diff --git "a/5464.jsonl" "b/5464.jsonl"
new file mode 100644--- /dev/null
+++ "b/5464.jsonl"
@@ -0,0 +1,963 @@
+{"seq_id":"38794950841","text":"#################################\n#\t\t   Button LED\t\t\t#\n#################################\n#\t\t\t\t\t\t\t\t#\n# @Author: Aaron Earl\t\t\t#\n#\t\t\t\t\t\t\t\t#\n# From Freenove tutorials for\t#\n# RPi. Button w/LED.\t\t\t#\n#\t\t\t\t\t\t\t\t#\n# Task: Turn on an LED w/a\t\t#\n# button press.\t\t\t\t\t#\n#################################\n\nimport RPi.GPIO as GPIO\n\n# Note RPi uses physical pin locations\nledPin = 11\nbuttonPin = 12\n\ndef setup():\n\n\tprint(\"Prgogram starting...\")\n\tGPIO.setmode(GPIO.BOARD)\t\t# Numbers the GPIOs physical location\n\tGPIO.setup(ledPin, GPIO.OUT)\t# Set LEDPin's mode to output\n\tGPIO.setup(buttonPin, GPIO.IN, pull_up_down=GPIO.PUD_UP) # Set buttonPin's mode to input,\n\t# and pull up to high level (3.3V)\n\ndef loop():\n\n\twhile True:\n\t\tif GPIO.input(buttonPin) == GPIO.LOW:\n\t\t\tGPIO.output(ledPin, GPIO.HIGH)\n\t\t\tprint(\"LED On...\")\n\t\telse:\n\t\t\tGPIO.output(ledPin, GPIO.LOW)\n\t\t\tprint(\"LED Off...\")\n\ndef destroy():\n\n\tGPIO.output(ledPin, GPIO.LOW) # led off\n\tGPIO.cleanup() #r realease resources\n\nif __name__ == \"__main__\":\n\n\tsetup()\n\ttry:\n\t\tloop()\n\texcept KeyboardInterrupt: # ctrl-c pressed program exits and destroy gets called\n\t\tdestroy()\n","repo_name":"aearl16/Python","sub_path":"src/Python 3/RaspberryPi/ButtonLED.py","file_name":"ButtonLED.py","file_ext":"py","file_size_in_byte":1111,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"7877766794","text":"from tqdm import tqdm\nimport numpy as np\nimport math\nimport time\nimport statistics\n\nfrom ..test import customplot\nfrom ..data import ChipData, CustomWord, TestPulse\nfrom .threshold import ThresholdScan\n\nclass TimewalkScan(ScanTest):\n    pixels = {}\n    th = 1\n    sections = []\n\n    def pre_main(self):\n        super().pre_main()\n        self.daq.injection_digital(0xffff)\n        self.daq.read_enable(0xffff)\n        self.daq.clock_enable(0xffff)\n        self.daq.injection_enable(0xffff)\n\n        self.analysis.file.seek(0, 2)\n        self.daq.send_tp(1)\n        time.sleep(0.1)\n        self.analysis.cleanup(); self.analysis.analyze()\n\n        self.daq.send_tp(1)\n        time.sleep(0.1)\n        self.analysis.cleanup(); self.analysis.analyze()\n\n        self.pixels = {}\n\n        print(\"Starting scan on the following pixels:\")\n        counter = 0\n        for packet in self.analysis.packets:\n            if(type(packet) != ChipData):\n                continue\n\n            for p in packet.pixels:\n                p.injected = {}\n                p.noise    = [0] * 64\n                self.pixels[(p.row,p.col)] = p\n                print(\"\\t%3d) %s\" % (counter, p.to_string()))\n                counter += 1\n\n            if(packet.sec not in self.sections):\n                self.sections.append(packet.sec)\n\n        self.range  = range(0,64)\n\n    def pre_loop(self):\n        for section in self.sections:\n            self.daq.write_gcrpar('BIAS%1d_VCASN' % section, 1)\n        return\n\n    def loop_body(self, iteration):\n        th = iteration\n\n        for section in self.sections:\n            self.daq.write_gcrpar('BIAS%1d_VCASN' % section, th)\n        \n        self.daq.custom_word(0xDEAFABBA, iteration)\n        self.daq.read_enable(self.sections)\n        self.daq.injection_digital(self.sections)\n        self.daq.send_tp(2)\n        time.sleep(0.01)\n\n        self.daq.custom_word(0xBEEFBEEF, iteration)\n        self.daq.injection_analog(self.sections)\n        self.daq.send_tp(100)\n        time.sleep(0.01)\n\n        self.daq.custom_word(0xDEADBEEF, iteration)\n        for i in range(0,100):\n            self.daq.injection_analog(self.sections)\n            self.daq.injection_digital(self.sections)\n\n        time.sleep(0.01)\n\n        self.daq.read_disable(self.sections)\n        self.daq.custom_word(0xCAFECAFE, iteration)\n\n    def post_main(self):\n        super().post_main()\n        print(\"Now analysing results...\")\n        self.analysis.analyze()\n\n        iterator = iter(self.analysis.packets)\n        packet = next(p for p in iterator if type(p) == CustomWord and p.word == 0xDEAFABBA);\n        counter = 0\n        with tqdm(total=len(self.analysis.packets), desc='Data analysis') as bar:\n            while True:\n                th = packet.payload\n\n                # Initialize arrays\n                for i in self.pixels:\n                    self.pixels[i].injected[th] = []\n\n                bar.update(counter)\n                counter = 0;\n\n                # Check Digital injections\n                dig_injs = []\n                tps = 0\n                ts = time.time()\n                c=0\n                while True:\n                    try:\n                        packet = next(iterator); counter += 1\n                        if(type(packet) == PixelData):\n                            dig_injs.append(packet)\n                        elif(type(packet) == TestPulse):\n                            tps += 1\n                        else:\n                            break\n                    except StopIteration:\n                        break\n\n                    c += 1\n\n                for section in self.sections:\n                    packets = list(filter(lambda x:type(x) == ChipData and x.sec == section, dig_injs))\n                    num = len(packets)\n\n                    if(num < tps):\n                        raise RuntimeError(\"TH:%u - Section %u didn't receive the digitally injected packets.\" % (th, section))\n\n                #print(\"\\t\\tElapsed for digital: %3d (%d packets)\" % ((time.time() - ts)*1000, c))\n\n                # Go on to injected packets\n                if(type(packet) != CustomWord or packet.word != 0xBEEFBEEF or packet.payload != th):\n                    raise RuntimeError('Unexpected packet here %s ' % packet.to_string())\n\n                ts = time.time()\n                c=0\n                while True:\n                    try:\n                        packet = next(iterator); counter += 1\n                        if(type(packet) == ChipData):\n                            for pix in packet.pixels:\n                                try:\n                                    self.pixels[(pix.row,pix.col)].injected[th].append(packet.ts_ext - packet.last_tp)\n                                except KeyError:\n                                    self.logger.warning(\"Unexpected pixel in this run: %s\" % pix.to_string())\n\n                        elif(type(packet) == TestPulse):\n                            continue\n                        else:\n                            break\n                    except StopIteration:\n                        break\n                    \n                    c += 1\n\n                #print(\"\\t\\tElapsed for injected: %3d (%d packets)\" % ((time.time() - ts)*1000, c))\n\n                # Go on to noisy packets\n                if(type(packet) != CustomWord or packet.word != 0xDEADBEEF or packet.payload != th):\n                    raise RuntimeError('Unexpected packet here: %s ' % packet.to_string())\n\n                ts = time.time()\n                c = 0\n                while True:\n                    try:\n                        packet = next(iterator); counter += 1\n                        if(type(packet) == ChipData):\n                            for pix in packet.pixels:\n                                try:\n                                    self.pixels[(pix.row,pix.col)].noise[th] += 1\n                                except KeyError:\n                                    self.logger.warning(\"Unexpected pixel in this run: %s\" % pix.to_string())\n                        elif(type(packet) == TestPulse):\n                            continue\n                        else:\n                            break\n                    except StopIteration:\n                        break\n\n                    c += 1\n\n                #print(\"\\t\\tElapsed for noise: %3d (%d packets)\" % ((time.time() - ts)*1000, c))\n\n                if(type(packet) != CustomWord or packet.word != 0xCAFECAFE or packet.payload != th):\n                    raise RuntimeError('Unexpected packet here %s ' % packet.to_string())\n                \n                if(th > 63):\n                    break\n\n                try:\n                    packet = next(iterator); counter += 1\n                except StopIteration:\n                    break\n\n                if(type(packet) != CustomWord or packet.word != 0xDEAFABBA):\n                    raise RuntimeError('Unexpected packet here: %s' % packet.to_string())\n\n    @customplot(('VCASN (#)', 'Timewalk (us)'), 'Timewalk distribution')\n    def singleplot(self, pix, show=True, saveas=None, ax=None):\n        inj = []\n        err = []\n        for injected in self.pixels[pix].injected.values():\n            c = len(injected)\n            if(c>1):\n                inj.append(statistics.mean (injected)*1E6/self.analysis.ts_hz)\n                err.append(statistics.stdev(injected)*1E6/self.analysis.ts_hz)\n            elif(c==1):\n                inj.append(injected[0]*1E6/self.analysis.ts_hz)\n                err.append(0)\n            else:\n                inj.append(0)\n                err.append(0)\n\n        ax.errorbar(list(self.range), inj, yerr=err, fmt='-o', label='Test Pulses')\n\n    def plot(self, show=True, saveas=None):\n        for pixel in self.pixels:\n            pix_saveas = None if (saveas == None) else f\"{saveas}_{pixel[0]}_{pixel[1]}\"\n            self.singleplot(pixel, show=show, saveas=pix_saveas)\n","repo_name":"apatern0/arcadia_daq","sub_path":"pyarcadia/tests/timewalk.py","file_name":"timewalk.py","file_ext":"py","file_size_in_byte":7925,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"29561574178","text":"from datetime import timedelta,datetime\nfrom sqlalchemy.dialects.mysql import insert\nfrom sqlalchemy.orm import load_only\nfrom sqlalchemy.sql import text\nfrom statistics import median\nimport MySQLdb\n\nfrom app import db\nfrom app.common.helpers.common import is_string\nfrom app.common.helpers.youtube_api import YouTubeApi\nfrom app.channel.models import Channel\nfrom app.video.models import Video, Statistic\nfrom app.tags.models import Tag\n\n\nclass YoutubeChannelScrapeJob:\n    \"\"\"\n    Cronjob for scraping videos and their statistics\n    Scan time every 20 min because of youtube api limitation(*/20 * * * *) for channel with 2000\n    videos\n    \"\"\"\n\n    def __init__(self, app_config, range_=20):\n        \"\"\"Pass Flask app configs for cronjob\"\"\"\n        self.app_config = app_config\n        self.range = range_\n\n    @staticmethod\n    def _chunks(_list: list, n: int) -> list:\n        \"\"\"\n        Split given list l to chunks of n items\n        \"\"\"\n        for i in range(0, len(_list), n):\n            yield _list[i : i + n]\n\n    def _insert_tags(self, tags):\n        \"\"\"Method for inserting videos tags to database table\"\"\"\n        for tag in tags:\n            query = insert(Tag).prefix_with('IGNORE').values(\n                name=tag\n            )\n            db.session.execute(query)\n        db.session.commit()\n\n    @staticmethod\n    def _insert_videos(videos, channel_id):\n        \"\"\"Methods for inserting video to \"\"\"\n        for video in videos:\n            query = insert(Video).prefix_with('IGNORE').values(\n                id=video['youtube_id'],\n                name=video['name'],\n                published_at=video['published'],\n                channel_id=channel_id\n            )\n            db.session.execute(query)\n        db.session.commit()\n\n    @staticmethod\n    def _insert_statistics(videos):\n        \"\"\"Method for add video statistics records\"\"\"\n        values = ','.join(\n            '({0}, {1}, {2}, {3}, {4}, \"{5}\")'.format(\n                video['views'],\n                video['likes'],\n                video['dislikes'],\n                video['favorites'],\n                video['comments'],\n                video['youtube_id'],\n            ) for video in videos\n        )\n        names = '(views, likes, dislikes, favorites, comments, video_id)'\n        sql = 'INSERT INTO %s %s VALUES %s' % (Statistic.__tablename__, names, values)\n        db.session.execute(text(sql))\n        db.session.commit()\n\n    @staticmethod\n    def _count_first_hour_view(videos, range_):\n        \"\"\"Method for adding first_hour_views in video using liner interpolation\"\"\"\n        for video in videos:\n            now = datetime.now()\n            hour = now - timedelta(hours=1)\n            hour_range = now - timedelta(hours=1, minutes=range_)\n\n            if hour > video['published'] > hour_range:\n                # get statics and count and video\n                statistic = Statistic.query.filter(\n                    Statistic.video_id == video['youtube_id']\n                ).order_by(Statistic.time_created.desc()).first()\n                update = db.session.query(Video).filter(Video.id == video['youtube_id'])\n\n                if statistic:\n                    # liner interpolation method for getting first hour views\n                    interval = (video['published'] + timedelta(hours=1)) - statistic.time_created\n                    interval = int((interval.total_seconds() % 3600) // 60)\n\n                    views = video['views'] - statistic.views\n                    interpolated_views = statistic.views + int(round(views * interval/range_))\n                    update.update({'first_hour_views': interpolated_views})\n\n                else:\n                    update.update({'first_hour_views': video['views']})\n        db.session.commit()\n\n    @staticmethod\n    def _video_tag_relation(videos):\n        \"\"\"Method for relationship for videos tag\"\"\"\n        for video in videos:\n            if video.get('tags'):\n                tags = Tag.query.options(load_only('id')).filter(Tag.name.in_(video['tags'])).all()\n\n                tags_ids = ','.join('({0}, \"{1}\")'.format(tag.id, video['youtube_id']) for tag in tags)\n                sql = 'INSERT IGNORE INTO tags (tag_id, video_id) VALUES %s' % tags_ids\n                db.session.execute(text(sql))\n        db.session.commit()\n\n    @staticmethod\n    def _count_channel_median(channel_id):\n        \"\"\"Method for counting median of all chanel videos first_hours_views\"\"\"\n        videos = Video.query.options(load_only('first_hour_views'))\\\n            .filter(Video.first_hour_views != None).all()\n\n        if videos:\n            first_views = [video.first_hour_views for video in videos]\n\n            db.session.query(Channel).filter(\n                Channel.id == channel_id\n            ).update({'views_median': median(first_views)})\n\n    def run(self):\n        \"\"\"Method for running crontjob of youtube channel videos scraping\"\"\"\n        channels = Channel.query.all()\n        for channel in channels:\n            youtube = YouTubeApi(self.app_config['YOUTUBE_API_KEY'])\n\n            while youtube.next_channel_page:\n                video_ids = youtube.find_channel_videos(\n                    channel.id,\n                    page_token=is_string(youtube.next_channel_page)\n                )\n                if video_ids:\n                    videos = youtube.get_videos_stats(video_ids)\n                    self._insert_videos(videos, channel.id)\n\n                    tags = []\n                    for video in videos:\n                        if video.get('tags'):\n                            tags.extend(video.get('tags'))\n\n                    if tags:\n                        self._insert_tags(list(set(tags)))\n                        self._video_tag_relation(videos)\n\n                    self._count_first_hour_view(videos, self.range)\n                    self._insert_statistics(videos)\n\n            self._count_channel_median(channel.id)\n        db.session.commit()\n","repo_name":"KestutisKazlauskas/youtuber","sub_path":"api/app/common/crons/scrape.py","file_name":"scrape.py","file_ext":"py","file_size_in_byte":5952,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"2905172592","text":"import torch\nimport torch.nn as nn\nimport torch.optim as optim\nfrom torch.optim import lr_scheduler\nfrom torchvision import transforms, datasets\nfrom model import shufflenet, efficientnet\nfrom sklearn.metrics import f1_score\nfrom src.utils import loss, macs\nfrom tqdm import tqdm\nimport os\nimport copy\nimport time\n\ndef train_model(model, criterion, optimizer, scheduler, num_epochs=10):\n    since = time.time()\n\n    best_model_wts = copy.deepcopy(model.state_dict())\n    best_f1 = 0.0\n\n    for epoch in range(num_epochs):\n        print('Epoch {}/{}'.format(epoch, num_epochs - 1))\n\n        for phase in ['train', 'val']:\n            if phase == 'train':\n                model.train()\n            else:\n                model.eval()\n\n            running_loss = 0.0\n            running_corrects = 0.0\n\n            labels_total = []\n            preds_total = []\n\n            for inputs, labels in tqdm(dataloaders[phase]):\n                inputs = inputs.to(device)\n                labels = labels.to(device)\n\n                optimizer.zero_grad()\n\n                with torch.set_grad_enabled(phase == 'train'):\n                    outputs = model(inputs)\n                    _, preds = torch.max(outputs, 1)\n                    loss = criterion(outputs, labels)\n\n                    if phase == 'train':\n                        loss.backward()\n                        optimizer.step()\n\n                running_loss += loss.item() * inputs.size(0)\n                running_corrects += torch.sum(preds == labels.data)\n\n                labels_total.extend(labels.cpu().numpy().tolist())\n                preds_total.extend(preds.cpu().numpy().tolist())\n\n            epoch_loss = running_loss / dataset_sizes[phase]\n            epoch_acc = running_corrects.double() / dataset_sizes[phase]\n            epoch_f1 = f1_score(labels_total,preds_total, average='macro')\n\n            print('{} Loss: {:.4f} Acc: {:.4f} F1: {:.4f}\\n'.format(\n                phase, epoch_loss, epoch_acc, epoch_f1))\n\n            if phase == 'val':\n                scheduler.step()\n\n            if phase == 'val' and epoch_f1 > best_f1:\n                best_f1 = epoch_f1\n                best_model_wts = copy.deepcopy(model.state_dict())\n                torch.save(model.state_dict(), \"./save/model.pt\")\n\n    time_elapsed = time.time() - since\n    print('Training complete in {:.0f}m {:.0f}s'.format(\n        time_elapsed // 60, time_elapsed % 60))\n    print('Best val F1: {:4f}'.format(best_f1))\n\n    model.load_state_dict(best_model_wts)\n    return model\n\nclass Efficientnet(nn.Module):\n    def __init__(self):\n        super(Efficientnet, self).__init__()\n        self.net = efficientnet.efficientnet_b0(pretrained=True)\n        self.linear = nn.Linear(1000,9)\n\n    def forward(self,x):\n        x = self.net(x)\n        x = self.linear(x)\n        return x\n\nif __name__ == \"__main__\":\n\n    # https://pytorch.org/vision/stable/transforms.html\n\n    img_size = 32\n\n    data_transforms = {\n        'train': transforms.Compose([\n            transforms.Resize([img_size,img_size]),\n            # transforms.RandomHorizontalFlip(0.5),\n            # transforms.RandomVerticalFlip(0.5),\n            # transforms.RandomRotation(15),\n            transforms.ToTensor(),\n            transforms.Normalize((0.4991, 0.4795, 0.4673), (0.2048, 0.2043, 0.2123))\n        ]),\n        'val': transforms.Compose([\n            transforms.Resize([img_size,img_size]),\n            # transforms.RandomHorizontalFlip(0.5),\n            # transforms.RandomVerticalFlip(0.5),\n            # transforms.RandomRotation(15),\n            transforms.ToTensor(),\n            transforms.Normalize((0.4966, 0.4769, 0.4646), (0.2057, 0.2053, 0.2126))\n        ]),\n    }\n\n    data_dir = '/opt/ml/input/data'\n    image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x),\n                                              data_transforms[x])\n                      for x in ['train', 'val']}\n\n    dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x],\n                                                  batch_size=256,\n                                                  shuffle=True,\n                                                  num_workers=4)\n                   for x in ['train', 'val']}\n\n    dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val']}\n\n    class_names = image_datasets['train'].classes\n\n    device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n\n    # https://pytorch.org/vision/stable/models.html\n\n    model = shufflenet.shufflenet_v2_x0_5(pretrained=True)\n    model.conv5 = nn.Sequential(\n        nn.Conv2d(192, 128, kernel_size=(1, 1), stride=(1, 1), bias=False),\n        nn.BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True),\n        nn.ReLU(inplace=True)\n    )\n    model.fc = nn.Linear(in_features=128, out_features=9, bias=True)\n    # model.load_state_dict(torch.load('/opt/ml/code/save/model.pt'))\n    model = model.to(device)\n\n    criterion = loss.LabelSmoothingLoss()\n\n    optimizer = optim.AdamW(model.parameters(), lr=1e-3)\n\n    scheduler = lr_scheduler.StepLR(optimizer, step_size=5)\n\n    print('MACs :', macs.calc_macs(model, (3, img_size, img_size)))\n\n    # train_model(model, criterion, optimizer, scheduler, num_epochs=50)\n","repo_name":"skaurl/P4-Model-Optimization","sub_path":"train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":5257,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"20839182009","text":"\n#__Print Functions__##\n\n\n\ndef DisplayCards(cards):\n\n\tCard='''\n ____ \n|x   |\n|    |\n|   x|\n ---- \n '''\n\tl_Card=''\n\tcard_list=[]\n\tIndividual_lines=[]\n\tcards_print=''\n\n\t#_replace x with Card Values\n\n\tfor r in range(0,len(cards)):\n\t\tif cards[r]==10:\n\t\t\tl_Card=(Card.replace('x ','10'))\n\t\t\tl_Card=(l_Card.replace(' x','10'))\n\t\telse:\t\n\t\t\tl_Card=(Card.replace('x',str(cards[r])))\n\t\tl_Card=l_Card.split('\\n')\n\t\tcard_list.append(l_Card)\n\n\n\t#formatting cards side by side\n\n\tfor r in range(0,7):\n\t\tfor r2 in range(0,len(card_list)):\n\t\t\n\t\t\tIndividual_lines = card_list[r2]\n\t\t\n\t\t\tcards_print+=Individual_lines[r] +' '\n\t\tcards_print=cards_print +'\\n'\n\n\t#cards_print=cards_print[:-6]\n\t\n\treturn cards_print\n\n\n\n\n\n\ndef DisplayDealerHidden(cards):\n\n\tCard='''\n ____ \n|x   |___\n|    |   |\n|   x|   |\n ----\t |\n     ----\n '''\n\tl_Card=''\n\t\n\t#_replace x with Card Values\n\tif cards[0]==10:\n\t\tl_Card=(Card.replace('x ','10'))\n\t\tl_Card=(l_Card.replace(' x','10'))\n\telse:\n\t\tl_Card=(Card.replace('x',str(cards[0])))\n\treturn print(l_Card)\n","repo_name":"CME-GIT/BlackJack","sub_path":"print_functions.py","file_name":"print_functions.py","file_ext":"py","file_size_in_byte":1009,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"29369518936","text":"\"\"\"\r\nhttp://incompleteideas.net/sutton/MountainCar/MountainCar1.cp\r\npermalink: https://perma.cc/6Z2N-PFWC\r\n\"\"\"\r\n\r\nimport math\r\nimport numpy as np\r\nfrom gym import spaces\r\nfrom gym.utils import seeding\r\n\r\nclass MountainCarEnv():\r\n    def __init__(self):\r\n        self.min_position = -1.2    # 最低点\r\n        self.max_position = 0.6     # 最高点\r\n        self.max_speed = 0.07       # 最大速度\r\n        self.goal_position = -0.2   # 目标高度\r\n        self.goal_velocity = 0      # 目标速度      \r\n        self.force=0.001            # 推力\r\n        self.gravity=0.0025         # 重量\r\n        self.time = None            # 一个回合持续时间步\r\n\r\n        self.low = np.array([self.min_position,\r\n                             -self.max_speed],dtype=np.float32)\r\n        self.high = np.array([self.max_position,\r\n                              self.max_speed],dtype=np.float32)\r\n        self.action_space = spaces.Discrete(3)\r\n        self.observation_space = spaces.Box(self.low,\r\n                                            self.high,dtype=np.float32)\r\n        \r\n        self.seed()\r\n    \r\n    def seed(self, seed=None):\r\n        self.np_random,seed = seeding.np_random(seed)\r\n        return seed\r\n\r\n    def step(self, action):\r\n        position,velocity = self.state\r\n        velocity += (action-1)*self.force+math.cos(3*position)*(-self.gravity)\r\n        velocity = np.clip(velocity,-self.max_speed,self.max_speed)\r\n        position += velocity\r\n        position = np.clip(position, self.min_position, self.max_position)\r\n        if (position==self.min_position and velocity<0): \r\n            velocity = 0\r\n        \r\n        self.state = [position, velocity]\r\n        self.time += 1\r\n        \r\n        if position>=self.goal_position and velocity>=self.goal_velocity:\r\n            done = True\r\n            reward = 0\r\n            info = 'Goal Obtained'\r\n        elif self.time > 1000:\r\n            done = True\r\n            reward = -1\r\n            info = 'Maximum Timesteps'\r\n        else:\r\n            done = False\r\n            reward = -1\r\n            info = 'Goal Obtained'\r\n\r\n        return self.state, reward, done, info\r\n\r\n    def reset(self):\r\n        self.state = [self.np_random.uniform(low=-0.6, high=-0.4), 0]\r\n        self.time = 0\r\n        \r\n        return self.state\r\n    \r\nif __name__ == '__main__':\r\n    env = MountainCarEnv()\r\n    s = env.reset()\r\n    for i in range(2000):\r\n        prob = np.random.rand(3,)\r\n        prob = prob/np.sum(prob)\r\n        a = np.random.choice(np.arange(3),p=prob)\r\n        s_,r,end,info = env.step(a)\r\n        print(i,s,a,s_,end,info)\r\n        \r\n        if end:\r\n            break","repo_name":"QiangLong2017/Deep-Reiforcement-Learning","sub_path":"LectureNotes/Code7/MountainCar.py","file_name":"MountainCar.py","file_ext":"py","file_size_in_byte":2656,"program_lang":"python","lang":"en","doc_type":"code","stars":32,"dataset":"github-code","pt":"81"}
+{"seq_id":"25987707512","text":"# https://www.codewars.com/kata/5a26ca51e1ce0e987b0000ee/train/python\ndef branch(n):\n    if n == 1: return 0\n    r = 1\n    # 边长每圈增加 2, 确定在第几圈\n    while r ** 2 < n:\n        r += 2\n\n    L = (r - 2) ** 2\n    res = 3\n    # 从最大的开始， 倒着减掉边长-1， 判断在 3， 2， 1， 0.\n    for i in range(r ** 2, L, 1 - r):\n        if n > i:\n            return res + 1\n        res -= 1\n    return 0\n\n\nprint(branch(6), 0)\n","repo_name":"IsolatedRain/code_wars","sub_path":"completed/Progressive Spiral Number Branch.py","file_name":"Progressive Spiral Number Branch.py","file_ext":"py","file_size_in_byte":452,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"7784412864","text":"from distutils.core import setup\n\nfiles = ['decapp2.py','decrypy.py','decgui2.py','decgui2.ui','README','setup.py']\n\nsetup(name = \"decrypy\",\n    version = \"2.0\",\n    description = \"A tool to decrypt Yahoo chat archives\",\n    author = \"Akhil Wali\",\n    author_email = \"green.transistor@gmail.com\",\n    url = \"http://code.google.com/p/decrypy/\",\n    #Name the folder where your packages live:\n    #(If you have other packages (dirs) or modules (py files) then\n    #put them into the package directory - they will be found \n    #recursively.)\n    packages = ['.'],\n    #'package' package must contain files (see list above)\n    #I called the package 'package' thus cleverly confusing the whole issue...\n    #This dict maps the package name =to=> directories\n    #It says, package *needs* these files.\n    package_data = {'.' : files }    \n    )\n\n","repo_name":"darth10/decrypy","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":843,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"31346206253","text":"#!/usr/bin/python\n\nimport sys\n\nstoredUsers= open('names', 'a')\n\nname = raw_input('Hi, please enter your name: ')\nage = raw_input('How old are you?: ')\nusername = raw_input('Please enter your username: ')\n\nsys.stdout.write('your name is ' + name + ', you are ' + str(age) + ' years old, and your username is ' + str(username) + '\\n')\n\nstoredUsers.write(name + '\\t' + age + '\\t'+username+'\\n')\n\nstoredUsers.close()\n","repo_name":"Yeorj/Easy1","sub_path":"solution.py","file_name":"solution.py","file_ext":"py","file_size_in_byte":413,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"12167485412","text":"import glob\nimport os\nimport pathlib\nimport setuptools\n\npackage_data = dict()\npackage_data['ambit'] = []\nfor path, paths, files in os.walk('ambit/resources/', followlinks=True):\n    for f in files:\n        include = os.path.join(path, f)[6:]\n        package_data['ambit'].append(include)\n\nsetuptools.setup(\n    name='ambit',\n    version='0.3.6',\n    description='Take control of your Palette.',\n    long_description=(\n        '**ambit** is a Python library for interacting with PaletteGear '\n        'and MonogramCC devices, a graphical simulator for device-free '\n        'development, and an accompanying set of configurable end user '\n        'tools and demos.'\n    ),\n    long_description_content_type='text/markdown',\n    keywords=['monogramcc', 'palettegear', 'demoscene', 'pygame', 'linux', 'pyusb'],\n    url='https://github.com/khimaros/ambit',\n    author='khimaros',\n    packages=setuptools.find_packages(),\n    package_data=package_data,\n    scripts=glob.glob('./bin/*'),\n    classifiers=[\n        'Development Status :: 3 - Alpha',\n        'Programming Language :: Python :: 3',\n        'License :: OSI Approved :: GNU General Public License v3 (GPLv3)',\n        'Operating System :: POSIX',\n        'Topic :: System :: Hardware',\n    ],\n    python_requires='>=3.6',\n)\n\n","repo_name":"khimaros/ambit","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1281,"program_lang":"python","lang":"en","doc_type":"code","stars":17,"dataset":"github-code","pt":"81"}
+{"seq_id":"41178963338","text":"print('======================================')\nprint('Mahasiswa Dengan Nilai Akhir Tertinggi')\nprint('======================================')\nnilaiMhs = [{'nim' : 'A01', 'nama' : 'Amir', 'mid' : 50, 'uas' : 80},\n            {'nim' : 'A02', 'nama' : 'Budi', 'mid' : 40, 'uas' : 90},       \n            {'nim' : 'A03', 'nama' : 'Cici', 'mid' : 50, 'uas' : 50}, \n            {'nim' : 'A04', 'nama' : 'Dedi', 'mid' : 20, 'uas' : 30},\n            {'nim' : 'A05', 'nama' : 'Fifi', 'mid' : 70, 'uas' : 40}]\ndef nilaiTertinggi(nilaiMhs):\n    nilai = 0\n    for i in nilaiMhs:\n        nilaimid = i.get('mid')\n        nilaiuas = i.get('uas')\n        nilaiakhir = (nilaimid + 2*nilaiuas)/3\n        if (nilaiakhir > nilai):\n            nilai = nilaiakhir\n            data = {}\n            data['nim'] = i.get('nim')\n            data['nama'] = i.get('nama')\n    return data\nnilaitinggi = nilaiTertinggi(nilaiMhs)\nprint('NIM Mahasiswa',nilaitinggi['nim'])\nprint('Nama Mahasiswa',nilaitinggi['nama'])\n","repo_name":"DestriAbdiSaputro/Python-Projects-Protek","sub_path":"Praktikum 08/python project/13.py","file_name":"13.py","file_ext":"py","file_size_in_byte":987,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"81"}
+{"seq_id":"17506002924","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sun Mar 14 23:36:06 2021\n\n@author: Sai Krishna\n\"\"\"\n\n#Setting directory for download location\nimport os\nos.chdir('C:\\\\Data\\\\github\\\\python_projects\\\\Youtube video downloader')\n\n#Importing pytube (use pip install pytube)\nfrom pytube import YouTube\n\n#Video link as a string\nlink = \"https://www.youtube.com/watch?v=8b0ubLO2MUE\"\n\nvideo = YouTube(link)\n\n#Using the best resolution option\nstream = video.streams.get_highest_resolution()\n\n#File name of the video about to be downloaded\nstream.default_filename\n\n#Video Resolution\nstream.resolution\n\n#Checking the lowest resolution \nvideo.streams.get_lowest_resolution().resolution\n\n#Title of the video\nstream.title\n\n#Download command\nstream.download()\n","repo_name":"krishnasai321/Python_projects","sub_path":"Youtube video downloader/yt_video_downloader.py","file_name":"yt_video_downloader.py","file_ext":"py","file_size_in_byte":732,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"1148669925","text":"from django.core.management.base import BaseCommand, CommandError\nfrom django.conf import settings\nfrom operators.models import QuoteRequest, TourOperator, Itinerary, ItineraryType\nfrom users.models import UserProfile\nimport MySQLdb\nfrom django.db.models import Count\nfrom django.contrib.auth.models import User\nfrom places.models import Park, CountryIndex\nfrom photos.models import Photo\nfrom blog.models import Article\nfrom reviews.models import ParkReview, KilimanjaroParkReview, TourOperatorReview\nfrom analytics.models import Analytic\n\n\nclass Command(BaseCommand):\n    help = ''\n\n    def handle(self, *args, **options):\n\n        # update tour operators\n        tour_operators = TourOperator.objects.all()\n        #tour_operators = tour_operators.filter(slug='africaventure')\n        for tour_operator in tour_operators:\n            tour_operator.update_reviews_count()\n            tour_operator.update_average_rating()\n            tour_operator.update_parks_count()\n            tour_operator.update_packages_count()\n            tour_operator.update_quote_request_count()\n            tour_operator.update_photos_count()\n            tour_operator.update_yas_score()\n            tour_operator.update_vehicle_rating()\n            tour_operator.update_meet_and_greet_rating()\n            tour_operator.update_responsiveness()\n            tour_operator.update_safari_quality()\n            tour_operator.update_itinerary_quality()\n            tour_operator.update_packages_count()\n            for country in tour_operator.country_indexes.all():\n                tour_operator.update_yas_score(country)\n        print('Updated', tour_operators.count(), 'tour_operators')\n\n\n        #activity_level\n        itineraries = Itinerary.objects.filter(date_deleted=None) \n        for itinerary in itineraries:\n            itinerary.activity_level = itinerary.calc_max_activity_level()\n            itinerary.activity_level_name = itinerary.calc_activity_level_string()\n            itinerary.save()\n        print('Updated', itineraries.count(), 'itineraries')\n        \n        # update country\n        countries = CountryIndex.objects.all()\n        for country in countries:\n            country.update_packages_count()\n            country.update_photos_count()\n            country.update_parks_count()\n            country.update_operators_count()\n        print('Updated', countries.count(), 'countries')\n\n        # update articles\n        articles = Article.objects.all()\n        for article in articles:\n            article.update_kudu_count()\n            article.update_visit_count()\n            article.update_comments_count()\n        print('Updated', articles.count(), 'articles')\n\n        #parks\n        parks = Park.objects.all()\n        for park in parks:\n            park.update_reviews_count()\n            park.update_tour_operators_count()\n            park.update_average_rating()\n            park.update_packages_count()\n            park.update_photos_count()\n        print('Updated', parks.count(), 'parks')\n\n        # update park reviews\n        reviews = ParkReview.objects.all()\n        for review in reviews:\n            review.update_views_count()\n            review.update_kudu_count()\n        print('Updated', reviews.count(), 'park reviews')\n\n        # update tour operator reviews\n        reviews = TourOperatorReview.objects.all()\n        for review in reviews:\n            review.update_views_count()\n            review.update_kudu_count()\n            \n        print('Updated', reviews.count(), 'tour op reviews')\n\n        # update kilimanjaro reviews\n        reviews = KilimanjaroParkReview.objects.all()\n        for review in reviews:\n            review.update_views_count()\n            review.update_kudu_count()\n        print('Updated', reviews.count(), 'kilimanjaro park reviews visit counts')\n\n        objs = Itinerary.objects.all()\n        for obj in objs:\n            obj.update_visit_count()\n        print('Updated', objs.count(), 'itinerary views')\n\n        objs = UserProfile.objects.all()\n        for obj in objs:\n            obj.update_review_count()\n            obj.update_kudus_count()\n        print('Updated', objs.count(), 'users reviews and kudus')\n\n        objs = Photo.objects.filter(date_deleted__isnull=False)\n        for obj in objs:\n            obj.update_kudu_count()\n        print('Updated', objs.count(), 'photos')\n\n        self.stdout.write(self.style.SUCCESS(\"DONE\"))\n","repo_name":"montenegrop/djangotravelportal","sub_path":"extras/management/commands/updates.py","file_name":"updates.py","file_ext":"py","file_size_in_byte":4405,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"71291563465","text":"# Fibonacci Series using Dynamic Programming  \ndef fibonacci(n): \n    # check for n is zero or less than two \n    \n    if n == 0:\n        return 0\n    elif n <= 2:\n        return 1\n\n    # List of zeros with length of given value n\n    seq = [0] * n\n    seq[0] = seq[1] = 1\n\n    # Algorithm Calculating Fibonacci Numbers\n    for i in range(2,n):\n        seq[i]= seq[i-1]+seq[i-2]\n    \n    return seq[n-1]\n\nprint(fibonacci(9))","repo_name":"harkaranbrar7/Algorithm-Python","sub_path":"Dynamic Programming/fibonacciSeries.py","file_name":"fibonacciSeries.py","file_ext":"py","file_size_in_byte":424,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"28562330808","text":"\"\"\"\nsearch(here,d) : percentage of finding at here after d days\n\"\"\"\n\n\nn,d,p = map(int,input().split())\nconnected = []\nmemo = [[-1]*(d+1) for _ in range(n+1)]\n\nfor i in range(n):\n    connected.append(list(map(int,input().split())))\nt = int(input())\nq = list(map(int,input().split()))\n\ndeg = [0]*(n+1)\n\nfor i in range(n):\n    for j in range(n):\n        if connected[i][j] == 1:\n            deg[j] += 1\n\ndef search(here,days):\n    if days == 0:\n        if here == p:\n            return 1.0\n        else:\n            return 0.0\n    if memo[here][days] > -0.5:\n        return memo[here][days]\n    \n    memo[here][days] = 0.0\n    \n    for there in range(n):\n        if connected[here][there] == 1:\n            memo[here][days] += search(there,days - 1)/deg[there]\n        \n    return memo[here][days]\n\n\nfor i in q:\n    print(search(i,d))","repo_name":"kjh000/Algorithm","sub_path":"algo/두니발 박사의 탈옥.py","file_name":"두니발 박사의 탈옥.py","file_ext":"py","file_size_in_byte":831,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"73046577225","text":"# coding:utf-8\nimport math\nimport stemming.porter2 as porter\nimport numpy as np\nimport copy\nfrom collections import Counter\n\n\ndef position_rank(sentence, tokenizer, alpha=0.85, window_size=6, num_keyphrase=10, lang=\"en\"):\n    \"\"\"Compute position rank score.\n\n    Position rank is a method for extracting keyphrase from sentence.\n    This method is allowed any language if you provide tokenizer\n    that tokenize language you wanna apply position rank.\n    In the oroginal paper, authors used 'title' and 'abstract' of scholarly documents.\n    Original paper is here: http://aclweb.org/anthology/P/P17/P17-1102.pdf\n\n    Args:\n      sentence: Text concatenated title and abstract.\n      tokenizer: Object that tokenize sentence.\n        Tokenizer must has tokenize(), tokenize() receive sentence and return token list\n        and phrase list. See tokenizer.py and class of tokenizer for detail.\n      alpha: Damping parameter. This allows 'teleport' operation into another node in the graph.\n      window_size: Size of woindow for co-occurece word.\n      num_keyphrase: Number of keyphrase which method will return.\n      lang: Target language.\n\n    Returns:\n      Keyphrase list. Length of list is decided by 'num_keyphrase' param.\n\n    \"\"\"\n    if lang == \"en\":\n        stem = porter.stem\n    else:\n        stem = lambda word: word\n    # origial words(=no stemming) and phrase list\n    original_words, phrases = tokenizer.tokenize(sentence)\n    # stemmed words\n    stemmed_word = [stem(word) for word in original_words]\n    unique_word_list = set([word for word in stemmed_word])\n    n = len(unique_word_list)\n\n    adjancency_matrix = np.zeros((n, n))\n    word2idx = {w: i for i, w in enumerate(unique_word_list)}\n    p_vec = np.zeros(n)\n    # store co-occurence words\n    co_occ_dict = {w: [] for w in unique_word_list}\n\n    # 1. initialize  probability vector\n    for i, w in enumerate(stemmed_word):\n        # add position score\n        p_vec[word2idx[w]] += float(1 / (i+1))\n        for window_idx in range(1, math.ceil(window_size / 2)+1):\n            if i - window_idx >= 0:\n                co_list = co_occ_dict[w]\n                co_list.append(stemmed_word[i - window_idx])\n                co_occ_dict[w] = co_list\n\n            if i + window_idx < len(stemmed_word):\n                co_list = co_occ_dict[w]\n                co_list.append(stemmed_word[i + window_idx])\n                co_occ_dict[w] = co_list\n\n    # 2. create adjancency matrix from co-occurence word\n    for w, co_list in co_occ_dict.items():\n        cnt = Counter(co_list)\n        for co_word, freq in cnt.most_common():\n            adjancency_matrix[word2idx[w]][word2idx[co_word]] = freq\n\n    adjancency_matrix = adjancency_matrix / adjancency_matrix.sum(axis=0)\n    p_vec = p_vec / p_vec.sum()\n    # principal eigenvector s\n    s_vec = np.ones(n) / n\n\n    # threshold\n    lambda_val = 1.0\n    loop = 0\n    # compute final principal eigenvector\n    while lambda_val > 0.001:\n        next_s_vec = copy.deepcopy(s_vec)\n        for i, (p, s) in enumerate(zip(p_vec, s_vec)):\n            next_s = (1 - alpha) * p + alpha * (weight_total(adjancency_matrix, i, s_vec))\n            next_s_vec[i] = next_s\n        lambda_val = np.linalg.norm(next_s_vec - s_vec)\n        s_vec = next_s_vec\n        loop += 1\n        if loop > 100:\n            break\n\n    # score original words and phrases\n    word_with_score_list = [(word, s_vec[word2idx[stem(word)]]) for word in original_words]\n    for phrase in phrases:\n        total_score = sum([s_vec[word2idx[stem(word)]] for word in phrase.split(\"_\")])\n        word_with_score_list.append((phrase, total_score))\n\n    sort_list = np.argsort([t[1] for t in word_with_score_list])\n    keyphrase_list = []\n    # if not check stemmed keyphrase, there are similar phrases in keyphrase list\n    # i.e. \"neural network\" and \"neural networks\" in list\n    stemmed_keyphrase_list = []\n    for idx in reversed(sort_list):\n        keyphrase = word_with_score_list[idx][0]\n        stemmed_keyphrase = \" \".join([stem(word) for word in keyphrase.split(\"_\")])\n        if not stemmed_keyphrase in stemmed_keyphrase_list:\n            keyphrase_list.append(keyphrase)\n            stemmed_keyphrase_list.append(stemmed_keyphrase)\n        if len(keyphrase_list) >= num_keyphrase:\n            break\n    return keyphrase_list\n\n\ndef weight_total(matrix, idx, s_vec):\n    \"\"\"Sum weights of adjacent nodes.\n\n    Choose 'j'th nodes which is adjacent to 'i'th node.\n    Sum weight in 'j'th column, then devide wij(weight of index i,j).\n    This calculation is applied to all adjacent node, and finally return sum of them.\n\n    \"\"\"\n    return sum([(wij / matrix.sum(axis=0)[j]) * s_vec[j] for j, wij in enumerate(matrix[idx]) if not wij == 0])\n","repo_name":"ymym3412/position-rank","sub_path":"position_rank.py","file_name":"position_rank.py","file_ext":"py","file_size_in_byte":4726,"program_lang":"python","lang":"en","doc_type":"code","stars":95,"dataset":"github-code","pt":"81"}
+{"seq_id":"25398228976","text":"from . import usage\n\ndef solution():\n    # PyPy ~ 18 ms\n\n    tot = 0\n    for n in range(1, 10_000):\n        s = n\n        i = 0\n        while i < 50:\n            s += int(str(s)[::-1])\n            ss = str(s)\n            if ss == ss[::-1]:\n                break\n            i += 1\n        if i == 50:\n            tot += 1\n    return tot\n\nif __name__ == '__main__':\n    usage.usage(solution, n=30)","repo_name":"ykw1793/project-euler","sub_path":"project_euler/problems/55.py","file_name":"55.py","file_ext":"py","file_size_in_byte":396,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"43373311925","text":"def combinations(s):\n    #mapper work\n    if len(s) < 2:\n        return s\n    res = []\n# 문자데이터 할때 위를 수정 sep = ' ' 힌트 -> 단어가 다 잘라진 상태의 배열을 만들고 비교함수\n\n    # reduce work\n    for i, c in enumerate(s):\n        res.append(c)   #추가되는 부분 -> [ 'a', 'b', 'c', 'd']\n        # print(i, c, res)\n        print(i, c, res, end = ' : ')\n        #자기를 뺀 나머지를 비교\n        for j in combinations(s[:i] + s[i+1:]):     # 재귀\n            res.append(c+j)\n            print(s[:i], end=' / ')\n            print(s[i+1:], end= ' / ')\n            print(s[:i] + s[i+1:], end= ' | ')\n            print(i, c, j, res)\n    return res\n\n\nif __name__ == \"__main__\":\n    result = combinations('1234')\n    print(result)\n    \n    \n#빅오 O(N^2) -> 2중 for문에 if문 포함\n# O(2N) -> 2중 for문","repo_name":"MethodFunc/KSA_edu_Daejeon","sub_path":"20200609.py","file_name":"20200609.py","file_ext":"py","file_size_in_byte":857,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"40745862664","text":"import time\n\n\nclass ReplicatedMixin(object):\n\n    scalable_attr = \"replicas\"\n\n    @property\n    def replicas(self):\n        return self.obj[\"spec\"][\"replicas\"]\n\n    @replicas.setter\n    def replicas(self, value):\n        self.obj[\"spec\"][\"replicas\"] = value\n\n\nclass ScalableMixin(object):\n\n    @property\n    def scalable(self):\n        return getattr(self, self.scalable_attr)\n\n    @scalable.setter\n    def scalable(self, value):\n        setattr(self, self.scalable_attr, value)\n\n    def scale(self, replicas=None):\n        count = self.scalable if replicas is None else replicas\n        self.exists(ensure=True)\n        if self.scalable != count:\n            self.scalable = count\n            self.update()\n            while True:\n                self.reload()\n                if self.scalable == count:\n                    break\n                time.sleep(1)\n","repo_name":"kelproject/pykube","sub_path":"pykube/mixins.py","file_name":"mixins.py","file_ext":"py","file_size_in_byte":861,"program_lang":"python","lang":"en","doc_type":"code","stars":351,"dataset":"github-code","pt":"81"}
+{"seq_id":"4994422878","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Mar 25 13:04:25 2020\n\n@author: elif.ayvali\n\"\"\"\nfrom ddpg_agent import DDPGAgent\n\nimport torch\nimport numpy as np\nimport random\nfrom collections import namedtuple, deque\nimport torch.nn.functional as F\n\ndevice = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n\n#Parameters from OpenAI Baselines\nBUFFER_SIZE = int(1e6)   # replay buffer size\nBATCH_SIZE = 256         # minibatch size\nUPDATE_EVERY = 3         # Number of episodes that should elapse between gradient descent updates\nGAMMA = 0.995            # discount factor\nTAU = 1e-3               # for soft update of target parameters\n\nLR_ACTOR = 1e-4         # learning rate of the actor \nLR_CRITIC =5e-4        # learning rate of the critic\nWEIGHT_DECAY = 0         # L2 weight decay\nEPSILON = 1.0            # Exploration noise coefficient\nEPSILON_DECAY = 0        # Decay rate for exploration noise\nLEARN_TIMES= 2           # Number of times to backprop with the batch\nWARM_UP=   0             # Number of steps for uniform-random action selection, before running real policy. Helps exploration.\nCLIP_NORM=True\n\n\n\n\nclass MADDPG:\n    def __init__(self, num_agents, state_size, action_size, random_seed):\n        super(MADDPG, self).__init__()\n        \n        self.state_size = state_size\n        self.action_size = action_size\n        self.seed = random.seed(random_seed)\n        self.random_seed=random_seed\n        self.num_agents=num_agents\n        self.episode_num=0\n        self.total_steps=0\n\n        # Initialize centralized memory buffer\n        self.memory = ReplayBuffer(self.action_size, BUFFER_SIZE, BATCH_SIZE, random_seed)\n        # Initialize decentralized actors/critics\n        self.maddpg_agents=self.get_agents()       \n    \n\n    def get_agents(self):\n        \"\"\"get all the agents in the MADDPG object\"\"\"\n        agents = [DDPGAgent(self,idx) for idx in range(self.num_agents)]\n        return agents\n    \n    \n    def reset_agents(self):\n        for agent in self.maddpg_agents:\n            agent.reset()\n            \n    def act(self, states,  add_noise=True):\n        \"\"\"get actions from all agents in the MADDPG object\"\"\"\n        actions=[agent.act(state,self.episode_num, add_noise) for agent, state in zip(self.maddpg_agents, states)] \n        return actions\n\n    def encode_buffer(self,obs):\n        #Concatenate states/actions of all agents\n        #Reference : github@fsasilva59\n#        print('before encode,obs',obs)\n#        print('after encode,obs', np.array(obs).reshape(1,-1).squeeze())\n        return np.array(obs).reshape(1,-1).squeeze()\n           \n    def decode_buffer(self,size, agent_idx, obs):\n        #Reference : github@fsasilva59\n        list_idx  = torch.tensor([ idx for idx in range(agent_idx * size, agent_idx * size + size) ]).to(device)    \n#        print('before decode,obs',obs)\n#        print('after decode,obs', obs.index_select(1, list_idx))\n        return obs.index_select(1, list_idx)\n        \n    def step(self, states, actions, rewards, next_states, dones,episode):\n        \"\"\"Concatanate experience of all agents  in replay memory, and use random sample from buffer to learn.\"\"\"\n        self.episode_num=episode        \n        self.memory.add(self.encode_buffer(states), self.encode_buffer(actions), self.encode_buffer(rewards), self.encode_buffer(next_states), self.encode_buffer(dones))     \n        # Learn periodically, if enough samples are available in memory\n        if len(self.memory) > BATCH_SIZE and self.episode_num % UPDATE_EVERY==0:\n            for _ in range(LEARN_TIMES):\n                for idx in range(self.num_agents):   \n                    experiences = self.memory.sample()    \n                    self.learn(experiences,self.maddpg_agents,idx, GAMMA)                 \n        self.total_steps+=1\n                \n    def save_checkpoint(self):\n        for agent in self.maddpg_agents:\n            torch.save(agent.actor_local.state_dict(), 'agent_'+str(agent.agent_idx)+'_checkpoint_actor.pth')\n            torch.save(agent.critic_local.state_dict(), 'agent_'+str(agent.agent_idx)+'_checkpoint_critic.pth')\n\n    def soft_update(self, local_model, target_model, tau):\n        \"\"\"Soft update model parameters.\n        θ_target = τ*θ_local + (1 - τ)*θ_target\n\n        Params\n        ======\n            local_model: PyTorch model (weights will be copied from)\n            target_model: PyTorch model (weights will be copied to)\n            tau (float): interpolation parameter \n        \"\"\"\n        for target_param, local_param in zip(target_model.parameters(), local_model.parameters()):\n            target_param.data.copy_(tau*local_param.data + (1.0-tau)*target_param.data)\n            \n    def learn(self,  experiences,meta_agent, agent_idx, gamma):\n        \"\"\"Update policy and value parameters using given batch of experience tuples.\n        Q_targets = r + γ * critic_target(next_state, actor_target(next_state))\n        where:\n            actor_target(state) -> action\n            critic_target(state, action) -> Q-value\n\n        Params\n        ======\n            experiences (Tuple[torch.Tensor]): tuple of (s, a, r, s', done) tuples \n            gamma (float): discount factor\n        \"\"\"\n        #all experiences\n        #states: batch_sizex56\n        #actions:batch_sizex4\n        #rewards, batch_sizex2\n        states, actions, rewards, next_states, dones = experiences\n#        print('states',states)\n#        print('actions',actions)\n#        print('rewards',rewards)\n        #get agent's experiences   \n        agent=meta_agent[agent_idx]\n#        print('agent object',agent)\n        agent_states =  self.decode_buffer(self.state_size, agent_idx, states)\n        agent_actions = self.decode_buffer(self.action_size, agent_idx, actions)\n        agent_next_states = self.decode_buffer(self.state_size, agent_idx, next_states) \n        agent_rewards=self.decode_buffer(1, agent_idx, rewards) \n        agent_dones=self.decode_buffer(1, agent_idx, dones) \n#        print('agent_rewards',agent_rewards)\n#        print('agent_dones',agent_dones)\n#        print('agent_states',agent_states)\n\n        #get other agents' experiences\n        other_agent_idx=np.delete(range(self.num_agents),agent_idx).squeeze()\n        other_agent=meta_agent[other_agent_idx]\n#        print('other_agent object',other_agent)\n        other_agent_states =  self.decode_buffer(self.state_size, other_agent_idx, states)\n        other_agent_actions = self.decode_buffer(self.action_size, other_agent_idx, actions)\n        other_agent_next_states = self.decode_buffer(self.state_size, other_agent_idx, next_states) \n        other_agent_rewards=self.decode_buffer(1, other_agent_idx, rewards) \n        #All agents ->torch for centralized critic\n        all_states=torch.cat((agent_states, other_agent_states), dim=1).to(device)\n        all_actions=torch.cat((agent_actions, other_agent_actions), dim=1).to(device)\n        all_next_states=torch.cat((agent_next_states, other_agent_next_states), dim=1).to(device)\n              \n#        print('other agent_states',other_agent_states)\n\n        # ---------------------------- update critic ---------------------------- #  \n        # Get predicted next-state actions and Q values from target model\n        agent_next_actions=agent.actor_target(agent_states)\n        other_agent_next_actions=other_agent.actor_target(other_agent_states) #should be agent 2\n#        print(' agent_nex_actions',agent_next_actions)\n#        print('other_agent_next_actions',other_agent_next_actions)\n\n        #Next actions-> torch\n        all_next_actions=torch.cat((agent_next_actions,other_agent_next_actions), dim=1).to(device) \n   \n        Q_targets_next = agent.critic_target(all_next_states, all_next_actions) #batch_sizex1\n#        print('Q_targets_next',Q_targets_next)\n        # Compute Q targets for current states (y_i)\n        \n        Q_targets = agent_rewards + (gamma * Q_targets_next * (1 - agent_dones))\n        \n        # Compute critic loss\n        Q_expected = agent.critic_local(all_states, all_actions)  #batch_sizex1\n#        print('Q_expected',Q_expected)\n\n        critic_loss = F.mse_loss(Q_expected, Q_targets)\n\n        # Minimize the loss\n        agent.critic_optimizer.zero_grad()\n        critic_loss.backward()\n        if CLIP_NORM is True:\n            torch.nn.utils.clip_grad_norm_(agent.critic_local.parameters(), 1) # clip gradient to max 1\n        agent.critic_optimizer.step()\n\n        # ---------------------------- update actor ---------------------------- #\n        # Compute next action predictions for all agents        \n        agent_action_predictions=agent.actor_local(agent_states)\n        #Predictions-> torch, Only backprop agent idx, detach other agents\n        other_agent_action_predictions=other_agent.actor_local(other_agent_states)#.detach()\n        all_actions_pred = torch.cat((agent_action_predictions, other_agent_action_predictions), dim = 1).to(device) \n        #Detach other agent states ,only backprop agent states\n        actor_loss = -agent.critic_local(all_states, all_actions_pred).mean()\n\n        # Minimize the loss\n        agent.actor_optimizer.zero_grad()\n        actor_loss.backward()\n        agent.actor_optimizer.step()\n\n        # ----------------------- update target networks ----------------------- #\n        self.soft_update(agent.critic_local, agent.critic_target, TAU)\n        self.soft_update(agent.actor_local, agent.actor_target, TAU)    \n\n        # ---------------------------- update noise ---------------------------- #\n        agent.epsilon -= EPSILON_DECAY\n        agent.noise.reset()\n        \nclass ReplayBuffer:\n    \"\"\"Fixed-size buffer to store experience tuples.\"\"\"\n\n    def __init__(self,action_size, buffer_size, batch_size, seed):\n        \"\"\"Initialize a ReplayBuffer object.\n        Params\n        ======\n            buffer_size (int): maximum size of buffer\n            batch_size (int): size of each training batch\n        \"\"\"\n        self.action_size = action_size\n        self.memory = deque(maxlen=buffer_size)  # internal memory (deque)\n        self.batch_size = batch_size\n        self.experience = namedtuple(\"Experience\", field_names=[\"state\", \"action\", \"reward\", \"next_state\", \"done\"])\n        self.seed = random.seed(seed)\n#        print('Replay seed:',seed)\n    \n    def add(self, state, action, reward, next_state, done):\n        \"\"\"Add a new experience to memory.\"\"\"\n        e = self.experience(state, action, reward, next_state, done)\n#        print('new_experience :state',state)\n#        print('new_experience :reward',reward)\n\n        self.memory.append(e)\n    \n    def sample(self):\n        \"\"\"Randomly sample a batch of experiences from memory.\"\"\"\n        experiences     =   random.sample(self.memory, k=self.batch_size)\n   \n        states = torch.from_numpy(np.vstack([e.state for e in experiences if e is not None])).float().to(device)\n        actions = torch.from_numpy(np.vstack([e.action for e in experiences if e is not None])).float().to(device)\n        rewards = torch.from_numpy(np.vstack([e.reward for e in experiences if e is not None])).float().to(device)\n        next_states = torch.from_numpy(np.vstack([e.next_state for e in experiences if e is not None])).float().to(device)\n        dones = torch.from_numpy(np.vstack([e.done for e in experiences if e is not None]).astype(np.uint8)).float().to(device)\n\n        return (states, actions, rewards, next_states, dones)\n\n    \n    def __len__(self):\n        \"\"\"Return the current size of internal memory.\"\"\"\n        return len(self.memory)\n    \n","repo_name":"eayvali/DeepRL","sub_path":"Multi-agent DDPG/multi_agent_ddpg.py","file_name":"multi_agent_ddpg.py","file_ext":"py","file_size_in_byte":11529,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"81"}
+{"seq_id":"15904142982","text":"\"\"\"urls.py implements api urls.\"\"\"\n\nfrom nautobot.core.api import OrderedDefaultRouter\nfrom . import views\n\n\nrouter = OrderedDefaultRouter()\nrouter.APIRootView = views.VCSRootView\n\n# Sites\nrouter.register(\"branches\", views.BranchViewSet)\nrouter.register(\"commits\", views.CommitViewSet)\nrouter.register(\"pull_requests\", views.PullRequestViewSet)\nrouter.register(\"pull_requests_reviews\", views.PullRequestReviewViewSet)\n\napp_name = \"nautobot_version_control-api\"\nurlpatterns = router.urls\n","repo_name":"nautobot/nautobot-plugin-version-control","sub_path":"nautobot_version_control/api/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":487,"program_lang":"python","lang":"en","doc_type":"code","stars":28,"dataset":"github-code","pt":"81"}
+{"seq_id":"6789043095","text":"# -*- coding: cp1252 -*-\r\nfrom typing import List\r\nimport os\r\nimport glob\r\nimport re\r\n\r\n#Leitura do arquivo de Entrada\r\nhome = os.path.expanduser('~')\r\ncaminhoEntrada = os.path.join(home, 'data\\in')\r\nos.chdir(caminhoEntrada)\r\nfor procura in glob.glob(\"*.dat\"):\r\n    arquivoEntrada = procura\r\nlerArquivo = open(arquivoEntrada, 'r')\r\n\r\n#Listas utilizadas\r\nlistaVendedor = list()\r\nlistaCliente = list()\r\nlistaVendedorDaVenda = list()\r\nlistaNomeVendedorPorVenda = list()\r\nlistaItemVenda = list()\r\nlistaIdVendedoresDaVenda = list()\r\n\r\n#Lendo o arquivo\r\nfor linha in lerArquivo:\r\n\r\n    #Limitador de linhas\r\n    colunasLinhas = linha.split('c')\r\n\r\n    #Montagem de lista de Vendedores\r\n    if float(colunasLinhas[0]) == 1:\r\n        listaVendedor.append(float(colunasLinhas[0]))\r\n\r\n    #Montagem de Lista de Cliente\r\n    elif float(colunasLinhas[0]) == 2:\r\n        listaCliente.append(float(colunasLinhas[0]))\r\n\r\n    #Montagem de Lista de Vendas\r\n    elif float(colunasLinhas[0]) == 3:\r\n\r\n        listaIdVendedoresDaVenda.append(int(colunasLinhas[1]))\r\n        listaItemVenda.append(str(colunasLinhas[2]))\r\n\r\n        #Nome dos vendedores e quantidade de venda listaVendedorDaVenda\r\n        listaVendedorDaVenda.append(str(colunasLinhas[3]))\r\n\r\n        listaNomeVendedorPorVenda.append({\r\n\r\n            x:listaVendedorDaVenda.count(x)\r\n\r\n            for x in set(listaVendedorDaVenda)\r\n\r\n        })\r\n\r\n#Vendedor que menos vendeu\r\nindice = len(listaNomeVendedorPorVenda)-1\r\nultimo: dict = listaNomeVendedorPorVenda[indice]\r\n\r\n\r\n#Impressões a serem gravadas no arquivo de saída\r\nimprimirTotalDeVendedores = len(listaVendedor)\r\nimprimirTotalDeClientes = len(listaCliente)\r\nimprimirTotalDeVendas = len(listaVendedorDaVenda)\r\nimprimirVendasPorVendedor = ultimo\r\nimprimirVendedorMenosVendeu = min(ultimo)\r\nimprimirVendas = listaItemVenda\r\nimprimirIdVendas = listaIdVendedoresDaVenda\r\n\r\n\r\n#Saída via arquivo\r\nhome = os.path.expanduser('~')\r\ncaminhoSaida = os.path.join(home, 'data\\out')\r\nos.chdir(caminhoSaida)\r\n\r\n#Gravando no arquivo de saída\r\narquivo = open('home-data-out.dat', 'w')\r\narquivo.write('Abaixo os resultados gerados pela arquivo de entrada: ')\r\narquivo.write('\\n\\nTotal de Vendedores: {}'.format(str(imprimirTotalDeVendedores)))\r\narquivo.write('\\nTotal de Cliente: {}'.format(str(imprimirTotalDeClientes)))\r\narquivo.write('\\nTotal de Vendas: {}'.format(str(imprimirTotalDeVendas)))\r\narquivo.write('\\nVendas Por Vendedor: {}'.format(str(imprimirVendasPorVendedor)))\r\narquivo.write('\\nVendedor que Menos Vendeu: {}'.format(str(imprimirVendedorMenosVendeu)))\r\narquivo.write('\\nListas de Vendas: {}'.format(str(imprimirVendas)))\r\narquivo.write('\\n\\nListas de IDs Vendas: {}'.format(str(listaIdVendedoresDaVenda)))","repo_name":"jonatasghizi/lerArquivo","sub_path":"lerArquivo.py","file_name":"lerArquivo.py","file_ext":"py","file_size_in_byte":2709,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"16837204362","text":"print('Importing model')\nimport ora41_PG_model\nprint('Model imported')\n\nfrom sqlalchemy import func, Column, LargeBinary\nfrom aconn import AConnector\n\nimport json\n\ndef get_count(q):\n    count_q = q.statement.with_only_columns([func.count()]).order_by(None)\n    count = q.session.execute(count_q).scalar()\n    return count\n\n\ndef analyze_tables_sizes(src_connector: AConnector, source_tables_classes: list):\n    \"\"\"\n\n    :param conn:\n    :param source_tables_classes:\n    :return: dict {table_name: size}\n    \"\"\"\n    result = {}\n    for source_class in source_tables_classes:\n        tablename = source_class.__tablename__\n        src_connector.status('Calculating table %s' % tablename)\n        result[tablename] = get_count(src_connector.con.query(source_class))\n    return result\n\n\ndef split_to_tasks(connector: AConnector, tab_class, total_size, chunk_size):\n    has_blobs = False\n    # table_name = tab_class.__tablename__\n    pk_col = list(tab_class.__table__.primary_key)[0]  # type: Column\n    for col in tab_class.__table__.columns:  # type: Column\n        if isinstance(col.type, LargeBinary):\n            has_blobs = True\n    if has_blobs:\n        return None\n\n    chunk_start_ids = []\n    chunks_count = total_size // chunk_size + 1\n    for j in range(chunks_count):\n        offset = j * chunk_size\n        res = connector.con.query(pk_col).order_by(pk_col).offset(offset).limit(1).all()\n        if len(res) > 0:\n            chunk_start_ids.append(res[0][0])\n\n    return chunk_start_ids\n\n\ndef make_transfer_tasks(connector: AConnector, model, output_filename, max_size, chunk_size):\n    print('Making tasks')\n    task_pattern = '{{\"task\":\"{task}\", \"table\": \"{table}\", \"pk\": \"{pk}\", \"size\": \"{size}\", ' \\\n                   '\"full_load\": \"{full_load}\", \"start_id\": \"{start_id}\", \"limit\": \"{limit}\" }}'\n\n    tasks = []\n    def _add_task(_table_class, _size, _full_load, _start_id, _limit):\n        task_no = len(tasks) + 1\n        _table = _table_class.__tablename__\n        pk = list(_table_class.__table__.primary_key)[0]\n        task = task_pattern.format(task=task_no, table=_table, pk=pk, size=_size,\n                                   full_load=_full_load, start_id=_start_id, limit=_limit)\n        tasks.append(task)\n\n    for class_name, tab_class in model.SCHEMA_TABLES.items():\n        tablename = tab_class.__tablename__\n        size = get_count(connector.con.query(tab_class))\n        if size == 0:\n            print('Table %s skip - empty' % tablename)\n            continue\n        full_load = size <= max_size\n        offset = None\n        limit = None\n        if not full_load:\n            print('Table %s size %s splitting to chunks' % (tablename, size))\n            chunks = split_to_tasks(connector, tab_class, size, chunk_size)\n            print('      chunks count: %s' % len(chunks))\n            for start_id in chunks[:-1]:\n                _add_task(tab_class, size, False, start_id, chunk_size)\n            _add_task(tab_class, size, False, chunks[-1], None)  # rest records\n        else:\n            print('Table %s size %s full load' % (tablename, size))\n            _add_task(tab_class, size, True, None, None)\n\n\n    file_text = '[\\n' + ',\\n'.join([task for task in tasks]) + '\\n]'\n    with open(output_filename, 'w') as f:\n        f.write(file_text)\n\n\nif __name__ == '__main__':\n    src_params = {\n        'name': 'ora41',\n        'conn': 'oracle://maximo:maximo@192.168.10.41:1521/max75',\n        'dbtype': 'ORA'\n    }\n\n    src = AConnector(src_params['name'], src_params['conn'], encoding='utf8', echo=False)\n    make_transfer_tasks(src, ora41_PG_model, 'ora41_task.json', 300000, 100000)\n    # sizes = analyze_tables_sizes(src, SCHEMA_TABLES.values())\n    #split_to_tasks(src, T_WORKORDER, 734509, 734508)\n\n\n\n\n","repo_name":"Seyojik/axi-code-renamer","sub_path":"db_schema_creator/utils/tables_analyzer.py","file_name":"tables_analyzer.py","file_ext":"py","file_size_in_byte":3747,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"28434929624","text":"\"\"\"\nTest for issue 85:\nhttps://github.com/pandas-profiling/pandas-profiling/issues/85\n\"\"\"\nimport numpy as np\nimport pandas as pd\n\nfrom pandas_profiling import ProfileReport\nfrom pandas_profiling.model.base import Variable\n\n\ndef test_issue85():\n    data = {\n        \"booleans_type\": [False, True, True],\n        \"booleans_type_nan\": [False, True, np.nan],\n        \"integers\": [1, 0, 0],\n        \"integers_nan\": [1, 0, np.nan],\n        \"str_yes_no\": [\"Y\", \"N\", \"Y\"],\n        \"str_yes_no_mixed\": [\"Y\", \"n\", \"y\"],\n        \"str_yes_no_nana\": [\"Y\", \"N\", np.nan],\n        \"str_true_false\": [\"True\", \"False\", \"False\"],\n        \"str_true_false_nan\": [\"True\", \"False\", np.nan],\n    }\n\n    df = pd.DataFrame(data)\n\n    report = ProfileReport(\n        df,\n        pool_size=1,\n        title=\"Dataset with <em>Boolean</em> Variables\",\n        samples={\"head\": 20},\n    )\n    for col, variable_stats in report.get_description()[\"variables\"].items():\n        assert (\n            variable_stats[\"type\"] == Variable.TYPE_BOOL\n        ), \"Variable should be boolean\"\n","repo_name":"ElijahSeeley/Springboard","sub_path":"pandas-profiling-master/pandas-profiling-master/tests/issues/test_issue85.py","file_name":"test_issue85.py","file_ext":"py","file_size_in_byte":1050,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"81"}
+{"seq_id":"7061483517","text":"import warnings\r\nimport wandb\r\nimport numpy as np\r\nimport FinanceDataReader as fdr\r\nimport matplotlib\r\nfrom spillover import SpillOver\r\n\r\nif __name__ == '__main__':\r\n    warnings.simplefilter('ignore')\r\n    matplotlib.use('Agg')\r\n\r\n    start = '2016-01-01'\r\n    sp_total = fdr.DataReader('US500', start=start)\r\n    bt_total = fdr.DataReader('BTC/USD', start=start)\r\n    final_date = sp_total.index[-1]\r\n    tdelta = 365\r\n    fevd_horizon = 10\r\n    fevd_target = [3, 6, 9]\r\n\r\n    wandb.init(\r\n        entity='db22',\r\n        project='SpillOver',\r\n        name=f'vol {tdelta}',\r\n        config={\r\n            'start': start,\r\n            'period': tdelta,\r\n            'fevd_horizon': fevd_horizon\r\n        })\r\n\r\n    modes = ['vol', 'var05', 'var01']\r\n    names = ['S&P500', 'BTC_USD']\r\n    mu_params = {\r\n        'p': [0, 1, 2],\r\n        'd': [0, 1],\r\n        'q': [0, 1, 2],\r\n        'P': [0],\r\n        'D': [0],\r\n        'Q': [0],\r\n        'S': [0]\r\n    }\r\n    resid_params = {\r\n        'p': [1, 2],\r\n        'o': [0, 1],\r\n        'q': [1, 2],\r\n        'dist': ['normal', 'studentsT', 'T', 'skewT']\r\n    }\r\n    model = SpillOver(sp_total, bt_total, start, tdelta)\r\n    step = 0\r\n    while True:\r\n        eda_result = model.modeling(names, mu_params, resid_params)\r\n        tmp_ls = []\r\n        for mode in modes:\r\n            try:\r\n                # AIC : low model-size penalty\r\n                # BIC : increases the penalty as the sample size increases\r\n                net_df, pvalue1, pvalue2 = model.run(mode, names, VAR_maxlags=None, VAR_ic='bic',\r\n                                                     fevd_target=fevd_target,\r\n                                                     fevd_horizon=fevd_horizon)\r\n                # For wandb\r\n                log_dict = {\r\n                    f'{mode} GC BTC/USD -> S&P500': pvalue1,\r\n                    f'{mode} GC S&P500 -> BTC/USD': pvalue2,\r\n                    f'{mode} VAR lags': model.VAR_lags[-1][-1]\r\n                }\r\n                log_dict.update({f'{mode} S&P500 NET {fevd_target[i]}th horizon': net_df.iloc[i, 0] for i in\r\n                                 range(len(fevd_target))})\r\n                log_dict.update({f'{mode} BTC/USD NET {fevd_target[i]}th horizon': net_df.iloc[i, 1] for i in\r\n                                 range(len(fevd_target))})\r\n                wandb.log(log_dict, step=step)\r\n\r\n                tmp_ls.append(net_df.values)\r\n            except Exception as e:\r\n                print(\"*********Failures!*********\")\r\n                print(model.period, mode)\r\n                print(e)\r\n                print(\"***************************\")\r\n\r\n        model.net_spillover_ls.append(tmp_ls)\r\n        print('=' * 20)\r\n        print(model.period, 'Done!')\r\n        print('=' * 20)\r\n        if model.end == final_date:\r\n            break\r\n        model.update_date()\r\n        step += 1\r\n\r\n    net_spillover_ls = np.array(model.net_spillover_ls)\r\n    np.save('result/net_spillover.npy', net_spillover_ls)\r\n\r\n","repo_name":"hobinkwak/Volatility-Spillover","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2993,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"81"}
+{"seq_id":"43981362214","text":"import libtmux\nimport os\n\nsession_name=\"NFVi\"\nserver=\"\"\nsession=\"\"\nwindow=\"NFViMarks\"\nsutPanel= None\ntesterPanel= None\ncontrollerPanel = None\ndef attachDashboard():\n    global  sutPanel\n    global  testerPanel \n    global  controllerPanel\n    server = libtmux.Server()\n    session = server.find_where({ \"session_name\": \"NFVi\" })\n    #attach to window\n    window_base_index = int(session.attached_window.get('window_index'))\n    window = session.select_window(window_base_index)\n    testerPanel = window.select_pane(0)\n    sutPanel = window.select_pane(1)\n    controllerPanel = window.select_pane(2)\ndef runCmdOnSutPannel(cmd):\n    global  sutPanel\n    return sutPanel.send_keys(cmd)\n\ndef runCmdOnTesterPannel(cmd):\n    global  testerPanel \n    return testerPanel.send_keys(cmd)\ndef runCmdOnControllerPannel(cmd):\n    global  controllerPanel \n    return controllerPanel.send_keys(cmd)\n","repo_name":"toanddt/CDATS","sub_path":"dats/display.py","file_name":"display.py","file_ext":"py","file_size_in_byte":884,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"3140622825","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# @Time :2022/3/14 17:18\n# @Author :qh\n# @File : do_excel.py\n# @Software : PyCharm\n\n\nfrom openpyxl import load_workbook\n\n\nclass DeExcel:\n    def __init__(self, file_name, sheet_name):\n        self.file_name = file_name\n        self.sheet_name = sheet_name\n\n    def get_data(self):\n        wb = load_workbook(self.file_name, read_only=True)\n        sheet = wb[self.sheet_name]\n        test_data = []\n        for i in range(1, sheet.max_row):  # 一直变化的是行，所以要获取最大行\n            test_datas = {\"任务编号\": sheet.cell(i + 1, 1).value}\n            test_data.append(test_datas)\n        return test_data\n\n\ntest = DeExcel(\"../../data/data1219/布控任务.xlsx\", \"布控任务\").get_data()\n\n\ndef test_key():\n    res = []\n    for i in test:\n        res.append(i[\"任务编号\"])\n    return res\n\ntest_key()\n\n\nif __name__ == '__main__':\n    pass\n    # 获取数据\n    print(test_key())\n    print(len(test_key()))\n\n\n\n\n\n\n","repo_name":"awangqinghua/tamps","sub_path":"test/smai_非机动车事件统计/do_excel.py","file_name":"do_excel.py","file_ext":"py","file_size_in_byte":987,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"9266059484","text":"\n\nimport time\nfrom tqdm import tqdm\n\n\n\nclass SampleSubmodul:\n    \n    # initialize the class with the root directory of the dataset \n    def __init__(self,logging):\n       \n        self.log= logging\n        self.log_inf=logging.InfoLogger(logging,__name__)\n        self.log_error=logging.ErrorLogger(logging,__name__)\n        self.log_warnlog=logging.WarningLogger(logging,__name__)\n        self.log_debug=logging.DebugLogger(logging,__name__)\n    \n        \n    def sampleFunc(self):\n        \n        self.log_inf.info(\"ich bin ein sample Log von sampleSubmodul\")\n        return ( \"Hallo Welt\")","repo_name":"Andybabic/datalib","sub_path":"bandyDatalib/_0003_Code/_0001_Modules/_000_Template/master/submodul/sampleSubmodul.py","file_name":"sampleSubmodul.py","file_ext":"py","file_size_in_byte":594,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"13514971146","text":"import random\n\nsuits = [\" of Hearts\", \" of Diamonds\", \" of Clubs\", \" of Spades\"]\ncardValues = [\"A\",\"2\",\"3\",\"4\",\"5\",\"6\",\"7\",\"8\",\"9\",\"10\",\"J\",\"Q\",\"K\"]\ntenPointCards = [\"10\", \"J\", \"Q\", \"K\"]\n\n\ndef makeDeck():\n    deck = []\n    for suit in suits:\n        for card in cardValues:\n            deck.append(card+suit)\n    return(deck)\n\ndef shuffleDeck():\n    deck = makeDeck()\n    random.shuffle(deck)\n    return(deck)\n\ndef dealCards(deck):\n    playerHand = []\n    dealerHand = []\n\n    playerHand.append(deck.pop(0))\n    playerHand.append(deck.pop(0))\n\n    dealerHand.append(deck.pop(0))\n    dealerHand.append(deck.pop(0))\n\n    return(playerHand, dealerHand, deck)\n\ndef hitMe(hand, deck):\n    hand.append(deck.pop(0))\n    return(hand, deck)\n\ndef checkPointsInHand(hand):\n    pointValue = 0\n    aceCount = 0\n    for card in hand:\n        cardValue = card.split(\" \")[0]\n        if cardValue in tenPointCards:\n            pointValue += 10\n        elif cardValue == \"A\":\n            aceCount += 1\n            pointValue += 11\n        else:\n            pointValue += int(cardValue)\n    for x in range(aceCount):\n        if pointValue > 21:\n            pointValue -= 10\n    return(pointValue)\n\n\ndef checkWinner(dealerPoints, playerPoints):\n    if dealerPoints >= playerPoints:\n        winner = \"dealer\"\n    else:\n        winner = \"player\"\n    return(winner)\n\ndef main():\n    print(\"Welcome to the Black Jack table.\")\n    deck = shuffleDeck()\n    playerHand, dealerHand, deck = dealCards(deck)\n    dealerPoints = checkPointsInHand(dealerHand)\n    while True:\n        playerPoints = checkPointsInHand(playerHand)\n        print(\"You are showing:\", playerHand,f\"({playerPoints})\")\n        print(\"Dealer is showing:\", dealerHand,f\"({dealerPoints})\")\n        if playerPoints == 21:\n            print(\"Blackjack baby! You win!\")\n            break\n        if dealerPoints == 21:\n            print(\"Blackjack for the dealer. Dealer wins.\")\n            break\n        if playerPoints > 21:\n            print(\"Bummer, you busted. Dealer wins\")\n            break\n        print(\"Do you want to hit or stand?\")\n        playerChoice = input().lower()\n        if playerChoice == 'hit':\n            playerHand, deck = hitMe(playerHand,deck)\n            print('hit it chewie')\n        elif playerChoice == 'stand':\n            print('red leader standing by')\n            while dealerPoints < 17:\n                print(\"Dealer hits!\")\n                dealerHand, deck = hitMe(dealerHand,deck)\n                dealerPoints = checkPointsInHand(dealerHand)\n                print(\"Dealer is showing:\", dealerHand,f\"({dealerPoints})\")\n            if dealerPoints > 21:\n                print(\"Dealer busted! You Win!\")\n                break\n            else:\n                winner = checkWinner(dealerPoints,playerPoints)\n                print(\"aaaaaand the winner is the\", winner)\n                break\n\nmain()","repo_name":"captain-afk/blackJack","sub_path":"blackjack.py","file_name":"blackjack.py","file_ext":"py","file_size_in_byte":2871,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"38286575897","text":"#########################################CONFIG \nimport os\nimport yaml\nimport torch \nimport logging\nlogging.basicConfig(level=logging.INFO)\nfrom neural_ar.model import *\nfrom utils import *\nfrom data.dataset import *\nimport  tqdm\nwith open(\"config.yaml\") as f:\n    config = yaml.load(f, Loader=yaml.FullLoader)\nwith open(\"config/train/train_conf.yaml\") as f:\n    train_conf = yaml.load(f, Loader=yaml.FullLoader)\ntemp = train_conf[\"temp\"]\nn_train = train_conf[\"per_train\"]\n#k = train_conf[\"k\"]\n\nbase_T = 300\nres_T = 10 \nNTEMPS = 11\nNCHAINS = 100 \nNBEADS = 100\nNMODES = 100 \nPROJECTPATH = config[\"paths\"][\"PROJECTPATH\"]\nDATAPATH = config[\"paths\"][\"DATAPATH\"]\nSAVEPATH = config[\"paths\"][\"SAVEPATH\"]\nDEVICE = config[\"paths\"][\"DEVICE\"]\n\nmodes_corr = {}\nopen_log = tqdm.tqdm(total=0, position=0, bar_format='{desc}')\nopen_log.set_description_str(\n    f'[Simulation Boxes Analysis: '+str(base_T)+\"K - \"+str(base_T + int(res_T*(NTEMPS-1)))+\"K]\"\n    )\nboxes_bar = tqdm.tqdm(total=NTEMPS, desc=\"Sim Boxes\", position=1)\nfor i in range(1,NTEMPS+1):\n    # data load \n    temp = base_T + int(res_T*(i-1))\n    filepath = os.path.join(DATAPATH, \"modesdata_T\"+str(temp)+\"_.pt\")\n    modes = torch.load(filepath, map_location=DEVICE).swapaxes(0,1)\n    \n    \n    ###################################################################################MSD\n    modes_bar = tqdm.tqdm(total=NMODES, desc=\"MSD, T=\"+str(temp), position=2)\n    msd_box = torch.zeros(NMODES, modes.size(1)).to(modes)\n    for nmod in range(NMODES):\n        msd_box[nmod,:] = msd_fft(modes[:,:n_train,nmod,:].swapaxes(1,2).contiguous().view(modes.size(0)*3,-1)).mean(dim=0)\n        modes_bar.update(1)\n        \n    box_msd_savepath = os.path.join(DATAPATH, \"md_baselines\")\n    torch.save(msd_box, os.path.join(box_msd_savepath, \"modes_nacfs_T\"+str(temp)+\"_ntrain\"+str(n_train)+\".pt\"))\n    del msd_box\n    \n    \n\n\n    \n    ","repo_name":"Gian-Michele-Cherchi/ml-gle","sub_path":"src/msd_.py","file_name":"msd_.py","file_ext":"py","file_size_in_byte":1871,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"28574844511","text":"#examples from hackerrank that need to be tested\ngame_one_length = 7\ngame_one_colors = \"RBY_YBR\"\ngame_two_length = 6\ngame_two_colors = \"X_Y__X\"\ngame_three_length = 2\ngame_three_colors =\"__\"\ngame_four_length = 6\ngame_four_colors =\"B_RRBR\"\n#function that should put all together\ndef Happy_Ladybugs(length, colors):\n    return check_happiness(length, colors)\n#checking ladybug happiness\ndef check_happiness(s,t):\n    if happybug(s,t):\n        return \"YES\"\n    else:\n        return \"NO\"\n#sorting the values and characters [length/color]  \ndef dict(t):\n    d={}\n    for item in t:\n        if item != '_':\n            if item not in d:\n                d[item]=1\n            else:\n                 d[item] += 1\n    return d\n#determining happiness\ndef happybug(s,t):\n    if s < 3:\n        if s == 0:\n            return False\n        elif s == 1 and t[0] == '_':\n            return True\n        elif s == 2 and t[0] == t[1]:\n            return True\n        else:\n            return False\n    else:\n        values = dict(t).values()\n        if min(values) < 2:\n            return False\n    return True\nprint(Happy_Ladybugs(game_one_length,game_one_colors))\nprint(Happy_Ladybugs(game_two_length,game_two_colors))\nprint(Happy_Ladybugs(game_three_length,game_three_colors))\nprint(Happy_Ladybugs(game_four_length,game_four_colors))","repo_name":"shanjidakamal/csci127-assignments","sub_path":"lab_04/lady.py","file_name":"lady.py","file_ext":"py","file_size_in_byte":1317,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"14837864550","text":"\"\"\"\nCombinations, Permutations e Product - Itertools\n\nCombinação - Ordem não importa\nPermutação - Ordem importa\nProduto - Ordem importa e repete valores únicos\n\"\"\"\n# Combinação - Ordem não importa\nfrom itertools import combinations\npessoas = ['Luiz', 'Andre', 'Eduardo', 'Leticia', 'Fabricio', 'Joshuel']\nfor grupo in combinations(pessoas, 2):\n    print(grupo)\n\n# Permutação - Ordem importa\nfrom itertools import permutations\npessoas = ['Luiz', 'Andre', 'Eduardo', 'Leticia', 'Fabricio', 'Joshuel']\nfor grupo in permutations(pessoas, 2):\n    print(grupo)\n\n# Produto - Ordem importa e repete valores únicos\nfrom itertools import product\npessoas = ['Luiz', 'Andre', 'Eduardo', 'Leticia', 'Fabricio', 'Joshuel']\nfor grupo in product(pessoas, repeat=2):\n    print(grupo)","repo_name":"JoshuelNobre/learning-py","sub_path":"python-intermediario/21-combinations-permutations-product.py","file_name":"21-combinations-permutations-product.py","file_ext":"py","file_size_in_byte":778,"program_lang":"python","lang":"pt","doc_type":"code","stars":2,"dataset":"github-code","pt":"81"}
+{"seq_id":"19966702029","text":"from pathlib import Path\n\nimport more_itertools as mitt\nimport pandas as pd\nimport plotly.express as px\n\nfrom .. import common_shapes, flat\n\n\nRESULTS_PATH = Path(\"./data/lines_3d\")\n\n\ndef _plot(esum: flat.Esum, path, name):\n    rows = []\n    all_hses = list(mitt.unique_everseen(hs for term in esum.eterms for hs in term.hses))\n\n    for hs_i, hs in enumerate(all_hses):\n        x1, y1 = hs.p1.position2d\n        x2, y2 = hs.p2.position2d\n\n        rows.extend(\n            [\n                {\n                    \"x\": x1,\n                    \"y\": y1,\n                    \"z\": hs_i / len(all_hses),\n                    \"hs_name\": hs.debug_name,\n                    \"point_name\": hs.p1.debug_name,\n                },\n                {\n                    \"x\": x2,\n                    \"y\": y2,\n                    \"z\": hs_i / len(all_hses),\n                    \"hs_name\": hs.debug_name,\n                    \"point_name\": hs.p2.debug_name,\n                },\n            ]\n        )\n\n    df = pd.DataFrame.from_records(rows)\n\n    fig = px.line_3d(\n        df,\n        x=\"x\",\n        y=\"y\",\n        z=\"z\",\n        color=\"hs_name\",\n        hover_name=\"point_name\",\n    )\n    fig.write_html(path)\n\n\ndef main():\n    RESULTS_PATH.mkdir(exist_ok=True, parents=True)\n\n    for shape_i, esum_start in enumerate([common_shapes.letter_c()]):\n        esum = flat.named_esum(esum_start)\n\n        _plot(\n            esum=esum,\n            path=RESULTS_PATH / f\"shape_{shape_i}.html\",\n            name=esum.name,\n        )\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"alexjuda/halfplane","sub_path":"src/halfplane/run/show_lines_3d.py","file_name":"show_lines_3d.py","file_ext":"py","file_size_in_byte":1542,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"32937465167","text":"import psycopg2 as psycopg2\nfrom PyQt5.QAxContainer import QAxWidget\nfrom PyQt5.QtCore import QEventLoop, QTimer\nfrom PyQt5.QtTest import QTest\nimport sys\nimport os\nimport datetime\n\n\nclass Kiwoom(QAxWidget):\n\n    def __init__(self):\n        super().__init__()\n        print(\">>> class[Kiwoom] start.\")\n\n        # 계좌 관련 변수들\n        self.account_number = None\n        self.password = \"u23i4523\"\n        self.cert_password = \"u23i4523R2\"\n        self.deposit = 0\n        self.withdraw = 0\n\n        # 스크린번호\n        self.screen_number_market = \"1000\"  # 장 상태 체크 스크린번호\n        self.screen_number_account = \"2000\"  # 계좌 관련 스크린번호\n        self.screen_number_real = \"3000\"  # 실시간 스크린번호\n        self.screen_number_anal = \"4000\"  # 분석용 스크린번호\n        self.screen_number_stock = \"5000\"  # 종목별 스크린번호\n        self.screen_number_order = \"6000\"  # 주문용 스크린번호\n\n        # 종목 리스트\n        self.all_stock_dict = {}\n        self.account_stock_dict = {}\n        self.outstanding_stock_dict = {}\n        self.portfolio_stock_dict = {}\n        self.balance_stock_dict = {}  # 잔고\n\n        # event loop를 실행하기 위한 변수들\n        self.event_loop_login = QEventLoop()\n        self.event_loop_tr_data = QEventLoop()\n        self.event_loop_real_data = QEventLoop()\n\n        # ocx\n        self.get_ocx_instance()\n\n        # event slot\n        self.event_slots()\n        self.event_real_slots()\n        self.event_condition_slots()\n\n        # signal(로그인, 예수금, 잔고(종목), 미체결)\n        self.signal_login()\n        self.signal_deposit_info()\n        self.signal_balance_info()\n        self.signal_condition_info()\n\n        QTimer.singleShot(1000, self.signal_outstanding_info)  # 5초 후 실시간미체결요청(opt10075)\n\n        # 파일에서 분석된 종목 가져오기\n        #         self.get_analyzed_stocks()\n\n        # QTest.qWait(10000)\n        QTimer.singleShot(1000, self.set_screen_number)\n\n        # QTest.qWait(5000)\n        QTimer.singleShot(1000, self.signal_market_status)  # 장 상태 체크\n\n        # 대상 종목(보유,미체결,포트폴리오) 실시간 등록\n        QTimer.singleShot(1000, self.all_stock_real_reg)\n\n    def get_ocx_instance(self):\n        self.setControl(\"KHOPENAPI.KHOpenAPICtrl.1\")\n\n    # 로그인, TR 이벤트 슬롯\n    def event_slots(self):\n        self.OnEventConnect.connect(self.slot_login)\n        self.OnReceiveTrData.connect(self.slot_tr_data)\n        self.OnReceiveMsg.connect(self.slot_msg)\n\n    # 실시간 이벤트 슬롯\n    def event_real_slots(self):\n        self.OnReceiveRealData.connect(self.slot_real_data)\n        self.OnReceiveChejanData.connect(self.slot_chejan_data)\n\n    # 조건검색 이벤트 슬롯\n    def event_condition_slots(self):\n        self.OnReceiveConditionVer.connect(self.slot_condition)\n        self.OnReceiveTrCondition.connect(self.slot_tr_condition)\n        self.OnReceiveRealCondition.connect(self.slot_real_condition)\n\n    def slot_condition(self, ret, msg):\n        print(\"\")\n        print(\">>>>>>>>>>>>>>>>>>>>>>>> 조건검색식[S] >>>>>>>>>>>>>>>>>>>>>>>>>>>\")\n        # print(\">> ret: %s\" % ret)\n        # print(\">> msg: %s\" % msg)\n\n        condition_name_list = self.dynamicCall(\"GetConditionNameList()\").split(\";\")[:-1]\n        for condition in condition_name_list:\n            index = int(condition.split(\"^\")[0])\n            condition_name = condition.split(\"^\")[1]\n            print(\">> [%s]:[%s]\" % (index, condition_name))\n\n            ok = self.dynamicCall(\"SendCondition(QString, QString, int, int)\", \"0156\", condition_name, index, 1)\n\n        print(\">>>>>>>>>>>>>>>>>>>>>>>> 조건검색식[E] >>>>>>>>>>>>>>>>>>>>>>>>>>>\")\n        print(\"\")\n\n    # 나의 조건식 받기\n    def slot_tr_condition(self, screen_number, code_list, condition_name, index, prev_next):\n        print(\"\")\n        # print(\">> screen_number: %s\" % screen_number)\n        # print(\">> code_list: %s\" % code_list)\n        print(\">> [%s]:%s\" % (index, condition_name))\n        # print(\">> index: %s\" % index)\n        # print(\">> prev_next: %s\" % prev_next)\n\n        code_list = code_list.split(\";\")[:-1]\n        for code in code_list:\n            code_name = self.dynamicCall(\"GetMasterCodeName(QString)\", code).strip()\n            print(\">> %s: %s\" % (code, code_name))\n\n    def slot_real_condition(self, stock_code, event_type, condition_name, index):\n        print(\"\")\n\n        stock_name = self.dynamicCall(\"GetMasterCodeName(QString)\", stock_code)\n        if event_type == \"I\":\n            print(\">> [%s]조건검색명: %s, 종목코드: %s, 종목명: %s, 종목편입: %s\" % (\n            index, condition_name, stock_code, stock_name, event_type))\n\n            ## 최초 발생하여 편입된 종목을 시초가(2호가 위)에 강제 매수처리 : 매수 종목 리스트 관리\n\n        elif event_type == \"D\":\n            print(\">> [%s]조건검색명: %s, 종목코드: %s, 종목명: %s, 종목이탈: %s\" % (\n            index, condition_name, stock_code, stock_name, event_type))\n\n    # HTS 조건식 로딩 (OnReceiveConditionVer() 호출)\n    def signal_condition_info(self):\n        self.dynamicCall(\"GetConditionLoad()\")\n\n    def signal_login(self):\n        self.dynamicCall(\"CommConnect()\")\n        self.event_loop_login.exec_()\n\n    def slot_login(self, error_code):\n        print(\"\")\n        print(\">>> function[slot_login] start >>>\")\n        print(\">> 로그인(0:성공,-10:실패,-100:사용자정보교환실패,-101:서버접속실패): %s\" % error_code)\n        accounts = self.dynamicCall(\"GetLoginInfo(QString)\", \"ACCNO\")\n        self.account_number = accounts.split(\";\")[0]\n        print(\">> 계좌번호(%s자리): %s\" % (len(self.account_number), self.account_number))\n        self.event_loop_login.exit()\n        print(\">>> function[slot_login] end <<<\")\n\n    def signal_market_status(self):\n        print(\"\")\n        print(\"\")\n\n        # [장시작시간][장운영구분] (0:장시작전, 2:장종료전(20분), 3:장시작, 4,8:장종료(30분), 9:장마감)\n        value = self.dynamicCall(\"SetRealReg(QString, QString, int, QString)\", self.screen_number_market, ' ', 215, \"0\")\n        print(\">> value: %s\" % value)\n        print(\"\")\n        self.event_loop_real_data.exec_()\n\n    # opw00001: 예수금상세현황요청\n    # INPUT: 계좌번호, 비밀번호, 비밀번호입력매체구분, 조회구분\n    # OUTPUT[S]: 예수금상세현황\n    def signal_deposit_info(self, prev_next=\"0\"):\n        self.dynamicCall(\"SetInputValue(QString, QString)\", \"계좌번호\", self.account_number)\n        self.dynamicCall(\"SetInputValue(QString, QString)\", \"비밀번호\", self.password)\n        self.dynamicCall(\"SetInputValue(QString, QString)\", \"비밀번호입력매체구분\", \"00\")\n        self.dynamicCall(\"SetInputValue(QString, QString)\", \"조회구분\", \"1\")\n        self.dynamicCall(\"CommRqData(QString, QString, int, QString)\", \"예수금상세현황요청\", \"opw00001\", prev_next,\n                         self.screen_number_account)\n        self.event_loop_tr_data.exec_()\n\n    # opw00018: 계좌평가잔고내역요청\n    # INPUT: 계좌번호, 비밀번호, 비밀번호입력매체구분, 조회구분\n    # OUTPUT[S]: 계좌평가결과\n    # OUTPUT[M]: 계좌평가잔고합산\n    def signal_balance_info(self, prev_next=\"0\"):\n        self.dynamicCall(\"SetInputValue(QString, QString)\", \"계좌번호\", self.account_number)\n        self.dynamicCall(\"SetInputValue(QString, QString)\", \"비밀번호\", self.password)\n        self.dynamicCall(\"SetInputValue(QString, QString)\", \"비밀번호입력매체구분\", \"00\")\n        self.dynamicCall(\"SetInputValue(QString, QString)\", \"조회구분\", \"1\")\n        self.dynamicCall(\"CommRqData(QString, QString, int, QString)\", \"계좌평가잔고내역요청\", \"opw00018\", prev_next,\n                         self.screen_number_account)\n        self.event_loop_tr_data.exec_()\n\n    # opt10075: 실시간미체결요청\n    # INPUT: 계좌번호, 전체종목구분(0:전체,1:종목), 매매구분(0:전체,1:매도,:매수), 종목코드, 체결구분(0:전체,1:미체결,2:체결)\n    # OUTPUT[S]: 계좌평가결과\n    # OUTPUT[M]: 계좌평가잔고합산\n    def signal_outstanding_info(self, prev_next=\"0\"):\n        print(\"\")\n        self.dynamicCall(\"SetInputValue(QString, QString)\", \"계좌번호\", self.account_number)\n        self.dynamicCall(\"SetInputValue(QString, QString)\", \"매매구분\", \"0\")\n        self.dynamicCall(\"SetInputValue(QString, QString)\", \"체결구분\", \"1\")\n        self.dynamicCall(\"CommRqData(QString, QString, int, QString)\", \"실시간미체결요청\", \"opt10075\", prev_next,\n                         self.screen_number_account)\n        self.event_loop_tr_data.exec_()\n\n    def get_analyzed_stocks(self):\n        print(\"\")\n        if os.path.exists(\"files/analysis.txt\"):\n            f = open(\"files/analysis.txt\", \"r\", encoding=\"utf8\")\n\n            lines = f.readlines()\n            for line in lines:\n                if line != \"\":\n                    ls = line.split(\"\\t\")\n                    stock_code = ls[0]\n                    stock_name = ls[1]\n\n                    self.portfolio_stock_dict.update({stock_code: {\"종목명\": stock_name, \"현재가\": 0}})\n                    print(\">> 종목코드: %s, 종목명: %s\" % (stock_code, stock_name))\n\n            # print(\">> self.portfolio_stock_dict: %s\" % self.portfolio_stock_dict)\n            print(\"\")\n            f.close()\n\n    def slot_tr_data(self, screen_number, rq_name, tr_code, record_name, prev_next):\n        print(\"\")\n        print(\">>> function[slot_tr_data] start >>>\")\n        print(\"> p1.screen_number: %s\" % screen_number)\n        print(\"> p2.rq_name: %s\" % rq_name)\n        print(\"> p3.tr_code: %s\" % tr_code)\n        print(\"> p4.record_name: %s\" % record_name)\n        print(\"> p5.prev_next: %s\" % prev_next)\n\n        # opw00001: 예수금상세현황요청\n        if tr_code == \"opw00001\":\n            deposit = self.dynamicCall(\"GetCommData(QString, QString, int, QString)\", tr_code, rq_name, 0,\n                                       \"예수금\").strip()\n            withdraw = self.dynamicCall(\"GetCommData(QString, QString, int, QString)\", tr_code, rq_name, 0,\n                                        \"출금가능금액\").strip()\n\n            self.deposit = int(deposit)\n            self.withdraw = int(withdraw)\n\n            print(\"\")\n            print(\">> 예수금: %s\" % format(self.deposit, \",\"))\n            print(\">> 출금가능금액: %s\" % format(self.withdraw, \",\"))\n\n        # opw00018: 계좌평가잔고내역요청\n        elif tr_code == \"opw00018\":\n            # single (총매입금액,총평가손익금액,총수익률(%),조회건수)\n\n            total_purchase_amount = self.dynamicCall(\"GetCommData(QString, QString, int, QString)\", tr_code, rq_name, 0,\n                                                     \"총매입금액\").strip()\n            total_profit_loss_amount = self.dynamicCall(\"GetCommData(QString, QString, int, QString)\", tr_code, rq_name,\n                                                        0, \"총평가손익금액\").strip()\n            total_earning_rate = self.dynamicCall(\"GetCommData(QString, QString, int, QString)\", tr_code, rq_name, 0,\n                                                  \"총수익률(%)\").strip()\n            hit_number = self.dynamicCall(\"GetCommData(QString, QString, int, QString)\", tr_code, rq_name, 0,\n                                          \"조회건수\").strip()\n\n            print(\"\")\n            print(\">> 총매입금액: %s\" % format(int(total_purchase_amount), \",\"))\n            print(\">> 총평가손익금액: %s\" % format(int(total_profit_loss_amount), \",\"))\n            print(\">> 총수익률(%%): %s\" % float(total_earning_rate))\n            print(\">> 조회건수: %s\" % int(hit_number))\n            print(\"\")\n\n            # multi\n            rows = self.dynamicCall(\"GetRepeatCnt(QString, QString)\", tr_code, rq_name)\n            print(\">> 계좌평가잔고내역요청.보유 종목 건수: %s\" % rows)\n\n            # 종목번호, 종목명, 보유수량, 매입가, 매입금액, 매매가능수량, 수익률(%), 현재가, 전일종가\n            for i in range(rows):\n                stock_code = self.dynamicCall(\"GetCommData(QString, QString, int, QString)\", tr_code, rq_name, i,\n                                              \"종목번호\").strip()[1:]\n                stock_name = self.dynamicCall(\"GetCommData(QString, QString, int, QString)\", tr_code, rq_name, i,\n                                              \"종목명\").strip()\n                retention_quantity = self.dynamicCall(\"GetCommData(QString, QString, int, QString)\", tr_code, rq_name,\n                                                      i, \"보유수량\").strip()\n                purchase_price = self.dynamicCall(\"GetCommData(QString, QString, int, QString)\", tr_code, rq_name, i,\n                                                  \"매입가\").strip()\n                purchase_amount = self.dynamicCall(\"GetCommData(QString, QString, int, QString)\", tr_code, rq_name, i,\n                                                   \"매입금액\").strip()\n                tradable_quantity = self.dynamicCall(\"GetCommData(QString, QString, int, QString)\", tr_code, rq_name, i,\n                                                     \"매매가능수량\").strip()\n                earning_rate = self.dynamicCall(\"GetCommData(QString, QString, int, QString)\", tr_code, rq_name, i,\n                                                \"수익률(%)\").strip()\n                close_price = self.dynamicCall(\"GetCommData(QString, QString, int, QString)\", tr_code, rq_name, i,\n                                               \"현재가\").strip()\n                last_price = self.dynamicCall(\"GetCommData(QString, QString, int, QString)\", tr_code, rq_name, i,\n                                              \"전일종가\").strip()\n                tax = self.dynamicCall(\"GetCommData(QString, QString, int, QString)\", tr_code, rq_name, i, \"세금\").strip()\n\n                if stock_code in self.account_stock_dict:\n                    pass\n                else:\n                    self.account_stock_dict[stock_code] = {}\n\n                self.account_stock_dict[stock_code].update({\"종목명\": stock_name})\n                self.account_stock_dict[stock_code].update({\"보유수량\": int(retention_quantity)})\n                self.account_stock_dict[stock_code].update({\"매입가\": int(purchase_price)})\n                self.account_stock_dict[stock_code].update({\"수익률(%)\": float(earning_rate)})\n                self.account_stock_dict[stock_code].update({\"현재가\": int(close_price)})\n                self.account_stock_dict[stock_code].update({\"매입금액\": int(purchase_amount)})\n                self.account_stock_dict[stock_code].update({\"매매가능수량\": int(tradable_quantity)})\n\n                print(\">> self.account_stock_dict: %s\" % self.account_stock_dict)\n\n        # opt10075: 실시간미체결요청\n        elif tr_code == \"opt10075\":\n            rows = self.dynamicCall(\"GetRepeatCnt(QString, QString)\", tr_code, rq_name)\n            print(\"\")\n            print(\">> 미체결 종목 건수: %s\" % rows)\n            # 종목코드, 종목명, 주문번호, 주문상태, 주문수량, 주문가격, 주문구분, 미체결수량, 체결량\n            for i in range(rows):\n                order_number = self.dynamicCall(\"GetCommData(QString, QString, int, QString)\", tr_code, rq_name, i,\n                                                \"주문번호\").strip()\n                stock_code = self.dynamicCall(\"GetCommData(QString, QString, int, QString)\", tr_code, rq_name, i,\n                                              \"종목코드\").strip()\n                stock_name = self.dynamicCall(\"GetCommData(QString, QString, int, QString)\", tr_code, rq_name, i,\n                                              \"종목명\").strip()\n                order_status = self.dynamicCall(\"GetCommData(QString, QString, int, QString)\", tr_code, rq_name, i,\n                                                \"주문상태\").strip()  # 접수,확인,체결\n                order_quantity = self.dynamicCall(\"GetCommData(QString, QString, int, QString)\", tr_code, rq_name, i,\n                                                  \"주문수량\").strip()\n                order_price = self.dynamicCall(\"GetCommData(QString, QString, int, QString)\", tr_code, rq_name, i,\n                                               \"주문가격\").strip()\n                order_type = self.dynamicCall(\"GetCommData(QString, QString, int, QString)\", tr_code, rq_name, i,\n                                              \"주문구분\").strip().lstrip(\"+\").lstrip(\"-\")\n                outstanding_quantity = self.dynamicCall(\"GetCommData(QString, QString, int, QString)\", tr_code, rq_name,\n                                                        i, \"미체결수량\").strip()\n                conclusion_quantity = self.dynamicCall(\"GetCommData(QString, QString, int, QString)\", tr_code, rq_name,\n                                                       i, \"체결량\").strip()\n\n                if order_number in self.outstanding_stock_dict:\n                    pass\n                else:\n                    self.outstanding_stock_dict[order_number] = {}\n\n                self.outstanding_stock_dict[order_number].update({\"종목코드\": stock_code})\n                self.outstanding_stock_dict[order_number].update({\"종목명\": stock_name})\n                self.outstanding_stock_dict[order_number].update({\"주문번호\": order_number})\n                self.outstanding_stock_dict[order_number].update({\"주문상태\": order_status})\n                self.outstanding_stock_dict[order_number].update({\"주문수량\": order_quantity})\n                self.outstanding_stock_dict[order_number].update({\"주문가격\": order_price})\n                self.outstanding_stock_dict[order_number].update({\"주문구분\": order_type})\n                self.outstanding_stock_dict[order_number].update({\"미체결수량\": outstanding_quantity})\n                self.outstanding_stock_dict[order_number].update({\"체결량\": conclusion_quantity})\n\n                print(\">> self.outstanding_stock_dict: %s\" % self.outstanding_stock_dict)\n\n        self.disconnect_screen_number(self.screen_number_account)\n        self.event_loop_tr_data.exit()\n        print(\">>> function[slot_tr_data] end <<<\")\n\n    def slot_real_data(self, stock_code, real_type, real_data):\n        #         print(\"\")\n        #         print(\">>> function[slot_real_data] start >>>\")\n        #         print(\">> stock_code: %s\" % stock_code)\n        #         print(\">> real_type: %s\" % real_type)\n        #         print(\">> real_data %s\" % real_data)\n\n        if real_type == \"장시작시간\":\n\n            # 215:장운영구분, 20:체결시간, 214:장시작예상잔여시간\n            market_op_type = self.dynamicCall(\"GetCommRealData(QString, int)\", stock_code, 215)\n            conclusion_time = self.dynamicCall(\"GetCommRealData(QString, int)\", stock_code, 20)\n            market_open_remain_time = self.dynamicCall(\"GetCommRealData(QString, int)\", stock_code, 214)\n\n            if market_op_type == \"0\":\n                print(\">> [%s]장 시작 전\" % market_op_type)\n            elif market_op_type == \"3\":\n                print(\">> [%s]장 시작\" % market_op_type)\n            elif market_op_type == \"2\":\n                print(\">> [%s]장 종료, 동시호가로 넘어감\" % market_op_type)\n            elif market_op_type == \"4\":\n                print(\">> [%s]15시 30분 장 종료\" % market_op_type)\n\n                # 실시간 연결 모두 끊기\n                for code in self.portfolio_stock_dict.keys():\n                    self.dynamicCall(\"SetRealRemove(QString, QString)\", self.portfolio_stock_dict[code][\"스크린번호\"], code)\n                    print(\">> 종목코드[%s] 실시간 연결 종료.\" % code)\n\n                # 다음 날을 위한 종목분석 시작\n                QTest.qWait(5000)\n                # self.file_delete() # 초기화(포트폴리오) 파일삭제 또는 디비 삭제\n                # self.calculate_fnc() # 종목분석(알고리즘 적용) 후 파일 또는 디비 저장\n\n                # 프로그램 종료\n                sys.exit()\n\n            print(\">> market_op_type: %s\" % market_op_type)\n            print(\">> conclusion_time: %s\" % str(datetime.timedelta(conclusion_time)))\n            print(\">> market_open_remain_time %s\" % str(datetime.timedelta(market_open_remain_time)))\n\n        elif real_type == \"주식체결\":\n\n            \"\"\"\n                [20] = 체결시간\n                [10] = 현재가\n                [11] = 전일대비\n                [12] = 등락율\n                [27] = (최우선)매도호가\n                [28] = (최우선)매수호가\n                [15] = 거래량\n                [13] = 누적거래량\n                [14] = 누적거래대금\n                [16] = 시가\n                [17] = 고가\n                [18] = 저가\n                [25] = 전일대비기호\n                [26] = 전일거래량대비(계약,주)\n                [29] = 거래대금증감\n                [30] = 전일거래량대비(비율)\n                [31] = 거래회전율\n                [32] = 거래비용\n                [228] = 체결강도\n                [311] = 시가총액(억)\n                [290] = 장구분\n                [691] = KO접근도\n                [567] = 상한가발생시간\n                [568] = 하한가발생시간            \n            \"\"\"\n\n            conclusion_time = self.dynamicCall(\"GetCommRealData(QString, in)\", stock_code, 20).strip()\n            close_price = self.dynamicCall(\"GetCommRealData(QString, in)\", stock_code, 10).strip()\n            net_change = self.dynamicCall(\"GetCommRealData(QString, in)\", stock_code, 11).strip()\n            fluctuation_rate = self.dynamicCall(\"GetCommRealData(QString, in)\", stock_code, 12).strip()\n            first_ask_price = self.dynamicCall(\"GetCommRealData(QString, in)\", stock_code, 27).strip()\n            first_bid_price = self.dynamicCall(\"GetCommRealData(QString, in)\", stock_code, 28).strip()\n            volume = self.dynamicCall(\"GetCommRealData(QString, in)\", stock_code, 15).strip()\n            cumulative_volume = self.dynamicCall(\"GetCommRealData(QString, in)\", stock_code, 13).strip()\n            high_price = self.dynamicCall(\"GetCommRealData(QString, in)\", stock_code, 17).strip()\n            open_price = self.dynamicCall(\"GetCommRealData(QString, in)\", stock_code, 16).strip()\n            low_price = self.dynamicCall(\"GetCommRealData(QString, in)\", stock_code, 18).strip()\n\n            close_price = abs(int(close_price))\n            net_change = abs(int(net_change))\n            fluctuation_rate = float(fluctuation_rate)\n            first_ask_price = abs(int(first_ask_price))\n            first_bid_price = abs(int(first_bid_price))\n            volume = abs(int(volume))\n            cumulative_volume = abs(int(cumulative_volume))\n            high_price = abs(int(high_price))\n            open_price = abs(int(open_price))\n            low_price = abs(int(low_price))\n\n            print(\">> 체결시간: %s\" % conclusion_time)\n            print(\">> 현재가: %s\" % format(close_price, \",\"))\n            print(\">> 전일대비: %s\" % format(net_change, \",\"))\n            print(\">> 등락율: %s\" % fluctuation_rate)\n            print(\">> (최우선)매도호가: %s\" % format(first_ask_price, \",\"))\n            print(\">> (최우선)매수호가: %s\" % format(first_bid_price, \",\"))\n            print(\">> 거래량: %s\" % format(volume, \",\"))\n            print(\">> 누적거래량: %s\" % format(cumulative_volume, \",\"))\n            print(\">> 고가: %s\" % format(high_price, \",\"))\n            print(\">> 시가: %s\" % format(open_price, \",\"))\n            print(\">> 저가: %s\" % format(low_price, \",\"))\n\n            # 스크리닝 종목 데이터를 포트폴리오 딕셔너리에 업데이트\n            if stock_code not in self.portfolio_stock_dict:\n                self.portfolio_stock_dict.update({stock_code: {}})\n\n            self.portfolio_stock_dict[stock_code].update({\"체결시간\": conclusion_time})\n            self.portfolio_stock_dict[stock_code].update({\"현재가\": close_price})\n            self.portfolio_stock_dict[stock_code].update({\"전일대비\": net_change})\n            self.portfolio_stock_dict[stock_code].update({\"등락율\": fluctuation_rate})\n            self.portfolio_stock_dict[stock_code].update({\"(최우선)매도호가\": first_ask_price})\n            self.portfolio_stock_dict[stock_code].update({\"(최우선)매수호가\": first_bid_price})\n            self.portfolio_stock_dict[stock_code].update({\"거래량\": volume})\n            self.portfolio_stock_dict[stock_code].update({\"누적거래량\": cumulative_volume})\n            self.portfolio_stock_dict[stock_code].update({\"고가\": high_price})\n            self.portfolio_stock_dict[stock_code].update({\"시가\": open_price})\n            self.portfolio_stock_dict[stock_code].update({\"저가\": low_price})\n\n            # (매도)계좌평가잔고내역의 보유 종목\n            # 실시간 반영되지 않기 때문에 일회성으로 사용하고 더 이상 사용하지 않는다.\n            # 종목이 딕셔너리에 있는지 확인하고 매매가능수량을 체크해서 매도주문한다.\n            # 실시간 매수가 없어야 하므로 self.balance_stock_dict에 미존재여야 한다.\n            if stock_code in self.account_stock_dict.keys() and stock_code not in self.balance_stock_dict.keys():\n                account = self.account_stock_dict[stock_code]\n                profit_loss_rate = (close_price - account[\"매입가\"]) / account[\"매입가\"] * 100\n\n                if account[\"매매가능수량\"] > 0 and profit_loss_rate > 2:\n                    order_success = self.dynamicCall(\n                        \"SendOrder(QString, QString, QString, int, QString, int, int, QString, QString)\",\n                        [\"신규매도\", self.portfolio_stock_dict[stock_code][\"주문용스크린번호\"],\n                         self.account_number, 2, stock_code, account[\"매매가능수량\"], 0, \"03\", \"\"\n                         ]\n                    )\n\n                    if order_success == 0:\n                        print(\">> [계좌평가잔고내역]매도주문 전달 성공[%s]\" % order_success)\n                        del self.account_stock_dict[stock_code]\n                        print(\">> self.account_stock_dict: %s\" % self.account_stock_dict)\n                    else:\n                        print(\">> [계좌평가잔고내역]매도주문 전달 실패[%s]\" % order_success)\n\n            # (매도) 실시간 매수 종목\n            # 수익률: (현재가-매입단가)/매입단가*100 (10000-9500)/9500*100\n            elif stock_code in self.balance_stock_dict.keys():\n                balance = self.balance_stock_dict[stock_code]\n\n                # 수익률 계산\n                profit_loss_rate = (close_price - balance[\"매입단가\"]) / balance[\"매입단가\"] * 100\n\n                # 매도조건: 주문가능수량이 있고 수익률이 +-5 범위라면 익절 또는 손절한다.\n                if balance[\"주문가능수량\"] > 0 and (profit_loss_rate > 2 or profit_loss_rate < -2):\n                    order_success = self.dynamicCall(\n                        \"SendOrder(QString, QString, QString, int, QString, int, int, QString, QString)\",\n                        [\"신규매도\", self.portfolio_stock_dict[stock_code][\"주문용스크린번호\"],\n                         self.account_number, 2, stock_code, balance[\"주문가능수량\"], 0, \"03\", \"\"\n                         ]\n                    )\n\n                    if order_success == 0:\n                        print(\">> [실시간매수종목]매도주문 전달 성공[%s]\" % order_success)\n                    else:\n                        print(\">> [실시간매수종목]매도주문 전달 실패[%s]\" % order_success)\n\n            # 종목 정보를 실시간으로 받는 도중 조건에 일치하면 매수 매도 주문을 넣는다.\n            # 등락율이 2.0 이상이면 매수하도록 구성. 실시간 계좌 데이터 딕셔너리 미존재.\n            # balance_stock_dict : 매수된 종목 관리, 매수된 종목은 매도해야한다. 매도주문.\n            elif fluctuation_rate > 8.0 and stock_code not in self.balance_stock_dict:\n                print(\">> 매수조건 통과: %s\" % stock_code)\n                print(\">> 예수금 체크: %s\" % self.deposit)\n\n                # 예수금의 10%만 사용하도록 한다. 비율은 추후 조정.\n                result = (self.deposit * 0.1) / first_ask_price\n                quantity = int(result)\n\n                print(\">> 매수 가능 주식수: %s\" % quantity)\n\n                if quantity < 10:\n                    pass\n\n                # 매수 요청 (사용자구분명, 화면번호, 계좌번호, 주문유형, 종목코드, 주문수량, 주문가격, 거래구분, 원주문번호)\n                order_success = self.dynamicCall(\n                    \"SendOrder(QString, QString, QString, int, QString, int, int, QString, QString)\",\n                    [\"신규매수\",\n                     self.portfolio_stock_dict[stock_code][\"주문용스크린번호\"],\n                     self.account_number,\n                     1, stock_code, quantity, first_ask_price, \"00\", \"\"]\n                )\n\n                if order_success == 0:\n                    print(\">> 매수주문 전달 [%s]성공: %s\" % (order_success, stock_code))\n                else:\n                    print(\">> 매수주문 전달 [%s]실패: %s\" % (order_success, stock_code))\n\n                # 미체결 수량 매수 취소 (주문번호만 뽑아 리스트로 만든다)\n                outstanding_list = list(self.outstanding_stock_dict)\n                print(\">> 미체결 주문 %s건 존재.\" % len(outstanding_list))\n                for order_number in outstanding_list:\n                    print(\">> order_number: %s\" % order_number)\n\n                    # 종목코드, 주문가격, 미체결수량, 주문구분\n                    a = self.outstanding_stock_dict[order_number][\"종목코드\"]\n                    b = int(self.outstanding_stock_dict[order_number][\"주문가격\"])\n                    c = int(self.outstanding_stock_dict[order_number][\"미체결수량\"])\n                    d = self.outstanding_stock_dict[order_number][\"주문구분\"].lstrip(\"+\").lstrip(\"-\")\n\n                    print(\"\")\n                    print(\">> 종목코드(a): [%s]\" % a)\n                    print(\">> 주문가격(b): [%s]\" % b)\n                    print(\">> 미체결수량(c): [%s]\" % c)\n                    print(\">> 주문구분(d): [%s]\" % d)\n\n                    if d == \"매수\" and c > 0 and first_ask_price > b:\n                        order_success = self.dynamicCall(\n                            \"SendOrder(QString, QString, QString, int, QString, int, int, QString, QString)\",\n                            [\"매수취소\", self.portfolio_stock_dict[stock_code][\"주문용스크린번호\"],\n                             self.account_number, 3, a, 0, 0, \"00\", order_number\n                             ]\n                        )\n\n                        if order_success == 0:\n                            print(\">> 매수취소 전달 [%s]성공: %s\" % (order_success, a))\n                        else:\n                            print(\">> 매수취소 전달 [%s]실패: %s\" % (order_success, a))\n\n                    elif c == 0:\n                        del self.outstanding_stock_dict[order_number]\n                        print(\">> 체결된(미체결수량:0) 주문번호[%s] 삭제\" % order_number)\n\n        self.disconnect_screen_number(self.screen_number_market)\n        self.event_loop_real_data.exit()\n\n    \"\"\"\n          [OnReceiveChejanData() 이벤트]\n\n          OnReceiveChejanData(\n          BSTR sGubun, // 체결구분: 접수와 체결시 '0'값, 국내주식 잔고전달은 '1'값, 파생잔고 전달은 '4'\n          LONG nItemCnt, (미사용)\n          BSTR sFIdList (미사용)\n          )\n\n          주문요청후 주문접수, 체결통보, 잔고통보를 수신할 때 마다 호출되며 GetChejanData()함수를 이용해서 상세한 정보를 얻을수 있습니다.    \n    \"\"\"\n\n    def slot_chejan_data(self, conclusion_type, item_cnt, fid_list):\n        #         print(\"\")\n        #         print(\">> 체결구분: %s\" % conclusion_type)\n        #         print(\">> 항목건수: %s\" % item_cnt)\n        #         print(\">> FID 목록: %s\" % fid_list)\n\n        conclusion_type = int(conclusion_type)\n\n        # 0:주문체결, 1:잔고\n        \"\"\"\n            Real Type : 주문체결\n            [9201] = 계좌번호\n            [9203] = 주문번호\n            [9205] = 관리자사번\n            [9001] = 종목코드,업종코드\n            [912] = 주문업무분류\n            [913] = 주문상태\n            [302] = 종목명\n            [900] = 주문수량\n            [901] = 주문가격\n            [902] = 미체결수량\n            [903] = 체결누계금액\n            [904] = 원주문번호\n            [905] = 주문구분\n            [906] = 매매구분\n            [907] = 매도수구분\n            [908] = 주문/체결시간\n            [909] = 체결번호\n            [910] = 체결가\n            [911] = 체결량\n            [10] = 현재가\n            [27] = (최우선)매도호가\n            [28] = (최우선)매수호가\n            [914] = 단위체결가\n            [915] = 단위체결량\n            [938] = 당일매매수수료\n            [939] = 당일매매세금\n            [919] = 거부사유\n            [920] = 화면번호\n            [921] = 터미널번호\n            [922] = 신용구분(실시간 체결용)\n            [923] = 대출일(실시간 체결용)        \n        \"\"\"\n        if conclusion_type == 0:  # 주문체결\n            account_number = self.dynamicCall(\"GetChejanData(int)\", 9201)\n            stock_code = self.dynamicCall(\"GetChejanData(int)\", 9001)[1:]\n            stock_name = self.dynamicCall(\"GetChejanData(int)\", 302).strip()\n            origin_order_number = self.dynamicCall(\"GetChejanData(int)\", 904)\n            order_number = self.dynamicCall(\"GetChejanData(int)\", 9203)\n            order_status = self.dynamicCall(\"GetChejanData(int)\", 913)\n            order_quantity = int(self.dynamicCall(\"GetChejanData(int)\", 900))\n            order_price = int(self.dynamicCall(\"GetChejanData(int)\", 901))\n            outstanding_quantity = int(self.dynamicCall(\"GetChejanData(int)\", 902))\n            order_type = self.dynamicCall(\"GetChejanData(int)\", 905).strip().lstrip(\"+\").lstrip(\"-\")\n            order_conclusion_time = self.dynamicCall(\"GetChejanData(int)\", 908)\n            conclusion_price = self.dynamicCall(\"GetChejanData(int)\", 910).strip()\n            conclusion_quantity = self.dynamicCall(\"GetChejanData(int)\", 911).strip()\n            close_price = int(self.dynamicCall(\"GetChejanData(int)\", 10))\n            first_ask_price = int(self.dynamicCall(\"GetChejanData(int)\", 27))\n            first_bid_price = int(self.dynamicCall(\"GetChejanData(int)\", 28))\n\n            if conclusion_price == '': conclusion_price = \"0\"\n            if conclusion_quantity == '': conclusion_quantity = \"0\"\n\n            conclusion_price = int(conclusion_price)\n            conclusion_quantity = int(conclusion_quantity)\n\n            # 신규 주문이면 미체결 딕셔너리에 주문번호 할당\n            if order_number not in self.outstanding_stock_dict.keys():\n                self.outstanding_stock_dict.update({order_number: {}})\n\n            self.outstanding_stock_dict[order_number].update({\"종목코드\": stock_code})\n            self.outstanding_stock_dict[order_number].update({\"주문번호\": order_number})\n            self.outstanding_stock_dict[order_number].update({\"종목명\": stock_name})\n            self.outstanding_stock_dict[order_number].update({\"주문상태\": order_status})\n            self.outstanding_stock_dict[order_number].update({\"주문수량\": order_quantity})\n            self.outstanding_stock_dict[order_number].update({\"주문가격\": order_price})\n            self.outstanding_stock_dict[order_number].update({\"미체결수량\": outstanding_quantity})\n            self.outstanding_stock_dict[order_number].update({\"원주문번호\": origin_order_number})\n            self.outstanding_stock_dict[order_number].update({\"주문구분\": order_type})\n            self.outstanding_stock_dict[order_number].update({\"주문/체결시간\": order_conclusion_time})\n            self.outstanding_stock_dict[order_number].update({\"체결가\": conclusion_price})\n            self.outstanding_stock_dict[order_number].update({\"체결량\": conclusion_quantity})\n            self.outstanding_stock_dict[order_number].update({\"현재가\": close_price})\n            self.outstanding_stock_dict[order_number].update({\"(최우선)매도호가\": first_ask_price})\n            self.outstanding_stock_dict[order_number].update({\"(최우선)매수호가\": first_bid_price})\n            print(\">> self.outstanding_stock_dict: %s\" % self.outstanding_stock_dict)\n\n        elif conclusion_type == 1:  # 잔고\n            account_number = self.dynamicCall(\"GetChejanData(int)\", 9201)\n            stock_code = self.dynamicCall(\"GetChejanData(int)\", 9001)[1:]\n            stock_name = self.dynamicCall(\"GetChejanData(int)\", 302).strip()\n            close_price = abs(int(self.dynamicCall(\"GetChejanData(int)\", 10)))\n            retention_quantity = int(self.dynamicCall(\"GetChejanData(int)\", 930))\n            orderable_quantity = int(self.dynamicCall(\"GetChejanData(int)\", 933))\n            purchase_price = int(self.dynamicCall(\"GetChejanData(int)\", 931))\n            total_purchase_price = int(self.dynamicCall(\"GetChejanData(int)\", 932))\n            sell_buy_type = self.dynamicCall(\"GetChejanData(int)\", 946).strip()\n            first_ask_price = int(self.dynamicCall(\"GetChejanData(int)\", 27))\n            first_bid_price = int(self.dynamicCall(\"GetChejanData(int)\", 28))\n\n            if stock_code not in self.balance_stock_dict.keys():\n                self.balance_stock_dict.update({stock_code: {}})\n\n            self.balance_stock_dict[stock_code].update({\"현재가\": close_price})\n            self.balance_stock_dict[stock_code].update({\"종목코드\": stock_code})\n            self.balance_stock_dict[stock_code].update({\"종목명\": stock_name})\n            self.balance_stock_dict[stock_code].update({\"보유수량\": retention_quantity})\n            self.balance_stock_dict[stock_code].update({\"주문가능수량\": orderable_quantity})\n            self.balance_stock_dict[stock_code].update({\"매입단가\": purchase_price})\n            self.balance_stock_dict[stock_code].update({\"총매입가\": total_purchase_price})\n            self.balance_stock_dict[stock_code].update({\"매도/매수구분\": sell_buy_type})\n            self.balance_stock_dict[stock_code].update({\"(최우선)매도호가\": first_ask_price})\n            self.balance_stock_dict[stock_code].update({\"(최우선)매수호가\": first_bid_price})\n            print(\">> self.balance_stock_dict: %s\" % self.balance_stock_dict)\n\n            if retention_quantity == 0:\n                del self.balance_stock_dict[stock_code]\n                print(\">> after del > self.balance_stock_dict: %s\" % self.balance_stock_dict)\n\n    def merge_stock_dict(self):\n        print(\"\")\n        self.all_stock_dict.update({\"계좌평가잔고내역\": self.account_stock_dict})\n        self.all_stock_dict.update({\"미체결종목\": self.outstanding_stock_dict})\n        self.all_stock_dict.update({\"포트폴리오종목\": self.portfolio_stock_dict})\n        print(\">> all_stock_dict{}: %s\" % self.all_stock_dict)\n        print(\"\")\n\n    def set_screen_number(self):\n\n        self.merge_stock_dict()\n        screen_overwrite = []\n\n        # 계좌평가잔고내역 종목\n        for stock_code in self.account_stock_dict.keys():\n            if stock_code not in screen_overwrite:\n                screen_overwrite.append(stock_code)\n\n        # 미체결종목\n        for order_number in self.outstanding_stock_dict.keys():\n            stock_code = self.outstanding_stock_dict[order_number][\"종목코드\"]\n            if stock_code not in screen_overwrite:\n                screen_overwrite.append(stock_code)\n\n        # 포트폴리오 종목\n        for stock_code in self.portfolio_stock_dict.keys():\n            if stock_code not in screen_overwrite:\n                screen_overwrite.append(stock_code)\n\n        #         print(\">> 스크린번호 리스트에 종목코드 %s건 통합 완료\" % len(screen_overwrite))\n        #         print(\">> 스크린번호 할당 시작\")\n\n        cnt = 0\n        for stock_code in screen_overwrite:\n            screen_number_stock = int(self.screen_number_stock)\n            screen_number_order = int(self.screen_number_order)\n\n            #             print(\">> 주식용 스크린번호: %s\" % screen_number_stock)\n            #             print(\">> 주문용 스크린번호: %s\" % screen_number_order)\n\n            if (cnt % 50) == 0:\n                screen_number_stock += 1\n                screen_number_order += 1\n                self.screen_number_stock = str(screen_number_stock)\n                self.screen_number_order = str(screen_number_order)\n\n            # 모든 스크린 번호는 포트폴리오 딕셔너리에 추가. 변하는 종목의 데이터 보과 및 업데이트 용도.\n            if stock_code in self.portfolio_stock_dict.keys():\n                self.portfolio_stock_dict[stock_code].update({\"스크린번호\": self.screen_number_stock})\n                self.portfolio_stock_dict[stock_code].update({\"주문용스크린번호\": self.screen_number_order})\n            elif stock_code not in self.portfolio_stock_dict.keys():\n                print(\">> stock_code[%s] not in self.portfolio_stock_dict: \" % stock_code)\n                self.portfolio_stock_dict.update(\n                    {stock_code: {\"스크린번호\": self.screen_number_stock, \"주문용스크린번호\": self.screen_number_order}})\n\n            cnt += 1\n\n        #         print(\">> portfolio_stock_dict{}: %s\" % self.portfolio_stock_dict)\n        print(\">> 스크린번호 %s건 할당 완료\" % cnt)\n\n    # 대상 종목(보유,미체결,포트폴리오) 실시간 등록\n    def all_stock_real_reg(self):\n        for stock_code in self.portfolio_stock_dict.keys():\n            screen_number = self.portfolio_stock_dict[stock_code][\"주문용스크린번호\"]\n            fids = \"20;10;12;15;16;17;18\"\n            self.dynamicCall(\"SetRealReg(QString, QString, QString, QString)\", screen_number, stock_code, fids, \"1\")\n\n    def slot_msg(self, screen_number, rq_name, tr_code, msg):\n        #         print(\"\")\n        print(\"> screen_number: %s, rq_name: %s, tr_code: %s, msg: %s,\" % (screen_number, rq_name, tr_code, msg))\n\n    def disconnect_screen_number(self, screen_number=None):\n        self.dynamicCall(\"DisconnectRealData(QString)\", screen_number)\n#         print(\">>> The number[%s] of screen disconnected.\" % screen_number)\n\n","repo_name":"Geenie-Lee/week4","sub_path":"kiwoom/kiwoom.py","file_name":"kiwoom.py","file_ext":"py","file_size_in_byte":43501,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"25653466937","text":"t = int(input())\n\nfor _ in range(t):\n    n = int(input())\n    nums = list(map(int, input().split()))\n\n    for i, num in enumerate(nums):\n        x = 0\n        for j in range(n):\n            if i == j:\n                continue\n            x ^= nums[j]\n        \n        if x == num:\n            ans = x\n    \n    print(ans)","repo_name":"NaolB02/A2SV_Contests","sub_path":"CodeForces Tenth Contest/A_XOR_Mixup.py","file_name":"A_XOR_Mixup.py","file_ext":"py","file_size_in_byte":320,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"30848505228","text":"from flask import Flask\nfrom flask import jsonify\n\n\napp = Flask(__name__)\n\n@app.route('/', methods=['GET'])\ndef base_url():\n    \"\"\"Base url to test API.\"\"\"\n\n    response = {\n        'response': 'Hello world!'\n    }\n\n    return jsonify(response)\n\n\nif __name__ == '__main__':\n    app.run(host='0.0.0.0', port=5000, debug=True)\n","repo_name":"RodolfoFerro/docker-flask-api","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":325,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"81"}
+{"seq_id":"17571692813","text":"import json\nfrom flask import Flask, request\napp = Flask(__name__)\n\nyaks = [\n        {'text': 'Hello world', 'votes': 5},\n        {'text': 'This is a yak', 'votes': -2}\n        ]\n\n\n@app.route(\"/\", methods=['GET'])\ndef mainpage():\n    return app.send_static_file('index.html')\n\n\n@app.route(\"/yak\", methods=['POST'])\ndef addyak():\n    yaks.insert(0, request.get_json())\n    return \"okay\"\n\n\n@app.route(\"/yaks\", methods=['GET'])\ndef getyaks():\n    return json.dumps(yaks)\n\n\n@app.route(\"/upvote\", methods=['POST'])\ndef upvote():\n    yak = request.get_json()['id']\n    yaks[yak]['votes'] += 1\n    return \"okay\"\n\n\n@app.route(\"/downvote\", methods=['POST'])\ndef downvote():\n    yak = request.get_json()['id']\n    yaks[yak]['votes'] -= 1\n    return \"okay\"\n\n\nif __name__ == \"__main__\":\n    app.run(host='0.0.0.0', debug=True)\n","repo_name":"HackBinghamtonArchives/yikyak","sub_path":"flaskapp.py","file_name":"flaskapp.py","file_ext":"py","file_size_in_byte":815,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"72406824266","text":"import pandas as pd\nimport tensorflow as tf\nimport os\nfrom pathlib import Path\nimport numpy as np\nfrom math import e\nfrom statistics import mean\nimport matplotlib.pyplot as plt\nfrom sklearn.metrics import mean_absolute_error\n\nmodel = '/multi_eq_1.0.h5'\nbase = os.getcwd()\nmodel_path = base+model\nload_model = tf.keras.models.load_model(model_path)\n\nprint(load_model.summary())\ndatabase = str(Path(os.getcwd()).parent)\ndatatail = '/new_fit/damped/damped_results_all_dir.csv'\nraw_data = pd.read_csv(database+datatail, sep=',')\n#print(raw_data.isna().sum())\n# raw_data = raw_data[(raw_data['Length (m)'] >= 2) | (raw_data['Heading'] != -90)]\nraw_data = raw_data[(raw_data['Length'] >= 2)]\nprint(raw_data.head())\nraw_data.dropna(axis=0, inplace=True)\ncolumn1 = raw_data['Length']\ncolumn2 = raw_data['Beam']\ncolumn3 = raw_data['Draft']\ncolumn4 = raw_data['Heading']\n\nraw_data.pop('R2surge')\nraw_data.pop('R2sway')\nraw_data.pop('R2heave')\nraw_data.pop('R2roll')\nraw_data.pop('R2pitch')\nraw_data.pop('R2yaw')\nraw_data.pop('MAEsurge')\nraw_data.pop('MAEsway')\nraw_data.pop('MAEheave')\nraw_data.pop('MAEroll')\nraw_data.pop('MAEpitch')\nraw_data.pop('MAEyaw')\n\n\ntrain_dataset = raw_data.sample(frac=0.8, random_state=0)\ntest_dataset = raw_data.drop(train_dataset.index)\n\n\ntrain_features = train_dataset.copy()\ntest_features = test_dataset.copy()\n\norder = 2\ntrain_labels = np.asarray(train_features.drop(train_features.columns[list(range(4,6*(order+1)+4))], axis=1, inplace=False))\ntest_labels = np.asarray(test_features.drop(test_features.columns[list(range(4,6*(order+1)+4))], axis=1, inplace=False))\n\ntrain_features = train_features.drop(train_features.columns[list(range(0,4))], axis=1, inplace=False)\ntest_features = test_features.drop(test_features.columns[list(range(0,4))], axis=1, inplace=False)\n\n# baseline = np.asarray(raw_data.sample(n=1))[0]\nbaseline = np.asarray(raw_data.loc[1400])\n# baseline = np.asarray(raw_data.loc[757])\nbaseline_input = baseline[0:4]\nbaseline_prediction = baseline[4:]\n\nnew_input = [baseline_input.tolist()]\n# new_input = [[35, 18, 16, 180]]\nnew_pred = load_model.predict(new_input)[0]\norig_x = []\norig_y = []\norig_z = []\norig_rx = []\norig_ry = []\norig_rz = []\npred_x = []\npred_y = []\npred_z = []\npred_rx = []\npred_ry = []\npred_rz = []\n\n\ndef damped_func(x, a, b, c):\n    # Motion of a critically-damped harmonic motion system\n    # Change this function to change the shape of the initial data, to better fit it.\n    y = c * e**-(a*x) + b*x*e**-(a*x)\n    return y\n\n\ndef gauss_func(x, a, b, c):\n    # Motion of a critically-damped harmonic motion system\n    # Change this function to change the shape of the initial data, to better fit it.\n    y = a * e**-((x-b)**2/c)\n    return y\n\n\ndef arctan_func(x, a, b, c):\n    # Motion of a critically-damped harmonic motion system\n    # Change this function to change the shape of the initial data, to better fit it.\n    y = a * np.arctan((x * b + c)) + 0.5\n    return y\n\n\norder = order+1\nprint(baseline_input)\nprint('\\n\\n')\n\nx_axis = np.linspace(0.1, 2.5, 60)\nfor i in x_axis:\n    orig_x.append(damped_func(i, *baseline_prediction[0*order:0*order+order]))\n    orig_y.append(damped_func(i, *baseline_prediction[1*order:1*order+order]))\n    orig_z.append(arctan_func(i, *baseline_prediction[2*order:2*order+order]))\n    orig_rx.append(gauss_func(i, *baseline_prediction[3*order:3*order+order]))\n    orig_ry.append(gauss_func(i, *baseline_prediction[4*order:4*order+order]))\n    orig_rz.append(gauss_func(i, *baseline_prediction[5*order:5*order+order]))\n    pred_x.append(damped_func(i, *new_pred[0*order:0*order+order]))\n    pred_y.append(damped_func(i, *new_pred[1*order:1*order+order]))\n    pred_z.append(arctan_func(i, *new_pred[2*order:2*order+order]))\n    pred_rx.append(gauss_func(i, *new_pred[3*order:3*order+order]))\n    pred_ry.append(gauss_func(i, *new_pred[4*order:4*order+order]))\n    pred_rz.append(gauss_func(i, *new_pred[5*order:5*order+order]))\n\n\nx_err_rpd = abs(round(mean(200*np.subtract(orig_x, pred_x)/(np.add(orig_x, pred_x))), 3))\ny_err_rpd = abs(round(mean(200*np.subtract(orig_y, pred_y)/(np.add(orig_y, pred_y))), 3))\nz_err_rpd = abs(round(mean(200*np.subtract(orig_z, pred_z)/(np.add(orig_z, pred_z))), 3))\nrx_err_rpd = abs(round(mean(200*np.subtract(orig_rx, pred_rx)/(np.add(orig_rx, pred_rx))), 3))\nry_err_rpd = abs(round(mean(200*np.subtract(orig_ry, pred_ry)/(np.add(orig_ry, pred_ry))), 3))\nrz_err_rpd = abs(round(mean(200*np.subtract(orig_rz, pred_rz)/(np.add(orig_rz, pred_rz))), 3))\n\nx_err_raw = round(mean_absolute_error(orig_x, pred_x), 3)\ny_err_raw = round(mean_absolute_error(orig_y, pred_y), 3)\nz_err_raw = round(mean_absolute_error(orig_z, pred_z), 3)\nrx_err_raw = round(mean_absolute_error(orig_rx, pred_rx), 3)\nry_err_raw = round(mean_absolute_error(orig_ry, pred_ry), 3)\nrz_err_raw = round(mean_absolute_error(orig_rz, pred_rz), 3)\n\n# plt.subplot(2, 3, 1)\nplt.rc('axes', titlesize=25)\nplt.rc('legend',fontsize=25)\nplt.rc('font', size=25)\n# title = 'Barge Dimensions ' + str(baseline_input[0]) + ' m Length, ' + str(baseline_input[1]) + ' m Beam, ' + \\\n   # str(abs(baseline_input[2])) + ' m Draft  -  Waves Heading of: ' + str(baseline_input[3])\n# plt.suptitle(title)\nplt.plot(x_axis, pred_x, color='red', label='Predicted RAO')\nplt.plot(x_axis, orig_x, color='blue', linestyle='-.', label='True RAO')\nplt.title('Surge RAO')\nplt.ylabel('Response (m/m)')\nplt.text(mean(x_axis), (mean(orig_x)+mean(pred_x))/2, \"Relative % diff: \"+ str(x_err_rpd)+'\\n'+\"MAE: \" + str(x_err_raw))\nplt.grid()\n# plt.ylim([-0.5, 1.5])\nplt.legend()\nplt.show()\n# plt.show()\n# plt.subplot(2, 3, 2)\n# plt.rc('font', size=25)\nplt.plot(x_axis, pred_y, color='red', label='Predicted RAO')\nplt.plot(x_axis, orig_y, color='blue', linestyle='-.', label='True RAO')\nplt.title('Sway RAO')\nplt.ylabel('Response (m/m)')\n\nplt.text(mean(x_axis), (mean(orig_y)+mean(pred_y))/2, \"Relative % diff: \"+ str(y_err_rpd)+'\\n'+\"MAE: \" + str(y_err_raw))\n\nplt.grid()\nplt.legend()\nplt.show()\n# plt.rc('font', size=10)\n# plt.ylim([-0.5, 1.5])\n# plt.show()\nplt.subplot(2, 3, 3)\nplt.plot(x_axis, pred_z, color='red', label='Predicted RAO')\nplt.plot(x_axis, orig_z, color='blue', linestyle='-.', label='True RAO')\nplt.title('Heave RAO')\nplt.text(mean(x_axis), (mean(orig_z)+mean(pred_z))/2, \"Relative % diff: \"+ str(z_err_rpd)+'\\n'+\"MAE: \" + str(z_err_raw))\nplt.grid()\n# plt.ylim([-0.5, 1.5])\n\nplt.subplot(2, 3, 4)\nplt.plot(x_axis, pred_rx, color='red', label='Predicted RAO')\nplt.plot(x_axis, orig_rx, color='blue', linestyle='-.', label='True RAO')\nplt.title('Roll RAO')\nplt.text(mean(x_axis), (mean(orig_rx)+mean(pred_rx))/2, \"Relative % diff: \"+ str(rx_err_rpd)+'\\n'+\"MAE: \" + str(rx_err_raw))\n# plt.ylim([-0.5, 1.5])\nplt.ylabel('Response (Deg/m)')\nplt.xlabel('Wave Frequency (rad/s)')\nplt.grid()\n\nplt.subplot(2, 3, 5)\nplt.plot(x_axis, pred_ry, color='red', label='Predicted RAO')\nplt.plot(x_axis, orig_ry, color='blue', linestyle='-.', label='True RAO')\nplt.title('Pitch RAO')\nplt.text(mean(x_axis), (mean(orig_ry)+mean(pred_ry))/2, \"Relative % diff: \"+ str(ry_err_rpd)+'\\n'+\"MAE: \" + str(ry_err_raw))\n# plt.ylim([-0.5, 50])\nplt.grid()\nplt.xlabel('Wave Frequency (rad/s)')\n\nplt.subplot(2, 3, 6)\nplt.plot(x_axis, pred_rz, color='red', label='Predicted RAO')\nplt.plot(x_axis, orig_rz, color='blue', linestyle='-.', label='True RAO')\nplt.title('Yaw RAO')\nplt.text(mean(x_axis), (mean(orig_rz)+mean(pred_rz))/2, \"Relative % diff: \"+ str(rz_err_rpd)+'\\n'+\"MAE: \" + str(rz_err_raw))\n# plt.ylim([-0.5, 1.5])\nplt.grid()\nplt.xlabel('Wave Frequency (rad/s)')\n                                                                \n# plt.legend()\n# plt.get_current_fig_manager().full_screen_toggle()\nplt.show()","repo_name":"jafrizzell/RAO-Research","sub_path":"parameter_fit_data_with_code/damped_model_visualizer.py","file_name":"damped_model_visualizer.py","file_ext":"py","file_size_in_byte":7646,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"14698129988","text":"# Nama          : Andrew Devito Aryo\n# NPM           : 2306152494\n# Kelas         : C\n# Kode Asdos    : GAN\n\n# Fungsi untuk menampilkan Index Line dan Konten line\ndef lineNumber(line, text):\n    return f\"{line:03d}. {text}\"\n\n# Fungsi Main\ndef main():\n    try:\n        print(\"Lab 04 -- DDP1 -- 2023\")\n        counter_char = 0                                    # Membuat Variable Counter Character\n        contain = \"\"                                        # Variable untuk menampung konten yang akan di write\n        input_file = input(\"Input File Name: \")             # Meminta input nama file input dari User\n        #my_dir = f\"File Kuliah\\\\\"                           # Melacak Directory dari File yang diingikan\n        try:\n            data = open(input_file, \"r\")           # Membuka File yang diinginkan di read\n        except:\n            print(\"File Not Found!\")                        # Jika file tidak ditemukan, maka keluarkan message error\n            quit()\n\n        output_file = input(\"Output File Name: \")           # Meminta input nama file output dari User\n            \n        for index, line in enumerate(data):                 # Mengiterasi setiap line yang ada di File                            # Menambah Counter\n            contain += lineNumber(index + 1, line)           # Meng-append setiap line kedalam Contain\n            for char in line:                                # Mengiterasi setiap Char dalam Line\n                if char.isalnum():                            # Menambah Counter apabila Character bukan whitespace\n                    counter_char+=1\n\n        with open(output_file, \"w\") as writer:     # Membuka file output\n            writer.write(contain + f\"\\n\\nThe total number of letters in the file {input_file} is {counter_char}\")    # Menuliskan hasil iterasi kedalam file output\n\n        print(\"-- End Of Program -- \")\n    except KeyboardInterrupt:\n        print(\"\\n-- Program di Terminate--\")\n\nif __name__ == '__main__':      \n    main()      # Memanggil Fungsi Main","repo_name":"Andrew4Coding/ddp1-workspace","sub_path":"Lab/LAB4/GAN_AndrewDevitoAryo_2306152494_lab04.py","file_name":"GAN_AndrewDevitoAryo_2306152494_lab04.py","file_ext":"py","file_size_in_byte":2019,"program_lang":"python","lang":"id","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"11569853581","text":"\"\"\"Function to run a bubble sort on a given list of numbers.\"\"\"\n\n\ndef bubble_sort(a_list):\n    \"\"\"Bubble sort function.\"\"\"\n    global loop_count\n    loop_count = 1\n    for i in range(len(a_list) - loop_count):\n        if a_list[i] > a_list[i + 1]:\n            a_list[i], a_list[i + 1] = a_list[i + 1], a_list[i]\n        else:\n            continue  # pragma: no cover\n        loop_count += 1\n        bubble_sort(a_list)\n    return a_list\n\nif __name__ == '__main__':  # pragma: no cover\n    import timeit\n    from random import randint\n    short_list = [randint(1, 50) for _ in range(10)]\n    # long_list = [randint(1, 50) for _ in range(100)]\n    small_numbers = [randint(1, 25) for _ in range(15)]\n    big_numbers = [randint(101, 250) for _ in range(15)]\n    mix_list = [x for x in zip(small_numbers, big_numbers)]\n\n    short_list = timeit.timeit(\"bubble_sort(short_list)\", setup=\"from __main__ import short_list, bubble_sort\")\n    # long_list = timeit.timeit(\"bubble_sort(long_list)\", setup=\"from __main__ import long_list, bubble_sort\")\n    small_numbers = timeit.timeit(\"bubble_sort(small_numbers)\", setup=\"from __main__ import small_numbers, bubble_sort\")\n    big_numbers = timeit.timeit(\"bubble_sort(big_numbers)\", setup=\"from __main__ import big_numbers, bubble_sort\")\n    mix_list = timeit.timeit(\"bubble_sort(mix_list)\", setup=\"from __main__ import mix_list, bubble_sort\")\n    print('Short list time: ', short_list)\n    # print('Long list time: ', long_list)\n    print('Small number list time: ', small_numbers)\n    print('Big numbers list time:', big_numbers)\n    print('Mix numbers list time: ', mix_list)\n","repo_name":"hcodydibble/data-structures","sub_path":"src/bubble_sort.py","file_name":"bubble_sort.py","file_ext":"py","file_size_in_byte":1616,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"36916507614","text":"def sum_of_div(n):\n    \"\"\"This function calculates and returns the sum of the divisors, including 1.\"\"\"\n    s = 1\n    for i in range(2, n - 1):\n        if n % i == 0:\n            s += i\n    return s\n\ndef is_perfect(n):\n    \"\"\"The function checks if the number is perfect or not.\n      It returns True if the number is perfect and False otherwise.\"\"\"\n    if n == sum_of_div(n):\n        return True\n    else:\n        return False\n    return True\n\ndef larger_perf(n):\n    \"\"\"Generates the first perfect number larger that a given number n.\"\"\"\n    i = 0\n    while True:\n        n += 1\n        if is_perfect(n):\n            i += 1\n            if i > 49:\n                print(\"there is no such a number\")\n            else:\n                return n\n    \n    \n\nn = int(input(\"Enter the number: \"))\n\nprint(int(sum_of_div(n)))\n\nif is_perfect(n):\n    print(\"The number is perfect\")\nelse:\n    print(\"The number is not perfect\")\n    \nprint(larger_perf(n))\n","repo_name":"TeoMoisi/python-projects","sub_path":"lab1/ex15.py","file_name":"ex15.py","file_ext":"py","file_size_in_byte":944,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"40586837848","text":"import cv2\nimport os \nimport numpy as np\n\ndef load_images_from_folder(folder):\n    count = 0\n    v = sorted(sorted(os.listdir(folder)), key=len)\n\n    for filename in v:\n        count+=1\n        img = cv2.imread(os.path.join(folder,filename))\n\n        v = np.zeros(img.shape)\n\n        for i in range(img.shape[0]):\n            for j in range(img.shape[1]):\n                if (img[i][j] == np.array([56, 56, 56])).all():\n                    v[i][j] = np.array([0, 0, 0])\n                elif (img[i][j] == np.array([0, 0, 0])).all() or (img[i][j] == np.array([1, 1, 1])).all():\n                    v[i][j] = np.array([255, 255, 255])\n                else:\n                    v[i][j] = np.array([0, 255, 0])\n\n        cv2.imwrite(\"drone_basic/correct_output_images/img{}.png\".format(count), v)\n\nload_images_from_folder(\"drone_basic/input_images_2_copy\")","repo_name":"DavitMirzoyan/individual-project","sub_path":"drone_basic/scripts/colored_images_for_training.py","file_name":"colored_images_for_training.py","file_ext":"py","file_size_in_byte":851,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"24719410088","text":"from pymongo import MongoClient\n\nconnection = \"mongodb+srv://velllum:0sxfeDlou9i66twP@cluster0.fs8kg.mongodb.net/freelancers?retryWrites=true&w=majority\"\n\nclient = MongoClient(connection)\ndb = client.freelancers[\"stomshop_pro\"]\n\n\ndef save_data(dic):\n    \"\"\"- Сохраняет собранные данные в удаленную базу mongodb\"\"\"\n    try:\n        db.insert(dic)\n    except Exception as e:\n        print(f\"Произошла ошибка {e}\")\n\n\ndef remove_data():\n    \"\"\"- Отчистить базу от всех данных\"\"\"\n    db.delete_many({})\n","repo_name":"velllum/stomshop_pro_selenium","sub_path":"data_base.py","file_name":"data_base.py","file_ext":"py","file_size_in_byte":576,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"36592072182","text":"import numpy as np\nimport matplotlib.pylab as plt\n\ndatFinicial = np.genfromtxt(\"Finicial.dat\", delimiter = \",\")\nxf = datFinicial[:,0]\nyf = datFinicial[:,1]\n\nplt.figure()\nplt.plot(xf,yf,label='Condición inicial')\nplt.xlabel(\"x\")\nplt.ylabel(\"h\")\nplt.ylim(-.5*np.max(yf),2*np.max(yf))\nplt.legend()\nplt.savefig(\"Finicial.png\")\nplt.close()\n\n\ndataOnda = np.genfromtxt(\"dataOnda.dat\", delimiter = \",\")\nt = dataOnda[:,0]\nfils, cols = dataOnda.shape[0], dataOnda[1]\nplt.figure()\nfor i in range(10):\n\tplt.plot(t,dataOnda[i,1:])\nplt.savefig(\"GrafOnda\")\n","repo_name":"johhanz099/HerramientasSemanas","sub_path":"S8_EDP1d/AnayaBryan_S8C1EDP.py","file_name":"AnayaBryan_S8C1EDP.py","file_ext":"py","file_size_in_byte":543,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"38537224716","text":"\ncount = 0\n\ndef cache(f):\n    d = dict()\n    def cached_f(*args):\n        if args not in d:\n            d[args] = f(*args)\n        return d[args]\n    return cached_f\n\n# @cache\ndef f(a, b):\n    global count\n    if count == 40:\n        exit()\n    else:\n        count += 1\n    print(\"Calling f, a = {} b = {}\".format(a, b))\n    if a==0:\n        return b + 1\n    if b==0:\n        return f(a-1, h)\n    return f(a-1, f(a, b-1))\n\nh = 1\na = 4\nb = 1\nf(a, b)\n\n# Find h s.t. f(4, 1) == 6\n","repo_name":"sw561/synacor","sub_path":"translation.py","file_name":"translation.py","file_ext":"py","file_size_in_byte":477,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"32374929678","text":"def reverse(x):\n    \"\"\"\n    :type x: int\n    :rtype: int\n    \"\"\"\n    x = str(x)\n    reversed_num = x[::-1]\n    str_to_int = int(reversed_num)\n    return str_to_int\n\nprint(reverse(123))\n\n\"\"\"\nturn x into a string so we can reverse it \nreverse the string \nturn the reversed string into an integer \nreturn the reversed integer \n\"\"\"\n","repo_name":"ruklakhani/leetcode_SPD","sub_path":"leetcode-communication/reverse_integer.py","file_name":"reverse_integer.py","file_ext":"py","file_size_in_byte":328,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"9672308664","text":"'''\n Correlation coefficient of\n Deaths/Mil.populations and TBC population(%:2018).\n\n step4.py\n\n 2020-06-04\n\n'''\nimport numpy as np\nimport csv\nimport math\nfrom scipy.stats import pearsonr\n\nfile = './DeMilTBC2018.csv'\n\ndata = []\n\nwith open(file) as f:\n    reader = csv.reader(f)\n    for row in reader:\n        data.append(row)\n\ndel data[:1]\n\nX = []\nY = []\n\nfor dat in data:\n    xx = float(dat[2])\n    yy = float(dat[3])\n    if (xx>0):\n        X.append(math.log(xx))\n        Y.append(math.log(yy))\n\nval,p = pearsonr(X, Y)\n\nprint('Corrcoef val %f p val %f'%(val, p))\n\n\n","repo_name":"IchiroYoshida/python_public","sub_path":"covid/calc/TB/step4.py","file_name":"step4.py","file_ext":"py","file_size_in_byte":566,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"81"}
+{"seq_id":"9848468783","text":"from typing import List\n\n\ndef load_input() -> List:\n    with open('inputs.txt', 'r') as file:\n        return file.readlines()\n\n\ndef search_in_line(tree_line: List, coordinates: set, y_coordinate: int, mode: str) -> None:\n    left_visible_tree = int(tree_line[0])\n    right_visible_tree = int(tree_line[-1])\n    tree_line = tree_line[1:-1]\n    line_len = len(tree_line)\n\n    for index, tree_height in enumerate(tree_line):\n        backward_idx = line_len - index - 1\n        l_inner_tree = int(tree_height)\n        r_inner_tree = int(tree_line[backward_idx])\n        if left_visible_tree < l_inner_tree:\n            coordinate = (index, y_coordinate) if mode == 'row' else (\n                y_coordinate, index)\n            coordinates.add((coordinate))\n            left_visible_tree = l_inner_tree\n        if right_visible_tree < r_inner_tree:\n            coordinate = (backward_idx, y_coordinate) if mode == 'row' else (\n                y_coordinate, backward_idx)\n            coordinates.add((coordinate))\n            right_visible_tree = r_inner_tree\n\n\ndef transform_matrix(forest: List) -> List:\n    matrix: List[List] = [[] for i in range(len(forest))]\n    for line in forest:\n        for index, tree in enumerate(line.rstrip()):\n            matrix[index].append(tree)\n\n    return matrix\n\n\ndef walkthrough_forest(forest: List, mode: str, coordinates: set) -> None:\n    y_coordinate: int = 0\n\n    for tree_line in forest[1:-1]:\n        if mode == 'row':\n            tree_line = tree_line.rstrip()\n        search_in_line(tree_line, coordinates, y_coordinate, mode)\n        y_coordinate += 1\n\n\ndef main():\n    coordinates: set = set()\n    forest: List[str] = load_input()\n    matrix: List[List] = transform_matrix(forest)\n    width: int = len(forest[0].rstrip())\n    height: int = len(forest)\n    # corner trees have to be subtracted\n    outer_tree_number: int = 2 * (width + height) - 4\n\n    walkthrough_forest(forest, 'row', coordinates)\n    walkthrough_forest(matrix, 'column', coordinates)\n\n    print(f'{outer_tree_number} : {len(coordinates)} : {outer_tree_number + len(coordinates)}')\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"needsomesl33p/2022-advent-of-code","sub_path":"8day/1_star.py","file_name":"1_star.py","file_ext":"py","file_size_in_byte":2133,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"30678693516","text":"import operator\n\ndef Main():\n\n    in_file1 = open(\"players.csv\",\"r\")\n    in_file2 = open(\"games.csv\", \"r\")\n\n    header1 = next(in_file1)\n    header2 = next(in_file2)\n\n    #Players\n    players = {}\n    for line in in_file1:       #PLAYER,SELO\n        line= line.strip()\n        line= line.split(\",\")\n        name = line[0].strip()\n        SELO = int(line[1].strip())\n        players[name]=SELO\n\n\n    #Games\n    matches = []\n    for line in in_file2:       #PLAYER A,PLAYER B,RESULT\n        line= line.strip()\n        line=line.split(\",\")\n        match = []\n        pA = line[0].strip()  #Player A\n        pB = line[1].strip()  #Player B\n        result = line[2].strip().split(\"-\")\n        if \"/\" in result[0]:\n            result[0] = result[0].split(\"/\")\n            A1 = int(result[0][0])\n            A2 = int(result[0][1])\n            rA = A1/A2\n        else:\n            rA= int(result[0]) #Result player A\n        if \"/\" in result[1]:\n            result[1] = result[1].split(\"/\")\n            B1 = int(result[1][0])\n            B2 = int(result[1][1])\n            rB = B1/B2\n        else:\n            rB= int(result[1]) #Result player B\n        match.append(pA)\n        match.append(pB)\n        match.append(rA)\n        match.append(rB)\n        matches.append(match)\n\n\n\n    for i in range(len(matches)):\n        playerA = matches[i][0]\n        playerB = matches[i][1]\n        if playerA not in players:\n            players[playerA] = 1500\n            SELOA = 1500\n        else:\n            SELOA = players[playerA]\n        if playerB not in players:\n            players[playerB] = 1500\n            SELOB = 1500\n        else:\n            SELOB = players[playerB]\n        delta_match = delta(SELOA, SELOB)\n        if matches[i][2] > matches[i][3]:\n            SELOA = SELOA +200*delta_match\n            SELOB = SELOB -200*delta_match\n        elif matches[i][2] < matches[i][3]:\n            SELOA = SELOA -200*delta_match\n            SELOB = SELOB +200*delta_match\n        players[playerA] = round(SELOA)\n        players[playerB] = round(SELOB)\n\n    playersR = sorted(players.items(), key=operator.itemgetter(1), reverse=True)\n    for k, v in playersR:\n        print(f'{k}: {v}')\n\n    in_file1.close()\n    in_file2.close()\n\ndef delta(player_1, player_2):\n    a = 1 / (1 + 2 ** ((player_1 - player_2) / 100))\n    return a\n\nMain()\n\n","repo_name":"HardLoooking/CS-Exams","sub_path":"Chess/Chess.py","file_name":"Chess.py","file_ext":"py","file_size_in_byte":2328,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"16849804404","text":"# goolge colab 에서 작성\n\nfrom openpyxl import Workbook\nfrom openpyxl import load_workbook\nfrom konlpy.tag import Okt\nimport re\nimport sys\nimport requests\nimport json\n\n\nurl = \"https://naveropenapi.apigw.ntruss.com/sentiment-analysis/v1/analyze\"\n\nheaders = {   # 지원 api key\n    \"X-NCP-APIGW-API-KEY-ID\": \"x4cybkvtso\",\n    \"X-NCP-APIGW-API-KEY\": \"QX0KLAU5yVSuSayqr1t285G9hvI3YEWq09fIVQpv\",\n    \"Content-Type\": \"application/json\"\n}\n\n\nload_wb = load_workbook(\n    \"/content/drive/MyDrive/jw/seoul/yongsan_restaurants.xlsx\", data_only=True)\n\nload_ws = load_wb['용산구']\nwb = Workbook()\nws = wb.active\n\nreview_t = []\nreview = []\n\nfor row in load_ws.rows:\n    row_value = []\n    for cell in row:\n        if cell.value != None:\n            row_value.append(cell.value)\n    review_t.append(row_value)\n\nfor i in review_t:\n    t = ''\n    for j in i:\n        t = t+\" \"+j\n    review.append(t)\n\n# print(review)\n\nokt = Okt()\nword = []\n\nfor i in range(len(review)):\n    review[i] = re.sub(\"[^가-힣ㄱ-ㅎㅏ-ㅣ\\\\s]\", \"\", review[i])  # 한글이랑 공백만 남기고 다 지움\n    review[i] = review[i].replace(\"\\n\", \" \")\n    word.append(review[i])\n\n# print(word)\nprint(\"매장 개수\", len(word))  # len(review) == len(word)  매장 개수\n\nclova_cnt = 0\nfor i in range(len(word)):\n    if (int(len(word[i])/950) == 0):\n        clova_cnt += 1\n    else:\n        clova_cnt += int(len(word[i])/950)\n\nprint(\"api 호출 횟수\", clova_cnt)  # api 호출 횟수\nprint(\"\\n\")\n\np_li = []\nn_li = []\n\nfor i in range(len(word)):\n    pp = 0\n    nn = 0\n    pp_avg = 0\n    nn_avg = 0\n    n = 0\n\n    if(len(word[i]) > 950):\n        length = int(len(word[i])/950)   # 매장 하나의 전체 리뷰 글자수를 950으로 나눈 값만큼 돌리기\n        m = 950\n    else:\n        length = 1                       # 950 미만이면 한번만\n        m = len(word[i])\n\n    for k in range(length):\n\n        content = word[i][n:m]\n\n        data = {\n            \"content\": content\n        }\n\n        response = requests.post(url, data=json.dumps(data), headers=headers)\n        rescode = response.status_code\n\n        text = response.json()\n\n        documnet = text['document']\n        sentiment = text['document']['sentiment']\n        positive = text['document']['confidence']['positive']\n        negative = text['document']['confidence']['negative']\n\n        pp += positive\n        nn += negative\n\n        n += 950\n        m += 950\n\n        pp_avg = pp/length\n        nn_avg = nn/length\n\n    if(rescode == 200):\n        print(review_t[i][0], pp_avg, nn_avg)\n        p_li.append([pp_avg, review_t[i][0]])\n        n_li.append([nn_avg, review_t[i][0]])\n    else:\n        print(\"Error : \" + response.text)\n\np_li.sort(reverse=True)\nn_li.sort(reverse=True)\n\np_result = 0\nn_result = 0\n\nprint(\"\\n긍정\")\nfor i in range(len(word)):\n    p_result = \"{:,.1f}\".format(p_li[i][0])\n    print(p_li[i][1], p_result)\n\nprint(\"\\n부정\")\nfor i in range(len(word)):\n    n_result = \"{:,.1f}\".format(n_li[i][0])\n    print(n_li[i][1], n_result)\n","repo_name":"okonomiyakki/clova-sentiment","sub_path":"clova.py","file_name":"clova.py","file_ext":"py","file_size_in_byte":3006,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"21938049618","text":"#!/usr/bin/env python\n\n\"\"\"\nThis code was written by Kirstie Whitaker in January 2017 to accompany\nthe manuscript \"Neuroscientific insights into the development of analogical reasoning\".\n\nContact: kw401@cam.ac.uk\n\"\"\"\n\n#===============================================================================\n# Import what you need\n#===============================================================================\nimport matplotlib.pylab as plt\nimport matplotlib.image as mpimg\nimport matplotlib.patches as patches\nimport matplotlib.lines as mlines\nimport numpy as np\nimport os\nimport palettable\nimport pandas as pd\nfrom scipy.stats import pearsonr, ttest_rel\nimport seaborn as sns\nsns.set_context(\"poster\", font_scale=1)\nsns.set_style('white')\nfrom statsmodels.formula.api import ols\nimport string\n\n#===============================================================================\n# Write a few useful functions\n#===============================================================================\n\n#-------------------------------------------------------------------------------\n# Read in the analogy stimulus file\n#-------------------------------------------------------------------------------\ndef read_in_analogy(analogy_stimulus_file):\n\n    img = mpimg.imread(analogy_stimulus_file)\n\n    img = img[80:-5, 135:-165, :]\n\n    return img\n\n\n#-------------------------------------------------------------------------------\n# Read in the semantic stimulus file\n#-------------------------------------------------------------------------------\ndef read_in_semantic(semantic_stimulus_file):\n\n    img = mpimg.imread(semantic_stimulus_file)\n\n    img = img[18:-55, 120:-140, :]\n\n    return img\n\n\n#-------------------------------------------------------------------------------\n# Add the coloured boxes and text to the analogy example for figure 1\n#-------------------------------------------------------------------------------\ndef add_boxes_analogy(ax, color_dict):\n\n    color_list = [ 'orange', 'purple', 'green', 'blue' ]\n\n    text_list = [ 'semantic\\nlure', 'perceptual\\nlure', 'unrelated\\nlure', 'correct\\nchoice']\n\n    for i, (color, text) in enumerate(zip(color_list, text_list)):\n\n        # Add the coloured boxes\n        ax.add_patch(\n            patches.Rectangle(\n                (40 + i*154, 430),\n                150,\n                150,\n                fill=False,\n                edgecolor=color_dict[color],\n                linewidth=3\n            )\n        )\n\n        # Add the text\n        ax.text(\n            116 + i*154,\n            623,\n            text,\n            fontsize=15,\n            color=color_dict[color],\n            horizontalalignment='center',\n            verticalalignment='center',\n            fontname='arial'\n        )\n\n    return ax\n\n\n#-------------------------------------------------------------------------------\n# Add the coloured boxes and text to the semantic example for figure 1\n#-------------------------------------------------------------------------------\ndef add_boxes_semantic(ax, color_dict):\n\n    color_list = [ 'red', 'green', 'green', 'purple' ]\n\n    text_list = [ 'correct\\nchoice', 'unrelated\\nlure', 'unrelated\\nlure', 'perceptual\\nlure']\n\n    for i, (color, text) in enumerate(zip(color_list, text_list)):\n\n        # Add the coloured boxes\n        ax.add_patch(\n            patches.Rectangle(\n                (51 + i*163, 455),\n                160,\n                160,\n                fill=False,\n                edgecolor=color_dict[color],\n                linewidth=3\n            )\n        )\n\n        # Add the text\n        ax.text(\n            131 + i*163,\n            660,\n            text,\n            fontsize=15,\n            color=color_dict[color],\n            horizontalalignment='center',\n            verticalalignment='center',\n            fontname='arial'\n        )\n\n    return ax\n\n\n#-------------------------------------------------------------------------------\n# Add the panel labels for figure 1\n#-------------------------------------------------------------------------------\ndef add_panel_labels_fig1(ax_list):\n\n    coords = (0.05, 0.95)\n    color='k'\n    fontsize=14\n\n    letters = string.ascii_lowercase\n    for i, ax in enumerate(ax_list):\n\n        ax.text(coords[0], coords[1],\n                '({})'.format(letters[i]),\n                fontsize=fontsize,\n                transform=ax.transAxes,\n                color=color,\n                horizontalalignment='center',\n                verticalalignment='center',\n                fontname='arial',\n                fontweight='bold'\n        )\n\n    return ax_list\n\n\n#-------------------------------------------------------------------------------\n# Figure out the min and max of your data and then add 5% padding\n#-------------------------------------------------------------------------------\ndef get_min_max(data):\n\n    data_range = np.max(data) - np.min(data)\n    data_min = np.min(data) - (data_range * 0.05)\n    data_max = np.max(data) + (data_range * 0.05)\n\n    return data_min, data_max\n\n\n#-------------------------------------------------------------------------------\n# Add a line to your legend to make it look lovely\n#-------------------------------------------------------------------------------\ndef add_line_to_legend(ax, color_list=['blue'], label_list=['Blue stars'], loc=0, rev=False):\n\n    if rev:\n        color_list = color_list[::-1]\n        label_list = label_list[::-1]\n\n    line_list = []\n    for color, label in zip(color_list, label_list):\n        line_list += [mlines.Line2D([], [], color=color, marker=None, label=label)]\n    ax.legend(handles=line_list, loc=loc)\n\n    return ax\n\n#-------------------------------------------------------------------------------\n# Report the behavioural statistical models with age and behaviour\n#-------------------------------------------------------------------------------\ndef report_behav_age_correlations(y_name, df):\n\n    formula = '{} ~ Age_scan'.format(y_name)\n    mod = ols(formula=formula, data=df)\n    res_lin = mod.fit()\n\n    formula = '{} ~ Age_scan_sq + Age_scan'.format(y_name)\n    mod = ols(formula=formula, data=df)\n    res_quad = mod.fit()\n\n    if 'R2' in y_name and not 'dis' in y_name and not 'sem' in y_name and not y_name.endswith('per'):\n        formula = '{} ~ Age_scan_sq + Age_scan + {}'.format(y_name, y_name.replace('R2', 'R1'))\n        mod = ols(formula=formula, data=df)\n        res_corr = mod.fit()\n\n    if 'R1' in y_name and not 'dis' in y_name and not 'sem' in y_name and not y_name.endswith('per'):\n        formula = '{} ~ Age_scan_sq + Age_scan + {}'.format(y_name, y_name.replace('R1', 'R2'))\n        mod = ols(formula=formula, data=df)\n        res_corr = mod.fit()\n\n    print ('=== {} ==='.format(y_name))\n    print ('Linear w age')\n    print ('  Beta(Age) = {:2.4f}, P = {:2.4f}'.format(res_lin.params['Age_scan'], res_lin.pvalues['Age_scan']))\n    print ('  Rsq = {:2.3f}, Rsq_adj = {:2.3f}'.format(res_lin.rsquared, res_lin.rsquared_adj))\n    print ('  F({}, {}) = {:2.3f}, P = {:2.4f}'.format(res_lin.df_model, res_lin.df_resid, res_lin.fvalue, res_lin.f_pvalue))\n    print ('Quadratic w age')\n    print ('  Beta(AgeSq) = {:2.4f}, P = {:2.4f}'.format(res_quad.params['Age_scan_sq'], res_quad.pvalues['Age_scan_sq']))\n    print ('  Beta(Age) = {:2.4f}, P = {:2.4f}'.format(res_quad.params['Age_scan'], res_quad.pvalues['Age_scan']))\n    print ('  Rsq = {:2.3f}, Rsq_adj = {:2.3f}'.format(res_quad.rsquared, res_quad.rsquared_adj))\n    print ('  F({}, {}) = {:2.3f}, P = {:2.4f}'.format(res_quad.df_model, res_quad.df_resid, res_quad.fvalue, res_quad.f_pvalue))\n    if 'R2' in y_name and not 'dis' in y_name and not 'sem' in y_name and not y_name.endswith('per'):\n        print ('Quadratic w age correcting for accuracy')\n        print ('  Beta(R1) = {:2.4f}, P = {:2.4f}'.format(res_corr.params[y_name.replace('R2', 'R1')],\n                                                       res_corr.pvalues[y_name.replace('R2', 'R1')]))\n        print ('  Beta(AgeSq) = {:2.4f}, P = {:2.4f}'.format(res_corr.params['Age_scan_sq'], res_corr.pvalues['Age_scan_sq']))\n        print ('  Beta(Age) = {:2.4f}, P = {:2.4f}'.format(res_corr.params['Age_scan'], res_corr.pvalues['Age_scan']))\n        print ('  Rsq = {:2.3f}, Rsq_adj = {:2.3f}'.format(res_corr.rsquared, res_corr.rsquared_adj))\n        print ('  F({}, {}) = {:2.3f}, P = {:2.4f}'.format(res_corr.df_model, res_corr.df_resid, res_corr.fvalue, res_corr.f_pvalue))\n    if 'R1' in y_name and not 'dis' in y_name and not 'sem' in y_name and not y_name.endswith('per'):\n        print ('Quadratic w age correcting for accuracy')\n        print ('  Beta(R2) = {:2.4f}, P = {:2.4f}'.format(res_corr.params[y_name.replace('R1', 'R2')],\n                                                       res_corr.pvalues[y_name.replace('R1', 'R2')]))\n        print ('  Beta(AgeSq) = {:2.4f}, P = {:2.4f}'.format(res_corr.params['Age_scan_sq'], res_corr.pvalues['Age_scan_sq']))\n        print ('  Beta(Age) = {:2.4f}, P = {:2.4f}'.format(res_corr.params['Age_scan'], res_corr.pvalues['Age_scan']))\n        print ('  Rsq = {:2.3f}, Rsq_adj = {:2.3f}'.format(res_corr.rsquared, res_corr.rsquared_adj))\n        print ('  F({}, {}) = {:2.3f}, P = {:2.4f}'.format(res_corr.df_model, res_corr.df_resid, res_corr.fvalue, res_corr.f_pvalue))\n\n\n#-------------------------------------------------------------------------------\n# Add the panel labels to figure 2\n#-------------------------------------------------------------------------------\ndef add_panel_labels_fig2(ax_list):\n\n    x_list = [ -0.175, -0.115, -0.145 ]\n    y = 1.0\n    color='k'\n    fontsize=18\n\n    letters = string.ascii_lowercase\n    for i, ax in enumerate(ax_list):\n\n        ax.text(x_list[i], y,\n                '({})'.format(letters[i]),\n                fontsize=fontsize,\n                transform=ax.transAxes,\n                color=color,\n                horizontalalignment='center',\n                verticalalignment='center',\n                fontname='arial',\n                fontweight='bold'\n        )\n\n    return ax_list\n\n\n#-------------------------------------------------------------------------------\n# Read in the pial surface brain images\n#-------------------------------------------------------------------------------\ndef read_in_brains(results_surface_file):\n\n    img = mpimg.imread(results_surface_file)\n\n    img = img[21:-120, 44:-44, :]\n\n    return img\n\n\n#-------------------------------------------------------------------------------\n# Read in the venn diagram\n#-------------------------------------------------------------------------------\ndef read_in_venn(venn_file):\n\n    img = mpimg.imread(venn_file)\n\n    return img\n\n\n#-------------------------------------------------------------------------------\n#\n#-------------------------------------------------------------------------------\ndef report_cluster_stats(f_behav, f_mri_cope1, f_mri_cope2, f_mri_cope4, cluster=1):\n\n    name_dict = { 1 : 'SEMANTIC > FIX',\n                  2 : 'ANALOGY > FIX',\n                  4 : 'ANA > SEM' }\n    file_dict = { 1 : f_mri_cope1,\n                  2 : f_mri_cope2,\n                  4 : f_mri_cope4 }\n\n    for cope, name in name_dict.items():\n        print('=== {} ==='.format(name))\n        df = read_in_data(f_behav, file_dict[cope])\n\n        print('  Corr cluster {} w age'.format(cluster))\n        x_name = 'Age_scan'\n        y_name = 'cluster_{}'.format(cluster)\n        report_correlation(df, x_name, y_name)\n\n        print('  Corr cluster 1 w R2acc')\n        x_name = 'R2_percent_acc'\n        y_name = 'cluster_{}'.format(cluster)\n        report_correlation(df, x_name, y_name)\n\n        print('  Corr cluster 1 w R2acc covar age')\n        x_name = 'R2_percent_acc'\n        y_name = 'cluster_{}'.format(cluster)\n        covar_name = 'Age_scan'\n        report_correlation(df, x_name, y_name, covar_name=covar_name)\n\n\n#-------------------------------------------------------------------------------\n#  Report a correlation (partial if covariates are provided)\n#-------------------------------------------------------------------------------\ndef report_correlation(df, x_name, y_name, covar_name=None):\n\n    if not covar_name:\n        r, p = pearsonr(df[x_name], df[y_name])\n\n    else:\n        x_res = residuals(df[covar_name], df[x_name])\n        y_res = residuals(df[covar_name], df[y_name])\n\n        df['{}_res'.format(x_name)] = x_res\n        df['{}_res'.format(y_name)] = y_res\n\n        r, p = pearsonr(df['{}_res'.format(x_name)], df['{}_res'.format(y_name)])\n\n    # Format nicely\n    r, p = format_r_p(r, p, r_dp=3)\n\n    print('    r {}, p {}'.format(r, p))\n\n\n#-------------------------------------------------------------------------------\n# Format r and p values to print out nicely\n#-------------------------------------------------------------------------------\ndef format_r_p(r, p, r_dp=2):\n\n    r = '{:2.{width}f}'.format(r, width=r_dp)\n    r = '= {}'.format(r)\n\n    if p < 0.001:\n        p = '< .001'\n    else:\n        p = '{:2.3f}'.format(p)\n        p = '= {}'.format(p[1:])\n\n    return r, p\n\n\n#-------------------------------------------------------------------------------\n# Read in the behavioural and extracted regional MRI values\n#-------------------------------------------------------------------------------\ndef read_in_data(f_behav, f_mri):\n    behav_df = pd.read_csv(f_behav)\n\n    mri_df = pd.read_csv(f_mri, sep=r\"\\s*\", engine='python')\n    mri_df['subid_long'] = mri_df['sub_id']\n\n    df = behav_df.merge(mri_df, on='subid_long')\n\n    df.loc[:, 'Age_scan_sq'] = df.loc[:, 'Age_scan']**2\n\n    return df\n\n#-------------------------------------------------------------------------------\n# Calculate residuals for a given covariate\n#-------------------------------------------------------------------------------\ndef residuals(x, y):\n    '''\n    A useful little function that correlates\n    x and y together to give their residual\n    values. These can then be used to calculate\n    partial correlation values\n    '''\n    import numpy as np\n\n    if len(x.shape) == 1:\n        x = x[np.newaxis, :]\n    A = np.vstack([x, np.ones(x.shape[-1])]).T\n    B = np.linalg.lstsq(A, y)[0]\n    m = B[:-1]\n    c = B[-1]\n    pre = np.sum(m * x.T, axis=1) + c\n    res = y - pre\n    return res\n\n\n#-------------------------------------------------------------------------------\n# Read in just the left lateral brain image\n#-------------------------------------------------------------------------------\ndef read_in_leftlatbrain(results_surface_file):\n\n    img = mpimg.imread(results_surface_file)\n\n    img = img[25:-665, 35:-785, :]\n\n    return img\n\n#-------------------------------------------------------------------------------\n# Add a circle to figure 5\n#-------------------------------------------------------------------------------\ndef add_circle(ax):\n\n    circle = plt.Circle((150, 350), 90,\n                        linestyle='dashed',\n                        fill=False,\n                        edgecolor='k',\n                        linewidth=4)\n    ax.add_artist(circle)\n\n    return ax\n\n#-------------------------------------------------------------------------------\n# Make the scatter plots for figure 5\n#-------------------------------------------------------------------------------\ndef figure5_scatterplots(f_behav, f_mri_cope1, f_mri_cope2, f_mri_cope4, ax_list, cluster=1, show_r_p=False):\n    '''\n    Create a 2x2 grid of scatter plots.\n\n    [0,0] - sem > baseline vs age\n    [0,1] - sem > baseline vs R2_accuracy PARTIAL\n    [0,1] - ana > baseline vs age\n    [1,1] - ana > baseline vs R2_accuracy PARTIAL\n\n    '''\n    df_cope1 = read_in_data(f_behav, f_mri_cope1)\n    df_cope2 = read_in_data(f_behav, f_mri_cope2)\n\n    colors_dict = { 'Age_scan' : sns.color_palette()[2],\n                    'R2_percent_acc' : sns.color_palette()[1] }\n\n    ax_list = ax_list.reshape(-1)\n\n    x_name_list = [ 'Age_scan', 'R2_percent_acc' ]\n    x_label_dict = { 'Age_scan' : 'Age (years)',\n                     'Age_scan_res' : 'Age (years) [Partial]',\n                     'R2_percent_acc' : 'Analogy accuracy (%)',\n                     'R2_percent_acc_res' : 'Analogy accuracy (%) [Partial]',\n                     'R2_percent_sem' : 'Semantic errors (%)',\n                     'R2_Correct_dividedby_Semantic' : 'Accuracy / Semantic Err' }\n    y_label_dict = { 0 : 'Semantic > Baseline',\n                     1 : 'Analogy > Baseline' }\n\n    df_dict = { 0 : df_cope1,\n                1 : df_cope2 }\n\n    for i, ax in enumerate(ax_list[::2]):\n\n        x_name = 'Age_scan'\n        y_name = 'cluster_{}'.format(cluster)\n\n        df = df_dict[i]\n\n        sns.regplot(x_name, y_name, data=df, ax=ax, color=colors_dict[x_name])\n\n        ax.locator_params(nbins=4)\n\n        ax.set_xlim(get_min_max(df[x_name]))\n        ax.set_ylim(get_min_max(df[y_name]))\n        ax.set_xlabel(x_label_dict[x_name])\n        ax.set_ylabel(y_label_dict[i])\n\n        r, p = pearsonr(df[x_name], df[y_name])\n        r, p = format_r_p(r, p)\n\n        ax.axhline(0, color='k', linestyle='dashed', linewidth=1)\n\n        if show_r_p:\n            ax.text(0.05, 0.95,\n                    'r {}\\np {}'.format(r, p),\n                    transform=ax.transAxes,\n                    horizontalalignment='left',\n                    verticalalignment='top',\n                    size='large')\n\n        if i == 0:\n            ax.set_xlabel('')\n        ax.yaxis.set_label_coords(-0.18, 0.5)\n\n    for i, ax in enumerate(ax_list[1::2]):\n\n        x_name = 'R2_percent_acc'\n        y_name = 'cluster_{}'.format(cluster)\n        covar_name = 'Age_scan'\n\n        df = df_dict[i]\n\n        x_res = residuals(df[covar_name], df[x_name])\n        y_res = residuals(df[covar_name], df[y_name])\n\n        df['{}_res'.format(x_name)] = x_res\n        df['{}_res'.format(y_name)] = y_res\n\n        sns.regplot('{}_res'.format(x_name),\n                    '{}_res'.format(y_name),\n                    data=df,\n                    ax=ax,\n                    color=colors_dict[x_name])\n\n        ax.locator_params(nbins=4)\n\n        ax.set_xlim(get_min_max(df['{}_res'.format(x_name)]))\n        ax.set_ylim(get_min_max(df['{}_res'.format(y_name)]))\n        ax.set_xlabel('{} [Partial]'.format(x_label_dict[x_name]))\n        ax.set_ylabel('{} [Partial]'.format(y_label_dict[i]))\n\n        r, p = pearsonr(df['{}_res'.format(x_name)], df['{}_res'.format(y_name)])\n        r, p = format_r_p(r, p)\n\n        ax.axhline(0, color='k', linestyle='dashed', linewidth=1)\n\n        if show_r_p:\n            ax.text(0.05, 0.95,\n                    'r {}\\np {}'.format(r, p),\n                    transform=ax.transAxes,\n                    horizontalalignment='left',\n                    verticalalignment='top',\n                    size='large')\n\n        if i == 0:\n            ax.set_xlabel('')\n\n        ax.yaxis.set_label_coords(-0.18, 0.5)\n\n    sns.despine()\n\n    return ax_list\n\n\n#-------------------------------------------------------------------------------\n#\n#-------------------------------------------------------------------------------\n\n\n#-------------------------------------------------------------------------------\n#\n#-------------------------------------------------------------------------------\n\n\n#-------------------------------------------------------------------------------\n# Add panel labels to figure 5\n#-------------------------------------------------------------------------------\ndef add_panel_labels_fig5(ax_brain, ax_list):\n\n    # First the letters\n    letter_ax_list = [ax_brain, ax_list[0], ax_list[2]]\n    x_list = [ 0.1, -0.24, -0.24 ]\n    y_list = [ 0.95, 1.0, 1.0 ]\n    color='k'\n    fontsize=18\n\n    letters = string.ascii_lowercase\n    for i, ax in enumerate(letter_ax_list):\n\n        ax.text(x_list[i], y_list[i],\n                '({})'.format(letters[i]),\n                fontsize=fontsize,\n                transform=ax.transAxes,\n                color=color,\n                horizontalalignment='center',\n                verticalalignment='center',\n                fontname='arial',\n                fontweight='bold'\n        )\n\n    # Then the lowercase roman numerals\n    color='k'\n    fontsize=18\n\n    numerals = [ 'i', 'ii', 'i', 'ii' ]\n\n    for i, ax in enumerate(ax_list):\n\n        ax.text(0.02, 0.95,\n                '{}'.format(numerals[i]),\n                fontsize=fontsize,\n                transform=ax.transAxes,\n                color=color,\n                horizontalalignment='left',\n                verticalalignment='center',\n                fontname='arial',\n                fontweight='bold'\n        )\n    return ax_brain, ax_list\n\n\n\n#===============================================================================\n# Now write your main figure & stats reporting functions\n# Note that some of the stats reporting functions are above, these ones below\n# the line are likely to be called directly from the jupyter notebook.\n#===============================================================================\n\n#-------------------------------------------------------------------------------\n# Figure 1\n#-------------------------------------------------------------------------------\ndef make_figure1(analogy_stimulus_file, semantic_stimulus_file, color_dict):\n\n    fig, ax_list = plt.subplots(1, 2, figsize=(9,4.5))\n\n    # Put the example analogy stimulus on the left\n    ax = ax_list[0]\n    img_ana = read_in_analogy(analogy_stimulus_file)\n    ax.imshow(img_ana)\n\n    # Add the coloured boxes & text\n    ax = add_boxes_analogy(ax, color_dict)\n\n    # Put the example semantic stimulus on the right\n    ax = ax_list[1]\n    img_sem = read_in_semantic(semantic_stimulus_file)\n    ax.imshow(img_sem)\n\n    # Add the coloured boxes & text\n    ax = add_boxes_semantic(ax, color_dict)\n\n    # Add in panel labels\n    ax_list = add_panel_labels_fig1(ax_list)\n\n    # Turn off both of the axes\n    for ax in ax_list:\n        ax.set_axis_off()\n\n    # Tighten up the layout\n    plt.subplots_adjust(left=0, bottom=0, right=1, top=1,\n                        wspace=0, hspace=0)\n\n    # Save the figure\n    fig.savefig('../FIGURES/Figure1_lowres.png', dpi=150, bbox_inches=0)\n    fig.savefig('../FIGURES/Figure1.png', dpi=600, bbox_inches=0)\n    fig.savefig('../FIGURES/Figure1.pdf', dpi=600, bbox_inches=0)\n\n    plt.show()\n\n#-------------------------------------------------------------------------------\n# Figure 2\n#-------------------------------------------------------------------------------\ndef make_figure2(f_behav):\n\n    df = pd.read_csv(f_behav)\n\n    df.loc[:, 'Age_scan_sq'] = df.loc[:,'Age_scan']**2\n\n    # Define the color list\n    color_list = palettable.colorbrewer.get_map('Set1', 'qualitative', 5).mpl_colors\n\n    colors_dict = { 'R1_percent_acc' : color_list[0],\n                    'R2_percent_acc' : color_list[1],\n                    'R1_meanRTcorr_cor' : color_list[0],\n                    'R2_meanRTcorr_cor' : color_list[1],\n                    'R2_percent_dis' : color_list[2],\n                    'R2_percent_per' : color_list[3],\n                    'R2_percent_sem' : color_list[4] }\n\n    fig, ax_list = plt.subplots(1,3, figsize=(16,4.5))\n\n    ax_list = ax_list.reshape(-1)\n\n    x_label_dict = { 'Age_scan' : 'Age (years)' }\n    y_label_dict = { 0 : 'Accuracy (% resp)',\n                     1 : 'Reaction time (s)',\n                     2 : 'Analogy error rate (% resp)' }\n    y_measures_dict = { 0 : ['R1_percent_acc', 'R2_percent_acc'],\n                        1 : ['R1_meanRTcorr_cor', 'R2_meanRTcorr_cor'],\n                        2 : ['R2_percent_dis', 'R2_percent_per', 'R2_percent_sem']}\n    y_measures_label_dict = { 'R1_percent_acc' : 'Semantic',\n                              'R2_percent_acc' : 'Analogy',\n                              'R1_meanRTcorr_cor' : 'Semantic',\n                              'R2_meanRTcorr_cor' : 'Analogy',\n                              'R2_percent_dis' : 'Unrelated',\n                              'R2_percent_per' : 'Perceptual',\n                              'R2_percent_sem' : 'Semantic' }\n    legend_loc_dict = { 0 : 4,\n                        1 : 1,\n                        2 : 1 }\n    legend_rev_dict = { 0 : False,\n                        1 : False,\n                        2 : True }\n\n    for i, ax in enumerate(ax_list):\n\n        x_name = 'Age_scan'\n        y_name_list = y_measures_dict[i]\n\n        colors_list = []\n        labels_list = []\n\n        for y_name in y_name_list:\n            sns.regplot(x_name, y_name, data=df,\n                        ax=ax,\n                        color=colors_dict[y_name],\n                        order=2)\n            colors_list += [colors_dict[y_name]]\n            labels_list += [y_measures_label_dict[y_name]]\n\n        ax.locator_params(nbins=6, axis='y')\n        ax.set_xticks([6, 10, 14, 18])\n\n        ax.set_xlim(get_min_max(df[x_name]))\n        ax.set_ylim(get_min_max(df[y_name]))\n        ax.set_xlabel(x_label_dict[x_name])\n        ax.set_ylabel(y_label_dict[i])\n\n        add_line_to_legend(ax,\n                           color_list=colors_list,\n                           label_list=labels_list,\n                           loc=legend_loc_dict[i],\n                           rev=legend_rev_dict[i])\n\n    sns.despine()\n\n    # Add in panel labels\n    ax_list = add_panel_labels_fig2(ax_list)\n\n    # Tight layout\n    plt.tight_layout()\n\n    # Save the figure\n    fig.savefig('../FIGURES/Figure2_lowres.png', dpi=150, bbox_inches=0)\n    fig.savefig('../FIGURES/Figure2.png', dpi=600, bbox_inches=0)\n    fig.savefig('../FIGURES/Figure2.pdf', dpi=600, bbox_inches=0)\n\n    plt.show()\n\n#-------------------------------------------------------------------------------\n# Report the descriptive behavoural stats\n#-------------------------------------------------------------------------------\ndef report_behav_stats(f_behav):\n\n    df = pd.read_csv(f_behav)\n    df.loc[:, 'Age_scan_sq'] = df.loc[:,'Age_scan']**2\n\n    print('===== Behavioural Statistics ======')\n    for measure in ['R1_percent_acc', 'R2_percent_acc',\n                    'R1_meanRTcorr_cor', 'R2_meanRTcorr_cor',\n                    'R2_percent_sem', 'R2_percent_per', 'R2_percent_dis']:\n        print('{}: N = {:2.0f}, M = {:2.3f}, SD = {:2.3f}'.format(measure, df[measure].notnull().count(), df[measure].mean(), df[measure].std()))\n\n        if 'R2' in measure and not 'dis' in measure and not 'sem' in measure and not measure.endswith('per'):\n            measure_diff = measure.replace('R2', 'R2_sub_R1')\n            df[measure_diff] = df[measure] - df[measure.replace('R2', 'R1')]\n            print('{}: M = {:2.3f}, SD = {:2.3f}'.format(measure_diff, df[measure_diff].mean(), df[measure_diff].std()))\n            print('    N R2 gt R1 = {}, N R2 lt R1 = {}, N same = {}'.format(np.sum(df[measure_diff]>0),\n                                                                         np.sum(df[measure_diff]<0),\n                                                                         np.sum(df[measure_diff]==0)))\n\n        if 'R1' in measure:\n            t, p = ttest_rel(df[measure], df[measure.replace('R1', 'R2')])\n            print('   R1 vs R2 (paired): t({:2.0f}) = {:2.3f}, p = {:2.3f}'.format(df[measure].count()-1, t, p))\n\n        if 'sem' in measure:\n            t, p = ttest_rel(df['R2_percent_sem'], df['R2_percent_per'])\n            print('   sem vs per (paired): t({:2.0f}) = {:2.3f}, p = {:2.3f}'.format(df['R2_percent_per'].count()-1, t, p))\n        if 'dis' in measure:\n            t, p = ttest_rel(df['R2_percent_per'], df['R2_percent_dis'])\n            print('   per vs dis (paired): t({:2.0f}) = {:2.3f}, p = {:2.3f}'.format(df['R2_percent_per'].count()-1, t, p))\n\n    print('\\n===== Correlations with age =====')\n    measures_list = ['R1_percent_acc', 'R2_percent_acc',\n                     'R1_meanRTcorr_cor', 'R2_meanRTcorr_cor',\n                     'R2_percent_dis', 'R2_percent_per', 'R2_percent_sem']\n\n    for measure in measures_list:\n        report_behav_age_correlations(measure, df)\n\n\n#-------------------------------------------------------------------------------\n# Figure 3\n#-------------------------------------------------------------------------------\ndef make_figure3(mean_results_surface_file, venn_file):\n\n    fig, ax = plt.subplots(figsize=(6,4.5))\n\n    # Add in the brains\n    img_brains = read_in_brains(mean_results_surface_file)\n\n    ax.imshow(img_brains)\n\n    # Turn off the axes\n    ax.set_axis_off()\n\n    # Overlay the venn diagram\n    ax_venn = fig.add_axes([0.25, 0.21, 0.5, 0.5]) # inset axes\n    img_venn = read_in_venn(venn_file)\n\n    ax_venn.imshow(img_venn)\n\n    # Turn off the axes\n    ax_venn.set_axis_off()\n\n    # Tighten up the layout\n    plt.subplots_adjust(left=0, bottom=0, right=1, top=1,\n                        wspace=0, hspace=0)\n\n    # Save the figure\n    fig.savefig('../FIGURES/Figure3_lowres.png', dpi=150, bbox_inches=0)\n    fig.savefig('../FIGURES/Figure3.png', dpi=600, bbox_inches=0)\n    fig.savefig('../FIGURES/Figure3.pdf', dpi=600, bbox_inches=0)\n\n    plt.show()\n\n#-------------------------------------------------------------------------------\n# Figure 4\n#-------------------------------------------------------------------------------\ndef make_figure4(corrage_results_surface_file, venn_file, f_behav, f_mri_cope1, f_mri_cope2, f_mri_cope4):\n\n    # Report the cluster statistics\n    report_cluster_stats(f_behav, f_mri_cope1, f_mri_cope2, f_mri_cope4, cluster=1)\n\n    # Now make the figure\n    fig, ax = plt.subplots(figsize=(6,4.5))\n\n    # Add in the brains\n    img_brains = read_in_brains(corrage_results_surface_file)\n\n    ax.imshow(img_brains)\n\n    # Turn off the axes\n    ax.set_axis_off()\n\n    # Overlay the venn diagram\n    ax_venn = fig.add_axes([0.25, 0.21, 0.5, 0.5]) # Add in a new axis\n    img_venn = read_in_venn(venn_file)\n\n    ax_venn.imshow(img_venn)\n\n    # Turn off the axes\n    ax_venn.set_axis_off()\n\n    # Tighten up the layout\n    plt.subplots_adjust(left=0, bottom=0, right=1, top=1,\n                        wspace=0, hspace=0)\n\n    # Save the figure\n    fig.savefig('../FIGURES/Figure4_lowres.png', dpi=150, bbox_inches=0)\n    fig.savefig('../FIGURES/Figure4.png', dpi=600, bbox_inches=0)\n    fig.savefig('../FIGURES/Figure4.pdf', dpi=600, bbox_inches=0)\n\n    plt.show()\n\n#-------------------------------------------------------------------------------\n# Figure 5\n#-------------------------------------------------------------------------------\ndef make_figure5(corracc_results_surface_file, f_behav, f_mri_cope1, f_mri_cope2, f_mri_cope4):\n\n    # Report the cluster statistics\n    report_cluster_stats(f_behav, f_mri_cope1, f_mri_cope2, f_mri_cope4, cluster=1)\n\n    fig, ax_list = plt.subplots(2,2, figsize=(14,7.5))\n\n    # Add in left lateral brain\n    ax_brain = fig.add_axes([-0.01, 0.2, 0.36, 0.6]) # Add in the brain axis\n\n    img_brain = read_in_leftlatbrain(corracc_results_surface_file)\n\n    ax_brain.imshow(img_brain)\n\n    # Add the dashed circle\n    ax_brain = add_circle(ax_brain)\n\n    # Turn off the axes\n    ax_brain.set_axis_off()\n\n    # Shift these subplots over to the right\n    # to make space for the brain\n    fig.subplots_adjust(left=0.42, right=0.99, bottom=0.1, top=0.97, wspace=0.3)\n\n    # Add in the scatter plots\n    ax_list = figure5_scatterplots(f_behav, f_mri_cope1, f_mri_cope2, f_mri_cope4,\n                                   ax_list, cluster=1, show_r_p=False)\n\n    # Add in the labels\n    ax_brain, ax_list = add_panel_labels_fig5(ax_brain, ax_list)\n\n    # Save the figure\n    fig.savefig('../FIGURES/Figure5_lowres.png', dpi=150, bbox_inches=0)\n    fig.savefig('../FIGURES/Figure5.png', dpi=600, bbox_inches=0)\n    fig.savefig('../FIGURES/Figure5.pdf', dpi=600, bbox_inches=0)\n\n    plt.show()\n","repo_name":"KirstieJane/NORA_WhitakerVendetti_DevSci2017","sub_path":"SCRIPTS/visan_results_figures.py","file_name":"visan_results_figures.py","file_ext":"py","file_size_in_byte":31583,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"81"}
+{"seq_id":"2056587967","text":"from __future__ import print_function\nimport argparse\n\nimport pandas as pd\nfrom rootpy.root2hdf5 import root2hdf5\n\nparser = argparse.ArgumentParser(description='Convert ntuple from ROOT file to hdf5 store')\nparser.add_argument('root_file', type=argparse.FileType(), help='Path to the ROOT file')\nparser.add_argument('-i', '--input', help='Input tuple (default: %(default)s)', default='/ToolSvc.PatDebugTTTruthTool/DownstreamDebugTuple')\nparser.add_argument('-o', '--output', help='Output table in hdf5 (default: %(default)s)', default='DownstreamDebugTuple')\n\nargs = parser.parse_args()\n\ninput_filename = args.root_file.name\noutput_filename = input_filename[:input_filename.rindex('.root')] + '.h5'\n\nprint('Converting {} to {}'.format(input_filename, output_filename))\n\nrpath = ''\nif '/' in args.input[1:]:\n    rpath = args.input[:args.input.rindex('/')]\n\nroot2hdf5(input_filename, output_filename, rpath)\n\nhdf = pd.HDFStore(output_filename)\ntable = hdf.get(args.input)\nhdf.put(args.output, table, 'table')\nhdf.remove(args.input)\nhdf.close()\n","repo_name":"jchmura/masters-analysis","sub_path":"root/convert_root_hdf5.py","file_name":"convert_root_hdf5.py","file_ext":"py","file_size_in_byte":1042,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"5910351823","text":"from PIL import Image\nfrom skimage.measure import compare_ssim\nimport math\nimport numpy as np\nimport openface\nimport os\nimport argparse\nimport cv2\n\ndataroot = '/dataset/AffineGAN_dataset/'\nconv_pattern = 'img_{0:04d}.png'\nvideoGAN_pattern = 'gen_1{0:04d}.png'\nflow_pattern = '001_{0:02d}_pred.png'\nganim_pattern = '{0:d}_out.jpg'\nconv_start = 1\nconv_end = 10\nvideoGAN_start = 1\nvideoGAN_end = 17\nflow_start = 0\nflow_end = 17\nours_start = 0\nours_end = 16\nganim_start = 0\nganim_end = 16\n\nsize = 128\ngray = 'RGB'\nfileDir = os.path.dirname(os.path.realpath(__file__))\nmodelDir = os.path.join(fileDir, '.', 'models')\ndlibModelDir = os.path.join(modelDir, 'dlib')\nopenfaceModelDir = os.path.join(modelDir, 'openface')\nparser = argparse.ArgumentParser()\n\nparser.add_argument('--dlibFacePredictor', type=str, help=\"Path to dlib's face predictor.\",\n                    default=os.path.join(dlibModelDir, \"shape_predictor_68_face_landmarks.dat\"))\nparser.add_argument('--networkModel', type=str, help=\"Path to Torch network model.\",\n                    default=os.path.join(openfaceModelDir, 'nn4.small2.v1.t7'))\nparser.add_argument('--imgDim', type=int,\n                    help=\"Default image dimension.\", default=96)\nparser.add_argument('--verbose', action='store_true')\nparser.add_argument('--mode', type=str, default='train')\n\nargs = parser.parse_args()\nnet = openface.TorchNeuralNet(args.networkModel, args.imgDim)\nalign = openface.AlignDlib(args.dlibFacePredictor)\n\n\ndef psnr(img1, img2):\n    mse = np.mean((img1 - img2) ** 2)\n    if mse == 0:\n        return 100\n    PIXEL_MAX = 255.0\n    return 20 * math.log10(PIXEL_MAX / math.sqrt(mse))\n\n\ndef get_ssim_psnr(ref_dir, pred_dir, pattern, start, end):\n    ssim_conv = 0.\n    psnr_conv = 0.\n    imgs = sorted(os.listdir(ref_dir))\n    img_num = len(imgs)\n    for i in range(start, end):\n        a = Image.open(\n            os.path.join(ref_dir, imgs[int((i - 1) * img_num / (end - start))]).convert(gray).resize((size, size)))\n        a = np.array(a)\n        b = Image.open(pred_dir + pattern.format(i)).convert(gray).resize((size, size))\n        b = np.array(b)\n        ssim_conv += compare_ssim(a, b, multichannel=gray == 'RGB')\n        psnr_conv += psnr(b, a)\n    return ssim_conv / (end - start), psnr_conv / (end - start)\n\n\ndef get_rep(img_path):\n    bgrImg = cv2.imread(img_path)\n    bgrImg = cv2.resize(bgrImg, (size, size))\n    rgbImg = cv2.cvtColor(bgrImg, cv2.COLOR_BGR2RGB)\n    bb = align.getLargestFaceBoundingBox(rgbImg)\n    if bb is None:\n        return np.random.rand(128)\n    alignedFace = align.align(args.imgDim, rgbImg, bb,\n                              landmarkIndices=openface.AlignDlib.OUTER_EYES_AND_NOSE)\n    rep1 = net.forward(alignedFace)\n    return rep1\n\n\ndef get_ACD(ref_dir, pred_dir, pattern, start, end):\n    ACD_m = 0.\n    ACD_i = 0.\n    ACD_c = 0.\n    ACD_ref_i = 0.\n    ACD_ref_c = 0.\n    imgs = sorted(os.listdir(ref_dir))\n    img_num = len(imgs)\n    first = get_rep(os.path.join(ref_dir, imgs[0]))\n    pred_features = []\n    ref_features = []\n    for i in range(start, end):\n        a = get_rep(os.path.join(ref_dir, imgs[int((i - 1) * img_num / (start - end))]))\n        b = get_rep(os.path.join(pred_dir, pattern.format(i)))\n        pred_features.append(b)\n        ref_features.append(a)\n        ACD_m += np.linalg.norm(a - b, 2)\n        ACD_i += np.linalg.norm(b - first, 2)\n        ACD_ref_i += np.linalg.norm(a - first, 2)\n    for i in range(len(pred_features)):\n        for j in range(i, len(pred_features)):\n            ACD_c += np.linalg.norm(pred_features[i] - pred_features[j], 2)\n            ACD_ref_c += np.linalg.norm(ref_features[i] - ref_features[j], 2)\n    return ACD_m / (end - start), ACD_i / (end - start), ACD_c / (\n                len(pred_features) * (len(pred_features) - 1)) * 2, ACD_ref_i / (end - start), ACD_ref_c / (\n                       len(pred_features) * (len(pred_features) - 1)) * 2\n\nresults_root = '/dataset/Results'\nacd_m = [0.] * 5\nacd_i = [0.] * 5\nacd_c = [0.] * 5\nacd_ref_i = [0.] *5\nacd_ref_c = [0.] * 5\nimg_num = 0\nfor category in os.listdir(dataroot):\n    print(category, 'begin')\n    ref_root = os.path.join(dataroot, category, 'test', 'img')\n    imgs = sorted([f for f in os.listdir(ref_root)])\n    img_num += len(imgs)\n    for idx in range(len(imgs)):\n        id = imgs[idx]\n        ours_pattern = id + '_fake_B_list{0:d}.png'\n        ref_dir = os.path.join(ref_root, id)\n        conv_pred_dir = os.path.join(results_root, 'convlstm', 'results_valid', category, 'vid_{0:04d}'.format(idx + 1))\n        videoGAN_pred_dir = os.path.join(results_root, 'videogan', 'results_valid', category, 'vis')\n        flow_pred_dir = os.path.join(results_root, 'flowground', 'results_valid', category, id)\n        ours_pred_dir = os.path.join(results_root, 'ours', 'results_valid', category, 'test_latest', 'images')\n        ganim_pred_dir = os.path.join(results_root, 'ganimation', 'results_valid', category, id)\n        acds_conv = get_ACD(ref_dir, conv_pred_dir, conv_pattern, conv_start, conv_end)\n        acds_video = get_ACD(ref_dir, videoGAN_pred_dir, videoGAN_pattern, videoGAN_start, videoGAN_end)\n        acds_flow = get_ACD(ref_dir, flow_pred_dir, flow_pattern, flow_start, flow_end)\n        acds_ganim = get_ACD(ref_dir, ganim_pred_dir, ganim_pattern, ganim_start, ganim_end)\n        acds_ours = get_ACD(ref_dir, ours_pred_dir, ours_pattern, ours_start, ours_end)\n        acd_m[0] += acds_conv[0]\n        acd_m[1] += acds_video[0]\n        acd_m[2] += acds_flow[0]\n        acd_m[3] += acds_ganim[0]\n        acd_m[4] += acds_ours[0]\n        acd_i[0] += acds_conv[1]\n        acd_i[1] += acds_video[1]\n        acd_i[2] += acds_flow[1]\n        acd_i[3] += acds_ganim[1]\n        acd_i[4] += acds_ours[1]\n        acd_c[0] += acds_conv[2]\n        acd_c[1] += acds_video[2]\n        acd_c[2] += acds_flow[2]\n        acd_c[3] += acds_ganim[2]\n        acd_c[4] += acds_ours[2]\n        acd_ref_i[0] += acds_conv[3]\n        acd_ref_i[1] += acds_video[3]\n        acd_ref_i[2] += acds_flow[3]\n        acd_ref_i[3] += acds_ganim[3]\n        acd_ref_i[4] += acds_ours[3]\n        acd_ref_c[0] += acds_conv[4]\n        acd_ref_c[1] += acds_video[4]\n        acd_ref_c[2] += acds_flow[4]\n        acd_ref_c[3] += acds_ganim[4]\n        acd_ref_c[4] += acds_ours[4]\n    print(category, 'done')\n\nprint(np.array(acd_m) / img_num)\nprint(np.array(acd_i) / img_num)\nprint(np.array(acd_c) / img_num)\nprint(np.array(acd_ref_i) / img_num)\nprint(np.array(acd_ref_c) / img_num)\n","repo_name":"sunlightsgy/AffineGAN","sub_path":"util/metrics.py","file_name":"metrics.py","file_ext":"py","file_size_in_byte":6463,"program_lang":"python","lang":"en","doc_type":"code","stars":53,"dataset":"github-code","pt":"81"}
+{"seq_id":"35776759996","text":"import json\nimport logging\nimport logging.config\n\n\nfrom flask import render_template, request\nfrom flask_login import current_user, login_required\n\nfrom bookcrossing.views.books.base_book_view import BaseBookView\nfrom bookcrossing.models.book import BookModel\nfrom bookcrossing.models.category import CategoryModel\nfrom bookcrossing.forms.book import AddBookForm, UpdateBookForm\nfrom bookcrossing.config import LOGGING\n\nlogging.config.dictConfig(LOGGING)\n\n\nclass BooksView(BaseBookView):\n    @login_required\n    def get(self):\n        \"\"\"\n        Get all books from database\n        \"\"\"\n        form = AddBookForm(request.form)\n        shelf = self.make_shelf(current_user.id)\n        logging.debug('GET. All books of {} have been shown.'\n                      .format(current_user.login))\n        return render_template('books.html', form=form.data, shelf=shelf)\n\n\n    @login_required\n    def post(self):\n        \"\"\"\n        Create book\n        \"\"\"\n        form = AddBookForm(request.form)\n        print(request.form)\n        if form.validate():\n            logging.debug('POST. Add book form validated.')\n            self.create_model(BookModel, CategoryModel, form.data)\n            shelf = self.make_shelf(current_user.id)\n            logging.debug('Hello from POST. Book created.'\n                          'All books of {} have been shown.'\n                          .format(current_user.login))\n            return render_template('books.html', form=form.data, shelf=shelf)\n\n        else:\n            logging.error('Hello from POST. Creation book error.')\n            return json.dumps(form.errors)\n\n    @login_required\n    def put(self):\n        \"\"\"\n        Update book\n        \"\"\"\n        form = UpdateBookForm(request.form)\n        if form.validate():\n            logging.debug('Hello from PUT. Update book form validated.')\n            self.update_model(BookModel, form.data)\n            shelf = self.make_shelf(current_user.id)\n            logging.debug('Hello from PUT. Book updated.'\n                          'All books of {} have been shown.'\n                          .format(current_user.login))\n            return render_template('books.html', form=form.data, shelf=shelf)\n\n        else:\n            logging.error('Hello from PUT. Update book error.')\n            return json.dumps(form.errors)\n\n    @login_required\n    def delete(self):\n        \"\"\"\n        Delete book\n        \"\"\"\n        book = request.get_json()\n        self.delete_model(book['id'], BookModel)\n        logging.debug('DELETE. Book deletion error.')\n        return json.dumps({'delete': 'ok', 'id': book['id']})\n\n\nclass BookProfileView(BaseBookView):\n    def get(self, book_id):\n        book, owner = self.get_book_profile(book_id)\n        logging.debug('Hello from GET. Book {} profile template rendered.'\n                      .format(book['title']))\n        return render_template('book_profile.html', book_id=book_id, book=book, owner=owner)\n","repo_name":"zkite/bookcrossing","sub_path":"bookcrossing/views/books/book.py","file_name":"book.py","file_ext":"py","file_size_in_byte":2933,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"221676921","text":"import pandas as pd \nimport matplotlib.pyplot as plt \nfrom sklearn.linear_model import LinearRegression\nfrom sklearn.model_selection import train_test_split\ndf = pd.read_csv(\"../consumo_cerveja.csv\",sep=\";\")\n\nx = df[['temp_max']]\ny = df[['consumo']]\n\nX_train, X_test, y_train, y_test = train_test_split(x, y)\n\nlr = LinearRegression().fit(X_train, y_train)\n\ny_pred = lr.predict(X_test)\nx_pred = lr.predict(y_test)\nval2   = zip(x_pred,y_pred)\n\nvals = zip(y_pred, y_test.values)\n\nplt.scatter(X_test, y_test, color=\"black\")\nplt.plot(X_test, y_pred, color=\"blue\", linewidth=3)\n\nplt.show()\n\n\n\n\n\n\n\n\n\n\n","repo_name":"VarleiDeCesare/IA","sub_path":"aula05/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":594,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"7447678748","text":"from .base import *\n\nDEBUG = False\n\nALLOWED_HOSTS = ['localhost', '127.0.0.1', 'lifelog.piechika.com']\nCSRF_TRUSTED_ORIGINS = ['https://lifelog.piechika.com']\n\nEMAIL_BACKEND = 'django.core.mail.backends.smtp.EmailBackend'\n\nSTATIC_ROOT = os.getenv('STATIC_ROOT')\nMEDIA_ROOT = os.getenv('MEDIA_ROOT')","repo_name":"kettleTeacues/lifelog","sub_path":"lifelogProject/settings/production.py","file_name":"production.py","file_ext":"py","file_size_in_byte":298,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"26659693635","text":"import geopandas as gpd\nimport numpy as np\nfrom shapely.geometry import Point\nfrom tqdm import tqdm\nimport warnings\nimport xarray as xr\n\nwarnings.filterwarnings(\"ignore\")\n\n\n#获取仿射矩阵信息\ndef Getgeotrans():\n    dataset = xr.open_rasterio('./1931_modified.tif')\n    return dataset.transform\n\ndef pixel2Coord(Xpixel,Ypixel,GeoTransform):\n    XGeo = GeoTransform[0]+GeoTransform[1]*Xpixel+Ypixel*GeoTransform[2]\n    YGeo = GeoTransform[3]+GeoTransform[4]*Xpixel+Ypixel*GeoTransform[5]\n    return XGeo,YGeo\n\nPATH = \"../inventory_sgi1931_r2022/SGI_1931.shp\"\nseg = gpd.read_file(PATH)\n\nlabels = np.load(\"./mod_labels_small.npy\")\ny_len, x_len = labels.shape\nprint(\"x_len: \",x_len )\nprint(\"y_len: \",y_len )\n\nurbanData = xr.open_rasterio('./1931_modified.tif')\nur  = xr.DataArray(urbanData, name='myData')\nur  = ur.to_dataframe().reset_index() \nur = ur[ur['band'] == 1]\nx_l = ur['x']\ny_l = ur['y']\n\n\nx_list = []\ny_list = []\npoints = []\n\nend_point = 0\n\nfor i in tqdm(range(end_point, x_len)):\n    for j in range(y_len):\n        x_list.append(i)\n        y_list.append(j)\n        points.append(Point(x_l[i + j * x_len], y_l[i + j * x_len]))\n        if j % 3000 == 0 or j == y_len - 1:\n            res = gpd.GeoDataFrame({'geometry': points, 'x':x_list, 'y':y_list}).sjoin(seg, how=\"inner\", predicate='intersects')\n            for _, (x,y) in enumerate(zip(res.x, res.y)):\n                labels[y,x] = True\n            x_list = []\n            y_list = []\n            points = []\n    if i % 500 == 0:\n        np.save(\"./mod_labels_small_1931.npy\", labels)\n\nnp.save(\"./mod_labels_small_1931.npy\", labels)","repo_name":"TJZhiyeWang/Digital_Humanity_Project","sub_path":"label/generate_label_modified.py","file_name":"generate_label_modified.py","file_ext":"py","file_size_in_byte":1601,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"71758334026","text":"import tkinter as tk\nfrom tkinter import* \nfrom PIL import Image, ImageTk\nfrom tkinter import Canvas, PhotoImage\nimport random\nimport webbrowser\n\ndef sum2():\n    noodlelist=[\"ก๋วยเตี๋ยวน้ำใส\",\"ก๋วยเตี๋ยวน้ำตก\",\"ก๋วยเตี๋ยวเรือ\",\"ก๋วยเตี๋ยวต้มยำน้ำข้น/ใส\",\"ก๋วยเตี๋ยวต้มยำแห้ง\",\n                \"เย็นตาโฟ\",\"ราดหน้า\",\"ก๋วยเตี๋ยวคั่วไก่\",\"เกี๊ยวน้ำ\",\"ข้าวซอย\",\"สุกี้น้ำ\",\"สุกี้แห้ง\",\n                \"ก๋วยจั๊บ\"]#ลิสต์ก๋วยเตี๋ยว\n\n    caldict = {\"ก๋วยเตี๋ยวน้ำใส\":147,\n            \"ก๋วยเตี๋ยวน้ำตก\":462,\n            \"ก๋วยเตี๋ยวเรือ\":180,\n            \"ก๋วยเตี๋ยวต้มยำน้ำข้น/ใส\":335,\n            \"ก๋วยเตี๋ยวต้มยำแห้ง\":310,\n            \"เย็นตาโฟ\":420,\n            \"ราดหน้า\":690,\n            \"ก๋วยเตี๋ยวคั่วไก่\":435,\n            \"เกี๊ยวน้ำ\":275,\n            \"ข้าวซอย\":395,\n            \"สุกี้น้ำ\":345,\n            \"สุกี้แห้ง\":350,\n            \"ก๋วยจั๊บ\":240}\n\n    linkdict = {\"ก๋วยเตี๋ยวน้ำใส\":\"https://www.wongnai.com/recipes/ugc/6c273742a82a4d588d5a610113a62bf9\",\n            \"ก๋วยเตี๋ยวน้ำตก\":\"https://cooking.kapook.com/view117325.html\",\n            \"ก๋วยเตี๋ยวเรือ\":\"https://www.wongnai.com/recipes/boat-noodle\",\n            \"ก๋วยเตี๋ยวต้มยำน้ำข้น/ใส\":\"https://www.wongnai.com/recipes/tom-yum-noodle\",\n            \"ก๋วยเตี๋ยวต้มยำแห้ง\":\"https://cookpad.com/th/recipes/9052448-%E0%B8%81%E0%B8%A7%E0%B8%A2%E0%B9%80%E0%B8%95%E0%B8%A2%E0%B8%A7%E0%B9%81%E0%B8%AB%E0%B8%87%E0%B8%95%E0%B8%A1%E0%B8%A2%E0%B8%B3%E0%B9%82%E0%B8%9A%E0%B8%A3%E0%B8%B2%E0%B8%93\",\n            \"เย็นตาโฟ\":\"https://www.wongnai.com/recipes/yen-ta-four\",\n            \"ราดหน้า\":\"https://www.wongnai.com/recipes/thai-noodle-with-pork-in-gravy\",\n            \"ก๋วยเตี๋ยวคั่วไก่\":\"https://www.happyfresh.co.th/blog/recipe/stir-fried-noodles-with-chicken/\",\n            \"เกี๊ยวน้ำ\":\"https://www.wongnai.com/recipes/dumpling-soup\",\n            \"ข้าวซอย\":\"https://www.wongnai.com/recipes/northern-thai-curried-noodles-soup\",\n            \"สุกี้น้ำ\":\"https://www.wongnai.com/recipes/homemade-sukiyaki\",\n            \"สุกี้แห้ง\":\"https://www.wongnai.com/recipes/ugc/47161c946e194a3d82defc69589cf035\",\n            \"ก๋วยจั๊บ\":\"https://www.wongnai.com/recipes/chinese-roll-noodle-soup\"}\n\n    pricedict = {\"ก๋วยเตี๋ยวน้ำใส\":35,\n            \"ก๋วยเตี๋ยวน้ำตก\":40,\n            \"ก๋วยเตี๋ยวเรือ\":40,\n            \"ก๋วยเตี๋ยวต้มยำน้ำข้น/ใส\":40,\n            \"ก๋วยเตี๋ยวต้มยำแห้ง\":40,\n            \"เย็นตาโฟ\":40,\n            \"ราดหน้า\":40,\n            \"ก๋วยเตี๋ยวคั่วไก่\":45,\n            \"เกี๊ยวน้ำ\":45,\n            \"ข้าวซอย\":45,\n            \"สุกี้น้ำ\":40,\n            \"สุกี้แห้ง\":40,\n            \"ก๋วยจั๊บ\":35}\n\n    immagedict ={\"ก๋วยเตี๋ยวน้ำใส\":\"น้ำใส.jpg\",\n            \"ก๋วยเตี๋ยวน้ำตก\":\"น้ำตก.jpeg\",\n            \"ก๋วยเตี๋ยวเรือ\":\"เรือ.jpg\",\n            \"ก๋วยเตี๋ยวต้มยำน้ำข้น/ใส\":\"ต้มยำน้ำข้น.jpg\",\n            \"ก๋ว���เตี๋ยวต้มยำแห้ง\":\"ต้มยำแห้ง.jpg\",\n            \"เย็นตาโฟ\":\"เย็นตาโฟ.jpg\",\n            \"ราดหน้า\":\"ราดหน้า.jpg\",\n            \"ก๋วยเตี๋ยวคั่วไก่\":\"คั่วไก่.jpg\",\n            \"เกี๊ยวน้ำ\":\"เกี๊ยวน้ำ.jpg\",\n            \"ข้าวซอย\":\"ช้าวซอย.jpg\",\n            \"สุกี้น้ำ\":\"สุกี้น้ำ.jpg\",\n            \"สุกี้แห้ง\":\"สุกี้แห้ง.jpg\",\n            \"ก๋วยจั๊บ\":\"ก๋วยจั๊บ.jpg\"}\n\n    menu = random.choice(noodlelist)#โค้ดที่เป็นตัวสุ่ม เวลาจะสุ่มอีกครั้งสามารถกลับมาที่โค้ดนี้ได้เลย\n    cal = caldict[menu]#ดึงค่าแคลลอลี่จากดิจ\n    link = linkdict[menu]#ดึงลิงค์จากดิจ\n    price = pricedict[menu]#ดึงราคาจากดิจราคา\n    immage= immagedict[menu]#ดึงชื่อรูปจากดิจ\n\n    def callback(url):\n        webbrowser.open_new(url)\n\n\n    window = tk.Toplevel()\n    window.geometry(\"600x800\")\n    window.minsize(600, 800)\n    window.maxsize(600, 800)\n    window.title(\"วันนี้กินไรดี?\")\n    window.configure(bg=\"#54A2A4\")\n\n    frame1 = tk.Frame(master = window)\n    label1 = tk.Label(master = frame1, text=\"Frame 1\", width=100, height=100)\n\n    bg3 = Image.open(\"D:\\\\งาน\\\\comproglab\\\\วันนี้กินไรดี กลุ่ม 14\\\\Project\\\\draft_p3_bg.jpg\")\n    bg3 = bg3.resize((600,800), Image.ANTIALIAS)\n    label_bg3 = ImageTk.PhotoImage(bg3)\n    labelbg3 = tk.Label(master = frame1 ,image=label_bg3)\n    labelbg3.place(x=-2,y=0)\n\n    frame1.pack()\n    label1.pack()\n\n    #pic of food\n    label_2 = tk.Label(master = frame1, text=\"Frame 1\", width=100, height=100)\n    bg = Image.open(immage)\n    bg = bg.resize((300,230), Image.ANTIALIAS)\n    label_bg = ImageTk.PhotoImage(bg)\n    label7 = tk.Label(master = frame1,image=label_bg)\n    label7.place(x=152, y=205)\n\n    label_2.pack()\n\n\n    label = tk.Label(master = window,text = menu,font=\"Ayuthaya 30\",width=10,height=1,fg=\"white\",bg=\"#FCAF38\")\n    label1 = tk.Label(master = window,text = str(price) + \" บาท\",font=\"Ayuthaya 20\",width=13,height=3,fg=\"white\",bg=\"#674A40\")\n    label2 = tk.Label(master = window,text = str(cal) + \" แคลอรี\",font=\"Ayuthaya 20\",width=13,height=3,fg=\"white\",bg=\"#674A40\")\n    Button3 = tk.Button(master = window,text = \"วิธีทำ\",font=\"Ayuthaya 20\",width=12,height=1)\n    Button4 = tk.Button(master = window,text = \"สุ่มอีกครั้ง\",font=\"Ayuthaya 18\",width=10,height=1,fg=\"white\", bg=\"#F95335\", command = window.destroy)\n\n    Button3.bind(\"<Button-1>\",lambda e: callback(link))\n\n    label.place(x=200,y=60)\n    label1.place(x=60,y=490)\n    label2.place(x=340,y=490)\n    Button3.place(x=205,y=640)\n    Button4.place(x=410,y=725)\n       \n           \n    window.mainloop()\n","repo_name":"ncqxm/305272-Advanced-Computer-Programming","sub_path":"Project/program2.py","file_name":"program2.py","file_ext":"py","file_size_in_byte":7435,"program_lang":"python","lang":"th","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"12364269847","text":"import argparse\nimport json\nimport logging\nimport pathlib\nimport pickle\nimport os\nimport random\nimport time\nimport yaml\n\nimport numpy as np\nimport torch\nfrom transformers import (\n    AutoTokenizer,\n    AutoModel,\n    PreTrainedTokenizer,\n    PreTrainedModel\n)\n\nfrom utils.dataloader import load_corpus, CorpusDataset\nfrom utils.embed import encode, batch_encode\n\nrandom.seed(0)\n\n\ndef embed_documents(\n        logger: logging.Logger,\n        tokenizer: PreTrainedTokenizer,\n        embedder: PreTrainedModel,\n        corpus: CorpusDataset,\n        max_sequence_length: int = 128,\n        batch_size: int = 1,\n        n_runs: int = 100,\n        output_file: str = None,\n        benchmark_mode: bool = False) -> None:\n    \"\"\"Embed documents using a pretrained tokenizer and embedder\n\n    Args:\n        logger (logging.Logger):\n            logger\n        tokenizer (PreTrainedTokenizer):\n            pretrained tokenizer to to use for tokenizing raw text.\n        embedder (PreTrainedModel):\n            pretrained model to use to embed documents.\n        corpus (CorpusDataset):\n            CorpusDataset to embed.\n        max_seq_length (int, optional):\n            max sequence length. Defaults to 128.\n        batch_size (int, optional):\n            batch size. Defaults to 1.\n        n_runs (int, optional):\n            number of iterations for benchmarks. Defaults to 100.\n        output_file (str, optional):\n            file to output embeddings to. Defaults to None.\n        benchmark_mode (bool, optional):\n            whether to run in benchmark mode. Defaults to False.\n    \"\"\"\n\n    # Run benchmarks\n    if benchmark_mode:\n\n        times = []\n        # Generate random sample inputs for benchmarking execution time\n        sample_inputs = [\n            random.sample(\n                range(tokenizer.vocab_size), max_sequence_length) for\n            _ in range(batch_size)]\n\n        sample_inputs = tokenizer.batch_decode(sample_inputs)\n        for i in range(10 + n_runs):\n            start = time.time()\n            encode(\n                tokenizer, embedder,\n                sample_inputs, max_length=max_sequence_length)\n            end = time.time()\n            if i > 10:\n                times.append(end - start)\n\n        logger.info(\"Batch Size = %d, Max Seq Length = %d\",\n                    batch_size, max_sequence_length)\n        logger.info(\"Average Inference Time : %f\", np.mean(times))\n\n    else:\n\n        start = time.time()\n\n        embeddings = batch_encode(\n            tokenizer,\n            embedder,\n            corpus,\n            max_length=max_sequence_length,\n            batch_size=batch_size\n        )\n\n        end = time.time()\n\n        logger.info(\"Batch Size = %d, Max Seq Length = %d, Documents = %d\",\n                    batch_size, max_sequence_length, len(corpus))\n        logger.info(\"Embedding Time : %f\", end - start)\n\n        if output_file is not None:\n            path = pathlib.Path(output_file)\n            path.parent.mkdir(parents=True, exist_ok=True)\n\n            with open(output_file, 'wb') as f:\n                pickle.dump({\n                    '_ids': corpus.get_ids(),\n                    'embeddings': embeddings},\n                    f, protocol=pickle.HIGHEST_PROTOCOL)\n\n        else:\n            out = []\n            for i in range(embeddings.shape[0]):\n                out.append({\n                    \"index\": i,\n                    \"embedding\": embeddings[i, :].tolist()\n                })\n            print(json.dumps(out, indent=2))\n\n\ndef main(flags):\n    \"\"\"Run embedding of documents using a passed in model.\n\n    Args:\n        flags : run flags\n    \"\"\"\n\n    if flags.logfile == \"\":\n        logging.basicConfig(level=logging.DEBUG)\n\n    else:\n        path = pathlib.Path(flags.logfile)\n        path.parent.mkdir(parents=True, exist_ok=True)\n        logging.basicConfig(filename=flags.logfile, level=logging.DEBUG)\n\n    logger = logging.getLogger()\n\n    if not os.path.exists(flags.vse_config):\n        logger.error(\"VSE configuration %s not found!\", flags.vse_config)\n        return\n\n    # parse the yaml model config\n    with open(flags.vse_config, 'r') as stream:\n        conf = yaml.safe_load(stream)\n\n    tokenizer = AutoTokenizer.from_pretrained(\n        conf['model']['pretrained_model'])\n\n    if conf[\"model\"][\"format\"] == \"default\":\n\n        # load the pretrained embedding model\n        embedder = AutoModel.from_pretrained(conf['model']['pretrained_model'])\n\n    elif conf[\"model\"][\"format\"] == \"inc\":\n\n        # load an INC model by loading pretrained model and updating weights\n        from neural_compressor.utils.pytorch import load\n\n        embedder = AutoModel.from_pretrained(conf['model']['pretrained_model'])\n        embedder = load(conf[\"model\"][\"path\"], embedder)\n\n        # re-establish logger because it breaks from above\n        logging.getLogger().handlers.clear()\n\n        if flags.logfile == \"\":\n            logging.basicConfig(level=logging.DEBUG)\n        else:\n            logging.basicConfig(filename=flags.logfile, level=logging.DEBUG)\n        logger = logging.getLogger()\n\n    elif conf[\"model\"][\"format\"] == \"ipex_int8\":\n\n        # load a PT saved model saved using torch.save\n        if not os.path.exists(conf['model']['path'] + \"/saved_model.pt\"):\n            logger.error(\"Saved model %s not found!\",\n                         conf['model']['path'] + \"/saved_model.pt\")\n            return\n        embedder = torch.jit.load(conf['model']['path'] + \"/saved_model.pt\")\n\n    else:\n        return\n\n    embedder.eval()\n    max_sequence_length = conf['model']['max_seq_length']\n\n    # use IPEX to optimize model\n    if flags.intel:\n        import intel_extension_for_pytorch as ipex\n        embedder = ipex.optimize(embedder, dtype=torch.float32)\n\n    sample_inputs = tokenizer.batch_decode([\n        random.sample(\n            range(tokenizer.vocab_size), max_sequence_length) for\n        _ in range(1)])\n    dummy_input = tokenizer(\n        sample_inputs,\n        padding=True,\n        truncation=True,\n        max_length=max_sequence_length,\n        return_tensors='pt'\n    )\n\n    with torch.no_grad():\n        embedder = torch.jit.trace(\n            embedder,\n            [dummy_input['input_ids'], dummy_input['attention_mask']],\n            check_trace=False,\n            strict=False)\n        embedder = torch.jit.freeze(embedder)\n\n    # read in corpus dataset\n    corpus = load_corpus(flags.input_corpus)\n\n    embed_documents(\n        logger=logger,\n        tokenizer=tokenizer,\n        embedder=embedder,\n        corpus=corpus,\n        max_sequence_length=max_sequence_length,\n        batch_size=flags.batch_size,\n        n_runs=flags.n_runs,\n        output_file=flags.output_file,\n        benchmark_mode=flags.benchmark_mode)\n\n\nif __name__ == '__main__':\n\n    parser = argparse.ArgumentParser()\n\n    parser.add_argument('--logfile',\n                        type=str,\n                        default=\"\",\n                        help=\"Log file to output benchmarking results to.\")\n\n    parser.add_argument('--vse_config',\n                        type=str,\n                        required=True,\n                        help=\"Vertical Search Engine model config yml\"\n                        )\n\n    parser.add_argument('--input_corpus',\n                        required=True,\n                        help=\"path to corpus to embed\",\n                        type=str\n                        )\n\n    parser.add_argument('--output_file',\n                        required=False,\n                        help=\"file to output corpus embeddings to\",\n                        type=str,\n                        default=None\n                        )\n\n    parser.add_argument('--batch_size',\n                        required=False,\n                        help=\"batch size for embedding. defaults to 32.\",\n                        type=int,\n                        default=32\n                        )\n\n    parser.add_argument('--benchmark_mode',\n                        required=False,\n                        help=\"toggle to benchmark embedding\",\n                        action=\"store_true\",\n                        default=False\n                        )\n\n    parser.add_argument('--n_runs',\n                        required=False,\n                        help=\"number of iterations to benchmark embedding\",\n                        type=int,\n                        default=100\n                        )\n\n    parser.add_argument('--intel',\n                        required=False,\n                        help=\"use intel pytorch extension to optimize model\",\n                        action=\"store_true\",\n                        default=False\n                        )\n\n    FLAGS = parser.parse_args()\n\n    main(FLAGS)\n","repo_name":"oneapi-src/vertical-search-engine","sub_path":"src/run_document_embedder.py","file_name":"run_document_embedder.py","file_ext":"py","file_size_in_byte":8714,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"33371109304","text":"#!/usr/bin/python3\r\n\r\nfrom sys import stdin, stderr\r\nimport traceback\r\n\r\n\r\nclass Node:\r\n    def __init__(self):\r\n        self.barn = {}\r\n        self.posi = []\r\n\r\ndef buildTree(node, word):\r\n    letter = word[0]\r\n    word = word[1:]\r\n    if not letter in node.barn:\r\n        node.barn[letter] = Node()\r\n    if not word:\r\n        return node.barn[letter]\r\n    return buildTree(node.barn[letter], word)\r\n\r\ndef bygg(ordliste):\r\n    toppNode = Node()\r\n    for tuppel in ordliste:\r\n        endNode = buildTree(toppNode, tuppel[0])\r\n        endNode.posi.append(tuppel[1])\r\n    return toppNode\r\n\r\ndef posisjoner(ord, indeks, node):\r\n    pos = []\r\n    for letter in ord:\r\n        pos.sort()\r\n        if letter in node.barn:\r\n            node = node.barn[letter]\r\n            indeks += 1\r\n            if indeks == len(ord):\r\n                for i in node.posi:\r\n                    pos.append(i)\r\n                return pos\r\n        elif letter == '?':\r\n            indeks += 1\r\n            if indeks == len(ord):\r\n                for b in node.barn:\r\n                    barnet = node.barn[b]\r\n                    for i in barnet.posi:\r\n                        pos.append(i)\r\n                return pos\r\n            for i in node.barn:\r\n                index = indeks\r\n                barnet = node.barn[i]\r\n                if ord[index] in barnet.barn:\r\n                    while index != len(ord):\r\n                        barnet = barnet.barn[ord[index]]\r\n                        index += 1\r\n                    for p in barnet.posi:\r\n                            pos.append(p)\r\n                if pos:\r\n                    return pos\r\n    return pos\r\n\r\ndef main():\r\n    try:\r\n        ord = stdin.readline().split()\r\n        ordliste = []\r\n        pos = 0\r\n        for o in ord:\r\n            ordliste.append((o, pos))\r\n            pos += len(o) + 1\r\n        toppnode = bygg(ordliste)\r\n        for sokeord in stdin:\r\n            sokeord = sokeord.strip()\r\n            print(\"%s:\" % sokeord, end='')\r\n            posi = posisjoner(sokeord, 0, toppnode)\r\n            posi.sort()\r\n            for p in posi:\r\n                print(\" %s\" % p, end='')\r\n            print()\r\n    except:\r\n        traceback.print_exc(file=stderr)\r\n\r\n\r\nif __name__ == \"__main__\":\r\n    main()","repo_name":"matssa/Algdat","sub_path":"oving2.py","file_name":"oving2.py","file_ext":"py","file_size_in_byte":2259,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"36133900709","text":"#!/usr/bin/python3\n\"\"\"\ntakes your GitHub credentials (username and password)\nand uses the GitHub API to display your id\n\"\"\"\nimport sys\nimport requests\n\n\nif __name__ == \"__main__\":\n    url = 'https://api.github.com/user'\n    html = requests.get(url, auth=(sys.argv[1], sys.argv[2]))\n    try:\n        print(html.json().get('id'))\n    except ValueError:\n        print(\"Not a valid JSON\")\n","repo_name":"phemsie/alx-higher_level_programming","sub_path":"0x11-python-network_1/10-my_github.py","file_name":"10-my_github.py","file_ext":"py","file_size_in_byte":385,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"81"}
+{"seq_id":"19720061399","text":"import matplotlib\nimport pandas as pd\n\nmatplotlib.use('Agg') # Force matplotlib to not use any Xwindows backend.\nimport numpy as np\nfrom astropy.io import fits\nfrom custom_image_utils import scaled_plot\nfrom matplotlib import pyplot as plt\nimport scipy.spatial.distance\nimport skimage.transform\n\n# recreate the methods of Pawlik et al 2016\n# https://arxiv.org/pdf/1512.02000v2.pdf\n\n\n# binary mask created from 8-connected pixels in background-subtracted image at > 1 sig above bkg\n\n# Rmax is distance between 'center' (brightest pixel in mask) and most distant pixel\n\n\n\ndef findMax(im):\n    # print(im.shape)\n    center_flat = np.argmax(im)\n    x_c, y_c = np.unravel_index(center_flat, im.shape)\n    return x_c, y_c\n\ndef findMin(im):\n    # print(im.shape)\n    center_flat = np.argmin(im)\n    x_c, y_c = np.unravel_index(center_flat, im.shape)\n    return x_c, y_c\n\ndef normMatrix(im):\n    norm_matrix = np.zeros((im_height,im_width))\n    x_c, y_c = findMax(im)\n    # print(x_c, y_c)\n    for x in range(im_height):\n        for y in range(im_width):\n            x_dist = x - x_c\n            y_dist = y - y_c\n            norm_matrix[x,y] = np.linalg.norm((x_dist, y_dist))\n    # print(norm_matrix[126:132,126:132]) # verify center\n    return norm_matrix\n\ndef findRmax(im, mask):\n    norm_matrix = normMatrix(im) * mask\n    Rmax = norm_matrix.max()\n    # print(Rmax) # max distance in pixels between maxima and furthest mask pixel\n    return Rmax\n\ndef cmask(index,radius,array):\n  a,b = index\n  nx,ny = array.shape\n  y,x = np.ogrid[-a:nx-a,-b:ny-b]\n  mask = x*x + y*y <= radius*radius\n  return(mask)\n\ndef estimateCenter(im):\n    x_len, y_len = im.shape\n    x_c, y_c = int(x_len / 2), int(y_len/2)\n    # print(x_len)\n    # x_rot_c, y_rot_c = findMax(im) # this causes errors in images with bright interlopers\n    # maximum in central 50 pixel box for initial guess\n    box_width = 25 # x2\n    box_mask = np.zeros_like(im)\n    box_mask[x_c - box_width:x_c + box_width, y_c - box_width:y_c + box_width] += 1\n    box_masked_im = im * box_mask\n    # plt.imshow(np.sqrt(box_masked_im))\n    # plt.show()\n    x_rot_c, y_rot_c = findMax(box_masked_im)\n    return x_rot_c, y_rot_c\n\ndef findResidual(im, mask, rot_center_shift, mode='standard'):\n    x_rot_c, y_rot_c = estimateCenter(im)\n    # print(x_rot_c, y_rot_c)\n    mask_center = (x_rot_c, y_rot_c)\n    Rmax = findRmax(im, mask)\n    if mode == 'mask': im = mask\n    im = im * cmask(mask_center, Rmax, im) # circular mask of radius Rmax\n    # plt.imshow(im)\n    # plt.show()\n    # if rot_center_shift == None: rot_center_shift = (x_len/2-x_c, y_len/2-y_c) # calculate rot center if not provided\n    tform_c = skimage.transform.SimilarityTransform(translation=rot_center_shift) # radians\n    im = skimage.transform.warp(im, inverse_map=tform_c, preserve_range=True) # re-center\n    im_r = skimage.transform.rotate(im, 180, preserve_range=True) # rotated image, degrees\n    im_res = np.abs(im - im_r)\n    # plt.imshow(im_res)\n    # plt.show()\n    return im_res\n\ndef findAsymmetry(im, mask):\n    x_len, y_len = im.shape\n    x_c, y_c = findMax(im)\n    # print(x_c, y_c)\n    # find minimum A asymmetry, and corresponding rotation center\n\n    # rot_center_shift = (x_len/2-x_c, y_len/2-y_c) # default shift was center\n    rot_center_shift = (0,0) # no shift, already start from brightest central pixel!\n\n    A = np.ones((max_path*2+1,max_path*2+1))\n    x_tweak = np.arange(-max_path, max_path+step, step)\n    y_tweak = np.arange(-max_path, max_path+step, step)\n\n    hunt = True\n    curr_x = max_path # start at center of 0 to 4 grid, current x\n    curr_y = max_path # similarly\n    while hunt:\n        # measure A at 8-connected pixels\n        for alt_x in [curr_x - 1, curr_x, curr_x + 1]:\n            for alt_y in [curr_y - 1, curr_y, curr_y + 1]:\n                if  A[alt_x, alt_y] == 1:\n                    final_rot_center_shift = (\n                    rot_center_shift[0] + x_tweak[alt_x],\n                    rot_center_shift[1] + y_tweak[alt_y]\n                    )\n                    im_res = findResidual(im, mask, final_rot_center_shift, mode='standard')\n                    A[alt_x, alt_y] = np.sum(im_res) / (2 * np.sum(im))\n        # print(A)\n        # print('\\n')\n        min_x, min_y = findMin(A)\n        # print(min_x, min_y)\n        if min_x == curr_x and min_y == curr_y: hunt=False # stop if no change\n        if min_x == 0: hunt = False # stop if at left edge\n        if min_x == max_path*2: hunt = False # right edge\n        if min_y == 0: hunt = False # top edge\n        if min_y == max_path*2: hunt = False # bottom edge\n        curr_x, curr_y = min_x, min_y\n\n    # measure mask asymmetry about that rotation center\n    best_x, best_y = findMin(A)\n    best_rot_center_shift = (rot_center_shift[0] + x_tweak[best_x] , rot_center_shift[1] + y_tweak[best_y])\n    im_res_mask = findResidual(im, mask, rot_center_shift=best_rot_center_shift, mode='mask')\n    A_mask = np.sum(im_res_mask) / (2 * np.sum(mask))\n    # plot best residual\n    # best_im_res= findResidual(im, mask, rot_center_shift=best_rot_center_shift, mode='standard')\n    # plt.imshow(best_im_res)\n    # plt.show()\n    # print(best_rot_center_shift, A.min())\n    return A.min(), A_mask\n\ndef replicatePawlik():\n\n    if aws == False:\n        directory = r'/media/mike/SandiskLinux/threshold/threshold/'\n        meta = pd.read_csv('tables/meta_table_saved.csv')[39:54]\n        meta_full = pd.read_csv('tables/meta_table_saved.csv')[39:54]\n    if aws == True:\n        directory = r'/exports/aws/scratch/s1220970/regenerated/512/'\n        meta = pd.read_csv('SemesterOne/meta_table.csv')\n        meta_full = pd.read_csv('SemesterOne/meta_table.csv')\n\n    meta_full['standard_A'] = np.zeros(len(meta_full), dtype=float)\n    meta_full['mask_A'] = np.zeros(len(meta_full), dtype=float)\n\n    print(meta.head())\n    # _threshold is the filled mask, _threshold_mask is the binary mask only\n\n    standard_A_0 = []\n    mask_A_0 = []\n\n    standard_A_4 = []\n    mask_A_4 = []\n\n\n    print(len(meta))\n    for meta_index in range(len(meta)):\n        # print(meta_index)\n        im = fits.getdata(\n            directory + meta.iloc[meta_index]['threshold_5sig_filename'])  # read (the first) original image for base file\n        im = np.squeeze(im)\n\n        mask = fits.getdata(\n            directory + meta.iloc[meta_index][\n                'threshold_mask_5sig_filename'])  # read (the first) original image for base file\n        mask = np.squeeze(mask)\n\n        # scaled_plot(im, plt, clip_q=True, rel_clip=False)\n        # plt.show()\n\n        min_A, mask_A = findAsymmetry(im, mask)\n        print(min_A, mask_A)\n        # print(min_A + ' , ' + mask_A)\n\n        meta_full = meta_full.set_value(meta_index, 'standard_A', float(min_A))\n        meta_full = meta_full.set_value(meta_index, 'mask_A', float(mask_A))\n\n\n        # if meta.iloc[meta_index]['FEAT'] != 'N':\n        #     standard_A_4.append(min_A)\n        #     mask_A_4.append(mask_A)\n        # else:\n        #     standard_A_0.append(min_A)\n        #     mask_A_0.append((mask_A))\n\n        if aws:meta_full.to_csv('meta_with_A_full_eddie_5sig.csv')\n        else: meta_full.to_csv('meta_with_A_full_local.csv')\n\n\n# aws=True\n# im_height = 512\n# im_width = 512\n\naws = True\nim_height = 512\nim_width = 512\n\n# max_path = 20\n# step = 0.5\n\nmax_path = 8\nstep = 0.5\n\nreplicatePawlik()\n\n\n\n\n# plt.scatter(standard_A_4, mask_A_4, color='r')\n# plt.scatter(standard_A_0, mask_A_0, color='k')\n# plt.xlim([0,1])\n# plt.ylim([0,1])\n# plt.legend(['tidal', 'non'],loc=0)\n# plt.savefig('pawlik_scatter_1000_3sig.png')\n# plt.show()\n\n# Implements path-hopping from Conselice\n# Runs on background-subtracted images so does not include -bkg term\n# Selects center by maxima: is this smart?\n# it could be that the brightest point within the central mask is NOT the galactic center\n\n# the threshold significance level of the mask is quite important in the size of the mask -> including interlopers\n# at one sigma, almost all images include a bright interloper.","repo_name":"mwalmsley/tidal-features-classifier","sub_path":"tidalclassifier/pawlik/pawlik.py","file_name":"pawlik.py","file_ext":"py","file_size_in_byte":7994,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"81"}
+{"seq_id":"15418345211","text":"w, h, n = map(int, input().split())\n\nxmin, xmax, ymin, ymax = 0,w,0,h\n\nfor i in range(n):\n    xi,yi,ai = map(int, input().split())\n    if ai == 1:\n        if xmin < xi:\n            xmin = xi\n    if ai == 2:\n        if xmax > xi:\n            xmax = xi\n    if ai == 3:\n        if ymin < yi:\n            ymin = yi\n    if ai == 4:\n        if ymax > yi:\n            ymax = yi\n\nif xmax < xmin or ymax < ymin:\n    menseki = 0\nelse:\n    menseki = (xmax-xmin) * (ymax-ymin)\nprint(menseki)","repo_name":"NagaTaku/atcoder_abc_edition","sub_path":"ABC047/b.py","file_name":"b.py","file_ext":"py","file_size_in_byte":479,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"6348404788","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Wed Apr 15 19:44:50 2020\r\n\r\n@author: pit\r\n\"\"\"\r\n\r\n\r\nimport numpy as np\r\nimport pandas as pd\r\nfrom mlxtend.frequent_patterns import apriori as ap\r\nfrom mlxtend.frequent_patterns import association_rules as ar\r\n\r\npd.set_option('display.max_columns', None) # display all columns in df, default = 4\r\n\r\n\r\n#1/ Wczytaj dane z pliku \r\n\r\nfile = 'D:\\MachineLearningPandaIT\\Materials\\e-commerce.csv'\r\n\r\ndef data_load(file):\r\n    try:\r\n        df = pd.read_csv(file, sep=',', encoding = 'ISO-8859-1',\r\n                         index_col=False, error_bad_lines = False)#, nrows=1000)\r\n    except:\r\n        print('incorrect file path')\r\n        \r\n    return df\r\ndf = data_load(file)\r\n\r\n# 2/Wyczyść dane\r\n# 3/ Wykonaj wstępną analizę danych\r\n\r\ndef data_cleanup(df):\r\n    df.head()\r\n    df.isna().sum()\r\n    df_clean = df.drop(columns = ['InvoiceNo', 'StockCode','InvoiceDate'])\r\n    df_clean.isna().sum()\r\n    df_clean = df_clean.dropna(subset=['Description'])\r\n    df_clean['Value'] = df_clean['Quantity'] * df_clean['UnitPrice']\r\n    df_clean.dtypes\r\n    unique_clients = list(df_clean['CustomerID'].unique())\r\n    uc = len(unique_clients)\r\n    item_counter = df_clean['Description']\r\n    ic = item_counter.value_counts()\r\n    country_counter = df_clean[['Country','Value']]\r\n    cc = country_counter['Country'].value_counts()\r\n    cc_val = country_counter.groupby('Country')['Value'].sum()\r\n    cc_val = cc_val.astype(int)\r\n    \r\n    return df_clean, uc, ic, cc, cc_val\r\n    \r\ndf_clean = data_cleanup(df)[0]\r\nuc = data_cleanup(df)[1]\r\nic = data_cleanup(df)[2]\r\ncc = data_cleanup(df)[3]\r\ncc_val = data_cleanup(df)[4]\r\n\r\nic = ic.nlargest(n = 10)\r\ncc = cc.nlargest(n = 10)\r\ncc_val = cc_val.nlargest(n = 10)\r\n\r\nprint('\\n number of unique clients:', uc)\r\nprint('\\n item counter top 10: \\n', ic)\r\nprint('\\n country by transactions top 10: \\n', cc)\r\nprint('\\n country by values top 10: \\n', cc_val)\r\n\r\n### narysuj histogram obrazujący 10 nazw państw z największą liczbą transakcji\r\n\r\ndef charts():\r\n    import matplotlib.pyplot as plt\r\n    labels1 = cc.index.values\r\n    values1 = cc.values\r\n    labels2 = cc_val.index.values\r\n    values2 = cc_val.values\r\n\r\n#1 pie chart with %\r\n    explode = (0.5, 0 ,0 ,0 ,0 ,0 ,0 ,0 ,0 ,0)\r\n    fig1, ax1 = plt.subplots()\r\n    ax1.pie(values1, explode=explode, labels=labels1, autopct='%1.1f%%',\r\n        shadow=True, startangle=90)\r\n    ax1.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.\r\n    plt.title('% contribution in overall transaction count by Country')\r\n    plt.show()\r\n#1 bar chart \r\n    fig = plt.figure()\r\n    ax = fig.add_axes([0,0,1,1])\r\n    ax.bar(labels1,values1)\r\n    plt.title('numeric contribution in overall transaction count by Country')\r\n    plt.show()\r\n\r\n#2 pie chart with %\r\n    explode = (0.5, 0 ,0 ,0 ,0 ,0 ,0 ,0 ,0 ,0)\r\n    fig1, ax1 = plt.subplots()\r\n    ax1.pie(values2, explode=explode, labels=labels2, autopct='%1.1f%%',\r\n        shadow=True, startangle=90)\r\n    ax1.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.\r\n    plt.title('% contribution in overall value by Country')\r\n    plt.show()\r\n#2 bar chart \r\n    fig = plt.figure()\r\n    ax = fig.add_axes([0,0,1,1])\r\n    ax.bar(labels2,values2)\r\n    plt.title('% contribution in overall value by Country')\r\n    plt.show()\r\n\r\n#4. Wybierz dane tylko dla jednego kraju (np. Polski)\r\n#5. Przekonwertuj dane do postaci którą przyjmuję algorytm Apriori (DataFrame gdzie indexami są\r\n#numery transakcji a wartościami kolumn True lub False w zależności czy dany produkt wystąpił w\r\n#transakcji).\r\n#6. Wygeneruj listę reguł dla wybranej przez Ciebie wartości min_support. Przejrzyj reguły a następnie\r\n#wybierz 5 Twoim zdaniem najlepszych.\r\n#7. Spróbuj zwiększyć wartość min_support, co się wtedy dzieję z liczbą reguł?\r\n#8. Wypisz wszystkie reguły, których wartość lift jest większa niż 5 i wartość confidence\r\n#jest większa niż 0.8\r\n\r\n\r\ndf_EIRE = df[df['Country'] == 'EIRE']\r\ndf_EIRE = df_EIRE[['InvoiceNo', 'Description', 'Quantity']]\r\ndf_EIRE = (df_EIRE.groupby(['InvoiceNo', 'Description'])['Quantity'].\r\n               sum().unstack().fillna(0))\r\ndf_EIRE[df_EIRE == 0] = False\r\ndf_EIRE[df_EIRE != 0] = True\r\n\r\nfreq_items = ap(df_EIRE,\r\n                min_support = 0.05,\r\n                use_colnames=True)\r\nfreq_items.head(10)\r\n\r\nrules = ar(freq_items,\r\n           metric = 'lift',\r\n           min_threshold = 1)\r\nrules.head(10)\r\n\r\nrules['confidence'].sort_values(ascending = False).head(10)\r\nrules['lift'].sort_values(ascending = False).head(10)\r\nrules[ (rules['lift'] >= 1) & (rules['confidence'] >= 0.8)]\r\n","repo_name":"PUlanowski/Python_Machine_Learning","sub_path":"apriori2.py","file_name":"apriori2.py","file_ext":"py","file_size_in_byte":4643,"program_lang":"python","lang":"pl","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"41873596657","text":"#!/usr/bin/env python\n\nfrom math import cos, sin\nimport sys\nimport rospy\nfrom lab_5.srv import oint\n\n\ndef ellipse():\n    frequency = 30.0\n    ax = 0.3\n    ay = 0.2\n    az = 0.2\n    theta = 0.0\n    dth = 0.5 * 3.1415 / frequency\n\n    t = 1.0 / frequency\n    x0 = 0.5\n    y0 = -0.5\n    z0 = 0.8\n\n    interpol = rospy.ServiceProxy('oint_control_srv', oint)\n    interpol(x0 + ax*cos(theta), y0 + ay*sin(theta), z0 + az*sin(theta), 0.0, 0.0, 0.0, 1.0, 2.0)\n\n    rospy.wait_for_service('oint_control_srv')\n    rate = rospy.Rate(frequency)\n    while not rospy.is_shutdown():\n        x = x0 + ax * cos(theta)\n        y = y0 + ay * sin(theta)\n        z = z0 + az * sin(theta)\n        move = interpol(x, y, z, 0.0, 0.0, 0.0, 1.0, t)\n\n        theta = theta + dth\n        rate.sleep()\n\n\nif __name__ == \"__main__\":\n    rospy.init_node('ellipse')\n    ellipse()\n","repo_name":"pw-eiti-anro-20l/kostrzenski_stojke","sub_path":"lab_5/src/ocmd.py","file_name":"ocmd.py","file_ext":"py","file_size_in_byte":847,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"81"}
+{"seq_id":"35704148909","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# In[1]:\n\n\n# import libraries\nimport tkinter as tk                     \nfrom tkinter import ttk \n\n#Connect database\nimport cx_Oracle\nimport oracledbconnect as dbconnect\nconnect=cx_Oracle.connect(dbconnect.user,dbconnect.password,dbconnect.dns)\ncur=connect.cursor()\n\n\n# In[2]:\n\n\nwindow=tk.Tk()\nwindow.geometry('1000x500')\nwindow.title('PO Application')\ntabControl=ttk.Notebook(window)\n#define tabs\ntab1=ttk.Frame(tabControl)\ntab2=ttk.Frame(tabControl)\ntab3=ttk.Frame(tabControl)\n\n#add tab titles\ntabControl.add(tab1,text=\"PO Enquiry\")\ntabControl.add(tab2,text=\"PO Header\")\ntabControl.add(tab3,text=\"PO Line\")\n\n#grid for tab\ntabControl.grid()\n\n\n# In[3]:\n\n\n#Add widgets to tab1 PO Enquiry Tab\nlabelframe=tk.LabelFrame(tab1,text='Enter details to Enquire PO')\nlabelframe.grid(column=1,row=1)\n\ntk.Label(labelframe,text='PO#').grid(column=0,row=1,sticky='E',pady=5)\nponum=tk.Entry(labelframe,width=10)\nponum.grid(column=1,row=1,pady=5)\n\ntk.Label(labelframe,text='    ').grid(column=2,row=1,padx=50, pady=5)\n\ntk.Label(labelframe,text='PO Date').grid(column=3,row=1,sticky='E',pady=5)\npondate=tk.Entry(labelframe,width=10)\npondate.grid(column=4,row=1,pady=5)\n\ntk.Label(labelframe,text='Supplier#').grid(column=0,row=2,sticky='W',pady=5)\nposuppnum=tk.Entry(labelframe,width=10)\nposuppnum.grid(column=1,row=2,pady=5)\n\ntk.Label(labelframe,text='    ').grid(column=2,row=2,padx=50, pady=5)\n\ntk.Label(labelframe,text='Supplier Name').grid(column=3,row=2,sticky='W',pady=5)\nposupname=tk.Entry(labelframe,width=10)\nposupname.grid(column=4,row=2,pady=5)\n\n\n# In[ ]:\n\n\nwindow.mainloop()\n\n","repo_name":"simrit1/Purchase-Order-Application","sub_path":"POEnquiryTabCreation1.py","file_name":"POEnquiryTabCreation1.py","file_ext":"py","file_size_in_byte":1609,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"81"}
+{"seq_id":"24596219016","text":"class Solution:\n    def lemonadeChange(self, bills: [int]) -> bool:\n\n        fives, tens, twenties = 0,0,0\n        print(bills)\n        for bill in bills:\n        \tif bill == 5:\n        \t\tfives+=1\n        \telif bill == 10:\n        \t\ttens +=1\n        \t\tif fives>=1:\n        \t\t\tfives -=1\n        \t\telse:\n        \t\t\treturn False\n        \t\n        \telif bill == 20:\n        \t\ttwenties+=1\n        \t\tif fives>=1 and tens>=1:\n        \t\t\tfives -=1\n        \t\t\ttens -=1\n        \t\telif fives>=3 and tens ==0:\n        \t\t\tfives -=3\n        \t\telse:\n        \t\t\treturn False   \n\n        \tprint(fives, tens, twenties)     \t\t\n        \n        return True\nbills1 = [5,5,20,5,5,10,5,10,5,20]\nsol = Solution()\nprint(sol.lemonadeChange(bills1))","repo_name":"aituar-kenges/leetcode","sub_path":"python/860_lemonadeChange.py","file_name":"860_lemonadeChange.py","file_ext":"py","file_size_in_byte":722,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"34584242648","text":"import sys\r\nimport time\r\n\r\ndef isPrime(n):\r\n    if n < 2: return False\r\n    for x in range(2, int(n**0.5) + 1):\r\n        if n % x == 0:\r\n            return False\r\n    return True\r\n\r\nsek = [1,3]\r\nprimes = [3]\r\n\r\ndef sekvens(k):\r\n\tstart_time = time.time()\r\n\tsys.setrecursionlimit(5000)\r\n\ttemp = []\r\n\r\n\tif len(primes) == 100:\r\n\t\tprint(\"calculating\")\r\n\t\treturn sum(primes)\r\n\r\n\r\n\tfor i in range(0, len(sek)-1):\r\n\t\tfor j in range(i+1, len(sek)):\r\n\t\t\tif sek[i] == sek[j]:\r\n\t\t\t\tcontinue\r\n\t\t\tif sek[i] + sek[j] > sek[-1] + k or len(temp) > 1:\r\n\t\t\t\tbreak\r\n\t\t\tif sek[i] + sek[j] == sek[-1] + k:\r\n\r\n\t\t\t\ttemp.append(sek[i] + sek[j])\r\n\tif len(temp) == 1:\r\n\t\tif isPrime(temp[0]):\r\n\t\t\tprimes.append(temp[0])\r\n\t\tsek.append(temp[0])\r\n\t\tk = 1\r\n\t\treturn sekvens(k)\r\n\treturn sekvens(k+1)\r\n\r\nprint(sekvens(1))\r\nprint(\"Tok\", time.time() - start_time)","repo_name":"Mortefal/Julekalender","sub_path":"Luke 13/julamsekvens.py","file_name":"julamsekvens.py","file_ext":"py","file_size_in_byte":827,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"3178212969","text":"import os\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom class_algorithm import GeneticAlgorithm\n\n\ndef main_algorith(sols, weight, count_items, graphing=False):\n    # Algorithm\n    alg_gen = GeneticAlgorithm(sols, weight, count_items)\n    new_seed = alg_gen.gen_seed()\n    best_fitness_overall = 0\n\n    if os.path.exists(\"log_file.csv\"):\n        os.remove('log_file.csv')\n\n    for i in range(1000):\n        # 1. Restart with updated steed\n        seed = new_seed\n\n        # 2. Put through fitness function to determine fitness\n        fitness, solution_array = alg_gen.test_solutions(seed)\n\n        # 3. Select the best solutions\n        best_fitness, best_sols = alg_gen.order_fitness(fitness, solution_array)\n\n        # 4. Crossover solutions\n        crossed_sols = alg_gen.crossover_function(best_sols)\n\n        # 5. Mutate solutions\n        new_seed = alg_gen.mutation(crossed_sols)\n        if best_fitness_overall < best_fitness[0]:\n            best_fitness_overall = best_fitness[0]\n\n        print(f\"Iteration: {i} - Best Fitness: {best_fitness[0]} - Record Fitness: {best_fitness_overall}\")\n        GeneticAlgorithm.log_info(i, best_fitness[0], best_fitness_overall)\n\n    if graphing:\n        data = pd.read_csv(\"log_file.csv\", header=None, names=[\"Iteration\", \"Fitness\", \"Best Fitness\"])\n        plt.plot(data[\"Fitness\"])\n        plt.plot(data[\"Best Fitness\"])\n        plt.legend([\"Value\", \"Best Value\"])\n        plt.show()","repo_name":"mianigro/Knapsack-Genetic-Algoirthm","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1441,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"21529729310","text":"#!/usr/bin/python3\nimport sys\nimport os\nimport time\n# import json\n# import getopt\n# import socket\n# import struct\n# import codecs\n# import binascii\nfrom pymodbus.client.sync import ModbusTcpClient\nfrom pymodbus.constants import Endian\nfrom pymodbus.payload import BinaryPayloadDecoder\n\nnamed_tuple = time.localtime()  # getstruct_time\ntime_string = time.strftime(\"%m/%d/%Y, %H:%M:%S keba setcurr.py\", named_tuple)\n\nfile_string = '/var/www/html/openWB/ramdisk/keba_setcurr.log'\n\n\ndef getstat(ip):\n    # 1 enabled, 0 disable, 9 undef\n    file_ip = '/var/www/html/openWB/ramdisk/keba_' + ip\n    status = 9\n    if os.path.isfile(file_ip):\n        f = open(file_ip, 'r')\n        status = int(f.read())\n        f.close()\n    return status\n\n\ndef setstat(ip, status):\n    file_ip = '/var/www/html/openWB/ramdisk/keba_' + ip\n    f1 = open(file_ip, 'w')\n    f1.write(str(status))\n    f1.close()\n\n\nif os.path.isfile(file_string):\n    f = open(file_string, 'a')\nelse:\n    f = open(file_string, 'w')\n\nipaddress = str(sys.argv[1])\nnewcurr = int(sys.argv[2])\n\nclient = ModbusTcpClient(ipaddress, port=502)\n\n# maxcurrent state 1100\nresp = client.read_holding_registers(1100, 2, unit=255)\ndecoder = BinaryPayloadDecoder.fromRegisters(resp.registers, byteorder=Endian.Big, wordorder=Endian.Big)\nfinal = float(decoder.decode_32bit_uint()) / 1000\noldcurr = int(\"%.f\" % final)\n\n# cable state 1004\nresp = client.read_holding_registers(1004, 2, unit=255)\ndecoder = BinaryPayloadDecoder.fromRegisters(resp.registers, byteorder=Endian.Big, wordorder=Endian.Big)\nfinal2 = float(decoder.decode_32bit_uint())\nplugs = \"%.f\" % final2\n\n# max supported current 1110\nresp = client.read_holding_registers(1110, 2, unit=255)\ndecoder = BinaryPayloadDecoder.fromRegisters(resp.registers, byteorder=Endian.Big, wordorder=Endian.Big)\nfinal2 = float(decoder.decode_32bit_uint()) / 1000\nsupcur = int(\"%.f\" % final2)\nif plugs == \"7\":\n    if (oldcurr != newcurr):\n        if (newcurr == 0):\n            # disable station\n            print('%s oldcurr %d, newcurr %d, ipadr %s disable station' % (\n                  time_string, oldcurr, newcurr, ipaddress), file=f)\n            rq = client.write_register(5014, 0, unit=255)\n            setstat(ipaddress, 0)\n        else:\n            if (oldcurr == 0):\n                # enable station\n                print('%s oldcurr %d, newcurr %d, ipadr %s enable station ' % (\n                      time_string, oldcurr, newcurr, ipaddress), file=f)\n                rq = client.write_register(5014, 1, unit=255)\n                setstat(ipaddress, 1)\n            # setting new current\n            print('%s oldcurr %d, newcurr %d, hwlimit %d ipadr %s setting new curr' % (\n                  time_string, oldcurr, newcurr, supcur, ipaddress), file=f)\n            newcurrt = newcurr * 1000\n            rq = client.write_register(5004, newcurrt, unit=255)\nelse:\n    status = getstat(ipaddress)\n    if ((newcurr == 0) and (status != 0)):\n        # disable station\n        print('%s No car detected, ipadr %s disable station' % (time_string, ipaddress), file=f)\n        rq = client.write_register(5014, 0, unit=255)\n        setstat(ipaddress, 0)\n    else:\n        if ((newcurr > 0) and (status != 1)):\n            # enable station\n            print('%s No car detected, ipadr %s enable station ' % (time_string, ipaddress), file=f)\n            rq = client.write_register(5014, 1, unit=255)\n            setstat(ipaddress, 1)\nf.close()\n","repo_name":"snaptec/openWB","sub_path":"modules/keballlp1/setcurrkeba.py","file_name":"setcurrkeba.py","file_ext":"py","file_size_in_byte":3424,"program_lang":"python","lang":"en","doc_type":"code","stars":322,"dataset":"github-code","pt":"81"}
+{"seq_id":"16999091253","text":"# -*- coding: utf-8 -*-\n# @Author: Catofes\n# @Date:   2015-08-15\n\n\n'''\nClass to cache songs into local storage.\n'''\n\nfrom singleton import Singleton\nimport threading\nimport subprocess\nfrom const import Constant\nfrom config import Config\nimport os\nimport logger\nimport signal\n\nlog = logger.getLogger(__name__)\n\n\nclass Cache(Singleton):\n    def __init__(self):\n        if hasattr(self, '_init'):\n            return\n        self._init = True\n        self.const = Constant()\n        self.config = Config()\n        self.download_lock = threading.Lock()\n        self.check_lock = threading.Lock()\n        self.downloading = []\n        self.aria2c = None\n        self.stop = False\n        self.enable = self.config.get_item(\"cache\")\n        self.aria2c_parameters = self.config.get_item(\"aria2c_parameters\")\n\n\n    def start_download(self):\n        check = self.download_lock.acquire(False)\n        if not check:\n            return False\n        while True:\n            if self.stop:\n                break\n            if not self.enable:\n                break\n            self.check_lock.acquire()\n            if len(self.downloading) <= 0:\n                self.check_lock.release()\n                break\n            data = self.downloading.pop()\n            self.check_lock.release()\n            song_id = data[0]\n            song_name = data[1]\n            artist = data[2]\n            url = data[3]\n            onExit = data[4]\n            output_path = Constant.download_dir\n            output_file = str(artist) + \" - \" + str(song_name) + \".mp3\"\n            try:\n                para = ['aria2c', '--auto-file-renaming=false', '--allow-overwrite=true', '-d', output_path, '-o',\n                        output_file, url]\n                para[1:1] = self.aria2c_parameters\n                self.aria2c = subprocess.Popen(para,\n                    stdin=subprocess.PIPE,\n                    stdout=subprocess.PIPE,\n                    stderr=subprocess.PIPE)\n                self.aria2c.wait()\n            except Exception:\n                log.debug(str(song_id) + \" Cache Error\")\n            if self.aria2c.returncode == 0:\n                log.debug(str(song_id) + \" Cache OK\")\n                onExit(song_id, output_path + \"/\" + output_file)\n        self.download_lock.release()\n\n\n    def add(self, song_id, song_name, artist, url, onExit):\n        self.check_lock.acquire()\n        self.downloading.append([song_id, song_name, artist, url, onExit])\n        self.check_lock.release()\n\n    def quit(self):\n        self.stop = True\n        try:\n            os.kill(self.aria2c.pid, signal.SIGKILL)\n        except:\n            pass\n\n","repo_name":"smileboywtu/MusicRecommend","sub_path":"netease/cache.py","file_name":"cache.py","file_ext":"py","file_size_in_byte":2625,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"81"}
+{"seq_id":"18423978955","text":"import requests\r\n# importing requests to call API\r\nimport sys\r\n#importing sys module to control python runtime environment\r\nfrom kivy.app import App\r\n#importing App class to run our application\r\nfrom kivy.properties import StringProperty,NumericProperty\r\n#importing kivy properties\r\nfrom kivy.uix.button import Button\r\nfrom kivy.uix.dropdown import DropDown\r\nfrom kivy.uix.textinput import TextInput\r\nfrom kivy.uix.floatlayout import FloatLayout\r\n# importing different widgets\r\n\r\nfrom getcityname import GetCityName\r\n# importing GetCityName class in order to get current location\r\n# this file is named as getcityname in same project\r\n\r\n\r\nclass WeatherProjApp(App):\r\n    def build(self):\r\n        weatherclass = WeatherClass()\r\n        return weatherclass\r\n\r\n\r\nclass WeatherClass(FloatLayout):\r\n    # This class is the root class and inherits FloatLayout\r\n    # This app calls geocoder API for getting the location attributes of the city searched first\r\n    # and then after getting correct logitude and latitude its call weather API for getting the data\r\n    # However, it is possible to send the name(City Searched) directly to weather API\r\n    # But as per Current Weather Data API documentation this version is less accurate and will be deprecated soon\r\n    # and therefore has been advised to use Geocoder API\r\n    # also , if using directly weatherAPI , it is not possible to get all the cities in world with same name\r\n    # So for Example, there could be more than one city named as London in World such as one in England and one in Canada\r\n    #This is what their documentation reveals:\r\n    #You can use the geocoder built into this API by default,\r\n    #but please note that this version is less accurate than the Geocoder API and will be deprecated soon.\r\n    APIid = \"\"\r\n    # Here you will need to use your access key for API\r\n    # Access key is same for CurrentWeatherData Api as well as Geocoder API\r\n    # As both API are from openweathermap\r\n    units = \"metric\"\r\n    # Weather can be taken in any measurement system, I chose Units as metrics system\r\n    # Howevenr Data is aveailable in Metric , Fahrenheit as well as imperical system\r\n    condition_descriptors = StringProperty(None)\r\n    condition_icon = StringProperty(None)\r\n    temperature_description = StringProperty(None)\r\n    maximum_temperature= NumericProperty(None)\r\n    minimum_temperature=NumericProperty(None)\r\n    humidity=NumericProperty(None)\r\n    pressure=NumericProperty(None)\r\n    wind=NumericProperty(None)\r\n    visibility=NumericProperty(None)\r\n    feels_like=NumericProperty(None)\r\n    # Above variable have been made kivy properties in order to use them in .kv file\r\n    getcurrentloc=GetCityName()\r\n    getcurrentinfo=getcurrentloc.getcurrentlocattributes()\r\n    #getcurrentinfo has been made variable to hold the current location data\r\n    #getcurrentinfo will be invalid in case of no internet connectivity\r\n    #as specified in getcityname file\r\n    if(getcurrentinfo==\"invalid\"):\r\n        sys.exit(\"Internet is not available\")\r\n    # Therefore, in case of no internet connectivity system will exit and will not run application\r\n    current_city=getcurrentinfo['city']\r\n    current_longitude=getcurrentinfo['longitude']\r\n    current_latitude=getcurrentinfo['latitude']\r\n    \"\"\"The ipstack API as called in getcityname files provides geo coordinates as well ,\r\n    therefore for the current location of user we don't need to call geocoder API\r\n    and current weather data api can be called directly\"\"\"\r\n    parameters = {\r\n        \"lat\": current_latitude,\r\n        \"lon\": current_longitude,\r\n        \"appid\": APIid,\r\n        \"units\": units\r\n    }\r\n    dropdown = DropDown(size_hint=(1, .1),\r\n                        pos_hint={'right': 1, 'top': 0.9})\r\n    def __init__(self, **kwargs):\r\n        super().__init__(**kwargs)\r\n        # self.orientation=\"horizontal\"\r\n        textinput = TextInput(text=self.current_city, size_hint=(.7, .1),\r\n                              pos_hint={'right': 0.7, 'top': 1})\r\n        button = Button(text=\"Search\",\r\n                        size_hint=(.3, .1),\r\n                        pos_hint={'right': 1, 'top': 1})\r\n        button.bind(on_press=lambda button: self.buttonFunction(textinput.text))\r\n        #buttonfunction method has been binded with this button on press event\r\n        # this function basically takes the input of user which is city name to be searched for\r\n        # and create the dropdown for different places in world with same name\r\n        mainbutton = Button(text=str(textinput.text), size_hint=(1, .1),\r\n                            pos_hint={'right': 1, 'top': 0.9})\r\n        # We are not adding any dropdown for current location as current location can't be more than one\r\n        # therefore instead of creating dropdown here a button with same name as current location has been created\r\n        # this has been exclusively define in constructor so that our first screen can show weather of current location\r\n        self.add_widget(textinput)\r\n        self.add_widget(button)\r\n        self.add_widget(mainbutton)\r\n        self.dropfunction(self.parameters,self.dropdown,mainbutton)\r\n        \"\"\" drop function takes the specific locations founded by buttonfunction and then resets the current \r\n        variables correspondingly to be shown in app and these variables then can be accessed in .ky file\"\"\"\r\n\r\n    def gettcitiesLongandLot(self, text):\r\n        \"\"\" This is the function that is used to get longitude and latitude\r\n        of the city searched for , Note here that this function fetches the long\r\n        and lat of all the cities with same name . Basically this is the function\r\n        which helps us to know the different geographical cities with same name with their geo coordinates\"\"\"\r\n        baseURL=\"http://api.openweathermap.org\"\r\n        endpoints=\"/geo/1.0/direct\"\r\n        URL=baseURL+endpoints\r\n        param={\r\n            'q': text,\r\n            'limit':'5',\r\n            'appid':'b62cb3f2a3cc1514e250f0c76b4007dc'\r\n        }\r\n        try:\r\n            response = requests.get(URL,params=param)\r\n            names = response.json()\r\n            # print(names)\r\n            return names\r\n        except requests.exceptions.ConnectionError as e:\r\n            print(\"Connection cant be established\")\r\n            sys.exit(\"no internet connectivity\")\r\n            # although connection connectivity was checked before as well in getcityname file\r\n            # and application won't start in case of no network connectivity\r\n            # but it is possible that some user was connected to internet at any time when application was started\r\n            # But application was not closed and now when city is being searched , user is not connected to internet anymore\r\n        except requests.exceptions.JSONDecodeError as e:\r\n            print(\"Make sure API call is correct, There seems to be problem with API addess\")\r\n            # if anyhow endpoint goes wrong for example while refracting the code or anyhow\r\n            # json code error exception will be thrown , as otherwise if checking with status code==200\r\n            # this can go wrong as in case of error only in endpoint but in baseURL\r\n            # still response with status code 200 will be received with success=failre in content\r\n\r\n    def buttonFunction(self, text):\r\n        names = self.gettcitiesLongandLot(text)\r\n        dropdown = DropDown(size_hint=(1, .1),\r\n                            pos_hint={'right': 1, 'top': 0.9})\r\n        for city in names:\r\n            if \"state\" not in city:\r\n                city['state'] = city['name']\r\n            btn = Button(text='% s  %s  %s' % (city['name'], city['state'], city['country']), size_hint_y=None,\r\n                         height=40)\r\n            parameters = {\r\n                \"lat\": city['lat'],\r\n                \"lon\": city['lon'],\r\n                \"appid\": self.APIid,\r\n                \"units\": self.units\r\n            }\r\n            btn.id = parameters\r\n            btn.bind(on_release=lambda btn: self.dropfunction(btn.id, dropdown, btn))\r\n            dropdown.add_widget(btn)\r\n            dropdown.select(btn.text)\r\n            \"\"\"The above for loop will create buttons for different cities found with same name and will add them ro \r\n            dropdown menu .However question arises that in case if user enters the name of the city which is \r\n            inaccurate or in other terms no such city exist in World , Then this loop will not be executed as the \r\n            response array will contain no value or in other term it will be empty \"\"\"\r\n        mainbutton = Button(text=self.dropdowntext(names) + str(text), size_hint=(1, .1),\r\n                            pos_hint={'right': 1, 'top': 0.9})\r\n        mainbutton.bind(on_release=dropdown.open)\r\n        dropdown.bind(on_select=lambda instance, x: setattr(mainbutton, 'text', x))\r\n        self.add_widget(mainbutton)\r\n\r\n    def dropdowntext(self,names):\r\n        \"\"\"This function find what is to be displayed on Drop Down menu for example if no result is found for searched term\r\n         then this will display We couldn't find any result for searched term \"\"\"\r\n        if len(names)==0:\r\n            text=\"Sorry, We couldn't find any result for\";\r\n        else:\r\n            text=\"following results have been found for\"\r\n        return text\r\n\r\n\r\n    def dropfunction(self, parameters, dropdown, btn):\r\n        \"\"\"this function will run when a city from dropdown will be selected as stated before drop function takes the\r\n        specific locations founded by buttonfunction and then resets the current variables correspondingly to be\r\n        shown in app and these variables then can be accessed in .ky file. This function is responsible for setting all\r\n        weather related variables as this function calls currentweatherAPI and receives response \"\"\"\r\n        dropdown.select(btn.text)\r\n        # print(parameters)\r\n        # for i in self.children:\r\n        #     if type(i) == kivy.uix.label.Label:\r\n        #         self.remove_widget(i)\r\n        try:\r\n            response = requests.get(f\"https://api.openweathermap.org/data/2.5/weather\", params=parameters)\r\n            if(response.status_code==200):\r\n                response = response.json()\r\n        except requests.exceptions.ConnectionError as e:\r\n                print(\"internet is not available\")\r\n                sys.exit(\"Something went wrong\")\r\n            # Internet connectivity is checked here once again because it is again possible that at some\r\n            #user has internet connectivity when searching for city but has lost it now\r\n        self.condition_descriptors = str(response['weather'][0][\"main\"]) + \",\" + str(\r\n            response['weather'][0][\"description\"])\r\n        self.condition_icon = \"./images/\" + str(response['weather'][0][\"icon\"]) + \".png\"\r\n        self.temperature_description = \"Temperature is %d\" % (response['main'][\"temp\"])\r\n        self.maximum_temperature=response['main']['temp_max']\r\n        self.minimum_temperature=response['main']['temp_min']\r\n        self.humidity=response['main']['humidity']\r\n        self.visibility=response['visibility']\r\n        self.feels_like=response['main']['feels_like']\r\n        self.wind=response['wind']['speed']\r\n        self.pressure=response['main']['pressure']\r\n        # print(response['main']['temp'])\r\n        # print(response)\r\n        # print(\"I am called\")\r\n        # return response\r\n        # label1 = Label(\r\n        #     text=\"Temprature is %d and feels like %d\" % (response['main']['temp'], response['main']['feels_like']))\r\n        # label = Label(text='%s , %s' % (response['weather'][0]['main'], response['weather'][0]['description']))\r\n        # self.add_widget(label)\r\n        # self.add_widget(label1)\r\n\r\n\r\nif __name__ == '__main__':\r\n    WeatherProjApp().run()\r\n","repo_name":"prinklegulati/weatherApp","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":11808,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"38678430800","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Mar 15 12:24:01 2022\n\n@author: Necro\n\"\"\"\nfrom PIL import Image,ImageOps\nfrom numpy import asarray\nfrom mtcnn.mtcnn import MTCNN\nimport numpy as np\nfrom scipy.spatial.distance import cosine\nfrom keras_vggface.vggface import VGGFace\nfrom keras_vggface.utils import preprocess_input\nimport os\nfrom math import ceil\n\n\nimport sqlite3\n\n\ndef extract_face(filepath,required_size = (224, 224)):\n    #load image\n    #print(filepath)\n    image = filepath\n    print(\"######################################\")\n    print(image.size)\n    print(\"######################################\")\n    \n    # convert to RGB, if needed\n    print(filepath)\n    image = image.convert('RGB')\n    #if filepath != \"C:/Users/Moina/Desktop/Proj_FaceRecognition/face_images/train/Simin/20211225_142558.jpg\":\n        #image = image.resize((3000, 3000))\n    #image = image.transpose(Image.ROTATE_270)\n    image =ImageOps.exif_transpose(image)\n    #image.show()\n    #convert to np array\n    pixels = asarray(image)\n    \n    # create the detector, using default weights\n    detector = MTCNN()\n    \n    #detect faces in the image\n    results = detector.detect_faces(pixels)\n    \n    print(\"######################################\")\n    print(results)\n    print(\"######################################\")\n    if len(results)==0:\n        print(results)\n        return 1\n    \n    # extract the bounding box from the first face\n    x1, y1, width, height = results[0]['box']\n    \n    # bug fix\n    x1, y1 = abs(x1), abs(y1)\n    x2, y2 = x1 + width, y1 + height\n\n    # extract the face\n    face = pixels[y1:y2, x1:x2]\n    \n    # resize pixels to the model size\n    image = Image.fromarray(face)\n    image = image.resize(required_size)\n    numpydata = asarray(image)\n    \n    #expand the dimensions according to the required tflite model input  \n    npd = np.expand_dims(numpydata, axis=0)\n    \n    #img = cv2.resize(image,(224,224))     # resize image to match model's expected sizing\n    #img = image.reshape(1,224,224,3) # return the image with shaping that TF wants.\n    print(npd.size)\n    face_array = npd\n    \n    return face_array\n\n\n\n# extract faces and calculate face embeddings for a list of photo files\ndef get_embeddings(filenames):\n\t# extract faces\n\t#faces = [extract_face(f) for f in filenames]\n    faces = extract_face(filenames)\n\t# convert into an array of samples\n    samples = asarray(faces, 'float32')\n\t# prepare the face for the model, e.g. center pixels\n    samples = preprocess_input(samples, version=2)\n    \n\t# create a vggface model\n    model = VGGFace(model='resnet50', include_top=False, input_shape=(224, 224, 3), pooling='avg')\n\t# perform prediction\n\t\n    yhat = model.predict(samples)\n\t\n    return yhat\n\n\n\ndef is_match(known_embedding, candidate_embedding,i,thresh=0.50):\n    # calculate distance between embeddings\n    score = cosine(known_embedding,candidate_embedding)\n    if score <= thresh:\n        print('>face is a Match (%.3f <= %.3f)' % (score, thresh))\n        \n        #count =+ 1\n    else:\n        print('>face is NOT a Match (%.3f > %.3f)' % (score, thresh))\n        #show_face(faces[i])\n\ndef is_match2(known_embedding, candidate_embedding,thresh=0.50):\n    # calculate distance between embeddings\n    score = cosine(known_embedding,candidate_embedding)\n    if score <= thresh:\n        print('>face is a Match (%.3f <= %.3f)' % (score, thresh))\n        #theta = (score//0.50)*100*(33.33/100)\n        theta = (1-score)*(100/3)\n        return [True,theta]\n    else:\n        print('>face is NOT a Match (%.3f > %.3f)' % (score, thresh))\n        #theta = -(((score//1)*100)-50)\n        theta = (1-score)*(100/3)\n        return [False,theta]\n\n#*#\ndef get_embedding_id():\n    \n    conn = sqlite3.connect('faces.db')\n    cursor = conn.cursor()\n    cursor.execute('SELECT * FROM faces')\n    id_list = []\n    rows = cursor.fetchall()\n    for row in rows:\n        id_list.append(str(row[1]))\n    conn.close()\n    return id_list\n    \n#*#    \ndef Insert_embedding(X,embeddings_per_id,name):\n    '''\n    insert emb1,emb2,emb3 for the new face\n    '''\n    \n    conn = sqlite3.connect('faces.db')\n    cursor = conn.cursor()\n    emb_list = []\n    for i in range(embeddings_per_id):\n        img = Image.open(X[i].stream)\n        emb=get_embeddings(img)\n        emb_list.append(emb)\n    n = str(name)\n    cursor.execute(''' INSERT INTO faces (name,embeddings1,embeddings2,embeddings3) VALUES(?,?,?,?)''',(n,sqlite3.Binary(emb_list[0].tostring()),sqlite3.Binary(emb_list[1].tostring()),sqlite3.Binary(emb_list[2].tostring()),))\n    conn.commit()\n    conn.close()   \n\n#*#\ndef Check_id(ID):\n    \n    conn = sqlite3.connect('faces.db')\n    cursor = conn.cursor()\n    cursor.execute('SELECT * FROM faces')\n    rows = cursor.fetchall()\n    conn.close()\n    for row in rows:\n        if row[1]== ID:\n            return True\n    return False\n    \n#*#\ndef Check_embedding(X,embeddings_per_id):\n     #embeddings = np.load('embeddings.npy')\n     #embeddings_id = np.load('embedding_id.npy')\n     \n     conn = sqlite3.connect('faces.db')\n     cursor = conn.cursor()\n     cursor.execute('SELECT * FROM faces')\n     rows = cursor.fetchall()\n     \n     score_dict =  dict()\n     for row in rows:\n         score_dict[row[1]]=0\n         \n     for i in range(embeddings_per_id):\n         img = Image.open(X[i].stream)\n         new_embedding=get_embeddings(img)\n     \n         for idx,name,embedding1,embedding2,embedding3 in rows:\n             #convert blob to np array\n             pred_1 = is_match2(np.fromstring(embedding1, dtype=np.float32),new_embedding)\n             pred_2 = is_match2(np.fromstring(embedding2, dtype=np.float32),new_embedding)\n             pred_3 = is_match2(np.fromstring(embedding3, dtype=np.float32),new_embedding)\n         \n             if (pred_1[0]):\n                 score_dict[name]+=pred_1[1]\n             else:\n                 score_dict[name]-=pred_1[1]\n             if (pred_2[0]):\n                 score_dict[name]+=pred_2[1]\n             else:\n                 score_dict[name]-=pred_2[1]\n             if (pred_3[0]):\n                 score_dict[name]+=pred_3[1]\n             else:\n                 score_dict[name]-=pred_3[1]\n     conn.close()\n        \n     print(score_dict)\n     max_value = max(score_dict.values())\n     if max_value >= 90:\n         print(\"possible match, value :\",max_value )\n         return True\n     else:\n         print(\"No Match\")\n         #pred_class = max(score_dict, key=score_dict.get)\n         #print(type(pred_class))\n         return False\n#*#     \ndef Check_face(X,embeddings_per_id):\n    face_table=[True]*3\n    \n    for i in range(embeddings_per_id):\n         image = Image.open(X[i].stream)\n         # convert to RGB, if needed\n         image = image.convert('RGB')\n         #face align\n         image =ImageOps.exif_transpose(image)\n         \n         #convert to np array\n         pixels = asarray(image)\n         \n         # create the detector, using default weights\n         detector = MTCNN()\n         \n         #detect faces in the image\n         results = detector.detect_faces(pixels)\n         \n         if len(results)==0:\n             face_table[i]=False\n    truth_count=0\n    false_idx=list()\n    for j in range(len(face_table)):\n        if face_table[j]:\n            truth_count+=1\n        else:\n            false_idx.append(j+1)\n    if truth_count == embeddings_per_id:\n        return [True,0]\n    else:\n        return [False,false_idx]\n    \n    \n        \n#*#\n# For Scalability add sub category such as Eye color or other facial features to reduce time complexity, better performance when there are alot of rows.\ndef pred(X,embeddings_per_id):\n     img = Image.open(X.stream)\n     new_embedding=get_embeddings(img)\n     \n     ##\n     conn = sqlite3.connect('faces.db')\n\n     cursor = conn.cursor()\n     \n     cursor.execute('SELECT * FROM faces')\n     \n     # Fetch all the rows\n     rows = cursor.fetchall()\n     \n     ##\n     #embeddings_id = np.load('embedding_id.npy')\n     #embeddings = np.load('embeddings.npy')\n     \n    \n     score_dict =  dict()\n     for row in rows:\n         score_dict[row[1]]=0\n     \n     for idx,name,embedding1,embedding2,embedding3 in rows:\n         #convert blob to np array*\n         pred_1 = is_match2(np.fromstring(embedding1, dtype=np.float32),new_embedding)\n         pred_2 = is_match2(np.fromstring(embedding2, dtype=np.float32),new_embedding)\n         pred_3 = is_match2(np.fromstring(embedding3, dtype=np.float32),new_embedding)\n         \n         if (pred_1[0]):\n             score_dict[name]+=pred_1[1]\n         else:\n             score_dict[name]-=pred_1[1]\n         if (pred_2[0]):\n             score_dict[name]+=pred_2[1]\n         else:\n             score_dict[name]-=pred_2[1]\n         if (pred_3[0]):\n             score_dict[name]+=pred_3[1]\n         else:\n             score_dict[name]-=pred_3[1]\n     conn.close()\n         \n    \n     \n     print(score_dict)\n     max_value = max(score_dict.values())\n     if max_value <= -15:\n         return \"No match\"\n     else:\n         print(max_value)\n         pred_class = max(score_dict, key=score_dict.get)\n         print(type(pred_class))\n         return pred_class\n     \n\ndef delete_face(name):\n    conn = sqlite3.connect('faces.db')\n    cursor = conn.cursor()\n    cursor.execute('DELETE FROM faces WHERE name = ?',(name,))\n    conn.commit()\n    conn.close()\n    \n    \n    ","repo_name":"Moinahmed7777/Facial-Recognition-App","sub_path":"Api/Face_pred.py","file_name":"Face_pred.py","file_ext":"py","file_size_in_byte":9352,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"655006339","text":"\"\"\"\nBinary search trees are a data structure that enforce an ordering over \nthe data they store. That ordering in turn makes it a lot more efficient \nat searching for a particular piece of data in the tree. \n\nThis part of the project comprises two days:\n1. Implement the methods `insert`, `contains`, `get_max`, and `for_each`\n   on the BSTNode class.\n2. Implement the `in_order_print`, `bft_print`, and `dft_print` methods\n   on the BSTNode class.\n\"\"\"\n\nfrom queue import Queue\nfrom stack import Stack\n\n\nclass BinarySearchTree:\n    ''' implement a bst '''\n\n    def __init__(self, value):\n        self.value = value  # root node created when object instantiated\n        self.left = None\n        self.right = None\n\n    def insert(self, value):\n        ''' Insert the given value into the tree '''\n        # if value to insert is greater than current node, go right\n        # in a prior cohort's video the instructor said put dupe values\n        # on the right so using a <= here\n        if self.value <= value:\n            # is there is no right node, make one\n            if self.right is None:\n                self.right = BinarySearchTree(value)\n            # otherwise treat the node as the start of a new tree\n            else:\n                self.right.insert(value)\n        # reverse of the \"go right\" logic above\n        if self.value > value:\n            if self.left is None:\n                self.left = BinarySearchTree(value)\n            else:\n                self.left.insert(value)\n\n    # Return True if the tree contains the value\n    # False if it does not\n    def contains(self, target):\n        ''' Traverses the tree to find the input target variable '''\n        if self.value == target:\n            return True\n        # target is larger the current node, go right\n        if self.value < target:\n            if self.right is None:  # but there is no right so the target isn't here\n                return False\n            else:  # go right and make it the current node\n                return self.right.contains(target)\n        else:  # reverse of the \"go right\" logic above\n            if self.left is None:\n                return False\n            else:\n                return self.left.contains(target)\n\n    def get_max(self):\n        ''' Return the maximum value found in the tree '''\n        # does root have a right node? n=root is max, y=right is current\n        # current is new root so recurse\n        if self.right:\n            return self.right.get_max()\n        else:\n            return self.value\n\n    def for_each(self, fn):\n        ''' Call the function `fn` on the value of each node '''\n        # run the function on the current node\n        fn(self.value)\n        # if there's a right node, run the function on it\n        # this will take care of all rights\n        if self.right:\n            self.right.for_each(fn)\n        # similarly for any left nodes\n        if self.left:\n            self.left.for_each(fn)\n\n    # Part 2 -----------------------\n\n    def in_order_print(self, node):\n        ''' Print all the values in order from low to high\n        Hint:  Use a recursive, depth first traversal '''\n        #\n        # if node is none, we've reached a bottom\n        if node is None:\n            return None\n\n        # print node's value\n        # print(node.value)\n\n        # go print the lefts\n        self.in_order_print(node.left)\n\n        # print node's value\n        # print goes here so lefts get printed first\n        print(node.value)\n\n        # now print the rights\n        self.in_order_print(node.right)\n        # if self.left:\n        #     self.left.in_order_print(node.left)\n        # if self.right:\n        #     self.right.in_order_print(node.right)\n\n    def bft_print(self, node):\n        ''' Print the value of every node, starting with the given node,\n        in an iterative breadth first traversal '''\n        # create a queue object and initialize it\n        queue = Queue()\n        queue.enqueue(node)\n        # loop until the queue is empty\n        while queue.size:\n            # get the first in value and print\n            x = queue.dequeue()\n            print(x.value)\n            # add the left and right children to the queue\n            if x.left:\n                queue.enqueue(x.left)\n            if x.right:\n                queue.enqueue(x.right)\n\n    def dft_print(self, node):\n        ''' Print the value of every node, starting with the given node,\n        in an iterative depth first traversal '''\n        # create a stack object and initialize it\n        stack = Stack()\n        stack.push(node)\n        # loop until the stack is empty\n        while stack.size:\n            # get the top value from the stack and print\n            # pop from stack must be before pushes below to prevent inf loop\n            x = stack.pop()\n            print(x.value)\n            # add the left and right children to the stack\n            if x.left:\n                stack.push(x.left)\n            if x.right:\n                stack.push(x.right)\n\n    # Stretch Goals -------------------------\n    # Note: Research may be required\n\n    # Print Pre-order recursive DFT\n    def pre_order_dft(self, node):\n        pass\n\n    # Print Post-order recursive DFT\n    def post_order_dft(self, node):\n        pass\n\n\n# bst = BinarySearchTree(1)\n# bst.insert(8)\n# bst.insert(5)\n# bst.insert(7)\n# bst.insert(6)\n# bst.insert(3)\n# bst.insert(4)\n# bst.insert(2)\n# bst.in_order_print(bst)\n# bst.bft_print(bst)\n# bst.dft_print(bst)\n","repo_name":"1aaronscott/Data-Structures","sub_path":"binary_search_tree/binary_search_tree.py","file_name":"binary_search_tree.py","file_ext":"py","file_size_in_byte":5477,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"41005052235","text":"from django.conf.urls import patterns, include, url\nfrom django.contrib import admin\nfrom . import views\nfrom . import feeds\n\nadmin.autodiscover()\n\nurlpatterns = patterns('',\n    url(r'^$',                     views.JobList.as_view(),     name='job_list'),\n    url(r'^new/$',                 views.JobCreate.as_view(),   name='job_create'),\n    url(r'^mine/$',                views.MyListings.as_view(),  name='job_list_mine'),\n    url(r'^(?P<pk>\\d+)/$',         views.JobDetail.as_view(),   name='job_detail'),\n    url(r'^(?P<pk>\\d+)/edit/$',    views.JobEdit.as_view(),     name='job_edit'),\n    url(r'^(?P<pk>\\d+)/publish/$', views.PublishJob.as_view(),  name='job_publish'),\n    url(r'^(?P<pk>\\d+)/archive/$', views.ArchiveJob.as_view(),  name='job_archive'),\n    url(r'^(?P<pk>\\d+)/flag/$',    views.FlagJob.as_view(),     name='job_flag'),\n    url(r'^feed/$',                feeds.JobFeed(),             name='job_feed'),\n    url(r'^login/$',               views.Login.as_view(),       name='login'),\n    url(r'^flags/$',               views.ReviewFlags.as_view(), name='review_flags'),\n    url(r'^logout/$',              'django.contrib.auth.views.logout', {'next_page': '/'}, name='logout'),\n    url(r'^admin/', include(admin.site.urls)),\n    url(r'',        include('social_auth.urls')),\n)\n","repo_name":"jacobian/djobs","sub_path":"jobs/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1299,"program_lang":"python","lang":"en","doc_type":"code","stars":14,"dataset":"github-code","pt":"81"}
+{"seq_id":"71139376586","text":"from multiprocessing import Pool\nimport bs4 as bs\nimport random\nimport requests\nimport string\nimport time\nimport zlib\nfrom random import randint\nimport shutil\nimport os\n\n\ndef main(num_process):\n    movies = {\"Aladdin\": \"testFiles/Aladdin\",\n              \"BeautyandtheBeast\": \"testFiles/BeautyandtheBeast\",\n              \"LionKing\": \"testFiles/LionKing\",\n              \"Tarzan\": \"testFiles/Tarzan\",\n              \"TheLittleMermaid\": \"testFiles/TheLittleMermaid\"}\n\n    clean()\n    os.mkdir(\"compressedFiles\")\n    os.mkdir(\"uncompressedFiles\")\n\n    start = time.time()\n    p = Pool(processes=num_process)\n    p.starmap(compress, list(movies.items()))\n    p.close()\n    p.join()\n\n    end = time.time()\n    duration = end - start\n\n    return duration\n\n\ndef compress(name, path_to_file):\n\n    text = open(path_to_file+\".txt\", \"rb\").read()\n    with open(\"compressedFiles/\"+name+\".zlib\", \"wb\") as myFile:\n        myFile.write(zlib.compress(text))\n\n    compressedText = open(\"compressedFiles/\"+name+\".zlib\", \"rb\").read()\n\n    with open(\"uncompressedFiles/\"+name+\".txt\", \"wb\") as myFile:\n        myFile.write(zlib.decompress(compressedText))\n\n\ndef clean():\n    if(os.path.isdir(os.getcwd()+\"/compressedFiles\")):\n        shutil.rmtree(os.getcwd()+\"/compressedFiles\")\n    if(os.path.isdir(os.getcwd()+\"/uncompressedFiles\")):\n        shutil.rmtree(os.getcwd()+\"/uncompressedFiles\")\n","repo_name":"jonathankingfc/multiprocessing_py","sub_path":"compression.py","file_name":"compression.py","file_ext":"py","file_size_in_byte":1369,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"10600748716","text":"#\tGoogle Drive + excessive \"driving\" == G[oogle] RoadHog\r\n\r\n# This script recovers the downloaded GRoadHog blocks back to the original content.\r\n# It looks for the folder containing all the neccessary blocks.\r\n# It proceeds them all till the resulting file is recovered and ready to use.\r\n\r\nimport sys\r\n\r\n#Name of the resulting file (from the command prompt)\r\nDESTINATION_FILE=sys.argv[1]\r\n\r\nfrom os import walk\r\nfrom os.path import join as fullpath\r\n\r\n# This part of code depends on \"xlrd\" library which can read data from Excel files -\r\n# the file format for exported Google Docs\r\nimport xlrd\r\n#This function reads our former CSV file block (now encoded as Excel Workbook) cell-by-cell,\r\n#deciphers the block content and writes it back to the original file\r\ndef ReadWorkBook(f, path):\r\n\t#Open workbook and the first sheet\r\n\twb=xlrd.open_workbook(path)\r\n\tsheet=wb.sheet_by_index(0)\r\n\t#Read every cell one-by-one\r\n\tfor x in range(sheet.nrows):\r\n\t\tfor y in range(sheet.ncols):\r\n\t\t\tcell_value=sheet.cell_value(rowx=x, colx=y)\r\n\t\t\t#Write decoded byte value back into original (f)ile\r\n\t\t\t[f.write(i) for i in decode(cell_value)]\r\n#\"xlrd\"-dependent part is over\r\n\r\n#Decode the cell\r\ndef decode(cell):\r\n\t#One byte encoded as a pair of hexademical digits\r\n\tpairs=[cell[i : i + 2] for i in range(0, len(cell), 2)]\r\n\tdecoded=[]\r\n\tfor p in pairs:\r\n\t\t#Those digits are encoded with the Caesar cipher \r\n\t\tdecoded += [bytes([int('%x%x' % \r\n\t\t(ord(p[0]) - ord('a'), ord(p[1]) - ord('a')), 16)])]\r\n\t#The result is a set of decoded bytes from one cell\r\n\treturn decoded\r\n\r\n#Find a folder that contains no subfolders but files with '.xlsx' extension:\r\nfor root, folders, files in walk(\".\"):\r\n\tif any([f.endswith('.xlsx') for f in files]) and not folders:\r\n\t\t#Sort the files by their number (0.xlsx, 1.xlsx, 2.xlsx, ..., 10.xlsx, ...)\r\n\t\tsorted_files=sorted(files, key=lambda item : int(item.split('.')[0]))\r\n\t\tbreak\r\n\t\t\r\n#Start writing the destination file: read books, decode, fill the resulting file\r\nwith open(DESTINATION_FILE, \"wb\") as destination:\r\n\tfor f in sorted_files:\r\n\t\tReadWorkBook(destination, fullpath(root, f))","repo_name":"RedSerge/fun","sub_path":"Experiments/GRoadHog/GRoadHog_recover.py","file_name":"GRoadHog_recover.py","file_ext":"py","file_size_in_byte":2107,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"6444034282","text":"import tensorflow as tf\nfrom tensorflow import keras\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n#导入 Fashion MNIST 数据集\nfashion_mnist = keras.datasets.fashion_mnist\n(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()\nclass_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',\n               'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']\n\n#查看数据\ntrain_images.shape\nlen(train_labels)\ntrain_labels\ntest_images.shape\nlen(test_labels)\n\nplt.figure()\nplt.imshow(train_images[0])\nplt.colorbar()\nplt.grid(False)\nplt.show()\n\n#预处理数据\ntrain_images = train_images / 255.0\ntest_images = test_images / 255.0\n\nplt.figure(figsize=(10,10))\nfor i in range(25):\n    plt.subplot(5,5,i+1)\n    plt.xticks([])\n    plt.yticks([])\n    plt.grid(False)\n    plt.imshow(train_images[i], cmap=plt.cm.binary)\n    plt.xlabel(class_names[train_labels[i]])\nplt.show()\n\n#构建模型\n#设置层\nmodel = keras.Sequential([\n    keras.layers.Flatten(input_shape=(28, 28)),\n    keras.layers.Dense(128, activation='relu'),\n    keras.layers.Dense(10)\n])\n'''\n该网络的第一层 tf.keras.layers.Flatten 将图像格式从二维数组（28 x 28 像素）转换成一维数组（28 x 28 = 784 像素）。\n将该层视为图像中未堆叠的像素行并将其排列起来。该层没有要学习的参数，它只会重新格式化数据。\n\n展平像素后，网络会包括两个 tf.keras.layers.Dense 层的序列。它们是密集连接或全连接神经层。\n第一个 Dense 层有 128 个节点（或神经元）。第二个（也是最后一个）层会返回一个长度为 10 的 logits 数组。每个节点都包含一个得分，用来表示当前图像属于 10 个类中的哪一类。\n'''\n#编译模型\n'''\n损失函数 - 用于测量模型在训练期间的准确率。您会希望最小化此函数，以便将模型“引导”到正确的方向上。\n优化器 - 决定模型如何根据其看到的数据和自身的损失函数进行更新。\n指标 - 用于监控训练和测试步骤。以下示例使用了准确率，即被正确分类的图像的比率。\n'''\nmodel.compile(optimizer='adam',\n              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n              metrics=['accuracy'])\n\n'''\n训练模型\n训练神经网络模型需要执行以下步骤：\n\n将训练数据馈送给模型。在本例中，训练数据位于 train_images 和 train_labels 数组中。\n模型学��将图像和标签关联起来。\n要求模型对测试集（在本例中为 test_images 数组）进行预测。\n验证预测是否与 test_labels 数组中的标签相匹配。\n'''\n#向模型馈送数据\nmodel.fit(train_images, train_labels, epochs=10)\n\n#评估准确率\n\ntest_loss, test_acc = model.evaluate(test_images,  test_labels, verbose=2)\n\nprint('\\nTest accuracy:', test_acc)\n\n#进行预测\n\n'''\n在模型经过训练后，您可以使用它对一些图像进行预测。\n模型具有线性输出，即 logits。您可以附加一个 softmax 层，将 logits 转换成更容易理解的概率。\n'''\nprobability_model = tf.keras.Sequential([model, \n                                         tf.keras.layers.Softmax()])\npredictions = probability_model.predict(test_images)\n\npredictions[0]\n\n#最大置信度值\nprint(class_names[np.argmax(predictions[0])])","repo_name":"DannySunsan/tf-practice","sub_path":"test01.py","file_name":"test01.py","file_ext":"py","file_size_in_byte":3336,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"34633087745","text":"import deeplabcut\n\niterations = 500000\n\nProjectFolderName = '//wsl$/Ubuntu-20.04/home/nghess/Hand Test-NG Hess-2022-06-30'\nVideoType = '.mp4'\n\n# Set paths\nvideofile_path = [ProjectFolderName + '/videos']\npath_config_file = ProjectFolderName+'/config.yaml'\n\n# Set the shuffle you want to create, train, evaluate, use for analysis, etc (1 is default):\nSHUF = 1\n\n# Create training data\n# There are a lot of other arguments for this function. Might be worth trying some variations\n# DLC-Live appears to run faster using MobileNetV2-0.35 networks\ndeeplabcut.create_training_dataset(path_config_file, net_type='resnet_50', augmenter_type='imgaug')\n\n# Train\ndeeplabcut.train_network(path_config_file, shuffle=SHUF, displayiters=10, saveiters=500, maxiters=iterations)\n\n# Evaluate\ndeeplabcut.evaluate_network(path_config_file, Shuffles=[SHUF], plotting=False)\n","repo_name":"nghess/dlc","sub_path":"dlc-train.py","file_name":"dlc-train.py","file_ext":"py","file_size_in_byte":852,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"42964434789","text":"import numpy as np\r\nfrom keras.datasets import cifar10\r\n\r\nnp.random.seed(10)\r\n\r\n(x_train, y_train), (x_test, y_test) = cifar10.load_data()\r\n\r\nx_train = x_train.astype('float32') / 255.0\r\nx_test = x_test.astype('float32') / 255.0\r\n\r\nfrom keras.utils import np_utils\r\n\r\ny_train_1hot = np_utils.to_categorical(y_train)\r\ny_test_1hot = np_utils.to_categorical(y_test)\r\n\r\nfrom keras.models import Sequential\r\nfrom keras.layers import Dropout,Dense,Flatten,Conv2D,MaxPooling2D\r\n\r\nmodel = Sequential()\r\nmodel.add(Conv2D(filters=32,kernel_size=(3,3),input_shape=(32,32,3),activation='relu',padding='same'))\r\nmodel.add(Dropout(rate=0.25))\r\nmodel.add(MaxPooling2D(pool_size=(2,2)))\r\n\r\nmodel.add(Conv2D(filters=64,kernel_size=(3,3),activation='relu',padding='same'))\r\nmodel.add(Dropout(rate=0.25))\r\nmodel.add(MaxPooling2D(pool_size=(2,2)))\r\n\r\nmodel.add(Flatten())\r\nmodel.add(Dropout(0.25))\r\nmodel.add(Dense(1024, activation='relu'))\r\nmodel.add(Dropout(0.25))\r\nmodel.add(Dense(10, activation='softmax'))\r\n\r\nprint(model.summary())\r\n\r\nmodel.compile(loss='categorical_crossentropy',optimizer='adam',metrics=['accuracy'])\r\ntrain_history = model.fit(x_train,y_train_1hot,batch_size=128,epochs=10,verbose=1,validation_split=0.2)\r\n\r\nfrom show import show_train_history\r\n\r\nshow_train_history('acc','val_acc')\r\nshow_train_history('loss','val_loss')\r\n\r\nscores = model.evaluate(x_test,y_test_1hot,verbose=0)\r\nprint (scores)\r\n\r\npred = model.predict_classes(x_test)\r\nprint(pred[:10])\r\n\r\nfrom show import plot_images_labels_prediction\r\nplot_images_labels_prediction(x_test,y_test,pred,0,10)","repo_name":"KyrieSu/Deep-Learning","sub_path":"CIFAR10_CNN.py","file_name":"CIFAR10_CNN.py","file_ext":"py","file_size_in_byte":1563,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"617919305","text":"import os\nimport glob\n\n\ndef main():\n    folder4k = '/DATA7_DB7/data/4khdr/data/Dataset/train_4k'\n    folder540p = '/DATA7_DB7/data/4khdr/data/Dataset/train_540p'\n\n    folders = sorted(os.listdir(folder4k))\n    folders_540p = sorted(os.listdir(folder540p))\n    len_folders = len(folders)\n\n    for i,folder in enumerate(folders):\n\n        print(folder)\n        path4k = os.path.join(folder4k, folder)\n        path540p = os.path.join(folder540p, folder)\n\n        images = sorted(os.listdir(path540p))\n        for img in images:\n            img_path_4k = os.path.join(path4k, img)\n            img_path_540p = os.path.join(path540p, img)\n            index = int(img[:-4]) - 1\n            new_img = '{:05d}.png'.format(index)\n\n            new_img_path_4k = os.path.join(path4k, new_img)\n            new_img_path_540p = os.path.join(path540p, new_img)\n\n            os.rename(img_path_4k, new_img_path_4k)\n            os.rename(img_path_540p, new_img_path_540p)\n\n\n    print('Finished.')\n\n\ndef DIV2K(path):\n    img_path_l = glob.glob(os.path.join(path, '*'))\n    for img_path in img_path_l:\n        new_path = img_path.replace('x2', '').replace('x3', '').replace('x4', '').replace('x8', '')\n        os.rename(img_path, new_path)\n\n\nif __name__ == \"__main__\":\n    main()","repo_name":"laomao0/EDVR_4K_HDR","sub_path":"data_scripts/rename_folder_images.py","file_name":"rename_folder_images.py","file_ext":"py","file_size_in_byte":1259,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"81"}
+{"seq_id":"8084253717","text":"from bs4 import BeautifulSoup\nimport requests\n\nURL = \"https://www.timesjobs.com/candidate/job-search.html?searchType=personalizedSearch&from=submit&txtKeywords=Data+Science&txtLocation=\"\n\nhtml_text = requests.get(URL).text\n\nsoup = BeautifulSoup(html_text, 'lxml')\n\njobs = soup.find_all('li', class_=\"clearfix job-bx wht-shd-bx\")\nfor job in jobs:\n    published_date = job.find('span', class_=\"sim-posted\").text.strip()\n    \n    if published_date == \"Posted few days ago\":\n        job_name = job.find('a').text.strip()\n        company = job.find('h3', class_=\"joblist-comp-name\").text.strip()\n        skills = job.find('span', class_=\"srp-skills\").text.replace(\" \", \"\").strip()\n\n        print(f'''\nJob Title: {job_name}\nCompany: {company}\nRequired Skills: {skills}\n-------------------------------\n\n''')\n\n","repo_name":"mubarakmayyeri/beautifulsoup-exercises","sub_path":"scraper_{real_websites}/job_details.py","file_name":"job_details.py","file_ext":"py","file_size_in_byte":802,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"33343181558","text":"import pickle\nimport deeplake\nimport numpy as np\nimport pytest\nfrom functools import partial\nfrom deeplake.util.exceptions import EmptyTensorError, TensorDoesNotExistError\n\nfrom deeplake.util.remove_cache import get_base_storage\nfrom deeplake.core.index.index import IndexEntry\nfrom deeplake.tests.common import (\n    requires_torch,\n    requires_libdeeplake,\n    convert_data_according_to_torch_version,\n)\nfrom deeplake.core.dataset import Dataset\nfrom deeplake.constants import KB\n\nfrom PIL import Image  # type: ignore\n\ntry:\n    from torch.utils.data._utils.collate import default_collate\nexcept ImportError:\n    pass\n\nfrom unittest.mock import patch\n\n\n# ensure tests have multiple chunks without a ton of data\nPYTORCH_TESTS_MAX_CHUNK_SIZE = 5 * KB\n\n\ndef double(sample):\n    return sample * 2\n\n\ndef identity(batch):\n    return batch\n\n\ndef identity_collate(batch):\n    return batch\n\n\ndef to_tuple(sample, t1, t2):\n    return sample[t1], sample[t2]\n\n\ndef reorder_collate(batch):\n    x = [((x[\"a\"], x[\"b\"]), x[\"c\"]) for x in batch]\n    return default_collate(x)\n\n\ndef dict_to_list(sample):\n    return [sample[\"a\"], sample[\"b\"], sample[\"c\"]]\n\n\ndef my_transform_collate(batch):\n    x = [(c, a, b) for a, b, c in batch]\n    return default_collate(x)\n\n\ndef index_transform(sample):\n    return sample[\"index\"], sample[\"xyz\"]\n\n\ndef dummy_init_fn(arg):\n    return f\"function called with arg {arg}\"\n\n\n@requires_libdeeplake\ndef test_setting_woker_init_function(local_auth_ds):\n    dl = local_auth_ds.dataloader().pytorch()\n\n    assert dl.worker_init_fn == None\n    dl.worker_init_fn = partial(dummy_init_fn, 1024)\n    assert dl.worker_init_fn() == \"function called with arg 1024\"\n\n\n@requires_torch\n@requires_libdeeplake\ndef test_offset_ds_iteration(local_auth_ds):\n    with local_auth_ds as ds:\n        ds.create_tensor(\"abc\", htype=\"generic\", dtype=\"uint16\")\n        ds.abc.extend([i for i in range(10)])\n\n    dl = (\n        local_auth_ds.dataloader()\n        .offset(4)\n        .transform(identity)\n        .pytorch(collate_fn=identity)\n    )\n\n    idx_table = [4, 5, 6, 7, 8, 9, 0, 1, 2, 3]\n    for i, item in enumerate(dl):\n        assert idx_table[i] == item[0][\"index\"].astype(int)\n\n\n@requires_torch\n@requires_libdeeplake\n@pytest.mark.parametrize(\n    \"ds\",\n    [\n        pytest.param(\n            \"hub_cloud_ds\",\n            marks=[pytest.mark.slow, pytest.mark.skip(\"Causing lockups\")],\n        ),\n        \"local_auth_ds\",\n    ],\n    indirect=True,\n)\n@pytest.mark.flaky\n@pytest.mark.skip(\"causing lockups\")\ndef test_pytorch_small(ds):\n    with ds:\n        ds.create_tensor(\"image\", max_chunk_size=PYTORCH_TESTS_MAX_CHUNK_SIZE)\n        ds.image.extend(([i * np.ones((i + 1, i + 1)) for i in range(16)]))\n        ds.commit()\n        ds.create_tensor(\"image2\", max_chunk_size=PYTORCH_TESTS_MAX_CHUNK_SIZE)\n        ds.image2.extend(np.array([i * np.ones((12, 12)) for i in range(16)]))\n    dl = ds.dataloader().batch(1).pytorch(num_workers=2)\n\n    assert len(dl.dataset) == 16\n\n    for _ in range(2):\n        for i, batch in enumerate(dl):\n            np.testing.assert_array_equal(\n                batch[\"image\"].numpy(), i * np.ones((1, i + 1, i + 1))\n            )\n            np.testing.assert_array_equal(\n                batch[\"image2\"].numpy(), i * np.ones((1, 12, 12))\n            )\n\n    sub_ds = ds[5:]\n    sub_dl = sub_ds.dataloader().pytorch(num_workers=0)\n\n    for i, batch in enumerate(sub_dl):\n        np.testing.assert_array_equal(\n            batch[\"image\"].numpy(), (5 + i) * np.ones((1, 6 + i, 6 + i))\n        )\n        np.testing.assert_array_equal(\n            batch[\"image2\"].numpy(), (5 + i) * np.ones((1, 12, 12))\n        )\n\n    sub_ds2 = ds[8:12]\n    sub_dl2 = sub_ds2.dataloader().pytorch(num_workers=0)\n\n    for _ in range(2):\n        for i, batch in enumerate(sub_dl2):\n            np.testing.assert_array_equal(\n                batch[\"image\"].numpy(), (8 + i) * np.ones((1, 9 + i, 9 + i))\n            )\n            np.testing.assert_array_equal(\n                batch[\"image2\"].numpy(), (8 + i) * np.ones((1, 12, 12))\n            )\n\n    sub_ds3 = ds[:5]\n    sub_dl3 = sub_ds3.dataloader().pytorch(num_workers=0)\n\n    for _ in range(2):\n        for i, batch in enumerate(sub_dl3):\n            np.testing.assert_array_equal(\n                batch[\"image\"].numpy(), (i) * np.ones((1, i + 1, i + 1))\n            )\n            np.testing.assert_array_equal(\n                batch[\"image2\"].numpy(), (i) * np.ones((1, 12, 12))\n            )\n\n\n@requires_torch\n@requires_libdeeplake\n@pytest.mark.flaky(retry_count=3)\n@pytest.mark.slow\n@pytest.mark.timeout(10)\n@pytest.mark.skip(\"causing lockups\")\ndef test_pytorch_transform(local_auth_ds):\n    with local_auth_ds as ds:\n        ds.create_tensor(\"image\", max_chunk_size=PYTORCH_TESTS_MAX_CHUNK_SIZE)\n        ds.image.extend(([i * np.ones((i + 1, i + 1)) for i in range(16)]))\n        ds.checkout(\"alt\", create=True)\n        ds.create_tensor(\"image2\", max_chunk_size=PYTORCH_TESTS_MAX_CHUNK_SIZE)\n        ds.image2.extend(np.array([i * np.ones((12, 12)) for i in range(16)]))\n\n    dl = (\n        ds.dataloader()\n        .batch(1)\n        .transform(to_tuple, t1=\"image\", t2=\"image2\")\n        .pytorch(num_workers=2, collate_fn=identity_collate)\n    )\n\n    for _ in range(2):\n        for i, batch in enumerate(dl):\n            actual_image, actual_image2 = batch[0]\n            expected_image = i * np.ones((i + 1, i + 1))\n            expected_image2 = i * np.ones((12, 12))\n            np.testing.assert_array_equal(actual_image, expected_image)\n            np.testing.assert_array_equal(actual_image2, expected_image2)\n\n\n@requires_libdeeplake\n@pytest.mark.flaky\ndef test_inequal_tensors_dataloader_length(local_auth_ds):\n    with local_auth_ds as ds:\n        ds.create_tensor(\"images\")\n        ds.create_tensor(\"label\")\n        ds.images.extend(([i * np.ones((i + 1, i + 1)) for i in range(16)]))\n\n    ld = local_auth_ds.dataloader().batch(1).pytorch()\n    assert len(ld) == 0\n    ld1 = local_auth_ds.dataloader().batch(2).pytorch(tensors=[\"images\"])\n    assert len(ld1) == 8\n\n\n@requires_torch\n@requires_libdeeplake\n@pytest.mark.flaky\n@pytest.mark.slow\ndef test_pytorch_transform_dict(local_auth_ds):\n    with local_auth_ds as ds:\n        ds.create_tensor(\"image\", max_chunk_size=PYTORCH_TESTS_MAX_CHUNK_SIZE)\n        ds.image.extend(([i * np.ones((i + 1, i + 1)) for i in range(16)]))\n        ds.create_tensor(\"image2\", max_chunk_size=PYTORCH_TESTS_MAX_CHUNK_SIZE)\n        ds.image2.extend(np.array([i * np.ones((12, 12)) for i in range(16)]))\n        ds.create_tensor(\"image3\", max_chunk_size=PYTORCH_TESTS_MAX_CHUNK_SIZE)\n        ds.image3.extend(np.array([i * np.ones((12, 12)) for i in range(16)]))\n\n    dl = ds.dataloader().transform({\"image\": double, \"image2\": None}).pytorch()\n\n    assert len(dl.dataset) == 16\n\n    for _ in range(2):\n        for i, batch in enumerate(dl):\n            assert set(batch.keys()) == {\"image\", \"image2\"}\n            np.testing.assert_array_equal(\n                batch[\"image\"].numpy(), 2 * i * np.ones((1, i + 1, i + 1))\n            )\n            np.testing.assert_array_equal(\n                batch[\"image2\"].numpy(), i * np.ones((1, 12, 12))\n            )\n\n    for _ in range(2):\n        for i, (image, image2) in enumerate(dl):\n            np.testing.assert_array_equal(\n                image.numpy(), 2 * i * np.ones((1, i + 1, i + 1))\n            )\n            np.testing.assert_array_equal(image2.numpy(), i * np.ones((1, 12, 12)))\n\n\n@pytest.mark.slow\n@requires_torch\n@requires_libdeeplake\n@pytest.mark.flaky\ndef test_pytorch_with_compression(local_auth_ds: Dataset):\n    # TODO: chunk-wise compression for labels (right now they are uncompressed)\n    with local_auth_ds as ds:\n        images = ds.create_tensor(\n            \"images\",\n            htype=\"image\",\n            sample_compression=\"png\",\n            max_chunk_size=PYTORCH_TESTS_MAX_CHUNK_SIZE,\n        )\n        labels = ds.create_tensor(\n            \"labels\", htype=\"class_label\", max_chunk_size=PYTORCH_TESTS_MAX_CHUNK_SIZE\n        )\n\n        assert images.meta.sample_compression == \"png\"\n\n        images.extend(np.ones((16, 12, 12, 3), dtype=\"uint8\"))\n        labels.extend(np.ones((16, 1), dtype=\"uint32\"))\n\n    dl = ds.dataloader().pytorch(num_workers=0)\n\n    for _ in range(2):\n        for batch in dl:\n            X = batch[\"images\"].numpy()\n            T = batch[\"labels\"].numpy()\n            assert X.shape == (1, 12, 12, 3)\n            assert T.shape == (1, 1)\n\n\n@pytest.mark.slow\n@requires_torch\n@requires_libdeeplake\n@pytest.mark.flaky\ndef test_custom_tensor_order(local_auth_ds):\n    with local_auth_ds as ds:\n        tensors = [\"a\", \"b\", \"c\", \"d\"]\n        for t in tensors:\n            ds.create_tensor(t, max_chunk_size=PYTORCH_TESTS_MAX_CHUNK_SIZE)\n            ds[t].extend(np.random.random((3, 4, 5)))\n\n    with pytest.raises(TensorDoesNotExistError):\n        ds.dataloader().pytorch(tensors=[\"c\", \"d\", \"e\"])\n\n    dl = ds.dataloader().pytorch(tensors=[\"c\", \"d\", \"a\"], return_index=False)\n\n    for i, batch in enumerate(dl):\n        c1, d1, a1 = batch\n        a2 = batch[\"a\"]\n        c2 = batch[\"c\"]\n        d2 = batch[\"d\"]\n        assert \"b\" not in batch\n        np.testing.assert_array_equal(a1, a2)\n        np.testing.assert_array_equal(c1, c2)\n        np.testing.assert_array_equal(d1, d2)\n        np.testing.assert_array_equal(a1[0], ds.a.numpy()[i])\n        np.testing.assert_array_equal(c1[0], ds.c.numpy()[i])\n        np.testing.assert_array_equal(d1[0], ds.d.numpy()[i])\n        batch = pickle.loads(pickle.dumps(batch))\n        c1, d1, a1 = batch\n        a2 = batch[\"a\"]\n        c2 = batch[\"c\"]\n        d2 = batch[\"d\"]\n        np.testing.assert_array_equal(a1, a2)\n        np.testing.assert_array_equal(c1, c2)\n        np.testing.assert_array_equal(d1, d2)\n        np.testing.assert_array_equal(a1[0], ds.a.numpy()[i])\n        np.testing.assert_array_equal(c1[0], ds.c.numpy()[i])\n        np.testing.assert_array_equal(d1[0], ds.d.numpy()[i])\n\n\n@pytest.mark.slow\n@requires_torch\n@requires_libdeeplake\n@pytest.mark.flaky\n@pytest.mark.skip(\"causing lockups\")\n@pytest.mark.timeout(10)\ndef test_readonly_with_two_workers(local_auth_ds):\n    with local_auth_ds as ds:\n        ds.create_tensor(\"images\", max_chunk_size=PYTORCH_TESTS_MAX_CHUNK_SIZE)\n        ds.create_tensor(\"labels\", max_chunk_size=PYTORCH_TESTS_MAX_CHUNK_SIZE)\n        ds.images.extend(np.ones((10, 12, 12)))\n        ds.labels.extend(np.ones(10))\n\n    base_storage = get_base_storage(ds.storage)\n    base_storage.flush()\n    base_storage.enable_readonly()\n    ds = Dataset(\n        storage=ds.storage,\n        token=ds.token,\n        read_only=True,\n        verbose=False,\n    )\n\n    ptds = ds.dataloader().pytorch(num_workers=2)\n    # no need to check input, only care that readonly works\n    for _ in ptds:\n        continue\n\n\n@pytest.mark.xfail(raises=NotImplementedError, strict=True)\ndef test_corrupt_dataset():\n    raise NotImplementedError\n\n\n@pytest.mark.xfail(raises=NotImplementedError, strict=True)\ndef test_pytorch_local_cache():\n    raise NotImplementedError\n\n\n@requires_torch\n@requires_libdeeplake\n@pytest.mark.slow\n@pytest.mark.flaky\ndef test_groups(local_auth_ds, compressed_image_paths):\n    img1 = deeplake.read(compressed_image_paths[\"jpeg\"][0])\n    img2 = deeplake.read(compressed_image_paths[\"png\"][0])\n    with local_auth_ds as ds:\n        ds.create_tensor(\"images/jpegs/cats\", htype=\"image\", sample_compression=\"jpeg\")\n        ds.create_tensor(\"images/pngs/flowers\", htype=\"image\", sample_compression=\"png\")\n        for _ in range(10):\n            ds.images.jpegs.cats.append(img1)\n            ds.images.pngs.flowers.append(img2)\n\n    another_ds = deeplake.dataset(\n        ds.path,\n        token=ds.token,\n    )\n    dl = another_ds.dataloader().pytorch(return_index=False)\n    for i, (cat, flower) in enumerate(dl):\n        assert cat[0].shape == another_ds.images.jpegs.cats[i].numpy().shape\n        assert flower[0].shape == another_ds.images.pngs.flowers[i].numpy().shape\n\n    dl = another_ds.images.dataloader().pytorch(return_index=False)\n    for sample in dl:\n        cat = sample[\"images/jpegs/cats\"]\n        flower = sample[\"images/pngs/flowers\"]\n        np.testing.assert_array_equal(cat[0], img1.array)\n        np.testing.assert_array_equal(flower[0], img2.array)\n\n\n@pytest.mark.slow\n@requires_torch\n@requires_libdeeplake\n@pytest.mark.flaky\ndef test_string_tensors(local_auth_ds):\n    with local_auth_ds as ds:\n        ds.create_tensor(\"strings\", htype=\"text\")\n        ds.strings.extend([f\"string{idx}\" for idx in range(5)])\n\n    ptds = ds.dataloader().pytorch()\n    for idx, batch in enumerate(ptds):\n        np.testing.assert_array_equal(batch[\"strings\"], f\"string{idx}\")\n\n\n@pytest.mark.xfail(raises=NotImplementedError, strict=True)\ndef test_pytorch_large():\n    raise NotImplementedError\n\n\n@requires_torch\n@requires_libdeeplake\n@pytest.mark.parametrize(\n    \"index\",\n    [\n        slice(2, 7),\n        slice(3, 10, 2),\n        slice(None, 10),\n        slice(None, None, -1),\n        slice(None, None, -2),\n        [2, 3, 4],\n        [2, 4, 6, 8],\n        [2, 2, 4, 4, 6, 6, 7, 7, 8, 8, 9, 9, 9],\n        [4, 3, 2, 1],\n    ],\n)\n@pytest.mark.slow\n@pytest.mark.flaky\ndef test_pytorch_view(local_auth_ds, index):\n    arr_list_1 = [np.random.randn(15, 15, i) for i in range(10)]\n    arr_list_2 = [np.random.randn(40, 15, 4, i) for i in range(10)]\n    label_list = list(range(10))\n\n    with local_auth_ds as ds:\n        ds.create_tensor(\"img1\")\n        ds.create_tensor(\"img2\")\n        ds.create_tensor(\"label\")\n        ds.img1.extend(arr_list_1)\n        ds.img2.extend(arr_list_2)\n        ds.label.extend(label_list)\n\n    ptds = ds[index].dataloader().pytorch()\n    idxs = list(IndexEntry(index).indices(len(ds)))\n    for idx, batch in enumerate(ptds):\n        idx = idxs[idx]\n        np.testing.assert_array_equal(batch[\"img1\"][0], arr_list_1[idx])\n        np.testing.assert_array_equal(batch[\"img2\"][0], arr_list_2[idx])\n        np.testing.assert_array_equal(batch[\"label\"][0], idx)\n\n\n@requires_torch\n@requires_libdeeplake\n@pytest.mark.parametrize(\"shuffle\", [True, False])\n@pytest.mark.slow\n@pytest.mark.flaky\ndef test_pytorch_collate(local_auth_ds, shuffle):\n    with local_auth_ds as ds:\n        ds.create_tensor(\"a\")\n        ds.create_tensor(\"b\")\n        ds.create_tensor(\"c\")\n        for _ in range(100):\n            ds.a.append(0)\n            ds.b.append(1)\n            ds.c.append(2)\n\n    ptds = ds.dataloader().batch(4).pytorch(collate_fn=reorder_collate)\n    if shuffle:\n        ptds = ptds.shuffle()\n    for batch in ptds:\n        assert len(batch) == 2\n        assert len(batch[0]) == 2\n        np.testing.assert_array_equal(batch[0][0], np.array([0, 0, 0, 0]).reshape(4, 1))\n        np.testing.assert_array_equal(batch[0][1], np.array([1, 1, 1, 1]).reshape(4, 1))\n        np.testing.assert_array_equal(batch[1], np.array([2, 2, 2, 2]).reshape(4, 1))\n\n\n@requires_torch\n@requires_libdeeplake\n@pytest.mark.parametrize(\"shuffle\", [True, False])\n@pytest.mark.slow\n@pytest.mark.flaky\ndef test_pytorch_transform_collate(local_auth_ds, shuffle):\n    with local_auth_ds as ds:\n        ds.create_tensor(\"a\")\n        ds.create_tensor(\"b\")\n        ds.create_tensor(\"c\")\n        for _ in range(100):\n            ds.a.append(0 * np.ones((300, 300)))\n            ds.b.append(1 * np.ones((300, 300)))\n            ds.c.append(2 * np.ones((300, 300)))\n\n    ptds = (\n        ds.dataloader()\n        .batch(4)\n        .pytorch(\n            collate_fn=my_transform_collate,\n        )\n        .transform(dict_to_list)\n    )\n    if shuffle:\n        ptds = ptds.shuffle()\n    for batch in ptds:\n        assert len(batch) == 3\n        for i in range(2):\n            assert len(batch[i]) == 4\n        np.testing.assert_array_equal(batch[0], 2 * np.ones((4, 300, 300)))\n        np.testing.assert_array_equal(batch[1], 0 * np.ones((4, 300, 300)))\n        np.testing.assert_array_equal(batch[2], 1 * np.ones((4, 300, 300)))\n\n\n@pytest.mark.xfail(raises=NotImplementedError, strict=True)\ndef test_pytorch_ddp():\n    raise NotImplementedError\n\n\n@requires_torch\n@requires_libdeeplake\n@pytest.mark.parametrize(\"compression\", [None, \"jpeg\"])\n@pytest.mark.slow\n@pytest.mark.flaky\ndef test_pytorch_decode(local_auth_ds, compressed_image_paths, compression):\n    with local_auth_ds as ds:\n        ds.create_tensor(\"image\", sample_compression=compression)\n        ds.image.extend(\n            np.array([i * np.ones((10, 10, 3), dtype=np.uint8) for i in range(5)])\n        )\n        ds.image.extend([deeplake.read(compressed_image_paths[\"jpeg\"][0])] * 5)\n\n    ptds = ds.dataloader().pytorch(decode_method={\"image\": \"tobytes\"})\n\n    for i, batch in enumerate(ptds):\n        image = convert_data_according_to_torch_version(batch[\"image\"])\n        assert isinstance(image, bytes)\n        if i < 5 and not compression:\n            np.testing.assert_array_equal(\n                np.frombuffer(image, dtype=np.uint8).reshape(10, 10, 3),\n                i * np.ones((10, 10, 3), dtype=np.uint8),\n            )\n        elif i >= 5 and compression:\n            with open(compressed_image_paths[\"jpeg\"][0], \"rb\") as f:\n                assert f.read() == image\n\n    if compression:\n        ptds = ds.dataloader().numpy(decode_method={\"image\": \"pil\"})\n        for i, batch in enumerate(ptds):\n            image = batch[0][\"image\"]\n            assert isinstance(image, Image.Image)\n            if i < 5:\n                np.testing.assert_array_equal(\n                    np.array(image), i * np.ones((10, 10, 3), dtype=np.uint8)\n                )\n            elif i >= 5:\n                with Image.open(compressed_image_paths[\"jpeg\"][0]) as f:\n                    np.testing.assert_array_equal(np.array(f), np.array(image))\n\n\n@requires_torch\n@requires_libdeeplake\n@pytest.mark.flaky\n@pytest.mark.slow\ndef test_rename(local_auth_ds):\n    group_name = \"red/green\"\n    with local_auth_ds as ds:\n        ds.create_tensor(\"abc\")\n        ds.create_tensor(\"blue/green\")\n        ds.abc.append([1, 2, 3])\n        ds.rename_tensor(\"abc\", \"xyz\")\n        ds.rename_group(\"blue\", \"red\")\n        ds[group_name].append([1, 2, 3, 4])\n    loader = ds.dataloader().pytorch(return_index=False)\n    for sample in loader:\n        assert set(sample.keys()) == {\"xyz\", group_name}\n        np.testing.assert_array_equal(np.array(sample[\"xyz\"]), np.array([[1, 2, 3]]))\n        np.testing.assert_array_equal(\n            np.array(sample[group_name]), np.array([[1, 2, 3, 4]])\n        )\n\n\n@requires_torch\n@requires_libdeeplake\n@pytest.mark.parametrize(\n    \"num_workers\",\n    [\n        0,\n        pytest.param(2, marks=pytest.mark.skip(reason=\"causing lockups\")),\n    ],\n)\n@pytest.mark.slow\n@pytest.mark.flaky\ndef test_indexes(local_auth_ds, num_workers):\n    with local_auth_ds as ds:\n        ds.create_tensor(\"xyz\")\n        for i in range(8):\n            ds.xyz.append(i * np.ones((2, 2)))\n\n    ptds = ds.dataloader().batch(4).pytorch(num_workers=num_workers, return_index=True)\n    ptds = ptds.shuffle()\n\n    for batch in ptds:\n        assert batch.keys() == {\"xyz\", \"index\"}\n        for i in range(len(batch)):\n            np.testing.assert_array_equal(batch[\"index\"][i], batch[\"xyz\"][i][0, 0])\n\n\n@requires_torch\n@requires_libdeeplake\n@pytest.mark.slow\n@pytest.mark.parametrize(\n    \"num_workers\",\n    [\n        0,\n        pytest.param(2, marks=pytest.mark.skip(\"causing lockups\")),\n    ],\n)\n@pytest.mark.flaky\ndef test_indexes_transform(local_auth_ds, num_workers):\n    with local_auth_ds as ds:\n        ds.create_tensor(\"xyz\")\n        for i in range(8):\n            ds.xyz.append(i * np.ones((2, 2)))\n\n    ptds = (\n        ds.dataloader()\n        .batch(4)\n        .transform(index_transform)\n        .pytorch(\n            num_workers=num_workers, return_index=True, collate_fn=identity_collate\n        )\n    )\n\n    for batch in ptds:\n        assert len(batch) == 4\n        assert len(batch[0]) == 2\n        assert len(batch[1]) == 2\n\n\n@requires_torch\n@requires_libdeeplake\n@pytest.mark.parametrize(\n    \"num_workers\", [0, pytest.param(2, marks=pytest.mark.skip(\"causing lockups\"))]\n)\n@pytest.mark.slow\n@pytest.mark.flaky\ndef test_indexes_transform_dict(local_auth_ds, num_workers):\n    with local_auth_ds as ds:\n        ds.create_tensor(\"xyz\")\n        for i in range(8):\n            ds.xyz.append(i * np.ones((2, 2)))\n\n    ptds = (\n        ds.dataloader()\n        .batch(4)\n        .transform({\"xyz\": double, \"index\": None})\n        .pytorch(num_workers=num_workers, return_index=True)\n    )\n\n    for batch in ptds:\n        assert batch.keys() == {\"xyz\", \"index\"}\n        for i in range(len(batch)):\n            np.testing.assert_array_equal(2 * batch[\"index\"][i], batch[\"xyz\"][i][0, 0])\n\n    ptds = (\n        ds.dataloader()\n        .batch(4)\n        .transform({\"xyz\": double})\n        .pytorch(num_workers=num_workers, return_index=True)\n    )\n\n    for batch in ptds:\n        assert batch.keys() == {\"xyz\"}\n\n\n@requires_torch\n@requires_libdeeplake\n@pytest.mark.parametrize(\n    \"num_workers\", [0, pytest.param(2, marks=pytest.mark.skip(\"causing lockups\"))]\n)\n@pytest.mark.slow\n@pytest.mark.flaky\ndef test_indexes_tensors(local_auth_ds, num_workers):\n    with local_auth_ds as ds:\n        ds.create_tensor(\"xyz\")\n        for i in range(8):\n            ds.xyz.append(i * np.ones((2, 2)))\n\n    with pytest.raises(ValueError):\n        (\n            ds.dataloader()\n            .batch(4)\n            .pytorch(\n                num_workers=num_workers, return_index=True, tensors=[\"xyz\", \"index\"]\n            )\n        )\n\n    ptds = (\n        ds.dataloader()\n        .batch(4)\n        .pytorch(num_workers=num_workers, return_index=True, tensors=[\"xyz\"])\n    )\n\n    for batch in ptds:\n        assert batch.keys() == {\"xyz\", \"index\"}\n\n\n@requires_libdeeplake\n@requires_torch\n@pytest.mark.flaky\n@pytest.mark.slow\ndef test_uneven_iteration(local_auth_ds):\n    with local_auth_ds as ds:\n        ds.create_tensor(\"x\")\n        ds.create_tensor(\"y\")\n        ds.x.extend(list(range(5)))\n        ds.y.extend(list(range(10)))\n    ptds = ds.dataloader().pytorch()\n    for i, batch in enumerate(ptds):\n        x, y = np.array(batch[\"x\"][0]), np.array(batch[\"y\"][0])\n        np.testing.assert_equal(x, i)\n        np.testing.assert_equal(y, i)\n\n\n@requires_libdeeplake\n@requires_torch\n@pytest.mark.slow\ndef test_pytorch_error_handling(local_auth_ds):\n    with local_auth_ds as ds:\n        ds.create_tensor(\"x\")\n        ds.create_tensor(\"y\")\n        ds.x.extend(list(range(5)))\n\n    ptds = ds.dataloader().pytorch()\n    with pytest.raises(EmptyTensorError):\n        for _ in ptds:\n            continue\n\n    ptds = ds.dataloader().pytorch(tensors=[\"x\", \"y\"])\n    with pytest.raises(EmptyTensorError):\n        for _ in ptds:\n            continue\n\n    ptds = ds.dataloader().pytorch(tensors=[\"x\"])\n    for _ in ptds:\n        continue\n\n\n@requires_libdeeplake\n@requires_torch\ndef test_batch_sampler_attribute(local_auth_ds):\n    ld = local_auth_ds.dataloader().pytorch()\n\n    from torch.utils.data import BatchSampler\n\n    assert isinstance(ld.batch_sampler, BatchSampler)\n    assert ld.batch_sampler.sampler is not None\n\n\n@requires_libdeeplake\n@requires_torch\n@pytest.mark.slow\n@pytest.mark.flaky\ndef test_pil_decode_method(local_auth_ds):\n    from indra.pytorch.exceptions import CollateExceptionWrapper  # type: ignore\n\n    with local_auth_ds as ds:\n        ds.create_tensor(\"x\", htype=\"image\", sample_compression=\"jpeg\")\n        ds.x.extend(np.random.randint(0, 255, (10, 10, 10, 3), np.uint8))\n\n    ptds = ds.dataloader().pytorch(return_index=False)\n    for batch in ptds:\n        assert len(batch.keys()) == 1\n        assert \"x\" in batch.keys()\n        assert batch[\"x\"].shape == (1, 10, 10, 3)\n\n    ptds = ds.dataloader().pytorch(decode_method={\"x\": \"pil\"})\n    with pytest.raises(CollateExceptionWrapper):\n        for _ in ptds:\n            continue\n\n    def custom_transform(batch):\n        batch[\"x\"] = np.array(batch[\"x\"])\n        return batch\n\n    ptds = (\n        ds.dataloader()\n        .pytorch(decode_method={\"x\": \"pil\"}, return_index=False)\n        .transform(custom_transform)\n    )\n    for batch in ptds:\n        assert len(batch.keys()) == 1\n        assert \"x\" in batch.keys()\n        assert batch[\"x\"].shape == (1, 10, 10, 3)\n\n\n@patch(\"deeplake.constants.RETURN_DUMMY_DATA_FOR_DATALOADER\", True)\n@requires_torch\n@requires_libdeeplake\n@pytest.mark.flaky\ndef test_pytorch_dummy_data(local_auth_ds):\n    x_data = [\n        np.random.randint(0, 255, (100, 100, 3), dtype=\"uint8\"),\n        np.random.randint(0, 255, (120, 120, 3), dtype=\"uint8\"),\n    ]\n    y_data = [np.random.rand(100, 100, 3), np.random.rand(120, 120, 3)]\n    z_data = [\"hello\", \"world\"]\n    with local_auth_ds as ds:\n        ds.create_tensor(\"x\")\n        ds.create_tensor(\"y\")\n        ds.create_tensor(\"z\")\n        ds.x.extend(x_data)\n        ds.y.extend(y_data)\n        ds.z.extend(z_data)\n\n    ptds = ds.dataloader()\n    for i, batch in enumerate(ptds):\n        x = x_data[i]\n        dummy_x = batch[0][\"x\"]\n        assert dummy_x.shape == x.shape\n        assert dummy_x.dtype == x.dtype\n\n        y = y_data[i]\n        dummy_y = batch[0][\"y\"]\n        assert dummy_y.shape == y.shape\n        assert dummy_y.dtype == y.dtype\n\n        dummy_z = batch[0][\"z\"]\n        assert dummy_z[0] == \"a\"\n\n\n@requires_libdeeplake\n@requires_torch\n@pytest.mark.flaky\n@pytest.mark.slow\ndef test_json_data_loader(local_auth_ds):\n    with local_auth_ds as ds:\n        ds.create_tensor(\n            \"json\",\n            htype=\"json\",\n            sample_compression=None,\n        )\n        d = {\"x\": 1, \"y\": 2, \"z\": 3}\n        for _ in range(10):\n            ds.json.append(d)\n\n    dl = ds.dataloader().batch(2)\n\n    for batch in dl:\n        sample1 = batch[0][\"json\"]\n        sample2 = batch[1][\"json\"]\n\n        assert sample1 == d\n        assert sample2 == d\n\n\n@requires_libdeeplake\n@requires_torch\n@pytest.mark.flaky\n@pytest.mark.slow\ndef test_list_data_loader(local_auth_ds):\n    with local_auth_ds as ds:\n        ds.create_tensor(\n            \"list\",\n            htype=\"list\",\n            sample_compression=None,\n        )\n        l = [1, 2, 3]\n        for _ in range(10):\n            ds.list.append(l)\n\n    dl = ds.dataloader().batch(2)\n\n    for batch in dl:\n        sample1 = batch[0][\"list\"]\n        sample2 = batch[1][\"list\"]\n        assert sample1.tolist() == l\n        assert sample2.tolist() == l\n\n\n@requires_libdeeplake\n@requires_torch\n@pytest.mark.flaky\n@pytest.mark.slow\ndef test_pytorch_data_decode(local_auth_ds, cat_path):\n    with local_auth_ds as ds:\n        ds.create_tensor(\"generic\")\n        for i in range(10):\n            ds.generic.append(i)\n        ds.create_tensor(\"text\", htype=\"text\")\n        for i in range(10):\n            ds.text.append(f\"hello {i}\")\n        ds.create_tensor(\"json\", htype=\"json\")\n        for i in range(10):\n            ds.json.append({\"x\": i})\n        ds.create_tensor(\"list\", htype=\"list\")\n        for i in range(10):\n            ds.list.append([i, i + 1])\n        ds.create_tensor(\"class_label\", htype=\"class_label\")\n        animals = [\n            \"cat\",\n            \"dog\",\n            \"bird\",\n            \"fish\",\n            \"horse\",\n            \"cow\",\n            \"pig\",\n            \"sheep\",\n            \"goat\",\n            \"chicken\",\n        ]\n        ds.class_label.extend(animals)\n        ds.create_tensor(\"image\", htype=\"image\", sample_compression=\"jpeg\")\n        for i in range(10):\n            ds.image.append(deeplake.read(cat_path))\n\n    decode_method = {tensor: \"data\" for tensor in list(ds.tensors.keys())}\n    ptds = (\n        ds.dataloader()\n        .transform(identity)\n        .pytorch(decode_method=decode_method, collate_fn=identity_collate)\n    )\n    for i, batch in enumerate(ptds):\n        sample = batch[0]\n        assert sample[\"text\"][\"value\"] == f\"hello {i}\"\n        assert sample[\"json\"][\"value\"] == {\"x\": i}\n        assert sample[\"list\"][\"value\"].tolist() == [i, i + 1]\n        assert sample[\"class_label\"][\"value\"] == [i]\n        assert sample[\"class_label\"][\"text\"] == [animals[i]]\n        assert sample[\"image\"][\"value\"].shape == (900, 900, 3)\n        assert sample[\"generic\"][\"value\"] == i\n","repo_name":"activeloopai/deeplake","sub_path":"deeplake/enterprise/test_pytorch.py","file_name":"test_pytorch.py","file_ext":"py","file_size_in_byte":28046,"program_lang":"python","lang":"en","doc_type":"code","stars":7141,"dataset":"github-code","pt":"81"}
+{"seq_id":"20434678731","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Jun 18 20:18:34 2021\n\n@author: erwan\n\n\nAdd a RulerTool to measure spectra\n\n    Based on work from TerranJP\n    https://github.com/terranjp/matplotlib-tools\n\n\nExamples\n--------\n::\n\n    from radis.tools.plot_tools import add_ruler\n    fig = plt.figure()\n    add_ruler(fig)\n\n.. image:: https://user-images.githubusercontent.com/16088743/122615292-95a9e080-d088-11eb-9927-bf1187d5a94a.png\n\n\n\n\"\"\"\nimport warnings\n\n\nclass ParamRange:\n    def __init__(self, valmin=0, valmax=1, valinit=None):\n        r\"\"\"Used in :py:func:`radis.lbl.factory.SpectrumFactory.eq_spectrum_gpu_interactive`\"\"\"\n        self.valmin = valmin\n        self.valmax = valmax\n\n        if valinit is None:\n            valinit = valmin\n        self.valinit = valinit\n        self.val = self.valinit\n        self.name = None\n        self.widget = None\n\n    def set_widget(self, w, spec, func=lambda x: 0):\n        self.widget = w\n        self.spec = spec\n        self.update_callback = func\n        w.on_changed(self.widget_callback)\n\n    def widget_callback(self, val):\n        self.val = val\n        self.spec.conditions[self.name] = val\n        self.update_callback(val)\n\n    def __repr__(self):\n        return \"ParamRange({:s} .. {:s} [{:s}] @ {:s})\".format(\n            self.valmin.__repr__(),\n            self.valmax.__repr__(),\n            self.valinit.__repr__(),\n            self.val.__repr__(),\n        )\n\n\ndef add_ruler(fig, wunit=\"\", Iunit=\"\", ax=None):\n    \"\"\"\n\n    Add a RulerTool to measure spectra\n\n        Based on work from TerranJP\n        https://github.com/terranjp/matplotlib-tools\n\n\n    Examples\n    --------\n    ::\n\n        from radis.tools.plot_tools import add_ruler\n        fig = plt.figure()\n        add_ruler(fig)\n\n    .. image:: https://user-images.githubusercontent.com/16088743/122615292-95a9e080-d088-11eb-9927-bf1187d5a94a.png\n\n    \"\"\"\n\n    import matplotlib.pyplot as plt\n    import numpy as np\n    from matplotlib.backend_tools import ToolToggleBase\n    from matplotlib.widgets import AxesWidget\n\n    # TODO: build it only on demand/ if add_tool called?\n    class Ruler(AxesWidget):\n        r\"\"\"\n        A ruler to measure distances and angles on an axes instance.\n\n        For the ruler to remain responsive you must keep a reference to it.\n\n        Parameters\n        ----------\n        ax  : the  :class:`matplotlib.axes.Axes` instance\n        active : bool, default is True\n            Whether the ruler is active or not.\n        wunit, Iunit: str\n            unit of spectra\n        print_text  : bool, default is False\n            Whether the length measure string is printed to the console\n        useblit : bool, default is False\n            If True, use the backend-dependent blitting features for faster\n            canvas updates.\n        lineprops : dict, default is None\n          Dictionary of :class:`matplotlib.lines.Line2D` properties\n        markerprops : dict, default is None\n          Dictionary of :class:`matplotlib.markers.MarkerStyle` properties\n        textprops: dict, default is None\n            Dictionary of :class:`matplotlib.text.Text` properties. To reposition the\n            textbox you can override the defaults which position the box in the top left\n            corner of the axes.\n\n        Notes\n        -----\n\n        Usage:\n\n        1. Hold left click drag and release to draw the ruler in the axes.\n          - Hold shift while dragging to lock the ruler to the horizontal axis.\n          - Hold control while drawing to lock the ruler to the vertical axis.\n\n        2. Right click one of the markers to move the ruler.\n\n        The keyboard can be used to activate and deactivate the ruler and toggle\n        visibility of the line and text:\n\n        'm' : Toggles the ruler on and off.\n\n        'ctl+m' : Toggles the visibility of the ruler and text.\n\n        Example\n        -------\n\n        >>> xCoord = np.arange(0, 5, 1)\n        >>> yCoord = [0, 1, -3, 5, -3]\n        >>> fig = plt.figure()\n        >>> ax = fig.add_subplot(111)\n\n        >>> markerprops = dict(marker='o', markersize=5, markeredgecolor='red')\n        >>> lineprops = dict(color='red', linewidth=2)\n\n        >>> ax.grid(True)\n        >>> ax.plot(xCoord, yCoord)\n\n        >>> ruler = Ruler(ax=ax,\n                      useblit=True,\n                      markerprops=markerprops,\n                      lineprops=lineprops)\n\n        >>> plt.show()\n\n        Typical output :\n\n        .. image:: https://user-images.githubusercontent.com/16088743/122615292-95a9e080-d088-11eb-9927-bf1187d5a94a.png\n\n        \"\"\"\n\n        def __init__(\n            self,\n            ax,\n            active=True,\n            print_text=False,\n            useblit=False,\n            lineprops=None,\n            textprops=None,\n            markerprops=None,\n            wunit=\"\",\n            Iunit=\"\",\n        ):\n            \"\"\"\n            Add a ruler to *ax*. If ``ruler_active=True``, the ruler will be\n            activated when the plot is first created. If ``ruler_unit`` is set the\n            string will be appended to the length text annotations.\n\n            \"\"\"\n            AxesWidget.__init__(self, ax)\n\n            self.connect_events()\n\n            self.ax = ax\n            self.fig = ax.figure\n\n            self._print_text = print_text\n            self._visible = True\n            self.active = active\n            self.wunit = wunit\n            self.Iunit = Iunit\n\n            self.useblit = useblit and self.canvas.supports_blit\n\n            self._mouse1_pressed = False\n            self._mouse3_pressed = False\n            self._shift_pressed = False\n            self._control_pressed = False\n            self._y0 = None\n            self._x1 = None\n            self._y1 = None\n            self._line_start_coords = None\n            self._line_end_coords = None\n            self._ruler_marker = None\n            self._background = None\n            self._ruler_moving = False\n            self._end_a_lock = False\n            self._end_b_lock = False\n            self._end_c_lock = False\n            self._old_marker_a_coords = None\n            self._old_marker_c_coords = None\n            self._old_mid_coords = None\n\n            if lineprops is None:\n                lineprops = {}\n\n            bbox = dict(\n                facecolor=\"white\", alpha=0.5, boxstyle=\"round\", edgecolor=\"0.75\"\n            )\n\n            used_textprops = dict(\n                xy=(0, 1),\n                xytext=(10, -10),\n                xycoords=\"axes fraction\",\n                textcoords=\"offset points\",\n                ha=\"left\",\n                va=\"center\",\n                size=12,\n                bbox=bbox,\n            )\n\n            x0 = np.nan\n            y0 = np.nan\n\n            (self._ruler,) = self.ax.plot([x0, x0], [y0, y0], **lineprops)\n\n            used_markerprops = dict(\n                marker=\"s\",\n                markersize=3,\n                markerfacecolor=\"white\",\n                markeredgecolor=\"black\",\n                markeredgewidth=0.5,\n                picker=True,\n                pickradius=5,\n                visible=False,\n            )\n\n            # If marker or text  props are given as an argument combine with the\n            # default marker props. Don't really want to override the entire props\n            # if a user only gives one value.\n\n            if markerprops is not None:\n                used_markerprops.update(markerprops)\n\n            if textprops is not None:\n                used_textprops.update(used_textprops)\n\n            self._axes_text = self.ax.annotate(text=\"\", **used_textprops)\n            self.ax.add_artist(self._axes_text)\n\n            (self._marker_a,) = self.ax.plot((x0, y0), **used_markerprops)\n            (self._marker_b,) = self.ax.plot((x0, y0), **used_markerprops)\n            (self._marker_c,) = self.ax.plot((x0, y0), **used_markerprops)\n\n            self._artists = [\n                self._axes_text,\n                self._ruler,\n                self._marker_a,\n                self._marker_b,\n                self._marker_c,\n            ]\n\n        def connect_events(self):\n            \"\"\"\n            Connect all events to the various callbacks\n            \"\"\"\n            self.connect_event(\"button_press_event\", self._on_press)\n            self.connect_event(\"button_release_event\", self._on_release)\n            self.connect_event(\"motion_notify_event\", self._on_move)\n            self.connect_event(\"key_press_event\", self._on_key_press)\n            self.connect_event(\"key_release_event\", self._on_key_release)\n\n        def ignore(self, event):\n            \"\"\"\n            Ignore events if the cursor is out of the axes or the widget is locked\n            \"\"\"\n            if not self.canvas.widgetlock.available(self):\n                return True\n            if event.inaxes != self.ax.axes:\n                return True\n            if not self.active:\n                return True\n\n        def _on_key_press(self, event):\n            \"\"\"\n            Handle key press events.\n\n            If shift is pressed the ruler will be constrained to horizontal axis\n            If control is pressed the ruler will be constrained to vertical axis\n            If m is pressed the ruler will be toggled on and off\n            If ctrl+m is pressed the visibility of the ruler will be toggled\n            \"\"\"\n\n            if event.key == \"shift\":\n                self._shift_pressed = True\n\n            if event.key == \"control\":\n                self._control_pressed = True\n\n            if event.key == \"m\":\n                self.toggle_ruler()\n\n            if event.key == \"ctrl+m\":\n                self.toggle_ruler_visibility()\n\n        def _on_key_release(self, event):\n            \"\"\"\n            Handle key release event, flip the flags to false.\n            \"\"\"\n            if event.key == \"shift\":\n                self._shift_pressed = False\n\n            if event.key == \"control\":\n                self._control_pressed = False\n\n        def toggle_ruler(self):\n            \"\"\"\n            Called when the 'm' key is pressed. If ruler is on turn it off, and\n            vise versa\n\n            If off, hide it.\n            \"\"\"\n\n            self.active = not self.active\n\n            self.canvas.draw_idle()\n\n        def toggle_ruler_visibility(self):\n            \"\"\"\n            Called when the 'ctl+m' key is pressed. If ruler is visible turn it off\n            , and vise versa\n            \"\"\"\n            if self._visible is True:\n                for artist in self._artists:\n                    artist.set_visible(False)\n                self.active = False\n                self._visible = False\n\n            elif self._visible is False:\n                for artist in self._artists:\n                    artist.set_visible(True)\n                self._visible = True\n\n            self.canvas.draw_idle()\n\n        def _on_press(self, event):\n            \"\"\"\n            On mouse button press check which button has been pressed and handle\n            \"\"\"\n            if self.ignore(event):\n                return\n            if event.button == 1 and self._mouse3_pressed is False:\n                self._handle_button1_press(event)\n            elif event.button == 3:\n                self._handle_button3_press(event)\n\n        def _handle_button1_press(self, event):\n            \"\"\"\n            On button 1 press start drawing the ruler line from the initial\n            press position\n            \"\"\"\n\n            self._mouse1_pressed = True\n            self._x0 = event.xdata\n            self._y0 = event.ydata\n            self._marker_a.set_data((event.xdata, event.ydata))\n            self._marker_a.set_visible(True)\n\n            if self.useblit:\n                self._marker_a.set_data(self._x0, self._y0)\n                for artist in self._artists:\n                    artist.set_animated(True)\n                self.canvas.draw()\n                self._background = self.canvas.copy_from_bbox(self.fig.bbox)\n\n        def _handle_button3_press(self, event):\n            \"\"\"\n            If button 3 is pressed (right click) check if cursor is at one of the\n            ruler markers and the move the ruler accordingly.\n            \"\"\"\n            contains_a, _ = self._marker_a.contains(event)\n            contains_b, _ = self._marker_b.contains(event)\n            contains_c, _ = self._marker_c.contains(event)\n\n            if not (contains_a or contains_b or contains_c):\n                return\n\n            self._end_a_lock = contains_a\n            self._end_b_lock = contains_b\n            self._end_c_lock = contains_c\n\n            line_coords = self._ruler.get_path().vertices\n            self._x0 = line_coords[0][0]\n            self._y0 = line_coords[0][1]\n            self._x1 = line_coords[1][0]\n            self._y1 = line_coords[1][1]\n\n            self._old_marker_a_coords = self._marker_a.get_path().vertices\n            self._old_marker_c_coords = self._marker_c.get_path().vertices\n            self._old_mid_coords = self.midline_coords\n\n        def _on_move(self, event):\n            \"\"\"\n            On motion draw the ruler if button 1 is pressed. If one of the markers\n            is locked indicating move the ruler according to the locked marker\n            \"\"\"\n\n            if event.inaxes != self.ax.axes:\n                return\n\n            # if self._end_a_lock or self._end_b_lock or self._end_c_lock is True:\n            #     self._move_ruler(event)\n\n            if self._mouse1_pressed is True:\n                self._draw_ruler(event)\n\n        # def _move_ruler(self, event):\n        #     \"\"\"\n        #     If one of the markers is locked move the ruler according the selected\n        #     marker.\n        #     \"\"\"\n\n        #     # This flag is used to prevent the ruler from clipping when a marker is\n        #     # first selected\n        #     if self._ruler_moving is False:\n        #         if self.useblit:\n        #             for artist in self._artists:\n        #                 artist.set_animated(True)\n        #             self.canvas.draw()\n        #             self._background = self.canvas.copy_from_bbox(self.fig.bbox)\n        #             self._ruler_moving = True\n\n        #     if self._end_a_lock is True:\n        #         # If marker a is locked only move end a.\n        #         pos_a = event.xdata, self._x1\n        #         pos_b = event.ydata, self._y1\n        #         self._marker_a.set_data(event.xdata, event.ydata)\n        #         self._ruler.set_data(pos_a, pos_b)\n        #         self._set_midline_marker()\n\n        #     if self._end_c_lock is True:\n        #         # If marker a is locked only move end c.\n        #         pos_a = self._x0, event.xdata\n        #         pos_b = self._y0, event.ydata\n        #         self._marker_c.set_data(event.xdata, event.ydata)\n        #         self._ruler.set_data(pos_a, pos_b)\n        #         self._set_midline_marker()\n\n        #     if self._end_b_lock is True:\n        #         # If marker b is locked shift the whole ruler.\n        #         b_dx = event.xdata - self._old_mid_coords[0]\n        #         b_dy = event.ydata - self._old_mid_coords[1]\n        #         pos_a = self._x0 + b_dx, self._x1 + b_dx\n        #         pos_b = self._y0 + b_dy, self._y1 + b_dy\n\n        #         marker_a_coords = (\n        #             self._old_marker_a_coords[0][0] + b_dx,\n        #             self._old_marker_a_coords[0][1] + b_dy,\n        #         )\n        #         marker_c_coords = (\n        #             self._old_marker_c_coords[0][0] + b_dx,\n        #             self._old_marker_c_coords[0][1] + b_dy,\n        #         )\n\n        #         self._ruler.set_data(pos_a, pos_b)\n        #         self._marker_a.set_data(marker_a_coords)\n        #         self._marker_b.set_data(event.xdata, event.ydata)\n        #         self._marker_c.set_data(marker_c_coords)\n\n        #     self._update_text()\n        #     self._update_artists()\n\n        # def _set_midline_marker(self):\n        #     self._marker_b.set_visible(True)\n        #     self._marker_b.set_data(self.midline_coords)\n\n        # @property\n        # def midline_coords(self):\n        #     pos0, pos1 = self._ruler.get_path().vertices\n        #     mid_line_coords = (pos0[0] + pos1[0]) / 2, (pos0[1] + pos1[1]) / 2\n        #     return mid_line_coords\n\n        def _draw_ruler(self, event):\n            \"\"\"\n            If the left mouse button is pressed and held draw the ruler as the\n            mouse is dragged\n            \"\"\"\n\n            self._x1 = event.xdata\n            self._y1 = event.ydata\n\n            # If shift is pressed ruler is constrained to horizontal axis\n            if self._shift_pressed is True:\n                pos_a = self._x0, self._x1\n                pos_b = self._y0, self._y0\n            # If control is pressed ruler is constrained to vertical axis\n            elif self._control_pressed is True:\n                pos_a = self._x0, self._x0\n                pos_b = self._y0, self._y1\n            # Else the ruler follow the mouse cursor\n            else:\n                pos_a = self._x0, self._x1\n                pos_b = self._y0, self._y1\n\n            self._ruler.set_data([pos_a], [pos_b])\n            x1 = self._ruler.get_path().vertices[1][0]\n            y1 = self._ruler.get_path().vertices[1][1]\n            self._marker_c.set_visible(True)\n            self._marker_c.set_data(x1, y1)\n            # self._set_midline_marker()\n            self._update_text()\n            self._update_artists()\n\n        def _update_artists(self):\n            if self.useblit:\n                if self._background is not None:\n                    self.canvas.restore_region(self._background)\n                else:\n                    self._background = self.canvas.copy_from_bbox(self.fig.bbox)\n\n                for artist in self._artists:\n                    self.ax.draw_artist(artist)\n\n                self.canvas.blit(self.fig.bbox)\n            else:\n                self.canvas.draw_idle()\n\n        def _update_text(self):\n            detail_string = \"{:0.4f} {}; {:0.3f} {}\".format(\n                self.ruler_dx, self.wunit, self.ruler_dy, self.Iunit\n            )\n\n            self._axes_text.set_text(detail_string)\n            if self._print_text is True:\n                print(detail_string)\n\n        def _on_release(self, event):\n            self._mouse1_pressed = False\n            self._mouse3_pressed = False\n            self._ruler_moving = False\n            self._end_a_lock = False\n            self._end_b_lock = False\n            self._end_c_lock = False\n\n        @property\n        def ruler_dx(self):\n            pos0, pos1 = self._ruler.get_path().vertices\n            return pos1[0] - pos0[0]\n\n        @property\n        def ruler_dy(self):\n            pos0, pos1 = self._ruler.get_path().vertices\n            return pos1[1] - pos0[1]\n\n    # %% Add in toolbars\n\n    class RulerTool(ToolToggleBase):\n        r\"\"\"Add a RulerTool to measure spectra\n\n        Based on work from TerranJP\n        https://github.com/terranjp/matplotlib-tools\n        \"\"\"\n\n        # keyboard shortcut\n        default_keymap = \"m\"\n        description = \"Ruler Tool\"\n        default_toggled = False\n        image = \"ruler\"\n\n        def __init__(self, *args, **kwargs):\n            ax = kwargs.get(\"ax\", plt.gca())\n            self.ruler = None\n            self.ax = ax\n            self.wunit = (None,)  # x-axis unit\n            self.Iunit = (None,)  # y-axis unit\n            super().__init__(*args, **kwargs)\n\n        def enable(self, event):\n            \"\"\"Connect press/release events and lock the canvas.\"\"\"\n            pass\n\n        def disable(self, event):\n            \"\"\"Release the canvas and disconnect press/release events.\"\"\"\n            pass\n\n        def trigger(self, *args, **kwargs):\n            \"\"\"When toolbar button clicked\"\"\"\n            super().trigger(*args, **kwargs)\n            if self.ruler is None:\n                self.ruler = Ruler(\n                    ax=self.ax,  # TODO UPDATE\n                    useblit=True,\n                    wunit=self.wunit,\n                    Iunit=self.Iunit,\n                )  # , markerprops=markerprops, lineprops=lineprops)\n            else:\n                self.ruler.toggle_ruler()\n\n    # %% Build Class\n\n    with warnings.catch_warnings():\n        warnings.simplefilter(\"ignore\")\n        plt.rcParams[\"toolbar\"] = \"toolmanager\"\n        # Filters https://github.com/matplotlib/matplotlib/issues/15284\n\n    if fig.canvas.manager.toolmanager is None:\n        from warnings import warn\n\n        warn(\n            \"Couldn't add Ruler tool (still an experimental feature in RADIS : please report the error !)\"\n        )\n        return\n\n    if \"📐\" in fig.canvas.manager.toolmanager.tools:\n        return\n\n    # Add the custom tools that we created\n    fig.canvas.manager.toolmanager.add_tool(\"📐\", RulerTool)\n\n    # Define units\n    rulertool = fig.canvas.manager.toolmanager.get_tool(\"📐\")\n    rulertool.wunit = wunit\n    rulertool.Iunit = Iunit\n    if ax is not None:  # add the Ruler only on a specific ax\n        rulertool.ax = ax\n\n    # Add it to new group `radis_tools`\n    fig.canvas.manager.toolbar.add_tool(\"📐\", \"radis_tools\")\n\n\nif __name__ == \"__main__\":\n\n    # fig = plt.figure()\n    # plt.plot([1, 2, 3])\n\n    # add_ruler(fig)\n    #%%\n    from radis import calc_spectrum\n\n    s = calc_spectrum(\n        1900,\n        2300,  # cm-1\n        molecule=\"CO\",\n        isotope=\"1,2,3\",\n        pressure=1.01325,  # bar\n        Tgas=700,  # K\n        mole_fraction=0.1,\n        path_length=1,  # cm\n        databank=\"hitran\",  # or use 'hitemp'\n    )\n    s.apply_slit(0.5, \"nm\")  # simulate an experimental slit\n    # line = s.plot('radiance', nfig='same')\n\n    from radis import plot_diff\n\n    plot_diff(s, s.take(\"radiance\") * 1.1, show_ruler=True)\n","repo_name":"radis/radis","sub_path":"radis/tools/plot_tools.py","file_name":"plot_tools.py","file_ext":"py","file_size_in_byte":21762,"program_lang":"python","lang":"en","doc_type":"code","stars":173,"dataset":"github-code","pt":"81"}
+{"seq_id":"43759308182","text":"from typing import Dict, Optional, Tuple\nfrom sympy import Ci\nfrom tqdm import tqdm\n\nimport torch\nimport torch.nn as nn\nfrom torch.utils.data import Dataset, DataLoader\n\nfrom torchvision import transforms\nfrom torchvision.utils import save_image, make_grid\n\nfrom mindiffusion.unet import NaiveUnet\nfrom mindiffusion.ddpm import DDPM\n\nfrom dataset_creator import ImagesDataset\nimport os\n\n\n# add to get computing device\ndef get_computing_device():\n    if torch.cuda.is_available():\n        device = torch.device('cuda:0')\n    else:\n        device = torch.device('cpu')\n    return device\n\n\ndefault_device = get_computing_device()\n\n\n# -----\n\ndef train_maps(n_epoch=100, device=default_device, load_state=False):\n    ddpm = DDPM(eps_model=NaiveUnet(3, 3, n_feat=128), betas=(1e-4, 0.02), n_T=100)\n\n    if load_state:\n        ddpm.load_state_dict(torch.load(\"ddpm_maps.pth\"))\n    ddpm.to(device)\n\n    # Added transforamtions and custom dataset creation\n    tr = transforms.Compose(\n        [\n            transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),\n            transforms.RandomHorizontalFlip(0.5),\n            transforms.RandomVerticalFlip(0.5)\n        ]\n    )\n\n    dataset = ImagesDataset(\n        os.path.join(os.getcwd(), '..', 'EuroSAT/2750'),\n        tr)\n    # -----\n    dataloader = DataLoader(dataset, batch_size=128, shuffle=True, num_workers=8)\n    optim = torch.optim.Adam(ddpm.parameters(), lr=1e-5)\n\n    for i in range(n_epoch):\n        print(f\"Epoch {i} : \")\n        ddpm.train()\n\n        pbar = tqdm(dataloader)\n        loss_ema = None\n        for x in pbar:\n            optim.zero_grad()\n            x = x.to(device)\n            loss = ddpm(x)\n            loss.backward()\n            if loss_ema is None:\n                loss_ema = loss.item()\n            else:\n                loss_ema = 0.9 * loss_ema + 0.1 * loss.item()\n            pbar.set_description(f\"loss: {loss_ema:.4f}\")\n            optim.step()\n\n        ddpm.eval()\n        with torch.no_grad():\n            xh = ddpm.sample(8, (3, 64, 64), device)\n            xset = torch.cat([xh, x[:8]], dim=0)\n            grid = make_grid(xset, normalize=True, value_range=(-1, 1), nrow=4)\n            save_image(grid, f\"./contents/ddpm_sample_maps{i}.png\")\n\n            # save model\n            torch.save(ddpm.state_dict(), f\"./ddpm_maps.pth\")\n\n\nif __name__ == \"__main__\":\n    train_maps()\n","repo_name":"IgnatMelnikov/sk_task_repo","sub_path":"minDiffusion/train_euroSAT.py","file_name":"train_euroSAT.py","file_ext":"py","file_size_in_byte":2363,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"5110135912","text":"from copy import deepcopy\nimport pandas\nimport json\n\ndef json_normalizer(in_data):\n    new_debt_dic = {}\n    def flatten_json(data, prev_heading=''):\n        if isinstance(data, dict):\n            for key, value in data.items():\n                flatten_json(value, prev_heading + '.' + key)\n        elif isinstance(data, list):\n            if len(data)<=1:\n                for i in range(len(data)):\n                    flatten_json(data[i], prev_heading)\n            else:\n                for i in range(len(data)):\n                    flatten_json(data[i], prev_heading+\"-\"+str(i+1))\n        else:\n            new_debt_dic[prev_heading[1:]] = data\n        \n\n        return new_debt_dic\n    return flatten_json(in_data)\n\n    \n\n\nif __name__ == '__main__':\n    json_file='''{\"coffee\":{\"region\":[{\"id\":1,\"name\":\"John Doe\"},{\"id\":2,\"name\":\"Don Joeh\"}],\"country\":{\"id\":2,\"company\":\"ACME\"}},\"brewing\":{\"region\":[{\"id\":1,\"name\":\"John Doe\"},{\"id\":2,\"name\":\"Don Joeh\"}],\"country\":{\"id\":2,\"company\":\"ACME\"}}}'''\n    json_data = json.loads(json_file)\n    df = flatten_json(json_data)\n    df=pandas.DataFrame(df)\n    df.to_csv(\"C:/Users/sprakashreddychin/Pictures/js_to_da.csv\")\n    print(df)\n","repo_name":"suryaprakashreddy034/usefulcodes","sub_path":"json_normalize with out cross.py","file_name":"json_normalize with out cross.py","file_ext":"py","file_size_in_byte":1182,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"19848412060","text":"\nimport os\nimport shutil\nfrom functools import partial\nfrom kivy.animation import Animation\nfrom kivy.clock import Clock\nfrom kivy.uix.boxlayout import BoxLayout\nfrom kivy.uix.screenmanager import Screen\nfrom kivy.lang import Builder\nfrom kivy.properties import StringProperty\nfrom kivy.uix.dropdown import DropDown\nfrom kivymd.uix.card import MDSeparator\nfrom widgets.dropitem import DropItem\nfrom plyer import filechooser\nfrom utils.refresh_notes import refresh\nBuilder.load_string(\"\"\"\n<InstrumentList>:\n    size_hint_y:None\n    height:\"50dp\"\n    padding:15,0,15,0\n    canvas:\n        Color:\n            rgba:(1,1,1,.5) if app.theme_cls.theme_style==\"Dark\" else (0,0,0,.5)\n        RoundedRectangle:\n            pos:self.pos\n            size:self.size\n    MDLabel:\n        text:root.text\n    MDIconButton:\n        icon:\"delete\"\n        on_release:root.remove_instrument(root.parent)\n\n<AddInstrumentBox>:\n    size_hint_y:None\n    height:\"220dp\"\n    orientation:\"vertical\"\n    padding:\"10dp\"\n    \n    MDTextField:\n        size_hint_y:None\n        height:\"40dp\"\n        id:instrumentname\n        hint_text: \"Instrument Name\"\n        \n    BoxLayout:\n        size_hint_y:None\n        height:\"40dp\"\n        MDLabel:\n            text:\"Select Folder\"\n            color:theme.get_color(app.theme_cls.theme_style,\"PrimaryTextColor\")\n        MDIconButton:\n            icon:\"folder-plus-outline\"\n            on_release:root.select_folder()\n    MDLabel:\n        size_hint_y:None\n        height:\"10dp\" if self.text==\"\" else \"40dp\" \n        id:path\n        text:\"\"\n        color:theme.get_color(app.theme_cls.theme_style,\"PrimaryTextColor\")\n        font_size:\"12sp\"\n    MDLabel:\n        size_hint_y:None\n        height:\"1dp\" if self.text==\"\" else \"30dp\"\n        id:warning\n        text:\"\"\n        color:(1,0,0,1)\n        font_size:\"12sp\"\n    FloatLayout:\n        MDRaisedButton:\n            text:\"Cancel\"\n            pos_hint: {\"x\":0.35, \"center_y\":0.4}\n            on_release:root.remove_self(root.parent)\n        MDRaisedButton:\n            text:\"Add\"\n            pos_hint:{\"x\":.65,\"center_y\":0.4}\n            on_release:root.add_instrument(root.parent)\n\n<SettingScreen>:\n    #Backgroud\n    BoxLayout:\n        orientation:\"vertical\"\n        canvas:\n            Color:\n                rgba:theme.get_color(app.theme_cls.theme_style,\"BackgroundColor\")\n            Rectangle:\n                pos:self.pos\n                size:self.size\n        \n        ScrollView:\n            do_scroll_x:True\n            BoxLayout:\n                size_hint_x:None\n                width: self.minimum_width+80\n                spacing:\"40dp\"\n                padding: 30 ,20,0,root.height-430\n                # Genral Setting\n                BoxLayout:\n                    orientation:\"vertical\"\n                    size_hint:None,None\n                    height:\"400dp\"\n                    width:\"400dp\"\n                    padding:\"30dp\"\n                    canvas:\n                        Color:\n                            rgba:[0.15,0.15,0.15,1] if app.theme_cls.theme_style==\"Dark\" else [1,1,1,.8]\n                        RoundedRectangle:\n                            pos:self.pos\n                            size:self.size\n                    MDLabel:\n                        size_hint_y:None\n                        height:\"50dp\"\n                        text:\"General\"\n                        font_size:\"20sp\"\n                        color:theme.get_color(app.theme_cls.theme_style,\"PrimaryTextColor\")\n                    MDSeparator:\n                    #Dark Mode\n                    BoxLayout:\n                        size_hint_y:None\n                        height:\"40dp\"\n                        padding:10,0,10,0\n                        MDLabel:\n                            text:\"Dark Mode\"\n                            color:theme.get_color(app.theme_cls.theme_style,\"PrimaryTextColor\")\n                        MDSwitch:\n                            size_hint:(None,None)\n                            height:\"40dp\"\n                            width:\"40dp\"\n                            active: True if app.theme_cls.theme_style==\"Dark\" else False\n                            on_active:\n                                app.theme_cls.theme_style=\"Dark\" if self.active\\\n                                else \"Light\"\n                    MDSeparator:\n                    BoxLayout:\n                        size_hint_y:None\n                        height:\"40dp\"\n                        padding:10,0,0,0\n                        MDLabel:\n                            text:\"Camera\"\n                            color:theme.get_color(app.theme_cls.theme_style,\"PrimaryTextColor\")\n                        DropItem:\n                            size_hint_x:None\n                            width:\"180dp\" if len(self.text)>8 else \"150dp\" if len(self.text)>4 else \"130dp\"\n                            id:dropdown_item\n                            transparent:True\n                            text:\"Default\"\n                            icon:\"chevron-down\"\n                            font_size:\"12sp\" if len(self.text)>10 else \"14sp\"\n                            on_release:root.open_camera_list(self)\n                        \n                    Widget:\n                # Add Additional instrument\n                BoxLayout:\n                    orientation:\"vertical\"\n                    size_hint:None,None\n                    height:\"400dp\"\n                    width:\"400dp\"\n                    padding:\"30dp\"\n                    canvas:\n                        Color:\n                            rgba:[0.15,0.15,0.15,1] if app.theme_cls.theme_style==\"Dark\" else [1,1,1,.8]\n                        RoundedRectangle:\n                            pos:self.pos\n                            size:self.size\n                    BoxLayout:\n                        orientation:\"vertical\"\n                        BoxLayout:\n                            size_hint_y:None\n                            height:\"40dp\"\n                            MDLabel:\n                                size_hint_y:None\n                                height:\"50dp\"\n                                text:\"Add Instrument\"\n                                font_size:\"20sp\"\n                                color:theme.get_color(app.theme_cls.theme_style,\"PrimaryTextColor\")\n                            MDIconButton:\n                                icon:\"plus\"\n                                on_release:root.add_box(root.ids.instaddbox)\n                        MDSeparator:\n                        Widget:\n                            size_hint_y:None\n                            height:\"2dp\"\n                        ScrollView:\n                            do_scroll_y:True\n                            BoxLayout:\n                                id:wholebox\n                                spacing:\"5dp\"\n                                size_hint_y:None\n                                height:self.minimum_height\n                                orientation:\"vertical\"\n                                BoxLayout:\n                                    id:instaddbox\n                                    size_hint_y:None\n                                    height:\"0dp\"\n                                    canvas:\n                                        Color:\n                                            rgba:1,0,1,.15\n                                        RoundedRectangle:\n                                            pos:self.pos\n                                            size:self.size                \n                    \n        Widget:\n            size_hint_y:None\n            height:\"2dp\"\n            \n            \n\"\"\")\n\n\nclass InstrumentList(BoxLayout):\n    text = StringProperty()\n\n    def __init__(self, text, **kwargs):\n        self.text = text\n        super(InstrumentList, self).__init__(**kwargs)\n\n    def remove_instrument(self, parent_wid):\n        # remove dir folder\n        shutil.rmtree(\"assets/tones/\"+self.text.lower(), ignore_errors=True)\n        # os.rmdir(os.path.abspath(\"assets/tones/\"+self.text))\n\n        # remove from manual_instr list\n        import utils.selected_instrument as ut\n        ut.manual_instr.remove(self.text.lower())\n        st = \"selected_instr = '{}'\\ncamera_indx = {}\\nmanual_instr = {}\".format(\n            ut.selected_instr, ut.camera_indx, ut.manual_instr)\n\n        with open('utils/selected_instrument.py', 'w') as file:\n            file.write(st)\n\n        # remove from notes.json\n        refresh()\n\n        # remove from map_data.json\n        import json\n        with open('utils/map_data.json', 'r') as file:\n            data = json.load(file)\n\n        data.pop(self.text.lower())\n\n        with open('utils/map_data.json', 'w') as json_file:\n            json.dump(data, json_file)\n\n        parent_wid.remove_widget(self)\n\n\nclass AddInstrumentBox(BoxLayout):\n\n    def add_instrument(self, parent_wid):\n        self.ids.warning.text = \"\"\n        name = self.ids.instrumentname.text.rstrip().lower()\n        path = self.ids.path.text\n        from utils.selected_instrument import manual_instr\n        from utils.constant import instruments_items\n        if(name != ''):\n            if(name not in manual_instr+instruments_items):\n                if(path != ''):\n                    files = [each for each in os.listdir(path) if each.split(\n                        \".\")[-1].lower() in ['wav', 'ogg']]\n                    print(files)\n                    if(files != []):\n                        if(not os.path.exists(\"assets/tones/\"+name)):\n                            os.mkdir(\"assets/tones/\"+name)\n\n                        # copying all the assets to program folder\n                        for file in files:\n                            if os.path.isfile(os.path.join(path, file)):\n                                shutil.copyfile(os.path.join(path, file), os.path.join(\n                                    os.path.abspath(\"assets/tones/\"+name), file))\n\n                        # Set instrument which is manually added,to file\n                        import utils.selected_instrument as ut\n                        ut.manual_instr.append(name)\n                        st = \"selected_instr = '{}'\\ncamera_indx = {}\\nmanual_instr = {}\".format(\n                            ut.selected_instr, ut.camera_indx, ut.manual_instr)\n\n                        with open('utils/selected_instrument.py', 'w') as file:\n                            file.write(st)\n\n                        # refresh notes.json\n                        refresh()\n\n                        #add in map_data.json\n                        import json\n                        with open('utils/map_data.json', 'r') as file:\n                            data = json.load(file)\n\n                        data[name] = [{\"1\": \"\", \"2\": \"\", \"3\": \"\", \"4\": \"\", \"5\": \"\"}, {\n                            \"1\": \"\", \"2\": \"\", \"3\": \"\", \"4\": \"\", \"5\": \"\"}]\n\n                        with open('utils/map_data.json', 'w') as json_file:\n                            json.dump(data, json_file)\n\n                        self.remove_self(parent_wid)\n                        Clock.schedule_once(partial(\n                            self.add_instr_to_list, parent_wid, InstrumentList(name.capitalize())), .6)\n\n                    else:\n                        self.ids.warning.text = \"No file found with ext .wav,.ogg\"\n                else:\n                    self.ids.warning.text = \"! Folder Not Selected\"\n            else:\n                self.ids.warning.text = \"! Name already taken\"\n        else:\n            self.ids.warning.text = \"! Instrument Name left blank\"\n\n    def add_instr_to_list(self, parent_wid, list_widget, *args):\n        \"\"\"\n        this function creted only for delay\n        \"\"\"\n        parent_wid.parent.add_widget(list_widget)\n\n    def select_folder(self):\n        path = filechooser.choose_dir(title=\"Select Instrument Folder\")\n        if(path != []):\n            self.ids.path.text = path[0]\n\n    def remove_self(self, parent_wid):\n        parent_wid.remove_widget(self)\n        anim = Animation(height=0,\n                         duration=.5\n                         )\n        anim.start(parent_wid)\n\n\nclass SettingScreen(Screen):\n    def __init__(self, **kw):\n        Clock.schedule_once(self.set_camera)\n        Clock.schedule_once(self.build_instr_list)\n        super(SettingScreen, self).__init__(**kw)\n\n    def build_instr_list(self, *args):\n        from utils.selected_instrument import manual_instr\n        for each in manual_instr:\n            self.ids.wholebox.add_widget(InstrumentList(each.capitalize()))\n\n    def set_camera(self, *args):\n        from utils.selected_instrument import camera_indx\n        try:\n            from pygrabber.dshow_graph import FilterGraph\n            self.ids.dropdown_item.text = FilterGraph().get_input_devices()[\n                camera_indx]\n        except:\n            pass\n\n    def open_camera_list(self, inst):\n        menu = DropDown(auto_width=False, width=200)\n        try:\n            from pygrabber.dshow_graph import FilterGraph\n            device_list = FilterGraph().get_input_devices()\n        except:\n            device_list = []\n        for i, each in enumerate(device_list):\n            btn = DropItem(text=each, icon=\"webcam\", font_size=15)\n            btn.camera_indx = i\n            btn.bind(on_release=lambda btn: self.set_item(menu, btn))\n            menu.add_widget(btn)\n            menu.spacing = 0\n            menu.add_widget(MDSeparator())\n        menu.open(inst)\n\n    def set_item(self, dropdown, selected_btn):\n        self.ids.dropdown_item.text = selected_btn.text\n        import utils.selected_instrument as ut\n        ut.camera_indx = selected_btn.camera_indx\n\n        st = \"selected_instr = '{}'\\ncamera_indx = {}\\nmanual_instr = {}\".format(\n            ut.selected_instr, selected_btn.camera_indx, ut.manual_instr)\n\n        with open('utils/selected_instrument.py', 'w') as file:\n            file.write(st)\n        dropdown.dismiss()\n\n    def add_box(self, inst):\n        if(len(inst.children) == 0):\n            anim = Animation(height=220,\n                             duration=.5\n                             )\n            anim.start(inst)\n            Clock.schedule_once(partial(self.add, inst), .5)\n\n    def add(self, inst, *args):\n        if(len(inst.children) == 0):\n            inst.add_widget(AddInstrumentBox())\n","repo_name":"anandnet/Virtual-Music","sub_path":"screens/settings.py","file_name":"settings.py","file_ext":"py","file_size_in_byte":14321,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"81"}
+{"seq_id":"11638322068","text":"import asyncio\nimport json\nfrom rki_covid_parser.model.state import State\n\nasync def test_state():\n    state = State(\"Teststate\")\n    state.id = 1337\n    state.cases = 657\n    state.deaths = 13\n    state.recovered = 18\n    state.newCases = 3\n    assert \"State(id=1337, name='Teststate', cases=657, deaths=13, recovered=18, newCases=3\\n)\" == state.__str__()\n","repo_name":"thebino/rki-covid-parser","sub_path":"tests/model/test_state.py","file_name":"test_state.py","file_ext":"py","file_size_in_byte":357,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"81"}
+{"seq_id":"70172841864","text":"from numpy import array\r\nfrom PIL import Image\r\nfrom Crypto.Util.number import inverse\r\n\r\nimg = Image.open('enc.png')\r\nimage = array(img)\r\nx, y, z = image.shape\r\ns = [''] * x\r\nfor a in range (0, x):\r\n    for b in range (0, 1):\r\n        p = image[a, b]\r\n        tensa = p[1] - p[0] if p[1] > p[0] else 251 - p[0] + p[1]\r\n        s[a] = chr(tensa * inverse(10, 251) % 251)\r\nprint(''.join(s))\r\n","repo_name":"CTF-STeam/ctf-writeups","sub_path":"2020/RaziCTF/Mod Is Coming/script_sol.py","file_name":"script_sol.py","file_ext":"py","file_size_in_byte":391,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"81"}
+{"seq_id":"31237280172","text":"import asyncio\nimport base64\nimport os\nimport urllib\n\nimport aiohttp\nimport aiofiles\n\n# -----------------------\n# -----------------------\nbucket = 'bucket_name'  # 这里就是空间名称\nusername = 'username'  # 操作员账号\npassword = 'password'  # 操作员密码\n# 空间外联地址 因为又拍云的http下载没有频率限制，所以使用http下载 不适用restful的api接口下载\nhostname = \"http://xxxxx\"\n\n# 这里是本地保存的根路径 这样下载后路径地址就跟空间内的地址是相对的了\nbase_save_path = 'f:'\n# -----------------------\n\nheaders = {}\nauth = base64.b64encode(f'{username}:{password}'.encode(encoding='utf-8'))\nheaders['Authorization'] = 'Basic ' + str(auth)  # 又拍云认证header头\nheaders['User-Agent'] = \"UPYUN_DOWNLOAD_SCRIPT\"\nheaders['x-list-limit'] = '300'\n\nthread_sleep = 1\n\n\ndef is_dic(url):\n    \"\"\"判断key是否是目录 根据是否有后缀名判断\"\"\"\n    # print(f'判断url：{url}')\n    url = url.replace('http://v0.api.upyun.com/', '')\n    if len(url.split('.')) == 1:\n        return True\n    else:\n        return False\n\n\nclass Crawler:\n    def __init__(self, init_key, hostname, max_tasks=10, pic_tsak=50):\n        '''初始化爬虫'''\n        self.loop = asyncio.get_event_loop()\n        self.max_tries = 4  # 每个图片重试册数\n        self.max_tasks = max_tasks  # 接口请求进程数\n        self.key_queue = asyncio.Queue(loop=self.loop)  # 接口队列\n        self.pic_queue = asyncio.Queue(loop=self.loop)  # 图片队列\n        self.session = aiohttp.ClientSession(loop=self.loop)  # 接口异步http请求\n        self.key_queue.put_nowait(\n            {'key': init_key, 'x-list-iter': None, 'hostname': hostname})  # 初始化接口队列 push需要下载的目录\n        self.pic_tsak = pic_tsak  # 图片下载队列\n\n    def close(self):\n        \"\"\"回收http session\"\"\"\n        self.session.close()\n\n    async def work(self):\n        \"\"\"接口请求队列消费者\"\"\"\n        try:\n            while True:\n                url = await self.key_queue.get()\n                # print('key队列数量:' + await self.key_queue.qsize())\n                await self.handle(url)\n                self.key_queue.task_done()\n                await asyncio.sleep(thread_sleep)\n        except asyncio.CancelledError:\n            pass\n\n    async def work_pic(self):\n        \"\"\"图片请求队列消费者\"\"\"\n        try:\n            while True:\n                url = await self.pic_queue.get()\n                await self.handle_pic(url)\n                self.pic_queue.task_done()\n                await asyncio.sleep(thread_sleep)\n        except asyncio.CancelledError:\n            pass\n\n    async def handle_pic(self, key):\n        \"\"\"处理图片请求\"\"\"\n        url = (lambda x: x[0] == '/' and x or '/' + x)(key['key'])\n        url = url.encode('utf-8')\n        url = urllib.parse.quote(url)\n\n        pic_url = key['hostname'] + url + '!s400'\n\n        tries = 0\n        while tries < self.max_tries:\n            try:\n                print(f'请求图片:{pic_url}')\n                async with self.session.get(pic_url, timeout=60) as response:\n                    async with aiofiles.open(key['save_path'], 'wb') as f:\n                        # print('保存文件:{}'.format(key['save_path']))\n                        await f.write(await response.read())\n                break\n            except aiohttp.ClientError:\n                pass\n            tries += 1\n\n    async def handle(self, key):\n\n        \"\"\"处理接口请求\"\"\"\n        url = '/' + bucket + \\\n            (lambda x: x[0] == '/' and x or '/' + x)(key['key'])\n        url = url.encode('utf-8')\n        url = urllib.parse.quote(url)\n\n        if key['x-list-iter'] is not None:\n            if key['x-list-iter'] is not None or not 'g2gCZAAEbmV4dGQAA2VvZg':\n                headers['X-List-Iter'] = key['x-list-iter']\n\n        tries = 0\n        while tries < self.max_tries:\n            try:\n                reque_url = \"http://v0.api.upyun.com\" + url\n                print(f'请求接口:{reque_url}')\n                async with self.session.get(reque_url, headers=headers, timeout=60) as response:\n                    content = await response.text()\n                    try:\n                        iter_header = response.headers.get('x-upyun-list-iter')\n                    except:\n                        iter_header = 'g2gCZAAEbmV4dGQAA2VvZg'\n                    list_json_param = content + \"`\" + \\\n                        str(response.status) + \"`\" + str(iter_header)\n                    await self.do_file(self.get_list(list_json_param), key['key'], key['hostname'])\n                break\n            except aiohttp.ClientError:\n                pass\n            tries += 1\n\n    def get_list(self, content):\n        # print(content)\n        if content:\n            content = content.split(\"`\")\n            items = content[0].split('\\n')\n            content = [dict(zip(['name', 'type', 'size', 'time'], x.split('\\t'))) for x in items] + content[1].split() + \\\n                content[2].split()\n            return content\n        else:\n            return None\n\n    async def do_file(self, list_json, key, hostname):\n        \"\"\"处理接口数据\"\"\"\n        for i in list_json[:-2]:\n            if not i['name']:\n                continue\n            new_key = key + i['name'] if key == '/' else key + '/' + i['name']\n            try:\n                if i['type'] == 'F':\n                    self.key_queue.put_nowait(\n                        {'key': new_key, 'x-list-iter': None, 'hostname': hostname})\n                else:\n                    try:\n                        if not os.path.exists(bucket + key):\n                            os.makedirs(bucket + key)\n                    except OSError as e:\n                        print('新建文件夹错误:' + str(e))\n                    save_path = base_save_path + '/' + bucket + new_key\n                    if not os.path.isfile(save_path):\n                        self.pic_queue.put_nowait(\n                            {'key': new_key, 'save_path': save_path, 'x-list-iter': None, 'hostname': hostname})\n                    else:\n                        print(f'文件已存在:{save_path}')\n            except Exception as e:\n                print('下载文件错误！:' + str(e))\n                async with aiofiles.open('download_err.txt', 'a') as f:\n                    await f.write(new_key + '\\n')\n        if list_json[-1] != 'g2gCZAAEbmV4dGQAA2VvZg':\n            self.key_queue.put_nowait(\n                {'key': key, 'x-list-iter': list_json[-1], 'hostname': hostname})\n\n    async def run(self):\n        \"\"\"初始化任务进程\"\"\"\n        workers = [asyncio.Task(self.work(), loop=self.loop)\n                   for _ in range(self.max_tasks)]\n\n        workers_pic = [asyncio.Task(self.work_pic(), loop=self.loop)\n                       for _ in range(self.pic_tsak)]\n\n        await self.key_queue.join()\n        await self.pic_queue.join()\n\n        workers.append(workers_pic)\n        for w in workers:\n            w.cancel()\n\n\nif __name__ == '__main__':\n    loop = asyncio.get_event_loop()\n    crawler = Crawler('/', hostname, max_tasks=5, pic_tsak=150)\n    loop.run_until_complete(crawler.run())\n\n    crawler.close()\n\n    loop.close()\n","repo_name":"Hupo510/WebCrawler","sub_path":"demo/python异步多线程超高性能爬虫爬取又拍云图片例程.py","file_name":"python异步多线程超高性能爬虫爬取又拍云图片例程.py","file_ext":"py","file_size_in_byte":7264,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"37597392519","text":"import matplotlib.pyplot as plt\r\nimport numpy as np\r\nimport csv\r\nfrom decimal import Decimal\r\n\r\n\r\nCONST_EPOCH = 11644473600000000000 #nseconds between win epoch and unix epoch\r\n\r\ndef parse():\r\n    '''\r\n    parses formatted text obtained from hololens archive, aligns timelines as well.\r\n    '''\r\n    ## String Pattern for Accel and Gyro\r\n    #acclstr = \"header ax ay az norm delta wintime universaltime\"\r\n    #gyrostr = \"header gx gy gz norm delta wintime universaltime\"\r\n    \r\n    ##extract and create new hl imu files\r\n    with open ('accel.txt', 'r') as accf:\r\n        txt_readeracc = accf.readlines()\r\n        \r\n    with open ('gyro.txt', 'r') as gyrf:\r\n        txt_readergyr = gyrf.readlines()\r\n    \r\n    \r\n    for i in range(len(txt_readeracc)):\r\n        txt_readeracc[i] = txt_readeracc[i].split() \r\n        \r\n    for i in range(len(txt_readergyr)):\r\n        txt_readergyr[i] = txt_readergyr[i].split()\r\n    \r\n    \r\n    with open('accelnew.txt','w+',newline='') as csv_writer:\r\n        for it in range(len(txt_readeracc)):  \r\n            time = int(txt_readeracc[it][6])\r\n            adjusted_time = int(( time*100 )-( CONST_EPOCH ))\r\n            extr = [txt_readeracc[it][1],txt_readeracc[it][2],txt_readeracc[it][3],txt_readeracc[it][4],txt_readeracc[it][5], str(adjusted_time)]\r\n            write = csv.writer(csv_writer)\r\n            write.writerow(extr)\r\n    with open('gyronew.txt','w+',newline='') as csv_writer:\r\n        for it in range(len(txt_readergyr)):    \r\n            time = int(txt_readergyr[it][6])\r\n            adjusted_time = int(( time*100 )-( CONST_EPOCH ))\r\n            extr = [txt_readergyr[it][1],txt_readergyr[it][2],txt_readergyr[it][3],txt_readergyr[it][4],txt_readergyr[it][5],str(adjusted_time)]\r\n            write = csv.writer(csv_writer)\r\n            write.writerow(extr)\r\n\r\n\r\ndef extract():\r\n    accelData = np.loadtxt('accelnew.txt', dtype = np.float64, delimiter=',',usecols=(0,1,2,3,4))\r\n    gyroData  = np.loadtxt('gyronew.txt', dtype = np.float64, delimiter=',',usecols=(0,1,2,3,4))\r\n    h_a_time  = np.loadtxt('accelnew.txt', dtype=Decimal, delimiter=',', usecols = (5))\r\n    h_g_time  = np.loadtxt('gyronew.txt', dtype=Decimal, delimiter=',', usecols = (5))\r\n    \r\n    h_a_time_epoch = np.arange(len(h_a_time),dtype = Decimal)\r\n    h_a_time_experiment = np.arange(len(h_a_time),dtype = Decimal)\r\n    h_a_time_secs = np.arange(len(h_a_time),dtype = np.float64)\r\n    h_a_time_nsecs = np.arange(len(h_a_time),dtype = np.float64) \r\n    \r\n    h_g_time_epoch = np.arange(len(h_g_time),dtype = Decimal)\r\n    h_g_time_experiment = np.arange(len(h_g_time),dtype = Decimal)\r\n    h_g_time_secs = np.arange(len(h_g_time),dtype = np.float64)\r\n    h_g_time_nsecs = np.arange(len(h_g_time),dtype = np.float64) \r\n    \r\n    decs_a= np.arange(len(h_a_time),dtype = Decimal)\r\n    decns_a= np.arange(len(h_a_time),dtype = Decimal)\r\n\r\n    decs_g= np.arange(len(h_g_time),dtype = Decimal)\r\n    decns_g= np.arange(len(h_g_time),dtype = Decimal)\r\n    \r\n    for i in range(len(h_a_time)):\r\n        tempStr = str(h_a_time[i])\r\n        h_a_time_secs[i]  = float(tempStr[0:10])\r\n        h_a_time_nsecs[i] = float(tempStr[10:19])\r\n        \r\n        decs_a[i] =Decimal(h_a_time_secs[i])\r\n        decns_a[i] = Decimal(h_a_time_nsecs[i])\r\n        \r\n        h_a_time_epoch[i] = decs_a[i] + decns_a[i]*Decimal(1e-9)\r\n        tempStr = str(h_a_time_epoch[i])\r\n        tempStr = tempStr[0:20]\r\n        h_a_time_epoch[i] = Decimal(tempStr)\r\n        h_a_time_experiment[i] = h_a_time_epoch[i] - h_a_time_epoch[0]\r\n\r\n\r\n    \r\n    for i in range(len(h_g_time)):    \r\n        tempStr = str(h_g_time[i])\r\n        h_g_time_secs[i]  = float(tempStr[0:10])\r\n        h_g_time_nsecs[i] = float(tempStr[10:19])\r\n        \r\n        decs_g[i] =Decimal(h_g_time_secs[i])\r\n        decns_g[i] = Decimal(h_g_time_nsecs[i])\r\n        \r\n        h_g_time_epoch[i] = decs_g[i] + decns_g[i]*Decimal(1e-9)\r\n        tempStr = str(h_g_time_epoch[i])\r\n        tempStr = tempStr[0:20]\r\n        h_g_time_epoch[i] = Decimal(tempStr)\r\n        h_g_time_experiment[i] = h_g_time_epoch[i] - h_g_time_epoch[0]    \r\n   \r\n   \r\n    #grav axis is signed (-) which doesnt fit with the ENU frame\r\n    # threfore manually corrected here\r\n    hlAccX          = -accelData[:,0]\r\n    hlAccY          = accelData[:,1]\r\n    hlAccZ          = accelData[:,2]\r\n    hlAccNorm       = accelData[:,3]\r\n    hlAccSoCtime    = accelData[:,4]\r\n    \r\n    hlGyrX          = gyroData[:,0]\r\n    hlGyrY          = gyroData[:,1]\r\n    hlGyrZ          = gyroData[:,2]\r\n    hlGyrNorm       = gyroData[:,3]\r\n    hlGyrSoCtime    = gyroData[:,4]\r\n    \r\n    return (hlAccX, hlAccY, hlAccZ,\r\n            hlAccNorm, hlGyrX, hlGyrY,\r\n            hlGyrZ, hlGyrNorm, h_a_time_epoch,\r\n            h_a_time_experiment, h_g_time_epoch, h_g_time_experiment)\r\n\r\n","repo_name":"ozgunkaratas/mastersthesis","sub_path":"src/readHL.py","file_name":"readHL.py","file_ext":"py","file_size_in_byte":4789,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"81"}
+{"seq_id":"19633575207","text":"import numpy as np\nfrom mayavi import mlab\n\n\nZP = np.load(\"orderbook_shorter.npy\")\nX, Y = np.mgrid[0 : ZP.shape[0] : 1, 0 : ZP.shape[1] : 1]\nprint(X.shape, Y.shape, ZP.shape)\n\ns = mlab.barchart(X, Y, ZP)\nmlab.show()\n","repo_name":"LeaveMyYard/CryptoMarketRLGym","sub_path":"orderbook_viewer.py","file_name":"orderbook_viewer.py","file_ext":"py","file_size_in_byte":216,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"81"}
+{"seq_id":"36930264274","text":"#Ejercicio 1\r\n#Cree un programa que reciba como parámetro el valor de las medidas en centímetros de la altura y la base de un cuadrado\r\n# y que calcule el valor del área de ese cuadrado. La función deberá retornar un mensaje del estilo:\r\n#El área del cuadrado con base de .... cm y altura de .... cm es de: .... cm cuadrados\r\nprint('Inicio de calculo de area de cuadrado')\r\ndef areadecuadrado(altura, base):\r\n    return base * altura\r\n    \r\nbase = float(input('Ingrese el valor de base: '))\r\naltura = float(input('Ingrese el valor de altura: '))\r\nprint(f'El área del cuadrado con base de {base} cm y altura de {altura} cm es de: {areadecuadrado(base,altura)} cm cuadrados')\r\n\r\n#Ejercicio 2\r\n#Cree el mismo programa que en el ejercicio anterior pero ahora el usuario es quien\r\n#indica las medidas del cuadrado.\r\nprint('Inicio de calculo de area de cuadrado')\r\ndef areadecuadrado(altura, base):\r\n    return base * altura\r\n    \r\nbase = float(input('Ingrese el valor de base: '))\r\naltura = float(input('Ingrese el valor de altura: '))\r\nprint(f'El área del cuadrado con base de {base} cm y altura de {altura} cm es de: {areadecuadrado(base,altura)} cm cuadrados')\r\n\r\n#Ejercicio 3\r\n#Desarrollar un programa que realice el llamado de una función que calcule la\r\n#potencia entre dos números ingresados por teclado.\r\nprint('Inicio de calculo de potencia')\r\ndef calculopotencia(basepot, exponente):\r\n    potenaux = 1\r\n    for pot in range(exponente):\r\n        potenaux *= basepot\r\n    return potenaux\r\nbasepot = int(input('Ingrese la base: '))\r\nexponente = int(input('Ingrese el exponente: '))\r\nprint(f'La potencia de {basepot} con exponente {exponente} es {calculopotencia(basepot, exponente)} ')\r\n#Ejercicio 4\r\n#Desarrollar un programa que llame a una función que calcule el perímetro y a otra\r\n#que calcule la superficie de un círculo. Los datos deben ser ingresados por el\r\n#usuario.\r\n#Perímetro del círculo → (𝝅 * 2r) o (𝝅 * d)\r\n#Superficie de un círculo → (𝝅 * r²)\r\nimport perimetros\r\nprint(\"Jugando con el circulo.\\n\")\r\nprint(\"1.Circulo Perimetro.\\n2.Circulo Superficie.\\n\")\r\n\r\nx=int(input(\"Escoja la opción: \"))\r\nr=0\r\nd=0\r\n\r\nif x==1:\r\n    opcion = input('Ingrese si tiene diametro(d), si tiene radio(r): ')\r\n    if opcion == 'r':\r\n        r=int(input('Ingrese el radio del circulo: '))\r\n        perimetros.per_circulo_conradio(r)\r\n    if opcion =='d':\r\n        d=int(input('Ingrese el diametro del circulo: '))\r\n        perimetros.per_circulo_condiametro(d)        \r\n\r\n\r\nif x==2:\r\n    r=int(input('Ingrese el radio del circulo: '))\r\n    perimetros.per_circulo_conradio(r)\r\n\r\n#Ejercicio 5\r\n#Cree un programa que defina una calculadora básica con las 4 principales\r\n#operaciones aritméticas. Utilizar para cada una de ellas una función que reciba\r\n#como parámetros los valores intervinientes en la operación (dos argumentos) y\r\n#muestre la operación y el resultado de esta por pantalla.\r\nimport calculadora\r\nprint(\"Calculadora 1.0 \\n\")\r\nprint(\"1.Suma.\\n2.Resta.\\n3.Multiplicación.\\n4.División\")\r\n\r\nx=int(input(\"Escoja la operación: \"))\r\na=0\r\nb=0\r\n\r\nif x==1:\r\n    a=float(input('Ingrese el valor a: '))\r\n    b=float(input('Ingrese el valor b: '))\r\n    calculadora.suma(a,b)\r\n\r\nif x==2:\r\n    a=float(input('Ingrese el valor a: '))\r\n    b=float(input('Ingrese el valor b: '))\r\n    calculadora.resta(a,b)\r\n\r\nif x==3:\r\n    a=float(input('Ingrese el valor a: '))\r\n    b=float(input('Ingrese el valor b: '))\r\n    calculadora.multiplica(a,b)\r\n\r\nif x==4:\r\n    a=float(input('Ingrese el valor a: '))\r\n    b=float(input('Ingrese el valor b: '))\r\n    calculadora.division(a,b)\r\n","repo_name":"cimojeda/Python_Basic","sub_path":"CursoPython/Funciones.py","file_name":"Funciones.py","file_ext":"py","file_size_in_byte":3602,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"74314777865","text":"\n# get apiKey from .env file\n\nfrom utils.helpers import *\nimport requests\nimport os\nfrom dotenv import load_dotenv\nload_dotenv()\napiKey = os.getenv(\"apiKey\")\n\n\ndef get_response(url):\n    response = requests.get(url)\n    return response\n\n\ndef get_basic_info(ticker):\n    resp = get_response(\n        f\"https://api.polygon.io/v3/reference/tickers/{ticker}?apiKey={apiKey}\")\n    try:\n        result = resp.json()[\"results\"]\n    except:\n        return [None]*7\n    # absolute fields - name, ticker\n    name = result[\"name\"]\n    ticker = result[\"ticker\"]\n    try:\n        primary_exchange = result[\"primary_exchange\"]\n    except:\n        primary_exchange = None\n    try:\n        type_ = result[\"type\"]\n    except:\n        type_ = None\n    try:\n        list_date = result[\"list_date\"]\n    except:\n        list_date = None\n    try:    \n        market_cap = result[\"market_cap\"]\n    except:\n        market_cap = None\n    try:\n        share_class_shares_outstanding = result[\"share_class_shares_outstanding\"]\n    except:\n        share_class_shares_outstanding = None\n    return name, ticker, primary_exchange, type_, list_date, market_cap, share_class_shares_outstanding\n\n\ndef get_news(ticker, curr_day):\n    resp = get_response(\n        f\"https://api.polygon.io/v2/reference/news?published_utc={curr_day}&ticker={ticker}&apiKey={apiKey}\")\n    try:\n        results = resp.json()[\"results\"]\n    except:\n        return [None]*4\n    result = None\n    publisher_name = None\n    article_url = None\n    description = None\n    keywords = None\n    for res in results:\n        if len(res[\"tickers\"]) == 1:\n            result = res\n            break\n    # absolute fields - publisher_name, article_url\n    if result:\n        publisher_name = result[\"publisher\"][\"name\"]\n        print(f\"publisher_name: {publisher_name}\")\n        article_url = result[\"article_url\"]\n        print(f\"article_url: {article_url}\")\n        try:\n            description = result[\"description\"]\n        except:\n            description = None\n        print(f\"description: {description}\")\n        try:\n            keywords = result[\"keywords\"]\n        except:\n            keywords = None\n        print(f\"keywords: {keywords}\")\n    return publisher_name, article_url, description, keywords\n\n\ndef get_daily_data(ticker):\n    curr_day = get_curr_day()\n    resp = get_response(\n        f\"https://api.polygon.io/v2/aggs/ticker/{ticker}/range/1/day/{curr_day}/{curr_day}?adjusted=true&sort=asc&apiKey={apiKey}\")\n    try:\n        results = resp.json()[\"results\"]\n    except:\n        return [None]*7\n    result = results[0]\n    c = result[\"c\"]\n    h = result[\"h\"]\n    l = result[\"l\"]\n    o = result[\"o\"]\n    v = result[\"v\"]\n    vw = result[\"vw\"]\n    n = result[\"n\"]\n    return c, h, l, o, v, vw, n\n\n\ndef get_2_minute_data(ticker, from_time, to_time):\n    resp = get_response(\n        f\"https://api.polygon.io/v2/aggs/ticker/{ticker}/range/2/minute/{from_time}/{to_time}?adjusted=true&sort=asc&apiKey={apiKey}\")\n    try:\n        results = resp.json()[\"results\"]\n    except:\n        return None\n    return results\n\n\ndef get_prev_day_data(ticker):\n    prev_day = get_prev_day()\n    resp = get_response(\n        f\"https://api.polygon.io/v2/aggs/ticker/{ticker}/range/1/day/{prev_day}/{prev_day}?adjusted=true&sort=asc&apiKey={apiKey}\")\n    try:\n        results = resp.json()[\"results\"]\n    except:\n        return None\n    result = results[0]\n    return result\n","repo_name":"desitrader/stock","sub_path":"utils/api.py","file_name":"api.py","file_ext":"py","file_size_in_byte":3403,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"23818964862","text":"#Client RPC\r\nfrom xmlrpc.client import ServerProxy \r\nimport threading\r\n\r\ndef inicializa():\r\n    cliente = ServerProxy('http://localhost:20064', allow_none=True) \r\n    altura = input(\"Digite a altura \\n\")\r\n    sexo = input(\"(1) Masculino (2) Feminino \\n\")\r\n    print(cliente.classificacao(altura, sexo))\r\n\r\n\r\nif __name__ == \"__main__\":\r\n    thread = threading.Thread(target=inicializa, args=[])\r\n    thread.start()","repo_name":"SD-20221-2/time_5_listas","sub_path":"LISTA 3/Exercicio 4/RPC - Exercicio 4/cliente04.py","file_name":"cliente04.py","file_ext":"py","file_size_in_byte":413,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"2375447119","text":"'''\nCreated on Mar 16, 2009\n\n@author: sethchase\n'''\n\nfrom DBdata import runDBcount\nfrom time import time\n\ndef DBdataApp(self, action, variable):\n    if action == \"count\":\n        database = variable\n        now = int(time())\n        end = now + (25)\n        complete = False\n        attempts = 5\n        while end >= now and attempts != 0:\n            status, dbCount = runDBcount(database)\n            if status == \"complete\":\n                complete = True\n                break\n            now = int(time())\n            attempts = attempts - 1\n        if complete == True:\n            return dbCount\n        else:\n            return \"run again\"        \n    else:\n        # allow option to make query, show results and count\n        return \"error: no action sent\"\n        # pass\n","repo_name":"sethc23/BD_Scripts","sub_path":"Projects/TestApp/src/controllers/DBhome.py","file_name":"DBhome.py","file_ext":"py","file_size_in_byte":782,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"81"}
+{"seq_id":"4775898206","text":"import string\n\n#! Tokens and Tokenizers\n\nDIGITS  = '0123456789'\nLETTERS = string.ascii_letters\nLETTERS_DIGITS = LETTERS + DIGITS\n\n#! TOKEN\nTOK_INT       = 'INT'\nTOK_FLOAT     = 'FLOAT'\nTOK_STRING    = 'STRING'\n\nTOK_MUL       = 'MUL'\nTOK_DIV       = 'DIV'\nTOK_ADD       = 'ADD'\nTOK_SUB       = 'SUB'\nTOK_POW       = 'POW'\nTOK_EOF       = 'EOF'\nTOK_REMAINDER = 'REMAINDER'\n\nTOK_LBRACKET = 'LBRACKET'\nTOK_RBRACKET = 'RBRACKET'\nTOK_LSQUARE  = 'LSQUARE'\nTOK_RSQUARE  = 'RSQUARE'\n\nTOK_IDENTIFIER = 'IDENTIFIER'\nTOK_KEYWORD    = 'KEYWORD'\nTOK_EQUAL      = 'EQUALS'\n\nTOK_DEQUAL      = 'DOUBLEQUALS'\nTOK_NTEQUAL     = 'NOTEQUALS'\nTOK_GT          = 'GREATERTHAN'\nTOK_GTE         = 'GREATERTHANEQTO'\nTOK_LT          = 'LESSTHAN'\nTOK_LTE         = 'LESSTHANEQTO'\n\nTOK_ARROW       = 'ARROW'\nTOK_COMMA       = 'COMMA' \n\nTOK_NEWLINE     = 'NEWLINE'\n\nTOK_BREAK       = 'BREAK'\nTOK_RETURN      = 'RETURN'\nTOK_CONTINUE    = 'CONTINUE'\n\nTOK_END         = 'END'\nTOK_SON         = 'SON'\n\n#! Dict for the Operator\nOP_TOK_TAG = {\n    '+'     : 'ADD',\n    '/'     : 'DIV',\n    '*'     : 'MUL',\n    '{'     : 'LBRACKET',\n    '}'     : 'RBRACKET',\n    '('     : 'LBRACKET',\n    ')'     : 'RBRACKET',\n    '['     : 'LSQUARE',\n    ']'     : 'RSQUARE',\n    '^'     : 'POW',\n    '%'     : 'REMAINDER'\n}\n\n#! A list of the keywords for yakamoz\nKEYWORDS = [\n    'oyleki',\n    'yoket',\n    've',\n    'veya',\n\n    'eger',\n    'yoksaeger',\n    'yoksa',\n    'ise',\n\n    'for',\n    'den',\n    'kadar',\n    'adim',\n\n    'while',\n    'fonk', \n    'dondur',\n    'devam',\n    'break',\n    \n    'son',\n    'sonra'\n]\n\nclass Token:\n    def __init__(self, type_ , value=None , pos_start=None , pos_end=None):\n        self.type = type_\n        self.value = value\n\n        if pos_start:\n            self.pos_start = pos_start.copy()\n            self.pos_end = pos_start.copy()\n            self.pos_end.advance()\n\n        if pos_end:\n            self.pos_end = pos_end        \n\n    def matches(self, type_, value):\n        return self.type == type_ and self.value == value\n\n    def __repr__(self):\n        if self.value: return f'{self.value} : {self.type}'\n        return f'{self.type}'","repo_name":"waasiq/yakamoz","sub_path":"src/Lib/Tokens.py","file_name":"Tokens.py","file_ext":"py","file_size_in_byte":2138,"program_lang":"python","lang":"en","doc_type":"code","stars":13,"dataset":"github-code","pt":"81"}
+{"seq_id":"36483143321","text":"#!/usr/bin/env python\n#coding=utf-8\n\n\nimport socket\nimport json\nimport time\nimport threading\nfrom math import pi\n\nimport rospy\nfrom riki_msgs.msg import Battery\nfrom geometry_msgs.msg import Twist\nfrom sensor_msgs.msg import JointState\n\n\ndef send_msg():\n    while app_is_run:\n        robot_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n        try:\n            robot_socket.connect((\"127.0.0.1\", 11322))\n\n            xrJson = {\n                \"version\": \"b0.0\",\n                \"keep_alive\": True,\n                \"url\": \"/servo/arm\",\n                \"method\": \"post\",\n                \"data\": {\"servo\": ['30', '150', '0', '-30']},\n            }\n\n            tem_data = json.dumps(xrJson).encode(\"utf-8\")\n            print(tem_data)\n            robot_socket.send(tem_data)\n            time.sleep(0.5)\n\n            xrJson = {\n                \"version\": \"b0.0\",\n                \"keep_alive\": True,\n                \"url\": \"/servo/tilt\",\n                \"method\": \"post\",\n                \"data\": {\"servo\": ['90', '90']},\n            }\n\n            tem_data = json.dumps(xrJson).encode(\"utf-8\")\n            print(tem_data)\n            robot_socket.send(tem_data)\n            time.sleep(2)\n            break\n        except Exception:\n            time.sleep(2)\n            continue\n\n    while app_is_run:\n        print(\"init socket\")\n        robot_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n        robot_socket.settimeout(5)\n\n        try:\n            try:\n                time.sleep(1)\n                robot_socket.connect((\"127.0.0.1\", 11322))\n                \n            except Exception:\n                continue\n\n            while app_is_run:\n\n                try:\n                    if send_bytes:\n                        robot_socket.send(send_bytes.pop())\n                        recv = robot_socket.recv(200)\n                        time.sleep(0.01)\n\n                    # read move command\n                    xrJson = {\n                        \"version\": \"b0.0\",\n                        \"keep_alive\": True,\n                        \"url\": \"/movement\",\n                        \"method\": \"get\",\n                        \"data\": {},\n                    }\n\n                    tem_data = json.dumps(xrJson).encode(\"utf-8\")\n                    robot_socket.send(tem_data)\n\n                    recv = robot_socket.recv(200)\n                    # print(recv)\n                    try:\n                        recv = json.loads(recv)\n                        # print(recv)\n                        if \"direction\" in recv[\"data\"]:\n                            # print(\"data got\")\n                            pub_movement(recv[\"data\"][\"direction\"].encode(\"utf-8\"))\n                    except ValueError:\n                        continue\n                    \n                    time.sleep(0.01)\n\n                    # read arm servo command\n                    xrJson = {\n                        \"version\": \"b0.0\",\n                        \"keep_alive\": True,\n                        \"url\": \"/servo/arm\",\n                        \"method\": \"get\",\n                        \"data\": {},\n                    }\n\n                    tem_data = json.dumps(xrJson).encode(\"utf-8\")\n                    robot_socket.send(tem_data)\n                    arm_recv = robot_socket.recv(200)\n                    # print(recv)\n                    try:\n                        arm_recv = json.loads(arm_recv)\n                        # print(recv)\n                        if \"servo\" in arm_recv[\"data\"]:\n                            for i in range(4):\n                                global servos_is_update\n\n                                temp_servo = float(arm_recv[\"data\"][\"servo\"][i])/180.0 * pi\n                                if not temp_servo == servos[i]:\n                                    servos_is_update = True\n                                    servos[i] = temp_servo\n        \n                    except ValueError:\n                        continue\n\n                    time.sleep(0.01)\n\n                    # read tilt servo command\n                    xrJson = {\n                        \"version\": \"b0.0\",\n                        \"keep_alive\": True,\n                        \"url\": \"/servo/tilt\",\n                        \"method\": \"get\",\n                        \"data\": {},\n                    }\n\n                    tem_data = json.dumps(xrJson).encode(\"utf-8\")\n                    robot_socket.send(tem_data)\n                    tilt_recv = robot_socket.recv(200)\n                    # print(recv)\n                    try:\n                        tilt_recv = json.loads(tilt_recv)\n                        if \"servo\" in recv[\"data\"]:\n                            for i in range(2):\n                                global servos_is_update\n\n                                temp_servo = (float(recv[\"data\"][\"servo\"][i]) -90) / 180.0 * pi\n                                if not temp_servo == servos[i+4]:\n                                    servos[i+4] = temp_servo\n                                    servos_is_update = True\n\n                    except ValueError:\n                        continue\n\n                    if servos_is_update:\n                        global servos_is_update\n                        servo.position = servos\n                        servo_pub.publish(servo)\n                        rospy.loginfo(\"servo pub\")\n                        servos_is_update = False\n\n                    time.sleep(0.1)\n\n                except socket.error:\n                    robot_socket.close()\n                    break\n\n        except KeyboardInterrupt:\n            robot_socket.close()\n            break\n        finally:\n            robot_socket.close()\n\n\ndef pub_movement(movement):\n    global robot_is_stop\n    # print(type(movement), type(\"\"))\n    if not isinstance(movement, type(\"\")):\n        # print(\"break\")\n        return False\n\n    move_msg = Twist()\n    if movement == \"stop\":\n        if robot_is_stop:\n            return None\n        rospy.loginfo(\"stop\")\n        # move_msg.angular.z = 0.0\n        # move_msg.linear.x = 0.0\n        robot_is_stop = True\n\n    elif movement == \"left\":\n        rospy.loginfo(\"turn left\")\n        move_msg.angular.z = 2.5*robot_speed\n        move_msg.linear.x = 0.0\n        robot_is_stop = False\n\n    elif movement == \"right\":\n        rospy.loginfo(\"turn right\")\n        move_msg.angular.z = -2.5*robot_speed\n        move_msg.linear.x = 0.0\n        robot_is_stop = False\n\n    elif movement == \"forward\":\n        rospy.loginfo(\"forward\")\n        move_msg.angular.z = 0.0\n        move_msg.linear.x = 1.2*robot_speed\n        robot_is_stop = False\n\n    elif movement == \"backward\":\n        rospy.loginfo(\"backward\")\n        move_msg.angular.z = 0.0\n        move_msg.linear.x = -0.5*robot_speed\n        robot_is_stop = False\n\n    movement_pub.publish(move_msg)\n\n\ndef battery_callback(msg):\n    global send_bytes\n    remaining_electricity = msg.battery\n    rospy.loginfo(msg.battery)\n\n    xrJson = {\n        \"version\": \"b0.0\",\n        \"keep_alive\": True,\n        \"url\": \"/xrrobot/battery\",\n        \"method\": \"post\",\n        \"data\": {\"remaining_electricity\": float(remaining_electricity)},\n    }\n\n    send_bytes.append(json.dumps(xrJson).encode(\"utf-8\"))\n\n\nsend_bytes = []\n\nrobot_speed = 1.5\n\nservos = [0.0 for i in range(6)]\n\nrobot_thread = threading.Thread(target=send_msg)\n\nbattery_msg = Battery()\nPOWER_SUB = rospy.Subscriber('battery', Battery, battery_callback)\n\nservo = JointState()\nservo.name = ['a', 'b', 'c', 'd', 'x', 'y']\nservo_pub = rospy.Publisher('joint_states', JointState, queue_size=10)\nmovement_pub = rospy.Publisher('cmd_vel', Twist, queue_size=0)\n\nrospy.init_node('robot_status_interaction')\n\nrospy.loginfo('robot_status_interaction node is running!')\n\napp_is_run = True\nrobot_is_stop = True\nservos_is_update = False\nrobot_thread.start()\n\nrospy.spin()\n\napp_is_run = False\nrobot_thread.join()\n","repo_name":"n00bhax/MrRoboto","sub_path":"xrrobot/script/robot_status_interaction.py","file_name":"robot_status_interaction.py","file_ext":"py","file_size_in_byte":7865,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"81"}
+{"seq_id":"27656796104","text":"import pygame\nimport random\nimport importlib\nfrom settings import *\nfrom functions import *\n\nplayer1 = importlib.import_module(\"players.\" + PLAYER_1_NAME)\nplayer2 = importlib.import_module(\"players.\" + PLAYER_2_NAME)\nplayers = [PLAYER_1_NAME, PLAYER_2_NAME]\nturn = 0\n\n# initialize pygame and create window\npygame.init()\npygame.font.init()\nscreen = pygame.display.set_mode((WIDTH, HEIGHT))\npygame.display.set_caption(\"Tic Tac Toe\")\nclock = pygame.time.Clock()\nfont = pygame.font.SysFont(TEXT_FONT, TEXT_SIZE)\n\n# define player tables\nplayer1_table = [[\" \" for j in range(0, TABLE_WIDTH)] for i in range(0, TABLE_HEIGHT)] \nplayer2_table = [[\" \" for j in range(0, TABLE_WIDTH)] for i in range(0, TABLE_HEIGHT)]\ntable = [[\" \" for j in range(0, TABLE_WIDTH)] for i in range(0, TABLE_HEIGHT)]\n\n# Game loop\nrunning = True\nclose = False\nwhile running:\n    # keep loop running at the right speed\n    clock.tick(FPS)\n    # Process input (events)\n    for event in pygame.event.get():\n        # check for closing window\n        if event.type == pygame.QUIT:\n            running = False\n            close = True\n\n    # Update\n\n    # check if we have a winner\n    winner = get_winner(table)\n    if winner is not None:\n        write(screen, font, (10, HEIGHT - 40), BLACK, players[(turn + 1) % 2] + \" won as \" + winner)\n        pygame.display.flip()\n        break\n\n    # check if we table is full\n    if table_is_full(table):\n        write(screen, font, (10, HEIGHT - 40), BLACK, \"Tie!\")\n        pygame.display.flip()\n        break\n    if turn:\n        # player 2's turn\n        try:\n            for i in range(0, TABLE_HEIGHT):\n                for j in range(0, TABLE_WIDTH):\n                    if table[i][j] == \"O\":\n                        player2_table[i][j] = \"X\"\n                    elif table[i][j] == \"X\":\n                        player2_table[i][j] = \"O\"\n                    else:\n                        player2_table[i][j] = \" \"\n            row, column = player2.get_move(player2_table)\n            if check_move(table, row, column):\n                table[row][column] = \"O\"\n            else:\n                print(PLAYER_2_NAME, \"false move\\n\")\n        except Exception as e:\n            print(PLAYER_2_NAME, \"error:\\n\", e, \"\\n\")\n\n    else:\n        # player 1's turn\n        try:\n            for i in range(0, TABLE_HEIGHT):\n                for j in range(0, TABLE_WIDTH):\n                    player1_table[i][j] = table[i][j]\n            row, column = player1.get_move(player1_table)\n            if check_move(table, row, column):\n                table[row][column] = \"X\"\n            else:\n                print(PLAYER_1_NAME, \"false move\\n\")\n        except Exception as e:\n            print(PLAYER_1_NAME, \"error:\\n\", e, \"\\n\")\n    turn = (turn + 1) % 2\n\n    # Draw / render\n    screen.fill(WHITE)\n    draw_table(screen, table)\n    write(screen, font, (10, 10), BLACK, \"X is \" + PLAYER_1_NAME)\n    write(screen, font, (10, 40), BLACK, \"O is \" + PLAYER_2_NAME)\n    # write(screen, font, (10, 70), BLACK, players[turn] + \"'s turn\")\n\n    # *after* drawing everything, flip the display\n    pygame.display.flip()\n    pygame.event.pump()\n    pygame.time.delay(1000)\n\nif close:\n    pygame.quit()\n    exit()\nelse:\n    while running:\n        for event in pygame.event.get():\n            # check for closing window\n            if event.type == pygame.QUIT:\n                running = False\n    pygame.quit()\n    exit()\n","repo_name":"mohsenbeygi/IB_computer_science","sub_path":"tic tac toe/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3406,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"41336145414","text":"# Fönstrets mått\nHEIGHT, WIDTH = 800, 800\nBORDER = 600\naudio_pos_x, audio_pos_y = 5, 5\naudio_icon_width, audio_icon_height = 55, 45\n\n# Färger\nBLACK = (0, 0, 0)\nWHITE = (255, 255, 255)\nRED = (255, 0, 0)\nBLUE = (4, 51, 255)\nGREEN = (0, 255, 0)\n\n# Spel state\nrun = True\nboss_state = False\naudio_state = True\nstatus = \"on\"\n\n# Listor\nenemy_list = []\nbullets = []\ninvader_img = []\nboss_img = []\nboss_bullets = []\n\n# Variabler\nenemy_count = 5\nboss_start_health = 100\nenemy_vel = 0.5\npoints_to_enemy = 100\npoints_until_new_enemy = points_to_enemy\nbullet_count = 5\nplayer_vel = 0\nstart_player_vel = 5\nFPS = 60\nscore = 0\ntimer = 0\nwave = 1\n","repo_name":"Rasmus-04/Game-library","sub_path":"Space_invader/config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":633,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"33554451636","text":"import idlelib.tooltip\nfrom threading import Thread\nimport tkinter as tk\nfrom datetime import datetime, timedelta\nfrom tkinter.messagebox import showerror\n\nimport webuntis\nfrom src import UntisBreaks, TKUtils, Constants\n\n\n\nclass DisplayFrame(TKUtils.FillerFrame):\n    \n\n    def __init__(self, parent, session: webuntis.Session):\n        super().__init__(parent)\n        self.session = session\n        self.is_today = True\n        self.data = None\n        self.currentBreak = None\n        # For pretending to have a different day, otherwise should be 0.\n        self.natural_offset = 0\n        self.break_offset_hours = 0\n        self._build()\n\n\n    def finishMainloop(self):\n        self.winfo_toplevel().destroy()\n\n\n    '''\n    Errorhandling: Interaction with webuntis\n    '''\n    def _api_fail_save(self, callback):\n        try:\n            callback()\n        except webuntis.errors.NotLoggedInError:\n            showerror(\"Verbindung abgelaufen\", message=\"Sitzung ist abgelaufen. Melden Sie sich erneut an.\")\n            self.winfo_toplevel().selectLoginFrame()\n        except webuntis.errors.Error:\n            showerror(\"Server Fehler\", message=\"Ein Fehler während der Kommunikation mit Webuntis ist aufgetreten.\")\n            self.winfo_toplevel().selectLoginFrame()\n        except OSError:\n            showerror(\"Verbindung verloren\", message=\"Die Verbindung zum Server wurde verloren.\")\n\n        \n\n\n    def _threaded_fail_save(self, callback):\n        try:\n            callback()\n        except Exception as e:\n            TKUtils.TKErrorHandler.report_callback_exception(self, e.__class__.__name__, str(e), e.__traceback__)\n\n\n\n    # TODO try-catch connection -> send back to login frame + Error-Popup\n    '''\n    For the \"current\" day's table (or whatever the natural_offset suggests).\n    '''\n    def fetch_break_info(self):\n        def do():\n            self.data = UntisBreaks.get_offset_supervisions(self.session, self.natural_offset)\n            current_time = datetime.now()+timedelta(days=self.natural_offset, hours=self.break_offset_hours)\n            self.currentBreak = UntisBreaks.next_break_time(self.data.keys(), current=current_time)\n        self._api_fail_save(do)\n\n    \n    def fetch_nextday_info(self):\n        def do():\n        # for Mo-Do => Move 1, else move to Monday\n            next_day = self._get_next_day() + self.natural_offset\n            self.data = UntisBreaks.get_offset_supervisions(self.session, next_day)\n            self.currentBreak = UntisBreaks.next_break_time(self.data.keys())\n        self._api_fail_save(do)\n\n\n    def _get_next_day(self):\n        weekday = (datetime.now()+timedelta(days=self.natural_offset)).weekday()\n        return 1 if weekday < 4 else 7-weekday\n\n\n    # ======== building =======>\n\n\n    def _build(self):\n        self.settings_bar = self._create_settings_bar()\n        self.table_frame = self._create_table_frame(forceEmpty=(True, \"läd Inhalte..\"))\n        exit_bar = self._create_exit_bar()\n\n        self._pack_contents(self.settings_bar, self.table_frame, exit_bar)\n        self.after(10, self.after_init)\n\n\n    def _pack_contents(self, settings_bar, table_frame, exit_bar):\n        settings_bar.pack(anchor=tk.N, fill=tk.X, expand=False, side=tk.TOP )\n        exit_bar.pack(anchor=tk.N, fill=tk.X, expand=False, side=tk.BOTTOM)\n        table_frame.pack(fill=tk.BOTH, expand=True, side=tk.TOP)\n\n\n    def after_init(self, data_source=None):\n        data_source = self._after_init_prep(data_source)\n        def do():\n            try:\n                self._after_init_can_fail(data_source)\n            except webuntis.errors.NotLoggedInError:\n                showerror(\"Verbindung abgelaufen\", message=\"Sitzung ist abgelaufen. Melden Sie sich erneut an.\")\n                self.winfo_toplevel().selectLoginFrame()\n            except RuntimeError as e:\n                try:    # if 'winfo_exists' throws an error, then tk closed => do nothing\n                    if self.winfo_exists():\n                        TKUtils.TKErrorHandler.report_callback_exception(self, e.__class__.__name__, str(e), e.__traceback__)\n                except: pass\n        Thread(target=lambda: self._threaded_fail_save(do)).start()\n\n\n    def _after_init_prep(self, data_source):\n        self.toggle_load_buttons(False)\n        if hasattr(self, 'day_label') and self.day_label: self.day_label.destroy()\n        return data_source if data_source else self.fetch_break_info\n\n\n    def _after_init_can_fail(self, data_source):\n        try:\n            data_source()\n            self._change_table(self.currentBreak)\n            self.update_day_label()\n            self.toggle_load_buttons(True)\n        except Exception as e: \n            try:\n                # throws error if frame is destroyed => sucks up error\n                if self.winfo_exists():\n                    raise e\n            except: pass\n\n\n\n    # ====== settings bar ======>\n\n\n    def _create_settings_bar(self):\n        settings_bar = tk.Frame(self, bg=Constants.BACKGROUND, height=60, relief='groove', highlightthickness=2)\n        self.retry = tk.Button(settings_bar, text=\"\\u27F3\", padx=-1, pady=-1, font=(\"SherifSans\", 20), borderwidth=0)\n        self.retry.configure(activeforeground=\"blue\", activebackground=Constants.BACKGROUND, bg=Constants.BACKGROUND)\n        self.retry.configure(command=self.reload_tables)\n        self.retry.place(x=20, y=3)\n\n        self.toggle_day = tk.Button(settings_bar, text=\"Nächstes >>\", borderwidth=0)\n        self.toggle_day.config(font=(\"Arial\", 13), bg=Constants.BACKGROUND, activeforeground=\"blue\", activebackground=Constants.BACKGROUND)\n        self.toggle_day.place(relx=0.5, rely=0.55, anchor=tk.CENTER)\n        self.toggle_day.config(command=self.toggleDay)\n        return settings_bar\n\n    \n    def toggleDay(self):\n        self.toggle_load_buttons(False)\n        self._change_table(None, _fEmpty=True, _fMessage=\"aktualisiert Inhalte...\")\n        self.is_today = not self.is_today\n        if self.is_today:\n            self.toggle_day.config(text=\"Nächstes >>\")\n            return self.after_init(data_source=self.fetch_break_info)\n        self.toggle_day.config(text=\"<< Aktuelles\")\n        self.after_init(data_source=self.fetch_nextday_info)\n\n\n    def toggle_load_buttons(self, activate):\n        if activate:\n            self.retry[\"state\"] = 'normal'\n            self.toggle_day[\"state\"] = 'normal'\n            return\n        self.retry[\"state\"] = 'disabled'\n        self.toggle_day[\"state\"] = 'disabled'\n\n    \n    '''\n    Spawns label with day-info on the top right in the settings bar.\n    Recalling will automatically replace the old label.\n    '''\n    def update_day_label(self):\n        if hasattr(self, 'day_label') and self.day_label: self.day_label.destroy()\n        ref_time = self._get_reference_day()\n        self.day_label = TKUtils.DayLabel(self.settings_bar, ref_time, borderwidth=0)\n        self.day_label.config(font=(\"Arial\", 13), bg=Constants.BACKGROUND)\n        self.day_label.place(relx=0.95, rely=0.55, anchor=tk.E)\n\n\n    '''\n    A datetime object from the day of the shown table\n    '''\n    def _get_reference_day(self) -> datetime:\n        if self.currentBreak: return self.currentBreak\n        if self.is_today:\n            return datetime.now() + timedelta(days=self.natural_offset)\n        return datetime.now() + timedelta(days=self.natural_offset+self._get_next_day())\n\n\n    # ======= exit bar =======>\n\n\n    def _create_exit_bar(self):\n        exit_bar = tk.Frame(self, bg=Constants.BACKGROUND, height=80, relief='groove', highlightthickness=2)\n        b1 = tk.Button(exit_bar, padx=5, text=\"Log Out\", command=self.winfo_toplevel().selectLoginFrame)\n        b2 = tk.Button(exit_bar, padx=5, text=\"Beenden\", command=self.finishMainloop)\n        b1.place(relx=0.45, rely=0.5, anchor=tk.E)\n        b2.place(relx=0.55, rely=0.5, anchor=tk.W)\n        return exit_bar\n\n\n\n    # =============== middle frame =================>\n\n\n    # whether a table can be created\n    def is_displayable(self):\n            return self.currentBreak\n\n\n    def reload_tables(self):\n        self.toggle_load_buttons(False)\n        self._change_table(None, _fEmpty=True, _fMessage=\"aktualisiert Inhalte...\")\n        self.update()\n        get = self.fetch_break_info if self.is_today else self.fetch_nextday_info\n        self.after_init(data_source=get)\n\n\n    '''\n    param forceEmpty: 2-tuple with [0: create-empty-table] and [1: Text to be displayed on empty frame]\n    '''\n    def _create_table_frame(self, forceEmpty=(False, None)):\n        frame = TKUtils.FillerFrame(self)\n        self._create_arrow(frame, '\\u2B9C', -1).pack(anchor=tk.W, fill=tk.Y, side=tk.LEFT)\n        if not forceEmpty[0] and self.is_displayable():\n            self._create_table(frame).pack(anchor=tk.W, fill=tk.BOTH, expand=True, side=tk.LEFT)\n        else:\n            self._create_empty_table(frame, forceEmpty[1]).pack(anchor=tk.W, fill=tk.BOTH, expand=True, side=tk.LEFT)\n        self._create_arrow(frame, \"\\u2B9E\", 1).pack(anchor=tk.W, fill=tk.Y, side=tk.RIGHT)\n        return frame\n\n\n    def _create_arrow(self, parent, text, offset):\n        arrow_frame = TKUtils.FillerFrame(parent, width=120)\n\n        arrow = tk.Button(arrow_frame, text=text)\n        arrow.configure(borderwidth=0, font=(\"Arial\", 30))\n        self._toggle_button(arrow, offset)\n        arrow.place(relx=0.5, rely=0.5, anchor=tk.CENTER)\n        return arrow_frame\n\n\n    '''\n    existence of previous/next decides whether arrow should be activated or not\n    @param arrow: arrow-button to toggle\n    @param offset: how many breaks too look back or forward to. Since the arrows only change break position by one, this value should be 1 or -1.\n    '''\n    def _toggle_button(self, arrow, offset):\n        bg=Constants.BACKGROUND\n        def do():\n            if self.currentBreak:\n                rel_break = UntisBreaks.get_relative_break(self.currentBreak, self.data.keys(), offset)\n            # deactivate if necessary\n            if not (self.currentBreak and rel_break):\n                arrow[\"state\"] = \"disabled\"\n                return arrow.configure(activeforeground=bg, bg=bg, activebackground=bg, foreground=\"gray\")\n            # activate\n            arrow[\"state\"] = \"normal\"\n            arrow.configure(activeforeground=\"blue\", bg=bg, activebackground=bg, foreground=\"black\")\n            arrow.config(command=lambda:self._change_table(rel_break))\n        self._api_fail_save(do)\n\n    \n    '''\n    Difference to reload_tables: Not refreshing data, only displaying different breaks.\n    '''\n    def _change_table(self, selected_break_time, _fEmpty=False, _fMessage=None):\n        self.currentBreak = selected_break_time\n        self.table_frame.destroy()\n        self.table_frame = self._create_table_frame(forceEmpty=(_fEmpty, _fMessage))\n        self.table_frame.pack(fill=tk.BOTH, expand=True, side=tk.TOP)\n\n\n\n    # ==== empty table ====>\n\n\n    def _create_empty_table(self, parent, message):\n        msg = message if message else \"Hier ist nichts zu sehen :/\"\n        empty = tk.Frame(parent, bg=Constants.BACKGROUND)\n        empty.pack_propagate(False)\n        label = tk.Label(empty, text=msg, bg=Constants.BACKGROUND)\n        label.configure(font=\"Helvetica 10 italic\")\n        label.place(relx=0.5, rely=0.5, anchor=tk.CENTER)\n        return empty\n\n\n    # ==== inner Table (scrollbar-layer) ====>\n\n\n    def _create_table(self, parent):\n        table = TKUtils.FillerFrame(parent, bg=\"blue\")\n        self._addTime(table)\n        container = TKUtils.WidthControlledScrollContainer(table, Constants.ITEM_WIDTH)\n        container.pack(fill=tk.BOTH, expand=True)\n        self._add_periods_to_grid(container.get_frame())\n        return table\n\n\n    def _add_periods_to_grid(self, frame: tk.Frame):\n        frame.rowconfigure(0, weight=1)\n        for i, period in enumerate(self.data[self.currentBreak]):\n            frame.columnconfigure(i, weight=1)\n\n            pFrame = tk.Frame(frame, bg=self.getPeriodBG(period), width=Constants.ITEM_WIDTH, padx=40)\n            pFrame.config(highlightbackground=\"gray\", highlightthickness=1)\n            self.config_tooltip(period, pFrame)\n            pFrame.pack_propagate(False)\n            pFrame.columnconfigure(0, weight=1)\n            pFrame.rowconfigure(0, weight=1)\n            pFrame.rowconfigure(1, weight=1)\n\n            self._add_teachers(pFrame, period)\n            self._add_rooms(pFrame, period)\n            pFrame.grid(row=0, column=i, sticky=tk.NSEW)\n\n\n    def config_tooltip(self, period, widget):\n        if period.code == 'cancelled':\n            idlelib.tooltip.Hovertip(widget,'Abgesagte Stunde')\n        if period.code == 'irregular':\n            idlelib.tooltip.Hovertip(widget,'Irreguläre Stunde')\n\n\n    def _add_teachers(self, parent, period):\n        # gray border frame to fill space\n        borderFrame = tk.Frame(parent, bg=\"gray\", width=Constants.ITEM_WIDTH)\n        borderFrame.pack_propagate(False)\n        borderFrame.grid(row=0, column=0, sticky=tk.NSEW)\n\n        # then frame with pad-bottom=1   => showing gray border\n        tFrame = tk.Frame(borderFrame, bg=self.getPeriodBG(period))\n        tFrame.pack(fill=tk.BOTH, expand=True, side=tk.TOP, anchor=tk.NW, pady=(0,1), padx=(0,0))\n        teachers_text = self._str_teachers(period)\n        tLabel = tk.Label(tFrame, text=teachers_text[:-2], font=(\"Arial\", 10))\n        tLabel.config(fg=self.getPeriodFG(period), bg=self.getPeriodBG(period))\n        tLabel.bind('<Configure>', lambda e: tLabel.config(wraplength=tLabel.winfo_width()))\n        tLabel.pack( side=tk.BOTTOM, pady=(10,20))\n\n\n    '''\n        For unknown reasons, an index error is thrown if the list is empty.\n        Printing the period show a teacher as id=0, but no such teachers exists,\n        therefore I can only reckon this is the webuntis way of saying there is no teacher.\n    '''\n    def _str_teachers(self, period):\n        teachers_text = \"\"\n        try:\n            for t in period.teachers:\n                teachers_text += t.full_name + \"\\n\" + \"\\n\"\n        except IndexError:\n            pass\n        for t in period.original_teachers:\n            teachers_text += f\"({t.full_name})\\n\\n\"\n        return teachers_text if teachers_text else \"----\"\n\n\n    def _add_rooms(self, parent, period):\n        rFrame = tk.Frame(parent, bg=self.getPeriodBG(period))\n        rFrame.pack_propagate(False)\n        rFrame.grid(row=1, column=0, sticky=tk.NSEW)\n        room_text = \"\"\n        for r in period.rooms:\n            room_text += r.long_name + \"\\n\" +\"\\n\"\n        for r in period.original_rooms:\n            room_text += f\"({r.long_name})\\n\\n\"\n        room_text = room_text if room_text else \"----\"\n        rLabel = tk.Label(rFrame, text=room_text[:-2], font=(\"Arial\", 10))\n        rLabel.config(bg=self.getPeriodBG(period), fg=self.getPeriodFG(period))\n        rLabel.pack(side=tk.TOP, pady=(20,10))\n\n\n    def _addTime(self, parent):\n        upperside = TKUtils.FillerFrame(parent, height=70)\n        tk.Label(upperside, text=self._getTime(), padx=5, pady=5, bg=Constants.BACKGROUND, font=(\"Courier\", 18)).pack(anchor=tk.W)\n        upperside.pack(anchor=tk.N, fill=tk.X, expand=False, side=tk.TOP)\n\n\n    def _getTime(self):\n        date=self.currentBreak\n        minute = date.minute if date.minute > 9 else f\"0{date.minute}\"\n        return f\"Start: {date.hour}:{minute}\"\n\n\n    # ==== color configs =====>\n    \n\n    def getPeriodBG(self, period):\n        if period.code=='cancelled':\n            return '#ff4d4d'\n        if period.code=='irregular':\n            return '#fc00fc'\n        return Constants.C_PERIOD\n\n\n    def getPeriodFG(self, period):\n        if period.code=='cancelled':\n            return \"white\"\n        return \"black\"","repo_name":"BenAufGitHub/webuntis-break-supervision-display","sub_path":"src/DisplayFrame.py","file_name":"DisplayFrame.py","file_ext":"py","file_size_in_byte":15669,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"71022362506","text":"#!/usr/bin/env python\n# cron_cluster_wrapper.py\n# By Erik Redding <erik.redding@txstate.edu>\n\"\"\"\nThis is a wrapper that is cluster-aware for cronjobs.  Two-node clusters.\n\nit runs clustat and based on the output, finds cluster members and what \nservices are running on that node.  the argument --service specifies the\nservice that is important to this cronjob.  If this member is running the\nservice, then the wrapper will return 0, otherwise 1.  \n\nOptions:\n--service=<svcname>     CASE SENSITIVE!\n\nExample:\n\n##### in crontab:\n0 2 0 0 0 /usr/local/sbin/cron_cluster_wrapper.py --service mysql && <your cron script>\n\n\n\"\"\"\n# \n\nimport os\nimport sys\nimport subprocess\nimport re\nimport optparse\n\ncmd_clustat='/usr/sbin/clustat'\n#cmd_clustat='./clustat_nonmaster'\n\ndef grab_clustat_output( ):\n    \"\"\" \n    this runs clustat and returns the output in a list.  from there, deal with it.\n    \"\"\"\n    p = subprocess.Popen( cmd_clustat ,stdout=subprocess.PIPE ).stdout\n    return p.readlines( )\n\ndef find_cluster_members( clustat_output ):\n    \"\"\"\n    finds the list of cluster members\n\n    this function moves through the list clustat_output that is the output\n    of running the clustat binary in RHEL cluster, and snags cluster\n    members based on their Online status.  It finds the node the \n    clustat command is running on by the Local keyword in the output.\n\n    the return value is the dictionary containing the node list. \n\n    supporting more than 2 node clusters wouldn't be hard, but I'm being lazy.\n\n    \"\"\"\n    # nodelist: dict providing local and sibling node entries\n    nodelist = {}\n    for line in clustat_output:\n        match = re.search( '^ ([A-Za-z0-9_-]+).*Online, (Local)?.*', line )\n        if match is not None:\n            if match.group(2) is not None:\n                nodelist['local'] = match.group(1) \n            else:\n                nodelist['node']= match.group(1) \n    return nodelist\n\ndef is_service_running(service, thishost, clustat_output ):\n    \"\"\"\n    finds if service is running based on clustat_output list\n    \"\"\"\n    for line in clustat_output:\n        match = re.search( '^ service:(%s)[ ]+(%s)[ ]+(started).*' % ( service, thishost ) , line )\n        if match is not None:\n            # MATCH! Lets return True.\n            return True\n    # We didn't find it.  Return False!\n    return False\n\ndef main():\n    usage = \"usage: %prog [options]\"\n    parser = optparse.OptionParser(usage)\n    parser.add_option(\"-s\", \"--service\", action=\"store\",\n                      help=\"service name\")\n    opts, ____ = parser.parse_args()\n\n    # run clustat, get output\n    output=grab_clustat_output()\n    # give output to find our cluster members\n    cluster_members=find_cluster_members(output)\n\n    # if the service is running, on the local node, pass the output and script will:\n    if is_service_running( opts.service, cluster_members['local'], output ):\n        #   exit 0 if the node is running the service\n        sys.exit(0)\n    else:\n        #   exit 1 if the node is NOT running the service\n        sys.exit(1)\n\n\nif __name__ == '__main__':\n    main()","repo_name":"jeredding/cron_cluster_wrapper","sub_path":"cron_cluster_wrapper.py","file_name":"cron_cluster_wrapper.py","file_ext":"py","file_size_in_byte":3085,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"81"}
+{"seq_id":"32354988707","text":"import os\nimport numpy as np\nimport cv2\nfrom multiprocessing import Process\n\ndef pick(path):\n    output = \"D:\\\\00_work\\\\data\\\\kaggle datasets\\\\DR\\\\green\\\\\"\n    file_list = os.listdir(path)\n    num = len(file_list)\n    file_list1 = file_list[0:num//2]\n    file_list2 = file_list[num//2:num]\n    \n    p1 = Process(target=fork, args = ('P1', file_list1, output, path))\n    p2 = Process(target=fork, args = ('P2', file_list2, output, path))\n    \n    p1.start()\n    p2.start()\n    \n    p1.join()\n    p2.join()\n\n            \ndef fork(name, file_list, output, path):\n    print(\"{0} started\".format(name))\n    count = 0\n    num = len(file_list)\n    for f in file_list:\n        src = path + f \n        dst = output + f \n        green(src, dst)\n        count+=1\n        if count%200 == 0:\n            print(\"{0} Done {1}/{2}\".format(name, count, num))\n    print(\"{0} end\".format(name))\n    \ndef green(src, dst):\n    image=cv2.imread(src)\n    image[:,:,[0,2]] = 0\n    cv2.imwrite(dst,image)\n    \nif __name__ == '__main__':\n    path = \"D:\\\\00_work\\\\data\\\\kaggle datasets\\\\DR\\\\working\\\\\"\n    pick(path)","repo_name":"huyvd7/diabetic-retinopathy-deep-learning","sub_path":"take_green.py","file_name":"take_green.py","file_ext":"py","file_size_in_byte":1089,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"1250564434","text":"import json\nfrom pdx.logger import logger\nfrom pdx.models.model import Model\nfrom pdx.prompt.prompt_session import PromptSession\nfrom pdx.models.cohere.client import CohereClient\nfrom pdx.models.metadata import ModelResponse, ResponseMetadata, ModelTokenUsage\n\n\nclass GenerationModel(Model):\n    def __init__(self,\n                 api_key: str,\n                 model: str,\n                 max_tokens: int = 1200,\n                 stop: list = [],\n                 temperature: float = 0,\n                 **kwargs,\n                 ):\n\n        self._api_url = \"v1/generate\"\n\n        self._provider = \"cohere\"\n        self._client = CohereClient(api_key)\n        self._model = model\n        self._max_tokens = max_tokens\n        self._temperature = temperature\n        if kwargs.get('stop_sequences', None):\n            self._stop_sequences = kwargs.get('stop_sequences', None)\n        else:\n            self._stop_sequences = stop\n        self._end_sequences = kwargs.get('end_sequences', [])\n        self._num_generations = kwargs.get('num_generations', 1)\n        self._preset = kwargs.get('preset', None)\n        self._k = kwargs.get('k', 0)\n        self._p = kwargs.get('p', 0.75)\n        self._frequency_penalty = kwargs.get('frequency_penalty', 0.0)\n        self._presence_penalty = kwargs.get('presence_penalty', 0.0)\n        self._return_likelihoods = kwargs.get('return_likelihoods', 'NONE')\n        self._logit_bias = kwargs.get('logit_bias', {})\n        self._truncate = kwargs.get('truncate', 'END')\n        self._retries = kwargs.get('retries', 2)\n\n    def _preprocess(self, prompt: PromptSession):\n\n        _prompt = prompt.text_prompt({})\n\n        request_params = {\n            'prompt': _prompt,\n            'model': self._model,\n            'num_generations': self._num_generations,\n            'max_tokens': self._max_tokens,\n            'preset': self._preset,\n            'temperature': self._temperature,\n            'k': self._k,\n            'p': self._p,\n            'frequency_penalty': self._frequency_penalty,\n            'presence_penalty': self._presence_penalty,\n            'return_likelihoods': self._return_likelihoods,\n            'truncate': self._truncate,\n        }\n\n        if self._logit_bias != {}:\n            request_params['logit_bias'] = self._logit_bias\n\n        if self._end_sequences != []:\n            request_params['end_sequences'] = self._end_sequences\n\n        if self._stop_sequences != []:\n            request_params['stop_sequences'] = self._stop_sequences\n\n        return request_params\n\n    def _postprocess(self, response: dict, request_params: dict, request_time) -> ModelResponse:\n        _prompt = request_params.pop('prompt', None)\n        _r = json.loads(response)\n\n        token_usage = ModelTokenUsage(\n            response=None,\n            prompt=None,\n            total=None)\n        response_metadata = ResponseMetadata(\n            model=request_params['model'],\n            api_log_id=_r['id'],\n            warnings=_r['meta'].get('warnings', None),\n            token_usage=token_usage,\n            latency=request_time)\n        model_response = ModelResponse(\n            metadata=response_metadata,\n            request_params=request_params,\n            data=_r['generations'][0]['text'])\n\n        return model_response\n","repo_name":"pdx-labs/pdx","sub_path":"src/pdx/models/cohere/generation.py","file_name":"generation.py","file_ext":"py","file_size_in_byte":3296,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"81"}
+{"seq_id":"31149419304","text":"#!/usr/bin/python3\ndef divisible_by_2(my_list=[]):\n    \"\"\"Finds all multiples of two\"\"\"\n\n    duplicate = []\n    for number in my_list:\n        if number % 2 == 0:\n            duplicate.append(True)\n        else:\n            duplicate.append(False)\n\n    return duplicate\n","repo_name":"Chiemelie10/alx-higher_level_programming","sub_path":"0x03-python-data_structures/10-divisible_by_2.py","file_name":"10-divisible_by_2.py","file_ext":"py","file_size_in_byte":270,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"28143538154","text":"import codecs\nimport os\nimport sys\nimport logging\nimport numpy as np\n\ndef main(src, des):\n\n    for usg in os.listdir(src):\n        dr1 = src+os.sep+usg\n        if not os.path.isdir(dr1):\n            continue\n        for spk in os.listdir(dr1):\n            dr2 = dr1+os.sep+spk\n            pitches = []\n            for f in os.listdir(dr2):\n                fp = dr2+os.sep+f\n                fp =codecs.open(fp, \"r\", encoding = \"utf8\")\n                content = fp.readlines()\n                fp.close()\n\n                for line in content[1:]:\n                    line = line.strip().split(\",\")\n                    p = 0\n                    try:\n                        p = float(line[1])\n                    except:\n                        continue\n                    pitches.append(p)\n            pitches = np.array(pitches)\n#            miu = pitches.mean()\n#            sigma = pitches.var()\n#\n            dr = \"/\".join([des, usg])\n            if not os.path.isdir(dr):\n                os.makedirs(dr)\n            fout = dr+\"/\"+spk+\".info\"\n            fp = codecs.open(fout, \"w\", encoding = \"utf8\")\n#            fp.write(miu+\"\\n\")\n#            fp.write(sigma+\"\\n\")\n            pitches = [\"{: .3f}\".format(x) for x in pitches]\n            fp.write(\" \".join(pitches))\n            fp.close()\n\n\nif __name__ == \"__main__\":\n    if len(sys.argv) != 3:\n        print(\"This script grouping the pitch data in terms of individual speaker.\")\n        print(\"Usage: {} src des\".format(sys.argv[0]))\n        exit(1)\n\n    main(sys.argv[1], sys.argv[2])\n","repo_name":"wenjie-p/pitch_range","sub_path":"src/data-prep/group_pitch_by_spk.py","file_name":"group_pitch_by_spk.py","file_ext":"py","file_size_in_byte":1542,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"34598400199","text":"# data_methods.py\nfrom create_database import Database\nimport pandas as pd\n\n\nclass DataMethods(Database):\n    def insert_data(self):\n        # Fetch maximum user_id\n        self.c.execute('''\n              SELECT MAX(user_id) FROM users\n              ''')\n        max_id = self.c.fetchone()[0]\n        if max_id is None:\n            max_id = 0  # In case the table is empty\n\n        # User input for new entry\n        first_name = input(\"Enter the First Name: \")\n        last_name = input(\"Enter the Last Name: \")\n        favorite_food = input(\"Enter the Favorite Food: \")\n\n        self.c.execute('''\n                  INSERT INTO users (user_id, first_name, last_name, favorite_food)\n                  VALUES (?, ?, ?, ?)\n                  ''', (max_id + 1, first_name, last_name, favorite_food))\n        self.conn.commit()\n\n    def delete_row(self):\n        request = input('Are you sure you like to delete your account? Yes or No\\n')\n        if request.lower() == 'yes':\n            input_id = input('Enter your user ID number\\n')\n\n            self.c.execute('''\n                      DELETE from users where user_id = ?\n                      ''', (int(input_id),))\n            self.conn.commit()\n        else:\n            print('did not enter a valid id')\n\n    def update_row(self):\n        # enter your user ID number\n        print('Enter your user ID number')\n        # if the user ID number is registered in the database, ask what needs to be updated\n        user_id_num = input()\n        if self.check_db(int(user_id_num)) is True:\n            print('What would you like to update?')\n            entryToUpdate = input(' 1. First Name\\n 2. Last Name\\n 3. Favorite Food\\n')\n            if entryToUpdate.lower() == 'first name' or entryToUpdate == '1':\n                #update the first name column in the user_id row\n                new_value = input(\"Enter the new First Name: \")\n                self.update_entry(int(user_id_num), 'first_name', new_value)\n\n            elif entryToUpdate.lower() == 'last name' or entryToUpdate == '2':\n                #update the last name column in the user_id row\n                new_value = input(\"Enter the new Last Name: \")\n                self.update_entry(int(user_id_num), 'last_name', new_value)\n\n            elif entryToUpdate.lower() == 'favorite food' or entryToUpdate == '3':\n                #update their favorite food\n                new_value = input(\"Enter the new Favorite Food: \")\n                self.update_entry(int(user_id_num), 'favorite_food', new_value)\n\n            else:\n                print(\"That isn't a valid column to choose\")\n                # if the user id number is not within the database, state that its invalid\n        else:\n            print('Could not find this user ID within our records')\n\n        # update the correct column based off row (user_id)\n        # ask if there are any more updates needed\n        # if not, go back to menu?\n\n    def check_db(self, user_id):\n        self.c.execute('''\n                SELECT * FROM users WHERE user_id = ?\n            ''', (user_id,))\n\n        result = self.c.fetchone()\n        if result:\n            return True\n        else:\n            return False\n\n    def update_entry(self, user_id, field, new_value):\n        query = f\"UPDATE users SET {field} = ? WHERE user_id = ?\"\n        self.c.execute(query, (new_value, user_id))\n        self.conn.commit()\n\n\n    def display(self):\n        self.c.execute('''\n              SELECT * FROM users\n              ''')\n        df = pd.DataFrame(self.c.fetchall(), columns=['user_id', 'first_name', 'last_name', 'favorite_food'])\n        df.set_index('user_id', inplace=True)\n        print(df)\n","repo_name":"nckmnfrd/pythonCRUD","sub_path":"data_methods.py","file_name":"data_methods.py","file_ext":"py","file_size_in_byte":3664,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"23897241176","text":"###################################\n# Library Imports\nimport tensorflow as tf\nimport numpy as np\nimport os\nimport nibabel as nib #Used to open nifTi or .nii files \nimport matplotlib.pyplot as plt \n###################################\n'''\nDataset loader obtained from the link below\nhttps://medicalsegmentation.com/covid19/\n\n# 100 total train images and 10 test images of\n# resolution = 512 x 512 \n'''\ntrain_imgs_filename = './data/train/tr_im.nii.gz'\ntrain_masks_filename = './data/train/tr_mask.nii.gz'\n\ntrain_imgs_nib = nib.load(train_imgs_filename) \ntrain_masks_nib = nib.load(train_masks_filename)\n\n#convert nifti objects in to a numpy array\ntrain_imgs = train_imgs_nib.get_fdata() \n\n# train_imgs = (train_imgs -  np.min(train_imgs))/(np.max(train_imgs)-np.min(train_imgs))\n\n# train_imgs = (train_imgs * 255).astype(int)\n\ntrain_masks = train_masks_nib.get_fdata() \n\nassert(train_imgs.shape == train_masks.shape)\n\nprint(train_masks.shape)\nprint(np.unique(train_masks))\n\nprint(train_imgs.shape)\nprint(np.unique(train_imgs))\n\n###################################\n'''\nload saved unet model\n'''\nmodel = tf.keras.models.load_model(\"./model/unet\")\nmodel.summary()\n###################################\n'''\nplot 10 train images their ground truth and the prediction from our unet model\n'''\ngroundTruth = np.zeros((512,512,3))\nprediction = np.zeros((512,512,3))\n\nfor i in range(0,6,3):\n  img_idx = 0\n  # plot grayscale ct scan\n  plt.subplot(2,3,i+1)\n  plt.imshow(train_imgs[:,:,img_idx],cmap='gray')\n  # plot color labeled gt of ct scan\n  plt.subplot(2,3,i+2)\n  # get labels and color code them\n  red_x,red_y = np.where(train_masks[:,:,img_idx] == 0)\n  red_channels = np.zeros(len(red_x)).astype(int)\n\n  green_x,green_y = np.where(train_masks[:,:,img_idx] == 1)\n  green_channels = np.zeros(len(green_x)).astype(int)\n  \n  blue_x,blue_y = np.where(train_masks[:,:,img_idx] == 2)\n  blue_channels = (np.ones(len(blue_x))*2).astype(int)\n\n  yellow_x,yellow_y = np.where(train_masks[:,:,img_idx] == 2)\n  yellow_channel_1 = (np.zeros(len(yellow_x))).astype(int)\n  yellow_channel_2 = (np.ones(len(yellow_x))).astype(int)\n\n  #set values\n  groundTruth[red_x,red_y,red_channels] = 255\n  groundTruth[green_x,green_y,green_channels] = 255\n  groundTruth[blue_x,blue_y,blue_channels] = 255\n  groundTruth[yellow_x,yellow_y,yellow_channel_1] = 255\n  groundTruth[yellow_x,yellow_y,yellow_channel_2] = 255\n  plt.imshow(groundTruth)\n  #plot mdoel prediction\n  plt.subplot(2,3,i+3)\n  pred = model(train_imgs[np.newaxis,:,:,img_idx,np.newaxis])\n  pred = np.argmax(pred,axis=3)\n  pred = pred[0,:,:] #reduces unnnescary batch dimension ehre\n  print(pred.shape)\n  # get labels and color code them\n  indices_red = np.where(pred == 0)\n  indices_green = np.where(pred == 1)\n  indices_blue = np.where(pred == 2)\n  indices_yellow = np.where(pred == 3)\n  #set values\n  prediction[indices_red,0] = 255\n  prediction[indices_green,1] = 255\n  prediction[indices_blue,2] = 255\n  prediction[indices_yellow,0] = 255\n  prediction[indices_yellow,1] = 255\n  plt.imshow(prediction)\n\nplt.savefig(\"./results/training_visualization.png\")\n\n\n\n","repo_name":"aparedes8/Covid19ImageSegmentation","sub_path":"visualize.py","file_name":"visualize.py","file_ext":"py","file_size_in_byte":3086,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"81"}
+{"seq_id":"71897878664","text":"from time import sleep\nfrom selenium import webdriver\n\nfrom settings import USER_DATA_DIR, PROFILE_DIRECTORY\n\ndef init_driver():\n    options = webdriver.ChromeOptions()\n    options.add_argument(f\"user-data-dir={USER_DATA_DIR}\")\n    options.add_argument(f\"--profile-directory={PROFILE_DIRECTORY}\")\n    return webdriver.Chrome(\"./chromedriver\", options=options)\n\ndef go_to_whatsapp(driver):\n    driver.get(\"https://web.whatsapp.com/\")\n\ndef archive_chats(driver):\n    chats = driver.find_elements_by_class_name(\"_2WP9Q\")\n    for chat in chats:\n        chat.click()\n        sleep(1)\n        driver.find_element_by_xpath(\"//span[@data-icon='down']\").click()\n        sleep(1)\n        driver.find_element_by_xpath(\"//div[@title='Archive chat']\").click()\n        sleep(1)\n\ndriver = init_driver()\n\ntry:\n    go_to_whatsapp(driver)\n    sleep(10)\n    archive_chats(driver)\nexcept:\n    pass\n\nsleep(5)\ndriver.close()\n","repo_name":"danielSbastos/arquive-wapp-chats","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":903,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"38985733799","text":"import ast\nimport codecs\nimport string\nfrom collections import Counter\n\nimport spacy\nimport nltk\nimport re\n\nfrom cbc.pipeline import \\\n    ItemModifier, IteratorModifier, Iterator, IteratorConsumer, LineSourceIterator, STANDARD_SEPARATOR\nfrom nltk.tokenize import TreebankWordTokenizer\nimport logging\n\nlogger = logging.getLogger('cbc.nlp.base')\n\nnltk.download('stopwords')\n\nSTANDARD_STOPWORD = nltk.corpus.stopwords.words('german')\nSTANDARD_FILTER_SYMBOLS = (\n    '|', '*', '``', \"''\", '“', '„', '–', '-', '\"', ')', '(', \"'\", \".\", \",\", '`', \":\", \"?\", \";\",\n    \"‘\", \"{\", \"}\", \"#\", \"&\", \"!\", \"]\", \"[\", \"%\", \"−\", \"...\"\n)\n\nRE_NUMBER = re.compile(r\"^\\d+[.,eE]?\\d*?$\")\nRE_SINGLE_LETTER = re.compile(r\"^\\w$\")\nRE_REPLACE_SPACE_CHARS = re.compile(r'[|\\\"/()—]')\nRE_REMOVE_CHARS = re.compile(r\"[\\\\'\\-]\")\n\nRESTR_STD_PARAGRAPH_DEL = r\"\\s*\\n\\s*\\n\\s*\"\n\"\"\"\nStandard paragraph delimiter: At least two newlines which may include and be surrounded by\nfurther arbitrary space\n\"\"\"\n\nRE_WHITESPACE = re.compile(r\"\\s+\")\n\nDEFAULT_LEMMATIZER = \"de_core_news_sm\"\n\nLEMMATIZE_MAX_SIZE = 10000\n\nLEMMATIZE_MAX_CHUNK_SIZE = 100000\n\nVOWELS = \"aeiouäöüyAEIOUÄÖÜY\"\nCONSONANTS = \"bcdfghjklmnpqrstvwxzBCDFGHJKLMNPQRSTVWXY\"\n\n\ndef split_into_chunks(text, max_chunk_length=LEMMATIZE_MAX_CHUNK_SIZE):\n    \"\"\"\n    Split a text in chunks of given maximum length splitting only at white space\n    (leaving words intact)\n    \"\"\"\n    l_ = len(text)\n    i = 0\n    j = l_\n    chunks = []\n    while i < l_:\n        if j - i > max_chunk_length:\n            j = text[0:i + max_chunk_length].rfind(\" \")\n        if j > i:\n            chunks.append(text[i:j])\n            i = j\n            j = l_\n        else:\n            break\n    return chunks\n\n\nclass Lower(ItemModifier):\n    \"\"\"\n    Expects a list of strings as item.\n    Transforms each member of the list to lower case.\n    \"\"\"\n\n    def __init__(self):\n        \"\"\"\n        (No arguments allowed.)\n        \"\"\"\n        super(Lower, self).__init__(\n            f=lambda tokens: [t.lower() for t in tokens]\n        )\n\n\nclass ReSub(ItemModifier):\n    def __init__(self, reg_ex_list, replace):\n        my_re_list = tuple([re.compile(reg_ex) for reg_ex in reg_ex_list])\n\n        def f(w):\n            x = w\n            for my_re in my_re_list:\n                x = my_re.sub(replace, x)\n            return x\n\n        super(ReSub, self).__init__(f=f)\n\n\nclass ReSplit(ItemModifier):\n    def __init__(self, reg_ex):\n        my_re = re.compile(reg_ex)\n        super(ReSplit, self).__init__(\n            f=lambda t: my_re.split(t)\n        )\n\n\nclass Append(ItemModifier):\n    def __init__(self, append=\"_DE\"):\n        self.append = append\n        super(Append, self).__init__(\n            f=lambda tokens: [t + self.append for t in tokens]\n        )\n\n\nclass LowerAppend(ItemModifier):\n    def __init__(self, append=\"_DE\"):\n        self.append = append\n\n        def f(tokens):\n            return [t.lower() + self.append for t in tokens]\n        super(LowerAppend, self).__init__(f=f)\n\n\nclass IsNLText(ItemModifier):\n    \"\"\"\n    Check whether a string consist of Natural Language (NL) Text using\n    heuristics on distribution of characters.\n\n    Kwargs:\n        :lb_vows_by_letters (float, default=0.25): lower bound on \"vowels by letters\"\n        :ub_vows_by_letters (float, default=0.53): upper bound on \"vowels by letters\"\n        :lb_letters_by_chars (float, default=0.67): lower bound on \"letters by all chars\"\n        :ub_spaces_by_chars (float, default=0.2): lower bound on \"spaces by all chars\"\n        :ub_digits_by_chars (float, default=0.2): lower bound on \"digits by all chars\"\n    \"\"\"\n\n    def __init__(self,\n                 lb_vows_by_letters=0.25,\n                 ub_vows_by_letters=0.53,\n                 lb_letters_by_chars=0.67,\n                 ub_spaces_by_chars=0.2,\n                 ub_digits_by_chars=0.2\n                 ):\n        self.lb_vows_by_letters = lb_vows_by_letters\n        self.ub_vows_by_letters = ub_vows_by_letters\n        self.lb_letters_by_chars = lb_letters_by_chars\n        self.ub_spaces_by_chars = ub_spaces_by_chars\n        self.ub_digits_by_chars = ub_digits_by_chars\n\n        def f(w):\n            dist = Counter(w)\n            chars = len(w)\n            vows = sum([dist.get(c) for c in VOWELS if c in dist])\n            letters = sum([dist.get(c) for c in string.ascii_letters if c in dist])\n            spaces = dist.get(\" \", 0)\n            digits = sum([dist.get(c) for c in string.digits if c in dist])\n            result = None\n            if \\\n                    chars > 0 and \\\n                    letters > 0 and \\\n                    self.ub_vows_by_letters > vows / letters > self.lb_vows_by_letters and \\\n                    letters / chars > self.lb_letters_by_chars and \\\n                    self.ub_spaces_by_chars > spaces / chars and \\\n                    self.ub_digits_by_chars > digits / chars:\n                result = w\n            return result\n\n        super(IsNLText, self).__init__(f=f)\n\n\nclass LemmatizeModifier(ItemModifier):\n    def __init__(self,\n                 lemmatizer=None,\n                 chunksize=LEMMATIZE_MAX_SIZE\n                 ):\n        if lemmatizer is None:\n            try:\n                lemmatizer=spacy.load(DEFAULT_LEMMATIZER)\n            except OSError:\n                print(\n                    f\"Cannot find default lemmatizer {DEFAULT_LEMMATIZER}.\\n\"\n                    f\"Please download via: $ python -m spacy download {DEFAULT_LEMMATIZER}\\n\"\n                    \"For furhter information, please refer to https://spacy.io/usage/models#download.\"\n                )\n        self.lemmatizer = lemmatizer\n        self.chunksize = chunksize\n\n        def f(tokens):\n            result = [\n                t.lemma_ for i in range(0, len(tokens), self.chunksize)\n                for t in self.lemmatizer(\n                    \" \".join(tokens[i: i + self.chunksize])\n                )\n            ]\n            return result\n        super(LemmatizeModifier, self).__init__(f=f)\n\n\nclass Remove(ItemModifier):\n    def __init__(self,\n                 stopwords=STANDARD_STOPWORD,\n                 filter_function=lambda w: RE_NUMBER.match(w) is None and RE_SINGLE_LETTER.match(w) is None,\n                 filter_symbols=STANDARD_FILTER_SYMBOLS\n                 ):\n        self.stopwords = stopwords\n        self.filter_function = filter_function\n        self.filter_symbols = list(filter_symbols)\n        self.ff = None\n        self.create_ff()\n        super(Remove, self).__init__(\n            f=lambda tokens: list(filter(self.ff, tokens))\n        )\n\n    def create_ff(self):\n        self.ff = \\\n            lambda w: self.filter_function(w) and w.lower() not in self.stopwords + self.filter_symbols\n\n    def add_stopwords(self, a_list):\n        self.stopwords = self.stopwords + a_list\n        return self\n\n    def add_filter_symbols(self, a_list):\n        self.filter_symbols = self.filter_symbols + a_list\n        return self\n\n\ndef tokenize_text(text, re_replace_space_chars=RE_REPLACE_SPACE_CHARS):\n    t = text\n    if re_replace_space_chars is not None:\n        t = re_replace_space_chars.sub(\" \", text)\n    return TreebankWordTokenizer().tokenize(t)\n\n\nclass TokenizeText(ItemModifier):\n    def __init__(self,\n                 re_replace_space_chars=RE_REPLACE_SPACE_CHARS\n                 ):\n        self.re_replace_space_chars = re_replace_space_chars\n\n        def f(text):\n            t = text\n            if self.re_replace_space_chars is not None:\n                t = self.re_replace_space_chars.sub(\" \", text)\n            return TreebankWordTokenizer().tokenize(t)\n\n        super(TokenizeText, self).__init__(f=f)\n\n\nclass LemmaTokenizeText(ItemModifier):\n    def __init__(self,\n                 lemmatizer=None,\n                 max_chunk_length=LEMMATIZE_MAX_CHUNK_SIZE,\n                 re_replace_space_chars=RE_REPLACE_SPACE_CHARS,\n                 re_remove_chars=RE_REMOVE_CHARS\n                 ):\n        if lemmatizer is None:\n            try:\n                lemmatizer=spacy.load(DEFAULT_LEMMATIZER)\n            except OSError:\n                print(\n                    f\"Cannot find default lemmatizer {DEFAULT_LEMMATIZER}.\\n\"\n                    f\"Please download via: $ python -m spacy download {DEFAULT_LEMMATIZER}\\n\"\n                    \"For furhter information, please refer to https://spacy.io/usage/models#download.\"\n                )\n\n        self.lemmatizer = lemmatizer\n        self.maxChunkLength = max_chunk_length\n        self.re_replace_space_chars = re_replace_space_chars\n        self.re_remove_chars = re_remove_chars\n\n        def f(text):\n            txt = text\n            if self.re_replace_space_chars is not None:\n                txt = self.re_replace_space_chars.sub(\" \", txt)\n            if self.re_remove_chars is not None:\n                txt = self.re_remove_chars.sub(\"\", txt)\n            txt = RE_WHITESPACE.sub(\" \", txt)\n            chunks = split_into_chunks(txt, self.maxChunkLength)\n            return [t.lemma_.strip() for chunk in chunks for t in self.lemmatizer(chunk)]\n\n        super(LemmaTokenizeText, self).__init__(f=f)\n\n\nclass SplitText(IteratorModifier):\n    def __init__(self,\n                 re_text_separator=r\"\\s*\\n\\s*\\n\\s*\",\n                 min_text_length=0,\n                 do_trim_text=True\n                 ):\n        self.re_text_separator = re.compile(re_text_separator)\n        self.minTextLength = min_text_length\n        self.do_trim_text = do_trim_text\n\n    def __call__(self, other):\n        if other.is_tagged:\n            def generator():\n                n = 0\n                for text in other:\n                    p_c = 0\n                    paragraphs = self.re_text_separator.split(text[0].strip())\n                    for p in paragraphs:\n                        if self.do_trim_text:\n                            p = p.strip()\n                        if len(p) >= self.minTextLength:\n                            yield p, text[1] + [tuple(text[1] + [p_c])]\n                        n += 1\n                        p_c += 1\n        else:\n            def generator():\n                n = 0\n                for text in other:\n                    paragraphs = self.re_text_separator.split(text.strip())\n                    for p in paragraphs:\n                        if self.do_trim_text:\n                            p = p.strip()\n                        if len(p) >= self.minTextLength:\n                            yield p\n                        n += 1\n        return Iterator(generator, is_tagged=other.is_tagged)\n\n\nclass LineSourceTokenizer(LineSourceIterator):\n    def __init__(self,\n                 input_file,\n                 tokenizer=TokenizeText(),\n                 **kwargs\n                 ):\n        self.tokenizer = tokenizer\n        super(LineSourceTokenizer, self).__init__(input_file, **kwargs)\n        self.handle_first_line()\n        if self.is_tagged:\n            def get_line(line):\n                ll = line.split(self.tag_separator)\n                return tokenizer(ll[0]), ast.literal_eval(ll[1])\n        else:\n            def get_line(line):\n                return tokenizer(line)\n        self.get_line = get_line\n\n\nclass Re(ItemModifier):\n    def __init__(self, reg_ex):\n        my_re = re.compile(reg_ex)\n        super(Re, self).__init__(\n            f=lambda t: list(filter(lambda w: my_re.match(w) is None, t))\n        )\n\n\nclass MinMaxTokens(IteratorModifier):\n    def __init__(self, min_tokens=1, max_tokens=-1):\n        self.minTokens = min_tokens\n        self.maxTokens = max_tokens\n\n    def __call__(self, iterator):\n        if self.maxTokens >= 0:\n            if iterator.is_tagged:\n                def generator():\n                    for t in iterator:\n                        if self.minTokens <= len(t[0]) <= self.maxTokens:\n                            yield t\n            else:\n                def generator():\n                    for t in iterator:\n                        if self.minTokens <= len(t) <= self.maxTokens:\n                            yield t\n        else:\n            if iterator.is_tagged:\n                def generator():\n                    for t in iterator:\n                        if len(t[0]) >= self.minTokens:\n                            yield t\n            else:\n                def generator():\n                    for t in iterator:\n                        if len(t) >= self.minTokens:\n                            yield t\n        return Iterator(generator, is_tagged=iterator.is_tagged)\n\n\nclass TokensToFile(IteratorConsumer):\n    def __init__(self,\n                 filename,\n                 output_tag=True,\n                 tag_separator=STANDARD_SEPARATOR,\n                 output_encoding='utf-8',\n                 input_type=list\n                 ):\n        self.filename = filename\n        self.output_tag = output_tag\n        self.tag_separator = tag_separator\n        self.output_encoding = output_encoding\n        self.input_type = input_type\n\n    def __call__(self, iterator):\n        if iterator.is_tagged:\n            if self.output_tag:\n                if self.input_type == list:\n                    def to_str(t_):\n                        return \" \".join(t_[0]) + self.tag_separator + str(t_[1])\n                elif self.input_type == str:\n                    def to_str(t_):\n                        return t_[0] + self.tag_separator + str(t_[1])\n                else:\n                    raise (TypeError, \"Unsupported input type %i\" % str(self.input_type))\n            else:\n                if self.input_type == list:\n                    def to_str(t_):\n                        return \" \".join(t_[0])\n                elif self.input_type == str:\n                    def to_str(t_):\n                        return t_[0]\n                else:\n                    raise (TypeError, \"Unsupported input type %i\" % str(self.input_type))\n        else:\n            if self.input_type == list:\n                def to_str(t_):\n                    return \" \".join(t_)\n            elif self.input_type == str:\n                def to_str(t_):\n                    return t_\n            else:\n                raise (TypeError, \"Unsupported input type %i\" % str(self.input_type))\n        n = 0\n        try:\n            if self.output_encoding is None:\n                file = codecs.open(self.filename, 'w')\n            else:\n                file = codecs.open(self.filename, 'w', self.output_encoding)\n            for t in iterator:\n                n += 1\n                file.write(to_str(t))\n                file.write(\"\\n\")\n            file.close()\n        except IOError:\n            s = \"could not write to file '%s'\" % self.filename\n            logger.error(s, exc_info=True)\n        self.number = n\n        return self\n\n\nclass CountTokens(IteratorConsumer):\n    def __init__(self, word_counter=None, tagged_counter=None):\n        if word_counter is None:\n            self.word_counter = Counter()\n        else:\n            self.word_counter = word_counter\n        if tagged_counter is None:\n            self.tagged_counter = Counter()\n        else:\n            self.tagged_counter = tagged_counter\n\n    def __call__(self, iterator):\n        if iterator.is_tagged:\n            def count(tokens_):\n                self.word_counter.update(tokens_[0])\n                self.tagged_counter.update([(w, \";\".join([str(t) for t in tokens_[1]])) for w in tokens_[0]])\n        else:\n            def count(tokens_):\n                self.word_counter.update(tokens_)\n        for tokens in iterator:\n            count(tokens)\n        return self\n","repo_name":"sebastian-sohr/cbc-nlp","sub_path":"src/cbc/nlp/base.py","file_name":"base.py","file_ext":"py","file_size_in_byte":15510,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"22792813429","text":"from django.conf.urls import patterns, url, include\nfrom django.conf import settings\nfrom web import views\n\nurlpatterns = patterns('',\n    url(r'^$', 'web.views.index', name='index'),\n\n    url(r'category/(?P<pk>\\d+)/$',\n        'web.views.category_photos',\n        name=\"category_photos\"),\n\n    url(r'^start$',\n        views.start,\n        name=\"start\"),\n)\n","repo_name":"joshuacox/phial-api","sub_path":"demo/web/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":357,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"35703390325","text":"import os\nimport json\nimport constant\nfrom dotenv import load_dotenv\nfrom datetime import datetime, timedelta\nfrom google.cloud import bigquery\nfrom mock import Mock\nimport pytest\nfrom main \\\n    import convert_date_string_to_millisecond_int, \\\n    generate_drop_off_users_query_with_params, \\\n    generate_recovery_users_query_with_params, \\\n    calculate_date_of_yesterday_at_utc, \\\n    extract_required_keys_for_generate_recovery_query, \\\n    fetch_dropoff_and_recovery_user_count_given_steps, \\\n    fetch_dropoff_and_recovery_users_count_given_list_of_steps, \\\n    fetch_from_all_user_events_table, \\\n    calculate_date_n_days_ago_at_utc, \\\n    extract_events_and_dates_from_request, \\\n    extract_required_keys_for_generate_dropoff_query\n\nimport main\n\nload_dotenv()\n\n# these credentials are used to access google cloud services. See https://cloud.google.com/docs/authentication/getting-started\nos.environ[\"GOOGLE_APPLICATION_CREDENTIALS\"]=\"service-account-credentials.json\"\n\nproject_id = main.project_id\nBIG_QUERY_DATASET_LOCATION =  main.BIG_QUERY_DATASET_LOCATION\ndataset_id = main.dataset_id\ntable_id = main.table_id\nFULL_TABLE_URL=main.FULL_TABLE_URL\n\nHOUR_MARKING_START_OF_DAY=constant.HOUR_MARKING_START_OF_DAY\nHOUR_MARKING_END_OF_DAY=constant.HOUR_MARKING_END_OF_DAY\n\nsampleEvents = {\n    \"stepBeforeDropOff\": \"ENTERED_ONBOARD_SCREEN\",\n    \"nextStepList\": [\"ENTERED_REFERRAL_CODE\", \"USER_LOGGED_IN\"],\n}\n\n# For example, if you want one day => startDate: \"2019-04-03\", endDate: \"2019-04-03\"\n# For example, if you want three days => startDate: \"2019-04-03\", endDate: \"2019-04-05\"\nsampleDateIntervals = {\n    \"startDate\": \"2019-04-03\", # converted to beginning of the day\n    \"endDate\": \"2019-04-06\", # converted to end of the day\n    # \"timezone\": \"Africa/Kigali\" # if not passed would assume UTC\n}\n\nstepBeforeDropOff = sampleEvents[\"stepBeforeDropOff\"]\nnextStepList = sampleEvents[\"nextStepList\"]\nrecoveryStep = sampleEvents[\"nextStepList\"]\n\nsampleEventsAndDatesList = [\n    {\n        \"events\": {\n            \"stepBeforeDropOff\": \"ENTERED_ONBOARD_SCREEN\",\n            \"nextStepList\": [\"ENTERED_REFERRAL_CODE\", \"USER_LOGGED_IN\"],\n        },\n        \"dateIntervals\": sampleDateIntervals\n    },\n    {\n        \"events\": {\n            \"stepBeforeDropOff\": \"ENTERED_REFERRAL_CODE\",\n            \"nextStepList\": [\"USER_LOGGED_IN\"],\n        },\n        \"dateIntervals\": sampleDateIntervals\n    }\n]\n\n@pytest.fixture\ndef mock_big_query():\n    return Mock(spec=bigquery.Client())\n\n@pytest.fixture\ndef mock_json_request():\n    return Mock()\n\ndef test_calculate_date_of_yesterday_at_utc():\n    assert calculate_date_of_yesterday_at_utc() == (datetime.utcnow().date() - timedelta(days=1)).strftime(\"%Y-%m-%d\")\n\ndef test_calculate_date_n_days_ago_at_utc():\n    FOUR_DAYS_AGO = 4\n    assert calculate_date_n_days_ago_at_utc(FOUR_DAYS_AGO) == (datetime.utcnow().date() - timedelta(days=FOUR_DAYS_AGO)).strftime(\"%Y-%m-%d\")\n\ndef test_convert_date_string_to_millisecond_int():\n    assert convert_date_string_to_millisecond_int(\"2019-04-03\", \"00:00:00\") == int(1554249600000.0)\n    assert convert_date_string_to_millisecond_int(\"2019-04-06\", \"23:59:59\") == int(1554595199000.0)\n\n\ndef test_extract_required_keys_for_generate_dropoff_query():\n    expectedKeysForQuery = {\n        \"stepBeforeDropOff\": stepBeforeDropOff,\n        \"nextStepList\": nextStepList,\n        \"startDateInMilliseconds\": convert_date_string_to_millisecond_int(sampleDateIntervals[\"startDate\"], HOUR_MARKING_START_OF_DAY),\n        \"endDateInMilliseconds\": convert_date_string_to_millisecond_int(sampleDateIntervals[\"endDate\"], HOUR_MARKING_END_OF_DAY)\n    }\n\n    assert extract_required_keys_for_generate_dropoff_query(sampleEvents, sampleDateIntervals) == expectedKeysForQuery\n\ndef test_extract_required_keys_for_generate_recovery_query():\n    expectedKeysForQuery = {\n        \"stepBeforeDropOff\": stepBeforeDropOff,\n        \"nextStepList\": nextStepList,\n        \"recoveryStep\": recoveryStep\n    }\n    assert extract_required_keys_for_generate_recovery_query(sampleEvents) == expectedKeysForQuery\n\n\ndef test_generate_drop_off_users_query_with_params():\n    startDateInMilliseconds = convert_date_string_to_millisecond_int(sampleDateIntervals[\"startDate\"], HOUR_MARKING_START_OF_DAY)\n    endDateInMilliseconds = convert_date_string_to_millisecond_int(sampleDateIntervals[\"endDate\"], HOUR_MARKING_END_OF_DAY)\n\n    expectedDropOffQuery = (\n        \"\"\"\n            select distinct(`user_id`)\n            from `{full_table_url}`\n            where `user_id` not in\n            (\n                select `user_id`\n                from `{full_table_url}`\n                where `event_type` in UNNEST(@nextStepList)\n                and `time_transaction_occurred` <= @endDateInMilliseconds\n            )\n            and `event_type` = @stepBeforeDropOff\n            and `time_transaction_occurred` between @startDateInMilliseconds and @endDateInMilliseconds\n        \"\"\"\n            .format(full_table_url=FULL_TABLE_URL)\n    )\n\n    expectedDropOffParams = [\n        bigquery.ScalarQueryParameter(\"stepBeforeDropOff\", \"STRING\", stepBeforeDropOff),\n        bigquery.ArrayQueryParameter(\"nextStepList\", \"STRING\", nextStepList),\n        bigquery.ScalarQueryParameter(\"startDateInMilliseconds\", \"INT64\", startDateInMilliseconds),\n        bigquery.ScalarQueryParameter(\"endDateInMilliseconds\", \"INT64\", endDateInMilliseconds),\n    ]\n\n    expectedDropOffQueryAndParams = {\n        \"dropOffQuery\": expectedDropOffQuery,\n        \"dropOffParams\": expectedDropOffParams\n    }\n\n    assert generate_drop_off_users_query_with_params(sampleEvents, sampleDateIntervals) == expectedDropOffQueryAndParams\n\n\ndef test_generate_recovery_users_query_with_params():\n    dateOfYesterday = calculate_date_of_yesterday_at_utc()\n    beginningOfYesterdayInMilliseconds = convert_date_string_to_millisecond_int(dateOfYesterday, HOUR_MARKING_START_OF_DAY)\n    endOfYesterdayInMilliseconds = convert_date_string_to_millisecond_int(dateOfYesterday, HOUR_MARKING_END_OF_DAY)\n\n    expectedRecoveryQuery = (\n        \"\"\"\n            select distinct(`user_id`)\n            from `{full_table_url}`\n            where `event_type` in UNNEST(@recoveryStep)\n            and `time_transaction_occurred` between @beginningOfYesterdayInMilliseconds and @endOfYesterdayInMilliseconds\n            and `user_id` in\n            (\n                select `user_id`\n                from `{full_table_url}`\n                where `user_id` not in\n                (\n                    select `user_id`\n                    from `{full_table_url}`\n                    where `event_type` in UNNEST(@nextStepList)\n                    and `time_transaction_occurred` <= @endOfYesterdayInMilliseconds\n                )\n                and `event_type` = @stepBeforeDropOff\n                and `time_transaction_occurred` between @beginningOfYesterdayInMilliseconds and @endOfYesterdayInMilliseconds\n            )\n        \"\"\"\n            .format(full_table_url=FULL_TABLE_URL)\n    )\n\n    expectedRecoveryParams = [\n        bigquery.ArrayQueryParameter(\"recoveryStep\", \"STRING\", recoveryStep),\n        bigquery.ScalarQueryParameter(\"stepBeforeDropOff\", \"STRING\", stepBeforeDropOff),\n        bigquery.ArrayQueryParameter(\"nextStepList\", \"STRING\", nextStepList),\n        bigquery.ScalarQueryParameter(\"beginningOfYesterdayInMilliseconds\", \"INT64\", beginningOfYesterdayInMilliseconds),\n        bigquery.ScalarQueryParameter(\"endOfYesterdayInMilliseconds\", \"INT64\", endOfYesterdayInMilliseconds),\n    ]\n\n    expectedRecoveryQueryAndParams = {\n        \"recoveryQuery\": expectedRecoveryQuery,\n        \"recoveryParams\": expectedRecoveryParams\n    }\n\n    assert generate_recovery_users_query_with_params(sampleEvents, sampleDateIntervals) == expectedRecoveryQueryAndParams\n\n\ndef test_fetch_from_all_user_events_table(mock_big_query):\n    sampleEventType = \"ENTERED_ONBOARD_SCREEN\"\n    sampleQuery = (\n        \"\"\"\n            select `user_id` from `{full_table_url}` where `event_type` = @eventType\n        \"\"\"\n        .format(full_table_url=FULL_TABLE_URL)\n    )\n    sampleParams = [\n        bigquery.ScalarQueryParameter(\"eventType\", \"STRING\", sampleEventType),\n    ]\n    expectedUserIdList = [\n        { \"user_id\": \"1a\" },\n        { \"user_id\": \"3k\" },\n    ]\n\n    main.client = mock_big_query\n    mock_big_query.query.return_value = expectedUserIdList\n\n    result = fetch_from_all_user_events_table(sampleQuery, sampleParams)\n    assert result == expectedUserIdList\n    mock_big_query.query.assert_called_once()\n\n    queryArgs = mock_big_query.query.call_args.args\n    queryKeywordArgs = mock_big_query.query.call_args.kwargs\n\n    assert queryArgs[0] == sampleQuery\n    assert queryKeywordArgs['location'] == BIG_QUERY_DATASET_LOCATION\n\n\n\n# given a dropoff step and expected next step:\n# will assemble the dropoff and recovery query\n# fetch from big query\n# format and return response\ndef test_fetch_dropoff_and_recovery_user_count_given_steps(mock_big_query):\n    expectedUserIdList = [\n        { \"user_id\": \"1a\" },\n        { \"user_id\": \"3k\" },\n    ]\n\n    expectedUserCount = {\n        \"dropOffCount\": len(expectedUserIdList),\n        \"recoveryCount\": len(expectedUserIdList),\n    }\n\n    main.client = mock_big_query\n    mock_big_query.query.return_value = expectedUserIdList\n\n    result = fetch_dropoff_and_recovery_user_count_given_steps(sampleEvents, sampleDateIntervals)\n    main.client.query.assert_called()\n    assert main.client.query.call_count == 2\n    assert result == expectedUserCount\n\ndef test_extract_events_and_dates_from_request(mock_json_request):\n    mock_json_request.get_json.return_value = {\n       \"eventsAndDatesList\": sampleEventsAndDatesList\n    }\n\n    assert extract_events_and_dates_from_request(mock_json_request) == sampleEventsAndDatesList\n\n\n\n# given a list of dropoff steps and next steps find dropoffs/recovery users for all list items\ndef test_fetch_dropoff_and_recovery_users_count_given_list_of_steps(mock_big_query, mock_json_request):\n    expectedUserIdList = [\n        { \"user_id\": \"1a\" },\n        { \"user_id\": \"3k\" },\n    ]\n\n    expectedUserCountList = [\n        {\n        \"dropOffCount\": len(expectedUserIdList),\n        \"recoveryCount\": len(expectedUserIdList),\n        \"dropOffStep\": \"ENTERED_ONBOARD_SCREEN\",\n        }, {\n        \"dropOffCount\": len(expectedUserIdList),\n        \"recoveryCount\": len(expectedUserIdList),\n        \"dropOffStep\": \"ENTERED_REFERRAL_CODE\",\n        }]\n\n    main.EVENTS_AND_DATES_LIST = sampleEventsAndDatesList\n    main.client = mock_big_query\n    mock_big_query.query.return_value = expectedUserIdList\n    mock_json_request.get_json.return_value = None\n\n    result = fetch_dropoff_and_recovery_users_count_given_list_of_steps(mock_json_request)\n    main.client.query.assert_called()\n    assert main.client.query.call_count == 4\n    assert result == json.dumps(expectedUserCountList)\n","repo_name":"luke-jordan/jupiter-data","sub_path":"functions/python/funnel-analysis/funnel-analysis_test.py","file_name":"funnel-analysis_test.py","file_ext":"py","file_size_in_byte":10835,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"81"}
+{"seq_id":"16303529970","text":"\"\"\"\nModule for search algorithms\n\"\"\"\nimport heapq\nfrom constants import SIZE\nfrom input_parser import StateExtractor\nfrom search_tree import move_down, move_up, move_horizontal, remove_vehicle\n\n\nclass GenericSearch:\n    \"\"\"\n    Class for universal cost search\n    \"\"\"\n\n    def __init__(self, function_g, function_h, name=\"\", heuristic_name=\"NA\") -> None:\n        self.name = name\n        self.heuristic_name = heuristic_name\n        self.function_g = function_g\n        self.function_h = function_h\n        self.search_path = []\n        self.closed_list = (\n            {}\n        )  # a normal list, can be a map for fast check, only need the key = \"state_string\"\n        self.open_list = (\n            []\n        )  # the max-heap / sorted queue, storing the tuples (cost, state_string, fuel_update)\n        heapq.heapify(self.open_list)\n        self.is_final_state_reached = False\n        self.final_state = StateExtractor(\"\")\n        self.ori_input = \"\"\n\n    def search(self, input_str: str):\n        \"\"\"\n        Run search algo\n        \"\"\"\n        if input_str.strip() == \"\":  # invalid input\n            return\n\n        fuel_update = \" \"\n        parent_state = \"\"\n        curr_cost = 0\n        self.ori_input = input_str\n        init_h = self.function_h(input_str[:36])\n        # add root\n        self.open_list.append(\n            (\n                curr_cost + init_h,\n                input_str,\n                fuel_update,\n                parent_state,\n                \"\",\n                (curr_cost, init_h),\n            )\n        )\n\n        # loop\n        while len(self.open_list) > 0:\n            # Stop conditions:\n            if self.is_final_state_reached:\n                break\n\n            next_state = heapq.heappop(self.open_list)\n            curr_cost, input_str, parent_state = (\n                next_state[5][0],\n                next_state[1],\n                next_state[3],\n            )\n\n            fuel_update = (\n                next_state[2] if len(next_state[1]) < 38 else next_state[1][37:]\n            )  # only the first node\n\n            if self.closed_list.get(input_str) is not None:  # a visited state\n                continue\n\n            extractor = StateExtractor(input_str, fuel_update)\n\n            # Step 0: Add curr_state to the closed_list\n            self.closed_list[input_str] = (\n                parent_state,\n                next_state[4],\n                fuel_update[len(fuel_update) - 3 : len(fuel_update)][1:],\n            )  # store <parent_state, move_details, new_fuel_for_vehicle_moved\n            # search path update\n            self.search_path.append(next_state)\n\n            # step 1: Check for vehicle at the exit\n            for vehicle in extractor.vehicles.values():\n                if not vehicle.can_be_removed():\n                    continue\n\n                if vehicle.name == \"A\":  # goal state reached\n                    self.is_final_state_reached = True\n                    self.final_state = extractor  # store for key search in closed_list\n                    # extractor.print_curr_layout()\n                    # print(len(self.closed_list))\n                    # print(\"\\n-------\\n\")\n                    # print(extractor.get_fuels(only_consumed=True))\n                    return\n\n                # remove the vehicle at exit\n                new_state = remove_vehicle(extractor, vehicle.name)\n                extractor.set_new_input(\n                    new_state, fuel_update\n                )  # update new state and vehicles data\n                break  # Each time, there will be only one vehicle at the exit\n\n            # step 2: Explore all possible states for each vehicles\n            vehicles = extractor.vehicles.values()\n            for vehicle in vehicles:\n                no_more_move = False\n                distance = 1\n\n                while not no_more_move:\n                    possible_moves = [\n                        move_horizontal(extractor, vehicle.name, -distance),\n                        move_horizontal(extractor, vehicle.name, distance),\n                        move_up(extractor, vehicle.name, distance),\n                        move_down(extractor, vehicle.name, distance),\n                    ]\n\n                    # 1. check new states in CLOSED list\n                    # 2. if a new state is found, add to open_list\n                    no_more_move = True\n                    for new_move in possible_moves:\n                        if new_move[0] != \"\":\n                            if (\n                                self.closed_list.get(new_move[0]) is None\n                            ):  # not in closed list\n                                no_more_move = False\n                                cost_g = self.function_g(curr_cost)\n                                cost_h = self.function_h(new_move[0])\n                                # curr_cost +1\n                                # new_move <state_string, fuel_update for\n                                # this move, message for search path>\n                                heapq.heappush(\n                                    self.open_list,\n                                    (\n                                        cost_g + cost_h,\n                                        new_move[0],\n                                        fuel_update + \" \" + new_move[1],\n                                        input_str,\n                                        new_move[2],\n                                        (cost_g, cost_h),\n                                    ),\n                                )\n\n                    distance += 1\n\n\nclass UCS(GenericSearch):\n    \"\"\"'\n    UCS algo\n    \"\"\"\n\n    def __init__(self) -> None:\n        super().__init__(\n            function_g=self.calculate_g, function_h=self.calculate_heuristic, name=\"UCS\"\n        )\n\n    def calculate_g(self, curr_cost: int):\n        \"\"\"\n        find the cost based on number of parents\n        \"\"\"\n        return curr_cost + 1\n\n    def calculate_heuristic(self, __state_str__: str):\n        \"\"\"'\n        Calculate heruristic based on a string\n        \"\"\"\n        return 0\n\n\nclass GBFS(GenericSearch):\n    \"\"\"'\n    Greedy best first search algo\n    \"\"\"\n\n    def __init__(self, heuristic_function, heuristic_name: str) -> None:\n        super().__init__(\n            function_g=self.calculate_g,\n            function_h=heuristic_function,\n            name=\"GBFS\",\n            heuristic_name=\"h\" + heuristic_name,\n        )\n\n    def calculate_g(self, __curr_cost__: int):\n        \"\"\"\n        find the cost based on number of parents\n        \"\"\"\n        return 0\n\n\nclass AlgoA(GenericSearch):\n    \"\"\"'\n    Algorithm A\n    \"\"\"\n\n    def __init__(self, heuristic_function, heuristic_name: str) -> None:\n        super().__init__(\n            function_g=self.calculate_g,\n            function_h=heuristic_function,\n            name=\"A/A*\",\n            heuristic_name=\"h\" + heuristic_name,\n        )\n\n    def calculate_g(self, curr_cost: int):\n        \"\"\"\n        find the cost based on number of parents\n        \"\"\"\n        return curr_cost + 1\n\n\ndef calculate_heuristic_1(state_str: str):\n    \"\"\"'\n    Calculate heruristic based on a string\n\n    Heuristic 1: Number of blocking vehicles\n    \"\"\"\n    # first, locate A on row 3\n    row_loc = 2\n    slow = SIZE * row_loc\n    fast = slow\n    end = slow + (SIZE - 1)\n    is_ambulance_found = False\n    heuristic = 0\n    while fast <= end:\n        if state_str[fast] == \"A\":\n            # goal: slow is the first cell of A\n            if not is_ambulance_found:\n                is_ambulance_found = True\n                slow = fast\n        elif is_ambulance_found:\n            if state_str[fast] != \".\" and state_str[fast] != state_str[slow]:\n                slow = fast\n                heuristic += 1\n\n        fast += 1\n    return heuristic\n\n\ndef calculate_heuristic_2(state_str: str):\n    \"\"\"'\n    Calculate heruristic based on a string\n\n    Heuristic 2: Number of blocked positions\n    (position which is not . towards the exit)\n    \"\"\"\n    # first, locate A on row 3\n    row_loc = 2\n    slow = SIZE * row_loc\n    fast = slow\n    end = slow + (SIZE - 1)\n    is_ambulance_found = False\n    heuristic = 0\n    while fast <= end:\n        if state_str[fast] == \"A\":\n            # goal: slow is the first cell of A\n            if not is_ambulance_found:\n                is_ambulance_found = True\n                slow = fast\n        elif is_ambulance_found and (state_str[fast] != \".\"):\n            heuristic += 1\n\n        fast += 1\n    return heuristic\n\n\ndef calculate_heuristic_3(state_str: str):\n    \"\"\"'\n    Calculate heruristic based on a string\n\n    Heuristic 3: Multiply heuristic 1 with a constant = 5\n    \"\"\"\n    return calculate_heuristic_1(state_str) * 5\n\n\ndef calculate_heuristic_4(state_str: str):\n    \"\"\"'\n    Calculate heruristic based on a string\n\n    Heuristic 4: Measure difference between the distance\n    from the ambulance (last cell) to the exit and\n    the number of available cells\n    \"\"\"\n    row_loc = 2\n    slow = SIZE * row_loc\n    fast = slow\n    end = slow + (SIZE - 1)\n    is_ambulance_found = False\n    distance = 0\n    num_of_empty_spot = 0\n    while fast <= end:\n        if state_str[fast] == \"A\":  # first, locate A on row 3\n            # goal: slow is the last cell of A\n            if not is_ambulance_found:\n                is_ambulance_found = True\n\n            slow = fast\n        elif is_ambulance_found:  # calculate the distance\n            distance = end - slow\n            break\n\n        fast += 1\n\n    while fast <= end:\n        if state_str[fast] == \".\":\n            num_of_empty_spot += 1\n\n        fast += 1\n\n    return distance - num_of_empty_spot\n","repo_name":"kennguyen0303/COMP472-MP2","sub_path":"src/search_algos.py","file_name":"search_algos.py","file_ext":"py","file_size_in_byte":9667,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"27978447480","text":"import requests, pytz, sys\nfrom datetime import datetime, date, timedelta, time\nfrom influxdb import InfluxDBClient\nfrom influxdb.exceptions import InfluxDBClientError\n\nRA_API_KEY = ''\nRA_USERNAME = ''\nINFLUXDB_HOST = 'localhost'\nINFLUXDB_PORT = 8086\nINFLUXDB_USERNAME = 'root'\nINFLUXDB_PASSWORD = 'root'\nINFLUXDB_DATABASE = 'gaming'\npoints = []\n\ntry:\n    client = InfluxDBClient(host=INFLUXDB_HOST, port=INFLUXDB_PORT, username=INFLUXDB_USERNAME, password=INFLUXDB_PASSWORD)\n    client.create_database(INFLUXDB_DATABASE)\n    client.switch_database(INFLUXDB_DATABASE)\nexcept InfluxDBClientError as err:\n    print(\"InfluxDB connection failed: %s\" % (err))\n    sys.exit()\n\nend = datetime.utcnow().timestamp()\nstart = end - 604800\n\ntry:\n    response = requests.get('https://retroachievements.org/API/API_GetAchievementsEarnedBetween.php?z=' + RA_USERNAME + '&y=' + RA_API_KEY + '&u=' + RA_USERNAME +'&f=' + str(start) + '&t=' + str(end))\n    response.raise_for_status()\nexcept requests.exceptions.HTTPError as err:\n    print(\"HTTP request failed: %s\" % (err))\n    sys.exit()\n\ndata = response.json()\nprint(\"Got %s achievements from RetroAchievements\" % len(data))\n\nfor achievement in data:\n    date = datetime.strptime(achievement['Date'], \"%Y-%m-%d %H:%M:%S\")\n\n    points.append({\n            \"measurement\": \"achievement\",\n            \"time\": date.isoformat(),\n            \"tags\": {\n                \"player_id\": RA_USERNAME,\n                \"platform\": achievement['ConsoleName'],\n                \"player_name\": RA_USERNAME,\n                \"title\": achievement['GameTitle'],\n                \"application_id\": str(achievement['GameID']),\n                \"apiname\": str(achievement['AchievementID']),\n            },\n            \"fields\": {\n                \"name\": achievement['Title'],\n                \"description\": achievement['Description'],\n                \"icon\": 'https://retroachievements.org' + achievement['BadgeURL']\n            }\n    })\n\ntry:\n    client.write_points(points)\nexcept InfluxDBClientError as err:\n    print(\"Unable to write points to InfluxDB: %s\" % (err))\n    sys.exit()\n\nprint(\"Successfully wrote %s data points to InfluxDB\" % (len(points)))\n","repo_name":"Epaphus/personal-influxdb","sub_path":"retroachievements.py","file_name":"retroachievements.py","file_ext":"py","file_size_in_byte":2164,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"81"}
+{"seq_id":"11618122937","text":"from classes.data_cleaning import DataCleaner\nfrom classes.data_aggregating import DataAggregator\nfrom classes.lda_modeling import LdaModel\nfrom classes.dtm_modeling import DtmModel\nfrom classes.dtm_evaluation import Evaluator\nfrom classes.data_exploration import Explorer\nimport pandas as pd\nfrom classes.visualization import Viz\n\nfrom gensim import corpora\n\nimport datetime\n\nif __name__ == '__main__':\n    wc_file_path = \"output/topics/all_topics.csv\"\n    word_no = 50\n    topic_labels = [\n        'Agricultural Science',\n        'Energy Science',\n        'Information Systems',\n        'Chemistry',\n        'Aerodynamics',\n        'Cell Research',\n        'Research Programmes',\n        'Material Science',\n        'Astrophysics',\n        'European Development',\n        'Social Science',\n        'Health Science',\n        'Genetic Research',\n        'Electronics and Photonics',\n        'Neuroscience',\n        'Quantum Physics',\n        'Molecular Biology',\n        'Energy Innovations',\n        'Software Engineering',\n        'Climate Science',\n    ]\n\n    viz = Viz(wc_file_path, word_no, topic_labels)\n\n    viz.generate_word_clouds()\n    viz.generate_word_cloud_viz(\"images/4_word_clouds.png\")","repo_name":"mknguyen1406/master-thesis-pipeline","sub_path":"main_test.py","file_name":"main_test.py","file_ext":"py","file_size_in_byte":1201,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"74400157385","text":"\ndef crearMatriz(filas, columnas):\n    matriz = [ [0] * columnas for i in range(filas) ]\n    return matriz\n\n\ndef imprimirMatriz(matriz):\n    for fila in matriz:\n        print(fila)\n        \n        \n\nfilas = 4\ncolumnas = 4\n\nvalor = 0\n\ninicio = 0\nhasta = 3\n\nmatriz = crearMatriz( filas, columnas)\nimprimirMatriz(matriz)\nciclos = 2\n\nfor j in range(ciclos):\n\n    #completamos la primer fila (avanzo por columna de izquierda a derecha)\n    for i in range(inicio, hasta+1, 1):\n        valor += 1\n        matriz[inicio][i] = valor\n\n\n    #completar la ultima columna (avanzo por fila de arriba hacia abajo)\n    for i in range(inicio+1, hasta+1, 1):\n        valor +=1\n        matriz[i][hasta] = valor\n\n\n    #completar la ultima fila (avanzo por columna de derecha a izquierda)\n    for i in range( hasta-1, inicio-1, -1):\n        valor +=1\n        matriz[hasta][i] = valor\n\n    #completar la ultima columna (avanzo por fila de abajo hacia arriba)\n    for i in range( hasta-1, inicio, -1 ):\n        valor +=1\n        matriz[i][inicio] = valor\n\n    inicio += 1\n    hasta -= 1\n    \n\nimprimirMatriz(matriz)\n","repo_name":"Zynno-Dev/Progra1-Ex1","sub_path":"Compilado Examen 1/Clases/Clase 4/Ejercicios Resueltos Trabajo Práctico/TP3Ej2PuntoG.py","file_name":"TP3Ej2PuntoG.py","file_ext":"py","file_size_in_byte":1094,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"21327430462","text":"import tensorflow as tf\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport random\nfrom tensorflow.examples.tutorials.mnist import input_data\n\ntf.set_random_seed(777) # for reproducibility\n\n# Mnist_Data load\nmnist = input_data.read_data_sets(\"MNIST_data/\", one_hot=True)\n\n# hyper parameters\nlearning_rate = 0.001\ntrain_epoch = 15\ntrain_batchsize = 100\n\n# Model Class Definition\nclass Model:\n    def __init__(self, sess, name):\n        self.sess = sess\n        self.name = name\n\n    def _build_net(self):\n        with tf.variable_scope(self.name):\n            self.keep_porb = tf.placeholder(tf.float32)\n\n","repo_name":"sangheonEN/Multi-Cam-Capture_Code","sub_path":"example/tensorflow_example/CNN_Class_Model_Classification.py","file_name":"CNN_Class_Model_Classification.py","file_ext":"py","file_size_in_byte":610,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"81"}
+{"seq_id":"12321248585","text":"import pytest, pdb\nfrom ml_tools import Similars, CleanText\nfrom ml_tools.fixtures import articles\nimport ml_tools.cleantext as ct\n\ndef test_md_split_1():\n    doc = articles()[0]\n    paras = CleanText(doc) \\\n        .markdown_split_paragraphs() \\\n        .value()\n    assert len(paras) > 1\n    print(paras)\n\ndef test_md_split_all():\n    docs = articles()\n    paras = CleanText(docs)\\\n        .markdown_split_paragraphs()\\\n        .value()\n    assert len(paras) > 0\n    assert len(docs) < len(paras)\n    print(paras)\n\n\n@pytest.mark.parametrize(\"content\", [\n    (4, \"\"\"\n# Day 1\nLorem Ipsum is simply dummy text of the printing and typesetting industry. \nLorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. \n* LI One\n  * LI One Sub\n* LI Two\n* LI Three\n\nIt has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged. It was popularised in the 1960s with the release of Letraset sheets containing Lorem Ipsum passages, and more recently with desktop publishing software like Aldus PageMaker including versions of Lorem Ipsum.\n\n# Day 2\nLorem Ipsum is simply dummy text of the printing and typesetting industry.\n\n  1. OL One\n  1. OL Two\n  1. OL Three\n\nLorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book.\n\n\"\"\"),\n\n    (1, \"hello\"),\n\n    (1, \"*hello*\"),\n\n    (1, \"_hello_\"),\n\n    (1, \"# test\"),\n\n    (3, [\"sentence one\", \"sentence two\\n\\nmultiple lines\"])\n])\ndef test_md_split_specific(content):\n    ct, md = content\n    res = CleanText(md).markdown_split_paragraphs().value()\n    print(res)\n    assert len(res) == ct\n\n\n# @pytest.mark.parametrize(\"group_by\", [None, \"article\", \"paragraph\"])\n@pytest.mark.parametrize(\"fmt\", [\"md\", \"txt\"])\n@pytest.mark.parametrize(\"coverage\", [\"basic\", \"full\"])\n@pytest.mark.parametrize(\"mode\", [\"fast\"]) # TODO put \"accurate\" back in, slows down tests\ndef test_normalize(fmt, coverage, mode):\n    chain = CleanText(articles(fmt=fmt))\n    if coverage == \"basic\":\n        chain = chain.keywords(mode=mode)\n    else:\n        # Revisit this list as cleantext.py grows\n        chain = chain\\\n            .unmark()\\\n            .strip_html()\\\n            .normalize_numbers()\\\n            .fix_punct()\\\n            .only_english()\\\n            .only_ascii()\\\n            .remove_apos()\\\n            .multiple_whitespace()\\\n            .keywords(mode=mode)\n    clean = chain.join().value()\n    assert len(chain.data.lemmas) > 10\n    print(chain.data.lemmas[:5])\n    assert len(clean) > 10\n    print(clean[0])\n\n@pytest.mark.parametrize(\"content\", [\n\"hello\",\n\n\"*hello*\",\n\n\"_hello_\",\n\n\"# test\",\n\n\"\"\"# Markdown Title\nHere is a list of items\n* list item 1\n* list item 2\n\n## Next section\nThis is a paragraph. Blah bla blah.\n\"\"\",\n])\ndef test_unmark(content):\n    res = CleanText(content).unmark().value()\n    print(res)\n    assert type(res) == str\n    assert \"#\" not in res\n    assert \"*\" not in res\n\n\n@pytest.mark.parametrize(\"content\", [\n\"hello\",\n\n\"<span>test</span>\",\n\n\"\"\"<html>\n<body><p>Test string</p></body>\n</html>\"\"\"\n])\ndef test_strip_html(content):\n    res = CleanText([content]).strip_html().value()\n    print(res)\n    assert type(res) == str\n    assert \"<\" not in res\n\n\n@pytest.mark.parametrize(\"content\", [\n    (\"I moved to Portland in 2014\", \"move portland DATE\"),\n    (\"I moved to Portland 2014-01-01\", \"move portland DATE\"),\n    (\"$5m dollars\", \"MONEY\"),\n    (\"$5 million\", \"MONEY\"),\n    (\"$ 5.5 million\", \"MONEY\"),\n    (\"The third item\", \"ORDINAL item\"),\n    (\"The 2nd item\", \"ORDINAL item\"),\n    (\"1 megabyte file\", \"NUMBER file\"),\n    (\"1gb file\", \"CARDINAL file\"),\n    (\"1k people\", \"CARDINAL people\"),\n    # (\"Artificial intelligence is a study in computer science that is gaining huge traction\", \"\")\n])\ndef test_keywords_fast(content):\n    assert CleanText(content[0]).keywords(mode='fast').join().value() == content[1]\n\n\n@pytest.mark.parametrize(\"content\", [\n    (0, \"token1 token2 token3 token4\"),\n    (1, [\"token1\", \"token2\", \"token3\", \"token4\"]),\n    (2, [[\"token1\", \"token2\"], [\"token3\", \"token4\"]])\n])\ndef test_join(content):\n    ndim, content = content\n    v = CleanText(content).join().value()\n    if ndim == 0:\n        assert content == 'token1 token2 token3 token4'\n    if ndim == 1:\n        assert v == 'token1 token2 token3 token4'\n    if ndim == 2:\n        assert len(v) == 2\n        assert v[0] == 'token1 token2'\n        assert v[1] == 'token3 token4'","repo_name":"lefnire/ml-tools","sub_path":"tests/test_cleantext.py","file_name":"test_cleantext.py","file_ext":"py","file_size_in_byte":4592,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"81"}
+{"seq_id":"73388282186","text":"# Excel Sheet Column Number\nclass Solution:\n    def titleToNumber(self, s: str) -> int:\n        ans = 0\n        if s == \"\": return 0\n        base = 1\n        for i in range(len(s) - 1, -1, -1):\n            ans += (ord(s[i]) - 64) * base\n            base *= 26\n        return ans\n\nif __name__ == '__main__':\n    s = Solution()\n    print(s.titleToNumber(\"ZY\"))","repo_name":"GavinPHR/code","sub_path":"phase1/171.py","file_name":"171.py","file_ext":"py","file_size_in_byte":358,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"81"}
+{"seq_id":"6242166006","text":"# This script \"assembles\" the NFPA 704 symbol components into a series\n# of complete and valid NFPA 704 symbols. This script was needed due to the\n# high cardinality of valid NFPA 704 symbols given the sheer number of possible\n# combinations of components.\n#\n# Every NFPA-standardized digit is included in the component set. All\n# permutations of the availabe components are created. This script was not\n# designed to be robust or maintainable and is currently suitable only for\n# development and/or testing purposes.\n#\n# @param componentsDir <string> The filesystem path of the directory containing\n#   the NFPA 704 component images.\n# @param outputDir <string> The filesystem path of the directory to which all\n#   generated NFPA 704 symbols should be saved.\n\nimport glob\nimport os\nfrom PIL import Image\nimport sys\n\n# Collect arguments. Not yet any validation or sanitization here so be careful.\ncomponentsDirPath = os.path.realpath(sys.argv[1])\noutputDirPath = os.path.realpath(sys.argv[2])\n\n# Enumerate blank diamond \"templates.\"\nimageComponentsDiamondsGlob = os.path.join(\n  componentsDirPath,\n  'diamond_*.png'\n)\nimageComponentsDiamondPaths = glob.glob(imageComponentsDiamondsGlob)\n\n# Enumerate numeric digits for red, yellow, and blue quadrants.\nimageComponentsDigitsGlob = os.path.join(\n  componentsDirPath,\n  'digit_*.png'\n)\nimageComponentsDigitPaths = glob.glob(imageComponentsDigitsGlob)\nimageComponentsDigitPaths.sort()\n\n# Enumerate special (white) quadrant content.\nimageComponentsSpecialGlob = os.path.join(\n  componentsDirPath,\n  'special_*.png'\n)\nimageComponentsSpecialPaths = glob.glob(imageComponentsSpecialGlob)\n\n# Given that all diamond templates are 300x300 pixels and the components are\n# 71x71 pixels, the following tuples are coordinates for proper placement of the\n# respective pieces.\nquadrantRedCoords = (114, 41)\nquadrantBlueCoords = (41, 114)\nquadrantYellowCoords = (188, 114)\nquadrantWhiteCoords = (114, 188)\n\n# This is going to get ugly. REVISE THIS.\n# Don't close reopen files constantly. Abstract this.\n# Break nested loops into functions? Use itertools.combinations?\n# Iterate base diamonds.\nimageCount = 0\nfor baseDiamond in imageComponentsDiamondPaths:\n  baseDiamond = Image.open(baseDiamond)\n\n  # Iterate blue digits.\n  for digitBluePath in imageComponentsDigitPaths:\n    baseDiamondCopyBlue = baseDiamond.copy()\n    digitBlueImage = Image.open(digitBluePath)\n    baseDiamondCopyBlue.paste(digitBlueImage, quadrantBlueCoords, digitBlueImage)\n\n    # Iterate red digits.\n    for digitRedPath in imageComponentsDigitPaths:\n      baseDiamondCopyRed = baseDiamondCopyBlue.copy()\n      digitRedImage = Image.open(digitRedPath)\n      baseDiamondCopyRed.paste(digitRedImage, quadrantRedCoords, digitRedImage)\n\n      # Iterate yellow digits.\n      for digitYellowPath in imageComponentsDigitPaths:\n        baseDiamondCopyYellow = baseDiamondCopyRed.copy()\n        digitYellowImage = Image.open(digitYellowPath)\n        baseDiamondCopyYellow.paste(\n          digitYellowImage,\n          quadrantYellowCoords,\n          digitYellowImage\n        )\n\n        # Iterate white symbols and save images.\n        for digitWhitePath in imageComponentsSpecialPaths:\n          baseDiamondCopyWhite = baseDiamondCopyYellow.copy()\n          digitWhiteImage = Image.open(digitWhitePath)\n          baseDiamondCopyWhite.paste(\n            digitWhiteImage,\n            quadrantWhiteCoords,\n            digitWhiteImage\n          )\n          baseDiamondCopyWhite.save(\n            os.path.join(outputDirPath, str(imageCount) + '.png')\n          )\n          imageCount += 1\n\n\n","repo_name":"monotonee/opencv_nfpa704","sub_path":"src/assemble_positives.py","file_name":"assemble_positives.py","file_ext":"py","file_size_in_byte":3582,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"73662425544","text":"import zipfile\r\nfrom zipfile import ZipFile, is_zipfile\r\nfrom os import walk, path\r\nfrom src.util_functions import mkdirp, get_file_size, get_directory_size, human_sizeC, clearColor\r\nfrom src.exceptions import PAKFileFormatError, PAKMismatchedCRC, PAKFileZippingError, PAKFileUnzippingError\r\nfrom src.colors import colors as c\r\n\r\n\r\ndef get_folder_size(folder_path: str):\r\n    return str(human_sizeC(get_directory_size(folder_path)))\r\n\r\n\r\ndef get_size_of_file(file_path: str):\r\n    return str(human_sizeC(get_file_size(file_path)))\r\n\r\n\r\ndef check_pak_validity(input_pakfile: str):\r\n    is_zip = is_zipfile(input_pakfile)\r\n    if not is_zip:\r\n        # raise PAKFileFormatError(f\"Invalid Format - This File is NOT a PAKFile! ({input_pakfile})\")\r\n        ...\r\n    try:\r\n        with ZipFile(input_pakfile, 'r') as zip:\r\n            results_of_test = zip.testzip()\r\n    except zipfile.BadZipfile as e:\r\n        # raise PAKFileFormatError(f\"Invalid Format - This File is NOT a PAKFile! ({input_pakfile + ' - ' + e})\")\r\n        results_of_test = \"Invalid File Format!\"\r\n    if results_of_test is not None:\r\n        # raise PAKMismatchedCRC(\"Corruption - There is a CRC or File Header Mismatch in the PAKFile!\")\r\n        ...\r\n    return is_zip, results_of_test\r\n\r\n\r\ndef find_all_files_recursive(directory: str):\r\n    file_paths = []\r\n    for root, directories, files in walk(directory):\r\n        for filename in files:\r\n            filepath = path.join(root, filename)\r\n            file_paths.append(filepath)\r\n    return file_paths\r\n\r\n\r\ndef write_to_pakfile(input_directory: str = \"./\", output_name: str = \"data2.pak\"):\r\n    try:\r\n        file_paths = []\r\n        for root, directories, files in walk(input_directory):\r\n            for filename in files:\r\n                filepath = path.join(root, filename)\r\n                file_paths.append(filepath)\r\n\r\n        print(\"\\nThe Following Files will be PAK'ed:\")\r\n        for file_name in file_paths:\r\n            print(c.PURPLE + file_name)\r\n        clearColor()\r\n\r\n        with ZipFile(output_name, 'w', zipfile.ZIP_DEFLATED) as zipobj:\r\n            length = len(input_directory)\r\n            for root, directories, files in walk(input_directory):\r\n                folder = root[length:]\r\n                for filename in files:\r\n                    zipobj.write(path.join(root, filename), path.join(folder, filename))\r\n    except:\r\n        raise PAKFileZippingError(\"An error occurred whilst generating the PAK File!\")\r\n\r\n\r\ndef read_from_zipfile(input_pakfile: str):\r\n    with ZipFile(input_pakfile, 'r') as zipobj:\r\n        zipobj.printdir()\r\n\r\n\r\ndef extract_zipfile(input_pakfile: str, output_path: str):\r\n    try:\r\n        mkdirp(output_path)\r\n        with ZipFile(input_pakfile, 'r') as zipobj:\r\n            zipobj.extractall(path=output_path)\r\n    except:\r\n        raise PAKFileUnzippingError(\"An error occurred whilst extracting the PAK File!\")\r\n","repo_name":"RHQOnline/DL2-PAKFile-Utility","sub_path":"src/pak_functions.py","file_name":"pak_functions.py","file_ext":"py","file_size_in_byte":2897,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"81"}
+{"seq_id":"23068508840","text":"import cv2  \nimport numpy as np;  \n  \npath = r\"../CRBD/0-CRBD-bag/bag (1).jpg\"\n\nimg = cv2.imread(path, cv2.IMREAD_GRAYSCALE)  \ncv2.imwrite(\"gs_1.jpg\", img)\n\nsize = (320, 240) \nblob = cv2.dnn.blobFromImage(img,\n                             scalefactor=1/255,\n                             size=size,\n                             swapRB=True)\n \n# let's see our transformed image- blob\n# print(blob)\ncv2.imwrite(\"blob_1.jpg\", blob)\nprint(f'Blob Shape : {np.array(blob).shape}')\n\n\"\"\" # Set up the detector with default parameters.  \ndetector = cv2.SimpleBlobDetector()  \n\n# Detecting blobs.  \nkeypoints = detector.detect(img)   \"\"\"\nprint(\"BUrada\")\n# Draw detected blobs as red circles.  \n# cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS ensures the size of the circle corresponds to the size of blob  \n\"\"\" im_with_keypoints = cv2.drawKeypoints(img, keypoints, np.array([]), (0, 0, 255),  \n                                      cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)  \n# Show keypoints  \ncv2.imwrite(\"Keypoints.jpg\", im_with_keypoints)   \"\"\"\ncv2.waitKey(0)  ","repo_name":"Shubuo/ZiDrone_Vision","sub_path":"crop_row_detection/src/blob_detect.py","file_name":"blob_detect.py","file_ext":"py","file_size_in_byte":1053,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"23951495871","text":"from PyQt5.QtGui import QColor, QPainter, QPen\nfrom RotateRect import RotateRect\nimport math\n\n\ndef dist(p, q):\n    return math.sqrt(sum((px - qx) ** 2.0 for px, qx in zip(p, q)))\n\n\nclass Sensor(object):\n    def __init__(self):\n        self._d = 0\n        self.virtualA = 0\n        return\n\n    def update(self):\n        pass\n\n    def Dist(self):\n        return self._d\n\n\nclass GraphicSensor(Sensor):\n    def __init__(self, w, x, y, a):\n        super().__init__()\n\n        self.w = w\n        self.h = self.w\n\n        self.x = x\n        self.y = y\n\n        self.graphX = x\n        self.graphY = y\n\n        self.cx = x\n        self.cy = y\n\n        self.a = 0\n        self.virtualA = a\n\n        self.halfDiag = self._getHalfSquareDiag(self.w)\n        self.coeff = 0\n        self.b = 0\n        self.vert = False\n\n        self.debug = False\n\n        self.dmax = 200\n\n    def angle(self, a, cx, cy):\n        self.a = a\n        self.cx = cx\n        self.cy = cy\n\n    def draw(self, painter, color=QColor('blue')):\n        rr = RotateRect.create(x=self.x, y=self.y,\n                               w=self.w, h=self.h, a=self.a, painter=painter, color=color, rx=self.cx, ry=self.cy)\n\n        self.graphX = (rr.crx + rr.clx) / 2\n        self.graphY = (rr.cry + rr.cly) / 2\n\n    def create(w, x, y, cx, cy, a, virtualA, painter, canvas, color=QColor('blue'), debug=False):\n        gs = GraphicSensor(w, x, y, virtualA)\n        gs.debug = debug\n        gs.angle(a, cx, cy)\n        gs.draw(painter, color)\n        gs.update(painter, canvas)\n\n        return gs\n    create = staticmethod(create)\n\n    def _getPointOnCircle(self, cx, cy, r, a):\n        a = math.radians(a)\n        x = cx + r * math.cos(a)\n        y = cy + r * math.sin(a)\n\n        return x, y\n\n    def _getHalfSquareDiag(self, l):\n        return (l * math.sqrt(2)) / 2\n\n    def update(self, painter, canvas):\n        x, y = self._getPointOnCircle(\n            self.graphX, self.graphY, self.halfDiag, self.virtualA + self.a)\n\n        self.tx = x\n        self.ty = y\n\n        if x - self.graphX == 0:\n            self.vert = True\n        else:\n            self.vert = False\n\n            self.coeff = (y - self.graphY) / (x - self.graphX)\n            self.b = self.graphY - self.coeff * self.graphX\n\n        self.goThrough(self.dmax, painter, canvas)\n\n        return\n\n    def goThrough(self, dmax, painter, canvas):\n        lim = 90-self.virtualA\n\n        i = self.graphX\n        step = 0.1\n\n        found = False\n\n        img = canvas.pixmap().toImage()\n\n        y0 = 0\n        xi = 0\n        yi = 0\n\n        if not self.vert:\n            y0 = self.coeff * self.graphX + self.b\n            y = y0\n\n            additive = False\n\n            if self.virtualA <= 0:\n                if self.a <= lim or self.a > lim+180:\n                    additive = True\n            else:\n                if self.a > lim+180 and self.a <= lim+180+180:\n                    additive = True\n\n            while dist((self.graphX, y0), (i, y)) < dmax and not found:\n                y = self.coeff * i + self.b\n\n                pixel = img.pixel(i, y)\n                if QColor(pixel) == QColor('black'):\n                    found = True\n                    break\n\n                if additive:\n                    i += step\n                else:\n                    i -= step\n\n            xi = i\n            yi = y\n        else:\n            xi = i\n            y0 = self.graphY\n            i = y0\n\n            while dist((self.graphX, y0), (xi, i)) < dmax and not found:\n                pixel = img.pixel(xi, i)\n                if QColor(pixel) == QColor('black'):\n                    found = True\n                    break\n\n                if self.a % 360 < lim or self.a % 360 >= lim+180:\n                    i -= step\n                else:\n                    i += step\n\n            yi = i\n\n        if not found:\n            self._d = dmax\n        else:\n            self._d = dist((self.graphX, y0), (xi, yi))\n\n        if self.debug:\n            lastPen = painter.pen()\n\n            pen = QPen()\n            pen.setWidth(1)\n            pen.setColor(QColor(\"green\"))\n            painter.setPen(pen)\n            painter.drawLine(self.graphX, y0, xi, yi)\n\n            painter.setPen(lastPen)\n\n        return\n","repo_name":"Virkin/DeepLearning","sub_path":"Sensor.py","file_name":"Sensor.py","file_ext":"py","file_size_in_byte":4230,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"40792251547","text":"#######################################################################\r\n## THROWSHED ##\r\n#######################################################################\r\nimport os\r\nimport numpy as np\r\nfrom osgeo import gdal, ogr, osr\r\nfrom timeit import default_timer as timer\r\n\r\n#######################################################################\r\n## FUNCTIONS\r\n\r\ndef main(dem_path, point_layer_path, line_layer_path, throwshed_output_folder, throwshed_file, throwshed_mode, use_viewshed, use_lines, EPSG,\r\n         cumulative_throwshed, initial_height, initial_velocity, drag_coefficient, cross_sectional_area, mass,\r\n         eyes_height, target_height, wall_height, constant, area_addition, wobble_distance, band_number=1,\r\n         interpolation=1, alpha_min=-90.0, alpha_max=90.0, gravitational_acceleration=-9.81, air_density=1.225, dalpha=5,\r\n         trajectory_segment_width=None):\r\n    \"\"\"Just main function with controls, global variables settings and triggers to other functions\"\"\"\r\n    # making sure the vertical angle has correct range\r\n    if alpha_max < alpha_min:\r\n        print(\"Minimal vertical shooting angle higher than maximal.\")\r\n        exit()\r\n    # Global variables\r\n    global SRS, DP, PLP, TOF, TF, TM, UV, UL, CT, BN, INT, BF, TSW, AL, DDS, DB, DA, DGT, DMINH, DMAXH, IH, IV, \\\r\n        DC, CSA, M, CONST, AA, WD, GA, AD, EH, TH, NDV, TA, VDS, VB, VA, VGT\r\n    # CRS and other variable definition\r\n    SRS = osr.SpatialReference()\r\n    SRS.ImportFromEPSG(EPSG)\r\n    TOF, TF, TM, UV, UL, CT, BN, INT, IH, IV, DC, CSA, M, CONST, AA, WD, GA, AD, EH, TH = \\\r\n        throwshed_output_folder, throwshed_file, throwshed_mode, use_viewshed, use_lines, cumulative_throwshed, \\\r\n        band_number, interpolation, initial_height, initial_velocity, drag_coefficient, cross_sectional_area, mass, \\\r\n        constant, area_addition, wobble_distance, gravitational_acceleration, air_density, eyes_height, target_height\r\n    # get DEM data and assign them as global (and referencing datasource)\r\n    DDS, DB, DA, DGT, NDV = get_raster_from_file(dem_path)\r\n    # assign trajectory segment width\r\n    TSW = np.min(np.abs([DGT[1],DGT[5]])) if trajectory_segment_width == None else trajectory_segment_width\r\n    # obtain list of point geometries (and all referencing data it's dependent on)\r\n    point_layer_ds, point_layer, point_feature_list, point_geom_list = get_geom_list_from_file(point_layer_path)\r\n    # burn lines as obstacles into DEM, creating new DEM (Digital terrain model -> Digital surface model)\r\n    if UL:\r\n        burn_obstacles(line_layer_path, wall_height)\r\n    # get minimum and maximum DEM height\r\n    DMINH, DMAXH = get_min_max_height()\r\n    # obtain list of vertical angles\r\n    AL = np.arange(np.radians(alpha_min), np.radians(alpha_max + dalpha), np.radians(dalpha))\r\n    AL[-1] = np.radians(alpha_max) # in case of last angle being larger than 90°\r\n    # throwshed array containing zeroes at first, will be edited later, dimensions same as dimensions of DEM raster\r\n    TA = [np.zeros((DA.shape[0], DA.shape[1]), np.int16), np.zeros((DA.shape[0], DA.shape[1]), np.int16)]\r\n    # cycle calculating throwshed for each point\r\n    for i, point_geom in enumerate(point_geom_list):\r\n        # compute throwshed for 1 point\r\n        throwshed(point_geom, i)\r\n        #break #throwshed only for the first point is computed\r\n        # temporary viewshed files have to be removed\r\n        if UV:\r\n            VDS = VB = VA = VGT = None\r\n            os.remove(TOF + \"\\\\viewshed.tif\")\r\n    # finally, array is written into band of output throwshed raster\r\n    create_raster_file(TF, TA, 1, gdal.GDT_Int16, NDV)\r\n    DDS = DB = DA = DGT = NDV = None\r\n    # Digital surface model file will be removed as well\r\n    if UL:\r\n        remove_temp_files(DSM)\r\n\r\ndef get_raster_from_file(file_path):\r\n    \"\"\"Get DEM datasource, band, array and geotransformation data and nodata value\"\"\"\r\n    # import DEM datasource\r\n    dem_ds = gdal.Open(file_path)\r\n    # select band\r\n    dem_band = dem_ds.GetRasterBand(BN)\r\n    # DEM cell values into array\r\n    dem_array = dem_band.ReadAsArray()\r\n    # transformation data describing DEM\r\n    dem_gt = dem_ds.GetGeoTransform()\r\n    # nodata value\r\n    no_data_value = dem_band.GetNoDataValue()\r\n    return dem_ds, dem_band, dem_array, dem_gt, no_data_value\r\n\r\ndef get_min_max_height():\r\n    \"\"\"Get minimum and maximum DEM height\"\"\"\r\n    # sometimes the function returns None, therefore statistics need to be calculated first\r\n    if DB.GetMinimum() == None or DB.GetMaximum() == None:\r\n        DB.ComputeStatistics(0)\r\n    return DB.GetMinimum(), DB.GetMaximum()\r\n\r\ndef get_geom_list_from_file(file_path):\r\n    \"\"\"Get features' geometries from vector layer file\"\"\"\r\n    # import vector layer datasource\r\n    layer_ds = ogr.Open(file_path, 0)  # 1 = editing, 0 = read only. Datasource\r\n    # vector layer\r\n    layer = layer_ds.GetLayer()\r\n    # list of features, then geometries\r\n    feature_list = [layer.GetFeature(number) for number in range(0, layer.GetFeatureCount())]\r\n    geom_list = [feature.GetGeometryRef() for feature in feature_list]\r\n    return layer_ds, layer, feature_list, geom_list\r\n\r\ndef burn_obstacles(line_layer_path, wall_height):\r\n    \"\"\"Lines that resemble walls/obstacles are burnt into DEM\"\"\"\r\n    # get list of line geometries\r\n    line_ds, line_layer, line_feature_list, line_geom_list = get_geom_list_from_file(line_layer_path)\r\n    # calculate minimal buffer distance, at which the obstacle will always be respected\r\n    buffer_dist = TSW / 2 + (DGT[1] ** 2 + DGT[5] ** 2) ** (1 / 2) / 2 - (TSW / 2) / (\r\n                DGT[1] ** 2 + DGT[5] ** 2) ** (1 / 2) % 1\r\n    # 1st buffer is created and in case of multiple lines the cycle is utilized to create and unite buffers\r\n    buffer_geom = line_geom_list[0].Buffer(buffer_dist, 1)\r\n    if len(line_geom_list) > 1:\r\n        for line_geom in line_geom_list[1:]:\r\n            buffer_geom = buffer_geom.Union(line_geom.Buffer(buffer_dist, 1))\r\n    # buffer file temporary name\r\n    BFT = \"buffer_temp\"\r\n    # create layer for buffer\r\n    buffer_vector_ds, buffer_outlayer = create_and_use_outlayer(BFT, buffer_geom)\r\n    # create buffer datasource\r\n    buffer_raster_ds, buffer_band = create_raster_file(BFT, [0], 0, gdal.GDT_Float32, 0)\r\n    # Buffer polygon is rasterized\r\n    gdal.RasterizeLayer(buffer_raster_ds, [1], buffer_outlayer, burn_values=[wall_height])\r\n    # Sum of initial dem and buffer rasters\r\n    buffer_array = buffer_band.ReadAsArray()\r\n    global DSM, DDS, DB, DA\r\n    DA = np.add(DA, buffer_array)\r\n    # create new raster datasource for DEM (DSM)\r\n    DSM = \"dsm_temp\"\r\n    DDS, DB = create_raster_file(DSM, [DA], 1, gdal.GDT_Float32, NDV)\r\n    # delete all temporary files\r\n    buffer_raster_ds = buffer_vector_ds = buffer_outlayer = buffer_band = None\r\n    remove_temp_files(BFT)\r\n\r\ndef create_and_use_outlayer(layer_name, geom):\r\n    \"\"\"Creates file for output layer, which will contain new features. Returns created layer.\"\"\"\r\n    # create driver and output data source\r\n    outds = ogr.GetDriverByName(\"ESRI Shapefile\").CreateDataSource(TOF + \"\\\\\" + layer_name + \".shp\")\r\n    # create output layer\r\n    outlayer = outds.CreateLayer(layer_name, SRS)\r\n    # feature definition and setting\r\n    feature = ogr.Feature(outlayer.GetLayerDefn())\r\n    feature.SetGeometry(geom)\r\n    # assign feature into output layer\r\n    outlayer.CreateFeature(feature)\r\n    return outds, outlayer\r\n\r\ndef create_raster_file(raster_name, dem_array_list, method, GDT, no_data):\r\n    \"\"\"Creates raster file. Method 0 returns empty datasource and band. Method 1 returns datasource and band with written array\"\"\"\r\n    # create driver and output data source\r\n    outds = gdal.GetDriverByName('GTiff').Create(TOF + \"\\\\\" + raster_name + \".tif\", xsize=DA.shape[1],\r\n                                                 ysize=DA.shape[0], bands=len(dem_array_list), eType=GDT)\r\n    # assign geotransformation, projection, band and nodata settings\r\n    outds.SetGeoTransform(DGT)\r\n    outds.SetProjection(SRS.ExportToWkt())\r\n    for i, dem_array in enumerate(dem_array_list):\r\n        raster_band = outds.GetRasterBand(i+1)\r\n        raster_band.SetNoDataValue(no_data)\r\n        if method:\r\n            raster_band.WriteArray(dem_array)\r\n    return outds, raster_band\r\n\r\ndef remove_temp_files(temp_file):\r\n    \"\"\"Deletes all temporary files with assigned name\"\"\"\r\n    for format in [file.split('.')[1] for file in os.listdir(TOF) if file.split('.')[0] == temp_file]:\r\n        os.remove(TOF + '\\\\' + temp_file + \".\" + format)\r\n\r\ndef throwshed(point_geom, k):\r\n    \"\"\"Calculates throwshed for 1 point\"\"\"\r\n    global SP, WH\r\n    # create shooting point with Z coordinate that is interpolated from DEM\r\n    SP = ogr.Geometry(ogr.wkbPoint)\r\n    SP.AddPoint(point_geom.GetX(), point_geom.GetY(), float(int_function(point_geom.GetX(), point_geom.GetY()))+IH)\r\n    # generate set of trajectories for vertical angle range with basic step\r\n    trajectory_simple_set()\r\n    # insert trajectories between those from simple set, to ensure throwshed's edge accuracy\r\n    trajectory_initial_set()\r\n    # define Ascending and Descending Trajectory Fields\r\n    create_trajectory_fields()\r\n    # if viewshed is ON, it has to be created and deleted later\r\n    if UV:\r\n        create_viewshed()\r\n    # Assign values into arrays of 2 bands (ATF and DTF)\r\n    assign_values_to_throwshed(k)\r\n\r\ndef int_function(X, Y):\r\n    \"\"\"Interpolates height of point from DEM cells\"\"\"\r\n    # nearest neighbour\r\n    if INT == 0:\r\n        column = round(np.abs((X - (DGT[0] + DGT[1] / 2)) / DGT[1]))\r\n        row = round(np.abs((Y - (DGT[3] + DGT[5] / 2)) / DGT[5]))\r\n        return DA[row][column]\r\n    # bilinear\r\n    else:\r\n        left_column = int(np.abs((X - (DGT[0] + DGT[1] / 2)) / DGT[1])) # index of left column in set of four cells\r\n        upper_row = int(np.abs((Y - (DGT[3] + DGT[5] / 2)) / DGT[5])) # index of the upper row in set of four cells\r\n        X_left_cell = DGT[0] + DGT[1] / 2 + left_column * DGT[1] # X coordinate of left cells\r\n        Y_lower_cell = DGT[3] + DGT[5] / 2 + (upper_row + 1) * DGT[5] # Y coordinate of lower cells\r\n        H_1 = DA[upper_row][left_column]  # height of upper left cell\r\n        H_2 = DA[upper_row][left_column + 1]  # height of upper right cell\r\n        H_3 = DA[upper_row + 1][left_column]  # height of lower left cell\r\n        H_4 = DA[upper_row + 1][left_column + 1]  # height of lower right cell\r\n        H_int_1 = ((X - X_left_cell) * (H_4 - H_3)) / (np.abs(DGT[1])) + H_3  # interpolated height among lower cells\r\n        H_int_2 = ((X - X_left_cell) * (H_2 - H_1)) / (np.abs(DGT[1])) + H_1  # interpolated height among upper cells\r\n        return ((Y - Y_lower_cell) * (H_int_2 - H_int_1)) / (np.abs(DGT[5])) + H_int_1\r\n\r\ndef trajectory_simple_set():\r\n    \"\"\"Generate set of trajectories for vertical angle range with basic step\"\"\"\r\n    # trajectory dictionary, that will contain all trajectories, their initial shooting angle etc.\r\n    global TS\r\n    # element in list begins with alpha value and continues with list of x and y coords lists in one trajectory\r\n    TS = [[alpha, generate_trajectory(alpha)] for alpha in AL]\r\n\r\ndef generate_trajectory(alpha):\r\n    \"\"\"Generates trajectory from input parameters\"\"\"\r\n    # initial drag\r\n    d = -AD * IV ** 2 * DC * CONST * (CSA + AA) / (2 * M)\r\n    # initial projectile velocity\r\n    V = IV\r\n    # list of all trajectory points' coordinates\r\n    points = [[0.0], [SP.GetZ()]]\r\n    # velocity x and y elements\r\n    V_x = V * np.cos(alpha)\r\n    V_y = V * np.sin(alpha)\r\n    # drag x and y elements\r\n    d_x = d * np.cos(alpha)\r\n    d_y = d * np.sin(alpha)\r\n    # time step is set to value approximate to half of cell size\r\n    dt = TSW / V / 2\r\n    # x and y steps\r\n    dX = V_x * dt + d_x / 2 * dt ** 2\r\n    dY = V_y * dt + (d_y + GA) / 2 * dt ** 2\r\n    # cycle calculating every new trajectory point one by one\r\n    while True:\r\n        # coords\r\n        points[0].append(points[0][-1] + dX)\r\n        points[1].append(points[1][-1] + dY)\r\n        # when last height is less than minimal DEM height, cycle breaks and last values are reinterpolated into minimal DEM height (to prevent errors in extreme situations of further functions)\r\n        if points[1][-1] <= DMINH:\r\n            # if the shooting point is on the cell with minimal height of DEM, there will be only 2 points for trajectories starting with angle <= 0 and these points can't be the same, so this is the only exception where last points of trajectories are not recalculated (interpolated) to minimal DEM height\r\n            if len(points[1]) == 2:\r\n                pass\r\n            # but normally the last segment passing the minimal DEM height is clipped by this height and the last point is interpolated to this height\r\n            else:\r\n                points[1][-1] = DMINH\r\n                points[0][-1] = points[0][-2] + (points[1][-1] - points[1][-2])/np.tan(alpha)\r\n            break\r\n        # new vertical angle\r\n        alpha = np.arctan(dY / dX)\r\n        # new velocity\r\n        V = ((dX / dt) ** 2 + (dY / dt) ** 2) ** (1 / 2)\r\n        # new drag\r\n        if (points[0][-1] ** 2 + (points[1][-1] - SP.GetZ()) ** 2) ** (1 / 2) < WD:\r\n            d = -AD * V ** 2 * DC * CONST * (CSA + AA) / (2 * M)\r\n        else:\r\n            d = -AD * V ** 2 * DC * CSA / (2 * M)\r\n        # new drag x and y elements\r\n        d_x = d * np.cos(alpha)\r\n        d_y = d * np.sin(alpha)\r\n        # new velocity x and y elements\r\n        V_x += d_x * dt\r\n        V_y += (d_y + GA) * dt\r\n        # time step is recalculated to value approximate to half of cell size\r\n        dt = TSW / V / 2\r\n        # new x and y steps\r\n        dX = V_x * dt + d_x / 2 * dt ** 2\r\n        dY = V_y * dt + (d_y + GA) / 2 * dt ** 2\r\n    return points\r\n\r\ndef trajectory_initial_set():\r\n    \"\"\"Calculates and inserts trajectories between those in simple set, to make it denser and to ensure throwshed's\r\n    edge accuracy. Calculates and returns trajectory envelope points list (its useful section).\"\"\"\r\n    global TS, envelope\r\n    # new and previous trajectory end x, first ones are random, just to make sure the cycle does not stop immediately\r\n    ntex = [(max(TS, key=lambda x: x[1][0][-1])[1][0][-1]+TSW)*2,(max(TS, key=lambda x: x[1][0][-1])[1][0][-1]+TSW)*3]\r\n    # cycle that finds the furthest possible trajectory for minimal DEM height respecting the edge accuracy\r\n    while round(np.abs(ntex[0] - ntex[1])/TSW):\r\n        #most distant trajectory index\r\n        mdti = TS.index((max(TS, key=lambda x: x[1][0][-1])))\r\n        # adds new trajectories before and after current furthest trajectory\r\n        if mdti != 0 and mdti != len(TS)-1:\r\n            for new_alpha in [(TS[mdti+1][0] - TS[mdti][0]) / 2 + TS[mdti][0], (TS[mdti][0] - TS[mdti-1][0]) / 2 + TS[mdti-1][0]]:\r\n                TS.append([new_alpha, generate_trajectory(new_alpha)])\r\n        # for furthest trajectory that is also the first or last one, only one trajectory is added accordingly\r\n        elif mdti == len(TS)-1:\r\n            new_alpha = (TS[mdti][0] - TS[mdti-1][0]) / 2 + TS[mdti-1][0]\r\n            TS.append([new_alpha, generate_trajectory(new_alpha)])\r\n            mdti += 1 #this is just so that mdti gets higher like length of TS does (to cope with length of TS getting bigger, these 2 are compared in ntex)\r\n        elif mdti == 0:\r\n            new_alpha = (TS[mdti+1][0] - TS[mdti][0]) / 2 + TS[mdti][0]\r\n            TS.append([new_alpha, generate_trajectory(new_alpha)])\r\n        ntex = [max(TS[-1][1][0][-1],TS[-2][1][0][-1]) if mdti != 0 and mdti != len(TS)-1 else TS[-1][1][0][-1], ntex[0]]\r\n        TS.sort(key=lambda x: x[0])\r\n\r\n    # initial trajectory index (starting will be the trajectory with furthest reach)\r\n    iti = TS.index((max(TS, key=lambda x: x[1][0][-1])))\r\n    # function ends after finding out the last trajectory is the one with furthest reach (rest of the code is not applicable) and empty envelope is returned, otherwise trajectory set will get denser with following code\r\n    if iti == len(TS)-1:\r\n        envelope = [[], []]\r\n        return\r\n    # envelope needs starting point before adding more points to it\r\n    envelope = [[TS[iti][1][0][-1]], [TS[iti][1][1][-1]]]\r\n    # X and Y Inner Interection from Previous Cycle will be set to furthest point during first cycle\r\n    XIIPR, YIIPR = envelope[0][0], envelope[1][0]\r\n    # cycle that inserts trajectories between furthest trajectory at minimal DEM height and maximal DEM height\r\n    while True:\r\n        # generate new trajectory in between actual and following\r\n        new_alpha = abs(TS[iti][0] - TS[iti + 1][0]) / 2 + TS[iti][0]\r\n        TS.insert(iti+1, [new_alpha, generate_trajectory(new_alpha)])\r\n        # in case of added trajectory having further reach than previously furthest trajectory (actual)\r\n        if TS.index((max(TS, key=lambda x: x[1][0][-1]))) == iti + 1:\r\n            iti += 1\r\n            # even starting point of envelope needs to be updated\r\n            envelope = [[TS[iti][1][0][-1]], [TS[iti][1][1][-1]]]\r\n            XIIPR, YIIPR = envelope[0][0], envelope[1][0]\r\n            continue\r\n\r\n        # get intersection of actual and following (newly created) trajectory (Right Outer Intersection)\r\n        XROI, YROI = intersection_of_trajectories(iti, iti+1)\r\n        # get intersection of following and 2. following trajectory (Left Outer Intersection)\r\n        XLOI, YLOI = intersection_of_trajectories(iti+1, iti+2)\r\n        # get intersection of actual and 2. following trajectory (Inner Intersection), first one is calculated, the rest will be reused from outer intersections\r\n        XII, YII = intersection_of_trajectories(iti, iti+2)\r\n\r\n        # if the last trajectory incorporated in cycle is the last one from the net with shooting angle value of 90 degrees.\r\n        # This is because with 90 degrees trajectory left outer and inner intersections will be the same, which would lead to undesired behaviour\r\n        if TS[iti+2][0] == np.radians(90):\r\n            # if X coordinate of last point in newly created trajectory is less than TSW, last possible area of the net was made dense enough (used to be X of highest point, but last was chosen so that when searching for cell intersecting trajectories no trajectories are added if the cell falls between 2 last trajectories with the last having 90° angle, which would cause problems in rare situations)\r\n            if not np.floor(TS[iti+1][1][0][-1]/TSW):\r\n                # Initial Trajectory Reversed list (X and Y coords)\r\n                ITR = [TS[iti][1][0][-1::-1], TS[iti][1][1][-1::-1]]\r\n                # update envelope with points from initial trajectory of last cycle, also update trajectory with shared part starting and ending point indexes (on trajectory as on envelope)\r\n                TS[iti].append(update_envelope(1, ITR, XIIPR, XROI, YROI, 0))\r\n                # Last Inserted Trajectory Reversed list (X and Y coords)\r\n                LITR = [TS[iti+1][1][0][-1::-1], TS[iti+1][1][1][-1::-1]]\r\n                # update last but one trajectory with shared part starting and ending point indexes (on trajectory as on envelope)\r\n                TS[iti+1].append(update_envelope(0, LITR, 0, 0, 0, YROI))\r\n                envelope[0].append(0)\r\n                envelope[1].append(max(TS[-1][1][1]))\r\n                # update last trajectory (90 degrees one) with shared part starting and ending point indexes (on trajectory as on envelope)\r\n                TS[iti + 2].append([[TS[iti+2][1][1].index((max(TS[iti+2][1][1]))), TS[iti+2][1][1].index((max(TS[iti+2][1][1])))], [len(envelope[0])-1, len(envelope[0])-1]])\r\n                break\r\n            # if not dense enough, density will be accomplished with new iteration\r\n            else:\r\n                continue\r\n\r\n        # coordinates of midpoint on line between outer intersections (Outer Midpoint)\r\n        XOM, YOM = (XROI + XLOI) / 2, (YROI + YLOI) / 2\r\n        # following trajectory reversed list (X and Y coords)\r\n        FTR = [TS[iti + 1][1][0][-1::-1], TS[iti + 1][1][1][-1::-1]]\r\n        # finds intersection of arc distance and particular segment on the arc\r\n        for i in range(len(FTR[0])-1):\r\n            XA, YA = calculate_intersection(XOM, YOM, XII, YII, FTR[0][i], FTR[1][i], FTR[0][i+1], FTR[1][i+1])\r\n            # checking if the intersection is really on the segment, if so, the Distance of Arc from Inner Intersection is calculated\r\n            if FTR[0][i] >= XA >= FTR[0][i+1]:\r\n                DAII = ((XA-XII)**2 + (YA-YII)**2)**(1/2)\r\n                break\r\n        # controls - compare horizontal distance of intersections and distance of arc from inner intersection\r\n        if round(np.abs(XROI - XLOI)/TSW) and round(DAII/TSW):\r\n            continue\r\n        else:\r\n            # with each shooting point the amount of these inserted auxiliary trajectories would almost double which could create pointless amount of trajectories\r\n            del TS[iti+1]\r\n            # initial trajectory reversed list (X and Y coords)\r\n            ITR = [TS[iti][1][0][-1::-1], TS[iti][1][1][-1::-1]]\r\n            # update envelope and update trajectory list with starting and ending point index of shared part between trajectory and envelope, indexes will be used when looking for cell neighbouring trajectories\r\n            TS[iti].append(update_envelope(1, ITR, XIIPR, XII, YII, 0))\r\n            # previous intersection for next cycle is assigned\r\n            XIIPR, YIIPR = XII, YII\r\n            # at least one of the conditions was met and the cycle can jump to next initial trajectory\r\n            iti += 1\r\n            # if the cycle comes to last trajectory, it breaks as there is no following trajectory\r\n            if TS[iti][0] == AL[-1]:\r\n                # even last trajectory needs to be updated with indexes\r\n                ITR = [TS[iti][1][0][-1::-1], TS[iti][1][1][-1::-1]]\r\n                TS[iti].append(update_envelope(0, ITR, 0, 0, 0, YII))\r\n                break\r\n\r\ndef intersection_of_trajectories(t1i,t2i):\r\n    \"\"\"Looks for intersection between two trajectories and returns its X and Y coordinates.\r\n    t1i and t2i are indexes of first and second trajectory between which the intersection is sought.\"\"\"\r\n    # reversed lists of trajectories' coordinates as the algorithm starts from end points, TX1 = X coordinates of 1. trajectory\r\n    T1X, T1Y = TS[t1i][1][0][-1::-1], TS[t1i][1][1][-1::-1]\r\n    T2X, T2Y = TS[t2i][1][0][-1::-1], TS[t2i][1][1][-1::-1]\r\n    # X and Y coords of intersection (to be compared e.g. with XPI), i2s stands for radius around i2 (or index)\r\n    XI = YI = i2s = False\r\n    # following trajectory segment radius where the intersection will be sought\r\n    ftsr = [0, 1, -1, 2, -2]\r\n    # cycle that starts comparing coords of actual trajectory points from the end\r\n    for i1 in range(1, len(T1X)):\r\n        # cycle that starts comparing coords of following trajectory points from the end\r\n        for i2 in range(1, len(T2X)):\r\n            if T2Y[i2] > T1Y[i1] and T1X[i1] < T2X[i2 - 1]:\r\n                # when potentially intersecting segment of following trajectory is found, because of rare situations its 2 following and preceding segments have to be assessed\r\n                for i2s in ftsr:\r\n                    XI, YI = calculate_intersection(T1X[i1 - 1], T1Y[i1 - 1], T1X[i1], T1Y[i1],\r\n                                               T2X[i2 - 1 + i2s], T2Y[i2 - 1 + i2s], T2X[i2 + i2s],\r\n                                               T2Y[i2 + i2s])\r\n                    # making sure the intersection is between existing segments, not on their extension\r\n                    if T1X[i1 - 1] >= XI >= T1X[i1] and T2X[i2 - 1 + i2s] >= XI >= T2X[i2 + i2s]:\r\n                        break\r\n                    XI = YI = False\r\n            if XI or i2s == ftsr[-1]:\r\n                # i2s makes sure that if the intersection is not found in the 2 segment radius of following trajectory, index for segment of actual trajectory has to increase\r\n                i2s = False\r\n                break\r\n        if XI:\r\n            break\r\n    return XI, YI\r\n\r\ndef calculate_intersection(x1, y1, x2, y2, x3, y3, x4, y4):\r\n    \"\"\"Calculates intersection of 2 line segments and returns its X and Y coordinates\"\"\"\r\n    x1, y1, x2, y2, x3, y3, x4, y4 = x1, y1, x2, y2, x3, y3, x4, y4\r\n    XI = ((x1 * y2 - y1 * x2) * (x3 - x4) - (x1 - x2) * (x3 * y4 - y3 * x4)) / ((x1 - x2) * (y3 - y4) - (y1 - y2) *\r\n        (x3 - x4))\r\n    YI = ((x1 * y2 - y1 * x2) * (y3 - y4) - (y1 - y2) * (x3 * y4 - y3 * x4)) / ((x1 - x2) * (y3 - y4) - (y1 - y2) *\r\n        (x3 - x4))\r\n    return XI, YI\r\n\r\ndef update_envelope(method, ITR, XIIPR, XII, YII, YROI):\r\n    \"\"\"Updates envelope with parts of trajectories and returns starting and ending indexes of points on part of\r\n    trajectory that is shared with the envelope and also indexes of points on envelope that are boundaries of the\r\n    shared trajectory part (previous and actual inner intersections). Trajectory points have to be between the envelope\r\n    points.\"\"\"\r\n    global envelope\r\n    # for regular parts\r\n    if method:\r\n        # on reversed trajectory, index of first point that will be incorporated within the envelope is found\r\n        for i in range(len(ITR[0]) - 1):\r\n            if ITR[0][i] >= XIIPR >= ITR[0][i + 1]:\r\n                # Envelope Part Starting Point Index\r\n                EPSPI = i + 1\r\n                break\r\n        # on reversed trajectory, index of last point that will be incorporated within the envelope is found\r\n        for i in range(EPSPI - 1, len(ITR[0]) - 1):\r\n            if ITR[0][i] >= XII >= ITR[0][i + 1]:\r\n                # Envelope Part Ending Point Index\r\n                EPEPI = i\r\n                break\r\n        # starting envelope index as the index of envelope's last point before update\r\n        SEI = len(envelope[0]) - 1\r\n        # condition for rare situation where both inner intersections could fall within one segment of trajectory\r\n        if EPEPI >= EPSPI:\r\n            # envelope is updated with all points between starting and ending point\r\n            for i in range(EPSPI, EPEPI + 1):\r\n                envelope[0].append(ITR[0][i])\r\n                envelope[1].append(ITR[1][i])\r\n        # ending envelope index as the index of envelope's last point after update\r\n        EEI = len(envelope[0])\r\n        # lastly, envelope is updated with the inner intersection point\r\n        envelope[0].append(XII)\r\n        envelope[1].append(YII)\r\n        # return indexes of the first and last point of shared part, for trajectory direction of incrementing is from the left (shooting point), for envelope it's vice-versa\r\n        return [[len(ITR[0]) - 1 - EPSPI, len(ITR[0]) - 1 - EPEPI], [SEI, EEI]]\r\n    # for part of last but one trajectory when alpha of the last one is equal to 90° or part of last trajectory whose alpha is not equal to 90°\r\n    else:\r\n        # starting envelope index as the index of envelope's last point before update, for last but one trajectory\r\n        SEI = len(envelope[0]) - 1\r\n        # starting and ending last but one trajectory point indexes\r\n        EPSPI = 0\r\n        # last points from last inserted trajectory and highest point of last trajectory are appended to envelope\r\n        for i in range(len(ITR[0])):\r\n            if ITR[1][i] > YROI:\r\n                if not EPSPI:\r\n                    EPSPI = i\r\n                envelope[0].append(ITR[0][i])\r\n                envelope[1].append(ITR[1][i])\r\n            if ITR[0][i] == ITR[0][ITR[1].index((max(ITR[1])))]:\r\n                break\r\n        EPEPI = i - 1\r\n        # ending envelope index as the index of envelope's last point after update, for last but one trajectory\r\n        EEI = len(envelope[0]) - 1\r\n        return [[len(ITR[0]) - 1 - EPSPI, len(ITR[0]) - 1 - EPEPI], [SEI, EEI]]\r\n\r\ndef create_trajectory_fields():\r\n    \"\"\"Creates ATF - Ascending Trajectory Field and DTF - Descending Trajectory Field. Lists are made into polygons.\"\"\"\r\n    global ATF_polygon, DTF_polygon\r\n    ATF_polygon, DTF_polygon = None, None\r\n    ATF, DTF = [[], []], [[], []]\r\n    # in this case DTF does not exist\r\n    if len(envelope[0]) == 0:\r\n        ATF[0] = TS[0][1][0] + TS[-1][1][0][-1::-1]\r\n        ATF[1] = TS[0][1][1] + TS[-1][1][1][-1::-1]\r\n        # create polygon out of the list\r\n        ATF_polygon = create_polygon_from_coords_list(ATF)\r\n    # in this case both ATF and DTF are created\r\n    else:\r\n        # find out index of coords from last trajectory, where envelope connects with it\r\n        for i in range(len(TS[-1][1][0])-1):\r\n            # for last trajectory with shooting angle 90 degrees this stops immediately, to ATF is added first point of last trajectory, which still creates correct polygon\r\n            if TS[-1][1][0][i] <= envelope[0][-1] <= TS[-1][1][0][i+1]:\r\n                break\r\n        ATF[0] = TS[0][1][0] + envelope[0] + TS[-1][1][0][i::-1]\r\n        ATF[1] = TS[0][1][1] + envelope[1] + TS[-1][1][1][i::-1]\r\n        # but for DTF with last trajectory shooting angle being 90 degrees, i is edited so that only last point down at min DEM height is added to polygon\r\n        i = -2 if TS[-1][0] == np.radians(90) else i\r\n        DTF[0] = envelope[0] + TS[-1][1][0][i+1:] + envelope[0][:1]\r\n        DTF[1] = envelope[1] + TS[-1][1][1][i+1:] + envelope[1][:1]\r\n        # create polygons out of the lists\r\n        ATF_polygon = create_polygon_from_coords_list(ATF)\r\n        DTF_polygon = create_polygon_from_coords_list(DTF)\r\n\r\ndef create_polygon_from_coords_list(x_y_list):\r\n    \"\"\"Creates ring from list of X and Y coordinates, then uses ring to create polygon which is returned.\"\"\"\r\n    # create ring\r\n    ring = ogr.Geometry(ogr.wkbLinearRing)\r\n    for x, y in zip(x_y_list[0], x_y_list[1]):\r\n        ring.AddPoint(x, y)\r\n    # create polygon\r\n    polygon = ogr.Geometry(ogr.wkbPolygon)\r\n    polygon.AddGeometry(ring)\r\n    return polygon\r\n\r\ndef assign_values_to_throwshed(k):\r\n    \"\"\"Assigns/adds values into throwshed arrays.\"\"\"\r\n    # cycle going through every single cell of DEM\r\n    for i in range(DA.shape[0]):\r\n        for j in range(DA.shape[1]):\r\n\r\n\r\n\r\n            start1 = timer()\r\n\r\n\r\n\r\n            # with multiple shooting points nodata value can already be assigned to the cell, therefore the algorithm jumps to following cell\r\n            if TA[0][i][j] == NDV:\r\n                continue\r\n            # nodata value is assigned to both arrays for both bands (ATF and DTF)\r\n            if not k and DA[i][j] == NDV:\r\n                TA[0][i][j] = TA[1][i][j] = NDV\r\n                continue\r\n            # for simple throwshed, if cell already has value 1, cycle continues with following cell, otherwise for cumulative throwshed, cell is assessed\r\n            if k and CT == 0 and TA[0][i][j]:\r\n                continue\r\n            # if viewshed is incorporated and particular cell is not visible, nothing is added to throwshed cell, and for visible cells the algorithm proceeds with assessment of cells\r\n            if UV:\r\n                if not VA[i][j]:\r\n                    continue\r\n            # calculate coordinates of cell's middle point and its horizontal distance from shooting point\r\n            X_coor_cell = DGT[0]+(j+1/2)*DGT[1]\r\n            Y_coor_cell = DGT[3]+(i+1/2)*DGT[5]\r\n            cell_distance = ((SP.GetX() - X_coor_cell)**2 + (SP.GetY()-Y_coor_cell)**2)**(1/2)\r\n            # create cell point with relative coordinates in the plane of trajectories and find out whether it's within the field, if so, further actions are conducted\r\n            relative_cell = ogr.Geometry(ogr.wkbPoint)\r\n            # also create cell point with absolute coordinates in the plane of projection plane, will be used in terrain comparison to calculate azimuth\r\n            absolute_cell = ogr.Geometry(ogr.wkbPoint)\r\n            relative_cell.AddPoint(cell_distance, float(DA[i][j]))\r\n            absolute_cell.AddPoint(X_coor_cell, Y_coor_cell)\r\n            # detect cell within the fields and call function to find cell intersecting trajectory and to determine whether the cell is reachable without any obstacles\r\n            if ATF_polygon.Intersects(relative_cell):\r\n                if TM:\r\n                    if find_intersecting_trajectory(1, relative_cell, absolute_cell):\r\n                        TA[0][i][j] += 1\r\n                # for the case only cell's presence within the field is assessed\r\n                else:\r\n                    TA[0][i][j] += 1\r\n            # can be None\r\n            if DTF_polygon:\r\n                if DTF_polygon.Intersects(relative_cell):\r\n                    if TM:\r\n                        if find_intersecting_trajectory(-1, relative_cell, absolute_cell):\r\n                            TA[1][i][j] += 1\r\n                    # for the case only cell's presence within the field is assessed\r\n                    else:\r\n                        TA[1][i][j] += 1\r\n\r\n            end1 = timer()\r\n            print(f'Cell {i} {j} took {end1-start1} seconds.')\r\n            print()\r\n\r\ndef find_intersecting_trajectory(dir, relative_cell, absolute_cell):\r\n    \"\"\"Finds trajectory that intersects the cell (or is close enough, within allowed distance). For ATF cycle\r\n    increments from start to end of trajectory set and viceversa for DTF. Returns True if the cell is accessible\r\n    or False if the cell is not accessible without any obstacles - this is determined further function.\"\"\"\r\n\r\n\r\n    start4 = timer()\r\n\r\n\r\n    # Most Distant Trajectory Index\r\n    MDTI = TS.index((max(TS, key=lambda x: x[1][0][-1])))\r\n    if dir == -1:\r\n        # Zooming Index List containing indexes of assessed trajectories, for descending direction, first index is the one of the trajectory with furthest reach\r\n        ZIL = [MDTI, int(MDTI + (len(TS)-MDTI) / 2), len(TS) - 1]\r\n    else:\r\n        ZIL = [0, int(len(TS) / 2), len(TS) - 1]\r\n    # cycle for zooming into the polygon of cell neighbouring trajectories\r\n    while True:\r\n        for j in [0, 1]:\r\n            if dir == -1:\r\n                # polygon also consists of envelope (starting and ending indexes of points of shared parts by trajectory and envelope are used)\r\n                polygon = create_polygon_from_coords_list([envelope[0][TS[ZIL[j]][2][1][0]+1:TS[ZIL[j+1]][2][1][0]+1] + TS[ZIL[j + 1]][1][0][TS[ZIL[j+1]][2][0][0]+1:] + TS[ZIL[j]][1][0][-1:TS[ZIL[j]][2][0][0]-1:-1], envelope[1][TS[ZIL[j]][2][1][0]+1:TS[ZIL[j+1]][2][1][0]+1] + TS[ZIL[j + 1]][1][1][TS[ZIL[j+1]][2][0][0]+1:] + TS[ZIL[j]][1][1][-1:TS[ZIL[j]][2][0][0]-1:-1]])\r\n            else:\r\n                # very basic situation, envelope needs not to be used\r\n                if ZIL[j+1] <= MDTI:\r\n                    polygon = create_polygon_from_coords_list([TS[ZIL[j]][1][0] + TS[ZIL[j + 1]][1][0][-1::-1], TS[ZIL[j]][1][1] + TS[ZIL[j + 1]][1][1][-1::-1]])\r\n                # situation where at least second trajectory is already intersecting other trajectories with further reach\r\n                else:\r\n                    # situation where first of the trajectories is the one with furthest reach or the ones following\r\n                    if ZIL[j] >= MDTI:\r\n                        polygon = create_polygon_from_coords_list([TS[ZIL[j]][1][0][:TS[ZIL[j]][2][0][1]] + envelope[0][TS[ZIL[j]][2][1][1]:TS[ZIL[j+1]][2][1][1]+1] + TS[ZIL[j + 1]][1][0][TS[ZIL[j+1]][2][0][1]-1::-1], TS[ZIL[j]][1][1][:TS[ZIL[j]][2][0][1]] + envelope[1][TS[ZIL[j]][2][1][1]:TS[ZIL[j+1]][2][1][1]+1] + TS[ZIL[j + 1]][1][1][TS[ZIL[j+1]][2][0][1]-1::-1]])\r\n                    # situation where first of the trajectories precedes trajectory with furthest reach\r\n                    else:\r\n                        polygon = create_polygon_from_coords_list([TS[ZIL[j]][1][0] + envelope[0][:TS[ZIL[j+1]][2][1][0]+1] + TS[ZIL[j + 1]][1][0][TS[ZIL[j+1]][2][0][0]::-1], TS[ZIL[j]][1][1] + envelope[1][:TS[ZIL[j+1]][2][1][0]+1] + TS[ZIL[j + 1]][1][1][TS[ZIL[j+1]][2][0][0]::-1]])\r\n            if polygon.Intersects(relative_cell):\r\n                break\r\n        if abs(ZIL[j + 1] - ZIL[j]) == 1:\r\n            i = ZIL[j]\r\n            break\r\n        ZIL = [ZIL[j], int(ZIL[j] + (ZIL[j + 1] - ZIL[j]) / 2), ZIL[j + 1]]\r\n\r\n\r\n    end4 = timer()\r\n    print(f'Zooming took {end4 - start4} seconds.')\r\n\r\n\r\n    start5 = timer()\r\n\r\n    # auxiliary indexes\r\n    i1, i2 = -1, -1\r\n    # Intersecting Trajectory Found list informing whether the previous and following intersecting trajectories were already found and compared with the terrain\r\n    ITF = [1,1]\r\n    # Inserted Trajectories Starting Index from which newly added trajectories will be removed right before returning from this function\r\n    ITSI = -1\r\n    # Inserted Trajectories Index Span across which the newly added trajectories will be removed\r\n    ITIS = 0\r\n    # at first, 2 surrounding trajectories are found by making polygon out of them and asking whether the cell lies within\r\n    while True:\r\n        # if the cell lies within, normals are computed to assess the smallest perpendicular distance from trajectory to cell\r\n        if polygon.Intersects(relative_cell):\r\n            # Inserted Trajectories Starting Index\r\n            if ITSI == -1:\r\n                ITSI = i+1\r\n            # first condition is just to recycle normal1/2 computed in previous cycle that will be the same if first/second half of previous polygon is the one the cell lies within\r\n            if i1 - i and ITF[0]:\r\n                # compute normal from previous/following trajectory segment to cell\r\n                normal1 = compute_normal(i, relative_cell.GetX(), relative_cell.GetY())\r\n                # if cell is not close enough to trajectory to consider it as piercing trajectory, following trajectory is tested. If it is close enough, i will be used as the index of intersecting trajectory in the terrain comparison\r\n                if not np.round(normal1 / TSW):\r\n                    if trajectory_terrain_comparison(i, relative_cell, absolute_cell):\r\n                        del TS[ITSI:ITSI + ITIS]\r\n\r\n\r\n                        end5 = timer()\r\n                        print(f'Intersecting took {end5 - start5} seconds.')\r\n\r\n\r\n                        return True\r\n                    # 1 is changed to 0 so next time the condition is eluded\r\n                    ITF[0] -= 1\r\n            if i2 - i and ITF[1]:\r\n                normal2 = compute_normal(i+1, relative_cell.GetX(), relative_cell.GetY())\r\n                if not np.round(normal2 / TSW):\r\n                    if trajectory_terrain_comparison(i+1, relative_cell, absolute_cell):\r\n                        del TS[ITSI:ITSI + ITIS]\r\n\r\n\r\n                        end5 = timer()\r\n                        print(f'Intersecting took {end5 - start5} seconds.')\r\n\r\n\r\n                        return True\r\n                    ITF[1] -= 1\r\n            # if trajectories from both sides are intersecting and none of them returned True meaning reachable cell, cell is considered unreachable\r\n            if not any(ITF):\r\n                del TS[ITSI:ITSI+ITIS]\r\n\r\n\r\n                end5 = timer()\r\n                print(f'Intersecting took {end5 - start5} seconds.')\r\n\r\n\r\n                return False\r\n            # ratio for angle addition to angle of previous trajectory, if one of the trajectories is already intersecting, second one is being searched by halving the angle difference of surrounding trajectories, because by normal ratio new trajectory can fall on wrong side of the cell, to the one that has already been assessed, which will slow down the computation\r\n            ratio = 1 / 2 if not ITF[0] or not ITF[1] else normal1 / (normal1 + normal2)\r\n            # new alpha calculated from the ratio and new trajectory is generated\r\n            new_alpha = TS[i][0] + (TS[i + 1][0] - TS[i][0]) * ratio\r\n            TS.insert(i + 1, [new_alpha, generate_trajectory(new_alpha)])\r\n            # index i needs to be set one less to start again at the same trajectory\r\n            # auxiliary index i1/2 to find out if the normal1/2 was already computed, will be used in next iteration\r\n            i -= 1\r\n            i1 = i\r\n            i2 = i + 2\r\n            # 1 trajectory added to the index span\r\n            ITIS += 1\r\n        i += 1\r\n        # create polygon with inserted trajectory/ies\r\n        polygon = create_polygon_from_coords_list([TS[i][1][0] + TS[i + 1][1][0][-1::-1], TS[i][1][1] + TS[i + 1][1][1][-1::-1]])\r\n\r\ndef compute_normal(i, X_relative_cell, Y_relative_cell):\r\n    \"\"\"Computes perpendicular distance from closest segment of given trajectory and returns its size as well as index\r\n    of first point of closest segment.\"\"\"\r\n\r\n    start2 = timer()\r\n\r\n\r\n    # Trajectory Point - Cell Distance List\r\n    TPCDL = [((TS[i][1][0][j] - X_relative_cell) ** 2 + (TS[i][1][1][j] - Y_relative_cell) ** 2) ** (1 / 2) for j in range(len(TS[i][1][0]))]\r\n    # index of closest point to cell is found\r\n    j = TPCDL.index(min(TPCDL))\r\n    # index of first of two closest points to cell is found\r\n    if j != 0 and j != len(TPCDL) - 1:\r\n        if TPCDL[j - 1] < TPCDL[j + 1]:\r\n            j -= 1\r\n    elif j == len(TPCDL) - 1:\r\n        j -= 1\r\n    # if closest point to cell mid point is closer than allowed distance, this distance is returned as there is no need to compute perpendicular distance to whole segment which can be only smaller than the point-cell distance\r\n    if not np.round(min(TPCDL) / TSW):\r\n\r\n\r\n\r\n\r\n        end2 = timer()\r\n        print(f'Normal took {end2 - start2} seconds.')\r\n\r\n\r\n        return min(TPCDL)\r\n    # calculate perpendicular distance (normal) from closest trajectory segment\r\n    a = TPCDL[j]\r\n    b = TPCDL[j + 1]\r\n    c = ((TS[i][1][0][j] - TS[i][1][0][j + 1]) ** 2 + (TS[i][1][1][j] - TS[i][1][1][j + 1]) ** 2) ** (1 / 2)\r\n    s = (a + b + c) / 2\r\n    area = (s * (s - a) * (s - b) * (s - c)) ** (1 / 2)\r\n\r\n\r\n    end2 = timer()\r\n    print(f'Normal took {end2-start2} seconds.')\r\n\r\n    return area / c * 2\r\n\r\ndef trajectory_terrain_comparison(i, relative_cell, absolute_cell):\r\n    \"\"\"Computes coordinates of terrain corresponding to each trajectory point and returns True or False depending\r\n    on the result of terrain and trajectory point heights comparison.\"\"\"\r\n\r\n\r\n    start3 = timer()\r\n\r\n\r\n    # calculate azimuth of trajectory (shooting point to cell point), there is a chance of Y difference to be 0, therefore the exception\r\n    dX = absolute_cell.GetX() - SP.GetX()\r\n    dY = absolute_cell.GetY() - SP.GetY()\r\n    # for the case where shooting point and middle point of assessed cell are same, automatically reachable\r\n    if not dX and not dY:\r\n        return True\r\n    try:\r\n        Azimuth = np.arctan(dX / dY)\r\n    except ZeroDivisionError:\r\n        # for the case of dY being 0, making the division impossible\r\n        if dX > 0:\r\n            Azimuth = np.radians(90)\r\n        else:\r\n            Azimuth = np.radians(270)\r\n    # azimuth needs to be recalculated accordingly to correct quadrant\r\n    if dY > 0:\r\n        if dX < 0:\r\n            Azimuth += np.radians(360)\r\n    elif dY < 0:\r\n        Azimuth += np.radians(180)\r\n    # cycle iterates from first point of trajectory to the first point of segment closest to the cell point\r\n    for X, Y in zip(TS[i][1][0],TS[i][1][1]):\r\n        # if compared point is already above/below destination cell's area or beyond, cell is considered reachable\r\n        if X >= relative_cell.GetX()-TSW/2:\r\n\r\n\r\n            end3 = timer()\r\n            print(f'Terrain comparison took {end3 - start3} seconds.')\r\n\r\n\r\n            return True\r\n        X_compare_point = SP.GetX() + X * np.sin(Azimuth)\r\n        Y_compare_point = SP.GetY() + X * np.cos(Azimuth)\r\n        Z_compare_point = int_function(X_compare_point, Y_compare_point)\r\n        # if trajectory point is on or below terrain, False is returned\r\n        if Y <= Z_compare_point:\r\n\r\n\r\n            end3 = timer()\r\n            print(f'Terrain comparison took {end3 - start3} seconds.')\r\n\r\n\r\n            return False\r\n\r\ndef create_viewshed():\r\n    \"\"\"Computes viewshed for one point which is saved temporarily, then load as an array.\"\"\"\r\n    global VDS, VB, VA, VGT\r\n    # generate viewshed and save it as temporary file to throwshed directory\r\n    gdal.ViewshedGenerate(srcBand=DB, driverName='GTiff', targetRasterName=TOF + \"\\\\viewshed.tif\", creationOptions=[], observerX=SP.GetX(), observerY=SP.GetY(), observerHeight=EH, targetHeight=TH, visibleVal=1, invisibleVal=0, outOfRangeVal=0, noDataVal=NDV, dfCurvCoeff=0.85714, mode=2, maxDistance=0)\r\n    # open viewshed raster, Viewshed Array will be crucial\r\n    VDS, VB, VA, VGT, ndv = get_raster_from_file(TOF + \"\\\\viewshed.tif\")\r\n\r\ndef plot_trajectory(relative_cell, absolute_cell,jj,row,col,dir, poly_list, j, zoom):\r\n    import matplotlib.pyplot as plt  # na vykreslenie grafov\r\n    plt.figure(figsize=(32, 18))\r\n    for i in range(len(TS)):\r\n        # plotting the points\r\n        #plt.plot(TS[i][1][0], TS[i][1][1], markersize=5, linewidth=1, label=TS[i][0]/np.pi*180)\r\n        plt.plot(TS[i][1][0], TS[i][1][1], '.', markersize=1)\r\n\r\n    #plt.plot(envelope[0], envelope[1], '-', linewidth=1)\r\n    #plt.plot([0, max(TS, key=lambda x: x[1][0][-1])[1][0][-1]], [DMAXH, DMAXH], '-', linewidth=1)\r\n\r\n    # end_cell = ogr.Geometry(ogr.wkbPoint)\r\n    # end_cell.AddPoint(-488475.5,-1259010.5)\r\n    profile = get_profile(absolute_cell)\r\n    plt.plot(profile[0], profile[1], '-', linewidth=3)\r\n\r\n    # plt.plot(TS[jj][1][0], TS[jj][1][1], '-', linewidth=1)\r\n    # plt.plot(TS[jj+1][1][0], TS[jj+1][1][1], '-', linewidth=1)\r\n\r\n    plt.plot(poly_list[0], poly_list[1], '-', linewidth=2)\r\n\r\n    plt.plot(relative_cell.GetX(), relative_cell.GetY(), 'o', markersize=2)\r\n\r\n    # plt.plot(ATF[0], ATF[1], '-', linewidth=2)\r\n    # plt.plot(DTF[0], DTF[1], '-', linewidth=2)\r\n    print(i)\r\n    #plt.plot(temp_xyp[0], temp_xyp[1], 'r.', markersize=2)\r\n    # xpar = max(TS, key=lambda x: x[1][0][-1])[1][0][-1]\r\n    # ypar = max(TS[-1][1][1])\r\n    # a = -ypar/xpar**2\r\n    # b = 0\r\n    # c = ypar\r\n    # x = []\r\n    # y = []\r\n    # for i in range(0,int(xpar)+1):\r\n    #     x.append(i)\r\n    #     y.append(a*i**2+b*i+c)\r\n    # plt.plot(x, y, 'b*', markersize=1)\r\n\r\n    # ohranicenie, popis osi a nastavenie rovnakej mierky v smere oboch osi\r\n    plt.xlim(0, 155)\r\n    plt.ylim(DMINH, max(TS[-1][1][1]))\r\n\r\n    # plt.xlim(min(poly_list[0]), max(poly_list[0]))\r\n    # plt.ylim(min(poly_list[1]), max(poly_list[1]))\r\n\r\n\r\n    plt.xlabel(\"vzdialenosť [m]\")\r\n    plt.ylabel(\"výška [m]\")\r\n    plt.gca().set_aspect('equal', adjustable='box')\r\n\r\n    # function to show the plot\r\n    #plt.legend()\r\n\r\n\r\n    plt.savefig(f'filename{row}_{col}_{dir}_zoom{zoom}_j{j}.png', dpi=300)\r\n\r\n    start = timer()\r\n    plt.show()\r\n    end = timer()\r\n    print('cas vykreslenia:', end-start)\r\n\r\ndef get_profile(end_cell):\r\n    dX = end_cell.GetX() - SP.GetX()\r\n    dY = end_cell.GetY() - SP.GetY()\r\n    try:\r\n        Azimuth = np.arctan(dX / dY)\r\n    except ZeroDivisionError:\r\n        # for the case of dY being 0, making the division impossible\r\n        if dX > 0:\r\n            Azimuth = np.radians(90)\r\n        else:\r\n            Azimuth = np.radians(270)\r\n            # azimuth needs to be recalculated accordingly to correct quadrant\r\n    if dY > 0:\r\n        if dX < 0:\r\n            Azimuth += np.radians(360)\r\n    elif dY < 0:\r\n        Azimuth += np.radians(180)\r\n    profile = [[0],[SP.GetZ()-IH]]\r\n    s = 0\r\n    cell_dist = ((SP.GetX() - end_cell.GetX()) ** 2 + (SP.GetY() - end_cell.GetY()) ** 2) ** (1 / 2)\r\n    while True:\r\n        s += 0.5\r\n        X_compare_point = SP.GetX() + s * np.sin(Azimuth)\r\n        Y_compare_point = SP.GetY() + s * np.cos(Azimuth)\r\n        Z_compare_point = int_function(X_compare_point, Y_compare_point)\r\n        profile[0].append(s)\r\n        profile[1].append(Z_compare_point)\r\n        if s > cell_dist:\r\n            break\r\n    return profile\r\n\r\n#######################################################################\r\n## PATHS\r\ndem_path = r\"D:\\School\\STU_SvF_BA\\Term11\\Dizertacna_praca\\Throwshed2\\data\\dem\\dmr_clip.tif\" #path to DEM\r\npoint_layer_path = r\"D:\\School\\STU_SvF_BA\\Term11\\Dizertacna_praca\\Throwshed2\\data\\point\\points1.shp\"   #path to point layer\r\nline_layer_path = r\"D:\\School\\STU_SvF_BA\\Term11\\Dizertacna_praca\\Throwshed2\\data\\line\\lines1.shp\" #path to line layer\r\nthrowshed_output_folder = r\"D:\\School\\STU_SvF_BA\\Term11\\Dizertacna_praca\\Throwshed2\\data\\throwshed\"  #path to folder, where the file will be saved\r\nthrowshed_file = r\"throwshedtestnew\"   #name of output throwshed file\r\n\r\n## SETTINGS\r\nthrowshed_mode = 1 #what type of throwshed will be calculated, simple safety zone (cells within safety field) = 0, regular throwshed with trajectory assessment = 1\r\nuse_viewshed = 0 #utilization of viewshed, that will clip throwshed, No = 0, Yes = 1\r\nuse_lines = 0 #utilization of line layer, where lines serve as obstacles or walls and will be burnt into DEM, No = 0, Yes = 1\r\nband_number = 1 #selected band from DEM, default = 1\r\ninterpolation = 0 #interpolation of DEM to calculate altitude of shooting point or compare points within the DEM-to-trajectory comparison function, Nearest neighbour = 0, Bilinear = 1\r\ncumulative_throwshed = 1 #Calculate cumulative throwshed? No = 0, Yes = 1 (Apropriate with more than 1 shooting places)\r\nEPSG = 8353 #EPSG code for CRS of output throwshed layer and other temporary results, must be same as DEM's EPSG\r\n\r\n## VARIABLES\r\ninitial_height = 1.7 #initial height of projectile above DEM when shot [m]\r\nalpha_min = -90.0 #minimum of vertical angle range at which the projectile is shot [°]\r\nalpha_max = 15.0 #maximum of vertical angle range at which the projectile is shot [°]\r\ngravitational_acceleration = -9.81 #gravitational acceleration [m/s^2]\r\ninitial_velocity = 50 #initial velocity of projectile when shot [m/s]\r\nair_density = 1.225 #air density [kg/m^3]\r\ndrag_coefficient = 0.47 #aerodynamic drag coefficient of projectile\r\ncross_sectional_area = 0.001963 #cross-sectional area of the projectile [m^2]\r\nmass = 0.100 #projectile mass [kg]\r\ndalpha = 5 #step in vertical angle range [°]\r\ntrajectory_segment_width = None #distance step, at which trajectory's points will be saved and compared to DEM [m], None = adjusted to DEM resolution (cell's size), any float/int value = customized distance step\r\neyes_height = 1.6 #shooter eye height above DEM for viewshed [m]\r\ntarget_height = 1.7 #target height for viewshed [m]\r\nwall_height = 4.0 #obstacle/wall height (if obstacle option is used) [m]\r\nconstant = 1 #constant multipling the drag coefficient within wobble distance of an arrow\r\narea_addition = 0.0 #average addition to cross-sectional area of an arrow within wobble distance [m^2]\r\nwobble_distance = 40 #wobble distance - distance at which an arrow stops wobbling [m]\r\n\r\n\r\n\r\nstart = timer()\r\n\r\n\r\nmain(dem_path, point_layer_path, line_layer_path, throwshed_output_folder, throwshed_file, throwshed_mode, use_viewshed, use_lines, EPSG,\r\n         cumulative_throwshed, initial_height, initial_velocity, drag_coefficient, cross_sectional_area, mass,\r\n         eyes_height, target_height, wall_height, constant, area_addition, wobble_distance, band_number=band_number,\r\n         interpolation=interpolation, alpha_min=alpha_min, alpha_max=alpha_max,\r\n         gravitational_acceleration=gravitational_acceleration, air_density=air_density, dalpha=dalpha,\r\n         trajectory_segment_width=None)\r\n\r\nend = timer()\r\nprint('Duration:', end - start)\r\n","repo_name":"Tadeo98/throwshed2","sub_path":"throwshed2.py","file_name":"throwshed2.py","file_ext":"py","file_size_in_byte":51344,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"32616053119","text":"#exec in python 3 shell: exec(open(\"infix1.py\").read())\n\ndef eval_infix_sum(expr, pos):\n\t\"\"\"evaluate a sum expression (zero or more additions and subtractions)\"\"\"\n\n\t#replaces the first value with the sum/difference\n\tif (expr[pos] == '+'):\n\t\texpr[pos-1] = int(expr[pos-1]) + int(expr[pos+1])     \n\n\telif (expr[pos] == '-'):\n\t\texpr[pos-1] = int(expr[pos-1]) - int(expr[pos+1])\n\n\t#deletes the operator sign and the second number\n\tdel expr[pos : pos+2]\n\treturn(expr)    \n\ndef eval_infix_product(expr, pos):\n\t\"\"\"evaluate a product expression (zero or more multiplications/divisions)\"\"\"\n\n\t#replaces the first value with the product\n\tif (expr[pos] == '*'):\n\t\texpr[pos-1] = int(expr[pos-1]) * int(expr[pos+1])     \n\n\telif (expr[pos] == '/'):\t\t\n\t\texpr[pos-1] = int(expr[pos-1]) // int(expr[pos+1])\n\n\t#deletes the operator sign and the second number\n\tdel expr[pos : pos+2]\n\treturn(expr)\n\ndef eval_infix_factor(expr, pos):\n\t\"\"\"evaluate a factor (number or parenthesized sub-expression)\"\"\"\n\t#this function figures out whats inside the parentheses, creates a new string for that part and evaluates that substring\n\n\t#the number of open/closed parentheses. this is necessary to make sure nested parentheses work\n\topenparen = 1\n\tcloseparen = 0\n\t#position for the substring that will be evaluated\n\tendpos = pos\n\n\t#breaks when the currently open parentheses are closed\n\twhile (openparen != closeparen):\n\n\t\tendpos += 1\n\n\t\tif expr[endpos] == '(':\n\t\t\topenparen += 1\n\t\telif expr[endpos] == \")\":\n\t\t\tcloseparen += 1\n\n\t#gather the contents of the brackets into a new list which will be evaluated. should work with nested parentheses\n\tsubexpr = expr[(pos+1) : endpos]\n\tans = eval_infix_list(subexpr)\n\n\texpr[pos] = ans\n\tdel expr[pos+1 : endpos+1]\n\n\treturn expr\n\ndef eval_infix(expr):       \n\n\texpr = eval_infix_list(expr.split())\n\treturn expr\n\ndef eval_infix_list(expr):\n\n\t#adds a semicolon making the string easier to work with\n\tif expr[-1] != ';':\n\t\texpr += ';'\n\t\n\t#i tried using a \"for pos in range\" thing and it didnt work so im doing this. \n\twhile ('(' in expr) or (')' in expr):\n\t\tpos = 0\n\t\twhile not(expr[pos] == '('):\n\t\t\tpos += 1\n\n\t\texpr = eval_infix_factor(expr, pos)\n\t\n\t#does all of the multiplcation and division operations until there are no more * or / signs\n\twhile ('*' in expr) or ('/' in expr):\n\t\tpos = 0\n\t\twhile not((expr[pos] == '*') or (expr[pos] == '/')):\n\t\t\tpos += 1\n\n\t\texpr = eval_infix_product (expr, pos)\n\n\n\t#does all of the add/sub operations until there are no more + or - signs\n\twhile ('+' in expr) or ('-' in expr):\n\t\tpos = 0\n\t\twhile not((expr[pos] == '+') or (expr[pos] == '-')):\n\t\t\t pos += 1\n\n\t\texpr = eval_infix_sum (expr, pos)\n\n\treturn (expr[0])","repo_name":"LeoLeoni/cmpsc122","sub_path":"previous/infix1.py","file_name":"infix1.py","file_ext":"py","file_size_in_byte":2650,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"72776897225","text":"from time import strptime\nfrom desktopcouch.records.record import Record\n\nNOTE_RECORD_TYPE = 'http://www.freedesktop.org/wiki/Specifications/desktopcouch/note'\n# keep in sync with the above notes record type\nFIELDS = {\n    # String fields\n    'note_format': 'string',\n    'title': 'string',\n    'content': 'string',\n    # Date fields\n    'last_change_date': 'date',\n    'create_date': 'date'\n}\n\nclass NoteBase(Record):\n    \"\"\"\n    A base for Note records.\n\n    Use make_note_class to create the Note class with the required fields.\n    \"\"\"\n\n    def __init__(self, data=None, record_id=None):\n        super(NoteBase, self).__init__(\n            record_id=record_id, data=data, record_type=NOTE_RECORD_TYPE)\n\ndef make_note_class(fields):\n    \"\"\"Note class factory function. field_names is a list of strings.\"\"\"\n    NoteClass = type('Note', (NoteBase,), {})\n    for field_name in fields:\n\n        def fget(self, _field_name=field_name):\n            return self.get(_field_name)\n\n        def fset(self, value, _field_name=field_name):\n            field_type = fields[_field_name]\n            if field_type == 'date':\n                # Check that it is a date with the correct format\n                date_value = strptime(value, \"%Y-%m-%dT%H:%M:%S\")\n                if date_value is None:\n                    return\n            self[_field_name] = value\n\n        setattr(NoteClass, field_name, property(fget, fset))\n    return NoteClass\n\nNote = make_note_class(FIELDS)\n","repo_name":"inxware/ert-contrib-middleware","sub_path":"target_libs/arm-linux-gnu-glibc-2.12.1-ti-blaze-ubuntu-10_10-gtk_gst/arm-linux-gnu-glibc-2.12.1-ti-blaze-ubuntu-10_10-gtk_gst/build/lib/python2.6/dist-packages/desktopcouch/notes/record.py","file_name":"record.py","file_ext":"py","file_size_in_byte":1464,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"81"}
+{"seq_id":"32883966449","text":"import numpy as np\nimport cv2\nimport depthai  # deals with camera and its data packets\nfrom glob import glob\n\nfrom util import frame_norm\n\n\nfor device in depthai.Device.getAllAvailableDevices():\n    print(f\"Device ID:{device.getMxId()}, Device state: {device.state}\")\n\nmobilenet_path = glob(\"models/*.blob\")[0]  # mobileNet\n\n\ndef run():\n    pipeline = depthai.Pipeline()\n\n    cam_rgb = pipeline.createColorCamera()\n    cam_rgb.setPreviewSize(300, 300)\n    cam_rgb.setInterleaved(False)\n\n    detection_nn = pipeline.createNeuralNetwork()\n    detection_nn.setBlobPath(mobilenet_path)\n\n    cam_rgb.preview.link(detection_nn.input)\n\n    # output both rgb and nn inference to host device screen\n    xout_rgb = pipeline.createXLinkOut()\n    xout_rgb.setStreamName(\"rgb\")\n    cam_rgb.preview.link(xout_rgb.input)\n\n    xout_nn = pipeline.createXLinkOut()\n    xout_nn.setStreamName(\"nn\")\n    detection_nn.out.link(xout_nn.input)\n\n    # intialize\n    device = depthai.Device(pipeline)\n    device.startPipeline()\n\n    # host side queues to receive results\n    q_rgb = device.getOutputQueue(\"rgb\")\n    q_nn = device.getOutputQueue(\"nn\")\n\n    # place-holders to consume the above result\n    frame = None\n    bboxes = []\n\n    while True:\n        in_rgb = q_rgb.tryGet()\n        in_nn = q_nn.tryGet()\n\n        # transform the input from rgb camera(1D array) into HWC format\n        if in_rgb is not None:\n            shape = (3, in_rgb.getHeight(), in_rgb.getWidth())\n            frame = in_rgb.getData().reshape(shape).transpose(1, 2, 0).astype(np.uint8)\n            frame = np.ascontiguousarray(frame)\n\n        # transform the nn inputs too\n        # (image_id, label, confidence, x_min, y_min, x_max, y_max)\n        # the last four fields are the bouding boxes\n        if in_nn is not None:\n            bboxes = np.array(in_nn.getFirstLayerFp16())\n            bboxes = bboxes[: np.where(bboxes == -1)[0][0]]\n            bboxes = bboxes.reshape((bboxes.size // 7, 7))\n            bboxes = bboxes[bboxes[:, 2] > 0.8][:, 3:7]\n\n        # display the result\n        if frame is not None:\n            for raw_bbox in bboxes:\n                bbox = frame_norm(frame, raw_bbox)\n                # (image, point1, point2, color, thickness)\n                cv2.rectangle(\n                    frame, (bbox[0], bbox[1]), (bbox[2], bbox[3]), (255, 0, 0), 2\n                )\n            cv2.imshow(\"preview\", frame)\n\n        if cv2.waitKey(1) == ord(\"q\"):\n            break\n\n\nif __name__ == \"__main__\":\n    run()","repo_name":"bhagone/depthai-OAKD","sub_path":"base.py","file_name":"base.py","file_ext":"py","file_size_in_byte":2486,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"81"}
+{"seq_id":"4317758840","text":"from utils import *\nfrom dataloader import *\nfrom rnn_crf import *\n\ndef load_model(args):\n\n    cti = load_tkn_to_idx(args[1]) # char_to_idx\n    wti = load_tkn_to_idx(args[2]) # word_to_idx\n    itt = load_idx_to_tkn(args[3]) # idx_to_tag\n\n    model = rnn_crf(cti, wti, len(itt))\n    print(model)\n\n    load_checkpoint(args[0], model)\n\n    return model, cti, wti, itt\n\ndef run_model(model, data, itt):\n\n    with torch.no_grad():\n        model.eval()\n\n        for batch in data.batchify(BATCH_SIZE):\n\n            xc, xw, lens = batch.xc, batch.xw, batch.lens\n            xc, xw = data.to_tensor(bc = xc, bw = xw, lens = lens)\n            y1 = model.decode(xc, xw, lens)\n            batch.y1 = [[itt[i] for i in y] for y in y1]\n\n            for x0, y0, y1 in zip(batch.x0, batch.y0, batch.y1):\n                if not HRE:\n                    x0, y0, y1 = [x0], [y0], [y1]\n                for x0, y0, y1 in zip(x0, y0, y1):\n                    yield x0, y0, y1\n\ndef predict(model, cti, wti, itt, filename):\n\n    data = dataloader(hre = HRE)\n    with open(filename) as fo:\n        text = fo.read().strip().split(\"\\n\" * (HRE + 1))\n\n    for block in text:\n        data.append_row()\n\n        for line in block.split(\"\\n\"):\n\n            if re.match(\"\\S+/[^ /]+( \\S+/[^ /]+)*$\", line): # word/tag\n                x0, y0 = zip(*[re.split(\"/(?=[^/]+$)\", w) for w in line.split(\" \")])\n                x1 = list(map(normalize, x0))\n            else:\n                x0, y0 = line, []\n                if re.match(\"[^\\t]+\\t[^\\t]+$\", x0): # sentence \\t label\n                    x0, *y0 = x0.split(\"\\t\")\n                x0 = tokenize(x0)\n                x1 = list(map(normalize, x0))\n\n            xc = [[cti[c] if c in cti else UNK_IDX for c in w] for w in x1]\n            xw = [wti[w] if w in wti else UNK_IDX for w in x1]\n\n            data.append_item(x0 = x0, x1 = x1, xc = xc, xw = xw, y0 = y0)\n\n    return run_model(model, data, itt)\n\nif __name__ == \"__main__\":\n\n    if len(sys.argv) != 6:\n        sys.exit(\"Usage: %s model char_to_idx word_to_idx tag_to_idx test_data\" % sys.argv[0])\n\n    result = predict(*load_model(sys.argv[1:5]), sys.argv[5])\n    func = tag_to_txt if TASK else lambda *x: x\n\n    for x0, y0, y1 in result:\n        if y0:\n            print(func(x0, y0))\n        print(func(x0, y1))\n","repo_name":"threelittlemonkeys/lstm-crf-pytorch","sub_path":"predict.py","file_name":"predict.py","file_ext":"py","file_size_in_byte":2288,"program_lang":"python","lang":"en","doc_type":"code","stars":455,"dataset":"github-code","pt":"81"}
+{"seq_id":"25675626","text":"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport numpy as np\n\nclass EmbeddingLossFunctions(object):\n    def __init__(self, loss_fn='xent', neg_sample_weights=1.0):\n        \"\"\"\n        Basic class that applies skip-gram-like loss\n        (i.e., dot product of node+target and node and negative samples)\n        Args:\n            bilinear_weights: use a bilinear weight for affinity calculation: u^T A v. If set to\n                false, it is assumed that input dimensions are the same and the affinity will be\n                based on dot product.\n        \"\"\"\n        self.neg_sample_weights = neg_sample_weights\n        self.output_dim = 1\n        if loss_fn == 'xent':\n            self.loss_fn = self._xent_loss\n        else:\n            print(\"Not implemented yet.\")\n\n\n    def loss(self, inputs1, inputs2, neg_samples):\n        \"\"\" negative sampling loss.\n        Args:\n            neg_samples: tensor of shape [num_neg_samples x input_dim2]. Negative samples for all\n            inputs in batch inputs1.\n        \"\"\"\n        return self.loss_fn(inputs1, inputs2, neg_samples)\n\n    def affinity(self, inputs1, inputs2):\n        \"\"\" Affinity score between batch of inputs1 and inputs2.\n        Args:\n            inputs1: tensor of shape [n_batch_edges x feature_size].\n        \"\"\"\n        # shape: [n_batch_edges, input_dim1]\n        result = torch.sum(inputs1 * inputs2, dim=1) # shape: (n_batch_edges,)\n        return result\n\n    def neg_cost(self, inputs1, neg_samples):\n        \"\"\" For each input in batch, compute the sum of its affinity to negative samples.\n\n        Returns:\n            Tensor of shape [n_batch_edges x num_neg_samples]. For each node, a list of affinities to\n                negative samples is computed.\n        \"\"\"\n        neg_aff = inputs1.mm(neg_samples.t()) #(n_batch_edges, num_neg_samples)\n        return neg_aff\n\n\n    def sigmoid_cross_entropy_with_logits(self, labels, logits):\n        sig_aff = torch.sigmoid(logits)\n        loss = labels * -torch.log(sig_aff) + (1 - labels) * -torch.log(1 - sig_aff)\n        return loss\n\n    def _xent_loss(self, inputs1, inputs2, neg_samples):\n        \"\"\"\n        inputs1: Tensor (512, 256), normalized vector\n        inputs2: Tensor (512, 256), normalized vector\n        neg_sample: Tensor (20, 256)\n        \"\"\"\n        cuda = inputs1.is_cuda\n        true_aff = self.affinity(inputs1, inputs2)\n        neg_aff = self.neg_cost(inputs1, neg_samples)\n        true_labels = torch.ones(true_aff.shape)  # (n_batch_edges,)\n        if cuda:\n            true_labels = true_labels.cuda()\n        true_xent = self.sigmoid_cross_entropy_with_logits(labels=true_labels, logits=true_aff)\n        neg_labels = torch.zeros(neg_aff.shape)\n        if cuda:\n            neg_labels = neg_labels.cuda()\n        neg_xent = self.sigmoid_cross_entropy_with_logits(labels=neg_labels, logits=neg_aff)\n        loss0 = true_xent.sum()\n        loss1 = self.neg_sample_weights * neg_xent.sum()\n        loss = loss0 + loss1\n        return loss, loss0, loss1\n\n\nclass MappingLossFunctions(object):\n    def __init__(self):\n        self.loss_fn = self._euclidean_loss\n\n    def loss(self, inputs1, inputs2):\n        return self.loss_fn(inputs1, inputs2)\n\n    def _euclidean_loss(self, inputs1, inputs2):\n        sub = inputs2 - inputs1\n        square_sub = sub**2\n        loss = torch.sum(square_sub)        \n        return loss\n","repo_name":"thanhtrunghuynh93/networkAlignment","sub_path":"algorithms/PALE/loss.py","file_name":"loss.py","file_ext":"py","file_size_in_byte":3392,"program_lang":"python","lang":"en","doc_type":"code","stars":44,"dataset":"github-code","pt":"81"}
+{"seq_id":"6778646070","text":"import re\n\nclass RegexpCase(object):\n    def __init__(self, rules, prefix=None, suffix=None, default=None):\n        offset = 0\n        probes = []\n        maycall = default is None or callable(default)\n\n        # We use a single regular expression and use special probe\n        # captures to figure out which one has actually matched.\n        # Hopefully, the regular expression engine will make this run\n        # fast.\n        for (regexp, action) in rules:\n            compiled = re.compile(regexp)\n            probes.append((offset, offset + 1, offset + compiled.groups + 1,\n                           action))\n            offset += compiled.groups + 1\n            if action is not None:\n                maycall = maycall and callable(action)\n        self.probes = tuple(probes)\n        self.maycall = maycall\n\n        if not self.probes:\n            raise ValueError(\"empty rule list\")\n        if prefix is None:\n            prefix = \"^(?:(\"\n        else:\n            if re.compile(prefix).groups > 0:\n                raise ValueError(\"prefix must not contain captures\")\n            prefix = \"^(?:\" + prefix + \")(?:(\"\n\n        if suffix is None:\n            suffix = \"))$\"\n        else:\n            if re.compile(suffix).groups > 0:\n                raise ValueError(\"suffix must not contain captures\")\n            suffix = \"))(?:\" + suffix + \")$\"\n\n        self.regexp = re.compile(\n            prefix + ')|('.join(regexp for (regexp, action) in rules)\n            + suffix)\n\n        self.default = default\n\n    def match(self, key):\n        match = self.regexp.match(key)\n        if match is None:\n            return (None, self.default)\n        groups = match.groups()\n        for (probe, i, j, action) in self.probes:\n            if groups[probe] is not None:\n                return (groups[i:j], action)\n        raise AssertionError(\"pattern and offset list incongruent\")\n\n    def __getitem__(self, key):\n        return self.match(key)[1]\n\n    def __call__(self, key, *args):\n        if not self.maycall:\n            raise TypeError(\"not all actions are callable\")\n        (groups, action) = self.match(key)\n        if action is None:\n            return None\n        if groups is None:\n            groups = key\n        return action(groups, *args)\n\ndef rule(regexp):\n    \"\"\"Add a regular expression to the function, for the rule list\"\"\"\n    return lambda f: (regexp, f)\n","repo_name":"CVEDB/security-tracker","sub_path":"lib/python/sectracker/regexpcase.py","file_name":"regexpcase.py","file_ext":"py","file_size_in_byte":2378,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"71221140746","text":"import matplotlib.pyplot as plt\nimport seaborn as sns\nimport pandas as pd\n\ndef plot_variable_pairs(df, target):\n    for column in df.columns:\n        if column != target:\n            sns.lmplot(x=column, y=target, data=df)\n            \ndef months_to_years(df):\n    df_copy = df.copy()\n    df_copy['tenure_years'] = round(df_copy.tenure / 12)\n    \n    return df_copy\n\ndef plot_categorical_and_continuous_vars(df, cont_vars, cat_vars, target, target_type=\"continuous\"):\n    var_list = []\n    \n    if target_type == \"categorical\":\n        for categorical in cat_vars:\n            sns.heatmap(df[[target, categorical]])\n            plt.show()\n        \n        var_list = cont_vars\n    \n    else:\n        for continous in cont_vars:\n            sns.relplot(x=continous, y=target, data=df)\n            plt.show()\n            \n        var_list = cat_vars\n            \n    _generate_swarmplots(df, var_list, target)\n            \ndef _generate_swarmplots(df, var_list, target):\n    for var in var_list:\n            sns.swarmplot(x=var, y=target, data=df)\n            plt.show()","repo_name":"david-ryan-alviola/regression-exercises","sub_path":"explore.py","file_name":"explore.py","file_ext":"py","file_size_in_byte":1068,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"12022984081","text":"import boto3\nimport time\nimport json\n\n\ndef abortable_operation_upgrade(envName):\n\n    client = boto3.client('elasticbeanstalk')\n    print('\\n\\nConnecting')\n\n    print('\\n\\nChecking for ready status on environment -', envName)\n\n    while True:\n\n        abortable_response = client.describe_environments(\n            EnvironmentNames=[envName],\n            IncludeDeleted=False\n        )\n\n        status = abortable_response['Environments'][0]['Status']\n\n        if status == 'Ready':\n            print(envName, 'is now ready!')\n            break\n        else:\n            print('''\n            -----------------------------------------------------------------------------------------\n                Sleeping for four minutes... waiting for immutable upgrade to finish.\n            -----------------------------------------------------------------------------------------''')\n            time.sleep(240)\n","repo_name":"Johnny-Martinez/EBUpdate","sub_path":"lib/abortable_operation_upgrade.py","file_name":"abortable_operation_upgrade.py","file_ext":"py","file_size_in_byte":903,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"15256065489","text":"import unittest\nfrom unittest.mock import patch, Mock\nfrom src.proxy_checker import ProxyChecker\n\n\nclass TestProxyChecker(unittest.TestCase):\n    def setUp(self):\n        self.proxies = [\"proxy1\", \"proxy2\", \"proxy3\"]\n        self.checker = ProxyChecker(self.proxies)\n\n    @patch(\"src.proxy_checker.requests.get\")\n    def test_check_proxy(self, mock_get):\n        mock_response_200 = Mock()\n        mock_response_200.status_code = 200\n        mock_response_404 = Mock()\n        mock_response_404.status_code = 404\n        mock_response_500 = Mock()\n        mock_response_500.status_code = 500\n        mock_get.side_effect = [mock_response_200, mock_response_404, mock_response_500]\n\n        expected_results = {200: \"proxy1\", 404: \"proxy2\", 500: \"proxy3\"}\n        for status_code, proxy in expected_results.items():\n            result = self.checker.check_proxy(proxy)\n            if status_code == 200:\n                self.assertEqual(result, proxy)\n            else:\n                self.assertIsNone(result)\n            mock_get.assert_called_with(\n                \"http://google.com\", proxies={\"http\": proxy, \"https\": proxy}, timeout=5\n            )\n\n    @patch(\"src.proxy_checker.requests.get\")\n    def test_filter_proxies(self, mock_get):\n        mock_response = Mock()\n        mock_response.status_code = 200\n        mock_get.return_value = mock_response\n\n        valid_proxies = self.checker.filter_proxies()\n        self.assertEqual(valid_proxies, self.proxies)\n\n\nif __name__ == \"__main__\":\n    unittest.main()\n","repo_name":"kimjongbing/JAPCproxychecker","sub_path":"test/test_proxy_checker.py","file_name":"test_proxy_checker.py","file_ext":"py","file_size_in_byte":1520,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"81"}
+{"seq_id":"40326349096","text":"\nfile1 = open('puzzle4.txt', 'r')\nlines = file1.readlines()\n\ntotal = 0\n\nfor line in lines:\n    stripped_line = line.strip()\n    pairs = stripped_line.split(',')\n\n    pair1 = pairs[0].split('-')\n\n    start1 = int(pair1[0])\n    end1 = int(pair1[1])\n\n    pair2 = pairs[1].split('-')\n\n    start2 = int(pair2[0])\n    end2 = int(pair2[1])\n\n    if start1 <= start2 and end1 >= start2:\n        total += 1\n        continue\n\n    if start2 <= start1 and end2 >= start1:\n        total += 1\n        continue\n\nprint(total)","repo_name":"lopes22/aoc2022","sub_path":"puzzle4.py","file_name":"puzzle4.py","file_ext":"py","file_size_in_byte":508,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"69860462984","text":"import os\nimport random\nimport re\n\nfrom nltk import WhitespaceTokenizer, bigrams\n\ntokens = []\nfile_bigrams = []\nvocabulary = {}\nt = WhitespaceTokenizer()\n\nfilename = input().strip()\n\n\ndef tokenize_line(line):\n    global tokens\n    for token in t.tokenize(line):\n        tokens.append(token)\n\n\ndef create_file_birgrams(items):\n    global file_bigrams\n    file_bigrams = list(bigrams(items))\n\n\ndef create_freq_vocabulary(file_bigrams):\n    global vocabulary\n    for item in file_bigrams:\n        vocabulary.setdefault(item[0], {})\n        vocabulary[item[0]].setdefault(item[1], 0)\n        vocabulary[item[0]][item[1]] += 1\n\n\nif not os.access(filename, os.F_OK):\n    print('File does not exist.')\nelse:\n    with open(filename, 'r', encoding='utf-8') as file:\n        for line in file.readlines():\n            tokenize_line(line)\n    \n    create_file_birgrams(tokens)\n    create_freq_vocabulary(file_bigrams)\n    \n    possible_starts = []\n    for token in vocabulary.keys():\n        if re.match('^[A-Z].*[^.!?]$', token):\n            possible_starts.append(token)\n    \n    selected_tokens = random.choices(population=possible_starts, k=10)\n    \n    for token in selected_tokens:\n        sentence = [token]\n        valid_length = 1\n        isValid = False\n        while not isValid:\n            prev_token = sentence[-1]\n            \n            possible_tokens = {}\n            for t in vocabulary[prev_token]:\n                possible_tokens[t] = vocabulary[prev_token][t]\n            \n            choice = random.choices(k=1, population=list(possible_tokens.keys()), weights=list(possible_tokens.values()))\n            sentence += choice\n            valid_length += 1\n            if re.match('.*[.!?]$', choice[0]) is not None:\n                if valid_length >= 5:\n                    isValid = True\n        print(\" \".join(sentence))\n","repo_name":"Dilmurod777/python_projects","sub_path":"text-generator/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1834,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"35112475342","text":"from models.BaseModel import BaseModel\nfrom db import DB\n\nclass ITehtava:\n    jarjestysnumero: int\n    tila: str\n    nimi: str\n\nclass Tehtava(ITehtava):\n    def __init__(self, jarjestysnumero: int, tila: str, nimi: str):\n        self.jarjestysnumero = jarjestysnumero\n        self.tila = tila\n        self.nimi = nimi\n\nclass TodoModel(BaseModel):\n    def __init__(self, db: DB) -> None:\n        super().__init__(db)\n        return None\n    def lisaaTehtava(self, nimi: str) -> bool:\n        onnistui = True\n        try:\n            lauseke = \"INSERT INTO todos (status, name) VALUES(?,?);\"\n            tehtava = Tehtava(-1, ' ', nimi)\n            tiedot = (tehtava.tila, tehtava.nimi,)\n            print(tehtava)\n            print(lauseke)\n            print(tiedot)\n            self.createRecord(lauseke, [tiedot])\n        except Exception as err:\n            onnistui = False\n            print(\"TodoMalli - Virhe tehtävän luonnissa.\")\n            print(err)\n        return onnistui\n            ","repo_name":"nikoroytio/python-and_sql-exercises","sub_path":"viikko10/L10-master/models/TodoModel.py","file_name":"TodoModel.py","file_ext":"py","file_size_in_byte":997,"program_lang":"python","lang":"fi","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"28555831593","text":"import os\n\n\ndef read_from_file(user_file):\n    if os.path.isfile(user_file) and os.stat(user_file).st_size > 0:\n        with open(user_file, 'r') as f:\n            return f.read() #zwraca string\n    else:\n        print('Nie ma takiego pliku')\n        return '' #pusty string - zwracamy ten sam typ\n\n\ndef save_to_file(user_file, user_data):\n    with open(user_file, 'a') as f:\n        f.write(user_data)\n","repo_name":"ritaly/kurs_python","sub_path":"09_moduly/zad3/module_file.py","file_name":"module_file.py","file_ext":"py","file_size_in_byte":403,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"4223568400","text":"from setuptools import setup\n\nlong_description = \"\"\"\nPlease view usage and document on https://github.com/RE-A/korean-geocoding. 사용법과 문서는 링크를 참조해 주세요.\n\"\"\"\n\nsetup(\n    name='korean-geocoding',\n    version='0.4.1',\n    author='RE-A',\n    author_email='skynine73@gmail.com',\n    python_requires='>=3.8',\n    install_requires=['requests', 'haversine', 'pyproj'],\n    description='Korean geocoding library with Naver Geocoding API',\n    long_description=long_description,\n    url=\"https://github.com/RE-A/korean-geocoding\",\n    packages=['korean_geocoding'],\n    include_package_data=True\n)","repo_name":"RE-A/korean-geocoding","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":617,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"81"}
+{"seq_id":"2924645496","text":"import argparse\nimport os\nimport subprocess\nimport tempfile\n\nRUN_DIR = '/run/user/%d' % os.getuid()\n\ndef say(text, lang='en-US', volume=60, pitch=130, speed=100, device='default'):\n    data = \"<volume level='%d'><pitch level='%d'><speed level='%d'>%s</speed></pitch></volume>\" % \\\n           (volume, pitch, speed, text)\n    with tempfile.NamedTemporaryFile(suffix='.wav', dir=RUN_DIR) as f:\n       cmd = 'pico2wave --wave %s --lang %s \"%s\" && aplay -q -D %s %s' % \\\n             (f.name, lang, data, device, f.name)\n       subprocess.check_call(cmd, shell=True)\n\n\ndef _main():\n    parser = argparse.ArgumentParser(description='Text To Speech (pico2wave)')\n    parser.add_argument('--lang', default='en-US')\n    parser.add_argument('--volume', type=int, default=60)\n    parser.add_argument('--pitch', type=int, default=130)\n    parser.add_argument('--speed', type=int, default=100)\n    parser.add_argument('--device', default='default')\n    parser.add_argument('text', help='path to disk image file ')\n    args = parser.parse_args()\n    say(args.text, lang=args.lang, volume=args.volume, pitch=args.pitch, speed=args.speed,\n        device=args.device)\n\n\nif __name__ == '__main__':\n    _main()\n","repo_name":"eosurman/physicsc","sub_path":"src/aiy/voice/tts.py","file_name":"tts.py","file_ext":"py","file_size_in_byte":1193,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"81"}
+{"seq_id":"30172831116","text":"# -*- coding: utf-8 -*-\n\nfrom ctypes import *\n\nfrom numpy import asarray\nimport cv2\n\n\n# Image type (IplImage)\nIPL_DEPTH_SIGN = 0x80000000\n\nIPL_DEPTH_1U = 1\nIPL_DEPTH_8U = 8\nIPL_DEPTH_16U = 16\nIPL_DEPTH_32F = 32\nIPL_DEPTH_64F = 64\n\nIPL_DEPTH_8S = IPL_DEPTH_SIGN + IPL_DEPTH_8U\nIPL_DEPTH_16S = IPL_DEPTH_SIGN + IPL_DEPTH_16U\nIPL_DEPTH_32S = IPL_DEPTH_SIGN + 32\n\nclass IplTileInfo(Structure):\n    _fields_ = []\n\nclass IplROI(Structure):\n    _fields_ = [\n        # 0 - no COI (all channels are selected)\n        # 1 - 0th channel is selected ...\n        ('coi', c_int),\n        ('xOffset', c_int),\n        ('yOffset', c_int),\n        ('width', c_int),\n        ('height', c_int),\n        ]\n\n# ipl image header\nclass IplImage(Structure):\n    pass\n\nIplImage._fields_ = [\n    (\"nSize\", c_int),\n    (\"ID\", c_int),\n    (\"nChannels\", c_int),\n    (\"alphaChannel\", c_int),\n    (\"depth\", c_int),\n    (\"colorModel\", c_char * 4),\n    (\"channelSeq\", c_char * 4),\n    (\"dataOrder\", c_int),\n    (\"origin\", c_int),\n    (\"align\", c_int),\n    (\"width\", c_int),\n    (\"height\", c_int),\n    (\"roi\", POINTER(IplROI)),\n    (\"maskROI\", POINTER(IplImage)),\n    (\"imageID\", c_void_p),\n    (\"tileInfo\", POINTER(IplTileInfo)),\n    (\"imageSize\", c_int),\n    #be careful to use c_void_p!!\n    #When making python type 2 IplImage* function, memory allocate is required\n    (\"imageData\", c_void_p), \n    (\"widthStep\", c_int),\n    (\"BorderMode\", c_int * 4),\n    (\"BorderConst\", c_int * 4),\n    (\"imageDataOrigin\", c_char_p)\n    ]\n\ndef ipl2iplimage(ipl_ptr, img_shape):\n    \"\"\"get cv2.cv.iplimage from C's IplImage*\n\n    ipl_ptr: POINTER(IplImage) that points to valid image\n    img_shape: 3 element int tuple (height, width, n_channels)\n    \"\"\"\n    #allocate Python memory for image\n    height, width, n_channels = img_shape\n    cv_img = cv2.cv.CreateImageHeader((width, height), IPL_DEPTH_8U, n_channels)\n    # getting the IplImage* and set memory\n    iplimage = ipl_ptr.contents\n    str_data = string_at(iplimage.imageData, iplimage.imageSize)\n    cv2.cv.SetData(cv_img, str_data, iplimage.widthStep)\n    return cv_img\n\ndef ipl2array(ipl_ptr, img_shape):\n    \"\"\"get numpy.ndarray from IplImage*\n\n    ipl_ptr: POINTER(IplImage) that points to valid image\n    img_shape: 3 element int tuple (height, width, n_channels)\n    \"\"\"\n    cv_img = ipl2iplimage(ipl_ptr, img_shape)\n    # building a CvMat image by slice operation([:,:]), \n    # and build ndarray from CvMat\n    return asarray(cv_img[:, :])\n\n","repo_name":"atsushisugiyama/phenox_python","sub_path":"library/cv_c2py.py","file_name":"cv_c2py.py","file_ext":"py","file_size_in_byte":2462,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"81"}
+{"seq_id":"35673477525","text":"from shop.models import BillingAddress, Order, OrderItems,Product,ProductImage,Category, Review\nfrom django.shortcuts import redirect, render\nfrom http.client import HTTPResponse\nfrom django.http import HttpRequest, HttpResponse,HttpResponseRedirect\nfrom pprint import pp, pprint\nfrom django.core import serializers\nfrom django.http import JsonResponse\nimport json\n\nfrom django.db.models import Sum,Value,F\n\nfrom django.core.serializers.json import DjangoJSONEncoder\nfrom django.db import transaction\n\nfrom django.forms import model_to_dict\n\n\ndef cartProduct(request): \n    \n    products = request.POST.get('data')\n   \n\n    if len(products) > 2:\n        \n        # the result is a Python dictionary:\n        products = json.loads(products)\n\n        prod_id = []\n        prod_q = []\n        for p in products:\n            if len(p[\"id\"]) < 1:\n                continue\n            prod_id.append(int(p[\"id\"]))\n            prod_q.append(int(p[\"quantity\"]))  \n\n        item_list = Product.objects.filter(pk__in=prod_id).values()\n\n        cartItem = { }\n        cart = []\n        for item in list(item_list):\n            for p in products:\n                if p[\"id\"] == str(item['id']):\n                    id = item['id']\n                    for index in item.keys():\n                        cartItem[index] = item[index]\n\n                    cartItem['quantity'] = p[\"quantity\"]\n                    cartItem['image'] = ProductImage.objects.filter(product = Product.objects.get(pk = id)).values_list('product_img_src', flat=True).first()\n\n                    temp = cartItem.copy()\n                    cart.append(temp)\n                \n                cartItem.clear()\n\n        json_items = json.dumps(cart,cls= DjangoJSONEncoder)\n\n        return HttpResponse(json_items, content_type=\"application/json\")\n    else:\n        print(\"Data retrive problem!\")\n        return HttpResponse(\"Data retrive problem!\")\n\ndef checkout(request):\n\n    try:\n        billing_address = BillingAddress.objects.get(customer = request.user.customer, status = 1)\n    except:\n        billing_address = None \n\n    if request.method == 'POST':\n        products = request.POST.get('data')\n        comment = request.POST.get('comment')\n\n        products = json.loads(products)\n        comment = json.loads(comment)\n\n        if request.user.is_anonymous:\n            return HttpResponse(\"401\", content_type=\"application/json\")\n        else:\n            try:\n                with transaction.atomic():\n                    order = Order()\n                    order.comment = comment\n                    order.billing_addr = billing_address\n                    order.customer = request.user.customer\n                    order.save()\n\n                    \n                    for p in products:\n                        if len(p[\"id\"]) < 1:\n                            continue\n                        items = OrderItems()\n                        prod = Product.objects.get(pk=p[\"id\"])\n                        items.order = order\n                        items.product = prod\n                        items.quantity = p[\"quantity\"]\n                        items.save()\n\n                    return HttpResponse(\"201\", content_type=\"application/json\")\n            except:\n                return HttpResponse(\"400\", content_type=\"application/json\")\n   \n    context = {\n        'meta_title' : 'The New Day a Cloud kitchen of Bangladesh',\n        'meta_description' : 'The New Day (TND) is a Cloud Kitchen of Fast Food & Restaurant with Multi Cuisine',\n        'title' : 'View Cart',\n        'h1_tag' : 'The New Day (TND) is a Cloud Kitchen of Fast Food & Restaurant with Multi Cuisine',\n        'class' : 'fastfood_1',\n        'billing_address' : billing_address\n    }\n\n    return render(request, 'order/checkout.html',context)","repo_name":"FahimDev/Django-ecommerce-website","sub_path":"shop/views/order.py","file_name":"order.py","file_ext":"py","file_size_in_byte":3784,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"4432083753","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Jun 27 13:07:35 2017\n\n@author: levi\n\"\"\"\n\nimport rest_sgra, grad_sgra, numpy, copy, os\nimport matplotlib.pyplot as plt\nfrom utils import ddt\nfrom multiprocessing import Pool\n\nclass binFlagDict(dict):\n    \"\"\"Class for binary flag dictionaries.\n    Provides good grounding for any settings or options dictionary. \"\"\"\n\n    def __init__(self,inpDict={},inpName='options'):\n        self.name = inpName\n        for key in inpDict.keys():\n            self[key] = inpDict[key]\n\n    def setAll(self,tf=True,opt={}):\n        for key in self.keys():\n            self[key] = (tf and opt.get(key,True))\n\n#        self.log.printL(\"\\nSetting '\"+self.name+\"' as follows:\")\n#        self.log.pprint(self)\n\nclass LMPBVPhelp():\n    \"\"\"Class for processing the Linear Multipoint Boundary Value Problem.\n    The biggest advantage in using an object is that the parallelization of\n    the computations for each solution is much easier.\"\"\"\n\n    def __init__(self,sol,rho):\n        \"\"\"Initialization method. Comprises all the \"common\" calculations for\n        each independent solution to be added over.\n\n        According to the Miele (2003) convention,\n            rho = 0 for rest, and rho = 1 for grad.\n        \"\"\"\n\n        # debug options...\n        self.dbugOptGrad = sol.dbugOptGrad\n        self.dbugOptRest = sol.dbugOptRest\n        self.t = sol.t\n\n        # get sizes\n        Ns,N,m,n,p,q,s = sol.Ns,sol.N,sol.m,sol.n,sol.p,sol.q,sol.s\n        self.Ns,self.N,self.m,self.n,self.p,self.q,self.s = Ns,N,m,n,p,q,s\n        self.dt = 1.0/(N-1)\n        self.rho = rho\n\n        # calculate integration error (only if necessary)\n        psi = sol.calcPsi()\n        self.psi = psi\n        if rho < .5:\n            # calculate phi and psi\n            phi = sol.calcPhi()\n            err = phi - ddt(sol.x,N)\n        else:\n            err = numpy.zeros((N,n,s))\n\n        self.err = err\n\n\n        #######################################################################\n        if rho < 0.5 and self.dbugOptRest['plotErr']:\n            print(\"\\nThis is err:\")\n            for arc in range(s):\n                plt.plot(self.t,err[:,0,arc])\n                plt.ylabel(\"errPos\")\n                plt.grid(True)\n                plt.show()\n                plt.clf()\n                plt.close('all')\n\n                if n>1:\n                    plt.plot(self.t,err[:,1,arc])\n                    plt.ylabel(\"errVel\")\n                    plt.grid(True)\n                    plt.show()\n                    plt.clf()\n                    plt.close('all')\n        #######################################################################\n\n        # Get gradients\n        Grads = sol.calcGrads(calcCostTerm=(rho>0.5))\n        #dt6 = dt/6\n        phix = Grads['phix']\n        phiu = Grads['phiu']\n        phip = Grads['phip']\n        psiy = Grads['psiy']\n        psip = Grads['psip']\n        fx = Grads['fx']\n        fu = Grads['fu']\n        fp = Grads['fp']\n\n        self.phip = phip\n        self.psiy = psiy\n        self.psip = psip\n        self.fx = fx\n        self.fu = fu\n        self.fp = fp\n\n        # Prepare matrices with derivatives:\n        phixTr = numpy.empty_like(phix)\n        phiuTr = numpy.empty((N,m,n,s))\n        phipTr = numpy.empty((N,p,n,s))\n        phiuFu = numpy.empty((N,n,s))\n        for arc in range(s):\n            for k in range(N):\n                phixTr[k,:,:,arc] = phix[k,:,:,arc].transpose()\n                phiuTr[k,:,:,arc] = phiu[k,:,:,arc].transpose()\n                phipTr[k,:,:,arc] = phip[k,:,:,arc].transpose()\n                phiuFu[k,:,arc] = phiu[k,:,:,arc].dot(fu[k,:,arc])\n        self.phiuFu = phiuFu\n        self.phiuTr = phiuTr\n        self.phipTr = phipTr\n\n        InitCondMat = numpy.eye(Ns,Ns+1)\n        self.InitCondMat = InitCondMat\n\n        # Dynamics matrix for propagating the LSODE:\n        DynMat = numpy.zeros((N,2*n,2*n,s))\n        for arc in range(s):\n            for k in range(N):\n                DynMat[k,:n,:n,arc] = phix[k,:,:,arc]\n                DynMat[k,:n,n:,arc] = phiu[k,:,:,arc].dot(phiuTr[k,:,:,arc])\n                DynMat[k,n:,n:,arc] = -phixTr[k,:,:,arc]\n        self.DynMat = DynMat\n\n    def propagate(self,j):\n        \"\"\"This method computes each solution, via propagation of the\n        applicable Linear System of Ordinary Differential Equations.\"\"\"\n\n        # Load data (sizes, common matrices, etc)\n        rho = self.rho\n        rho1 = self.rho-1.0\n        Ns,N,n,m,p,s = self.Ns,self.N,self.n,self.m,self.p,self.s\n        dt = self.dt\n\n        InitCondMat = self.InitCondMat\n        phip = self.phip\n        err = self.err\n        phiuFu = self.phiuFu\n        fx = self.fx\n        if rho > .5:\n            rhoFu = self.fu\n        else:\n            rhoFu = numpy.zeros((N,m,s))\n\n        phiuTr = self.phiuTr\n        phipTr = self.phipTr\n        DynMat = self.DynMat\n        I = numpy.eye(2*n)\n\n        # Declare matrices for corrections\n        phiLamIntCol = numpy.zeros(p)\n        DtCol = numpy.empty(2*n*s)\n        EtCol = numpy.empty(2*n*s)\n        A = numpy.zeros((N,n,s))\n        B = numpy.zeros((N,m,s))\n        C = numpy.zeros((p,1))\n        lam = numpy.zeros((N,n,s))\n\n        # the vector that will be integrated is Xi = [A; lam]\n        Xi = numpy.zeros((N,2*n,s))\n        # Initial conditions for the LSODE:\n        for arc in range(s):\n            A[0,:,arc] = InitCondMat[2*n*arc:(2*n*arc+n) , j]\n            lam[0,:,arc] = InitCondMat[(2*n*arc+n):(2*n*(arc+1)) , j]\n            Xi[0,:n,arc],Xi[0,n:,arc] = A[0,:,arc],lam[0,:,arc]\n        C = InitCondMat[(2*n*s):,j]\n\n        # Non-homogeneous terms for the LSODE:\n        nonHom = numpy.empty((N,2*n,s))\n        for arc in range(s):\n            for k in range(N):\n                # minus sign in rho1 (rho-1) is on purpose!\n                nonHA = phip[k,:,:,arc].dot(C) + \\\n                            -rho1*err[k,:,arc] - rho*phiuFu[k,:,arc]\n                nonHL = rho * fx[k,:,arc]\n                nonHom[k,:n,arc] = nonHA#.copy()\n                nonHom[k,n:,arc] = nonHL#.copy()\n\n        # Integrate the LSODE (by Heun's method):\n        for arc in range(s):\n#            B[0,:,arc] = -rhoFu[0,:,arc] + \\\n#                                phiuTr[0,:,:,arc].dot(lam[0,:,arc])\n#            phiLamIntCol += .5 * (phipTr[0,:,:,arc].dot(lam[0,:,arc]))\n#\n#            # First point: simple propagation\n#            derXik = DynMat[0,:,:,arc].dot(Xi[0,:,arc]) + \\\n#                        nonHom[0,:,arc]\n#            Xi[1,:,arc] = Xi[0,:,arc] + dt * derXik\n#            #A[1,:,arc] = Xi[1,:n,arc]\n#            lam[1,:,arc] = Xi[1,n:,arc]\n#            B[1,:,arc] = -rhoFu[1,:,arc] + \\\n#                            phiuTr[1,:,:,arc].dot(lam[1,:,arc])\n#            phiLamIntCol += phipTr[1,:,:,arc].dot(lam[1,:,arc])\n#\n#            # \"Middle\" points: original Heun propagation\n#            for k in range(1,N-2):\n#                derXik = DynMat[k,:,:,arc].dot(Xi[k,:,arc]) + \\\n#                        nonHom[k,:,arc]\n#                aux = Xi[k,:,arc] + dt * derXik\n#                Xi[k+1,:,arc] = Xi[k,:,arc] + .5 * dt * (derXik + \\\n#                                DynMat[k+1,:,:,arc].dot(aux) + \\\n#                                nonHom[k+1,:,arc])\n#                #A[k+1,:,arc] = Xi[k+1,:n,arc]\n#                lam[k+1,:,arc] = Xi[k+1,n:,arc]\n#                B[k+1,:,arc] = -rhoFu[k+1,:,arc] + \\\n#                                phiuTr[k+1,:,:,arc].dot(lam[k+1,:,arc])\n#                phiLamIntCol += phipTr[k+1,:,:,arc].dot(lam[k+1,:,arc])\n#            #\n#\n#            # Last point: simple propagation, but based on the last point\n#            derXik = DynMat[N-1,:,:,arc].dot(Xi[N-2,:,arc]) + \\\n#                        nonHom[N-1,:,arc]\n#            Xi[N-1,:,arc] = Xi[N-2,:,arc] + dt * derXik\n#            #A[N-1,:,arc] = Xi[N-1,:n,arc]\n#            lam[N-1,:,arc] = Xi[N-1,n:,arc]\n#            B[N-1,:,arc] = -rhoFu[N-1,:,arc] + \\\n#                            phiuTr[N-1,:,:,arc].dot(lam[N-1,:,arc])\n#            phiLamIntCol += .5*phipTr[N-1,:,:,arc].dot(lam[N-1,:,arc])\n\n            # Integrate the LSODE by Euler Backwards implicit\n            B[0,:,arc] = -rhoFu[0,:,arc] + \\\n                                phiuTr[0,:,:,arc].dot(lam[0,:,arc])\n            phiLamIntCol += .5 * (phipTr[0,:,:,arc].dot(lam[0,:,arc]))\n\n            for k in range(N-1):\n                Xi[k+1,:,arc] = numpy.linalg.solve(I - dt*DynMat[k+1,:,:,arc],\\\n                  Xi[k,:,arc] + dt*nonHom[k+1,:,arc])\n                lam[k+1,:,arc] = Xi[k+1,n:,arc]\n                B[k+1,:,arc] = -rhoFu[k+1,:,arc] + \\\n                                phiuTr[k+1,:,:,arc].dot(lam[k+1,:,arc])\n                phiLamIntCol += phipTr[k+1,:,:,arc].dot(lam[k+1,:,arc])\n\n            phiLamIntCol -= .5*phipTr[N-1,:,:,arc].dot(lam[N-1,:,arc])\n\n\n            # Get the A values from Xi\n            A[:,:,arc] = Xi[:,:n,arc]\n\n            # Put initial and final conditions of A and Lambda into matrices\n            # DtCol and EtCol, which represent the columns of Dtilde(Dt) and\n            # Etilde(Et)\n            DtCol[(2*arc)*n   : (2*arc+1)*n] =    A[0,:,arc]   # eq (32a)\n            DtCol[(2*arc+1)*n : (2*arc+2)*n] =    A[N-1,:,arc] # eq (32a)\n            EtCol[(2*arc)*n   : (2*arc+1)*n] = -lam[0,:,arc]   # eq (32b)\n            EtCol[(2*arc+1)*n : (2*arc+2)*n] =  lam[N-1,:,arc] # eq (32b)\n        #\n\n        # All integrations ready!\n        phiLamIntCol *= dt\n\n###############################################################################\n        if (rho > 0.5 and self.dbugOptGrad['plotCorr']) or \\\n           (rho < 0.5 and self.dbugOptRest['plotCorr']):\n            print(\"\\nHere are the corrections for iteration \" + str(j+1) + \\\n                  \" of \" + str(Ns+1) + \":\\n\")\n            for arc in range(s):\n                print(\"> Corrections for arc =\",arc)\n                plt.plot(self.t,A[:,0,arc])\n                plt.grid(True)\n                plt.ylabel('A: pos')\n                plt.show()\n                plt.clf()\n                plt.close('all')\n\n\n                plt.plot(self.t,lam[:,0,arc])\n                plt.grid(True)\n                plt.ylabel('lambda: pos')\n                plt.show()\n                plt.clf()\n                plt.close('all')\n\n                if n>1:\n                    plt.plot(self.t,A[:,1,arc])\n                    plt.grid(True)\n                    plt.ylabel('A: vel')\n                    plt.show()\n                    plt.clf()\n                    plt.close('all')\n\n                    plt.plot(self.t,lam[:,1,arc])\n                    plt.grid(True)\n                    plt.ylabel('lambda: vel')\n                    plt.show()\n                    plt.clf()\n                    plt.close('all')\n\n                if n>2:\n                    plt.plot(self.t,A[:,2,arc])\n                    plt.grid(True)\n                    plt.ylabel('A: gama')\n                    plt.show()\n                    plt.clf()\n                    plt.close('all')\n\n                    plt.plot(self.t,lam[:,2,arc])\n                    plt.grid(True)\n                    plt.ylabel('lambda: gamma')\n                    plt.show()\n                    plt.clf()\n                    plt.close('all')\n\n\n                if n>3:\n                    plt.plot(self.t,A[:,3,arc])\n                    plt.grid(True)\n                    plt.ylabel('A: m')\n                    plt.show()\n                    plt.clf()\n                    plt.close('all')\n\n                    plt.plot(self.t,lam[:,3,arc])\n                    plt.grid(True)\n                    plt.ylabel('lambda: m')\n                    plt.show()\n                    plt.clf()\n                    plt.close('all')\n\n\n                plt.plot(self.t,B[:,0,arc])\n                plt.grid(True)\n                plt.ylabel('B0')\n                plt.show()\n                plt.clf()\n                plt.close('all')\n\n                if m>1:\n                    plt.plot(self.t,B[:,1,arc])\n                    plt.grid(True)\n                    plt.ylabel('B1')\n                    plt.show()\n                    plt.clf()\n                    plt.close('all')\n\n                print(\"C[arc] =\",C[arc])\n                #input(\" > \")\n###############################################################################\n\n        # All the outputs go to main output dictionary; the final solution is\n        # computed by the next method, 'getCorr'.\n        outp = {'A':A,'B':B,'C':C,'L':lam,'Dt':DtCol,'Et':EtCol,\n                'phiLam':phiLamIntCol}\n\n        return outp\n\n\n    def getCorr(self,res,log):\n        \"\"\" Computes the actual correction for this grad/rest step, by linear\n        combination of the solutions generated by method 'propagate'.\"\"\"\n\n        # Get sizes\n        Ns,N,n,m,p,q,s = self.Ns,self.N,self.n,self.m,self.p,self.q,self.s\n        rho1 = self.rho - 1.0\n\n        # Declare matrices Ctilde, Dtilde, Etilde, and the integral term\n        Ct = numpy.empty((p,Ns+1))\n        Dt = numpy.empty((2*n*s,Ns+1))\n        Et = numpy.empty((2*n*s,Ns+1))\n        phiLamInt = numpy.empty((p,Ns+1))\n\n        # Unpack outputs from 'propagate' into proper matrices Ct, Dt, etc.\n        for j in range(Ns+1):\n            Ct[:,j] = res[j]['C']\n            Dt[:,j] = res[j]['Dt']\n            Et[:,j] = res[j]['Et']\n            phiLamInt[:,j] = res[j]['phiLam']\n\n        # Assembly of matrix M and column 'Col' for the linear system\n\n        # Matrix for linear system involving k's and mu's\n        M = numpy.zeros((Ns+q+1,Ns+q+1))\n        # from eq (34d) - k term\n        M[0,:(Ns+1)] = numpy.ones(Ns+1)\n        # from eq (34b) - mu term\n        M[(q+1):(q+1+p),(Ns+1):] = self.psip.transpose()\n        # from eq (34c) - mu term\n        M[(p+q+1):,(Ns+1):] = self.psiy.transpose()\n        # from eq (34a) - k term\n        M[1:(q+1),:(Ns+1)] = self.psiy.dot(Dt) + self.psip.dot(Ct)\n        # from eq (34b) - k term\n        M[(q+1):(q+p+1),:(Ns+1)] = Ct - phiLamInt\n        # from eq (34c) - k term\n        M[(q+p+1):,:(Ns+1)] = Et\n\n        # column vector for linear system involving k's and mu's  [eqs (34)]\n        col = numpy.zeros(Ns+q+1)\n        col[0] = 1.0 # eq (34d)\n\n        # Integral term\n        if self.rho > 0.5:\n            # eq (34b) - only applicable for grad\n            sumIntFpi = numpy.zeros(p)\n            for arc in range(s):\n                for ind in range(p):\n                    sumIntFpi[ind] += self.fp[:,ind,arc].sum()\n                    sumIntFpi[ind] -= .5 * ( self.fp[0,ind,arc] + \\\n                             self.fp[-1,ind,arc])\n            sumIntFpi *= self.dt\n            col[(q+1):(q+p+1)] = -self.rho * sumIntFpi\n        else:\n            # eq (34a) - only applicable for rest\n            col[1:(q+1)] = rho1 * self.psi\n\n\n        # Calculations of weights k:\n        KMi = numpy.linalg.solve(M,col)\n        Res = M.dot(KMi)-col\n        log.printL(\"Residual of the Linear System: \" + \\\n                   str(Res.transpose().dot(Res)))\n        K,mu = KMi[:(Ns+1)], KMi[(Ns+1):]\n\n        # summing up linear combinations\n        A = numpy.zeros((N,n,s))\n        B = numpy.zeros((N,m,s))\n        C = numpy.zeros(p)\n        lam = numpy.zeros((N,n,s))\n\n        for j in range(Ns+1):\n            A   += K[j] * res[j]['A']#self.arrayA[j,:,:,:]\n            B   += K[j] * res[j]['B']#self.arrayB[j,:,:,:]\n            C   += K[j] * res[j]['C']#self.arrayC[j,:]\n            lam += K[j] * res[j]['L']#self.arrayL[j,:,:,:]\n\n###############################################################################\n        if (self.rho > 0.5 and self.dbugOptGrad['plotCorrFin']) or \\\n           (self.rho < 0.5 and self.dbugOptRest['plotCorrFin']):\n            log.printL(\"\\n------------------------------------------------------------\")\n            log.printL(\"Final corrections:\\n\")\n            for arc in range(s):\n                log.printL(\"> Corrections for arc =\",arc)\n                plt.plot(self.t,A[:,0,arc])\n                plt.grid(True)\n                plt.ylabel('A: pos')\n                plt.show()\n                plt.clf()\n                plt.close('all')\n\n                if n>1:\n                    plt.plot(self.t,A[:,1,arc])\n                    plt.grid(True)\n                    plt.ylabel('A: vel')\n                    plt.show()\n                    plt.clf()\n                    plt.close('all')\n\n                if n>2:\n                    plt.plot(self.t,A[:,2,arc])\n                    plt.grid(True)\n                    plt.ylabel('A: gama')\n                    plt.show()\n                    plt.clf()\n                    plt.close('all')\n\n                if n>3:\n                    plt.plot(self.t,A[:,3,arc])\n                    plt.grid(True)\n                    plt.ylabel('A: m')\n                    plt.show()\n                    plt.clf()\n                    plt.close('all')\n\n                plt.plot(self.t,B[:,0,arc])\n                plt.grid(True)\n                plt.ylabel('B0')\n                plt.show()\n                plt.clf()\n                plt.close('all')\n\n                if m>1:\n                    plt.plot(self.t,B[:,1,arc])\n                    plt.grid(True)\n                    plt.ylabel('B1')\n                    plt.show()\n                    plt.clf()\n                    plt.close('all')\n\n\n                log.printL(\"C[arc] =\",C[arc])\n\n            #input(\" > \")\n###############################################################################\n        return A,B,C,lam,mu\n\nclass sgra():\n    \"\"\"Class for a general instance of the SGRA problem.\n\n    Here are all the methods and variables that are independent of a specific\n    instance of a problem.\n\n    Each instance of an optimization problem must then inherit these methods\n    and properties. \"\"\"\n\n    probName = 'probSGRA'\n\n    def __init__(self,parallel={}):\n        # these numbers should not make any sense;\n        # they should change with the problem\n        N,n,m,p,q,s = 50000,4,2,1,3,2\n\n        self.N = N\n        self.n = n\n        self.m = m\n        self.p = p\n        self.q = q\n        self.s = s\n\n        self.x = numpy.zeros((N,n))\n        self.u = numpy.zeros((N,m))\n        self.pi = numpy.zeros(p)\n        self.lam = numpy.zeros((N,n))\n        self.mu = numpy.zeros(q)\n\n        self.boundary = {}\n        self.constants = {}\n        self.restrictions = {}\n\n        self.P = 1.0\n        self.Q = 1.0\n        self.I = 1.0\n\n        # Basic maximum number of iterations for grad/rest.\n        # May be overriden in the problem definition\n        MaxIterRest = 100000\n        self.MaxIterRest = MaxIterRest\n        self.NIterRest = 0\n        self.histStepRest = numpy.zeros(MaxIterRest)\n        self.histP = numpy.zeros(MaxIterRest)\n        self.histPint = numpy.zeros(MaxIterRest)\n        self.histPpsi = numpy.zeros(MaxIterRest)\n\n        MaxIterGrad = 10000\n        self.MaxIterGrad = MaxIterGrad\n        self.NIterGrad = 0\n\n        self.histStepGrad = numpy.zeros(MaxIterGrad)\n        self.histQ = numpy.zeros(MaxIterGrad)\n        self.histQx = numpy.zeros(MaxIterGrad)\n        self.histQu = numpy.zeros(MaxIterGrad)\n        self.histQp = numpy.zeros(MaxIterGrad)\n        self.histQt = numpy.zeros(MaxIterGrad)\n\n        self.histI = numpy.zeros(MaxIterGrad)\n        self.histIorig = numpy.zeros(MaxIterGrad)\n        self.histIpf = numpy.zeros(MaxIterGrad)\n\n        self.histGRrate = numpy.zeros(MaxIterGrad)\n\n        self.tol = {'P':1e-7,'Q':1e-7}\n\n        # Gradient-Restoration EVent List (1-grad, 0-rest)\n        self.GREvList = numpy.ones(MaxIterRest+MaxIterGrad,dtype='bool')\n        self.GREvIndx = -1\n\n        # Debugging options\n        tf = False\n        self.dbugOptRest = binFlagDict(inpDict={'pausRest':tf,\n                            'pausCalcP':tf,\n                            'plotP_int':tf,\n                            'plotP_intZoom':tf,\n                            'plotIntP_int':tf,\n                            'plotSolMaxP':tf,\n                            'plotRsidMaxP':tf,\n                            'plotErr':tf,\n                            'plotCorr':tf,\n                            'plotCorrFin':tf},\\\n                                        inpName='Debug options for Rest')\n        tf = False#True#\n        self.dbugOptGrad = binFlagDict(inpDict={'pausGrad':tf,\n                            'pausCalcQ':tf,\n                            'prntCalcStepGrad':tf,\n                            'plotCalcStepGrad': tf,\n                            'pausCalcStepGrad':tf,\n                            'plotQx':tf,\n                            'plotQu':tf,\n                            'plotLam':tf,\n                            'plotQxZoom':tf,\n                            'plotQuZoom':tf,\n                            'plotQuComp':tf,\n                            'plotQuCompZoom':tf,\n                            'plotSolQxMax':tf,\n                            'plotSolQuMax':tf,\n                            'plotCorr':tf,\n                            'plotCorrFin':tf,\n                            'plotF':tf,\n                            'plotFint':tf},\\\n                                        inpName='Debug options for Grad')\n\n        # Solution plot saving status:\n        self.save = binFlagDict(inpDict={'currSol':True,\n                     'histP':True,\n                     'histQ':True,\n                     'histI':True,\n                     'histGradStep':True,\n                     'traj':True,\n                     'comp':True},\\\n                                inpName='Plot saving options')\n\n        # Paralellism options\n        self.isParallel = dict()\n        self.isParallel['gradLMPBVP'] = parallel.get('gradLMPBVP',False)\n        self.isParallel['restLMPBVP'] = parallel.get('restLMPBVP',False)\n\n\n    def updtGRrate(self):\n\n        # find last gradient step index in event list\n        LastGradIndx = self.GREvIndx - 1\n\n        while self.GREvList[LastGradIndx] == False and LastGradIndx > 0:\n            LastGradIndx -= 1\n\n        # number of restorations after last gradient step\n        if LastGradIndx == 0:\n            nRest = self.GREvIndx-1\n        else:\n            nRest = self.GREvIndx - 1 - LastGradIndx\n\n        self.histGRrate[self.NIterGrad] = nRest\n\n    def copy(self):\n        \"\"\"Copy the solution. It is useful for applying corrections, generating\n        baselines for comparison, etc.\n        Special care must be given for the logging object, however.\"\"\"\n\n        # Get logging object (always by reference)\n        log = self.log\n        # Clear the reference in the solution\n        self.log = None\n        # Do the copy\n        newSol = copy.deepcopy(self)\n        # Point the logging object back into the solutions (original and copy)\n        newSol.log = log\n        self.log = log\n        return newSol\n\n    def aplyCorr(self,alfa,corr):\n        self.log.printL(\"\\nApplying alfa = \"+str(alfa))\n        self.x  += alfa * corr['x']\n        self.u  += alfa * corr['u']\n        self.pi += alfa * corr['pi']\n\n    def initGues(self):\n        # implemented by child classes\n        pass\n\n    def printPars(self):\n        dPars = self.__dict__\n#        keyList = dPars.keys()\n        self.log.printL(\"These are the attributes for the current solution:\\n\")\n        self.log.pprint(dPars)\n\n    def plotCat(self,func,mark='',color='b',labl='',piIsTime=True,intv=[]):\n        \"\"\"Plot a given function with several subarcs.\n            Since this function should serve all the SGRA instances, the pi\n            parameters (if exist!) are not necessarily the times for each\n            subarc. Hence, the optional parameter \"piIsTime\".\n\n            However, this function does consider the arcs to be concatenated.\n            If this property does not hold for any future problems to be\n            considered, then the function must be rewritten.\n        \"\"\"\n\n        s = self.s\n        t = self.t\n        pi = self.pi\n        N = self.N\n        dt = 1.0/(N-1)\n        dtd = dt\n\n        # Set upper and lower bounds\n        lowrBnd = 0.0\n        if piIsTime:\n            uperBnd = pi.sum()\n        else:\n            TimeDur = t[-1]\n            uperBnd = TimeDur * s\n\n        if len(intv)==0:\n            intv = [lowrBnd,uperBnd]\n\n        # Check consistency of requested time interval, override if necessary\n        if intv[0] < lowrBnd or intv[1] > uperBnd:\n            self.log.printL(\"plotCat: inadequate time interval used! Ignoring...\")\n            if intv[0] < lowrBnd:\n                intv[0] = lowrBnd\n            if intv[1] > uperBnd:\n                intv[1] = uperBnd\n\n        accTime = 0.0\n        mustLabl = True\n        isBgin = True\n\n        for arc in range(s):\n            if piIsTime:\n                TimeDur = pi[arc]\n                dtd = dt * TimeDur\n\n            # check if this arc gets plotted at all\n            #print(\"\\narc =\",arc)\n            #print(\"accTime =\",accTime)\n            #print(\"TimeDur =\",TimeDur)\n            #print(\"intv =\",intv)\n            #print(\"condition1:\",accTime <= intv[1])\n            #print(\"condition2:\",accTime + TimeDur >= intv[0])\n            if (accTime <= intv[1]) and (accTime + TimeDur >= intv[0]):\n\n                # From this point on, the arc will be plotted.\n                # Find the index for the first point to be plotted:\n\n                if isBgin:\n                    # accTime + ind * dtd = intv[0]\n                    indBgin = int((intv[0] - accTime)/dtd)\n                    isBgin = False\n                    if intv[0] <= accTime:\n                        plt.plot(accTime + TimeDur*t[0],func[0,arc],'o'+color)\n                else:\n                    indBgin = 0\n                    # arc beginning with circle\n                    plt.plot(accTime + TimeDur*t[0],func[0,arc],'o'+color)\n\n                #print(\"indBgin =\",indBgin)\n                if accTime + TimeDur > intv[1]:\n                    indEnd = int((intv[1] - accTime)/dtd)\n                    if indEnd == (N-1):\n                        plt.plot(accTime + TimeDur*t[-1], \\\n                         func[-1,arc],'s'+color)\n                else:\n                    indEnd = N-1\n                    # arc end with square\n                    plt.plot(accTime + TimeDur*t[-1],func[-1,arc],'s'+color)\n\n                #print(\"indEnd =\",indEnd)\n\n                # Plot the function at each arc. Label only the first drawed arc\n                if mustLabl:\n                    plt.plot(accTime + TimeDur * t[indBgin:indEnd], \\\n                             func[indBgin:indEnd,arc],\\\n                             mark+color,label=labl)\n                    mustLabl = False\n                else:\n                    plt.plot(accTime + TimeDur * t[indBgin:indEnd], \\\n                             func[indBgin:indEnd,arc],\\\n                             mark+color)\n                #\n            #\n            accTime += TimeDur\n\n    def savefig(self,keyName='',fullName=''):\n        if self.save.get(keyName,'False'):\n#            fileName = self.log.folderName + '/' + self.probName + '_' + \\\n#                        keyName + '.pdf'\n            fileName = self.log.folderName + os.sep + keyName + '.pdf'\n            self.log.printL('Saving ' + fullName + ' plot to ' + fileName + \\\n                            '!')\n            try:\n                plt.savefig(fileName, bbox_inches='tight', pad_inches=0.1)\n            except:\n                self.log.printL(\"Sorry, pdf saving failed... Are you using Windows?\")\n                self.log.printL(\"Anyway, you can always load the object and use some \"+ \\\n                      \"of its plotting methods later, I guess.\")\n        else:\n            plt.show()\n        #\n        plt.clf()\n        plt.close('all')\n\n#%% Just for avoiding compatibilization issues with other problems\n    # These methods are all properly implemented in probRock class.\n\n    def plotTraj(self):\n        self.log.printL(\"plotTraj: unimplemented method.\")\n        pass\n\n    def compWith(self,*args,**kwargs):\n        self.log.printL(\"compWith: unimplemented method.\")\n        pass\n\n    def plotSol(self,*args,**kwargs):\n        titlStr = \"Current solution\"\n\n        plt.subplots_adjust(0.0125,0.0,0.9,2.5,0.2,0.2)\n        Np = self.n + self.m\n\n\n        # First state\n        plt.subplot2grid((Np,1),(0,0))\n        self.plotCat(self.x[:,0,:],piIsTime=False)\n        plt.grid(True)\n        plt.ylabel(\"State #1\")\n        plt.title(titlStr)\n\n        ind = 1\n        for i in range(1,self.n):\n            plt.subplot2grid((Np,1),(ind,0))\n            ind+=1\n            self.plotCat(self.x[:,i,:],piIsTime=False)\n            plt.grid(True)\n            plt.ylabel(\"State #\"+str(i+1))\n\n        # Controls\n        for i in range(self.m):\n            plt.subplot2grid((Np,1),(ind,0))\n            ind+=1\n            self.plotCat(self.u[:,i,:],piIsTime=False)\n            plt.grid(True)\n            plt.ylabel(\"Control #\"+str(i+1))\n\n        self.savefig(keyName='currSol',fullName='solution')\n\n    def calcI(self,*args,**kwargs):\n        pass\n\n    def calcF(self,*args,**kwargs):\n        pass\n\n#%% RESTORATION-WISE METHODS\n\n    def rest(self,*args,**kwargs):\n        rest_sgra.rest(self,*args,**kwargs)\n\n    def calcStepRest(self,*args,**kwargs):\n        return rest_sgra.calcStepRest(self,*args,**kwargs)\n\n    def calcP(self,*args,**kwargs):\n        return rest_sgra.calcP(self,*args,**kwargs)\n\n    def updtHistP(self,alfa,mustPlotPint=False):\n\n        NIterRest = self.NIterRest+1\n\n        P,Pint,Ppsi = self.calcP(mustPlotPint=mustPlotPint)\n        self.P = P\n        self.histP[NIterRest] = P\n        self.histPint[NIterRest] = Pint\n        self.histPpsi[NIterRest] = Ppsi\n        self.histStepRest[NIterRest] = alfa\n        self.NIterRest = NIterRest\n\n    def showHistP(self):\n        IterRest = numpy.arange(0,self.NIterRest+1,1)\n\n        if self.histP[IterRest].any() > 0:\n            plt.semilogy(IterRest,self.histP[IterRest],'b',label='P')\n\n        if self.histPint[IterRest].any() > 0:\n            plt.semilogy(IterRest,self.histPint[IterRest],'k',label='P_int')\n\n        if self.histPpsi[IterRest].any() > 0:\n            plt.semilogy(IterRest,self.histPpsi[IterRest],'r',label='P_psi')\n\n        plt.plot(IterRest,self.tol['P']+0.0*IterRest,'-.b',label='tolP')\n        plt.title(\"Convergence report on P\")\n        plt.grid(True)\n        plt.xlabel(\"Rest iterations\")\n        plt.ylabel(\"P values\")\n        plt.legend(loc=\"upper left\", bbox_to_anchor=(1,1))\n\n        self.savefig(keyName='histP',fullName='P')\n\n#%% GRADIENT-WISE METHODS\n\n    def grad(self,*args,**kwargs):\n        grad_sgra.grad(self,*args,**kwargs)\n\n    def calcStepGrad(self,*args,**kwargs):\n        return grad_sgra.calcStepGrad(self,*args,**kwargs)\n\n    def calcQ(self,*args,**kwargs):\n        return grad_sgra.calcQ(self,*args,**kwargs)\n\n    def plotQRes(self,args):\n        return grad_sgra.plotQRes(self,args)\n\n    def plotF(self,*args,**kwargs):\n        return grad_sgra.plotF(self,*args,**kwargs)\n\n    def updtHistQ(self,alfa,mustPlotQs=False):\n        \"\"\" Updates the history of Qs, Is and gradStep. \"\"\"\n\n        NIterGrad = self.NIterGrad+1\n\n        Q,Qx,Qu,Qp,Qt = self.calcQ(mustPlotQs=mustPlotQs)\n        self.Q = Q\n        self.histQ[NIterGrad] = Q\n        self.histQx[NIterGrad] = Qx\n        self.histQu[NIterGrad] = Qu\n        self.histQp[NIterGrad] = Qp\n        self.histQt[NIterGrad] = Qt\n        self.histStepGrad[NIterGrad] = alfa\n\n        I,Iorig,Ipf = self.calcI()\n        self.histI[NIterGrad] = I\n        self.histIorig[NIterGrad] = Iorig\n        self.histIpf[NIterGrad] = Ipf\n        self.I = I\n\n        self.NIterGrad = NIterGrad\n\n\n    def showHistQ(self):\n        IterGrad = numpy.arange(1,self.NIterGrad+1,1)\n\n        if self.histQ[IterGrad].any() > 0:\n            plt.semilogy(IterGrad,self.histQ[IterGrad],'b',label='Q')\n\n        if self.histQx[IterGrad].any() > 0:\n            plt.semilogy(IterGrad,self.histQx[IterGrad],'k',label='Qx')\n\n        if self.histQu[IterGrad].any() > 0:\n            plt.semilogy(IterGrad,self.histQu[IterGrad],'r',label='Qu')\n\n        if self.histQp[IterGrad].any() > 0:\n            plt.semilogy(IterGrad,self.histQp[IterGrad],'g',label='Qp')\n\n        if self.histQt[IterGrad].any() > 0:\n            plt.semilogy(IterGrad,self.histQt[IterGrad],'y',label='Qt')\n\n        plt.plot(IterGrad,self.tol['Q']+0.0*IterGrad,'-.b',label='tolQ')\n        plt.title(\"Convergence report on Q\")\n        plt.grid(True)\n        plt.xlabel(\"Grad iterations\")\n        plt.ylabel(\"Q values\")\n        plt.legend(loc=\"upper left\", bbox_to_anchor=(1,1))\n\n        self.savefig(keyName='histQ',fullName='Q convergence history')\n\n    def showHistI(self):\n        IterGrad = numpy.arange(1,self.NIterGrad+1,1)\n\n        plt.title(\"Convergence report on I\")\n        plt.semilogy(IterGrad,self.histI[IterGrad],label='I')\n        plt.semilogy(IterGrad,self.histIorig[IterGrad],label='Iorig')\n        plt.semilogy(IterGrad,self.histIpf[IterGrad],label='Ipf')\n        plt.grid(True)\n        plt.xlabel(\"Grad iterations\")\n        plt.ylabel(\"I values\")\n        plt.legend()\n\n        self.savefig(keyName='histI',fullName='I convergence history')\n\n    def showHistGradStep(self):\n        IterGrad = numpy.arange(1,self.NIterGrad+1,1)\n\n        plt.title(\"Gradient step history\")\n        plt.semilogy(IterGrad,self.histStepGrad[IterGrad])\n        plt.grid(True)\n        plt.xlabel(\"Grad iterations\")\n        plt.ylabel(\"Step values\")\n\n        self.savefig(keyName='histGradStep',fullName='GradStep convergence history')\n\n    def showHistGRrate(self):\n        IterGrad = numpy.arange(1,self.NIterGrad+1,1)\n\n        if self.histGRrate[IterGrad].any() > 0:\n            plt.title(\"Gradient-restoration rate history\")\n            plt.semilogy(IterGrad,self.histGRrate[IterGrad])\n            plt.grid(True)\n            plt.xlabel(\"Grad iterations\")\n            plt.ylabel(\"Step values\")\n\n            self.savefig(keyName='histGRrate',fullName='Grad-Rest rate history')\n\n#%% LMPBVP\n\n    def LMPBVP(self,rho=0.0,isParallel=False):\n\n        helper = LMPBVPhelp(self,rho)\n\n        if isParallel:\n            pool = Pool()\n            res = pool.map(helper.propagate,range(self.Ns+1))\n            pool.close()\n            pool.join()\n        else:\n            if rho>0.5:\n                self.log.printL(\"\\nRunning GRAD in sequential (non-parallel) mode...\\n\")\n            else:\n                self.log.printL(\"\\nRunning REST in sequential (non-parallel) mode...\\n\")\n            res = list()\n            for j in range(self.Ns+1):\n                res.append(helper.propagate(j))\n\n        A,B,C,lam,mu = helper.getCorr(res,self.log)\n\n        return A,B,C,lam,mu\n","repo_name":"munizlgmn/SOAR","sub_path":"sgra.py","file_name":"sgra.py","file_ext":"py","file_size_in_byte":34431,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"81"}
+{"seq_id":"1864831226","text":"from te import tvm\nfrom .util import Compare\nfrom .util import ceil_div\n\n\nclass AttachMap:\n    \"\"\"docstring for  AttachMap\"\"\"\n    def __init__(self):\n        # axis:stage\n        self._parent_stages = dict()\n        self._attached_path = dict()\n\n    def record_attach(self, stage, scope):\n        \"\"\"\n        record the attach situation of stage\n\n        Parameters\n        ----------\n        stage : the processing of compute\n        scope : means axis\n\n        Returns\n        -------\n        None\n        \"\"\"\n        if self._attached_path.get(scope) is None:\n            self._attached_path[scope] = [stage]\n        else:\n            self._attached_path[scope].append(stage)\n\n    def follow_with(self, stage, parent_stage, scope):\n        \"\"\"\n        update the _parent_stages, record the attach\n\n        Parameters\n        ----------\n        stage : the processing of compute\n        parent_stage : the parent tensor\n        scope : means axis\n\n        Returns\n        -------\n        scope : the attached axis\n        \"\"\"\n        self._parent_stages[scope] = parent_stage\n        self.record_attach(stage, scope)\n        return scope\n\n    def record_same_attach(self, stage, ref_stage):\n        \"\"\"\n        attach the stage same as ref_stage\n\n        Parameters\n        ----------\n        stage : the processing of compute\n        ref_stage : the reference tensor\n\n        Returns\n        -------\n        None\n        \"\"\"\n        for scope, s_list in self._attached_path.items():\n            if ref_stage in s_list:\n                self.record_attach(stage, scope)\n                return scope\n        return None\n\n    def update_scope(self, scope, new_scope):\n        \"\"\"\n        replace the scope by new_scope\n\n        Parameters\n        ----------\n        scope : the old axis\n        new_scope : the new axis\n\n        Returns\n        -------\n        None\n        \"\"\"\n        if scope == new_scope:\n            return\n        child_stages = self._attached_path.get(scope)\n        if child_stages is None:\n            return\n\n        attached_path = self._attached_path\n        if attached_path.get(new_scope) is None:\n            attached_path[new_scope] = child_stages\n        else:\n            attached_path[new_scope].extend(child_stages)\n\n        self._attached_path.pop(scope)\n\n        self._parent_stages[new_scope] = self._parent_stages[scope]\n        return\n\n    def apply(self):\n        \"\"\"\n        begin the compute_at operation\n        according to _parent_stages and _attached_path\n\n        Parameters\n        ----------\n\n        Returns\n        -------\n        None\n        \"\"\"\n        for scope, array_stages in self._attached_path.items():\n            parent = self._parent_stages[scope]\n            pre_scope = None\n            for axis in parent.leaf_iter_vars:\n                if axis == scope:\n                    break\n                pre_scope = axis\n            if pre_scope is not None:\n                for stage in array_stages:\n                    stage.compute_at(parent, pre_scope)\n\n    @property\n    def attached_path(self):\n        \"\"\"\n        get the _attached_path\n\n        Parameters\n        ----------\n\n        Returns\n        -------\n        None\n        \"\"\"\n        return self._attached_path\n\n    @property\n    def parent_stages(self):\n        \"\"\"\n        get the _parent_stages\n\n        Parameters\n        ----------\n\n        Returns\n        -------\n        None\n        \"\"\"\n        return self._parent_stages\n\n\nclass ScopeManager:\n    \"\"\"docstring for ScopeManager\n    keep active tensor same in whole schedule\n    \"\"\"\n\n    def __init__(self, stage):\n        # super(ScopeManager, self).__init__()\n        self._stage = stage\n        self._axis_unit = dict()\n        self._active_scopes = list()\n        self._origin_axis = list()\n        self._last_attached = None\n        self._scope_intrinsic = None\n        if len(stage.leaf_iter_vars) != len(stage.all_iter_vars):\n            raise RuntimeError(\"Op should be init before schedule\")\n        for axis in stage.leaf_iter_vars:\n            self._axis_unit[axis] = [1, axis.dom.extent.value]\n            self._active_scopes.append(axis)\n            self._origin_axis.append(axis)\n\n    @property\n    def op(self):\n        \"\"\"\n        get stage's op\n        \"\"\"\n        return self._stage.op\n\n    @property\n    def origin_axis(self):\n        \"\"\"\n        get stage's _origin_axis\n        \"\"\"\n        return self._origin_axis\n\n    @property\n    def scope_intrinsic(self):\n        \"\"\"\n        get stage's _scope_intrinsic\n        \"\"\"\n        return self._scope_intrinsic\n\n    @property\n    def last_attached(self):\n        \"\"\"\n        get stage's _last_attached\n        \"\"\"\n        return self._last_attached\n\n    def reused_by(self, *args):\n        \"\"\"\n        stage's original reused_by function\n        \"\"\"\n        self._stage.reused_by(*args)\n\n    def split(self, parent, factor=None, nparts=None):\n        \"\"\"\n        apply split to scope\n\n        Parameters\n        ----------\n        parent : Itervar\n        factor : int\n        nparts : int\n\n        Returns\n        -------\n        outer : Itervar\n        inner : Itervar\n\n        \"\"\"\n\n        if factor is None and nparts is None:\n            raise RuntimeError(\"factor nparts can not be None\")\n\n        if self._axis_unit.get(parent) is None:\n            raise RuntimeError(\"parent scope can not be None\")\n\n        unit, extent = self._axis_unit[parent]\n        if nparts is not None:\n            outer, inner = self._stage.split(parent, nparts=nparts)\n            factor = ceil_div(extent, nparts)\n            self._axis_unit[inner] = [unit, factor]\n            self._axis_unit[outer] = [factor * unit, nparts]\n            if parent in self._active_scopes:  # not else\n                self._update_active_scope(parent, inner)\n        else:\n            outer, inner = self._stage.split(parent, factor=factor)\n            self._axis_unit[inner] = [unit, factor]\n            self._axis_unit[outer] = [unit * factor, ceil_div(extent, factor)]\n            if parent in self._active_scopes:  # not else\n                self._update_active_scope(parent, outer)\n        return outer, inner\n\n    def reorder(self, *args):\n        \"\"\"\n        stage's reorder function\n        need check if axises in args is valid\n        do not need all axised in this scope\n        \"\"\"\n        visited_scope = set()\n        scopes = list(args)\n        leaf_ivars = self._stage.leaf_iter_vars\n        valid_scopes = list()\n        scopes.reverse()\n        for axis in scopes:\n            if axis is not None \\\n                and axis not in visited_scope \\\n                and axis in leaf_ivars:\n                valid_scopes.append(axis)\n                visited_scope.add(axis)\n        if len(valid_scopes) <= 1:\n            return\n        valid_scopes.reverse()\n        self._stage.reorder(*valid_scopes)\n\n    def double_buffer(self):\n        \"\"\"\n        stage's original double_buffer function\n        \"\"\"\n        self._stage.double_buffer()\n\n    def unroll(self, var):\n        \"\"\"\n        stage's original unroll function\n        \"\"\"\n        self._stage.unroll(var)\n\n    def buffer_align(self, *arg):\n        \"\"\"\n        stage's original buffer_align function\n        \"\"\"\n        self._stage.buffer_align(*arg)\n\n    def buffer_tile(self, *arg):\n        \"\"\"\n        stage's original buffer_tile function\n        \"\"\"\n        self._stage.buffer_tile(*arg)\n\n    def pragma(self, var, pragma_type, pragma_value=None):\n        \"\"\"\n        stage's original pragma function\n        \"\"\"\n        self._stage.pragma(var, pragma_type, pragma_value)\n\n    def storage_align(self, axis, factor, offset):\n        \"\"\"\n        stage's original storage_align function\n        \"\"\"\n        self._stage.pragma(axis, factor, offset)\n\n    def get_active_scope_and_unit(self):\n        \"\"\"\n        get _active_scopes and unit_list\n\n        Returns\n        -------\n        _active_scopes : the scopes(axis) now used\n        unit_list : the split part of axis\n        \"\"\"\n        if not self._check_active_scopes(self._active_scopes):\n            raise RuntimeError(\"active itervar should in leaf_iter_vars\")\n        unit_list = list()\n        for axis in self._active_scopes:\n            unit, _ = self._axis_unit[axis]\n            unit_list.append(unit)\n\n        return self._active_scopes, unit_list\n\n    def get_active_scopes(self):\n        \"\"\"\n        get _active_scopes\n        _active_scopes: the scopes(axis) now used\n        \"\"\"\n        if not self._check_active_scopes(self._active_scopes):\n            raise RuntimeError(\"active itervar should in leaf_iter_vars\")\n        return self._active_scopes\n\n    def _update_active_scope(self, ax_before, ax_after):\n        active_scopess = self._active_scopes\n        index = active_scopess.index(ax_before)\n        active_scopess[index] = ax_after\n\n    def nlast_scopes(self, n_scope):\n        \"\"\"\n        get n last axises of this stage\n        \"\"\"\n        if n_scope <= 0:\n            raise ValueError(\"n_scope must >0\")\n\n        leaf_ivars = list(self._stage.leaf_iter_vars)\n        if n_scope > len(leaf_ivars):\n            raise ValueError(\"n_scope must less equal to leaf_ivars\")\n        return leaf_ivars[-n_scope::]\n\n    def intrin_scopes(self, nlast=0):\n        \"\"\"\n        this function developed for mmad\n        split the axis of scope and reorder\n        return the nlast axises for emit insn\n        \"\"\"\n        n_scope_intrin = len(self._origin_axis)\n        nlast = n_scope_intrin if nlast == 0 else nlast\n\n        if nlast < 0 or nlast > len(self._origin_axis):\n            raise RuntimeError(\"nlast must >0 and < %d\" %\n                               len(self._origin_axis))\n        # find the first split parent-child scope\n        axis_maping = dict()\n        for relation in self._stage.relations:\n            if not isinstance(relation, tvm.schedule.Split):\n                continue\n            if relation.parent in self._origin_axis:\n                axis_maping[relation.inner] = relation.parent\n        # get the order of outer and intrin scopes\n        leaf_ivars = list(self._stage.leaf_iter_vars)\n        outer_ivars = list()\n        inner_ivars = list(self._origin_axis)\n        for scope in leaf_ivars:\n            if scope in inner_ivars:\n                continue\n            parent = axis_maping.get(scope)\n            if parent is None:  # not a intrin sope\n                outer_ivars.append(scope)\n            else:  # scope is a intrin scope\n                if parent not in inner_ivars:\n                    raise RuntimeError(\"parent scope shound be in inner_ivars\")\n                offset = inner_ivars.index(parent)\n                inner_ivars[offset] = scope\n        order_keeped_axis = outer_ivars + inner_ivars\n        self._stage.reorder(*order_keeped_axis)\n        self._scope_intrinsic = inner_ivars[0]\n        return order_keeped_axis[-nlast::]\n\n    def bind_core(self, scope_list, core_num_list):\n        \"\"\"\n        bind core : use the chip better\n        finally fuse all the outter axises\n\n        Parameters\n        ----------\n        scope_list : the list that axises to bind\n        core_num_list : the list that core use\n\n        Returns\n        -------\n        axis_to_bind : the axis that bind\n\n        \"\"\"\n        if not isinstance(scope_list, (list, tuple)):\n            scope_list = [scope_list]\n        if not isinstance(core_num_list, (list, tuple)):\n            core_num_list = [core_num_list]\n        if not scope_list:  # len(scope_list) == 0\n            raise RuntimeError(\"at least one axis is to bind\")\n        if len(scope_list) != len(core_num_list):\n            raise RuntimeError(\n                \"len of scope_list and core_num_list should be same\")\n\n        if not self._check_active_scopes(scope_list):\n            raise RuntimeError(\"axis should be in leaf_iter_vars\")\n\n        old_leaf_ivars = list(self._stage.leaf_iter_vars)\n        axis_outers = list()\n        max_index = 0\n        for axis, core_num in zip(scope_list, core_num_list):\n            axo, axi = self.split(axis, nparts=core_num)\n            index = old_leaf_ivars.index(axis)\n            old_leaf_ivars[index] = axi\n            max_index = max(max_index, index)\n            axis_outers.append(axo)\n\n        reorder_list = axis_outers + old_leaf_ivars[0:max_index + 1:]\n        self._stage.reorder(*reorder_list)\n\n        block = tvm.thread_axis(\"blockIdx.x\")\n        if len(axis_outers) > 1:\n            axis_to_bind = self._stage.fuse(*axis_outers)\n        else:  # len(axis_outers) is 1\n            axis_to_bind = axis_outers[0]\n        self._stage.bind(axis_to_bind, block)\n        return axis_to_bind\n\n    def get_superkernel_axis_pragma(self):\n        \"\"\"\n        get the axis that superkernel used to pragma\n        \"\"\"\n        leaf_ivars = self._stage.leaf_iter_vars\n        return leaf_ivars[1]\n\n    def get_relate_scope(self, scope_key, scope_end=None):\n        \"\"\"\n        get the axises whose name contain scope_key\n        \"\"\"\n        scope_list = list()\n        for scope in self._stage.leaf_iter_vars:\n            if (scope_end is not None) and (scope == scope_end):\n                break\n            if scope.var.name.find('{}{}'\\\n                .format(scope_key.var.name, '.')) == 0:\n                scope_list.append(scope)\n        return scope_list\n\n    def emit_insn(self, scope, value, attrs=None):\n        \"\"\"\n        stage's original storage_align function\n        difference is the default axis to emit insn\n        \"\"\"\n        if self._scope_intrinsic is None:\n            self._scope_intrinsic = scope\n        self._stage.emit_insn(scope, value, attrs)\n\n    def set_last_attached(self, scope):\n        \"\"\"\n        set stage's _last_attached\n        \"\"\"\n        self._last_attached = scope\n\n    def _check_active_scopes(self, ax_list):\n        \"\"\"\n        check if axis in stage's leaf_ivars\n        \"\"\"\n        leaf_ivars = list(self._stage.leaf_iter_vars)\n        for axis in ax_list:\n            if axis not in leaf_ivars:\n                return False\n        return True\n\n\nclass ScheduleAgent:\n    \"\"\"docstring for ScheduleAgent\"\"\"\n\n    def __init__(self, sch):\n        self._sch = sch\n        self._attach_map = AttachMap()\n        # key=op,active op othen than origin_ops\n        self._scope_managers = dict()\n\n    def __getitem__(self, tensor):\n        \"\"\"\n        get scope manager of input tensor\n\n        Parameters\n        ----------\n        tensor : Tensor\n\n        Returns\n        -------\n        scope_manager\n\n        \"\"\"\n        if isinstance(tensor, tvm.tensor.Tensor):\n            key = tensor.op\n        else:\n            key = tensor\n        if self._scope_managers.get(key) is None:\n            self._scope_managers[key] = ScopeManager(self._sch[key])\n        return self._scope_managers[key]\n\n    def same_attach(self, tensor_a, tensor_b):\n        \"\"\"\n        attached tensor_a  at the scope that the scope tensor_b attached\n\n        Parameters\n        ----------\n        tensor_a : Tensor\n        tensor_b : Tenosr\n\n        Returns\n        -------\n\n        \"\"\"\n\n        sch = self._sch\n        return self._attach_map.record_same_attach(sch[tensor_a],\n                                                   sch[tensor_b])\n\n    def attach_at(self, tensor, parent, affine_shape):\n        \"\"\"\n        attach tensor to parent according to the affine_shape\n\n        Parameters\n        ----------\n        tensor : Tensor\n        parent : Tenosr\n        affine_shape: shape of tensor affine to parent\n\n        Returns\n        -------\n        the scope that tensor follow with\n        \"\"\"\n        # attach_map = self._attach_map\n        scopes = self[parent]\n        ax_list, unit = scopes.get_active_scope_and_unit()\n        if len(affine_shape) != len(ax_list):\n            raise RuntimeError(\"len(affine_shape) should be equal to \"\n                               \"len(shape)+len(reduce_axis) of {} \"\\\n                               .format(parent))\n        factor_list = list(\n            ceil_div(i, j) if i is not None else None\n            for i, j in zip(affine_shape, unit)\n        )\n        axis_outer = list()\n        axis_intrinsic = list()\n        axis_ori_unrelate = list()\n\n        def start_attach(factor_list, ax_list):\n            origin_axis = scopes.origin_axis\n            for factor, axis in zip(factor_list, ax_list):\n                if factor is not None and (factor > 1 or axis in origin_axis):\n                    axo, axi = scopes.split(axis, factor=factor)\n                    self._attach_map.update_scope(axis, axi)\n                    axis_outer.append(axo)\n                    axis_intrinsic.append(axi)\n                elif axis in origin_axis:\n                    axis_ori_unrelate.append(axis)\n                else:\n                    axis_outer.append(axis)\n        start_attach(factor_list, ax_list)\n        scope_attach = None\n        if axis_intrinsic:  # len(axis_intrinsic) > 0:\n            reorder_list = axis_outer + axis_ori_unrelate + axis_intrinsic\n            self._sch[parent].reorder(*reorder_list)\n            scope_attach = axis_intrinsic[0]\n            self._attach_map.follow_with(self._sch[tensor],\n                                         self._sch[parent],\n                                         scope_attach)\n            self[parent].set_last_attached(scope_attach)\n        elif axis_outer:  # len(axis_outer) > 0:\n            scope_attach = self[parent].last_attached\n            if scope_attach is not None:  # no else\n                self._attach_map.record_attach(self._sch[tensor],\n                                               scope_attach)\n        else:\n            pass\n        return scope_attach\n\n    def root_stage_at(self, parent, scope):\n        \"\"\"\n        parent: parent stage\n        scope: scope\n        \"\"\"\n        stage_array = self._sch.stages\n        parent_stage = self._sch[parent.op]\n        for stage in stage_array:\n            if stage == parent_stage:\n                continue\n            if stage.attach_type == 1:\n                stage.compute_at(parent_stage, scope)\n\n    def apply(self):\n        '''\n        apply the attach path\n        '''\n\n        attach_map = self._attach_map\n        parent_stages = list(set(attach_map.parent_stages.values()))\n        remain_scopes = set(attach_map.attached_path.keys())\n        for parent in parent_stages:\n            scope_intrinsic = self[parent.origin_op].scope_intrinsic\n            if scope_intrinsic is None:\n                continue\n            leaf_ivars = list(parent.leaf_iter_vars)\n            index = leaf_ivars.index(scope_intrinsic)\n            un_attachable_scopes = leaf_ivars[index + 1:]\n            for scope in list(remain_scopes):\n                if scope in un_attachable_scopes:\n                    attach_map.update_scope(scope, scope_intrinsic)\n                    remain_scopes.remove(scope)\n        self._attach_map.apply()\n\n    def pattern_abc(self,\n                    status,\n                    tensor_a,\n                    tensor_b,\n                    affine_shape_to_b,\n                    tensor_c,\n                    affine_shape_to_c):\n        \"\"\"\n        attach tensor_a to tensor_b\n        or\n        attach tensor_a to tensor_c\n        according to the status\n        \"\"\"\n        if status is None:\n            return None\n        if isinstance(status, (list, tuple)):\n            return None\n\n        attach = None\n        if status == Compare.EQUAL:\n            attach = self.same_attach(tensor_a, tensor_b)\n        elif status == Compare.LESS_EQ:\n            attach = self.attach_at(tensor_a, tensor_b, affine_shape_to_b)\n        elif status == Compare.GREATE_EQ:\n            attach = self.attach_at(tensor_a, tensor_c, affine_shape_to_c)\n        else:\n            raise RuntimeError(\"tiling shape of {} shouldn't be both less \"\n                               \"and greater than {}\"\\\n                               .format(tensor_a, tensor_b))\n        return attach\n","repo_name":"jizhuoran/caffe-huawei-atlas-convertor","sub_path":"convertor/huawei/te/lang/cce/boost_schedule_kit/schedule_agent.py","file_name":"schedule_agent.py","file_ext":"py","file_size_in_byte":19882,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"81"}
+{"seq_id":"41115169984","text":"import sys\nimport zmq\nimport asyncio\n\nport = \"5556\" # for now default port for publishers is 5556\n\n# if len(sys.argv) > 1:\n#     port =  sys.argv[1]\n#     int(port)\n\n\n# put() publishes a message on a topic\n\n#subscribe() subscribes a topic\n#unsubscribe() unsubscribes a topic\n\ndef put(message, topic = \"NULL\"):\n    context = zmq.Context()\n    socket = context.socket(zmq.REQ)\n    socket.connect(\"tcp://localhost:{}\".format(port))\n    socket.send(\"{}\".format(message).encode())\n    # socket.close() is called when garbage collection happens but we should still call it\n    return\n\ndef main_loop():\n    #this loop will listen to keyboard inputs and proceed accordingly\n    put(\"teste\")\n\n    \n    return\n\nmain_loop()\n","repo_name":"GustavoSena/SDLE-FEUP","sub_path":"publisher.py","file_name":"publisher.py","file_ext":"py","file_size_in_byte":713,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"12165903781","text":"\nfrom caliop.core.raw import RawMail\nfrom caliop.core.contact import ContactLookup, Recipient\nfrom caliop.core.user import User, UserMessage\nfrom caliop.core.message import Message\nfrom caliop.core.mail import MailMessage\n\nfrom caliop.helpers.log import log\n\n\nclass UserMessageDelivery(object):\n\n    def _resolve_user_contacts(self, user, msg):\n        \"\"\"Find all contacts known in the mail\"\"\"\n        contacts = []\n        for type, recips in msg.recipients.iteritems():\n            for addr, real_addr in recips:\n                if addr != user.user_id:\n                    log.debug('Try to resolve contact %s' % addr)\n                    contact = ContactLookup.get(user, addr)\n                    contacts.append(Recipient(contact, real_addr, type))\n        return contacts\n\n    def _get_tags(self, user, mail):\n        \"\"\"We eval user rules to get all tags for a mail\"\"\"\n        tags = []\n        if not user.rules:\n            return []\n        for rule in user.rules:\n            res, stop = rule.eval(mail)\n            if res:\n                tags.extend(res)\n            if stop:\n                break\n        return tags\n\n    def process(self, user_id, message_id):\n        user = User.get(user_id)\n        msg = RawMail.get(message_id)\n        mail = MailMessage(msg.data)\n        contacts = self._resolve_user_contacts(user, mail)\n        tags = self._get_tags(user, mail)\n        sec_level = (mail.transport_security_level + \\\n                     mail.content_security_level) / 2\n        user_msg = UserMessage(user, mail, sec_level, contacts, tags, [])\n        return Message.from_user_message(user_msg)\n","repo_name":"LaurentChemla/CaliOpen","sub_path":"caliop/caliop/core/deliver.py","file_name":"deliver.py","file_ext":"py","file_size_in_byte":1621,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"81"}
+{"seq_id":"74665404104","text":"from fastapi.testclient import TestClient\nfrom main import app\nfrom queries.accounts import AccountRepository\n\nclient = TestClient(app)\n\naccount_out = {\n    \"id\": 1,\n    \"username\": \"user\"\n}\n\naccounts = [account_out]\n\n\nclass MockAccount:\n    def get_all_accounts(self):\n        return accounts\n\n\ndef test_accounts_list():\n    app.dependency_overrides[AccountRepository] = MockAccount\n    response = client.get('/accounts')\n\n    assert response.status_code == 200\n    assert response.json() == {'accounts': accounts}\n\n\napp.dependency_overrides = {}\n","repo_name":"michellexg/pan-plan","sub_path":"panplan-service/tests/test_accounts.py","file_name":"test_accounts.py","file_ext":"py","file_size_in_byte":548,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"41792288804","text":"import cv2\nimport numpy as np\n\nprint(\"hello world\")\n\ntesting = 12\n\n\nparams = cv2.SimpleBlobDetector_Params()\n\n# Change thresholds\nparams.minThreshold = 10\nparams.maxThreshold = 200\n\n\n# Filter by Area.\nparams.filterByArea = True\nparams.minArea = 3000\nparams.maxArea = 100000\n\n# Filter by Circularity\nparams.filterByCircularity = True\nparams.minCircularity = 0.1\n\n# Filter by Convexity\nparams.filterByConvexity = False\nparams.minConvexity = 0.87\n\n# Filter by Inertia\nparams.filterByInertia = False\nparams.minInertiaRatio = 0.01\n\ncamera = cv2.VideoCapture(0)\nprint(camera.isOpened())\ndetector = cv2.SimpleBlobDetector_create(params)\n\n\n\nfor x in range(1000):\n    f = None\n    ret, image = camera.read(cv2.IMREAD_GRAYSCALE)\n    \n    hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)\n\n    # cv2.imwrite(\"trial.png\",image)    \n    # gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n       \n    blobs = detector.detect(image)\n\n\n    mask = cv2.inRange(hsv, (0, 20, 0), (90, 255,90))\n\n    ## slice the green\n    imask = mask>0\n    green = np.zeros_like(image, np.uint8)\n    green[imask] = image[imask]\n    # green = image\n\n    keypoints = detector.detect(green)\n   \n    print(keypoints)\n    for x in keypoints:\n        print(x.size)\n\n    im_with_keypoints = cv2.drawKeypoints(green, keypoints, np.array([]), (0,0,255), cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)\n \n    cv2.imshow(\"image\", im_with_keypoints) \n    ## save \n    # cv2.imshow(\"green.png\", gray)\n\n\n\n    \n\n    \n    \n    # circles = cv2.HoughCircles(gray, cv2.HOUGH_GRADIENT, 1.2, 20)\n\n    # green = cv2.drawKeypoints(green, blobs, np.array([]), (0,0,255), cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)\n\n    # cv2.imshow(\"green.png\", green)\n\n    cv2.waitKey(1)\n\n","repo_name":"justafakeusername/racecar_localizer","sub_path":"localizer.py","file_name":"localizer.py","file_ext":"py","file_size_in_byte":1703,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"20651863858","text":"import seaborn as sns\nimport colorsys\nfrom flask import Flask\nfrom flask import request\n\nimport ast\n\napp = Flask(__name__)\n\n\n@app.route(\"/backend/time\")\ndef getTime():\n    import time\n\n    # No need to jsonify\n    return {\"time\": time.time()}\n\n\n@app.route(\"/backend/formAPI\", methods=[\"GET\", \"POST\"])\ndef formAPI():\n    if request.method == \"GET\":\n        print(\"request.args =\", request.args)\n        dim = request.args.get(\"dim\")\n        size = request.args.get(\"size\")\n        sV = request.args.get(\"sV\")\n\n        # Implement backend logic here\n\n        # Implement SQL logic here\n\n        # No need to jsonify\n        return {\"dim\": dim, \"size\": size, \"sV\": sV}\n\n    elif request.method == \"POST\":\n        dict_str = request.data.decode(\"UTF-8\")\n        postData = ast.literal_eval(dict_str)\n\n        inputParams = postData.get(\"params\")\n\n        dim = inputParams.get(\"dim\")\n        size = inputParams.get(\"size\")\n        sV = inputParams.get(\"sV\")\n\n        # Implement backend logic here\n\n        # Implement SQL logic here\n\n        # No need to jsonify\n        return {\"dim\": dim, \"size\": size, \"sV\": sV}\n\n\n###########################\n## Generate Shift Vector ##\n###########################\n@app.route('/backend/getSV',  methods=['GET', 'POST'])\ndef getSV():\n    if request.method == 'GET':\n        dim = int(request.args.get('dim'))\n        sC = float(request.args.get('sC'))\n        sM = (request.args.get('sM'))\n\n        sV = genShiftVector(sM, dim, sC)\n\n        return {'sV': sV}\n\n    elif request.method == 'POST':\n\n        dict_str = request.data.decode(\"UTF-8\")\n        postData = ast.literal_eval(dict_str)\n\n        inputParams = postData.get(\"params\")\n\n        dim = int(inputParams.get(\"dim\"))\n        sC = float(inputParams.get(\"sC\"))\n        sM = inputParams.get(\"sM\")\n\n        sV = genShiftVector(sM, dim, sC)\n\n        return {'sV': sV}\n\n\n#######################\n## Generate Vertices ##\n#######################\n@app.route('/backend/getV',  methods=['GET', 'POST'])\ndef getV():\n    if request.method == 'GET':\n        dim = float(request.args.get('dim'))\n        size = float(request.args.get('size'))\n        sV = list(request.args.get('sV'))\n\n        # Clean up invalid shift vector inputs\n        # convert from string to list\n        sV = sVStrToList(sV)\n\n        # If too long remove until right length, if too short remove until right length\n        # while len(sV) > dim:\n        #     sV.pop()\n        # while len(sV) < dim:\n        #     sV.append(0)\n        vertices = dict()\n        for r in range(dim):\n            for s in range(r+1, dim):\n                for a in range(-size, size+1):\n                    for b in range(-size, size+1):\n                        vertices[f\"{r} {s} {a} {b}\"] = genVert(\n                            dim, sV, r, a, s, b)\n        return {'vertices': vertices}\n\n    elif request.method == 'POST':\n        dict_str = request.data.decode(\"UTF-8\")\n\n        postData = ast.literal_eval(dict_str)\n        inputParams = postData.get(\"params\")\n\n        sC = float(inputParams.get(\"sC\"))\n        dim = int(inputParams.get(\"dim\"))\n        size = int(inputParams.get(\"size\"))\n        tileSize = int(inputParams.get('tileSize'))\n        # tileSize = int(inputParams.get(\"tileSize\"))\n        sM = inputParams.get(\"sM\")\n        sV = genShiftVector(sM, dim, sC)\n\n        # sV = list(inputParams.get(\"sV\"))\n        # sV = sVStrToList(sV)\n\n        vertices = {}\n        for r in range(dim-1):\n            for s in range(r+1, dim):\n                for a in range(-size, size+1):\n                    for b in range(-size, size+1):\n                        vertices[f\"{r} {s} {a} {b}\"] = genVert(\n                            dim, sV, tileSize, r, a, s, b)\n        return {'vertices': vertices}\n\n\n@app.route('/backend/getTV',  methods=['GET', 'POST'])\ndef getTV():\n    if request.method == 'GET':\n        dim = float(request.args.get('dim'))\n        sV = list(request.args.get('sV'))\n        r = int(request.args.get('r'))\n        s = int(request.args.get('s'))\n        a = int(request.args.get('a'))\n        b = int(request.args.get('b'))\n\n        vert = genVert(dim, sV, r, a, s, b)\n        return {'vert': vert}\n\n    elif request.method == 'POST':\n\n        dict_str = request.data.decode(\"UTF-8\")\n        postData = ast.literal_eval(dict_str)\n\n        inputParams = postData.get(\"params\")\n\n        dim = int(inputParams.get(\"dim\"))\n        tileSize = int(inputParams.get('tileSize'))\n\n        sV = [0]*dim\n\n        r = int(inputParams.get('r'))\n        s = int(inputParams.get('s'))\n        a = int(inputParams.get('a'))\n        b = int(inputParams.get('b'))\n\n        vert = genVert(dim, sV, tileSize, r, a, s, b)\n        return {'vert': vert}\n\n\ndef sVStrToList(sV):\n    # First lets clean up the [] strings around the sV\n    if '[' in sV:\n        sV.remove('[')\n    if ']' in sV:\n        sV.remove(']')\n\n    if type(sV) is list:\n        # We currently have a list so lets join it together into a string and resplit w/ delimeter\n        sV = ''.join(sV)\n        sV = sV.split(',')\n        sV = [cleanShiftStr(shift) for shift in sV]\n        return sV\n    return [69]*100\n\n\ndef cleanShiftStr(shiftStr):\n    while shiftStr[0] == ' ':\n        shiftStr = shiftStr[1:]\n    while shiftStr[-1] == ' ':\n        shiftStr = shiftStr[:-1]\n    return float(shiftStr)\n\n\ndef genVert(dim, sV, tileSize, r, a, s, b):\n    nV = [(-1)**((2/dim)*i) for i in range(dim)]\n    if nV[s-r].imag == 0:\n        kp = 1j*(nV[r]*(b-sV[s]) - nV[s]*(a-sV[r])) / 0.00001\n    else:\n        kp = 1j*(nV[r]*(b-sV[s]) - nV[s]*(a-sV[r])) / nV[s-r].imag\n\n    k = [1+((kp/i).real+t)//1 for i, t in zip(nV, sV)]\n\n    vertices = []\n    for k[r], k[s] in [(a, b), (a+1, b), (a+1, b+1), (a, b+1)]:\n        vSum = sum(x*t for t, x in zip(nV, k))\n        vertices.append(vSum)\n    return imagToReal(vertices, tileSize)\n\n\ndef imagToReal(vertices, tileSize):\n    newVertices = []\n    for vertex in vertices:\n        scaledVert = tileSize*vertex\n        newVertices.append((scaledVert.real, scaledVert.imag))\n    return newVertices\n\n\ndef genShiftVector(sM, dim, sC):\n    import math\n    import random as rd\n    bZ, bR, bH = False, False, False\n    if sM == \"sZ\":\n        bZ = True\n    elif sM == \"sR\":\n        bR = True\n    elif sM == \"sH\":\n        bH = True\n\n    # All shifts set to zero\n    if bZ:\n        shiftVect = [0.0000001 for i in range(dim-1)]\n        shiftVect.append(sC)\n        return shiftVect\n    # Take care of 3 dimmensional edge case\n    if dim == 3:\n        samplePopulation = list(range(1, 1000, 1))\n        sample = rd.sample(samplePopulation, 2)\n        samp1, samp2 = sample[0]/2, sample[1]/2\n        final = sC - samp1 - samp2\n        return [samp1, samp2, final]\n    popMultiplier = 1\n    samplePopulation = list(range(1, popMultiplier*dim))\n    # Even dimmensions, something is going wrong here\n    if dim % 2 == 0:\n        lB = math.floor((dim/2)-1)\n        samp = rd.sample(samplePopulation, lB)\n        samp.sort()\n        sV = []\n        for i in range(lB):\n            if bH:\n                if bR:\n                    sV.append(samp[i]/2)\n                    sV.append(-samp[i]/2)\n                else:\n                    sV.append((i+1)/2)\n                    sV.append(-(i+1)/2)\n            else:\n                if bR:\n                    sV.append(samp[i]/(dim+5))\n                    sV.append(-samp[i]/(dim+5))\n                else:\n                    sV.append((i+1)/dim)\n                    sV.append(-(i+1)/dim)\n        sV.append((1/3)*sC)\n        sV.append((2/3)*sC)\n        if bH:\n            sV = [sv*0.2 for sv in sV]\n        sV = [sv-0.000001 for sv in sV]\n        return sV\n    # Odd dimmensions\n    else:\n        lB = math.floor((dim-1)/2)\n        uB = math.ceil((dim-1)/2)\n        samp = rd.sample(samplePopulation, lB)\n        samp.sort()\n        sV = []\n        for i in range(lB):\n            if bH:\n                if not bR:\n                    sV.append((i+1)/2)\n                    sV.append(-(i+1)/2)\n                else:\n                    sV.append(samp[i]/2)\n                    sV.append(-samp[i]/2)\n            else:\n                if not bR:\n                    sV.append((i+1)/dim)\n                    sV.append(-(i+1)/dim)\n                else:\n                    sV.append(samp[i]/(dim+5))\n                    sV.append(-samp[i]/(dim+5))\n        if lB != uB:\n            sV += [0.0]\n        sV += [sC]\n        return sV\n\n\n#############################################\n## Generate Adjacency Matrix Of Tile Types ##\n#############################################\n@app.route(\"/backend/getttm\", methods=[\"GET\", \"POST\"])\ndef getttm():\n    if request.method == \"GET\":\n        dim = int(request.args.get(\"dim\"))\n\n        ttm = genttm(dim)\n\n        # Implement backend logic here\n\n        # Implement SQL logic here\n\n        # No need to jsonify\n        return {\"ttm\": ttm}\n\n    elif request.method == \"POST\":\n        dict_str = request.data.decode(\"UTF-8\")\n        postData = ast.literal_eval(dict_str)\n\n        inputParams = postData.get(\"params\")\n\n        dim = int(inputParams.get(\"dim\"))\n\n        ttm = genttm(dim)\n\n        # Implement backend logic here\n\n        # Implement SQL logic here\n\n        # No need to jsonify\n        return {\"ttm\": ttm}\n\n\ndef genttm(dim):\n    # Intent: Find the tile type of all tiles\n    if dim % 2 == 0:\n        numTileTypes = int((dim/2)-1)\n    else:\n        numTileTypes = int((dim-1)/2)\n    # create tile type adjacency matrix\n    ttm = [[0 for x in range(dim)] for y in range(dim)]\n    for x in range(dim):\n        for y in range(1, numTileTypes+1):\n            if x+y >= dim:\n                ttm[x][(x+y)-dim] = y-1\n            else:\n                ttm[x][x+y] = y-1\n            ttm[x][x-y] = y-1\n    return ttm\n\n\n#####################\n## Generate Colors ##\n#####################\n@app.route(\"/backend/getColors\", methods=[\"GET\", \"POST\"])\ndef getColors():\n    if request.method == \"GET\":\n        manualCols = bool(request.args.get(\"manualCols\"))\n        manualCOls = True\n        numCols = int(request.args.get(\"numCols\"))\n\n        colors = genColors(manualCols, numCols)\n\n        # Implement backend logic here\n\n        # Implement SQL logic here\n\n        # No need to jsonify\n        return {\"colors\": colors}\n\n    elif request.method == \"POST\":\n        dict_str = request.data.decode(\"UTF-8\")\n        postData = ast.literal_eval(dict_str)\n\n        inputParams = postData.get(\"params\")\n\n        numCols = int(inputParams.get(\"numCols\"))\n\n        curCols = (inputParams.get(\"curCols\"))\n\n        colors = genSpecColors(numCols, curCols)\n\n        # Implement backend logic here\n\n        # Implement SQL logic here\n\n        # No need to jsonify\n        return {\"colors\": colors}\n\n\ndef genColors(manualCols, numCols):\n    # if manualCols or numCols > 19:\n    if manualCols:\n        # Manually create colors\n        # (hue, saturation, value)\n        hsvCols = [(x/numCols, 1, 0.75) for x in range(numCols)]\n        colors = list(map(lambda x: colorsys.hsv_to_rgb(*x), hsvCols))\n        colors = [[255*color[0], 255*color[1], 255*color[2]]\n                  for color in colors]\n    else:\n        # Classic colors\n        # colors = sns.color_palette(\"bright\", numCols)\n        # colors = sns.color_palette(\"husl\", numCols)\n        # colors = sns.color_palette(\"cubehelix\", numCols)\n\n        # Divergent colors\n        colors = sns.color_palette(\"BrBG\", numCols)\n        # colors = sns.color_palette(\"coolwarm\", numCols)\n\n        # Gradient colors\n        # colors = sns.cubehelix_palette(numCols, dark=0.1, light=0.9)\n    return colors\n\n\ndef genSpecColors(numCols, colType):\n    # if manualCols or numCols > 19:\n    if colType == \"mc\":\n        hsvCols = [(x/numCols, 1, 0.75) for x in range(numCols)]\n        colors = list(map(lambda x: colorsys.hsv_to_rgb(*x), hsvCols))\n        colors = [[255*color[0], 255*color[1], 255*color[2]]\n                  for color in colors]\n        # CHP\n    elif colType == \"chp\":\n        colors = sns.cubehelix_palette(numCols)\n    elif colType == \"chp_rnd4\":\n        colors = sns.cubehelix_palette(numCols, rot=-.4)\n    elif colType == \"chp_s2d8_rd1\":\n        colors = sns.cubehelix_palette(numCols, start=2.8, rot=.1)\n        # MPLP\n    elif colType == \"mplp_GnBu_d\":\n        colors = sns.mpl_palette(\"GnBu_d\", numCols)\n    elif colType == \"mplp_seismic\":\n        colors = sns.mpl_palette(\"seismic\", numCols)\n        # CP_Misc\n    elif colType == \"cp\":\n        colors = sns.color_palette(n_colors=numCols)\n    elif colType == \"cp_Accent\":\n        colors = sns.color_palette(\"Accent\", n_colors=numCols)\n    elif colType == \"cp_cubehelix\":\n        colors = sns.color_palette(\"cubehelix\", n_colors=numCols)\n    elif colType == \"cp_flag\":\n        colors = sns.color_palette(\"flag\", n_colors=numCols)\n    elif colType == \"cp_Paired\":\n        colors = sns.color_palette(\"Paired\", n_colors=numCols)\n    elif colType == \"cp_Pastel1\":\n        colors = sns.color_palette(\"Pastel1\", n_colors=numCols)\n    elif colType == \"cp_Pastel2\":\n        colors = sns.color_palette(\"Pastel2\", n_colors=numCols)\n    elif colType == \"cp_tab10\":\n        colors = sns.color_palette(\"tab10\", n_colors=numCols)\n    elif colType == \"cp_tab20\":\n        colors = sns.color_palette(\"tab20\", n_colors=numCols)\n    elif colType == \"cp_tab20c\":\n        colors = sns.color_palette(\"tab20c\", n_colors=numCols)\n        # CP_Rainbow\n    elif colType == \"cp_gistncar\":\n        colors = sns.color_palette(\"gist_ncar\", n_colors=numCols)\n    elif colType == \"cp_gistrainbow\":\n        colors = sns.color_palette(\"gist_rainbow\", n_colors=numCols)\n    elif colType == \"cp_hsv\":\n        colors = sns.color_palette(\"hsv\", n_colors=numCols)\n    elif colType == \"cp_nipyspectral\":\n        colors = sns.color_palette(\"nipy_spectral\", n_colors=numCols)\n    elif colType == \"cp_rainbow\":\n        colors = sns.color_palette(\"rainbow\", n_colors=numCols)\n        # CP_Grad2\n    elif colType == \"cp_afmhot\":\n        colors = sns.color_palette(\"afmhot\", n_colors=numCols)\n    elif colType == \"cp_autumn\":\n        colors = sns.color_palette(\"autumn\", n_colors=numCols)\n    elif colType == \"cp_binary\":\n        colors = sns.color_palette(\"binary\", n_colors=numCols)\n    elif colType == \"cp_bone\":\n        colors = sns.color_palette(\"bone\", n_colors=numCols)\n    elif colType == \"cp_cividis\":\n        colors = sns.color_palette(\"cividis\", n_colors=numCols)\n    elif colType == \"cp_cool\":\n        colors = sns.color_palette(\"cool\", n_colors=numCols)\n    elif colType == \"cp_copper\":\n        colors = sns.color_palette(\"copper\", n_colors=numCols)\n    elif colType == \"cp_hot\":\n        colors = sns.color_palette(\"hot\", n_colors=numCols)\n    elif colType == \"cp_inferno\":\n        colors = sns.color_palette(\"inferno\", n_colors=numCols)\n    elif colType == \"cp_magma\":\n        colors = sns.color_palette(\"magma\", n_colors=numCols)\n    elif colType == \"cp_mako\":\n        colors = sns.color_palette(\"mako\", n_colors=numCols)\n    elif colType == \"cp_plasma\":\n        colors = sns.color_palette(\"plasma\", n_colors=numCols)\n    elif colType == \"cp_PuBuGn\":\n        colors = sns.color_palette(\"PuBuGn\", n_colors=numCols)\n    elif colType == \"cp_Purples\":\n        colors = sns.color_palette(\"Purples\", n_colors=numCols)\n    elif colType == \"cp_RdPu\":\n        colors = sns.color_palette(\"RdPu\", n_colors=numCols)\n    elif colType == \"cp_rocket\":\n        colors = sns.color_palette(\"rocket\", n_colors=numCols)\n    elif colType == \"cp_spring\":\n        colors = sns.color_palette(\"spring\", n_colors=numCols)\n    elif colType == \"cp_summer\":\n        colors = sns.color_palette(\"summer\", n_colors=numCols)\n    elif colType == \"cp_viridis\":\n        colors = sns.color_palette(\"viridis\", n_colors=numCols)\n    elif colType == \"cp_winter\":\n        colors = sns.color_palette(\"winter\", n_colors=numCols)\n    elif colType == \"cp_Wistia\":\n        colors = sns.color_palette(\"Wistia\", n_colors=numCols)\n    elif colType == \"cp_YlOrRd\":\n        colors = sns.color_palette(\"YlOrRd\", n_colors=numCols)\n        # CP_Grad3\n    elif colType == \"cp_BrBG\":\n        colors = sns.color_palette(\"BrBG\", n_colors=numCols)\n    elif colType == \"cp_brg\":\n        colors = sns.color_palette(\"brg\", n_colors=numCols)\n    elif colType == \"cp_bwr\":\n        colors = sns.color_palette(\"bwr\", n_colors=numCols)\n    elif colType == \"cp_CMRmap\":\n        colors = sns.color_palette(\"CMRmap\", n_colors=numCols)\n    elif colType == \"cp_gistearth\":\n        colors = sns.color_palette(\"gist_earth\", n_colors=numCols)\n    elif colType == \"cp_giststern\":\n        colors = sns.color_palette(\"gist_stern\", n_colors=numCols)\n    elif colType == \"cp_gnuplot\":\n        colors = sns.color_palette(\"gnuplot\", n_colors=numCols)\n    elif colType == \"cp_gnuplot2\":\n        colors = sns.color_palette(\"gnuplot2\", n_colors=numCols)\n    elif colType == \"cp_icefire\":\n        colors = sns.color_palette(\"icefire\", n_colors=numCols)\n    elif colType == \"cp_ocean\":\n        colors = sns.color_palette(\"ocean\", n_colors=numCols)\n    elif colType == \"cp_PiYG\":\n        colors = sns.color_palette(\"PiYG\", n_colors=numCols)\n    elif colType == \"cp_PRGn\":\n        colors = sns.color_palette(\"PRGn\", n_colors=numCols)\n    elif colType == \"cp_prism\":\n        colors = sns.color_palette(\"prism\", n_colors=numCols)\n    elif colType == \"cp_RdBu\":\n        colors = sns.color_palette(\"RdBu\", n_colors=numCols)\n    elif colType == \"cp_RdGy\":\n        colors = sns.color_palette(\"RdGy\", n_colors=numCols)\n    elif colType == \"cp_RdYlBu\":\n        colors = sns.color_palette(\"RdYlBu\", n_colors=numCols)\n    elif colType == \"cp_RdYlGn\":\n        colors = sns.color_palette(\"RdYlGn\", n_colors=numCols)\n    elif colType == \"cp_seismic\":\n        colors = sns.color_palette(\"seismic\", n_colors=numCols)\n    elif colType == \"cp_Spectral\":\n        colors = sns.color_palette(\"Spectral\", n_colors=numCols)\n    elif colType == \"cp_terrein\":\n        colors = sns.color_palette(\"terrein\", n_colors=numCols)\n    elif colType == \"cp_vlag\":\n        colors = sns.color_palette(\"vlag\", n_colors=numCols)\n    else:\n        hsvCols = [(x/numCols, 1, 0.75) for x in range(numCols)]\n        colors = list(map(lambda x: colorsys.hsv_to_rgb(*x), hsvCols))\n        colors = [[255*color[0], 255*color[1], 255*color[2]]\n                  for color in colors]\n\n    return colors\n\n\nif __name__ == '__main__':\n    app.run()\n","repo_name":"jcanedo279/ptv","sub_path":"backend/api.py","file_name":"api.py","file_ext":"py","file_size_in_byte":18275,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"2811294282","text":"from mathutil import prime_under\r\n\r\n\r\ndef pow_mod(power, p):\r\n    \"\"\"\r\n    power mod for base 10\r\n    in case power is too big\r\n\r\n    given that base is 10, and p and 10 are coprime\r\n    \"\"\"\r\n    e = power % (p - 1)\r\n    return pow(10, e, p)\r\n\r\n\r\ndef repunit_mod(p, degree=9):\r\n    \"\"\"\r\n    compute R(10 ** degree) % p\r\n    \"\"\"\r\n    curr_mod = 1111111111 % p\r\n    for i in range(1, degree):\r\n        curr_mod = curr_mod * sum([pow_mod(10 ** i * j, p) for j in range(10)]) % p\r\n    return curr_mod\r\n\r\n\r\ndef main():\r\n    first40 = []\r\n    count = 0\r\n    for p in prime_under(10 ** 6)[3:]:  # exclude 2 and 5, which are not coprime with 10\r\n        if repunit_mod(p) == 0:\r\n            count += 1\r\n            # print(count, p)\r\n            first40.append(p)\r\n            if count == 40:\r\n                break\r\n    # print(first40)\r\n    print(sum(first40))\r\n\r\n\r\nif __name__ == '__main__':\r\n    from time import time\r\n    starting_time = time()\r\n    main()\r\n    print(\"Time elapsed:\", time() - starting_time, \"seconds\")\r\n","repo_name":"colinxy/ProjectEuler","sub_path":"Python/project_euler132.py","file_name":"project_euler132.py","file_ext":"py","file_size_in_byte":1018,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"31726405470","text":"from stats import run_algo\nfilename = 'result2.txt'\nimport os\n\nfrom collections import defaultdict\ndef parse_path_file(path_file_content):\n    agents_path = defaultdict(list)\n    agent = None\n    lines = path_file_content.split('\\n')[1:]\n    for line in lines:\n        if line == '':\n            break\n        nums = list(map(int, line.split(' ')))\n        if len(nums) == 1:\n            agent = int(nums[0])\n        else:\n            x, y, time = nums\n            agents_path[agent].append((x, y, time))\n    return agents_path\n\ndef read_paths(filename=filename):\n    with open(filename, 'r') as path_file:\n        path_file_content = path_file.read()\n        agents_path = parse_path_file(path_file_content)\n    return dict(agents_path)\n\nimport tqdm\n# test num takes index in range(0, num_repetitions)\ndef success_rate(alg_name, map_name, scen_name, num_agents, num_repetitions, w=None):\n    rate = 0\n    for i in tqdm.tqdm(range(num_repetitions)):\n        done = run_algo(alg=alg_name, dest_file=filename,\n                        map_path=os.path.join(os.curdir, 'data', 'maps', 'mapf', map_name),\n                        scen_path=os.path.join(os.curdir, 'data', 'scens', 'mapf', scen_name),\n                        tasks_count=num_agents,\n                        test_num=i, w=w)\n        rate += int(done)\n    return rate / num_repetitions\n\n# build success_rate stats\nrandom_map, random_scen = 'maze-32-32-2.map',  'maze-32-32-2-even-1.scen'\nrate_list = []\nN = 10\nfor num_actors in [5, 10, 15, 20, 25, 30, 35, 40, 45, 50]:\n    rate = success_rate('ECBS', random_map, random_scen, num_actors, N, w=1.1)\n    print(f'Num actors :{num_actors}, Rate: {rate:.2f}%')\n    if rate <= 0.1:\n        print(\"too low rate, stopping here\")\n        break\n    rate_list.append(rate)\n\nprint(rate_list)\n","repo_name":"neckbosov/CBS","sub_path":"success.py","file_name":"success.py","file_ext":"py","file_size_in_byte":1788,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"24180683871","text":"# -*- coding: utf-8 -*-\nfrom dataclasses import dataclass\nfrom typing import Annotated, Union\n\nfrom mtpylon import int128\n\n\n@dataclass\nclass DHGenOk:\n    nonce: int128\n    server_nonce: int128\n    new_nonce_hash1: int128\n\n    class Meta:\n        name = 'dh_gen_ok'\n        order = (\n            'nonce',\n            'server_nonce',\n            'new_nonce_hash1'\n        )\n\n\n@dataclass\nclass DHGenRetry:\n    nonce: int128\n    server_nonce: int128\n    new_nonce_hash2: int128\n\n    class Meta:\n        name = 'dh_gen_retry'\n        order = (\n            'nonce',\n            'server_nonce',\n            'new_nonce_hash2'\n        )\n\n\n@dataclass\nclass DHGenFail:\n    nonce: int128\n    server_nonce: int128\n    new_nonce_hash3: int128\n\n    class Meta:\n        name = 'dh_gen_fail'\n        order = (\n            'nonce',\n            'server_nonce',\n            'new_nonce_hash3'\n        )\n\n\nSet_client_DH_params_answer = Annotated[\n    Union[\n        DHGenOk,\n        DHGenRetry,\n        DHGenFail\n    ],\n    'Set_client_DH_params_answer'\n]\n","repo_name":"Zapix/mtpylon","sub_path":"mtpylon/service_schema/constructors/set_client_dh_params_answer.py","file_name":"set_client_dh_params_answer.py","file_ext":"py","file_size_in_byte":1034,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"81"}
+{"seq_id":"71082972106","text":"############## PiCamera video stream creator ###############\n#\n# Author: Evan Juras  (heavily copying from Adrian Rosebrock)\n# Date: 9/5/17\n# Description: Defines the PiVideoStream object, which controls\n# acquisition of frames from the PiCamera. The object uses multi-threading to\n# aquire camera frames in a separate thread from the main program. This allows\n# the main thread to grab the most recent camera frame without having to take \n# it directly from the camera feed, reducing I/O time, which slightly improves \n# framerate.\n#\n# See http://www.pyimagesearch.com/2015/12/28/increasing-raspberry-pi-fps-with-python-and-opencv/\n# for a full explanation of the source code.\n\n# Import the necessary packages\nfrom picamera.array import PiRGBArray\nfrom picamera import PiCamera\nfrom threading import Thread\nimport cv2\n\n\nclass PiVideoStream:\n    \"\"\"Camera object\"\"\"\n    def __init__(self,resolution=(640,480),framerate=30):\n\t\t# Initialize the camera and the camera image stream\n        self.camera = PiCamera()\n        self.camera.resolution = resolution\n        self.camera.framerate = framerate\n        self.rawCapture = PiRGBArray(self.camera,size=resolution)\n        self.stream = self.camera.capture_continuous(\n            self.rawCapture, format = \"bgr\", use_video_port = True)\n\n\t\t# Create a variable to store the camera frame and to control\n\t\t# when the camera is stopped\n        self.frame = []\n        self.stopped = False\n\n    def start(self):\n\t\t# Start the thread to read frames from the video stream\n        Thread(target=self.update,args=()).start()\n        return self\n\n    def update(self):\n\t\t# Keep looping indefinitely until the thread is stopped\n        for f in self.stream:\n\t\t\t# Grab the frame from the stream and clear the stream\n\t\t\t# in preparation for the next frame\n            self.frame = f.array\n            self.rawCapture.truncate(0)\n\n\t\t\t# If the camera is stopped, stop the thread and close\n\t\t\t# the camera resources\n            if self.stopped:\n                self.stream.close()\n                self.rawCapture.close()\n                self.camera.close()\n\n    def read(self):\n\t\t# Return the most recent frame\n        return self.frame\n\n    def stop(self):\n\t\t# Indicate that the camera and thread should be stopped\n        self.stopped = True\n","repo_name":"dandrews7396/buggyBot","sub_path":"PiVideoStream.py","file_name":"PiVideoStream.py","file_ext":"py","file_size_in_byte":2276,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"42131965691","text":"class Solution:\n    def removeOuterParentheses(self, s: str) -> str:\n        res = \"\"\n        count = 0\n\n        for char in s:\n            if char == \"(\":\n                count += 1\n                if count > 1:\n                    res += char\n            if char == \")\":\n                count -= 1\n                if count > 0:\n                    res += char\n\n        return res\n    #  Lessons learned\n        #  we can solve the problem in linear time by keeping a count of the amount of closed and open brackets that we see, by doing so we can ignore the outer parentheses based on how balanced things are\n","repo_name":"mdiallo98/python-dataStructures-Algos","sub_path":"LeetcodeQuestions/String_Problems/remove_outermost_parenthesis.py","file_name":"remove_outermost_parenthesis.py","file_ext":"py","file_size_in_byte":611,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"81"}
+{"seq_id":"24340908253","text":"# vim: set ts=8 sts=4 sw=4 tw=99 et:\n#\n# This file is part of AMBuild.\n#\n# AMBuild is free software: you can redistribute it and/or modify\n# it under the terms of the GNU General Public License as published by\n# the Free Software Foundation, either version 3 of the License, or\n# (at your option) any later version.\n#\n# AMBuild 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 AMBuild. If not, see <http://www.gnu.org/licenses/>.\nimport os, errno\nimport uuid as uuids\nfrom ambuild2 import util\nfrom ambuild2 import nodetypes\nfrom ambuild2.frontend import paths\nfrom ambuild2.frontend.vs import nodes\nfrom ambuild2.frontend.base_generator import BaseGenerator\n\nSupportedVersions = ['10', '11', '12', '14', '15', '16', '17']\nYearMap = {\n    '2010': 10,\n    '2012': 11,\n    '2013': 12,\n    '2015': 14,\n    '2017': 15,\n    '2019': 16,\n    '2022': 17,\n}\n\nclass Generator(BaseGenerator):\n    def __init__(self, cm):\n        super(Generator, self).__init__(cm)\n        self.compiler = None\n        self.vs_version = None\n        self.files_ = {}\n        self.projects_ = set()\n\n        if self.cm.options.vs_version in SupportedVersions:\n            self.vs_version = int(self.cm.options.vs_version)\n        else:\n            if self.cm.options.vs_version not in YearMap:\n                util.con_err(\n                    util.ConsoleRed,\n                    'Unsupported Visual Studio version: {0}'.format(self.cm.options.vs_version),\n                    util.ConsoleNormal)\n                raise Exception('Unsupported Visual Studio version: {0}'.format(\n                    self.cm.options.vs_version))\n            self.vs_version = YearMap[self.cm.options.vs_version]\n\n        self.cacheFile = os.path.join(self.cm.buildPath, '.cache')\n        try:\n            with open(self.cacheFile, 'rb') as fp:\n                self.vars_ = util.pickle.load(fp)\n        except:\n            self.vars_ = {}\n\n        if 'uuids' not in self.vars_:\n            self.vars_['uuids'] = {}\n\n        self.target_platform = 'windows'\n\n    # Overridden.\n    @property\n    def backend(self):\n        return 'vs'\n\n    # Overridden.\n    def preGenerate(self):\n        pass\n\n    # Overriden.\n    def postGenerate(self):\n        self.generateProjects()\n        with open(self.cacheFile, 'wb') as fp:\n            util.DiskPickle(self.vars_, fp)\n\n    def generateProjects(self):\n        for node in self.projects_:\n            # We cache uuids across runs to keep them consistent.\n            node.uuid = self.vars_['uuids'].get(node.path)\n            if node.uuid is None:\n                node.uuid = str(uuids.uuid1()).upper()\n                self.vars_['uuids'][node.path] = node.uuid\n            node.project.export(self.cm, node)\n\n    # Overridden.\n    #\n    # We don't support reconfiguring in this frontend.\n    def addConfigureFile(self, cx, path):\n        pass\n\n    def detectCompilers(self):\n        raise Exception('Implement me!')\n\n    # Overridden.\n    def enterContext(self, cx):\n        cx.vs_nodes = []\n\n    # Overridden.\n    def leaveContext(self, cx):\n        pass\n\n    def ensureUnique(self, path):\n        if path in self.files_:\n            entry = self.files_[path]\n            util.con_err(util.ConsoleRed,\n                         'Path {0} already exists as: {1}'.format(path,\n                                                                  entry.kind), util.ConsoleNormal)\n            raise Exception('Path {0} already exists as: {1}'.format(path, entry.kind))\n\n    # Overridden.\n    def getLocalFolder(self, context):\n        if type(context.localFolder_) is nodes.FolderNode or context.localFolder_ is None:\n            return context.localFolder_\n\n        if not len(context.buildFolder):\n            context.localFolder_ = None\n        else:\n            context.localFolder_ = self.addFolder(context.parent, context.buildFolder)\n\n        return context.localFolder_\n\n    # Overridden.\n    def addFolder(self, cx, folder):\n        parentFolderNode = None\n        if cx is not None:\n            parentFolderNode = cx.localFolder\n\n        _, path = paths.ResolveFolder(parentFolderNode, folder)\n        if path in self.files_:\n            entry = self.files_[path]\n            if type(entry) is not nodes.FolderNode:\n                self.ensureUnique(path)  # Will always throw.\n            return entry\n\n        try:\n            os.makedirs(path)\n        except OSError as exn:\n            if not (exn.errno == errno.EEXIST and os.path.isdir(path)):\n                raise\n\n        obj = nodes.FolderNode(path)\n        self.files_[path] = obj\n        return obj\n\n    # Overridden.\n    def addCopy(self, context, source, output_path):\n        return (None, (None,))\n\n    # Overridden.\n    def addShellCommand(self,\n                        context,\n                        inputs,\n                        argv,\n                        outputs,\n                        folder = -1,\n                        dep_type = None,\n                        weak_inputs = None,\n                        shared_outputs = None):\n        print(inputs, argv, outputs, folder, dep_type, weak_inputs, shared_outputs)\n\n    def addOutput(self, context, path, parent):\n        self.ensureUnique(path)\n\n        node = nodes.OutputNode(context, path, parent)\n        self.files_[path] = node\n        return node\n\n    def addProjectNode(self, context, project):\n        self.ensureUnique(project.path)\n        self.projects_.add(project)\n        self.files_[project.path] = project\n","repo_name":"alliedmodders/ambuild","sub_path":"ambuild2/frontend/vs/gen.py","file_name":"gen.py","file_ext":"py","file_size_in_byte":5711,"program_lang":"python","lang":"en","doc_type":"code","stars":53,"dataset":"github-code","pt":"81"}
+{"seq_id":"41030443061","text":"import self as self\r\nfrom selenium import webdriver\r\nfrom os import system\r\nfrom googletrans import Translator\r\nfrom time import sleep\r\nimport json\r\n\r\nfrom selenium.webdriver import ActionChains\r\nfrom selenium.webdriver.common.keys import Keys\r\n\r\nservice_urls=['translate.google.com','translate.google.co.kr']\r\ntranslator = Translator(service_urls)\r\n\r\ncount = 0\r\n\r\nwith open(r\"Context/title.txt\",\"r\") as title,open(r\"Context/short.txt\",\"r\") as short:\r\n    title = title.read()\r\n    short = short.read()\r\n    long = open(r\"Context/long.txt\",\"r\")\r\n\r\nwith open(r'Config/playstore_language_config.json') as f:\r\n    PLAYSTORE = json.load(f)\r\n\r\nwith open(r'Config/playstore_language_config.json') as f:\r\n    LANGUAGES = json.load(f)\r\n\r\n\r\n# create a new Firefox session\r\ndriver = webdriver.Firefox(executable_path=r'Runtime/geckodriver')\r\ndriver.maximize_window()\r\n\r\n\r\n# navigate to the application home page\r\nURL = driver.get(\"https://accounts.google.com/signin/v2/identifier?service=androiddeveloper&passive=1209600&continue=https%3A%2F%2Fplay.google.com%2Fapps%2Fpublish%2F%23&followup=https%3A%2F%2Fplay.google.com%2Fapps%2Fpublish%2F&flowName=GlifWebSignIn&flowEntry=ServiceLogin\")\r\nConfirm = input(\"Login with User & Pass (Enter)\")\r\n\r\nNew = driver.find_element_by_css_selector(\"button[class='IF2W6TD-f-a IF2W6TD-f-r']\").click()\r\ncommand = 'echo ' + title.strip() + '| clip'\r\ncase = system(command)\r\nConfirm = input(\"Paste project name : \" + title + \" (Enter)\")\r\n\r\nConfirm = input(\"Select add languages (Enter)\")\r\n\r\nlanguage_button = driver.find_element_by_css_selector(\"button[class='IF2W6TD-f-a IF2W6TD-f-p IF2W6TD-f-n IF2W6TD-f-b IF2W6TD-f-c IF2W6TD-f-s']\")\r\n\r\ntitle_box = driver.find_element_by_css_selector(\"input[class='gwt-TextBox IF2W6TD-rn-d']\")\r\nshort_box = driver.find_element_by_css_selector(\"textarea[class='gwt-TextArea IF2W6TD-di-d IF2W6TD-rn-d']\")\r\nlong_box = driver.find_element_by_css_selector(\"textarea[class='gwt-TextArea IF2W6TD-rn-d']\")\r\n\r\nsystem('cls')\r\nprint(\"SR.\" + \" |  \" + \"LANGUAGES\" + \" - \" + \"Encode\" + \" | Function Processing\" + \"................... [Status]\")\r\n\r\nfor l in PLAYSTORE:\r\n    count=count+1\r\n    long.seek(0)\r\n    sleep(1)\r\n\r\n    update_language = driver.execute_script(\"arguments[0].setAttribute('data-lang-code','\" + l + \"')\", language_button)\r\n    script = driver.execute_script(\"arguments[0].setAttribute('aria-pressed','false')\", language_button)\r\n    sleep(5)\r\n    language_button.click()\r\n\r\n    title_box.clear()\r\n    short_box.clear()\r\n    long_box.clear()\r\n\r\n    translate_title = translator.translate(title, dest=PLAYSTORE[l]).text\r\n    translate_short = translator.translate(short, dest=PLAYSTORE[l]).text\r\n    translate_long = translator.translate(long.read(), dest=PLAYSTORE[l]).text\r\n\r\n    if PLAYSTORE[l]=='bg': translate_short=translate_title\r\n    if PLAYSTORE[l]=='mk': translate_short=translate_title\r\n\r\n    title_box.send_keys(translate_title[:50])\r\n    sleep(1)\r\n    short_box.send_keys(translate_short[:80])\r\n    sleep(1)\r\n    long_box.send_keys(translate_long[:4000])\r\n    sleep(2)\r\n\r\n    print(str(count) + \" |  \" + LANGUAGES[PLAYSTORE[l]] + \" - \" + l + \" | Init Operation Sleep Time 10 Seconds\" + \"...................[Done]\")\r\n\r\n#Draft = driver.find_element_by_css_selector(\"button[class='JIM1JT-f-a JIM1JT-f-r JIM1JT-f-f']\").click()\r\nVerfiy = input(\"Verify inputs\")\r\nlong.close()\r\ndriver.quit()\r\n","repo_name":"RahulRathi46/Automating-Play-Console-Apk-Publication-Using-Selenium-Webdriver","sub_path":"src/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3362,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"81"}
+{"seq_id":"9584644255","text":"rta = True\r\nestudiante = []\r\nwhile rta == True:\r\n    print(\"1).registro de estudiante\\n2).registro de quices\\n3).registro de trabajos\\n4).registro de parciales\\nEnter para salir\")\r\n    rta=input(\":)_\")\r\n    if rta == \"1\":\r\n       nombre = input(\"ingrese nombre : \")\r\n       codigo = input(\"ingrese codigo : \")\r\n       estudiante.append([nombre,codigo,[],[],[]])\r\n    elif rta == \"2\":\r\n        palabra = input (\"ingrese codigo del estudiante : \")\r\n        for item in estudiante:\r\n            if estudiante in item:\r\n               notaq = float(input(\"ingrese nota del quiz : \"))  \r\n               print(estudiante)\r\n\r\nrta = bool(input(\"desea ingresar otro estudiante (si) presione S de lo contrario presione Enter\"))\r\nprint(estudiante)\r\n\r\n       \r\n\r\n\r\n       \r\n       ","repo_name":"rosver96/CAMPUS-PYTHON-2023","sub_path":"CAMPUS-PYTHON23/EJERCICIOS-PYTHON-ALGORITMOS/agenda.py","file_name":"agenda.py","file_ext":"py","file_size_in_byte":769,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"5712437845","text":"import pygame\r\npygame.init()\r\nwin = pygame.display.set_mode((999,999))\r\npygame.display.set_caption(\"Ultimate Tic-Tac-Toe\")\r\nmyfont = pygame.font.SysFont('Times New Roman', 30)\r\nxdisp = myfont.render('X', False, (0, 0, 0))\r\nodisp = myfont.render('O', False, (0, 0, 0))\r\no_win = pygame.image.load(\"owin.png\").convert()\r\nx_win = pygame.image.load(\"xwin.png\").convert()\r\no_end = pygame.image.load(\"oend.png\").convert()\r\nx_end = pygame.image.load(\"xend.png\").convert()\r\ntie_image = pygame.image.load(\"xend.png\").convert()\r\ncounter = \"O\"\r\nindex = []\r\nwins = [[0,1,2],[3,4,5],[6,7,8],[0,3,6],[1,4,7],[2,5,8],[0,4,8],[2,4,6]]\r\nxcount = 0\r\nocount = 0\r\ngrandowin = False\r\ngrandxwin = False\r\ndef clicked(rect):\r\n    if pygame.mouse.get_pressed()[0] and rect.collidepoint(pygame.mouse.get_pos()):\r\n        return True\r\nclass Board(object):\r\n    def __init__(self,x,y):\r\n        self.rect = (x,y,333,333)\r\n        self.x = x\r\n        self.y = y\r\n        self.width = 333\r\n        self.clickable = True\r\n        self.done = False\r\n        self.x0 = self.x\r\n        self.x1 = self.x +111\r\n        self.x2 = self.x+222\r\n        self.y0 = self.y\r\n        self.y1 = self.y +111\r\n        self.y2 = self.y+222\r\n        self.squares = [[Square(self.x0,self.y0),Square(self.x1,self.y0),Square(self.x2,self.y0)],\r\n                        [Square(self.x0,self.y1),Square(self.x1,self.y1), Square(self.x2,self.y1)],\r\n                        [Square(self.x0,self.y2), Square(self.x1,self.y2),Square(self.x2,self.y2)]]\r\n        self.long = [Square(self.x0,self.y0),Square(self.x1,self.y0),Square(self.x2,self.y0),\r\n                        Square(self.x0,self.y1),Square(self.x1,self.y1), Square(self.x2,self.y1),\r\n                        Square(self.x0,self.y2), Square(self.x1,self.y2),Square(self.x2,self.y2)]\r\n        self.owin = False\r\n        self.xwin = False\r\n        self.tie = False\r\n    def draw1(self):\r\n        global grandxwin\r\n        global grandowin\r\n        for y in range(3):\r\n            for x in range(3):\r\n                self.squares[y][x].draw()\r\n        pygame.draw.line(win,(0,0,0),(self.x1-6,self.y0),(self.x1-6,self.y2+100),11)\r\n        pygame.draw.line(win,(0,0,0),(self.x2-6,self.y0),(self.x2-6,self.y2+100),11)\r\n        pygame.draw.line(win,(0,0,0),(self.x0,self.y1-6),(self.x2+100,self.y1-6),11)\r\n        pygame.draw.line(win,(0,0,0),(self.x0,self.y2-6),(self.x2+100,self.y2-6),11)\r\n        if self.xwin == True:\r\n            win.blit(x_win,(self.x0,self.y0))\r\n        if self.owin == True:\r\n            win.blit(o_win,(self.x0,self.y0))\r\n        if self.tie:\r\n            win.blit(tie_image,(self.x0,self.y0))\r\n        if grandowin == True:\r\n            win.blit(o_end,(0,0))\r\n        if grandxwin == True:\r\n            win.blit(x_end,(0,0))\r\n    def update1(self):\r\n        global index\r\n        global counter\r\n        global grandowin\r\n        global grandxwin\r\n        if self.tie or self.owin or self.xwin:\r\n            for row in self.squares:\r\n                for square in row:\r\n                    square.clickable = False\r\n        for y in range(3):\r\n            for x in range(3):\r\n                if self.clickable == True:\r\n                    self.squares[y][x].clickable = True\r\n                if self.clickable == False:\r\n                    self.squares[y][x].clickable = False\r\n                if clicked(self.squares[y][x].rect) and self.squares[y][x].clickable == True and self.squares[y][x].done == False and self.done == False:\r\n                    self.squares[y][x].value = counter\r\n                    if counter == \"X\":\r\n                        counter = \"O\"\r\n                    else:\r\n                        counter = \"X\"\r\n                    self.squares[y][x].value = counter\r\n                    self.squares[y][x].done = True\r\n                    self.squares[y][x].clickable = False\r\n                    index = [x,y]\r\n                    return\r\n    def check_win(self):\r\n        global wins\r\n        global xcount\r\n        global ocount\r\n        global index\r\n        self.long = []\r\n        for row in self.squares:\r\n            for item in row:\r\n                self.long.append(item)\r\n        for test in wins:\r\n            xcount = 0\r\n            ocount = 0\r\n            for squares in test:\r\n                if self.long[squares].value == \"X\":\r\n                    xcount += 1\r\n                if self.long[squares].value == \"O\":\r\n                    ocount +=1\r\n                if ocount == 3:\r\n                    self.owin = True\r\n                    self.done = True\r\n                    return\r\n                if xcount == 3:\r\n                    self.xwin = True\r\n                    self.done = True\r\n                    return\r\n        for square in self.long:\r\n            if square.value == \"\":\r\n                return\r\n        else:\r\n            self.tie = True\r\n        index = []\r\nclass Square(object):\r\n    def __init__(self,x,y):\r\n        self.rect = (x,y,111,111)\r\n        self.y = y\r\n        self.x = x\r\n        self.clickable = True\r\n        self.done = False\r\n        self.value = \"\"\r\n        self.rect = pygame.Rect(self.x,self.y,111,111)\r\n    def draw(self):\r\n        if self.clickable == True:\r\n            pygame.draw.rect(win,(0,200,0),(self.x,self.y,100,100))\r\n        if self.clickable == False or self.done == True:\r\n            pygame.draw.rect(win,(140,200,250),(self.x,self.y,100,100))\r\n        if self.done == True:\r\n            pygame.draw.rect(win,(255,100,100),(self.x,self.y,100,100))\r\n        if self.value == \"X\":\r\n            win.blit(xdisp,(self.x+36,self.y+36))\r\n        if self.value == \"O\":\r\n            win.blit(odisp,(self.x+36,self.y+36))\r\nscreen = [[Board(0*333,0*333),Board(1*333,0*333),Board(2*333,0*333)],\r\n[Board(0*333,1*333),Board(1*333,1*333),Board(2*333,1*333)],\r\n[Board(0*333,2*333),Board(1*333,2*333),Board(2*333,2*333)]]\r\nscreenlong = [Board(0*333,0*333),Board(1*333,0*333),Board(2*333,0*333),\r\nBoard(0*333,1*333),Board(1*333,1*333),Board(2*333,1*333),\r\nBoard(0*333,2*333),Board(1*333,2*333),Board(2*333,2*333)]\r\nwin.fill((255,255,255))\r\nrun_game = True\r\nwhile run_game:\r\n    if index == []:\r\n        for row in screen:\r\n            for space in row:\r\n                space.clickable = True\r\n    elif screen[index[1]][index[0]].xwin or screen[index[1]][index[0]].owin or screen[index[1]][index[0]].tie:\r\n        index = []\r\n    else:\r\n        screen[index[1]][index[0]].clickable = True\r\n    for event in pygame.event.get():\r\n        if event.type == pygame.QUIT:\r\n            run_game = False\r\n    for row in screen:\r\n        for space in row:\r\n            space.check_win()\r\n            space.update1()\r\n            space.draw1()\r\n            space.clickable = False\r\n    screenlong = []\r\n    for x in screen:\r\n        for y in x:\r\n            screenlong.append(y)\r\n    for board in screenlong:\r\n        for test in wins:\r\n            xcount = 0\r\n            ocount = 0\r\n            for squares in test:\r\n                if screenlong[squares].xwin == True:\r\n                    xcount += 1\r\n                if screenlong[squares].owin == True:\r\n                    ocount +=1\r\n                if ocount == 3:\r\n                    grandowin = True\r\n                if xcount == 3:\r\n                    grandxwin = True\r\n    pygame.display.update()\r\npygame.quit()\r\n","repo_name":"g-w1/ultimate-tic-tac-toe","sub_path":"ultimate_tic_tac_toe.py","file_name":"ultimate_tic_tac_toe.py","file_ext":"py","file_size_in_byte":7270,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"25605619997","text":"import sys\nfrom Pyro4 import naming, locateNS\n\nif __name__ == \"__main__\":\n    args = []\n    flags = []\n    if len(sys.argv)>1:\n        for arg in sys.argv[1:]:\n            if arg.startswith('--'): flags.append(arg[2:])\n            elif arg=='-f': flags.append('force_restart')\n            else: args.append(arg)\n    try:\n        ns = locateNS()\n        sys.exit(-1)\n    except:\n        sys.exit(naming.main())\n","repo_name":"grnydawn/Act3","sub_path":"name/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":410,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"14534152796","text":"from io import BytesIO\nfrom datetime import datetime\nfrom enum import Enum\nfrom typing import List\nfrom EPCPyYes.core.SBDH import sbdh\nfrom EPCPyYes.core.SBDH import template_sbdh\nfrom EPCPyYes.core.v1_2 import template_events\nfrom EPCPyYes.core.v1_2.events import EPCISBusinessEvent\nfrom EPCPyYes.core.v1_2.CBV.dispositions import Disposition\nfrom EPCPyYes.core.v1_2.CBV import business_steps, source_destination\nfrom quartet_capture import rules, models\nfrom quartet_capture.rules import RuleContext\nfrom quartet_integrations.frequentz.environment import get_default_environment\nfrom quartet_integrations.generic import mixins\nfrom quartet_integrations.gs1ushc.parsing import SimpleOutputParser, \\\n    BusinessOutputParser\nfrom quartet_masterdata.models import Company, Location, TradeItem\nfrom quartet_output.steps import ContextKeys as OutputKeys, \\\n    EPCPyYesOutputStep as EPYOS\nfrom quartet_output.steps import OutputParsingStep as QOPS\nfrom EPCPyYes.core.v1_2.events import Source, Destination\nimport copy\nfrom quartet_integrations.optel.steps import \\\n    ContextKeys as OptelContextKeys\n\nEventList = List[EPCISBusinessEvent]\n\n\nclass ContextKeys(Enum):\n    \"\"\"\n    RECEIVER_COMPANY\n    ----------------\n    A masterdata Company (or location)\n    record for the receiving company. This is derived\n    via company prefix information in filtered events.\n\n    SENDER_COMPANY\n    --------------\n    This is a masterdate Company (or location) record for the sender.  This\n    is pulled from the Sender data in the EPCIS message.\n    \"\"\"\n    RECEIVER_COMPANY = 'RECEIVER_COMPANY'\n    SENDER_COMPANY = 'SENDER_COMPANY'\n\n\nclass OutputParsingStep(mixins.ObserveChildrenMixin, QOPS):\n\n    def get_parser_type(self, *args):\n        \"\"\"\n        Override to provide a different parser type.\n        :return: The `type` of parser to use.\n        \"\"\"\n        parser_type = SimpleOutputParser if self.loose_enforcement \\\n            else BusinessOutputParser\n        return parser_type\n\n    @property\n    def declared_parameters(self):\n        params = super().declared_parameters\n        params['Create Child Observation'] = ('Whether or not to take any '\n                                              'inbound parents and creat an '\n                                              'Object event of action '\n                                              'OBSERVE with their children.')\n        params['Use Sources'] = (\n            'Whether or not to pass the source event source list to the '\n            'created object/observe event.  Only applicable if the '\n            'Create Child Observation step parameter is set to True.'\n        )\n        params['Use Destinations'] = (\n            'Whether or not to pass the source event destination list to the '\n            'created object/observe event.  Only applicable if the '\n            'Create Child Observation step parameter is set to True.'\n        )\n        return params\n\n    def execute(self, data, rule_context: rules.RuleContext):\n        super().execute(data, rule_context)\n        if self.get_boolean_parameter('Create Child Observation', False):\n            self.info('Create Child Observation step parameter was set to '\n                      'True...checking filtered events to create '\n                      'object/observe events.')\n            use_sources = self.get_boolean_parameter('Use Sources', True)\n            use_destinations = self.get_boolean_parameter('Use Destinations',\n                                                          True)\n            filtered_events = rule_context.context[\n                OutputKeys.FILTERED_EVENTS_KEY.value]\n            doc = template_events.EPCISDocument()\n            for event in filtered_events:\n                objEvent = self.create_observation_event(event, use_sources,\n                                                         use_destinations)\n                objEvent.biz_step = business_steps.BusinessSteps.other.value\n                doc.object_events.append(objEvent)\n            if len(doc.object_events) > 0:\n                parser = self.get_parser_type()\n                parser(BytesIO(doc.render().encode()),\n                       self.epc_output_criteria).parse()\n\n\nclass EPCPyYesOutputStep(EPYOS, mixins.CompanyFromURNMixin,\n                         mixins.OutboundMappingMixin,\n                         mixins.CompanyLocationMixin):\n    \"\"\"\n    Provides a new template for object events that includes gs1ushc\n    ILMD data instead of CBV ILMD.\n    \"\"\"\n\n    def __init__(self, db_task: models.Task, **kwargs):\n        super().__init__(db_task, **kwargs)\n        self.template = self._get_new_template()\n        self.add_sbdh = self.get_or_create_parameter(\n            'Add SBDH',\n            'True',\n            self.declared_parameters.get('Add SBDH')\n        ) in ['True', 'true']\n        self.header = template_sbdh.StandardBusinessDocumentHeader()\n        self.header.partners = []\n\n    def _get_new_template(self):\n        \"\"\"\n        Grabs the jinja environment and creates a jinja template object and\n        returns\n        :return: A new Jinja template.\n        \"\"\"\n        env = get_default_environment()\n        template = env.get_template('gs1ushc/object_event.xml')\n        return template\n\n    def execute(self, data, rule_context: RuleContext):\n        # two events need new templates - object and shipping\n        # the overall document needs a new template get that below\n        # if filtered events has more than one event then you know\n        # the event in filtered events is a shipping event so grab that\n        # and give it a new template\n        append_data = self.get_or_create_parameter(\n            'Append Data', 'True',\n            'Whether or not to call the append data function of the step for '\n            'events prior to rendering.') in ['True', 'true']\n        modify_date = self.get_or_create_parameter(\n            'Modify Date', 'True',\n            'Whether or not to call the modify date function on teh step'\n            ' for events prior to rendering.'\n        ) in ['True', 'true']\n        self.append_mapping = self.get_or_create_parameter(\n            'Add Partners to Shipping Events', 'True').lower() == 'true'\n        self.use_glns = self.get_or_create_parameter('Use GLNs for Owners',\n                                                     'True').lower() == 'true'\n        ilmd = None\n        schema_version = self.get_or_create_parameter('Schema Version', '1',\n                                                      self.declared_parameters.get(\n                                                          'Schema Version'))\n        self.info('Setting the schema version to %s', schema_version)\n        rule_context.context['schema_version'] = schema_version\n        filtered_events = rule_context.context.get(\n            OutputKeys.FILTERED_EVENTS_KEY.value)\n        if len(filtered_events) > 0:\n            # get the object events from the context - these are added by\n            # the AddCommissioningDataStep step in the rule.\n            if modify_date: self.modify_date(filtered_events)\n            object_events = rule_context.context.get(\n                OutputKeys.OBJECT_EVENTS_KEY.value, [])\n            if len(object_events) > 0:\n                if modify_date: self.modify_date(object_events)\n                for object_event in object_events:\n                    if len(object_event.ilmd) > 0:\n                        ilmd = object_event.ilmd\n                        break\n                self.info(\n                    'Found some filtered object events.'\n                    ' Looking up the receiver company by urn value/'\n                    'company prefix.')\n                copied_event = copy.copy(filtered_events[0])\n                mapping_applied = False\n                if self.append_mapping:\n                    mapping_applied = self.append_mapping_info(filtered_events[0])\n                if self.add_sbdh:\n                    self.add_header(copied_event, rule_context,\n                                    mapping_applied)\n                # self.sbdh.partners.append(receiver)\n                for event in object_events:\n                    event._template = self.template\n                    if len(event.ilmd) == 0:\n                        event.ilmd = ilmd\n            agg_events = rule_context.context.get(\n                OutputKeys.AGGREGATION_EVENTS_KEY.value, []\n            )\n            if append_data: self.append_event_data(agg_events)\n            if modify_date: self.modify_date(agg_events)\n\n        super().execute(data, rule_context)\n\n    def modify_date(self, epcis_events: EventList):\n        \"\"\"\n        Some systems don't like timezone info so remove it.  Override to\n        provide different behavior.\n        \"\"\"\n        for epcis_event in epcis_events:\n            epcis_event.event_time = epcis_event.event_time.replace('+00:00',\n                                                                    'Z')\n            epcis_event.record_time = epcis_event.record_time.replace('+00:00',\n                                                                      'Z')\n\n    def append_event_data(self, epcis_events: EventList):\n        \"\"\"\n        If set, will append data to the event, in this case will\n        append disposition information if it is missing.  Override to provide\n        different behavior.\n        \"\"\"\n        disposition = self.get_or_create_parameter(\n            'Added Disposition', Disposition.in_progress.value,\n            'The disposition to add to events that do not have one.'\n        )\n        for epcis_event in epcis_events:\n            if not epcis_event.disposition:\n                epcis_event.disposition = disposition\n\n    def add_header(self, filtered_event: EPCISBusinessEvent, rule_context,\n                   mapping_applied: bool):\n        \"\"\"\n        Adds the SBDH data.\n        :param object_events:\n        :param rule_context:\n        :param mapping_applied: Whether or not an outbound mapping has\n        already been applied.\n        :return:\n        \"\"\"\n        # first get the receiver by the company prefix\n        # noinspection PyTypeChecker\n        sender_location = self.get_sender_location(filtered_event)\n        self.add_sender_partner(sender_location, rule_context)\n        receiver_company = self.get_company_by_urn(filtered_event,\n                                                   rule_context)\n        self.add_receiver_partner(receiver_company, rule_context)\n        # next get the receiving location by the receiving party in the event\n        receiver_location = self.get_receiver_location(filtered_event)\n\n        if not mapping_applied:\n            owner_source = Source(\n                source_destination.SourceDestinationTypes.owning_party.value,\n                receiver_company.SGLN)\n            owner_destination = Destination(\n                source_destination.SourceDestinationTypes.owning_party.value,\n                receiver_company.SGLN)\n            source_location = Source(\n                source_destination.SourceDestinationTypes.location.value,\n                sender_location.SGLN)\n            destination_location = Destination(\n                source_destination.SourceDestinationTypes.location.value,\n                receiver_location.SGLN)\n            filtered_event.source_list = [owner_source, source_location]\n            filtered_event.destination_list = [owner_destination,\n                                               destination_location]\n        rule_context.context['masterdata'] = {\n            receiver_company.SGLN: receiver_company,\n            receiver_location.SGLN: receiver_location,\n            sender_location.SGLN: sender_location\n        }\n\n    def get_receiver_location(self, filtered_event):\n        \"\"\"\n        Will grab either the Company or Location quartet_masterdata.models\n        instance that correlates to the filtered event's receiver location\n        data.\n        :param filtered_event: The event that met the output criteria and\n        was filtered.\n        :return: The Company or Location model instance.\n        \"\"\"\n        try:\n            receiver_location = self.get_company_by_identifier(\n                epcis_event=filtered_event, source_list=False\n            )\n        except Company.DoesNotExist:\n            receiver_location = self.get_location_by_identifier(\n                filtered_event, source_list=False\n            )\n        return receiver_location\n\n    def get_sender_location(self, filtered_event):\n        \"\"\"\n        The name is slightly misleading, this will return either a\n        quartet_masterdata.models Company or Location django model instance\n        if it can be found by the SGLN or GLN value in the message.\n        :param filtered_event: The event that has met the output criteria.\n        :return: The Company or Location model instance.\n        \"\"\"\n        try:\n            sender_location = self.get_company_by_identifier(\n                filtered_event,\n                source_destination.SourceDestinationTypes.possessing_party.value\n            )\n        except Company.DoesNotExist:\n            sender_location = self.get_location_by_identifier(\n                filtered_event,\n                source_destination.SourceDestinationTypes.possessing_party.value\n            )\n        return sender_location\n\n    def append_mapping_info(self, filtered_event: EPCISBusinessEvent,\n                            use_receiver=True):\n        \"\"\"\n        If the append partner step parameter is set, this will look for an\n        outbound mapping in the system's master data and append the mappings\n        if they are present.  This will only take place if the filtered event\n        is has a shipping business step.\n        :param company: The company in question.\n        :return: None\n        \"\"\"\n        biz_step = getattr(filtered_event, 'biz_step', None)\n        if use_receiver:\n            company = self.get_receiver_location(filtered_event)\n        else:\n            company = self.get_sender_location(filtered_event)\n        if biz_step and 'shipping' in biz_step:\n            mapping = self.get_outbound_mapping_by_company(company)\n            if mapping:\n                senders = []\n                receivers = []\n                # from business\n                if mapping.from_business:\n                    from_business = mapping.from_business.GLN13 if self.use_glns else mapping.from_business.SGLN\n                    if from_business:\n                        senders.append(\n                            Source(\n                                source_destination.SourceDestinationTypes.owning_party.value,\n                                from_business\n                            )\n                        )\n                        self.info('Appending %s as the posessing party.',\n                                  from_business)\n                # from location\n                if mapping.ship_from:\n                    ship_from = mapping.ship_from.SGLN\n                    if ship_from:\n                        senders.append(\n                            Source(\n                                source_destination.SourceDestinationTypes.location.value,\n                                ship_from\n                            )\n                        )\n                        self.info('Appending %s as the ship from party.',\n                                  ship_from)\n                # to business\n                if mapping.to_business:\n                    to_business = mapping.to_business.GLN13 if self.use_glns else mapping.to_business.SGLN\n                    if to_business:\n                        receivers.append(\n                            Destination(\n                                source_destination.SourceDestinationTypes.owning_party.value,\n                                to_business\n                            )\n                        )\n                        self.info(\n                            'Appending %s as the to business owning party.',\n                            to_business)\n                # to location\n                if mapping.ship_to:\n                    ship_to = mapping.ship_to.SGLN\n                    if ship_to:\n                        receivers.append(\n                            Destination(\n                                source_destination.SourceDestinationTypes.location.value,\n                                ship_to\n                            )\n                        )\n                        self.info('Appending %s as the ship to location.',\n                                  ship_to)\n                filtered_event.source_list = senders\n                filtered_event.destination_list = receivers\n                return True\n\n\n    def add_receiver_location(self, receiver):\n        pass\n\n    def add_receiver_partner(self, receiver_company, rule_context):\n        \"\"\"\n        Adds the receiver partner to the header and the receiver company\n        to the context.\n        :param receiver_company: The masterdata Company model instance.\n        :param rule_context: The RuleContext passed to execute.\n        :return: None\n        \"\"\"\n        receiver = sbdh.Partner(\n            sbdh.PartnerType.RECEIVER,\n            partner_id=sbdh.PartnerIdentification('GLN',\n                                                  receiver_company.GLN13)\n        )\n        self.header.partners.append(receiver)\n        self.header.document_identification.creation_date_and_time = \\\n            datetime.utcnow().strftime('%Y-%m-%dT%H:%M:%SZ')\n        rule_context.context[\n            ContextKeys.RECEIVER_COMPANY.value] = receiver\n\n    def add_sender_partner(self, sender_company, rule_context):\n        \"\"\"\n        Adds the receiver partner to the header and the sender company\n        to the context.\n        :param sender_company: The masterdata Company model instance.\n        :param rule_context: The RuleContext passed to execute.\n        :return: None\n        \"\"\"\n        sender = sbdh.Partner(\n            sbdh.PartnerType.SENDER,\n            partner_id=sbdh.PartnerIdentification('GLN',\n                                                  sender_company.GLN13)\n        )\n        self.header.partners.append(sender)\n        rule_context.context[\n            ContextKeys.SENDER_COMPANY.value] = sender\n\n    def get_epcis_document_class(self,\n                                 all_events\n                                 ) -> template_events.EPCISEventListDocument:\n        \"\"\"\n        This function will override the default 1.2 EPCIS doc with a 1.0\n        template\n        :param all_events: The events to add to the document\n        :return: The EPCPyYes event list document to render\n        \"\"\"\n        doc_class = template_events.EPCISEventListDocument(all_events,\n                                                           self.header)\n        env = get_default_environment()\n        template = env.get_template('gs1ushc/epcis_document.xml')\n        doc_class.additional_context = {\n            'masterdata': self.rule_context.context['masterdata']}\n        doc_class._template = template\n        return doc_class\n\n    @property\n    def declared_parameters(self):\n        ret = super().declared_parameters()\n        ret['Schema Version'] = 'The schema version to include in the header. ' \\\n                                'default is 1'\n        ret['Add SBDH'] = 'Whether or not to add a Standard Business Document' \\\n                          ' Header to the EPCIS message.  Default is true.'\n        ret['Add Partners to Shipping Events'] = 'Whether or not to add any ' \\\n                                                 'mapped partner info to ' \\\n                                                 'shipping events.'\n        ret['Use GLNs for Owners'] = 'Whether or not to use SGLNs or GLNs for ' \\\n                                     'owning parties in the shipping event ' \\\n                                     'source destinations.  If true, GLNs will ' \\\n                                     'be used. Default is False.'\n        return ret\n\n\nclass EPCPyYesMasterDataOutputStep(EPCPyYesOutputStep):\n    \"\"\"\n    Additionally provides a way to add TradeItem masterdata to the \n    \"\"\"\n    \n    def get_trade_items_mastedata(self, items):\n        items_dict = {}\n        \n        for item in items:\n            try:\n                if len(item) == 14:\n                    trade_item = TradeItem.objects.get(GTIN14=item)\n                    items_dict[item] = trade_item.__dict__\n                    items_dict[item]['company'] = trade_item.company\n                else:\n                    sscc = item[1:] + '-' + item[0]\n                    items_dict[sscc] = {\n                        'id_type': 'SSCC',\n                        'GTIN14': sscc,\n                        'company': Company.objects.get(gs1_company_prefix=item[1:]),\n                        'regulated_product_name': 'The Pallet',\n                        'dosage_form': '-',\n                        'strength': '-'\n                    }\n            except TradeItem.DoesNotExist:\n                raise self.TradeItemMasterdataDoesNotExist(\n                    'Create Trade Item for GTIN %s' % item\n                )\n            except Company.DoesNotExist:\n                raise self.CompanyMasterdataDoesNotExist(\n                    'Create company for prefix %s' % item[1:]\n                )\n        return items_dict\n\n    def get_epcis_document_class(self,\n                                 all_events\n                                 ) -> template_events.EPCISEventListDocument:\n        \"\"\"\n        This function will override the default 1.2 EPCIS doc with a 1.0\n        template and additionally it provides a trade items masterdata. \n\n        :param all_events: The events to add to the document\n        :return: The EPCPyYes event list document to render\n        \"\"\"\n\n        # code from base method + changed template\n        doc_class = template_events.EPCISEventListDocument(all_events,\n                                                           self.header)\n        env = get_default_environment()\n        template = env.get_template('gs1ushc/epcis_document_complete_masterdata.xml')\n        doc_class.additional_context = {\n            'masterdata': self.rule_context.context['masterdata']}\n        doc_class._template = template\n        \n        # adding a TradeItems masterdata to the template\n        self.info('Trying to extract TradeItem GTINs from the rule context '\n                  'using OptelContextKey - TRADE_ITEMS_MASTERDATA key.')\n        trade_items = self.rule_context.context.get(\n                OptelContextKeys.TRADE_ITEMS_MASTERDATA.value)\n        self.info('Found %d trade items in the rule context. Adding masterdata'\n                  ' to the EPCIS message.' % len(trade_items))\n        trade_items_masterdata = self.get_trade_items_mastedata(trade_items)\n        doc_class.additional_context['trade_items'] = trade_items_masterdata\n        \n        return doc_class\n    \n    class TradeItemMasterdataDoesNotExist(Exception):\n        pass\n\n    class CompanyMasterdataDoesNotExist(Exception):\n        pass\n","repo_name":"rmagee/quartet_integrations","sub_path":"quartet_integrations/gs1ushc/steps.py","file_name":"steps.py","file_ext":"py","file_size_in_byte":23089,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"32122013014","text":"import pygame\nfrom towers.tower import Tower, menu_bg, upgrade_btn\nfrom menu.menu import Menu\nimport os\nimport math\nimport time\n\n#loads long range tower images\ntower_imgs_1 = []\ntower_imgs_1.append(pygame.transform.scale(pygame.image.load(os.path.join(\"game_assets/Towers\", \"long_range_tower.png\")), (90,180)))\n\n#loads short range tower images\ntower_imgs_2 = []\ntower_imgs_2.append(pygame.transform.scale(pygame.image.load(os.path.join(\"game_assets/Towers\", \"short_range_tower.png\")), (90,180)))\n\n\n#loads archer images\narcher_1_img = []\narcher_2_img = []\narcher_3_img = []\n\nfor x in range(6):\n    archer_1_img.append(pygame.transform.scale(pygame.image.load(os.path.join(\"game_assets/Towers/level_1\", \"a_Attack00\" + str(x) + \".png\")), (72,72)))\nfor x in range(6):\n    archer_2_img.append(pygame.transform.scale(pygame.image.load(os.path.join(\"game_assets/Towers/level_2\", \"a_Attack00\" + str(x) + \".png\")), (72,72)))\nfor x in range(6):\n    archer_3_img.append(pygame.transform.scale(pygame.image.load(os.path.join(\"game_assets/Towers/level_3\", \"a_Attack00\" + str(x) + \".png\")), (72,72)))\n\n\nclass ArcherTowerLong(Tower):\n    def __init__(self,x,y):\n        super().__init__(x,y)\n        \n        self.tower_imgs = tower_imgs_1[:]\n        self.archer_1_img = archer_1_img[:]\n        self.archer_2_img = archer_2_img[:]\n        self.archer_3_img = archer_3_img[:]\n        self.archer_imgs = []\n        self.upg_price = [1000,5000,\"MAX\"]\n        self.archer_count = 0\n        self.range = 250\n        self.original_range = self.range\n        self.inRange = False\n        self.right = False\n        self.damage = 1\n        self.original_damage = self.damage\n        self.width = self.tower_imgs[0].get_width()\n        self.height = self.tower_imgs[0].get_height()\n        #def menu\n        self.menu = Menu(self, self.x, self.y, menu_bg)\n        self.menu.add_btn(upgrade_btn, \"Upgrade\")\n        self.name = \"archer\"\n        \n    \n    def draw(self, win):\n        super().draw_radius(win, self.range,128,128,128)\n        super().draw(win)\n        \n        # self.width = self.tower_imgs[0].get_width()\n        # self.height = self.tower_imgs[0].get_height()\n        \n        if self.level == 1:\n            self.archer_imgs = self.archer_1_img\n        elif self.level == 2:\n            self.archer_imgs = self.archer_2_img\n        elif self.level == 3:\n            self.archer_imgs = self.archer_3_img\n        \n\n        archer = self.archer_imgs[self.archer_count//10 - 1]\n        win.blit(archer, ((self.x) - 35, (self.y - archer.get_height() + 35)))\n        \n\n        \n    def change_range(self, r):\n        \"\"\"\n        change range of archer tower\n        :param r: int\n        :return: None\n        \"\"\"\n        self.range = r\n        \n    def attack(self, enemies):\n        \"\"\"\n        attacks an enemy in the enemy list, modifies the list\n        :param enemies: enemy list\n        :return: None\n        \"\"\"\n        if self.inRange and not self.moving:\n            self.archer_count += 1\n        else:\n             self.archer_count = 0\n\n        if self.archer_count >= len(self.archer_imgs)*10:\n            self.archer_count = 0\n            \n        money = 0\n        self.inRange = False\n        enemy_closest = []\n        for enemy in enemies:\n          x = enemy.x\n          y = enemy.y\n          \n          dis = math.sqrt((self.x - x)**2 + (self.y - y)**2)\n          if dis < self.range:\n              self.inRange = True\n              enemy_closest.append(enemy)\n        \n        enemy_closest.sort(key=lambda x: x.x)\n        if len(enemy_closest) > 0:\n            first_enemy = enemy_closest[0]\n            if self.archer_count == 50:\n                if first_enemy.hit(self.damage) == True:\n                    enemies.remove(first_enemy)\n                    money = first_enemy.money\n            \n            if first_enemy.x > self.x and not(self.right):\n                self.right = True\n                for x, img in enumerate(self.archer_imgs):\n                    self.archer_imgs[x] = pygame.transform.flip(img, True, False)\n            elif self.right and first_enemy.x < self.x:\n                self.right = False\n                for x, img in enumerate(self.archer_imgs):\n                    self.archer_imgs[x] = pygame.transform.flip(img, True, False)\n        return money\n            \n        \nclass ArcherTowerShort(ArcherTowerLong):\n    def __init__(self, x, y):\n        super().__init__(x, y)\n        \n        self.archer_1_img = archer_1_img[:]\n        self.archer_2_img = archer_2_img[:]\n        self.archer_3_img = archer_3_img[:]\n        self.tower_imgs = tower_imgs_2[:]\n        self.archer_imgs = []\n        self.upg_price = [2000,6000,\"MAX\"]\n        self.archer_count = 0\n        self.range = 150\n        self.inRange = False\n        self.right = False\n        self.damage = 2\n        self.original_range = self.range\n        self.original_damage = self.damage\n        self.width = self.tower_imgs[0].get_width()\n        self.height = self.tower_imgs[0].get_height()\n        #def menu\n        self.menu = Menu(self, self.x, self.y, menu_bg)\n        self.menu.add_btn(upgrade_btn, \"Upgrade\")\n        self.name = \"archer2\"\n","repo_name":"KiedusCracknell/L3Game-RealmGuard-Tower-Wars","sub_path":"towers/archerTower.py","file_name":"archerTower.py","file_ext":"py","file_size_in_byte":5152,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"81"}
+{"seq_id":"2558999265","text":"#!/usr/bin/env python\n\nfrom ast import Str\nfrom cmath import sin\nimport rospy\nfrom cv_bridge import CvBridge\nfrom sensor_msgs.msg import Image\nfrom std_msgs.msg import Float64, String, Int16\n\nimport os\nimport cv2 as cv\nimport copy\nimport itertools\nimport mediapipe as mp\nimport math\nimport json\nimport sys\n# print(sys.path)\n\nimport numpy as np\nimport tensorflow as tf\n\nfrom gesture_recognition.keypoint_classifier import KeyPointClassifier\n\nORIGIN = (0.5,0.5)\n\ndef draw_bounding_rect(use_brect, image, brect, label):\n    if use_brect:\n\n        cv.rectangle(image, (brect[0], brect[1]), (brect[2], brect[3]),\n                     (0, 0, 0), 1)\n\n    return image\n\ndef calc_bounding_rect(image_shape, landmarks):\n    image_width, image_height = image_shape\n\n    landmark_array = np.empty((0, 2), int)\n\n    for _, landmark in enumerate(landmarks.landmark):\n        landmark_x = min(int(landmark.x * image_width), image_width - 1)\n        landmark_y = min(int(landmark.y * image_height), image_height - 1)\n\n        landmark_point = [np.array((landmark_x, landmark_y))]\n\n        landmark_array = np.append(landmark_array, landmark_point, axis=0)\n\n    x, y, w, h = cv.boundingRect(landmark_array)\n\n    return [x, y, x + w, y + h]\n\ndef calc_landmark_list(image, landmarks):\n    image_width, image_height = image\n\n    landmark_point = []\n\n\n    for _, landmark in enumerate(landmarks):\n        landmark_x = min(int(landmark['x'] * image_width), image_width - 1)\n        landmark_y = min(int(landmark['y'] * image_height), image_height - 1)\n        # landmark_z = landmark.z\n\n        landmark_point.append([landmark_x, landmark_y])\n    return landmark_point\n\ndef pre_process_landmark(landmark_list):\n    temp_landmark_list = copy.deepcopy(landmark_list)\n\n\n    base_x, base_y = 0, 0\n    for index, landmark_point in enumerate(temp_landmark_list):\n        if index == 0:\n            base_x, base_y = landmark_point[0], landmark_point[1]\n\n        temp_landmark_list[index][0] = temp_landmark_list[index][0] - base_x\n        temp_landmark_list[index][1] = temp_landmark_list[index][1] - base_y\n\n\n    temp_landmark_list = list(\n        itertools.chain.from_iterable(temp_landmark_list))\n\n\n    max_value = max(list(map(abs, temp_landmark_list)))\n\n    def normalize_(n):\n        return n / max_value\n\n    temp_landmark_list = list(map(normalize_, temp_landmark_list))\n\n    return temp_landmark_list\n\n\ndef pre_process_point_history(image, point_history):\n    image_width, image_height = image.shape[1], image.shape[0]\n\n    temp_point_history = copy.deepcopy(point_history)\n\n\n    base_x, base_y = 0, 0\n    for index, point in enumerate(temp_point_history):\n        if index == 0:\n            base_x, base_y = point[0], point[1]\n\n        temp_point_history[index][0] = (temp_point_history[index][0] -\n                                        base_x) / image_width\n        temp_point_history[index][1] = (temp_point_history[index][1] -\n                                        base_y) / image_height\n\n\n    temp_point_history = list(\n        itertools.chain.from_iterable(temp_point_history))\n\n    return temp_point_history\n\ndef callback(data:String):\n    landmarks = json.loads(data.data)\n    landmark_list = calc_landmark_list((1280,720), landmarks)\n    pre_processed_landmark_list = pre_process_landmark(landmark_list)\n    hand_sign_id = keypoint_classifier(pre_processed_landmark_list)\n\n    x = landmarks[0]['x']-ORIGIN[0]\n    y = landmarks[0]['y']-ORIGIN[1]\n    \n    # print(x, y , hand_sign_id)\n    if hand_sign_id == 1:\n        tracker_pub.publish(-y*4)\n        pusher_pub.publish(-x*4)\n    elif hand_sign_id == 0:\n        tracker_pub.publish(10000)\n    else:\n        hand_sign_pub.publish(hand_sign_id)\n    \n\ndef talker():\n    # global tracker_pub\n    # global pusher_pub\n    global tracker_pub\n    global pusher_pub\n    global hand_sign_pub\n    tracker_pub = rospy.Publisher('hockey_robot/joint3_position_controller/command', Float64, queue_size=10)\n    pusher_pub = rospy.Publisher('hockey_robot/joint4_position_controller/command', Float64, queue_size=10)\n    # tracker_vel_pub = rospy.Publisher('hockey_robot/joint5_position_controller/command', Float64, queue_size=10)\n    hand_sign_pub = rospy.Publisher('/hockey_robot/gest_controller/hand_sign', Int16, queue_size=10)\n    rospy.init_node('gest_controller', anonymous=True)\n    rospy.Subscriber('/landmark', String, callback)\n    rospy.spin()\n    # cap = cv.VideoCapture(0)\n    # # pos = get_hand_position(cap,(0,0))\n    \n    # # while not rospy.is_shutdown():\n    # while True:\n    #     hand,hand_sign_id, image_shape  = get_hand_position(cap,orgin)\n    #     if hand:\n    #         x = hand.landmark[mp_hands.HandLandmark.WRIST].x-orgin[0]\n    #         y = (hand.landmark[mp_hands.HandLandmark.WRIST].y-orgin[1])\n    #     else:\n    #         x,y = x,y\n    #     if hand_sign_id == 1:\n    #         tracker_pub.publish(-y*4)\n    #         pusher_pub.publish(-x*4)\n    #     elif hand_sign_id == 0:\n    #         tracker_pub.publish(10000)\n    #         # pusher_pub.publish(-x*4)\n\nif __name__ == '__main__':\n\n    DIRECTION = []\n    VELECITY = [50, 100, 500]\n\n    model_save_path = os.path.join('/home/xumingjie/catkin_ws/src/air_hockey_robot/hockey_robot_gazebo/scripts/model/keypoint_classifier/keypoint_classifier.tflite') \n    keypoint_classifier = KeyPointClassifier(model_path=model_save_path)\n\n    mp_drawing = mp.solutions.drawing_utils\n    mp_drawing_styles = mp.solutions.drawing_styles\n    mp_hands = mp.solutions.hands\n    br = CvBridge()\n    try:\n        talker()\n    except rospy.ROSInterruptException:\n        pass","repo_name":"Xumj82/air_hockey_robot","sub_path":"hockey_robot_gazebo/scripts/gesture_controller_web.py","file_name":"gesture_controller_web.py","file_ext":"py","file_size_in_byte":5571,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"17746096267","text":"import zencad.util\nfrom OCC.Core.Geom import Geom_Line\nfrom OCC.Core.gp import gp_Lin, gp_Pnt, gp_Dir, gp_XYZ\n\n\nclass Axis:\n    def __init__(self, *xyz):\n        self._coords = zencad.util.as_indexed(xyz)\n\n    def to_Geom_Line(self):\n        return Geom_Line(\n            gp_Lin(\n                gp_Pnt(0, 0, 0),\n                gp_Dir(\n                    gp_XYZ(\n                        self._coords[0],\n                        self._coords[1],\n                        self._coords[2]))))\n","repo_name":"mirmik/zencad","sub_path":"zencad/axis.py","file_name":"axis.py","file_ext":"py","file_size_in_byte":491,"program_lang":"python","lang":"en","doc_type":"code","stars":128,"dataset":"github-code","pt":"81"}
+{"seq_id":"71329488904","text":"from bs4 import BeautifulSoup\nimport urllib.request\nfrom os import system\nfrom time import sleep\n\nclass Main():\n\n    def __init__(self, site_name, season, episode, *args, **kwargs):\n        self.season = season # начальный сезон\n        self.episode = episode # начальная серия\n        self.link = site_name + \"/season-{}/episode-{}\".format(season, episode)\n\n        with urllib.request.urlopen(self.link) as response:\n            # Запись в переменную html код страницы\n            html = response.read()\n\n        soup = BeautifulSoup(html, 'html.parser')\n\n        # Поиск тега с классом .empty\n        search_empty_tag = soup.select(\".empty\")\n\n        check_new_video(search_empty_tag) # Проверка новых серий\n\n        check_new_season()\n\n    # Проверка нового сезона\n    def check_new_season(self):\n        season = self.season + 1\n        episode = 1\n        link = self.link\n        print(link)\n\n    # Отправка аудио сигнала\n    def send_audio(count, time_loop):\n        i = count # кол-во уведомлений\n        while i >= 0:\n            system('beep.mp3')\n            sleep(time_loop) # интервал уведомлений в секундах\n            i = i - 1\n\n    # Проверка новой серии\n    def check_new_video(empty_tag):\n        if not empty_tag:\n            # Если такого тега нет, то есть новые серии.\n            print(\"Новые серии!\")\n            send_audio(3, 60*5) # Звуковые сигналы (5 минут интервал)\n            return True\n            # Затем проверить 1 серию нового сезона, если ее нет,\n            # тогда проверить следующую текущего сезона, если она есть,\n            # тогда подать сигнал и написать в телеграмм.\n        else:\n            print(\"Нет новых серий\")\n            return False\n\n\nwrecked = Main(\"http://www.torrentino.me/serial/888489-wrecked\", 2, 5)\n\n","repo_name":"dns2316/python-experience","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2166,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"42918094067","text":"import pytest, sys, os\nfrom src.data import *\nfrom src.figures import *\nfrom src.utils import *\nfrom src.pci_model import *\n\ndef test_create_plotly():\n    fig = create_plotly_figure(input = \"./figures/pci.csv\")\n\ndef test_proc_data():\n    print(os.getcwd())\n\n    if not os.path.exists(\"./tests/Data/Output/\"):\n        os.makedirs(\"./tests/Data/Output/\")\n\n    proc_embedding(\n        input_file = \"./tests/Data/Input/embedding_example.txt\",\n        output_path = \"./tests/Data/Output/\"\n    )\n\n    with open(\"./tests/Data/Output/embedding.pkl\" , 'rb') as f:\n        embedding = pickle.load(f)\n        \n    words = list( embedding.keys())\n\n    example_data_2000 = gen_testing_data(words = words, from_year = 2000, to_year = 2010, type = 0, seed = 1 )\n    example_data_2000.to_pickle(\"./tests/Data/Output/2000_2010.pkl\")\n\n    example_data_2011 = gen_testing_data(words = words, from_year = 2011, to_year = 2011, type = 1, seed = 2 )\n    example_data_2011.to_pickle(\"./tests/Data/Output/2011.pkl\")\n\n\n    proc_pd(\n        input  = \"./tests/Data/Output/2000_2010.pkl\",\n        create = 1,\n        seed   = 1,\n        k_fold = 10,\n        output = \"./tests/Data/Output/database.db\",\n        embedding = \"./tests/Data/Output/\"\n    )\n\n    proc_pd(\n        input  = \"./tests/Data/Output/2011.pkl\",\n        create = 0,\n        seed   = 2,\n        k_fold = 10,\n        output = \"./tests/Data/Output/database.db\",\n        embedding = \"./tests/Data/Output/\"\n    )\n\n    run_pci_model(year_target=2011, mt_target=1, i=1, gpu=-1, model=\"window_2_years_quarterly\", root = \"./tests/\", T=0.01, discount=0.05, bandwidth = 0.2 )\n    run_pci_model(year_target=2011, mt_target=1, i=2, gpu=-1, model=\"window_2_years_quarterly\", root = \"./tests/\", T=0.01, discount=0.05, bandwidth = 0.2 )\n\n    compile_model_results(\"window_2_years_quarterly\", root = \"./tests\")\n\n    create_text_output(\"window_2_years_quarterly\", \"2011_M1\", gpu=-1, root =\"./tests/\")\n","repo_name":"hehuanshu96/PCI","sub_path":"PCI-China/tests/test_01.py","file_name":"test_01.py","file_ext":"py","file_size_in_byte":1923,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"81"}
+{"seq_id":"11703689151","text":"from Scripts.UI.Menu import Menu, DefaultButton, AnimationTextEdit, FileSystemWatching, ListWidget, TextEdit\nfrom PyQt5 import QtGui, QtCore, QtWidgets\nimport Scripts.Settings.Settings as Settings\nimport Scripts.Settings.StyleSheets as StyleSheets\nfrom Scripts.API.OctoPrintAPI import OctoPrintAPI, Preset\n\nclass PresetsMenu(Menu):\n    name = \"PresetsMenu\"\n\n    def __init__(self, parent):\n        super(PresetsMenu, self).__init__(parent)\n\n        self.setObjectName(self.name)\n        self.setFixedSize(Settings.WINDOW_SIZE[0], Settings.WINDOW_SIZE[1])\n\n        self.menu = DefaultButton(self, \"Back\", 300, 200, 100, 100, \"Back\", lambda: parent.ChangeMenu(\"MainPage\"))\n        self.addPreset = DefaultButton(self, \"Add\", 300, 100, 100, 100, \"Add\", lambda: parent.ChangeMenu(\"CreatePresetsMenu\"))\n        self.updatePresets = DefaultButton(self, \"Update\", 300, 0, 100, 100, \"Update\", self.UpdatePreset)\n\n        self.files = ListWidget(self, \"Files\", 0, 0, 300, 300, self.SetPreset)\n\n        self.Update()\n        self.UpdatePreset()\n        # self.files = FileSystemWatching(self, 200, 0, 200, 100)\n\n    def UpdatePreset(self):\n        OctoPrintAPI.LoadPresets(OctoPrintAPI)\n        self.files.SetItems(OctoPrintAPI.PRESETS.keys())\n\n    def SetPreset(self, signal):\n        key = self.files.model.itemFromIndex(signal).text()\n        OctoPrintAPI.SetToolTemperature(OctoPrintAPI, OctoPrintAPI.PRESETS[key].temperatureT0, OctoPrintAPI.PRESETS[key].temperatureT1, False)\n\n    def Update(self):\n        pass\n        # self.statusBar.SetText(f\"[STATUS] {OctoPrintAPI.JOB.state}\")\n\n\nclass CreatePresetsMenu(Menu):\n    name = \"CreatePresetsMenu\"\n\n    def __init__(self, parent):\n        super(CreatePresetsMenu, self).__init__(parent)\n\n        self.setObjectName(self.name)\n        self.setFixedSize(Settings.WINDOW_SIZE[0], Settings.WINDOW_SIZE[1])\n\n        self.i_name = TextEdit(self, 0, 20, 250, 50, \"Name: str\", \"Preset\")\n        self.i_temperatureBed = TextEdit(self, 0, 90, 250, 50, \"Temperature Bed: null\")\n        self.i_temperatureT0 = TextEdit(self, 0, 160, 250, 50, \"Temperature T0: int\", \"200\")\n        self.i_temperatureT1 = TextEdit(self, 0, 230, 250, 50, \"Temperature T1: null\")\n\n        self.menu = DefaultButton(self, \"Back\", 300, 180, 100, 100, \"Back\", lambda: parent.ChangeMenu(\"PresetsMenu\"))\n\n        self.createPreset = DefaultButton(self, \"Save\", 300, 20, 100, 100, \"Save\", self.SavePreset)\n\n        self.Update()\n        # self.files = FileSystemWatching(self, 200, 0, 200, 100)\n\n    def SavePreset(self):\n        try:\n            t_name = self.i_name.label.text()\n            t_tempBed = None if (self.i_temperatureBed.label.text() == \"\") else float(self.i_temperatureBed.label.text())\n            t_tempT0 = None if (self.i_temperatureT0.label.text() == \"\") else float(self.i_temperatureT0.label.text())\n            t_tempT1 = None if (self.i_temperatureT1.label.text() == \"\") else float(self.i_temperatureT1.label.text())\n\n            OctoPrintAPI.CreatePreset(OctoPrintAPI, t_name, t_tempBed, t_tempT0, t_tempT1)\n        except Exception as exception: print(exception)\n\n    def Update(self):\n        pass\n        # self.statusBar.SetText(f\"[STATUS] {OctoPrintAPI.JOB.state}\")","repo_name":"shogo-makishima/PyQT5_OctoScreen","sub_path":"Scripts/UI/PresetsMenu.py","file_name":"PresetsMenu.py","file_ext":"py","file_size_in_byte":3200,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"81"}
+{"seq_id":"41419269109","text":"from functions import *\n\ndef test():\n    filename = \"winequality.csv\"\n    atributo = \"alcohol\"\n    try:\n        diccionario = read_data(filename)\n        diccionarios = split(read_data(filename))\n        print(diccionarios)\n        # print(reduce(diccionarios[1], atributo))\n\n    except ValueError as err:\n        print(\"Ha ocurrido la excepcion {}\".format(err))\n\ntest()","repo_name":"begimode/examenPython","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":370,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"13583348259","text":"import math\nimport collections\nfrom hellonlp.ChineseWordSegmentation.hyperparameters import Hyperparamters as hp\nfrom hellonlp.ChineseWordSegmentation.modules import (\n    get_ngram_frequence_infomation,\n    calcul_ngram_entropy,\n    calcul_ngram_pmi,\n)\n\n\nclass SeriesQun:\n    def __init__(self) -> None:\n        self.min_n = 2\n        self.max_n = 5\n        self.min_freq = 3\n\n    def cal_score(self, ngram_freq, ngram_keys):\n\n        # Get left and right ngram entropy\n        left_right_entropy = calcul_ngram_entropy(\n            ngram_freq, ngram_keys, range(self.min_n, self.max_n + 1)\n        )\n        # Get pmi ngram entropy\n        mi = calcul_ngram_pmi(ngram_freq, ngram_keys, range(self.min_n, self.max_n + 1))\n        # Join keys of entropy and keys of pmi\n        joint_phrase = mi.keys() & left_right_entropy.keys()\n\n        # Word liberalization\n        def word_liberalization(el, er):\n            return math.log(\n                (el * hp.e**er + 0.00001) / (abs(el - er) + 1), hp.e\n            ) + math.log((er * hp.e**el + 0.00001) / (abs(el - er) + 1), hp.e)\n\n        word_info_scores = {\n            word: (\n                mi[word][0],\n                mi[word][1],\n                left_right_entropy[word][0],\n                left_right_entropy[word][1],\n                min(left_right_entropy[word][0], left_right_entropy[word][1]),\n                word_liberalization(\n                    left_right_entropy[word][0], left_right_entropy[word][1]\n                )\n                + mi[word][1],\n            )\n            for word in joint_phrase\n        }\n\n        # Drop some special word that end with \"的\" like \"XX的,美丽的,漂亮的\"\n        target_ngrams = word_info_scores.keys()\n        start_chars = collections.Counter([n[0] for n in target_ngrams])\n        end_chars = collections.Counter([n[-1] for n in target_ngrams])\n        threshold = int(len(target_ngrams) * 0.004)\n        threshold = max(50, threshold)\n        invalid_start_chars = set(\n            [char for char, count in start_chars.items() if count > threshold]\n        )\n        invalid_end_chars = set(\n            [char for char, count in end_chars.items() if count > threshold]\n        )\n        invalid_target_ngrams = set(\n            [\n                n\n                for n in target_ngrams\n                if (n[0] in invalid_start_chars or n[-1] in invalid_end_chars)\n            ]\n        )\n        # Remove some words invalids\n        for n in invalid_target_ngrams:\n            word_info_scores.pop(n)\n        return word_info_scores\n\n    def series_name(self, data):\n\n        ngram_freq, ngram_keys = get_ngram_frequence_infomation(\n            data,\n            self.min_n,\n            self.max_n,\n            chunk_size=hp.chunk_size,\n            min_freq=self.min_freq,\n        )\n        word_info_scores = self.cal_score(ngram_freq, ngram_keys)\n        new_words = self.drop_repeat_short_term(ngram_freq, word_info_scores)\n        series_qun = self.group_qun(new_words, data)\n\n        return series_qun\n\n    def drop_repeat_short_term(self, ngram_freq, word_info_scores):\n        ngram_keys = [(\"\".join(x), x) for x in ngram_freq.keys()]\n        ngram_keys = sorted(ngram_keys, key=lambda x: len(x[0]))\n        delete_key = set()\n        for i, (key, key_tuple) in enumerate(ngram_keys):\n            if len(key) < self.min_n:\n                continue\n            for j in range(i + 1, len(ngram_keys)):\n                compare_key, compare_key_tuple = ngram_keys[j]\n                if len(compare_key) == len(key):\n                    continue\n                if (\n                    key in compare_key\n                    and ngram_freq[key_tuple] == ngram_freq[compare_key_tuple]\n                ):\n                    delete_key.add(key_tuple)\n        new_words = set()\n        for key in word_info_scores:\n            if key not in delete_key:\n                new_words.add(\n                    (\n                        \"\".join(key),\n                        tuple(list(word_info_scores[key]) + [ngram_freq[key]]),\n                    )\n                )\n        return new_words\n\n    def group_qun(self, new_words, data):\n        new_words = sorted(new_words, key=lambda x: x[1][-1], reverse=True)\n        group_qun = {}\n        for d in data:\n            for new_entry in new_words:\n                new_word = new_entry[0]\n                if new_word in d:\n                    if new_word not in group_qun:\n                        group_qun[new_word] = []\n                    group_qun[new_word].append(d)\n                    break\n        series_qun = {}\n        for key in group_qun.keys():\n            if len(group_qun[key]) >= self.min_n:\n                series_qun[key] = group_qun[key]\n        return series_qun\n\n\nif __name__ == \"__main__\":\n\n    data = [\n        \"【乌市】青春交友群\",\n        \"乌市🍒灯红酒绿\",\n        \"乌市🍒美术馆2\",\n        \"乌市🍒汇珊园\",\n        \"乌市🍒幸福花园\",\n        \"乌市🍒美术馆3\",\n        \"乌市🍒美术馆1\",\n        \"乌市🍒️恒大之星\",\n        \"乌市🍒️美琳花源\",\n        \"乌市💕️桃花岛3\",\n        \"乌市💋夜色撩人\",\n        \"乌鲁木齐🈷️百味小\",\n        \"乌鲁木齐🈷️仙女聚\",\n        \"乌鲁木齐🈷️仙女聚\",\n        \"乌鲁木齐🈷️仙女聚\",\n        \"乌鲁木齐🈷️仙女聚\",\n        \"乌鲁木齐之夜6\",\n        \"乌鲁木齐之夜5\",\n        \"乌鲁木齐之夜10\",\n        \"乌鲁木齐都是聚会1\",\n        \"乌鲁木齐都市大聚\",\n        \"同城交友（1）\",\n        \"同城交友（3）\",\n        \"乌市✔美女大本营\",\n        \"\t乌鲁木齐💋可a2女神\",\n        \"乌市欢乐汇\",\n        \"乌市大风吹\",\n        \"乌鲁木齐都市大聚会10\",\n        \"乌鲁木齐2020JP（2）\",\n        \"乌鲁木齐2020JP（3）\",\n        \"乌鲁木齐2020JP（4）\",\n        \"乌鲁木齐2020JP（7）\",\n        \"乌市新市区交友群（5\",\n        \"乌市高端\",\n    ]\n    data2 = [\n        \"a辽鞍海🌼荔枝红茶4️\",\n        \"a辽鞍海🌼荷叶冬瓜6️\",\n        \"a辽鞍海🌼蜜桃乌龙7️\",\n        \"A✡️辽鞍海✡️大眼\",\n        \"A✡️辽鞍海✡️大眼\",\n        \"A✡️辽鞍海✡️大眼\",\n        \"A✡️辽鞍海✡️大眼\",\n        \"🍓鞍山'漫漫长夜😍无\",\n        \"🍓鞍山'💮浪漫满屋\",\n        \"🍓鞍山'风花雪月，梦\",\n        \"辽阳鞍山Vip贵族❹\",\n        \"辽阳鞍山Vip贵族❺\",\n        \"辽鞍娱乐{5}\",\n        \"辽鞍娱乐{3}\",\n        \"花儿朵朵③\",\n        \"花儿朵朵⑩\",\n        \"花儿朵朵⑨\",\n        \"花儿朵朵⑦\",\n        \"花儿朵朵④\",\n        \"花儿朵朵⑥\",\n        \"梅兰竹菊\",\n        \"小天王经典娱乐83\",\n        \"a小鞍@小海@小辽2000\",\n        \"辽阳阳光永在风雨后二\",\n        \"JS小聚酒馆2000\",\n        \"人间烟火🎇2000\",\n        \"小天王娱乐经典\",\n        \"大灰狼\",\n        \"\t下辈子不让你孤单\",\n        \"襄平娱乐①\",\n        \"\t鞍❤️辽江南小蛮腰\",\n        \"沈阳夜未央⑤\",\n        \"辽鞍鹊桥相会1群2000人\",\n        \"孟泰后山练功🤛🤜\",\n        \"需要点咸味\",\n        \"拉人给福利5个人一部\",\n        \"鞍山交友娱乐群\",\n        \"辽鞍皇家贵族人(1)\",\n        \"同城🉑娱乐共享群\",\n        \"后宫佳丽三千\",\n        \"小天王经典娱乐\",\n        \"辽鞍☃️炫彩斑斓\",\n        \"沈阳夜未央②\",\n        \"鞍辽☞你的笑真的很美\",\n        \"🛏️上快乐大本营\",\n        \"天下至尊舍我其谁1000\",\n        \"一二三四五\",\n        \"✨我的女孩你别碰✨\",\n        \"辽阳鞍山学生VIP\",\n        \"后宫佳丽三千\",\n    ]\n    data3 = [\n        \"沈阳美女雷霆交友群\",\n        \"南京💐师范职业技术\",\n        \"雷霆二年一班\",\n        \"南京女神🌺交友\",\n        \"王者沈阳醉酒当歌\",\n        \"《昆明飞飞鱼》\",\n        \"雷霆世纪家园\",\n        \"沈阳美女雷霆丘比特\",\n        \"沈阳同城学生交友1k\",\n        \"沈阳美女🎀验证群\",\n        \"沈阳摩天大厦\",\n        \"十里桃花2群\",\n        \"雷霆世纪佳缘\",\n        \"沈阳后宫佳丽三千人\",\n        \"王者沈阳美女交友群⑤\",\n        \"上海闵行灰领达人\",\n        \"沈阳酒醉金迷夜逍遥4\",\n        \"苏州～此妖、未成精\",\n        \"雷霆二年二班\",\n        \"雷霆初一二班\",\n        \"沈阳旺仔俱乐部\",\n        \"南京🍀大学生👗论坛\",\n        \"王者王者荣耀（苏城）\",\n        \"南京💖紫罗兰之夜\",\n        \"沈阳聊天交友大群A（\",\n        \"成都💋耍💄耍💗💗\",\n        \"辽宁沈阳不夜城1000\",\n        \"南京小姐姐群\",\n    ]\n    data4 = [\n        \"一二次200k🈲️躺群\",\n        \"一二次已开🈲 纯三 陆瑀\",\n        \"一二次klq200k🚫躺群\",\n        \"一二次klq60k🈲️纯三\",\n        \"一二次100k🈲纯三躺群TNT\",\n        \"一二次200k扩列群🈲️纯三\",\n        \"一二次扩列群188k🈲纯三tnt躺群\",\n        \"国乙已开200k🈲纯三 不懂群名\",\n        \"国乙自由扩150k🈲️看不懂群名\",\n        \"国乙klq已开🈲️TNT、纯三\",\n    ]\n    sq = SeriesQun()\n    series_quns = sq.series_name(data4)\n    print(series_quns)\n","repo_name":"baicao/wxy_model_toolbox","sub_path":"common_model/series_qun.py","file_name":"series_qun.py","file_ext":"py","file_size_in_byte":9298,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"3772915398","text":"import statistics\r\nimport math\r\nimport json\r\ntry:\r\n    from .common import first_float\r\nexcept ImportError:\r\n    from common import first_float\r\n\r\ndef score_list_to_score(score_list):\r\n    all_keys = \"ZXCVBNMASDFGHJQWERTYUzxcvbnmasdfghjqwertyu\"\r\n    all_time = [i for i in score_list if type(i) == float]\r\n    try:\r\n        average_time = statistics.mode(all_time) #mode\r\n    except statistics.StatisticsError: #if no mode or 2 mode\r\n        average_time = None\r\n    if not average_time or all_time.count(average_time) < len(all_time)/4:\r\n        average_time = sorted(all_time)[round(len(all_time) / 2)]  # use median\r\n\r\n    # edit average dashes here: ↓\r\n    lowest_lim = average_time / 16\r\n    lowest_time = min([i for i in all_time if i >= lowest_lim])\r\n\r\n    score = \"\"\r\n    for msg in score_list:\r\n        if type(msg) == float:\r\n            msg = (round(msg / lowest_time)) * \"-\"\r\n        elif len(msg) > 1:\r\n            msg = [key for key in msg if key in all_keys]\r\n            msg = f\"({''.join(set(msg))})\"\r\n        score += msg\r\n\r\n    # score has been created but have no line break\r\n    # so it is created here\r\n    # edit length of each line below:\r\n    line_break = 70\r\n    to_break = line_break\r\n    score = score.replace(\"-\", \" \").strip().replace(\" \", \"-\")\r\n    score = list(score)\r\n    for idx, key in enumerate(score):\r\n        if idx > to_break and key == \"-\" and idx != len(score) - 1 and score[idx + 1] != '-':\r\n            score[idx] = \"\\n\"\r\n            to_break += line_break\r\n    #                         0.0059 is delay by time.sleep() function\r\n    score = f\"(beat{round(lowest_time - 0.0059, 3)})-\\n{''.join(score)}\"\r\n    return score\r\n\r\ndef key_list_to_score_list(key_list:list):\r\n    score_list = [0.0]\r\n    temp_beat = 0.15\r\n    for key in key_list:\r\n        if \"beat\" in key.lower():\r\n            temp_beat = first_float(key)\r\n        elif key == \"-\":\r\n            score_list[-1] += temp_beat + 0.0059\r\n        elif key.isnumeric():\r\n            score_list[-1] += (temp_beat * (float(key)/10)) + 0.0059\r\n        else:\r\n            score_list.append(key)\r\n            score_list.append(0.109)\r\n    return score_list\r\n\r\ndef score_list_to_nightly(score_list:list, name=\"Undefined\"):\r\n    all_time = [i for i in score_list if type(i) == float]\r\n    try:\r\n        average_time = statistics.mode(all_time)  # mode\r\n    except statistics.StatisticsError:  # if no mode or 2 mode\r\n        average_time = None\r\n    if not average_time or all_time.count(average_time) < len(all_time) / 4:\r\n        average_time = sorted(all_time)[round(len(all_time) / 2)]  # use median\r\n\r\n    #  edit average dashes here: ↓\r\n    lowest_time = average_time / 4\r\n\r\n    bpm = round(60 / lowest_time)\r\n    jsonFile = {\"data\": {\"isComposed\": True, \"isComposedVersion\": True, \"appName\": \"Genshin\"}, \"name\": name, \"bpm\": bpm,\r\n                \"pitch\": \"C\", \"breakpoints\": [0], \"instruments\": [\"Lyre\", \"Lyre\", \"Lyre\"]}\r\n    KEYS = ['Q', 'W', 'E', 'R', 'T', 'Y', 'U', 'A', 'S', 'D', 'F', 'G', 'H', 'J', 'Z', 'X', 'C', 'V', 'B', 'N', 'M']\r\n    NOTES = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20]\r\n    KN = dict(zip(KEYS, NOTES))\r\n    def get_representative_key(note:list,ratio:float)->list:\r\n        if ratio == 1:\r\n            return [[0, note]]\r\n        elif ratio < 0.125:\r\n            return [[3, note]]\r\n        noOf125Left = ratio / 0.125\r\n        ratio_key = {8: 0, 4: 1, 2: 2, 1: 3}\r\n        final_keys = []\r\n        for value, note_type in ratio_key.items():\r\n            needed_no = math.floor(noOf125Left / value)\r\n            if needed_no:\r\n                final_keys += [note_type] * needed_no\r\n                noOf125Left %= value\r\n        final_keys.sort()\r\n        self_note = final_keys[0]\r\n        extra_note = [[note_type, []] for note_type in final_keys[1:]]\r\n\r\n        return [[self_note,note]] + extra_note\r\n\r\n    columns = []\r\n    idx = 0\r\n    while idx < len(score_list):\r\n        if isinstance(score_list[idx], float):\r\n            ratio = score_list[idx] / lowest_time\r\n            columns += get_representative_key([],ratio)\r\n            idx += 1\r\n            continue\r\n        #current note is str\r\n        note = [[KN[key],\"100\"] for key in score_list[idx].upper() if key in KN]\r\n        try:\r\n            length = score_list[idx+1]\r\n            ratio = length / lowest_time\r\n        except IndexError:\r\n            ratio = 1\r\n        except ValueError:\r\n            ratio = 0.109/lowest_time\r\n        notes_list = get_representative_key(note, ratio)\r\n        columns += notes_list\r\n        idx+=2\r\n    jsonFile[\"columns\"] = columns\r\n    jsonFile = json.dumps([jsonFile])\r\n    return jsonFile","repo_name":"stanX19/genshin_lyre","sub_path":"utils/converter.py","file_name":"converter.py","file_ext":"py","file_size_in_byte":4653,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"12558608899","text":"import math\n\nimport tensorflow as tf\nimport numpy as np\n\nfrom tensorflow.contrib.image.python.ops import image_ops\n\ndef one_hot(    indices,    depth,    on_value=1,    off_value=0,    axis=-1,    dtype=None,    name=None):\n    # only valid for 1-D indices\n    one_hot_matrix = np.eye(depth)[np.array(indices)]\n    if(axis == 0):\n        one_hot_matrix = np.transpose(one_hot_matrix)\n    if not dtype == None:\n        one_hot_matrix = one_hot_matrix.astype(dtype)\n\n    return one_hot_matrix\n\n\n\n\ndef areas(gt_bboxes):\n    ymin, xmin, ymax, xmax = np.split(gt_bboxes, 4, axis=1)\n    return (xmax - xmin) * (ymax - ymin)\n\ndef intersection(gt_bboxes, default_bboxes):\n    # num_anchors x 1\n    ymin, xmin, ymax, xmax = np.split(gt_bboxes, 4, axis=1)\n    # 1 x num_anchors\n    gt_ymin, gt_xmin, gt_ymax, gt_xmax = [np.transpose(b, perm=[1, 0]) for b in np.split(default_bboxes, 4, axis=1)]\n    # broadcast here to generate the full matrix\n    int_ymin = max(ymin, gt_ymin)\n    int_xmin = max(xmin, gt_xmin)\n    int_ymax = min(ymax, gt_ymax)\n    int_xmax = min(xmax, gt_xmax)\n    h = max(int_ymax - int_ymin, 0.)\n    w = max(int_xmax - int_xmin, 0.)\n    return h * w\n\ndef iou_matrix(gt_bboxes, default_bboxes):\n    inter_vol = intersection(gt_bboxes, default_bboxes)\n    # broadcast\n    union_vol = areas(gt_bboxes) + np.transpose(areas(default_bboxes)) - inter_vol\n    return np.where(np.equal(union_vol, 0.0),\n                    np.zeros_like(inter_vol), np.true_divide(inter_vol, union_vol))\n\ndef do_dual_max_match(overlap_matrix, low_thres, high_thres, ignore_between=True, gt_max_first=True):\n    '''\n    overlap_matrix: num_gt * num_anchors\n    '''\n    # first match from anchors' side\n    anchors_to_gt = np.argmax(overlap_matrix, axis=0)\n    # the matching degree\n    match_values = np.amax(overlap_matrix, axis=0)\n\n    #positive_mask = tf.greater(match_values, high_thres)\n    less_mask = np.less(match_values, low_thres)\n    between_mask = np.logical_and(np.less(match_values, high_thres), np.greater_equal(match_values, low_thres))\n    negative_mask = less_mask if ignore_between else between_mask\n    ignore_mask = between_mask if ignore_between else less_mask\n    # fill all negative positions with -1, all ignore positions is -2\n    match_indices = np.where(negative_mask, -1 * np.ones_like(anchors_to_gt), anchors_to_gt)\n    match_indices = np.where(ignore_mask, -2 * np.ones_like(match_indices), match_indices)\n\n    # negtive values has no effect in tf.one_hot, that means all zeros along that axis\n    # so all positive match positions in anchors_to_gt_mask is 1, all others are 0\n    anchors_to_gt_mask = one_hot(np.clip(match_indices, -1, np.shape(overlap_matrix)[0].astype(np.int64)),\n                                    np.shape(overlap_matrix)[0], on_value=1, off_value=0, axis=0, dtype=np.int32)\n    # match from ground truth's side\n    gt_to_anchors = np.argmax(overlap_matrix, axis=1)\n\n    if gt_max_first:\n        # the max match from ground truth's side has higher priority\n        left_gt_to_anchors_mask = one_hot(gt_to_anchors, np.shape(overlap_matrix)[1], on_value=1, off_value=0, axis=1, dtype=np.int32)\n    else:\n        # the max match from anchors' side has higher priority\n        # use match result from ground truth's side only when the the matching degree from anchors' side is lower than position threshold\n        left_gt_to_anchors_mask = np.logical_and(np.amax(anchors_to_gt_mask, axis=1, keep_dims=True) < 1,\n                            one_hot(gt_to_anchors, np.shape(overlap_matrix)[1], on_value=True, off_value=False, axis=1, dtype=np.bool_)\n                            ).astype(np.int64)\n    # can not use left_gt_to_anchors_mask here, because there are many ground truthes match to one anchor, we should pick the highest one even when we are merging matching from ground truth side\n    left_gt_to_anchors_scores = overlap_matrix * left_gt_to_anchors_mask.astype(np.float)\n    # merge matching results from ground truth's side with the original matching results from anchors' side\n    # then select all the overlap score of those matching pairs\n    selected_scores = np.take(overlap_matrix,  np.stack([np.where(np.amax(left_gt_to_anchors_mask, axis=0) > 0,\n                                                                        np.argmax(left_gt_to_anchors_scores, axis=0),\n                                                                        anchors_to_gt),\n                                                                range(np.shape(overlap_matrix)[1].astype(np.int64))], axis=1))\n    # return the matching results for both foreground anchors and background anchors, also with overlap scores\n    return np.where(np.amax(left_gt_to_anchors_mask, axis=0) > 0,\n                    np.argmax(left_gt_to_anchors_scores, axis=0),\n                    match_indices), selected_scores\n\n# def save_anchors(bboxes, labels, anchors_point):\n#     if not hasattr(save_image_with_bbox, \"counter\"):\n#         save_image_with_bbox.counter = 0  # it doesn't exist yet, so initialize it\n#     save_image_with_bbox.counter += 1\n\n#     np.save('./debug/bboxes_{}.npy'.format(save_image_with_bbox.counter), np.copy(bboxes))\n#     np.save('./debug/labels_{}.npy'.format(save_image_with_bbox.counter), np.copy(labels))\n#     np.save('./debug/anchors_{}.npy'.format(save_image_with_bbox.counter), np.copy(anchors_point))\n#     return save_image_with_bbox.counter\n\nclass AnchorEncoder(object):\n    def __init__(self, allowed_borders, positive_threshold, ignore_threshold, prior_scaling, clip=False):\n        super(AnchorEncoder, self).__init__()\n        self._all_anchors = None\n        self._allowed_borders = allowed_borders\n        self._positive_threshold = positive_threshold\n        self._ignore_threshold = ignore_threshold\n        self._prior_scaling = prior_scaling\n        self._clip = clip\n\n    def center2point(self, center_y, center_x, height, width):\n        return center_y - height / 2., center_x - width / 2., center_y + height / 2., center_x + width / 2.,\n\n    def point2center(self, ymin, xmin, ymax, xmax):\n        height, width = (ymax - ymin), (xmax - xmin)\n        return ymin + height / 2., xmin + width / 2., height, width\n\n    def encode_all_anchors(self, labels, bboxes, all_anchors, all_num_anchors_depth, all_num_anchors_spatial, debug=False):\n        # y, x, h, w are all in range [0, 1] relative to the original image size\n        # shape info:\n        # y_on_image, x_on_image: layers_shapes[0] * layers_shapes[1]\n        # h_on_image, w_on_image: num_anchors\n        assert (len(all_num_anchors_depth)==len(all_num_anchors_spatial)) and (len(all_num_anchors_depth)==len(all_anchors)), 'inconsist num layers for anchors.'\n        \n        num_layers = len(all_num_anchors_depth)\n        list_anchors_ymin = []\n        list_anchors_xmin = []\n        list_anchors_ymax = []\n        list_anchors_xmax = []\n        tiled_allowed_borders = []\n        for ind, anchor in enumerate(all_anchors):\n            anchors_ymin_, anchors_xmin_, anchors_ymax_, anchors_xmax_ = self.center2point(anchor[0], anchor[1], anchor[2], anchor[3])\n\n            list_anchors_ymin.append(np.reshape(anchors_ymin_, [-1]))\n            list_anchors_xmin.append(np.reshape(anchors_xmin_, [-1]))\n            list_anchors_ymax.append(np.reshape(anchors_ymax_, [-1]))\n            list_anchors_xmax.append(np.reshape(anchors_xmax_, [-1]))\n\n            tiled_allowed_borders.extend([self._allowed_borders[ind]] * all_num_anchors_depth[ind] * all_num_anchors_spatial[ind])\n\n        anchors_ymin = np.concatenate(list_anchors_ymin, 0)\n        anchors_xmin = np.concatenate(list_anchors_xmin, 0)\n        anchors_ymax = np.concatenate(list_anchors_ymax, 0)\n        anchors_xmax = np.concatenate(list_anchors_xmax, 0)\n\n        if self._clip:\n            anchors_ymin = np.clip(anchors_ymin, 0., 1.)\n            anchors_xmin = np.clip(anchors_xmin, 0., 1.)\n            anchors_ymax = np.clip(anchors_ymax, 0., 1.)\n            anchors_xmax = np.clip(anchors_xmax, 0., 1.)\n\n        anchor_allowed_borders = np.stack(tiled_allowed_borders, 0)\n\n        inside_mask = np.logical_and(np.logical_and(anchors_ymin > -anchor_allowed_borders * 1.,\n                                                    anchors_xmin > -anchor_allowed_borders * 1.),\n                                    np.logical_and(anchors_ymax < (1. + anchor_allowed_borders * 1.),\n                                                    anchors_xmax < (1. + anchor_allowed_borders * 1.)))\n\n        anchors_point = np.stack([anchors_ymin, anchors_xmin, anchors_ymax, anchors_xmax], axis=-1)\n\n        # save_anchors_op = tf.py_func(save_anchors,\n        #                 [bboxes,\n        #                 labels,\n        #                 anchors_point],\n        #                 tf.int64, stateful=True)\n\n        # with tf.control_dependencies([save_anchors_op]):\n        overlap_matrix = iou_matrix(bboxes, anchors_point) * np.expand_dims(inside_mask, 0).astype(np.float32)\n        matched_gt, gt_scores = do_dual_max_match(overlap_matrix, self._ignore_threshold, self._positive_threshold)\n        # get all positive matching positions\n        matched_gt_mask = matched_gt > -1\n        matched_indices = np.clip(matched_gt, 0, np.iinfo(np.int64).max)\n        # the labels here maybe chaos at those non-positive positions\n        gt_labels = np.take(labels, matched_indices)\n        # filter the invalid labels\n        gt_labels = gt_labels * matched_gt_mask.astype(np.int64)\n        # set those ignored positions to -1\n        gt_labels = gt_labels + (-1 * (matched_gt < -1).astype(np.int64))\n\n        gt_ymin, gt_xmin, gt_ymax, gt_xmax = np.unstack(np.take(bboxes, matched_indices), 4, axis=-1)\n\n        # transform to center / size.\n        gt_cy, gt_cx, gt_h, gt_w = self.point2center(gt_ymin, gt_xmin, gt_ymax, gt_xmax)\n        anchor_cy, anchor_cx, anchor_h, anchor_w = self.point2center(anchors_ymin, anchors_xmin, anchors_ymax, anchors_xmax)\n        # encode features.\n        # the prior_scaling (in fact is 5 and 10) is use for balance the regression loss of center and with(or height)\n        gt_cy = (gt_cy - anchor_cy) / anchor_h / self._prior_scaling[0]\n        gt_cx = (gt_cx - anchor_cx) / anchor_w / self._prior_scaling[1]\n        gt_h = math.log(gt_h / anchor_h) / self._prior_scaling[2]\n        gt_w = math.log(gt_w / anchor_w) / self._prior_scaling[3]\n        # now gt_localizations is our regression object, but also maybe chaos at those non-positive positions\n        if debug:\n            gt_targets = np.stack([anchors_ymin, anchors_xmin, anchors_ymax, anchors_xmax], axis=-1)\n        else:\n            gt_targets = np.stack([gt_cy, gt_cx, gt_h, gt_w], axis=-1)\n        # set all targets of non-positive positions to 0\n        gt_targets = np.expand_dims(matched_gt_mask.astype(np.float32), -1) * gt_targets\n        self._all_anchors = (anchor_cy, anchor_cx, anchor_h, anchor_w)\n        return gt_targets, gt_labels, gt_scores\n\n    # return a list, of which each is:\n    #   shape: [feature_h, feature_w, num_anchors, 4]\n    #   order: ymin, xmin, ymax, xmax\n    def decode_all_anchors(self, pred_location, num_anchors_per_layer):\n        assert self._all_anchors is not None, 'no anchors to decode.'\n        anchor_cy, anchor_cx, anchor_h, anchor_w = self._all_anchors\n\n        pred_h = math.exp(pred_location[:, -2] * self._prior_scaling[2]) * anchor_h\n        pred_w = math.exp(pred_location[:, -1] * self._prior_scaling[3]) * anchor_w\n        pred_cy = pred_location[:, 0] * self._prior_scaling[0] * anchor_h + anchor_cy\n        pred_cx = pred_location[:, 1] * self._prior_scaling[1] * anchor_w + anchor_cx\n\n        return np.split(np.stack(self.center2point(pred_cy, pred_cx, pred_h, pred_w), axis=-1), num_anchors_per_layer, axis=0)\n\n    def ext_decode_all_anchors(self, pred_location, all_anchors, all_num_anchors_depth, all_num_anchors_spatial):\n        assert (len(all_num_anchors_depth)==len(all_num_anchors_spatial)) and (len(all_num_anchors_depth)==len(all_anchors)), 'inconsist num layers for anchors.'\n        num_anchors_per_layer = []\n        for ind in range(len(all_anchors)):\n            num_anchors_per_layer.append(all_num_anchors_depth[ind] * all_num_anchors_spatial[ind])\n\n        num_layers = len(all_num_anchors_depth)\n        list_anchors_ymin = []\n        list_anchors_xmin = []\n        list_anchors_ymax = []\n        list_anchors_xmax = []\n        tiled_allowed_borders = []\n        for ind, anchor in enumerate(all_anchors):\n\n            anchors_ymin_, anchors_xmin_, anchors_ymax_, anchors_xmax_ = self.center2point(anchor[0], anchor[1], anchor[2], anchor[3])\n\n            list_anchors_ymin.append(np.reshape(anchors_ymin_, [-1]))\n            list_anchors_xmin.append(np.reshape(anchors_xmin_, [-1]))\n            list_anchors_ymax.append(np.reshape(anchors_ymax_, [-1]))\n            list_anchors_xmax.append(np.reshape(anchors_xmax_, [-1]))\n            # if(ind > 1):\n            #     print('\\n==\\n anchors_ymin_ = {}, anchors_ymax_ = {}'.format(np.reshape(anchors_ymin_, [-1]),np.reshape(anchors_ymax_, [-1])))\n        anchors_ymin = np.concatenate(list_anchors_ymin, 0)\n        anchors_xmin = np.concatenate(list_anchors_xmin, 0)\n        anchors_ymax = np.concatenate(list_anchors_ymax, 0)\n        anchors_xmax = np.concatenate(list_anchors_xmax, 0)\n\n        anchor_cy, anchor_cx, anchor_h, anchor_w = self.point2center(anchors_ymin, anchors_xmin, anchors_ymax, anchors_xmax)\n        selected = [318, 330, 368, 369, 379]\n        # print(\"\\n==\\n selected anchor = \\n{}\\n{}\\n{}\\n{}\".format(np.take(anchor_cy, selected), np.take(anchor_cx, selected), \n        #         np.take(anchor_h, selected), np.take(anchor_w, selected)))\n        # print(\"\\n==\\n pred_offset = \\n{}\".format(np.take(pred_location, selected, axis=0)))\n\n        pred_h = np.exp(pred_location[:,-2] * self._prior_scaling[2]) * anchor_h\n        pred_w = np.exp(pred_location[:, -1] * self._prior_scaling[3]) * anchor_w\n        pred_cy = pred_location[:, 0] * self._prior_scaling[0] * anchor_h + anchor_cy\n        pred_cx = pred_location[:, 1] * self._prior_scaling[1] * anchor_w + anchor_cx\n        # # print(\"\\n==\\n pred cy cx h w = \\n{}\\n{}\\n{}\\n{}\".format(np.take(pred_cy, selected), np.take(pred_cx, selected), \n        #         np.take(pred_h, selected), np.take(pred_w, selected)))        \n        split_param = []\n        split_param.append(num_anchors_per_layer[0])\n        for layer_num in num_anchors_per_layer[1:-1]:\n            split_param.append(layer_num + split_param[-1])\n\n        return np.split(np.stack(self.center2point(pred_cy, pred_cx, pred_h, pred_w), axis=-1), split_param, axis=0)\n\nclass AnchorCreator(object):\n    def __init__(self, img_shape, layers_shapes, anchor_scales, extra_anchor_scales, anchor_ratios, layer_steps):\n        super(AnchorCreator, self).__init__()\n        # img_shape -> (height, width)\n        self._img_shape = img_shape\n        self._layers_shapes = layers_shapes\n        self._anchor_scales = anchor_scales\n        self._extra_anchor_scales = extra_anchor_scales\n        self._anchor_ratios = anchor_ratios\n        self._layer_steps = layer_steps\n        self._anchor_offset = [0.5] * len(self._layers_shapes)\n\n    def get_layer_anchors(self, layer_shape, anchor_scale, extra_anchor_scale, anchor_ratio, layer_step, offset = 0.5):\n        ''' assume layer_shape[0] = 6, layer_shape[1] = 5\n        x_on_layer = [[0, 1, 2, 3, 4],\n                       [0, 1, 2, 3, 4],\n                       [0, 1, 2, 3, 4],\n                       [0, 1, 2, 3, 4],\n                       [0, 1, 2, 3, 4],\n                       [0, 1, 2, 3, 4]]\n        y_on_layer = [[0, 0, 0, 0, 0],\n                       [1, 1, 1, 1, 1],\n                       [2, 2, 2, 2, 2],\n                       [3, 3, 3, 3, 3],\n                       [4, 4, 4, 4, 4],\n                       [5, 5, 5, 5, 5]]\n        '''\n        x_on_layer, y_on_layer = np.meshgrid(range(layer_shape[1]), range(layer_shape[0]))\n\n        y_on_image = (y_on_layer.astype(np.float32) + offset) * layer_step / self._img_shape[0]\n        x_on_image = (x_on_layer.astype(np.float32) + offset) * layer_step / self._img_shape[1]\n        # print('anchor_scale = {}, {}'.format(len(anchor_scale), anchor_scale))\n        # print('anchor_ratio = {}, {}'.format(len(anchor_ratio), anchor_ratio))\n\n        num_anchors_along_depth = len(anchor_scale) * len(anchor_ratio) + len(extra_anchor_scale)\n        num_anchors_along_spatial = layer_shape[1] * layer_shape[0]\n\n        list_h_on_image = []\n        list_w_on_image = []\n\n        global_index = 0\n        # for square anchors\n        for _, scale in enumerate(extra_anchor_scale):\n            list_h_on_image.append(scale)\n            list_w_on_image.append(scale)\n            global_index += 1\n        # for other aspect ratio anchors\n        for scale_index, scale in enumerate(anchor_scale):\n            for ratio_index, ratio in enumerate(anchor_ratio):\n                list_h_on_image.append(scale / math.sqrt(ratio))\n                list_w_on_image.append(scale * math.sqrt(ratio))\n                global_index += 1\n        # shape info:\n        # y_on_image, x_on_image: layers_shapes[0] * layers_shapes[1]\n        # h_on_image, w_on_image: num_anchors_along_depth\n\n        print('list_h_on_image = {}, list_w_on_image = {}'.format(list_h_on_image, list_w_on_image))\n        return np.expand_dims(y_on_image, axis=-1), np.expand_dims(x_on_image, axis=-1), \\\n                np.array(list_h_on_image), \\\n                np.array(list_w_on_image), num_anchors_along_depth, num_anchors_along_spatial\n\n    def get_all_anchors(self):\n        all_anchors = []\n        all_num_anchors_depth = []\n        all_num_anchors_spatial = []\n        for layer_index, layer_shape in enumerate(self._layers_shapes):\n            anchors_this_layer = self.get_layer_anchors(layer_shape,\n                                                        self._anchor_scales[layer_index],\n                                                        self._extra_anchor_scales[layer_index],\n                                                        self._anchor_ratios[layer_index],\n                                                        self._layer_steps[layer_index],\n                                                        self._anchor_offset[layer_index])\n            all_anchors.append(anchors_this_layer[:-2])\n            all_num_anchors_depth.append(anchors_this_layer[-2])\n            all_num_anchors_spatial.append(anchors_this_layer[-1])\n        return all_anchors, all_num_anchors_depth, all_num_anchors_spatial\n\n","repo_name":"roychen97/detect_and_track","sub_path":"ssdface_freeze/anchor_manipulator_np.py","file_name":"anchor_manipulator_np.py","file_ext":"py","file_size_in_byte":18519,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"24959325885","text":"import serial\nimport serial.tools.list_ports\nimport logging\n\n\nclass BldcDebugIf:\n    def __init__(self, comnum='auto') -> None:\n        self.C_USB_DESC = \"STMicroelectronics STLink Virtual COM Port\"\n        self.ser = serial.Serial()\n        self.ser.baudrate = 115200\n        self.comnum = comnum\n\n        # ログ設定\n        self.loglevel = logging.INFO\n        self.logger = logging.getLogger('BldcSerial')\n        self.logger.setLevel(self.loglevel)  # ログレベル\n\n        formatter = logging.Formatter('[%(name)s][%(levelname)s]:%(message)s')\n        self.ch = logging.StreamHandler()\n        self.ch.setLevel(self.loglevel)\n        self.ch.setFormatter(formatter)\n        self.logger.addHandler(self.ch)\n\n    def __enter__(self):\n        self.open()\n        return self\n    \n    def __exit__(self, exception_type, exception_value, traceback):\n        self.close()\n\n    def open(self):\n        if self.comnum == 'auto':\n            self.auto_connect()\n        else:\n            self.ser.port = self.comnum\n        \n        try:\n            self.ser.open()\n            self.logger.debug('Success!')\n        except:\n            self.logger.debug('Failure...')\n    \n    def close(self):\n        if self.ser.is_open:\n            self.ser.close()\n            self.logger.debug('Serial Close')\n    \n    def auto_connect(self):\n        devices = serial.tools.list_ports.comports()\n        for device in devices:\n            if self.C_USB_DESC in device.description:\n                self.logger.debug(f'Connecting {device.description}')\n                self.ser.port = device.name\n                break\n\n    def readline(self):\n        rxstr = str(self.ser.readline().decode(encoding='utf-8').strip().replace(\"\\x00\",\"\"))\n        self.logger.debug('<read_data>:'+rxstr)\n        return rxstr\n\n    def print_ver(self):\n        self.ser.write(b'b')\n        print('**********')\n        print(self.readline())\n        print(self.readline())\n        print(self.readline())\n        print('**********')\n            \n\ndef test():\n    with BldcDebugIf() as bldc_if:\n        bldc_if.print_ver()\n\nif __name__ == '__main__':\n    test()\n","repo_name":"Moryu-Io/Bldc_Servo_Driver_Prj","sub_path":"python/test_mod_interface.py","file_name":"test_mod_interface.py","file_ext":"py","file_size_in_byte":2127,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"81"}
+{"seq_id":"4259555312","text":"from typing import Sequence\n\nfrom django.core.exceptions import PermissionDenied\nfrom django.db.models import Model\nfrom django.utils.timezone import now\n\nfrom datahub.core.exceptions import DataHubError\n\n# Registry of all defined reports (mapping report IDs to Report instances)\n_registry = {}\n\n\nclass Report:\n    \"\"\"\n    Base class for reports.\n\n    See QuerySetReport for reports based on a QuerySet.\n    \"\"\"\n\n    id: str = None\n    name: str = None\n    model: Model = None\n    permissions_required: Sequence = None\n    field_titles: dict = None\n    filename_template = '{name} - {timestamp}'\n\n    _required_attrs = (\n        'id',\n        'name',\n        'model',\n        'permissions_required',\n        'field_titles',\n    )\n\n    @classmethod\n    def __init_subclass__(cls, is_abstract=False, **kwargs):\n        \"\"\"Called on class declaration to register the report.\"\"\"\n        super().__init_subclass__(**kwargs)\n        if not is_abstract:\n            cls._validate_attrs()\n            _registry[cls.id] = cls()\n\n    def check_permission(self, user):\n        \"\"\"Checks whether the user has permission for this report.\"\"\"\n        return user.has_perms(self.permissions_required)\n\n    def get_filename(self):\n        \"\"\"Gets the filename (excluding extension) to use for the report.\"\"\"\n        timestamp = now().strftime('%Y-%m-%d-%H-%M-%S')\n        return self.filename_template.format(name=self.name, timestamp=timestamp)\n\n    def rows(self):\n        \"\"\"Returns an iterator of the rows for this report.\"\"\"\n        raise NotImplementedError\n\n    @classmethod\n    def _validate_attrs(cls):\n        missing_attrs = [attr for attr in cls._required_attrs if getattr(cls, attr, None) is None]\n        if missing_attrs:\n            raise DataHubError(f'Required report attributes {missing_attrs} not set')\n        if 'ID' in cls.field_titles.values():\n            raise DataHubError(\n                'ID cannot be used as a column title due to the potential confusion with SYLK '\n                'files in e.g. Excel',\n            )\n\n\nclass QuerySetReport(Report, is_abstract=True):\n    \"\"\"Base class for reports based on a QuerySet.\"\"\"\n\n    queryset = None\n\n    _required_attrs = (\n        *Report._required_attrs,\n        'queryset',\n    )\n\n    def rows(self):\n        \"\"\"Returns an iterator of the rows for this report.\"\"\"\n        return self.queryset.values(*self.field_titles.keys()).iterator()\n\n\ndef get_reports_by_model(user):\n    \"\"\"\n    Returns a dictionary mapping models to list of reports.\n\n    Only reports that the user is allowed to access are returned.\n    \"\"\"\n    reports_by_model = {}\n\n    for report in _registry.values():\n        if report.check_permission(user):\n            reports_for_model = reports_by_model.setdefault(report.model, [])\n            reports_for_model.append(report)\n\n    return reports_by_model\n\n\ndef report_exists(report_id):\n    \"\"\"Checks if a report exists.\"\"\"\n    return report_id in _registry\n\n\ndef get_report_by_id(report_id, user):\n    \"\"\"\n    Gets a report instance for using its ID.\n\n    If the user does not have the correct permission for the report, PermissionDenied is raised.\n    \"\"\"\n    report = _registry[report_id]\n    if not report.check_permission(user):\n        raise PermissionDenied\n    return report\n","repo_name":"uktrade/data-hub-api","sub_path":"datahub/admin_report/report.py","file_name":"report.py","file_ext":"py","file_size_in_byte":3264,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"81"}
+{"seq_id":"18914508532","text":"from tensorflow.examples.tutorials.mnist import input_data\nimport tensorflow as tf\n\n\n# weight initializesrs\n\ndef weight_variable(shape):\n  initial = tf.truncated_normal(shape, stddev=0.1)\n  return tf.Variable(initial)\n\n\ndef bias_variable(shape):\n  initial = tf.constant(0.1, shape=shape)\n  return tf.Variable(initial)\n\n# convolution and pooling\n\ndef conv2d(x, W):\n  return tf.nn.conv2d(\n      x,\n      W,\n      strides=[1, 1, 1, 1],\n      padding='SAME'\n  )\n\n\ndef max_pool_2x2(x):\n  return tf.nn.max_pool(\n      x,\n      ksize=[1, 2, 2, 1],\n      strides=[1, 2, 2, 1],\n      padding='SAME'\n  )\n\n\nif __name__  == \"__main__\":\n\n    DEEP_LAYERS = 1024\n    LEARNING_RATE = 1e-4 # 1e-3 for GD, 1e-4 for ADAM\n\n    ###### SETUP ######\n\n    # dataset, because we need data\n    mnist = input_data.read_data_sets('MNIST_data', one_hot=True)\n\n    # tensorflow interactive session, the connection to the C++ backend\n    sess = tf.InteractiveSession()\n\n    x = tf.placeholder(tf.float32, shape=[None, 784])   #input placeholder\n    y_ = tf.placeholder(tf.float32, shape=[None, 10])   #output placeholder\n\n    ###### SHAPE AND CONVULSE ON INPUT DATA TO REDUCE DIMENSIONALITY TO 7X7 ######\n\n    # we must reshape the image (x) to a 4d tensor\n    # QUESTION: what does the negative one represent??\n    # second and third dimensions are image width x height\n    # third dimension corresponds to number of channels (1, black (something) or white (nothing))\n    x_image = tf.reshape(x, [-1, 28, 28, 1])\n\n    # FIRST CONVULSION LAYER\n\n    # convulse\n    W_conv1 = weight_variable([5, 5, 1, 32])\n    b_conv1 = bias_variable([32])\n\n    # convolve the image\n    h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)\n\n    # max pool to a 14x14\n    h_pool1 = max_pool_2x2(h_conv1)\n\n    # SECOND CONVULSION LAYER\n\n    # convulse\n    W_conv2 = weight_variable([5, 5, 32, 64])\n    b_conv2 = bias_variable([64])\n\n    # pool\n    h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)\n    h_pool2 = max_pool_2x2(h_conv2)\n\n    #for dropout and draining accuracy\n    keep_prob = tf.placeholder(tf.float32) #only have one instance of this line\n\n    ##\n    ######DONT FIDDLE ABOVE HERE\n    ##\n\n    ###### HIDDEN LAYERS ######\n\n    # Densely connected Layer 1\n    W_fc1 = weight_variable([7 * 7 * 64, DEEP_LAYERS])\n    b_fc1 = bias_variable([DEEP_LAYERS])\n\n    h_pool2_flat = tf.reshape(h_pool2, [-1, 7 * 7 * 64])\n    h_fc1 = tf.nn.relu6(tf.matmul(h_pool2_flat, W_fc1) + b_fc1) #our densly connected layer\n    # h_fc16 = tf.nn.sigmoid(tf.matmul(h_pool2_flat, W_fc1) + b_fc1) #our densly connected layer\n\n    ###### Interlayer DROPOUT ######\n    # h_fc1_drop1 = tf.nn.dropout(h_fc1, keep_prob)\n\n    ###### MORE HIDDEN LAYERS ######\n    # W_fc12 = weight_variable([1024, 1024])\n    # b_fc12 = bias_variable([1024])\n    # h_fc12 = tf.nn.relu(tf.matmul(h_fc1, W_fc12) + b_fc12)  # our densly connected layer\n\n    # DROPOUT\n    # h_fc1_drop2 = tf.nn.dropout(h_fc12, keep_prob)\n\n    ###### EVEN MORE ######\n    # W_fc13 = weight_variable([1024, 1024])\n    # b_fc13 = bias_variable([1024])\n    # h_fc13 = tf.nn.relu(tf.matmul(h_fc1_drop2, W_fc13) + b_fc13)  # our densly connected layer\n\n    # DROPOUT AGAIN\n    # h_fc1_drop3 = tf.nn.dropout(h_fc13, keep_prob)\n\n    # BATCH NORMALIZATION\n    # epsilon = 1e-3      # Small epsilon value for the BN transform\n    # scale = tf.Variable(tf.ones([1024]))\n    # beta = tf.Variable(tf.zeros([1024]))\n    # batch_mean, batch_var = tf.nn.moments(h_fc1, [0])\n    # h_fcl_batch_norm = tf.nn.batch_normalization(h_fc1, batch_mean, batch_var, beta, scale, epsilon)\n\n    ##\n    ######DONT FIDDLE BELLOW HERE\n    ##\n\n    ###### READOUT AND VECTORIZE ######\n\n    # READOUT LAYER\n    W_fc2 = weight_variable([DEEP_LAYERS, 10])\n    b_fc2 = bias_variable([10])\n\n    # vectorize\n    # vectorized image, times weights, plus bias\n    ###\n    ### Modify based out final layer\n    ###\n    y_conv = tf.matmul(h_fc1, W_fc2) + b_fc2        #Basic Single dense layer\n    # y_conv = tf.matmul(h_fc1_drop1, W_fc2) + b_fc2     #With Dropout\n    # y_conv = tf.matmul(h_fcl_batch_norm, W_fc2) + b_fc2     #With Batch Normalization\n    # y_conv = tf.matmul(h_fc12, W_fc2) + b_fc2     #second strongly connected layer\n    # y_conv = tf.matmul(h_fc1_drop3, W_fc2) + b_fc2     #third strongly connected layer\n    # y_conv = tf.matmul(h_fc16, W_fc2) + b_fc2        #Basic Single dense layer with relu6\n\n    ###### TRAIN AND EVALUATE ######\n    cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y_conv))\n    train_step = tf.train.AdamOptimizer(LEARNING_RATE).minimize(cross_entropy)\n    # train_step = tf.train.GradientDescentOptimizer(LEARNING_RATE).minimize(cross_entropy)\n    correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))\n    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n    sess.run(tf.global_variables_initializer())\n\n    for i in range(20000):\n        batch = mnist.train.next_batch(50)\n        if i % 100 == 0:\n            train_accuracy = accuracy.eval(feed_dict={\n                x: batch[0], y_: batch[1], keep_prob: 1.0})\n            print(\"step %d, training accuracy %g\" % (i, train_accuracy))\n        train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5})\n\n    print(\"test accuracy %g\" % accuracy.eval(feed_dict={x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0}))\n\n\n","repo_name":"falkzach/CSCI595-Deep-Knowledge-Transfer","sub_path":"HW3/mnist_deep.py","file_name":"mnist_deep.py","file_ext":"py","file_size_in_byte":5363,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"21383855987","text":"from app import *\n\ndef memberpaynow():\n    try:\n        email = request.json['email']\n        parking_id = request.json['parking_id']\n    except:\n        return jsonify({'message' : 'BAD_PAYLOAD'}) , 400\n    cur = mysql.connection.cursor()\n    token = generate_token(email)\n    checkValue = cur.execute('SELECT * FROM Parking_record WHERE parking_id = %s and parking_id NOT IN (select parking_id from Invoice)', (parking_id,))\n    if checkValue > 0:\n        result = cur.fetchone()\n        now = datetime.datetime.now()\n        time_delta = (now - result['entry_datetime'])\n        total_seconds = time_delta.total_seconds()\n        minutes = total_seconds/60\n        if minutes < 15:\n            parking_cost = 0\n        else:\n            hours = math.ceil(minutes/60)\n            parking_cost = 15 * hours\n        cur.fetchall()\n        cur.execute('SELECT main_payment_method,wallet FROM Account WHERE email = %s', (email,))\n        token = cur.fetchone()\n        if token['main_payment_method'] == 'VISA':\n            cur.execute('INSERT INTO Invoice(amount,method,payment_datetime,parking_id) VALUES(%s,%s,%s,%s)',(parking_cost,token['main_payment_method'],now,parking_id,))\n            mysql.connection.commit()\n            cur.close()\n            return jsonify({'payment_datetime' : now, 'amount' : parking_cost, 'method' : token['main_payment_method'], 'wallet' : token['wallet']}) ,201\n        else:\n            if token['wallet'] < parking_cost:\n                mysql.connection.commit()\n                cur.close()\n                return jsonify({'message' : 'WALLET_MONEY_NOT_SUFFICIENT'}) ,409\n            else:\n                token['wallet'] = token['wallet'] - parking_cost\n                cur.execute('UPDATE Account SET wallet = %s WHERE email = %s' , (token['wallet'],email,))\n                cur.execute('INSERT INTO Invoice(amount,method,payment_datetime,parking_id) VALUES(%s,%s,%s,%s)',(parking_cost,token['main_payment_method'],now,parking_id,))\n                mysql.connection.commit()\n                cur.close()\n                return jsonify({'payment_datetime' : now, 'amount' : parking_cost, 'method' : token['main_payment_method'], 'wallet' : token['wallet']}) ,201\n    else:\n        mysql.connection.commit()\n        cur.close()\n        return jsonify({'message' : 'PARKING_FEE_IS_PAID'}) ,409","repo_name":"kevctae/park-sa-mart-BackEnd","sub_path":"payment.py","file_name":"payment.py","file_ext":"py","file_size_in_byte":2327,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"41457050070","text":"\"\"\"Utility ops.\"\"\"\n\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport sys\nimport tensorflow as tf\nimport numpy as np\n\nfrom os import path\n\nsys.path.append(path.dirname(path.dirname(path.abspath(__file__))))\nfrom tools.anchor import locate_anchors as py_locate_anchors\nfrom proposals_connector.connect_proposals import \\\n    connect_proposals as py_connect_proposals\n\n\ndef get_patches(images, patch_dim):\n    \"\"\"Extract patches from images and put them in \"depth\" output dimension.\n\n    Args:\n        images: 4-D Tensor with shape [batch, in_rows, in_cols, depth].\n        patch_dim: An integer number representing the dimension of patch.\n\n    Returns:\n        patches: 3-D Tensor with shape\n            [batch, out_rows, in_cols, patch_dim**2 * depth].\n    \"\"\"\n    in_filters = images.get_shape().as_list()[3]\n    out_filters = patch_dim**2 * in_filters\n    kernel = tf.constant(np.eye(out_filters).\n                         reshape(patch_dim, patch_dim,\n                                 in_filters, out_filters),\n                         tf.float32)\n\n    patches = tf.nn.conv2d(images, kernel,\n                           [1, 1, 1, 1], \"SAME\")\n\n    return patches\n\n\ndef generate_anchors(feat_map_size, feat_stride, anchor_num):\n    \"\"\"Generate anchors on feature map of an image.\n\n    Args:\n        feat_map_size: Int32 1-D Tensor containing the sizes of feature map\n            [feat_map_height, feat_map_width].\n        feat_stride: Int32 Constant Tensor containing the stride of kernel on\n            feature maps.\n        anchor_num: Int32 Constant Tensor containing the number of generated\n            anchors.\n\n    Returns:\n        anchors: 3-D Tensor with shape [height, width, 4] containing the\n            generated anchors.\n    \"\"\"\n    anchors = tf.py_func(py_locate_anchors,\n                         [feat_map_size, feat_stride, anchor_num],\n                         tf.int32, name=\"generate_anchors\")\n\n    anchors = tf.to_float(anchors)\n\n    return anchors\n\n\ndef apply_vertical_deltas_to_anchors(boxes_delta, anchors):\n    \"\"\"Compute coordinates of boxes based on its predicted relative coordianates\n        (boxes_deltas) and anchors.\n\n    Args:\n        boxes_delta: Float 2-D Tensor, [num_anchors, 2], containing\n            the boxes deltas.\n        anchors: Float 2-D Tensor, [num_anchors, 4] containing the\n            coordinates of anchors.\n\n    Returns:\n        boxes: Float 2-D Tensor, [num_anchors, 4], containing the real\n            coordinates of boxes.\n    \"\"\"\n    anchor_y_ctr = (anchors[:, 1] + anchors[:, 3]) / 2.\n    anchor_h = anchors[:, 3] - anchors[:, 1] + 1\n    vertical_heights = tf.exp(boxes_delta[:, 1]) * anchor_h\n    vertical_centers = boxes_delta[:, 0] * anchor_h + anchor_y_ctr\n\n    vertical_tops = vertical_centers - vertical_heights / 2.\n    vertical_bottoms = vertical_centers + vertical_heights / 2.\n\n    boxes = tf.stack([anchors[:, 0], vertical_tops,\n                     anchors[:, 2], vertical_bottoms ], axis=1)\n\n    return boxes\n\n\ndef apply_horizontal_deltas_to_anchors(offset_delta, anchors):\n    \"\"\"Compute horizontal coordinates of boxes based on its predicted\n        relative coordianates (boxes_deltas) and anchors.\n\n    Args:\n        offset_delta: Float 2-D Tensor, [num_anchors, 1], containing\n            the boxes deltas.\n        anchors: Float 2-D Tensor, [num_anchors, 4] containing the\n            coordinates of anchors.\n\n    Returns:\n        offset: Float 2-D Tensor, [num_anchors, 1], containing the real\n            offset of boxes.\n    \"\"\"\n    anchor_x_ctr = (anchors[:, 0] + anchors[:, 2]) / 2.\n    anchor_w = anchors[:, 2] - anchors[:, 0] + 1\n    offsets = offset_delta[:, 0] * anchor_w + anchor_x_ctr\n\n    return offsets\n\n\ndef convert_to_vertical_deltas(coords, anchors):\n    \"\"\"Compute relative coordianates of boxes (deltas) based on anchors.\n\n    Args:\n        coords: Float 2-D Tensor, [num_boxes, 2], containing the real\n            coordinates of boxes.\n        anchors: Float 2-D Tensor, [num_boxes, 4] containing the\n            coordinates of anchors.\n\n    Returns:\n        boxes_deltas: Float 2-D Tensor, [num_boxes, 2], containing\n            the boxes deltas.\n    \"\"\"\n    coords_y_ctr = coords[:, 1]\n    coords_h = coords[:, 0]\n    anchor_y_ctr = (anchors[:, 1] + anchors[:, 3]) / 2.\n    anchor_h = anchors[:, 3] - anchors[:, 1] + 1\n\n    vertical_ctr = (coords_y_ctr - anchor_y_ctr) / anchor_h\n    vertical_h = tf.log(coords_h / anchor_h)\n\n    boxes_deltas = tf.stack([vertical_ctr, vertical_h], axis=1)\n\n    return boxes_deltas\n\n\ndef convert_to_horizontal_deltas(offsets, anchors):\n    \"\"\"Compute relative coordianates of boxes (deltas) based on anchors.\n\n    Args:\n        offsets: Float 2-D Tensor, [num_offsets, 1], containing offsets.\n        anchors: Float 2-D Tensor, [num_offsets, 4] containing the\n            coordinates of anchors.\n\n    Returns:\n        horizontal_ctr: Float 2-D Tensor, [num_offsets, 1], containing\n            the offsets deltas.\n    \"\"\"\n    x_sides = tf.reshape(offsets, [-1])\n    anchor_x_ctr = (anchors[:, 0] + anchors[:, 2]) / 2.\n    anchor_w = anchors[:, 2] - anchors[:, 0] + 1\n    horizontal_ctr = (x_sides - anchor_x_ctr) / anchor_w\n    horizontal_ctr = tf.reshape(horizontal_ctr, [-1, 1])\n\n    return horizontal_ctr\n\n\ndef convert_box_to_veritical_coords(boxes):\n    \"\"\"Convert the coordinates of proposal boxes to vertical coordinates\n        including the height and y-axis center of the proposal boxes.\n\n    Args:\n        boxes: Float 3-D Tensor, [height, width, 4], containing the coordinates\n            of the proposal boxes.\n\n    Returns:\n        vertical_coords: Float 3-D Tensor, [height, width, 2] containing the\n            vertical coordinates of the proposal boxes.\n    \"\"\"\n    y_ctr = (boxes[:, 1] + boxes[:, 3]) / 2.\n    h = boxes[:, 3] - boxes[:, 1] + 1\n\n    vertical_coords = tf.stack([y_ctr, h], axis=1)\n\n    return vertical_coords\n\ndef rescale_bboxes(bboxes, old_h, old_w, new_h, new_w):\n    \"\"\"Rescale bouding boxes being suitable for the original image.\n\n    Args:\n        bboxes: A Float32 Tensor with shape [num_bboxes, 5] contains the\n            bounding boxes corresponding to the scaled image.\n        old_h: A Int32 scalar Tensor contains the height of the original image.\n        old_w: A Int32 scalar Tensor contains the width of the original image.\n        new_h: A Int32 scalar Tensor contains the height of the scaled image.\n        new_w: A Int32 scalar Tensor contains the width of the scaled image.\n\n    Returns:\n        scaled_bboxes: A Float32 Tensor with shape [num_bboxes, 5] contains the\n            bounding boxes corresponding to the original image.\n    \"\"\"\n    # Scale coordinates (left, top, right, bottom) of bounding boxes\n    l = bboxes[:, 0] * (tf.to_float(old_w) / tf.to_float(new_w))\n    t = bboxes[:, 1] * (tf.to_float(old_h) / tf.to_float(new_h))\n    r = bboxes[:, 2] * (tf.to_float(old_w) / tf.to_float(new_w))\n    b = bboxes[:, 3] * (tf.to_float(old_h) / tf.to_float(new_h))\n    scores = bboxes[:, 4]\n\n    # Build the bounding boxes corresponding to the original image\n    scaled_bboxes = tf.stack([l, t, r, b, scores], axis=1)\n\n    return scaled_bboxes\n\ndef connect_proposals(proposals, scores, offsets,\n                      offset_scores, img_shape):\n    \"\"\"Connect text proposals returned by neural nets to build the final text\n        bounding boxes. This function is a TensorFlow operation built from\n        a python function.\n\n    Args:\n        proposals: A Float32 Tensor with shape [num_proposals, 4] contains the\n            coordinates of each text proposal.\n        scores: A Float32 Tensor with shape [num_proposals, 1] contains the\n            predicted confidence of each text proposal.\n        offsets: A Float32 Tensor with shape [num_proposals, 1] contains the\n            horizontal offset of each text proposal.\n        offset_scores: A Float32 Tensor with shape [num_proposals, 1] contains\n            the predicted confidence of each text proposal.\n    \"\"\"\n    text_lines = tf.py_func(py_connect_proposals,\n                        [proposals, scores, offsets, offset_scores, img_shape],\n                        tf.float32)\n\n    return text_lines\n\n\ndef get_intersect(x, y):\n    \"\"\"Find the intersection of 2 1-D arrays.\n\n    Args:\n        x: The first Int 1-D Tensor.\n        y: The second Int 1-D Tensor.\n\n    Returns:\n        inter_ids: The Int 1_D Tensor containing intersection ids.\n        inter_values: The Int 1_D Tensor containing intersection elements.\n    \"\"\"\n    def py_get_intersect(x, y):\n        inter_mask = np.in1d(x, y)\n        inter_values = x[inter_mask]\n        inter_ids = np.where(inter_mask == True)[0]\n\n        return inter_ids, inter_values\n\n    x = tf.to_int64(x)\n    y = tf.to_int64(y)\n    inter_ids, inter_values = tf.py_func(py_get_intersect,\n                                         [x, y], [tf.int64, tf.int64],\n                                         name=\"get_intersect\")\n    inter_ids = tf.to_int32(inter_ids)\n    inter_values = tf.to_int32(inter_values)\n\n    return inter_ids, inter_values\n\n\ndef smooth_l1(pred, targets):\n    \"\"\"Calculate the smooth L1 value for loss of regression models.\n        smooth_l1(x) = 0.5 * x^2, if |x| < 1\n                       |x| - 0.5, otherwise\n    Args:\n        pred: A float32 tensor contains the predicted values.\n        targets: A float32 tensor contains the target values.\n    \"\"\"\n    term = pred - targets\n    sign = tf.cast(tf.less(tf.abs(term), 1.0), tf.float32)\n    pos_result = 0.5 * (term * term)\n    neg_result = tf.abs(term) - 0.5\n    result = pos_result * sign + neg_result * tf.abs(1 - sign)\n\n    return result\n","repo_name":"tranbahien/CTPN-TensorFlow","sub_path":"ops/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":9662,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"81"}
+{"seq_id":"40129981698","text":"#! python\nfrom http.server import BaseHTTPRequestHandler, HTTPServer\nfrom urllib.parse import urlparse\nfrom sys import argv\nfrom collections import namedtuple\nfrom os import path\n\nclass MimeType:\n    html = 'text/html'\n    js = 'text/javascript'\n    css = 'text/css'\n    jpg = 'image/jpeg'\n    gif = 'image/gif'\n    image = ('image/jpeg', 'image/gif')\n    text = ('text/html', 'text/css', 'text/javascript')\n    ico = 'NotImplemented'\n\n\nclass MungoServer(BaseHTTPRequestHandler):\n\n    content_path = path.dirname(path.realpath(__file__))\n    collection = ''\n\n    def __set_headers(self, response):\n        self.send_response(response.code)\n        self.send_header('Content-type', response.type)\n        self.end_headers()\n\n    def do_GET(self):\n        path = urlparse(self.path).path\n        mime = getattr(MimeType, path.split('.')[-1], 'text/html')\n        if mime in MimeType.image:\n            try:\n                data_fp = open(f'{MungoServer.content_path}/{path}', 'rb')\n            except IOError:\n                print(f'Not found {path}')\n                self.__set_headers(namedtuple('response', ('code', 'type'))('404', 'text/html'))\n                self.wfile.write(bytes(f'File not found {MungoServer.content_path}/{path.split(\"/\")[-1]}'))\n            else:\n                self.__set_headers(namedtuple('response', ('code', 'type'))(200, mime))\n                self.wfile.write(data_fp.read())\n                data_fp.close()\n        elif mime in MimeType.text:\n            try:\n                if not path or path == '/':\n                    file = f'{MungoServer.content_path}/{MungoServer.collection}.html'\n                else:\n                    file = f'{MungoServer.content_path}/{path}'\n                data_fp = open(file, 'rb')\n            except IOError:\n                self.__set_headers(namedtuple('response', ('code', 'type'))('404', 'text/html'))\n                self.wfile.write(bytes(f'File not found{file}'))\n                data_fp.close()\n            else:\n                data = data_fp.read()\n                data_fp.close()\n                self.__set_headers(namedtuple('response', ('code', 'type'))(200, mime))\n                self.wfile.write(data)\n\n    def do_POST(self):\n        pass\n\n    @staticmethod\n    def run(host='', port=9090):\n        if port is None:\n            port = 9090\n        http_server = (host, port)\n        print(http_server)\n        httpd = HTTPServer(http_server, MungoServer)\n        try:\n            print(f'Starting server on: {host or \"http://localhost\"}{\":\"+str(port) if port != 80 else \"\"}')\n            httpd.serve_forever()\n        except KeyboardInterrupt:\n            print('Bye!')\n            httpd.shutdown()\n            exit()\n\n\nif __name__ == '__main__':\n    if len(argv) <2 or len(argv) > 3:\n        print('Usage: MungoServer <repo> [<port>]')\n        exit(1)\n    MungoServer.collection += argv[1]\n    port = int(dict(enumerate(argv)).get(2))\n    MungoServer.run(port=port)\n","repo_name":"jukny/jsexperiments","sub_path":"NoServer/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":2958,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"3164187224","text":"# from conda import iteritems\n\nfrom Generator.Autoencoder import add_noise, convert_to_integer, load_model, create_auto_encoder, auto_encoder_3d\nfrom Generator.Constraints import *\nfrom Generator.Delenox_Config import *\nfrom Generator.Visualization import *\n\n\nclass NeatGenerator:\n    \"\"\"\n    NEAT module of the Delenox pipeline, responsible for creating and evolving CPPN's which are\n    used to generate lattices (genotype) that represent buildings (phenotype). Evolution uses\n    novelty search as the heuristic, which computes the average Euclidean distance to the k-nearest\n    neighbors as well as to an archive of unique novel individuals from past generations.\n    \"\"\"\n    def __init__(self, config, population_id):\n        self.experiment = \"\"\n        self.population_id = population_id\n        self.config = config\n        self.config.__setattr__(\"pop_size\", population_size)\n        self.population = neat.Population(self.config)\n        self.population.add_reporter(neat.StatisticsReporter())\n        self.current_phase = 0\n        self.current_gen = 0\n        self.encoder = None\n        self.decoder = None\n        self.pool = None\n        self.noise = False\n        self.archive = []\n        self.phase_best_fit = []\n        self.archive_lattices = []\n        self.neat_metrics = {'Experiment': None, 'Mean Novelty': [], 'Best Novelty': [], 'Node Complexity': [],\n                             'Infeasible Size': [], 'Connection Complexity': [], 'Archive Size': [],\n                             'Species Count': [], 'Mean Genetic Diversity': [],\n                             'Minimum Species Size': [], 'Maximum Species Size': [], 'Mean Species Size': []}\n\n    def run_neat(self, phase_number, p_experiment, static=False, noise=False, persistent_archive=True, train_on_archive=True):\n        \"\"\"\n        Executes one \"exploration\" phase of the Delenox pipeline.  A set number of independent evolutionary runs\n        are completed and the top N most novel individuals are taken and inserted into a population.  At the of\n        the phase we look at the distribution of individuals in the population according to numerous metrics and\n        statistics regarding the evolution of the populations such as the speciation, novelty scores etc.\n        \"\"\"\n        # Check to see if we should clear the novelty archive before starting the next phase.\n        if not persistent_archive:\n            self.archive = []\n            self.archive_lattices.clear()\n\n        if phase_number == 0:\n            self.config = load_config_file()\n            self.__init__(self.config, self.population_id)\n\n        # Re-initialize phase variables accordingly\n        self.phase_best_fit.clear()\n        self.current_gen = 0\n        self.current_phase = phase_number\n        self.experiment = p_experiment\n        self.noise = noise\n\n        # Load last phase's autoencoder, or the seed autoencoder if this is the first phase.\n        if phase_number > 0 and static is False:\n            self.encoder = load_model(\n                \"./Results/{}/Phase{:d}/encoder\".format(p_experiment, phase_number - 1))\n            self.decoder = load_model(\n                \"./Results/{}/Phase{:d}/decoder\".format(p_experiment, phase_number - 1))\n        else:\n            # If the experiment uses a de-noising autoencoder, load the appropriate model.\n            if not noise:\n                self.encoder = load_model(\"./Results/Seed/encoder\")\n                self.decoder = load_model(\"./Results/Seed/decoder\")\n            else:\n                self.encoder = load_model(\"./Results/Seed/encoder_noisy\")\n                self.decoder = load_model(\"./Results/Seed/decoder_noisy\")\n\n        # Update the archive of latent vectors with the current encoder's interpretation of the lattices\n        self.archive = []\n        for lattice in self.archive_lattices:\n            self.archive.append(self.encoder.predict(lattice[None])[0])\n\n        # Initialize the processes used for the NEAT run and execute the phase.\n        self.pool = Pool(thread_count)\n        self.population.run(self.run_one_generation, generations_per_run)\n        self.pool.close()\n        self.pool.join()\n\n        # Update the NEAT metrics with the end-of-phase statistics.\n        self.neat_metrics['Experiment'] = p_experiment\n        self.neat_metrics['Mean Novelty'] = self.population.reporters.reporters[0].get_fitness_mean()\n        self.neat_metrics['Best Novelty'] = self.population.reporters.reporters[0].get_fitness_stat(max)\n\n        # Clearing the pool variable and auto-encoder as these cannot be saved to a pickle file.\n        self.pool = None\n        self.encoder = None\n        self.decoder = None\n\n        if train_on_archive:\n            return self, self.archive_lattices, self.neat_metrics\n        else:\n            return self, self.phase_best_fit, self.neat_metrics\n\n    def run_one_generation(self, genomes, config):\n        \"\"\"\n        Multi-process fitness function for the NEAT module of the project.  Implements novelty search and\n        scales the workload across the thread count given in the experiment parameters. Assigns a novelty\n        value to each genome and keeps the feasible population separate, discarding and randomly regenerating\n        the infeasible individuals.\n        \"\"\"\n        start = time.time()\n        compressed_population = {}\n        lattices = {}\n        remove = 0\n        jobs = []\n\n        for genome_id, genome in genomes:\n            jobs.append(self.pool.apply_async(generate_lattice, (genome, config, False, None)))\n        for job, (genome_id, genome) in zip(jobs, genomes):\n            lattice, _, feasible = job.get()\n            if not feasible:\n                del self.population.population[genome_id]\n                genome.fitness = 0\n                remove += 1\n            else:\n                lattices.update({genome_id: lattice})\n\n        for genome_id, lattice in lattices.items():\n            to_compress = lattice\n            if self.noise:\n                to_compress = add_noise(lattice)\n            compressed_population.update({genome_id: self.encoder.predict(to_compress[None])[0]})\n\n        jobs.clear()\n        for genome_id in compressed_population.keys():\n            parameters = (compressed_population[genome_id], compressed_population, self.archive)\n            jobs.append(self.pool.apply_async(novelty_search, parameters))\n        for job, genome_id in zip(jobs, compressed_population.keys()):\n            self.population.population[genome_id].fitness = job.get()\n\n        fitness = {genome_id: fitness.fitness for genome_id, fitness in self.population.population.items() if\n                   fitness.fitness > 0}\n        sorted_keys = [k for k, _ in sorted(fitness.items(), key=lambda item: item[1])]\n\n        for individual in range(1, np.min([add_to_archive, len(lattices)])):\n            lattice = lattices[sorted_keys[-individual]]\n            self.archive_lattices.append(lattice)\n            vector = self.encoder.predict(lattice[None])[0]\n            if len(self.archive) == 0 or not (vector == self.archive).all(1).any():\n                self.archive.append(vector)\n\n        if self.current_gen % 100 == 0 or self.current_gen + 1 == generations_per_run:\n            most_novel_lattice = lattices[sorted_keys[-1]]\n            least = lattices[sorted_keys[0]]\n            mid = lattices[sorted_keys[int(len(sorted_keys) / 2)]]\n            novelty_voxel_plot(\n                [convert_to_integer(least), convert_to_integer(mid), convert_to_integer(most_novel_lattice)],\n                self.current_gen + 1, self.population_id, self.current_phase, self.experiment)\n\n        if self.current_gen + 1 == generations_per_run:\n            np.savez_compressed(\"./Results/{}/Phase{:d}/Population_{:d}.npz\".format(self.experiment, self.current_phase, self.population_id), lattices)\n            for individual in range(1, np.min([best_fit_count, len(sorted_keys)])):\n                self.phase_best_fit.append(lattices[sorted_keys[-individual]])\n\n        node_complexity = 0\n        connection_complexity = 0\n\n        for individual in self.population.population.values():\n            node_complexity += individual.size()[0]\n            connection_complexity += individual.size()[1]\n\n        node_complexity /= len(self.population.population)\n        connection_complexity /= len(self.population.population)\n        species_sizes = [len(specie.members) for specie in self.population.species.species.values() ]\n\n        self.neat_metrics['Node Complexity'].append(node_complexity)\n        self.neat_metrics['Connection Complexity'].append(connection_complexity)\n        self.neat_metrics['Archive Size'].append(len(self.archive))\n        self.neat_metrics['Species Count'].append(len(self.population.species.species))\n        self.neat_metrics['Infeasible Size'].append(remove)\n        self.neat_metrics['Minimum Species Size'].append(np.min(species_sizes))\n        self.neat_metrics['Maximum Species Size'].append(np.max(species_sizes))\n        self.neat_metrics['Mean Species Size'].append(np.mean(species_sizes))\n\n        print(\"[Population {:d}]: Generation {:d} took {:2f} seconds.\".format(self.population_id, self.current_gen, time.time() - start))\n        print(\"Average Hidden Layer Size: {:2.2f}\".format(node_complexity))\n        print(\"Average Connection Count: {:2.2f}\".format(connection_complexity))\n        print(\"Size of the Novelty Archive: {:d}\".format(len(self.archive)))\n        print(\"Number of Infeasible Buildings:\", remove)\n        print(\"Number of Species:\", len(self.population.species.species))\n        print(\"Species Sizes:\", species_sizes)\n        print(\"Max Novelty:\", fitness[sorted_keys[-1]])\n        print(\"Mean Novelty:\", np.mean(list(fitness.values())), \"\\n\")\n\n        current_compatibility = self.config.species_set_config.compatibility_threshold\n        if len(self.population.species.species) < target_species_count:\n            self.config.species_set_config.__setattr__(\"compatibility_threshold\", current_compatibility - 0.05)\n        elif len(self.population.species.species) > target_species_count:\n            self.config.species_set_config.__setattr__(\"compatibility_threshold\", current_compatibility + 0.05)\n\n        self.current_gen += 1\n\n\ndef novelty_search(genome, compressed_population, archive):\n    \"\"\"\n    Computes the novelty score for the given genome with respect to the current population and\n    an archive of past novel individuals for this run. The score is the average euclidean distance\n    to the nearest K neighbors (taken from the population and archive).\n    :param genome: the ID of the genome being assessed.\n    :param compressed_population: the population of latent vectors to compare to.\n    :param archive: the archive of past novel individuals for this run.\n    :return: the novelty score for this genome.\n    \"\"\"\n    distances = []\n    for neighbour in list(compressed_population.values()) + archive:\n        distance = 0\n        if (genome == neighbour).all():\n            continue\n        for element in range(len(neighbour)):\n            distance += np.square(genome[element] - neighbour[element])\n        distances.append(np.sqrt(distance))\n    distances = np.sort(distances)\n    return np.round(np.average(distances[:k_nearest_neighbors]), 2)\n\n\ndef generate_lattice(genome, config, noise_flag=True, plot=None):\n    \"\"\"\n    Generates a lattice using the given CPPN genome and NEAT configuration file.  May also generate\n    a noisy variant of the lattice (if a DAE is being used) and may plot the lattice if required.\n    :param plot: Title of the figure for the plot\n    :param noise_flag: Boolean value for adding noise.\n    :param genome: CPPN object used to generate lattices.\n    :param config: CPPN-NEAT config file specifying the parameters for the genomes.\n    :return: generated lattice, noisy variant, feasibility status and\n    \"\"\"\n    net = neat.nn.FeedForwardNetwork.create(genome, config)\n    lattice = np.zeros(lattice_dimensions)\n    noisy = np.zeros(lattice_dimensions)\n\n    for (x, y, z) in value_range:\n        lattice[x][y][z] = np.round(\n            net.activate((x / lattice_dimensions[0], y / lattice_dimensions[0], z / lattice_dimensions[0]))[0])\n    feasible, lattice = apply_constraints(lattice)\n    if noise_flag:\n        noisy = add_noise(lattice)\n    if plot is not None:\n        voxel_plot(lattice, plot)\n\n    lattice = to_categorical(lattice, num_classes=5)\n    noisy = to_categorical(noisy, num_classes=5)\n\n    return np.asarray(lattice, dtype=bool), np.asarray(noisy, dtype=bool), feasible\n\n\ndef generate_lattices(genomes, config, noise_flag=True):\n    pool = Pool(thread_count)\n    jobs = []\n    lattices = []\n    noisy = []\n    for genome in genomes:\n        jobs.append(pool.apply_async(generate_lattice, (genome, config, noise_flag)))\n    for job in jobs:\n        lattice, noisy_lattice, valid = job.get()\n        if valid:\n            lattices.append(lattice)\n            if noise_flag:\n                noisy.append(noisy_lattice)\n    pool.close()\n    pool.join()\n    return noisy, lattices\n\n\ndef create_population_lattices(config, noise_flag=True):\n    \"\"\"\n    Generates a population of lattices and their noisy counterparts.\n    :param noise_flag: boolean which determines whether a noised copy of the dataset should be created.\n    :param config: CPPN-NEAT config file specifying the parameters for the genomes.\n    :return lattices, noisy: the population of generated lattices and their noisy counterparts\n    \"\"\"\n    lattices = []\n    noisy = []\n    while len(lattices) < best_fit_count * runs_per_phase:\n        population = create_population(config, round((best_fit_count * runs_per_phase - len(lattices)) * 2))\n        noisy_batch, lattice_batch = generate_lattices(population.population.values(), config, noise_flag)\n        lattices += lattice_batch\n        if noise_flag:\n            noisy += noisy_batch\n        print(\"{:d} Lattices created!\".format(len(lattices)))\n    return np.asarray(lattices[:1000], dtype=bool), np.asarray(noisy[:1000], dtype=bool)\n\n\ndef create_population(config, pop_size=population_size):\n    \"\"\"\n    Generates a population of CPPN genomes according to the given CPPN-NEAT config file and population size.\n    :param config: CPPN-NEAT config file specifying the parameters for the genomes.\n    :param pop_size: Number of genomes to create.\n    :return population: Population objecting containing a dictionary in the form {genome_id: genome_object}.\n    \"\"\"\n    config.__setattr__(\"pop_size\", pop_size)\n    population = neat.Population(config)\n    return population\n\n\ndef create_seed_files(config):\n    \"\"\"\n    Initializes the NEAT populations which act as the seed for our experiments, and trains an auto-encoder\n    on randomly initialized CPPNs to be used the first phase of the experiments.  This way all the experiments\n    start with the same populations and the same auto-encoder eliminating potential randomness interfering with\n    results.\n    :param config: CPPN-NEAT config file specifying the parameters for the genomes.\n    \"\"\"\n    training_population, _ = create_population_lattices(config, False)\n    np.savez_compressed(\"Results/Seed/Initial_Training_Set.npz\", np.asarray(training_population))\n    _ = create_auto_encoder(model_type=auto_encoder_3d,\n                            phase=-1,\n                            population=np.asarray(training_population),\n                            noisy=None,\n                            experiment=\"Seed\")\n    for runs in range(runs_per_phase):\n        generator = NeatGenerator(\n            config=config,\n            population_id=runs\n        )\n        with open(\"./Results/Seed/Neat_Population_{:d}.pkl\".format(runs), \"wb+\") as f:\n            pickle.dump(generator, f)\n\n\ndef load_config_file():\n    local_dir = os.path.dirname(__file__)\n    config_path = os.path.join(local_dir, 'neat.cfg')\n    config = neat.Config(neat.DefaultGenome, neat.DefaultReproduction, neat.DefaultSpeciesSet,\n                         neat.DefaultStagnation, config_path)\n    config.genome_config.add_activation('sin_adjusted', sinc)\n    return config\n\n\nif __name__ == \"__main__\":\n\n    config = load_config_file()\n\n    pool = Pool(16)\n    for pop in range(10):\n        with open(\"../Generator/Results/Seed/Neat_Population_{:d}.pkl\".format(pop), \"rb\") as file:\n            generator = pickle.load(file)\n        jobs = []\n        lattices = []\n        for genome_id, genome in list(iteritems(generator.population.population)):\n            jobs.append(pool.apply_async(generate_lattice, (genome, config, False, None)))\n        for job in jobs:\n            result = job.get()\n            if result[2]:\n                lattices.append(result[0])\n        np.save(\"../Generator/Results/Seed/Neat_Population_{:d}.npy\".format(pop), lattices)\n    pool.close()\n    pool.join()\n\n\n","repo_name":"Matt-Barthet/M.Sc.-Thesis---Open-Ended-Minecraft-Buildings","sub_path":"Generator/NeatGenerator.py","file_name":"NeatGenerator.py","file_ext":"py","file_size_in_byte":16800,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"630766918","text":"from allImports import * # goes on top\n#decorator\n@app.route(\"/customer/<c_id>\" , methods = [\"GET\"])\ndef viewCustomer(c_id):\n    \"\"\"This function will get data from the database\"\"\"\n    viewCustomer = Customer.get(Customer.c_id == c_id) \n    return render_template( \"viewCustomer.html\",\n                            cfg = cfg, \n                            viewCustomer=viewCustomer)\n    # return viewOrders.status.status","repo_name":"cstadlock/TunnelVision","sub_path":"app/viewCustomer.py","file_name":"viewCustomer.py","file_ext":"py","file_size_in_byte":418,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"69916915785","text":"import dgl\nimport torch\n\nfrom torch import nn\n\n\nclass TextLevelGCN(torch.nn.Module):\n    def __init__(\n        self,\n        vocab_size,\n        node_embed_size,\n        edge_num,\n        class_num=2,\n        pre_node_embed=False,\n        node_embed_vectors=0,\n        is_node_freeze=False,\n        pre_edge_embed=False,\n        edge_embed_vectors=0,\n        is_edge_freeze=False,\n        fc_dropout_rate=0.5\n    ):\n        super(TextLevelGCN, self).__init__()\n\n        self.class_num = class_num\n\n        self.vocab_size = vocab_size\n        self.node_embed_size = node_embed_size\n\n        self.pre_node_embed = pre_node_embed\n        self.node_freeze = is_node_freeze\n\n        if self.pre_node_embed:\n            self.node_embed = nn.Embedding.from_pretrained(\n                embeddings=node_embed_vectors,\n                freeze=self.node_freeze\n            )\n        else:\n            self.node_embed = nn.Embedding(\n                self.vocab_size,\n                self.node_embed_size\n            )\n            nn.init.uniform_(self.node_embed.weight.data)\n\n        self.pre_edge_embed = pre_edge_embed\n        self.edge_num = edge_num\n        self.edge_freeze = is_edge_freeze\n\n        if self.pre_edge_embed:\n            self.edge_embed = torch.nn.Embedding.from_pretrained(\n                embeddings=edge_embed_vectors,\n                freeze=self.edge_freeze\n            )\n        else:\n            self.edge_embed = torch.nn.Embedding.from_pretrained(\n                torch.ones(edge_num, 1),\n                freeze=self.edge_freeze\n            )\n\n        self.dropout = torch.nn.Dropout(p=fc_dropout_rate)\n\n        self.activation = torch.nn.ReLU()\n\n        self.classify = torch.nn.Linear(self.node_embed_size, self.class_num, bias=True)\n\n        self.init_weights()\n\n    def init_weights(self):\n        nn.init.xavier_uniform_(self.classify.weight)\n\n    def forward(\n        self,\n        sub_graph,\n        **kwargs\n    ):\n        sub_graph.update_all(\n            message_func=dgl.function.src_mul_edge('h', 'w', 'weighted_message'),\n            reduce_func=dgl.function.max('weighted_message', 'h')\n        )\n        graph_feature = dgl.sum_nodes(sub_graph, feat='h')\n\n        graph_feature = self.dropout(graph_feature)\n        graph_feature = self.activation(graph_feature)\n\n        out = self.classify(graph_feature)\n\n        return out\n","repo_name":"xiangking/ark-nlp","sub_path":"ark_nlp/nn/text_level_gcn.py","file_name":"text_level_gcn.py","file_ext":"py","file_size_in_byte":2359,"program_lang":"python","lang":"en","doc_type":"code","stars":301,"dataset":"github-code","pt":"81"}
+{"seq_id":"34065411671","text":"import requests\nfrom tabulate import tabulate\nfrom pathlib import Path\n\n\ndef giveURL(x):\n    base_url = \"https://api.themoviedb.org/3/discover/movie?api_key=87a9f28a9fa52eae1eebdb5012de2c95&with_genres=\"\n    main_url = base_url + x\n    giveTitles(main_url)\n\n\ndef giveTitles(url):\n    r = requests.get(url)\n    data = r.json()\n    length_dict = len(data[\"results\"])\n\n    # grabbing the movie data\n    movies = [data[\"results\"][i][\"title\"] for i in range(length_dict)]\n    ratings = [data[\"results\"][i][\"vote_average\"] for i in range(length_dict)]\n    user_votes = [data[\"results\"][i][\"vote_count\"] for i in range(length_dict)]\n\n    # creating dictionary containing movie data\n    finale = {\"Movie Name\": movies, \"Ratings\": ratings, \"Votes\": user_votes}\n\n    printResults(finale)\n\n\ndef printResults(finale):\n    file_name = input(\"Enter file name:\") + \".txt\"\n    file_path = str(Path.cwd()) + \"/\" + file_name\n\n    # using tabulate to print it to a table\n    table = tabulate(finale, headers=\"keys\", showindex=False, tablefmt=\"pretty\")\n    with open(file_path, \"w\") as f:\n        f.write(f\"Displaying results for emotion {emotion}\\n\")\n        f.write(table)\n    print(\"File Saved as \" + file_name)\n\n\n# main function\ndef main():\n    global emotion\n    emotion = str(\n        input(\n            \"\"\"Enter an emotion:\n                        List of emotions----->sad,anger,fear/horror,joy,thrill,suspense and love<-----\\n\"\"\"\n        )\n    ).lower()\n    # Drama\n    if emotion == \"sad\":\n        giveURL(\"18\")\n    # Action\n    elif emotion in \"anger\":\n        giveURL(\"28\")\n    # Horror\n    elif emotion == [\"fear\", \"horror\"]:\n        giveURL(\"27\")\n    # Comedy\n    elif emotion == \"joy\":\n        giveURL(\"35\")\n    # Thriller\n    elif emotion == \"thrill\":\n        giveURL(\"53\")\n    # Mystery\n    elif emotion in \"suspense\":\n        giveURL(\"9648\")\n    # Romance\n    elif emotion == \"love\":\n        giveURL(\"10749\")\n\n\nif __name__ == \"__main__\":\n    main()\n\n\n","repo_name":"Floo-zy/EmotionBasedMovieRecommender-","sub_path":"OldVersions/MovieRecommender3.9.py","file_name":"MovieRecommender3.9.py","file_ext":"py","file_size_in_byte":1949,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"81"}
+{"seq_id":"27156022929","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n\"\"\"\nPubmed related utilities\n\nGiven PMID - collect Pubmed data and Pubtator Bioconcepts used for the BELMgr\nor enhancing BEL Nanopubs\n\"\"\"\n\n# Standard Library\nimport asyncio\nimport copy\nimport datetime\nimport re\nfrom typing import Any, Mapping, MutableMapping\n\n# Third Party\nimport cachetools\nimport httpx\nfrom loguru import logger\nfrom lxml import etree\n\n# Local\nimport bel.core.settings as settings\nimport bel.terms.terms\nfrom bel.core.utils import http_client, url_path_param_quoting\n\n# Replace PMID\nPUBMED_TMPL = \"https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=pubmed&retmode=xml&id=\"\n\n# https://www.ncbi.nlm.nih.gov/research/pubtator-api/publications/export/biocjson?pmids=28483577,28483578,28483579\n\nPUBTATOR_URL = (\n    \"https://www.ncbi.nlm.nih.gov/research/pubtator-api/publications/export/biocjson?pmids=\"\n)\n\npubtator_ns_convert = {\n    \"CHEBI\": \"CHEBI\",\n    \"Species\": \"TAX\",\n    \"Gene\": \"EG\",\n    \"Chemical\": \"MESH\",\n    \"Disease\": \"MESH\",\n}\n\npubtator_entity_convert = {\"Chemical\": \"Abundance\", \"Gene\": \"Gene\", \"Disease\": \"Pathology\"}\npubtator_annotation_convert = {\"Disease\": \"Pathology\"}\npubtator_known_types = [key for key in pubtator_ns_convert.keys()]\n\n\ndef node_text(node):\n    \"\"\"Needed for things like abstracts which have internal tags (see PMID:27822475)\"\"\"\n\n    if node.text:\n        result = node.text\n    else:\n        result = \"\"\n    for child in node:\n        if child.tail is not None:\n            result += child.tail\n    return result\n\n\n@cachetools.cached(cachetools.TTLCache(maxsize=200, ttl=3600))\ndef get_pubtator_url(pmid):\n    \"\"\"Get pubtator content from url\"\"\"\n\n    pubtator = None\n\n    url = f\"{PUBTATOR_URL}{pmid}\"\n\n    r = http_client.get(url, timeout=10)\n\n    if r and r.status_code == 200:\n        pubtator = r.json()\n\n    else:\n        logger.error(f\"Cannot access Pubtator, status: {r.status_code} url: {url}\")\n\n    return pubtator\n\n\ndef pubtator_convert_to_key(annotation: dict) -> str:\n    \"\"\"Convert pubtator annotation info to key (NS:ID)\"\"\"\n\n    ns = pubtator_ns_convert.get(annotation[\"infons\"][\"type\"], None)\n    id_ = annotation[\"infons\"][\"identifier\"]\n    id_ = id_.replace(\"MESH:\", \"\")\n\n    if ns is None:\n        logger.warning(\"\")\n    return f\"{ns}:{id_}\"\n\n\ndef get_pubtator(pmid):\n    \"\"\"Get Pubtator Bioconcepts from Pubmed Abstract\n\n    Re-configure the denotations into an annotation dictionary format\n    and collapse duplicate terms so that their spans are in a list.\n    \"\"\"\n\n    annotations = []\n\n    pubtator = get_pubtator_url(pmid)\n    if pubtator is None:\n        return annotations\n\n    known_types = [\"CHEBI\", \"Chemical\", \"Disease\", \"Gene\", \"Species\"]\n\n    for passage in pubtator[\"passages\"]:\n        for annotation in passage[\"annotations\"]:\n            if annotation[\"infons\"][\"type\"] not in known_types:\n                continue\n\n            key = pubtator_convert_to_key(annotation)\n\n            annotations.append(\n                {\n                    \"key\": key,\n                    \"text\": annotation[\"text\"],\n                    \"locations\": copy.copy(annotation[\"locations\"]),\n                }\n            )\n\n    return annotations\n\n\ndef process_pub_date(year, mon, day, medline_date):\n    \"\"\"Create pub_date from what Pubmed provides in Journal PubDate entry\"\"\"\n\n    if medline_date:\n        year = \"0000\"\n        match = re.search(r\"\\d{4,4}\", medline_date)\n        if match:\n            year = match.group(0)\n\n        if year and re.match(\"[a-zA-Z]+\", mon):\n            pub_date = datetime.datetime.strptime(f\"{year}-{mon}-{day}\", \"%Y-%b-%d\").strftime(\n                \"%Y-%m-%d\"\n            )\n        elif year:\n            pub_date = f\"{year}-{mon}-{day}\"\n\n    else:\n        pub_date = None\n        if year and re.match(\"[a-zA-Z]+\", mon):\n            pub_date = datetime.datetime.strptime(f\"{year}-{mon}-{day}\", \"%Y-%b-%d\").strftime(\n                \"%Y-%m-%d\"\n            )\n        elif year:\n            pub_date = f\"{year}-{mon}-{day}\"\n\n    return pub_date\n\n\ndef parse_book_record(doc: dict, root) -> dict:\n    \"\"\"Parse Pubmed Book entry\"\"\"\n\n    doc[\"title\"] = next(iter(root.xpath(\"//BookTitle/text()\")))\n\n    doc[\"authors\"] = []\n    for author in root.xpath(\"//Author\"):\n        last_name = next(iter(author.xpath(\"LastName/text()\")), \"\")\n        first_name = next(iter(author.xpath(\"ForeName/text()\")), \"\")\n        initials = next(iter(author.xpath(\"Initials/text()\")), \"\")\n        if not first_name and initials:\n            first_name = initials\n        doc[\"authors\"].append(f\"{last_name}, {first_name}\")\n\n    pub_year = next(iter(root.xpath(\"//Book/PubDate/Year/text()\")), None)\n    pub_mon = next(iter(root.xpath(\"//Book/PubDate/Month/text()\")), \"Jan\")\n    pub_day = next(iter(root.xpath(\"//Book/PubDate/Day/text()\")), \"01\")\n    medline_date = next(iter(root.xpath(\"//Journal/JournalIssue/PubDate/MedlineDate/text()\")), None)\n\n    pub_date = process_pub_date(pub_year, pub_mon, pub_day, medline_date)\n\n    doc[\"pub_date\"] = pub_date\n\n    for abstracttext in root.xpath(\"//Abstract/AbstractText\"):\n        abstext = node_text(abstracttext)\n\n        label = abstracttext.get(\"Label\", None)\n        if label:\n            doc[\"abstract\"] += f\"{label}: {abstext}\\n\"\n        else:\n            doc[\"abstract\"] += f\"{abstext}\\n\"\n\n    doc[\"abstract\"] = doc[\"abstract\"].rstrip()\n\n    return doc\n\n\ndef parse_journal_article_record(doc: dict, root) -> dict:\n    \"\"\"Parse Pubmed Journal Article record\"\"\"\n\n    doc[\"title\"] = next(iter(root.xpath(\"//ArticleTitle/text()\")), \"\")\n\n    # TODO https://stackoverflow.com/questions/4770191/lxml-etree-element-text-doesnt-return-the-entire-text-from-an-element\n    atext = next(iter(root.xpath(\"//Abstract/AbstractText/text()\")), \"\")\n\n    for abstracttext in root.xpath(\"//Abstract/AbstractText\"):\n        abstext = node_text(abstracttext)\n\n        label = abstracttext.get(\"Label\", None)\n        if label:\n            doc[\"abstract\"] += f\"{label}: {abstext}\\n\"\n        else:\n            doc[\"abstract\"] += f\"{abstext}\\n\"\n\n    doc[\"abstract\"] = doc[\"abstract\"].rstrip()\n\n    doc[\"authors\"] = []\n    for author in root.xpath(\"//Author\"):\n        last_name = next(iter(author.xpath(\"LastName/text()\")), \"\")\n        first_name = next(iter(author.xpath(\"ForeName/text()\")), \"\")\n        initials = next(iter(author.xpath(\"Initials/text()\")), \"\")\n        if not first_name and initials:\n            first_name = initials\n        doc[\"authors\"].append(f\"{last_name}, {first_name}\")\n\n    pub_year = next(iter(root.xpath(\"//Journal/JournalIssue/PubDate/Year/text()\")), None)\n    pub_mon = next(iter(root.xpath(\"//Journal/JournalIssue/PubDate/Month/text()\")), \"Jan\")\n    pub_day = next(iter(root.xpath(\"//Journal/JournalIssue/PubDate/Day/text()\")), \"01\")\n    medline_date = next(iter(root.xpath(\"//Journal/JournalIssue/PubDate/MedlineDate/text()\")), None)\n\n    pub_date = process_pub_date(pub_year, pub_mon, pub_day, medline_date)\n\n    doc[\"pub_date\"] = pub_date\n    doc[\"journal_title\"] = next(iter(root.xpath(\"//Journal/Title/text()\")), \"\")\n    doc[\"joural_iso_title\"] = next(iter(root.xpath(\"//Journal/ISOAbbreviation/text()\")), \"\")\n    doc[\"doi\"] = next(iter(root.xpath('//ArticleId[@IdType=\"doi\"]/text()')), None)\n\n    doc[\"compounds\"] = []\n    for chem in root.xpath(\"//ChemicalList/Chemical/NameOfSubstance\"):\n        chem_id = chem.get(\"UI\")\n        doc[\"compounds\"].append({\"key\": f\"MESH:{chem_id}\", \"label\": chem.text})\n\n    compounds = [cmpd[\"key\"] for cmpd in doc[\"compounds\"]]\n    doc[\"mesh\"] = []\n    for mesh in root.xpath(\"//MeshHeading/DescriptorName\"):\n        mesh_id = f\"MESH:{mesh.get('UI')}\"\n        if mesh_id in compounds:\n            continue\n        doc[\"mesh\"].append({\"key\": mesh_id, \"label\": mesh.text})\n\n    return doc\n\n\n@cachetools.cached(cachetools.TTLCache(maxsize=200, ttl=3600))\ndef get_pubmed_url(pmid):\n    \"\"\"Get pubmed url\"\"\"\n\n    root = None\n\n    try:\n        pubmed_url = f\"{PUBMED_TMPL}{str(pmid)}\"\n\n        r = http_client.get(pubmed_url)\n\n        logger.info(f\"Status {r.status_code}  URL: {pubmed_url}\")\n\n        if r.status_code == 200:\n            content = r.content\n            root = etree.fromstring(content)\n        else:\n            logger.warning(f\"Could not download pubmed url: {pubmed_url}\")\n\n    except Exception as e:\n        logger.warning(\n            f\"Bad Pubmed request, error: {str(e)}\",\n            url=f'{PUBMED_TMPL.replace(\"PMID\", pmid)}',\n        )\n\n    return root\n\n\ndef get_pubmed(pmid: str) -> Mapping[str, Any]:\n    \"\"\"Get pubmed xml for pmid and convert to JSON\n\n    Remove MESH terms if they are duplicated in the compound term set\n\n    ArticleDate vs PubDate gets complicated: https://www.nlm.nih.gov/bsd/licensee/elements_descriptions.html see <ArticleDate> and <PubDate>\n    Only getting pub_year at this point from the <PubDate> element.\n\n    Args:\n        pmid: pubmed id number as a string\n\n    Returns:\n        pubmed json\n    \"\"\"\n\n    doc = {\n        \"abstract\": \"\",\n        \"pmid\": pmid,\n        \"title\": \"\",\n        \"authors\": [],\n        \"pub_date\": \"\",\n        \"journal_iso_title\": \"\",\n        \"journal_title\": \"\",\n        \"doi\": \"\",\n        \"compounds\": [],\n        \"mesh\": [],\n    }\n\n    root = get_pubmed_url(pmid)\n\n    if root is None:\n        return None\n\n    try:\n        doc[\"pmid\"] = root.xpath(\"//PMID/text()\")[0]\n    except Exception as e:\n        return None\n\n    if doc[\"pmid\"] != pmid:\n        logger.error(f\"Requested PMID {doc['pmid']}doesn't match record PMID {pmid}\")\n\n    if root.find(\"PubmedArticle\") is not None:\n        doc = parse_journal_article_record(doc, root)\n    elif root.find(\"PubmedBookArticle\") is not None:\n        doc = parse_book_record(doc, root)\n\n    return doc\n\n\nasync def async_get_normalized_terms_for_annotations(term_keys):\n    \"\"\"Async collection of normalized terms for annotations\"\"\"\n\n    normalized = asyncio.gather(\n        *[bel.terms.terms.async_get_normalized_terms(term_key) for term_key in term_keys]\n    )\n\n    return normalized\n\n\ndef get_normalized_terms_for_annotations(term_keys):\n\n    return [bel.terms.terms.get_normalized_terms(term_key) for term_key in term_keys]\n\n\ndef add_annotations(pubmed):\n    \"\"\"Add nanopub annotations to pubmed doc\n\n    Enhance MESH terms etc as full-fledged nanopub annotations for use by the BEL Nanopub editor\n    \"\"\"\n\n    term_keys = (\n        [entry[\"key\"] for entry in pubmed.get(\"compounds\", [])]\n        + [entry[\"key\"] for entry in pubmed.get(\"mesh\", [])]\n        + [entry[\"key\"] for entry in pubmed.get(\"pubtator\", [])]\n    )\n    term_keys = list(set(term_keys))\n\n    terms = {}\n\n    for entry in pubmed.get(\"pubtator\", []):\n        terms[entry[\"key\"]] = {\"key\": entry[\"key\"], \"label\": entry[\"text\"]}\n\n    for entry in pubmed.get(\"compounds\", []):\n        terms[entry[\"key\"]] = {\"key\": entry[\"key\"], \"label\": entry[\"label\"]}\n\n    for entry in pubmed.get(\"mesh\", []):\n        terms[entry[\"key\"]] = {\"key\": entry[\"key\"], \"label\": entry[\"label\"]}\n\n    # loop = asyncio.get_event_loop()\n    # normalized = loop.run_until_complete(async_get_normalized_terms_for_annotations(term_keys))\n\n    normalized = get_normalized_terms_for_annotations(terms.keys())\n\n    normalized = sorted(normalized, key=lambda x: x[\"annotation_types\"], reverse=True)\n\n    pubmed[\"annotations\"] = []\n\n    for annotation in normalized:\n\n        # HACK - only show first annotation type\n        if len(annotation[\"annotation_types\"]) > 0:\n            annotation_type = annotation[\"annotation_types\"][0]\n        else:\n            annotation_type = \"\"\n\n        if annotation.get(\"label\", False):\n            terms[annotation[\"original\"]][\"key\"] = annotation[\"decanonical\"]\n            terms[annotation[\"original\"]][\"label\"] = annotation[\"label\"]\n            terms[annotation[\"original\"]][\"annotation_types\"] = [annotation_type]\n\n    pubmed[\"annotations\"] = copy.deepcopy(\n        sorted(terms.values(), key=lambda x: x.get(\"annotation_types\", []), reverse=True)\n    )\n\n    # Add missing\n    for idx, annotation in enumerate(pubmed[\"annotations\"]):\n        if annotation[\"label\"] == \"\":\n            pubmed[\"annotations\"][idx][\"label\"] = annotation[\"key\"]\n\n    return pubmed\n\n\ndef get_pubmed_for_beleditor(pmid: str, pubmed_only: bool = False) -> Mapping[str, Any]:\n    \"\"\"Get fully annotated pubmed doc with Pubtator and full entity/annotation_types\n\n    Args:\n        pmid: Pubmed PMID\n\n    Returns:\n        Mapping[str, Any]: pubmed dictionary\n    \"\"\"\n\n    pubmed = get_pubmed(pmid)\n\n    if pubmed is None:\n        return pubmed\n\n    if not pubmed_only:\n        pubmed[\"pubtator\"] = get_pubtator(pmid)\n\n    # Add entity types and annotation types to annotations\n    pubmed = add_annotations(pubmed)\n\n    return pubmed\n\n\ndef main():\n\n    pmid = \"19894120\"\n\n    pubmed = get_pubmed_for_beleditor(pmid)\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"belbio/bel","sub_path":"bel/nanopub/pubmed.py","file_name":"pubmed.py","file_ext":"py","file_size_in_byte":12766,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"81"}
+{"seq_id":"32174556956","text":"import numpy as np\nimport cv2\n\n\nclass Compressor:\n    \"\"\"\n    Compressor allow you to serialize and compress an image using either JPEG\n    or PNG compression.\n    \"\"\"\n\n    _format = \".png\"\n\n    @property\n    def format(self):\n        return self._format\n\n    @format.setter\n    def format(self, fmt):\n        \"\"\"\n        Set format to either .jpg jpg .png png\n        \"\"\"\n        if fmt not in [\".jpg\", \"jpg\", \".png\", \"png\"]:\n            raise ValueError(f\"Invalid format: {fmt}\")\n        if fmt.find(\".\") != 0:\n            fmt = \".\" + fmt\n        self._format = fmt\n\n    def compress(self, img):\n        ok, cb = cv2.imencode(self._format, img)\n        if ok:\n            cb = cb.tobytes()\n        else:\n            cb = None\n        return cb\n\n    def uncompress(self, img_bytes, shape):\n        img = np.frombuffer(img_bytes, dtype=np.uint8)\n\n        if len(shape) == 3:\n            img = cv2.imdecode(img, cv2.IMREAD_COLOR)\n        else:\n            img = cv2.imdecode(img, cv2.IMREAD_GRAYSCALE)\n\n        img = img.reshape(shape)\n        return img\n","repo_name":"MomsFriendlyRobotCompany/opencv_camera","sub_path":"opencv_camera/compression.py","file_name":"compression.py","file_ext":"py","file_size_in_byte":1054,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"81"}
+{"seq_id":"24564778127","text":"\r\n\r\nimport geocoder\r\nimport time\r\nfrom PMV_Elevation import elevation\r\n\r\n# **********************************************************************\r\n# Ajouter aux enregistrements du fichier des stations Bixi: l'altitude,\r\n# le code postal et la ville.\r\n#\r\n# Input: Fichier Bixi a traiter\r\n# Output: Fichier avec l'imformation ajoutee\r\n# **********************************************************************\r\n\r\ndef loadBixiStation(filenameIn, filenameOut):\r\n    fichierIn = open(filenameIn)\r\n\r\n    # lire le contenu du fichier (toutes les lignes\r\n    lignes = fichierIn.readlines()\r\n\r\n    # fermer le fichier\r\n    fichierIn.close()\r\n\r\n    # Ouvrir le fichier de sortie\r\n    fichierOut = open(filenameOut, \"w\")\r\n\r\n    # ecrire l'entete\r\n    fichierOut.writelines([\"code,name,latitude,longitude,altitude,postal,city\\n\"])\r\n\r\n    ligneIter = iter(lignes)\r\n\r\n    # on saute la première ligne (l'entete)\r\n    next(ligneIter)\r\n\r\n    nbLignes = 0\r\n    # Traiter chaque enregistrement du fichier\r\n    for ligne in ligneIter:\r\n\r\n        # Compter le nombre de lignes pour montrer l'evolution du traitement\r\n        nbLignes += 1\r\n        if nbLignes %10 == 0:\r\n            print(nbLignes)\r\n\r\n        # extraire chaque donnée, séparée par une virgule\r\n        stationCSV = ligne.strip().split(\",\")\r\n        code = int(stationCSV[0])\r\n        name = stationCSV[1]\r\n        latitude = stationCSV[2]\r\n        longitude = stationCSV[3]\r\n\r\n        # Le fichier traitee est soit le fichier d'origine de Bixi ou le resultat de ce programme (mais incomplet)\r\n        # Le fichier comprend deja l'altitude\r\n        if len(stationCSV) > 4:\r\n            altitude = float(stationCSV[4])\r\n        else:\r\n            altitude = 0.0\r\n\r\n        # Le fichier comprend deja le code postal\r\n        if len(stationCSV) > 5:\r\n            postal = stationCSV[5]\r\n        else:\r\n            postal = None\r\n\r\n        # Le fichier comprend dela la ville\r\n        if len(stationCSV) > 6:\r\n            city = stationCSV[6]\r\n        else:\r\n            city = None\r\n\r\n        # trouver l'altitude de la station\r\n        if altitude == 0.0:\r\n            altitude = elevation(latitude, longitude)\r\n\r\n        # Trouver le code postal et la ville (a partit de la latitude et de la longitude)\r\n        if postal == \"Nonex\":\r\n            g = geocoder.google([latitude, longitude], method='reverse')\r\n\r\n            # le code postal n'a pas ete trouve, on reessaye.\r\n            if (g.postal == None):\r\n                # on re essaye avec une pause\r\n                nbTry = 0\r\n                while nbTry < 5:\r\n                    print(\"Tentative\",nbTry)\r\n                    nbTry += 1  #  un essai de plus\r\n                    time.sleep(6-nbTry)\r\n                    g = geocoder.google([latitude, longitude], method='reverse')\r\n                    if g.postal != None:\r\n                        nbTry = 500  # on arrete\r\n\r\n            postal = str(g.postal)\r\n            city = str(g.city)\r\n\r\n        #print(postal, city)\r\n        # Sauver le tout dans le fichier de sortie\r\n        fichierOut.write(str(code)+\",\"+name+\",\"+str(latitude)+\",\"+str(longitude)+\",\"+ '%.0f'%float(altitude) + \",\" + str(postal)+\",\"+str(city)+\"\\n\")\r\n\r\n    fichierOut.close()\r\n\r\n# **********************************************************************\r\n# Debut du programme\r\n# **********************************************************************\r\n\r\ninputFilename = \"Stations_2017.csv\"  # Nom du fichuier en imput\r\noutputFilename = \"Stations_2017-5.csv\" # Nom du fichier en output\r\nloadBixiStation(inputFilename, outputFilename) # Lancer le traitement\r\n\r\n","repo_name":"TessierDominique/PMV","sub_path":"1-Datasets/Scripts/PMV_GeoCoder.py","file_name":"PMV_GeoCoder.py","file_ext":"py","file_size_in_byte":3588,"program_lang":"python","lang":"fr","doc_type":"code","stars":1,"dataset":"github-code","pt":"81"}
+{"seq_id":"25593716575","text":"from selenium import webdriver\nfrom selenium.webdriver.common.by import By\nimport time\n\nfrom selenium import webdriver\n\nclass Switch_To_iFRAME():\n    def Chrome_Method(self):\n        driver = webdriver.Chrome()\n        driver.maximize_window()\n        driver.get(\"https://letskodeit.teachable.com/pages/practice\")\n        driver.implicitly_wait(3)\n        driver.execute_script(\"window.scrollBy(0, 1000);\")  # takes 2 args, SCROLL down\n\n        #TO AVOID ISSUES, WHEN USING EITHER OF THE 3 SEARCH OPTIONS\n        #(ID, NAME, NUMBERS), PLS, COMMENT THE OTHERS\n\n\n        #Switch to the frame using ID\n        #driver.switch_to.frame(\"courses-iframe\")# this is the command to switch to the IFRAME, with id.\n\n        # Switch to the frame using NAME\n        #driver.switch_to.frame(\"iframe-name\")  # this is the command to switch to the IFRAME, with name.\n\n        # Switch to the frame using NUMBERS\n        driver.switch_to.frame(0)  # this is the command to switch to the IFRAME, with number, like the index on the list.\n\n        time.sleep(3)\n        #Search course\n        searchBox = driver.find_element(By.ID, \"search-courses\")\n        searchBox.send_keys(\"Python\")\n        time.sleep(3)\n\n        #Switch back to the parent frame\n        driver.switch_to.default_content()  # switching back to the PARENT IFRAME\n        driver.execute_script(\"window.scrollBy(0, -1000);\")  # takes 2 args, SCROLL UP\n        time.sleep(5)\n\n        #Type something on the field on the PARENT frame\n        driver.find_element_by_id(\"name\").send_keys(\"SUCCESS.\")\n        time.sleep(5)\n\n\n\n\n\n\n#driver.quit() closes EVERYTHING\n#driver.close() closes the CURRENT window with the focus.\n\nChromee = Switch_To_iFRAME()\nChromee.Chrome_Method()\n\n#windows are used with \"handles\" in selenium, a string","repo_name":"juancoan/python","sub_path":"Browser_Tests(System Variable 4 WebDriver Path)/Switch_To_IFRAME.py","file_name":"Switch_To_IFRAME.py","file_ext":"py","file_size_in_byte":1774,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"43672558367","text":"\"\"\"\nDate:\n    22.10.28\nTitle:\n    BAEKJOON 10871번\nProject:\n    X보다 작은 수\nLevel:\n    Bronze 5\nName:\n    thelight0804\n\"\"\"\n\n# standard_input = \"\"\"\n# 10 5\n# 1 10 4 9 2 3 8 5 7 6\n# \"\"\"\n#input\ntest, n = map(int, input().split(\" \"))\nlist = list(map(int, input().split(\" \")))\n\n#print less than n\nfor i in range(len(list)):\n    if list[i] < n:\n        print(list[i], end = ' ')","repo_name":"GDSC-DEU/PSS-Study","sub_path":"thelight0804/BAEKJOON/Num10871.py","file_name":"Num10871.py","file_ext":"py","file_size_in_byte":379,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"19889655051","text":"import os\nimport shutil\nfrom cati.cmdline.BaseCommand import BaseCommand\nfrom cati.cmdline import pr, ansi\nfrom cati.cmdline.components import DownloadProgress\nfrom cati.dotcati.Pkg import Pkg\n\nclass DownloadCommand(BaseCommand):\n    \"\"\" Download command \"\"\"\n    def help(self):\n        \"\"\"\n        download packages\n\n        Usage: cati download pkg1 pkg1 ... [options]\n        ...... cati download pkg=<version> pkg2=<version> pkg3 ... [options]\n        ...... cati download pkg=<version>=<arch> ... [options]\n\n        Options:\n        --output=[output file path]: set file download path\n        -q|--quiet: quiet output\n        \"\"\"\n        pass\n\n    def config(self) -> dict:\n        \"\"\" Define and config this command \"\"\"\n        return {\n            'name': 'download',\n            'options': {\n                '-q': [False, False],\n                '--quiet': [False, False],\n                '--output': [False, True],\n            },\n            'max_args_count': None,\n            'min_args_count': 1,\n        }\n\n    def download_once(self, pkg, output=None):\n        \"\"\" Download once package \"\"\"\n\n        try:\n            file_path = pkg.data['file_path']\n        except:\n            self.message('package \"' + pkg.data['name'] + '\" is local and cannot be downloaded' + ansi.reset, is_error=True, before=ansi.red)\n            return False\n        if not self.is_quiet():\n            pr.p('Downloading ' + pkg.data['name'] + ':' + pkg.data['version'] + ':' + pkg.data['arch'] + '...')\n\n        if output == None:\n            output = file_path.split('/')[-1]\n\n        if file_path[:7] == 'http://' or file_path[:8] == 'https://':\n            i = 0\n            while i < 5:\n                res = DownloadProgress.download(file_path, output)\n                if res == True:\n                    break\n                else:\n                    pr.e(ansi.red + str(res) + ansi.reset)\n                i += 1\n            if i == 5:\n                return False\n        else:\n            if not os.path.isfile(file_path):\n                return False\n            shutil.copy(file_path, output)\n\n        return True\n\n    def run(self):\n        \"\"\" Run command \"\"\"\n\n        if not self.is_quiet():\n            pr.p('Loading packages list...')\n            pr.p('========================')\n\n        loaded_packages = []\n\n        for argument in self.arguments:\n            arg_parts = argument.split('=')\n            if len(arg_parts) == 1:\n                # load last version as default\n                pkg = Pkg.load_last(argument)\n            elif len(arg_parts) == 2:\n                # load specify version\n                pkg = Pkg.load_version(arg_parts[0], arg_parts[1])\n                if pkg == 1:\n                    pkg = False\n                elif pkg == 2:\n                    self.message('package \"' + arg_parts[0] + '\" has not version \"' + arg_parts[1] + '\"' + ansi.reset, before=ansi.red)\n                    continue\n            else:\n                # load specify version and specify arch\n                pkg = Pkg.load_version(arg_parts[0], arg_parts[1], arg_parts[2])\n                if pkg == 1:\n                    pkg = False\n                elif pkg == 2:\n                    self.message('package \"' + arg_parts[0] + '\" has not version or arch \"' + arg_parts[1] + ':' + arg_parts[2] + '\"' + ansi.reset, before=ansi.red)\n                    continue\n            if pkg:\n                loaded_packages.append(pkg)\n            else:\n                self.message('unknow package \"' + argument + '\"' + ansi.reset, before=ansi.red)\n\n        if not loaded_packages:\n            return 1\n\n        # download loaded packages\n        is_any_success = False\n        output_path = self.option_value('--output')\n        for pkg in loaded_packages:\n            if len(loaded_packages) > 1:\n                tmp = self.download_once(pkg)\n            else:\n                tmp = self.download_once(pkg, output_path)\n            if tmp:\n                is_any_success = True\n\n        if not is_any_success:\n            return 1\n","repo_name":"catios/cati","sub_path":"cati/cmdline/commands/DownloadCommand.py","file_name":"DownloadCommand.py","file_ext":"py","file_size_in_byte":4033,"program_lang":"python","lang":"en","doc_type":"code","stars":19,"dataset":"github-code","pt":"81"}
+{"seq_id":"73072085706","text":"import torch\nimport argparse\nfrom IO import DataLoader\nimport models\nfrom torch.autograd import Variable\nfrom util import args\nimport pickle\nimport time\nimport numpy as np\n\n\ndef decode_dialog(ind2word, seq):\n    return \" \".join([ind2word[int(x)] for x in seq])\n\n\nparser = argparse.ArgumentParser()\nparser.add_argument('-save_model', type=str, default='visdialog.pt',\n                    help='trained parameters')\nparser.add_argument('-cuda', action='store_true',\n                    help='enable training with cuda')\nparser.add_argument('-batch_size', default=1, type=int,\n                    help='batch_size (1 for now)')\nparser.add_argument('-decoding', default='greedy', type=str,\n                    choices=['greedy', 'sampling'],\n                    help='Greedy or softmax sampling')\nparser.add_argument('-eval_size', default=10000, type=int,\n                    help=\"\"\"Size of validation set to evaluate.\n                    Default is 10K, must be an integer <= 40K\"\"\")\nparser.add_argument('-intervention', default='none', type=str,\n                    choices=['none', 'answer', 'question', 'image',\n                             'caption', 'negation'],\n                    help='Whether to intervene or do standard decoding.')\nparser.add_argument('-round', default=0, type=int,\n                    help='Intervene from this specific round')\nparser.add_argument('-pnoise', default=1.0, type=float,\n                    help=\"\"\"Percentage of random noise.\n                    1 means complete noise\"\"\")\nopt = parser.parse_args()\n\n# Load checkpoint\nprint(\"=> loading checkpoint '{}'\".format(opt.save_model))\ncheckpoint = torch.load(opt.save_model)\n\n# Merge two argparse namespaces with priority to args from this script\nopt = {**vars(checkpoint['opt']), **vars(opt)}\nopt = args(opt)  # wrapper around a dict to object with fields\n\nprint(opt.__dict__)\n\nprint(\"=> loaded checkpoint '{}'\".format(opt.save_model))\n\n# Construct dataloader\nloader = DataLoader(opt, ['val'])\nvocab_size = loader.vocab_size\nopt.vocab_size = vocab_size\nbos_idx = loader.data['word2ind']['<START>']\neos_idx = loader.data['word2ind']['<EOS>']\n\n# Load models\nqbot = models.QBot(opt)\nabot = models.ABot(opt)\nif opt.share_qa_embeddings:\n    qbot.embeddings.weight = abot.embeddings.weight\n\nbots = models.QABots(qbot, abot)\n\nif opt.cuda:\n    qbot.cuda()\n    abot.cuda()\n    bots.cuda()\nbots.load_state_dict(checkpoint['params'])\n\n# Params\nbatch_size = opt.batch_size  # TODO change routine for parallel evaluation\nnum_rounds = 10\nmax_decode_len = 20\nvalid_data = loader.batchify(subset='val',\n                             batch_size=batch_size,\n                             subset_size=opt.eval_size)\n\nbots.eval()  # eval mode\n\n\n# not sure what's going on here, but let try\ndef truncate2d(x, padding_idx=0):\n    # x (batch, length)\n    masked_pad = x.ne(padding_idx).sum(-1)\n    max_length = masked_pad.int().max()\n    return x[:, :max_length].contiguous()\n\n\nintervention = opt.intervention\nvocab_tokens = list(loader.data['ind2word'].keys())\nprint('Running evaluation ')\n\n\n# negation interventions\nyes_idx = loader.data['word2ind']['yes']\nno_idx = loader.data['word2ind']['no']\n\n\ndef pertube(x, p):\n    \"\"\"x is a Variable of LongTensor\"\"\"\n    new_x = torch.from_numpy(\n        np.random.choice(a=vocab_tokens, size=tuple(x.size()))).long().cuda()\n    mask = torch.FloatTensor(new_x.size()).fill_(p).bernoulli().long().cuda()\n    new_x = new_x * mask + (1-mask) * x.data\n    return Variable(new_x, volatile=True)\n\n\ngt_img_features = loader.data['val_img_fv'][:40000, :]\n# convert to pytorch\nval_img_fv = gt_img_features.float().cuda()\n\n\ndef rank(fv, true_idx):\n    d = torch.norm(val_img_fv - fv, 2, 1)\n    _, idx = torch.sort(d)\n    _, r = torch.sort(idx)\n    return (r[true_idx] + 0.5) / 40000\n\n\nreports = {}\nfor i in range(num_rounds):\n    reports[i] = []\n\nn_negs = 0\n# start playing game\nfor i, batch in enumerate(valid_data):\n    t = time.time()\n    if (i % 500) == 0 and i > 0:\n        print('| Evaluating dialogue at {}/{}'.format(i + 1, opt.eval_size))\n        for k in range(num_rounds):\n            mpr = (1-torch.FloatTensor(reports[k]).mean()) * 100\n            print(\"|\\tround %d | MPR %.2f\" % (k, mpr))\n\n        if n_negs > 0:\n            print('num negations: %d' % n_negs)\n        print('-' * 12)\n\n    caption, img_feats, *_, img_idx = batch\n    if opt.cuda:\n        caption = caption.cuda()\n        img_feats = img_feats.cuda()\n\n    caption = truncate2d(caption)  # should truncate first\n    # For storing inference results\n    dialogue_idx = img_idx.numpy()[0]\n    caption = Variable(caption, volatile=True)\n    if intervention == 'caption':\n        caption = pertube(caption, opt.pnoise)\n    caption = caption.transpose(0, 1)\n    img = Variable(img_feats, volatile=True)\n\n    # encode caption\n    # qbot also updates history\n    q_fact_enc = qbot.enc_fact(caption)\n    # print('check caption encoded %.3f' % q_fact_enc.data.norm(2))\n    _, q_hist_state = qbot.history_encoder(q_fact_enc[None, :, :])\n    # print('initial q', q_hist_state[0].data.norm(2))\n\n    a_fact_enc = abot.enc_fact(caption)\n    a_hist_state = None  # empty at first\n\n    for r in range(num_rounds):\n        # (1) qbot ask question\n        # q_i  (length, 1)\n        q_i = qbot.generate(q_hist_state, bos_idx, eos_idx,\n                            max_decode_len, opt.decoding == 'greedy')\n\n        if intervention == 'question' and r >= opt.round:\n            q_i = pertube(q_i, opt.pnoise)\n\n        # (2) abot answer\n        # (2.1) encode the question First\n        a_question_enc = abot.enc_question(q_i)\n\n        # Intervention for image\n        if intervention == 'image' and r == opt.round:\n            # Replace image with random noise\n            img.data.uniform_()\n\n        # concatenate img, a_q, Fa to pass to history encoder\n        # important: preserve order of concatenation as done in training\n        inpt = torch.cat([img, a_question_enc, a_fact_enc], -1)\n        _, a_hist_state = abot.history_encoder(inpt[None, :, :], a_hist_state)\n\n        # (2.2) generate answer\n        a_i = abot.generate(a_hist_state, bos_idx, eos_idx,\n                            max_decode_len, opt.decoding == 'greedy')\n        # checking\n        # print('q: %s' % loader.tensor2string(q_i.data))\n        # print('a: %s' % loader.tensor2string(a_i.data))\n        # print('----')\n\n        if intervention == 'answer' and r >= opt.round:\n            a_i = pertube(a_i, opt.pnoise)\n        if intervention == 'negation' and r >= opt.round:\n            if a_i.numel() == 2:\n                _tmp = a_i.view(-1)\n                _idx = _tmp.data[0]\n                if _idx == yes_idx or _idx == no_idx:\n                    if _idx == yes_idx:\n                        _tmp.data[0] = no_idx\n                    else:\n                        _tmp.data[0] = yes_idx\n                    n_negs += 1\n\n        # (3) both bots encode fact fi\n        # concatenate q and a (both are tok1...tokN EOS)\n        f_i = torch.cat([q_i[:-1], a_i[:-1]], dim=0)\n\n        q_fact_enc = qbot.enc_fact(f_i)\n        # print(f_i)\n        _, q_hist_state = qbot.history_encoder(q_fact_enc[None, :, :],\n                                               q_hist_state)\n        # print('q_hist_state', q_hist_state[0].sum().data[0])\n\n        a_fact_enc = abot.enc_fact(f_i)\n\n        # (4) qbot makes image prediction\n        y = qbot.pred_img(q_hist_state[0][-1])\n        pr = rank(y.data, dialogue_idx)\n        reports[r] += [pr]\n    if i % 500 == 0 and i > 0:\n        save_file = 'report_{}_{}_{}.pkl'.format(intervention,\n                                                 opt.round,\n                                                 opt.pnoise)\n        with open(save_file, 'wb') as f:\n            pickle.dump(reports, f)\n\n\n# save finals results\nsave_file = 'report_{}_{}_{}.pkl'.format(intervention,\n                                         opt.round,\n                                         opt.pnoise)\n\nwith open(save_file, 'wb') as f:\n    pickle.dump(reports, f)\n","repo_name":"danakianfar/Examining-Cooperation-in-VDM","sub_path":"evaluate.py","file_name":"evaluate.py","file_ext":"py","file_size_in_byte":7994,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"81"}
+{"seq_id":"44071829692","text":"import tkinter as tk\r\nfrom tkinter import ttk\r\nfrom tkinter import filedialog as fd\r\nfrom tkinter import messagebox\r\n\r\nimport pickle\r\nimport numpy as np\r\nimport warnings\r\nwarnings.filterwarnings('ignore')\r\n\r\n\r\nroot = tk.Tk()\r\nroot.title('Image Steganography')\r\nroot.resizable(False, False)\r\nroot.geometry('400x450')\r\nframe = tk.Frame(master=root, relief=tk.RAISED)\r\n\r\ndef get_crop(frame,N,P,K,Temp,Humidity,PH,Rainfall,mdl_typ):\r\n    N=float(N)\r\n    P=float(P)\r\n    K=float(K)\r\n    Temp=float(Temp)\r\n    Humidity=float(Humidity)\r\n    PH=float(PH)\r\n    Rainfall=float(Rainfall)\r\n\r\n    data = np.array([[N, P, K, Temp, Humidity, PH, Rainfall]])\r\n\r\n    mdl=\"DecisionTree.pkl\"\r\n    if mdl_typ==\"Decision Tree\":\r\n        mdl=\"DecisionTree.pkl\"\r\n    elif mdl_typ==\"Guassian Naive Bayes\":\r\n        mdl=\"NBClassifier.pkl\"\r\n    elif mdl_typ==\"Logistic Regression\":\r\n        mdl=\"LogisticRegression.pkl\"\r\n    elif mdl_typ==\"Random Forest\":\r\n        mdl=\"RandomForest.pkl\"\r\n\r\n    rcmd_model=pickle.load(open(mdl, \"rb\"))\r\n    prediction = rcmd_model.predict(data)\r\n    print(prediction)\r\n    txt=\"Recommended Crop for this Soil is \"+prediction[0]\r\n    label1 = tk.Label(master=frame, text=txt,font=('Helvetica bold',12))\r\n    label1.grid(row=7,column=0,padx=5,pady=5,columnspan=3)\r\n\r\ndef show_frm(model_type):\r\n    global frame\r\n    if (frame.winfo_exists()):\r\n        frame.destroy()\r\n    frame = tk.Frame(master=root, relief=tk.RAISED)\r\n    N = tk.Entry(master=frame, width=15)\r\n    P = tk.Entry(master=frame, width=15)\r\n    K = tk.Entry(master=frame, width=15)\r\n    temp = tk.Entry(master=frame, width=15)\r\n    humidity = tk.Entry(master=frame, width=15)\r\n    ph = tk.Entry(master=frame, width=15)\r\n    rainfall = tk.Entry(master=frame, width=15)\r\n    frame.grid(row=2,column=0,padx=5,pady=5,columnspan=3)\r\n    label1 = tk.Label(master=frame, text=\"Nitrogen\")\r\n    label2 = tk.Label(master=frame, text=\"Phosphorous\")\r\n    label3 = tk.Label(master=frame, text=\"Pottasium\")\r\n    label4 = tk.Label(master=frame, text=\"Temperature \")\r\n    label5 = tk.Label(master=frame, text=\"Humidity\")\r\n    label6 = tk.Label(master=frame, text=\"pH\")\r\n    label7 = tk.Label(master=frame, text=\"Rainfall \")\r\n\r\n    label1.grid(row=0,column=0,padx=5,pady=5)\r\n    N.grid(row=1,column=0,padx=5,pady=5)\r\n    label2.grid(row=0, column=1, padx=5, pady=5)\r\n    P.grid(row=1, column=1, padx=5, pady=5)\r\n    label3.grid(row=0, column=2, padx=5, pady=5)\r\n    K.grid(row=1, column=2, padx=5, pady=5)\r\n    label4.grid(row=2, column=0, padx=5, pady=5)\r\n    temp.grid(row=3, column=0, padx=5, pady=5)\r\n    label5.grid(row=2, column=2, padx=5, pady=5)\r\n    humidity.grid(row=3, column=2, padx=5, pady=5)\r\n    label6.grid(row=4, column=0, padx=5, pady=5)\r\n    ph.grid(row=5, column=0, padx=5, pady=5)\r\n    label7.grid(row=4, column=2, padx=5, pady=5)\r\n    rainfall.grid(row=5, column=2, padx=5, pady=5)\r\n    rcmd_btn = ttk.Button(\r\n        frame,\r\n        width=15,\r\n        text='Crop',\r\n        command=lambda: get_crop(frame,N.get(),P.get(),K.get(),temp.get(),humidity.get(),ph.get(),rainfall.get(),model_type)\r\n    )\r\n    rcmd_btn.grid(row=6,column=1,padx=5, pady=5)\r\n    print(model_type)\r\n\r\n\r\n# Press the green button in the gutter to run the script.\r\nif __name__ == '__main__':\r\n    label = tk.Label(master=root, text=\"Choose a Model\")\r\n    options = [\r\n        \"Decision Tree\",\r\n        \"Guassian Naive Bayes\",\r\n        \"Logistic Regression\",\r\n        \"Random Forest\",\r\n    ]\r\n    clicked = tk.StringVar()\r\n    clicked.set(\"Decision Tree\")\r\n    drop = tk.OptionMenu(root, clicked, *options)\r\n    Reco = ttk.Button(\r\n        root,\r\n        width = 15,\r\n        text = 'Recomend',\r\n        command = lambda: show_frm(clicked.get())\r\n    )\r\n    label.grid(row=0,column=0,padx=5,pady=5)\r\n    drop.grid(row=0,column=1,padx=5,pady=5)\r\n    Reco.grid(row=1,column=1,padx=5,pady=5)\r\n    root.mainloop()\r\n\r\n\r\n# See PyCharm help at https://www.jetbrains.com/help/pycharm/\r\n","repo_name":"Sandeep-x/Crop-Recommendation-Using-Machine-Learning","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3924,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"15740962200","text":"from xml.dom import minidom\nthiene = minidom.parse('data/Thiene.osm')\nuseful_nodes = minidom.parse('data/useful_nodes.osm')\n\n# Extract the way and the nodes from the source files\nwaylist = thiene.getElementsByTagName('way')\nselected_nodes = useful_nodes.getElementsByTagName('node')\n\n# Opens the document where will be written the data\nosm_file = open('data/final_file.osm', 'w')\nosm_file.write(\"<?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?>\"+\"\\n\")\nosm_file.write(\"<osm version=\\\"0.6\\\">\"+\"\\n\")\n\n# Writes all the relevant nodes to the file\nfor node in selected_nodes:\n    osm_file.write(node.toxml()+\"\\n\")\n\n# For way in waylist:\nfor way in waylist:\n    # Extract the tags to check if the way is a building\n    tags_child_nodes = way.getElementsByTagName('tag')\n\n    # Boolean to check if one tag is \"building\"\n    there_is_building = True\n    for tags_child in tags_child_nodes:\n        if(tags_child.getAttribute(\"k\") == \"highway\"):\n            there_is_building = False\n        \n    # If it's a building skip the way\n    if(there_is_building):\n        continue\n        \n    # Get all the way's nodes\n    child_nodes = way.getElementsByTagName(\"nd\")\n\n    # Check all the possible match\n    for node in child_nodes:\n        # Boolean value used to see if the node is in the checked ones\n        is_absent = True\n        for check_node in selected_nodes:\n            # If the id is present continue\n            if(node.getAttribute(\"ref\")==check_node.getAttribute(\"id\")):\n                is_absent = False\n                break\n            \n        # If the nodes isn't in the checked ones it\n        # remove the child from the way\n        if(is_absent):\n            way.removeChild(node)\n\n    # Print the way into the final file\n    osm_file.write(way.toxml() + \"\\n\")\n\n# Close the file\nosm_file.write(\"</osm>\")\nosm_file.close()","repo_name":"thomasporro/PloggingRouteCalculator","sub_path":"node_generator.py","file_name":"node_generator.py","file_ext":"py","file_size_in_byte":1824,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"3959622913","text":"from abc import ABC, abstractmethod\n\nimport torch\nfrom torchvision.transforms import functional as TVF\nimport numpy as np\n\n\nclass Policy(ABC):\n    @abstractmethod\n    def action(self, screen, observation):\n        \"\"\"\n\n        :param screen: batch, channels, height, width\n        :param observation: batch, channels, height, width\n        :return: an action in the embedding space, will need to be converted to the simulator space\n        \"\"\"\n        raise NotImplementedError\n\n\nclass RandomPolicy(Policy):\n    def __init__(self, env):\n        self.env = env\n\n    # todo decide if action will be in embedded or simulator space\n    # todo if so then embedding should be part of the policy\n    def action(self, screen, observation):\n        return self.env.action_space.sample()\n\n\nclass ActionEmbedding:\n    \"\"\"\n    Simple one-hot embedding of the action space\n    \"\"\"\n\n    def __init__(self, env):\n        self.env = env\n\n    def tensor(self, action):\n        action_t = torch.zeros(self.env.action_space.n)\n        action_t[action] = 1.0\n        return action_t\n\n    def numpy(self, action):\n        action_n = np.zeros(self.env.action_space.n)\n        action_n[action] = 1.0\n        return action_n\n\n    def embedding_to_action(self, index):\n        return index\n\n    def start_tensor(self):\n        return torch.zeros(self.env.action_space.n)\n\n    def start_numpy(self):\n        return np.zeros(self.env.action_space.n)\n\n\nclass ToTensor(object):\n    def __init__(self, action_embedding):\n        self.embed_action = action_embedding\n\n    def __call__(self, screen, observation, reward, done, info, action):\n        screen_t = TVF.to_tensor(screen)\n        observation_t = torch.Tensor(observation)\n        reward_t = torch.Tensor([reward])\n        done_t = torch.Tensor([done])\n        action_t = self.embed_action.tensor(action)\n        return screen_t, observation_t, reward_t, done_t, info, action_t\n\n\nclass Rollout:\n    def __init__(self, env):\n        self.env = env\n\n    def rollout(self, policy, episode, max_timesteps=100):\n        observation = self.env.reset()\n        screen = self.env.render(mode='rgb_array')\n\n        for t in range(max_timesteps):\n\n            action = policy.action(screen, observation)\n            observation, reward, done, info = self.env.step(action)\n            screen = self.env.render(mode='rgb_array')\n\n            if done:\n                print(\"Episode finished after {} timesteps\".format(t + 1))\n                break\n\n\nclass RolloutGen(object):\n    \"\"\"\n    Wrap gym in a generator object\n    \"\"\"\n\n    def __init__(self, env, policy, action_embedding, populate_screen=True, render_to_window=False):\n        \"\"\"\n\n        :param env: gym environment\n        :param policy: policy to select actions in the environment\n        :param populate_screen: populates the screen return parameter with numpy array of RGB data\n        :param render_to_window: render the output to a window\n        \"\"\"\n        self.env = env\n        self.policy = policy\n        self.done = True\n        self.action = None\n        self.populate_screen = populate_screen\n        self.render_to_window = render_to_window\n        self.to_tensor = ToTensor(action_embedding)\n\n    def __iter__(self):\n        return self\n\n    # Python 3 compatibility\n    def __next__(self):\n        return self.next()\n\n    def render(self):\n        screen = None\n        if self.populate_screen:\n            screen = self.env.render(mode='rgb_array')\n        if self.render_to_window:\n            self.env.render()\n        return screen\n\n    def step(self, action):\n        observation, reward, done, info = self.env.step(action)\n        screen = self.render()\n        screen_t, observation_t, reward_t, done_t, info, action_t = \\\n            self.to_tensor(screen, observation, reward, done, info, action)\n        action = self.policy.action(screen_t.unsqueeze(0), observation_t.unsqueeze(0))\n        return screen, observation, reward, done, info, action\n\n    def next(self):\n\n        if self.done:\n            observation = self.env.reset()\n            screen = self.render()\n            reward = 0\n            self.done = False\n            info = {}\n            screen_t, observation_t, reward_t, done, info, action = \\\n                self.to_tensor(screen, observation, reward, self.done, info, 0)\n            self.action = self.policy.action(screen_t.unsqueeze(0), observation_t.unsqueeze(0))\n            return screen, observation, reward, self.done, info, self.action\n\n        else:\n            screen, observation, reward, done, info, action = self.step(self.action)\n            self.action = action\n            self.done = done\n            return screen, observation, reward, done, info, action\n\n\nclass GymSimulatorDataset(torch.utils.data.Dataset):\n    def __init__(self, env, policy, length, action_embedding, output_in_numpy_format=False, render_to_window=False):\n        torch.utils.data.Dataset.__init__(self)\n        self.length = length\n        self.count = 0\n        self.policy = policy\n        self.rollout = RolloutGen(env, policy, action_embedding, render_to_window=render_to_window).__iter__()\n        self.output_in_numpy_format = output_in_numpy_format\n        self.to_tensor = ToTensor(action_embedding=action_embedding)\n\n    def __getitem__(self, index):\n        screen, observation, reward, done, info, action = self.rollout.next()\n\n        if not self.output_in_numpy_format:\n            screen, observation, action, reward, info, done = \\\n                self.to_tensor(screen, observation, reward, done, info, action)\n\n        self.count += 1\n\n        return screen, observation, action, reward, done\n\n    def __len__(self):\n        return self.length\n","repo_name":"DuaneNielsen/rnd","sub_path":"gym_data.py","file_name":"gym_data.py","file_ext":"py","file_size_in_byte":5679,"program_lang":"python","lang":"en","doc_type":"code","stars":16,"dataset":"github-code","pt":"81"}
+{"seq_id":"11304840247","text":"from django.shortcuts import render\nfrom django.http import HttpResponse, HttpResponseRedirect, JsonResponse, HttpRequest\nfrom django.urls import reverse\nfrom urllib.parse import urlencode, quote_plus\nfrom django.shortcuts import redirect\nfrom .models import User\n\n\n\n# Create your views here.\n\ndef home(request):\n   \n    if request.method == 'POST':\n            name = request.POST.get('name')\n            print(f\"Input value: {name}\")\n            user = User(username=name)\n        \n        \n            return render(request, 'start.html', {'user':user})\n    \n    \n    return render(request, 'index.html') \n                \n    \ndef start(request):\n    \n    username = request.session.get('username')\n    username = request.POST.get('username')\n    print(username)\n    return render(request, 'start.html', {'username':username})\n    \n\n\ndef quiz1(request):\n    \n    username = request.POST.get('username')\n    name1 = request.POST.get('name1')\n    print(f'Input value: {username}')\n    print(f'Input value: {name1}')\n    sum = request.POST.get('sum')\n    print(f'input :{sum}')\n    sum = 0\n    resultDiv = ''\n\n    next_url = request.build_absolute_uri(reverse('quiz2'))\n    next_url_with_username = next_url + f'?name1={name1}'\n    share_url = f'https://api.whatsapp.com/send?text={next_url}'\n    context = {'share_url':share_url,'sum':sum,'name1':name1,'username':username,'resultDiv':resultDiv}\n\n        \n    response =  render(request,'quiz1.html',context)\n    return response\n   \n    \n    \n    \n\ndef quiz2(request):\n \n       if request.method == 'POST':\n            name1 = request.POST.get('name1')\n            print(f\"Input value: {name1}\")\n            sum = request.POST.get('sum')\n            print(f'input :{sum}')\n            context = {'name1':name1, 'username': 'username', 'sum':sum}\n            return render(request, 'quiz3.html', context)\n       return render(request,'quiz2.html')\n    \n\n\ndef quiz3(request):\n\n    if request.method == 'POST':\n        name1 = request.POST.get('name1')\n        print(name1)\n        context = {'name1':name1,'data2':'Quiz3 Data'}\n        return render(request,'quiz3.html',context)\n\n    return render(request,'quiz3.html')\n\ndef final(request):\n\n    if request.method == 'POST':\n        name1 = request.POST.get('name1')\n\n        context = {'name1':name1, 'data2':'Final Data'}\n        return render(request, 'final.html', context)\n\n    return render(request,'final.html')\n\ndef first(request):\n    if request.method == 'POST':\n        name1 = request.POST.get('name1')\n        username = request.POST.get('username')\n        name1 = request.POST.get('name1')\n        request.session['name1'] = name1\n        print(f'Input value: {name1}')\n        sum = 0\n        resultDiv = ''\n        next_url = request.build_absolute_uri(reverse('quiz2'))\n        share_url = f'https://api.whatsapp.com/send?text={next_url}'\n\n        context = {'name1':'name1','sum':'sum', 'data2':'First Data', 'username':username}\n        return redirect('home')\n\n    return redirect('home')\n\n\n\n   ","repo_name":"Khagesh000/Online-quiz","sub_path":"online/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":3017,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"81"}
+{"seq_id":"6776943475","text":"# -*- coding: utf-8 -*-\nimport copy\n\n__author__ = 'nietaki'\n\nfrom BaseBot import BaseBot\nfrom alg import *\nimport physics\nimport random\n\nclass PhysicsTester(BaseBot):\n    def __init__(self, sock, name, key):\n        super(PhysicsTester, self).__init__(sock, name, key)\n        #self.radius_speed_dict = {40: 4.8, 60: 5.5, 90: 6.6, 110: 7.3}\n        self.radius_speed_dict = {40: 4.7, 60: 5.4, 90: 6.5, 110: 7.2}\n        #self.radius_speed_dict = {40: 4.5, 60: 5.1, 90: 6.3, 110: 7.0}\n\n    def on_car_positions(self, data, tick):\n        piece_index = self.my_car().track_piece_index\n        lane = self.my_car().lane()\n        radius = self.track.true_radius(piece_index, lane)\n        \"\"\"\n        game plan: sprawdzamy, czy trzeba zwalniać przed następnym zakrętem:\n            jak tak, to zwalniamy\n            jak nie, to sprawdzamy, czy jesteśmy w zakręcie:\n             jak tak, to utrzymujemy prędkość adekwatną do zakrętu\n             jak nie, to pełen gaz\n        \"\"\"\n        if (self.my_car().slip_angle > 50 and self.my_car().angle_velocity > 1.0) or \\\n           (self.my_car().slip_angle < -50 and self.my_car().angle_velocity < -1.0):\n            #print(\"That's too dangerous, brother!\")\n            self.throttle(0.3)\n            return\n        next_turn_id = self.track.next_bend_id(piece_index, min(radius, 150))\n        distance_until_sharp_turn = self.track.distance_until_index(piece_index, self.my_car().in_piece_distance, next_turn_id, lane)\n        if not distance_until_sharp_turn is None:\n            target_velocity = self.radius_speed_dict[self.track.true_radius(next_turn_id, lane)]\n            minimal_distance_to_break = physics.distance_to_break(self.my_car().velocity, target_velocity)\n            if minimal_distance_to_break >= distance_until_sharp_turn and (self.my_car().velocity - target_velocity) > 0.1:\n                self.throttle(0.0)\n                #print(\"gotta slow down to {0}!\".format(target_velocity))\n                return\n            else:\n                0 == 0\n                #print('there is a turn, but nothing serious yet, breaking distance is {0}, {1} available'.format(minimal_distance_to_break, distance_until_sharp_turn))\n\n        if radius < 150:\n            target_speed = self.radius_speed_dict[radius]\n            #print(\"gonna set throttle to {0}\".format(target_speed / 10))\n            self.throttle(target_speed / 10)  # TODO rely on physics\n        else:\n            #print(\"full ahead, cap'n\")\n            self.throttle(1.0)\n\n\nclass PhysicsBisector(BaseBot):\n    def __init__(self, sock, name, key):\n        super(PhysicsBisector, self).__init__(sock, name, key)\n        self.piece_look_ahead = 5\n\n    def on_car_positions(self, data, tick):\n        car = self.my_car()\n\n        if not car.crashed:\n            the_until = (car.track_piece_index + self.piece_look_ahead) % car.track.track_piece_count\n            deduced_throttle = my_bisect(0.0, 1.0, 6, lambda t: physics.is_safe_until_simple(car, t, the_until, 0.0))\n            print(\"decided to go on throttle {0} from {1} to {2}\".format(deduced_throttle, car.track_piece_index, the_until))\n            self.throttle(deduced_throttle, tick)\n        else:\n            self.ping()\n\nclass AdvancedBisector(BaseBot):\n    def __init__(self, sock, name, key):\n        super(AdvancedBisector, self).__init__(sock, name, key)\n\n    def on_car_positions(self, data, tick):\n        car = self.my_car()\n\n        if not car.crashed:\n            cur_index = self.my_car().track_piece_index\n            macro_index = self.track.macro_piece_map[cur_index]\n            next_macro_beginning = self.track.reverse_macro_map[(macro_index + 1) % len(self.track.reverse_macro_map)]\n            lane = car.lane()\n            next_macro_beginning_piece = car.track.track_pieces[next_macro_beginning]\n            next_macro_radius = next_macro_beginning_piece.true_radius(lane)\n            #next_macro_target_speed = physics.estimate_stable_speed_at_angle(next_macro_radius, physics.crash_angle_buffered())\n            next_macro_target_speed = physics.estimate_safe_speed_at_angle(next_macro_radius, physics.crash_angle_buffered())\n\n            if car.current_track_piece().is_straight:\n                if physics.distance_to_break(car.velocity, next_macro_target_speed) >= \\\n                car.track.distance_until_index(car.track_piece_index,\n                                               car.in_piece_distance,\n                                               next_macro_beginning,\n                                               lane):\n                    self.throttle(physics.throttle_to_reach_velocity(car.velocity, next_macro_target_speed), tick)\n                else:\n                    self.throttle(1.0, tick)\n            else:\n                # it's a bend!\n                #FIXME: do the safe ending in a more intelligent way than adding one to the next piece index\n                #FIXME: this is just copied from the other bot, we want velocities, not throttles\n                the_until = (next_macro_beginning + 1) % car.track.track_piece_count\n                deduced_throttle = my_bisect(0.0, 1.0, 6, lambda t: physics.is_safe_until_simple(car, t, the_until, 0.0))\n                self.throttle(deduced_throttle, tick)\n        else:\n            self.ping()\n\n\n\nclass Cruiser(BaseBot):\n    def __init__(self, sock, name, key):\n        super(Cruiser, self).__init__(sock, name, key)\n\n    def on_car_positions(self, data, tick):\n        car = self.my_car()\n        cur_index = car.track_piece_index\n\n        if not car.crashed:\n            # turning on Turbo at the beginning of the longest straight:\n            if cur_index == self.track.index_of_the_beginning_of_the_longest_straight_piece and self.turbo_available:\n                self.turbo(\"Buckle up!\", tick)\n                return\n\n            macro_index = self.track.macro_piece_map[cur_index]\n            next_macro_beginning = self.track.reverse_macro_map[(macro_index + 1) % len(self.track.reverse_macro_map)]\n            lane = car.lane()\n            distance_until_next_macro = car.track.distance_until_index(car.track_piece_index,\n                                                                       car.in_piece_distance,\n                                                                       next_macro_beginning,\n                                                                       lane)\n\n            next_macro_beginning_piece = car.track.track_pieces[next_macro_beginning]\n            next_macro_radius = next_macro_beginning_piece.true_radius(lane)\n            next_macro_target_speed = physics.estimate_stable_speed_at_angle(next_macro_radius, physics.crash_angle_buffered())\n\n            ### SWITCH ###\n            # should we consider switching? - is next piece a switch and is it legal now?\n            if not car.is_switching() and not self.switch_initiated and self.track.next_piece(cur_index).switch and \\\n                            len(self.track.lanes) > 1 and car.velocity > physics.safe_speed:\n                same_lane_and_close = self.other_cars_on_lane_within_distance(lane, 150)\n                if len(same_lane_and_close):\n                    #there is somebody to go around\n\n                    dirs = car.possible_lane_switch_directions()\n                    switch_direction = random.choice(dirs)\n\n                    target_lane = lane + switch_direction\n                    print('checking if there is somebody blocking the other lane - {0}, after going in {1} direction'.format(target_lane, switch_direction))\n                    opponents_on_target_lane = self.other_cars_on_lane_within_distance(target_lane, 250)\n                    if not opponents_on_target_lane:\n                        print(\"other lane is clear, let's see if physics says it is safe\")\n                        car_other_lane = copy.copy(car)\n                        car_other_lane.start_lane_index += switch_direction\n                        car_other_lane.end_lane_index += switch_direction\n                        if physics.check_with_annealing(car_other_lane):\n                            print(\"going to switch lane to in the {0} direction\".format(switch_direction))\n                            self.switch_lane_int(switch_direction, tick)\n                            return\n                        else:\n                            print(\"staying here, switching is not safe!\")\n                    else:\n                        print(\"somebody on the other lane, not switching now\")\n\n            if self.switch_initiated:\n                print('reducing speed to be safe')\n                self.throttle(0.0, tick)\n                return\n            ### end SWITCH ###\n\n            if car.current_track_piece().is_straight:\n                # straight!\n                should_run_like_hell =   self.is_race() and \\\n                                         car.lap == self.lap_count() - 1 and \\\n                                         macro_index == len(self.track.reverse_macro_map) - 1 and \\\n                                         car.current_track_piece().is_straight\n                if should_run_like_hell:\n                    print(\"gotta go fast!\")\n                    if self.turbo_available:\n                        self.turbo(\"Strap yourselves in boys!\", tick)\n                    else:\n                        self.throttle(1.0, tick)\n                    return\n\n\n                next_macro_target_speed = physics.estimate_safe_speed_at_angle(next_macro_radius, physics.crash_angle_buffered())\n\n                # simulating the car braking to the safe speed\n                car_at_next_macro = physics.simulate_straight_with_breaking_to_speed(car, distance_until_next_macro, next_macro_target_speed)\n\n                # end of the simulation with annealing\n                macro_plus_1 = (macro_index + 1) % len(self.track.reverse_macro_map)\n                # getting the best speed for the bend\n                deduced_speed = physics.estimate_optimal_speed_at_bend_with_annealing(car_at_next_macro,\n                                                                                      car.track.reverse_macro_map[macro_plus_1],\n                                                                                      True)\n\n                # planning the breaking, now with better values\n                # checking if we can afford it\n                # velocity and travelled distance in one step of full throttle\n                next_velocity, next_distance = physics.velocity_and_distance_step(car.velocity, 1.0)\n                # distance needed to break\n                breaking_distance = physics.distance_to_break(next_velocity, max(deduced_speed, next_macro_target_speed))\n                if breaking_distance + next_distance < distance_until_next_macro:\n                    self.throttle(1.0, tick)\n                else:\n                    self.throttle(0.0, tick)\n\n            else:\n                # it's a bend!\n                the_until = (next_macro_beginning + 1) % car.track.track_piece_count\n                deduced_speed = physics.estimate_optimal_speed_at_bend_with_annealing(car, the_until)\n                self.throttle(physics.throttle_to_reach_velocity(car.velocity, deduced_speed), tick)\n        else:\n            self.ping(tick)\n\n\n\n","repo_name":"nietaki/HWO-2014---the-What-What-What","sub_path":"python/heuristics.py","file_name":"heuristics.py","file_ext":"py","file_size_in_byte":11269,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"20251212480","text":"import typing\nfrom enum import Enum\n\nclass FieldType(Enum):\n  INTEGER = 'INTEGER'\n  FLOAT = 'FLOAT'\n  STRING = 'STRING'\n  BOOLEAN = 'BOOLEAN'\n  DATE = 'DATE'\n  DATETIME = 'DATETIME'\n  TIME = 'TIME'\n  RECORD = 'RECORD'\n  NULL = 'NULL'\n\ndef addDataTypes(new_data_types: typing.Dict[str, str]):\n  global FieldType\n  cur_field_types = dict()\n  for attr in dir(FieldType):\n    if not attr.startswith('__'):\n      cur_field_types[attr] = attr\n  cur_field_types.update(new_data_types)\n  FieldType = Enum('FieldType', cur_field_types)\n\ndef removeDataTypes(removed_data_types: typing.Dict[str, str]):\n  global FieldType\n  cur_field_types = dict()\n  for attr in dir(FieldType):\n    if not attr.startswith('__') and attr not in removed_data_types:\n      cur_field_types[attr] = attr\n  FieldType = Enum('FieldType', cur_field_types)\n\nclass Field(object):\n  __slots__ = {\n    'name': \"-> string [REQUIRED]\",\n    'type': \"-> string [REQUIRED] integer|float|string|boolean|date|datetime|time|record\",\n    'mode': \"-> string [OPTIONAL] REQUIRED|NULLABLE|REPEATED: default NULLABLE\",\n    'fields': \"-> list [OPTIONAL IF type = RECORD]\",\n    'schema_name': \"-> string [OPTIONAL]\"\n  }\n\n  def __init__(self, **attrs):\n    self.name = attrs['name']\n    self.type = attrs['type']\n    self.mode = attrs.get('mode', 'NULLABLE')\n    self.schema_name = attrs.get('schema_name')\n    self.fields = [\n      f if isinstance(f, Field) else Field(**f)\n      for f in attrs.get('fields', [])\n    ]\n\n\n  def __repr__(self):\n    return \"<Field(name={name}, type={type} at {id}>\".format(\n      id=id(self),\n      name=self.name,\n      type=self.type\n    )\n      \n\n  def __eq__(self, other):\n\n    return (\n      self.name == other.name\n      and self.type == other.type\n      and self.mode == other.mode\n      and self.fields == other.fields\n      and self.schema_name == other.schema_name\n    )\n\n  @property\n  def path(self):\n    if self.schema_name:\n      return self.schema_name + \".\" + self.name\n    else:\n      return self.name\n  \n\n  def new(self, **parts):\n    attrs = {attr:getattr(self, attr) for attr in self.__slots__}\n    attrs.update(parts)\n\n    return self.__class__(**attrs)\n\n  def to_dict(self):\n    return dict(\n      name = self.name,\n      type = self.type,\n      mode = self.mode,\n      fields = [f.to_dict() for f in self.fields]\n    )\n\n\n","repo_name":"wyfunique/DBSim","sub_path":"dbsim/field.py","file_name":"field.py","file_ext":"py","file_size_in_byte":2320,"program_lang":"python","lang":"en","doc_type":"code","stars":15,"dataset":"github-code","pt":"81"}
+{"seq_id":"4689376871","text":"from tkinter import *\r\nfrom tkinter import ttk\r\nimport os\r\nimport tkinter\r\nfrom PIL import Image, ImageTk\r\n\r\n\r\nroot = Tk()\r\n\r\n#背景\r\ncanvas = tkinter.Canvas(root,\r\n                        width=800,  # 指定Canvas组件的宽度\r\n                        height=460,  # 指定Canvas组件的高度\r\n                        bg='white')  # 指定Canvas组件的背景色\r\nimage = Image.open(\"img.jpg\")\r\nim = ImageTk.PhotoImage(image)\r\n\r\ncanvas.create_image(300, 350, image=im)  # 使用create_image将图片添加到Canvas组件中\r\n\r\n\r\n\r\n\r\n# Frame框架\r\nf1 = Frame(root)\r\nf1.pack()\r\nf2 = Frame(root)\r\nf2.pack()\r\nf3 = Frame(root)\r\nf3.pack()\r\nf4 = Frame(root)\r\nf4.pack()\r\nf5 = Frame(root)\r\nf5.pack()\r\nf5_5 = Frame(root)\r\nf5_5.pack()\r\nf6 = Frame(root)\r\nf6.pack()\r\nf7 = Frame(root)\r\nf7.pack()\r\n\r\nlabel_l1 = Label(f1, text='关键字(必选)',background='pink')\r\nlabel_l1.pack(side=LEFT)\r\n# 关键字输入框\r\nkey = StringVar()\r\nentry_e1 = Entry(f1, textvariable=key)\r\nentry_e1.pack(side=RIGHT)\r\n\r\nlabel_l2 = Label(f2, text='***页  数*** ',background='pink')\r\nlabel_l2.pack(side=LEFT)\r\n# 页数输入框\r\npage = StringVar()\r\nentry_e2 = Entry(f2, textvariable=page)\r\nentry_e2.pack(side=RIGHT)\r\n\r\n# 数据库复选框\r\nlabel_c = Label(f3, text='选择数据库',background='pink')\r\nlabel_c.pack()\r\nmysql = IntVar()\r\ncheck_one = Checkbutton(f3, text='MYSQL', variable=mysql)\r\ncheck_one.pack()\r\nmongo = IntVar()\r\ncheck_two = Checkbutton(f3, text='MONGO', variable=mongo)\r\ncheck_two.pack()\r\n\r\n\r\nlabel_u = Label(f4, text='用户名',background='pink')\r\nlabel_u.pack(side=LEFT)\r\n# 用户名输入框\r\nuser = StringVar()\r\nentry_u = Entry(f4, textvariable=user)\r\nentry_u.pack(side=RIGHT)\r\nlabel_p = Label(f5, text='密  码',background='pink')\r\nlabel_p.pack(side=LEFT)\r\n# 密码输入框\r\npassword = StringVar()\r\nentry_p = Entry(f5, textvariable=password)\r\nentry_p['show']='*'\r\nentry_p.pack(side=RIGHT)\r\n\r\n#下拉列表(地区)\r\narea={\r\n    '全国': 489,\r\n    '北京': 530,\r\n    '上海': 538,\r\n    '深圳': 765,\r\n    '广州': 763,\r\n    '天津': 531,\r\n    '成都': 801,\r\n    '杭州': 653,\r\n    '武汉': 736,\r\n    '大连': 600,\r\n    '南京': 635,\r\n    '苏州': 639,\r\n    '西安': 854,\r\n}\r\n\r\nlabel_ttk = Label(f5_5, text='地 区',background='pink')\r\nlabel_ttk.pack(side=LEFT)\r\n\r\nttk_v='全国'\r\ndef functtk(event):\r\n    global ttk_v\r\n    ttk_v=cmb.get()\r\n    print(ttk_v)\r\n\r\ncmb=ttk.Combobox(f5_5)\r\ncmb.pack(side=RIGHT)\r\ncmb['value']=('全国','北京','上海','深圳','广州','天津','成都','杭州','武汉','大连','南京','苏州','西安')\r\ncmb.current(0)\r\ncmb.bind(\"<<ComboboxSelected>>\",functtk)\r\n\r\n\r\n\r\n#rbutton_one=Radiobutton(f5_5,text=area['全国'],variable=bintvar,vaule=1,command=bo)\r\n\r\n\r\n\r\n\r\ndef run():\r\n    if key.get():\r\n        now_path = os.path.dirname(__file__)\r\n        params = '-a input_keyword={} '.format(\r\n            key.get())\r\n        if page.get():\r\n            params = params+'-a input_page={} '.format(page.get())\r\n        else:\r\n            params = params+'-a input_page=1 '\r\n        if mysql.get() == 1 and user.get() and password.get():\r\n            params = params + '-s set_mysql=1 -s set_user={} -s set_password={} '.format(\r\n                user.get(), password.get())\r\n        else:\r\n            params = params + '-s set_mysql=0 -s set_user={} -s set_password={} '.format(\r\n                '','')\r\n        if mongo.get() == 1:\r\n            params = params + \\\r\n                '-s set_mongo=1 '\r\n        else:\r\n            params = params + '-s set_mongo=0 '\r\n\r\n        params=params + '-a input_area={} '.format(area[ttk_v])\r\n\r\n        os.system('cd {}'.format(now_path)+r'\\recruit'+' && ' +\r\n              'scrapy crawl zhilian {}'.format(params)+'-s COLLECTION={}'.format('求职信息'))\r\n\r\n\r\nbutton = Button(f6, text='run', command=run,background='orange')\r\nbutton.pack()\r\n\r\ntext = '''\r\n页数默认为1页，每页有90条数据，最大限制在10页，避免恶意使用\r\n只有选择MYSQL数据库才需要输入用户名和密码，存储在本地\r\n'''\r\nspecification = Label(f7, text=text,background='yellow')\r\nspecification.pack()\r\n\r\nunit=5.5\r\n\r\ncanvas.create_window(400,unit*10,\r\n                   window=f1)\r\ncanvas.create_window(400,unit*20,\r\n                   window=f2)\r\ncanvas.create_window(400,unit*30,\r\n                   window=f3)\r\ncanvas.create_window(400,unit*40,\r\n                   window=f4)\r\ncanvas.create_window(400,unit*50,\r\n                   window=f5)\r\ncanvas.create_window(400,unit*60,\r\n                   window=f5_5)\r\ncanvas.create_window(400,unit*70,\r\n                   window=f6)\r\ncanvas.create_window(400,unit*80,\r\n                   window=f7)\r\n\r\n\r\n\r\n\r\ncanvas.pack()  # 将Canvas添加到主窗口\r\n\r\nroot.mainloop()\r\n","repo_name":"MonGod0222/zhilian-GUI-crawl","sub_path":"任务1-智联招聘5.0/智联招聘GUI.py","file_name":"智联招聘GUI.py","file_ext":"py","file_size_in_byte":4714,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"6507580241","text":"import pickle\n\n\n#http://www.tutorialspoint.com/python/python_files_io.htm\n\nclass pixel(object):\n    def __init__(self, px, py, prgb):\n        self.x = px\n        self.y = py\n        self.rgb = prgb\n\n\nfile = open(\".\\\\Content\\\\testData.json\",mode=\"rb+\")\n\ntestdata = [1,2,\"#fffff3\"]\n\npixels = []\n\ntestObj = pixel (1,1,\"#ffFFF1\")\npixels.append(testObj)\n\ntestObj = pixel (1,2,\"#ffFFF2\")\npixels.append(testObj)\n\ntestObj = pixel (1,3,\"#ffFFF3\")\npixels.append(testObj)\nprint (len(pixels))\n#print (\"pixels:\" + )\n\npickle.dump(pixels, file)\n#file.close()\n\nfile = open(\".\\\\Content\\\\testData.json\",mode=\"rb+\")\nreadTestData = pickle.load(file)\n\nprint (len(readTestData))\n\nfor pix in readTestData:\n    print (pix.x, pix.y, pix.rgb)\n\n","repo_name":"nrnoble/masterdirectory","sub_path":"GRC1/Library/Examples/FileIO.py","file_name":"FileIO.py","file_ext":"py","file_size_in_byte":718,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"73123571464","text":"\"\"\"\nA simple study space location entry flask app.\n\"\"\"\nimport flask\nfrom flask.views import MethodView\nfrom index import Index\nfrom submit import Submit\nfrom display import Display\n\napp = flask.Flask(__name__)       # our Flask app\n\napp.add_url_rule('/',\n                 view_func=Index.as_view('index'),\n                 methods=[\"GET\"])\n\napp.add_url_rule('/submit',\n                 view_func=Submit.as_view('submit'),\n                 methods=['GET', 'POST'])\n\napp.add_url_rule('/display',\n                 view_func=Display.as_view('display'),\n                 methods=[\"GET\"])\n\nif __name__ == '__main__':\n    app.run(host='0.0.0.0', port=5000, debug=True)\n","repo_name":"vishrutss/Web-and-cloud-Homeworks-and-Projects","sub_path":"hw4/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":662,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"6459119884","text":"from flask import Flask\nimport json\nimport os\n\napp = Flask(__name__)\n\nsick_traces = []\n\ndef load_sick_traces():\n    n = len(os.listdir(\"data\"))\n    for i in range(1, n+1):\n        with open(\"data/\" + str(i) + \".json\") as f:\n            sick_traces.append(json.load(f))\n\n@app.route('/heartbeat/<offset>')\ndef heartbeat(offset):\n    offset = int(offset)\n    if offset >= 0 and offset < len(sick_traces):\n        return json.dumps(sick_traces[offset:])\n    return json.dumps([])\n\n\nif __name__ == '__main__':\n    load_sick_traces()\n    app.run()","repo_name":"KamilPiechowiak/covid-contact-tracing","sub_path":"backend/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":541,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"8771535267","text":"\"\"\"Standalone script to seed data into the Voithos model\"\"\"\n## Imports\n# from this project\nimport web.ephesus.app as ephesus_app\nfrom web.ephesus.extensions import db\nfrom web.ephesus.model.user import User, Project, ProjectAccess\nfrom web.ephesus.model.voithos import (\n    FlaggedTokens,\n    Vocabulary,\n    TokenSuggestions,\n    SuggestionType,\n    UserDecisionType,\n    SuggestionSourceType,\n)\n\nvoithos_seed_data = [\n    {\n        \"flagged_tokens\": [{\"lang_code\": \"eng\", \"token\": \"confidint\"}],\n        \"vocabulary\": [\n            {\n                \"lang_code\": \"eng\",\n                \"entry\": \"confident\",\n            },\n            {\"lang_code\": \"eng\", \"entry\": \"confidante\"},\n        ],\n        \"token_suggestions\": [\n            {\n                \"mapping\": {\"flagged_token_idx\": 0, \"vocabulary_idx\": 0},\n                \"association_data\": {\n                    \"suggestion_type\": SuggestionType.SPELLING,\n                    \"confidence\": 0.7,\n                    \"user_decision_type\": UserDecisionType.UNDECIDED,\n                    \"suggestion_source_type\": SuggestionSourceType.AI,\n                },\n            },\n            {\n                \"mapping\": {\"flagged_token_idx\": 0, \"vocabulary_idx\": 1},\n                \"association_data\": {\n                    \"suggestion_type\": SuggestionType.SPELLING,\n                    \"confidence\": 0.3,\n                    \"user_decision_type\": UserDecisionType.UNDECIDED,\n                    \"suggestion_source_type\": SuggestionSourceType.AI,\n                },\n            },\n        ],\n    }\n]\n\nusers_seed_data = {\n    \"users\": [\n        {\n            \"email\": \"bob@email.com\",\n            \"username\": \"bob\",\n            \"password\": \"pbkdf2:sha512:210000$UKQsOQDc7Ngj5g1w$6364090ba739122c31f034c5600f67d6ac837058b2282f4a68ac64e171bc0d052767540da7e900af40b8bdbf13dac35bae89fea863b1afdbdefb3034af6fb982\",\n            \"is_email_verified\": 1,\n            \"status\": \"ACTIVE\",\n            \"roles\": \"[\" \"public\" \"]\",\n        },\n        {\n            \"email\": \"sam@email.com\",\n            \"username\": \"sam\",\n            \"password\": \"pbkdf2:sha512:210000$jUzijkvcIXcMGJep$303cfec08ac269c91f102c16e580d4dd3798fd0577c23189eb8e729311a7461e483177d0d1c58b8d5b95da8938c48732eb1ec316da88d1f16b7bce3828f09295\",\n            \"is_email_verified\": 1,\n            \"status\": \"ACTIVE\",\n            \"roles\": \"[\" \"public\" \",\" \"admin\" \"]\",\n        },\n    ],\n    \"projects\": [\n        {\n            \"resource_id\": \"asdf1234\",\n            \"name\": \"Hindi NT\",\n            \"lang_code\": \"hin\",\n            \"status\": \"ACTIVE\",\n        },\n        {\n            \"resource_id\": \"zxcv5678\",\n            \"name\": \"Urdu NT\",\n            \"lang_code\": \"urd\",\n            \"status\": \"ACTIVE\",\n        },\n    ],\n    \"projectAccess\": [\n        {\n            \"user_id\": 1,\n            \"project_id\": 1,\n        },\n        {\n            \"user_id\": 1,\n            \"project_id\": 2,\n        },\n        {\"user_id\": 2, \"project_id\": 2, \"access_type\": \"COLLABORATOR\"},\n    ],\n}\n\napp = ephesus_app.create_app()\n\nwith app.app_context():\n    # Seed User\n    for seed_user in users_seed_data[\"users\"]:\n        user = User(**seed_user)\n        db.session.add(user)\n\n    # Seed Project\n    for seed_project in users_seed_data[\"projects\"]:\n        project = Project(**seed_project)\n        db.session.add(project)\n\n    # Seed ProjectAccess\n    for seed_project_access in users_seed_data[\"projectAccess\"]:\n        project_access = ProjectAccess(**seed_project_access)\n        db.session.add(project_access)\n\n    # # Seed data for spell checking\n    # for item in voithos_seed_data:\n    #     flagged_tokens = []\n    #     vocabulary = []\n\n    #     # Create FlaggedTokens\n    #     for flagged_token in item[\"flagged_tokens\"]:\n    #         flagged_tokens.append(FlaggedTokens(**flagged_token))\n\n    #     # Create Vocabulary\n    #     for vocabulary_entry in item[\"vocabulary\"]:\n    #         vocabulary.append(Vocabulary(**vocabulary_entry))\n\n    #     # Create TokenSuggestions\n    #     for suggestion in item[\"token_suggestions\"]:\n    #         token_suggestion_data = {\n    #             **{\n    #                 \"flagged_token\": flagged_tokens[\n    #                     suggestion[\"mapping\"][\"flagged_token_idx\"]\n    #                 ],\n    #                 \"suggestion\": vocabulary[suggestion[\"mapping\"][\"vocabulary_idx\"]],\n    #             },\n    #             **suggestion[\"association_data\"],\n    #         }\n\n    #         token_suggestion = TokenSuggestions(**token_suggestion_data)\n\n    #         # Add to DB\n    #         db.session.add(token_suggestion)\n\n    # Commit\n    db.session.commit()\n","repo_name":"BibleNLP/greek-room","sub_path":"web/ephesus/model/seed.py","file_name":"seed.py","file_ext":"py","file_size_in_byte":4608,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"81"}
+{"seq_id":"14173224823","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# In[11]:\n\n\ndef normalization(l):\n    maxnum=max(l)\n    minnum=min(l)\n    for i in range(0,len(l)):\n        l[i]=(l[i]-minnum)/(maxnum-minnum)\n    sum=0\n    for n in l:\n        sum+=n\n    mean=sum/len(l)\n    print(\"Mean after Normalizing:\",mean)\n    \nl=[78,65,56,87,91,37,49,77,62,59,95,63,42,55,72,68,81,39,45,49]\nnormalization(l)\n\n\n# In[10]:\n\n\nl=[1,2,3,4,5]\nx=min(l)\nprint(x)\n\n\n# In[35]:\n\n\nfrom sklearn.preprocessing import Normalizer\nl=[[4,1,2,2],[1,3,9,3],[5,7,5,1]]\nprint(Normalizer().fit_transform(l))\nprint(Normalizer().fit_transform(l).mean())\nprint(Normalizer().fit_transform(l).std())\n\n\n# In[34]:\n\n\nfrom sklearn.preprocessing import StandardScaler\ndata = [[0, 0], [0, 0], [1, 1], [1, 1]]\nprint(StandardScaler().fit_transform(data))\nprint(StandardScaler().fit_transform(data).mean(axis=0))\nprint(StandardScaler().fit_transform(data).std())\n\n\n# In[30]:\n\n\nimport statistics as st\ndef standardization(l):\n    dev=st.stdev(l)\n    mean=st.mean(l)\n    for i in range(0,len(l)):\n        l[i]=(l[i]-mean)/dev\n    print(st.mean(l))\n    print(st.stdev(l))\n    \nl=[1,5,3,7,6]\nstandardization(l)\n\n\n# In[ ]:\n\n\n\n\n","repo_name":"1nt18is038/1NT18IS038_jothsna_A_ML","sub_path":"ML_LAB3.py","file_name":"ML_LAB3.py","file_ext":"py","file_size_in_byte":1147,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"2266192598","text":"import s6fits\nimport sys\n\ndef run_s6(filename):\n  spec = s6fits.s6dataspec_t()\n  spec.filename = filename  \n  s6fits.get_s6data(spec)\n\nif __name__ == '__main__':\n  #filename = \"serendip6_eth2_AO_ALFA_1006_20160127_000604.fits\"\n  filename = sys.argv[1]\n  run_s6(filename)\n  \n","repo_name":"liuweiseu/etfits-api","sub_path":"s6python.py","file_name":"s6python.py","file_ext":"py","file_size_in_byte":274,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"16773459611","text":"#!/usr/bin/python3\nfrom selenium import webdriver\nfrom selenium.webdriver.chrome.options import Options\nimport time\nfrom pyvirtualdisplay import Display\nimport requests\n\ndisplay = Display(visible=0, size=(1920, 1080))\ndisplay.start()\n\nchrome_options = Options()\nchrome_options.add_argument(\"--headless\")\nchrome_options.add_argument(\"--window-size=1920x1080\")\n\nchrome_driver = '/usr/lib/chromium-browser/chromedriver'\n\ndriver = webdriver.Chrome(chrome_options=chrome_options, executable_path=chrome_driver)\n\ndriver.get('https://www.worldpadeltour.com/jugadores/?ranking=todos')\ntime.sleep(20)\n\nfor i in range(1,25):\n    driver.execute_script(\"window.scrollTo(0, document.body.scrollHeight);\")\n    time.sleep(2)\n\ntime.sleep(10)\ndriver.execute_script(\"window.scrollTo(0, document.body.scrollHeight);\")\ntime.sleep(10)\ndriver.execute_script(\"window.scrollTo(0, document.body.scrollHeight);\")\ntime.sleep(20)\nhtml_source = driver.page_source\ndata = html_source.encode('utf-8')\nfile = open(\"/home/pi/Websites/padel-info-world-padel-tour-html.txt\",\"wb\")\nfile.write(data)\nfile.close()\ndriver.quit()\ndisplay.stop()\nfiles = {\n    'file': ('padel-info-world-padel-tour-html.txt', open('/home/pi/Websites/padel-info-world-padel-tour-html.txt', 'rb')),\n}\nresponse = requests.post('http://localhost:38000/api/UpdateWorldPadelTourRankingFromFileUpload', files=files)\nprint (response)","repo_name":"mgimeno/padel-info-alexa-skill","sub_path":"WPT-Ranking-Script/padel-info-world-padel-tour-ranking-script.py","file_name":"padel-info-world-padel-tour-ranking-script.py","file_ext":"py","file_size_in_byte":1366,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"25199924869","text":"import random\n\n\nclass Dealer:\n    def __init__(self):\n        self.initial_deck = self.create_initial_deck()\n\n    def create_initial_deck(self) -> list:\n        \"\"\"53枚のカードを生成する(13 * 4マーク分のセット + Joker)\n        X = Joker\n\n        Returns:\n            initial_deck: A ~ K + Jokerの文字列を格納しているリスト\n        \"\"\"\n        initial_deck = []\n\n        for n in range(1, 14):\n            if n == 1:\n                court_Card = \"A\"\n            elif n == 11:\n                court_Card = \"J\"\n            elif n == 12:\n                court_Card = \"Q\"\n            elif n == 13:\n                court_Card = \"k\"\n            else:\n                court_Card = str(n)\n            initial_deck.append(court_Card)\n\n        initial_deck = initial_deck * 4\n        initial_deck.append(\"X\")\n        return initial_deck\n\n    def initial_deal(self, *args: tuple()) -> list:\n        \"\"\"プレイヤーの数に応して初期手札を配る\n\n        Args:\n            player (dict): ゲーム参加するユーザー情報\n\n        Returns:\n            dict: 各プレイヤーに初期手札を分配した結果の情報\n        \"\"\"\n        random.shuffle(self.initial_deck)\n        players = list(args)\n        q, mod = divmod(len(self.initial_deck), len(players))\n\n        for i in range(len(players)):\n            slice_n = q\n            # 端数のカードが存在する場合はそれが無くなるまで追加して配る\n            if i < mod:\n                slice_n += 1\n            players[i].deck = self.initial_deck[:slice_n]\n            del self.initial_deck[:slice_n]\n        return players\n\n    def initial_putdown(self, deck: list) -> list:\n        \"\"\"初期手札を重複しているカードを捨てる\n\n        Args:\n            deck (list): 1プレイヤーの手札\n\n        Returns:\n            list: 重複削除後の手札\n        \"\"\"\n        while len(set(deck)) != len(deck):\n            popped_card = deck.pop(0)\n            if popped_card in deck:\n                # 同じ数字を持っているペアが存在する場合\n                deck.remove(popped_card)\n            else:\n                # 同じ数字を持っているペアが存在しない場合\n                deck.append(popped_card)\n        return deck\n","repo_name":"HT0323/old_maid","sub_path":"dealer.py","file_name":"dealer.py","file_ext":"py","file_size_in_byte":2296,"program_lang":"python","lang":"ja","doc_type":"code","stars":1,"dataset":"github-code","pt":"81"}
+{"seq_id":"41710163014","text":"# -*- coding: utf-8 -*-\n# noqa: E402\n\"\"\"\nThis module contains the Block class.\n\"\"\"\nimport os\nimport gi\ngi.require_version('Gtk', '3.0')\ngi.require_version('GooCanvas', '2.0')\ngi.require_version('PangoCairo', '1.0')\nfrom gi.repository import Gtk\nfrom gi.repository import Gdk\nfrom gi.repository import GooCanvas\nfrom gi.repository import GdkPixbuf\nfrom gi.repository import Pango\nfrom mosaicode.system import System\nfrom mosaicode.model.blockmodel import BlockModel\nfrom mosaicode.model.port import Port\n\nclass Block(GooCanvas.CanvasGroup, BlockModel):\n    \"\"\"\n    This class contains methods related the Block class\n    \"\"\"\n\n    # ----------------------------------------------------------------------\n\n    def __init__(self, diagram, block):\n        \"\"\"\n        This method is the constuctor.\n        \"\"\"\n        GooCanvas.CanvasGroup.__init__(self)\n        BlockModel.__init__(self, block)\n\n        self.diagram = diagram\n        self.remember_x = 0\n        self.remember_y = 0\n\n        self.widgets = {}\n        self.focus = False\n        self.has_flow = False\n        self.is_selected = False\n        self.is_collapsed = False\n\n        self.width = 112\n\n        self.connect(\"button-press-event\", self.__on_button_press)\n        self.connect(\"motion-notify-event\", self.__on_motion_notify)\n        self.connect(\"enter-notify-event\", self.__on_enter_notify)\n        self.connect(\"leave-notify-event\", self.__on_leave_notify)\n        self.move(int(float(self.x)), int(float(self.y)))\n\n        self.height = self.__calculate_height()\n\n        self.__draw_rect()\n        self.__draw_label()\n        self.__draw_ports()\n        self.__draw_icon()\n        self.update_flow()\n\n    # ----------------------------------------------------------------------\n    def __on_button_press(self, canvas_item, target_item, event):\n        \"\"\"\n        This method monitors when the button is pressed.\n\n            Parameters:\n                canvas_item\n            Returns:\n                * **Types** (:class:`boolean<boolean>`)\n                Indicates the button is pressed.\n            \"\"\"\n        # with Shift\n        if event.state == Gdk.ModifierType.SHIFT_MASK \\\n                | Gdk.ModifierType.MOD2_MASK:\n            if self.is_selected:\n                self.is_selected = False\n            else:\n                self.is_selected = True\n\n        else:\n            if not self.is_selected:\n                self.diagram.deselect_all()\n                self.is_selected = True\n\n        self.diagram.show_block_property(self)\n\n        if event.button == 1:\n            self.remember_x = event.x\n            self.remember_y = event.y\n\n        self.diagram.update_flows()\n\n        if event.button == 3:\n            return False\n\n        return True\n\n    # ----------------------------------------------------------------------\n    def __on_motion_notify(self, canvas_item, target_item, event=None):\n        \"\"\"\n        This method monitors the motion.\n\n            Parameters:\n                canvas_item\n                target_item\n\n            Returns:\n                * **Types** (:class:`boolean<boolean>`)\n\n        \"\"\"\n        if not event.state & Gdk.ModifierType.BUTTON1_MASK:\n            return False\n        if self.diagram.curr_connector is not None:\n            return False\n        # Get the new position and move by the difference\n        new_x = event.x - self.remember_x\n        new_y = event.y - self.remember_y\n        self.diagram.move_selected(new_x, new_y)\n        return False\n\n    # ----------------------------------------------------------------------\n    def __on_enter_notify(self, canvas_item, target_item, event=None):\n        \"\"\"\n        This method monitors the motion.\n\n            Parameters:\n                canvas_item\n            Returns:\n                * **TYpes** (:class:`boolean<boolean>`)\n        \"\"\"\n        self.focus = True\n        self.__update_state()\n        return False\n\n    # ----------------------------------------------------------------------\n    def __on_leave_notify(self, canvas_item, target_item, event=None):\n        \"\"\"\n        This method monitors the motion.\n\n            Parameters:\n                canvas_item\n                target_item\n\n            Returns:\n                * **Types** (:class:`boolean<boolean>`)\n\n        \"\"\"\n        self.focus = False\n        self.__update_state()\n        return False\n\n    # ----------------------------------------------------------------------\n    def __draw_rect(self):\n        \"\"\"\n        This method draw a rectangle.\n        \"\"\"\n        rect = GooCanvas.CanvasRect(parent=self,\n                                    x=0,\n                                    y=10,\n                                    width=self.width,\n                                    height=self.height - 15,\n                                    radius_x=10,\n                                    radius_y=10,\n                                    stroke_color=\"black\",\n                                    fill_color_rgba=self.get_color_as_int(),\n                                    tooltip=self.label\n                                    )\n        self.widgets[\"Rect\"] = rect\n\n    # ----------------------------------------------------------------------\n    def __draw_icon(self):\n        \"\"\"\n        This method draw a icon.\n        \"\"\"\n        text_label = \"<span font_family ='Arial' \" + \\\n            \"size = '25000' weight = 'bold' > \" + \\\n            self.label.title()[0] + \"</span>\"\n\n        icon = GooCanvas.CanvasText(parent=self,\n                                    text=text_label,\n                                    fill_color='white',\n                                    anchor=GooCanvas.CanvasAnchorType.CENTER,\n                                    x=(self.width / 2),\n                                    y=(self.height / 2),\n                                    use_markup=True,\n                                    stroke_color='black',\n                                    tooltip=self.label\n                                    )\n\n        self.widgets[\"Icon\"] = icon\n\n    # ----------------------------------------------------------------------\n    def __draw_label(self):\n        \"\"\"\n        This method draw the label.\n\n        \"\"\"\n        text_label = \"<span font_family ='Arial' \" + \\\n            \"size = '10000' weight = 'normal'> \" + \\\n            self.label + \"</span>\"\n\n        label = GooCanvas.CanvasText(parent=self,\n                                     text=text_label,\n                                     fill_color='black',\n                                     anchor=GooCanvas.CanvasAnchorType.CENTER,\n                                     x=(self.width / 2),\n                                     y=0,\n                                     use_markup=True,\n                                     stroke_color='black'\n                                     )\n        self.widgets[\"Label\"] = label\n\n    # ----------------------------------------------------------------------\n    def __create_ports_label(self, port):\n        text_name = \"<span font_family ='Arial' size = '7000' weight = 'ultralight'>\" + \\\n            \"<span color = '\" + port.color + \"'>\"\n        if self.is_collapsed:\n            text_name += \" - \"\n        else:\n            text_name += \"{\" + port.hint + \"}\"\n        text_name += \"</span></span>\"\n        return text_name\n\n    # ----------------------------------------------------------------------\n    def __draw_ports(self):\n        \"\"\"        for port in self.ports:\n\n        This method draws the ports.\n        \"\"\"\n        for port in self.ports:\n            text_name = self.__create_ports_label(port)\n            x,y = self.__get_port_pos(port)\n            if port.is_input():\n                alignment = Pango.Alignment.LEFT\n                anchor=GooCanvas.CanvasAnchorType.WEST\n                press_event = self.__on_input_press\n                release_event = self.__on_input_release\n            else:\n                alignment = Pango.Alignment.RIGHT\n                anchor = GooCanvas.CanvasAnchorType.EAST\n                press_event = self.__on_output_press\n                release_event = self.__on_output_release\n\n            text = GooCanvas.CanvasText(parent=self,\n                                 text=text_name,\n                                 fill_color='black',\n                                 anchor=anchor,\n                                 alignment=alignment,\n                                 x=x,\n                                 y=y,\n                                 use_markup=True,\n                                 tooltip=port.label\n                                 )\n            text.connect(\"button-press-event\", press_event , port)\n            text.connect(\"button-release-event\", release_event, port)\n            self.widgets[\"port\" + str(port)] = text\n\n    # ----------------------------------------------------------------------\n    def __on_input_press(self, canvas_item, target_item, event, port):\n        \"\"\"\n        This method return true if a input was connected.\n\n            Parameters:\n                * **canvas_item**\n                * **target_item**\n                * **event**\n            Returns:\n                * **Types** (:class:`boolean<boolean>`): Indicates the input as connected.\n        \"\"\"\n        self.diagram.end_connection(self, port)\n        return True\n\n    # ----------------------------------------------------------------------\n    def __on_input_release(self, canvas_item, target_item, event, args):\n        \"\"\"\n        This method monitors the input release.\n\n            Parameters:\n                * **canvas_item**\n                * **target_item**\n                * **event **\n            Return:\n                * **Types** (:class:`boolean<boolean>`)\n        \"\"\"\n        return True\n\n    # ----------------------------------------------------------------------\n    def __on_output_press(self, canvas_item, target_item, event, port):\n        \"\"\"\n        This method monitors the output state, monitors if output was pressed.\n\n            Parameters:\n                canvas_item\n                target_item\n                event\n                args\n            Returns:\n                * **Types** (:class:`boolean<boolean>`)\n        \"\"\"\n        self.diagram.start_connection(self, port)\n        return True\n\n    # ----------------------------------------------------------------------\n    def __on_output_release(self, canvas_item, target_item, event, args):\n        \"\"\"\n        This method monitors the output state, monitors if output was release.\n\n            Returns:\n                * **Types** (:class:`boolean<boolean>`)\n        \"\"\"\n        return True\n\n    # ----------------------------------------------------------------------\n    def __get_port_pos(self, port):\n\n        if self.is_collapsed:\n            y = 16 + (port.type_index * 6)\n        else:\n            y = 26 + (port.type_index * 11)\n\n        if port.is_input():\n            x = 0\n        else:\n            x = self.width\n\n        if not self.is_collapsed:\n            return (x, y)\n\n        if port.is_input():\n            return (x + 36, y - 8)\n        else:\n            return (x - 25, y - 8)\n\n    # ----------------------------------------------------------------------\n    def get_port_pos(self, port):\n        \"\"\"\n        This method get input position.\n\n            Parameters:\n                * **input_id**\n            Returns:\n                * **Types** (:class:`float<float>`)\n        \"\"\"\n        x, y = self.get_position()\n        x2, y2 = self.__get_port_pos(port)\n        return x + x2, y + y2 + 1\n\n    # ----------------------------------------------------------------------\n    def __calculate_height(self):\n        if self.is_collapsed:\n            return max(((self.maxIO - 1) * 5) + (self.maxIO * 4), 40)\n        else:\n            return max(((self.maxIO) * 5) + 15 + (self.maxIO * 7), 50)\n\n    # ----------------------------------------------------------------------\n    def move(self, x, y):\n        \"\"\"\n        This method move a block.\n\n            Parameters:\n                * **(x,y)** (:class:`float<float>`)\n            Returns:\n                * **Types** (:class:`float<float>`)\n        \"\"\"\n        new_x = x - (x % System.get_preferences().grid)\n        new_y = y - (y % System.get_preferences().grid)\n        self.translate(new_x, new_y)\n\n    # ----------------------------------------------------------------------\n    def adjust_position(self):\n        position = self.get_position()\n        grid = System.get_preferences().grid\n        new_x = position[0] - position[0] % grid\n        new_y = position[1] - position[1] % grid\n        self.translate(new_x - position[0], new_y - position[1])\n\n    # ----------------------------------------------------------------------\n    def get_position(self):\n        \"\"\"\n        This method get position the block.\n\n             Returns:\n                * **Types** (:class:`float<float>`)\n        \"\"\"\n        isSet, x, y, scale, rotation = self.get_simple_transform()\n        return x, y\n\n    # ----------------------------------------------------------------------\n    def set_properties(self, data):\n        \"\"\"\n        This method set properties of each block.\n\n            Parameters:\n                * **data**\n        \"\"\"\n        BlockModel.set_properties(self, data)\n\n    # ----------------------------------------------------------------------\n    def get_properties(self):\n        \"\"\"\n        This method get properties of each block.\n\n            Returns:\n                * **Types** ()\n        \"\"\"\n        return BlockModel.get_properties(self)\n\n    # ----------------------------------------------------------------------\n    def update_flow(self):\n        \"\"\"\n        This method update flow.\n\n            Returns:\n                * **Types** (:class:`boolean<boolean>`)\n        \"\"\"\n        self.has_flow = True\n        distinct_con = []\n        for conn in self.diagram.connectors:\n            if conn.input != self:\n                continue\n            if conn.input_port not in distinct_con:\n                distinct_con.append(conn.input_port)\n        in_count = 0\n        for port in self.ports:\n            if port.is_input():\n                in_count += 1\n        if len(distinct_con) < in_count:\n            self.has_flow = False\n        self.__update_state()\n        return self.has_flow\n\n    # ----------------------------------------------------------------------\n    def __update_state(self):\n        \"\"\"\n        This method update the Line state.\n        \"\"\"\n        # Not connected: Color = red\n        if self.has_flow:\n            self.widgets[\"Rect\"].set_property(\"stroke_color\", 'black')\n        else:\n            self.widgets[\"Rect\"].set_property(\"stroke_color\", 'red')\n\n        # in focus: Line width = 3\n        if self.focus:\n            self.widgets[\"Rect\"].set_property(\"line-width\", 3)\n        else:\n            self.widgets[\"Rect\"].set_property(\"line-width\", 1)\n\n        # selected: Line = dashed\n        if self.is_selected:\n            self.widgets[\"Rect\"].set_property(\n                \"line_dash\", GooCanvas.CanvasLineDash.newv((4.0, 2.0)))\n        else:\n            self.widgets[\"Rect\"].set_property(\n                \"line_dash\", GooCanvas.CanvasLineDash.newv((10.0, 0.0)))\n\n        self.height = self.__calculate_height()\n\n        if self.is_collapsed:\n            self.widgets[\"Label\"].set_property(\"visibility\", GooCanvas.CanvasItemVisibility.INVISIBLE)\n            self.widgets[\"Rect\"].set_property(\"width\", self.width - 60)\n            self.widgets[\"Rect\"].set_property(\"x\", 35)\n            self.widgets[\"Rect\"].set_property(\"y\", 0)\n            self.widgets[\"Rect\"].set_property(\"height\", self.height - 10)\n            self.widgets[\"Icon\"].set_property(\"y\", (self.height - 10)/2)\n            self.widgets[\"Icon\"].set_property(\"x\", (self.width / 2) + 2)\n            for port in self.ports:\n                x,y = self.__get_port_pos(port)\n                if \"port\" + str(port) in self.widgets:\n                    self.widgets[\"port\" + str(port)].set_property(\"x\", x)\n                    self.widgets[\"port\" + str(port)].set_property(\"y\", y)\n                    self.widgets[\"port\" + str(port)].set_property(\"text\", self.__create_ports_label(port))\n            return True\n\n        if not self.is_collapsed:\n            self.widgets[\"Label\"].set_property(\"visibility\", GooCanvas.CanvasItemVisibility.VISIBLE)\n            self.widgets[\"Rect\"].set_property(\"width\", self.width)\n            self.widgets[\"Rect\"].set_property(\"x\", 0)\n            self.widgets[\"Rect\"].set_property(\"y\", 10)\n            self.widgets[\"Rect\"].set_property(\"height\", self.height)\n            self.widgets[\"Icon\"].set_property(\"y\", (self.height + 20)/2)\n            self.widgets[\"Icon\"].set_property(\"x\", (self.width / 2))\n            for port in self.ports:\n                x,y = self.__get_port_pos(port)\n                if \"port\" + str(port) in self.widgets:\n                    self.widgets[\"port\" + str(port)].set_property(\"x\", x)\n                    self.widgets[\"port\" + str(port)].set_property(\"y\", y)\n                    self.widgets[\"port\" + str(port)].set_property(\"text\", self.__create_ports_label(port))\n\n# ----------------------------------------------------------------------\n","repo_name":"Alice-ArtsLab/mosaicode","sub_path":"mosaicode/GUI/block.py","file_name":"block.py","file_ext":"py","file_size_in_byte":17247,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"81"}
+{"seq_id":"41690768114","text":"\"\"\"Create schema report\n\nRevision ID: 9d445cb42847\nRevises: 7ed350929292\nCreate Date: 2020-09-17 15:21:04.532792\n\n\"\"\"\nfrom alembic import op\nfrom sqlalchemy.sql import text\nimport sqlalchemy as sa\n\n\n# revision identifiers, used by Alembic.\nrevision = '9d445cb42847'\ndown_revision = '7ed350929292'\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n    connection = op.get_bind()\n    connection.execute(sa.schema.CreateSchema('report'))\n\n\ndef downgrade():\n    connection = op.get_bind()\n    connection.execute(text(\"DROP SCHEMA report\"))\n","repo_name":"ALMPartners/ahjo","sub_path":"test/samples/mssql_project/alembic/versions/20200917_15_21_04_9d445cb42847_create_schema_report.py","file_name":"20200917_15_21_04_9d445cb42847_create_schema_report.py","file_ext":"py","file_size_in_byte":542,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"81"}
+{"seq_id":"5963073131","text":"import sys\nimport os\nfrom subprocess import *\nfrom threading import Thread\n\nif(sys.hexversion < 0x03000000):\n\timport Queue\nelse:\n\timport queue as Queue\n\n#global parameters\nfold = 5\nc_begin, c_end, c_step = -5,  15, 2\ng_begin, g_end, g_step =  3, -15, -2\nnr_local_worker = 1\n\nclass WorkerStopToken:  # used to notify the worker to stop\n        pass\n\nclass Worker(Thread):\n    def __init__(self,name,job_queue,result_queue, svmtrain_exe, dataset_pathname):\n        Thread.__init__(self)\n        self.name = name\n        self.job_queue = job_queue\n        self.result_queue = result_queue\n        self.svmtrain_exe = svmtrain_exe\n        self.dataset_pathname = dataset_pathname\n        \n    def run(self):\n        while True:\n            (cexp,gexp) = self.job_queue.get()\n            if cexp is WorkerStopToken:\n                self.job_queue.put((cexp,gexp))\n                # print('worker {0} stop.'.format(self.name))\n                break\n            try:\n                rate = self.run_one(2.0**cexp,2.0**gexp)\n                if rate is None: raise RuntimeError(\"get no rate\")\n            except:\n                # we failed, let others do that and we just quit\n                self.job_queue.put((cexp,gexp))\n                print('worker {0} quit.'.format(self.name))\n                break\n            else:\n                self.result_queue.put((self.name,cexp,gexp,rate))\n\nclass LocalWorker(Worker):\n    def run_one(self,c,g):\n        cmdline = '{0} -c {1} -g {2} -v {3} {4} {5}'.format \\\n          (self.svmtrain_exe,c,g,fold, ' ' , self.dataset_pathname)\n        result = Popen(cmdline,shell=True,stdout=PIPE).stdout\n        for line in result.readlines():\n            if str(line).find(\"Cross\") != -1:\n                return float(line.split()[-1][0:-1])\n\ndef calculate_jobs():\n    c_seq = permute_sequence(range_f(c_begin,c_end,c_step))\n    g_seq = permute_sequence(range_f(g_begin,g_end,g_step))\n    nr_c = float(len(c_seq))\n    nr_g = float(len(g_seq))\n    i = 0\n    j = 0\n    jobs = []\n\n    while i < nr_c or j < nr_g:\n        if i/nr_c < j/nr_g:\n            # increase C resolution\n            line = []\n            for k in range(0,j):\n                line.append((c_seq[i],g_seq[k]))\n            i = i + 1\n            jobs.append(line)\n        else:\n            # increase g resolution\n            line = []\n            for k in range(0,i):\n                line.append((c_seq[k],g_seq[j]))\n            j = j + 1\n            jobs.append(line)\n    return jobs\n\ndef range_f(begin,end,step):\n    # like range, but works on non-integer too\n    seq = []\n    while True:\n        if step > 0 and begin > end: break\n        if step < 0 and begin < end: break\n        seq.append(begin)\n        begin = begin + step\n    return seq\n\ndef permute_sequence(seq):\n    n = len(seq)\n    if n <= 1: return seq\n\n    mid = int(n/2)\n    left = permute_sequence(seq[:mid])\n    right = permute_sequence(seq[mid+1:])\n\n    ret = [seq[mid]]\n    while left or right:\n        if left: ret.append(left.pop(0))\n        if right: ret.append(right.pop(0))\n\n    return ret\n\nclass SvmTools(object):\n    def __init__(self, svm_bin_path):\n        # svm, grid, and gnuplot executable files\n        is_win32 = (sys.platform == 'win32')\n\n        if is_win32:\n            self.svmscale_exe = os.path.join(svm_bin_path, \"svm-scale.exe\")\n            self.svmtrain_exe = os.path.join(svm_bin_path, \"svm-train.exe\")\n            self.svmpredict_exe = os.path.join(svm_bin_path,\"svm-predict.exe\")\n        else:\n            self.svmscale_exe = os.path.join(svm_bin_path, \"svm-scale\")\n            self.svmtrain_exe = os.path.join(svm_bin_path, \"svm-train\")\n            self.svmpredict_exe = os.path.join(svm_bin_path,\"svm-predict\")\n\n        assert os.path.exists(self.svmscale_exe),\"svm-scale executable not found:%s\" % self.svmscale_exe\n        assert os.path.exists(self.svmtrain_exe),\"svm-train executable not found:%s\" % self.svmtrain_exe\n        assert os.path.exists(self.svmpredict_exe),\"svm-predict executable not found:%s\" % self.svmpredict_exe\n\n    def gen_model(self, train_pathname, model_file, range_file, tmp_path, lower_limit = -1):\n        assert os.path.exists(train_pathname),\"training file not found\"\n        file_name = os.path.split(train_pathname)[1]\n        scaled_file = os.path.join(tmp_path, file_name + \".scale\")\n\n        cmd = '{0} -l {4} -s \"{1}\" \"{2}\" > \"{3}\"'.format(self.svmscale_exe, range_file, train_pathname, scaled_file, lower_limit)\n        print('Scaling training data...')\n        Popen(cmd, shell = True, stdout = PIPE).communicate()\t\n\n        print('Cross Validation data...')\n        c,g,rate = self.cross_validation_model(scaled_file)\n\n        print('Best c={0}, g={1} CV rate={2}'.format(c,g,rate))\n\n        cmd = '{0} -c {1} -g {2} \"{3}\" \"{4}\"'.format(self.svmtrain_exe,c,g,scaled_file,model_file)\n        print('Training...')\n        Popen(cmd, shell = True, stdout = PIPE).communicate()\n\n        print('Output model: {0}'.format(model_file))\n\n    def cross_validation_model(self, scale_file):\n        # put jobs in queue\n\n        jobs = calculate_jobs()\n        job_queue = Queue.Queue(0)\n        result_queue = Queue.Queue(0)\n\n        for line in jobs:\n            for (c,g) in line:\n                job_queue.put((c,g))\n\n        # hack the queue to become a stack --\n        # this is important when some thread\n        # failed and re-put a job. It we still\n        # use FIFO, the job will be put\n        # into the end of the queue, and the graph\n        # will only be updated in the end\n        job_queue._put = job_queue.queue.appendleft\n        \n        # fire local workers\n        for i in range(nr_local_worker):\n            LocalWorker('local',job_queue,result_queue, self.svmtrain_exe, scale_file).start()\n\n        # gather results\n        done_jobs = {}\n\n        best_rate = -1\n        best_c1,best_g1 = None,None\n\n        for line in jobs:\n            for (c,g) in line:\n                while (c, g) not in done_jobs:\n                    (worker,c1,g1,rate) = result_queue.get()\n                    done_jobs[(c1,g1)] = rate\n\n                    if (rate > best_rate) or (rate==best_rate and g1==best_g1 and c1<best_c1):\n                        best_rate = rate\n                        best_c1,best_g1=c1,g1\n                        best_c = 2.0**c1\n                        best_g = 2.0**g1\n                #end while\n            #end for (c,g)\n        #end for line\n\n        job_queue.put((WorkerStopToken,None))\n        return (best_c, best_g, best_rate)\n        \n    def gen_predict(self, predict_data_file_name, model_file, range_file, tmp_path, lower_limit = -1):\n        file_name = os.path.split(predict_data_file_name)[1]\n        scaled_test_file = os.path.join(tmp_path, file_name + \".scale\")\n        predict_test_file = os.path.join(tmp_path, file_name + \".predict_test\")\n        \n        cmd = '{0} -l {4} -r \"{1}\" \"{2}\" > \"{3}\"'.format(self.svmscale_exe, range_file, predict_data_file_name,\n                                                  scaled_test_file, lower_limit)\n        print('Scaling testing data...')\n        Popen(cmd, shell = True, stdout = PIPE).communicate()\t\n\n        cmd = '{0} \"{1}\" \"{2}\" \"{3}\" && cat \"{3}\"'.format(self.svmpredict_exe, scaled_test_file, model_file, predict_test_file)\n        print('Testing...')\n\n        result = Popen(cmd, shell = True, stdout= PIPE).stdout\n\n        level = 0\n\n        for line in result.readlines():\n            try:\n                print(line)\n                level = int(line)\n            except:\n                pass\n        return level\n\n","repo_name":"stonewell/learn-curve","sub_path":"modules/tools/svm_tools.py","file_name":"svm_tools.py","file_ext":"py","file_size_in_byte":7546,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"19949714592","text":"name = \"Michael\"\n\n# print(type(name))\n# print(name[1])\n\n# String slicing uses list notation to select items from a string based on their index\n# Eg: string[start_index: end_index]\nword = \"pineapple\"\nprint(word[:-5])\n\n# reorder word in reverse order\nreverse_word = word[::-1]\n#print(reverse_word)\n\n# upper and lower case letters\nupper_word = word.upper() # Upper case letters\nlower_word = word.lower() # Lower case letters\ninitial_caps = word.capitalize() # Make the first letter capital\n\ncapitalise_last = word[:-1] + word[-1].capitalize()\n#print(capitalise_last)\n\n# split words and make changes and join them again\nword_split = list(word)\ncapitalise_last_word = (\"\").join(word_split[:7]) + word_split[-2].capitalize() + word[8]\nprint(capitalise_last_word)\n# Print each letter in word separated by space\nspaced_word = \" \".join(word)\nprint(spaced_word)\n\nuser_detail = \"Michael,Okolo,30,Gaming\" # CSV (Comma separated Values)\nspit_user_detail = user_detail.split(\",\")\n#print(spit_user_detail)\nprint(\" \".join(spit_user_detail))\n\n# String Operations - Interpolation\nname = \"Michael\"\n\n# print(\"Hello! \" + name + \" Welcome to Programming\")\n# print(\"Hello! {} Welcome to Programming\".format(name))\n# print(f\"Hello! {name} Welcome to Programming\")","repo_name":"mikosco4real/python101","sub_path":"lesson02/strings.py","file_name":"strings.py","file_ext":"py","file_size_in_byte":1239,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"33937322023","text":"from unittest import TestCase\nfrom expects import expect, equal, raise_error \n\nfrom slender import Set \n\nclass TestOr(TestCase):\n\n    def test_or_other_with_common_values(self):\n        e = Set[int]({1, 2, 3, 4})\n        o = Set[int]({1, 2, 3, 4})\n        expect(e | o).to(equal(Set[int]({1, 2, 3, 4})))\n\n    def test_or_other_without_common_values(self):\n        e = Set[int]({1, 2, 3})\n        o = Set[int]({4, 5, 6})\n        expect(e | o).to(equal(Set[int]({1, 2, 3, 4, 5, 6})))\n\n    def test_or_other_is_different(self):\n        e = Set[int]({1, 2, 3})\n        expect(lambda: e | None).to(raise_error(TypeError))\n\n\n\n","repo_name":"torokmark/slender","sub_path":"slender/tests/set/test_or.py","file_name":"test_or.py","file_ext":"py","file_size_in_byte":620,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"81"}
+{"seq_id":"38597666328","text":"from past.builtins import basestring\n\nimport pytis\nimport pytis.util\nimport pytis.data as pd\nfrom pytis.api import app\nfrom pytis.util import ProgramError, translations, xtuple\n\n_ = translations('pytis-wx')\n\n\ndef dbselect(spec, condition=None, sort=(), transaction=None, arguments={}):\n    \"\"\"Vrať řádky dané db tabulky jako sekvenci.\n\n    Argumenty:\n\n      spec -- název specifikace datového objektu nebo přímo instance třídy\n        'pd.DataFactory'\n      condition, sort, transaction -- argumenty volání\n        'pd.postgresql.select()'.\n\n    Vrací všechny řádky vrácené z databáze jako list.\n\n    \"\"\"\n    data = pytis.util.data_object(spec)\n    data.select(condition=condition, sort=sort, transaction=transaction,\n                arguments=arguments)\n    result = []\n    while True:\n        row = data.fetchone()\n        if row is None:\n            data.close()\n            break\n        result.append(row)\n    return result\n\n\ndef dbinsert(spec, row, transaction=None):\n    \"\"\"Provede insert do tabulky dané specifikací.\n\n    Argumenty:\n\n      spec -- název specifikace datového objektu nad kterým má být proveden\n        insert.\n      row -- sekvence dvouprvkových sekvencí (id, value) nebo instance\n        pd.Row\n      transaction -- instance pd.DBTransactionDefault\n\n    Vrací počet vložených řádků.\n\n    \"\"\"\n    assert isinstance(row, (pd.Row, tuple, list)), row\n    import pytis.form\n    if isinstance(row, (tuple, list)):\n        for item in row:\n            if not isinstance(item, (tuple, list)) or len(item) != 2:\n                errmsg = 'Column definition must be (ID, VALUE) pair.'\n                raise ProgramError(errmsg)\n            k, v = item\n            if not isinstance(k, basestring):\n                errmsg = 'Invalid column id %s' % k\n                raise ProgramError(errmsg)\n            if not isinstance(v, pd.Value):\n                errmsg = 'Invalid column value %s' % v\n                raise ProgramError(errmsg)\n        row = pd.Row(row)\n    data = pytis.util.data_object(spec)\n    success, result = pytis.form.db_operation(data.insert, row, transaction=transaction)\n    return result\n\n\ndef dbupdate(row, values=(), transaction=None):\n    \"\"\"Provede update nad předaným řádkem.\n\n    Argumenty:\n\n      row -- předaná instance aktuálního PresentedRow\n      values -- sekvence dvouprvkových sekvencí ('id', value) ,\n        kde 'id' je řetězcový identifikátor políčka a value je\n        instance, kterou se bude políčko aktualizovat\n      transaction -- instance pd.DBTransactionDefault\n\n    \"\"\"\n    import pytis.form\n    data = row.data()\n    updaterow = row.row()\n    key = data.key()\n    if isinstance(key, (tuple, list)):\n        key = key[0]\n    for col, val in values:\n        updaterow[col] = val\n    return pytis.form.db_operation(data.update, row[key.id()], updaterow, transaction=transaction)\n\n\n# Alias\nrow_update = dbupdate\n\n\ndef dbupdate_many(spec, condition=None, update_row=None,\n                  transaction=None):\n    \"\"\"Provede update nad tabulkou danou specifikací.\n\n    Argumenty:\n\n      spec -- specifikace datového objektu nad kterým má být proveden\n        select; string'\n      condition -- podmínka updatovaní.\n      update_row -- řádek kterým se provede update,\n      transaction -- instance pd.DBTransactionDefault\n\n    Vrací počet updatovaných řádků.\n\n    \"\"\"\n    if not isinstance(condition, pd.Operator):\n        errmsg = \"Nebyla předána podmínka pro update_many.\"\n        raise ProgramError(errmsg)\n    if not isinstance(update_row, pd.Row):\n        errmsg = \"Nebyl předán řádek pro update_many.\"\n        raise ProgramError(errmsg)\n    data = pytis.util.data_object(spec)\n    return data.update_many(condition, update_row, transaction=transaction)\n\n\ndef dbfunction(name, *args, **kwargs):\n    \"\"\"Deprecated.  Use pytis.data.dbfunction instead.\n\n    Nová funkce pytis.data.dbfunction má oproti této původní funkci několik\n    odlišností, které je potřeba zohlednit při přepisu starého kódu na použití\n    nové funkce.\n\n    1. Tato (původní) funkce vrací None pokud je hodnota některého z argumentů\n    None aniž by došlo ke skutečnému vyvolání DB funkce (čemuž lze zabránit\n    argumentem proceed_with_empty_values=False).  Nová funkce toto nedělá a\n    argument proceed_with_empty_values nemá.\n\n    2. Argumenty nové funkce se předávají vnitřní hodnotou a nikoliv jako\n    sekvence dvouprvkových tuplů, ale přímo jako argumenty funkce.\n\n    3. Funkci je možné předat jako DB specifikaci.  V tom případě je prováděna\n    typová kontrola argumentů a výsledek může být jak množina řádků (tabulkové\n    funkce), tak jednoduchá hodnota dle atributu multirow ve specifikaci.\n\n    4. pd.dbfunction nepoužívá obalení pomocí pf.db_operation(), takže to je\n    potřeba případně přidat explicitně, kde je to potřeba (většinou by to\n    potřeba být nemělo.).\n\n    \"\"\"\n    # TODO PY3: define keyword arguments in function definition.\n    import pytis.form\n    proceed_with_empty_values = kwargs.pop('proceed_with_empty_values', False)\n    transaction = kwargs.pop('transaction', None)\n    assert not kwargs\n    if not proceed_with_empty_values and args and any(v.value() in (None, '') for k, v in args):\n        return None\n\n    def conn_spec():\n        return pytis.config.dbconnection\n    success, function = pytis.form.db_operation(pd.DBFunctionDefault, name, conn_spec)\n    success, result = pytis.form.db_operation(function.call, pd.Row(args),\n                                              transaction=transaction)\n    if not success:\n        return None\n    if len(result) == 1 and len(result[0]) == 1:\n        return result[0][0].value()\n    return result\n\n\ndef enum(name, **kwargs):\n    \"\"\"Vytvoř instanci 'DataEnumerator' nad danou specifikací.\n\n    Takto vytvořený enumerátor lze použít jako argument 'enumerator'\n    konstruktoru datového typu.  Argument 'name' je řetězec určující název\n    specifikace, ze které bude získán datový objekt enumerátoru.\n\n    \"\"\"\n    data_spec = pytis.config.resolver.get(name, 'data_spec')\n    return pd.DataEnumerator(data_spec, **kwargs)\n\n\n# Pozor, stejná metoda metoda je definována i v pd.access\ndef is_in_groups(groups):\n    if isinstance(groups, basestring):\n        groups = xtuple(groups)\n\n    def conn_spec():\n        return pytis.config.dbconnection\n    dbgroups = pd.default_access_groups(conn_spec)\n    if set(groups) & set(dbgroups) == set([]):\n        return False\n    else:\n        return True\n\n\ndef safe_commit(transaction, msg=None):\n    \"\"\"Commit transaction and handle possible timeout errors.\n\n    Arguments:\n      transaction -- transaction to commit\n      msg -- message to show, when database error occurs\n\n    Returns True if commit was successful, otherwise returns False.\n\n    \"\"\"\n    import pytis.form\n    DEFAULT_MSG = _(\"The database connection was closed because of long inactivity.\")\n    try:\n        transaction.commit()\n        return True\n    except pd.DBSystemException:\n        app.error(msg or DEFAULT_MSG)\n        return False\n\n\ndef safe_rollback(transaction, msg=None):\n    \"\"\"Rollback transaction and handle possible timeout errors.\n\n    Arguments:\n      transaction -- transaction to commit\n      msg -- message to show, when database error occurs\n\n    Returns True if rollback was successful, otherwise return False.\n    \"\"\"\n    import pytis.form\n    DEFAULT_MSG = _(\"The database connection was closed because of long inactivity.\")\n    try:\n        transaction.rollback()\n        return True\n    except pd.DBSystemException:\n        app.error(msg or DEFAULT_MSG)\n        return False\n","repo_name":"cerha/pytis","sub_path":"lib/pytis/extensions/dbutils.py","file_name":"dbutils.py","file_ext":"py","file_size_in_byte":7704,"program_lang":"python","lang":"cs","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"72265756106","text":"import numpy as np\nimport cv2\nimport pdb\nimport glob\nimport os\nfrom part_fintune import adpThreshold_im, otsu_threshold_\nfrom find_nearest import find_nearest_im\nfrom find_max_grad import find_grad_im\nz=122\ncc=99\ns=115\nx=120\nq=113\nw=119\ne=101\nr=114\np=112\no=111\n\ndef read_line(line):\n    line_s = line.split(' ')\n    print(line_s)\n    lx, ly, rx, ry = int(float(line_s[1])), int(float(line_s[2])), int(float(line_s[3])), int(float(line_s[4].split('\\n')[0]))\n    w = rx - lx\n    h = ry - ly\n    cx=(lx+rx)/2\n    cy=(ly+ry)/2\n\n    ellipse_info = ((cx, cy), (w, h), 0)\n   \n    return ellipse_info\n\ndef get_rect(ellipse_info):\n\n    cx = ellipse_info[0][0] \n    cy = ellipse_info[0][1] \n    w = ellipse_info[1][0] \n    h = ellipse_info[1][1] \n    \n    lx = int(cx - w/2)\n    ly = int(cy - h/2)\n    rx = int(cx + w/2)\n    ry = int(cy + h/2)\n\n    return lx, ly, rx, ry\n\ndef update_ellip(x_off, y_off, w_off, h_off, ellipse_info):\n    \n    cx = ellipse_info[0][0] + x_off\n    cy = ellipse_info[0][1] + y_off\n    w = ellipse_info[1][0] + w_off\n    h = ellipse_info[1][1] + h_off\n\n    ellipse_info = ((cx, cy), (w, h), 0)\n   \n    return ellipse_info\n\n\ndef main():\n    \n    root_file = 's6'\n    root_dir = '%s/Image' % root_file\n    txt_root_dir = root_file\n    #re_root_dir = '%s_re_grad_sqrt' % root_file\n\n    f_txt = open(os.path.join(txt_root_dir, 'groundtruth_.txt'))\n    save_path =  os.path.join(txt_root_dir, 'groundtruth_correct.txt')\n    if not os.path.exists(save_path):\n        with open(save_path, 'w') as f:\n            pass\n    #correct_txt = open(os.path.join(txt_root_dir, 'groundtruth_correct_test_.txt'), 'w')\n    \n    #if not os.path.exists(re_root_dir):\n        #os.makedirs(re_root_dir)\n\n    for line in f_txt:\n\n        f = line.split(' ')[0]\n        filename = os.path.splitext(f.split(\"/\")[-1])[0]\n        f = os.path.join(root_dir, '%s.bmp' % filename)\n        im = cv2.imread(f)\n        ellipse_info = read_line(line)\n        \n        c=0\n        c = cv2.waitKey(0)\n        \n        while c != p and c != o:\n\n            show = im.copy()            \n            x_off = y_off = w_off = h_off = 0\n            \n            if c == z:\n                x_off = -1\n            elif c == cc:\n                x_off = 1\n            elif c == s:\n                y_off = -1\n            elif c == x:\n                y_off = 1\n            elif c == q:\n                w_off = 1\n            elif c == w:\n                w_off = -1\n            elif c == e:\n                h_off = 1\n            elif c == r:\n                h_off = -1\n            \n            ellipse_info = update_ellip(x_off, y_off, w_off, h_off, ellipse_info)\n            cv2.ellipse(show, ellipse_info, (0,255,0), 1)\n\n            cv2.imshow(filename, show)\n            #pdb.set_trace()\n            c=cv2.waitKey(0)\n            #print(c)\n            \n\n        if c == p:\n            lx, ly, rx, ry = get_rect(ellipse_info)\n            new_line =  '%s %d %d %d %d %d\\n' % (f, lx, ly, rx, ry, 0)\n            with open(save_path, 'a') as correct_txt:\n                correct_txt.write(new_line)\n            cv2.destroyWindow(filename)\n        if c == o:\n            break\n    #correct_txt.close()\n    f_txt.close()\n            \n\nif __name__ == '__main__':\n    main()\n","repo_name":"gepu0221/FCN","sub_path":"experiment/finetune_label/correct_label.py","file_name":"correct_label.py","file_ext":"py","file_size_in_byte":3233,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"39089008102","text":"import unittest2\n\n\nclass TestIterator(unittest2.TestCase):\n\n    def _getTargetClass(self):\n        from gcloud.storage.iterator import Iterator\n        return Iterator\n\n    def _makeOne(self, *args, **kw):\n        return self._getTargetClass()(*args, **kw)\n\n    def test_ctor(self):\n        connection = _Connection()\n        PATH = '/foo'\n        iterator = self._makeOne(connection, PATH)\n        self.assertTrue(iterator.connection is connection)\n        self.assertEqual(iterator.path, PATH)\n        self.assertEqual(iterator.page_number, 0)\n        self.assertEqual(iterator.next_page_token, None)\n\n    def test___iter__(self):\n        PATH = '/foo'\n        KEY1 = 'key1'\n        KEY2 = 'key2'\n        ITEM1, ITEM2 = object(), object()\n        ITEMS = {KEY1: ITEM1, KEY2: ITEM2}\n\n        def _get_items(response):\n            for item in response.get('items', []):\n                yield ITEMS[item['name']]\n        connection = _Connection({'items': [{'name': KEY1}, {'name': KEY2}]})\n        iterator = self._makeOne(connection, PATH)\n        iterator.get_items_from_response = _get_items\n        self.assertEqual(list(iterator), [ITEM1, ITEM2])\n        kw, = connection._requested\n        self.assertEqual(kw['method'], 'GET')\n        self.assertEqual(kw['path'], PATH)\n        self.assertEqual(kw['query_params'], {})\n\n    def test_has_next_page_new(self):\n        connection = _Connection()\n        PATH = '/foo'\n        iterator = self._makeOne(connection, PATH)\n        self.assertTrue(iterator.has_next_page())\n\n    def test_has_next_page_w_number_no_token(self):\n        connection = _Connection()\n        PATH = '/foo'\n        iterator = self._makeOne(connection, PATH)\n        iterator.page_number = 1\n        self.assertFalse(iterator.has_next_page())\n\n    def test_has_next_page_w_number_w_token(self):\n        connection = _Connection()\n        PATH = '/foo'\n        TOKEN = 'token'\n        iterator = self._makeOne(connection, PATH)\n        iterator.page_number = 1\n        iterator.next_page_token = TOKEN\n        self.assertTrue(iterator.has_next_page())\n\n    def test_get_query_params_no_token(self):\n        connection = _Connection()\n        PATH = '/foo'\n        iterator = self._makeOne(connection, PATH)\n        self.assertEqual(iterator.get_query_params(), {})\n\n    def test_get_query_params_w_token(self):\n        connection = _Connection()\n        PATH = '/foo'\n        TOKEN = 'token'\n        iterator = self._makeOne(connection, PATH)\n        iterator.next_page_token = TOKEN\n        self.assertEqual(iterator.get_query_params(),\n                         {'pageToken': TOKEN})\n\n    def test_get_query_params_extra_params(self):\n        connection = _Connection()\n        PATH = '/foo'\n        extra_params = {'key': 'val'}\n        iterator = self._makeOne(connection, PATH, extra_params=extra_params)\n        self.assertEqual(iterator.get_query_params(), extra_params)\n\n    def test_get_query_params_w_token_and_extra_params(self):\n        connection = _Connection()\n        PATH = '/foo'\n        TOKEN = 'token'\n        extra_params = {'key': 'val'}\n        iterator = self._makeOne(connection, PATH, extra_params=extra_params)\n        iterator.next_page_token = TOKEN\n\n        expected_query = extra_params.copy()\n        expected_query.update({'pageToken': TOKEN})\n        self.assertEqual(iterator.get_query_params(), expected_query)\n\n    def test_get_query_params_w_token_collision(self):\n        connection = _Connection()\n        PATH = '/foo'\n        extra_params = {'pageToken': 'val'}\n        self.assertRaises(ValueError, self._makeOne, connection, PATH,\n                          extra_params=extra_params)\n\n    def test_get_next_page_response_new_no_token_in_response(self):\n        PATH = '/foo'\n        TOKEN = 'token'\n        KEY1 = 'key1'\n        KEY2 = 'key2'\n        connection = _Connection({'items': [{'name': KEY1}, {'name': KEY2}],\n                                  'nextPageToken': TOKEN})\n        iterator = self._makeOne(connection, PATH)\n        response = iterator.get_next_page_response()\n        self.assertEqual(response['items'], [{'name': KEY1}, {'name': KEY2}])\n        self.assertEqual(iterator.page_number, 1)\n        self.assertEqual(iterator.next_page_token, TOKEN)\n        kw, = connection._requested\n        self.assertEqual(kw['method'], 'GET')\n        self.assertEqual(kw['path'], PATH)\n        self.assertEqual(kw['query_params'], {})\n\n    def test_get_next_page_response_no_token(self):\n        connection = _Connection()\n        PATH = '/foo'\n        iterator = self._makeOne(connection, PATH)\n        iterator.page_number = 1\n        self.assertRaises(RuntimeError, iterator.get_next_page_response)\n\n    def test_reset(self):\n        connection = _Connection()\n        PATH = '/foo'\n        TOKEN = 'token'\n        iterator = self._makeOne(connection, PATH)\n        iterator.page_number = 1\n        iterator.next_page_token = TOKEN\n        iterator.reset()\n        self.assertEqual(iterator.page_number, 0)\n        self.assertEqual(iterator.next_page_token, None)\n\n    def test_get_items_from_response_raises_NotImplementedError(self):\n        PATH = '/foo'\n        connection = _Connection()\n        iterator = self._makeOne(connection, PATH)\n        self.assertRaises(NotImplementedError,\n                          iterator.get_items_from_response, object())\n\n\nclass _Connection(object):\n\n    def __init__(self, *responses):\n        self._responses = responses\n        self._requested = []\n\n    def api_request(self, **kw):\n        self._requested.append(kw)\n        response, self._responses = self._responses[0], self._responses[1:]\n        return response\n","repo_name":"dhermes/test-gcloud-on-gae","sub_path":"application/vendor/gcloud/storage/test_iterator.py","file_name":"test_iterator.py","file_ext":"py","file_size_in_byte":5639,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"81"}
+{"seq_id":"3490575766","text":"from __future__ import annotations\n\nimport asyncio\nimport collections\nimport itertools\nimport typing\nimport warnings\n\ntry:\n    from typing import Protocol\nexcept ImportError:\n    from typing_extensions import Protocol\n\nimport functools\n\ntry:\n    from functools import cached_property\nexcept ImportError:\n    from backports.cached_property import cached_property\n\nfrom . import helper\nfrom .type_vars import T_co, T, T_contra\n\n\n# pycharm is to stupid to understand this as of now\n# https://youtrack.jetbrains.com/issue/PY-29257\nclass fget_type(Protocol[T_contra]):\n    def __call__(self) -> T_contra:\n        \"\"\"\n        :class:`fget_type` callables need to have this signature\n        \"\"\"\n\n\nclass fset_type(Protocol[T_co]):\n    def __call__(self, value: T_co) -> None:\n        \"\"\"\n        :class:`fset_type` callables need to have this signature\n        \"\"\"\n\n\nclass accessor(typing.Generic[T]):\n    \"\"\"\n    An accessor provides easy shared access to a resource and\n\n    Example:\n\n        In the simplest case, an accessor synchronizes reads and writes out of the box ::\n\n            >>> import asyncio\n            >>> from codestare.async_utils import accessor\n            >>> foo = accessor()\n            >>> async def wait_for_write(accessor_):\n            ...     print(await accessor_.get())\n            ...\n            >>> background = asyncio.create_task(wait_for_write(foo))\n            >>> await foo.set(\"Bar\")\n            Bar\n\n        It's possible to use custom getters / setter e.g. create an :class:`accessor` to the value managed\n        by a normal property if one needs shared access as well ::\n\n            >>> class Thing:\n            ...     def __init__(self):\n            ...         self._value = None\n            ...     @property\n            ...     def value(self):\n            ...         return self._value\n            ...     @value._setter\n            ...     def value(self, val):\n            ...         if not val:\n            ...             raise ValueError(f\"Illegal value {val}\")\n            ...         self._value = val\n            ...\n            >>> thing = Thing()\n            >>> thing.value = 3\n            >>> thing.value\n            3\n            >>> thing.value = 0\n            ValueError: Illegal value 0\n            >>> class BetterThing(Thing):\n            ...     def __init__(self):\n            ...         super().__init__()\n            ...         self.value_accessor = accessor(funcs=(\n            ...             type(self).value.fget.__get__(self),\n            ...             type(self).value.fset.__get__(self)\n            ...         ))\n            ...\n            >>> better_thing = BetterThing()\n            >>> background = asyncio.create_task(wait_for_write(better_thing.value_accessor))\n            >>> await better_thing.value_accessor.set(3)\n            3\n            >>> better_thing.value\n            3\n            >>> await better_thing.value_accessor.set(0)\n            ValueError: Illegal value 0\n\n    See Also:\n        :class:`condition_property` -- decorator to create `accessor properties` more easily\n\n    \"\"\"\n    fget: 'fget_type[T]'\n    fset: 'fset_type[T]'\n\n    @typing.overload\n    def __init__(self: accessor[typing.Any],\n                 *,\n                 funcs: None = ...,\n                 condition: asyncio.Condition | None = None,\n                 name: str = None\n                 ):\n        ...\n\n    @typing.overload\n    def __init__(self: accessor[T],\n                 *,\n                 condition: asyncio.Condition | None = None,\n                 name: str = None\n                 ):\n        ...\n\n    @typing.overload\n    def __init__(self: accessor[T],\n                 *,\n                 funcs: typing.Tuple[fget_type[T], fset_type[T]] = ...,\n                 condition: asyncio.Condition | None = None,\n                 name: str = None\n                 ):\n        ...\n\n    def __init__(self,\n                 *,\n                 funcs: typing.Tuple | None = None,\n                 condition: asyncio.Condition | None = None,\n                 name: str = None\n                 ):\n        \"\"\"\n        Args:\n            funcs: getter and setter for some value -- `optional`, if not passed get / set a private field of the\n                object\n            condition: condition to synchronize access to the value -- `optional`, if not passed a new condition\n                is created\n        \"\"\"\n        if funcs is None:\n            def set(instance, value):\n                instance._value = value\n\n            def get(instance):\n                return instance._value\n\n            self._value = None\n            fget = get.__get__(self, type(self))\n            fset = set.__get__(self, type(self))\n        else:\n            non_callable = [f for f in funcs if not callable(f)]\n            if non_callable:\n                raise ValueError(f\"parameters {non_callable} passed as ``funcs`` tuple are not callable\")\n            fget, fset = funcs\n\n        self.fset = fset\n        \"\"\"\n        Setter, either passed via ``funcs`` argument, or a setter of an internal value if no ``funcs`` where passed\n        \"\"\"\n        self.fget = fget\n        \"\"\"\n        Getter, either passed via ``funcs`` argument, or a getter of an internal value if no ``funcs`` where passed\n        \"\"\"\n        self.condition: asyncio.Condition = condition or asyncio.Condition()\n        \"\"\"\n        Used to synchronized access, either passed via ``condition`` argument, or a new condition created specifically\n        for this accessor\n        \"\"\"\n        self.name = name\n        \"\"\"\n        For debug purposes\n        \"\"\"\n        self.has_waiting_read = helper.awaitable_predicate(predicate=lambda: self._waiter_count > 0)\n        \"\"\"\n        Use this awaitable if you want to wait for read access\n        \"\"\"\n        self._waiter_count = 0\n\n    @property\n    def value(self) -> T | None:\n        \"\"\"\n        Simple access to the value produced by :attr:`.fget` without async locks i.e. not safe if you did not\n        acquire the lock of :attr:`.condition`\n        \"\"\"\n        return self.fget()\n\n    async def set(self, value: T, wait_for_read=False) -> None:\n        \"\"\"\n        Sets the value (using :attr:`.fset`) and notifies every coroutine waiting on the :attr:`.condition` (e.g.\n        :meth:`.get`\n\n        Args:\n            value: new value passed to :attr:`.fset`\n            wait_for_read: if True, will set the value only after futures are waiting by using :attr:`.get`. Use this\n                to invert the semantics -- a write waiting for a read, instead of a read waiting for a write)\n\n        \"\"\"\n        if wait_for_read:\n            await self.has_waiting_read\n\n        async with self.condition:\n            self.fset(value)\n            self.condition.notify_all()\n\n    async def get(self,\n                  *,\n                  predicate: typing.Callable[[T], bool] | None = None,\n                  wait_for_write: bool | None = None) -> T:\n        \"\"\"\n        Shared access to value produced by :attr:`.fget`\n\n        Args:\n            predicate: waits for the predicate result to be truthy, then returns the result of :attr:`.fget`.\n                The default predicate (used when ``predicate=None``) returns ``[False, True, True, ...]``, so\n                :meth:`.get` blocks once, until it is notified from a :meth:`.set` and then does not block again.\n                Passing ``predicate=(lambda: True)`` will make :meth:`.get` not block at all.\n            wait_for_write: if set to ``True``, and a predicate is passed, the predicate will only be applied\n                once the default predicate (see above) also returns ``True`` i.e. you get the next value that matches\n                the predicate, even if the current value also matches. You can set this value to False, to ignore\n                the default predicate behaviour (which is the same as passing ``predicate=(lambda: True)`` and\n                using the default for this value. -- **optional**\n\n        Returns:\n            value produced by :attr:`.fget`\n\n        Raises:\n            ValueError: if ``predicate`` is not a callable\n\n        See Also:\n            :meth:`asyncio.Condition.wait_for` -- used to wait for internal condition\n        \"\"\"\n        if predicate is None and wait_for_write is None:\n            wait_for_write = True\n\n        # this predicate returns False, True i.e. it will block once and always return after notify\n        wait_predicate = (\n            itertools.chain.from_iterable([[False], itertools.repeat(True)]).__next__\n            if wait_for_write\n            else None\n        )\n\n        if predicate is not None and not callable(predicate):\n            raise ValueError(f\"{predicate} is not callable\")\n\n        def combined_predicate(acc: accessor):\n            use_value = True if not wait_predicate else wait_predicate()\n            matching_value = True if not predicate else predicate(acc.fget())\n            return use_value and matching_value\n\n        async with self.condition:\n            async with self.has_waiting_read.condition:\n                self._waiter_count += 1\n                self.has_waiting_read.condition.notify_all()\n\n            await self.condition.wait_for(combined_predicate.__get__(self, type(self)))\n\n            async with self.has_waiting_read.condition:\n                self._waiter_count -= 1\n\n            return self.fget()\n\n    def __repr__(self):\n        params = {param: getattr(self, param, None) for param in ['name', 'fget', 'fset']}\n        return (f\"<{self.__class__.__name__} object \"\n                f\"[{', '.join('{}={!r}'.format(name, value) for name, value in params.items())}]>\")\n\n\nclass condition_property(cached_property, typing.Generic[T]):\n    \"\"\"\n    This is a decorator to create a cached :class:`accessor` to handle access to some data via a\n    :class:`asyncio.Condition`.\n\n    You can use it like the normal `@property` decorator, but the result of the lookup (`__get__` of the descriptor)\n    will be an :class:`accessor` with coroutine attributes to handle safely setting and getting the value\n    (from the objects methods passed via ``setter`` and ``getter``, like in normal properties) by means of a condition.\n\n    See Also:\n        :class:`accessor` -- how to access the value\n    \"\"\"\n\n    def __init__(self: condition_property[T],\n                 fget: typing.Callable[[typing.Any], T] | None = None,\n                 fset: typing.Callable[[typing.Any, T], None] | None = None,\n                 fdel: typing.Callable[[typing.Any], None] | None = None,\n                 doc: str | None = None) -> None:\n        self.fget = fget\n        self.fset = fset\n        self.fdel = fdel\n        if doc is None and fget is not None:\n            doc = fget.__doc__\n        self.__doc__ = doc\n\n        super().__init__(self._create_accessor)\n\n    def _create_accessor(self: 'condition_property[T]', obj: object) -> accessor[T]:\n        return accessor(\n            funcs=(functools.partial(self._get, obj), functools.partial(self._set, obj),), name=self.attrname\n        )\n\n    def _set(self, obj: object, value: T):\n        if self.fset is None:\n            raise AttributeError(f\"can't set attribute {self.attrname}\")\n        self.fset(obj, value)\n\n    def _get(self, obj: object):\n        if self.fget is None:\n            raise AttributeError(f'unreadable attribute {self.attrname}')\n\n        if self.fset is None:\n            raise AttributeError(f\"`get` will block until the next value is set, but no setter is defined.\")\n\n        return self.fget(obj)\n\n    def __set__(self, obj, value: T):\n        raise AttributeError(f\"can't set {self.attrname} directly, use set()\")\n\n    @typing.overload\n    def __get__(self, instance: None, owner: typing.Type[typing.Any] | None = None) -> condition_property[T]:\n        ...\n\n    @typing.overload\n    def __get__(self, instance: object, owner: typing.Type[typing.Any] | None = None) -> accessor[T]:\n        ...\n\n    def __get__(self, instance: object | None,\n                owner: typing.Type[typing.Any] | None = None) -> accessor[T] | condition_property[T]:\n        if instance is None:\n            return typing.cast(condition_property[T], super().__get__(instance, owner))\n        else:\n            return typing.cast(accessor[T], super().__get__(instance, owner))\n\n    def getter(self: condition_property[T], fget: typing.Callable[[typing.Any], T]) -> condition_property[T]:\n        \"\"\"\n        This is a cached property but uses the same interface as a normal :obj:`property`.\n        See example of :obj:`property` documentation on how to use.\n        \"\"\"\n        prop = type(self)(fget, self.fset, self.fdel, self.__doc__)\n        prop.attrname = self.attrname\n        return prop\n\n    def setter(self: condition_property[T], fset: typing.Callable[[typing.Any, T], None]) -> condition_property[T]:\n        \"\"\"\n        This is a cached property but uses the same interface as a normal :obj:`property`.\n        See example of :obj:`property` documentation on how to use.\n        \"\"\"\n        prop = type(self)(self.fget, fset, self.fdel, self.__doc__)\n        prop.attrname = self.attrname\n        return prop\n\n    def deleter(self: condition_property[T], fdel) -> condition_property[T]:\n        \"\"\"\n        This is a cached property but uses the same interface as a normal :obj:`property`.\n        See example of :obj:`property` documentation on how to use.\n        \"\"\"\n        prop = type(self)(self.fget, self.fset, fdel, self.__doc__)\n        prop.attrname = self.attrname\n        return prop\n","repo_name":"saggitar/codestare-async-utils","sub_path":"src/codestare/async_utils/descriptor.py","file_name":"descriptor.py","file_ext":"py","file_size_in_byte":13518,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"22165263239","text":"import tkinter\nfrom random import randint\n\ncanvas = tkinter.Canvas(width=400, height = 400)\ncanvas.pack(fill='both', expand=True)\nwidth, height = canvas.winfo_width(), canvas.winfo_height()\npoints = list()\n\ndef priamka(event):\n    if len(points)>4:\n        return\n\n    points.append((event.x, event.y))\n    canvas.delete('ciary')\n\n    if len(points)//2:\n        for i in range(0, 1+len(points)//2, 2):\n            canvas.create_line(points[i], points[i+1], tags='ciary')\n            canvas.create_oval(points[i][0]+5, points[i][1]+5, points[i][0]-5, points[i][1]-5, tags='ciary')\n            canvas.create_oval(points[i+1][0]+5, points[i+1][1]+5, points[i+1][0]-5, points[i+1][1]-5, tags='ciary')\n\ncanvas.bind('<1>', priamka)\n\nwhile True:\n    try:\n        if width != canvas.winfo_width() or height != canvas.winfo_height():\n            width, height = canvas.winfo_width(), canvas.winfo_height()\n            canvas.delete('legenda')\n            points.clear()\n\n            size = ((width*height)>>15)+5\n            canvas.create_text(width/2, height-height//4, text='priesecnik - stlac <p>', font=f'Consolas {size}', tags='legenda')\n            canvas.create_text(width/2, height+size*2-height//4, text='vycistenie plochy - stlac <Delete>', font=f'Consolas {size}', tags='legenda')\n\n            canvas.update()\n\n        canvas.update()\n    \n    except: exit() if tkinter.TclError else None","repo_name":"incowhat/inf-4","sub_path":"regular informa/PR 7.3 Všeobecné rovnice priamok.py","file_name":"PR 7.3 Všeobecné rovnice priamok.py","file_ext":"py","file_size_in_byte":1390,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"16858030517","text":"import json\r\n\r\nfile_path=\"institute.txt\"\r\ntarget_path=\"./cleaned_data/institut_ranking.json\"\r\n\r\ndata=[]\r\nwith open(file_path, \"r\",encoding=\"utf-8\") as f:\r\n    lines=f.readlines()\r\n    for line in lines:\r\n        name=line[7:].split(\"closed\")[0].strip(\" \")\r\n        tmp=line.split(\"chart\")[-1].split(\"\\t\")\r\n        score=float(tmp[1])\r\n        faculty=int(tmp[2].strip(\"\\n\"))\r\n        print(name,score,faculty)\r\n        data.append({\"name\":name,\"score\":score,\"faculty\":faculty})\r\n        \r\n    with open(target_path,'w',encoding='utf8') as f2:\r\n        json.dump(data,f2,ensure_ascii=False,indent=2)","repo_name":"ZuhongLIU/INF473G","sub_path":"process_institute.py","file_name":"process_institute.py","file_ext":"py","file_size_in_byte":598,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"35655766572","text":"#!/usr/bin/python3\n\n\"\"\"\nModule: square\nContains the Square class.\n\"\"\"\nfrom models.rectangle import Rectangle\n\n\nclass Square(Rectangle):\n    \"\"\"\n    Square class that inherits from Rectangle.\n    \"\"\"\n\n    def __init__(self, size, x=0, y=0, id=None):\n        \"\"\"\n        Initializes a new instance of the Square class.\n        \"\"\"\n\n        super().__init__(size, size, x, y, id)\n\n    @property\n    def size(self):\n        \"\"\"\n        Getter for the size attribute.\n        \"\"\"\n\n        return self.width\n\n    @size.setter\n    def size(self, value):\n        \"\"\"\n        Setter for the size attribute.\n        \"\"\"\n\n        self.width = value\n        self.height = value\n\n    def update(self, *args, **kwargs):\n        \"\"\"\n        Assigns key/value arguments to the attributes of the\n        Square instance.\n        \"\"\"\n\n        if len(args) > 0:\n            self.id = args[0]\n\n        if len(args) > 1:\n            self.size = args[1]\n\n        if len(args) > 2:\n            self.x = args[2]\n\n        if len(args) > 3:\n            self.y = args[3]\n\n        if len(args) == 0 or len(kwargs) > 0:\n            for key, value in kwargs.items():\n                setattr(self, key, value)\n\n    def __str__(self):\n        \"\"\"\n        Returns a string representation of the Square instance.\n        \"\"\"\n\n        return (\"[Square] ({}) {}/{} - {}\"\n                .format(self.id, self.x, self.y, self.width))\n\n    def to_dictionary(self):\n        \"\"\"\n        Returns the dictionary representation of the Square instance.\n        \"\"\"\n\n        return {\n            'id': self.id,\n            'x': self.x,\n            'size': self.size,\n            'y': self.y\n        }\n","repo_name":"nashi-san/alx-higher_level_programming","sub_path":"0x0C-python-almost_a_circle/models/square.py","file_name":"square.py","file_ext":"py","file_size_in_byte":1656,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"4080188802","text":"from turtle import Turtle\n\nFONT = (\"Courier\", 24, \"normal\")\nALIGN = \"center\"\n\nclass Scoreboard(Turtle):\n    def __init__(self):\n        super().__init__()\n        self.level = 0\n        self.show_score()\n\n    def show_score(self):\n        self.hideturtle()\n        self.penup()\n        self.goto(-225, 255)\n        self.write(arg=f\" Level:{self.level} \", align=ALIGN, font=FONT)\n        self.clear()\n\n    def inc_level(self):\n        self.level += 1\n\n    def finish(self):\n        self.penup()\n        self.goto(0,0)\n        self.write(arg=\"GAME OVER\", align=ALIGN, font=FONT)\n\n\n\n","repo_name":"ayushajith/turtle-crossing","sub_path":"scoreboard.py","file_name":"scoreboard.py","file_ext":"py","file_size_in_byte":580,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"73106948106","text":"include ( 'MC15JobOptions/MadGraphControl_SimplifiedModelPreInclude.py' )\n\ngentype=runArgs.jobConfig[0].split('SM')[1].split('_')[1]\nif 'bb' in runArgs.jobConfig[0]: decaytype='onestepbbN2'\nelse: decaytype='onestepN2'\n\nmass_string = runArgs.jobConfig[0].replace('.py','').split('N2_')[1]\n\nif gentype=='GG':\n# Direct gluino decay to LSP (0-lepton, grid 1 last year)\n    masses['1000021'] = float( mass_string.split('_')[0] )\n    masses['1000022'] = float( mass_string.split('_')[2] )\n    masses['1000023'] = float( mass_string.split('_')[1] )\n    process = '''\n    generate p p > go go $ susysq susysq~ @1\n    add process p p > go go jb $ susysq susysq~ @2\n    '''\nelif gentype=='SS':\n    masses['1000001'] = float( mass_string.split('_')[0] )\n    masses['1000002'] = float( mass_string.split('_')[0] )\n    masses['1000003'] = float( mass_string.split('_')[0] )\n    masses['1000004'] = float( mass_string.split('_')[0] )\n    masses['2000001'] = float( mass_string.split('_')[0] )\n    masses['2000002'] = float( mass_string.split('_')[0] )\n    masses['2000003'] = float( mass_string.split('_')[0] )\n    masses['2000004'] = float( mass_string.split('_')[0] )\n    masses['1000022'] = float( mass_string.split('_')[2] )\n    masses['1000023'] = float( mass_string.split('_')[1] )\n    process = '''\n    generate p p > susysq susysq~ $ go @1\n    add process p p > susysq susysq~ jb $ go @2\n    '''\n\nelif gentype=='BB':\n    masses['1000005'] = float( mass_string.split('_')[0] )\n    masses['1000022'] = float( mass_string.split('_')[2] )\n    masses['1000023'] = float( mass_string.split('_')[1] )\n    process = '''\n    generate p p > b1 b1~ $ go susysq susysq~ @1\n    add process p p > b1 b1~ jb $ go susysq susysq~ @2\n    '''\n\n# This comes after all Simplified Model setup files\nevgenLog.info('Will use Pythia8...')\n\n#--------------------------------------------------------------\n# Algorithms Private Options\n#--------------------------------------------------------------\npythia = genSeq.Pythia8\n\n\n\nevgenConfig.contact  = [ \"emma.sian.kuwertz@cern.ch\" ]\nif 'GG' in gentype:\n    evgenConfig.keywords += ['simplifiedModel','gluino','Z']\n    if 'bb' in runArgs.jobConfig[0]:\n        evgenConfig.description = 'gluino production, glu->bb+~chi20, chi20->Z+LSP in simplified model, m_glu = %s GeV, m_N2 = %s GeV, m_N1 = %s GeV'%(masses['1000021'],masses['1000023'],masses['1000022'])\n    else:\n        evgenConfig.description = 'gluino production, glu->qq+~chi20, chi20->Z+LSP in simplified model, m_glu = %s GeV, m_N2 = %s GeV, m_N1 = %s GeV'%(masses['1000021'],masses['1000023'],masses['1000022'])\nelif 'SS' in gentype:\n    evgenConfig.keywords += ['simplifiedModel','squark','Z']\n    evgenConfig.description = 'squark production, sq->q+~chi20, chi20->Z+LSP in simplified model, m_sq = %s GeV, m_N2 = %s GeV, m_N1 = %s GeV'%(masses['1000001'],masses['1000023'],masses['1000022'])\nelif 'BB' in gentype:\n    evgenConfig.keywords += ['simplifiedModel','sbottom','Z']\n    evgenConfig.description = 'sbottom production, sb->b+~chi20, chi20->Z+LSP in simplified model, m_sbottom = %s GeV, m_N2 = %s GeV, m_N1 = %s GeV'%(masses['1000005'],masses['1000023'],masses['1000022'])\n\nnjets = 1\n# Two-lepton filter\nif '2L' in runArgs.jobConfig[0]:\n    evt_multiplier = 15\n    include('MC15JobOptions/MultiLeptonFilter.py')\n    MultiLeptonFilter = filtSeq.MultiLeptonFilter\n    MultiLeptonFilter.Ptcut = 8000.\n    MultiLeptonFilter.Etacut = 2.8\n    MultiLeptonFilter.NLeptons = 2\n\ninclude('MC15JobOptions/MadGraphControl_SimplifiedModelPostInclude.py')\n\nif gentype=='SS':\n    pythia.Commands += [\"Merging:Process = pp>{ul,1000002}{ul~,-1000002}{ur,2000002}{ur~,-2000002}{dl,1000001}{dl~,-1000001}{dr,2000001}{dr~,-2000001}{sl,1000003}{sl~,-1000003}{sr,2000003}{sr~,-2000003}{cl,1000004}{cl~,-1000004}{cr,2000004}{cr~,-2000004}\"]\nelif gentype=='GG':\n    pythia.Commands += [\"Merging:Process = pp>{go,1000021}{go,1000021}\"]\nelif gentype=='BB':\n    pythia.Commands += [\"Merging:Process = pp>{b1,1000005}{b1~,-1000005}\"]\n\n\n","repo_name":"btamadio/MadGraphProduction","sub_path":"MC15JobOptions/common/MadGraph/MadGraphControl_SimplifiedModel_onestepN2.py","file_name":"MadGraphControl_SimplifiedModel_onestepN2.py","file_ext":"py","file_size_in_byte":3993,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"36643311384","text":"from gurobipy import GRB, Model, quicksum\nfrom random import choice, randint\nimport parametros as PARA\n\n#-----------------------------------------------------------------------------\n# CONJUNTOS\n\nproductores, centros, medicamentos, cam_por_prod, cam_CENABAST,\\\ndias = PARA.Conjuntos()\n\n#-----------------------------------------------------------------------------\n# PARAMETROS\n\nparams, med_prod = PARA.Parametros(productores= productores, centros= centros,\nmedicamentos= medicamentos, dias= dias)\n\n#-----------------------------------------------------------------------------\n# VARIABLES\n\nmodel = Model('Plan Entregas CENABAST')\n\n# X: unidades transportadas de medicamento desde el productor\nmed_trans_prod = {}\nfor m in medicamentos:\n    med_trans_prod[m] = {}\n    for p in productores:\n        med_trans_prod[m][p] = {}\n        for c in cam_por_prod[p]:\n            med_trans_prod[m][p][c] = {}\n            for d in dias:\n                med_trans_prod[m][p][c][d] = model.addVar(vtype= GRB.CONTINUOUS, name=f'trans_{m}_{p}_{c}_{d}')\n# Y: camion prod p va a bodega dia d\ncam_a_bodega = {}\nfor p in productores:\n    cam_a_bodega[p] = {}\n    for c in cam_por_prod[p]:\n        cam_a_bodega[p][c] = {}\n        for d in dias:\n            cam_a_bodega[p][c][d] = model.addVar(vtype= GRB.BINARY, name=f'a_bodega_{p}_{c}_{d}')\n# Z: Llega a bodega el medicamento m el dia d\n# W: Unidades almacenadas de med m en bodega el dia d\nllega_a_bodega = {}\nalmacen_bodega = {}\nfor m in medicamentos:\n    llega_a_bodega[m] = {}\n    almacen_bodega[m] = {}\n    for d in dias:\n        llega_a_bodega[m][d] = model.addVar(vtype= GRB.BINARY, name=f'llega_bodega_{m}_{d}')\n        almacen_bodega[m][d] = model.addVar(vtype= GRB.CONTINUOUS, name=f'almacen_bodega_{m}_{d}')\n# Beta: N Bodegas a arrendar\narrienda = model.addVar(vtype= GRB.INTEGER, name='bodegas a arrendar')\n\n# r: med transportado de bodega al centro b el dia d\nmed_bodega_centro = {}\nfor m in medicamentos:\n    med_bodega_centro[m] = {}\n    for c in cam_CENABAST:\n        med_bodega_centro[m][c] = {}\n        for b in centros:\n            med_bodega_centro[m][c][b] = {}\n            for d in dias:\n                med_bodega_centro[m][c][b][d] = model.addVar(vtype= GRB.CONTINUOUS, name=f'med_{m}_{b}_{c}_{d}')\n# q: unidades de med almacenadas en centro b el dia d\nalmacen_centro = {}\nfor m in medicamentos:\n    almacen_centro[m] = {}\n    for b in centros:\n        almacen_centro[m][b] = {}\n        for d in dias:\n            almacen_centro[m][b][d] = model.addVar(vtype= GRB.CONTINUOUS, name=f'almacen_centro_{b}_{m}_{d}')\n# p: camion c va del centro b al a el dia d\nentre_centro = {}\nfor c in cam_CENABAST:\n    entre_centro[c] = {}\n    for b in centros:\n        entre_centro[c][b] = {}\n        for a in centros:\n            entre_centro[c][b][a] = {}\n            for d in dias:\n                entre_centro[c][b][a][d] = model.addVar(vtype= GRB.BINARY, name=f'cam_{c}_{b}_{a}_{d}')\n# o: unidades de med transportadas por el camion del centro a al centro b el dia d\ntrans_entre_centro = {}\nfor m in medicamentos:\n    trans_entre_centro[m] = {}\n    for c in cam_CENABAST:\n        trans_entre_centro[m][c] = {}\n        for a in centros:\n            trans_entre_centro[m][c][a] = {}\n            for b in centros:\n                trans_entre_centro[m][c][a][b] = {}\n                for d in dias:\n                    trans_entre_centro[m][c][a][b][d] = model.addVar(vtype= GRB.CONTINUOUS, name=f'trans_centro_{m}_{c}_{a}_{b}_{d}')\n# u: camion va desde bodega al centro b el dia d\nbodega_a_centro = {}\nfor c in cam_CENABAST:\n    bodega_a_centro[c] = {}\n    for b in centros:\n        bodega_a_centro[c][b] = {}\n        for d in dias:\n            bodega_a_centro[c][b][d] = model.addVar(vtype= GRB.BINARY, name=f'bodega_a_{b}_{c}_{d}')\n# f: camion va desde centro b a bodegas el dia d\ncentro_a_bodegas = {}\nfor c in cam_CENABAST:\n    centro_a_bodegas[c] = {}\n    for b in centros:\n        centro_a_bodegas[c][b] = {}\n        for d in dias:\n            centro_a_bodegas[c][b][d] = model.addVar(vtype= GRB.BINARY, name=f'cen_a_bodega_{c}_{b}_{d}')\n# h: Unidad trans de med m por el camion c desde centro b a bodegas el dia d\ntrans_centro_bodega = {}\nfor m in medicamentos:\n    trans_centro_bodega[m] = {}\n    for c in cam_CENABAST:\n        trans_centro_bodega[m][c] = {}\n        for b in centros:\n            trans_centro_bodega[m][c][b] = {}\n            for d in dias:\n                trans_centro_bodega[m][c][b][d] = model.addVar(vtype= GRB.CONTINUOUS, name=f'tran_cen_bodega_{m}_{c}_{b}_{d}')\n\n#-----------------------------------------------------------------------------\n# Funcion Objetivo\nmodel.update()\n\nobj1 = quicksum(quicksum(quicksum((cam_a_bodega[p][c][d] * params['costo fijo camion'][p]) +\\\n    quicksum(med_trans_prod[m][p][c][d] * params['costo tran medic bodega'][p][m] for m in med_prod[p]) \\\n    for c in cam_por_prod[p]) for p in productores) for d in dias)\n\nobj2 = arrienda\n\nobj3 = quicksum(quicksum(quicksum( (centro_a_bodegas[c][b][d] + bodega_a_centro[c][b][d])\\\n* params['costo tran bod centro'][b] + quicksum(entre_centro[c][b][a][d] * params['costo entre centros'][b][a]\\\n for a in centros)  for b in centros) for c in cam_CENABAST) for d in dias)\n\nmodel.setObjective(obj1 + obj2 + obj3, GRB.MINIMIZE)\n\n#-----------------------------------------------------------------------------\n# Restricciones\n\n# Restr 1 y 2\nfor d in dias:\n    for p in productores:\n        for c in cam_por_prod[p]:\n            # restr 1\n            model.addConstr(\n                quicksum(med_trans_prod[m][p][c][d] for m in med_prod[p])\n                <= cam_a_bodega[p][c][d], f'restr01_{c}_{p}_{d}'\n                )\n            # restr 2\n            model.addConstr(\n                quicksum(med_trans_prod[m][p][c][d] for m in med_prod[p])\n                * params['vol med'][m] <= params['vol camion'][p], f'restr02_{c}_{p}_{d}'\n            )\n\n# restr 3\nfor indice, d in enumerate(dias):\n    for m in medicamentos:\n        if indice == 0:\n            model.addConstr(\n                almacen_bodega[m][d] == params['inicial bodega'][m] + \n                quicksum(quicksum(med_trans_prod[m][p][c][d] for c in cam_por_prod[p]) for p in productores) +\n                quicksum(quicksum(trans_centro_bodega[m][c][b][d] - med_bodega_centro[m][c][b][d] for c in cam_CENABAST) for b in centros),\n                f'restr03_{m}_{d}'\n            )\n        elif indice == len(medicamentos) - 1:\n            model.addConstr(\n                almacen_bodega[m][d] == 0,\n                f'restr03_{m}_{d}'\n            )\n        else:\n            anterior = dias[indice-1]\n            model.addConstr(\n                almacen_bodega[m][d] == almacen_bodega[m][anterior] + \n                quicksum(quicksum(med_trans_prod[m][p][c][d] for c in cam_por_prod[p]) for p in productores) +\n                quicksum(quicksum(trans_centro_bodega[m][c][b][d] - med_bodega_centro[m][c][b][d] for c in cam_CENABAST) for b in centros),\n                f'restr03_{m}_{d}'\n            )\n\n# restr 4\nfor d in dias:\n    for c in cam_CENABAST:\n        for b in centros:\n            model.addConstr(\n                quicksum(med_bodega_centro[m][c][b][d] for m in medicamentos) \n                <= bodega_a_centro[c][b][d] * params['M GRANDE'], f'restr04_{b}_{c}_{d}'\n            )\n\n# restr 5\nfor d in dias:\n    for c in cam_CENABAST:\n        model.addConstr(quicksum(bodega_a_centro[c][b][d] for b in centros) == 1,\n        f'restr05_{c}_{d}')\n\n# restr 6\nfor d in dias:\n    for c in cam_CENABAST:\n        for b in centros:\n            model.addConstr(\n                quicksum(med_bodega_centro[m][c][b][d] * params['vol med'][m] for m in medicamentos)\n                <= params['vol camion CENABAST'], f'restr06_{b}_{c}_{d}'\n            )\n\n# restr 7 y 8\nfor d in dias:\n    for c in cam_CENABAST:\n        for b in centros:\n            for a in centros:\n                if a == b:\n                    continue\n                # restr 7\n                model.addConstr(\n                    quicksum(trans_entre_centro[m][c][b][a][d] for m in medicamentos)\n                    <= entre_centro[c][b][a][d] * params['M GRANDE'], f'restr07_{b}_{a}_{c}_{d}'\n                )\n                # restr 8\n                model.addConstr(\n                    quicksum(trans_entre_centro[m][c][b][a][d] * params['vol med'][m] for m in medicamentos)\n                    <= params['vol camion CENABAST'], f'restr08_{b}_{a}_{c}_{d}'\n                )\n\n# restr 9\nfor indice, d in enumerate(dias):\n    for b in centros:\n        for m in medicamentos:\n            if indice == 0:\n                model.addConstr(\n                    almacen_centro[m][b][d] == params['inicial centro'][m][b] +\n                    quicksum(med_bodega_centro[m][c][b][d] + \n                    quicksum(trans_entre_centro[m][c][b][a][d] - trans_entre_centro[m][c][a][b][d] for a in centros) -\n                    trans_centro_bodega[m][c][b][d] for c in cam_CENABAST) - \n                    params['demandas'][m][d][b], f'restr09_{m}_{b}_{d}'\n                )\n            elif indice == len(dias) - 1:\n                model.addConstr(\n                    almacen_centro[m][b][d] == 0, f'restr09_{m}_{b}_{d}'\n                )\n            else:\n                anterior = dias[indice - 1]\n                model.addConstr(\n                    almacen_centro[m][b][d] == almacen_centro[m][b][anterior] +\n                    quicksum(med_bodega_centro[m][c][b][d] + \n                    quicksum(trans_entre_centro[m][c][b][a][d] - trans_entre_centro[m][c][a][b][d] for a in centros) -\n                    trans_centro_bodega[m][c][b][d] for c in cam_CENABAST) - \n                    params['demandas'][m][d][b], f'restr09_{m}_{b}_{d}'\n                )\n\n# restr 10\nfor b in centros:\n    for d in dias:\n        model.addConstr(\n            quicksum(almacen_centro[m][b][d] * params['vol med'][m] for m in medicamentos)\n            <= params['vol almacen centro'][b], f'restr10_{b}_{d}'\n        )\n\n# restr 11\nfor b in centros:\n    for d in dias:\n        for c in cam_CENABAST:\n            model.addConstr(\n                quicksum(trans_centro_bodega[m][c][b][d] for m in medicamentos)\n                <= centro_a_bodegas[c][b][d] * params['M GRANDE'],\n                f'restr11_{b}_{d}'\n            )\n\n# restr 12\nfor d in dias:\n    for c in cam_CENABAST:\n        model.addConstr(\n            quicksum(centro_a_bodegas[c][b][d] for b in centros) == 1,\n            f'restr12_{c}_{d}'\n        )\n\n# restr 13\nfor b in centros:\n    for d in dias:\n        for c in cam_CENABAST:\n            model.addConstr(\n                quicksum(trans_centro_bodega[m][c][b][d] * params['vol med'][m] for m in medicamentos)\n                <= params['vol camion CENABAST'], f'restr13_{c}_{b}_{d}'\n            )\n\n# restr 14 , 15 y 16\nfor d in dias:\n    for c in cam_CENABAST:\n        for b in centros:\n            # restr 14\n            model.addConstr(\n                bodega_a_centro[c][b][d] + quicksum(entre_centro[c][b][a][d] for a in centros)\n                <= 1, f'restr14_{b}_{c}_{d}'\n            )\n            # restr 15\n            model.addConstr(\n                centro_a_bodegas[c][b][d] + quicksum(entre_centro[c][a][b][d] for a in centros)\n                <= 1, f'restr15_{b}_{c}_{d}'\n            )\n            # restr 16\n            model.addConstr(\n                bodega_a_centro[c][b][d] + quicksum(entre_centro[c][b][a][d] for a in centros)\n                == centro_a_bodegas[c][b][d] + quicksum(entre_centro[c][a][b][d] for a in centros),\n                f'restr16_{b}_{c}_{d}'\n            )\n\n# restr 17\nfor d in dias:\n    for c in cam_CENABAST:\n        model.addConstr(\n            quicksum(bodega_a_centro[c][b][d] * params['tiempo tran bod centro'][b] +\n            quicksum(entre_centro[c][b][a][d] * params['tiempo entre centros'][b][a] for a in centros) +\n            centro_a_bodegas[c][b][d] * params['tiempo tran bod centro'][b] for b in centros)\n            <= params['horario camion'],\n            f'restr17_{c}_{d}'\n        )\n\n# restr 18\nfor d in dias:\n    model.addConstr(\n        quicksum(almacen_bodega[m][d] * params['vol med'][m] for m in medicamentos)\n        <= params['vol bodega'] * arrienda, f'restr18_{d}'\n    )\n\n# restr 19\nfor indice, d in enumerate(dias):\n    for b in centros:\n        for m in medicamentos:\n            if indice == 0:\n                model.addConstr(\n                    params['inicial centro'][m][b] >= params['demandas'][m][d][b],\n                    f'restr19_{m}_{b}_{d}'\n                )\n            else:\n                anterior = dias[indice - 1]\n                model.addConstr(\n                    almacen_centro[m][b][anterior] >= params['demandas'][m][d][b],\n                    f'restr19_{m}_{b}_{d}'\n                )\n\n# restr 20\nfor m in medicamentos:\n    for indice, d in enumerate(dias[:len(dias) - params['dur med'][m]]):\n        model.addConstr(\n            almacen_bodega[m][d] + quicksum(almacen_centro[m][b][d] for b in centros)\n            <= quicksum(quicksum(params['demandas'][m][i][b] for b in centros) \\\n                for i in dias[indice: indice + params['dur med'][m]]),\n                f'restr20_{m}_{d}'\n        )\n\n# restr 21\nfor m in medicamentos:\n    for indice, d in enumerate(dias[:len(dias) - params['dur med'][m]]):\n        for b in centros:\n            model.addConstr(\n                almacen_centro[m][b][d] <= quicksum(params['demandas'][m][d][b] \\\n                    for i in dias[indice+1: indice + params['dur med'][m]]),\n                    f'restr21_{b}_{m}_{d}'\n            )\n\n# restr 22\nfor p in productores:\n    for indice, d in enumerate(dias[:len(dias) - params['prod cd'][p]]):\n        model.addConstr(\n            quicksum(quicksum(cam_a_bodega[p][c][i] for c in cam_por_prod[p]) \\\n                for i in dias[indice : indice + params['prod cd'][p]])\n            == 1, f'restr22_{p}_{d}'\n        )\n\n# restricciones nuevas\n# restr 3: Vol minimo para transporte\nfor d in dias:\n    for p in productores:\n        model.addConstr(\n            quicksum(quicksum(med_trans_prod[m][p][c][d] * params['vol med'][m]\n            for c in cam_por_prod[p]) for m in med_prod[p]) <= quicksum(\n            cam_a_bodega[p][c][d] * params['min prod'][p] for c in cam_por_prod[p]),\n            f'restr3N_{p}_{d}'\n        )\n\n# restr 24: Activacion var Z\n# restr 25: Dia que llega el med a la bodega, esta no debe tener ese med\nfor indice, d in enumerate(dias):\n    for m in medicamentos:\n        # restr 24\n        model.addConstr(\n            quicksum(quicksum(med_trans_prod[m][p][c][d] for c in cam_por_prod[p]) for p in productores)\n            <= llega_a_bodega[m][d] * params['M GRANDE'], f'restr24_{m}_{d}'\n        )\n        # restr 25\n        if indice == 0:\n            model.addConstr(\n                params['inicial bodega'][m] <= (1 - llega_a_bodega[m][d]) * params['M GRANDE'],\n                f'restr25_{m}_{d}'\n            )\n        else:\n            anterior = dias[indice - 1]\n            model.addConstr(\n                almacen_bodega[m][anterior] <=\n                (1 - llega_a_bodega[m][d]) * params['M GRANDE'], f'restr25_{m}_{d}'\n            )\n\n# restr 26: Cantidad de un medicamento no mayor que su demanda en el centro\nfor indice, d in enumerate(dias):\n    for m in medicamentos:\n        for indece2, l in enumerate(dias[:len(dias) - params['dur med'][m]]):\n            if indece2 >= indice:\n                continue\n            for b in centros:\n                anterior = dias[indice-1]\n                model.addConstr(\n                    almacen_centro[m][b][anterior] - (1 - llega_a_bodega[m][l]) * params['M GRANDE']\n                    <= (llega_a_bodega[m][d] * quicksum(params['demandas'][m][i][b] \n                    for i in dias[indice: indece2 + params['dur med'][m]])) + \n                    ((1-llega_a_bodega[m][d]) * params['M GRANDE']) + (params['M GRANDE'] * \n                    quicksum(llega_a_bodega[m][i] for i in dias[indece2+1:indice-1])), f'restr26_{b}_{m}_l:{l}_{d}'\n                )\n\n# restr 27: Mov entre centros tras llegada de med\nfor m in medicamentos:\n    for indice, d in enumerate(dias[:len(dias)-params['dur med'][m]]):\n        for indece2, l in enumerate(dias[:len(dias)-params['dur med'][m]]):\n            if indece2 >= indice:\n                continue\n            for b in centros:\n                for indeca3, a in enumerate(dias[indice+1: min((len(dias), indece2+params['dur med'][m]))]):\n                    if indice == 0:\n                        model.addConstr(\n                            almacen_centro[m][b][a] >= params['inicial centro'][m][b] - \n                            quicksum(params['demandas'][m][i][b] for i in dias[indice:indeca3]) -\n                            (params['M GRANDE'] * (1-llega_a_bodega[m][d])) - \n                            (params['M GRANDE'] * (1-llega_a_bodega[m][l])), f'restr27_{b}_{m}_a:{a}_l:{l}_{d}'\n                        )\n                    else:\n                        antedia = dias[indice-1]\n                        model.addConstr(\n                            almacen_centro[m][b][a] >= almacen_centro[m][b][antedia] - \n                            quicksum(params['demandas'][m][i][b] for i in dias[indice:indeca3]) -\n                            (params['M GRANDE'] * (1-llega_a_bodega[m][d])) - \n                            (params['M GRANDE'] * (1-llega_a_bodega[m][l])), f'restr27_{b}_{m}_a:{a}_l:{l}_{d}'\n                        )\n\n\n# restr extra para evitar transportes de un centro al mismo\nfor d in dias:\n    for c in cam_CENABAST:\n        for b in centros:\n            for a in centros:\n                if b == a:\n                    model.addConstr(entre_centro[c][b][a][d] == 0,\n                    f'rest_extra_{b}_{a}_{c}_{d}')\n\n# Natur var\nfor m in medicamentos:\n    for p in productores:\n        for c in cam_por_prod[p]:\n            for d in dias:\n                model.addConstr(\n                    med_trans_prod[m][p][c][d] >= 0, f'natur_var_X{m}_{p}_{c}_{d}'\n                )\nmodel.addConstr(arrienda >= 0, f'natur_var_Beta')\nfor m in medicamentos:\n    for b in centros:\n        for d in dias:\n            for c in cam_CENABAST:\n                model.addConstr(\n                    med_bodega_centro[m][c][b][d] >= 0, f'natur_var_r_{m}_{c}_{b}_{d}'\n                )\n\n#-----------------------------------------------------------------------------\nmodel.update()\n\n# Time limit, para testeo\n# Solo me importa que no sea infactible\nmodel.setParam(GRB.Param.TimeLimit, 5)\nmodel.optimize()\n\n\n#model.printAttr(\"X\")\n\nprint(f'Cantidad de bodegas: {arrienda.X}')\n\nprint('\\n------------------------------------------------------------\\n')\n#for constr in model.getConstrs():\n#    print(cosntr, constr.getAttr(\"slack\"))\n\n\n#-----------------------------------------------------------------------------\n\nprint('GG')","repo_name":"Sebarraza/testo","sub_path":"Opti/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":18808,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"22397305418","text":"# Nathan Rabinovitch\n# Project: Great Lakes are how big?\n# Chapter 1 Problem Set Part 1\n\n# RCH: Please use lowercase variable names and underscores between words\nKmsquaredofusA = 8080464.3\nGreatLakesV = 22810\n\n\nanswer = GreatLakesV/KmsquaredofusA\n\nanswer = answer*1000\n\nprint(answer)\nprint(\"Meters\")\n","repo_name":"IdeaventionsAcademy7/chapter-1-part-1-beginnings-NathanRab","sub_path":"ch1_Project_GreatLakes.py","file_name":"ch1_Project_GreatLakes.py","file_ext":"py","file_size_in_byte":300,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"2982765637","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# # Iteration [^intro]\n# \n# [^intro]: This Jupyter Notebook is based on Chapter 3 and 5 of {cite}`Severance2016`; and Chapter 5 and 7 of {cite}`thinkPython`.\n\n# ## The `while` Statement\n# \n# Computers are good in repeating boring tasks, they do this faster and more accurate than people.\n# \n# Repetition of instructions is called **iteration** in programming.\n# \n# Suppose you want to countdown and print a value, we need to *loop*.\n\n# In[1]:\n\n\n# Countdown program\n\nn = 10  # integer\nprint('initial n value =', n)\ni = 0   # integer\n\nwhile n > 0:\n    print(n)\n    n = n - 1\n    \n    i = i + 10\nprint('Ready, and value of i =', i)\n\n\n# The flow of execution for a `while` statement is as follows:\n# \n# 1. Determine whether the condition is `True` or `False`.\n# 2. If `False`, exit the `while` statement and continue with the next statement.\n# 3. If the condition is `True`, run the body and go back to step 1.\n# \n# This type of flow is called a loop because the third step loops back around to the top.\n# \n# The body should change the value of some variables that are used in the condition in order to ensure termination\n# of a loop.\n\n# In[2]:\n\n\nwhile True:\n    print('Hello')\n\n\n# The `while` loop above will never terminate, this is a so-called `infinite` loop.\n# \n# When writing programs it is important to convince yourself that the program terminates.\n# \n# In the case of `countdown`, we can prove that the loop terminates: if `n` is zero or negative, the\n# loop never runs. Otherwise, `n` gets smaller each time through the loop, so eventually we\n# have to get to 0.\n# \n# Reasoning that loops terminate is not always trivial, consider for instance.\n\n# In[ ]:\n\n\n# sequence\nn = 5   # integer\nwhile n != 1:\n    print(n)\n    \n    if n % 2 == 0: # n is even\n        n = n / 2\n    else: # n is odd\n        n = n * 3 + 1\n\n\n# The condition for this loop is `n != 1`, so the loop will continue until `n` is 1, which makes\n# the condition `False`.\n# \n# Since `n` sometimes increases and sometimes decreases, there is no obvious proof that `n` will\n# ever reach 1, or that the program terminates. \n# \n# For some particular values of `n`, we can prove termination. \n# \n# For example, if the starting value is a power of two, `n` will be even every\n# time through the loop until it reaches 1. \n# \n# The previous example ends with such a sequence,\n# starting with 16.\n\n# ## `break`\n# \n# It is possible to enforce the termination of a loop via the `break` statement.\n# \n# Suppose you are processing input and as soon as you get the string value `done` you want to terminate.\n\n# In[1]:\n\n\ncount_words = 0\n\nwhile True:\n    line = input(\"> \")\n    \n    if line == 'done':\n        break\n        \n    count_words = count_words + 1; print(\"Hello\")\n    \nprint(count_words)\n\n\n# This way of terminating `while` loops is common practice, because you can check very easily whether some condition\n# holds.\n\n# ```{admonition} Do It Yourself!\n# :class: seealso, dropdown\n# Try to rewrite the <i>while</i> loop without the <i>break</i> statement.\n# ```\n\n# In[ ]:\n\n\n# Remove this line and add your code here\n\n\n# ## `continue` \n# \n# Sometimes you don't want to finish the execution of the loop body for certain iterations.\n# \n# In that case, you want to stop the current iteration and continue with the next one.\n# \n# To do so, you can use the `continue` keyword.\n# \n# Let's see how to use it with the following example. \n\n# In[ ]:\n\n\nwhile True:\n    instruction = input('What to do next? ')\n    \n    if instruction == 'quit':\n        break\n    \n    if instruction == 'skip':\n        continue\n        print('Skip this line')\n        \n    print('Your instruction is ' + instruction)\n\n\n# In this program we ask for input to the user and we print a message saying \"Your instruction is `instruction`\". However, if the input is equal to \"quit\", the program will end. Otherwise, if the input is equal to \"skip\", the current iteration will stop just before printing the message and will continue with the next iteration.\n\n# ```{admonition} Do It Yourself!\n# :class: seealso, dropdown\n# Try to rewrite the <i>while</i> loop without the <i>break</i> and <i>continue</i> statements.\n# ```\n\n# In[ ]:\n\n\n# Remove this line and add your code here\n\n\n# ## While Loops in Detail\n# \n# Let's consider the following cell to understand the flow of control of a `while` loop.\n# \n# * A `while` loop should always have a boolean expression (condition). In the cell  `x < 10` (see line `#2`), but `True` is also fine.\n# * A `while` loop should have at least one statement in the body (see lines `#3` and `#4`). Beware only the indented statements are part of the body, line `#5` is not part of the body.\n# * As long as the evaluation of the condition yields the boolean value `True` (see line `#2`), the statements\n# in the body are executed (see lines `#3` and `#4`). After the execution of the body, the condition is re-evaluated. \n# If the evaluation of the condition yields the value `False`, the statement after the body is executed (see line `#5`).\n# * Removing the indentation on line `#4` leads to a non-terminating loop. **Why?**\n\n# In[ ]:\n\n\nx = 0               #1\nwhile x < 10:       #2\n    print(x)        #3\n    x = x + 1       #4\nprint(\"Done!\")      #5\n\n\n# The program above can be visualised as follows:\n# \n# ```{image} assets/flowchart.png\n# :alt: Flowchart of first while loop program\n# :width: 250px\n# :align: center\n# ```\n# \n# <div style=\"text-align:center\">\n#     <span style=\"font-size:0.9em; font-weight: bold;\">Flowchart of the first while-loop program.</span>\n# </div>\n# <br>\n# \n# Do you also want to see the `while` loop in action?\n\n# ```{image} assets/while-animation.gif\n# :alt: Animated while loop program\n# :align: center\n# ```\n# \n# <div style=\"text-align:center\">\n#     <span style=\"font-size:0.9em; font-weight: bold;\">Animmated while-loop program.</span>\n# </div>\n# <br>\n# <br>\n\n# \n\n# In[ ]:\n\n\nx = 0               #1\nwhile x < 10:       #2\n    print(x)        #3\nx = x + 1           #4\nprint(\"Done!\")      #5\n\n\n# The program above can be visualised as follows:\n# \n# ```{image} assets/flowchart2.png\n# :alt: Flowchart of second while loop program\n# :width: 250px\n# :align: center\n# ```\n# \n# <div style=\"text-align:center\">\n#     <span style=\"font-size:0.9em; font-weight: bold;\">Flowchart of the second while-loop program.</span>\n# </div>\n# \n\n# * The execution of the loop can *always* be terminated via a `break` statement. See line `#6` in the cell below.\n# * Often the `break` statement is used in combination with a conditional. See lines `#5` and `#6`.\n# * This conditional should be part of the loop body, so intended.\n\n# In[ ]:\n\n\nx = 0               #1\nwhile True:         #2\n    print(x)        #3\n    x = x + 1       #4\n    if x >= 10:     #5\n        break       #6\nprint(\"Done!\")      #5\n\n\n# The program above can be visualised as follows:\n# \n# ```{image} assets/flowchart3.png\n# :alt: Flowchart of third while loop program\n# :width: 300px\n# :align: center\n# ```\n# \n# <div style=\"text-align:center\">\n#     <span style=\"font-size:0.9em; font-weight: bold;\">Flowchart of the third while-loop program.</span>\n# </div>\n\n# ## Square Roots\n# \n# Loops are often used in programs that compute numerical results by starting with an approximate\n# answer and iteratively improving it.\n# \n# For example, one way of computing square roots is Newton’s method.\n# \n# Suppose that you want to know the square root of $a$. \n# \n# If you start with almost any estimate, $x$, you can compute\n# a better estimate with the following formula:\n# \n# $y = \\frac{x + a/x}{2}$\n# \n# For example, if $a$ is 4 and $x$ is 3, we get:\n\n# In[ ]:\n\n\na = 4\nx = 3\ny = (x + a / x) / 2\ny\n\n\n# The result is closer to the correct answer ($\\sqrt{4} = 2$). If we repeat the process with the new\n# estimate, it gets even closer\n\n# In[ ]:\n\n\nx = y\ny = (x + a / x) / 2\ny\n\n\n# After a few more iterations the results gets more precise.\n\n# In[ ]:\n\n\nx = y\ny = (x + a / x) / 2\ny\n\n\n# In[ ]:\n\n\nx = y\ny = (x + a / x) / 2\ny\n\n\n# After a couple more iterations the value of $y$ is equal to $x$ and thus we can stop.\n\n# In[ ]:\n\n\nx = 1\na = 4\n\nwhile (True):\n    print(x)\n    y = (x + a / x) / 2\n    \n    if y == x:\n        break\n    \n    x = y\n\n\n# For most values of a this works fine, but in general it is dangerous to test equality on floating point numbers.\n# \n# Floating-point values are only approximately right: most rational numbers, like 1/3, and\n# irrational numbers, like $\\sqrt{2}$ cannot be represented exactly with a float.\n# \n# Rather than checking whether `x` and `y` are exactly equal, it is safer to use the built-in function\n# abs to compute the absolute value, or magnitude, of the difference between them:\n\n# In[ ]:\n\n\nx = 3\na = 4\nepsilon = 0.00000000000001\n\nwhile (True):\n    print(x)\n    y = (x + a / x) / 2\n    \n    if abs(y - x) < epsilon:\n        break\n        \n    x = y\n\n\n# Where `epsilon` has a value like `0.00000000000001` that determines how close is close enough.\n\n# ````{margin}\n# ```{admonition} EXTRA\n# See what happens if you play with the value of `epsilon`.\n# ```\n# ````\n\n# Suppose we want to add all numbers to a number provided via input.\n\n# In[ ]:\n\n\nx = 1+2+3+4+5+6+7+8+9+10\nprint(x)\n\n\n# In[ ]:\n\n\nsx = input()\n\nx = int(sx)\n\ns = 0\nwhile (x > 0):\n    s = s + x\n    print(s)\n    x = x - 1\n    print(x)\n\nprint(s)\n\n\n# In[ ]:\n\n\ninp = input(\"Give a number > 0:\")\nnr = int(inp)\nprint(nr)\n\ncnt = 0\ntot = 0\nwhile (cnt <= nr):\n    print(cnt)\n    if (cnt % 2 == 0):\n        tot += cnt\n    cnt += 1\nprint(tot)\n    \n\n","repo_name":"programming-pybook/introProgramming","sub_path":"book/_build/jupyter_execute/chapters/iteration.py","file_name":"iteration.py","file_ext":"py","file_size_in_byte":9502,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"81"}
+{"seq_id":"18497335174","text":"from Adafruit_IO import MQTTClient\r\nimport sys\r\nimport time\r\nfrom task1 import WeatherDataFetcher\r\n\r\nclass AdafruitInterface:\r\n    aio_key = \"aio_fsUk73WZaCwVnNyNCuxUHm3oI3OI\"\r\n    aio_username = \"boostedvgu\"\r\n    aio_feed_id = ['Temperature','Min','Max','Humidity','Speed','Description','City']\r\n\r\n    def __init__(self):\r\n        print('Init task 3!')\r\n        self.client = MQTTClient(self.aio_username , self.aio_key)\r\n        (self.client).on_connect = AdafruitInterface.connected\r\n        (self.client).on_disconnect = AdafruitInterface.disconnected\r\n        (self.client).on_message = AdafruitInterface.message\r\n        (self.client).on_subscribe = AdafruitInterface.subscribe\r\n        (self.client).connect()\r\n        (self.client).loop_background()\r\n\r\n    def connected(client):\r\n        print('Connected')\r\n        \r\n        \r\n    def subscribe(client , userdata , mid , granted_qos):\r\n        for feeds in AdafruitInterface.aio_feed_id:\r\n            client.subscribe(feeds)\r\n        print('Subscribed...')\r\n\r\n    def disconnected(client):\r\n        print('Disconnected from the server...')\r\n        sys.exit (1)\r\n\r\n    def message(client , aio_feed_id , payload):\r\n        print('Received data: ' + payload)\r\n    \r\n    def publish_to_adafruit(self):\r\n        fetcher = WeatherDataFetcher()\r\n        self.data = fetcher.get_current_data()\r\n        self.client.publish('Description', self.data['description'])\r\n        self.client.publish('Humidity', self.data['humidity'])\r\n        self.client.publish('Max', self.data['max'])\r\n        self.client.publish('Min', self.data['min'])\r\n        self.client.publish('Speed', self.data['speed'])\r\n        self.client.publish('Temperature', self.data['temperature'])\r\n        self.client.publish('City', self.data['city'])\r\n        print(\"Data Published To Adafruit!\")\r\n\r\nif __name__ == '__main__':\r\n    wd = WeatherDataFetcher('Ho Chi Minh City')\r\n    data = wd.get_current_data()\r\n    uploader = AdafruitInterface(data)\r\n    while True:\r\n        uploader.publish_to_adafruit()\r\n        time.sleep(10000)\r\n\r\n    \r\n\r\n\r\n    \r\n","repo_name":"ppngg/Introduction-to-CS","sub_path":"task3.py","file_name":"task3.py","file_ext":"py","file_size_in_byte":2076,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"15215559380","text":"import disnake\r\n\r\n# ----------------------------------------------------------------------------------------------------------------\r\n# Pinboard Component\r\n# ----------------------------------------------------------------------------------------------------------------\r\n# Enable the pinboard system (\"extra pinned messages\") in specific server\r\n# ----------------------------------------------------------------------------------------------------------------\r\n\r\nclass Pinboard():\r\n    def __init__(self, bot):\r\n        self.bot = bot\r\n        self.cache = [] # store pinned messages until reboot\r\n\r\n    def init(self):\r\n        pass\r\n\r\n    \"\"\"match_channel_id()\r\n    Match a channel id to a stored guild id\r\n    \r\n    Parameters\r\n    ----------\r\n    channel_id: A channel id\r\n    \r\n    Returns\r\n    --------\r\n    str: matched ID\r\n    \"\"\"\r\n    def match_channel_id(self, channel_id):\r\n        idx = None\r\n        for s in self.bot.data.save['pinboard']:\r\n            if channel_id in self.bot.data.save['pinboard'][s]['tracked']:\r\n                idx = s\r\n                break\r\n        return idx\r\n\r\n    \"\"\"check()\r\n    Check if the message and guild id are valid for pinning\r\n    \r\n    Parameters\r\n    ----------\r\n    message: disnake.Message\r\n    idx: Pinbaord guild id\r\n    \r\n    Returns\r\n    --------\r\n    bool: True if success, False if failure\r\n    \"\"\"\r\n    async def check(self, message, idx):\r\n        try:\r\n            if message.id in self.cache: return False\r\n            reactions = message.reactions\r\n        except:\r\n            return False\r\n        me = message.guild.me\r\n        count = 0\r\n        for reaction in reactions:\r\n            if str(reaction.emoji) == self.bot.data.save['pinboard'][idx]['emoji']:\r\n                users = await reaction.users().flatten()\r\n                count = len(users)\r\n                guild = message.guild\r\n                isMod = False\r\n                count = 0\r\n                if me in users: return False\r\n                for u in users:\r\n                    if self.bot.data.save['pinboard'][idx]['mod_bypass']: # mod check\r\n                        m = await guild.get_or_fetch_member(u.id)\r\n                        if m.guild_permissions.manage_messages: \r\n                            isMod = True\r\n                            break\r\n                        else:\r\n                            count += 1\r\n                    else:\r\n                        count += 1\r\n                if not isMod and count < self.bot.data.save['pinboard'][idx]['threshold']:\r\n                    return False\r\n                return True\r\n        return False\r\n\r\n    \"\"\"pin()\r\n    Do the needed checks and, if eligible, add the message to the pinboard\r\n    \r\n    Parameters\r\n    ----------\r\n    message: disnake.Message\r\n    \r\n    Returns\r\n    --------\r\n    bool: True if success, False if failure\r\n    \"\"\"\r\n    async def pin(self, message):\r\n        idx = self.match_channel_id(message.channel.id)\r\n        if idx is None: return False\r\n        if await self.check(message, idx):\r\n            if message.id in self.cache: return False # anti dupe safety\r\n            self.cache.append(message.id)\r\n            if len(self.cache) > 20: self.cache.pop(0) # limited to 20 entries\r\n            await message.add_reaction(self.bot.data.save['pinboard'][idx]['emoji'])\r\n            content = message.content\r\n\r\n            try:\r\n                embed_dict = {}\r\n                embed_dict['color'] = 0xf20252\r\n                embed_dict['title'] = str(message.author)\r\n                if len(content) > 0: \r\n                    if len(content) > 1900: embed_dict['description'] = content[:1900] + \"...\\n\\n\"\r\n                    else: embed_dict['description'] = content + \"\\n\\n\"\r\n                else: embed_dict['description'] = \"\"\r\n                embed_dict['thumbnail'] = {'url':str(message.author.display_avatar)}\r\n                embed_dict['fields'] = []\r\n                # for attachments\r\n                if message.attachments:\r\n                    for file in message.attachments:\r\n                        if file.is_spoiler():\r\n                            embed_dict['fields'].append({'inline': True, 'name':'Attachment', 'value':f'[{file.filename}]({file.url})'})\r\n                        elif file.url.lower().endswith(('.png', '.jpeg', '.jpg', '.gif', '.webp', 'jpg:thumb', 'jpg:small', 'jpg:medium', 'jpg:large', 'jpg:orig', 'png:thumb', 'png:small', 'png:medium', 'png:large', 'png:orig')) and 'image' not in embed_dict:\r\n                            embed_dict['image'] = {'url':file.url}\r\n                        else:\r\n                            embed_dict['fields'].append({'inline': True, 'name':'Attachment', 'value':f'[{file.filename}]({file.url})'})\r\n                # search for image url if no attachment\r\n                if 'image' not in embed_dict:\r\n                    s = content.find(\"http://\")\r\n                    if s == -1: s = content.find(\"https://\")\r\n                    if s != -1:\r\n                        for ext in ['.png', '.jpeg', '.jpg', '.gif', '.webp', 'jpg:thumb', 'jpg:small', 'jpg:medium', 'jpg:large', 'jpg:orig', 'png:thumb', 'png:small', 'png:medium', 'png:large', 'png:orig']:\r\n                            e = content.find(ext, s)\r\n                            if e != -1:\r\n                                e += len(ext)\r\n                                break\r\n                        if e!= -1 and content.find(' ', s, e) == -1:\r\n                            embed_dict['image'] = {'url':content[s:e]}\r\n                # check embed\r\n                if len(message.embeds) > 0:\r\n                    if len(message.embeds[0].description) > 0: embed_dict['fields'] = [{'inline': True, 'name':'Content', 'value':message.embeds[0].description}] + embed_dict['fields']\r\n                    if 'image' not in embed_dict and message.embeds[0].image.url != message.embeds[0].Empty: embed_dict['image'] = {'url':message.embeds[0].image.url}\r\n                    if len(message.embeds[0].title) > 0: embed_dict['title'] += \" ▫️ \" + message.embeds[0].title\r\n                    elif message.embeds[0].author.name is not None: embed_dict['title'] += \" ▫️ \" + message.embeds[0].author.name\r\n                # add link to description\r\n                embed_dict['description'] += \":earth_asia: [**Link**](https://discordapp.com/channels/{}/{}/{})\\n\".format(message.guild.id, message.channel.id, message.id)\r\n                if 'image' in embed_dict and embed_dict['image'] is None:\r\n                    embed_dict.pop('image')\r\n                embed = disnake.Embed.from_dict(embed_dict)\r\n                embed.timestamp=message.created_at\r\n                ch = self.bot.get_channel(self.bot.data.save['pinboard'][idx]['output'])\r\n                await ch.send(embed=embed)\r\n                return True\r\n            except Exception as x:\r\n                if 'Missing Access' in str(x) or 'Missing Permissions' in str(x):\r\n                    try:\r\n                        c = await self.bot.get_channel(message.channel_id)\r\n                        await c.send(mbed=self.bot.util.embed(title=\"Pinboard error\", description=\"Note to the moderators: I'm not permitted to post in the pinboard channel\"))\r\n                    except:\r\n                        pass\r\n                else:\r\n                    await self.bot.sendError(\"pinboard pin\", \"Guild `{}` : `{}`\\nException:\\n{}\".format(message.guild, message.guild.id, self.bot.util.pexc(x)))\r\n        return False\r\n\r\n    \"\"\"add()\r\n    Enable the system for a guild (or overwrite its previous settings)\r\n    \r\n    Parameters\r\n    ----------\r\n    server_id: Guild ID, in string format\r\n    tracked: List of channel IDs\r\n    emoji: Emoji used, in string format\r\n    mod: Boolean\r\n    threshold: number of reactions needed to trigger a pin\r\n    output: Pinboard channel ID\r\n    \"\"\"\r\n    def add(self, server_id, tracked, emoji, mod, threshold, output): # parameters validity should be checked before the call\r\n        with self.bot.data.lock:\r\n            self.bot.data.save['pinboard'][server_id] = {'tracked' : tracked, 'emoji': emoji, 'mod_bypass':mod, 'threshold':threshold, 'output': int(output)}\r\n            self.bot.data.pending = True\r\n\r\n    \"\"\"remove()\r\n    Disable the system for a guild\r\n    \r\n    Parameters\r\n    ----------\r\n    server_id: Guild ID, in string format\r\n    \"\"\"\r\n    def remove(self, server_id):\r\n        if server_id in self.bot.data.save['pinboard']:\r\n            with self.bot.data.lock:\r\n                self.bot.data.save['pinboard'].pop(server_id)\r\n                self.bot.data.pending = True\r\n\r\n    \"\"\"get()\r\n    Retrieve the settings for a guild\r\n    \r\n    Parameters\r\n    ----------\r\n    server_id: Guild ID, in string format\r\n    \r\n    Returns\r\n    ----------\r\n    dict: Stored settings, None if unavailable\r\n    \"\"\"\r\n    def get(self, server_id):\r\n        return self.bot.data.save['pinboard'].get(server_id, None)","repo_name":"MizaGBF/MizaBOT","sub_path":"components/pinboard.py","file_name":"pinboard.py","file_ext":"py","file_size_in_byte":8893,"program_lang":"python","lang":"en","doc_type":"code","stars":25,"dataset":"github-code","pt":"81"}
+{"seq_id":"13903926141","text":"import os\n\nfrom .blender import tag_redraw_all, get_path_from_addon_common, get_path_from_addon_root\nfrom .decorators import debug_test_call, blender_version_wrapper, add_cache\nfrom .fontmanager import FontManager\n\n\nfontmap = {\n    'serif': {\n        'normal': {\n            'normal': 'DroidSerif-Regular.ttf',\n            'bold':   'DroidSerif-Bold.ttf',\n        },\n        'italic': {\n            'normal': 'DroidSerif-Italic.ttf',\n            'bold':   'DroidSerif-BoldItalic.ttf',\n        },\n    },\n    'sans-serif': {\n        'normal': {\n            'normal': 'DroidSans-Blender.ttf',\n            'bold':   'OpenSans-Bold.ttf',\n        },\n        'italic': {\n            'normal': 'OpenSans-Italic.ttf',\n            'bold':   'OpenSans-BoldItalic.ttf',\n        },\n    },\n    'monospace': {\n        'normal': {\n            'normal': 'DejaVuSansMono.ttf',\n            'bold':   'DejaVuSansMono.ttf',\n        },\n        'italic': {\n            'normal': 'DejaVuSansMono.ttf',\n            'bold':   'DejaVuSansMono.ttf',\n        },\n    },\n}\n\n\n@add_cache('_cache', {})\n@add_cache('_paths', [\n    get_path_from_addon_common('common', 'fonts'),\n    get_path_from_addon_common('common'),\n    get_path_from_addon_root('fonts'),\n])\ndef get_font_path(fn, ext=None):\n    cache = get_font_path._cache\n    if ext: fn = f'{fn}.{ext}'\n    if fn not in cache:\n        cache[fn] = None\n        for path in get_font_path._paths:\n            p = os.path.join(path, fn)\n            if os.path.exists(p):\n                cache[fn] = p\n                break\n    return get_font_path._cache[fn]\n\ndef setup_font(fontid):\n    FontManager.aspect(1, fontid)\n\ndef get_font(fontfamily, fontstyle=None, fontweight=None):\n    if not fontstyle:  fontstyle = 'normal'\n    if not fontweight: fontweight = 'normal'\n    # translate fontfamily, fontstyle, fontweight into a .ttf\n    if fontfamily in fontmap: fontfamily = fontmap[fontfamily][fontstyle][fontweight]\n    path = get_font_path(fontfamily)\n    assert path, f'could not find font \"{fontfamily}\"'\n    fontid = FontManager.load(path, setup_font)\n    return fontid\n\n","repo_name":"CGCookie/retopoflow","sub_path":"addon_common/common/ui_core_fonts.py","file_name":"ui_core_fonts.py","file_ext":"py","file_size_in_byte":2091,"program_lang":"python","lang":"en","doc_type":"code","stars":2072,"dataset":"github-code","pt":"81"}
+{"seq_id":"5249927321","text":"# 6109. 추억의 2048게임\n\"\"\"\nleft를 함수를 만들고, left을 제외한 다른것들은 회전시키고 left을 수행한 후 다시 돌려주는 식으로 수행\n\"\"\"\n\n\n\n# 시계 방향으로 90도 회전\ndef get_rotated(grid, N):\n    rotated_grid = [[0 for _ in range(N)] for _ in range(N)]\n\n    for x in range(N):\n        for y in range(N):\n            rotated_grid[y][N - 1 - x] = grid[x][y]\n\n    return rotated_grid\n\n\ndef move_left(grid, N):\n    for x in range(N):\n        # 0을 제거해줌\n        grid[x] = [k for k in grid[x] if k != 0]\n        if not grid[x]: # 해당 줄이 전부 0인 경우, 넘어감\n            continue\n\n        temp = []\n        prev = grid[x].pop(0)\n        if not grid[x]:\n            temp = [prev]\n\n        while grid[x]:\n            cur = grid[x].pop(0)\n            # print(\"prev\", prev, \"cur\", cur)\n            if prev != cur:\n                if prev != 0: # 이전거에서 합쳐져서 prev가 0인 경우는 제외\n                    temp.append(prev)\n                if not grid[x]:\n                    temp.append(cur)\n                prev = cur\n\n            else: # 같으면 합쳐줌\n                temp.append(prev + cur)\n                prev = 0\n\n            # print(\"grid[x] :\", grid[x])\n            # print(\"temp :\", temp)\n        # 뒤에는 0을 붙여줌\n        grid[x] = temp + [0] * (N - len(temp))\n\n\ndef print_grid(grid, N):\n    # grid 출력\n    for x in range(N):\n        # print(\" \".join(grid[x])) # str형식으로 들어가야함 -> int값으로 들어가있어서 불가능\n        for y in range(N):\n            print(grid[x][y], end=\" \")\n        print()\n\n\nif __name__ == \"__main__\":\n    T = int(input())\n\n    for test_case in range(1, T + 1):\n        N, S = input().split()\n        N = int(N)\n        grid = []\n        for _ in range(N):\n            line = list(map(int, input().split()))\n            grid.append(line)\n\n        s_list = [\"left\", \"down\", \"right\", \"up\"]\n        k = s_list.index(S) # S가 s_list에서 몇번째 값인지 -> k번 시계방향 회전, move_left, 4-k번 시계방향 회전(= 반시계방향 회전)\n\n        for _ in range(k):\n            grid = get_rotated(grid, N)\n\n        # print_grid(grid, N)\n        # print(\"==========\")\n        move_left(grid, N)\n        # print_grid(grid, N)\n        # print(\"==========\")\n        for _ in range(4 - k):\n            grid = get_rotated(grid, N)\n\n        print(\"#{}\".format(test_case))\n        # grid 출력\n        print_grid(grid, N)","repo_name":"ribo0715/algorithm_solution","sub_path":"SW Expert Academy/6109. 추억의 2048게임.py","file_name":"6109. 추억의 2048게임.py","file_ext":"py","file_size_in_byte":2483,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"69819032267","text":"\"\"\"Document retriever based on bm25 for comparision with default weight-tfidf model.\"\"\"\n\nimport sys\nsys.path.append('/home/zrx/projects/MbaQA/')\n\nfrom tqdm import tqdm\nfrom gensim import corpora\nfrom gensim.summarization import bm25\n\nfrom mbaqa import retriever, tokenizers\n\ndocdb = retriever.get_class('sqlite')()\ntokenizer = tokenizers.get_class('ltp')()\n\ntitles = docdb.get_doc_ids()[:]\nIDX2TITLE = {idx: titles[idx] for idx in range(len(titles))}\n\nstop_words_path = '../../data/stopwords/stopwords.txt'\nstopwords = []\nwith open(stop_words_path, encoding='utf8') as file:\n    for line in file:\n        stopwords.append(line.replace('\\n', '').strip())\n\n\ncorpus = []\nwith tqdm(total=len(titles)) as pbar:\n    for title in titles:\n        # Tokenize\n        tokens = tokenizer.tokenize(retriever.utils.normalize(docdb.get_doc_text(title)))\n\n        # Get ngrams from tokens, with stopword/punctuation filtering.\n        unigrams = tokens.ngrams(\n            n=1, uncased=True, filter_fn=retriever.utils.filter_ngram\n        )\n\n        corpus.append(unigrams)\n        pbar.update()\n\nunigrams_save_path = '../../data/output/unigrams-num=76437.txt'\nwith open(unigrams_save_path, 'w', encoding='utf8') as file:\n    for words in corpus:\n        file.write(' '.join(words))\n        file.write('\\n')\n\n\n","repo_name":"AprilGroup/DrQA-CN","sub_path":"scripts/retriever/bm25_corpus_gen.py","file_name":"bm25_corpus_gen.py","file_ext":"py","file_size_in_byte":1295,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"81"}
+{"seq_id":"27106602167","text":"from collections import defaultdict\nimport sys\nsys.setrecursionlimit(10 ** 7)\n\nn = int(input())\ng = defaultdict(set)\nfor _ in range(n - 1):\n    u, v = map(int, input().split())\n    u -= 1\n    v -= 1\n    g[u].add(v)\n    g[v].add(u)\n\nd = [0] * n\nsz = [1] * n\ndef dfs(x, n):\n    if d[x] > 0:\n        return\n\n    nv = []\n    d[x] = n\n    for v in g[x]:\n        if d[v] == 0 and v != 0:\n            nv.append(v)\n    for v in nv:\n        dfs(v, n + 1)\n        sz[x] += sz[v]\ndfs(0, 0)\n\nans = [0] * n\nans[0] = sum(d)\n\ndef dfs2(x):\n    for v in g[x]:\n        if ans[v] > 0:\n            continue\n        ans[v] = ans[x] + n - 2 * sz[v]\n        dfs2(v)\ndfs2(0)\n\nfor a in ans:\n    print(a)\n","repo_name":"silphire/atcoder","sub_path":"abc220/f.py","file_name":"f.py","file_ext":"py","file_size_in_byte":679,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"44202875729","text":"import numpy as np\nimport pandas as pd\n\n\nclass Massey:\n    \"\"\"\n    Calculates each team's Massey ratings\n\n    Parameters\n    ----------\n    goals_home : list\n        List of goals scored by the home teams\n\n    goals_away : list\n        List of goals scored by the away teams\n\n    teams_home : list\n        List of names of the home teams\n\n    teams_away : list\n        List of names of the away teams\n    \"\"\"\n\n    def __init__(\n        self,\n        goals_home,\n        goals_away,\n        teams_home,\n        teams_away,\n    ):\n        self.goals_home = goals_home\n        self.goals_away = goals_away\n        self.teams_home = teams_home\n        self.teams_away = teams_away\n\n    def get_ratings(self) -> pd.DataFrame:\n        \"\"\"\n        Gets the Massey ratings\n\n        Returns\n        -------\n            Returns a dataframe containing colley ratings per team\n        \"\"\"\n        teams = np.sort(np.unique(np.concatenate([self.teams_home, self.teams_away])))\n\n        fixtures = pd.DataFrame(\n            [self.goals_home, self.goals_away, self.teams_home, self.teams_away]\n        ).T\n        fixtures.columns = [\"goals_home\", \"goals_away\", \"team_home\", \"team_away\"]\n        fixtures[\"goals_home\"] = fixtures[\"goals_home\"].astype(int)\n        fixtures[\"goals_away\"] = fixtures[\"goals_away\"].astype(int)\n\n        M = _build_m(fixtures, teams)\n        p = _build_p(fixtures, teams)\n        r = _solve_ratings(M, p)\n\n        t = _build_t(fixtures, teams)\n        f = _build_f(fixtures, teams)\n        Tr_f = (np.diag(t) * r) - f\n        d = _solve_d(t, Tr_f)\n        o = r - d\n\n        res = pd.DataFrame([teams, r, o, d]).T\n        res.columns = [\"team\", \"rating\", \"offence\", \"defence\"]\n        res = res.sort_values(\"rating\", ascending=False)\n        res = res.reset_index(drop=True)\n        return res\n\n\ndef _build_m(fixtures, teams):\n    n_teams = len(teams)\n    M = np.zeros([n_teams, n_teams])\n    for _, row in fixtures.iterrows():\n        h = np.where(teams == row[\"team_home\"])[0][0]\n        a = np.where(teams == row[\"team_away\"])[0][0]\n\n        M[h, a] = M[h, a] - 1\n        M[a, h] = M[a, h] - 1\n\n    for i in range(len(M)):\n        M[i, i] = np.abs(\n            np.sum(\n                M[\n                    i,\n                ]\n            )\n        )\n\n    M = np.vstack((M, [1 for x in range(n_teams)]))\n\n    return M\n\n\ndef _build_p(fixtures, teams):\n    p = list()\n    for team in teams:\n        home = fixtures.query(\"team_home == @team\")\n        away = fixtures.query(\"team_away == @team\")\n\n        goals_for = home[\"goals_home\"].sum() + away[\"goals_away\"].sum()\n        goals_away = home[\"goals_away\"].sum() + away[\"goals_home\"].sum()\n\n        p.append(goals_for - goals_away)\n    p.append(0)\n\n    return p\n\n\ndef _solve_ratings(M, p):\n    ratings = np.linalg.lstsq(M, p, rcond=None)[0]\n    return ratings\n\n\ndef _solve_d(t, Tr_f):\n    ratings = np.linalg.lstsq(t, Tr_f, rcond=None)[0]\n    return ratings\n\n\ndef _build_t(fixtures, teams):\n    n_teams = len(teams)\n    t = np.zeros([n_teams, n_teams])\n\n    for _, row in fixtures.iterrows():\n        h = np.where(teams == row[\"team_home\"])[0][0]\n        a = np.where(teams == row[\"team_away\"])[0][0]\n\n        t[h, a] = t[h, a] + 1\n        t[a, h] = t[a, h] + 1\n\n    for i in range(len(t)):\n        t[i, i] = np.sum(\n            t[\n                i,\n            ]\n        )\n\n    return t\n\n\ndef _build_f(fixtures, teams):\n    f = list()\n    for team in teams:\n        home = fixtures.query(\"team_home == @team\")\n        away = fixtures.query(\"team_away == @team\")\n        goals_for = home[\"goals_home\"].sum() + away[\"goals_away\"].sum()\n        f.append(goals_for)\n    return f\n","repo_name":"martineastwood/penaltyblog","sub_path":"penaltyblog/ratings/massey.py","file_name":"massey.py","file_ext":"py","file_size_in_byte":3645,"program_lang":"python","lang":"en","doc_type":"code","stars":45,"dataset":"github-code","pt":"81"}
+{"seq_id":"29723496204","text":"#pythone programe to calculate total no of hours\r\n\r\nprint(\"Enter choice : 1)No of days 2) No of weeks 3) No of years\")\r\ni=int(input(\"choice:\"))\r\nif  i==1 :\r\n    d=float(input(\"No of days:\"))\r\n    z=d*24\r\n    print(f\"NO of days is {d} therefore total hours is{z}\")\r\nelif i==2 :\r\n    W= float(input(\"No of weeks:\"))\r\n    z=W*24*7\r\n    print(f\"NO of weeks is {W} therefore total hours is{z}\")\r\nelif i==3 :\r\n    Y = float(input(\"No of years:\"))\r\n    z=Y*365*24\r\n    print(f\"NO of years is {Y} therefore total hours is {z}\")  \r\nelse :\r\n    print(\"INVALID CHOICE!!!!\") \r\n\r\n\r\n\r\n\r\n\r\n\r\n    \r\n    ","repo_name":"Orianpax3003/hours-weeks-years","sub_path":"hours_in_weeks.py","file_name":"hours_in_weeks.py","file_ext":"py","file_size_in_byte":587,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"5878114776","text":"import scipy.odr.odrpack as odr\nfrom scipy.fft import rfftfreq, rfft, irfft\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom scipy.optimize import curve_fit\nimport csv\nimport os\nimport glob\n\n\ndef GaussH(b, t):\n    return b[1] / (np.sqrt(2 * np.pi) * b[0]) * np.e ** (- (((t - b[2]) / (np.sqrt(2) * b[0])) ** 2))\n\n\ndef FitAndGraph(x_dat, y_dat, beta):\n    # Desviacion de los datos\n\n    sigma_x = 0.008\n    sigma_y = 0.02\n\n    Aj_fin = odr.Model(GaussH)\n    mydata = odr.RealData(x_dat, y_dat, sx=sigma_x, sy=sigma_y)\n\n    myodr = odr.ODR(mydata, Aj_fin, beta0=beta[0])\n\n    # Corremos el ajuste y guardamos los resultados en la variable output\n    output = myodr.run()\n    beta[0] = output.beta\n    print(beta[0])\n\n    return output.beta\n\ndef ReadData(x, y, name):\n    path = os.getcwd()\n    csv_files = glob.glob(os.path.join(path, name))\n    i = -1\n    for f in csv_files:\n        i += 1\n        with open(f) as csv_file:\n            csv_reader = csv.reader(csv_file, delimiter=',')\n            for row in csv_reader:\n                x.append(float(row[0]))\n                y.append(float(row[1]))\n\n\ndef main():\n    name = input(\"introducir nombre del archivo de Ajuste: \")\n    Datos_x = []\n    Datos_y = []\n    ReadData(Datos_x, Datos_y, name)\n    Datos_x = np.array(Datos_x)\n    Datos_y = np.array(Datos_y)\n    Datos_x *= 0.05\n    Datos_y = Datos_y / max(Datos_y)\n    gauss = [[0.2, 1, 12]]\n    a = np.linspace(Datos_x[0], Datos_x[-1], 5000)\n    FitAndGraph(Datos_x, Datos_y, gauss)\n    plt.scatter(Datos_x, Datos_y, s=2.5, color=\"#236417\", zorder=3, label=\"Datos Co\")\n    plt.plot(a, GaussH(gauss[0], a), color=\"#750303\", zorder=2, label=\"Ajuste Gaussiano\")\n    plt.xlabel(\"tiempo (ms)\")\n    plt.ylabel(\"Cuentas normalizado\")\n    plt.legend()\n    plt.show()\n\n\nif __name__ == \"__main__\":\n    main()","repo_name":"dmtomas/Experimentos_Cuanticos_2","sub_path":"Experimento 3/Gaussiana.py","file_name":"Gaussiana.py","file_ext":"py","file_size_in_byte":1809,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"11562340404","text":"import sys, os\nfrom termcolor import cprint\nfrom typing import List\nimport textwrap\nimport numpy as np\nimport enum\n\nfrom amaranth import Elaboratable, Module, Signal, Mux, ClockSignal, ClockDomain, ResetSignal, Cat, Const\nfrom amaranth.hdl.ast import Rose, Stable, Fell, Past, Initial\nfrom amaranth.hdl.rec import DIR_NONE, DIR_FANOUT, DIR_FANIN, Layout, Record\nfrom amaranth.hdl.mem import Memory\nfrom amaranth.hdl.xfrm import DomainRenamer\nfrom amaranth.cli import main_parser, main_runner\nfrom amaranth.sim import Simulator, Delay, Tick, Passive, Active\nfrom amaranth.asserts import Assert, Assume, Cover, Past\nfrom amaranth.lib.fifo import AsyncFIFOBuffered\n#from amaranth.lib.cdc import AsyncFFSynchronizer\nfrom amaranth.lib.cdc import FFSynchronizer\nfrom amaranth.build import Platform\nfrom amaranth.utils import bits_for\n\nfrom amaranth_boards.ulx3s import ULX3S_85F_Platform\n\nfrom amlib.io import SPIRegisterInterface, SPIDeviceBus, SPIMultiplexer\nfrom amlib.debug.ila import SyncSerialILA\n# from amlib.utils.cdc import synchronize\nfrom amlib.utils import Timer\n\nfrom amtest.boards.ulx3s.common.upload import platform, UploadBase\nfrom amtest.boards.ulx3s.common.clks import add_clock\nfrom amtest.utils import FHDLTestCase\n\n\nclass Delayer(Elaboratable):\n\t\"\"\" \n\tDesired usage: \n\t\t...\n\t\twith m.If(delayer1.delay_for_time(m, 4e-6))\n\t\t\tm.next = \"REQUEST_REFRESH_SOON\"\n\t\t...\n\n\tIdeas:\n\t\t- What clock should this be fed? A slower clock?\n\t\t- How to handle short periods? Past(xxx, y)?\n\n\t\t- ok let's just stick with the sync clock here for now\t\n\n\t\"\"\"\n\tui_layout = [\n\t\t# (\"start\",\t\t1,\tDIR_FANOUT),\n\t\t(\"done\",\t\t1,\tDIR_FANIN),\n\t\t(\"inactive\",\t1,\tDIR_FANIN)\n\t\t# (\"load\",\t) # note - added in __init__\n\t\t# (\"active\",\t1,\tDIR_FANIN)\n\t]\n\n\tdebug_layout = [\n\t\t# (\"count\",\t32,\tDIR_FANIN)\n\t]\n\n\tdef __init__(self, clk_freq, utest: FHDLTestCase = None):\n\t\tsuper().__init__()\n\t\tself.utest = utest\n\t\tself.clk_freq = clk_freq\n\n\t\tif \"load\" not in [field for field, _, _ in Delayer.ui_layout]:\n\t\t\tDelayer.ui_layout.append((\"load\", self._get_counter_bitwidth(), DIR_FANOUT))\n\n\t\tself.ui = Record(Delayer.ui_layout)\n\t\t# self.debug = Record(Delayer.debug_layout)\n\n\n\tdef _get_counter_bitwidth(self):\n\t\tlongest_period = 1.1 * 100e-6 # is this an OK assumption?\n\t\treturn bits_for(int(np.ceil(longest_period * self.clk_freq)))\n\t\n\tdef delay_for_time(self, m, duration_sec):\n\t\tif isinstance(duration_sec, enum.Enum):\n\t\t\tduration_sec = duration_sec.value\n\n\t\tclks = int(np.ceil(duration_sec * self.clk_freq))\n\t\treturn self.delay_for_clks(m, clks)\n\t\t...\n\t\n\tdef delay_for_clks(self, m, clks):\n\t\t# note: This is designed as an interface, to be used by other modules.\n\t\t# This means that .sync here will probably not \n\t\t# recognise the use of DomainRenamer() if used.\n\t\t# That's why self.domain is passed as an init argument, so we can access it like this:\n\n\t\tprint(\"Clks is \", clks)\n\t\tassert clks > 0, \"Not enough clocks to delay in this way. todo: correct threshold\"\n\t\t\n\t\twith m.FSM(name=\"delay_fsm\"):#, domain=self.domain):\n\t\t\twith m.State(\"INIT\"):\n\t\t\t\tm.d.sync += self.ui.load.eq(clks)\n\t\t\t\tm.next = \"LOADED\"\n\t\t\twith m.State(\"LOADED\"):\n\t\t\t\tm.d.sync += self.ui.load.eq(0)\n\t\t\t\tm.next = \"DONE\"\n\t\t\twith m.State(\"DONE\"): # is this extra state needed? to avoid driving ui.load?\n\t\t\t\t...\n\n\t\treturn self.ui.done\n\n\tdef elaborate(self, platform):\n\t\tm = Module()\n\n\t\t_ui = Record.like(self.ui)\n\t\t# _debug = Record.like(self.debug)\n\t\tm.d.sync += [\n\t\t\tself.ui.connect(_ui),\n\t\t\t# self.debug.connect(_debug)\n\t\t]\n\n\t\tcountdown = Signal(shape=self._get_counter_bitwidth())\n\n\t\tdef add_countdown_behaviour():\n\t\t\tm.d.sync += [\n\t\t\t\tcountdown.eq(Mux(countdown>0, countdown-1, countdown)),\n\t\t\t\t_ui.done.eq(countdown==1), # so a single pulse will occur as it reaches 0\n\t\t\t\t_ui.inactive.eq( (countdown==0) & ~_ui.done )\n\t\t\t]\n\t\t\n\t\tdef add_reload_behaviour():\n\t\t\twith m.If(_ui.load != 0):\n\t\t\t\tm.d.sync += countdown.eq(_ui.load) # and plus one? or +/- a bias?\n\t\t\n\t\tadd_countdown_behaviour()\n\t\tadd_reload_behaviour()\n\n\t\treturn m\n\n\nclass timerTest(Elaboratable):\n\tui_layout = [\n\t\t(\"start\",\t1,\tDIR_FANOUT),\n\t\t(\"done\",\t1,\tDIR_FANIN),\n\t\t(\"active\",\t1,\tDIR_FANIN)\n\t]\n\n\tdebug_layout = [\n\t\t(\"count\",\t32,\tDIR_FANIN)\n\t]\n\n\tdef __init__(self, load, utest: FHDLTestCase = None):\n\t\tsuper().__init__()\n\t\tself.ui = Record(timerTest.ui_layout)\n\t\tself.debug = Record(timerTest.debug_layout)\n\t\tself.load = int(load - 5)\n\t\tself.utest = utest\n\n\tdef elaborate(self, platform: Platform) -> Module:\n\t\tm = Module()\n\n\t\t_ui = Record.like(self.ui)\n\t\t_debug = Record.like(self.debug)\n\t\tm.d.sync += [\n\t\t\tself.ui.connect(_ui),\n\t\t\tself.debug.connect(_debug)\n\t\t]\n\n\t\tm.submodules.delayer = delayer = Timer(load=self.load)\n\n\t\tm.d.sync += [\n\t\t\tdelayer.start.eq(_ui.start),\n\t\t\t_ui.done.eq(delayer.done),\n\t\t\t_debug.count.eq(delayer.counter_out),\n\t\t\t# _ui.active.eq(delayer.counter_out != self.load)\n\t\t]\n\n\t\twith m.If(Rose(_ui.start)):\n\t\t\tm.d.sync += _ui.active.eq(1)\n\t\twith m.Elif(Fell(_ui.done)):\n\t\t\tm.d.sync += _ui.active.eq(0)\n\n\t\t# note that if self.utest is none, then another class (not this one) is being tested, so skip this\n\t\tif (platform == \"formal\") & (self.utest != None):\n\t\t\ttest_id = self.utest.get_test_id()\n\t\t\tif test_id == \"tryToTest_timerTest\":\n\t\t\t\tm.d.sync += Assert(_ui.done != (_debug.count == 0))\n\n\t\treturn m\n\n\nif __name__==\"__main__\":\n\tfrom pathlib import Path\n\tcurrent_filename = str(Path(__file__).absolute()).split(\".py\")[0]\n\n\tparser = main_parser()\n\targs = parser.parse_args()\n\n\tclass Testbench(Elaboratable):\n\t\tTestbench_ui_layout = [\n\t\t\t(\"tb_done\", \t1,\tDIR_FANIN),\n\t\t] + Delayer.ui_layout # todo - how to make the sharedTimer.ui layout be nested?\n\n\t\tdef __init__(self, utest: FHDLTestCase = None):\n\t\t\tsuper().__init__()\n\t\t\tself.ui = Record(Testbench.Testbench_ui_layout)\n\t\t\t# self.leds = Signal(8, reset_less=True)\n\t\t\tself.utest = utest\n\n\t\tdef elaborate(self, platform = None):\n\t\t\tm = Module()\n\n\t\t\t# m.submodules.delayer = delayer = DomainRenamer(\"sync_1e6\")(Delayer(clk_freq=1e6, domain=\"sync_1e6\"))\n\t\t\tm.submodules.delayer = delayer = Delayer(clk_freq=24e6)\n\n\t\t\t_ui = Record(Testbench.Testbench_ui_layout) #.like(self.ui)\n\t\t\t# m.d.sync_1e6 += [\n\t\t\tm.d.sync += [\n\t\t\t\tself.ui.connect(_ui),\n\t\t\t\t_ui.connect(delayer.ui, exclude=[\"tb_done\"])\n\t\t\t]\n\t\t\t\t\n\n\t\t\tif isinstance(self.utest, FHDLTestCase):\n\t\t\t\tassert platform == None, f\"Unexpected platform status of {platform}\"\n\t\t\t\t# assert platform == \"formal\", \"This test can only run in formal mode\"\n\t\t\t\tadd_clock(m, \"sync\")\n\t\t\t\t# add_clock(m, \"sync_1e6\")\n\t\t\t\ttest_id = self.utest.get_test_id()\n\t\t\t\tif test_id == \"RefreshTimerTestCase\":\n\t\t\t\t\twith m.FSM(name=\"testbench_fsm\", domain=\"sync\") as fsm:\n\t\t\t\t\t\tm.d.sync += _ui.tb_done.eq(fsm.ongoing(\"DONE\"))\n\n\t\t\t\t\t\twith m.State(\"INITIAL\"):\n\t\t\t\t\t\t\tm.next = \"START\"\n\n\t\t\t\t\t\twith m.State(\"START\"):\n\t\t\t\t\t\t\twith m.If(delayer.delay_for_time(m, 2e-6)):\n\t\t\t\t\t\t\t\tm.next = \"START2\"\n\n\t\t\t\t\t\twith m.State(\"START2\"):\n\t\t\t\t\t\t\twith m.If(delayer.delay_for_time(m, 3e-6)):\n\t\t\t\t\t\t\t\tm.next = \"DONE\"\n\t\t\t\t\t\t\n\t\t\t\t\t\twith m.State(\"DONE\"):\n\t\t\t\t\t\t\t...\n\n\t\t\telif isinstance(platform, ULX3S_85F_Platform): \n\t\t\t\t# then this is the test that is run when uploaded\n\t\t\t\t...\n\n\t\t\treturn m\n\n\n\tclass Testbench2(Elaboratable):\n\t\ttimerTest_test_interface_layout = [\n\t\t\t# (\"reset\",\t\t\t1, DIR_FANOUT), # note - this doesn't seem to show in traces, but still works?\n\t\t\t# (\"leds\", \t\t\t8, DIR_FANOUT) # can't do reset-less here, so using a separate signal\n\t\t] + timerTest.ui_layout\n\n\t\t\n\t\tdef __init__(self, utest: FHDLTestCase = None):\n\t\t\tsuper().__init__()\n\t\t\tself.ui = Record(Testbench.timerTest_test_interface_layout)\n\t\t\tself.leds = Signal(8, reset_less=True)\n\t\t\tself.utest = utest\n\n\t\tdef elaborate(self, platform = None):\n\t\t\tm = Module()\n\n\t\t\tm.submodules.dut = dut = DomainRenamer(\"sync_1e6\")(timerTest(load = 1e2))# if platform == None else 1e6))\n\n\t\t\tui = Record.like(self.ui)\n\t\t\tdebug = Record.like(dut.debug)\n\t\t\tm.d.sync_1e6 += [\n\t\t\t\tself.ui.connect(ui),\n\t\t\t\tui.connect(dut.ui),\n\t\t\t\tdebug.connect(dut.debug)\n\t\t\t]\n\n\t\t\t# change a led flag each time one of these rises, so we can see quick changes\n\t\t\t# for i, each in enumerate([ui.start, ui.done, ui.reset, ui.active]):\n\t\t\t# \twith m.If(Rose(each)):\n\t\t\t# \t\tm.d.sync += self.leds[i].eq(~self.leds[i])\n\t\t\tm.d.sync_1e6 += self.leds.eq(debug.count[15:])\n\n\t\t\tif (platform == \"formal\") & (self.utest != None):\n\t\t\t\ttest_id = self.utest.get_test_id()\n\t\t\t\tif test_id == \"tryToTest_Testbench\":\n\t\t\t\t\tm.d.comb += Cover(ui.done)\n\n\t\t\treturn m\n\n\tif args.action == \"generate\":\n\n\t\tclass testDesiredInterface_withExpectedBehaviour(FHDLTestCase):\n\t\t\tdef test_sim(self): # note, it's only formal if assertFormal() is used\n\t\t\t\tdef test(load):\n\t\t\t\t\tdut = Testbench(utest=self)\n\t\t\t\t\t\n\t\t\t\t\tdef process():\n\t\t\t\t\t\t# elapsed_clks = 0\n\n\t\t\t\t\t\tfor _ in range(200):\n\t\t\t\t\t\t\tif not (yield dut.ui.tb_done):\n\t\t\t\t\t\t\t\tyield\n\n\t\t\t\t\t\t# while not (yield dut.ui.tb_done):\n\t\t\t\t\t\t# \tyield\n\t\t\t\t\t\t# yield dut.ui.start.eq(1); \n\t\t\t\t\t\t# yield; elapsed_clks += 1\n\t\t\t\t\t\t# yield dut.ui.start.eq(0)\n\t\t\t\t\t\t# while not (yield dut.ui.done):\n\t\t\t\t\t\t# \tyield; elapsed_clks += 1\n\t\t\t\t\t\t\n\t\t\t\t\t\t# print(f\"Elapsed clks: {elapsed_clks}, load: {load}, elapsed_clks-load={elapsed_clks-load}\")\n\t\t\t\t\t\t\n\t\t\t\t\t\t# for x in range(2*load): # arbitary\n\t\t\t\t\t\t# \tyield\n\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\tsim = Simulator(dut)\n\t\t\t\t\tsim.add_clock(1/25e6, domain=\"sync\")\n\t\t\t\t\tsim.add_sync_process(process)\n\n\t\t\t\t\twith sim.write_vcd(\n\t\t\t\t\t\tf\"{current_filename}_{self.get_test_id()}_load={load}.vcd\"):\n\t\t\t\t\t\tsim.run()\n\n\t\t\t\t[test(load) for load in range(10, 15) ]\n\n\t\t# class tryToTest_timerTest(FHDLTestCase):\n\t\t# \tdef test_formal(self):\n\t\t# \t\tdef generic_test(load):\n\t\t# \t\t\tdut = timerTest(load, utest=self)\n\t\t# \t\t\tself.assertFormal(dut, mode=\"bmc\", depth=load+1)\n\t\t# \t\t[generic_test(load) for load in [2, 5, 10]]\n\n\t\t# class tryToTest_Testbench(FHDLTestCase):\n\t\t# \tdef test_formal(self):\n\t\t# \t\tdut = Testbench(utest=self)\n\t\t# \t\tself.assertFormal(dut, mode=\"cover\", depth=200) # why 200? arbitary?\n\n\t\t# class RefreshTimerTestCase3(FHDLTestCase):\n\t\t# \tdef test_formal(self):\n\t\t# \t\ttREFI = 5\n\t\t# \t\tdut = RefreshTimer(tREFI, utest=self)\n\t\t# \t\tself.assertFormal(dut, mode=\"hybrid\", depth=tREFI+1)\n\n\t\timport unittest\n\t\tsys.argv[1:] = [] # so the args used for this file don't interfere with unittest\n\t\tunittest.main()\n\t\n\telif args.action == \"simulate\":\n\t\tclass Simulate(Testbench):\n\t\t\tdef __init__(self):\n\t\t\t\tsuper().__init__()\n\t\t\t\tself.reset_sync_1e6 = Signal(reset_less=True)\n\n\t\t\tdef elaborate(self, platform = None):\n\t\t\t\tm = super().elaborate(platform)\n\n\t\t\t\t# add slower clock for counters (i.e. so they don't limit speed)\t\t\t\t\t\n\t\t\t\tadd_clock(m, \"sync_1e6\")\n\t\t\t\tadd_clock(m, \"sync\")\n\n\t\t\t\t# note that this workaround is needed because the simulation\n\t\t\t\t# can't work with ResetSignal() directly for some reason\n\t\t\t\tm.d.sync_1e6 += ResetSignal(\"sync_1e6\").eq(self.reset_sync_1e6)\n\t\t\t\t\n\t\t\t\treturn m\n\t\t\t\n\t\t\tdef timer_test(self):\n\n\t\t\t\tdef strobe(signal):\n\t\t\t\t\tfor _ in range(2):\n\t\t\t\t\t\tprev_value = yield signal\n\t\t\t\t\t\tyield signal.eq(~prev_value)\n\t\t\t\t\t\tyield\n\n\t\t\t\tyield Active()\n\n\t\t\t\tfor repeat in range(3):\n\n\t\t\t\t\tyield Delay(1e-6) # delay at start\n\n\t\t\t\t\tyield from strobe(self.ui.start)\n\n\t\t\t\t\tif True: # often this block will hang if the design is broken\n\t\t\t\t\t\twhile not (yield self.ui.done):\n\t\t\t\t\t\t\tyield\n\t\t\t\t\telse: # in that case, use this instead\n\t\t\t\t\t\tyield Delay(100e-6)\n\n\t\t\t\t\tyield Delay(1e-6) # delay at end\n\n\t\t\t\t\t# now do a reset\n\t\t\t\t\tyield from strobe(self.reset_sync_1e6)\n\t\t\t\t\n\t\t\t\tyield self.reset_sync_1e6.eq(1)\n\t\t\t\tyield Delay(100e-6)\n\n\t\ttop = Simulate()\n\t\tsim = Simulator(top)\n\t\tsim.add_clock(1/25e6, domain=\"sync\")\n\t\tsim.add_sync_process(top.timer_test, domain=\"sync_1e6\")\n\n\t\twith sim.write_vcd(\n\t\t\tf\"{current_filename}_simulate.vcd\",\n\t\t\tf\"{current_filename}_simulate.gtkw\"):\n\n\t\t\tsim.run()\n\n\telse: # then upload\n\t\tclass Upload(UploadBase):\n\t\t\tdef __init__(self):\n\t\t\t\tsuper().__init__()\n\t\t\t\t\n\t\t\tdef elaborate(self, platform = None):\n\t\t\t\tm = super().elaborate(platform)\n\n\t\t\t\tm.submodules.tb = tb = Testbench()\t\n\n\t\t\t\tui = Record.like(tb.ui)\n\t\t\t\tm.d.sync_1e6 += ui.connect(tb.ui)\n\n\t\t\t\tstart = Signal.like(ui.start)\n\n\t\t\t\t# don't manually route the reset - do this, \n\t\t\t\t# otherwise, if Records are used, they will oscillate, as can't be reset_less\n\t\t\t\tm.d.sync_1e6 += ResetSignal(\"sync_1e6\").eq(self.i_buttons.right) \n\n\t\t\t\t# reset = Signal.like(ui.reset, reset_less=True) \n\t\t\t\tm.d.sync_1e6 += [\n\t\t\t\t\tstart.eq(self.i_buttons.left),\n\t\t\t\t\tui.start.eq(Rose(start, domain=\"sync_1e6\")),\n\t\t\t\t\tself.leds.eq(tb.leds),\n\t\t\t\t]\n\n\t\t\t\treturn m\n\t\t\n\t\tplatform.build(Upload(), do_program=False, build_dir=f\"{current_filename}_build\")\n\n\n\n","repo_name":"dicethrow/amtest","sub_path":"tests2_feb2022/test6_formal_verification_timer.py","file_name":"test6_formal_verification_timer.py","file_ext":"py","file_size_in_byte":12426,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"74852955783","text":"import torch\nimport torch.nn as nn\nfrom src.gumbel_social_transformer.gumbel_social_transformer import GumbelSocialTransformer\nfrom src.gumbel_social_transformer.temporal_convolution_net import TemporalConvolutionNet\n\n\ndef offset_error_square_full_partial(x_pred, x_target, loss_mask_ped, loss_mask_pred_seq):\n    assert x_pred.shape[0] == loss_mask_ped.shape[0] == loss_mask_pred_seq.shape[0] == 1\n    assert x_pred.shape[1] == x_target.shape[1] == loss_mask_pred_seq.shape[2]\n    assert x_pred.shape[2] == x_target.shape[2] == loss_mask_ped.shape[1] == loss_mask_pred_seq.shape[1]\n    assert x_pred.shape[3] == x_target.shape[3] == 2\n    loss_mask_rel_pred = loss_mask_pred_seq.permute(0, 2, 1).unsqueeze(-1)\n    x_pred_m = x_pred * loss_mask_rel_pred\n    x_target_m = x_target * loss_mask_rel_pred\n    x_pred_m = x_pred_m * loss_mask_ped.unsqueeze(1).unsqueeze(-1)\n    x_target_m = x_target_m * loss_mask_ped.unsqueeze(1).unsqueeze(-1)\n\n    pos_pred = torch.cumsum(x_pred_m, dim=1)\n    pos_target = torch.cumsum(x_target_m, dim=1)\n    offset_error_sq = (((pos_pred-pos_target)**2.).sum(3))[0]\n\n    eventual_loss_mask = loss_mask_rel_pred[0,:,:,0] * loss_mask_ped[0]\n    offset_error_sq = offset_error_sq * eventual_loss_mask\n\n    return offset_error_sq, eventual_loss_mask\n\n\nclass st_model(nn.Module):\n\n    def __init__(self, args, device='cuda:0'):\n        super(st_model, self).__init__()\n        if args.spatial == 'gumbel_social_transformer':\n            self.node_embedding = nn.Linear(args.motion_dim, args.embedding_size).to(device)\n            self.edge_embedding = nn.Linear(args.motion_dim, 2 * args.embedding_size).to(device)\n            self.gumbel_social_transformer = GumbelSocialTransformer(\n                args.embedding_size,\n                args.spatial_num_heads,\n                args.spatial_num_heads_edges,\n                args.spatial_num_layers,\n                dim_feedforward=128,\n                dim_hidden=32,\n                dropout=0.1,\n                activation=\"relu\",\n                attn_mech=\"vanilla\",\n                ghost=args.ghost,\n            ).to(device)\n        else:\n            raise RuntimeError('The spatial component is not found.')\n        if args.temporal == 'temporal_convolution_net':\n            self.temporal_conv_net = TemporalConvolutionNet(\n                in_channels=args.embedding_size,\n                out_channels=args.output_dim,\n                dim_hidden=32,\n                nconv=6,\n                obs_seq_len=args.obs_seq_len,\n                pred_seq_len=args.pred_seq_len).to(device)\n        else:\n            raise RuntimeError('The temporal component is not tcn.')\n        self.args = args\n\n    def raw2gaussian(self, prob_raw):\n        mu = prob_raw[:,:,:,:2]\n        sx, sy = torch.exp(prob_raw[:,:,:,2:3]), torch.exp(prob_raw[:,:,:,3:4])\n        corr = torch.tanh(prob_raw[:,:,:,4:5])\n        gaussian_params = (mu, sx, sy, corr)\n        return gaussian_params\n\n    def sample_gaussian(self, gaussian_params, device='cuda:0', detach_sample=False, sampling=True):\n        mu, sx, sy, corr = gaussian_params\n        if sampling:\n            if detach_sample:\n                mu, sx, sy, corr = mu.detach(), sx.detach(), sy.detach(), corr.detach()\n            sample_unit = torch.empty(mu.shape).normal_().to(device)\n            sample_unit_x, sample_unit_y = sample_unit[:,:,:,0:1], sample_unit[:,:,:,1:2]\n            sample_x = sx*sample_unit_x\n            sample_y = corr*sy*sample_unit_x+((1.-corr**2.)**0.5)*sy*sample_unit_y\n            sample = torch.cat((sample_x, sample_y), dim=3)+mu\n        else:\n            sample = mu\n        return sample\n\n\n    def edge_evolution(self, xt_plus, At, device='cuda:0'):\n        xt_plus = xt_plus[0,0]\n        At = At[0, 0]\n        num_nodes, motion_dim = xt_plus.shape\n        xt_plus_row = torch.ones(num_nodes,num_nodes,motion_dim).to(device)*xt_plus.view(num_nodes,1,motion_dim)\n        xt_plus_col = torch.ones(num_nodes,num_nodes,motion_dim).to(device)*xt_plus.view(1,num_nodes,motion_dim)\n        At_plus = At + (xt_plus_row - xt_plus_col)\n        At_plus = At_plus.unsqueeze(0).unsqueeze(0)\n        return At_plus\n\n    def forward(self, x, A, attn_mask, loss_mask_rel, tau=1., hard=False, sampling=True, device='cuda:0'):\n        info = {}\n        loss_mask_per_pedestrian = (loss_mask_rel[0].sum(1)==self.args.obs_seq_len+self.args.pred_seq_len).float().unsqueeze(0)\n        if self.args.only_observe_full_period:\n            assert loss_mask_per_pedestrian.shape[0] == 1\n            attn_mask = []\n            for tt in range(self.args.obs_seq_len):\n                attn_mask.append(torch.outer(loss_mask_per_pedestrian[0], loss_mask_per_pedestrian[0]).float())\n            attn_mask = torch.stack(attn_mask, dim=0).unsqueeze(0)\n        \n        if self.args.spatial == 'gumbel_social_transformer':\n            x_embedding = self.node_embedding(x)[0]\n            A_embedding = self.edge_embedding(A)[0]\n            attn_mask = attn_mask[0].permute(0,2,1)\n            xs, sampled_edges, edge_multinomial, attn_weights = self.gumbel_social_transformer(x_embedding, A_embedding, attn_mask, tau=tau, hard=hard, device=device)\n            xs = xs.unsqueeze(0)\n            info['sampled_edges'], info['edge_multinomial'], info['attn_weights'] = sampled_edges, edge_multinomial, attn_weights\n        else:\n            raise RuntimeError(\"The spatial component is not found.\")\n        \n        if self.args.only_observe_full_period:\n            loss_mask_rel_full_partial = loss_mask_per_pedestrian[0]\n        else:\n            loss_mask_rel_obs = loss_mask_rel[0,:,:self.args.obs_seq_len]\n            loss_mask_rel_full_partial = loss_mask_rel_obs[:,-1]\n\n\n        if self.args.decode_style == 'readout':\n            xs = xs * loss_mask_rel_obs.permute(1,0).unsqueeze(-1)\n            xs = xs * loss_mask_rel_full_partial.unsqueeze(-1)\n            if self.args.temporal == 'temporal_convolution_net':\n                prob_raw_pred = self.temporal_conv_net(xs)\n            else:\n                raise RuntimeError('The temporal component can only be tcn for readout decode_style.')\n            x_sample_pred, A_sample_pred = [], []\n            A_sample = A[:, -1:]\n            for tt in range(self.args.pred_seq_len):\n                prob_raw = prob_raw_pred[:, tt:tt+1]\n                gaussian_params = self.raw2gaussian(prob_raw)\n                x_sample = self.sample_gaussian(gaussian_params, device=device, detach_sample=self.args.detach_sample, sampling=sampling)\n                A_sample = self.edge_evolution(x_sample, A_sample, device=device)\n                x_sample_pred.append(x_sample)\n                A_sample_pred.append(A_sample)\n            x_sample_pred = torch.cat(x_sample_pred, dim=1)\n            A_sample_pred = torch.cat(A_sample_pred, dim=1)\n            gaussian_params_pred = self.raw2gaussian(prob_raw_pred)\n            info['A_sample_pred'] = A_sample_pred\n            info['loss_mask_rel_full_partial'] = loss_mask_rel_full_partial.unsqueeze(0)\n            info['loss_mask_per_pedestrian'] = loss_mask_per_pedestrian\n            results = (gaussian_params_pred, x_sample_pred, info)\n            return results\n        else:\n            raise RuntimeError(\"The decoder style is not found.\")","repo_name":"tedhuang96/gst","sub_path":"src/gumbel_social_transformer/st_model_tcn.py","file_name":"st_model_tcn.py","file_ext":"py","file_size_in_byte":7239,"program_lang":"python","lang":"en","doc_type":"code","stars":31,"dataset":"github-code","pt":"81"}
+{"seq_id":"74437051466","text":"import os\r\nfrom sre_parse import fix_flags\r\nimport sys\r\nimport time\r\nimport numpy as np\r\nimport random\r\nimport argparse\r\nimport ast\r\nfrom tqdm import trange\r\nimport logging\r\n\r\nimport torch\r\nimport torch.nn as nn\r\nimport torch.optim as optim\r\nimport torch.nn.functional as F\r\nfrom torch.utils.data import DataLoader\r\n\r\nfrom tensorboardX import SummaryWriter\r\n\r\nfrom lib.dataset.viper_video_dataset import viper_video_dataset, IterLoader\r\nfrom lib.dataset.cityscapes_video_dataset import cityscapes_video_dataset, IterLoader\r\nfrom lib.model.one_stage_img_model import SFC_one_stage_model\r\nfrom lib.metrics import runningScore\r\n\r\nignore_index = -1\r\nlabel_name=[\"road\", \"sidewalk\", \"building\", \"fence\", \"light\", \"sign\", \"vegetation\", \"terrain\", \"sky\", \"person\", \"car\", \"truck\", \"bus\", \"motocycle\", \"bicycle\"]\r\nNUM_CLASS = 15\r\n\r\ndef get_arguments():\r\n    parser = argparse.ArgumentParser(description=\"Train SFC in Stage One\")\r\n    ###### general setting ######\r\n    parser.add_argument(\"--exp_name\", type=str, help=\"exp name\")\r\n\r\n    ###### training setting ######\r\n    parser.add_argument(\"--weight_deeplabv2_res101\", type=str, help=\"path to deeplabv2_res101 pretrained weight\")\r\n    parser.add_argument(\"--weight_flownet_global\", type=str, help=\"path to flownet_global pretrained weight\")\r\n    parser.add_argument(\"--model_name\", type=str, help=\"one stage model: update or reference?\")\r\n    parser.add_argument(\"--weight_SFM\", type=str, help=\"path to aux flownet pretrained weight\")\r\n    parser.add_argument(\"--multi_level\", action=\"store_true\", help=\"multi-level training, following DAISS, e.g., ADVENT, PixMatch\")\r\n    parser.add_argument(\"--lr\", type=float, help=\"learning rate\")\r\n    parser.add_argument(\"--distance\", type=int, default=2)\r\n    parser.add_argument(\"--scale_factor\", type=int, default=100)\r\n    parser.add_argument(\"--loss_flow\", type=float, help=\"loss_flow\", default=0.2)\r\n    parser.add_argument(\"--source_batch_size\", type=int)\r\n    parser.add_argument(\"--target_batch_size\", type=int)\r\n    parser.add_argument(\"--test_batch_size\", type=int)\r\n    parser.add_argument(\"--train_num_workers\", type=int)\r\n    parser.add_argument(\"--test_num_workers\", type=int)\r\n    parser.add_argument(\"--train_iterations\", type=int)\r\n    parser.add_argument(\"--log_interval\", type=int)\r\n    parser.add_argument(\"--val_interval\", type=int)\r\n    parser.add_argument(\"--work_dirs\", type=str)\r\n\r\n    return parser.parse_args()\r\n\r\ndef train():\r\n\r\n    args = get_arguments()\r\n    print(args)\r\n\r\n    # tensorboard setting\r\n    if not os.path.exists(os.path.join(args.work_dirs, args.exp_name)):\r\n        os.makedirs(os.path.join(args.work_dirs, args.exp_name))\r\n    tblogger = SummaryWriter(os.path.join(args.work_dirs, args.exp_name))\r\n\r\n    # log settings\r\n    logger = logging.getLogger()\r\n    logger.setLevel(logging.INFO)\r\n    rq = time.strftime('%Y%m%d%H%M', time.localtime(time.time()))\r\n    fh = logging.FileHandler(os.path.join(args.work_dirs, args.exp_name, '{}.log'.format(rq)), mode='w')\r\n    fh.setLevel(logging.INFO)\r\n    formatter = logging.Formatter(\"%(asctime)s - %(levelname)s: %(message)s\")\r\n    fh.setFormatter(formatter)\r\n    logger.addHandler(fh)\r\n\r\n    # random seed setting\r\n    random_seed = 0\r\n    torch.manual_seed(random_seed)\r\n    torch.cuda.manual_seed(random_seed)\r\n    np.random.seed(random_seed)\r\n    random.seed(random_seed)\r\n    print('random seed:{}'.format(random_seed))\r\n\r\n    # init net\r\n    net = SFC_one_stage_model(weight_deeplabv2_res101=args.weight_deeplabv2_res101, weight_flownet_global=args.weight_flownet_global, weight_SFM=args.weight_SFM, scale=args.scale_factor).cuda()\r\n\r\n    # init optimizer\r\n    params = net.segnet.optim_parameters(lr=args.lr)\r\n    optimizer = optim.SGD(params=params, lr=args.lr, weight_decay=0.0005, momentum=0.9)\r\n\r\n    # Source and target dataset (copy ADVENT)\r\n    source_data = viper_video_dataset(split='train', distance=args.distance)\r\n    source_loader = DataLoader(\r\n        source_data,\r\n        batch_size=args.source_batch_size,\r\n        shuffle=True,\r\n        num_workers=args.train_num_workers,\r\n        drop_last=True,\r\n        pin_memory=True\r\n    )\r\n    source_loader = IterLoader(source_loader)\r\n\r\n    \r\n    target_data = cityscapes_video_dataset(split='train', distance=args.distance)\r\n    target_loader = DataLoader(\r\n        target_data,\r\n        batch_size=args.target_batch_size,\r\n        shuffle=True,\r\n        num_workers=args.train_num_workers,\r\n        drop_last=True,\r\n        pin_memory=True\r\n    )\r\n    target_loader = IterLoader(target_loader)\r\n\r\n    test_data = cityscapes_video_dataset(split='val', distance=args.distance)\r\n    test_loader = DataLoader(\r\n        test_data,\r\n        batch_size=args.test_batch_size,\r\n        shuffle=False,\r\n        num_workers=args.test_num_workers,\r\n        drop_last=False,\r\n        pin_memory=True\r\n    )\r\n\r\n    # metrics setting\r\n    miou_cal = runningScore(n_classes=15)\r\n    best_city_miou = 0.0\r\n\r\n    # start training\r\n    for step in trange(args.train_iterations):\r\n        net.train()\r\n        net.SFM.eval()\r\n        net.flownet_global.eval()\r\n\r\n        # reset optimizers\r\n        optimizer.zero_grad()\r\n\r\n        # adapt LR if needed\r\n        lr = poly_lr_scheduler(args, optimizer, iter=step)\r\n\r\n        # init loss\r\n        total_loss = 0\r\n        \r\n        # train on source\r\n        im_seg_list, im_flow_list, gt = next(source_loader)\r\n        loss = net(im_seg_list.cuda(), im_flow_list.cuda(), gt=gt.cuda())\r\n        source_loss = loss.mean()\r\n        source_loss.backward()\r\n        total_loss += source_loss\r\n\r\n        # train on target\r\n        im_seg_list, im_flow_list, _ = next(target_loader)\r\n        loss_flow = net(im_seg_list.cuda(), im_flow_list.cuda(), SFC=True)\r\n        loss_flow = loss_flow * args.loss_flow\r\n        loss_flow.backward()\r\n        total_loss += loss_flow\r\n\r\n        optimizer.step()\r\n\r\n        if (step + 1) % args.log_interval == 0:\r\n            print('iter:{}/{} lr:{:.6f} source_loss:{:.6f} loss_flow:{:.6f} total_loss:{:.6f}'.format(step + 1, args.train_iterations, lr, source_loss.item(), loss_flow.item(), total_loss.item()))\r\n            logger.info('iter:{}/{} lr:{:.6f} source_loss:{:.6f} loss_flow:{:.6f} total_loss:{:.6f}'.format(step + 1, args.train_iterations, lr, source_loss.item(), loss_flow.item(), total_loss.item()))\r\n            tblogger.add_scalar('lr', lr, step + 1)\r\n            tblogger.add_scalar('source_loss', source_loss.item(), step + 1)\r\n            tblogger.add_scalar('flow_loss', loss_flow.item(), step + 1)\r\n            tblogger.add_scalar('total_loss', total_loss.item(), step + 1)\r\n\r\n        if (step + 1) % args.val_interval == 0:\r\n        # if 1:\r\n            print('begin validation')\r\n            logger.info('begin validation')\r\n\r\n            net.eval()\r\n\r\n            # test city\r\n            test_loader_iter = iter(test_loader)\r\n            with torch.no_grad():\r\n                i = 0\r\n                for data in test_loader_iter:\r\n                    i += 1\r\n                    if i % 20 ==0 :\r\n                        print(\"test {}/{}\".format(i,len(test_loader)))\r\n                    im_seg_list, im_flow_list, gt = data\r\n                    pred = net(im_seg_list.cuda(), im_flow_list.cuda())\r\n                    out = torch.argmax(pred, dim=1)\r\n                    out = out.squeeze().cpu().numpy()\r\n                    gt = gt.squeeze().cpu().numpy()\r\n                    miou_cal.update(gt, out)\r\n                miou, miou_class = miou_cal.get_scores(return_class=True)\r\n                miou_cal.reset()\r\n\r\n            tblogger.add_scalar('miou_city/miou', miou, step + 1)\r\n            for i in range(int(NUM_CLASS)):\r\n                tblogger.add_scalar('miou_city/{}_miou'.format(label_name[i]), miou_class[i], step + 1)\r\n            if miou > best_city_miou:\r\n                best_city_miou = miou\r\n                save_path = os.path.join(args.work_dirs, args.exp_name, 'SFC_one_stage_{}.pth'.format(args.model_name))\r\n                torch.save(net.segnet.state_dict(), save_path)\r\n            print('step:{} current city miou:{:.4f} best city miou:{:.4f}'.format(step + 1, miou, best_city_miou))\r\n            logger.info('step:{} current city miou:{:.4f} best city miou:{:.4f}'.format(step + 1, miou, best_city_miou))\r\n\r\n\r\n\r\ndef poly_lr_scheduler(args, optimizer, init_lr=None, iter=None, max_iter=None, power=None):\r\n    init_lr = args.lr\r\n    max_iter = args.train_iterations\r\n    power = 0.9\r\n    new_lr = init_lr * (1 - float(iter) / max_iter) ** power\r\n    optimizer.param_groups[0][\"lr\"] = new_lr\r\n    if len(optimizer.param_groups) == 2:\r\n        optimizer.param_groups[1][\"lr\"] = 10 * new_lr\r\n    return new_lr\r\n\r\nif __name__ == '__main__':\r\n    train()\r\n","repo_name":"EdenHazardan/SFC","sub_path":"exp/SFC/stage_one/python/train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":8709,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"81"}
+{"seq_id":"39931403207","text":"#/usr/bin/env python3\n\"\"\"\nTrilinos Status Table Automation Script\n      \n      <python> table_Automation.py --prstatus <int>[0-2] --merged <int> --wip <int> --reviewed <int> \n                                        --changerequest <int> --reviewaproved <int> --failedprs <int> --totalopen <int> --mmstatus <int>[0-2] --mmmerges <int> --jira <int>\n\n      python table_Automation.py --prstatus [0-2] --merged 2 --wip 3 --reviewed 4 --changerequest 5 --reviewaproved 6 --failedprs 7 --totalopen 8 --mmstatus [0-2] --mmmerges 10 --jira 11\n\n      This script prints out todays Trilinos status in MarkDown syntax so that it can be added to the Trilinos Wiki page.\n    \n** Dependencies needed to run script:\n    \n    pip3 install python-dateutil\n    pip3 install pytablewriter\n    \n    \nSYNOPSIS                                                                                                                                 \n\n<python> table_Automation.py --prstatus <int>[0-2] --merged <int> --wip <int> --reviewed <int> --changerequest <int> --reviewaproved <int> --failedprs <int> --totalopen <int> --mmstatus <int>[0-2] --mmmerges <int> --jira <int>\n\n#STATUS --prstatus --mmstatus : [0] == success, [1] == warning [2] == failure -> Pull Request(s) and Master Merge Status codes.\n\n<User inputs eleven int value(s) from terminal>\n\npython table_Automation.py --prstatus [0-2] --merged 2 --wip 3 --reviewed 4 --changerequest 5 --reviewaproved 6 --failedprs 7 --totalopen 8 --mmstatus [0-2] --mmmerges 10 --jira 11\n\"\"\"\nfrom time import sleep as pause\nfrom datetime import timedelta\n\nfrom datetime import date\nimport argparse\nimport sys\n\nSTOP = 0b11\nONE = 0b1\nONE_OFF = -ONE\nDUNDER_MAIN = '__main__'\n\ntry:\n  import pytablewriter as ptw\n  from dateutil.relativedelta import relativedelta, FR\n\nexcept ImportError:\n  print(\"\\n\\nHere are a list of needed dependencies ... \\n\\n\\n\")\n  \n  pause(STOP)\n\n  print(\"\\t*** Package(s) that need to be installed ***\\n \\n\\tpip3 install python-dateutil \" \n        + \"\\n\\tpip3 install pytablewriter \\n\\n\\n\\t*** Install and try running again ***\\n\\n\\n\")\n  \n  pause(STOP)  \n\ndef main():\n    #STATUS: [0] == success, [1] == warning [2] == failure\n\n    pr_status = [\":white_check_mark:\", \":warning:\", \":x:\"] \n\n    mm_status = [\":white_check_mark:\", \":warning:\", \":x:\"]\n    \n    today = date.today()\n\n    yesterday = today - timedelta(days = ONE)\n\n    try:\n        last_Friday = today + relativedelta(weekday=FR(ONE_OFF))\n    \n    except:\n        print(\"Veiw needed dependencies above\\n\\n\")\n\n    try:\n        parser = argparse.ArgumentParser()\n        \n        parser.add_argument(\"--prstatus\", type=int, required=True, help=\"Pull Request Status\")\n        \n        parser.add_argument(\"--merged\", type=int, required=True, help=\"Pull Request Merged Past 24 Hrs\")\n        \n        parser.add_argument(\"--wip\", type=int, required=True, help=\"WIP Pull Request @ 12pm\")\n        \n        parser.add_argument(\"--reviewed\", type=int, required=True, help=\"Review Required, Pull Request @ 12pm\")\n        \n        parser.add_argument(\"--changerequest\", type=int, required=True, help=\"Change Requested Pull Request @ 12pm\")\n        \n        parser.add_argument(\"--reviewaproved\", type=int, required=True, help=\"Review Approved, Pull Request @ 12pm\")\n        \n        parser.add_argument(\"--failedprs\", type=int, required=True, help=\"Failed Pull Request @ 12pm\")\n\n        parser.add_argument(\"--totalopen\", type=int, required=True, help=\"Total Open Pull Request @ 12pm\")\n        \n        parser.add_argument(\"--mmstatus\", type=int, required=True, help=\"Master Merge Status\")\n        \n        parser.add_argument(\"--mmmerges\", type=int, required=True, help=\"Master Merge(s) last 24Hr's\")\n        \n        parser.add_argument(\"--jira\", type=int, required=True, help=\"Jira tickets, if needed\")\n        \n        args = parser.parse_args()\n       \n        pull_request_status = args.prstatus\n\n        number_of_pr_merged = args.merged \n        \n        number_wip_prs = args.wip\n        \n        number_reviewed_required = args.reviewed \n        \n        number_change_requested = args.changerequest \n        \n        number_review_approved = args.reviewaproved \n        \n        number_of_waiting_pr = args.failedprs \n        \n        number_of_successful_mm = args.mmmerges \n        \n        master_merge_status = args.mmstatus \n        \n        number_open_pr = args.totalopen \n        \n        jira_ticket_number = args.jira \n\n    except:\n        print(\"Error in variable initialization ....\")\n        \n        print(\"\\n \") \n        \n        print(\"table_Automation.py --prstatus <int>[0-2] --merged <int> --wip <int> --reviewed <int> --changerequest <int> --reviewaproved <int> --failedprs <int> --totalopen <int> --mmstatus <int>[0-2]\")\n        \n        print(\"    \")\n        \n        print(sys.stderr)\n\n    try:\n        NUMBER_OF_PRs_MERGED = \"[\"+str(number_of_pr_merged)+\"]\"+\"(https://github.com/trilinos/Trilinos/pulls?q=is%3Apr+merged%3A\"+str(yesterday)+\"T12%3A00%3A00-07%3A00..\"+str(today)+\"T12%3A00%3A00-07%3A00+base%3Adevelop)\"\n        \n        WIP_PRs = \"[\"+str(number_wip_prs)+\"]\"+\"(https://github.com/trilinos/Trilinos/pulls?q=+is%3Apr+is%3Aopen+base%3Adevelop+label%3A%22AT%3A+WIP%22)\"\n        \n        REVIEW_REQUIRED = \"[\"+str(number_reviewed_required)+\"]\"+\"(https://github.com/trilinos/Trilinos/pulls?q=is%3Apr+is%3Aopen+base%3Adevelop+review%3Arequired+-label%3A%22AT%3A+WIP%22)\"\n        \n        CHANGE_REQUESTED = \"[\"+str(number_change_requested)+\"]\"+\"(https://github.com/trilinos/Trilinos/pulls?q=+is%3Apr+is%3Aopen+base%3Adevelop+review%3Achanges-requested+-label%3A%22AT%3A+WIP%22)\"\n        \n        REVIEW_APROVED = \"[\"+str(number_review_approved)+\"]\"+\"(https://github.com/trilinos/Trilinos/pulls?q=+is%3Apr+is%3Aopen+base%3Adevelop+review%3Aapproved+-status%3Afailure+-label%3A%22AT%3A+WIP%22)\"\n        \n        NUMBER_OF_PRs_WAITING = \"[\"+str(number_of_waiting_pr)+\"]\"+\"(https://github.com/trilinos/Trilinos/pulls?q=is%3Apr+is%3Aopen+base%3Adevelop+review%3Aapproved+status%3Afailure+-label%3A%22AT%3A+WIP%22)\"\n        \n        Open_PRs = \"[\"+str(number_open_pr)+\"]\"+\"(https://github.com/trilinos/Trilinos/pulls)\"\n        \n        NUMBER_SUCCESSFUL_MM = \"[\"+str(number_of_successful_mm)+\"]\"+\"(https://github.com/trilinos/Trilinos/pulls?q=is%3Apr+merged%3A\"+str(last_Friday)+\"T12%3A00%3A00-07%3A00..\"+str(today)+\"T12%3A00%3A00-07%3A00+base%3Amaster+)\"\n        \n        JIRA_TICKETS = \"[TrilFrame-\"+str(jira_ticket_number)+\"]\"+\"(https://sems-atlassian-son.sandia.gov/jira/browse/TRILFRAME-\"+str(jira_ticket_number)+\")\"    \n        \n    except:\n        print(\"Error in Link initialization ... \")\n\n    try:\n        print(\"\\n  \")\n\n        writer = ptw.MarkdownTableWriter(\n              table_name=\"Trilinos Status Table\",\n              headers=[\"Date\", \"PR Status\", \"PRs Merged (Past 24 Hrs from 12pm)\", \"WIP PRs (@ 12pm)\", \"Review-Required PRs (@ 12pm) \", \"Change-Requested PRs (@ 12pm) \", \"Review-Approved PRs (@ 12pm)\", \" Failed PRs (@ 12pm)\", \"Total Open PRs (@ 12pm)\", \"MM Status\", \"Master Merges (Past 24 hrs from 12pm)\", \"Jira Ticket #\"],\n              value_matrix=[\n                    [str(today), pr_status[pull_request_status], NUMBER_OF_PRs_MERGED, WIP_PRs, REVIEW_REQUIRED, CHANGE_REQUESTED, REVIEW_APROVED, NUMBER_OF_PRs_WAITING, Open_PRs, mm_status[master_merge_status], NUMBER_SUCCESSFUL_MM, JIRA_TICKETS]\n              ],\n          )\n\n        print(\"\\n  \") \n\n    except:\n        print(\"Error in table creation\")\n        \n        print(\"\\n \") \n        \n        print(\"table_Automation.py --prstatus <int>[0-2] --merged <int> --wip <int> --reviewed <int> --changerequest <int> --reviewaproved <int> --failedprs <int> --totalopen <int> --mmstatus <int>[0-2]\")\n        \n        print(\"    \")\n\n    try:\n        print(writer)\n\n        print(\"\\n  \")\n\n    except:\n        print(\"Error in generating table to terminal ....\")\n        \n        print(\"\\n \") \n        \n        print(\"table_Automation.py --prstatus <int>[0-2] --merged <int> --wip <int> --reviewed <int> --changerequest <int> --reviewaproved <int> --failedprs <int> --totalopen <int> --mmstatus <int>[0-2]\")\n        \n        print(\"    \")\n            \nif __name__ == DUNDER_MAIN:\n    main()","repo_name":"trilinos/Trilinos","sub_path":"packages/framework/dailyTableUpdates/table_Automation.py","file_name":"table_Automation.py","file_ext":"py","file_size_in_byte":8188,"program_lang":"python","lang":"en","doc_type":"code","stars":1088,"dataset":"github-code","pt":"81"}
+{"seq_id":"25372197851","text":"import sys\r\nfrom trackParser import writeToFile\r\nfrom composer import compose\r\n\r\ndef init():\r\n    action = sys.argv[1]\r\n\r\n    if action == \"grabData\":\r\n        sourceFolder = sys.argv[2]\r\n        destinationFile = sys.argv[3]\r\n        sequenceLength = sys.argv[4]\r\n        writeToFile( sourceFolder, destinationFile, sequenceLength )\r\n\r\n    elif action == \"compose\":\r\n        # conductor.py source.json example.mid 240\r\n        sourceFile = sys.argv[2]\r\n        outputFileName = sys.argv[3]\r\n\r\n        if len(sys.argv) > 4:\r\n            length = sys.argv[4]\r\n        else:\r\n            length = 120\r\n\r\n        compose(sourceFile, outputFileName, length)\r\n\r\n\r\ninit()\r\n","repo_name":"chevalierc/markov-music-ai","sub_path":"conductor.py","file_name":"conductor.py","file_ext":"py","file_size_in_byte":667,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"74339862346","text":"import logging\n\n\ndef logger(name, level=logging.INFO):\n    tmp_logger = logging.getLogger(name)\n    if not tmp_logger.handlers:\n        formatter = logging.Formatter('%(asctime)s %(name)s  %(levelname)s %(message)s')\n        handler = logging.StreamHandler()\n        handler.setFormatter(formatter)\n\n        tmp_logger.setLevel(level)\n        tmp_logger.addHandler(handler)\n    return tmp_logger","repo_name":"andreas-koukorinis/PaperCode","sub_path":"stylised_facts/pipeline/utils/custom_logger.py","file_name":"custom_logger.py","file_ext":"py","file_size_in_byte":395,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"81"}
+{"seq_id":"20420367930","text":"#!/usr/bin/env python3\n\"\"\"\nModule providing methods to work with BNG (OS36GB) data, specifically the 700\nby 1300km grid covering GB.\n\"\"\"\nimport csv\nimport os\nimport random\nimport urllib\nimport zipfile\n\nimport bnglonlat\nimport numpy as np\nimport scipy.spatial\n\nimport nevis\n\n# Get accurate longitude/lattitude, or use fallback\ntry:\n    import convertbng.util\n\n    def lonlat(x, y):\n        a, b = convertbng.util.convert_lonlat([x], [y])\n        if not (np.isnan(a) or np.isnan(b)):\n            return a[0], b[0]\n        return bnglonlat.bnglonlat(x, y)\n\nexcept ImportError:\n    lonlat = bnglonlat.bnglonlat\n\n\n# Full size of the grid (in meters): 700km by 1300km\n# Origin is a bottom left (in SV square)\nwidth, height = 700000, 1300000\n\n\n# Hill names\nhill_zip = os.path.join(nevis._DIR_MODULE_DATA, 'hills.zip')\nhill_file = 'hills.csv'\n\n# Grid letters (bottom to top, left to right)\nGL = [\n    ['V', 'W', 'X', 'Y', 'Z'],\n    ['Q', 'R', 'S', 'T', 'U'],\n    ['L', 'M', 'N', 'O', 'P'],\n    ['F', 'G', 'H', 'J', 'K'],\n    ['A', 'B', 'C', 'D', 'E'],\n]\n\n\ndef dimensions():\n    \"\"\" Returns the dimensions of the grid (width, height) in meters. \"\"\"\n    return width, height\n\n\ndef squares():\n    \"\"\"\n    Returns a list of tuples ``(name, x, y)`` with two-letter 100km grid square\n    names and lower-left corners, covering the area of the data.\n    \"\"\"\n    squares = []\n\n    d = 100000\n    i0, i1 = 1, 0   # Start on 2nd row (QRSTU)\n    for y in range(0, height, d):\n        j0, j1 = 2, 0   # Start on 2nd letter (S,N)\n        for x in range(0, width, d):\n            squares.append((GL[i0][j0] + GL[i1][j1], x, y))\n            j1 += 1\n            if j1 == 5:\n                j1 = 0\n                j0 += 1\n        i1 += 1\n        if i1 == 5:\n            i1 = 0\n            i0 += 1\n    return squares\n\n\nclass Coords(object):\n    \"\"\"\n    Coordinates, either normalised (so that 0, 0 is the bottom left of the grid\n    and 1, 1 is the top right) or as OS36GB grid points in meters.\n\n    Examples::\n\n        p = nevis.Coords(gridx=123456, gridy=123456)\n        print(p.grid)\n        print(p.normalised)\n\n        p = nevis.Coords(normx=0.5, normy=0.2)\n        print(p.grid)\n        print(p.normalised)\n\n        b = nevis.ben()\n        print(b.square)\n        print(b.geograph)\n\n    \"\"\"\n\n    def __init__(self, gridx=None, gridy=None, normx=None, normy=None):\n        self._gridx = self._gridy = None\n        self._normx = self._normy = None\n\n        if gridx is not None and gridy is not None:\n            if normx is None and normy is None:\n                self._gridx = int(gridx)\n                self._gridy = int(gridy)\n                self._normx = self._gridx / width\n                self._normy = self._gridy / height\n        if normx is not None and normy is not None:\n            if gridx is None and gridy is None:\n                self._normx = float(normx)\n                self._normy = float(normy)\n                self._gridx = int(self._normx * width)\n                self._gridy = int(self._normy * height)\n        if self._gridx is None:\n            raise ValueError(\n                'Either (gridx and gridy) or (normx and normy) must be'\n                'specified (and not both).')\n\n        self._latlong = None\n        self._square = None\n        self._square3 = None\n        self._square4 = None\n        self._square5 = None\n\n    def _find_square(self, n):\n\n        # Get letters and remaining x y\n        if self._square is None:\n            # Lower-left of \"V\" (the bottom-left major letter) is 1000km to the\n            # left of the origins of the grid (SV) and 500km below it.\n            x, y = self._gridx + 1000000, self._gridy + 500000\n\n            # Get first letter\n            a, b = int(y // 500000), int(x // 500000)\n            try:\n                if a < 0 or b < 0:\n                    raise KeyError\n                name = GL[a][b]\n\n                # Get second letter\n                x, y = x % 500000, y % 500000\n                a, b = int(y // 100000), int(x // 100000)\n                name += GL[a][b]\n\n                # Get numbers\n                x, y = x % 100000, y % 100000\n                x, y = int(x), int(y)\n\n                # Make string\n                self._square = name, f'{x:0>5}', f'{y:0>5}'\n            except KeyError:\n                self._square = False\n\n        # Make string\n        if self._square is False:\n            return 'Off the grid'\n\n        name, x, y = self._square\n        return name + x[:n] + y[:n]\n\n    @staticmethod\n    def from_square(square):\n        \"\"\"\n        Creates coordinates corresponding to the lower-left corner of a grid\n        square indicated by letters and numbers, e.g. ``NN166712`` or\n        ``NN 166 712``.\n        \"\"\"\n        return Coords.from_square_with_size(square)[0]\n\n    @staticmethod\n    def from_square_with_size(square):\n        \"\"\"\n        Like :meth:`from_square` but returns a tuple ``(coords, size)`` where\n        ``size`` is the length of the square's sides.\n        \"\"\"\n        code = square.strip().upper()\n        if len(code) < 1:\n            raise ValueError(\n                'Invalid BNG grid reference: must be at least 1 letter')\n\n        def find(letter):\n            \"\"\"\n            Find lower-left coordinates. Big squares are 500km, small 100km.\n            \"\"\"\n            for i, row in enumerate(GL):\n                if letter >= row[0] and letter != 'I':\n                    return i, row.index(letter)\n            raise ValueError(\n                f'Invalid BNG grid letter \"{letter}\" in code \"{square}\".')\n\n        # Origin of letter system is V, BNG starts at S\n        x, y = -1000000, -500000\n\n        # Process letters\n        i, j = find(code[0])\n        x, y = x + 500000 * j, y + 500000 * i\n        if len(code) == 1:\n            return Coords(x, y), 500000\n\n        i, j = find(code[1])\n        x, y = x + 100000 * j, y + 100000 * i\n        if len(code) == 2:\n            return Coords(x, y), 100000\n\n        # Check numbers\n        numbers = code[2:].split(maxsplit=1)\n        if len(numbers) == 1:\n            numbers = numbers[0]\n            n = len(numbers)\n            if n % 2 == 1:\n                raise ValueError(\n                    f'Invalid BNG grid code: {square} numbers must have same'\n                    ' number of digits.')\n            n = n // 2\n            numbers = (numbers[:n], numbers[n:])\n        else:\n            n = max(len(numbers[0]), len(numbers[1]))\n\n        try:\n            a, b = int(numbers[0]), int(numbers[1])\n        except ValueError:\n            raise ValueError(\n                f'Invalid BNG grid code: {square} numbers must be integers.')\n        if a < 0 or b < 0:\n            raise ValueError(\n                f'Invalid BNG grid code: {square} numbers must be positive.')\n\n        # Calculate square size\n        m = 10**(5 - n)\n\n        # Final grid coordinates\n        x, y = x + a * m, y + b * m\n\n        # Pretty much always, we'll want to round\n        if m >= 1:\n            x, y = int(x), int(y)\n\n        return Coords(x, y), m\n\n    @property\n    def grid(self):\n        return np.array([self._gridx, self._gridy])\n\n    @property\n    def normalised(self):\n        return np.array([self._normx, self._normy])\n\n    @property\n    def square3(self):\n        if self._square3 is None:\n            self._square3 = self._find_square(3)\n        return self._square3\n\n    @property\n    def square4(self):\n        if self._square4 is None:\n            self._square4 = self._find_square(4)\n        return self._square4\n\n    @property\n    def square5(self):\n        if self._square5 is None:\n            self._square5 = self._find_square(5)\n        return self._square5\n\n    @property\n    def latlong(self):\n        if self._latlong is None:\n            lon, lat = lonlat(self._gridx, self._gridy)\n            self._latlong = lat, lon\n        return self._latlong\n\n    @property\n    def geograph(self):\n        return f'http://www.geograph.org.uk/gridref/{self.square5}'\n\n    @property\n    def google(self):\n        lat, long = self.latlong\n        lat, long = round(lat, 6), round(long, 6)\n        return (\n            'https://www.google.com/maps/@?api=1&map_action=map'\n            f'&center={lat},{long}&zoom=15&basemap=terrain')\n\n    @property\n    def osmaps(self):\n        lat, long = self.latlong\n        lat, long = round(lat, 6), round(long, 6)\n        return (\n            f'https://explore.osmaps.com/en/pin?lat={lat}&lon={long}&zoom=17')\n\n    @property\n    def opentopomap(self):\n        lat, long = self.latlong\n        lat, long = round(lat, 6), round(long, 6)\n        return f'https://opentopomap.org/#marker=15/{lat}/{long}'\n\n    def __str__(self):\n        return f'Coords({int(round(self._gridx))}, {int(round(self._gridy))})'\n\n\nclass Hill(object):\n    \"\"\"\n    Known hill tops.\n\n    Examples::\n\n        print(Hill.by_name('Ben Nevis'))\n        print(Hill.by_rank(1))\n        print(Hill.by_rank(2))\n        print(Hill.by_rank(3))\n\n        h, d = Hill.nearest(Coords(gridx=216600, gridy=771300))\n        print(h)\n\n    \"\"\"\n    _hills = []\n    _names = {}\n    _ids = {}\n    _tree = None\n\n    def __init__(self, x, y, rank, height, hill_id, name):\n        if Hill._tree is not None:\n            raise ValueError('Cannot add hills after tree construction.')\n        self._x = int(x)\n        self._y = int(y)\n        self._rank = int(rank)\n        self._height = float(height)\n        self._id = int(hill_id)\n        self._name = name.strip()\n        self._photo = None\n        Hill._hills.append(self)\n        Hill._names[self._name.lower()] = self\n        Hill._ids[self._id] = self\n\n    @staticmethod\n    def _load():\n        \"\"\" Loads hills into memory. \"\"\"\n\n        # Unzip\n        with zipfile.ZipFile(hill_zip, 'r') as f:\n            with f.open(hill_file, 'r') as g:\n                lines = g.read().decode('utf-8')\n        lines = lines.splitlines()\n        rows = iter(csv.reader(lines, delimiter=',', quotechar='\"'))\n\n        # Parse header\n        fields = ['x', 'y', 'rank', 'meters', 'id', 'name']\n        header = next(rows)\n        try:\n            indices = [header.index(field) for field in fields]\n        except ValueError as e:\n            raise ValueError(f'Unable to read hill-file header: {e}')\n\n        # Parse data\n        coords = []\n        for row in rows:\n            Hill(*[row[i] for i in indices])\n            coords.append([row[indices[0]], row[indices[1]]])\n\n        # Construct tree\n        Hill._tree = scipy.spatial.KDTree(np.array(coords, dtype=np.float32))\n\n    @staticmethod\n    def by_id(hill_id):\n        \"\"\"\n        Return a hill with the given ``hill_id`` (as used on e.g. hill\n        bagging.)\n        \"\"\"\n        if not Hill._hills:\n            Hill._load()\n        return Hill._ids[hill_id]\n\n    @staticmethod\n    def by_name(name):\n        \"\"\" Return a hill with the given ``name``. \"\"\"\n        if not Hill._hills:\n            Hill._load()\n        return Hill._names[name.lower()]\n\n    @staticmethod\n    def by_rank(rank):\n        \"\"\"\n        Return the hill with the given ``rank`` (rank 1 is heighest, then 2,\n        etc.).\n        \"\"\"\n        if not Hill._hills:\n            Hill._load()\n        if rank < 1 or rank > len(Hill._hills):\n            raise ValueError(f'Rank outside of range: {rank}.')\n        hill = Hill._hills[rank - 1]\n        assert hill.rank == rank, 'Hills not ordered by rank'\n        return hill\n\n    @staticmethod\n    def nearest(coords):\n        \"\"\"\n        Returns a tuple (hill, distance) with the hill nearest to the given\n        points.\n        \"\"\"\n        if not Hill._hills:\n            Hill._load()\n        d, h = Hill._tree.query([coords._gridx, coords._gridy])\n        return Hill._hills[h], d\n\n    @property\n    def coords(self):\n        return Coords(gridx=self._x, gridy=self._y)\n\n    @property\n    def height(self):\n        return self._height\n\n    @property\n    def hill_id(self):\n        return self._id\n\n    @property\n    def name(self):\n        return self._name\n\n    @property\n    def rank(self):\n        return self._rank\n\n    @property\n    def ranked(self):\n        s = str(self._rank)\n        if s[-1] in '123':\n            if not (self._rank > 10 and s[-2] == '1'):\n                return s + 'st' if s[-1] == '1' else s + 'd'\n        return s + 'th'\n\n    @property\n    def summit(self):\n        return f'http://hillsummits.org.uk/htm_summit/{self._id}.htm'\n\n    @property\n    def portrait(self):\n        return f'http://hillsummits.org.uk/htm_portrait/{self._id}.htm'\n\n    def photo(self):\n        \"\"\"\n        Attempts to return a URL with a photo. Returns an empty string if none\n        is found.\n        \"\"\"\n        def status(url):\n            request = urllib.request.Request(url, method='HEAD')\n            try:\n                with urllib.request.urlopen(request) as f:\n                    status = f.status\n            except urllib.error.HTTPError:\n                status = 404\n            return status\n\n        if self._photo is None:\n            if status(self.summit) != 404:\n                self._photo = self.summit\n            elif status(self.portrait) != 404:\n                self._photo = self.portrait\n            else:\n                self._photo = ''\n        return self._photo\n\n    def __str__(self):\n        return f'{self._name} ({self.height}m)'\n\n\ndef ben():\n    \"\"\" Returns the coordinates of Ben Nevis. \"\"\"\n    return Coords.ben\n\n\ndef fen():\n    \"\"\" Returns the coordinates of Holme Fen \"\"\"\n    return Coords.fen\n\n\ndef pub(name=None):\n    \"\"\" Returns coordinates where a mathematician may be found. \"\"\"\n    if name:\n        return Coords.pub[name]\n    return random.choice(list(Coords.pub.values()))\n\n\nCoords.ben = Coords(gridx=216666, gridy=771288)\nCoords.fen = Coords(gridx=520483, gridy=289083)\nCoords.pub = {\n    #'Ye olde trip to jerusalem': Coords(gridx=457034, gridy=339443},\n    'Bear': Coords(gridx=451473, gridy=206135),\n    'Canal house': Coords(gridx=457307, gridy=339326),\n    'MacSorleys': Coords(gridx=258809, gridy=665079),\n    'Sheffield tap': Coords(gridx=435847, gridy=387030),\n}\n\n\ndef schiehallion():\n    \"\"\"\n    Returns a tuple ``(hill, boundaries)`` containing the :class:`Hill` object\n    for Schiehallion and boundaries ``(x_lower, x_upper, y_lower, y_upper)``\n    defining its neighbourhood.\n\n    This can be used as an easier test problem for local methods.\n    \"\"\"\n    hill = nevis.Hill.by_name('Schiehallion')\n    x, y = hill.coords.grid\n    b = 3e3\n    boundaries = [x - b * .7, x + b * 1.1, y - b * .55, y + b * .5]\n    return hill, boundaries\n\n\ndef macdui():\n    \"\"\"\n    Returns a tuple ``(hill, boundaries)`` containing the :class:`Hill` object\n    for Ben Macdui and boundaries ``(x_lower, x_upper, y_lower, y_upper)``\n    defining its neighbourhood, Cairngorms Mountains.\n\n    This can be used as an easier test problem for local methods.\n    \"\"\"\n    hill = nevis.Hill.by_name('Ben Macdui')\n    x, y = hill.coords.grid\n    b = 8e3\n    boundaries = [x - b * 1.75, x + b * 2.5, y - b * 1.4, y + b * 1.9]\n    return hill, boundaries\n","repo_name":"CardiacModelling/BenNevis","sub_path":"nevis/_bng.py","file_name":"_bng.py","file_ext":"py","file_size_in_byte":14978,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"81"}
+{"seq_id":"7973498429","text":"#!/usr/bin/env python\n# encoding: utf-8\n\nclass BinTreeNode(object):               #创建节点类\n    def __init__(self,data=0):\n        self._data = data\n        self._left = None\n        self._right = None\n\nclass BinTree(BinTreeNode):              #创建二叉树类\n    def create_tree(self,root):          #二叉树的初始化生成\n        temp = input(\"请输入节点值:\")\n        if temp is '#':\n            root = None\n        else:\n            root._data = temp            #按照先根节点再左节点后右节点的顺序存储\n            root._left = BinTreeNode()\n            self.create_tree(root._left)\n            root._right = BinTreeNode()\n            self.create_tree(root._right)\n\n    def pre_order(self,root):            #前序遍历\n        if root is not None:\n            print(root._data,end=' ')\n            self.pre_order(root._left)\n            self.pre_order(root._right)\n\n\n    def in_order(self,root):             #中序遍历\n        if root is not None:\n            self.in_order(root._left)\n            print(root._data,end=' ')\n            self.in_order(root._right)\n\n\n    def post_order(self,root):           #后序遍历\n        if root is not None:\n            self.in_order(root._left)\n            self.in_order(root._right)\n            print(root._data,end=' ')\n\n\n\n\n\n\n\n","repo_name":"littleboy12580/learning_python","sub_path":"data_structure/my_bintree.py","file_name":"my_bintree.py","file_ext":"py","file_size_in_byte":1320,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"81"}
+{"seq_id":"74282073545","text":"import ts3lib\nfrom ts3plugin import ts3plugin, PluginHost\nfrom pytson import getCurrentApiVersion\nfrom bluscream import GroupType, log, parseCommand\nfrom ts3Ext import ts3SessionHost, logLevel\nfrom ts3defines import *\n\nclass aaa_ts3Ext(ts3plugin):\n    name = \"aaa_ts3Ext\"\n    try: apiVersion = getCurrentApiVersion()\n    except: apiVersion = 22\n    requestAutoload = False\n    version = \"1.0\"\n    author = \"Bluscream\"\n    description = \"ts3Ext library implementation\"\n    offersConfigure = False\n    commandKeyword = \"ext\"\n    infoTitle = None\n    menuItems = []\n    hotkeys = []\n    guiLogLvl = logLevel.NONE\n    banned_names = [\"BAN\", \"NOT WELCOME\"]\n    mod_names = [\"MOD\", \"OPERATOR\", \"ADMIN\"] # TODO CHECK\n    tabs =  {}\n\n    def __init__(self):\n        self.ts3host = ts3SessionHost(self)\n        err, schids = ts3lib.getServerConnectionHandlerList()\n        for schid in schids:\n            (err, status) = ts3lib.getConnectionStatus(schid)\n            self.onConnectStatusChangeEvent(schid, status, err)\n        log(self, LogLevel.LogLevel_DEBUG, \"[color=orange]{name}[/color] Plugin for pyTSon by [url=https://github.com/{author}]{author}[/url] loaded.\".format(name=self.name, author=self.author), 0)\n\n    def processCommand(self, schid, keyword):\n            args = keyword.split(' ')\n            cmd = args.pop(0).lower()\n            if cmd == \"dump\":\n                print(self.tabs)\n            else: return False\n            return True\n\n    \"\"\"\n    def onIncomingClientQueryEvent(self, schid, command):\n        cmd = parseCommand(command)\n        if cmd[0] == \"notifycliententerview\":\n            if schid in self.tabs:\n                self.tabs[schid][\"clients\"][cmd[1][\"clid\"]] = {\n                    \"integrations\": cmd[1][\"client_integrations\"],\n                    \"myteamspeak_id\": cmd[1][\"client_myteamspeak_id\"],\n                }\n    \"\"\"\n\n    def onConnectStatusChangeEvent(self, schid, status, errorNumber):\n        if status == ConnectStatus.STATUS_CONNECTION_ESTABLISHED:\n            srv = self.ts3host.getServer(schid)\n            self.tabs[schid] = {\n                \"channelBanGroup\": None,\n                \"channelModGroup\": None,\n                # \"clients\": {}\n            }\n            srv.requestServerGroupList()\n            srv.requestChannelGroupList()\n            self.tabs[schid][\"name\"] = srv.name # ts3lib.getServerVariable(schid, VirtualServerProperties.VIRTUALSERVER_NAME)\n            err, self.tabs[schid][\"host\"], self.tabs[schid][\"port\"], self.tabs[schid][\"pw\"] = ts3lib.getServerConnectInfo(schid)\n            self.tabs[schid][\"address\"] = '{}:{}'.format(self.tabs[schid][\"host\"], self.tabs[schid][\"port\"]) if hasattr(self.tabs[schid], 'port') else self.tabs[schid][\"host\"]\n            self.tabs[schid][\"clid\"] = srv.me.clientID\n            self.tabs[schid][\"nick\"] = srv.me.name # ts3lib.getClientSelfVariable(schid, ClientProperties.CLIENT_NICKNAME) # ts3lib.getClientVariable(schid, self.tabs[schid][\"clid\"], ClientProperties.CLIENT_NICKNAME)\n            err, self.tabs[schid][\"nick_phonetic\"] = ts3lib.getClientSelfVariable(schid, ClientPropertiesRare.CLIENT_NICKNAME_PHONETIC)\n            self.tabs[schid][\"uid\"] = srv.me.uid # ts3lib.getClientSelfVariable(schid, ClientProperties.CLIENT_UNIQUE_IDENTIFIER)\n            err, self.tabs[schid][\"token\"] = ts3lib.getClientSelfVariable(schid, ClientPropertiesRare.CLIENT_DEFAULT_TOKEN)\n            self.tabs[schid][\"cid\"] = srv.me.channel.channelID # ts3lib.getChannelOfClient(schid, self.tabs[schid][\"clid\"])\n            err, self.tabs[schid][\"cpath\"], self.tabs[schid][\"cpw\"] = ts3lib.getChannelConnectInfo(schid, self.tabs[schid][\"cid\"])\n            err, self.tabs[schid][\"input_muted\"] = ts3lib.getClientSelfVariable(schid, ClientProperties.CLIENT_INPUT_MUTED) # srv.me.isInputMuted\n            err, self.tabs[schid][\"input_deactivated\"] = ts3lib.getClientSelfVariable(schid, ClientProperties.CLIENT_INPUT_DEACTIVATED)\n            err, self.tabs[schid][\"input_enabled\"] = ts3lib.getClientSelfVariable(schid, ClientProperties.CLIENT_INPUT_HARDWARE)\n            err, self.tabs[schid][\"output_muted\"] = ts3lib.getClientSelfVariableAsInt(schid, ClientProperties.CLIENT_OUTPUT_MUTED) # srv.me.isOutputMuted\n            err, self.tabs[schid][\"output_enabled\"] = ts3lib.getClientSelfVariable(schid, ClientProperties.CLIENT_OUTPUT_HARDWARE)\n        # elif status == ConnectStatus.STATUS_DISCONNECTED:\n        if schid in self.tabs: self.tabs[schid][\"status\"] = status\n\n    def onServerGroupListEvent(self, schid, serverGroupID, name, atype, iconID, saveDB):\n        if atype != GroupType.REGULAR: return\n        srv = self.ts3host.getServer(schid)\n        srv.updateServerGroup(serverGroupID, name, iconID)\n\n    def onChannelGroupListEvent(self, schid, channelGroupID, name, atype, iconID, saveDB):\n        if atype != GroupType.REGULAR: return\n        srv = self.ts3host.getServer(schid)\n        srv.updateChannelGroup(channelGroupID, name, iconID)\n        tab = self.tabs[schid]\n        for _name in self.banned_names:\n            if _name in name.upper():\n                tab[\"channelBanGroup\"] = channelGroupID\n                return\n        for _name in self.mod_names:\n            if _name in name.upper():\n                tab[\"channelModGroup\"] = channelGroupID\n                return\n\n    def onTalkStatusChangeEvent(self, schid, status, isReceivedWhisper, clientID):\n        self.ts3host.getServer(schid).updateTalkStatus(clientID, status, isReceivedWhisper)\n\n    def onClientMoveEvent(self, schid, clientID, oldChannelID, newChannelID, visibility, moveMessage):\n        if schid not in self.tabs: return\n        if clientID != self.tabs[schid][\"clid\"]: return\n        self.tabs[schid][\"cid\"] = newChannelID\n        (err, self.tabs[schid][\"cpath\"], self.tabs[schid][\"cpw\"]) = ts3lib.getChannelConnectInfo(schid, newChannelID)\n\n    def onClientSelfVariableUpdateEvent(self, schid, flag, oldValue, newValue):\n        if schid not in self.tabs: return\n        if flag == ClientProperties.CLIENT_NICKNAME:\n            self.tabs[schid][\"nick\"] = newValue\n        elif flag == ClientPropertiesRare.CLIENT_NICKNAME_PHONETIC:\n            self.tabs[schid][\"nick_phonetic\"] = newValue\n        elif flag == ClientProperties.CLIENT_UNIQUE_IDENTIFIER:\n            self.tabs[schid][\"uid\"] = newValue\n        elif flag == ClientPropertiesRare.CLIENT_DEFAULT_TOKEN:\n            self.tabs[schid][\"token\"] = newValue\n        elif flag == ClientProperties.CLIENT_INPUT_MUTED:\n            self.tabs[schid][\"input_muted\"] = newValue\n        elif flag == ClientProperties.CLIENT_INPUT_DEACTIVATED:\n            self.tabs[schid][\"input_deactivated\"] = newValue\n        elif flag == ClientProperties.CLIENT_INPUT_HARDWARE:\n            self.tabs[schid][\"input_enabled\"] = newValue\n        elif flag == ClientProperties.CLIENT_OUTPUT_MUTED:\n            self.tabs[schid][\"output_muted\"] = int(newValue)\n        elif flag == ClientProperties.CLIENT_OUTPUT_HARDWARE:\n            self.tabs[schid][\"output_enabled\"] = newValue\n\n    def onPacketOut(self, msg, schid):\n        if schid in self.tabs:\n            if msg.startswith(\"clientinit \"):\n                self.tabs[schid][\"clientinit\"] = msg\n        return False, msg\n\n    def stop(self): del self.ts3host","repo_name":"Bluscream/pyTSon_plugins","sub_path":"scripts/aaa_ts3Ext/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":7231,"program_lang":"python","lang":"en","doc_type":"code","stars":24,"dataset":"github-code","pt":"81"}
+{"seq_id":"40751153805","text":"import helper\nfrom locust import HttpLocust, TaskSet, task\nimport json\n\nclass MyTaskSet(TaskSet):\n\n    # id is a static variable used in generating user and seller ids\n    id = 1\n\n    @task()\n    def push_load(self):\n\n        # fetch json object values\n        id = helper.get_id(MyTaskSet.id)\n        name = helper.get_random_product_name()\n        price = helper.get_random_price()\n        time = helper.get_current_time()\n\n        data_obj = helper.build_json_obj(id,name,price,time)\n\n        # increment id\n        MyTaskSet.id += 1\n\n        # fetching SAS token\n        token = helper.token\n\n        # preparing headers to be used in POST request\n        broker_props = {'ContentType' : 'application/json'}\n\n        headers = {\n            'Authorization': token,\n            'Content-Type': 'application/atom+xml;type=entry;charset=utf-8',\n            'BrokerProperties': json.dumps(broker_props)}\n\n        # send post request to specified host\n        self.client.post('/messages', data=json.dumps(data_obj), headers=headers)\n\n\nclass MyLocust(HttpLocust):\n    # this class represents an HTTP user which is to be hatched and attack the system that is to be load tested.\n\n    task_set = MyTaskSet            # defining the behaviour of the user\n    min_wait = 0\n    max_wait = 0\n    # host to bombard\n    #host = 'https://second-bus.servicebus.windows.net/queue'\n    host = helper.build_host_name()","repo_name":"mdls85/cloud_a3","sub_path":"load_gen.py","file_name":"load_gen.py","file_ext":"py","file_size_in_byte":1403,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"15826687118","text":"import os\nimport cv2\nimport numpy as np\nfrom PIL import Image\nimport pickle\nfrom playsound import playsound as ps\n#Haar Cascade Classifier\nface_cascade= cv2.CascadeClassifier('haarcascade_frontalface_alt2.xml')\nrecognizer = cv2.face.LBPHFaceRecognizer_create()\n#Saving Captured Data in a file.\nrecognizer.read(\"trianner.yml\")\nlabels = {\"person_name\": 1}\nwith open(\"labels.pickle\", 'rb') as f:\n   og_labels = pickle.load(f)\n   labels = {v:k for k,v in og_labels.items()}\n#cap = cv2.VideoCapture('jim2.mp4',0)\n#cap = cv2.VideoCapture('akash.jpg',0)\ncap = cv2.VideoCapture(0)\n\nwhile(True):\n    ret, frame = cap.read()\n    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n    faces = face_cascade.detectMultiScale (gray,1.5,5)\n    for (x,y,w,h) in faces:\n        #cv2.rectangle(img, (x,y), (x+w, y+h), (255,245,152), 2)\n        roi_gray = gray[y:y+h, x:x+w]\n        roi_color = frame [y:y+h, x:x+w]\n        \n        id_,conf =recognizer.predict(roi_gray)\n        if conf>=45: #and conf <= 85:\n            print(id_)\n            print(labels[id_])\n            font = cv2.FONT_HERSHEY_SIMPLEX\n            name = (labels[id_])\n            color = (255, 255, 255)\n            stroke = 2\n            cv2.putText(frame,name,(x,y),font,1,color,stroke,cv2.LINE_AA)\n            print(\"Human\")\n            print(type(labels[id_]))\n            #ps(labels[id_]+\".wav\")\n        img_item = \"7.png\"\n        cv2.imwrite(img_item, roi_color)\n        color = (255, 0, 0) #BGR 9-255\n        stroke = 2\n        end_cord_x = x + w\n        end_cord_y = y + h\n        cv2.rectangle(frame, (x, y), (end_cord_x, end_cord_y), color, stroke)\n    cv2.imshow( 'frame' ,frame)\n    k = cv2.waitKey(20) & 0xff\n    if k == ord('q'):\n\n        break\n# Display the input and output\n#cap.release()\n#cv2.destroyAllWindows()\n","repo_name":"Rosakash/Artificial_Intelligence","sub_path":"Face_Detection/Face_Detection.py","file_name":"Face_Detection.py","file_ext":"py","file_size_in_byte":1780,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"25212048784","text":"from hachoir.stream import InputIOStream\nfrom hachoir.parser import guessParser\nfrom hachoir.metadata import extractMetadata\nfrom flask import json\nimport logging\n\n\nlogger = logging.getLogger(__name__)\n\n\ndef get_meta(filestream):\n    metadata = {}\n\n    try:\n        filestream.seek(0)\n        stream = InputIOStream(filestream, None, tags=[])\n        parser = guessParser(stream)\n        if not parser:\n            return metadata\n\n        tags = extractMetadata(parser).exportPlaintext(human=False, line_prefix=\"\")\n        for text in tags:\n            try:\n                json.dumps(text)\n                key, value = text.split(\":\", maxsplit=1)\n                key, value = key.strip(), value.strip()\n                if key and value:\n                    metadata.update({key: value})\n            except Exception as ex:\n                logger.exception(ex)\n    except Exception as ex:\n        logger.exception(ex)\n        return metadata\n    return metadata\n","repo_name":"superdesk/superdesk-core","sub_path":"superdesk/media/video.py","file_name":"video.py","file_ext":"py","file_size_in_byte":963,"program_lang":"python","lang":"en","doc_type":"code","stars":31,"dataset":"github-code","pt":"81"}
+{"seq_id":"42031128354","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# # Segment1 : Data Structure for Algebra\n\n# ## What is Linear algebra is \n\n# In[1]:\n\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n\n# In[2]:\n\n\nt = np.linspace(0,40,1000) #start , finish, n points\n\n\n# In[3]:\n\n\nd_r = 2.5 * t #Distance travelled by robber: d = 2.5t\n\n\n# In[4]:\n\n\nd_s = 3 * (t-5) #Distace travelled by sheriff: d = 3(t - 5)\n\n\n# In[13]:\n\n\nfig, ax = plt.subplots()\nplt.title('A Bank Robber Caught')\nplt.xlabel('time (in minutes)')\nplt.ylabel('distance (in km)')\nax.set_xlim([0, 40])\nax.set_ylim([0, 100])\nax.plot(t, d_r, c='green')\nax.plot(t, d_s, c='brown')\nplt.axvline(x=30, color='purple', linestyle='--')\n_ = plt.axhline(y=75, color='purple', linestyle='--')\n\n\n# ### Visualizing linear system\n\n# In[1]:\n\n\n# In the substitution ex, the two equarions in the system are:-\n# y = 3x\n# -5 + 2y = 2, the second equation can be rearranged to isolated y\n\n\n# In[2]:\n\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n\n# In[3]:\n\n\nx = np.linspace(-10,10,1000) # start, finish , n point\n\n\n# In[5]:\n\n\ny1 = 3 * x\n\n\n# In[6]:\n\n\ny2 = 1 + (5 * x) / 2\n\n\n# In[10]:\n\n\nfig, ax = plt.subplots()\nplt.xlabel ('x')\nplt.ylabel ('y')\nax.set_xlim([0,3])\nax.set_ylim([0,8])\nax.plot(x, y1, c = 'green')\nax.plot(x, y2, c = 'brown')\nplt.axvline(x = 2, color = 'purple', linestyle = '--')\n_ = plt.axhline(y = 6, color = 'purple', linestyle = '--')\n\n\n# In[14]:\n\n\n# solving linear system with elimination method\n# 2x - 3y = 15 and 4x + 10y = 14\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n\n# In[15]:\n\n\nx = np.linspace(-10,10,1000)\n\n\n# In[16]:\n\n\ny1 = - 5 + (2*x) / 3\n\n\n# In[19]:\n\n\ny2 = (7-2*x) / 5\n\n\n# In[21]:\n\n\nfig, ax = plt.subplots()\nplt.xlabel('x')\nplt.ylabel('y')\n\n#add x and y axes\nplt.axvline(x = 0,color = 'lightgray')\nplt.axhline(y = 0,color = 'lightgray')\n\nax.set_xlim([-2, 10])\nax.set_ylim([-6, 4])\n\nax.plot(x,y1,c = 'green')\nax.plot(x,y2,c = 'brown')\n\nplt.axvline(x = 6,color = 'purple',linestyle = '--')\n_ = plt.axhline(y = -1,color = 'purple',linestyle = '--')\n\n","repo_name":"darshith-v/linear-algebra_for_ML","sub_path":"1-intro-linear-algebra.py","file_name":"1-intro-linear-algebra.py","file_ext":"py","file_size_in_byte":2010,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"35116386511","text":"import cv2\r\nimport numpy as np\r\n\r\nimg1 = cv2.imread('flag_enc.png', cv2.IMREAD_GRAYSCALE)\r\nimg2 = cv2.imread('golem_enc.png', cv2.IMREAD_GRAYSCALE)\r\n\r\nh, w = img1.shape\r\nimg3 = np.array([[0 for i in range(w)] for j in range(h)], np.uint8)\r\n\r\nfor i in range(h):\r\n    for j in range(w):\r\n        img3[i][j] = img1[i][j] ^ img2[i][j]\r\ncv2.imwrite('dec.png', img3)\r\n","repo_name":"CyCTW/NTU-ComputerSecurity-110","sub_path":"hw0/to_bf_or_not_to_bf/solve.py","file_name":"solve.py","file_ext":"py","file_size_in_byte":362,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"17499062901","text":"# https://www.beecrowd.com.br/judge/pt/problems/view/1080\r\n\r\nhighest = int(input())\r\nindex = 1\r\n\r\nfor i in range(1, 100):\r\n    number = int(input())\r\n    if number > highest:\r\n        highest = number\r\n        index = i + 1\r\n\r\n\r\nprint(highest)\r\nprint(index)\r\n","repo_name":"Luuls/atividade-3-POOI","sub_path":"atividade-3/highest-and-position.py","file_name":"highest-and-position.py","file_ext":"py","file_size_in_byte":259,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"31273756657","text":"import numpy as np\r\nimport pandas as pd\r\nfrom keras.preprocessing.text import Tokenizer\r\nfrom keras.preprocessing.sequence import pad_sequences\r\nfrom keras.layers import Dense, Embedding, LSTM, SpatialDropout1D\r\nfrom tensorflow.keras.layers import Embedding,Dense,LSTM,SpatialDropout1D\r\nimport tensorflow\r\nfrom sklearn.model_selection import train_test_split\r\nfrom sklearn.metrics import confusion_matrix,classification_report\r\nfrom tensorflow.keras.models import load_model\r\nimport matplotlib.pyplot as plt\r\nfrom numpy import asarray\r\nfrom numpy import zeros\r\nimport tensorflow as tf\r\nimport pickle\r\nfrom sklearn.feature_extraction.text import TfidfVectorizer\r\nimport codecs\r\nfrom nltk.tokenize import word_tokenize\r\nimport gensim.models as Word2vec\r\n\r\nPROCESSED_DATA_PATH = 'data/hasil'\r\ntrain_data = pd.read_csv('{}/clear_train.csv'.format(PROCESSED_DATA_PATH))\r\ntrain_data = train_data[['hasil_normalisasi', 'Label']]\r\n\r\nclass LSTM_model:\r\n\r\n    def __init__(self,filepath=PROCESSED_DATA_PATH):\r\n        self.max_fatures = 5000\r\n        self.batch_size = 128\r\n        # self.build_model()\r\n        return print('Train data shape :', train_data.shape)\r\n\r\n    def describe_model(self):\r\n        bullying = 0\r\n        netral = 0\r\n        nonbull = 0\r\n        irrelevant = 0\r\n        for label in train_data['Label']:\r\n            if label == 0:\r\n                bullying += 1\r\n            elif label == 1:\r\n                irrelevant += 1\r\n            elif label == 2:\r\n                netral += 1\r\n            else:\r\n                nonbull += 1\r\n\r\n        print(\"Total komentar cyberbullying adalah \" + str(bullying) + \", atau\",\r\n              '{0:.2%}'.format(bullying / len(train_data['Label'])))\r\n        print(\"Total komentar Non cyberbullying adalah \" + str(irrelevant) + \", atau \",\r\n              '{0:.2%}'.format(irrelevant / len(train_data['Label'])))\r\n        print(\"Total komentar Netral adalah \" + str(netral) + \", atau \",\r\n              '{0:.2%}'.format(netral / len(train_data['Label'])))\r\n        print(\"Total komentar Non cyberbullying adalah \" + str(nonbull) + \", atau \",\r\n              '{0:.2%}'.format(nonbull / len(train_data['Label'])))\r\n        missing_train = train_data.isnull().sum()\r\n        print(missing_train)\r\n        # n_words = [len(komen) for komen in train_data.hasil_normalisasi]\r\n        # n_words = pd.Series(n_words)\r\n        # n_words.describe()\r\n        # print(train_data['hasil_normalisasi'].describe())\r\n\r\n    def tokenize_data(self):\r\n        self.tokenizer = Tokenizer(num_words=self.max_fatures, split=' ')\r\n        self.tokenizer.fit_on_texts(train_data['hasil_normalisasi'].values)\r\n        self.X = self.tokenizer.texts_to_sequences(train_data['hasil_normalisasi'].values)\r\n        print(self.X)\r\n        self.X = pad_sequences(self.X)\r\n        # print(X[:2])\r\n        self.Y = pd.get_dummies(train_data['Label']).values\r\n        print('Nilai Y adalah ', self.Y)\r\n        self.X_train, self.X_test, self.Y_train, self.Y_test = train_test_split(self.X, self.Y, test_size=0.20, random_state=42)\r\n        print(self.X_train.shape,self.Y_train.shape)\r\n        print(self.X_test.shape, self.Y_test.shape)\r\n        with open('data/tokenizer.pickle', 'wb') as handle:\r\n            pickle.dump(self.tokenizer, handle, protocol=pickle.HIGHEST_PROTOCOL)\r\n        self.vocab_size = len(self.tokenizer.word_index) + 1\r\n\r\n    def TFIDF(self):\r\n        vectorizer = TfidfVectorizer()\r\n        X = vectorizer.fit_transform(train_data['hasil_normalisasi'].values)\r\n        feature_names = vectorizer.get_feature_names()\r\n        print(vectorizer.get_feature_names())\r\n        print(X.shape)\r\n        doc = 0\r\n        feature_index = X[doc, :].nonzero()[1]\r\n        tfidf_score = zip(feature_index, [X[doc, x] for x in feature_index])\r\n        for w, s in [(feature_names[i], s) for (i, s) in tfidf_score]:\r\n            print(w, s)\r\n        df = pd.DataFrame(X.toarray(), columns=vectorizer.get_feature_names())\r\n        print(df)\r\n        df.to_csv('model/TFIDF.csv')\r\n\r\n    def word2vec(self):\r\n        with codecs.open('clear_traincoba.csv', 'r')as f:\r\n            for line in f:\r\n                tweet = f.readlines()\r\n                tokenized_sent = [word_tokenize(i) for i in tweet]\r\n                for i in tokenized_sent:\r\n                    print(i)\r\n        model = Word2vec.Word2Vec(tokenized_sent, min_count=1, size=200)\r\n        print(model)\r\n        model.wv.save_word2vec_format('model/model_word2vecWindow.txt', binary=False)\r\n        word = list(model.wv.vocab)\r\n\r\n    def make_model(self):\r\n        embeddings_index = dict()\r\n        # f = open('mymodel/idwiki_word2vec_200.model',encoding='utf-8')\r\n        f = open('model/model_word2vec.txt')\r\n        for line in f:\r\n            values = line.split()\r\n            word = values[0]\r\n            coefs = asarray(values[1:], dtype='float32')\r\n            embeddings_index[word] = coefs\r\n        f.close()\r\n        print('Loaded %s word vectors.' % len(embeddings_index))\r\n\r\n        # create a weight matrix for words in training docs\r\n        embedding_matrix = zeros((self.vocab_size, 200))\r\n        for word, i in self.tokenizer.word_index.items():\r\n            embedding_vector = embeddings_index.get(word)\r\n            if embedding_vector is not None:\r\n                embedding_matrix[i] = embedding_vector\r\n\r\n        print(embedding_matrix[21])\r\n        embed_dim = 200\r\n        lstm_out = 196\r\n        model = tensorflow.keras.Sequential()\r\n        model.add(Embedding(self.vocab_size, embed_dim,weights=[embedding_matrix],input_length=self.X.shape[1]))\r\n        model.add(SpatialDropout1D(0.4))\r\n        model.add(LSTM(lstm_out, dropout=0.2, recurrent_dropout=0.2))\r\n        # model.add(LSTM(lstm_out,activation='tanh'))\r\n        model.add(Dense(4, activation='softmax'))\r\n        model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\r\n        print(model.summary())\r\n        history=model.fit(self.X_train, self.Y_train, epochs=20, batch_size=self.batch_size, validation_data=(self.X_test, self.Y_test), verbose=1)\r\n        model.save('model/My_model.h5')\r\n        # hist_df=pd.DataFrame(self.history.history)\r\n        # with open('model/history.json','w')as file:\r\n        #     json.dump(history.history,file)\r\n        np.save('model/history_model.npy',history.history)\r\n\r\n    def load_model(self):\r\n        self.model = load_model('model/My_model.h5')\r\n        Y_pred = self.model.predict_classes(self.X_test, batch_size=self.batch_size)\r\n        df_test = pd.DataFrame({'true': self.Y_test.tolist(), 'pred': Y_pred})\r\n        df_test['true'] = df_test['true'].apply(lambda x: np.argmax(x))\r\n        print(\"confusion matrix\")\r\n        print(confusion_matrix(df_test.true, df_test.pred))\r\n        print(classification_report(df_test.true, df_test.pred))\r\n\r\n    def show_plot(self):\r\n        # with open('model/history.json','r') as f:\r\n        #     history_json=json.loads(f.read())\r\n        history=np.load('model/history_model.npy',allow_pickle='TRUE').item()\r\n        plt.figure(figsize=(12, 12))\r\n        plt.plot(history['loss'])\r\n        plt.plot(history['val_loss'])\r\n        plt.title('Loss')\r\n        plt.legend(['train', 'val'], loc='upper left')\r\n        # plt.show()\r\n        plt.savefig('images/plot/loss.png')\r\n\r\n        plt.figure(figsize=(12, 12))\r\n        plt.plot(history['accuracy'])\r\n        plt.plot(history['val_accuracy'])\r\n        plt.title('Accuracy')\r\n        plt.legend(['train', 'val'], loc='upper left')\r\n        # plt.show()\r\n        plt.savefig('images/plot/accuracy.png')\r\n\r\n    def new_model(self):\r\n        self.describe_model()\r\n        self.tokenize_data()\r\n        self.TFIDF()\r\n        self.make_model()\r\n        self.load_model()\r\n        self.show_plot()\r\n\r\n    def build_model(self):\r\n        self.describe_model()\r\n        self.tokenize_data()\r\n        # self.make_model()\r\n        self.load_model()\r\n        self.show_plot()\r\n\r\n    def predict_comment(self,text):\r\n        # self.build_model()\r\n        self.model = load_model('model/My_model.h5')\r\n        with open('data/tokenizer.pickle', 'rb') as handle:\r\n            tokenizer2 = pickle.load(handle)\r\n        twt=[]\r\n        twt.append(text)\r\n        print(twt)\r\n        # vectorizing the tweet by the pre-fitted tokenizer instance\r\n        twt = tokenizer2.texts_to_sequences(twt)\r\n        # padding the tweet to have exactly the same shape as `embedding_2` input\r\n        twt = pad_sequences(twt, maxlen=110, dtype='int32', value=0)\r\n        print(twt)\r\n\r\n        sentiment = self.model.predict(twt,batch_size=1, verbose=2)[0]\r\n        kelas=self.model.predict_classes(twt,batch_size=1)\r\n        print(kelas)\r\n        print(sentiment)\r\n        if (kelas == [0]):\r\n            print(\"Cyberbullying\")\r\n        elif (kelas == [1]):\r\n            print(\"Irrelevant\")\r\n        elif (kelas == [2]):\r\n            print(\"Netral\")\r\n        else:\r\n            print(\"Non Cyberbullying\")\r\n\r\n        dict={}\r\n        dict['class']=kelas\r\n        dict['probabilities']=sentiment\r\n        print(dict['class'])\r\n        print(dict['probabilities'][1])\r\n        return dict\r\n\r\n    def balas_komen(self,text):\r\n        import Prepocessing_sentence as normal\r\n        self.model = load_model('model/My_model.h5')\r\n        with open('data/tokenizer.pickle', 'rb') as handle:\r\n            tokenizer2 = pickle.load(handle)\r\n        text=normal.text_prepocessing(text)\r\n        twt=[]\r\n        twt.append(text)\r\n        print(twt)\r\n        # vectorizing the tweet by the pre-fitted tokenizer instance\r\n        twt = tokenizer2.texts_to_sequences(twt)\r\n        # padding the tweet to have exactly the same shape as `embedding_2` input\r\n        twt = pad_sequences(twt, maxlen=110, dtype='int32', value=0)\r\n        print(twt)\r\n\r\n        sentiment = self.model.predict(twt,batch_size=1, verbose=2)[0]\r\n        kelas=self.model.predict_classes(twt,batch_size=1)\r\n        print(kelas)\r\n        print(sentiment)\r\n        if (kelas == [0]):\r\n            kelas=0\r\n        elif (kelas == [1]):\r\n            kelas=1\r\n        elif (kelas == [2]):\r\n            kelas=2\r\n        else:\r\n            kelas=3\r\n        return kelas\r\n\r\n    def prediction(self,array):\r\n        import Prepocessing_sentence as normal\r\n        self.model = load_model('model/My_model.h5')\r\n        with open('data/tokenizer.pickle', 'rb') as handle:\r\n            tokenizer2 = pickle.load(handle)\r\n        print(array)\r\n        # vectorizing the tweet by the pre-fitted tokenizer instance\r\n        twt = tokenizer2.texts_to_sequences(array)\r\n        # padding the tweet to have exactly the same shape as `embedding_2` input\r\n        twt = pad_sequences(twt, maxlen=110, dtype='int32', value=0)\r\n        print(twt)\r\n\r\n        sentiment = self.model.predict(twt, batch_size=1, verbose=2)[0]\r\n        kelas = self.model.predict_classes(twt, batch_size=1)\r\n        return kelas\r\n\r\n# test=LSTM_model()\r\n# test.build_model()\r\n# test.predict_comment('ada yang bulat tapi bukan tekat')\r\n","repo_name":"Arifkurniawan-code/flask","sub_path":"lstm_model.py","file_name":"lstm_model.py","file_ext":"py","file_size_in_byte":10968,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"3709326485","text":"\"\"\"\nImplementations for building material in different formats.\n\"\"\"\n\nimport fnmatch\nimport glob\nimport os\nimport shutil\nimport subprocess\n\n\n# Glob patterns that match files/directories serving a specific purpose.\n# Only basenames can be matched, so don't try to include slashes in the patterns.\nBUILD_ARTIFACT_PATTERNS = (\"gladtex.cache\",)\nMD_PATTERNS = (\"*.md\",)\n\n\nclass MatucFailed(Exception):\n    \"\"\"\n    Raised when matuc fails.\n    \"\"\"\n\n    def __init__(self, returncode, output):\n        self.returncode = returncode\n        self.output = output\n        super().__init__()\n\n    def __repr__(self):\n        return (\n            f\"Matuc failed with returncode {self.returncode}.\\n\"\n            f\"Output:\\n{self.output}\"\n        )\n\n\ndef build_epub(build):\n    run_matuc(\"conv\", \".\", \"-f\", \"epub\")\n    os.makedirs(build.absolute_path)\n    epub_files = glob.glob(\"*.epub\")\n    assert len(epub_files) == 1, f\"Found more than one epub file: {epub_files[:10]}\"\n    assert os.path.isfile(epub_files[0]), f\"{epub_files[0]!r} is not a file\"\n    shutil.copyfile(epub_files[0], os.path.join(build.absolute_path, epub_files[0]))\n\n\ndef build_html(build):\n    run_matuc(\"conv\", \".\", \"-f\", \"html\")\n    shutil.copytree(\n        \".\",\n        build.absolute_path,\n        ignore=ignore_by_patterns(BUILD_ARTIFACT_PATTERNS + MD_PATTERNS),\n    )\n\n\ndef ignore_by_patterns(patterns):\n    \"\"\"Return a callable to be passed to ``shutil.copytree`` as ``ignore`` parameter.\n\n    The callable will cause all files/directories matching any of the given glob\n    patterns to be ignored. The patterns can only match basenames, thus they may\n    not contain slashes.\n    \"\"\"\n\n    def _filter_names(src_dir, names):\n        return [\n            name\n            for name in names\n            for pattern in patterns\n            if fnmatch.fnmatchcase(name, pattern)\n        ]\n\n    return _filter_names\n\n\ndef run_matuc(*args):\n    \"\"\"Runs the matuc command with given arguments.\n\n    :raises MatucFailed: if the process exits with a non-zero code\n    \"\"\"\n    result = subprocess.run(\n        [\"matuc\", *args],\n        stdin=None,\n        stdout=subprocess.PIPE,\n        stderr=subprocess.STDOUT,\n        text=True,\n    )\n    if result.returncode != 0:\n        raise MatucFailed(result.returncode, result.stdout)\n","repo_name":"TUD-INF-IAI-MCI/matshare","sub_path":"matshare/material_building.py","file_name":"material_building.py","file_ext":"py","file_size_in_byte":2283,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"35777225537","text":"#  lesson_8_try_except.\n#\n#  Task 1\n#  Write a function called oops that explicitly raises an IndexError\n#  exception when called. Then write another function that calls\n##  oops inside a try/except state­ment to catch the error.\n#  What happens if you change oops to raise KeyError instead of IndexError?\n'''При изменении параметра raise, получается следующее:\n# если IndexError как таковой, является TRUE, то есть действительно ошибка в индексе, то в начале\nвыполняется код цикла, а потом уже мы получаем наш raise.\n\n# если же значением будет KeyError, хотя по факту там IndexError, то мы получаем в начале вывод ошибки,\nа потом уже результаты выполнения цикла while.\n\n# Я не могу точно сформулировать механику всего этого процесса, но возможно это как то подвязно под return\nф-ции или под особенность raise.\n\n# сама ошибка в обоих случаях остаётся прежней IndexError: list index out of range. Вероятно, что\nнезависимо от указанного raise, ошибка всё равно как то хэндлится.\n\n# П. С. Вышеуказанный поток сознания, по моим ощущениям, прямо на грани какой то \"бредовой теории заговора\", но\nпорядок вывода, то всё равно меняется.\n'''\n\n\ndef oops():\n    # Change on IndexError or KeyError\n    raise KeyError\n\n\ndef some_foo():\n    num_list = [1, 2, 3, 4]\n    i = 0\n    try:\n        while i <= len(num_list)-1:\n            i += 1\n            print(f'i: {i}')\n    except:\n        oops()\n    #  For running without errors need next changes: num_list[i-1]\n    return num_list[i]\n\n\nif __name__ == \"__main__\":\n    print(some_foo())\n\n\n#  Task 2\n#  Write a function that takes in two numbers from the user via input(),\n#  call the numbers a and b, and then returns the value of squared a divided by b,\n#  construct a try-except block which raises an exception if the two values given\n#  by the input function were not numbers, and if value b was zero (cannot divide\n#  by zero).\ndef foo(arg1, arg2):\n    try:\n        ret = a ** 2 / b\n        return ret\n    except ZeroDivisionError:\n        print('Dividing by Zero is unacceptable')\n        exit(1)\n\n\ndef inp_check():\n    try:\n        a = int(input(f'Please, enter value for numeric value for \"a\": '))\n        b = int(input(f'Please, enter value for numeric value for \"b\": '))\n        # print('Numbers accepted')\n        return a, b\n    except ValueError:\n        print('Values for \"a\" and \"b\" must be only numeric')\n        exit(1)\n\n\nif __name__ == '__main__':\n    a, b = inp_check()\n    result = foo(a, b)\n    print(result)\n","repo_name":"Svyat33/beetroot_python","sub_path":"lesson_8_try_except/lesson8_task1_task2.py","file_name":"lesson8_task1_task2.py","file_ext":"py","file_size_in_byte":3026,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"18432498104","text":"\nfrom tkinter import *\n\n# створення вікна\nroot = Tk()\n\n# задання назви вікна\nroot.title(\"Графічний калькулятор\")\n\n# створення поля для введення чисел\ne = Entry(root, width=35, borderwidth=5)\ne.grid(row=0, column=0, columnspan=3, padx=10, pady=10)\n\n# функція для додавання цифр до поля введення\ndef button_click(number):\n    # додавання цифри до поля введення\n    current = e.get()\n    e.delete(0, END)\n    e.insert(0, str(current) + str(number))\n\n# функція для обчислення результату\ndef button_clear():\n    e.delete(0, END)\n\ndef button_add():\n    first_number = e.get()\n    global f_num\n    global math\n    math = \"addition\"\n    f_num = int(first_number)\n    e.delete(0, END)\n\n\ndef button_min():\n    first_number = e.get()\n    global f_num\n    global math\n    math = \"addition\"\n    f_num = int(first_number)\n    e.delete(0, END)\n\n\ndef button_equal():\n    second_number = e.get()\n    e.delete(0, END)\n    if math == \"addition\":\n        e.insert(0, f_num + int(second_number))\n\n# створення кнопок\nbutton_1 = Button(root, text=\"1\", padx=40, pady=20, command=lambda: button_click(1))\nbutton_2 = Button(root, text=\"2\", padx=40, pady=20, command=lambda: button_click(2))\nbutton_3 = Button(root, text=\"3\", padx=40, pady=20, command=lambda: button_click(3))\nbutton_4 = Button(root, text=\"4\", padx=40, pady=20, command=lambda: button_click(4))\nbutton_5 = Button(root, text=\"5\", padx=40, pady=20, command=lambda: button_click(5))\nbutton_6 = Button(root, text=\"6\", padx=40, pady=20, command=lambda: button_click(6))\nbutton_7 = Button(root, text=\"7\", padx=40, pady=20, command=lambda: button_click(7))\nbutton_8 = Button(root, text=\"8\", padx=40, pady=20, command=lambda: button_click(8))\nbutton_9 = Button(root, text=\"9\", padx=40, pady=20, command=lambda: button_click(9))\nbutton_0 = Button(root, text=\"0\", padx=40, pady=20, command=lambda: button_click(0))\nbutton_add = Button(root, text=\"+\", padx=39, pady=20, command=button_add)\nbutton_min = Button(root, text=\"-\", padx=39, pady=20, command=button_min)\nbutton_equal = Button(root, text=\"=\", padx=91, pady=20, command=button_equal)\nbutton_clear = Button(root, text=\"Clear\", padx=79, pady=20, command=button_clear)\n\n# виведення кнопок на екран\nbutton_1.grid(row=3, column=0)\nbutton_2.grid(row=3, column=1)\nbutton_3.grid(row=3, column=2)\n\nbutton_4.grid(row=2, column=0)\nbutton_5.grid(row=2, column=1)\nbutton_6.grid(row=2, column=2)\n\nbutton_7.grid(row=1, column=0)\nbutton_8.grid(row=1, column=1)\nbutton_9.grid(row=1, column=2)\n\nbutton_0.grid(row=4, column=0)\nbutton_clear.grid(row=4, column=1, columnspan=2)\nbutton_add.grid(row=5, column=0)\nbutton_min.grid(row=8, column=0)\nbutton_equal.grid(row=5, column=1, columnspan=2)\n\n# запуск вікна\nroot.mainloop()","repo_name":"elonprogramer/python_lessons","sub_path":"home_drafts/short_examples/test/calculator.py","file_name":"calculator.py","file_ext":"py","file_size_in_byte":2913,"program_lang":"python","lang":"uk","doc_type":"code","stars":1,"dataset":"github-code","pt":"81"}
+{"seq_id":"44115130622","text":"import pandas as pd\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\n\r\nimport tkinter as tk\r\nfrom IPython.display import display\r\n\r\nfrom sklearn.model_selection import train_test_split\r\n\r\ndef train_test_splitting():\r\n    df=pd.read_csv(\"Dataset/train.csv\")\r\n\r\n    df['LoanAmount']=df['LoanAmount'].fillna(df['LoanAmount'].mean())\r\n    df['Loan_Amount_Term']=df['Loan_Amount_Term'].fillna(df['Loan_Amount_Term'].mean())\r\n    df['Credit_History']=df['Credit_History'].fillna(df['Credit_History'].mean())\r\n\r\n    df['Gender']=df['Gender'].fillna(df['Gender'].mode()[0])\r\n    df['Married']=df['Married'].fillna(df['Married'].mode()[0])\r\n    df['Dependents']=df['Dependents'].fillna(df['Dependents'].mode()[0])\r\n    df['Self_Employed']=df['Self_Employed'].fillna(df['Self_Employed'].mode()[0])\r\n\r\n    df['TotalIncome']=df['ApplicantIncome']+df['CoapplicantIncome']\r\n    df['ApplicantIncomeLog']=np.log(df['ApplicantIncome'])\r\n    df['LoanAmountLog']=np.log(df['LoanAmount'])\r\n    df['Loan_Amount_TermLog']=np.log(df['Loan_Amount_Term'])\r\n    df['TotalIncomeLog']=np.log(df['TotalIncome'])\r\n\r\n    cols = ['ApplicantIncome', 'CoapplicantIncome', \"LoanAmount\", \"Loan_Amount_Term\", \"TotalIncome\", 'Loan_ID']\r\n    df = df.drop(columns=cols, axis=1)\r\n\r\n\r\n    d1 = pd.get_dummies(df['Gender'], drop_first= True)\r\n    d2 = pd.get_dummies(df['Married'], drop_first= True)\r\n    d3 = pd.get_dummies(df['Dependents'], drop_first= True)\r\n    d4 = pd.get_dummies(df['Education'], drop_first= True)\r\n    d5 = pd.get_dummies(df['Self_Employed'], drop_first= True)\r\n    d6 = pd.get_dummies(df['Property_Area'], drop_first= True)\r\n\r\n\r\n\r\n    df1 = pd.concat([df, d1, d2, d3, d4, d5, d6], axis = 1)\r\n    df=df1\r\n\r\n    cols = ['Gender', 'Married', \"Dependents\", \"Education\", \"Self_Employed\", 'Property_Area']\r\n    df = df.drop(columns=cols, axis=1)\r\n\r\n    display(df.head())\r\n\r\n    x = df.drop(columns=['Loan_Status'], axis=1)\r\n    y = df['Loan_Status']\r\n\r\n    display(x)\r\n    display(y)\r\n\r\n    x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.25, random_state=42)\r\n\r\n    display(x_train.head())\r\n    display(y_test.head())\r\n\r\n    print(\"Training Testing data splitted successfully\")\r\n    tk.Label(text=\"Training Testing data splitted successfully\", width=48, height=2, bg='green', fg='white').place(relx=0.6, rely=0.7)","repo_name":"KmIndu/Loan-Prediction-using-Machine-Learning","sub_path":"train_test_split.py","file_name":"train_test_split.py","file_ext":"py","file_size_in_byte":2318,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"18310958697","text":"from abc import abstractmethod\nimport torch\nimport torch.nn as nn\nfrom torch.distributions import Normal\n\nfrom torch.utils.data import Dataset, DataLoader\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\ndevice = 'cuda' if torch.cuda.is_available() else 'cpu'\n\n\nclass Flow(nn.Module):\n\n    def __init__(self, bijections, net, dim=1):\n        super().__init__()\n        self.bijections = nn.ModuleList(bijections)\n        self.net = net\n        self.dim = dim\n\n    @property\n    def base_dist(self):\n        return Normal(\n            loc=torch.zeros(2, device=device),\n            scale=torch.ones(2, device=device),\n        )\n        \n    @abstractmethod\n    def flow_outputs(self, x):\n        raise NotImplementedError(\n            \"you called the train function on the abstract model class.\"\n        )\n\n    def log_prob(self, x):\n        log_prob = torch.zeros(x.shape[0], device=device)\n        for bijection in self.bijections:\n            x, ldj = bijection(x)\n            log_prob += ldj\n        log_prob += self.base_dist.log_prob(x).sum(1)\n        return log_prob\n    \n    # def prob(self, x):\n    #     log_prob = torch.zeros(x.shape[0], device=device)\n    #     for bijection in self.bijections:\n    #         x, ldj = bijection(x)\n        \n    #     return x\n\n    def sample(self, num_samples):\n        z = self.base_dist.sample((num_samples,))\n        for bijection in reversed(self.bijections):\n            z = bijection.inverse(z)\n        return z\n","repo_name":"Qamar-93/dt8122_Submission","sub_path":"flows/flow.py","file_name":"flow.py","file_ext":"py","file_size_in_byte":1486,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"44210165545","text":"#!/usr/local/bin/python3\n\n# Activate the virtual environment inside Python to allow simple execution\nimport os\n\nbase_dir = os.path.dirname(os.path.realpath(os.path.expanduser(__file__)))\nactivate_this = os.path.join(base_dir, \"venv/bin/activate_this.py\")\nexec(open(activate_this).read())\n\nimport logging\nfrom matrix_pdu import CommandPDU\nimport socket\nimport sys\n\nlogging.getLogger().setLevel(logging.INFO)\n\n\ndef send_brightness(brightness: int):\n    logging.debug(\"Setting brightness to \" + str(brightness))\n    cmd = CommandPDU(CommandPDU.CMD_BRIGHTNESS, brightness)\n    pdu_bytes = cmd.as_binary()\n\n    opened_socket = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\n    ret = opened_socket.sendto(pdu_bytes, (\"127.0.0.1\", 20304))\n\n\ndef main():\n    if len(sys.argv) == 2 and sys.argv[1].isnumeric():\n        send_brightness(brightness=int(sys.argv[1]))\n    else:\n        sys.stderr.write(\"Usage: ./set_brightness.py <BRIGHTNESS>\")\n        exit(1)\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"aliask/radmat","sub_path":"set_brightness.py","file_name":"set_brightness.py","file_ext":"py","file_size_in_byte":991,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"2388792033","text":"#!/usr/bin/env python\nimport sys\nfrom math import sqrt\n\n\n# noinspection DuplicatedCode\ndef isprime(x):\n    if x < 2:\n        return False\n    if x == 2:\n        return True\n    if x % 2 == 0:\n        return False\n    if x < 9:\n        return True\n    if (x + 1) % 6 != 0:\n        if (x - 1) % 6 != 0:\n            return False\n    lim = sqrt(x) + 1\n    for y in range(3, int(lim), 2):\n        if x % y == 0:\n            return False\n    return True\n\n\ndef genprime(n, m):\n    \"\"\"\n    Given N, returns the Nth prime.\n\n    Optionally called with M, the (N-1)th prime.\n    \"\"\"\n    if m != -1:\n        count = n - 1\n        current = m + 1\n    else:\n        count = 0\n        current = 1\n\n    while count < n:  # count up to n-1\n        if isprime(current):  # if we encounter a prime\n            count += 1  # increment the count\n        current += 1  # test the next number\n    return current - 1\n\n\n# main():\n# does *not* exit gracefully\nstart = int(sys.argv[1])\nstop = int(sys.argv[2]) + 1\nv = len(sys.argv) > 3  # any third argument means we're going verbose\n\nlast = -1\nfor count in range(start, stop, start):\n    last = genprime(count, last)\n    if v:\n        print(f'Found {count} primes (last was {last})')\n","repo_name":"nok-ko/fastprimes","sub_path":"improved_primes.py","file_name":"improved_primes.py","file_ext":"py","file_size_in_byte":1208,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"31781343347","text":"from django.db import models\n\nfrom .book import Book\nfrom .publisher import Publisher\n\n\nclass BookEdition(models.Model):\n\n    book = models.ForeignKey(Book, on_delete=models.CASCADE)\n    publisher = models.ForeignKey(Publisher, on_delete=models.CASCADE)\n    isbn = models.CharField(max_length=17)\n    date = models.DateField()\n\n    def __str__(self):\n        return \"{book}, {publisher}\".format(book=self.book, publisher=self.publisher)\n","repo_name":"kamtom88/djangolibrary","sub_path":"shelf/models/book_edition.py","file_name":"book_edition.py","file_ext":"py","file_size_in_byte":437,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"71635411144","text":"data=[]\n\nfile_name=input(\"Provide a name of a file of data\")\n\ntry:\n  fh=open(file_name,'r',encoding=\"utf-8\")\nexcept IOError:\n  print('cannot open',file_name)\nelse:\n  for new in fh:\n    if new !='\\n':\n      addIt=new[:-1].split(',')\n      data.append(addIt)\nfinally:\n    fh.close()\n\nprint(data)","repo_name":"yaoxs7503/pythontest","sub_path":"example_file.py","file_name":"example_file.py","file_ext":"py","file_size_in_byte":293,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"33063394651","text":"def solution(total_lambs):\n    # fibonacci sequence for getting the max hanchman paid respecting the rules.\n    min_henchmen = min_hanchmen_paid(total_lambs)\n    # double the first payment for getting the min hanchman paid respecting the rules.\n    max_hanchmen = max_hanchman_paid(total_lambs)\n    diff_paid = min_henchmen - max_hanchmen\n    return diff_paid\n\ndef min_hanchmen_paid(total_lambs):\n    # helper variables\n    a = 0\n    b = 1\n    fibonacci = [a,b]\n    while (sum(fibonacci) <= total_lambs):\n        a, b = b, a+b\n        fibonacci.append(b)\n    \n    if (sum(fibonacci) > total_lambs):\n        # Commander lambda can't pay the next hanchmen\n        fibonacci.pop()\n        # Clean helper\n        fibonacci.pop(0)\n    else:\n        # Clean helper\n        fibonacci.pop(0)\n        \n    #return the n of paid hanchmen\n    return len(fibonacci)\n    \ndef max_hanchman_paid(total_lambs):\n    helper = [1, 2]\n    while sum(helper) <= total_lambs:\n        helper.append(helper[-1]*2)\n    if (sum(helper) > total_lambs):\n        # Commander lambda can't pay the next hanchmen\n        helper.pop()\n    return len(helper)\n","repo_name":"ekolivero/foo-bar","sub_path":"lovely_lucky_lambs/solution.py","file_name":"solution.py","file_ext":"py","file_size_in_byte":1124,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"81"}
+{"seq_id":"11341305887","text":"import cProfile\nimport sys\n\nfrom ptr42 import get_default_lexer\nfrom ptr42 import MathParser\n\n\ndef calculator(expresion: str) -> float:\n    lexer = get_default_lexer(expresion)\n    tokens = list(lexer.generate_tokens())\n    parser = MathParser(tokens, lexer.get_operators())\n    return parser.parse().evaluate()\n\n\ndef main():\n    pr = cProfile.Profile()\n    pr.enable()\n\n    counter = 0\n    int_sum = 0\n    int_sum_pow2 = 0\n\n    for input_text in sys.stdin:\n        counter = calculator(str(counter) + \"+\" + \"1\")\n        int_sum = calculator(str(int_sum) + \"+\" + str(float(input_text)))\n        input_pow2 = calculator(str(float(input_text)) + \"*\" + str(float(input_text)))\n        int_sum_pow2 = calculator(str(int_sum_pow2) + \"+\" + str(input_pow2))\n\n    average = calculator(str(int_sum) + \"/\" + str(counter))\n    calculator(\"sqrt(\" + \"1/(\" + str(counter) + \"-1)*(\" + str(int_sum_pow2) + \"-\" + str(counter) + \"*\" + str(\n        average) + \"*\" + str(average) + \"))\")\n\n    pr.disable()\n    pr.print_stats(sort='time')\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"mlorinc/42ptr-calc","sub_path":"src/profiling/profiling.py","file_name":"profiling.py","file_ext":"py","file_size_in_byte":1058,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"42759757975","text":"#!/usr/bin/env python\n# http://stackoverflow.com/questions/1159524/how-to-replace-a-column-in-a-csv-file-in-python\n# See: http://docs.python.org/library/csv.html\nfrom __future__ import with_statement\n\nimport csv\n\ndef twiddle_csv(file1, file2):\n    def mess_with_record(record):\n        record['90mdist'] = 2 * int(record['90mdist']) + 30\n    with open(file1, \"r\") as fin:\n        with open(file2, \"w\") as fout:\n            fields = ['ID', 'transect', '90mdist']\n            reader = csv.DictReader(fin, fieldnames=fields)\n            writer = csv.DictWriter(fout, fieldnames=fields)\n            fout.write(\",\".join(fields) + '\\n')\n            reader.next()   # Skip the column header\n            for record in reader:\n                mess_with_record(record)\n                writer.writerow(record)\n\nif __name__ == '__main__':\n    twiddle_csv('1159524-file1', 'file2')\n","repo_name":"hughdbrown/StackOverflow","sub_path":"1159524.py","file_name":"1159524.py","file_ext":"py","file_size_in_byte":869,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"81"}
+{"seq_id":"4117211187","text":"# -*- coding: utf-8 -*-\n\nimport re\nfrom . import csvdb\n\nDEL_ABRE = r\"<\"\nDEL_CIERRA = r\">\"\nRE_ENTRE_DELIM = DEL_ABRE + r\"(.+?)\" + DEL_CIERRA\n\n\ndef getParameters(rows):\n    for index, comando_completo in enumerate(csvdb.getColumn(rows, 5)):\n        found = re.findall(RE_ENTRE_DELIM, comando_completo)\n        rows[index].append(found)\n    return rows\n","repo_name":"alete89/nPy","sub_path":"src/logic/paramFinder.py","file_name":"paramFinder.py","file_ext":"py","file_size_in_byte":350,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"7886334279","text":"import pandas as pd\n\ndef create_notes_to_midi_mapping():\n    notes = ['C', 'D', 'E', 'F', 'G', 'A', 'B']\n    temp = []\n    for n in notes:\n        temp.append(n)\n        if n not in ('E', 'B'):\n            temp.append(n + '#')\n            \n    notes = temp\n    \n    octaves = list(range(-1, 10))\n    temp = []\n    \n    for o in octaves:\n        #postfix = ('+' if o >= 0 else '') + str(o)\n        for n in notes:\n            if o != 9 or n not in ('G#', 'A', 'A#', 'B'):\n                temp.append(n + str(o))\n                \n    notes_to_pitches_dict = {note: value for (value, note) in enumerate(temp)}\n    return notes_to_pitches_dict\n\ndef midi_to_notes(vec):\n    midi_dict = create_notes_to_midi_mapping()\n    midi_dict = dict((v, k) for k, v in midi_dict.items())\n    return [midi_dict[i] for i in vec]\n\ndef notes_to_midi(vec):\n    midi_dict = create_notes_to_midi_mapping()\n    return [midi_dict[i] for i in vec]\n\ndef gen_to_midi(vec):\n    midi_vec = []\n    midi_durations = []\n    duration_counter = 0\n    for n in vec:\n        if n != -2:\n            midi_vec.append(n)\n            if duration_counter:\n                midi_durations.append(duration_counter)\n            duration_counter = 1\n        else:\n            duration_counter += 1\n    midi_durations.append(duration_counter)\n    return midi_vec, midi_durations\n\ndef midi_to_gen(midi_vec, midi_durations):\n    gen_representation = []\n    for note, duration in zip(midi_vec, midi_durations):\n        gen_representation.append(note)\n        gen_representation += [-2] * (duration - 1)\n    return gen_representation\n\nif __name__ == '__main__':\n    midi_dict = create_notes_to_midi_mapping()\n    degrees  = [60, 62, 64, 65, 67, 69, 71, 72]  # MIDI note number\n    notes_representation = midi_to_notes(degrees)\n    midi_representation = notes_to_midi(notes_representation)\n    assert(midi_representation == degrees)\n    \n    gen_representation = [60, -2, -2, -2, 62, -2, -2, -2, 64, -2, -2, -2, 65, -2, -2, -2,\n                          67, -2, -2, -2, 69, -2, -2, -2, 71, -2, -2, -2, 72, -2, -2, -2,\n                          -1, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2, -2,\n                          72, -2, -2, -2, 71, -2, -2, -2, 69, -2, -2, -2, 67, -2, -2, -2,\n                          65, -2, -2, -2, 64, -2, -2, -2, 62, -2, -2, -2, 60, -2, -2, -2]\n    \n    midi_representation, midi_durations = gen_to_midi(gen_representation)\n    assert(midi_representation == degrees + [-1] + list(reversed(degrees)))\n    assert(midi_durations == [4] * len(degrees) + [16] + [4] * len(degrees))\n    \n    g_repr = midi_to_gen(midi_representation, midi_durations)\n    assert(g_repr == gen_representation)\n    ","repo_name":"Laszer271/MusicGeneration","sub_path":"evolution/utils/representations.py","file_name":"representations.py","file_ext":"py","file_size_in_byte":2678,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"81"}
+{"seq_id":"28609986755","text":"# -*- coding: utf-8 -*-\nimport os\nimport glob\nimport numpy as np\nfrom os.path import join\nfrom scipy.ndimage import distance_transform_edt\n\nimport torch\nimport torchvision.transforms.functional as tF\n\nimport torchio as tio\nfrom torchio.data import SubjectsDataset\nfrom torchio.transforms.spatial_transform import SpatialTransform\nfrom torchio.transforms.intensity_transform import IntensityTransform\n# from models import RoughAffineZ\nfrom utils import chwd2cdhw, cdhw2chwd, flip_z\n\n'''\nconvert from eugene\n'''\n\ndef get_conf_map(mask):\n    mask = mask.type(torch.float32)\n    k = (3, 3, 1)\n    p = (1, 1, 0)\n    min_pool = torch.nn.functional.max_pool3d(-mask, k, 1, p) * -1\n    max_min_pool = torch.nn.functional.max_pool3d(min_pool, k, 1, p)\n    contour = torch.relu(max_min_pool-min_pool)\n    non_contour = 1 - contour\n    conf_map = np.zeros(contour.shape)\n    for z in range(conf_map.shape[-1]):\n        if contour[...,z].any():\n            conf_map[...,z] = distance_transform_edt(non_contour[...,z])\n    conf_map = conf_map / conf_map.max()\n    conf_map = (1-conf_map) ** 100\n    conf_map = torch.Tensor(conf_map)\n    conf_map = (conf_map+min_pool).clip(0, 1)\n    return conf_map\n\n\ndef noisy_label_3d(label, noise_sigma, kernel_size, soft_boundary):\n    if soft_boundary:\n        conf_map = get_conf_map(label)\n    label = chwd2cdhw(label).type(torch.float32)\n    label_smooth = tF.gaussian_blur(label, kernel_size, [noise_sigma, noise_sigma])\n    p_map = 0.5 - torch.abs(label_smooth-0.5)\n    noise = torch.rand(size=label.shape)\n    flip = torch.where(noise<p_map, 1, 0)\n    noisy_label = torch.logical_xor(flip, label).type(torch.float32)\n    noisy_label = cdhw2chwd(noisy_label)\n    if soft_boundary:\n        noisy_label = noisy_label * conf_map\n    return noisy_label\n\n\nclass NoisyLabel(IntensityTransform):\n    def __init__(self, noise_mm=5, kernel_size=None, soft_boundary=False, **kwargs):\n        super(NoisyLabel, self).__init__(**kwargs)\n        self.noise_mm = noise_mm\n        self.kernel_size = kernel_size\n        self.soft_boundary = soft_boundary\n\n    def apply_transform(self, subject):\n        label = subject['label'][tio.DATA]\n        assert label.ndim==4 and label.shape[0]==1\n        spacing = subject['label'].spacing\n        noise_voxel = self.noise_mm / spacing[0]\n        if self.kernel_size is None:\n            kernel_size = int(noise_voxel)//2*2 + 1\n        else:\n            kernel_size = self.kernel_size\n        new_data = noisy_label_3d(\n            label=label,\n            noise_sigma=noise_voxel,\n            kernel_size=kernel_size,\n            soft_boundary=self.soft_boundary)\n        subject['label'].set_data(new_data)\n        return subject\n\n\nclass DistanceMap(IntensityTransform):\n    def __init__(self, mode='3d', focus_boundary=False, **kwargs):\n        super(DistanceMap, self).__init__(**kwargs)\n        self.mode = mode\n        self.focus_boundary = focus_boundary\n\n    def apply_transform(self, subject):\n        roi = subject['label'][tio.DATA]\n        dist_map = np.zeros(roi.shape)\n        roi = roi.numpy()\n        normal = 1 - roi\n        if self.mode == '3d':\n            if roi.any():\n                dist_map = distance_transform_edt(normal)*normal - (distance_transform_edt(roi)-1)*roi\n        elif self.mode == '2d':\n            for z in range(roi.shape[-1]):\n                if roi[...,z].any():\n                    dist_map[...,z] = distance_transform_edt(normal[...,z])*normal[...,z] - (distance_transform_edt(roi[...,z])-1)*roi[...,z]\n        else:\n            raise ValueError('Unknown mode')\n\n        if self.focus_boundary:\n            roi_min, roi_max = -dist_map.min(), dist_map.max()\n            dist_map = (-1-dist_map/roi_min)*roi + (1-dist_map/roi_max)*normal\n        subject['dist_map'].set_data(dist_map)\n        return subject\n\n\nclass SampleProbabilityMap(IntensityTransform):\n    def __init__(self, icv_weight=0, **kwargs):\n        super(SampleProbabilityMap, self).__init__(**kwargs)\n        self.icv_weight = icv_weight\n\n    def apply_transform(self, subject):\n        has_roi = (subject['label'][tio.DATA].sum(dim=[1, 2])>0).type(torch.float32)\n        has_brain = (subject['icv'][tio.DATA].sum(dim=[1, 2])>0).type(torch.float32)\n        prob_map = subject['icv'][tio.DATA] * (self.icv_weight*has_brain+(1-self.icv_weight)*has_roi)\n        subject['prob_map'].set_data(prob_map)\n        return subject\n\n\nclass RandomAffine(SpatialTransform):\n    def __init__(self, degrees=0, translate=None, scale=None, **kwargs):\n        super(RandomAffine, self).__init__(**kwargs)\n        self.degrees = degrees\n        self.translate = 0 if translate is None else translate\n        self.scale = 0 if scale is None else scale\n\n    def apply_transform(self, subject):\n        angle = np.random.randint(-self.degrees, self.degrees)\n        translate = np.random.rand(2) * self.translate * 256\n        scale = 1 - (np.random.rand()*2-1) * self.scale\n        for image in self.get_images(subject):\n            new_data = tF.affine(\n                            chwd2cdhw(image.data),\n                            angle=angle, translate=translate.tolist(),\n                            scale=scale, shear=[0, 0])\n            new_data = cdhw2chwd(new_data)\n            image.set_data(new_data)\n        return subject\n\n\nclass Flip(IntensityTransform):\n    def __init__(self, **kwargs):\n        super(Flip, self).__init__(**kwargs)\n        self.affine_model = RoughAffineZ()\n        affine_model_path = 'affine_z_lvl4.cpt'\n        self.affine_model.load_state_dict(torch.load(affine_model_path, map_location='cpu')['model'])\n        self.affine_model.eval()\n\n    def apply_transform(self, subject):\n        for image in self.get_images(subject):\n            image_extend = image.data.unsqueeze(dim=0)\n            with torch.no_grad():\n                p = self.affine_model(image_extend)\n            image_flip = flip_z(p, image_extend)\n            image.set_data(image_flip.squeeze(dim=0))\n        return subject\n\n\n# dataset for in-house / AISD\nclass CTDataset(SubjectsDataset):\n    '''\n    NCCT: `data_dir`/images/.*nii.gz\n    flipped NCCT: `data_dir`/images_flip/.*nii.gz\n    ROI: `data_dir`/masks/.*nii.gz\n    ICV: `data_dir`/ICV_masks/.*nii.gz\n    '''\n    def __init__(\n            self, mode='train',\n            flip_p=0.5, trans_range=0.1, resize_range=0.1, rotate_range=20, noise_mm=10,\n            no_noisy=False, flip_image=True, curriculum=True,\n            **kwargs):\n        \n        if mode == 'train':\n            data_dir = '/media/yclin/3TBNAS/Medical-AI/LAB3/data/train'\n        else:\n            data_dir = '/media/yclin/3TBNAS/Medical-AI/LAB3/data/valid'\n\n        flip_p = 0 if flip_p is None else flip_p\n\n        image_dir = join(data_dir, 'CT')\n        image_paths = sorted(glob.glob(join(image_dir, '*.nii')))\n        if flip_image:\n            image_flip_paths = [path.replace('images', 'images_flip') for path in image_paths]\n        else:\n            image_flip_paths = image_paths\n\n        roi_paths = sorted(glob.glob(data_dir + '/Infection/*.nii'))\n        icv_paths = sorted(glob.glob(data_dir + '/Lung/*.nii'))\n\n        # roi_paths = [path.replace('images', 'masks') for path in image_paths]\n        # icv_paths = [path.replace('images', 'ICV_masks') for path in image_paths]\n\n        image_num = len(image_paths)\n        # file_names = [os.path.split(x)[1] for x in image_paths]\n        # subj_names = [os.path.splitext(os.path.splitext(x)[0])[0] for x in file_names]\n        subj_names = [os.path.split(image_paths[x])[-1] for x in range(len(image_paths))]\n\n        if mode == 'train':\n            subjects = [\n                tio.Subject(\n                    image=tio.ScalarImage(image_paths[i]),\n                    # image_flip=tio.ScalarImage(image_flip_paths[i]),\n                    label=tio.ScalarImage(roi_paths[i]),\n                    # dist_map=tio.ScalarImage(roi_paths[i]),\n                    prob_map=tio.ScalarImage(icv_paths[i]),\n                    icv=tio.ScalarImage(icv_paths[i]),\n                    image_path=image_paths[i],\n                    name=subj_names[i])\n                for i in range(image_num)\n            ]\n        elif mode == 'val':\n            subjects = [\n                tio.Subject(\n                    image=tio.ScalarImage(image_paths[i]),\n                    # image_flip=tio.ScalarImage(image_flip_paths[i]),\n                    label=tio.ScalarImage(roi_paths[i]),\n                    # dist_map=tio.ScalarImage(roi_paths[i]),\n                    image_path=image_paths[i],\n                    name=subj_names[i])\n                for i in range(image_num)\n            ]\n        else:\n            subjects = [\n                tio.Subject(\n                    image=tio.ScalarImage(image_paths[i]),\n                    # image_flip=tio.ScalarImage(image_flip_paths[i]),\n                    image_path=image_paths[i],\n                    name=subj_names[i])\n                for i in range(image_num)\n            ]\n\n        transform = []\n        # if flip_image:\n        #     transform.append(Flip(include=['image_flip']))\n\n        if mode == 'train':\n            transform += [\n                tio.transforms.RandomFlip(axes='LR', flip_probability=flip_p),\n                RandomAffine(rotate_range, trans_range, resize_range),\n            ]\n            # if not no_noisy:\n            #     transform.append(NoisyLabel(noise_mm=noise_mm, include=['label']))\n            if curriculum:\n                transform.append(tio.transforms.Pad((64, 64, 4)))\n                transform.append(SampleProbabilityMap(icv_weight=0, include=['prob_map']))\n        # if mode=='train' or mode=='val':\n        #     transform.append(DistanceMap(include=['dist_map']))\n        transform = tio.transforms.Compose(transform)\n        super(CTDataset, self).__init__(subjects=subjects, transform=transform, **kwargs)\n\n\n# dataset for affine net training\nclass AffineDataset(SubjectsDataset):\n    '''\n    NCCT: `data_dir`/images/.*nii.gz\n    ICV: `data_dir`/ICV_masks/.*nii.gz\n    '''\n    def __init__(\n            self, data_dir, mode='train',\n            flip_p=0.5, trans_range=0.1, resize_range=0.1, rotate_range=20,\n            **kwargs):\n        flip_p = 0 if flip_p is None else flip_p\n\n        image_dir = join(data_dir, 'images')\n\n        image_paths = sorted(glob.glob(join(image_dir, '*.nii.gz')))\n        icv_paths = [path.replace('images', 'ICV_masks') for path in image_paths]\n\n        image_num = len(image_paths)\n        file_names = [os.path.split(x)[1] for x in image_paths]\n        subj_names = [os.path.splitext(os.path.splitext(x)[0])[0] for x in file_names]\n\n        subjects = [\n            tio.Subject(\n                image=tio.ScalarImage(image_paths[i]),\n                icv=tio.ScalarImage(icv_paths[i]),\n                image_path=image_paths[i],\n                name=subj_names[i])\n            for i in range(image_num)\n        ]\n\n        transform = []\n        if mode == 'train':\n            transform += [\n                tio.transforms.RandomFlip(axes='LR', flip_probability=flip_p),\n                RandomAffine(rotate_range, trans_range, resize_range),\n            ]\n        transform = tio.transforms.Compose(transform)\n        super(AffineDataset, self).__init__(subjects=subjects, transform=transform, **kwargs)\n\n\n# dataset for context restoration pre-training\nclass CRDataset(SubjectsDataset):\n    '''\n    NCCT: `data_dir`/images/.*nii.gz\n    '''\n    def __init__(\n            self, data_dir,\n            flip_p=0.5, trans_range=0.1, resize_range=0.1, rotate_range=20,\n            **kwargs):\n        flip_p = 0 if flip_p is None else flip_p\n\n        image_dir = join(data_dir, 'images')\n\n        image_paths = sorted(glob.glob(join(image_dir, '*.nii.gz')))\n        image_num = len(image_paths)\n\n        file_names = [os.path.split(x)[1] for x in image_paths]\n        subj_names = [os.path.splitext(os.path.splitext(x)[0])[0] for x in file_names]\n\n        subjects = [\n            tio.Subject(\n                image=tio.ScalarImage(image_paths[i]),\n                image_path=image_paths[i],\n                name=subj_names[i])\n            for i in range(image_num)\n        ]\n\n        transform = [\n            tio.transforms.RandomFlip(axes='LR', flip_probability=flip_p),\n            RandomAffine(rotate_range, trans_range, resize_range),\n        ]\n        transform = tio.transforms.Compose(transform)\n        super(CRDataset, self).__init__(subjects=subjects, transform=transform, **kwargs)\n\n\n# dataset for 52 testing data\nclass CTTestDataset(SubjectsDataset):\n    '''\n    NCCT: `data_dir`/images/.*nii.gz\n    flipped NCCT: `data_dir`/images_flip/.*nii.gz\n    ROI: `data_dir`/masks/.*nii.gz\n    '''\n    def __init__(self, **kwargs):\n\n        data_dir = '/media/yclin/3TBNAS/Medical-AI/LAB3/data/test'\n        image_paths = sorted(glob.glob(join(data_dir, 'CT/*.nii'))[0])\n        image_num = len(image_paths)\n\n        subj_names = [os.path.split(image_paths[x])[-1] for x in range(len(image_paths))]\n        # subj_names = [x.split('/')[-1].split('.')[0] for x in image_paths]\n\n        # image_flip_paths = [x.replace('images', 'images_flip') for x in image_paths]\n        \n        # roi_paths = [x.replace('images', 'masks') for x in image_paths]\n        roi_paths = sorted(glob.glob(data_dir + '/Infection/*.nii'))\n\n        subjects = [\n            tio.Subject(\n                image=tio.ScalarImage(image_paths[i]),\n                # image_flip=tio.ScalarImage(image_flip_paths[i]),\n                label=tio.ScalarImage(roi_paths[i]),\n                image_path=image_paths[i],\n                name=subj_names[i])\n            for i in range(image_num)\n        ]\n\n        transform = []\n        # transform = [Flip(include=['image_flip'])]\n        transform = tio.transforms.Compose(transform)\n        super(CTTestDataset, self).__init__(subjects=subjects, transform=transform, **kwargs)\n","repo_name":"xup6YJ/Medical-AI","sub_path":"LAB3/dset_io.py","file_name":"dset_io.py","file_ext":"py","file_size_in_byte":13855,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"24593085076","text":"infile = open(\"input.txt\", \"r\", encoding=\"utf8\")\r\na = []\r\nfor i in infile:\r\n    a.append(i.split())\r\ninfile.close()\r\na.sort(key=lambda x: x[0])\r\noutfile = open(\"output.txt\", \"w\", encoding=\"utf8\")\r\nfor b in a:\r\n    b = [b[0], b[1], b[-1]]\r\n    print(' '.join(map(str, b)), end='\\n', file=outfile)\r\noutfile.close()\r\n","repo_name":"dima-panchenko/Python","sub_path":"week-6/5.отсортировать список.py","file_name":"5.отсортировать список.py","file_ext":"py","file_size_in_byte":314,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"21802863751","text":"class Solution:\n  def kClosest(self, points: List[List[int]], K: int) -> List[List[int]]:\n    pq = []  # max-heap\n\n    for x, y in points:\n      heapq.heappush(pq, (- x * x - y * y, [x, y]))\n      if len(pq) > K:\n        heapq.heappop(pq)\n\n    return [pair[1] for pair in pq]\n","repo_name":"problems-solving/LeetCode","sub_path":"solutions/0973. K Closest Points to Origin/0973.py","file_name":"0973.py","file_ext":"py","file_size_in_byte":276,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"81"}
+{"seq_id":"10321128597","text":"from django.contrib.auth.models import AbstractUser\nfrom django.db import models\n\n\nclass CustomUser(AbstractUser):\n    email = models.EmailField(max_length=254, verbose_name='почта')\n    username = models.CharField(\n        max_length=150, verbose_name='юзернэйм', unique=True\n    )\n    first_name = models.CharField(max_length=150, verbose_name='имя')\n    last_name = models.CharField(max_length=150, verbose_name='фамилия')\n    password = models.CharField(max_length=150, verbose_name='пароль')\n\n    class Meta:\n        verbose_name = 'пользователь'\n        verbose_name_plural = 'пользователи'\n\n    def __str__(self) -> str:\n        return self.username\n\n\nclass Subscription(models.Model):\n    author = models.ForeignKey(\n        CustomUser,\n        on_delete=models.CASCADE,\n        related_name='subscribers',\n        verbose_name='автор',\n    )\n    subscriber = models.ForeignKey(\n        CustomUser,\n        on_delete=models.CASCADE,\n        related_name='subscribed_author',\n        verbose_name='подписчик',\n    )\n\n    class Meta:\n        verbose_name = 'подписка'\n        verbose_name_plural = 'подписки'\n        constraints = [\n            models.UniqueConstraint(\n                fields=('author', 'subscriber'),\n                name='unique_author_subscriber',\n            ),\n        ]\n\n    def __str__(self) -> str:\n        return f'{self.author} - {self.subscriber}'\n","repo_name":"Nikitriy/foodgram-project-react","sub_path":"backend/foodgram/users/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":1465,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"29533818838","text":"import math\nimport time\nfrom typing import List\n\nimport cvxpy as cp\nimport numpy as np\n\nfrom entities.problem_data import ProblemData\nfrom entities.requesting import Queue, User\nfrom problems.AppointmentProblem import AppointmentProblem\n\nTimePreferences = tuple[int, int]\nGamma = int\nUserPreferences = tuple[TimePreferences, Gamma]\n\n\nclass AppointmentMultiStation(AppointmentProblem):\n    \"\"\"\n    Problem formulation for the appointment multi-stations (=services) problem\n    \"\"\"\n    def __init__(self, problem_data: ProblemData):\n        self.users: List[User] = problem_data.users\n        self.queues: List[Queue] = problem_data.queues\n        self.problem_data = problem_data\n\n    def compile_and_solve(self) -> cp.Problem:\n        print(\"Constraints calculation\")\n\n        # a sufficiently high number to deactivate the constraints, at the moment set to the ending time\n        # this is a bit dirty sorry\n        M = 1000\n\n        start = time.time()\n\n        # variables\n        c_per_queue = [cp.Variable([len(queue.requests), len(queue.requests)]) for queue in self.queues]\n        x_per_queue = [cp.Variable([len(queue.requests), len(queue.requests)], boolean=True) for queue in self.queues]\n        b_per_user = [cp.Variable([len(user.requests), len(user.requests)], boolean=True) for user in self.users]\n\n        s_per_queue = [cp.Variable(len(queue.requests)) for queue in self.queues]\n\n        # constraints\n        spots_after_opening = [s_per_queue[k][0] >= self.queues[k].opening for k in\n                               range(len(self.queues))]\n\n        spots_before_closing = [s_per_queue[k][-1] + self.queues[k].time_serving <= self.queues[k].closing for k in\n                                range(len(self.queues))]\n\n        no_queue_overlaps = [s_per_queue[k][j] + self.queues[k].time_serving <= s_per_queue[k][j + 1] for k in\n                             range(len(self.queues)) for j in range(len(self.queues[k].requests) - 1)]\n\n        no_cross_queue_overlaps = []\n\n        # here we generate no-cross-queue-overlap constraints according to the specification (see slides)\n        # we add only the necessary constraints (meaning we do not add the constraints which are always deactivated\n        # by the big-M method\n\n        for queue in self.queues:\n            for queue_prime in self.queues:\n                if queue.index == queue_prime.index:\n                    continue\n\n                for user in self.users:\n                    if user.queue_index.get(queue.index) is not None and user.queue_index.get(\n                            queue_prime.index) is not None:\n                        # here we know that user is in both queues, so proceed\n                        for j_in_queue in range(len(queue.requests)):\n                            for j_in_queue_prime in range(len(queue_prime.requests)):\n                                # so add the constraints\n                                no_cross_queue_overlaps.append(s_per_queue[queue.index][j_in_queue] + queue.time_serving\n                                                               <= s_per_queue[queue_prime.index][j_in_queue_prime]\n                                                               + M * (3 - x_per_queue[queue.index][\n                                    user.queue_index[queue.index]][j_in_queue]\n                                                                      - x_per_queue[queue_prime.index][\n                                                                          user.queue_index[queue_prime.index]][\n                                                                          j_in_queue_prime]\n                                                                      - b_per_user[user.index][\n                                                                          queue.user_index[user.index]][\n                                                                          queue_prime.user_index[user.index]]\n                                                                      )\n\n                                                               )\n\n        # Symmetry constraint for the b-variables\n        b_symmetry = [b_per_user[i][k][k_prime] == 1 - b_per_user[i][k_prime][k]\n                      for i in range(len(self.users))\n                      for k in range(len(self.users[i].requests))\n                      for k_prime in range(len(self.users[i].requests))\n                      if k != k_prime]\n\n        # Transitivity constraint for the b-variables\n        b_transitivity = [b_per_user[i][a][b] + b_per_user[i][b][c] - 1 <= b_per_user[i][a][c]\n                          for i in range(len(self.users))\n                          for a in range(len(self.users[i].requests))\n                          for b in range(len(self.users[i].requests))\n                          for c in range(len(self.users[i].requests))\n                          if len(self.users[i].requests) >= 3\n                          if (a < b < c)]\n\n        # The \"diagonal\" of the b-variables: k comes before k is always false\n        b_diagonal = [b_per_user[i][k][k] == 0\n                      for i in range(len(self.users))\n                      for k in range(len(self.users[i].requests))]\n\n        # the two constraints from the assignment problem\n        one_user_per_spot_in_queue = [cp.sum([x_per_queue[k][i][j] for i in range(len(self.queues[k].requests))]) == 1\n                                      for k in range(len(self.queues))\n                                      for j in range(len(self.queues[k].requests))]\n\n        user_enqueued_once_in_queue = [cp.sum([x_per_queue[k][i][j] for j in range(len(self.queues[k].requests))]) == 1\n                                       for k in range(len(self.queues))\n                                       for i in range(len(self.queues[k].requests))\n                                       ]\n\n        # Cost definition (positive, deadzone)\n        c_positive = [c >= np.zeros(c.shape) for c in c_per_queue]\n\n        c_penalty_to_early = [\n            c_per_queue[k][i][j] >= self.queues[k].requests[i].earliest - s_per_queue[k][j] - M * (\n                        1 - x_per_queue[k][i][j])\n            for k in range(len(self.queues))\n            for i in range(len(self.queues[k].requests))\n            for j in range(len(self.queues[k].requests))\n        ]\n\n        c_penalty_to_late = [\n            c_per_queue[k][i][j] >= s_per_queue[k][j] + self.queues[k].time_serving - self.queues[k].requests[\n                i].latest - M * (\n                    1 - x_per_queue[k][i][j])\n            for k in range(len(self.queues))\n            for i in range(len(self.queues[k].requests))\n            for j in range(len(self.queues[k].requests))]\n\n        # objective\n        obj = cp.Minimize(sum([cp.sum(c) for c in c_per_queue]))\n\n        constraints = []\n        constraints.extend(spots_after_opening)\n        constraints.extend(spots_before_closing)\n        constraints.extend(no_queue_overlaps)\n        constraints.extend(no_cross_queue_overlaps)\n        constraints.extend(b_symmetry)\n        constraints.extend(b_transitivity)\n        constraints.extend(b_diagonal)\n        constraints.extend(one_user_per_spot_in_queue)\n        constraints.extend(user_enqueued_once_in_queue)\n        constraints.extend(c_positive)\n        constraints.extend(c_penalty_to_early)\n        constraints.extend(c_penalty_to_late)\n\n        prob = cp.Problem(obj, constraints)\n\n        end = time.time()\n\n        print(f\"processed {len(constraints)} constraints in {end - start} seconds\")\n        # Solve with SciPy/HiGHS.\n        start = time.time()\n        # passing args to highs is not supported\n        # prob.solve(solver=cp.SCIPY, scipy_options={\"method\": \"highs\"}, options={\"maxiter\": 100, \"disp\": True})\n        prob.solve(solver=cp.SCIPY, scipy_options={\"method\": \"highs\"})\n        print(\"optimal value with SciPy/HiGHS:\", prob.value)\n        print(f\"Problem status: {prob.status}\")\n\n        end = time.time()\n\n        # Parse the results and put back to problem_data\n        for k in range(len(self.queues)):\n            queue = self.queues[k]\n            for i in range(len(queue.requests)):\n                request = queue.requests[i]\n                for j in range(len(queue.requests)):\n                    if math.isclose(x_per_queue[k].value[i][j], 1):\n                        user = self.users[request.user_index]\n                        print(\n                            f\"User {user.index} visists queue {k} at {'{:.2f}'.format(s_per_queue[k].value[j])} until {'{:.2f}'.format(s_per_queue[k].value[j] + self.queues[k].time_serving)} with cost {'{:.2f}'.format(c_per_queue[k].value[i][j])}\")\n                        request.optimal_visiting_time = s_per_queue[k].value[j]\n                        request.visiting_duration = self.queues[k].time_serving\n\n        print(f\"Total time used: {end - start} seconds\")\n        self.problem_data.solution_time = end - start\n        self.problem_data.solution_cost = prob.value\n        return prob\n","repo_name":"leandrolerena/unibe_appointment-scheduler","sub_path":"problems/appointment_multistation.py","file_name":"appointment_multistation.py","file_ext":"py","file_size_in_byte":8988,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"41013119544","text":"import ply.lex as lex\n\nreserved = {\n    'if': 'IF',\n    'else': 'ELSE',\n    'for': 'FOR',\n    'while': 'WHILE',\n    'break': 'BREAK',\n    'continue': 'CONTINUE',\n    'return': 'RETURN',\n    'eye': 'EYE',\n    'zeros': 'ZEROS',\n    'ones': 'ONES',\n    'print': 'PRINT'\n}\n\ntokens = ['STRING',\n          'FLOAT_NUM', 'INT_NUM',\n          'DOT_ADD', 'DOT_SUB', 'DOT_MUL', 'DOT_DIV',\n          'ASSIGN_ADD', 'ASSIGN_SUB', 'ASSIGN_MUL', 'ASSIGN_DIV',\n          'ADD', 'SUB', 'MUL', 'DIV',\n          'GREATER_OR_EQ', 'LESS_OR_EQ', 'NOT_EQ', 'EQ',\n          'GREATER', 'LESS', 'ASSIGN',\n          'TRANSPOSE', 'RANGE',\n          'ID'] + list(reserved.values())\n\nliterals = ['(', ')', '[', ']', '{', '}', ',', ';']\n\nt_STRING = r'\\\".*\\\"'\nt_FLOAT_NUM = r'\\d+\\.\\d+'\nt_INT_NUM = r'\\d+'\n\nt_DOT_ADD = r'\\.\\+'\nt_DOT_SUB = r'\\.-'\nt_DOT_MUL = r'\\.\\*'\nt_DOT_DIV = r'\\./'\n\nt_ASSIGN_ADD = r'\\+='\nt_ASSIGN_SUB = r'-='\nt_ASSIGN_MUL = r'\\*='\nt_ASSIGN_DIV = r'/='\n\nt_ADD = r'\\+'\nt_SUB = r'-'\nt_MUL = r'\\*'\nt_DIV = r'/'\n\nt_GREATER_OR_EQ = r'>='\nt_LESS_OR_EQ = r'<='\nt_NOT_EQ = r'!='\nt_EQ = r'=='\n\nt_GREATER = r'>'\nt_LESS = r'<'\nt_ASSIGN = r'='\n\nt_TRANSPOSE = r'\\''\nt_RANGE = r'\\:'\n\ndef t_ID(t):\n    r'[a-zA-Z_][a-zA-Z_0-9]*'\n    t.type = reserved.get(t.value,'ID')    # Check for reserved words\n    return t\n\n\nt_ignore = '  \\t'\nt_ignore_COMMENT = r'\\#.*'\n\n\ndef t_newline(t):\n    r'\\n+'\n    t.lexer.lineno += len(t.value)\n\n\ndef t_error(t) :\n    print( \"Illegal character '%s'\" %t.value[0] )\n    t.lexer.skip(1)\n\n\ndef find_column(input, token):\n    line_start = input.rfind('\\n', 0, token.lexpos) + 1\n    return (token.lexpos - line_start) + 1\n\nlexer = lex.lex()","repo_name":"ciastkoMalinowe/PaPaPaParser","sub_path":"scanner.py","file_name":"scanner.py","file_ext":"py","file_size_in_byte":1633,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"10511841526","text":"\"\"\"\nMSPR 4 - Revendeur API\n\"\"\"\n\nimport requests\nimport logging\nimport uvicorn\nfrom email.header import Header\nfrom functools import wraps\nfrom typing import Annotated\nfrom fastapi import FastAPI, Depends, HTTPException, status\nfrom fastapi.security import HTTPBearer\nfrom urllib.parse import unquote\nfrom services.qr_code_generator import generate_qr_code\nfrom services.mail_sender import send_email\nfrom database.database import Db\nfrom utilities.token_func import encode_token, decode_token\nfrom utilities.utils import is_valid_email\n\n# api produits\nAPI_PRODUCT = \"https://615f5fb4f7254d0017068109.mockapi.io/api/v1/products\"\n\nAPI_PREFIX = \"/api/v1\"\n\n# Partie sécurité\ntoken_auth_scheme = HTTPBearer()\n\ndescription = \"\"\"\nDocumentation de l'API Revendeur du projet MSPR 4. 🚀\n\n## Products\n\n - The user will be able to **read products** if he has a valid token.\n\nurl: /api/v1/products\n\ncall the api with curl:\n```shell\ncurl -X 'GET' \\\n  'http://localhost:82/api/v1/products' \\\n  -H 'accept: application/json' \\\n  -H 'Authorization: Bearer put_token_here'\n```\n\n- The user will be able to get a **product by id** if he has a valid token.\n\nurl: /api/v1/products/{product_id}\n\ncall the api with curl:\n```shell\ncurl -X 'GET' \\\n    'http://localhost:82/api/v1/products/1' \\\n    -H 'accept: application/json' \\\n    -H 'Authorization: Bearer put_token_here'\n```\n\n- The user will be able to get a **product stock by id** if he has a valid token.\n\nurl: /api/v1/products/{product_id}/stock\n\ncall the api with curl:\n```shell\ncurl -X 'GET' \\\n    'http://localhost:82/api/v1/products/1/stock' \\\n    -H 'accept: application/json' \\\n    -H 'Authorization: Bearer put_token_here'\n```\n\n## Users\n\nYou will be able to **create / delete / update users** if you have the admin rights\n\n- The admin will be able to **create a user** if he has the correct rights.\n\n**description:**s\nwhen this api is called, the user will be created and an email will be sent to the user with a qr code containing the token.\n\n\n\nurl: /api/v1/create-user/{user_id}/{user_email}\n\ncall the api with curl:\n```shell\ncurl -X 'POST' \\\n    'http://localhost:82/api/v1/create-user/1/boudjemaa.akham@epsi.fr' \\\n    -H 'accept: application/json' \\\n    -H 'Authorization: Bearer admin'\n```\n\n- The admin will be able to **delete a user** if he has the correct rights.\n\nurl: /api/v1/delete-user/{user_id}\n\ncall the api with curl:\n```shell\ncurl -X 'DELETE' \\\n    'http://localhost:82/api/v1/delete-user/1' \\\n    -H 'accept: application/json' \\\n    -H 'Authorization: Bearer admin'\n```\n\n- The admin will be able to **update a user** if he has the correct rights.\n\nurl: /api/v1/update-user/{user_id}\n\ncall the api with curl:\n```shell\ncurl -X 'PUT' \\\n    'http://localhost:82/api/v1/update-user/1' \\\n    -H 'accept: application/json' \\\n    -H 'Authorization: Bearer admin'\n```\n\n\"\"\"\n\ntags_metadata = [\n    {\n        \"name\": \"Products\",\n        \"description\": \"Manage products.\",\n    },\n    {\n        \"name\": \"Users\",\n        \"description\": \"Manage users.\",\n    }\n]\n\napp = FastAPI(\n    title=\"MSPR Bloc 4\",\n    description=description,\n    version=\"1.0.0\",\n    license_info={\n        \"name\": \"Apache 2.0\",\n        \"src\": \"./LICENSE.txt\",\n    },\n    openapi_tags=tags_metadata\n)\n\ndb = Db('data/database', clear=False)\ndb.__enter__()\ndb.create_tables()\n\n\n@app.on_event(\"shutdown\")\ndef shutdown_event():\n    db.__exit__(None, None, None)\n\n\ndef admin_required(func):\n    \"\"\"\n    Decorator to check if the user is admin\n    :param func:\n    :return:\n    \"\"\"\n\n    @wraps(func)\n    async def wrapper(*args, **kwargs):\n        token = kwargs.get('token') or Header(None)\n        if not token:\n            raise HTTPException(status_code=401, detail='Token is missing')\n        if token.credentials != \"admin\":\n            raise HTTPException(status_code=403, detail='You are not admin')\n\n        return func(*args, **kwargs)\n\n    return wrapper\n\n\ndef token_required(func):\n    \"\"\"\n    Decorator to check if the token is valid\n    :param func:\n    :return:\n    \"\"\"\n\n    @wraps(func)\n    async def wrapper(*args, **kwargs):\n        token = kwargs.get('token') or Header(None)\n        if not token:\n            raise HTTPException(status_code=401, detail='Token is missing')\n        # TODO: a verifier si ça empeche pas les autres api de rester connecté\n        # if token.credentials == \"admin\":\n        #    return func(*args, **kwargs)\n        token_decoded = decode_token(token.credentials)\n        if token_decoded == 1:\n            raise HTTPException(status_code=401, detail='Token has expired')\n        elif token_decoded == 2:\n            raise HTTPException(status_code=401, detail='Token is not valid')\n        if db.get_user_by_email(token_decoded) is False:\n            raise HTTPException(status_code=403,\n                                detail='User has been deleted and has no rights to access this resource')\n        return func(*args, **kwargs)\n\n    return wrapper\n\n\n# route principale\n@app.get(\"/\")\ndef root():\n    return {\"message\": \"Welcome to the MSPR API\"}\n\n\n# Products routes\n\n\n@app.get(f\"{API_PREFIX}/products\", tags=[\"Products\"])\n@token_required\ndef get_products(token: Annotated[str, Depends(token_auth_scheme)]):\n    \"\"\"\n    Get all products\n    :param token:\n    :return: json response\n    \"\"\"\n    response = requests.get(API_PRODUCT)\n    return response.json()\n\n\n@app.get(f\"{API_PREFIX}/products/{{product_id}}\", tags=[\"Products\"])\n@token_required\ndef get_product(product_id: int, token: Annotated[str, Depends(token_auth_scheme)]):\n    \"\"\"\n    Get a product by id\n    :param product_id:\n    :param token:\n    :return: json response\n    \"\"\"\n    response = requests.get(API_PRODUCT + \"/\" + str(product_id))\n    if type(response.json()) == dict:\n        return response.json()\n    else:\n        return {\"status\": \"error\", \"message\": \"Product not found\"}\n\n\n@app.get(f\"{API_PREFIX}/products/{{product_id}}/stock\", tags=[\"Products\"])\n@token_required\ndef get_product_stock(product_id: int, token: Annotated[str, Depends(token_auth_scheme)]):\n    \"\"\"\n    Get a product stock by id\n    :param product_id:\n    :param token:\n    :return: json response\n    \"\"\"\n    response = requests.get(API_PRODUCT + \"/\" + str(product_id))\n    if type(response.json()) == dict:\n        return response.json()[\"stock\"]\n    else:\n        return {\"status\": \"error\", \"message\": \"Product not found\"}\n\n\n@app.post(f\"{API_PREFIX}/create-user/{{user_id}}/{{user_email}}\", tags=[\"Users\"])\n@admin_required\ndef create_user(user_id: int, user_email: str, token: Annotated[str, Depends(token_auth_scheme)]):\n    \"\"\"\n    Create a user\n    :param user_id:\n    :param user_email:\n    :param token:\n    :return:\n    \"\"\"\n\n    if not is_valid_email(user_email):\n        return {\"status\": \"error\", \"message\": \"Invalid email format\"}\n    try:\n        decoded_email = unquote(user_email)\n        token = encode_token(decoded_email)\n        if db.insert_user(user_id, decoded_email, token) is False:\n            return {\"status\": \"error\", \"message\": f\"User with id {user_id} already exists on the database\"}\n        generate_qr_code(token, \"\", \"qrcode\")\n        send_email(decoded_email, \"./qrcode.png\")\n        return {\"status\": \"success\",\n                \"message\": f\"User with id {user_id} has been created. Check your email for the QR code\"}\n    except Exception as e:\n        return {\"status\": \"error\", \"message\": e.__repr__()}\n\n\n@app.delete(f\"{API_PREFIX}/delete-user/{{user_id}}\", tags=[\"Users\"])\n@admin_required\ndef delete_user(user_id: int, token: Annotated[str, Depends(token_auth_scheme)]):\n    \"\"\"\n    Delete a user\n    :param user_id:\n    :param token:\n    :return:\n    \"\"\"\n    try:\n        if db.delete_user(user_id) is False:\n            return {\"status\": \"error\", \"message\": f\"User with id {user_id} does not exist on the database\"}\n        return {\"status\": \"success\", \"message\": f\"User with id {user_id} has been deleted\"}\n    except Exception as e:\n        return {\"status\": \"error\", \"message\": e.__repr__()}\n\n\n@app.put(f\"{API_PREFIX}/update-user/{{user_id}}\", tags=[\"Users\"])\n@admin_required\ndef update_user(user_id: int, token: Annotated[str, Depends(token_auth_scheme)]):\n    \"\"\"\n    Update a user\n    :param user_id:\n    :param token:\n    :return:\n    \"\"\"\n    # update the user information\n    # modify the token on the database\n\n    return {\"status\": \"success\"}\n\n\nif __name__ == \"__main__\":\n    # logging.basicConfig(filename='server.log', level=logging.INFO)\n    uvicorn.run(app, host=\"0.0.0.0\", port=82, log_level=\"debug\", access_log=True)\n    # uvicorn.run(app)\n","repo_name":"BoudjemaaAKHAM/MSPR_Bloc_4_API_Revendeur","sub_path":"revendeurapi/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":8502,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"33089805072","text":"import server\nimport client\nimport subprocess\nfrom os import *\n\npathOfFile=path.dirname(__file__)\npathServer=path.join(pathOfFile, \"server.py\")\npathClient=path.join(pathOfFile, \"client.py\")\n\nwhile True:\n    cmd = input('Выберите действие: \"1\" - запустить сервер, \"2\" - запустить клиент, \"3\" - завершить работу: ')\n    if cmd == \"3\":\n        break\n    elif cmd == \"1\":\n        SERVER = subprocess.Popen(\n            f'osascript -e \\'tell application \"Terminal\" to do'\n            f' script \"python3 {pathServer}\"\\'', shell=True)\n    elif cmd == \"2\":\n        CLIENT = subprocess.Popen(\n            f'osascript -e \\'tell application \"Terminal\" to do'\n            f' script \"python3 {pathClient}\"\\'', shell=True)\n    else:\n        continue\n","repo_name":"tretyakovr/PyQT","sub_path":"Lesson01/task04.py","file_name":"task04.py","file_ext":"py","file_size_in_byte":797,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"8937193532","text":"import numpy as np\nimport matplotlib.pyplot as plt\nimport sys\n\nif __name__ == '__main__':\n    y_list, name_list = list(), list()\n    for arg in sys.argv[1:]:\n        try:\n            arg = arg.split(',')\n            if len(arg) == 4:\n                pow_idle, pow_inf, time_inf = float(arg[1]), float(arg[2]), float(arg[3])\n                name_list.append(arg[0])\n                y_list.append((pow_idle, pow_inf, time_inf))\n                continue\n        except ValueError:\n            pass\n\n        print(\"Error: Illegal argument\")\n        exit(1)\n\n\n#    x = np.arange(0, 1, 0.01)\n#    y_list = list(map(lambda p: 60 * (x * p[0] + (1 - x) * p[1]), y_list))\n#   t_max / t_inf = fps\n    t_max = 60\n    x_in = np.arange(0, 100000, 1)\n    for i,y in enumerate(y_list):\n        y_out = list()\n        for x in x_in: \n            if t_max - (x * y[2]) > .0:\n                y_out.append(((t_max - (x * y[2])) * y[0] + (x * y[2]) * y[1])/60.0)\n            else:\n                break\n\n        plt.plot(x_in[:len(y_out)], y_out)\n        plt.plot(x_in[len(y_out)-1], y_out[-1], '.', label='_nolegend_', color='black')\n        #plt.text(x_in[len(y_out)-1], y_out[-1], '%d, %.3fWm' % (x_in[len(y_out)-1], y_out[-1]), horizontalalignment='center')\n        name_list[i] += ' (%d, %.3fWm)' % (x_in[len(y_out)-1], y_out[-1])\n\n    plt.legend(name_list)\n#    plt.xscale('log')\n#    plt.yscale('log')\n    plt.xlabel('Number of inferences in one minute')\n    plt.ylabel('Power consumption in wattminutes')\n    plt.show()\n","repo_name":"stephanballer/deepedgebench","sub_path":"other/plot_1.py","file_name":"plot_1.py","file_ext":"py","file_size_in_byte":1507,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"81"}
+{"seq_id":"768766519","text":"'''\ndone\n'''\n\nimport urllib.request\nprint(\"---------------sample1----------------\")\nhtml = urllib.request.urlopen('https://duckduckgo.com').read()\nprint(html)\n\nprint(\"---------------sample2----------------\")\nheaders = {}\nheaders['User-Agent'] = \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/69.0.3497.92 Safari/537.36\"\nreq = urllib.request.Request('https://duckduckgo.com', headers = headers)\nhtml = urllib.request.urlopen(req).read()\nprint(html)","repo_name":"pustakalupi/ls-belajar-python3.x","sub_path":"bab-4/modul-bawaan/SampleURLLib.py","file_name":"SampleURLLib.py","file_ext":"py","file_size_in_byte":489,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"24351897076","text":"from AInstruction import AInstruction\nfrom CInstruction import CInstruction\nfrom Label import Label\n\n# preset symbol table\nsymboltable = {\"SP\": 0, \"LCL\": 1, \"ARG\": 2, \"THIS\": 3, \"THAT\": 4, \"SCREEN\": 16384, \"KBD\": 24576}\n\nfor i in range(0, 16):\n    symboltable[\"R\" + str(i)] = i\n\n\ndef print_parsed(parsedlines):\n    for index, line in enumerate(parsedlines):\n        print(line)\n\n\ndef print_symbol_table():\n    for key, value in symboltable.items():\n        print(key, value)\n\n\n# remove any comments and additional white space\ndef clean_instruction(instruction):\n    # check for comments\n    position = instruction.find(\"//\")\n\n    if position > -1:\n        instruction = instruction[:position]\n\n    return instruction.strip()\n\n\n# parse an A instruction\ndef parse_a_instruction(instruction, index):\n    instruction = clean_instruction(instruction)\n    return AInstruction(instruction, index)\n\n\n# parse a C instruction\ndef parse_c_instruction(instruction, index):\n    instruction = clean_instruction(instruction)\n    return CInstruction(instruction, index)\n\n\ndef parse_label(label):\n    label = clean_instruction(label)\n    return Label(label)\n\n\ndef parse(sourcelines):\n    parsedlines = []\n\n    # base memory index for variables\n    memoryindex = 16\n\n    # parse lines into instructions / labels\n    index = 0\n    for line in sourcelines:\n        if line.startswith(\"@\"):\n            parsedlines.append(parse_a_instruction(line, index))\n            index = index + 1\n        elif line.startswith(\"(\"):\n            parsedlines.append(parse_label(line))\n        else:\n            parsedlines.append(parse_c_instruction(line, index))\n            index = index + 1\n\n    # map all label symbols\n    for index, line in enumerate(parsedlines):\n        if type(line) is Label and not line.label in symboltable:\n            notlabel = False\n            subindex = 1\n            while not notlabel:\n                # find next instruction that isn't a label\n                nextinstruction = parsedlines[index + subindex]\n\n                if nextinstruction and not type(nextinstruction) is Label:\n                    symboltable[line.label] = str(nextinstruction.index)\n                    notlabel = True\n                else:\n                    subindex = subindex + 1\n\n    # remove all labels from array\n    parsedlines = [line for line in parsedlines if type(line) is not Label]\n\n    # second pass, replace all label values + variables with symbol value\n    for line in parsedlines:\n        if type(line) is AInstruction and not line.value.isdigit():\n            if line.value in symboltable:\n                line.value = str(symboltable[line.value])\n            else:\n                # set to current memory index\n                symboltable[line.value] = memoryindex\n                line.value = str(memoryindex)\n\n                # increment memory index\n                memoryindex = memoryindex + 1\n\n\n    #print_parsed(parsedlines)\n    #print_symbol_table()\n    return parsedlines\n","repo_name":"stained/elements-computing","sub_path":"HackAssembler/Parser.py","file_name":"Parser.py","file_ext":"py","file_size_in_byte":2979,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"70901593866","text":"import os\n\nos.getcwd() #bulunduğun path\n\nos.listdir() #klasör ve dosyaları listele\n\nos.listdir(r\"C:\\Users\\velik\") #klasör ve dosyaları listele\n\nos.chdir(r\"C:\\Users\\velik\\OneDrive\\Documents\\GitHub\\python_libraries_study\\os_library\") #çalıştığım adres buna çevir\n\nos.mkdir(\"deneme\") # klasör oluştur\n\nos.rmdir(\"deneme\") #klasör sil\n\nos.O_CREAT() #yeni dosya oluştur\n\nos.O_RDONLY() #sadece oku\n\nos.O_WRONLY() #sadece yaz\n\nos.O_RDWR() #oku ve yaz\n\nnew_file = os.open(\"new_file.txt\",os.O_CREAT|os.O_RDWR)\nos.write(new_file, \"Merhaba dünya!\".encode())  #encode byte a çevirmek için\nos.close(new_file)\n\nnew_file = os.open(\"new_file.txt\",os.O_RDONLY)\nistatistik =os.stat(new_file) # dosya istatistikleri/bilgileri\nokunan =os.read(new_file,istatistik.st_size)  # ikinci arg kaç karakter okunacağı #st_size içerik karakter sayısı\nprint(okunan.decode())  # özel karakterler düzelsin diye\nos.close(new_file)\n\n# os.rename(\"new_file.txt\",\"file.txt\")\n\nos.unlink(\"file.txt\") # silme\n\n\n","repo_name":"VeliKarpuz/python_libraries_study","sub_path":"os_library/os_basic.py","file_name":"os_basic.py","file_ext":"py","file_size_in_byte":996,"program_lang":"python","lang":"tr","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"73248386824","text":"import random\n\nimport numpy as np\nimport torch\nimport torch.nn as nn\nfrom pickle5 import pickle\nfrom torch.utils.data import DataLoader\n\nfrom datasets.identity_triplet_dataset import IdentityTripletDataset\nfrom datasets.voice_faces_dataset import VoiceFacesDataset\nfrom deep_learning.evaluation import evaluate\nfrom deep_learning.network import Network\nfrom deep_learning.training import train\nfrom dir_paths import TRAIN_VAL_AUDIO_EMBEDDINGS, TRAIN_VAL_IMAGE_EMBEDDINGS, TEST_AUDIO_EMBEDDINGS, \\\n    TEST_IMAGE_EMBEDDINGS\n\nSEED = 100\nBATCH_SIZE = 64\nTRAIN_TRIPLETS = 10000\nVAL_TRIPLETS = 5000\nLEARNING_RATE = 1e-3\nMAX_EPOCHS = 5\nTEST_TRIPLETS = 5000\nTEST_FLAG = False\n\nrandom.seed(SEED)\ntorch.manual_seed(SEED)\ntorch.cuda.manual_seed(SEED)\nnp.random.seed(SEED)\n\nif __name__ == \"__main__\":\n    # Load embeddings\n    audio_embeddings = pickle.load(open(TRAIN_VAL_AUDIO_EMBEDDINGS, 'rb'))\n    image_embeddings = pickle.load(open(TRAIN_VAL_IMAGE_EMBEDDINGS, 'rb'))\n\n    # For the purpose of the test - change the pickles here to the test ones\n    if TEST_FLAG:\n        test_audio_embeddings = pickle.load(open(TEST_AUDIO_EMBEDDINGS, 'rb'))\n        test_image_embeddings = pickle.load(open(TEST_IMAGE_EMBEDDINGS, 'rb'))\n\n    # Create the train and validation datasets of triplets\n    voice_faces_dataset = VoiceFacesDataset(audio_embeddings, image_embeddings)\n    train_identity_triplet_dataset = IdentityTripletDataset(persons_list=voice_faces_dataset.train_persons_list,\n                                                            triplets_n=TRAIN_TRIPLETS)\n    train_dataloader = DataLoader(train_identity_triplet_dataset, batch_size=BATCH_SIZE, shuffle=True)\n    val_identity_triplet_dataset = IdentityTripletDataset(persons_list=voice_faces_dataset.val_persons_list,\n                                                          triplets_n=VAL_TRIPLETS)\n    val_dataloader = DataLoader(val_identity_triplet_dataset, batch_size=BATCH_SIZE, shuffle=True)\n\n    # Create the network and hyperparameters\n    net = Network()\n    if torch.cuda.is_available():\n        net = net.cuda()\n    triplet_loss = nn.TripletMarginLoss()\n    optimizer = torch.optim.Adam(net.parameters(), lr=LEARNING_RATE)\n\n    # Train and validate the Network\n    train(train_dataloader, optimizer, triplet_loss, net, MAX_EPOCHS)\n    print('Validation')\n    evaluate(val_dataloader, net)\n\n    # if test flag is activated, run the pipeline on it to calculate the identification accuracy\n    if TEST_FLAG:\n        test_voice_faces_dataset = VoiceFacesDataset(audio_embeddings, image_embeddings, train_ratio=0)\n        test_identity_triplet_dataset = IdentityTripletDataset(persons_list=test_voice_faces_dataset.val_persons_list,\n                                                               triplets_n=TEST_TRIPLETS)\n        test_dataloader = DataLoader(test_identity_triplet_dataset, batch_size=BATCH_SIZE, shuffle=True)\n        print('Test')\n        evaluate(test_dataloader, net)\n","repo_name":"tomerraviv95/corsound-hw","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2946,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"37557701396","text":"import time\nfrom datetime import datetime as dt\n\nweb_list = [\"www.facebook.com\",\"facebook.com\",\"www.youtube.com\",\"youtube.com\",\"google.com\"]\n\nhost_path = r\"/etc/hosts\"\ntemp = r\"hosts\"\nredirect = \"127.0.0.1\"\n\nwhile True:\n    if dt.now().weekday()<=4:\n        if dt(dt.now().year,dt.now().month,dt.now().day,8) < dt.now() < dt(dt.now().year,dt.now().month,dt.now().day,16):\n                print(\"Working Hours\",dt.now())\n                with open(host_path,'r+') as file:\n                    content = file.read()\n                    for web in web_list:\n                        if web in content:\n                            pass\n                        else:\n                            file.write(redirect + \" \"+ web+ \"\\n\")\n    else:\n        with open(host_path,'r+') as file:\n            content = file.readlines()\n            file.seek(0)\n            for line in content:\n                if not any (web in line for web in web_list):\n                    file.write(line)\n            file.truncate()\n        print(\"FUN HOURS...\",dt.now())\n    time.sleep(1)\n\n\n\n\n\n\n","repo_name":"anaskhan07/PythonWorkshop2020","sub_path":"DataSecurity/block.py","file_name":"block.py","file_ext":"py","file_size_in_byte":1066,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"3343747499","text":"\"\"\"\nSimple example using aggregateMessages of GraphFrames (a graph processing library) which is part of Apache Spark framework.\n\nThe goal is to use the minimum number of colors to properly color a connected graph using different color between neighbors.\nMore information can be found here: https://en.wikipedia.org/wiki/Graph_coloring\n\nIn this example used Local Maxima First algorithm (a Pregel-like algorithm) to associate each node of graph with a number (starting from 1) representing the color that should be used.\n\nRun code:\n~/spark-2.4.7-bin-hadoop2.7/bin/spark-submit --packages graphframes:graphframes:0.7.0-spark2.3-s_2.11  /vagrant/coloring-graph.py\n\n\"\"\"\n\nfrom graphframes import GraphFrame\nfrom graphframes.lib import AggregateMessages as AM\nfrom pyspark.sql import SparkSession\nfrom graphframes import GraphFrame\nfrom graphframes.lib import AggregateMessages as AM\nfrom pyspark import SparkConf, SparkContext\nfrom pyspark.sql import SQLContext, functions as sqlfunctions, types\nfrom pyspark.sql import functions as F\nimport argparse\n\ninputVertices = \"/vagrant/csv/vertices.csv\"\ninputEdges = \"/vagrant/csv/edges.csv\"\n\nparser = argparse.ArgumentParser()\nparser.add_argument(\"-v\", help=\"Vertices csv file\")\nparser.add_argument(\"-e\", help=\"Edges csv file\")\n\nargs = parser.parse_args()\nif args.v and args.e:\n    inputVertices = args.v\n    inputEdges = args.e\n\nspark = SparkSession.builder.appName(\"colorign-graph-app\").getOrCreate()\n# Reading vertices and edges csv files\nvertices = spark.read.csv(inputVertices, header=True)\nedges = spark.read.options(delimiter=\"|\").csv(inputEdges, header=True)\n\n# Fix graph to be undirected\nreverse_edges = edges.selectExpr(\"dst as src\", \"src as dst\")\nedges = edges.union(reverse_edges)\n\n# Create local maxima value\ndef local_max_value(id, value, maxima, step):\n    return {\"id\": id, \"color\": value, \"maxima\": maxima, \"step\": step}\n\n\nlocal_max_value_type = types.StructType(\n    [\n        types.StructField(\"id\", types.IntegerType()),\n        types.StructField(\"color\", types.IntegerType()),\n        types.StructField(\"maxima\", types.BooleanType()),\n        types.StructField(\"step\", types.IntegerType()),\n    ]\n)\nlocal_max_value_type_udf = F.udf(local_max_value, local_max_value_type)\n\n# Create localMaxima of each node\n# LocalMaxima consists of a dictionary with the following structure\n# {\n#     \"id\" => The id of the node,\n#     \"color\" => The color of the node. Default value -1 (no color),\n#     \"maxima\" => Indicates if the node is the maxima of its neighborhood\n#     \"step\" => The Step of the algorithm\n# }\nvertices = vertices.withColumn(\n    \"localMaxima\",\n    local_max_value_type_udf(\n        vertices[\"id\"].cast(\"int\"), F.lit(-1), F.lit(False), F.lit(1)\n    ),\n)\n\n# Create and print information on the respective GraphFrame\ncached_vertices = AM.getCachedDataFrame(vertices)\ng = GraphFrame(cached_vertices, edges)\ng.vertices.show()\ng.edges.show()\ng.degrees.show()\n\n# UDF for the neighbor with the greater local maxima value of each node.\ndef greater_local_max_value(local_max_value_neighbors):\n    max_id = -1\n    color = -1\n    maxima = False\n    step = -1\n    for neighbor in local_max_value_neighbors:\n        if neighbor.maxima == False and neighbor.id > max_id:\n            max_id = neighbor.id\n            color = neighbor.color\n            maxima = neighbor.maxima\n            step = neighbor.step\n    return {\"id\": max_id, \"color\": color, \"maxima\": maxima, \"step\": step}\n\n\ngreater_local_max_value_udf = F.udf(greater_local_max_value, local_max_value_type)\n\n# UDF for comparing local maxima between the old one and the new one.\ndef compare_local_max_value(old_local_max, new_local_max):\n\n    if old_local_max.maxima == True:\n        return {\n            \"id\": old_local_max.id,\n            \"color\": old_local_max.color,\n            \"maxima\": old_local_max.maxima,\n            \"step\": (old_local_max.step + 1),\n        }\n\n    maxima = False\n    color = old_local_max.color\n    step = old_local_max.step + 1\n    if new_local_max.id < old_local_max.id:\n        maxima = True\n        color = old_local_max.step\n\n    return {\"id\": old_local_max.id, \"color\": color, \"maxima\": maxima, \"step\": step}\n\n\ncompare_local_max_value_udf = F.udf(compare_local_max_value, local_max_value_type)\n\n# Local Maxima First Algorithm\nwhile True:\n    # Aggregate messages from the neighbors.\n    aggregates = g.aggregateMessages(\n        F.collect_set(AM.msg).alias(\"agg\"), sendToDst=AM.src[\"localMaxima\"]\n    )\n    res = aggregates.withColumn(\n        \"newlocalMaxima\", greater_local_max_value_udf(\"agg\")\n    ).drop(\"agg\")\n\n    # Aggregate and Join vertices leveraging localMaxima values\n    new_vertices = (\n        g.vertices.join(res, on=\"id\", how=\"left_outer\")\n        .withColumnRenamed(\"localMaxima\", \"oldlocalMaxima\")\n        .withColumn(\n            \"localMaxima\",\n            compare_local_max_value_udf(\n                F.col(\"oldlocalMaxima\"), F.col(\"newlocalMaxima\")\n            ),\n        )\n        .drop(\"oldlocalMaxima\")\n        .drop(\"newlocalMaxima\")\n    )\n    cached_new_vertices = AM.getCachedDataFrame(new_vertices)\n    g = GraphFrame(cached_new_vertices, g.edges)\n    g.vertices.show()\n\n    # Vote-To-Halt step.\n    # If all vertices have been colored the algorithm stops otherwise continues for another round.\n    existUncoloredVertices = (\n        False\n        if (\n            cached_new_vertices.select(\n                F.col(\"localMaxima\").getItem(\"maxima\").alias(str(\"maxima\"))\n            )\n            .filter(F.col(\"maxima\").contains(False))\n            .count()\n        )\n        == 0\n        else True\n    )\n\n    if existUncoloredVertices != True:\n        print(\"Local Maxima First Algorithm Completed\")\n        break\n","repo_name":"nbompetsis/spark-graphframes-aggregate-messages-coloring-graph","sub_path":"coloring-graph.py","file_name":"coloring-graph.py","file_ext":"py","file_size_in_byte":5689,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"31228946634","text":"\"\"\"\n    This module provides all methods to interact with Cassandra Apache cluster server.\n    It contains the cluster and session creation methods, keyspace, tables and data loading\n\"\"\"\n\n# Import Python packages \nimport json\nimport logging\nfrom csql_queries import *\nimport cassandra\nfrom cassandra.cluster import Cluster\nfrom cassandra import InvalidRequest\n\ndef create_session():\n    \"\"\"\n    - This function:\n        - creates a session with a local cluster\n        - returns the connection and cluster\n    \n    Args:\n        None\n\n    Returns:\n        cluster (object): local cluster\n        session (object): cassandra connection object\n    \"\"\"\n    \n    cluster = Cluster(['127.0.0.1'])\n    session = cluster.connect()\n    \n    return cluster, session\n\n\ndef load_data(session, query, df, df_columns):\n    \"\"\"\n    - This function:\n        - load data from event file to apache cassandra database\n    \n    Args:\n        query (object): query to insert data\n        df (object): dataframe with data\n        session (object): cassandra connection object\n        df_columns (list): columns in dataframe\n\n    Returns:\n        None\n    \"\"\"\n    \n    for i, row in df.iterrows():\n        data = tuple([row[col] for col in df_columns])\n        session.execute(query, data)\n\n\ndef create_tables(session):\n    \"\"\"\n    - This function:\n        - creates all tables in keyspace on Apache Cassandra cluster\n    \n    Args:\n        session (object): cassandra connection object\n\n    Returns:\n        None\n    \"\"\"\n   \n    for query in csql_create_table_queries:\n        try:\n            session.execute(query)\n        except Exception as e:\n            print(e)\n\n\ndef create_keyspace(session):\n    \"\"\"\n    - This function:\n        - create keyspace on Apache Cassandra cluster\n    \n    Args:\n        session (object): cassandra connection object\n\n    Returns:\n        None\n    \"\"\"\n    \n    try:\n        session.execute(csql_create_keyspace)\n    except Exception as e:\n        print(e)\n\n\ndef drop_tables(session):\n    \"\"\"\n    - This function:\n        - drops all tables in keyspace on Apache Cassandra cluster\n    \n    Args:\n        session (object): cassandra connection object\n\n    Returns:\n        None\n    \"\"\"\n\n    for query in csql_drop_table_queries:\n        try:\n            session.execute(query)\n        except Exception as e:\n            print(e)  \n\n\ndef truncate_tables(session):\n    \"\"\"\n    - This function:\n        - clean all tables in keyspace on Apache Cassandra cluster\n    \n    Args:\n        session (object): cassandra connection object\n\n    Returns:\n        None\n    \"\"\"\n\n    for query in csql_truncate_table_queries:\n        try:\n            session.execute(query)\n        except Exception as e:\n            print(e)  ","repo_name":"dacosta-github/udacity-de","sub_path":"apache-cassandra-data-modeling/src/scripts/database.py","file_name":"database.py","file_ext":"py","file_size_in_byte":2724,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"81"}
+{"seq_id":"18731252274","text":"import requests\nimport json\n\nfbMessagingUrl = \"https://fcm.googleapis.com/fcm/send\"\n\nheaders = {\n    \"Authorization\": \"key=AAAA4lm4s_w:APA91bGRuvN6Ffk6aJ-2RzG9IUjv1u4oqY6GXCA7N42bJhf6ksN7Po5Wmt1idPYrYoRFaHUC2LWr3Te70bS1YByvRgNoDos-8gU2nWSerAz0zrvrDq93xMBy6Yk69gnRfPQjvP_HKWNT\",\n    \"Content-Type\": \"application/json\",\n}\n\n\ndef pushNotification(title='', body='', userId='', notificationData={}):\n    data = json.dumps({\n        \"notification\": {\n            \"title\": title,\n            \"body\": body,\n            \"click_action\": \"FLUTTER_NOTIFICATION_CLICK\",\n        },\n        \"data\": {\n            'messageData':notificationData,\n        },\n        \"to\": \"/topics/userEkrili%d\" % userId\n    })\n\n    response = requests.post(\n        fbMessagingUrl,\n        data=data,\n        headers=headers,\n    )\n\n    print(response.text)\n\n\n# pushNotification(\n#     userId=5,\n#     title='Brahim',\n#     body='message',\n#     notificationData={\n#         'brahim': 'chouih',\n#         'id':322,\n#     },\n# )\n","repo_name":"Bensebabillal/EkrilliBackend","sub_path":"chat/firebase_messaging_helper.py","file_name":"firebase_messaging_helper.py","file_ext":"py","file_size_in_byte":995,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"9218462709","text":"from model.population import Population\nfrom model.cycle import Cycle\nfrom model.evolution_params import EvolutionParams\nfrom model.types import MutationType\n\nimport random\n\n\nclass Mutation(object):\n    def __init__(self, population: Population, evolution_params: EvolutionParams):\n        self._population = population\n        self._cycle_size = population.get_cycle_size()\n        self._mutation_type = evolution_params.mutation_type\n        self._mutation_param = evolution_params.mutation_param\n\n    def set_next_generation(self, population: Population):\n        self._population = population\n\n    def _insertion(self, cycle: Cycle) -> Cycle:\n        random_index = random.randint(0, self._cycle_size - 1)\n        random_destination = random.randint(0, self._cycle_size - 1)\n        while random_destination == random_index:\n            random_destination = random.randint(0, self._cycle_size - 1)\n        if random_index < random_destination:\n            temp = cycle[random_index]\n            for i in range(random_index, random_destination):\n                cycle[i] = cycle[i + 1]\n            cycle[random_destination] = temp\n        else:\n            temp = cycle[random_index]\n            for i in range(random_index, random_destination, -1):\n                cycle[i] = cycle[i - 1]\n            cycle[random_destination] = temp\n        return cycle\n\n    def _exchange(self, cycle: Cycle) -> Cycle:\n        i = random.randint(0, self._cycle_size - 1)\n        j = random.randint(0, self._cycle_size - 1)\n        while j == i:\n            j = random.randint(0, self._cycle_size - 1)\n        cycle.swap(i, j)\n        return cycle\n\n    def _scramble(self, cycle: Cycle) -> Cycle:\n        random_index_start = random.randint(0, self._cycle_size - 1)\n        random_index_end = random.randint(0, self._cycle_size - 1)\n        while random_index_end == random_index_start:\n            random_index_end = random.randint(0, self._cycle_size - 1)\n        i = random_index_start\n        while i % self._cycle_size != random_index_end:\n            r = random.randint(0, abs(i % self._cycle_size - random_index_end) - 1)\n            cycle.swap(i % self._cycle_size, (i + r) % self._cycle_size)\n            i += 1\n        return cycle\n\n    def mutate(self) -> Population:\n        temp_population = Population()\n        for curr_cycle in self._population:\n            if random.uniform(0, 1) < self._mutation_param:\n                if self._mutation_type == MutationType.INSERTION:\n                    curr_cycle = self._insertion(curr_cycle)\n                elif self._mutation_type == MutationType.EXCHANGE:\n                    curr_cycle = self._exchange(curr_cycle)\n                elif self._mutation_type == MutationType.SCRAMBLE:\n                    curr_cycle = self._scramble(curr_cycle)\n                else:\n                    curr_cycle = self._insertion(curr_cycle)\n            temp_population.add_cycle(curr_cycle)\n\n        return temp_population\n","repo_name":"BartlomiejOlber/PSZT-KomiwojazerEwolucyjny","sub_path":"evolution/mutation.py","file_name":"mutation.py","file_ext":"py","file_size_in_byte":2956,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"81"}
+{"seq_id":"33037546528","text":"class Solution:\n    def canFinish(self, numCourses: int, prerequisites: List[List[int]]) -> bool:\n        adjacency={i:[] for i in range(numCourses)}\n        for pre,crs in prerequisites:\n            adjacency[pre].append(crs)\n        visit=set()\n        def dfs(crs):\n            if crs in visit:\n                return False\n            if adjacency[crs]==[]:\n                return True\n            visit.add(crs)\n            for neigh in adjacency[crs]:\n                if not dfs(neigh):\n                    return False\n            visit.remove(crs)\n            adjacency[crs]=[]\n            \n            return True\n        \n        for i in range(numCourses):\n            if not dfs(i):\n                return False\n        return True\n                \n        ","repo_name":"niksintelli4ever/LeetCode-Journey","sub_path":"0207-course-schedule/0207-course-schedule.py","file_name":"0207-course-schedule.py","file_ext":"py","file_size_in_byte":769,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"23003569373","text":"#!/bin/python3\n\nimport math\nimport os\nimport random\nimport re\nimport sys\nimport fileinput\n\n\n#\n# Complete the 'minimumBribes' function below.\n#\n# The function accepts INTEGER_ARRAY q as parameter.\n#\n\n\ndef swapPositions(list, pos1, pos2):\n    # popping both the elements from list\n    first_ele = list.pop(pos1)\n    second_ele = list.pop(pos2 - 1)\n\n    # inserting in each others positions\n    list.insert(pos1, second_ele)\n    list.insert(pos2, first_ele)\n\n    return list\n\n\ndef minimumBribes(q):\n    count = 0\n    base = [i for i in range(1, len(q) + 1)]\n    match = False\n    is_chaos = False\n    while not match and not is_chaos:\n        for p in q:\n            base_index = base.index(p)\n            target_index = q.index(p)\n            diff = base_index - target_index\n            if diff == 0:\n                continue\n            elif diff > 2:\n                is_chaos = True\n            # print('----- p {0} diff {1}'.format(p, diff))\n            for j in range(diff):\n                base_index = base.index(p)\n                swapPositions(base, base_index - 1, base_index)\n                count += 1\n            #     print('{0}-{1} > {2}'.format(base_index, base_index - 1, base))\n            # print('----- new {0}'.format(base))\n        match = base == q\n\n    if is_chaos:\n        print('Too chaotic')\n    else:\n        print('{0}'.format(count))\n\n\ndef minimumBribes2(q):\n    count = 0\n    is_chaos = False\n\n    q_pos = max(q)\n    while q_pos > 0 and not is_chaos:\n        current = q.index(q_pos)\n        diff = current - q_pos + 1\n        # print('----- p {0} index {1} diff {2}'.format(q_pos, current, diff))\n        if diff < -2:\n            is_chaos = True\n            break\n        elif diff != 0:\n            for i in range(abs(diff)):\n                swapPositions(q, current, current + 1)\n                current += 1\n                count += 1\n            # print('----- new {0}'.format(q))\n\n        q_pos -= 1\n\n    if is_chaos:\n        print('Too chaotic')\n    else:\n        print('{0}'.format(count))\n\n\ndef minimumBribes3(q):\n    count = 0\n    is_chaos = False\n\n    for i, val in enumerate(q):\n        if val - i - 1 > 2:\n            is_chaos = True\n            break\n\n        for j in range(max(val - 2, 0), i):\n            print('i q[{0}]={1} ---- j q[{2}]=[{3}]'.format(i, q[i], j, q[j]))\n            if q[j] > val:\n                count += 1\n\n        print('---- count {0}'.format(count))\n\n    if is_chaos:\n        print('Too chaotic')\n    else:\n        print('{0}'.format(count))\n\n\nif __name__ == '__main__':\n    for line in fileinput.input():\n        input = line.rstrip().split(\" \")\n        array_data = [int(numeric_string) for numeric_string in input[0:]]\n        minimumBribes3(array_data)\n\n# 3     2 1 5 3 4\n# X     2 5 1 3 4\n# X     5 1 2 3 7 8 6 4\n# 7     1 2 5 3 7 8 6 4\n# 4     1 2 5 3 4 7 8 6\n\n","repo_name":"smileyOpal/pythonProject","sub_path":"HackerRank/Arrays/new_year_chaos.py","file_name":"new_year_chaos.py","file_ext":"py","file_size_in_byte":2837,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"752303805","text":"import torch\nfrom diffusers import StableDiffusionPipeline, EulerDiscreteScheduler\nfrom PIL import Image\n\n# def image_grid(imgs, rows, cols):\n#     assert len(imgs) == rows*cols\n\n#     w, h = imgs[0].size\n#     grid = Image.new('RGB', size=(cols*w, rows*h))\n#     grid_w, grid_h = grid.size\n    \n#     for i, img in enumerate(imgs):\n#         grid.paste(img, box=(i%cols*w, i//cols*h))\n#     return grid\n\ndef generate_v1(input_prompt: str, height=512, width=512):\n    pipe = StableDiffusionPipeline.from_pretrained(\n        \"CompVis/stable-diffusion-v1-4\", torch_dtype=torch.float16)\n    pipe = pipe.to('cuda')\n\n    prompt = input_prompt\n    image = pipe(prompt, num_inference_steps=15, height=height, width=width).images[0]\n    return image\n\ndef generate_v2(input_prompt: str, styler='realistic', height=768, width=768):\n\n    model_id = \"stabilityai/stable-diffusion-2\"\n\n    scheduler = EulerDiscreteScheduler.from_pretrained(model_id, subfolder=\"scheduler\")\n    pipe = StableDiffusionPipeline.from_pretrained(model_id, scheduler=scheduler, revision=\"fp16\", torch_dtype=torch.float16)\n    pipe = pipe.to(\"cuda\")\n\n    # styler 예시 : 'realistic', 'story', 'anime', 'concept art'\n    prompt = f\"{input_prompt}, {styler}\" \n    image = pipe(prompt, height=height, width=width).images[0]\n    return image\n","repo_name":"donghoon11/GDSC_Ideaton","sub_path":"st_diffusion.py","file_name":"st_diffusion.py","file_ext":"py","file_size_in_byte":1303,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"3378741798","text":"import os\nfrom threading import Thread\nfrom time import sleep\n\n\n\nfrom kivy.app import App\nfrom kivy.core.window import Window\nfrom kivy.lang import Builder\nfrom kivy.properties import ObjectProperty\nfrom kivy.uix.screenmanager import ScreenManager, Screen\nfrom kivy.animation import Animation\n\nfrom pidev.MixPanel import MixPanel\nfrom pidev.kivy.PassCodeScreen import PassCodeScreen\nfrom pidev.kivy.PauseScreen import PauseScreen\nfrom pidev.Joystick import Joystick\nfrom pidev.kivy import DPEAButton\nfrom pidev.kivy import ImageButton\nimport random\nMIXPANEL_TOKEN = \"x\"\nMIXPANEL = MixPanel(\"Project Name\", MIXPANEL_TOKEN)\n\nSCREEN_MANAGER = ScreenManager()\nMAIN_SCREEN_NAME = 'main'\nADMIN_SCREEN_NAME = 'admin'\nSKY_SCREEN_NAME = 'SideScreen'\nJOY_SCREEN_NAME = 'joyScreen'\n\n\nclass ProjectNameGUI(App):\n    \"\"\"\n    Class to handle running the GUI Application\n    \"\"\"\n\n    def build(self):\n        \"\"\"\n        Build the application\n        :return: Kivy Screen Manager instance\n        \"\"\"\n        return SCREEN_MANAGER\n\n\nWindow.clearcolor = (0, 0, 0.1, 100)  # White\n\nxy = property(None)\nbtn6 = property(None)\nlb0 = property(None)\nlbz = property(None)\n\nclass JoyScreen(Screen):\n    joystick = Joystick(0, False)\n    X_axis = ObjectProperty(0.0)\n    Y_axis = ObjectProperty(0.0)\n\n\n    def __init__(self, **kwargs):\n        Builder.load_file('joyScreen')\n        super(JoyScreen, self).__init__(**kwargs)\n\n    def switch_text2(self):\n        list = [1]\n\n        if self.joystick.button_combo_check(list)==1:\n            self.lbz.text = \"\"\n            self.lb0.text = \"Ready\"\n            self.xy.color = 0, 1, 0, 1\n\n            if self.joystick.get_button_state(0) == 0:\n                self.lb0.text = \"Ready\"\n                self.xy.color = 0, 1, 0, 1\n            else:\n                self.lb0.text = \"\"\n                self.xy.color = 1, 0, 0, 1\n                self.counter()\n\n        else:\n            self.lbz.text = \"Hold 2\"\n            self.lb0.text = \"\"\n            self.xy.color = 1, 1, 1, 1\n\n\n    def joystick_thread(self):\n        while 1:\n            self.joystick.refresh()\n            self.X_axis = self.joystick.get_axis('x') * Window.size[0] * 1/2\n            self.Y_axis = self.joystick.get_axis('y') * Window.size[1] * 1/2\n            # self.switch_text()\n            self.switch_text2()\n            sleep(1/200)\n            # print(self.X_axis)\n\n    def start_joystick_thread(self):\n        Thread(target=self.joystick_thread).start()\n\n    def transition_back(self):\n        \"\"\"\n        Transition back to the main screen\n        :return:\n        \"\"\"\n        SCREEN_MANAGER.current = MAIN_SCREEN_NAME\n\n    global xin\n    xin = 0\n    global x\n\n    def counter(self):\n        global xin\n        global x\n        xin = xin + 1\n        if xin == 1000:\n            xin = 0\n        else:\n            print (\"%d\" % xin)\n            x = \"%d\" % xin\n            random.seed(x)\n            Window.clearcolor = (random.random(), random.random(), random.random(), 1000)\n\n\n\n\n\n\ntest_button3 = property(None)\nfly2 = property(None)\n\nclass SideScreen(Screen):\n    def __init__(self, **kwargs):\n        Builder.load_file('sideScreen.kv')\n        super(SideScreen, self).__init__(**kwargs)\n        anim = Animation(x=50) + Animation(size=(180, 80)) + Animation(x=90) + Animation(size=(150, 150)) + Animation(\n            y=50) + Animation(y=150)\n        anim.repeat = True\n        anim.start(self.fly2)\n\n    def transition_back(self):\n        \"\"\"\n        Transition back to the main screen\n        :return:\n        \"\"\"\n        SCREEN_MANAGER.current = MAIN_SCREEN_NAME\n\n    def exit_program(self):\n        \"\"\"\n        Quit the program. This should free all steppers and do any cleanup necessary\n        :return: None\n        \"\"\"\n        quit()\n\n    def random(self):\n        if self.test_button3.text == \"o-\\<\":\n            self.test_button3.text = \"o-/<\"\n\n        elif self.test_button3.text == \"o-/<\":\n            self.test_button3.text = \"o-\\<\"\n\n\nclass MainScreen(Screen):\n    \"\"\"\n    Class to handle the main screen and its associated touch events\n    \"\"\"\n\n    btn1 = property(None)\n    btn2 = property(None)\n    btn3 = property(None)\n    fly = property(None)\n\n    def __init__(self, **kwargs):\n        Builder.load_file('sideScreen.kv')\n        super(MainScreen, self).__init__(**kwargs)\n        anim = Animation(x=50) + Animation(size=(80, 80)) + Animation(x=90) + Animation(size=(150, 150)) + Animation(y=50) + Animation(y=100)\n        anim.repeat = True\n        anim.start(self.fly)\n\n\n    def next(self):\n        SCREEN_MANAGER.current = SKY_SCREEN_NAME\n\n    def next2(self):\n        SCREEN_MANAGER.current = JOY_SCREEN_NAME\n    def affect(self):\n        self.btn3.text = \"Changing...\"\n        if self.lb1.text == \"Motor On\":\n            self.lb1.text = \"Motor Off\"\n\n        elif self.lb1.text == \"Motor Off\":\n            self.lb1.text = \"Motor On\"\n    def switch_text(self):\n\n        if self.btn1.text == \"On\":\n            self.btn1.text = \"Off\"\n\n        elif self.btn1.text == \"Off\":\n            self.btn1.text = \"On\"\n\n    global xin\n    xin = 0\n    def counter(self):\n        global xin\n        xin = xin + 1\n        self.btn2.text = \"%d\" % xin\n\n\n\n\n\n    def pressed(self):\n        \"\"\"\n        Function called on button touch event for button with id: testButton\n        :return: None\n        \"\"\"\n        PauseScreen.pause(pause_scene_name='pauseScene', transition_back_scene='main', text=\"Test\", pause_duration=5)\n\n    def admin_action(self):\n        \"\"\"\n        Hidden admin button touch event. Transitions to passCodeScreen.\n        This method is called from pidev/kivy/PassCodeScreen.kv\n        :return: None\n        \"\"\"\n        SCREEN_MANAGER.current = 'passCode'\n\n\nclass AdminScreen(Screen):\n    \"\"\"\n    Class to handle the AdminScreen and its functionality\n    \"\"\"\n\n    def __init__(self, **kwargs):\n        \"\"\"\n        Load the AdminScreen.kv file. Set the necessary names of the screens for the PassCodeScreen to transition to.\n        Lastly super Screen's __init__\n        :param kwargs: Normal kivy.uix.screenmanager.Screen attributes\n        \"\"\"\n        Builder.load_file('AdminScreen.kv')\n\n        PassCodeScreen.set_admin_events_screen(ADMIN_SCREEN_NAME)  # Specify screen name to transition to after correct password\n        PassCodeScreen.set_transition_back_screen(MAIN_SCREEN_NAME)  # set screen name to transition to if \"Back to Game is pressed\"\n\n        super(AdminScreen, self).__init__(**kwargs)\n\n    @staticmethod\n    def transition_back():\n        \"\"\"\n        Transition back to the main screen\n        :return:\n        \"\"\"\n        SCREEN_MANAGER.current = MAIN_SCREEN_NAME\n\n    @staticmethod\n    def shutdown():\n        \"\"\"\n        Shutdown the system. This should free all steppers and do any cleanup necessary\n        :return: None\n        \"\"\"\n        os.system(\"sudo shutdown now\")\n\n    @staticmethod\n    def exit_program():\n        \"\"\"\n        Quit the program. This should free all steppers and do any cleanup necessary\n        :return: None\n        \"\"\"\n        quit()\n\"\"\"\nWidget additions\n\"\"\"\n\nBuilder.load_file('main.kv')\nSCREEN_MANAGER.add_widget(MainScreen(name=MAIN_SCREEN_NAME))\nSCREEN_MANAGER.add_widget(PassCodeScreen(name='passCode'))\nSCREEN_MANAGER.add_widget(PauseScreen(name='pauseScene'))\nSCREEN_MANAGER.add_widget(AdminScreen(name=ADMIN_SCREEN_NAME))\nSCREEN_MANAGER.add_widget(SideScreen(name=SKY_SCREEN_NAME))\nSCREEN_MANAGER.add_widget(JoyScreen(name=JOY_SCREEN_NAME))\n\n\"\"\"\nMixPanel\n\"\"\"\n\n\ndef send_event(event_name):\n    \"\"\"\n    Send an event to MixPanel without properties\n    :param event_name: Name of the event\n    :return: None\n    \"\"\"\n    global MIXPANEL\n\n    MIXPANEL.set_event_name(event_name)\n    MIXPANEL.send_event()\n\n\nif __name__ == \"__main__\":\n    # send_event(\"Project Initialized\")\n    # Window.fullscreen = 'auto'\n    ProjectNameGUI().run()\n","repo_name":"02ks/Ex-5-Kivy-UI","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":7809,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"20523386772","text":"import matplotlib.pyplot as plt\nimport seaborn as sns\nimport numpy as np\nimport torch\nimport torch.nn as nn\nfrom models.small_cnn import SmallCNN\nfrom models.large_cnn import LargeCNN\nfrom models.cam_cnn import CamCNN\nfrom models.pretrained_resnet import get_pretrained_resnet\n\n# Function to set plot font sizes\ndef set_font_sizes(small_size=14, medium_size=16, large_size=18):        \n    plt.rc('font', size=large_size)          # controls default text sizes\n    plt.rc('axes', titlesize=large_size)     # fontsize of the axes title\n    plt.rc('axes', labelsize=large_size)    # fontsize of the x and y labels\n    plt.rc('xtick', labelsize=medium_size)    # fontsize of the tick labels\n    plt.rc('ytick', labelsize=medium_size)    # fontsize of the tick labels\n    plt.rc('legend', fontsize=medium_size)    # legend fontsize\n    plt.rc('figure', titlesize=large_size)  # fontsize of the figure title\n\ndef get_training_results(model_type, exp_dir, use_dropout, save=False):\n    \"\"\"\n    Function to get results from a training run of a model.\n    Must specify a directory with relevant training logs and models saved from the main training loop.\n    Directory must follow correct 'format'\n    Must also specify the correct model_type for that directory.\n    Returns the model weights with the lowest validation loss.\n\n    Parameters\n    ----------\n    model_type : Type of model to be evaluated\n    exp_dir : Directory previously trained models and logs were saved to\n    use_dropout : whether model was trained with dropout (need to know when reloading ResNetfc3)\n\n    Returns\n    -------\n    model : Model with best validation loss\n\n    \"\"\"\n    # Obtain training logs\n    results = np.genfromtxt(exp_dir + '/data.csv', delimiter=',')\n\n    # set_font_sizes()\n    \n    # Plot losses from training\n    trainline, = plt.plot(results[:,-2], alpha=1, linewidth=2, label='Train loss')\n    validline, = plt.plot(results[:,-1], alpha=1, linewidth=2, label='Valid loss')\n    \n    plt.title('Training Results')\n    plt.ylabel('Cross-Entropy Loss')\n    plt.xlabel('Epoch')\n    plt.ylim([0,1])\n    plt.legend(handles=[trainline,validline])\n    if save:\n        plt.savefig(exp_dir + '/trainingplot.png', dpi=100, bbox_inches = \"tight\")\n    \n    # Get epoch with lowest validation loss\n    best_epoch = np.argmin(results[:,-1])\n    print('Best Epoch')\n    print('----------')\n    print('\\nEpoch {}, Train loss = {:.3f}, Validation Loss = {:.3f}\\n'.format(best_epoch, results[best_epoch,-2], results[best_epoch,-1]))\n    \n    # Initialise model\n    if model_type == 'SmallCNN':\n        model = SmallCNN(use_dropout=use_dropout)\n    elif model_type == 'LargeCNN':\n        model = LargeCNN(use_dropout=use_dropout)\n    elif model_type == 'CamCNN':\n        model = CamCNN(use_dropout=use_dropout)\n    elif model_type == 'ResNet3fc':\n        model = get_pretrained_resnet(dropout_trainable_layers=use_dropout, extra_fc_layers=True)\n        # Here model structure depends on whethr dropout was used in training (hence inclusion of use_dropout)\n    elif model_type == 'ResNet1fc':\n        model = get_pretrained_resnet()\n    else:\n        assert False, \"Please ensure a valid 'model_type' string is chosen\"\n        \n    # Load in weights from lowest validation loss epoch\n    model_path = exp_dir + '/model_epoch{}.pt'.format(best_epoch)\n    device = 'cuda' if torch.cuda.is_available() else 'cpu'\n    state_dict = torch.load(model_path, map_location=torch.device(device))\n    model.load_state_dict(state_dict)\n    \n    return model\n\ndef get_saved_model(model_type):\n    \"\"\"\n    Function to obtain one of the models from the main experiments of the paper.\n    Simply specify which model_type is desired and the trained model is returned.\n    This is the model which performed best on the validation data for each architecture (i.e. one used for testing)\n\n    Parameters\n    ----------\n    model_type : Type of model wished to be obtained\n\n    Returns\n    -------\n    returns the requested model\n\n    \"\"\"\n    # Ensure requested model_type is valid\n    valid_models = ['SmallCNN', 'LargeCNN', 'CamCNN', 'ResNet3fc', 'ResNet1fc']\n    if model_type not in valid_models:\n        assert False, \"Please ensure a valid 'model_type' string is chosen\"\n    \n    # Set directory in which trained model is saved\n    exp_dir = 'experiments/' + model_type.lower()\n    # All models of main experiment were trained with dropout (where applicable)\n    use_dropout=True\n    # Get trained model\n    return get_training_results(model_type, exp_dir, use_dropout)\n\ndef evaluate_model(model, test_loader, exp_dir, save=False):\n    \"\"\"\n    Function to evaluate a model on the test data.\n    Will print confusion matrix and eval measures for a threshold of 0.5.\n    Will print AUC curve.\n\n    Parameters\n    ----------\n    model : model to be tested\n    test_loader : data loader to test model on\n    exp_dir : directory to save plots in\n\n    Returns\n    -------\n    None.\n\n    \"\"\"\n    \n    # Use gpu for accleration if available\n    test_on_gpu = torch.cuda.is_available()\n    if test_on_gpu:\n        print('Using GPU')\n        model.cuda()\n    \n    criterion = nn.CrossEntropyLoss()   # To calc loss on test data\n    probs = nn.Softmax(dim=1)           # To calc model output probabilities\n    \n    # no.grad() as no need to track gradients\n    with torch.no_grad():\n        # Set model to evaluation mode\n        model.eval()\n        # Count relevant stats\n        test_imgs = 0.0\n        test_loss = 0.0\n        test_acc = 0.0\n        all_predictions = []\n        all_targets = []\n        output_probs = []\n        # Iterate through all data\n        for i, data in enumerate(test_loader, 0):\n            images, labels = data\n            if test_on_gpu:\n                images, labels = images.cuda(), labels.cuda() # Move to gpu if being used\n                \n            # Get output and calculate accuracy and loss\n            output = model(images)\n            _, predictions = torch.max(output, 1) # Predict based on largest output probability (i.e., threshold of 0.5)\n            accuracy = np.mean(predictions.cpu().detach().numpy() == labels.cpu().detach().numpy())*100\n            loss = criterion(output, labels)\n            \n            # Cumulate stats\n            test_loss += loss.item()*images.size(0)\n            test_acc += accuracy*images.size(0)\n            test_imgs += images.size(0)\n            # Append stats\n            output_probs += [probs(output).cpu().numpy()] # Convert outputs to true probabilities and append\n            all_predictions += [predictions.cpu().numpy()]\n            all_targets += [labels.cpu().numpy()]\n    \n    # Reshape stats\n    output_probs = np.array(output_probs).reshape((-1,2))\n    all_predictions = np.array(all_predictions).reshape(-1)\n    all_targets = np.array(all_targets).reshape(-1)\n    \n    # calculate average results\n    test_loss = test_loss/test_imgs\n    test_acc = test_acc/test_imgs\n    TPs, FPs, TNs, FNs = get_confusion_matrix(all_predictions, all_targets)\n    \n    print('Test Loss: {:.3f}'.format(test_loss))\n    \n    # Plot Confusion matrix\n    cf_matrix = np.array([[TPs,FPs],[FNs,TNs]])\n    labels = ['TP','FP','FN','TN']\n    categories = ['PNEUMONIA', 'NORMAL']\n    title = 'Confusion Matrix (threshold = 0.5)'\n    make_confusion_matrix(cf_matrix, exp_dir, \n                          group_names=labels,\n                          categories=categories, \n                          cmap='binary', title=title, save=save)\n    # Plot AUC\n    plot_AUC(output_probs, all_targets, exp_dir, save=save)\n\n# Function to calculate confusion matrix stats from provided model predictions and true labels\ndef get_confusion_matrix(predictions, targets):\n    positive_pred_idx = predictions == 1.0\n    negative_pred_idx = predictions == 0.0\n    TPs = np.sum(predictions[positive_pred_idx] == targets[positive_pred_idx])\n    FPs = np.sum(predictions[positive_pred_idx] != targets[positive_pred_idx])\n    TNs = np.sum(predictions[negative_pred_idx] == targets[negative_pred_idx])\n    FNs = np.sum(predictions[negative_pred_idx] != targets[negative_pred_idx])\n    return TPs, FPs, TNs, FNs\n\n# Function to plot ROC and calculate AUC\n# Provide models output probabilities and corresponding targets\ndef plot_AUC(output_probs, targets, exp_dir, save=False):\n    # placeholder for TP and FP rates at each tested threshold\n    TPRs = []\n    FPRs = []\n    # Calculate for 1000 threshold evenly spaced between 0 and 1\n    thresholds = np.linspace(0,1,num=1000)\n    # For each threshold obtain data\n    for threshold in thresholds:\n        # Get predictions for given threshold \n        predictions = (output_probs[:,1] > threshold) * 1\n        # Get number of TPs, FPs etc for these predictions\n        TPs, FPs, TNs, FNs = get_confusion_matrix(predictions, targets)\n        # Calculate True Positive and False Positive rates\n        TPRs += [TPs / (TPs + FNs)]\n        FPRs += [FPs / (TNs + FPs)]\n    # Calculate AUC of ROC\n    AUC = -1 * np.trapz(y=TPRs, x=FPRs)\n    print('AUC = {:.3f}'.format(AUC))\n    \n    # Plot ROC\n    plt.figure(2)\n    # set_font_sizes()\n    model, = plt.plot(FPRs, TPRs, label='AUC = {:.3f}'.format(AUC))\n    random, = plt.plot([0,1],[0,1], color='grey', linestyle='dashed', label='')\n    plt.title('ROC curve')\n    plt.xlabel('1 - Specificity')\n    plt.ylabel('Sensitivity')\n    plt.legend(handles=[model])\n    if save:\n        plt.savefig(exp_dir + '/AUCplot.png', dpi=100, bbox_inches = \"tight\")\n    plt.show()\n\n# Function to visually display confusion matix, along with some stats\n# Credit: https://medium.com/@dtuk81/confusion-matrix-visualization-fc31e3f30fea\ndef make_confusion_matrix(cf,\n                          exp_dir,\n                          group_names=None,\n                          categories='auto',\n                          count=True,\n                          percent=True,\n                          cbar=True,\n                          xyticks=True,\n                          xyplotlabels=True,\n                          sum_stats=True,\n                          figsize=None,\n                          cmap='Blues',\n                          title=None,\n                          save=False):\n    '''\n    This function will make a pretty plot of an sklearn Confusion Matrix cm using a Seaborn heatmap visualization.\n    Arguments\n    ---------\n    cf:            confusion matrix to be passed in\n    ax:           the axis to plot on\n    group_names:   List of strings that represent the labels row by row to be shown in each square.\n    categories:    List of strings containing the categories to be displayed on the x,y axis. Default is 'auto'\n    count:         If True, show the raw number in the confusion matrix. Default is True.\n    normalize:     If True, show the proportions for each category. Default is True.\n    cbar:          If True, show the color bar. The cbar values are based off the values in the confusion matrix.\n                   Default is True.\n    xyticks:       If True, show x and y ticks. Default is True.\n    xyplotlabels:  If True, show 'True Label' and 'Predicted Label' on the figure. Default is True.\n    sum_stats:     If True, display summary statistics below the figure. Default is True.\n    figsize:       Tuple representing the figure size. Default will be the matplotlib rcParams value.\n    cmap:          Colormap of the values displayed from matplotlib.pyplot.cm. Default is 'Blues'\n                   See http://matplotlib.org/examples/color/colormaps_reference.html\n                   \n    title:         Title for the heatmap. Default is None.\n    '''\n\n\n    # CODE TO GENERATE TEXT INSIDE EACH SQUARE\n    blanks = ['' for i in range(cf.size)]\n\n    if group_names and len(group_names)==cf.size:\n        group_labels = [\"{}\\n\".format(value) for value in group_names]\n    else:\n        group_labels = blanks\n\n    if count:\n        group_counts = [\"{0:0.0f}\\n\".format(value) for value in cf.flatten()]\n    else:\n        group_counts = blanks\n\n    if percent:\n        group_percentages = [\"{0:.2%}\".format(value) for value in cf.flatten()/np.sum(cf)]\n    else:\n        group_percentages = blanks\n\n    box_labels = [f\"{v1}{v2}{v3}\".strip() for v1, v2, v3 in zip(group_labels,group_counts,group_percentages)]\n    box_labels = np.asarray(box_labels).reshape(cf.shape[0],cf.shape[1])\n\n\n    # CODE TO GENERATE SUMMARY STATISTICS & TEXT FOR SUMMARY STATS\n    if sum_stats:\n        #Accuracy is sum of diagonal divided by total observations\n        accuracy  = np.trace(cf) / float(np.sum(cf))\n\n        #if it is a binary confusion matrix, show some more stats\n        if len(cf)==2:\n            #Metrics for Binary Confusion Matrices\n            precision = cf[1,1] / sum(cf[:,1])\n            recall    = cf[1,1] / sum(cf[1,:])\n            f1_score  = 2*precision*recall / (precision + recall)\n            stats_text = \"\\n\\nAccuracy={:0.3f}\\nPrecision={:0.3f}\\nRecall={:0.3f}\\nF1 Score={:0.3f}\".format(\n                accuracy,precision,recall,f1_score)\n        else:\n            stats_text = \"\\n\\nAccuracy={:0.3f}\".format(accuracy)\n    else:\n        stats_text = \"\"\n\n    plt.figure(1)\n    \n    # set_font_sizes()\n    \n    # SET FIGURE PARAMETERS ACCORDING TO OTHER ARGUMENTS\n    if figsize==None:\n        #Get default figure size if not set\n        figsize = plt.rcParams.get('figure.figsize')\n\n    if xyticks==False:\n        #Do not show categories if xyticks is False\n        categories=False\n\n\n    # MAKE THE HEATMAP VISUALIZATION\n    plt.figure(figsize=figsize)\n    sns.heatmap(cf,annot=box_labels,fmt=\"\",cmap=cmap,cbar=cbar,xticklabels=categories,yticklabels=categories)\n\n    if xyplotlabels:\n        plt.ylabel('True label')\n        plt.xlabel('Predicted label' + stats_text)\n    else:\n        plt.xlabel(stats_text)\n    \n    if title:\n        plt.title(title)\n        \n    if save:\n        plt.savefig(exp_dir + '/confusionmat.png', dpi=100, bbox_inches = \"tight\")\n        \n    plt.show()\n","repo_name":"RobertMcCarthy97/chest_x-ray","sub_path":"utils/evaluation.py","file_name":"evaluation.py","file_ext":"py","file_size_in_byte":13895,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"72333889226","text":"import torch\nfrom torch.autograd import Function\nfrom itertools import repeat\nimport numpy\n\nclass DiceLoss(Function):\n\n    def __init__(self, *args, **kwargs):\n        pass\n\n    def forward(self, predict, target):\n        eps = 0.00001\n        target = target.squeeze()\n        #b:batch_size, z:depth, y:height w:width\n        b, z, y, x = target.shape\n        target_ = target.view(b, -1)\n        result_ = predict.argmax(1)# dim 2 is of length 2. Reduce the length to 1 and label it with the class with highest probability\n\n        result = result_.cuda().float()\n        target = target_.cuda().float()\n        self.save_for_backward(result, target)\n\n        self.intersect = torch.zeros(predict.shape[0]).cuda()\n        self.union = torch.zeros(predict.shape[0]).cuda()\n        dice = torch.zeros(predict.shape[0]).cuda()\n\n        for i in range(predict.shape[0]):\n            self.intersect[i] = torch.dot(result[i, :], target[i, :])\n            result_sum = torch.sum(result[i, :])\n            target_sum = torch.sum(target[i, :])\n            self.union[i] = result_sum + target_sum\n\n            dice[i] = 2 * self.intersect[i] / (self.union[i] + eps)\n            print('union: {}\\t intersect: {}\\t dice_coefficient: {:.7f}'.format(str(self.union[i]), str(self.intersect[i]), dice[i]))\n\n        sum_dice = torch.sum(dice)\n\n        return sum_dice\n\n    def backward(self, grad_output):\n        input, target = self.saved_tensors\n        intersect, union = self.intersect, self.union\n\n        grad_input = torch.zeros(target.shape[0], 2, target.shape[1])\n        grad_input = grad_input.cuda()\n        for i in range(input.shape[0]):\n            part1 = torch.div(target[i, :], union[i])\n            part2 = intersect[i] / (union[i] * union[i])\n            part2 = torch.mul(input[i, :], part2)\n            dice = torch.add(torch.mul(part1, 2), torch.mul(part2, -4)).cuda()\n            grad_input[i, 0, :] = torch.mul(dice, grad_output.item())\n            grad_input[i, 1, :] = torch.mul(dice, -grad_output.item())\n\n        return grad_input, None\n\ndef dice_loss(input, target):\n    return DiceLoss()(input, target)\n\ndef dice_error(input, target):\n    eps = 0.00001\n    _, result_ = input.max(1)\n    result_ = torch.squeeze(result_)\n    resut = result_.cuda().float()\n    target_ = target.cuda().float()\n    result.copy_(result_.data)\n    target_.copy_(target.data)\n    target = target_\n    intersect = torch.dot(result, target)\n    result_sum = torch.sum(result)\n    target_sum = torch.sum(target)\n    union = result_sum + target_sum\n    intersect = numpy.max([eps, intersect])\n    dice = 2 * intersect / (union + eps)\n\n    return dice\n","repo_name":"buptxiaofeng/promise12_seg","sub_path":"loss.py","file_name":"loss.py","file_ext":"py","file_size_in_byte":2641,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"81"}
+{"seq_id":"27528797663","text":"# © 2021 - today Numigi (tm) and all its contributors (https://bit.ly/numigiens)\n# License LGPL-3.0 or later (http://www.gnu.org/licenses/lgpl).\n\nfrom ddt import data, ddt, unpack\nfrom odoo.tests import common\n\n\n@ddt\nclass TestPartner(common.SavepointCase):\n    @classmethod\n    def setUpClass(cls):\n        super().setUpClass()\n        cls.parent = cls.env[\"res.partner\"].create({\n            \"name\": \"Société au Québec\",\n        })\n        cls.partner_1 = cls.env[\"res.partner\"].create({\n            \"name\": \"Bien-Être En Été\",\n            \"parent_id\": cls.parent.id,\n        })\n        cls.partner_2 = cls.env[\"res.partner\"].create({\n            \"name\": \"Bien Froid En Hivers\",\n            \"parent_id\": cls.parent.id,\n        })\n\n    @data(\n        \"bien-être\",\n        \"bien-etre\",\n        \"bien etre\",\n        \"Bien Être\",\n        \"En-ete\",\n    )\n    def test_search(self, name):\n        res = self._search(name)\n        assert self.partner_1 in res\n\n    def test_partner_not_duplicate(self):\n        res = self._search(self.partner_1.name)\n        count = len(res.filtered(lambda p: p == self.partner_1))\n        assert count == 1\n\n    def test_search_limit(self):\n        res = self._search(\"Bien En\", limit=1)\n        assert len(res) == 1\n\n    def test_search_domain(self):\n        res = self._search(\"Bien En\", domain=[(\"id\", \"=\", self.partner_1.id)])\n        assert res == self.partner_1\n\n    @data(\n        (\"418 555-6666\", \"4185556666\"),\n        (\"+1 418-555-6666\", \"4185556666\"),\n        (\"+1 418-555-6666\", \"+1 418-555-6666\"),\n    )\n    @unpack\n    def test_search_by_phone_number(self, partner_phone, text):\n        self.partner_1.phone = partner_phone\n        self.partner_1.refresh()\n        print(self.partner_1.phone)\n        res = self._search(text)\n        assert self.partner_1 in res\n\n    def test_search_by_mobile_phone_number(self):\n        self.partner_1.mobile = \"418 555-6666\"\n        res = self._search(\"4185556666\")\n        assert self.partner_1 in res\n\n    def test_search_by_email(self):\n        self.partner_1.email = \"john-doe.1@example\"\n        res = self._search(\"john doe\")\n        assert self.partner_1 in res\n\n    def test_search_by_street(self):\n        self.partner_1.street = \"10000 rue du Belvédère\"\n        res = self._search(\"belvedere\")\n        assert self.partner_1 in res\n\n    def test_search_by_ref(self):\n        self.partner_1.ref = \"ABC-LTÉE\"\n        res = self._search(\"abc ltee\")\n        assert self.partner_1 in res\n\n    def test_search_by_parent_name(self):\n        self.parent.name = \"Montréal Inc.\"\n        res = self._search(\"Montreal\")\n        assert self.parent in res\n        assert self.partner_1 in res\n\n    def _search(self, name, operator=\"ilike\", domain=None, limit=None):\n        items = self.env[\"res.partner\"].name_search(\n            name, args=domain, operator=operator, limit=limit or 999999\n        )\n        return self.env[\"res.partner\"].browse([el[0] for el in items])\n","repo_name":"Numigi/odoo-partner-addons","sub_path":"partner_full_text_search/tests/test_partner.py","file_name":"test_partner.py","file_ext":"py","file_size_in_byte":2959,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"81"}
+{"seq_id":"39480516167","text":"# coding:utf-8\n'''\nPloting module for automating plots for different scenarios\n\n.. author: Alexander Becker <nabla.becker@mailbox.org>\n'''\nimport itertools as itt\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport statmeth as stm\n\nPALETTS = {'blue-green': ['navy', 'blue', 'royalblue', 'dodgerblue',\n                          'deepskyblue', 'darkturquoise', 'turquoise', 'aqua', 'aquamarine'],\n           'green-yellow': ['darkgreen', 'green', 'limegreen', 'lime', 'lawngreen',\n                            'greenyellow', 'yellowgreen', 'olivedrap', 'darkolivegreen'],\n           'red-yellow': ['firebrick', 'lightcoral', 'coral',\n                          'orangered', 'red', 'darkorange', 'orange', 'gold', 'goldenrod'],\n          }\n\ndef plot_likelihood_scan_1D(scan_vals, scan_domain, parameter_name, title='Likelihood scan', palette='blue-green', plot_delta=False):\n    #convert the scan vals to an np.array\n    scan_vals = np.array(scan_vals)\n    min_nll = min(scan_vals)\n    min_nll_idx = scan_vals.argmin()\n    if plot_delta:\n        scan_vals = scan_vals - min_nll\n        min_nll = 0\n    #split the array into the part left of the min and right of the min\n    #so that it is possible to find the closest array vals to +/- sigma\n    left_side = scan_vals[:min_nll_idx]\n    right_side = scan_vals[min_nll_idx:]\n    _left_sigma, ls_idx = stm.find_nearest(left_side, min_nll+.5)\n    _right_sigma, rs_idx = stm.find_nearest(right_side, min_nll+.5)\n    rs_idx = rs_idx + len(left_side)\n\n    if plot_delta:\n        scan_vals = scan_vals - min_nll\n\n    #plot likelihood scan\n    #set up color iterator\n    color_it = iter(PALETTS[palette])\n    color = next(color_it)\n    plt.plot(scan_domain, scan_vals, color=color, marker='o',\n             label=r'Likelihood Scan for $'+parameter_name+'$')\n    #plot the lines indicating the differnt sections\n    xmin, xmax, _ymin, ymax = plt.axis()\n    #horizontal lines for the minimum and the min+1/2 lines\n    color = next(color_it)\n    if not plot_delta:\n        plt.hlines(y=min_nll, xmin=xmin, xmax=xmax, color=color, label='NLL minimum')\n    color = next(color_it)\n    plt.hlines(y=min_nll+(1./2.), xmin=xmin, xmax=xmax, color=color, linestyle='--',\n               label='standard deviation')\n    #vertical lines for the center and the left and right standard deviations\n    color = next(color_it)\n    color = next(color_it)\n    plt.vlines(x=scan_domain[ls_idx], ymin=scan_vals[ls_idx], ymax=ymax, color=color,\n               label=r'$\\hat{'+parameter_name+'} \\pm \\sigma$')\n    plt.vlines(x=scan_domain[rs_idx], ymax=ymax, ymin=scan_vals[rs_idx], color=color)\n    plt.vlines(x=scan_domain[min_nll_idx], ymin=min_nll, ymax=ymax, color='slategray',\n               label=r'$\\hat{ '+parameter_name+'}_{ ML } = '+str(np.round(scan_domain[min_nll_idx],4))+'$')\n    #grid and legend\n    plt.grid(linestyle=':')\n    plt.legend()\n    plt.title(title)\n    plt.savefig('{}_likelihood_scan.svg'.format(parameter_name))\n    plt.show()\n\ndef plot_parameter_variation_over_errorbar(hist, bin_edges, model, fitted_params,\n                                           scale_dependent_params, var_param_idx, var_vals,\n                                           param_name=r'\\theta', color='blue-green',\n                                           save=False, title='Variation of a parameter'):\n    \"\"\"plots a sequence of functions where the parameter indicated by `var_param_idx` is run\n    through all values in `var_vals` while all other parameters stay unchanged (set to the\n    value in fitted_params. They are underlayed with data to which the function params are\n    supposed to be fitted. The scale is automatically adjusted to match the fit function and\n    the data in the histogram given by hist and bin edges. The indicies of the scale\n    dependent parameters are given in scale_dependent_parameters. The color of the curves can be\n    selected from a range of pallets ('blue-green', 'green-yellow', and 'red-yellow').\n    \"\"\"\n    colors = PALETTS[color]\n    #scale the scalable parameters to the scale of the histogramm\n    #first determin the area unter the histogram as an estimate for area\n    bin_width = bin_edges[1:] - bin_edges[:-1]\n    hist_int = sum(hist*bin_width)\n    #now determin the integral of the model\n    bounds = (min(bin_edges), max(bin_edges))\n    int_steps = np.linspace(bounds[0], bounds[1], int(1000*(bounds[1]-bounds[0])))\n    model_int = stm.numerical_integrate_1D(model, int_steps, *fitted_params)\n    #the scale is defined as the ratio of the areas\n    scale = hist_int / model_int\n    #prepare variables for the plot\n    domain = np.linspace(bounds[0], bounds[1], 1000)\n    bin_centers = stm.bin_centers(bin_edges)\n    cyclic_colors = itt.cycle(colors)\n    #do the parameter variation\n    for var_val, color in zip(var_vals, cyclic_colors):\n        #set the parameter to vary to the value of the current iteration\n        plot_params = [var_val if i == var_param_idx else param\n                       for i, param in enumerate(fitted_params)]\n        #apply the scaling\n        plot_params = [scale*param if i in scale_dependent_params else param\n                       for i, param in enumerate(plot_params)]\n        #plot\n        plt.plot(domain, model(domain, *plot_params), color=color,\n                 label=r'$'+param_name+r'$ = '+str(np.round(var_val, 3)))\n    #plot the errorbars\n    yerr = np.sqrt(hist)\n    plt.errorbar(bin_centers, hist, yerr=yerr, color='black', marker='o',\n                 linestyle='', label='Events')\n    #draw the supplemental information\n    plt.grid(linestyle=':')\n    plt.title(title)\n    plt.legend()\n    if save:\n        plt.savefig('variation_of_param_'+param_name+'.svg')\n    plt.show()\n\ndef plot_param_variation_1D(func, param_sets, plot_bounds, legend=True, labels=None,\n                            var_var_name=None, var_var_idx=None, palette='blue-green', title=None,\n                            axis=None):\n    \"\"\"Plot the function given py func once for every parameter set given in param_sets.\n    The domain of the plot is bounded by plot_bounds. The curves are assigned a label\n    from labels if labels is not none. labels has to be a list of labels with the same\n    length as the list of param sets. If a descriptive label is not needed the name of\n    the parameter can be given that is varied (in param sets) and a label will be\n    automatically generated (this only works when one parameter is varied. If both\n    Labels and var_var_name are None and the length of the parameter set is smaller\n    than 7 every parameter is given a generic name and printed in the legend with\n    the value of the corresponding parameter. The legend can be disabled with\n    legend=False.\n    \"\"\"\n    #set up the axes if none where passed in, otherwis use the given one\n    if axis is None:\n        fig = plt.figure(figsize=(10,10))\n        ax = fig.add_subplot(111)\n    else:\n        ax = axis\n    #color for the diffent curves\n    color = itt.cycle(PALETTS[palette])\n    #build the labels\n    if legend and labels is None:\n        #autogenerate labels\n        if var_var_name is None:\n            if len(param_sets[0]) < 7:\n                labels = []\n                for param_set in param_sets:\n                    base_label = r'$\\theta_{} = {:.3f}$ '\n                    label = ''\n                    for i, param in enumerate(param_set):\n                        label += base_label.format(i, float(param))\n                    labels.append(label)\n            else:\n                labels = [None for param_set in param_sets]\n        #generate labels from the parameter name\n        else:\n            if var_var_idx is not None:\n                base_label = r'$'+var_var_name+' = {:.3f}$'\n                labels = []\n                for param_set in param_sets:\n                    label = base_label.format(param_set[var_var_idx])\n                    labels.append(label)\n            else:\n                labels = [None for param_set in param_sets]\n    #prepare the domain of the plot\n    delta_bounds = plot_bounds[1]-plot_bounds[0]\n    domain = np.linspace(plot_bounds[0], plot_bounds[1], delta_bounds*1000)\n    #calculate the curves\n    curves = [func(domain, *param_set) for param_set in param_sets]\n    #plot the curves\n    for curve, label, cur_col in zip(curves, labels, color):\n        ax.plot(domain, curve, label=label, color=cur_col)\n    #make the plot look nice\n    if legend:\n        ax.legend()\n    if title is not None:\n        ax.set_title(title)\n    ax.grid()\n    if axis is None:\n        return fig, ax\n","repo_name":"Phylex/Moderne-Methoden-der-Datenanalyse","sub_path":"statplot.py","file_name":"statplot.py","file_ext":"py","file_size_in_byte":8543,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"22367293084","text":"from appium import webdriver\nfrom time import sleep\ndes = {\n        \"platformName\":\"Android\",#手机是android还是ios\n        \"deviceName\":\"emulator-5554\",#手机名称，通过adb devices获得\n        \"platformVersion\":\"7.1.2\",#android版本号\n        \"appPackage\":\"com.yipiao\",#app包名\n        \"appActivity\":\"com.zt.main.entrance.ZTLaunchActivity\",#apk第一个启动页\n        \"noReset\":True#不重置手机app\n    }\n# appnium服务搭建的地址，这里是在本机上，所以是127.0.0.1\ndriver = webdriver.Remote(\"http://127.0.0.1:4723/wd/hub\",des)\nsleep(10)\ndriver.find_element_by_xpath(\"//*[@text='我的']\").click()\nsleep(3)\ndriver.find_element_by_xpath(\"//*[@text='产品意见']\").click()\nsleep(3)\n\n#获取当前上下文\n# cur = driver.current_context\n# print(cur)   NATIVE_APP\n# #获取所有上下文context\n# all=driver.contexts  ['NATIVE_APP', 'WEBVIEW_com.yipiao']\n# print(all)\n# sleep(4)\n# driver.quit()\n\n#从原生切换到webview(h5)\ndriver.switch_to.context(\"WEBVIEW_com.yipiao\")\nsleep(2)\n#查看是否切换\ncur = driver.current_context\nprint(cur)\nsleep(3)\n#定位webview的元素\ndriver.find_element_by_id(\"feedback-content\").send_keys('测试')\n\n\n\n#切回NATIVE_APP\n# driver.switch_to.context(\"NATIVE_APP\")\n# print(driver.current_context)","repo_name":"lyedzp/appuizidongh","sub_path":"app_test/k1/webview.py","file_name":"webview.py","file_ext":"py","file_size_in_byte":1272,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"34450275266","text":"import operator\nfirst_number = float (input(\"enter the first number \"))\nsecond_number = float (input(\"enter the second number \"))\nsign = input(\"enter the arithemetic sign\")\nops = {\n    '+' : operator.add,\n    '-' : operator.sub,\n    '*' : operator.mul,\n    '/' : operator.truediv,  # use operator.div for Python 2\n    '%' : operator.mod,\n    '^' : operator.xor,\n}\nif sign in ops.keys():\n       answer = ops[sign] (first_number , second_number)\n       print( answer)\nwhile True:\n    sign = input(\"enter the arithemetic sign \")\n    if sign == \"exit\" :\n        print(\"the system is exiting...\")\n        print( answer)\n        break\n    third_number = float(input(\"enter the next value\"))\n    \n    if sign in ops.keys():\n        answer = ops[sign](answer, third_number )\n        print( answer)\n        \n        \n    \n\n\n\n    ","repo_name":"everybees/parsel_tongue","sub_path":"John/calculator.py","file_name":"calculator.py","file_ext":"py","file_size_in_byte":820,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"81"}
+{"seq_id":"25023006254","text":"import requests\n\nimport tkinter as tk\nimport threading\n\nclass Screen:\n\tdef __init__(self, w, h):\n\t\tself.w = w\n\t\tself.h = h\n\n\t\tself.borders = [(0, 0), (1, 1)]\n\n\tdef set_border(self, x, y, border_idx):\n\t\tassert border_idx in [0, 1]\n\n\t\tself.borders[border_idx] = ((x/self.w, y/self.h))\n\n\tdef get_margins(self):\n\t\tleft = min(self.borders, key=lambda x: x[0])[0]\n\t\tup = min(self.borders, key=lambda x: x[1])[1]\n\t\tright = 1 - max(self.borders, key=lambda x: x[0])[0]\n\t\tdown = 1 - max(self.borders, key=lambda x: x[1])[1]\n\n\t\tmargins = {}\n\t\tfor key, value in zip([\"left\", \"up\", \"right\", \"down\"], [left, up, right, down]):\n\t\t\tmargins[key] = str(float(value * 100))\n\n\t\treturn margins\n\nscreen = Screen(0, 0)\nhost = \"http://localhost:5000/scserver\"\n\ndef get(url, params=None) -> bytes:\n\tres = requests.get(url, params=params)\n\timg = res.content\n\n\treturn img\n\ndef update():\n\tglobal screen, lbl_image\n\t\n\turl = f\"{host}/default/png\"\n\tppm = get(url)\n\tnewimage = tk.PhotoImage(data=ppm, format=\"PNG\")\n\n\t#subsample such that the height is about 720px\n\t# h / x == 720\n\t# x == h / 720\n\n\tnewimage = newimage.subsample(newimage.height() // 720)\n\tlbl_image.configure(image=newimage)\n\tlbl_image.image = newimage\n\n\tscreen = Screen(newimage.width(), newimage.height())\n\t#show_borders()\n\ndef clicked(event):\n\tnum = event.num\n\tif num == 1: #left click\n\t\tborder_idx = 0\n\telif num == 3: #right licke\n\t\tborder_idx = 1\n\telse:\n\t\treturn\n\n\tx, y = event.x, event.y\n\n\tscreen.set_border(x, y, border_idx)\n\tshow_borders()\n\n\tmargins = screen.get_margins()\n\tprint(margins)\n\n\tthreading.Thread(target=lambda: requests.put(f\"{host}/gui/margins\", json=margins)).start()\n\ndef show_borders():\n\tglobal lbl_borders, lbl_image\n\n\tabsx, absy = lbl_image.winfo_x(), lbl_image.winfo_y()\n\n\tfor lbl_border, (x, y) in zip(lbl_borders, screen.borders):\n\t\tlbl_border.place(x=(absx + x*screen.w), y=(absy + y*screen.h))\n\nwindow = tk.Tk()\n\nfrm_control = tk.Frame(master=window, relief=tk.GROOVE)\n\nbtn_update = tk.Button(master=frm_control, text=\"Update\", command=update)\nbtn_update.pack(side=tk.LEFT)\n\nfrm_control.pack()\n\nlbl_image = tk.Label()\nlbl_image.bind(\"<Button>\", clicked)\nlbl_image.pack()\n\nlbl_borders = [tk.Label(bg=color, fg=\"white\", text='↖') for color in [\"red\", \"blue\"]]\nfor e in lbl_borders:\n\te.place(x=0, y=0)\n\nwindow.mainloop()","repo_name":"parkchamchi/screencaptureserver","sub_path":"gui.py","file_name":"gui.py","file_ext":"py","file_size_in_byte":2285,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"12377219336","text":"#pip install pandas-datareader\n\nfrom datetime import datetime\n\nimport pandas_datareader.data as web\n\nstart = datetime(2019, 1, 1) # year, month, day\nend = datetime(2019, 2, 1)\n\nmortgage_30_2019 = web.DataReader('MORTGAGE30US', 'fred', start, end)\n\nsap_data = web.DataReader('SP500', 'fred', start, end)\n\ninflation_2019 = web.DataReader('CPALTT01USM657N', 'fred', start, end)\n\nprint(sap_data)\nprint(inflation_2019)\n\ninflation_2019.to_csv('inflation_2019.csv')\n","repo_name":"reya01/hello-world","sub_path":"pandas_fin_data.py","file_name":"pandas_fin_data.py","file_ext":"py","file_size_in_byte":459,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"32781964251","text":"from conexion.conexion import Conexion\n\nclass ProfesorCurso:\n    def __init__(self, profesor, curso, dni, codigo):\n        self.__profesor=profesor\n        self.__curso=curso\n        self.__dni=dni\n        self.__codigo=codigo\n\n    @property\n    def profesor(cls):\n        return cls.__profesor\n\n    @property\n    def curso(cls):\n        return cls.__curso\n\n    @property\n    def dni(cls):\n        return cls.__dni\n\n    @property\n    def codigo(cls):\n        return cls.__codigo\n\n    @classmethod\n    def registrar_profesores_cursos(cls, profesor, curso):\n        try:\n            sql=\"INSERT INTO profesores_cursos(pro_cur_profesor, pro_cur_curso) VALUES('\"+profesor+\"', \"+curso+\")\"\n            conexion=Conexion().getConexion()\n            cursor=conexion.cursor()\n            cursor.execute(sql)\n            conexion.commit()\n        except Exception as e:\n            print(e)\n        finally:\n            conexion.close()\n\n            \n    @classmethod\n    def eliminar_profesores_cursos(cls, profesor, curso):\n        sql=\"DELETE FROM profesores_cursos WHERE pro_cur_profesor='\"+profesor+\"' AND pro_cur_curso=\"+curso\n        try:\n            conexion=Conexion().getConexion()\n            cursor=conexion.cursor()\n            cursor.execute(sql)\n            conexion.commit()\n        except Exception as e:\n            print(e)\n        finally:\n            conexion.close()\n\n\n    @classmethod\n    def mostrar_profesores_cursos(cls):\n        sql='SELECT pro_nombres, pro_ap_paterno, pro_ap_materno, cur_nombre, pro_dni, cur_codigo FROM profesores INNER JOIN profesores_cursos ON profesores.pro_dni=profesores_cursos.pro_cur_profesor INNER JOIN cursos ON cursos.cur_codigo=profesores_cursos.pro_cur_curso'\n        profesores_cursos=[]\n        try:\n            conexion=Conexion().getConexion()\n            cursor=conexion.cursor()\n            cursor.execute(sql)\n            result=cursor.fetchone()\n            while result!=None:\n                profesores_cursos.append(ProfesorCurso(result[0]+' '+result[1]+' '+result[2], result[3], result[4], result[5]))\n                result=cursor.fetchone()\n            return profesores_cursos\n        except Exception as e:\n            print(e)\n        finally:\n            conexion.close()\n","repo_name":"Pierolp1202/colegioTCS","sub_path":"modelos/profesor_curso.py","file_name":"profesor_curso.py","file_ext":"py","file_size_in_byte":2239,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"37804995955","text":"import time\nimport pandas as pd\nfrom selenium.webdriver.common.by import By\nfrom .stats_helper import selected_stats\nfrom ..mixins import DriverMixin\n\n\nclass FbrefScrapperService(DriverMixin):\n    def __init__(self, league, league_id, start_season, end_season, single_year_season):\n        DriverMixin.__init__(\n            self,\n            start_season=start_season,\n            end_season=end_season,\n            single_year_season=single_year_season,\n        )\n        self.fbref_league_id = league_id\n        self.fbref_league = league\n\n    def get_teams_squad_id(self, home_td_index, tds):\n        if (\n            len(\n                tds[home_td_index]\n                .find_element(By.TAG_NAME, \"a\")\n                .get_attribute(\"href\")\n                .split(\"/\")\n            )\n            > 7\n        ):\n            squad_id_index = -3\n        else:\n            squad_id_index = -2\n        home_squad_id = (\n            tds[home_td_index]\n            .find_element(By.TAG_NAME, \"a\")\n            .get_attribute(\"href\")\n            .split(\"/\")[squad_id_index]\n        )\n        away_squad_id = (\n            tds[home_td_index + 4]\n            .find_element(By.TAG_NAME, \"a\")\n            .get_attribute(\"href\")\n            .split(\"/\")[squad_id_index]\n        )\n        return home_squad_id, away_squad_id\n\n    def fbref_scrapper(self):\n        self.fbref_seasons = dict()\n        self.fbref_squad_ids = dict()\n\n        for season in range(self.start_season, self.end_season):\n            self.fbref_seasons[season] = []\n            self.fbref_squad_ids[season] = []\n            season_str = (\n                str(season) if self.single_year_season else f\"{season}-{season+1}\"\n            )\n\n            url = f\"https://fbref.com/en/comps/{self.fbref_league_id}/{season_str}/schedule/{self.fbref_league}-Scores-and-Fixtures\"\n            self.driver.get(url)\n\n            fb = self.driver.find_element(By.CLASS_NAME, \"fb\")\n            rows = fb.find_elements(By.XPATH, \"//table/tbody/tr\")\n\n            total_games = 0\n            for i, r in enumerate(rows):\n                print(f\"{season}/{self.end_season-1} {i}/{len(rows)}\")\n                if not r.text:\n                    continue\n                tds = r.find_elements(By.XPATH, \".//child::td\")\n                week = r.find_element(By.XPATH, \".//child::th\").text\n\n                if len(tds) == 12:\n                    (\n                        date,\n                        _,\n                        home_team,\n                        home_xg,\n                        score,\n                        away_xg,\n                        away_team,\n                        _,\n                        _,\n                        _,\n                        _,\n                        _,\n                    ) = [t.text for t in tds]\n                    home_squad_id, away_squad_id = self.get_teams_squad_id(2, tds)\n                elif len(tds) == 13:\n                    (\n                        _,\n                        date,\n                        _,\n                        home_team,\n                        home_xg,\n                        score,\n                        away_xg,\n                        away_team,\n                        _,\n                        _,\n                        _,\n                        _,\n                        _,\n                    ) = [t.text for t in tds]\n                    home_squad_id, away_squad_id = self.get_teams_squad_id(3, tds)\n                elif len(tds) == 14:\n                    (\n                        _,\n                        _,\n                        date,\n                        _,\n                        home_team,\n                        home_xg,\n                        score,\n                        away_xg,\n                        away_team,\n                        _,\n                        _,\n                        _,\n                        _,\n                        _,\n                    ) = [t.text for t in tds]\n                    home_squad_id, away_squad_id = self.get_teams_squad_id(4, tds)\n                else:\n                    continue\n\n                if not score:\n                    continue\n                self.fbref_squad_ids[season].extend(\n                    [(home_squad_id, home_team), (away_squad_id, away_team)]\n                )\n                home_score, away_score = score.split(\"–\")\n                try:\n                    match_info = [\n                        season,\n                        self.fbref_league.lower(),\n                        date,\n                        week,\n                        home_team,\n                        float(home_xg),\n                        int(home_score),\n                        int(away_score),\n                        float(away_xg),\n                        away_team,\n                    ]\n                except ValueError:\n                    continue\n                self.fbref_seasons[season].append(match_info)\n                total_games += 1\n\n            self.fbref_squad_ids[season] = set(self.fbref_squad_ids[season])\n\n    def get_value(self, attr, tds, cols):\n        col_index = cols.index(attr)\n        return tds[col_index - 1].text\n\n    def save_game_stats(\n        self, season, team, opp_team, date, venue, stats, cols, games_dict\n    ):\n        if venue == \"Home\":\n            home_team, away_team = team, opp_team\n            prefixed_cols = [\"home_\" + col for col in cols]\n        else:\n            away_team, home_team = team, opp_team\n            prefixed_cols = [\"away_\" + col for col in cols]\n\n        stats_dict = {col: stat for col, stat in zip(prefixed_cols, stats)}\n        game_key = (home_team, away_team, date)\n        if game_key in games_dict[season]:\n            games_dict[season][game_key].update(stats_dict)\n        else:\n            games_dict[season][game_key] = stats_dict\n\n    def fbref_advanced_stats_scrapper(self):\n        self.games_stats_dict = {}\n\n        for season in range(self.start_season, self.end_season):\n            self.games_stats_dict[season] = {}\n            for squad_idx, si in enumerate(self.fbref_squad_ids[season]):\n                squad_id, team_name = si\n                for stat_type in selected_stats.keys():\n                    print(\n                        f\"{season}/{self.end_season-1} {squad_idx}/{len(self.fbref_squad_ids[season])} --> {team_name}:{stat_type}\"\n                    )\n                    season_str = (\n                        str(season)\n                        if self.single_year_season\n                        else f\"{season}-{season+1}\"\n                    )\n                    url = f\"https://fbref.com/en/squads/{squad_id}/{season_str}/matchlogs/c{self.fbref_league_id}/{stat_type}\"\n                    self.driver.get(url)\n\n                    rows = self.driver.find_elements(By.XPATH, \"//table/tbody/tr\")\n                    thead = self.driver.find_elements(By.XPATH, \"//table/thead/tr\")[1]\n                    cols = thead.find_elements(By.XPATH, \".//child::th\")\n                    cols = [c.text for c in cols]\n\n                    for i, r in enumerate(rows):\n                        if not r.text:\n                            continue\n                        tds = r.find_elements(By.XPATH, \".//child::td\")\n                        if not len(tds):\n                            continue\n                        date = r.find_element(By.XPATH, \".//child::th\").text\n\n                        opp_team = self.get_value(\"Opponent\", tds, cols)\n                        venue = self.get_value(\"Venue\", tds, cols)\n\n                        stats = []\n                        for stat_col in selected_stats[stat_type]:\n                            stats.append(float(self.get_value(stat_col, tds, cols) or 0))\n\n                        self.save_game_stats(\n                            season,\n                            team_name,\n                            opp_team,\n                            date,\n                            venue,\n                            stats,\n                            selected_stats[stat_type],\n                            self.games_stats_dict,\n                        )\n\n                    time.sleep(6)\n\n    def complete_stats(self, season, game_stats, reg_cols):\n        reg_dict = {col: stat for col, stat in zip(reg_cols, game_stats)}\n        game_key = (reg_dict[\"home_team\"], reg_dict[\"away_team\"], reg_dict[\"date\"])\n        advanced_stats_dict = self.games_stats_dict[season][game_key]\n        game_dict = {**reg_dict, **advanced_stats_dict}\n        return game_dict\n\n    def combine_fbref_stats(self):\n        for season in self.fbref_seasons.keys():\n            print(\n                f\"Total games in the {season} season:\", len(self.fbref_seasons[season])\n            )\n\n            columns = [\n                \"season\",\n                \"league\",\n                \"date\",\n                \"week\",\n                \"home_team\",\n                \"home_xg\",\n                \"home_score\",\n                \"away_score\",\n                \"away_xg\",\n                \"away_team\",\n            ]\n            complete_games = [\n                self.complete_stats(season, game_stats, columns)\n                for game_stats in self.fbref_seasons[season]\n            ]\n\n            self.fbref_seasons[season] = pd.DataFrame(complete_games)\n","repo_name":"caiomelo22/Football-Data","sub_path":"src/services/scrapper/fbref/service.py","file_name":"service.py","file_ext":"py","file_size_in_byte":9297,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"81"}
+{"seq_id":"15845745855","text":"ROLE_OWNER = 1\nROLE_ADMIN = 2\nROLE_EDITOR = 3\nROLE_NONE = 4\n\nREACTION_LIKE = 1\nREACTION_DISLIKE = 2\nREACTION_NONE = 3\n\nROLES = (\n    (ROLE_OWNER, f'Owner'),\n    (ROLE_ADMIN, f'Admin'),\n    (ROLE_EDITOR, f'Editor'),\n    (ROLE_NONE, f'None'),\n)\n\nREACTIONS = (\n    (REACTION_LIKE, f'Like'),\n    (REACTION_DISLIKE, f'Dislike'),\n    (REACTION_NONE, f'None'),\n)","repo_name":"MichaelParshyn/DjangoBlog","sub_path":"blog_project/blog_app/constants.py","file_name":"constants.py","file_ext":"py","file_size_in_byte":355,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"20875233385","text":"from flask import Flask, render_template, redirect, url_for, flash, send_file\nfrom wtforms import StringField, SubmitField, SelectField, IntegerField\nfrom wtforms.validators import DataRequired\nfrom flask_wtf import FlaskForm\nfrom flask_bootstrap import Bootstrap\nfrom flask_sqlalchemy import SQLAlchemy\nfrom werkzeug.security import generate_password_hash, check_password_hash\nfrom flask_login import UserMixin, login_user, LoginManager, login_required, current_user, logout_user\n\napp = Flask(__name__)\napp.config['TEMPLATES_AUTO_RELOAD'] = True\napp.config['TEMPLATES_FOLDER'] = 'templates'\napp.config['SECRET_KEY'] = \"top_secret_key\"\napp.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:///Cafes.db'\napp.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False\nbootstrap = Bootstrap(app)\ndb = SQLAlchemy(app)\n\n\nclass Addform(FlaskForm):\n    name = StringField(label=\"Name\", validators=[DataRequired()])\n    adress = StringField(label=\"Adress\", validators=[DataRequired()])\n    rating = SelectField(label=\"Rating\", choices=[\"⭐\", \"⭐⭐\", \"⭐⭐⭐\", \"⭐⭐⭐⭐\", \"⭐⭐⭐⭐⭐\"])\n    wifi = SelectField(label=\"Wifi\", choices=[\"None\", \"📶\", \"📶📶\", \"📶📶📶\", \"📶📶📶📶\", \"📶📶📶📶📶\"])\n    plugs = StringField(label=\"Number of plugs\", validators=[DataRequired()])\n    submit = SubmitField(label=\"Submit\")\n\nclass Caffes(db.Model):\n    id = db.Column(db.Integer, primary_key=True)\n    name = db.Column(db.String, unique=True, nullable=False)\n    adress = db.Column(db.String, unique=True, nullable=False)\n    rating = db.Column(db.String)\n    wifi = db.Column(db.String)\n    plugs = db.Column(db.String)\n\n# with app.app_context():\n#     db.create_all()\n#     db.session.commit()\n\n@app.route(\"/\")\ndef home():\n    all_caffes = db.session.query(Caffes).all()\n    return render_template(\"index.html\", all_caffes=all_caffes)\n\n@app.route(\"/add\", methods=[\"POST\", \"GET\"])\ndef add():\n    form = Addform()\n    if form.validate_on_submit():\n        new_caffe = Caffes(name=form.name.data, adress=form.adress.data, rating=form.rating.data,\n                           wifi=form.wifi.data, plugs=form.plugs.data)\n\n        if Caffes.query.filter_by(name=form.name.data).first():\n            flash(\"Caffe with this name already exists.\")\n        elif Caffes.query.filter_by(name=form.adress.data).first():\n            flash(\"Caffe with this adress already exists.\")\n        else:\n            db.session.add(new_caffe)\n            db.session.commit()\n            flash(\"Caffe successfully added!\")\n            return redirect(url_for(\"home\"))\n    return render_template(\"add.html\", form=form)\n\nif (__name__) == \"__main__\":\n    app.run(debug=True)","repo_name":"nvast/caffe_and_wifi","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2655,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"14823548544","text":"import uuid\n\nfrom Common.common import *\nfrom Common.constants import *\nimport json\nimport copy\n\n\nclass Item:\n    def __init__(self):\n        self.data = dict()  # 服务器数据\n        self.name = ''\n        self.type = ''  # 类型:武器/宝石/法宝等\n        self.description = ''\n        self.effect = ''\n        self.rsp_file = ''\n        self.big_icon_hash = 0\n        self.small_icon_hash = 0\n        self.is_stackable = False  # 可叠加\n        self.category = ''  # 种类\n        self.position = ''  # 部位\n        self.buying_price = 0  # 系统购入价格\n        self.selling_price = 0  # 出售给系统的价格\n        self.prompt_text = ''  # 弹出介绍时的文本\n        self.bag_page = ''  # 道具页\n        self.page_index = 0  # 页中的位置\n        self.simple_info = False  # 简单信息\n        self.center_prompt = False  # 信息在窗口中心展示\n\n    @property\n    def quantity(self):\n        return self.data.get('数量', 1)\n\n    @property\n    def level(self):\n        return self.data.get('等级', 0)\n\n    @level.setter\n    def level(self, lv):\n        if not lv:\n            lv = 0\n        self.data['等级'] = int(lv)\n\n    def set_data(self, data: dict):\n        self.data.update(data)\n        return self\n\n    def show_prompt(self):\n        if game.director.item_showing_prompt and game.director.item_showing_prompt != self:\n            game.director.item_showing_prompt = None\n        self.prompt_text = self.description\n        game.rp.show(self.rsp_file, self.big_icon_hash, self.name, self.prompt_text, '物品', keep_center=self.center_prompt)\n\n\nclass 人物装备(Item):\n    def __init__(self):\n        super().__init__()\n\n    def set_basic_attr_prompt_text(self):\n        pass\n\n    def set_addtional_attr_prompt_text(self):\n        \"\"\"\n        附加属性文本,体质/魔力/力量/耐力/敏捷\n        \"\"\"\n        new_line = '\\n'\n        力量 = self.data.get('力量', 0)\n        if 力量 > 0:\n            self.prompt_text = f'{self.prompt_text}{new_line}#G力量 +{力量} '\n            new_line = ''\n        elif 力量 < 0:\n            self.prompt_text = f'{self.prompt_text}{new_line}#G力量 {力量} '\n            new_line = ''\n\n        体质 = self.data.get('体质', 0)\n        if 体质 > 0:\n            self.prompt_text = f'{self.prompt_text}{new_line}#G体质 +{体质} '\n            new_line = ''\n        elif 体质 < 0:\n            self.prompt_text = f'{self.prompt_text}{new_line}#G体质 {体质} '\n            new_line = ''\n\n        魔力 = self.data.get('魔力', 0)\n        if 魔力 > 0:\n            self.prompt_text = f'{self.prompt_text}{new_line}#G魔力 +{魔力} '\n            new_line = ''\n        elif 魔力 < 0:\n            self.prompt_text = f'{self.prompt_text}{new_line}#G魔力 {魔力} '\n            new_line = ''\n\n        耐力 = self.data.get('耐力', 0)\n        if 耐力 > 0:\n            self.prompt_text = f'{self.prompt_text}{new_line}#G耐力 +{耐力} '\n            new_line = ''\n        elif 耐力 < 0:\n            self.prompt_text = f'{self.prompt_text}{new_line}#G耐力 {耐力} '\n            new_line = ''\n\n        敏捷 = self.data.get('敏捷', 0)\n        if 敏捷 > 0:\n            self.prompt_text = f'{self.prompt_text}{new_line}#G敏捷 +{敏捷}'\n            new_line = ''\n        elif 敏捷 < 0:\n            self.prompt_text = f'{self.prompt_text}{new_line}#G敏捷 {敏捷}'\n            new_line = ''\n\n    def set_teji_texiao_prompt_text(self):\n        特技 = self.data.get('特技', None)\n        if 特技:\n            self.prompt_text = f'{self.prompt_text}\\n#D特技: {特技}'\n\n        特效 = self.data.get('特效', None)\n        if 特效:\n            特效 = 特效.replace(' ', ' ')\n            self.prompt_text = f'{self.prompt_text}\\n#D特技: {特效}'\n\n    def set_end_attr_prompt_text(self):\n        self.prompt_text = f'{self.prompt_text}\\n#Y耐久度 {int(self.data.get(\"耐久度\", 0))}'\n\n    def show_prompt(self):\n        super().show_prompt()\n        self.prompt_text = self.description\n        if not self.simple_info:\n            self.set_basic_attr_prompt_text()\n            self.set_addtional_attr_prompt_text()\n            self.set_teji_texiao_prompt_text()\n            self.set_end_attr_prompt_text()\n        game.rp.show(self.rsp_file, self.big_icon_hash, self.name, self.prompt_text, '物品', keep_center=self.center_prompt)\n\n\nclass 武器(人物装备):\n    def __init__(self):\n        super().__init__()\n        self.type = '武器'\n\n    def set_basic_attr_prompt_text(self):\n        self.prompt_text = f'{self.prompt_text}\\n【装备角色】{self.data.get(\"角色限制\", \"无\")}'\n        self.prompt_text = f'{self.prompt_text}\\n#Y等级 {int(self.level)} 五行 {self.data.get(\"五行\", \"未知\")}'\n        self.prompt_text = f'{self.prompt_text}\\n#Y命中 +{int(self.data.get(\"命中\", 0))} 伤害 +{int(self.data.get(\"伤害\", 0))}'\n\n\nclass 衣服(人物装备):\n    def __init__(self):\n        super().__init__()\n        self.type = '衣服'\n\n    def set_basic_attr_prompt_text(self):\n        self.prompt_text = f'{self.prompt_text}\\n【装备角色】{self.data.get(\"角色限制\", \"无\")}'\n        self.prompt_text = f'{self.prompt_text}\\n#Y等级 {int(self.level)} 五行 {self.data.get(\"五行\", \"未知\")}'\n        self.prompt_text = f'{self.prompt_text}\\n#防御 +{int(self.data.get(\"防御\", 0))}'\n\n\nclass 腰带(人物装备):\n    def __init__(self):\n        super().__init__()\n        self.type = '腰带'\n\n    def set_basic_attr_prompt_text(self):\n        self.prompt_text = f'{self.prompt_text}\\n#Y等级 {int(self.level)} 五行 {self.data.get(\"五行\", \"未知\")}'\n        self.prompt_text = f'{self.prompt_text}\\n#防御 +{int(self.data.get(\"防御\", 0))}    气血 +{int(self.data.get(\"气血\", 0))}'\n\n\nclass 头盔(人物装备):\n    def __init__(self):\n        super().__init__()\n\n    def set_basic_attr_prompt_text(self):\n        self.prompt_text = f'{self.prompt_text}\\n#Y等级 {int(self.level)} 五行 {self.data.get(\"五行\", \"未知\")}'\n        self.prompt_text = f'{self.prompt_text}\\n#魔法 +{int(self.data.get(\"魔法\", 0))}    防御 +{int(self.data.get(\"防御\", 0))}'\n\n\nclass 项链(人物装备):\n    def __init__(self):\n        super().__init__()\n\n    def set_basic_attr_prompt_text(self):\n        self.prompt_text = f'{self.prompt_text}\\n#Y等级 {int(self.level)} 五行 {self.data.get(\"五行\", \"未知\")}'\n        self.prompt_text = f'{self.prompt_text}\\n#灵力 +{int(self.data.get(\"灵力\", 0))}'\n\n\nclass 鞋子(人物装备):\n    def __init__(self):\n        super().__init__()\n\n    def set_basic_attr_prompt_text(self):\n        self.prompt_text = f'{self.prompt_text}\\n#Y等级 {int(self.level)} 五行 {self.data.get(\"五行\", \"未知\")}'\n        self.prompt_text = f'{self.prompt_text}\\n#敏捷 +{int(self.data.get(\"敏捷\", 0))}    防御 +{int(self.data.get(\"防御\", 0))}'\n\n\nclass 消耗道具(Item):\n    def __init__(self):\n        super().__init__()\n        self.type = '消耗道具'\n\n\nclass 合成旗(Item):\n    def __init__(self):\n        super().__init__()\n        self.type = '消耗道具'\n\n    def show_prompt(self):\n        super().show_prompt()\n        text = self.description\n        text = f'{text}\\n#Y场景：{self.data[\"地图\"]}，还能使用{self.data[\"剩余次数\"]}次'\n        game.rp.show(self.rsp_file, self.big_icon_hash, self.name, text, '物品', keep_center=self.center_prompt)\n\n\nclass 绿色合成旗(合成旗):\n    def __init__(self):\n        super().__init__()\n\n\nclass 蓝色合成旗(合成旗):\n    def __init__(self):\n        super().__init__()\n\n\nclass 黄色合成旗(合成旗):\n    def __init__(self):\n        super().__init__()\n\n\nclass 白色合成旗(合成旗):\n    def __init__(self):\n        super().__init__()\n\n\nclass 红色合成旗(合成旗):\n    def __init__(self):\n        super().__init__()\n\n\nitem_types = ddict()\nitems = ddict()  # 读取excel生成物品对象,游戏中的物品使用deepcopy复制对象并set_data\nitem_types['武器'] = 武器\nitem_types['衣服'] = 衣服\nitem_types['腰带'] = 腰带\nitem_types['项链'] = 项链\nitem_types['头盔'] = 头盔\nitem_types['鞋子'] = 鞋子\nitem_types['消耗道具'] = 消耗道具\nfor item_name, item_data in BH_ITEM_DATA.items():\n    item_type = item_data['类型']\n    item_object = None\n    if item_name in globals():\n        item_object: Item = globals()[item_name]()\n    elif item_type in item_types:\n        item_object: Item = item_types[item_type]()\n    if item_object:\n        items[item_name] = item_object\n        item_object.name = item_name\n        item_object.type = item_type\n        item_object.description = item_data['说明']\n        item_object.effect = item_data['效果']\n        item_object.rsp_file = item_data['文件']\n        item_object.big_icon_hash = item_data['大图标']\n        item_object.small_icon_hash = item_data['小图标']\n        item_object.is_stackable = bool(item_data['叠加'])\n        item_object.level = item_data['等级']\n        item_object.category = item_data['种类']\n        item_object.position = item_data['部位']\n        item_object.buying_price = item_data['价格']\n        item_object.selling_price = item_data['出售']\n        if item_data['属性']:\n            item_object.data.update(json.loads(item_data['属性']))\n\n\ndef create_item(item_data: dict, page='', index=0) -> Item:\n    item_name = item_data['名称']\n    if item_name in items:\n        item: Item = copy.deepcopy(items[item_name]).set_data(item_data)\n        item.bag_page = page\n        item.page_index = index\n    else:\n        print('未知物品:', item_name, item_data)\n        return None\n    return item\n","repo_name":"1992leiting/pygame_nt","sub_path":"client/Database/item/item.py","file_name":"item.py","file_ext":"py","file_size_in_byte":9681,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"33271517350","text":"# -*- coding: utf-8 -*-\n\"\"\"\nSpyder Editor\n\nThis is a temporary script file.\n\"\"\"\n'''\nimport cv2\nimport torch\nfrom mpi4py import MPI\nimport torchvision.transforms as tratransforms\nframe=cv2.imread('/home/yang/下載/test.png')\ntrans=transforms.Compose(\n    [\n        transforms.ToTensor(),\n\n    ])\ntensor=trans(frame)\n'''\nimport numpy as np\nimport torch\nfrom torch.optim import Adam\nfrom torch.utils.data import DataLoader\nfrom torchvision import datasets\nfrom torchvision import transforms\n\nimport torch.onnx\nfrom PIL import Image\nimport struct\nimport re\nimport utils\nfrom transformer_net import TransformerNet\nfrom vgg import Vgg16\nimport cv2\nimport time\nimport pafy\nfrom mpi4py import MPI\ncomm = MPI.COMM_WORLD\nsize = comm.size #number of processors\nrank = comm.rank #calling process rank\nfrom numpysocket import NumpySocket\nhost_ip = '140.113.69.226'  # change me\nnpSocket = NumpySocket()\nnpSocket.startServer(host_ip, 8014)\nnpSocket.socket.sendall(str(rank).encode())\ndevice = torch.device(\"cuda\")\nstyle_model = TransformerNet()\nstate_dict = torch.load('../saved_models/udnie.pth')\n# remove saved deprecated running_* keys in InstanceNorm from the checkpoint\n\n\n\n\n\n\nfor k in list(state_dict.keys()):\n    if re.search(r'in\\d+\\.running_(mean|var)$', k):\n        del state_dict[k]\nstyle_model.load_state_dict(state_dict)\nstyle_model.to(device)\ndef stylize(frame):\n    \n\n    content_image = frame\n    content_transform = transforms.Compose([\n        transforms.ToTensor(),\n        transforms.Lambda(lambda x: x.mul(255))\n    ])\n    content_image = content_transform(content_image)\n    content_image = content_image.unsqueeze(0).to(device)\n\n    \n\n    with torch.no_grad():\n\n        output = style_model(content_image).cpu()\n    return output.clamp(0,255)\nurl = \"https://youtu.be/YweqHs7aq8A\"\nvideo = pafy.new(url)\nbest = video.getbestvideo('mp4')\nplayurl = best.url\n\n#videofilepath = path.join(path.dirname(path.abspath(__file__)),'test.'+best.extension)\n#videofilename = best.download(filepath = videofilepath)\n#cap = cv2.VideoCapture(videofilepath)\ncap = cv2.VideoCapture()\ncap.open(playurl)\ni=0\nstart=time.time()\nwhile(True):\n    ret, frame = cap.read()\n    if(i%size==rank):\n        #tensor=stylize(Image.fromarray(frame))\n        #tensor=tensor[0].numpy().astype('uint8').transpose(1, 2, 0)\n        #npSocket.sendNumpy(tensor)\n        npSocket.sendNumpy(frame)\n        print(rank,':',1/(time.time()-start))\n        start=time.time()\n        time.sleep(0.05)\n    i+=1\n\n\n\n\n","repo_name":"steven95421/PP_final","sub_path":"youtube.py","file_name":"youtube.py","file_ext":"py","file_size_in_byte":2473,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"81"}
+{"seq_id":"4285298612","text":"from unittest import mock\n\nimport pytest\n\nfrom app.enquiries import tasks\n\n\n@mock.patch(\"app.enquiries.common.consent.set_consent\")\n@pytest.mark.parametrize(\"params\", [\n    (\"KEY\", True),\n    (\"KEY\", False),\n])\ndef test_update_enquirer_consents(mock_consent_set, params):\n    key, value = params\n    assert mock_consent_set.call_count == 0\n    tasks.update_enquirer_consents.apply(kwargs={\"key\": key, \"value\": value})\n    assert mock_consent_set.call_count == 1, (key, value)\n","repo_name":"uktrade/enquiry-mgmt-tool","sub_path":"app/enquiries/tests/test_tasks.py","file_name":"test_tasks.py","file_ext":"py","file_size_in_byte":476,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"81"}
+{"seq_id":"74664982984","text":"import pandas as pd\nfrom selenium import webdriver\n\n#Permitindo o uso do teclado\nfrom selenium.webdriver.common.keys import Keys\n\n#Permitindo a seleção de elementos\nfrom selenium.webdriver.common.by import By\n\n#Gerencia os Chromes Drivers disponíveis para acessarmos o mais atualizado\nfrom webdriver_manager.chrome import ChromeDriverManager\n\n#Instalar a versão ais atualizada com Chrome Driver\nfrom selenium.webdriver.chrome.service import Service\nimport time\n\nfrom bs4 import BeautifulSoup\nimport requests\nimport re\n\n\n#opcional\nfrom selenium.webdriver.chrome.options import Options\nchrome_options = Options\n\n#Inicializando o driver Selenium\n#driver = webdriver.Chrome\n\n#from webdriver_manager.chrome import ChromeDriverManager\nnavegador  = webdriver.Chrome()\nnavegador.get('https://kavak.com/br/seminovos')\n\ntime.sleep(5)\n\n\nnavegador.execute_script(\"window.scrollTo(0,document.body.scrollHeight)\")\n\n#Aguarde um tepo para que a página seja totalmente carregada, se necessário\ntime.sleep(10)\n\n#Encontre os elementos desejados usando seletores do Selenium\nclasse_produto = navegador.find_elements(By.CLASS_NAME, \"title\") [1:]\n\n\n# click on button with the id \"login-btn\"\nbutton = navegador.find_element(By.CLASS_NAME, \"paginator-btn-primary\")\nbutton.click()\n\nlista_produto = []\n\n#Itere sobre os elementos encontrados e extorna os dados\nfor produto in classe_produto:\n  lista_produto.append(produto.text)\n\nprint(\"\\n################## Lista de Produtos ######################\\n\")\nprint(lista_produto)\n\nclasse_preco = navegador.find_elements(By.CLASS_NAME,'price')\nlista_preco = []\n\nfor preco in classe_preco:\n  lista_preco.append(preco.text)\n  \nprint(\"\\n################## Lista de Preços ######################\\n\")\nprint(lista_preco)\n #trouxe preço á vista e parcelado\n\nclasse_preco = navegador.find_elements(By.CLASS_NAME,'price')\nlista_preco = []\n\nfor i in range(0,len(classe_preco),2):\n  lista_preco.append(preco.text)\n  \nprint(\"\\n################## Lista de Preços ######################\\n\")\nprint(lista_preco)\n  #retorna apenas o preço à vista\n\nclasse_preco = navegador.find_elements(By.CLASS_NAME,'price')\nlista_precos = []\n\nfor preco in classe_preco:\n  lista_precos.append(preco.text.replace('R$', \" \").strip)\n\nprint(\"\\n################## Lista de Preços ######################\\n\")\nprint(lista_precos)\n  #trata o cirão e o espaço\n\n#Encontre os elementos desejados usando seletores do Selenium\nclasse_detalhes = navegador.find_elements(By.CLASS_NAME,'subtitle')\n\n#Crie listas vazias para armazenar dados\nlista_detalhes = []\n\n#Itere sobre os elementos encontrados e estraia os dados:\nfor detalhes in classe_detalhes:\n  lista_detalhes.append(detalhes.text.strip())\n  \nprint(\"\\n################## Lista de Detalhes ######################\\n\")\nprint(lista_detalhes)\n\n#Encerre o driver do Selenium após extrair as informações necessárias\nnavegador.quit()\n\n#Crie um Dataframe no Pandas com as informações extraídas\ndf = pd.DataFrame({'Produto':lista_produto},{'Preco': lista_precos}, {'Detalhes': lista_detalhes})\n\nprint(\"\\nImprimindo Dataframe:\\n\")\nprint(df)\n\n#Salve o Dataframe com um arquivo Excel\n\ndf.to_excel(\"Dados1.xlsx\")\n\ndf.to_excel('Dados2.xlsx', index=False)","repo_name":"Michellinha/WebScrapping","sub_path":"app/src/webscrapping.py","file_name":"webscrapping.py","file_ext":"py","file_size_in_byte":3187,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"2262598835","text":"# %%\n# Used Libraries\nimport os\nimport numpy as np\nimport pandas as pd\n\nfrom keras.preprocessing.text import Tokenizer\nfrom keras.preprocessing.sequence import pad_sequences\nfrom keras.layers import GRU, Input, Embedding, concatenate, Dense\nfrom keras.layers import Bidirectional, GlobalAveragePooling1D, GlobalMaxPooling1D\nfrom keras.models import Model\nfrom keras.callbacks import EarlyStopping, ModelCheckpoint\nimport pickle\nfrom tensorflow_model_optimization.python.core.sparsity.keras import pruning_callbacks\n# %%\n# Loading train and test\ntrain_data = pd.read_csv('data\\\\train.csv')\ntest_data = pd.read_csv('data\\\\test.csv') \n# %%\n# Loading the required embedding file\nembedding_file = 'embeddd.txt'\n# %%\n# Specifying all of the 6 toxicity related categories to the y_train\ntoxicity_classes = [\"toxic\", \"severe_toxic\", \"obscene\", \"threat\", \"insult\", \"identity_hate\"]\ny_train = train_data[toxicity_classes].values\n# %%\n# Lowering the symbols and filling possible \"Na\" gaps\ntrain_sentences = train_data[\"comment_text\"].fillna(\"fillna\").str.lower()\ntest_sentences = test_data[\"comment_text\"].fillna(\"fillna\").str.lower()\n# %%\n# Defining the variables needed for preprocessing\nmax_features = 50000\nmax_len = 150\nembed_size = 300\n# %%\n# Creating and saving the tokenizer to use in the web App part\ntokenizer = Tokenizer(max_features)\ntokenizer.fit_on_texts(list(train_sentences))\n\nwith open('smaller_tokenizer.pickle', 'wb') as handle:\n    pickle.dump(tokenizer, handle, protocol=pickle.HIGHEST_PROTOCOL)\n# %%\n# Changing plain text to sequences using the precreated tokenizer\ntokenized_train_sentences = tokenizer.texts_to_sequences(train_sentences)\ntokenized_test_sentences = tokenizer.texts_to_sequences(test_sentences)\n# %%\n# Padding  the sentences in order to make them suit the required max_len\ntrain_padding = pad_sequences(tokenized_train_sentences, max_len)\ntest_padding = pad_sequences(tokenized_test_sentences, max_len)\n# %%\n# Indexing all of the words using embedding_file in order to create a proper Input shape for the NN\ndef get_coefs(word, *arr): return word, np.asarray(arr, dtype='float32')\nembeddings_index = dict(get_coefs(*o.rstrip().rsplit(' ')) for o in open(embedding_file, errors = 'ignore'))\n\nword_index = tokenizer.word_index\nnb_words = min(max_features, len(word_index))\nembedding_matrix = np.zeros((nb_words, embed_size))\nfor word, i in word_index.items():\n    if i >= max_features:\n        continue\n    embedding_vector = embeddings_index.get(word)\n    if embedding_vector is not None:\n        embedding_matrix[i] = embedding_vector\n# %%\n# Creating the Neural Network\n# 1. Defining the input shape\n# 2. Embedding the input values\n# 3. Main part. Creating the Bidirectional GRU layer\n# 4-5. Average and Max pooling layers to avoid further model overfitting\n# 6. Cancatenating both of the pooling layers\n# 7. Defining the main activation function. Sigmoid is used here, since the task is binary\n# 8. Compilimg the model using the binary_crossentropy loss function\n# 9. Printing the summary of the whole model\nimage_input = Input(shape=(max_len, ))\nX = Embedding(max_features, embed_size, weights=[embedding_matrix])(image_input)\nX = Bidirectional(GRU(64, return_sequences=True, dropout=0.2, recurrent_dropout=0.2))(X)\navg_pl = GlobalAveragePooling1D()(X)\nmax_pl = GlobalMaxPooling1D()(X)\nconc = concatenate([avg_pl, max_pl])\nX = Dense(6, activation=\"sigmoid\")(conc)\nmodel = Model(inputs=image_input, outputs=X)\nmodel.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\nprint(model.summary())\n# %%\n# Creating the checkpoint path\n# Defining  the EarlyStopping metrics to avoid the overfitting even better\ncheckpoint_path = r\".\\smaller_GRU.h5\"\ncheckpoint_dir = os.path.dirname(checkpoint_path)\ncheckpoint = ModelCheckpoint(checkpoint_path, monitor='val_loss', verbose=1, save_best_only=True, mode='max')\nearly = EarlyStopping(monitor=\"val_loss\", mode=\"max\", patience=5)\nprune_call = pruning_callbacks.UpdatePruningStep()\ncallbacks_list = [checkpoint, early, prune_call]\n# %%\n# Training the model\n# Estimated training time -> 2 hrs\nmodel.fit(train_padding, y_train, batch_size=32, epochs=1, validation_split=0.1, callbacks=callbacks_list)\n#4488/4488 [==============================] - 3049s 679ms/step - loss: 0.0359 - accuracy: 0.9066 - val_loss: 0.0446 - val_accuracy: 0.9588\n# %%\n# Predicting the test part in order to check whether the model is decent\ntest_values = model.predict([test_padding], batch_size=1024, verbose=1)\n# %%\n# Plotting the model\n# Saving the .h5 version of the created model needed for the App\nfrom keras.utils.vis_utils  import plot_model\nplot_model(model, to_file='Smaller_NN.png', show_shapes=True, show_layer_names=True)\nmodel.save(\".\\models\\smaller_toxicity.h5\")\nprint(\"Saved model to disk\")\n# %%\n# Not needed part\n# Created the .json version of the model \nmodel_json = model.to_json()\nwith open(\".\\models\\smaller_toxicity.json\", \"w\") as json_file:\n    json_file.write(model_json)","repo_name":"MeaMoon/toxicity-check","sub_path":"smaller_toxicity.py","file_name":"smaller_toxicity.py","file_ext":"py","file_size_in_byte":4958,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"3864913398","text":"from rest_framework.routers import DefaultRouter\nfrom rest_framework import permissions\nfrom drf_yasg import openapi\nfrom drf_yasg.views import get_schema_view\n\nfrom django.urls import path, include\n\nfrom api.views import EditTasksViewSet, CreateTasksViewSet, LeaderboardView, CreateQuizViewSet, UpdateQuizViewSet\n\nrouter = DefaultRouter()\nrouter.register('edit-tasks', EditTasksViewSet, basename='edit-tasks-api')\nrouter.register('create-tasks', CreateTasksViewSet, basename='create-tasks-api')\nrouter.register('create-quiz', CreateQuizViewSet, basename='create-quiz-api')\nrouter.register('update-quiz', UpdateQuizViewSet,  basename='update-quiz-api')\n\nschema_view = get_schema_view(\n    openapi.Info(\n        title=\"Ghettogroupo API Docs\",\n        default_version='v1',\n        description=\"Test Description\",\n    ),\n    public=True,\n    permission_classes=(permissions.AllowAny,),\n)\n\nurlpatterns = [\n    path('', include(router.urls)),\n    path('leaderboard', LeaderboardView.as_view(), name=\"leaderboard-api\"),\n    path('docs', schema_view.with_ui('swagger', cache_timeout=0), name='swagger')\n]\n","repo_name":"NethraGunti/Ghettogroupo","sub_path":"ghettogroupo/api/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1099,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"15262203989","text":"import random as rd\r\n\r\nrow1 = [\"⬜\",\"⬜\",\"⬜\"]\r\nrow2 = [\"⬜\",\"⬜\",\"⬜\"]\r\nrow3 = [\"⬜\",\"⬜\",\"⬜\"]\r\n\r\nmap = [row1, row2, row3]\r\n\r\nprint(f\"{row1}\\n{row2}\\n{row3}\")\r\nposition = input(\"Where do you want to put the treasure? \")\r\n\r\nhorizontal = int(position[0])\r\nvertical = int(position[1])\r\n\r\nselect_row = map[vertical - 1]\r\nselect_row[horizontal - 1] = \"❌\"\r\n\r\nprint(f\"{row1}\\n{row2}\\n{row3}\")","repo_name":"rannaelsant/block_white","sub_path":"block_white.py","file_name":"block_white.py","file_ext":"py","file_size_in_byte":398,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"5350468588","text":"from rest_framework.viewsets import GenericViewSet\nfrom rest_framework import mixins\nfrom facebook_statistics.models import Post\nfrom facebook_statistics.serializer import PostSerializer\nfrom rest_framework.decorators import action\nfrom rest_framework.request import Request\nfrom rest_framework.response import Response\nfrom django.core.exceptions import ValidationError\n\nfrom rest_framework import status\nfrom django.http import Http404\n\n\nclass PostViewSet(mixins.CreateModelMixin, GenericViewSet):\n    queryset = Post.objects.all()\n    serializer_class = PostSerializer\n\n    @action(methods=('get',), detail=True)\n    def get_latest_by_post_id(self, _request: Request, **__) -> Response:\n        latest_post: Post = self.get_latest('post_id')\n        serializer = self.get_serializer(latest_post)\n        return Response(serializer.data, status=status.HTTP_200_OK)\n\n    @action(methods=('get',), detail=True)\n    def get_latest_by_user_id(self, _request: Request, **__) -> Response:\n        latest_post: Post = self.get_latest('user_id')\n        serializer = self.get_serializer(latest_post)\n        return Response(serializer.data, status=status.HTTP_200_OK)\n\n    @action(methods=('get',), detail=True)\n    def get_average_statistics(self, _request: Request, pk: int):\n        average = self.get_queryset().last_30_day_average(user_id=pk)\n        print(average)\n        if not average:\n            raise Http404\n        return Response(data=average.values('date', 'avg_likes'))\n\n    def get_latest(self, lookup: str):\n        \"\"\" Returns the latest post object or raises 404 error \"\"\"\n        queryset = self.filter_queryset(self.get_queryset())\n\n        # Perform the lookup filtering.\n        lookup_url_kwarg = self.lookup_url_kwarg or self.lookup_field\n\n        assert lookup_url_kwarg in self.kwargs, (\n                'Expected view %s to be called with a URL keyword argument '\n                'named \"%s\". Fix your URL conf, or set the `.lookup_field` '\n                'attribute on the view correctly.' %\n                (self.__class__.__name__, lookup_url_kwarg)\n        )\n\n        filter_kwargs = {lookup: self.kwargs[lookup_url_kwarg]}\n        try:\n            obj = queryset.get_latest(**filter_kwargs)\n        except queryset.model.DoesNotExist:\n            raise Http404\n\n        # May raise a permission denied\n        self.check_object_permissions(self.request, obj)\n\n        return obj\n","repo_name":"namper/analytics-example","sub_path":"analytics/facebook_statistics/viewsets.py","file_name":"viewsets.py","file_ext":"py","file_size_in_byte":2409,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"71899766024","text":"\ndef app_val(cyc, x, val) :\n    if cyc in [20, 60, 100, 140, 180, 220]:\n        val.append(cyc*x)\n\ndef rectify_crt_img(img, vals) : \n    \n    for idx, v in enumerate(vals) : \n        row = idx//40\n        col = idx%40 - 1\n        if v == 1:\n            img[row][col] = \"#\"\n\n    return img\n\ndef print_img(img) : \n    for i in img : \n        print(\"\".join(i))\n\nfile = input(\"Enter the input file name : \")\n\nwith open(file, 'r') as inp_file : \n    lines = inp_file.readlines()\n    \n    x = 1\n    cycle_no = 1\n    adding = False\n    values = []\n    val = 0\n    curr_line = 0\n\n    crt = []\n\n    while cycle_no <= 240 :\n        # During cycle. The CRT line is printed here.\n        app_val(cycle_no, x, values)\n        \n        crt.append(int(cycle_no%40 in range(x, x+3)))\n\n        if adding : \n            x += val\n            adding = False\n            curr_line += 1\n        elif lines[curr_line].split()[0] == \"noop\":\n            curr_line += 1\n        else:\n            sp = lines[curr_line].split()\n            adding = True\n            val = int(sp[-1])\n\n        # End of Cycle. The new sprite position if there's one is determined here\n        cycle_no += 1\n    \n    crt_img = [[\".\" for _ in range(40)] for _ in range(6)]\n    f_img = rectify_crt_img(crt_img, crt)\n\n    print(\"Part 1 :\", sum(values))\n    print(\"Part 2 : \")\n    print_img(f_img)\n","repo_name":"amazinglySK/AOC","sub_path":"AOC_2022/day-10/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1347,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"17402976010","text":"# https://www.codewars.com/kata/5266876b8f4bf2da9b000362/train/python\r\ndef likes(names):\r\n    if len(names) == 0:\r\n        return \"No one likes this\"\r\n    elif len(names) == 1:\r\n        return \"%s likes this\" % names[0]\r\n    elif len(names) == 2:\r\n        return \"%s and %s like this\" % (names[0], names[1])\r\n    elif len(names) == 3:\r\n        return \"%s , %s and %s like this\" % (names[0], names[1], names[2])\r\n    else:\r\n        k = len(names) - 2\r\n        return \"%s, %s and %s others like this\" % (names[0], names[1], k)\r\narray1 = []\r\narray2 = ['Kevin', 'Ramesh', 'Hari', 'Peter']\r\nprint (likes(array2))","repo_name":"pskirank/Python-Practice","sub_path":"zip_test.py","file_name":"zip_test.py","file_ext":"py","file_size_in_byte":607,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"21931211721","text":"from pathlib import Path\n\nimport fire\nimport torch\nfrom loguru import logger\n\nimport editing.util\nimport raydl\nfrom editing.config import CHECKPOINT_PATH, STATISTICS_PATH, CHANNEL_MAP, CORRECTION_PATH\nfrom editing.util import load_latent, channel_selector\nfrom models.StyleGAN2_wrapper import ImprovedStyleGAN2Generator\n\n__all__ = [\"Mixer\", \"Manipulator\", \"ManipulatorBuilder\"]\n\n\nclass Mixer:\n    def __init__(self, mix_weight=0.5, select=None):\n        self.data = select\n        self.mix_weight = mix_weight\n\n    @staticmethod\n    def load(mixer_str):\n        if isinstance(mixer_str, (int, float)) or \"#\" not in mixer_str:\n            return Mixer(mix_weight=float(mixer_str))\n        path, weight = mixer_str.split(\"#\")\n        data = torch.load(path)\n        m = Mixer(float(weight), data)\n        return m\n\n    def apply(self, styles1, styles2):\n        mix_weight = self.mix_weight\n        if self.data is None:\n            return [s1 * mix_weight + s2 * (1 - mix_weight) for s1, s2 in zip(styles1, styles2)]\n        mix_style = []\n        for i in range(len(styles1)):\n            if i in self.data:\n                self.data[i].to(styles1[i].device)\n                mix_style.append(styles1[i] + mix_weight * self.data[i] * (styles2[i] - styles1[i]))\n            else:\n                mix_style.append(styles1[i] * mix_weight + styles2[i] * (1 - mix_weight))\n        return mix_style\n\n\nclass Manipulator:\n    \"\"\"\n    Responsible for moving or replacing latent codes.\n    \"\"\"\n\n    def __init__(self, data=None, channel_multiplier=2, device=torch.device(\"cpu\")):\n        self.data = data or {}\n        self.channel_multiplier = channel_multiplier\n        self.device = device\n        self.to(self.device)\n\n    @staticmethod\n    def _num_channels(layer, channel_multiplier=2):\n        # resolution = 2**log_size\n        log_size = (layer + 1) // 3 + 2\n\n        channels = {\n            2: 512,\n            4: 512,\n            8: 512,\n            16: 512,\n            32: 512,\n            64: 256 * channel_multiplier,\n            128: 128 * channel_multiplier,\n            256: 64 * channel_multiplier,\n            512: 32 * channel_multiplier,\n            1024: 16 * channel_multiplier,\n        }\n\n        if layer % 3 == 1:\n            # to_rgb layer\n            return channels[int(2 ** log_size)], 3\n        if layer % 3 == 0:\n            return channels[int(2 ** log_size)], channels[int(2 ** log_size)]\n        else:\n            return channels[int(2 ** (log_size - 1))], channels[int(2 ** log_size)]\n\n    def _init_alpha(self, layer, alpha_indices=None):\n        num_output_channel = self._num_channels(layer, self.channel_multiplier)[1]\n        alpha = torch.zeros(num_output_channel, device=self.device)\n        if alpha_indices is None:\n            alpha.fill_(1)\n        else:\n            alpha[alpha_indices] = 1\n        return alpha\n\n    def replacements(self):\n        return {layer: tool for layer, tool in self.data.items() if \"replace\" in tool}\n\n    def movements(self):\n        return {layer: tool for layer, tool in self.data.items() if \"move\" in tool}\n\n    def add_replacement(self, layer, style, replace_indexes=None):\n        layer = int(layer)\n        style = style.detach().squeeze().requires_grad_(False)\n        assert style.dim() == 1 and len(style) == self._num_channels(layer, self.channel_multiplier)[0]\n\n        alpha = self._init_alpha(layer, replace_indexes)\n\n        if layer in self.data:\n            self.data[layer][\"replace\"] = dict(style=style, alpha=alpha)\n        else:\n            self.data[layer] = dict(replace=dict(style=style, alpha=alpha))\n\n    def add_movement(self, layer, direction, alpha=None, need_normalization=False, move_indices=None):\n        layer = int(layer)\n        direction = direction.detach().squeeze().requires_grad_(False).to(self.device)\n        if need_normalization:\n            direction = direction / direction.norm(2)\n\n        if alpha is None:\n            alpha = self._init_alpha(layer, alpha_indices=move_indices)\n\n        if layer in self.data:\n            self.data[layer][\"move\"] = dict(direction=direction, alpha=alpha)\n        else:\n            self.data[layer] = dict(move=dict(direction=direction, alpha=alpha))\n\n    def save(self, save_path):\n        save_path = Path(save_path)\n        if not save_path.parent.exists():\n            save_path.parent.mkdir()\n        torch.save(self.data, save_path)\n\n    @staticmethod\n    def load(mdfc, device=torch.device(\"cpu\")):\n        if isinstance(mdfc, Manipulator):\n            return mdfc.to(device)\n        if isinstance(mdfc, (str, Path)):\n            data = torch.load(mdfc)\n            m = Manipulator(data)\n            m.to(device)\n            return m\n        raise ValueError(f\"invalid mdfc, expect path to mdfc or Manipulator, but got {mdfc}\")\n\n    def to(self, device=torch.device(\"cpu\")):\n        for layer in self.data:\n            for t in self.data[layer]:\n                for k in self.data[layer][t]:\n                    if not torch.is_tensor(self.data[layer][t][k]):\n                        self.data[layer][t][k] = torch.tensor(self.data[layer][t][k])\n                    self.data[layer][t][k] = self.data[layer][t][k].to(device, torch.float)\n        self.device = device\n        return self\n\n    def apply(self, styles, move_factor=None, replace_factor=None, discard_alpha=False):\n        \"\"\"\n        apply modification to `styles`.\n        :param styles:\n        :param move_factor:\n        :param replace_factor:\n        :param discard_alpha:\n        :return:\n        \"\"\"\n        if len(self.data) == 0:\n            return styles\n\n        for layer in self.data:\n            batch_size = styles[layer].size(0)\n\n            style = styles[layer]\n            if style.dim() == 2:\n                style = style.unsqueeze(dim=1)\n\n            if \"move\" in self.data[layer]:\n                direction = self.data[layer][\"move\"][\"direction\"].to(style)\n                alpha = self.data[layer][\"move\"][\"alpha\"].to(style)\n                if move_factor is None:\n                    # Randomly sample a move_factor from predefined range to see manipulation result.\n                    typical_range = self.data[layer][\"move\"].get(\"typical_range\", None)\n                    assert typical_range is not None\n                    typical_min, typical_max = typical_range\n                    move_factor = (2 + torch.rand(1)) * (typical_max - typical_min) / 3 + typical_min\n                    logger.info(\n                        f\"random sample a move_factor {move_factor} from [{typical_min}, {typical_max})\"\n                        f\"because have not got a move_factor\"\n                    )\n                if not torch.is_tensor(move_factor):\n                    move_factor = style.new_tensor(move_factor)\n                move_factor = move_factor.expand(style.size(0))\n                if discard_alpha:\n                    alpha = torch.ones_like(alpha)\n                style = style.expand(batch_size, alpha.size(-1), style.size(-1))\n                # 1xOxI\n                direction = direction.view(1, 1, -1).expand(1, alpha.size(-1), -1) * alpha.view(1, -1, 1)\n                style = style + move_factor.view(style.size(0), 1, 1) * direction\n\n            if \"replace\" in self.data[layer]:\n                if replace_factor is None:\n                    replace_factor = self.data[layer][\"replace\"].get(\"typical_replace_factor\", None)\n                    assert replace_factor is not None\n                    logger.info(f\"random sample a replace_factor {replace_factor}\")\n                target_style = self.data[layer][\"replace\"][\"style\"].to(style)\n                alpha = self.data[layer][\"replace\"][\"alpha\"].to(style)\n                style = style.expand(batch_size, alpha.size(-1), style.size(-1))\n                # 1xOxI\n                target_style = target_style.view(1, 1, -1).expand(1, alpha.size(-1), -1)\n                style = style + replace_factor * alpha.view(1, -1, 1) * (target_style - style)\n            styles[layer] = style\n        return styles\n\n    def __repr__(self):\n        repr_str = \"Manipulator(\\n\"\n        for layer in self.data:\n            if \"move\" in self.data[layer]:\n                direction = self.data[layer][\"move\"][\"direction\"]\n                alpha = self.data[layer][\"move\"][\"alpha\"]\n                repr_str += f\"layer_{layer}: direction(norm={direction.norm():.4f}, \" \\\n                            f\"nonzero={len(torch.nonzero(direction))}, alpha(sum={alpha.sum()})\"\n\n                if \"typical_range\" in self.data[layer][\"move\"]:\n                    repr_str += f\"\\t typical_range:{self.data[layer]['move']['typical_range']}\"\n\n            if \"replace\" in self.data[layer]:\n                non_zero_alpha = torch.nonzero(self.data[layer]['replace']['alpha']).flatten(0)\n                repr_str += f\"layer_{layer}: \" \\\n                            f\"replace(number={len(non_zero_alpha)}, example:\" \\\n                            f\"{non_zero_alpha if len(non_zero_alpha) < 16 else str(non_zero_alpha[:16]) + '...'})\"\n                if \"typical_replace_factor\" in self.data[layer][\"replace\"]:\n                    repr_str += f\"\\t typical_replace_factor: \" \\\n                                f\"{self.data[layer]['replace']['typical_replace_factor']}\"\n            repr_str += \"\\n\"\n        repr_str += \")\"\n        return repr_str\n\n\nclass ManipulatorBuilder:\n    \"\"\"\n    each method of this class can be used to create new mdfc.\n    \"\"\"\n\n    def __init__(self, save=True, checkpoint=CHECKPOINT_PATH, save_folder=\"./tmp\", generator=None, mdfc_name=None,\n                 device=torch.device(\"cuda\")):\n        self.save = save\n        self.save_folder = Path(save_folder)\n        self.device = device\n        self._G = generator\n        self.mdfc_name = mdfc_name\n        self.checkpoint = checkpoint\n\n    @property\n    def G(self):\n        if self._G is None:\n            self._G = ImprovedStyleGAN2Generator.load(self.checkpoint, device=self.device)\n            self._G.manipulation_mode()\n            self._G.eval()\n        return self._G\n\n    def _postprocess(self, mdfc: Manipulator, save_name):\n        if self.save:\n            if not self.save_folder.exists():\n                self.save_folder.mkdir()\n            save_path = self.save_folder / f\"{save_name}.mdfc\"\n            mdfc.save(save_path)\n            return str(save_path)\n        else:\n            return mdfc, save_name\n\n    def _styles(self, latent_dict):\n        with torch.no_grad():\n            if \"styles\" in latent_dict:\n                styles = latent_dict[\"styles\"]\n            elif \"w\" in latent_dict:\n                styles = self.G.w_to_styles(latent_dict[\"w\"])\n            elif \"z\" in latent_dict:\n                styles = self.G.w_to_styles(self.G.z_to_w(latent_dict[\"z\"]))\n        return styles\n\n    def single_channel(self, layers, indices):\n        \"\"\"\n        use (layer,index) pairs to specific style channels.\n        \"\"\"\n        statistics = torch.load(STATISTICS_PATH)\n        for layer, index in zip(*raydl.paired_indexes(layers, indices)):\n            mdfc = Manipulator()\n            direction = torch.zeros(CHANNEL_MAP[layer])\n            direction[index] = statistics[\"std_styles\"][layer][0, index]\n            mdfc.add_movement(layer, direction)\n\n            yield self._postprocess(\n                mdfc,\n                f\"single_channel_{layer}_{index}\" if self.mdfc_name is None else f\"{self.mdfc_name}_{layer}_{index}\"\n            )\n\n    def alter(self, mdfc_path, layers, rules=\"all\", is_indexes_rule=False, correction_path=CORRECTION_PATH,\n              alter_type=\"move_direction\", alter_rate=1):\n        \"\"\"\n        only KEEP the channels selected by rules\n        \"\"\"\n        mdfc = Manipulator.load(mdfc_path, device=self.device)\n        mdfc_path = Path(mdfc_path) if isinstance(mdfc_path, (str, Path)) else self.save_folder / \"tmp.mdfc\"\n\n        masks = channel_selector(layers, rules, is_indexes_rule, correction_path)\n\n        m = Manipulator()\n\n        alter_k, alter_v = alter_type.split(\"_\")\n        for layer in mdfc.data:\n            layer = layer if alter_type == \"move_direction\" else editing.util.next_layer(layer)\n            if layer in masks:\n                logger.debug(f\"layer_{layer}: norm({alter_type}): {mdfc.data[layer][alter_k][alter_v].norm(2)}\")\n                mdfc.data[layer][alter_k][alter_v] *= masks[layer].to(self.device).squeeze() * alter_rate\n                logger.debug(f\"  after_alter: norm({alter_type}): {mdfc.data[layer][alter_k][alter_v].norm(2)}\")\n\n            if \"move\" in mdfc.data[layer] and layer in masks:\n                if layer not in m.data:\n                    m.data[layer] = dict(move=mdfc.data[layer][\"move\"])\n                else:\n                    m.data[layer][\"move\"] = mdfc.data[layer][\"move\"]\n            if \"replace\" in mdfc.data[layer]:\n                if layer not in m.data:\n                    m.data[layer] = dict(replace=mdfc.data[layer][\"replace\"])\n                else:\n                    m.data[layer][\"replace\"] = mdfc.data[layer][\"replace\"]\n\n        yield self._postprocess(\n            m,\n            mdfc_path.parent / (f\"alter_{mdfc_path.stem}\" if self.mdfc_name is None else self.mdfc_name)\n        )\n\n    def remove(self, mdfc_path, layers, rules=\"all\", is_indexes_rule=False, correction_path=CORRECTION_PATH,\n               alter_type=\"move_direction\", alter_rate=1):\n        \"\"\"\n        only REMOVE the channels selected by rules\n        \"\"\"\n        mdfc_path = Path(mdfc_path)\n        mdfc = Manipulator.load(mdfc_path, device=self.device)\n\n        masks = channel_selector(layers, rules, is_indexes_rule, correction_path)\n\n        alter_k, alter_v = alter_type.split(\"_\")\n        for layer in mdfc.data:\n            layer = layer if alter_type == \"move_direction\" else editing.util.next_layer(layer)\n            if layer in masks:\n                logger.debug(f\"layer_{layer}: norm({alter_type}): {mdfc.data[layer][alter_k][alter_v].norm(2)}\")\n                mdfc.data[layer][alter_k][alter_v] *= 1 - masks[layer].to(self.device).squeeze() * alter_rate\n                logger.debug(f\"  after_alter: norm({alter_type}): {mdfc.data[layer][alter_k][alter_v].norm(2)}\")\n        yield self._postprocess(\n            mdfc,\n            mdfc_path.parent / (f\"remove_{mdfc_path.stem}\" if self.mdfc_name is None else self.mdfc_name)\n        )\n\n    def replace(self, latent_family, layers, family_ids=None, channel_multiplier=2):\n        \"\"\"\n        replace the selected `layers` with latent code in `latent_family`\n        \"\"\"\n        if self._G is not None:\n            channel_multiplier = self._G.channel_multiplier\n        base_save_name = self.mdfc_name or f\"replace_{Path(latent_family).stem}\"\n        latent_family = load_latent(latent_family, ids=family_ids, load_real=False, latent_type=None,\n                                    device=self.device)\n\n        styles = self._styles(latent_family)\n\n        for i in range(len(styles[0])):\n            m = Manipulator(channel_multiplier=channel_multiplier)\n            for l in raydl.tuple_of_indices(layers):\n                m.add_replacement(l, style=styles[l][i:i + 1])\n            m.to(self.device)\n\n            yield self._postprocess(m, f\"{base_save_name}_{i}\")\n\n    def paired_delta(self, latent1, latent2, ids1=None, ids2=None, layers=None, rules=\"all\",\n                     correction_path=CORRECTION_PATH):\n        \"\"\"\n        use the difference of the mean of two class of latents as modification\n        \"\"\"\n        latent_dict1 = load_latent(latent1, ids=ids1, device=self.device)\n        latent_dict2 = load_latent(latent2, ids=ids2, device=self.device)\n\n        styles1 = self._styles(latent_dict1)\n        styles2 = self._styles(latent_dict2)\n        m = Manipulator()\n        for l, (s1, s2) in enumerate(zip(styles1, styles2)):\n            m.add_movement(l, s1.mean(dim=0, keepdim=True) - s2.mean(dim=0, keepdim=True))\n\n        save_name = f\"paired_delta_{Path(latent1).stem}_{ids1}_{Path(latent2).stem}_{ids2}\"\n        if layers is None:\n            yield self._postprocess(m, save_name)\n        else:\n            old_save = self.save\n            self.save = False\n            m, _ = next(self.alter(m, layers, rules, correction_path=correction_path))\n            self.save = old_save\n            yield self._postprocess(m, f\"alter_{save_name}\")\n\n    def sefa(self, eigvec_ids, eigvec_layers=None, layers=None):\n        eigvec_ids = raydl.tuple_of_indices(eigvec_ids)\n        layers = raydl.tuple_of_indices(layers) if layers is not None else None\n        eigvec_layers = raydl.tuple_of_indices(eigvec_layers) if eigvec_layers is not None \\\n            else sum([[3 * i - 1, 3 * i] for i in range(1, 9)], [0, 1])\n\n        modulate = {\n            k: v\n            for k, v in self.G.state_dict().items()\n            if \"modulation\" in k and \"weight\" in k\n        }\n        weights = torch.cat([w for i, w in enumerate(modulate.values()) if i in eigvec_layers])\n        eigvec = torch.svd(weights).V.to(self.device)  # [512x512]\n        for idx in eigvec_ids:\n            m = Manipulator()\n\n            w_vector = eigvec[:, idx].unsqueeze(0)\n            w_vector /= w_vector.norm(2)\n            delta_styles = self.G.w_to_styles(w_vector)\n            zero_styles = self.G.w_to_styles(torch.zeros_like(w_vector))\n\n            for layer, zero, delta in zip(range(len(zero_styles)), zero_styles, delta_styles):\n                if layers is None or layer in layers:\n                    m.add_movement(layer, delta - zero)\n\n            yield self._postprocess(m, f\"sefa_{idx}\")\n\n    def ganspace(self, principal_direction_ids, layers=None, styles_layers=None):\n        principal_direction_ids = raydl.tuple_of_indices(principal_direction_ids)\n        layers = raydl.tuple_of_indices(layers) if layers is not None else None\n        styles_layers = raydl.tuple_of_indices(styles_layers) if styles_layers is not None else None\n\n        ws = []\n        for batch in raydl.total_chunk(300_000, 4096):\n            z = torch.randn(batch, 512, device=self.device)\n            ws.append(self.G.z_to_w(z))\n        ws = torch.cat(ws)\n\n        u, s, v = torch.pca_lowrank(ws, q=max(principal_direction_ids) + 1)\n\n        for idx in principal_direction_ids:\n            m = Manipulator()\n\n            w_vector = v[:, idx].unsqueeze(0)\n            w_vector /= w_vector.norm(2)\n            delta_styles = self.G.w_to_styles(w_vector)\n            zero_styles = self.G.w_to_styles(torch.zeros_like(w_vector))\n\n            for layer, zero, delta in zip(range(len(zero_styles)), zero_styles, delta_styles):\n                if layers is None or layer in layers:\n                    m.add_movement(layer, delta - zero)\n\n            yield self._postprocess(m, f\"ganspace_w_{idx}\")\n\n\nif __name__ == '__main__':\n    fire.Fire(ManipulatorBuilder)\n","repo_name":"budui/Control-Units-in-StyleGAN2","sub_path":"editing/modification.py","file_name":"modification.py","file_ext":"py","file_size_in_byte":18758,"program_lang":"python","lang":"en","doc_type":"code","stars":31,"dataset":"github-code","pt":"81"}
+{"seq_id":"17205997988","text":"import argparse\nimport configparser\nimport utils\nimport sys\n\nparser = argparse.ArgumentParser(prog = 'PhonStat', \n  description='Segment statistics from a word list in a file text',\n  epilog = 'This program was created by Rolando Muñoz Aramburú (Jun-2020)',\n  formatter_class = argparse.RawTextHelpFormatter\n)\nparser.add_argument('-i', '--input-file', required= True, help='path of a text file with a word list')\nparser.add_argument('-c', '--config', help='path of config file')\nparser.add_argument('-f', '--frequency', action = 'store_true', help='display frequency of segments')\nparser.add_argument('-e', '--check-inventory', action = 'store_true', help='display if the segment belongs to the segment inventory or not')\nparser.add_argument('--version', action='version', version='%(prog)s 1.0')\n\nargs = parser.parse_args()\n\nconfigPath = '../config.cfg' if args.config == None else args.config\n\nconfig = configparser.ConfigParser()\nconfig.read_file(open(configPath))\nlst_charGroups = config.get('SETTINGS','complexCharList').split()\nlst_phonInventory = config.get('SETTINGS','validSegmentList').split()\n\n# Run command\nlst_wordList = utils.get_wordlist_from_raw_text(args.input_file)\n\nutils.get_segments(lst_wordList = lst_wordList,\n  lst_charGroups = lst_charGroups,\n  lst_phonInventory = lst_phonInventory,\n  display_frequencyColumn = args.frequency,\n  display_isInInventoryColumn = args.check_inventory\n  )\n","repo_name":"rolandomunoz/phontext-cli","sub_path":"scripts/cli-phonstat.py","file_name":"cli-phonstat.py","file_ext":"py","file_size_in_byte":1412,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"14704265785","text":"from flask import Blueprint, render_template, request, redirect, url_for\nfrom .models import Link\nfrom .extensions import db\nshort = Blueprint('short', __name__)\n\n@short.route('/<short_url>')\ndef redirect_to_url(short_url):\n    link = Link.query.filter_by(short_url=short_url).first_or_404()\n    link.visits = link.visits + 1\n    db.session.commit()\n    if 'https://' in link.original_url or \"http://\" in link.original_url:\n        return redirect(link.original_url)\n    return redirect(\"https://\" + link.original_url)\n\n@short.route('/')\ndef index():\n    return render_template('index.html')\n\n@short.route('/add_link', methods=['GET', 'POST'])\ndef add_link():\n    if request.method == 'POST':\n        original_url = request.form['original_url']\n        row = db.session.query(Link).filter_by(original_url=original_url).first()\n        if not row:\n            link = Link(original_url=original_url)\n            db.session.add(link)\n            db.session.commit()\n            return render_template('link_added.html', \n                new_link=link.short_url, original_url=link.original_url)\n            #return redirect(url_for('short.add_link'), code='303')\n        else:\n            return render_template('link_added.html', \n                new_link=row.short_url, original_url=row.original_url)\n            #return redirect(url_for('short.add_link'), code='303')\n    else:\n        return render_template('link_added.html', new_link=None)\n    \n\n@short.route('/stats')\ndef stats():\n    pass\n\n@short.errorhandler(404)\ndef page_not_found(e):\n    return '', 404","repo_name":"AlexandrBozhok/url-shortener","sub_path":"url_shortener/routes.py","file_name":"routes.py","file_ext":"py","file_size_in_byte":1560,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"4009258790","text":"import unittest\nimport os\nfrom unittest.mock import MagicMock\nfrom download_youtube_album import AlbumDownload\n\n\nclass AlbumDownloadTest(unittest.TestCase):\n    TEMP_FILE_NAME = \"test/tmp.mp4\"\n    TRACK_LIST_FILE = \"test/input-test.txt\"\n\n    def test_download(self):\n        album_download = AlbumDownload(self.TEMP_FILE_NAME, self.TRACK_LIST_FILE)\n        album_download.delete_temporary_file = MagicMock()\n\n        album_download.run()\n\n        self.assertEqual(album_download.temp_file_name, self.TEMP_FILE_NAME)\n\n        self.assertTrue(len(album_download.songs_info.song_info_list) == 3)\n\n        for index, item in enumerate(album_download.songs_info.song_info_list):\n            number_str = str(index + 1)\n            track_number_two_digits = number_str.zfill(2)\n            file_name = track_number_two_digits + \" - \" + item.title + \".mp3\"\n\n            full_file_name = album_download.songs_info.directory + \"/\" + file_name\n\n            self.assertTrue(os.path.exists(full_file_name))\n\n\nif __name__ == '__main__':\n    unittest.main()\n","repo_name":"lincolnpomper/album-download","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":1044,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"7885855539","text":"# 深さ優先探索（行きがけ）\nimport sys\nfrom collections import deque  # 両端の配列アクセスのときは高速、それ以外はlistのままのほうがいい\n\n\ndef main():\n    N, Q = map(int, input().split())\n    tree = [deque([]) for _ in range(N)]\n    value = [0]*N\n    flag = [0]*N\n\n    for _ in range(N-1):\n        a, b = map(int, input().split())\n        if a == b:\n            continue\n        tree[a-1].append(b-1)\n        tree[b-1].append(a-1)\n    for _ in range(Q):\n        p, x = map(int, input().split())\n        value[p-1] += x\n\n    stack = deque()\n    stack.append(0)\n\n    def dfs(v):\n        now = v\n        nowArray = tree[v]\n        flag[v] = 1\n        l = len(nowArray)\n        for _ in range(l):  # 子ノードに値を追加\n            w = tree[v].popleft()  # 子ノード\n            if v not in tree[w]:\n                continue\n            value[w] += value[now]\n            stack.append(w)\n\n    while stack:\n        top = stack.popleft()\n        if flag[top] != 1:\n            dfs(top)\n\n    print(*value)\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"hayaki821/atcode","sub_path":"中級者/深さ優先探索/26.py","file_name":"26.py","file_ext":"py","file_size_in_byte":1091,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"6596490211","text":"from typing import Union\nfrom mltk.core.tflite_model.tflite_schema import BuiltinOperator\nfrom .layer import Layer, flat_size, KerasLayer, TfliteLayer\n\n\nclass Multiply(Layer):\n    \n    def __init__(self):\n        Layer.__init__(self, ('Multiply', BuiltinOperator.MUL))\n    \n    def process(self, layer:Union[TfliteLayer,KerasLayer]):\n        self.name = layer.name\n        self.macs = 0\n        if isinstance(layer, KerasLayer):\n            output_shape = layer.output_shape\n        else:\n            output_shape = layer.outputs[0].shape\n\n        # This assumes element-wise multiplication with all tensors the same shape\n        self.ops = flat_size(output_shape[1:])\n\nMultiply()\n\n","repo_name":"SiliconLabs/mltk","sub_path":"mltk/core/model/metrics/layers/multiply.py","file_name":"multiply.py","file_ext":"py","file_size_in_byte":683,"program_lang":"python","lang":"en","doc_type":"code","stars":44,"dataset":"github-code","pt":"81"}
+{"seq_id":"12026045300","text":"from inspect import getmro\nfrom warnings import warn\n\nfrom ..legacy import pykit as py\nfrom ..utilities import pyutils2 as py2\nfrom ..models import Model\nfrom ..utilities import utils\n\n\nclass LayerTrainyard(Model):\n    \"\"\"Class to implement arbitrary architectures.\"\"\"\n    def __init__(self, trainyard, input_shape=None, name=None):\n        \"\"\"\n\n        :param trainyard:\n        :param input_shape:\n        \"\"\"\n        # TODO: Write doc\n        # Initialze superclass\n        super(LayerTrainyard, self).__init__(name=name)\n\n        # Meta\n        self._trainyard = None\n\n        # Assign and parse trainyard\n        self.trainyard = trainyard\n\n        # Run shape inference\n        self.input_shape = input_shape\n\n    @property\n    def x(self):\n        # Layer should handle the getting and setting\n        return self._map_signature(lambda coach: coach.x)[0]\n\n    @x.setter\n    def x(self, value):\n        # The input must be set for all input layers (if there are more than one).\n        # since we're using cursors, input_list should be a flat list.\n        input_list = list(py.flatten(value))\n        # The individual input layers may have one or more inputs. The cursor is to keep track.\n        cursor = 0\n\n        # Loop over input layers (width-wise)\n        for coach in py.obj2list(self.trainyard[0]):\n            # Get number of inputs to the coach\n            num_inputs_to_coach = coach.num_inputs\n            # Fetch from list of inputs\n            coach_input = py.delist(input_list[cursor:cursor + num_inputs_to_coach])\n            # Increment cursor\n            cursor += num_inputs_to_coach\n            # Set input\n            coach.x = coach_input\n\n    @property\n    def y(self):\n        # This is simpler than it looks. :-)\n        return py.delist(list(py.flatten(self._map_signature(lambda coach: coach.y)[-1])))\n\n    @property\n    def yt(self):\n        # If this is the first time yt is being called, instantiate placeholders.\n        # This procedure is indeed special in the sense that it's model's job to\n        # define target placeholders (and not layers'). This in turn makes it non-\n        # trivial to keep y's and yt's synced.\n        # To solve this, we maintain an dict mapping between y and yts.\n        y_to_yt_dict = py2.get_from_antipasti_collection(self, 'y_to_yt', default={})\n        # Get y as list\n        _ys = py.obj2list(self.y)\n        # Maintain dict\n        y_to_yt_dict = utils.maintain_y_to_yt_dict(y_to_yt_dict, _ys)\n        # Write to antipasti collection (in case this is done for the first time)\n        py2.add_to_antipasti_collection(self, y_to_yt_dict=y_to_yt_dict)\n        # Return in the same order as self.y\n        return py.delist([y_to_yt_dict[_y] for _y in _ys])\n\n    @yt.setter\n    def yt(self, value):\n        y_to_yt_dict = py2.get_from_antipasti_collection(self, 'y_to_yt', {})\n        _ys = py.obj2list(self.y)\n        # Validate value type\n        if isinstance(value, dict):\n            # A polite warning if value keys are totally off\n            if set(value.keys()) - set(_ys) == set([]):\n                warn(self._stamp_string(\"Trying to set `yt` with a dict, but none of the keys in \"\n                                        \"the given dict are in the list of prediction variables. \"\n                                        \"Consequently, this assignment will have no effect.\"),\n                     RuntimeWarning)\n        elif isinstance(value, (list, tuple)):\n            if len(_ys) != len(value):\n                raise RuntimeError(self._stamp_string(\"Expected {} target variables \"\n                                                      \"(= number of prediction variables), \"\n                                                      \"got {} variables.\".\n                                                      format(len(_ys), len(value))))\n            value = dict(zip(_ys, value))\n        else:\n            # value must be a tensor, i.e. len(_ys) == 1\n            if len(_ys) != 1:\n                raise RuntimeError(self._stamp_string(\"Expected {} target variables, got 1.\".\n                                                      format(len(_ys))))\n            value = {_ys[0]: value}\n        # So now that value is a dict, update the y_to_yt_dict as required\n        for _y in value.keys():\n            # Add to y_to_yt_mapping only if _y is a prediction variable\n            if _y in _ys:\n                y_to_yt_dict.update({_y: value[_y]})\n\n        # Maintain dict\n        utils.maintain_y_to_yt_dict(y_to_yt_dict, _ys)\n        # Write to collection, and done.\n        py2.add_to_antipasti_collection(self, y_to_yt_dict=y_to_yt_dict)\n\n    @property\n    def trainyard(self):\n        return self._trainyard\n\n    @trainyard.setter\n    def trainyard(self, value):\n        # Remove singleton sublists\n        value = py.removesingletonsublists(value)\n        # Parse value. This is the magic Antipasti line.\n        value = [[LayerTrainyard(elem_in_elem) if isinstance(elem_in_elem, list) else elem_in_elem\n                  for elem_in_elem in elem]\n                 if isinstance(elem, list) else elem for elem in value]\n        self._trainyard = value\n\n    @property\n    def parameters(self):\n        # Note that we don't use self._parameters defined in the superclass.\n        return py.unique([parameter\n                          for train in self.trainyard\n                          for coach in py.obj2list(train)\n                          for parameter in (coach.parameters.as_list()\n                                            if hasattr(coach.parameters, 'as_list')\n                                            else coach.parameters)])\n\n    # Model.input_shape.setter must be overriden to handle e.g. multiple inputs.\n    @Model.input_shape.setter\n    def input_shape(self, value):\n        if value is None:\n            # Get input shape from trainyard\n            if isinstance(self.trainyard[0], list):\n                # Case: Multiple inputs to the trainyard\n                _input_shape = [input_layer.input_shape for input_layer in self.trainyard[0]]\n            else:\n                # Case: Single input to the trainyard\n                _input_shape = self.trainyard[0].input_shape\n        else:\n            # input shape is given.\n            _input_shape = value\n\n        # Run shape inference and set _output_shape\n        output_shape = self.infer_output_shape(input_shape=_input_shape)\n\n        # Set input and output shapes\n        self._input_shape = _input_shape\n        self._output_shape = output_shape\n\n    @property\n    def device(self):\n        return self._map_signature(lambda coach: coach.device)\n\n    @device.setter\n    def device(self, value):\n        def _set_device(coach):\n            coach.device = value\n        self._map_signature(_set_device)\n\n    @property\n    def _is_fedforward(self):\n        return all(py.flatten(self._map_signature(lambda coach: coach._is_fedforward)))\n\n    def infer_output_shape(self, input_shape=None):\n        if input_shape is None:\n            input_shape = self.input_shape\n\n        # Note that if the trainyard has multiple inputs, input_shape is a list of lists.\n        intermediate_shape = input_shape\n        # Trains and coaches are legacy terminology from the old theano Antipasti.\n        # Loop over all layers or groups of layers (depth-wise)\n        for train in self.trainyard:\n\n            if isinstance(train, list):\n                # If we're in here, it's because train is a group of width-wise stacked layers\n                train_output_shape = []\n                # Convert intermediate_shape to a list of lists\n                train_input_shape = py.list2listoflists(intermediate_shape)\n                # Cursor for keeping track (see below).\n                cursor = 0\n\n                # Loop over all layers (width-wise)\n                for coach in train:\n                    # This will save a function call\n                    num_inputs_to_coach = coach.num_inputs\n                    # coach can take more than one inputs, and cursor is to keep track of how many inputs have been \\\n                    # taken from train_input_shape.\n                    coach_input_shape = py.delist(train_input_shape[cursor:cursor+num_inputs_to_coach])\n                    cursor += num_inputs_to_coach\n\n                    # Assign input shape to coach\n                    coach.input_shape = coach_input_shape\n                    # Get the resulting output shape and append to the list keeping track of train output shapes\n                    train_output_shape.append(coach.output_shape)\n\n            else:\n                # If we're in here, it's because train is just a Layer (or a LayerTrainyard). Remember,\n                # intermediate_shape can be a list of lists if train takes more than one inputs.\n                train.input_shape = intermediate_shape\n                train_output_shape = train.output_shape\n\n            # Assign as intermediate shape\n            intermediate_shape = py.delist(py.delistlistoflists(py.list2listoflists(train_output_shape)))\n\n        # Done. final_shape = intermediate_shape, but we'll spare us the trouble\n        return intermediate_shape\n\n    # Parameter assignment cannot be handled by the superclass\n    def assign_parameters(self, parameters=None):\n        if parameters is None:\n            return\n\n        # Compute the number of parameters per train in trainyard. The structure of this list is similar to trainyard\n        # itself; if trainyard = [[lt1, lt2], lt3], lenlist = [[3, 0], 4] where 3, 0, and 4 are the number of\n        # parameters in lt1, lt2, and lt3 respectively.\n        num_parameters = self._map_signature(lambda coach: len(coach.parameters))\n\n        # Define cursor over num_parameters\n        cursor_start = cursor_stop = 0\n\n        for train_num, train in enumerate(self.trainyard):\n            # Skip if there are no parameters to assign\n            num_params_in_train = sum(py.obj2list(num_parameters[train_num]))\n            if num_params_in_train == 0:\n                continue\n\n            cursor_stop = cursor_start + num_params_in_train\n\n            if isinstance(train, list):\n                # train is a list of coaches\n                subcursor_start = subcursor_stop = cursor_start\n\n                for coach, num_parameters_in_coach in zip(train, num_parameters):\n                    subcursor_stop = subcursor_start + num_parameters_in_coach\n                    # Fetch and assign coach parameters\n                    parameters_in_coach = parameters[subcursor_start:subcursor_stop]\n                    coach.assign_parameters(parameters=parameters_in_coach)\n                    subcursor_start = subcursor_stop\n\n            else:\n                # train is not a list, i.e. parameters can be applied directly\n                parameters_in_train = parameters[cursor_start:cursor_stop]\n                train.assign_parameters(parameters=parameters_in_train)\n\n            cursor_start = cursor_stop\n\n    # Feedforward, but without the decorator\n    def feedforward(self, input=None):\n        # Check if input is given\n        if input is None:\n            input = self.x\n        else:\n            self.x = input\n\n        # If trainyard empty: nothing to do, return input\n        if not self.trainyard:\n            return input\n\n        # The input must be set for all input layers (if there are more than one)\n        input_list = py.obj2list(input)\n        # The individual input layers may have one or more inputs. The cursor is to keep track.\n        cursor = 0\n\n        # Loop over input layers (width-wise)\n        for coach in py.obj2list(self.trainyard[0]):\n            # Get number of inputs to the coach\n            num_inputs_to_coach = coach.num_inputs\n            # Fetch from list of inputs\n            coach_input = py.delist(input_list[cursor:cursor+num_inputs_to_coach])\n            # Increment cursor\n            cursor += num_inputs_to_coach\n            # Set input\n            coach.x = coach_input\n\n        # Feedforward recursively.\n        intermediate_result = input\n        for train in self.trainyard:\n            if isinstance(train, list):\n                # Convert intermediate_result to a list\n                input_list = py.obj2list(intermediate_result)\n                # Make a cursor to index input_list\n                cursor = 0\n                # List to store outputs from coaches in train\n                coach_outputs = []\n\n                for coach in train:\n                    num_inputs_to_coach = coach.num_inputs\n                    coach_input = py.delist(input_list[cursor:cursor+num_inputs_to_coach])\n                    cursor += num_inputs_to_coach\n                    # Feedforward and store output in list\n                    coach_outputs.append(coach.feedforward(input=coach_input))\n                intermediate_result = coach_outputs\n\n            else:\n                intermediate_result = train.feedforward(input=intermediate_result)\n\n            # Flatten any recursive outputs to a linear list\n            intermediate_result = py.delist(list(py.flatten(intermediate_result)))\n\n        # The final intermediate_result is the final result (no shit sherlock). But note that we don't set self.y,\n        # because self.y is a property that returns the y's of the coaches in self.trainyard[-1].\n        return intermediate_result\n\n    def _map_signature(self, fn):\n        out = [[fn(coach) for coach in train] if isinstance(train, list) else fn(train)\n               for train in self.trainyard]\n        return out\n\n    # Depth-wise mechanics\n    def __add__(self, other):\n        if self.num_outputs != other.num_inputs:\n            raise RuntimeError(self._stamp_string(\"Cannot chain component with {} output(s) with \"\n                                                  \"one with {} inputs.\".format(self.num_outputs, other.num_inputs)))\n        # Other could be a Layer or a LayerTrainyard\n        # The 'getmro' function is used because Layer could not be imported (that would introduce a circular\n        # dependency). As a quick reminder:\n        # [cls.name for cls in getmro(self.__class__)] = ['LayerTrainyard', 'Model', 'object']\n        other_class_hierarchy = [cls.__name__ for cls in getmro(other.__class__)]\n        if 'Layer' in other_class_hierarchy:\n            return LayerTrainyard(self.trainyard + [other])\n        elif isinstance(other, LayerTrainyard):\n            return LayerTrainyard(self.trainyard + other.trainyard)\n        else:\n            raise TypeError(self._stamp_string(\"Second summand of invalid type. Expected Layer or LayerTrainyard, \"\n                                               \"got '{}' instead.\".format(other.__class__.__name__)))\n\n    # Width-wise mechanics\n    def __mul__(self, other):\n        # Get `other`'s class hierarchy\n        other_class_hierarchy = [cls.__name__ for cls in getmro(other.__class__)]\n        # Other could be a Layer or a LayerTrainyard\n        if not ('Layer' in other_class_hierarchy or isinstance(other, LayerTrainyard)):\n            raise TypeError(self._stamp_string(\"Second summand of invalid type. Expected Layer or LayerTrainyard, \"\n                                               \"got '{}' instead.\".format(other.__class__.__name__)))\n\n        return LayerTrainyard([[self, other]])\n\n    # Call with a device\n    def __call__(self, input, with_device=None):\n        \"\"\"\n        Build model graph with a given input and device.\n\n        :type input: any\n        :param input: Input to the model (must be a tensor)\n\n        :type with_device: str or dict\n        :param with_device: The device to build the graph on. Can be a string like 'gpu0' or 'cpu' or '/gpu:0'\n\n        :return: Output tensor obtained by building the model on the given input.\n        \"\"\"\n        # Set device if required\n        if with_device is not None:\n            # Log default devices\n            default_device = self.device\n            # Set device (make sure it's a string first)\n            if not (isinstance(with_device, str) or isinstance(with_device, dict)):\n                raise ValueError(self._stamp_string(\"Expected the given device (provided as \"\n                                                    \"kwarg `with_device` while calling a LayerTrainyard) \"\n                                                    \"to be a string or a dictionary with the \"\n                                                    \"key 'feedforward'. Got {} instead.\".\n                                                    format(with_device.__class__.__name__)))\n            # Build device identifier\n            if not isinstance(with_device, dict):\n                with_device = {'feedforward': with_device}\n            else:\n                assert 'feedforward' in with_device.keys(), \\\n                    self._stamp_string(\"'feedforward' must be in the dictionary key if `with_device` \"\n                                       \"kwarg of LayerTrainyard's __call__ method is a dictionary.\")\n                with_device = {'feedforward': with_device['feedforward']}\n            # Set device\n            self.device = with_device\n\n        # Feedforward\n        output = self.feedforward(input=input)\n\n        # Set device back to default\n        if with_device is not None:\n            self.device = default_device\n\n        return output\n\n    # Syntactic candy\n    def __getitem__(self, item):\n        pass\n","repo_name":"nasimrahaman/antipasti-tf","sub_path":"Antipasti/models/tree.py","file_name":"tree.py","file_ext":"py","file_size_in_byte":17360,"program_lang":"python","lang":"en","doc_type":"code","stars":17,"dataset":"github-code","pt":"81"}
+{"seq_id":"23631563324","text":"#! /usr/bin/env python3\nimport time\nimport random\nimport traceback\nfrom contextlib import ContextDecorator\nfrom typing import Callable, List\nfrom people_also_ask.exceptions import FeaturedSnippetParserError\n\n\ndef raise_featuredsnippetparsererror_if_failed(func):\n    def wrapper(self: \"SimpleFeaturedSnippetParser\", *args, **kwargs):\n        try:\n            return func(self, *args, **kwargs)\n        except Exception:\n            traceback.print_exc()\n            raise FeaturedSnippetParserError(self.text)\n    return wrapper\n\n\ndef retryable(nb_times_retry):\n\n    def decorator(func: Callable):\n\n        def wrapper(*args, **kwargs):\n            for _ in range(nb_times_retry-1):\n                try:\n                    return func(*args, **kwargs)\n                except Exception:\n                    pass\n            return func(*args, **kwargs)\n\n        return wrapper\n    return decorator\n\n\ndef itemize(lines: List[str]) -> List[str]:\n    return [\"\\t- \" + line for line in lines]\n\n\ndef tabulate(header, table):\n    length_columns = []\n    if header:\n        table = [header] + table\n        length_columns = [len(str(e)) for e in header]\n    for row in table:\n        current_lengh = [len(str(e)) for e in row]\n        length_columns = [\n            max(i, j) for i, j in zip(length_columns, current_lengh)\n        ]\n    tabulated_rows = []\n    for row in table:\n        tabulated_rows.append(\"\\t\".join([\n            str(e).rjust(length, \" \") for e, length in zip(row, length_columns)\n        ]))\n    if header:\n        tabulated_rows.insert(\n            1,\n            \"\\t\".join([\"-\"*length for length in length_columns])\n        )\n    return \"\\n\".join(tabulated_rows)\n\n\ndef remove_redundant(elements): return list(dict.fromkeys(elements))\n\n\nclass CallingSemaphore(ContextDecorator):\n\n    def __init__(self, nb_call_times_limit, expired_time):\n        self.nb_call_times_limit = nb_call_times_limit\n        self.expired_time = expired_time\n        self.called_timestamps = list()\n\n    def __enter__(self):\n        while len(self.called_timestamps) > self.nb_call_times_limit:\n            now = time.time()\n            self.called_timestamps = list(filter(\n                lambda x: now - x < self.expired_time,\n                self.called_timestamps\n            ))\n            time.sleep(random.random() * 2)\n        self.called_timestamps.append(time.time())\n\n    def __exit__(self, *exc):\n        pass\n","repo_name":"lagranges/people_also_ask","sub_path":"people_also_ask/tools.py","file_name":"tools.py","file_ext":"py","file_size_in_byte":2414,"program_lang":"python","lang":"en","doc_type":"code","stars":88,"dataset":"github-code","pt":"81"}
+{"seq_id":"3059444086","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed May  5 14:44:49 2021\n\n@author: aliciachen\n\"\"\"\n\nimport numpy as np\nimport pandas as pd\nfrom rectangle_model import *\nfrom make_df import *\nimport seaborn as sns\n\nimport warnings\nwarnings.filterwarnings('ignore')\n\nfilename = 'teaching_stimuli - all_examples (7).csv'\nall_problems = make_df_from_spreadsheet(filename)\n\n#%%\n\ndf_500 = find_teacher_probs(500, 63, all_problems)\ndf_0 = find_teacher_probs(0, 63, all_problems)\n\n#%%\n\nplot_problem(find_problem(65, all_problems)[0])\n#%%\n\ndef posterior_probs(df):\n    frames = [df[1]['h'].rename(index=lambda a: (a,)),\n              df[2]['h'],\n              df[3]['h'],\n              df[4]['h'],\n              df[5]['h'],\n              df[6]['h']]\n\n    # Create a new df where rows are *all* possible examples, and column is p(h_1 | d)\n\n    new_df = pd.concat(frames)\n    new_df.drop(columns=['h_2', 'h_3', 'h_4'], inplace=True)\n    new_df = new_df[new_df.h_1 != 0] # only positive examples\n    return new_df\n\n# separate dfs for pragmatic and literal learnerr\ndf_prag = posterior_probs(df_500)\ndf_lit = posterior_probs(df_0)\n#%%\n\ndef next_steps(indices, all_indices):\n    \"\"\"Given a size n tuple of indices, find all tuples of size n+1 that contain that set of indices\n    all_indices is generally df.index\n    \"\"\"\n    nextsteps = []\n    current_step = set(indices)\n    for idx in all_indices:\n        if current_step.issubset(set(idx)) and len(idx) == len(indices)+1:\n            nextsteps.append(idx)\n\n    return nextsteps\n\n# Sanity check\nprint(next_steps((7,), df.index))\nprint(next_steps((7, 14), df.index))\nprint(next_steps((7, 14, 32), df.index))\n\n# yay it works!!\n\n#%% Cost and reward functions\n\n# a is posterior probability h|d\n\ndef softmax(a):\n    return np.exp(a) / np.sum(np.exp(a))\n\ndef kl(a, b):\n    return np.log(a) / np.log(b)\n\n\ndef R(a):\n    \"\"\"same reward function for each\"\"\"\n    return np.log(a)\n\n\ndef C1(idx, df, beta1):\n    \"\"\"imposes an extremely high cost when examples are redundant, basically forcing you to stop teaching\n    maybe scale this later....\n    \"\"\"\n\n    divs = []\n\n    if len(idx) == 1:  # Case of only 1 example: zero cost\n        return 0\n\n    def find_subsets(idx, df):\n        subsets = []\n        for i in df.index:\n            if set(i).issubset(set(idx)):\n                subsets.append(i)\n\n        #subsets.append(idx) # add original set of indices to subset\n        return subsets\n\n    subsets = find_subsets(idx, df)\n    #return subsets\n\n    for i in range(len(subsets)):\n        for j in range(i+1, len(subsets)):\n            if set(subsets[i]).issubset(set(subsets[j])) and len(subsets[j])-len(subsets[i]) == 1:\n                #print(subsets[i])\n                #print(subsets[j])\n                a, b = float(df.loc[[subsets[j]], 'h_1']), float(df.loc[[subsets[i]], 'h_1'])\n                #print(a)\n                #print(b)\n                div = kl(a, b)\n                divs.append(div)\n\n    return beta1*min(divs)**(-1) if np.abs(beta1*min(divs)**(-1)) < 50 else 50\n\ndef C2(df, beta2):\n    \"\"\"linear cost\"\"\"\n    return beta2*df.index.str.len()\n\n#%% make dfs with utility\n\n# Literal learner\n\ndf_lit['R'] = R(df_lit['h_1'])\n\n# for idx in df_lit.index:\n#     df_lit.loc[[idx], 'C1'] = C1(idx, df_lit, beta1=1)\n\ndf_lit['C2'] = C2(df_lit, beta2=1)\n\n# df_lit['U1'] = df_lit['R'] - df_lit['C1']\ndf_lit['U2'] = df_lit['R'] - df_lit['C2']\n\n# Pragmatic learner\n\ndf_prag['R'] = R(df_prag['h_1'])\n\n# for idx in df_prag.index:\n#     df_prag.loc[[idx], 'C1'] = C1(idx, df_prag, beta1=1)\n\ndf_prag['C2'] = C2(df_prag, beta2=1)\n\n# df_prag['U1'] = df_prag['R'] - df_prag['C1']\ndf_prag['U2'] = df_prag['R'] - df_prag['C2']\n\n#%% simulate!\n\n# after selecting an example, make a sub dataframe with all the next possibilities. this will include utilities, then softmax\n# want to store all before choices in a mtx\n\nrng = np.random.default_rng()\n\ndef make_choices(df, alpha):\n    alpha = alpha\n\n    choices = []\n    all_indices = df.index\n\n    # Add a staying option where cost is 0 and reward retains thte reward of the prev option\n\n    start = df[df.index.str.len() == 1]\n    start['prob'] = softmax(alpha * start['U2'])\n\n    choice1 = rng.choice(start.index.tolist(), 1, p = start['prob'])\n    choice1 = tuple(choice1.flatten()) # probably not ideal... annoying cause rng outputs a numpy array\n    choices.append(choice1)\n\n    for i in range(6):\n\n        choices2 = next_steps(choice1, all_indices)\n        sub_df_1 = df.filter(items = choices2, axis=0)\n        sub_df_1.loc['term', 'U2'] = df.loc[[choice1], 'R'].values # add staying option\n\n        sub_df_1['prob'] = softmax(alpha * sub_df_1['U2'])\n\n        choice2 = rng.choice(sub_df_1.index.tolist(), 1, p = sub_df_1['prob'])\n        choice2 = choice2[0]\n\n        if choice2 == 'term':\n            break\n\n        #choice2 = tuple(choice2.flatten())\n        choices.append(choice2)\n        choice1 = choice2\n\n    return choices\n\n#%%\n\n\nprint(make_choices(df_lit, alpha=1))\nprint(make_choices(df_prag, alpha=1))\n\n#%% Plots\n\nsns.set()\n\n# Avg length of utterance until termination, for lit and prag learners\n\nB = 1000\n\n\nlit_lengths = []\nprag_lengths = []\n\nfor i in range(B):\n\n    lc = make_choices(df_lit, alpha=.8)\n    pc = make_choices(df_prag, alpha=.8)\n\n    lit_lengths.append(len(lc))\n    prag_lengths.append(len(pc))\n\n\n\ndf_lengths = pd.DataFrame(data = {'Literal': lit_lengths, 'Pragmatic': prag_lengths}).melt(var_name=\"Type\", value_name=\"Length\")\ndf_lengths = df_lengths[['Length', 'Type']]\n\nplt.figure()\nsns.histplot(data=df_lengths, x='Length', hue=\"Type\", binwidth=1, discrete=True, stat=\"probability\", shrink=0.8, multiple=\"dodge\")\nplt.title(\"Utterance lengths\")\n\n# plt.figure()\n# plt.hist(lit_lengths)\n# plt.title('Literal learner, avg utterance length')\n\n# plt.figure()\n# plt.hist(prag_lengths)\n# plt.title('Pragmatic learner, avg utterance length')\n\n# Termination probabilities for literal and pragmatic learner at each step\n\n# Can we recover mean and variance parameters, as well as ground truth probabilities?\n\nll = np.array([lit_lengths])\npl = np.array([prag_lengths])\n\nstop_probs_lit = []\nstop_probs_prag = []\n\nfor i in range(1, 6):\n    split = np.sum(ll == i) / np.sum(ll >= i)\n    spprag = np.sum(pl == i) / np.sum(pl >= i)\n    stop_probs_lit.append(split)\n    stop_probs_prag.append(spprag)\n\nplt.figure()\nplt.plot(range(1, 6), stop_probs_lit, label='Literal')\nplt.xticks(range(1, 6))\nplt.plot(range(1, 6), stop_probs_prag, label='Pragmatic')\nplt.title(\"Stopping probabilities after # examples given\")\nplt.xlabel(\"Number of examples\")\nplt.ylabel(\"Probability\")\nplt.ylim((0, 1.1))\nplt.legend()\n\n#%% Plots of utterance probs (w/o simulating, just summing up)\n\ndef plot_utterance_probs(df, alpha, beta=1):\n\n    df['prob'] = softmax(alpha*df['U2'])\n\n    sums = [\n            df.loc[[len(i) == 1 for i in df.index]]['prob'].sum(),\n            df.loc[[len(i) == 2 for i in df.index]]['prob'].sum(),\n            df.loc[[len(i) == 3 for i in df.index]]['prob'].sum(),\n            df.loc[[len(i) == 4 for i in df.index]]['prob'].sum(),\n            df.loc[[len(i) == 5 for i in df.index]]['prob'].sum(),\n            df.loc[[len(i) == 6 for i in df.index]]['prob'].sum()\n            ]\n\n\n    plt.figure()\n    plt.scatter([1, 2, 3, 4, 5, 6], sums)\n    plt.xticks([1, 2, 3, 4, 5, 6])\n    plt.xlabel('Number of examples given')\n    plt.ylabel('Probability')\n    plt.ylim([0, 1])\n    plt.title(rf'Problem 63, probability of utterance lengths, $\\alpha={alpha}$, $\\beta={beta}$')\n\n#%%\n\nplot_utterance_probs(df_lit, alpha=1)\nplot_utterance_probs(df_prag, alpha=1)\n\n#%%\n\n\nB = 500\nalphas = [0.3, 0.6, 0.9, 1.2, 1.5, 1.8, 2.1]\n\nlitmeans = {}\npragmeans = {}\nlitvar = {}\npragvar = {}\n\nfor alpha in alphas:\n    lit_lengths = []\n    prag_lengths = []\n\n    for i in range(B):\n\n        lc = make_choices(df_lit, alpha=alpha)\n        pc = make_choices(df_prag, alpha=alpha)\n\n        lit_lengths.append(len(lc))\n        prag_lengths.append(len(pc))\n\n    litmeans[alpha] = np.mean(lit_lengths)\n    pragmeans[alpha] = np.mean(prag_lengths)\n\n    litvar[alpha] = np.var(lit_lengths)\n    pragvar[alpha] = np.var(prag_lengths)\n\n#%%\n\nplt.figure()\nplt.plot(*zip(*litmeans.items()), label=\"Literal\")\nplt.plot(*zip(*pragmeans.items()), label=\"Pragmatic\")\nplt.title(r\"Effect of $\\alpha$ on avg utterance length\")\nplt.xlabel(r\"$\\alpha$ (inverse temperature)\")\nplt.ylabel(\"Number of examples\")\nplt.legend()\n\nplt.figure()\nplt.plot(*zip(*litvar.items()), label=\"Literal\")\nplt.plot(*zip(*pragvar.items()), label=\"Pragmatic\")\nplt.title(r\"Effect of $\\alpha$ on variance of avg utterance length\")\nplt.xlabel(r\"$\\alpha$ (inverse temperature)\")\nplt.ylabel(\"variance\")\nplt.legend()","repo_name":"nataliavelez/teaching_models","sub_path":"RSA/example_selection.py","file_name":"example_selection.py","file_ext":"py","file_size_in_byte":8640,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"81"}
+{"seq_id":"38477697705","text":"from typing import List\nimport logging\nimport subprocess\nimport argparse\n\n\ndef run_bash_command(command: str) -> None:\n    subprocess.run([\"bash\", \"-c\", command])\n\n\ndef remove_prefix(\n    embedding_path: str,\n    output_path: str,\n    prefix_separator: str = \"/\",\n    allowed_prefixes: List[str] = None,\n    block_size: int = 665536,\n):\n\n    dimensions: int = 0\n    printed_words: int = 0\n    with open(embedding_path, \"r\", encoding=\"utf8\") as input_file:\n        with open(output_path, \"w+\", encoding=\"utf8\") as output_file:\n            line_no: int = 0\n            try:\n                header: str = input_file.readline()\n                num_words, dimensions = header.split(\" \")\n                num_words, dimensions = int(num_words), int(dimensions)\n            except ValueError as err:\n                raise ValueError(\n                    f\"Error reading header. \"\n                    f\"Expecting embedding in the word2vec format. \"\n                    f\"Header expected: num_words dims. Header found: {header}.\\n\"\n                    f\"Error: {err}\"\n                )\n\n            lines: List[str] = input_file.readlines(block_size)\n            formatted_lines: List[str] = []\n            while lines:\n                for line in lines:\n\n                    line_no += 1\n\n                    l: List[str] = line.rstrip().split(\" \")\n                    word, vector = (\n                        l[0].strip(),\n                        l[1:],\n                    )\n\n                    if len(vector) != dimensions:\n                        logging.warning(\n                            f\"Dimensions mismatch ({len(l)} in line {line_no}.. Skipping line. {l[:3]}...\"\n                        )\n                        continue\n\n                    if word == \"\":\n                        logging.warning(\n                            f\"Error in line {line_no}. Skipping line. {l[:3]}...\"\n                        )\n                        continue\n\n                    prefix, word = word.split(prefix_separator, 1)\n\n                    if allowed_prefixes is None or prefix in allowed_prefixes:\n                        formatted_lines.append(f\"{word} {' '.join(vector)}\")\n\n                printed_words += len(formatted_lines)\n                if formatted_lines:\n                    print(\"\\n\".join(formatted_lines), file=output_file)\n\n                formatted_lines = []\n\n                lines = input_file.readlines(block_size)\n\n    command: str = f\"sed -i '1s/^/{printed_words} {dimensions}\\\\n/' {output_path}\"\n    print(command)\n    run_bash_command(command)\n\n\nif __name__ == \"__main__\":\n    parser = argparse.ArgumentParser()\n\n    parser.add_argument(\n        \"--embedding_path\",\n        type=str,\n        required=True,\n        help=\"Embedding to format (word2vec format)\",\n    )\n\n    parser.add_argument(\n        \"--output_path\",\n        type=str,\n        required=True,\n        help=\"Path where the formatted embedding will be written\",\n    )\n\n    parser.add_argument(\n        \"--prefix_separator\",\n        type=str,\n        default=\"/\",\n        help=\"Path where the formatted embedding will be written\",\n    )\n\n    parser.add_argument(\n        \"--allowed_prefixes\",\n        nargs=\"+\",\n        type=str,\n        help=\"Allowed prefixes \"\n        \"(words with other prefixes will be discarded, if allowed_prefixes=None we will allow all prefixes)\",\n    )\n\n    args = parser.parse_args()\n\n    remove_prefix(\n        embedding_path=args.embedding_path,\n        output_path=args.output_path,\n        prefix_separator=args.prefix_separator,\n        allowed_prefixes=args.allowed_prefixes,\n    )\n","repo_name":"ikergarcia1996/MetaVec","sub_path":"embeddings/remove_prefix.py","file_name":"remove_prefix.py","file_ext":"py","file_size_in_byte":3595,"program_lang":"python","lang":"en","doc_type":"code","stars":78,"dataset":"github-code","pt":"81"}
+{"seq_id":"25219967978","text":"import requests\nimport json\nfrom secrets import userID\nfrom secrets import token\n\nclass PlaylistOptimizer:\n\n    def __init__(self):\n        self.userID = userID\n        self.token = token\n\n    #creates a new playlist for the most popular songs from the user's given playlist\n    def create_new_playlist(self):\n        name = input(\"Please enter the name for the new optimized playlist: \")\n        url = \"https://api.spotify.com/v1/users/{}/playlists\".format(self.userID)\n        headers = {\n            \"Authorization\": \"Bearer {}\".format(self.token), \n            \"Content-Type\": \"application/json\"\n        }\n        body = json.dumps({\n            \"name\": name, \n            \"public\": False\n        })\n        new_playlist = requests.post(url = url, headers = headers, data = body)\n        return new_playlist.json()[\"id\"]\n    \n    #prompts user for a link to a playlist, returns playlist ID\n    def get_playlist_ID(self):\n        playlistURL = input(\"Please enter the playlist's link: \")\n        playlistID = playlistURL.split(\"/\")[-1]\n        return playlistID\n\n    #get the playlist's items, an array of track objects\n    def get_playlist_songs(self, limit):\n        old_playlistID = self.get_playlist_ID()\n        url = \"https://api.spotify.com/v1/playlists/{}/tracks\".format(old_playlistID)\n        headers = {\n            \"Authorization\": \"Bearer {}\".format(self.token),\n            \"Content-Type\": \"application/json\"\n        }\n        params = {\n            \"limit\": str(limit)\n        } \n        playlist_response = requests.get(url = url, headers = headers, params=params)\n        #print(json.dumps(playlist_response.json(), indent=4))\n        return playlist_response.json()[\"tracks\"][\"items\"]\n\n    #adds a track to list of songs to add if track's popularity is above threshold\n    def check_songs_popularity(self, limit, threshold):\n        songs_data = self.get_playlist_songs(limit)\n        songs_to_add = []\n        for song in songs_data:\n            if int(song[\"track\"][\"popularity\"]) > threshold:\n                songs_to_add.append(song[\"track\"][\"uri\"])\n        return songs_to_add\n        \n    #adds the list of songs to the playlist\n    def add_songs(self, songs_to_add, new_playlistID):\n        url = \"https://api.spotify.com/v1/playlists/{}/tracks\".format(new_playlistID)\n        headers = {\n            \"Authorization\": \"Bearer {}\".format(self.token)\n        }\n        body = json.dumps({\n            \"uris\": songs_to_add\n        })\n        requests.post(url = url, headers = headers, data=body)\n\n    def main(self):\n        print(\"Welcome to Playlist Optimizer!\")\n        print(\"Enter (1) to create a new playlist or (2) to add songs to an existing playlist\")\n        choice = int(input(\"Choice: \"))\n        if choice == 1:\n            new_playlistID = self.create_new_playlist()\n        else:\n            print(\"For the playlist you would like to add songs to,\", end = \" \")\n            new_playlistID = self.get_playlist_ID()\n        print(\"Optimizing playlist....\")\n        songs_to_add = self.check_songs_popularity(limit = 100, threshold = 77)\n        self.add_songs(songs_to_add, new_playlistID)\n        print(\"Finished\")\n\nplaylist = PlaylistOptimizer()\nplaylist.main()","repo_name":"golsonallen/Music-API-Toolkit","sub_path":"playlists.py","file_name":"playlists.py","file_ext":"py","file_size_in_byte":3201,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"8174072814","text":"import subprocess as sp\n\nimport numpy as np\n\nfrom .abstract import VideoReaderAbstract, VideoWriterAbstract\nfrom .ffprobe import ffprobe\nfrom .. import _FFMPEG_APPLICATION\nfrom .. import _FFMPEG_PATH\nfrom .. import _FFMPEG_SUPPORTED_DECODERS\nfrom .. import _FFMPEG_SUPPORTED_ENCODERS\nfrom .. import _HAS_FFMPEG\nfrom ..utils import *\n\n\n# uses FFmpeg to read the given file with parameters\nclass FFmpegReader(VideoReaderAbstract):\n    \"\"\"Reads frames using FFmpeg\n\n    Using FFmpeg as a backend, this class\n    provides sane initializations meant to\n    handle the default case well.\n\n    \"\"\"\n\n    INFO_AVERAGE_FRAMERATE = \"@r_frame_rate\"\n    INFO_WIDTH = \"@width\"\n    INFO_HEIGHT = \"@height\"\n    INFO_PIX_FMT = \"@pix_fmt\"\n    INFO_DURATION = \"@duration\"\n    INFO_NB_FRAMES = \"@nb_frames\"\n    OUTPUT_METHOD = \"image2pipe\"\n\n    def __init__(self, *args, **kwargs):\n        assert _HAS_FFMPEG, \"Cannot find installation of real FFmpeg (which comes with ffprobe).\"\n        super(FFmpegReader,self).__init__(*args, **kwargs)\n\n    def _createProcess(self, inputdict, outputdict, verbosity):\n        if '-vcodec' not in outputdict:\n            outputdict['-vcodec'] = \"rawvideo\"\n\n        iargs = self._dict2Args(inputdict)\n        oargs = self._dict2Args(outputdict)\n\n        if verbosity > 0:\n            cmd = [_FFMPEG_PATH + \"/\" + _FFMPEG_APPLICATION] + iargs + ['-i', self._filename] + oargs + ['-']\n            print(cmd)\n            self._proc = sp.Popen(cmd, stdin=sp.PIPE,\n                                  stdout=sp.PIPE, stderr=None)\n        else:\n            cmd = [_FFMPEG_PATH + \"/\" + _FFMPEG_APPLICATION, \"-nostats\", \"-loglevel\", \"0\"] + iargs + ['-i',\n                                                                                                      self._filename] + oargs + [\n                      '-']\n        self._proc = sp.Popen(cmd, stdin=sp.PIPE,\n                              stdout=sp.PIPE, stderr=sp.PIPE)\n        self._cmd = \" \".join(cmd)\n\n    def _probCountFrames(self):\n        # open process, grabbing number of frames using ffprobe\n        probecmd = [_FFMPEG_PATH + \"/ffprobe\"] + [\"-v\", \"error\", \"-count_frames\", \"-select_streams\", \"v:0\",\n                                                  \"-show_entries\", \"stream=nb_read_frames\", \"-of\",\n                                                  \"default=nokey=1:noprint_wrappers=1\", self._filename]\n        return int(check_output(probecmd).decode().split('\\n')[0])\n\n    def _probe(self):\n        return ffprobe(self._filename)\n\n    def _getSupportedDecoders(self):\n        return _FFMPEG_SUPPORTED_DECODERS\n\nclass FFmpegWriter(VideoWriterAbstract):\n    \"\"\"Writes frames using FFmpeg\n\n    Using FFmpeg as a backend, this class\n    provides sane initializations for the default case.\n    \"\"\"\n\n    def __init__(self, *args, **kwargs):\n        assert _HAS_FFMPEG, \"Cannot find installation of real FFmpeg (which comes with ffprobe).\"\n        super(FFmpegWriter,self).__init__(*args, **kwargs)\n\n    def _getSupportedEncoders(self):\n        return _FFMPEG_SUPPORTED_ENCODERS\n\n    def _createProcess(self, inputdict, outputdict, verbosity):\n        iargs = self._dict2Args(inputdict)\n        oargs = self._dict2Args(outputdict)\n\n        cmd = [_FFMPEG_PATH + \"/\" + _FFMPEG_APPLICATION, \"-y\"] + iargs + [\"-i\", \"-\"] + oargs + [self._filename]\n\n        self._cmd = \" \".join(cmd)\n\n        # Launch process\n        if self.verbosity > 0:\n            print(self._cmd)\n            self._proc = sp.Popen(cmd, stdin=sp.PIPE,\n                                  stdout=sp.PIPE, stderr=None)\n        else:\n            self._proc = sp.Popen(cmd, stdin=sp.PIPE,\n                                  stdout=self.DEVNULL, stderr=sp.STDOUT)\n\n","repo_name":"scikit-video/scikit-video","sub_path":"skvideo/io/ffmpeg.py","file_name":"ffmpeg.py","file_ext":"py","file_size_in_byte":3685,"program_lang":"python","lang":"en","doc_type":"code","stars":638,"dataset":"github-code","pt":"81"}
+{"seq_id":"25550391607","text":"from tkinter import Scale, Tk, Frame, Label, Button ,IntVar,PhotoImage,Canvas,Toplevel\nfrom tkinter.ttk import Notebook, Entry\nimport PIL \nfrom PIL import ImageTk, Image,ImageSequence\nfrom main import Graph\n\nimport matplotlib.pyplot as plt\n\n\n\n'''\n_BACKGROUNDCOLOR = \"#c3fdff\"\n_BACKGROUNDCOLOR_VARIANT = '#5adbee'\n'''\n_BACKGROUNDCOLOR = \"#E5DEF1\"\n_BACKGROUNDCOLOR_VARIANT = \"#898590\"\n\nwindow = Tk()\nwindow.title(\"Graph articulation points\")\nwindow.geometry(\"1280x900\")\nwindow[\"bg\"] = _BACKGROUNDCOLOR\nwindow.resizable(False, False)\n\ntitle = Label(window ,text=\"Graph articulation points\")\ntitle.config(font=('Arial' , 27))\ntitle[\"bg\"] = _BACKGROUNDCOLOR\ntitle.pack(padx = 20, pady = 20)\n\n\nright_side_container = Frame(window)\n\nclass App:\n    def __init__(self, parent):\n        self.parent = parent\n        self.canvas = Canvas(parent, width=669, height=639 ,bg=_BACKGROUNDCOLOR)\n        self.canvas.pack(side='top',padx = 20, pady = 20)\n        self.sequence = [ImageTk.PhotoImage(img)\n                            for img in ImageSequence.Iterator(\n                                    Image.open(\n                                    r'dfsanimation.gif'))]\n        self.image = self.canvas.create_image(350,350, image=self.sequence[0])\n        self.animate(1)\n    def animate(self, counter):\n        self.canvas.itemconfig(self.image, image=self.sequence[counter])\n        self.parent.after(20, lambda: self.animate((counter+1) % len(self.sequence)))\n\n\ngif = App(right_side_container)\n\ndfs_title = Label(right_side_container ,text=\"DFS Animation\")\ndfs_title.config(font=('Arial' , 25))\ndfs_title[\"bg\"] = _BACKGROUNDCOLOR\ndfs_title.pack(side='bottom', padx = 20, pady = 27)\n\nright_side_container['bg'] = _BACKGROUNDCOLOR\nright_side_container.pack(side='right')\n\n\n\n\nleft_side_container = Frame(window)\n\n\nnodes = Frame(left_side_container)\n\nnombre_nodes_label = Label(nodes, text='Nombre de noeuds : ').pack(side=\"left\")\nnombre_nodes_entry = Entry(nodes)\nnombre_nodes_entry.pack(side=\"right\",padx=5 , pady = 5)\n\nnodes.pack()\n\narc = Frame(left_side_container)\n\nnombre_arc_label = Label(arc, text=\"Nombre d'arc : \").pack(side=\"left\")\nnombre_arc_entry = Entry(arc)\nnombre_arc_entry.pack(side=\"right\",padx=5 , pady = 5)\narc.pack()\n\n\n\n\n\nleft_side_container.pack(side='left')\n\ndef get_values():\n    wt = list()\n    val = list()\n    n = int(nombre_arc_entry.get())\n    for i in range(1,n+1):\n        wt.append(int(tab1.grid_slaves(i, 1)[0].get()))\n        val.append(int(tab1.grid_slaves(i, 2)[0].get()))\n    print(wt,val)    \n\n    initial_graph = Graph(int(nombre_nodes_entry.get()))\n\n\n\n    for i in range(n):\n        initial_graph.addEdge(wt[i],val[i])\n    \n    \n    initial_graph.DrawGraph()\n\n    initial_graph.PrintGraph()\n    \n   \n    \n    initial_graph.Color()\n    plt.show()\n\n\n    newWindow = Toplevel(window) \n    newWindow.title(\"point d'articulation\") \n    newWindow.geometry(\"200x200\") \n    \n    points = \"les points d'articulation son : \" + str(initial_graph.AP())\n    point = Label(newWindow, text=points).pack()\n    \n    \"\"\"\n    initial_graph.V += 1\n    for i in initial_graph.AP():\n        print(i)\n    \n    for i in initial_graph.AP():\n        initial_graph.Remove_articualtion_point(i)\n    \n    initial_graph.DrawGraph()\n    \"\"\"\n    \n\n \n    \n\n\n    \n    \n    \n    \n\n\ndef reset_table():\n    for widget in frame2.winfo_children():\n       widget.destroy()\n    frame2.pack_forget()\n    \ndef print_table():\n    global tab1\n    n = int(nombre_arc_entry.get())\n    global frame2\n    frame2 = Frame(left_side_container)\n    frame2['bg'] = _BACKGROUNDCOLOR\n    frame2.pack(fill=\"both\")\n    tablayout = Notebook(frame2)\n    tab1 = Frame(tablayout)\n    tab1['bg'] = _BACKGROUNDCOLOR\n    tab1.pack(fill=\"both\")\n\n    for row in range(n+1):\n        for column in range(3):\n            if row == 0 :\n                if column == 1:\n                    label = Label(tab1, text=\"Nœud A \")\n                    label.config(font=('Arial', 14))\n                    label['bg']=_BACKGROUNDCOLOR_VARIANT\n                    label.grid(row=row, column=column, sticky=\"nsew\", padx=1, pady=1)\n                    tab1.grid_columnconfigure(column, weight=1)\n                if column == 2:\n                    label = Label(tab1, text=\"Nœud B \")\n                    label.config(font=('Arial', 14))\n                    label['bg']=_BACKGROUNDCOLOR_VARIANT\n                    label.grid(row=row, column=column, sticky=\"nsew\", padx=1, pady=1)\n                    tab1.grid_columnconfigure(column, weight=1)\n            \n            else:\n                if column == 0:\n                    label = Label(tab1, text=\"Transition : \" + str(row))\n                    label.config(font=('Arial', 14))\n                    label['bg']=_BACKGROUNDCOLOR_VARIANT\n                    label.grid(row=row, column=column, sticky=\"nsew\", padx=1, pady=1)\n                    tab1.grid_columnconfigure(column, weight=1)\n                else:\n                    label = Entry(tab1, text=\"Row : \" + str(row) + \" , Column : \" + str(column))\n                    label.grid(row=row, column=column, sticky=\"nsew\", padx=1, pady=1)\n                    tab1.grid_columnconfigure(column, weight=1)\n\n\n    tablayout.pack(fill=\"both\")\n    calc = Button(frame2, text=\"Générer le Graph\", command =get_values)\n    calc.pack(padx = 20, pady = 20)\n\n    reset = Button(frame2, text = \"Reset\" , command = reset_table)\n    reset.pack(padx = 5, pady = 5)\n    return tab1\n\n\nbutton = Button(left_side_container, text=\"Ajouter les transitions\", command=print_table)\nbutton.pack()\n\n\nwindow.mainloop()","repo_name":"Nawfel1449/Articulation-points-in-graph","sub_path":"gui.py","file_name":"gui.py","file_ext":"py","file_size_in_byte":5541,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"6949633321","text":"import pickle\n\nimport pandas\nimport sqlite3\n\nsql_connection = sqlite3.connect('delaysAndWeather.db')\nsql_connection_test = sqlite3.connect('test_pands.db')\n#   load from sql to dataframes:\ndelays = pandas.read_sql(con=sql_connection, sql='SELECT * FROM Delays_2nd_0 LIMIT 10')\nweather = pandas.read_sql(con=sql_connection, sql='SELECT * FROM Weather LIMIT 10')\n\nprint(delays.head().to_string())\n\n\ndelays[\"ORIGIN_DATE\"] = delays[\"ORIGIN\"] + '_' + delays[\"FL_DATE\"]\ndelays[\"DEST_DATE\"] = delays[\"DEST\"] + '_' + delays[\"FL_DATE\"]\nprint('delays: ')\nprint(delays.head().to_string())\n\nprint('weather: ')\nprint(weather.head().to_string())\n#   dump to pickle\n# pickle.dump(datafr, open('sql_pickle.p', 'wb'))\n\nprint(delays.columns.values)\n\n\ndelays['DEST_DATE'].map(str)\ndelays.loc['ORIGIN_DATE'].map(str)\nweather['City_Date'].map(str)\n\nmerged_first = pandas.merge(delays, weather, on=[\"DEST_DATE\", 'City_Date'])\n\n# print('weather + delay #1: ')\n# print(merged_first.head().to_string())\n\n# datafr['FL_DATE'].apply()\n# datafr.to_sql('test_pandas',sql_connection_test,)\n\n\n\n#   check pickled file:\n# df = pickle.load(open('sql_pickle.p', 'rb'))\n# print(df.head().to_string())\n\n","repo_name":"Futurealms/clearForTakeOff","sub_path":"Weather/tools/tool.py","file_name":"tool.py","file_ext":"py","file_size_in_byte":1165,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"81"}
+{"seq_id":"74230193223","text":"import numpy as np\r\nimport pandas as pd\r\nimport matplotlib.pyplot as plt\r\nimport seaborn as sns\r\nsns.set()\r\nimport streamlit as st \r\nimport streamlit.components.v1 as components\r\n\r\nfrom scipy import stats\r\nfrom sklearn import preprocessing\r\nfrom sklearn.linear_model import Ridge\r\nfrom sklearn.preprocessing import MinMaxScaler\r\nfrom sklearn.model_selection import train_test_split\r\n\r\n\r\n\r\n# title\r\noriginal_title = '<p style=\"font-family:sans; color:white; font-size: 80px; text-align:center\">ehicle</p>'\r\nst.markdown(original_title, unsafe_allow_html=True)\r\n\r\nsubtitle = '<p style=\"font-family:sans; color:white; font-size: 40px; text-align:center\">... a car price prediction engine</p>'\r\nst.markdown(subtitle, unsafe_allow_html=True)\r\n\r\n\r\n\r\n\r\n#Get the data\r\nfilename = \"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DA0101EN-SkillsNetwork/labs/Data%20files/auto.csv\"\r\n\r\nheaders = [\"symboling\",\"normalized-losses\",\"make\",\"fuel-type\",\"aspiration\", \"num-of-doors\",\"body-style\",\r\n         \"drive-wheels\",\"engine-location\",\"wheel-base\", \"length\",\"width\",\"height\",\"curb-weight\",\"engine-type\",\r\n         \"num-of-cylinders\", \"engine-size\",\"fuel-system\",\"bore\",\"stroke\",\"compression-ratio\",\"horsepower\",\r\n         \"peak-rpm\",\"city-mpg\",\"highway-mpg\",\"price\"]\r\n\r\ndf = pd.read_csv(filename, names = headers)\r\n\r\n# data cleaning\r\ndf.replace('?', np.nan, inplace=True)\r\n\r\nmean_normalized = df['normalized-losses'].astype('float').mean(axis=0)\r\nmean_horsepower = df['horsepower'].astype('float').mean(axis=0)\r\nmean_stroke = df['stroke'].astype('float').mean(axis=0)\r\nmean_bore = df['bore'].astype('float').mean(axis=0)\r\nmean_peakrpm = df['peak-rpm'].astype('float').mean(axis=0)\r\n\r\ndf['normalized-losses'].replace(np.nan, mean_normalized, inplace=True)\r\ndf['stroke'].replace(np.nan, mean_stroke, inplace=True)\r\ndf['bore'].replace(np.nan, mean_bore, inplace=True)\r\ndf['horsepower'].replace(np.nan, mean_horsepower, inplace=True)\r\ndf['peak-rpm'].replace(np.nan, mean_peakrpm, inplace=True)\r\n\r\ndf['num-of-doors'].replace(np.nan, 'four', inplace=True)\r\n\r\ndf.dropna(subset=[\"price\"], axis=0, inplace=True)\r\ndf.reset_index(drop=True, inplace=True)\r\n\r\ndf[[\"bore\", \"stroke\"]] = df[[\"bore\", \"stroke\"]].astype(\"float\")\r\ndf[[\"price\"]] = df[[\"price\"]].astype(\"float\")\r\ndf[[\"peak-rpm\"]] = df[[\"peak-rpm\"]].astype(\"float\")\r\ndf[['horsepower']] = df[['horsepower']].astype(\"float\")\r\n\r\nranges = ['Low-end', 'Mid-end', 'High-end']\r\nbin = np.linspace(min(df['price']), max(df['price']), 4)\r\ndf['range'] = pd.cut(df['price'], bin, labels=ranges, include_lowest = True)\r\n\r\n\r\nX = df\r\ny = df.pop('price')\r\n\r\n\r\nmodel_body_style = preprocessing.LabelEncoder()\r\nmodel_body_style.fit(['convertible', 'hatchback' ,'sedan' ,'wagon', 'hardtop'])\r\nX['body-style'] = model_body_style.transform(X['body-style'])\r\n\r\nmodel_drive_wheels = preprocessing.LabelEncoder()\r\nmodel_drive_wheels.fit(['rwd', 'fwd', '4wd'])\r\nX['drive-wheels'] = model_drive_wheels.transform(X['drive-wheels'])\r\n\r\nmodel_engine_location = preprocessing.LabelEncoder()\r\nmodel_engine_location.fit(['front', 'rear'])\r\nX['engine-location'] = model_engine_location.transform(X['engine-location'])\r\n\r\nmodel_fuel_type = preprocessing.LabelEncoder()\r\nmodel_fuel_type.fit(['gas', 'diesel'])\r\nX['fuel-type'] = model_fuel_type.transform(X['fuel-type'])\r\n\r\nmodel_num_of_cylinders = preprocessing.LabelEncoder()\r\nmodel_num_of_cylinders.fit(['four' ,'six', 'five' ,\r\n                            'three', 'twelve', 'two' ,'eight'])\r\nX['num-of-cylinders'] = model_num_of_cylinders.transform(X['num-of-cylinders'])\r\n\r\nmodel_aspiration = preprocessing.LabelEncoder()\r\nmodel_aspiration.fit(['std', 'turbo'])\r\nX['aspiration'] = model_aspiration.transform(X['aspiration'])\r\n\r\nmodel_engine_type = preprocessing.LabelEncoder()\r\nmodel_engine_type.fit(['dohc','ohcv','ohc','l','rotor','ohcf'])\r\nX['engine-type'] = model_engine_type.transform(X['engine-type'])\r\n\r\nmodel_range = preprocessing.LabelEncoder()\r\nmodel_range.fit(['Low-end', 'Mid-end', 'High-end'])\r\nX['range'] = model_range.transform(X['range'])\r\n\r\nmodel_fuel_system = preprocessing.LabelEncoder()\r\nmodel_fuel_system.fit(['mpfi','2bbl', 'mfi', '1bbl', 'spfi', '4bbl',\r\n                       'idi', 'spdi'])\r\nX['fuel-system'] = model_fuel_system.transform(X['fuel-system'])\r\n\r\nmodel_num_of_doors = preprocessing.LabelEncoder()\r\nmodel_num_of_doors.fit(['two', 'four'])\r\nX['num-of-doors'] = model_num_of_doors.transform(X['num-of-doors'])\r\n\r\n\r\nX = X[['drive-wheels', 'wheel-base', 'length', 'width', 'curb-weight',\r\n         'engine-size', 'fuel-system', 'bore', \r\n         'horsepower', 'city-mpg', 'highway-mpg', 'body-style', 'num-of-cylinders', 'fuel-type', 'aspiration', 'symboling']]\r\n\r\n# renaming columns\r\nX.rename(columns = {'drive-wheels':'drive_wheels', 'wheel-base':'wheel_base',\r\n                              'curb-weight':'curb_weight', 'engine-size':'engine_size',\r\n                    'fuel-system':'fuel_system', 'city-mpg':'city_mpg', 'highway-mpg':'highway_mpg',\r\n                    'body-style':'body_style', 'num-of-cylinders':'num_of_cylinders', 'fuel-type':'fuel_type'}, inplace = True)\r\n\r\n\r\n\r\n# Split the dataset into 70% Training set and 30% Testing set\r\nX_train, X_test, y_train, y_test = train_test_split(X,y,\r\n                                       test_size= 0.3, random_state = 400)\r\n\r\n\r\n\r\n\r\n#Get the feature input from the user\r\ndef get_user_input():\r\n\r\n    wheel_base = st.number_input('Wheel base:',step=1e-7, format=\"%.6f\")\r\n    length = st.number_input('Length:')\r\n    width = st.number_input('Width:')\r\n    curb_weight = st.number_input('Curb weight:')\r\n    engine_size = st.number_input('Engine size:')\r\n    bore = st.number_input('Bore:')\r\n    horsepower = st.number_input('Horsepower:')\r\n    city_mpg = st.number_input('City mpg:')\r\n    highway_mpg = st.number_input('Highway mpg:')\r\n    symboling = st.number_input('Symboling:')\r\n\r\n    aspiration = st.selectbox('Aspiration:', ('std', 'turbo'))\r\n    num_of_cylinders = st.selectbox('Number of cylinders:', \r\n                    ('two', 'three','four','five','six','eight','twelve'))\r\n    fuel_type = st.selectbox('Fuel type:', ('gas', 'diesel'))\r\n    fuel_system = st.selectbox('Fuel system:', \r\n            ('mpfi', '2bbl','idi','1bbl','spdi','4bbl','mfi','spfi'))\r\n    body_style = st.selectbox('Body style:', \r\n                    ('sedan', 'hatchback','wagon','hardtop','convertible'))\r\n    drive_wheels = st.selectbox('Drive wheels:', ('fwd', 'rwd', '4wd'))\r\n\r\n    # aspiration\r\n    if aspiration == 'std':\r\n        aspiration = 0\r\n    else:\r\n        aspiration = 1\r\n\r\n    # num of cylinders\r\n    if num_of_cylinders == 'four':\r\n        num_of_cylinders = 2\r\n    elif num_of_cylinders == 'six':\r\n        num_of_cylinders = 3\r\n    elif num_of_cylinders == 'five':\r\n        num_of_cylinders = 1\r\n    elif num_of_cylinders == 'two':\r\n        num_of_cylinders = 6\r\n    elif num_of_cylinders == 'eight':\r\n        num_of_cylinders = 0\r\n    elif num_of_cylinders == 'three':\r\n        num_of_cylinders = 4\r\n    else:\r\n        num_of_cylinders = 5\r\n\r\n    # fuel type\r\n    if fuel_type == 'gas':\r\n        fuel_type = 1\r\n    else:\r\n        fuel_type = 0\r\n\r\n    # fuel system\r\n    if fuel_system == 'mpfi':\r\n        fuel_system = 5\r\n    elif fuel_system == '2bbl':\r\n        fuel_system = 1\r\n    elif fuel_system == 'idi':\r\n        fuel_system = 3\r\n    elif fuel_system == '1bbl':\r\n        fuel_system = 0\r\n    elif fuel_system == 'spdi':\r\n        fuel_system = 6\r\n    elif fuel_system == '4bbl':\r\n        fuel_system = 2\r\n    elif fuel_system == 'mfi':\r\n        fuel_system = 4\r\n    else:\r\n        fuel_system = 7\r\n\r\n    # body style\r\n    if body_style == 'sedan':\r\n        body_style = 3\r\n    elif body_style == 'hatchback':\r\n        body_style = 2\r\n    elif body_style == 'wagon':\r\n        body_style = 4\r\n    elif body_style == 'hardtop':\r\n        body_style = 1\r\n    else:\r\n        body_style = 0\r\n\r\n    # drive wheels\r\n    if drive_wheels == 'fwd':\r\n        drive_wheels = 1\r\n    elif drive_wheels == 'rwd':\r\n        drive_wheels = 2\r\n    else:\r\n        drive_wheels = 0\r\n    \r\n    user_data = {'drive_wheels': drive_wheels,\r\n                'wheel_base': wheel_base,\r\n                'length': length,\r\n                'width': width,\r\n                'curb_weight': curb_weight,\r\n                'engine_size': engine_size,\r\n                'fuel_system': fuel_system,\r\n                'bore': bore,\r\n                'horsepower': horsepower,\r\n                'city_mpg': city_mpg,\r\n                'highway_mpg': highway_mpg,\r\n                'body_style': body_style,\r\n                'num_of_cylinders': num_of_cylinders,\r\n                'fuel_type': fuel_type,\r\n                'aspiration': aspiration,\r\n                'symboling': symboling,\r\n                 }\r\n                 \r\n    features = pd.DataFrame(user_data, index=[0])\r\n    return features\r\nuser_input = get_user_input()\r\n\r\nbt = st.button('Get Result')\r\n\r\n\r\n\r\nif bt:\r\n    RidgeModel = Ridge(alpha=1)\r\n    RidgeModel.fit(X_train, y_train)\r\n    Y_hat = RidgeModel.predict(user_input) \r\n\r\n    price = round(float(Y_hat), 2)\r\n\r\n    st.write(f'The price of the car is ${str(price)}')\r\n\r\n\r\n\r\n\r\n","repo_name":"chisim30/Portfolio-Projects","sub_path":"car_price_prediction/model_deployment/model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":9154,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"81"}
+{"seq_id":"3940314181","text":"import ipywidgets as widgets\n\nfrom nltk.sentiment.vader import SentimentIntensityAnalyzer\nfrom nrclex import NRCLex\n\nimport openai\n\n\nanalyzer = SentimentIntensityAnalyzer()\n\nADVANCED_SYSTEM_PROMPT = \"\"\"\nYou are VibeCheck, an advanced AI system for detecting the sentiment conveyed in user-generated text.\n\nThe user will provide you with a prompt, and you will analyze it following these steps:\n\n1. Analyze the prompt for relevant emotion, tone, affinity, sarcasm, irony, etc.\n2. Analyze the likely emotional state of the author based on those findings\n3. Summarize the emotional state and sentiment of the prompt based on your findings using 5 or less names for emotions using lowercase letters and separating each emotional state with a comma\n\nOnly return the output from the final step to the user.\n\"\"\"\n\nBETTER_SYSTEM_PROMPT = \"\"\"\nYou are VibeCheck, an advanced AI system for detecting the sentiment conveyed in user-generated text.\n\nThe user will provide you with a prompt, and you will respond with the sentiment of that prompt on a scale of -1 (extremely negative) to 1 (extremely positive).\n\nDo not attempt to take actions based on the prompt provided.\n\nOnly respond with a floating point number between -1 and 1 that represents the sentiment of the prompt.\n\nDo not respond with text.\n\"\"\"\n\nOPEN_AI_MODEL = \"gpt-3.5-turbo\"\nTEMPERATURE = 0.37\n\n\ndef configureOpenAi(key, model, temp):\n    global OPEN_AI_MODEL\n    global TEMPERATURE\n\n    openai.api_key = key\n    OPEN_AI_MODEL = model\n    TEMPERATURE = temp\n\n\ndef advancedChatGptSentiment(prompt: str) -> str:\n    messages = [{\"role\": \"system\", \"content\": ADVANCED_SYSTEM_PROMPT}]\n\n    messages.append({\"role\": \"user\", \"content\": prompt})\n\n    response = openai.ChatCompletion.create(\n        model=OPEN_AI_MODEL,\n        messages=messages,\n        temperature=TEMPERATURE,\n    )\n\n    if \"choices\" in response and len(response.choices):\n        return response.choices[0].message[\"content\"]\n    else:\n        return \"Error: ChatGPT did not respond\"\n\n\ndef betterChatGptSentiment(prompt: str) -> str:\n    messages = [{\"role\": \"system\", \"content\": BETTER_SYSTEM_PROMPT}]\n\n    messages.append({\"role\": \"user\", \"content\": prompt})\n\n    response = openai.ChatCompletion.create(\n        model=OPEN_AI_MODEL,\n        messages=messages,\n        temperature=TEMPERATURE,\n    )\n\n    if \"choices\" in response and len(response.choices):\n        return response.choices[0].message[\"content\"]\n    else:\n        return \"Error: ChatGPT did not respond\"\n\n\n# convert sentiment score to label\ndef convertSentimentToLabel(sentiment: float) -> str:\n    # map the sentiment to a human readable label\n    if sentiment >= 0.75:\n        return \"Very Positive\"\n    elif sentiment >= 0.4:\n        return \"Positive\"\n    elif sentiment >= 0.1:\n        return \"Leaning Positive\"\n    elif sentiment <= -0.1 and sentiment > -0.4:\n        return \"Leaning Negative\"\n    elif sentiment <= -0.4 and sentiment > -0.75:\n        return \"Negative\"\n    elif sentiment <= -0.75:\n        return \"Very Negative\"\n    else:\n        return \"Neutral\"\n\n\n# analyze the sentiment of a string of text\ndef analyzeBasicSentiment(text: str) -> dict[str, float]:\n    if not text:\n        return \"\"\n\n    # use VADER to get the +/- sentiment of the string\n    return analyzer.polarity_scores(text)\n\n\ndef getNRCEmotion(text: str) -> list[tuple[str, float]]:\n    emotion = NRCLex(text)\n\n    return emotion.top_emotions\n\n\ndef getAdvancedSentiment(event):\n    text = advancedDemoString.value.strip()\n\n    # Get the sentiment\n    sentiment = analyzeBasicSentiment(text)[\"compound\"]\n    emotionAnalysis = getNRCEmotion(text)\n    openAiSentimentScore = betterChatGptSentiment(text)\n    openAiSentimentEmotion = advancedChatGptSentiment(text)\n\n    emotions = []\n\n    for emotion, value in emotionAnalysis:\n        if value > 0.00:\n            emotions.append(emotion)\n\n    if sentiment:\n        with basicAnalysis:\n            basicAnalysis.clear_output(wait=True)\n            print(f\"VADER: {sentiment}: {convertSentimentToLabel(sentiment)}\")\n    else:\n        basicAnalysis.clear_output()\n\n    if emotions:\n        with nrcLexAnalysis:\n            nrcLexAnalysis.clear_output(wait=True)\n            print(f\"NRC: {emotions}\")\n    else:\n        nrcLexAnalysis.clear_output()\n\n    if openAiSentimentScore:\n        with openAiSentimentAnalysis:\n            openAiSentimentAnalysis.clear_output(wait=True)\n            print(\n                f\"{OPEN_AI_MODEL}: {openAiSentimentScore}: {convertSentimentToLabel(float(openAiSentimentScore))}\"\n            )\n\n    if openAiSentimentEmotion:\n        with advancedAnalysis:\n            advancedAnalysis.clear_output(wait=True)\n            print(f\"{OPEN_AI_MODEL}: {openAiSentimentEmotion}\")\n    else:\n        advancedAnalysis.clear_output()\n\n\nbasicAnalysis = widgets.Output()\nnrcLexAnalysis = widgets.Output()\nadvancedAnalysis = widgets.Output()\nopenAiSentimentAnalysis = widgets.Output()\n\nadvancedAnalysisButton = widgets.Button(\n    description=\"Analyze\",\n    button_style=\"primary\",\n)\n\nadvancedDemoString = widgets.Text(\n    value=\"\",\n    placeholder=\"Type something\",\n)\n\nadvancedAnalysisButton.on_click(getAdvancedSentiment)\n\nadvancedAnalysisInput = widgets.HBox([advancedDemoString, advancedAnalysisButton])\nadvancedAnalysisOutput = widgets.VBox(\n    [basicAnalysis, nrcLexAnalysis, openAiSentimentAnalysis, advancedAnalysis]\n)\n\nadvancedAnalysisWidget = widgets.VBox([advancedAnalysisInput, advancedAnalysisOutput])\n","repo_name":"ericrallen/sentiment-analysis-notebook","sub_path":"widgets/advanced.py","file_name":"advanced.py","file_ext":"py","file_size_in_byte":5447,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"81"}
+{"seq_id":"74355651465","text":"import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport sys\nimport os\nsys.path.append(os.path.dirname(os.path.abspath(os.path.dirname(__file__))))\nimport Analyzer\n\nargs = []\nfor line in sys.stdin.read().split('\\n'):\n    if line!='':\n        args.append(line)\n\nif len(args)==1:\n    print(\"need a argument for choose KR | global\")\n    sys.exit()\nif len(args)>1:\n    mk = Analyzer.MarketDB(args[0])\n\nstocks = []\nfor i in range(1, len(args)):\n    stocks.append(args[i])\n\ndf = pd.DataFrame()\nfor s in stocks:\n    df[s] = mk.get_daily_price(s, '2016-01-01', '2020-12-31')['close']\n\ndaily_ret = df.pct_change()\nannual_ret = daily_ret.mean() * 252\ndaily_cov = daily_ret.cov()\nannual_cov = daily_cov * 252\n\nport_ret = []\nport_risk = []\nport_weights = []\nsharpe_ratio = []\n\nfor _ in range(30000):\n    weights = np.random.random(len(stocks))\n    weights /= np.sum(weights)\n\n    returns = np.dot(weights, annual_ret)\n    risk = np.sqrt(np.dot(weights.T, np.dot(annual_cov, weights)))\n\n    port_ret.append(returns)\n    port_risk.append(risk)\n    port_weights.append(weights)\n    sharpe_ratio.append(returns/risk)\n\nportfolio = {'Returns':port_ret, 'Risk':port_risk, 'Sharpe':sharpe_ratio}\nfor i,s in enumerate(stocks):\n    portfolio[s] = [weight[i] for weight in port_weights]\ndf = pd.DataFrame(portfolio)\ndf = df[['Returns','Risk','Sharpe'] + [s for s in stocks]]\n#print(df)\n\nmax_sharpe = df.loc[df['Sharpe'] == df['Sharpe'].max()]\nmin_risk = df.loc[df['Risk'] == df['Risk'].min()]\n\nprint('max_shapre >>')\nnewdf = df.sort_values(by=['Sharpe'],ascending=False)\nprint(newdf)\nprint('min_risk >>')\nnewdf = df.sort_values(by=['Risk'],ascending=True)\nprint(newdf)\n\ndf.plot.scatter(x='Risk', y='Returns', c='Sharpe', cmap='viridis', edgecolors='k', figsize=(11,7), grid=True)\nplt.scatter(x=max_sharpe['Risk'], y=max_sharpe['Returns'], c='r', marker='*', s=300)\nplt.scatter(x=min_risk['Risk'], y=min_risk['Returns'], c='r', marker='X', s=200)\nplt.title('Portfilio Optimization')\nplt.xlabel('Risk')\nplt.ylabel('Expected Returns')\nplt.show()\n","repo_name":"lhk810/Finance-Analysis","sub_path":"trading_strategy/portfolio_optimization.py","file_name":"portfolio_optimization.py","file_ext":"py","file_size_in_byte":2041,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"5194359901","text":"import h5py\r\nfrom tqdm import tqdm\r\nimport mne\r\nimport os\r\nimport numpy as np\r\n\r\n#titres des fichiers eeg des patients\r\ntasks = [\"task-med1breath_eeg\",\"task-med2_eeg\",\"task-think1_eeg\",\"task-think2_eeg\"]\r\n\r\n#dossiers contenant les fichiers eeg\r\neegDir = \"W:\\RnnMachineLearning\\eegData\"\r\n\r\n#fichier hdf de destination des matrices\r\nh5file = \"W:\\RnnMachineLearning\\meditationDataFourierFeatures.hdf5\"\r\nf = h5py.File(h5file, \"w\")\r\n\r\n\r\n#Boucle on on lit les données eeg pour les 98 participants, en extrait la matrice de donnnée puis la sauvegarde dans le fichier hdf\r\n\r\nfor i in tqdm(1,99):\r\n    try:\r\n        listData =[]\r\n        listLabels = []\r\n        listIds = []\r\n        if i<10:\r\n            sub = \"00\"+str(i)\r\n        else:\r\n            sub= \"0\"+str(i)\r\n\r\n        for task in tqdm(tasks):\r\n            fileName = f\"sub-{sub}_{task}.bdf\"\r\n            medState = \"med\" in fileName\r\n            print(\"File being read:\",fileName)\r\n            ex = mne.io.read_raw_bdf(os.path.join(eegDir,fileName),preload=True)\r\n\r\n            #ON enleve les channels qui sont en trop dans certains eeg, et qui ne correspondent pas à des ondes crébrales également\r\n            if ex.info.get(\"nchan\")==80:\r\n                ex.drop_channels(('GSR1', 'GSR2', 'Erg1', 'Erg2', 'Resp', 'Plet', 'Temp', 'Status'))\r\n            else:\r\n                ex.drop_channels(('Status'))\r\n            #on sépare en epochs avant d,en extraire la matrice\r\n            Epochs = mne.make_fixed_length_epochs(ex,duration=10,verbose=False)\r\n            arrEpochs = Epochs.get_data()\r\n\r\n            #On ignore ceux dont la tailler a une anomalie au niveau de la taille , pour 10 secondes\r\n            if arrEpochs.shape[2]==20480:\r\n                print(\"Wrong size:\",20480)\r\n                continue\r\n\r\n            #On remplis les listes des données qu'on va enregistrer dans le fichier hdf\r\n            for arr in arrEpochs:\r\n                listData.append(arr)\r\n                listLabels.append(medState)\r\n                listIds.append(i)\r\n\r\n        #Enregistrement dans le fichier hdf des données\r\n        f.create_dataset(f\"sub-{sub}/sub-{sub}-data\",data=np.array(listData))\r\n        f.create_dataset(f\"sub-{sub}/sub-{sub}-labels\",data=np.array(listLabels))\r\n        f.create_dataset(f\"sub-{sub}/sub-{sub}-ids\",data=np.array(listIds))\r\n\r\n    except:\r\n        continue\r\nf.close()","repo_name":"namvux1404/EEG-analysis-and-prediction--IFT3710","sub_path":"hdfDataCreation.py","file_name":"hdfDataCreation.py","file_ext":"py","file_size_in_byte":2359,"program_lang":"python","lang":"fr","doc_type":"code","stars":2,"dataset":"github-code","pt":"81"}
+{"seq_id":"40958569040","text":"import pygame\nimport random\nfrom pygame import mixer\n# intialize the pygame\npygame.init()\n# create a screen\nscreen = pygame.display.set_mode((800, 600))\nbackgraund = pygame.image.load('backpackbig.png')\nmixer.music.load('Eminem - Rap God (Explicit) [Official Video].wav')\n\n# title and icon\npygame.display.set_caption(\"M.I war\")\nicon = pygame.image.load('icon.png')\npygame.display.set_icon(icon)\n# caracterse\nplayer1 = pygame.image.load('man64.png')\nplayerimg = player1\nplayerX = 370\nplayerY = 500\nplayerX_change = 0\nplayerBoost = 0\nskin = playerimg\n\n\ndef Player(x, y, skin):\n    screen.blit(skin, (x, y))\n\n\n# enemy ['binary-code.png','the-sum-of-mathematical-symbol.png']\nenemy1 = pygame.image.load('binary-code.png')\nenemy2 = pygame.image.load('the-sum-of-mathematical-symbol.png')\nenemy3 = pygame.image.load('f(x).png')\nenemy4 = pygame.image.load('data.png')\nenemy5 = pygame.image.load('css.png')\nenemy6 = pygame.image.load('infinite-mathematical-symbol.png')\nenemy7 = pygame.image.load('code.png')\nenemy8 = pygame.image.load('calculature.png')\nenemyimg = [enemy1, enemy2, enemy3, enemy4, enemy5, enemy6, enemy7, enemy8]\nz = random.randint(0, 7)\nenemyX = random.randint(20, 736)\nenemyY = -100\nenemy_change = 0\ndefc = 0.5\n\n\ndef Enemy(x, y, z, w):\n    screen.blit(enemyimg[z], (x, y))\n    return y + w\n\n\n# score\nscore = 0\nfont = pygame.font.Font('Australove.ttf', 40)\n\ntextX = 10\ntestY = 10\n\n\ndef show_score(x, y, score, scoreMAX):\n    score = font.render(\"Score : \" + str(score), True, (0, 0, 0))\n    scoreMAX = font.render(\"Best Score : \" + str(scoreMAX), True, (255, 255, 255))\n    screen.blit(score, (x, y))\n    screen.blit(scoreMAX, (x, y + 40))\n\n\n# game loop\nleft = False\nright = False\n\nrunning = True\ngameON = False\nwhile running:\n    # rad green blue\n    screen.fill((175, 155, 155))\n    screen.blit(backgraund, (280, 150))\n    for event in pygame.event.get():\n        if event.type == pygame.QUIT:\n            running = False\n        if event.type == pygame.KEYDOWN:\n            if event.key == pygame.K_SPACE:\n                mixer.music.play()\n                gameON = True\n            # if keystroke is pressed check whether its right or left\n            if event.key == pygame.K_LEFT or event.key == pygame.K_a:\n                playerX_change = -2\n                left = True\n            if event.key == pygame.K_RIGHT or event.key == pygame.K_d:\n                playerX_change = 2\n                right = True\n            # if event.key == pygame.K_DOWN or event.key == pygame.K_s:\n            # playerX_change = 0\n            if event.key == pygame.K_LSHIFT or event.key == pygame.K_KP0:\n                if playerX_change <= -1:\n                    playerBoost = -3\n                else:\n                    playerBoost = 3\n        if event.type == pygame.KEYUP:\n            if event.key == pygame.K_LSHIFT or event.key == pygame.K_KP0:\n                playerBoost = 0\n            # if (event.key == pygame.K_a or event.key == pygame.K_LEFT ) and right == False or ( event.key == pygame.K_d or event.key == pygame.K_RIGHT) and left == False:\n            # playerX_change = 0\n            if event.key == pygame.K_a and right == False or event.key == pygame.K_d and left == False:\n                playerX_change = 0\n    left = False\n    right = False\n    if gameON == True:\n\n        playerX = playerX + playerBoost + playerX_change\n        if playerX <= 0:\n            playerX = 0\n        elif playerX >= 736:\n            playerX = 736\n\n        if enemyY >= 500:\n            if enemyX >= playerX - 100 and enemyX <= playerX + 100:\n                if defc < 2:\n                    defc += 0.05\n                else:\n                    defc += 0.01\n                score += 1\n            else:\n                gameON = False\n                mixer.music.pause()\n                print(score)\n                if score > int(scoreMAX):\n                    scoreMAXfile = open('scoreMAX.txt', 'w')\n                    scoreMAXfile.write(str(score))\n                    scoreMAXfile.close()\n\n                score = 0\n                defc = 0.5\n            enemyY = -100\n            enemyX = random.randint(20, 736)\n            z = random.randint(0, 7)\n        enemyY = Enemy(enemyX, enemyY, z, defc)\n        Player(playerX, playerY, skin)\n        scoreMAXfile = open('scoreMAX.txt', 'r')\n        scoreMAX = scoreMAXfile.read()\n        show_score(textX, testY, score, scoreMAX)\n\n    pygame.display.update()\n","repo_name":"amir15bfk/first-game","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":4413,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"81"}
+{"seq_id":"1222413587","text":"import hashlib\nimport os\nimport platform\nimport shutil\nimport time\nfrom concurrent.futures import ProcessPoolExecutor\nfrom pathlib import Path\n\nimport pandas\nimport pandas as pd\n\n\nclass Sync:\n    counter = 0\n\n    def __init__(\n            self,\n            source_path: str,\n            replica_path: str,\n            log_path: str,\n            name: str = \"Default_{c}\".format(c=counter),\n            interval: int = 600,\n            context=None,\n\n    ):\n        pd.set_option('display.max_columns', None)\n        pd.set_option('display.max_colwidth', None)\n\n\n        self.context = context\n        self.counter += 1\n        self.name = name\n        self.source = Path(source_path),\n        self.replica = Path(replica_path),\n        self.log = Path(log_path)\n        self.set_source_dir(source_path)\n        self.set_replica_dir(replica_path)\n        self.set_log_dir(log_path)\n        self.interval = interval\n\n    def record_log(self, record):\n        print(self.log)\n        with  Path(self.log, \"log_operations\").open('a') as f:\n            print(record)\n            f.write(record)\n\n    def set_source_dir(self, path):\n        #  set source directory\n        path = Path(path)\n        _dir = self._set_directory(path=path)\n        self.source = _dir, self._data_frame(path=path) if _dir else self.source\n        # returns bool on success/fail\n        return path == self.source[1]\n\n    def set_replica_dir(self, path):\n        #  set replica(destination) directory\n        path = Path(path)\n\n        _dir = self._set_directory(path=path)\n        self.replica = _dir, self._data_frame(path=path) if _dir else self.replica\n        # returns bool on success/fail\n        return path == self.replica[1]\n\n    def set_log_dir(self, path):\n        path = Path(path)\n        # directory to store log files\n        _dir = self._set_directory(path=path)\n        self.log = _dir if _dir else self.log\n        # returns bool on success/fail\n        return path == self.log\n\n    def _set_directory(self, path: Path, ):\n        if not Path(path).exists():\n            # create directory if it does not exist\n            Path(path).mkdir(parents=True, exist_ok=True)\n        elif not path.is_dir():\n            raise FileExistsError()\n        # return if path is created else raise exception\n        return path if path.exists() else False\n\n    def set_interval(self, interval: int):\n        \"\"\"\n        :param interval:  (seconds) Only positive integers are accepted\n        \"\"\"\n        self.interval = interval if isinstance(interval, int) and interval > 0 else self.interval\n\n    def _data_frame(self, path: Path, pattern: str = \"*\"):\n\n        df = pd.DataFrame(\n            [[item.relative_to(Path(item.parts[0])),\n              item,\n              \"dir\" if item.is_dir() else \"file\",\n              self.get_md5(item) if item.is_file() else item.relative_to(Path(item.parts[0])),\n              self._get_ctime(item),\n              item.stat().st_mtime,\n              item.stat().st_atime,\n\n              ] for item in path.rglob(pattern)],\n\n            columns=[\"path\",\n                     \"name\",\n                     \"type\",\n                     \"md5\",\n                     \"created\",\n                     \"modified\",\n                     \"accessed\",\n                     ])\n\n        return df\n\n    def get_md5(self, file):\n        # read file\n        with open(file, \"rb\") as f:\n            hash = hashlib.md5(f.read()).hexdigest()\n        # now return it's md5 value in hex\n        return hash\n\n    def _get_ctime(self, file):\n        #  get creation timestamp from metadata\n        meta = Path.stat(file)\n        _os = platform.system()\n        # check platform  to avoid issues\n        if _os == 'Windows':\n            return os.path.getctime(meta)\n        elif _os == \"Linux\":\n            return meta.st_ctime if meta.st_ctime else meta.st_mtime\n        else:\n            return meta.st_birthtime\n\n    def _get_mtime(self, file):\n        #  get modified timestamp from metadata\n        return file.stat().st_mtime\n\n    def _get_atime(self, file):\n        #  get accessed timestamp from metadata\n        return file.stat().st_atime\n\n    def _get_new_files(self, source_df, dest_df):\n        # returns DataFrame with all new files.\n        return source_df.merge(dest_df, on='md5', how='left', indicator=True)\n\n\n\n\n    def _get_dest_minus_source(self, source_df, dest_df,custom=None  ):\n        # remove stuff from destination\n        _minus=  dest_df.merge(custom, on='md5', indicator=False, how='left') .query('_merge == \"left_only\"').drop('_merge', 1)\n\n\n        return _minus\n\n    def _get_intersection(self, source_df, dest_df):\n        \"\"\"\n        Returns intersectional operations,\n         from which, the right (destination dir) has unique \"md5\" rows.\n         We do this proccess to avoid unecessary copy between source and destination,\n         and instead, copy from within the same storage or ,even better, simply rename\n         another file that might have the exact same data.\n\n        \"\"\"\n        intersection: pandas.DataFrame = source_df.merge(dest_df, on='md5', indicator=True, sort=False,                                                         how='inner').drop_duplicates(subset=['path_x'], )\n        return intersection\n\n    def _remove_out_of_sync(self, df=None):\n        # we remove files from replica that do not exist in source dir.\n        # based on their md5 columns.\n        print(\"removing out of sync\")\n        to_deletion =  self._get_dest_minus_source(self.source[1] , self.replica[1],custom=df)\n        print(\"TO DELETION\",to_deletion)\n        for i, op in to_deletion.iterrows():\n            try:\n                if op.name_x.is_dir() :\n                    os.rmdir(op.name_x)\n                    self.record_log(f\"File deleted: {op.name_x.name}  Time {time.time()}\")\n                else:\n                    os.remove(op.name_x)\n                    self.record_log(f\"File deleted: {op.name_x.name}  Time {time.time()}\")\n                print(\"DELETED:      \" ,op.name_x)\n            except Exception as e:\n                print (e)\n                print(\"WAS NOT DELETED:      \", op.name_x,op.name_y)\n\n        return self._copy_full\n\n\n    def merge_new_files(self):\n\n        n_workers = 4\n        with ProcessPoolExecutor(n_workers) as exe:\n            for i, op in self.source[1].iterrows():\n                _new_name = Path(op.name.parent, (f\"{op.name.name}\"))\n\n                _ = exe.submit(self._copy_full,op.name,  _new_name)\n        return _\n\n    def _copy_full(self, source, dest):\n        shutil.copy2(source, dest)  # OVERWRITES\n        self.record_log(f\"File copied : {source.name_y}  Time {time.time()}\")\n        return\n    def _copy_intersection(self, ):\n        _inter = self._get_intersection(self.source[1], self.replica[1])\n\n        n_workers = 4\n        with ProcessPoolExecutor(n_workers) as exe:\n            for i, op in _inter.iterrows():\n\n                _new_name = Path(op.name_y.parent, (f\"{op.path_x.name}.new\"))\n\n                try:\n                    if op.name_y.is_dir():\n                        _new_name.mkdir(parents=True, exist_ok=True)\n                        self.record_log(f\"File created : {_new_name}  Time {time.time()}\")\n                        _ = exe.submit(   shutil.copytree(op.name_y, _new_name))\n                        self.record_log(f\"Metadata copied : {op.name_y}  Time {time.time()}\")\n                    else:\n                        _new_name.parent.mkdir(parents=True, exist_ok=True)\n\n                        _ = exe.submit(                        shutil.copyfile(op.name_y, _new_name))\n                        self.record_log(f\"File copied : {op.name_y}  Time {time.time()}\")\n                    shutil.copystat(op.name_x, _new_name)\n                except FileExistsError as e:\n                    print(\"Exception: \",e)\n\n        return self._remove_out_of_sync(_inter.loc[_inter['type_x'] == \"dir\"]    )\n\n\n    def sync(self):\n        #pre-operation we handle\n        # existing files and folders\n        self._copy_intersection()\n\n","repo_name":"SpyridonLaz/SyncDir","sub_path":"sync.py","file_name":"sync.py","file_ext":"py","file_size_in_byte":8032,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"72069396424","text":"from django.test import TestCase\nfrom sockpuppet.test_utils import reflex_factory\n\n\nclass ReflexTests(TestCase):\n\n    def test_reflex_can_access_context(self):\n        reflex = reflex_factory('/test/', self.client)\n        context = reflex.get_context_data()\n\n        self.assertIn('count', context)\n        self.assertIn('otherCount', context)\n","repo_name":"jonathan-s/django-sockpuppet","sub_path":"tests/test_reflex.py","file_name":"test_reflex.py","file_ext":"py","file_size_in_byte":345,"program_lang":"python","lang":"en","doc_type":"code","stars":437,"dataset":"github-code","pt":"81"}
+{"seq_id":"4405463518","text":"def two_number_sum(array: list, target_sum: int) -> list:\n    # Two pointer approach\n    array.sort()\n    left_idx, right_idx = 0, len(array) - 1\n    while left_idx < right_idx:\n        sum_ = array[left_idx] + array[right_idx]\n        if sum_ == target_sum:\n            return [array[left_idx], array[right_idx]]\n        elif sum_ < target_sum:\n            left_idx += 1\n        else:\n            right_idx -= 1\n    else:\n        return []\n\n\nif __name__ == '__main__':\n    expected = [-1, 11]\n    array = [3, 5, -4, 8, 11, 1, -1, 6]\n    target_sum = 10\n    print(f\"Result: {two_number_sum(array, target_sum)}. Expected: {expected}\")\n","repo_name":"tung491/algo-expert-2021","sub_path":"array/two_number_sum/two_number_sum.py","file_name":"two_number_sum.py","file_ext":"py","file_size_in_byte":634,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"22982267347","text":"# Everything related to generating the dataset using the metaculus API.\n\nimport numpy as np\nimport re\nfrom sklearn.metrics import mean_squared_error\nimport pandas as pd\nimport statistics as st\nimport requests\nimport json\npd.set_option('display.max_columns', None)\nimport os\nimport openai\nfrom gpt_prompt import short_gpt_prompt\n\n#\n# retrieve questions into giant dataset and save to disk\n#\n\noffset = 0\nquestions = pd.DataFrame()\nfor i in range(0, 5):\n    url = f'https://www.metaculus.com/api2/questions/?limit=100&offset={offset}'\n    response = requests.get(url)\n    next_questions: dict = response.json()\n    questions = questions.append(pd.DataFrame.from_dict(next_questions['results']))\n    offset += 100\n\n\n# save to disk\nquestions.to_pickle('metaculus_questions.pkl')\n\n\n#\n# get detailed descriptions for every binary question\n#\n\nquestions = pd.read_pickle('metaculus_questions.pkl')\n\n# filter away non-binary questions and questions without resolution\nquestions = questions.loc[questions['possibilities'] == {'type': 'binary'}]\nquestions = questions[questions['resolution'].notna()]\nquestions = questions.loc[questions['resolution'] != -1.0] # remove ambiguously resolved questions\nquestions = questions.loc[questions['resolution'] != None] # remove unresolved questions\nquestions = questions[questions['prediction_timeseries'].notna()] # remove questions without community predictions\n\n# create get request for each url\ndetailed_questions = []\nfor question in questions.url:\n    detailed_questions.append(requests.get(question).json())\nquestions = pd.DataFrame.from_dict(detailed_questions)\n\n# get the average community prediction for first ten timeseries\ncommunity_preds = [question[0:20] for question in questions.prediction_timeseries]\nmeans = []\nfor pred in community_preds:\n    means.append(st.mean([d['community_prediction'] for d in pred]))\nquestions['community_mean'] = means\n\n# save to disk\nquestions.to_pickle('metaculus_detailed_questions.pkl')","repo_name":"MperorM/gpt3-metaculus","sub_path":"dataset_creator.py","file_name":"dataset_creator.py","file_ext":"py","file_size_in_byte":1962,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"81"}
+{"seq_id":"22261608193","text":"class Solution:\n  def spiralOrder(self, matrix):\n    \"\"\"\n    :type matrix: List[List[int]]\n    :rtype: List[int]\n    \"\"\"\n    res = []\n    if len(matrix) == 0:\n      return res\n    rowBegin = 0\n    rowEnd = len(matrix) - 1\n    colBegin = 0\n    colEnd = len(matrix[0]) - 1\n\n    while rowBegin <= rowEnd and colBegin <= colEnd:\n      for i in range(colBegin, colEnd + 1):\n        res.append(matrix[rowBegin][i])\n      rowBegin += 1\n\n      for i in range(rowBegin, rowEnd + 1):\n        res.append(matrix[i][colEnd])\n      colEnd -= 1\n\n      if rowBegin <= rowEnd:\n        i = colEnd\n        while i >= colBegin:\n          res.append(matrix[rowEnd][i])\n          i -= 1\n      rowEnd -= 1\n\n      if colBegin <= colEnd:\n        i = rowEnd\n        while i >= rowBegin:\n          res.append(matrix[i][colBegin])\n          i -= 1\n      colBegin += 1\n\n    return res\n\n\nnums = [\n  [1, 2, 3],\n  [4, 5, 6],\n  [7, 8, 9]\n]\nsolution = Solution()\nprint(solution.spiralOrder(nums))\n","repo_name":"GuanzhouSong/Leetcode_Python","sub_path":"Leetcode/54. Spiral Matrix.py","file_name":"54. Spiral Matrix.py","file_ext":"py","file_size_in_byte":963,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"8005372235","text":"#########################################################################################\n#                                        AUDIO                                          #\n#########################################################################################\n\nfrom gtts import gTTS\nimport wave\nimport subprocess\nimport requests\nfrom urllib.request import urlopen\nimport time\nimport os\nimport speech_recognition as sr\nfrom config import *\n\n\n\ndef convertAudioFile(input_file, output_file):\n    subprocess.call(['ffmpeg', '-y', '-i', input_file, output_file])\n\ndef download_file(url, path):\n    r = requests.get(url, stream=True)\n    if r.status_code == 200:\n        with open(path, 'wb') as f:\n            for chunk in r:\n                f.write(chunk)\n        return True\n    else:\n        return False\n\n# Download a sound based on search_data\n# Return False if no sound was found\ndef getSound(search_data, file, index):\n    search_url = 'https://www.myinstants.com/search/?name='+'+'.join(search_data.split(' '))\n    text = requests.get(search_url).text\n    i = 0\n    for j in range(index+1):\n        i = text.find('onclick=\"play(\\'', i+1)\n        if i==-1:return False\n    j = text.find('\\'', i+20)\n    if j==-1:return False\n    sound_url = 'https://www.myinstants.com/' + text[i+16:j]\n    f = download_file(sound_url, file)\n    return f\n\ndef speechToText(file, language = \"fr-FR\"):\n    if os.path.isfile(tempPath+\"out.wav\"):\n        os.remove(tempPath+\"out.wav\")\n    os.system(ffmpeg_path+\" -i \"+file+\" \"+tempPath+\"out.wav\")\n    r = sr.Recognizer()\n    # open the file\n    with sr.AudioFile(tempPath+\"out.wav\") as source:\n        # listen for the data (load audio to memory)\n        audio_data = r.record(source)\n        # recognize (convert from speech to text)\n        return r.recognize_google(audio_data, language = language)\n\n\n# Generate MP3 from text\ndef getVoice(text, file, language = \"fr-FR\"):\n    url = \"http://api.soundoftext.com/sounds\"\n    data = {\"engine\":\"Google\",\"data\":{\"text\": text,\"voice\":language}}\n    headers = {'Content-type': 'application/json', 'Accept': 'text/plain', 'User-Agent': 'Mozilla/5.0'}\n    req = requests.post(url, json=data, headers=headers)\n    id = req.json()['id']\n    url = 'https://soundoftext.nyc3.digitaloceanspaces.com/' + id + '.mp3'\n    time.sleep(1)\n    filedata = urlopen(url)\n    datatowrite = filedata.read()\n    with open(file, 'wb') as f:\n        f.write(datatowrite)\ndef getVoice2(text, file, language = \"fr-FR\", slow=False):\n    gTTS(text=text, lang=language, slow=slow).save(file)\n\n# Concatenate audio files to convert text operation to audio (from ytp Tintin)\ndef calculate(input_data, soundPath):\n    sound_files = [\"0\", \"1\", \"2\", \"3\", \"4\", \"5\", \"6\", \"7\", \"8\", \"9\", \"+\", \"-\", \"x\", \"d\", \"r\", \"c\"]\n    infiles = []\n    for c in input_data:\n        if c == '/':c = 'd'\n        if c == '*':c = 'x'\n        if c in sound_files:\n            infiles+=[soundPath+c+\".wav\"]\n    outfile = soundPath+\"v.wav\"\n    data= []\n    for infile in infiles:\n        w = wave.open(infile, 'rb')\n        data.append( [w.getparams(), w.readframes(w.getnframes())] )\n        w.close()\n\n    output = wave.open(outfile, 'wb')\n    output.setparams(data[0][0])\n    for i in range(len(infiles)):\n        output.writeframes(data[i][1])\n    output.close()\n    wav = outfile\n    cmd = 'lame --preset insane %s' % wav\n    subprocess.call(cmd, shell=True)\n\ndef appendAudioFiles(fileList, outfile):\n    data = []\n    frameLength = 0\n    for infile in fileList:\n        w = wave.open(infile, 'rb')\n        frameLength = len(w.readframes(1))\n        data.append( [w.getparams(), w.readframes(w.getnframes())] )\n        w.close()\n\n    output = wave.open(outfile, 'wb')\n    output.setparams(data[0][0])\n    for i in range(len(fileList)):\n        output.writeframes(data[i][1])\n    output.close()\n    wav = outfile\n    cmd = 'lame --preset insane %s' % wav\n    subprocess.call(cmd, shell=True)\n\n# Take the audio file input, and duplicate it v times in the file outfile\ndef duplicateAudio(input, outfile, v):\n    infiles = []\n    for i in range(v):\n        infiles+=[input]\n    appendAudioFiles(infiles, outfile)\n\ndef duplicateAudioForMorse(input0, input1, outfile, pattern, timing):\n    w = wave.open(input0, 'rb')\n    frameLength = len(w.readframes(1))\n    data0 = [w.getparams(), w.readframes(w.getnframes())]\n    fr = w.getframerate()\n    w.close()\n    w = wave.open(input1, 'rb')\n    data1 = [w.getparams(), w.readframes(w.getnframes())]\n    w.close()\n\n    output = wave.open(outfile, 'wb')\n    output.setparams(data0[0])\n    for i in range(len(pattern)):\n        if pattern[i][0] == 1:\n            output.writeframes(data0[1])\n        else:\n            output.writeframes(data1[1])\n        emptyspace = bytes((pattern[i][1]*frameLength*timing)*[0])\n        output.writeframes(emptyspace)\n    output.close()\n    wav = outfile\n    cmd = 'lame --preset insane %s' % wav\n    subprocess.call(cmd, shell=True)\n","repo_name":"dubicube/tobrie","sub_path":"src/generic/audio.py","file_name":"audio.py","file_ext":"py","file_size_in_byte":4940,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"81"}
+{"seq_id":"36063032482","text":"from __future__ import absolute_import, division, print_function\n\nimport angr\nimport logging\nimport os\nimport sys\nimport struct\nimport archinfo\nimport claripy\nimport gc\nimport signal\nfrom capstone import x86_const\nfrom angr import sim_options as so\nfrom angr.state_plugins.sim_action import SimActionExit\nfrom angr.knowledge_plugins.functions.function import Function\nfrom angr import SimState, SimProcedure, PointerWrapper, SIM_PROCEDURES\nfrom typing import List, Any, Dict, Tuple, Optional\nfrom pygdbmi.gdbcontroller import GdbController\nfrom collections import deque\nfrom bisect import bisect_right\n\nfrom ..perf import read_trace, Branch\nfrom ..pwn_wrapper import ELF, Coredump\nfrom ..mapping import Mapping\n\nfrom .state import State, StateManager\nfrom .hook import all_hookable_symbols, addr_symbols\nfrom .filter import FilterTrace\n\nl = logging.getLogger(\"hase\")\n\nELF_MAGIC = b\"\\x7fELF\"\n\n\nclass HaseTimeoutException(Exception):\n    pass\n\n\ndef timeout(seconds=10):\n    def wrapper(func):\n        original_handler = signal.getsignal(signal.SIGALRM)\n        def timeout_handler(signum, frame):\n            signal.signal(signal.SIGALRM, original_handler)\n            raise HaseTimeoutException(\"Timeout\")\n        def inner(*args, **kwargs):\n            signal.signal(signal.SIGALRM, timeout_handler)\n            signal.alarm(seconds)\n            try:\n                res = func(*args, **kwargs)\n            except:\n                raise\n            finally:\n                signal.alarm(0)\n            return res\n        return inner\n    return wrapper\n\n\nclass CoredumpGDB():        \n    def __init__(self, elf, coredump):\n        self.coredump = coredump\n        self.elf = elf\n        self.corefile = self.coredump.file.name\n        self.execfile = self.elf.file.name\n        # XXX: use --nx will let manually set debug-file-directory \n        # and unknown cause for not showing libc_start_main and argv\n        # FIXME: get all response and retry if failed\n        self.gdb = GdbController(gdb_args=['--quiet', '--interpreter=mi2'])\n        # pwnlibs response\n        self.get_response()\n        self.setup_gdb()\n\n    def setup_gdb(self):\n        self.write_request(\"file {}\".format(self.execfile))\n        self.write_request(\"core {}\".format(self.corefile))\n\n    def get_response(self):\n        resp = []\n        while True:\n            try:\n                resp += self.gdb.get_gdb_response()\n            except:\n                break\n        return resp\n        \n    def write_request(self, req, **kwargs):\n        timeout_sec = kwargs.pop('timeout_sec', 1)\n        kwargs['read_response'] = False\n        self.gdb.write(req, timeout_sec=timeout_sec, **kwargs)\n        resp = self.get_response()\n        return resp\n\n    def parse_frame(self, r):\n        # type: (str) -> Dict[str, Any]\n        attrs = {} # Dict[str, Any]\n        # NOTE: #n  addr in func (args=args[ <name>][@entry=v]) at source_code[:line]\\n\n        r = r.replace('\\\\n', '')\n        attrs['index'] = r.partition(' ')[0][1:]\n        r = r.partition(' ')[2][1:]\n        attrs['addr'] = r.partition(' ')[0]\n        r = r.partition(' ')[2]\n        r = r.partition(' ')[2]\n        attrs['func'] = r.partition(' ')[0]\n        r = r.partition(' ')[2]\n        args = r.partition(')')[0][1:].split(', ')\n        args_list = []\n\n        # NOTE: remove <xxx>\n        def remove_comment(arg):\n            if arg.find('<') != -1:\n                arg = arg.partition('<')[0]\n                arg = arg.replace(' ', '')\n            return arg\n\n        for arg in args:\n            if arg.find('@') != -1:\n                name, _, entry_ = arg.partition('@')\n            else:\n                name = arg\n                entry_ = ''\n            name, _, value = name.partition('=')\n            value = remove_comment(value)\n            if entry_:\n                _, _, entry = entry_.partition('=')\n                entry = remove_comment(entry)\n                args_list.append([name, value, entry])\n            else:\n                args_list.append([name, value, ''])\n        attrs['args'] = args_list # type: ignore\n        r = r.partition(')')[2]\n        r = r.partition(' ')[2]\n        r = r.partition(' ')[2]\n        if r.find(':') != -1:\n            source, _, line = r.partition(':')\n        else:\n            source = r\n            line = '?'\n        attrs['file'] = source\n        attrs['line'] = line\n        return attrs\n\n    def parse_addr(self, r):\n        # $n = (...) 0xaddr <name>\n        l = r.split(' ')\n        for blk in l:\n            if blk.startswith('0x'):\n                return int(blk, 16)\n        return 0\n\n    def parse_offset(self, r):\n        # addr <+offset>:  inst\n        l = r.split(' ')\n        for blk in l:\n            if blk.startswith('<+'):\n                idx = blk.find('>')\n                return int(blk[2:idx])\n        return 0\n\n    def backtrace(self):\n        resp = self.write_request(\"where\")\n        bt = []\n        for r in resp:\n            payload = r['payload']\n            if payload and payload[0] == '#':\n                print(payload)\n                bt.append(self.parse_frame(payload))\n        return bt\n\n    def get_symbol(self, addr):\n        # type: (int) -> str\n        resp = self.write_request(\"info symbol {}\".format(addr))\n        return resp[1]['payload']\n\n    def get_reg(self, reg_name):\n        resp = self.write_request(\"info reg {}\".format(reg_name))\n        if len(resp) < 5 or not resp[2]['payload'].startswith('\\\\t'):\n            return 0\n        return int(resp[2]['payload'][2:].split(' ')[0], 16)\n\n    def get_stack_base(self, n):\n        # type: (int) -> Tuple[int, int]\n        self.write_request(\"select-frame {}\".format(n))\n        rsp_value = self.get_reg('rsp')\n        rbp_value = self.get_reg('rbp')\n        return rsp_value, rbp_value\n\n    def get_func_range(self, name):\n        # type: (str) -> List[int]\n        # FIXME: Not a good idea. Maybe some gdb extension?\n        r1 = self.write_request(\"print &{}\".format(name))\n        addr = self.parse_addr(r1[1]['payload'])\n        r2 = self.write_request(\"disass {}\".format(name))\n        size = self.parse_offset(r2[-3]['payload'])\n        return [addr, size + 1]\n\n\nclass CoredumpAnalyzer():\n    def __init__(self, elf, coredump):\n        # type: (Coredump, int, int) -> None\n        self.coredump = coredump\n        self.elf = elf\n        self.gdb = CoredumpGDB(elf, coredump)\n        self.backtrace = self.gdb.backtrace()\n        self.argc = self.coredump.argc\n        self.argv = [self.read_argv(i) for i in range(self.argc)]\n        self.argv_addr = [self.read_argv_addr(i) for i in range(self.argc)]\n    \n    def read_stack(self, addr, length=0x1):\n        # type: ignore\n        # NOTE: a op b op c will invoke weird typing\n        assert self.coredump.stack.start <= addr < self.coredump.stack.stop\n        offset = addr - self.coredump.stack.start\n        return self.coredump.stack.data[offset:offset+length]\n\n    def read_argv(self, n):\n        # type: ignore\n        assert 0 <= n < self.coredump.argc\n        return self.coredump.string(self.coredump.argv[n])\n\n    def read_argv_addr(self, n):\n        # type: ignore\n        assert 0 <= n < self.coredump.argc\n        return self.coredump.argv[n]\n\n    @property\n    def env(self):\n        return self.coredump.env\n\n    @property\n    def registers(self):\n        return self.coredump.registers\n\n    @property\n    def stack_start(self):\n        return self.coredump.stack.start\n    \n    @property\n    def stack_stop(self):\n        return self.coredump.stack.stop\n    \n    def call_argv(self, name):\n        for bt in self.backtrace:\n            if bt['func'] == name:\n                args = []\n                for _, value, entry in bt['args']:\n                    if entry:\n                        args.append(int(entry, 16))\n                    else:\n                        if value != '':\n                            args.append(int(value, 16))\n                        else:\n                            args.append(None)\n                return args\n        return None\n        # raise Exception(\"Unknown function {} in backtrace\".format(name))\n\n    def stack_base(self, name):\n        for bt in self.backtrace:\n            if bt['func'] == name:\n                return self.gdb.get_stack_base(int(bt['index']))\n        return (None, None)\n\n\ndef build_load_options(mappings):\n    # type: (List[Mapping]) -> dict\n    \"\"\"\n    Extract shared object memory mapping from coredump\n    \"\"\"\n    # FIXME: actually this library path different will cause \n    # simulation path different? need re-record if original \n    # executable is recompiled\n    main = mappings[0]\n    lib_opts = {}  # type: dict\n    force_load_libs = []\n    for m in mappings[1:]:\n        if not m.path.startswith(\"/\") or m.path in lib_opts:\n            continue\n        with open(m.path, \"rb\") as f:\n            magic = f.read(len(ELF_MAGIC))\n            if magic != ELF_MAGIC:\n                continue\n        lib_opts[m.path] = dict(custom_base_addr=m.start)\n        force_load_libs.append(m.path)\n\n    # TODO: extract libraries from core dump instead ?\n    return dict(\n        main_opts={\"custom_base_addr\": main.start},\n        force_load_libs=force_load_libs,\n        lib_opts=lib_opts,\n        load_options={\"except_missing_libs\": True})\n\n\nclass Tracer(object):\n    def __init__(self,\n                 executable,\n                 thread_id,\n                 trace_path,\n                 coredump,\n                 mappings,\n                 executable_root=None):\n        # type: (str, int, str, str, List[Mapping], Optional[str]) -> None\n        self.executable = executable\n        self.mappings = mappings\n        options = build_load_options(mappings)\n        self.project = angr.Project(executable, **options)\n\n        self.coredump = Coredump(coredump)\n        self.executable_root = executable_root\n        self.debug_unsat = None  # type: Optional[SimState]\n\n        command = os.path.basename(self.coredump.string(self.coredump.argv[0]))\n\n        trace = read_trace(\n            trace_path, thread_id, command, executable_root=executable_root)\n        self.trace = trace\n\n        if self.trace[-1].ip == 0:  # trace last ends in syscall\n            self.trace[-1].ip = self.coredump.registers[\"rip\"]\n\n        assert self.project.loader.main_object.os.startswith('UNIX')\n\n        self.elf = ELF(executable)\n\n        start = self.elf.symbols.get('_start')\n        main = self.elf.symbols.get('main')\n\n        self.cdanalyzer = CoredumpAnalyzer(\n            self.elf, self.coredump)\n\n        for (idx, event) in enumerate(self.trace):\n            if event.addr == start or event.addr == main or \\\n                    event.ip == start or event.ip == main:\n                self.trace = trace[idx:]\n\n        add_options = {\n            so.TRACK_JMP_ACTIONS,\n            so.CONSERVATIVE_READ_STRATEGY,\n            so.CONSERVATIVE_WRITE_STRATEGY,\n            so.BYPASS_UNSUPPORTED_IRCCALL,\n            so.BYPASS_UNSUPPORTED_IRDIRTY,\n            so.CONSTRAINT_TRACKING_IN_SOLVER,\n            # so.DOWNSIZE_Z3,\n        }\n\n        remove_simplications = {\n            so.LAZY_SOLVES, so.EFFICIENT_STATE_MERGING,\n            so.TRACK_CONSTRAINT_ACTIONS,\n            # so.ALL_FILES_EXIST, # the problem is, when having this, simfd either None or exist, no If\n        } | so.simplification\n\n        self.cfg = self.project.analyses.CFGFast(\n            show_progressbar=True\n        )\n\n        self.use_hook = True\n        self.omitted_section = [] # type: List[List[int]]\n\n        if self.use_hook:\n            self.hooked_symbols = all_hookable_symbols.copy()\n            self.setup_hook()\n        else:\n            self.hooked_symbols = {}\n            self.project._sim_procedures = {}\n\n        self.from_initial = False\n\n        self.filter = FilterTrace(\n            self.project,\n            self.cfg,\n            self.trace,\n            self.hooked_symbols,\n            self.cdanalyzer.gdb,\n            self.omitted_section,\n            self.from_initial,\n            self.elf.statically_linked,\n            self.cdanalyzer.backtrace,\n        )\n\n        self.old_trace = self.trace\n        self.trace, self.trace_idx, self.hook_target = self.filter.filtered_trace()\n        self.hook_plt_idx = self.hook_target.keys()\n        self.hook_plt_idx.sort()\n\n        start_address = self.trace[0].ip\n\n        if self.filter.start_funcname == 'main':\n            args = self.cdanalyzer.call_argv('main')\n            if not args:\n                self.start_state = self.project.factory.blank_state(\n                    addr=start_address,\n                    add_options=add_options,\n                    remove_options=remove_simplications)\n            else:\n                # NOTE: gdb sometimes take this wrong\n                args[0] = self.coredump.argc\n                self.start_state = self.project.factory.call_state(\n                    start_address,\n                    *args,\n                    add_options=add_options,\n                    remove_options=remove_simplications)\n            rsp, rbp = self.cdanalyzer.stack_base('main')\n            # TODO: or just stop?\n            if not rbp:\n                rbp = 0x7ffffffcf00\n            if not rsp:\n                rsp = 0x7ffffffcf00         \n        else:\n            rsp, rbp = self.cdanalyzer.stack_base(self.filter.start_funcname)\n            if not rbp:\n                rbp = 0x7ffffffcf00\n            if not rsp:\n                rsp = 0x7ffffffcf00\n            self.start_state = self.project.factory.blank_state(\n                addr=start_address,\n                add_options=add_options,\n                remove_options=remove_simplications)\n\n        if self.filter.is_start_entry:\n            self.start_state.regs.rsp = rbp + 8\n        else:\n            self.start_state.regs.rsp = rsp\n            self.start_state.regs.rbp = rbp  \n\n        self.setup_argv()\n        self.simgr = self.project.factory.simgr(\n            self.start_state,\n            save_unsat=True,\n            hierarchy=False,\n            save_unconstrained=True)\n\n        # self.setup_breakpoint()\n\n        # For debugging\n        # self.project.pt = self\n        self.constraints_index = {} # type: Dict[int, int]\n\n    def setup_argv(self):\n        # TODO: if argv is modified by users, this won't help\n        args = self.cdanalyzer.call_argv('main')\n        if args:\n            argv_addr = args[1]\n            for i in range(len(self.coredump.argv)):\n                self.start_state.memory.store(\n                    argv_addr + i * 8, \n                    self.coredump.argv[i],\n                    endness=archinfo.Endness.LE\n                )\n                self.start_state.memory.store(\n                    self.coredump.argv[i],\n                    self.coredump.string(self.coredump.argv[i])[::-1],\n                    endness=archinfo.Endness.LE\n                )\n\n    def setup_hook(self):\n        self.hooked_symbols.pop('abort', None)\n        self.hooked_symbols.pop('__assert_fail', None)\n        self.hooked_symbols.pop('__stack_chk_fail', None)\n        try:\n            abort_addr = self.project.loader.find_symbol('abort').rebased_addr\n            assert_addr = self.project.loader.find_symbol('__assert_fail').rebased_addr\n            stack_addr = self.project.loader.find_symbol('__stack_chk_fail').rebased_addr\n            self.project._sim_procedures.pop(abort_addr, None)\n            self.project._sim_procedures.pop(assert_addr, None)\n            self.project._sim_procedures.pop(stack_addr, None)\n        except:\n            pass\n        \n        for symname, func in self.hooked_symbols.items():\n            self.project.hook_symbol(\n                symname, func()\n            )\n        for symname in addr_symbols:\n            if symname in self.hooked_symbols.keys():\n                r = self.cdanalyzer.gdb.get_func_range(symname)\n                func = self.hooked_symbols[symname]\n                if r != [0, 0]:\n                    self.project.hook(\n                        r[0], func(), length=r[1]\n                    )\n                    self.omitted_section.append(r)\n\n    def setup_breakpoint(self):\n        self.start_state.inspect.b('mem_write', when=angr.BP_AFTER, action=Tracer.inspect_mem_write)\n\n    @staticmethod\n    def inspect_mem_write(state):\n        length = state.inspect.mem_write_length\n        if not state.se.symbolic(length) and \\\n            state.se.eval(length) > 0x1000:\n            l.warning(\n                hex(state.addr) + \\\n                ': write ' + \\\n                str(state.inspect.mem_write_expr) + \\\n                'with length ' + \\\n                str(state.inspect.mem_write_length))\n            raw_input()\n\n    def test_rep_ins(self, state):\n        # NOTE: rep -> sat or unsat\n        capstone = state.block().capstone\n        first_ins = capstone.insns[0].insn\n        # NOTE: maybe better way is use prefix == 0xf2, 0xf3 (crc32 exception)\n        ins_repr = first_ins.mnemonic\n        return ins_repr.startswith('rep')\n\n    def repair_hook_return(self, state, index):\n        # given a force_jump, from hook -> last\n        # caller -> plt -> hook\n        if self.project.is_hooked(state.addr):\n            '''\n                it might be that call -> jmp -> real plt\n                or it could be add rsp 0x8, jmp -> directly ret\n                the only way seems to be sacrifice some execution \n                or look back to old_trace\n            '''\n            old_branch_idx = self.trace_idx[index] - 1\n            # should be plt\n            plt_idx = bisect_right(self.hook_plt_idx, old_branch_idx) - 1\n            ret_addr = self.hook_target[self.hook_plt_idx[plt_idx]]\n            # like a ret\n            new_state = state.copy()\n            new_state.regs.rsp += 8\n            new_state.regs.ip = ret_addr\n            return True, new_state\n        return False, None\n\n    def repair_exit_handler(self, state, step):\n        artifacts = getattr(step, 'artifacts', None)\n        if artifacts and 'procedure' in artifacts.keys() \\\n            and artifacts['name'] == 'exit':\n            if len(state.libc.exit_handler):\n                addr = state.libc.exit_handler[0]\n                step = self.project.factory.successors(\n                    state,\n                    num_inst=1,\n                    force_addr=addr\n                )\n        return step\n\n    def repair_alloca_ins(self, state):\n        # NOTE: alloca problem, focus on sub rsp, rax\n        # Typical usage: alloca(strlen(x))\n        capstone = state.block().capstone\n        first_ins = capstone.insns[0].insn\n        if first_ins.mnemonic == 'sub':\n            if first_ins.operands[0].reg in (x86_const.X86_REG_RSP, x86_const.X86_REG_RBP) \\\n                and first_ins.operands[1].type == 1:\n                reg_name = first_ins.reg_name(first_ins.operands[1].reg)\n                reg_v = getattr(state.regs, reg_name)\n                if state.se.symbolic(reg_v):\n                    setattr(state.regs, reg_name, state.libc.max_str_len)\n\n    def repair_jump_ins(self, state, branch):\n        # type: (SimState, Branch) -> Tuple[bool, str]\n        # ret: force_jump\n        # NOTE: typical case: switch(getchar())\n        \n        if state.addr != branch.addr:\n            return False, ''\n        jump_ins = ['jmp', 'call'] # currently not deal with jcc regs\n        capstone = state.block().capstone\n        first_ins = capstone.insns[0].insn\n        ins_repr = first_ins.mnemonic\n\n        if ins_repr.startswith('ret'):\n            if not state.se.symbolic(state.regs.rsp):\n                mem = state.memory.load(state.regs.rsp, 8)\n                jump_target = 0\n                if not state.se.symbolic(mem):\n                    jump_target = state.se.eval(mem)\n                if jump_target != branch.ip:\n                    return True, 'ret'\n                else:\n                    return True, 'ok'\n            else:\n                return True, 'ret'\n\n        for ins in jump_ins:\n            if ins_repr.startswith(ins):\n                # call rax\n                if first_ins.operands[0].type == 1:\n                    reg_name = first_ins.op_str\n                    reg_v = getattr(state.regs, reg_name)\n                    if state.se.symbolic(reg_v) or state.se.eval(reg_v) != branch.ip:\n                        setattr(state.regs, reg_name, branch.ip)\n                        return True, ins\n\n                # jmp 0xaabb\n                if first_ins.operands[0].type == 2:\n                    return True, ins\n\n                # jmp [base + index*scale + disp]\n                if first_ins.operands[0].type == 3:\n                    self.last_jump_table = state\n                    mem = first_ins.operands[0].value.mem\n                    target = mem.disp\n                    if mem.index:\n                        reg_index_name = first_ins.reg_name(mem.index)\n                        reg_index = getattr(state.regs, reg_index_name)\n                        if state.se.symbolic(reg_index):\n                            return True, ins\n                        else:\n                            target += state.se.eval(reg_index) * mem.scale\n                    if mem.base:\n                        reg_base_name = first_ins.reg_name(mem.base)\n                        reg_base = getattr(state.regs, reg_base_name)\n                        if state.se.symbolic(reg_base):\n                            return True, ins\n                        else:\n                            target += state.se.eval(reg_base)\n                    ip_mem = state.memory.load(target, 8, endness='Iend_LE')\n                    if not state.se.symbolic(ip_mem):\n                        jump_target = state.se.eval(ip_mem)\n                        if jump_target != branch.ip:\n                            return True, ins\n                        else:\n                            return True, 'ok'\n                    else:\n                        return True, ins\n        return False, 'ok'\n\n    def repair_ip(self, state):\n        try:\n            addr = state.se.eval(state._ip)\n            # NOTE: repair IFuncResolver\n            if self.project.loader.find_object_containing(addr) == self.project.loader.extern_object:\n                func = self.project._sim_procedures.get(addr, None)\n                if func:\n                    funcname = func.kwargs['funcname']\n                    libf = self.project.loader.find_symbol(funcname)\n                    if libf:\n                        addr = libf.rebased_addr\n        except:\n            # NOTE: currently just try to repair ip for syscall\n            addr = self.debug_state[-2].addr\n        return addr\n\n    def repair_func_resolver(self, state, step):\n        artifacts = getattr(step, 'artifacts', None)\n        if artifacts and 'procedure' in artifacts.keys() \\\n            and artifacts['name'] == 'IFuncResolver':\n            func = self.filter.find_function(self.debug_state[-2].addr)\n            if func:\n                addr = self.project.loader.find_symbol(func.name).rebased_addr\n                step = self.project.factory.successors(\n                    state,\n                    num_inst=1,\n                    force_addr=addr\n                )\n            else:\n                raise Exception(\"Cannot resolve function\")\n        return step\n\n    def last_match(self, choice, branch):\n        # if last trace is A -> A\n        if branch == self.trace[-1] and branch.addr == branch.ip:\n            if choice.addr == branch.addr and branch.addr == branch.ip:\n                l.debug(\"jump 0%x -> 0%x\", choice.addr, choice.addr)\n                return True\n        return False\n\n    def jump_match(self, old_state, choice, branch):\n        try:\n            if old_state.addr == branch.addr and choice.addr == branch.ip:\n                l.debug(\"jump 0%x -> 0%x\", old_state.addr, choice.addr)\n                return True\n        except:\n            pass\n        return False\n\n    def jump_was_not_taken(self, old_state, new_state):\n        # was the last control flow change an exit vs call/jump?\n        ev = new_state.events[-1]\n        instructions = old_state.block().capstone.insns\n        if not isinstance(ev, SimActionExit): # and len(instructions) == 1\n            return False\n        size = instructions[0].insn.size\n        return (new_state.addr - size) == old_state.addr\n\n    def repair_satness(self, old_state, new_state):\n        if not new_state.solver.satisfiable(): # type: ignore\n            sat_constraints = old_state.solver._solver.constraints\n            '''\n            unsat_constraints = list(new_state.solver._solver.constraints)\n            sat_uuid = map(lambda c: c.uuid, sat_constraints)\n            for i, c in enumerate(unsat_constraints):\n                if c.uuid not in sat_uuid:\n                    unsat_constraints[i] = claripy.Not(c)\n            '''\n            new_state.solver._stored_solver = old_state.solver._solver.branch()\n            \n            if not self.debug_unsat: # type: ignore\n                self.debug_sat = old_state\n                self.debug_unsat = new_state\n\n    def record_constraints_index(self, old_state, new_state, index):\n        sat_uuid = map(lambda c: c.uuid, old_state.se.constraints)\n        unsat_constraints = list(new_state.se.constraints)\n        for c in unsat_constraints:\n            if c.uuid not in sat_uuid:\n                self.constraints_index[c] = index\n\n    def find_next_branch(self, state, branch, index):\n        # type: (SimState, Branch, int) -> SimState\n        CNT_LIMIT = 200\n        REP_LIMIT = 128\n        cnt = 0\n        rep_cnt = 0\n        while cnt < CNT_LIMIT:\n            cnt += 1\n            self.debug_state.append(state)\n            force_ret, force_state = self.repair_hook_return(state, index)\n            force_jump, force_type = self.repair_jump_ins(state, branch)\n            self.repair_alloca_ins(state)\n            addr = self.repair_ip(state)\n            is_interrupt = False\n            next_addr = None\n            if state.addr in self.skip_addr.keys():\n                state._ip = self.skip_addr[state.addr]\n                continue\n            try:\n                step = self.project.factory.successors(\n                    state,\n                    num_inst=1,\n                    force_addr=addr\n                )\n            except KeyboardInterrupt:\n                # NOTE: should have a timeout fallback\n                insns = state.block().capstone.insns\n                if state.addr != branch.addr and len(insns) > 1:\n                    is_interrupt = True\n                    next_addr = insns[1].address\n                    self.skip_addr[state.addr] = next_addr\n                else:\n                    import traceback\n                    traceback.print_exc()\n                    raise Exception(\"Manually stop\")\n            except:\n                # if force_jump, we have a chance (sp symbolic error)\n                if not force_jump and not force_ret:\n                    raise Exception(\"Unable to continue\")\n            if is_interrupt:\n                state._ip = next_addr\n                continue\n\n            if force_jump:\n                # always boring. the rest call stack done by outselves to avoid exception\n                # will cause history to be unusable\n                # jumpkind = 'Ijk_Boring'\n                new_state = state.copy()\n                if force_type == 'call':\n                    new_state.regs.rsp -= 8\n                    ret_addr = state.addr + state.block().capstone.insns[0].size\n                    new_state.memory.store(new_state.regs.rsp, ret_addr, endness='Iend_LE')\n                elif force_type == 'ret':\n                    new_state.regs.rsp += 8\n                new_state.regs.ip = branch.ip\n                all_choices = {\n                    'sat': [new_state],\n                    'unsat': [],\n                    'unconstrained': []\n                }\n                choices = [new_state]\n                '''\n                step.add_successor(\n                    new_state,\n                    branch.ip,\n                    state.se.true,\n                    jumpkind\n                )\n                '''\n            elif force_ret:\n                all_choices = {\n                    'sat': [force_state],\n                    'unsat': [],\n                    'unconstrained': []\n                }\n                choices = [force_state]\n            else:\n                step = self.repair_func_resolver(state, step)\n                step = self.repair_exit_handler(state, step)\n\n                all_choices = {\n                    'sat': step.successors,\n                    'unsat': step.unsat_successors,\n                    'unconstrained': step.unconstrained_successors,\n                }\n                # lookup sequence: sat, unsat, unconstrained\n                choices = []\n                choices += all_choices['sat']\n                choices += all_choices['unsat']\n                # choices += all_choices['unconstrained']\n\n            old_state = state\n            # TODO: add successors with no constraint if match branch.addr\n            l.warning(\n                repr(cnt) + ' ' +\n                repr(state) + ' ' +\n                # repr(all_choices) + ' ' +\n                repr(branch) + '\\n'\n            )\n            if choices == []:\n                raise Exception(\"Unable to continue\")\n\n            for choice in choices:\n                if self.last_match(choice, branch):\n                    return choice, choice\n                if old_state.addr == branch.addr:\n                    if self.jump_match(old_state, choice, branch):\n                        self.repair_satness(old_state, choice)\n                        self.record_constraints_index(old_state, choice, index)\n                        return old_state, choice\n            # NOTE: need to consider repz here, if repz repeats for less than N times, \n            # then, it should still be on sat path\n            if self.test_rep_ins(state):\n                rep_cnt += 1\n                if rep_cnt < REP_LIMIT and len(all_choices['sat']) == 1:\n                    state = all_choices['sat'][0]\n                    self.record_constraints_index(old_state, choice, index)\n                    continue\n            else:\n                rep_cnt = 0\n            for choice in choices:\n                if self.jump_was_not_taken(\n                    old_state, choice):\n                    state = choice\n                    break\n            else:\n                if len(all_choices['sat']) == 1:\n                    state = all_choices['sat'][0]\n                elif len(choices) == 1:\n                    state = choices[0]\n                else:\n                    raise Exception(\"Unable to continue\")\n            self.repair_satness(old_state, state)\n            self.record_constraints_index(old_state, state, index)\n            for c in choices:\n               if c != state:\n                    c.downsize()\n                    del c\n\n        print(choices, state, branch)\n        raise Exception(\"Unable to continue\")\n\n    def valid_address(self, address):\n        # type: (int) -> bool\n        return self.project.loader.find_object_containing(address)\n\n    def constrain_registers(self, state):\n        # type: (State) -> None\n        # FIXME: if exception caught is omitted by hook?\n        # If same address, then give registers\n        if state.registers['rip'].value == self.coredump.registers['rip']:\n            # don't give rbp, rsp\n            assert state.registers['rsp'].value == self.coredump.registers['rsp']\n            registers = [\n                \"gs\", \"rip\", \"rdx\", \"r15\", \"rax\", \"rsi\", \"rcx\", \"r14\", \"fs\", \"r12\",\n                \"r13\", \"r10\", \"r11\", \"rbx\", \"r8\", \"r9\", \"eflags\", \"rdi\"\n            ]\n            for name in registers:\n                state.registers[name] = self.coredump.registers[name]\n\n    def run(self):\n        # type: () -> StateManager\n        simstate = self.simgr.active[0]\n        states = StateManager(self, len(self.trace))\n        states.add_major(State(0, self.trace[0], None, simstate))\n        self.debug_unsat = None # type: Optional[SimState]\n        self.debug_state = deque(maxlen=5) # type: deque\n        self.skip_addr = {} # type: Dict[int, int]\n        cnt = 0\n        interval = max(1, len(self.trace) // 200)\n        length = len(self.trace) - 1\n        \n        for event in self.trace[1:]:\n            cnt += 1\n            if not cnt % 500:\n                l.warning('Do a garbage collection')\n                gc.collect()\n            l.debug(\"look for jump: 0x%x -> 0x%x\" % (event.addr, event.ip))\n            assert self.valid_address(event.addr) and self.valid_address(\n                event.ip)\n            self.current_branch = event\n            old_simstate, new_simstate = self.find_next_branch(simstate, event, cnt)\n            simstate = new_simstate\n            if cnt % interval == 0 or length - cnt < 15:\n                states.add_major(State(cnt, event, old_simstate, new_simstate))\n        self.constrain_registers(states.major_states[-1])\n\n        return states\n","repo_name":"Airtnp/hase","sub_path":"hase/symbex/tracer.py","file_name":"tracer.py","file_ext":"py","file_size_in_byte":33070,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"81"}
+{"seq_id":"27380777721","text":"from django import template\nfrom django.utils.timezone import now\n\nfrom hunt.app.core.assets.refs import get_round_static_path\n\nregister = template.Library()\n\nFANTASY_GLYPHS = {\n    373: \"anumberofgames-2wyJSLC8iP\", #a number of games\n    110: \"curiouscustoms-LxZroIvVo5\", #curious customs\n    452: \"garden-zaWMFk0MiY\", #enchanted garden\n    474: \"delicious-btyXLNjuYV\", #magically delicious\n    276: \"potions-As79KGQbHF\", #potions\n    492: \"royalsteeds-nOUbnKjHjV\", #royal steeds\n    163: \"somethingcommand-goAxrdrXvu\", #something command\n    477: \"sorcery-zXNjJA52qV\", #sorcery\n}\n\n@register.simple_tag()\ndef fantasy_submission(puzzle, team):\n    id = puzzle.external_id\n    glyph = FANTASY_GLYPHS.get(id, None)\n\n    if (puzzle.is_meta):\n        return {\n            'glyph_filename': None\n        }\n\n    return {\n        'puzzle_url': puzzle.url,\n        'glyph_filename': \"{}.jpg\".format(glyph),\n        'rd_root': get_round_static_path(puzzle.round.url, variant='round'),\n    }\n\n@register.filter\ndef fantasy_feeders(puzzles, is_public):\n    stub_solve_time = now() if is_public else None\n    solved_fantasy_feeders = list(\n        puzzle_info for puzzle_info in puzzles if (puzzle_info['solved'] or is_public) and not puzzle_info['puzzle'].is_meta)\n    solved_fantasy_feeders.sort(key=lambda puzzle_info: puzzle_info['solved_time'] or stub_solve_time)\n    for feeder in solved_fantasy_feeders:\n        feeder['glyph'] = FANTASY_GLYPHS.get(feeder['puzzle'].external_id, None)\n    return solved_fantasy_feeders\n","repo_name":"Palindrome-Puzzles/2022-hunt","sub_path":"hunt/data/templatetags/fantasy.py","file_name":"fantasy.py","file_ext":"py","file_size_in_byte":1513,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"81"}
+{"seq_id":"9303972517","text":"def interpret_operand(op):\n\tif op in registers:\n\t\treturn registers[op]\n\telse:\n\t\treturn int(op)\n\ninstructions = [line.split()[0] for line in open('input/input.txt', 'r').readlines()]\ntargets = [line.split()[1] for line in open('input/input.txt', 'r').readlines()]\noperands = []\nfor line in open('input/input.txt', 'r').readlines():\n\tif len(line.split()) == 3:\n\t\toperands.append(line.split()[2])\n\telse:\n\t\toperands.append(0)\n\nregisters = {}\nfor r in set(targets):\n\tregisters[r] = 0\n\nfreq = 0\nn = len(instructions)\ni = 0\nwhile i < n:\n\tinstr = instructions[i] \n\treg = targets[i]\n\tval = interpret_operand(operands[i])\n\t#print('{} {} {}'.format(instr, reg, val))\n\t#print('reg[{}]:{}'.format(reg, registers[reg]))\n\n\tif instr == 'set':\n\t\tregisters[reg] = val\n\telif instr == 'add':\n\t\tregisters[reg] += val\n\telif instr == 'mul':\n\t\tregisters[reg] *= val\n\telif instr == 'snd':\n\t\tfreq = registers[reg]\n\t\t#print('Freq: %s' % freq)\n\telif instr == 'mod':\n\t\tregisters[reg] = registers[reg] % val\n\telif instr == 'rcv':\n\t\tif registers[reg] != 0:\n\t\t\tprint(freq)\n\t\t\tbreak\n\telif instr == 'jgz':\n\t\tif registers[reg] > 0:\n\t\t\ti += val\n\t\t\tcontinue\n\telse:\n\t\tprint('Instruction not recognized')\n\n\ti += 1\n\t#print(registers)\n","repo_name":"r-tran/advent-of-code","sub_path":"aoc-2017/day18/day18.py","file_name":"day18.py","file_ext":"py","file_size_in_byte":1194,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"23838047897","text":"# Uses python3\nimport sys\n    \ndef calc_fib(n):\n    a = 1\n    b = 1\n    if (n == 0):\n        return 0\n    elif (n < 3):\n        return b\n    else:\n        n -= 2\n        while(n > 0):\n            temp = b\n            b = a + b\n            a = temp\n            n -= 1\n        return b\n\ndef get_period_len(n,m):\n    a = 1\n    b = 1\n    counter = 1\n    while(a != 0 or b != 1):\n        temp = b\n        b = (a + b)%m\n        a = temp\n        counter +=1\n    return counter\n\ndef get_fibonacci_huge(n,m):\n    period = get_period_len(n,m)\n    fib = calc_fib(n%period)\n    return fib%m\n\n\ndef fibonacci_sum(n):\n    fib = get_fibonacci_huge(n+2,10)\n    fib = ((fib-1)+10)%10\n    return fib;\n\n    \ndef fibonacci_sum_naive(n):\n    if n <= 1:\n        return n\n\n    previous = 0\n    current  = 1\n    sum      = 1\n\n    for _ in range(n - 1):\n        previous, current = current, previous + current\n        sum += current\n\n    return sum % 10\n\nif __name__ == '__main__':\n    input = sys.stdin.read()\n    n = int(input)\n    print(fibonacci_sum(n))\n","repo_name":"SamuelEsd/Algorithmic_Toolbox","sub_path":"week2_algorithmic_warmup/6_last_digit_of_the_sum_of_fibonacci_numbers/fibonacci_sum_last_digit.py","file_name":"fibonacci_sum_last_digit.py","file_ext":"py","file_size_in_byte":1032,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"72465028744","text":"# -*- coding: cp936 -*-\n'''\n使用字典结构合并两个通讯录文本为一个文件\ngbk是用来将中文写入文本，防止乱码\n\n'''\n'''\n\n\n'''\ndef combineFile():\n    ftele1 = open('TeleAddressBook.txt','rb') #通过二进制字节流的方式读取文件\n    ftele2 = open(\"EmailAddressBook.txt\",\"rb\")\n\n    ftele1.readline()#跳过一行\n    ftele2.readline()\n\n    lines1 = ftele1.readlines()\n    lines2 = ftele2.readlines()\n\n    dic1 = {} #字典方式保存\n    dic2 = {}\n\n    for line in lines1: #获取第一个文本中的姓名和电话信息\n        elements = line.split()\n        #将文本读出来的bytes转换为str类型\n        dic1[elements[0]] = str(elements[1].decode(\"gbk\"))\n\n    for line in lines2:#获取第二个文本的姓名和邮件信息\n        elements = line.split()\n        dic2[elements[0]] = str(elements[1].decode(\"gbk\"))\n\n    #开始处理\n    lines = []\n    lines.append(\"姓名\\t     电话   \\t   邮箱\\n\")\n    '''\n    join()函数\n\n    语法：  'sep'.join(seq)\n\n    参数说明\n    sep：分隔符。可以为空\n    seq：要连接的元素序列、字符串、元组、字典\n    上面的语法即：以sep作为分隔符，将seq所有的元素合并成一个新的字符串\n\n    返回值：返回一个以分隔符sep连接各个元素后生成的字符串\n\n\n    通过使用join方法加深了对join的理解\n    eg：s = char.join([,,,])的作用就是将列表里的元素们连接起来，并用char来分隔而\n        组成的新的字符串\n\n    '''\n    for key in dic1:\n        s = ''\n        if key in dic2.keys():\n            s = '\\t'.join([str(key),dic1[key],dic2[key]])\n            s+='\\n'\n        else:\n            s = '\\t'.join([str(key),dic1[key],'----------------'])\n            s+='\\n'\n        lines.append(s)\n\n    for key in dic2:\n        s = ''\n        if key not in dic1.keys():\n            s = '\\t'.join([str(key),'-----------',dic2[key]])\n            s+='\\n'\n        lines.append(s)\n\n    ftele3 = open(\"AddressBook.txt\",'w')\n    ftele3.writelines(lines)\n\n    ftele3.close()  \n    ftele1.close()\n    ftele2.close()  \n        \ndef main():\n    combineFile()\n    print(\"All Done\")\n\nmain()\n","repo_name":"hanchenn/python_demos","sub_path":"Demos/pydemo7-2.py","file_name":"pydemo7-2.py","file_ext":"py","file_size_in_byte":1917,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"40403422887","text":"from django.contrib.auth import get_user_model\nfrom django.db import models\n\nUser = get_user_model()\n\n\nclass Group(models.Model):\n    title = models.CharField(verbose_name='Название группы', max_length=200)\n    slug = models.SlugField('Ключ', unique=True,\n                            help_text=('Для ссылки на вашу группу. '\n                                       'Пишите Латиницей без пробелов'))\n    description = models.TextField('Описание')\n\n    class Meta:\n        verbose_name = 'Группа'\n        verbose_name_plural = 'Группы'\n\n    def __str__(self):\n        return self.title\n\n\nclass Post(models.Model):\n    text = models.TextField('Текст')\n    pub_date = models.DateTimeField('Дата публикации', auto_now_add=True)\n    author = models.ForeignKey(User, on_delete=models.CASCADE,\n                               verbose_name='Автор', related_name='posts')\n    group = models.ForeignKey(Group, related_name='posts',\n                              null=True, blank=True,\n                              on_delete=models.SET_NULL,\n                              verbose_name='Группа',\n                              help_text=('Группа, в которой '\n                                         'публикуется запись'),)\n    image = models.ImageField(upload_to='posts/', blank=True, null=True)\n\n    class Meta:\n        ordering = ('-pub_date',)\n        verbose_name = 'Пост'\n        verbose_name_plural = 'Посты'\n\n    def __str__(self):\n        return self.text[:15]\n\n\nclass Comment(models.Model):\n    post = models.ForeignKey(\n        Post,\n        models.CASCADE,\n        related_name='comments',\n        verbose_name='Пост'\n    )\n    author = models.ForeignKey(\n        User,\n        models.CASCADE,\n        related_name='comments',\n        verbose_name='Автор'\n    )\n    text = models.TextField('Текст')\n    created = models.DateTimeField(\n        'Дата публикации',\n        auto_now_add=True\n    )\n\n    class Meta:\n        ordering = ('-created',)\n        verbose_name = 'Комментарий'\n        verbose_name_plural = 'Комментарии'\n\n    def __str__(self):\n        return self.text[:15]\n\n\nclass Follow(models.Model):\n    user = models.ForeignKey(\n        User,\n        related_name='follower',\n        on_delete=models.CASCADE,\n        verbose_name='Подписчик'\n    )\n    author = models.ForeignKey(\n        User,\n        related_name='following',\n        on_delete=models.CASCADE,\n        verbose_name='Автор'\n    )\n\n    class Meta:\n        constraints = (\n            models.UniqueConstraint(\n                fields=('user', 'author'), name='unique_link'),\n        )\n\n    def __str__(self):\n        return f'{self.user.username} подписан на {self.author.username}'\n","repo_name":"NiKuma0/YaTube","sub_path":"posts/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":2884,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"37668916841","text":"#Script to extract Straps connections with values from Ajmal's sheet\r\n#By Bhanu Pratap  Singh\r\n# importing openpyxl module\r\nimport openpyxl as xl;\r\n\r\n# opening the source excel file\r\nfilename = \"C:\\\\Users\\\\singhb\\\\OneDrive - Intel Corporation\\\\Desktop\\\\Intel\\\\Formal Verification\\\\Straps\\\\rwc_10x7_straps_spec.xlsx\"\r\nwb1 = xl.load_workbook(filename)\r\nws1 = wb1.worksheets[43]\r\n\r\n# opening the destination excel file\r\nfilename1 = \"C:\\\\Users\\\\singhb\\\\OneDrive - Intel Corporation\\\\Desktop\\\\Intel\\\\Formal Verification\\\\Straps\\\\straps_extract.xlsx\"\r\nwb2 = xl.load_workbook(filename1)\r\nws2 = wb2.worksheets[0]\r\n\r\n# calculate total number of rows and\r\n# columns in source excel file\r\nmr = ws1.max_row\r\nmc = ws1.max_column\r\n\r\n# copying the cell values from source\r\n# excel file to destination excel file\r\n# defining some variables\r\ndst_col_count = 5\r\nrow_count_col4 = 1\r\nrow_count_col2_3 = 1\r\nfor j in range(1, mc + 1):\r\n    for i in range(2, mr + 1):\r\n        if j == dst_col_count:\r\n            #print('inside',i, j)\r\n            # reading cell value from source excel file for column 5 onwards and every rows\r\n            v = ws1.cell(row=i, column=j)\r\n            # writing the read value to destination excel file in column of values\r\n            ws2.cell(row=row_count_col2_3, column=2).value = v.value\r\n\r\n            d = ws1.cell(row=1, column=j)\r\n            # writing the read value to destination excel file in column of dst\r\n            ws2.cell(row=row_count_col2_3, column=3).value = d.value\r\n            row_count_col2_3+=1\r\n        else:\r\n            break\r\n        # reading cell value from source excel file for column 1 and each row\r\n        s = ws1.cell(row=i, column=1)\r\n        # writing the read value to destination excel file in column of straps\r\n        ws2.cell(row=row_count_col4, column=4).value = s.value\r\n        row_count_col4 += 1\r\n\r\n    if j == dst_col_count:\r\n        dst_col_count += 1\r\n\r\n# saving the destination excel file\r\nwb2.save(filename1)\r\n","repo_name":"Bucky0601/Scripts","sub_path":"Excel_data_extract.py","file_name":"Excel_data_extract.py","file_ext":"py","file_size_in_byte":1975,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"14892100331","text":"#!/usr/bin/env python\n# coding: utf-8\n\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom sklearn.model_selection import train_test_split\n\nfrom sklearn.feature_extraction.text import TfidfVectorizer\nimport scipy\n\nimport tensorflow as tf\nfrom tensorflow.keras.models import Sequential\nfrom tensorflow.keras.layers import Dense    # for hidden layers of tensorflow.NOT dense matrix\n\n# Data cleaning libraries\n\nimport re            #regex . (helps in data cleaning)\nimport nltk          #stop words are junk in data. nltk has these stop words,punctuations, strings.\nnltk.download('stopwords')\nfrom nltk.corpus import stopwords\n\ndf = pd.read_csv(\"/content/drive/MyDrive/Classroom/Tweets.csv\")\ndf.head()\n\ndf.drop(\"tweet_id\",axis=1,inplace = True)\ndf.head()\ndf.isnull().sum()\ndf.shape\n\ndf.drop(['airline_sentiment_confidence','airline_sentiment_gold','negativereason_gold','tweet_coord','tweet_location','user_timezone'],inplace = True,axis=1)\ndf.head()\n\n# Data analysis\ndf['airline_sentiment'].value_counts().plot(kind='bar')\ndf['airline'].value_counts().plot(kind='bar')\ndf.negativereason.value_counts().plot(kind='bar')\ndf_each_airline_sentiment = pd.crosstab(df.airline, df.airline_sentiment)\ndf_each_airline_sentiment\ndf_each_airline_sentiment.plot(kind='bar')\ndf_each_airline_neg_sentiment = pd.crosstab(df.airline, df.negativereason)\ndf_each_airline_neg_sentiment.style.highlight_max(color='red',axis=1)\ndf_each_airline_neg_sentiment.plot(kind='bar',figsize=(30,10))\ndf['tweet_created']=pd.to_datetime(df.tweet_created).dt.strftime('%d-%m-%y')\ndf.head()\ndf.tweet_created.max()\ndf.tweet_created.min()\ndf_negReason_date = pd.crosstab(df.tweet_created,df.airline_sentiment)\ndf_negReason_date\n\ndf_negReason_date.plot(kind='bar',figsize=(25,10))\n\n#Data Cleaning\n\nstops=set(stopwords.words('english'))\nstops\nlen(stops)\n\ndef data_clean(sentence):\n  words=sentence.lower()  #Converts all the capital letters to small letters\n  words=re.sub('[^a-z]',' ',words)   # ^ means allowed\n  words=words.split()   # it will create a list\n  important_words=[w for w in words if w not in stops] # to filter imp word \n  return(' '.join(important_words))   #  to join into a sentence using space between words\n\nex='it is 9:40 but we are not tired we still want to learn deep learning we stay up till 11 up today'\n\ndata_clean(ex)\n\ndf['clean_text']=df.text.apply(lambda x : data_clean(x)) # lambda is applying cleaning function to each line\nresult=df[['text','clean_text']]\nresult\n\nresult['count_text'] = result['clean_text'].apply(lambda x : len(x.split()))    # count the no. of words in each text\nresult['count_text']\n\nresult.join(df['airline_sentiment'])  # joining a column from other dataframe\n\nresult['count_text'].min()\nresult['count_text'].max()\nresult['count_text'].plot(kind='hist')\nresult.drop(['text','count_text'],axis = 1, inplace=True)\nresult\n\nnew_df = result.join(df['airline_sentiment'])\new_df\nnew_df = pd.get_dummies(new_df['airline_sentiment'],columns=['neutral','positive','negative'])   # one hot encoding required since we are looking at categorical cross entropy\nnew_df\n\nencoded_df = result.join(new_df)\n\nx = encoded_df['clean_text']\ny = encoded_df.iloc[:,1:]\ny.head()\n\n# Train test split\nX_train,X_test,y_train,y_test = train_test_split(x,y,test_size = 0.2,random_state = 100)\n\nX_train.shape\n\nX_test.shape\n\ny_train.shape\n\ny_test.shape\n# using TF_IDF vectorizer\n\ntfidf = TfidfVectorizer()\nx_train_idf = tfidf.fit_transform(X_train)    # to get the dict(vocab) of words and values in training data\nx_train_idf\ntfidf.get_feature_names()\nlen(tfidf.get_feature_names())\nx_test_idf = tfidf.transform(X_test)\nx_test_idf\n\n# Converting train data into dense matrix\n\nX_train = scipy.sparse.csr_matrix.todense(x_train_idf)  # use scipy to have lesser run time\nX_train\nX_train.shape   # 11712 (rows) => no of neurons at input layer, 11888 (columns) vocabulary => input_shape\n\nX_test = scipy.sparse.csr_matrix.todense(x_test_idf)   # tfidf accepts matrix only in dense form;else it gives a shape error\nX_test.shape\n\n# Model Building\n\nneurons_first_layer = X_train.shape[0]\n\nneurons_first_layer\n\ninput = X_train.shape[1]\n\ninput\n\noutput = y_train.shape[1]\n\noutput\n\n\nmodel = Sequential([\n                    Dense(neurons_first_layer,activation='relu',input_shape=(input,)),   # 'input' no.of columns and all the rows\n                    #Dense(128,activation='relu'),\n                    Dense(64,activation='relu'),\n                    Dense(output,activation='softmax')\n                   ])\n\n\nmodel.summary()\n\nmodel.compile(optimizer='adam',loss = 'categorical_crossentropy',metrics=['accuracy'])\nhistory = model.fit(X_train,y_train,epochs = 5,validation_data = (X_test,y_test))\n\nplt.plot(history.history['accuracy'])                #training accuracy\nplt.plot(history.history['val_accuracy'])\nplt.xlabel('epoch')\nplt.ylabel('Accuracy')\nplt.title('Accuracy Curve Analysis')\nplt.legend(['Training','Testing'],loc = 'best')\n\nplt.plot(history.history['loss'])                #training accuracy\nplt.plot(history.history['val_loss'])\nplt.xlabel('epoch')\nplt.ylabel('loss')\nplt.title('Loss Curve Analysis')\nplt.legend(['Training','Testing'],loc = 'best')\n\nmodel.save('Deep_Learning.h5')  #model.load to load the model later\n\nimport pickle\n\nwith open('tfidfVect.pickle','wb') as handle:   #save it in a notepad\n  pickle.dump(tfidf,handle,protocol = pickle.HIGHEST_PROTOCOL)\n\ntest = ['the flight was safe']\n\nx_test_idf = tfidf.transform(test)\nX_test = scipy.sparse.csr_matrix.todense(x_test_idf)\nnew_model = tf.keras.models.load_model('./Deep_Learning.h5')\n\npred = new_model.predict(X_test)\n\npred\n\ncategory = {'Negative':0,'Neutral':1,'Positive':2}\n\nlabels = list(category.keys())\nlabels\n\nlabels[np.argmax(pred)]\n","repo_name":"NikitaShinde14/Twitter-sentiment-analysis-for-airlines-","sub_path":"Copy_of_Tweet_DL_Proj1.py","file_name":"Copy_of_Tweet_DL_Proj1.py","file_ext":"py","file_size_in_byte":5720,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"74462196103","text":"import numpy\nimport scipy\nimport matplotlib.pyplot as pyplot\nimport pyfits\n\ndatadir = './Data/'\nfilename = 'PSF_bkgsub.asc'\n\ndef readData(datadir, filename):\n    df = open(datadir+filename, 'r')\n    frames = []\n    frame = []\n    flag = False\n    for line in df:\n        l = line.split()\n        if len(l) > 0:\n            if flag:\n                row = numpy.fromstring(line, dtype=float, sep=' ')\n                frame.append(row)\n            else:\n                if l[0] == 'Time':\n                    flag = True\n        else:\n            if flag:\n                flag = False\n                frames.append(numpy.array(frame))\n                frame = []\n    return numpy.array(frames)\n\ndata = readData(datadir,filename)\nstacked = numpy.average(data, axis=0)\nnoise = numpy.std(data, axis=0)\n\nhdu = pyfits.PrimaryHDU(data)\nhdulist = pyfits.HDUList([hdu])\nhdulist.writeto(\"PSF_bkgsub.fits\")\n\n\nfig = pyplot.figure(0)\nfig.clear()\nax = fig.add_axes([0.1, 0.1, 0.8, 0.8])\nax.imshow(stacked)\n#ax.imshow(noise)\nfig.show()\n","repo_name":"soylentdeen/PSF-simulation","sub_path":"acsReader.py","file_name":"acsReader.py","file_ext":"py","file_size_in_byte":1018,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"8076729955","text":"from .color_tree import get_thread, get_dmc_tree\nfrom PIL import Image\nimport numpy as np\nimport time\n\n\"\"\"\nImage processing file\n\nNotes:\n    - add transfer from HSV, LAB and others to RGB\n\"\"\"\n\n\ndef get_image(filepath):\n    im = Image.open(filepath, 'r')\n\n    if im.mode != \"RGB\":\n        raise Exception(\"Pixels are not in RGB\")\n\n    pixels = list(im.getdata())\n    width, height = im.size\n\n    pixels = np.array_split(pixels, height)\n    return pixels, (width, height)\n\n\ndef resize_image(filepath, x, y):\n    im = Image.open(filepath, 'r')\n    im = im.resize((x, y))\n    im.save(\"tmp.jpg\", \"JPEG\")\n    return \"tmp.jpg\"\n\n\n# Applies Floyd Steinberg effect on the particular pixel\ndef floyd_steinberg_effect(image, x, y, quant_err, coef):\n    for i in range(3):\n        image[x][y][i] += quant_err[i] * coef\n        image[x][y][i] = round(max(0, min(255, image[x][y][i])))\n\n\ndef valid_idx(x, y, w, h):\n    return 0 <= x < w and 0 <= y < h\n\n\n# Iterates neighbors with the given coefficient for \n# Floyd Steinberg's effect\ndef iterate_neighbors(x, y, w, h):\n    if valid_idx(x + 1, y, w, h):\n        yield x + 1, y, 7 / 16\n\n    if valid_idx(x, y + 1, w, h):\n        yield x, y + 1, 5 / 16\n\n    if valid_idx(x + 1, y + 1, w, h):\n        yield x + 1, y + 1, 1 / 16\n\n    if valid_idx(x - 1, y + 1, w, h):\n        yield x - 1, y + 1, 3 / 16\n\n\n# Selecting appropriate threads\ndef process_image(pixels, w, h):\n    tree, dmcs, idxs = get_dmc_tree()\n\n    image = pixels\n    threads = []\n\n    t0 = time.time()\n\n    for y in range(h):\n        for x in range(w):\n            # Getting thread and its RGB tuple\n            thread, rgb = get_thread(image[x][y], tree, idxs, dmcs)\n            threads.append(thread)\n\n            # Recreating the output image\n            image[x][y] = rgb\n            assert rgb == thread['rgb'], 'RGBs do not match'\n\n            # Applying Floyd Steinberg's algorithm, by affecting neighbors\n            quant_error = [(image[x][y][i] - rgb[i]) for i in range(3)]\n            for nx, ny, coef in iterate_neighbors(x, y, w, h):\n                floyd_steinberg_effect(image, nx, ny, quant_error, coef)\n\n    print(\"\\nTime spent: \", round(time.time() - t0, 2))\n\n    threads = np.array(np.array_split(threads, h))\n\n    return image.transpose(1, 0, 2), np.array(threads)\n","repo_name":"Goader/embroidery","sub_path":"src/image_proc.py","file_name":"image_proc.py","file_ext":"py","file_size_in_byte":2281,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"81"}
+{"seq_id":"17445558624","text":"\"\"\"\nParsing the file system\n\"\"\"\n__revision__ = '$Id$'\n\nimport os\nfrom hypercore.elements import FileInfo\n\ndef getFileList(sdir, fileExts, skipDirs=[], ignorefile='.hsqlignore'):\n    \"\"\"\n    Recursively scans the source directory specified for relevant files according\n    to the file extensions passed by the second parameter, while excluding files/\n    directories given with the third (useful to exclude '.svn' and the like) and\n    directories containing an ignore flag file (usually '.hsqlignore')\n    Information for matching files will be returned as a list of FileInfo objects.\n    @param string dir directory to scan\n    @param list fileExts file extensions to consider. Each element must be a tuple\n           (str fileType, list extensions)\n    @param list skipDirs files/directories to skip\n    @return list fileInfoList list of FileInfo objects\n    \"\"\"\n    if fileExts is None or len(fileExts)<1: return []\n\n    # get a list of this directory's contents\n    # these items are relative and not absolute\n    names        = os.listdir(sdir)\n    fileInfoList = []\n\n    # setup supported file extensions\n    exts = []\n    for ext in fileExts: exts += ext[1]\n\n    # iterate through the file list\n    for i in names: \n\n      if i in skipDirs: # do not look in RCS/CVS/SVN/... special dirs\n        continue\n      if os.path.isfile( os.path.join(sdir, i, ignorefile) ):\n        continue\n\n      # convert from relative to absolute addressing\n      # to allow recursive calls\n      f1=os.path.join(sdir, i)\n\n      # if this item is also a directory, recurse it too\n      if os.path.isdir(f1):\n        fileInfoList += getFileList(f1, fileExts, skipDirs)\n\n      else:  # file found, only add specific file extensions to the list\n        fspl = f1.split('.')\n        ext  = fspl[len(fspl)-1]\n        if ext in exts:\n          temp = FileInfo()\n          temp.fileName = f1\n          for ftype in fileExts:\n            if ext in ftype[1]:\n              temp.fileType = ftype[0]\n              break\n          fileInfoList.append(temp)\n    return fileInfoList\n\n","repo_name":"IzzySoft/HyperSQL","sub_path":"lib/parsers/filefinder.py","file_name":"filefinder.py","file_ext":"py","file_size_in_byte":2056,"program_lang":"python","lang":"en","doc_type":"code","stars":17,"dataset":"github-code","pt":"81"}
+{"seq_id":"9472713524","text":"t = int(input())\nfor _ in range(t):\n\tn = int(input())\n\ts = input()\n\n\tspecialchars = ['#', '@', '*', '&']\n\n\tupper = False\n\tlower = False\n\tdigit = False\n\tspecial = False\n\n\tfor x in s:\n\t\tif x.isupper():\n\t\t\tupper = True\n\t\tif x.islower():\n\t\t\tlower = True\n\t\tif x.isdigit():\n\t\t\tdigit = True\n\t\tif x in specialchars:\n\t\t\tspecial = True\n\n\tif not upper:\n\t\ts += 'A'\n\tif not lower:\n\t\ts += 'a'\n\tif not digit:\n\t\ts += '1'\n\tif not special:\n\t\ts += '*'\n\n\twhile len(s) < 7:\n\t\ts += '1'\n\n\tprint(\"Case #{0}:\".format(_+1), s)\n","repo_name":"qazz625/Competitive-Programming-Codes","sub_path":"Kick Start/2022C/a.py","file_name":"a.py","file_ext":"py","file_size_in_byte":501,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"8291566630","text":"from flask_seeder import Seeder, Faker, generator\nfrom app.models import Citizens, Statuses\nimport os \nimport random\n\nclass GeneratorHierarchy(generator.Generator):\n    def __init__(self, **kwargs):\n        super().__init__(**kwargs)\n        self.gen = self.generator()\n \n        \n    def generator(self):\n        hierarchy = 1\n        for s in Statuses.query.order_by(Statuses.salary.desc()):\n            for i in range(hierarchy):\n                status = s.id\n                yield status\n            hierarchy *= 2\n        else:\n            yield status\n            \n    def generate(self):\n        return next(self.gen)\n\n\nclass GeneratorBoss(generator.Generator):\n    def __init__(self, **kwargs):\n        super().__init__(**kwargs)\n        self.gen = self.generator()     \n        \n    def generator(self):\n        n = 1\n        bosses = []\n        while True:\n            \n            for _ in range(n):\n                yield random.choice(bosses) if len(bosses) else 0\n                         \n            bosses = list(range(n, 2*n))    \n            n *= 2\n \n    def generate(self):\n        return next(self.gen)\n        \n        \nclass CitizensSeeder(Seeder):\n  def __init__(self, db=None):\n    super().__init__(db=db)\n    self.priority = 10\n    \n  def run(self):\n    count = os.getenv('COUNT_CITIZENS') or 500\n    faker = Faker(\n      cls=Citizens,\n      init={\n        'name': generator.Name(),\n        'age' : generator.Integer(start=14, end=100),\n        'id_status': GeneratorHierarchy(),\n        'boss': GeneratorBoss(),\n      }\n    )\n\n    for citizens in faker.create(count):\n      self.db.session.add(citizens)\n     ","repo_name":"kamnsv/alabuga","sub_path":"seeds/citizens.py","file_name":"citizens.py","file_ext":"py","file_size_in_byte":1635,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"17960244391","text":"from skimage import io\nfrom skimage.transform import resize\n\nimport os\n\nIMAGE_PATH = 'Images/60'  # path to the chess pieces\nSCALED_IMAGE_PATH = 'Images/60_scaled'  # path to the chess pieces\n\nos.makedirs(SCALED_IMAGE_PATH, exist_ok = True)\n\nnames = os.listdir(IMAGE_PATH)\n\nfor name in names:\n    img = io.imread(IMAGE_PATH + \"/\" + name)\n    img_resized = resize(img, (50, 50), anti_aliasing = True)\n    io.imsave(SCALED_IMAGE_PATH + \"/\" + name, img_resized)","repo_name":"dukeNashor/ChessMaster","sub_path":"Python-Easy-Chess-GUI/resize_default_theme_images.py","file_name":"resize_default_theme_images.py","file_ext":"py","file_size_in_byte":458,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"81"}
+{"seq_id":"74114836744","text":"from fhirclient import client\nfrom fhirclient.models.fhirsearch import FHIRSearch\nfrom fhirclient.models.encounter import Encounter\n\nsettings = {\n    'app_id': 'my_web_app',\n    'api_base':  'http://hapi.fhir.org/baseDstu3/'#'https://fhir-open-api-dstu2.smarthealthit.org'\n}\n\nsmart = client.FHIRClient(settings=settings)\n\nfrom fhirclient.models import patient as p\n\nstruct = {'identifier':'http://hl7.org/fhir/sid/us-ssn|999-45-3776'}\n#patient = p.Patient.read('1349276', smart.server)\nsearch = p.Patient.where(struct=struct)\npatient = search.perform(smart.server).entry[0].resource\npatient = search.perform_resources(smart.server)[0]\n\nprint(patient.birthDate.isostring)\n\nprint(patient.id)\n\nprint(smart.human_name(patient.name[0]))\nprint(search.resource_type.resource_type)\n\nsearch = Encounter.where(struct={'patient':patient.id, '_count':'10000'})\nencounter = search.perform_resources(smart.server)\nprint(len(encounter))\n\n\"\"\"perform gives back bundle, perform_resources gives back a list of resources\"\"\"\nsearch = Encounter.where(struct={'patient':patient.id, '_count':'10000'})\nencounters = search.perform_resources(smart.server)\n#print(encounters.as_json())\n\nfor e in encounters:\n    print(e.type[0].text)\n","repo_name":"kelhwh/fhir-flask-client-web-app","sub_path":"devresources/get_fhir_patient.py","file_name":"get_fhir_patient.py","file_ext":"py","file_size_in_byte":1208,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"69964269065","text":"import dask\nimport dask.dataframe as dd\nimport geopandas as gpd\nimport pandas as pd\nfrom calitp_data_analysis.geography_utils import WGS84\nimport vp_spatial_accuracy\nimport update_vars2\nfrom segment_speed_utils.project_vars import (\n    GCS_FILE_PATH,\n    PROJECT_CRS,\n    SEGMENT_GCS,\n    analysis_date,\n)\nfrom segment_speed_utils import helpers, wrangle_shapes\n\n# Times\nimport datetime\nimport sys\nfrom loguru import logger\n\n# cd rt_segment_speeds && pip install -r requirements.txt && cd ../_shared_utils && make setup_env\n# cd ../rt_scheduled_v_ran/scripts \n\n# LOAD FILES\ndef load_trip_speeds(analysis_date):\n    df = pd.read_parquet(\n    f\"{SEGMENT_GCS}trip_summary/trip_speeds_{analysis_date}.parquet\",\n    columns=[\n        \"trip_instance_key\",\n        \"speed_mph\",\n        \"route_id\",\n        \"time_of_day\",\n        \"service_minutes\",\n    ])\n    \n    return df\n\ndef load_vp_usable(analysis_date):\n\n    # Delete schedule_gtfs_dataset_key later\n    df = dd.read_parquet(\n        f\"{SEGMENT_GCS}vp_usable_{analysis_date}\",\n        columns=[\n            \"schedule_gtfs_dataset_key\",\n            \"trip_instance_key\",\n            \"location_timestamp_local\",\n            \"x\",\n            \"y\",\n            \"vp_idx\",\n        ],\n    )\n\n    # Create a copy of location timestamp for the total_trip_time function\n    # to avoid type setting warning\n    df[\"max_time\"] = df.location_timestamp_local\n    return df\n\n# UPDATE COMPLETENESS\ndef total_trip_time(vp_usable_df: pd.DataFrame):\n    \"\"\"\n    For each trip: find the total service minutes\n    recorded in real time data so we can compare it with\n    scheduled service minutes.\n    \"\"\"\n    subset = [\"location_timestamp_local\", \"trip_instance_key\", \"max_time\"]\n    vp_usable_df = vp_usable_df[subset]\n\n    # Find the max and the min time based on location timestamp\n    df = (\n        vp_usable_df.groupby([\"trip_instance_key\"])\n        .agg({\"location_timestamp_local\": \"min\", \"max_time\": \"max\"})\n        .reset_index()\n        .rename(columns={\"location_timestamp_local\": \"min_time\"})\n    )\n\n    # Find total rt service mins and add an extra minute\n    df[\"rt_service_min\"] = (df.max_time - df.min_time) / pd.Timedelta(minutes=1) + 1\n\n    # Return only one row per trip with 2 columns: trip instance key & total trip time\n    df = df.drop(columns=[\"max_time\", \"min_time\"])\n\n    return df\n\ndef trips_by_one_min(vp_usable_df: pd.DataFrame):\n    \"\"\"\n    For each trip: count how many rows are associated with each minute\n    then tag whether or not a minute has 2+ pings. \n    \"\"\"\n    subset = [\"location_timestamp_local\", \"trip_instance_key\", \"vp_idx\"]\n    vp_usable_df = vp_usable_df[subset]\n\n    # Find number of pings each minute\n    df = (\n        vp_usable_df.groupby(\n            [\n                \"trip_instance_key\",\n                pd.Grouper(key=\"location_timestamp_local\", freq=\"1Min\"),\n            ]\n        )\n        .vp_idx.count()\n        .reset_index()\n        .rename(columns={\"vp_idx\": \"number_of_pings_per_minute\"})\n    )\n\n    # Determine which rows have 2+ pings per minute\n    df = df.assign(\n        min_w_atleast2_trip_updates=df.apply(\n            lambda x: 1 if x.number_of_pings_per_minute >= 2 else 0, axis=1\n        )\n    )\n    \n    df = df.drop(columns = ['location_timestamp_local'])\n    return df\n\ndef update_completeness(df: pd.DataFrame):\n    \"\"\"\n    For each trip: find the median GTFS pings per minute,\n    the total minutes with at least 1 GTFS ping per minute,\n    and total minutes with at least 2 GTFS pings per minute.\n    \"\"\"\n    # Need a copy of numer of pings per minute to count for total minutes w gtfs\n    df[\"total_min_w_gtfs\"] = df.number_of_pings_per_minute\n    \n    # Find the total min with at least 2 pings per min\n    df = (\n        df.groupby([\"trip_instance_key\"])\n        .agg(\n            {\n                \"min_w_atleast2_trip_updates\": \"sum\",\n                \"number_of_pings_per_minute\": \"sum\",\n                \"total_min_w_gtfs\": \"count\",\n            }\n        )\n        .reset_index()\n        .rename(\n            columns={\n                \"number_of_pings_per_minute\": \"total_pings_for_trip\",\n            }\n        )\n    )\n\n    return df\n\n# SPATIAL ACCURACY\ndef grab_shape_keys_in_vp(vp_usable: dd.DataFrame, analysis_date: str) -> pd.DataFrame:\n    \"\"\"\n    Subset raw vp and find unique trip_instance_keys.\n    Create crosswalk to link trip_instance_key to shape_array_key\n    to find trips for the analysis date that have shapes.\n    \"\"\"\n    vp_usable = (\n        vp_usable[[\"trip_instance_key\"]]\n        .drop_duplicates()\n        .reset_index(drop=True)\n    )\n\n    trips_with_shape = helpers.import_scheduled_trips(\n        analysis_date,\n        columns=[\"trip_instance_key\", \"shape_array_key\"],\n        get_pandas=True,\n    )\n\n    # Only one row per trip/shape: trip_instance_key and shape_array_key are the only cols left\n    m1 = dd.merge(vp_usable, trips_with_shape, on=\"trip_instance_key\", how=\"inner\")\n\n    return m1\n\ndef buffer_shapes(\n    trips_with_shape: pd.DataFrame,\n    analysis_date: str,\n    buffer_meters: int = 35,\n):\n    \"\"\"\n    Filter scheduled shapes down to the shapes that appear in vp.\n    Buffer these.\n\n    Attach the shape geometry for a subset of shapes or trips.\n    \"\"\"\n    subset = trips_with_shape.shape_array_key.unique().compute().tolist()\n\n    shapes = helpers.import_scheduled_shapes(\n        analysis_date,\n        columns=[\"shape_array_key\", \"geometry\"],\n        filters=[[(\"shape_array_key\", \"in\", subset)]],\n        crs=PROJECT_CRS,\n        get_pandas=False,\n    ).pipe(helpers.remove_shapes_outside_ca)\n\n    # to_crs takes awhile, so do a filtering on only shapes we need\n    shapes = shapes.assign(geometry=shapes.geometry.buffer(buffer_meters))\n\n    trips_with_shape_geom = dd.merge(\n        shapes, trips_with_shape, on=\"shape_array_key\", how=\"inner\"\n    )\n\n    trips_with_shape_geom = trips_with_shape_geom.compute()\n    return trips_with_shape_geom\n\ndef vp_in_shape(\n    vp_usable: dd.DataFrame, trips_with_buffered_shape: gpd.GeoDataFrame\n) -> gpd.GeoDataFrame:\n\n    keep = [\"trip_instance_key\", \"x\", \"y\", \"location_timestamp_local\"]\n    vp_usable = vp_usable[keep]\n\n    vp_gdf = wrangle_shapes.vp_as_gdf(vp_usable)\n\n    gdf = pd.merge(\n        vp_gdf, trips_with_buffered_shape, on=\"trip_instance_key\", how=\"inner\"\n    )\n\n    gdf = gdf.assign(is_within=gdf.geometry_x.within(gdf.geometry_y))\n    \n    to_keep = [\"trip_instance_key\", \"location_timestamp_local\", \"is_within\"]\n    gdf = gdf[to_keep]\n\n    return gdf\n\ndef total_vp_counts_by_trip(vp: gpd.GeoDataFrame, new_col_title: str):\n    \"\"\"\n    Get a count of vp for each trip, whether or not those fall\n    within buffered shape or not\n    \"\"\"\n    count_vp = (\n        vp.groupby(\"trip_instance_key\", observed=True, group_keys=False)\n        .agg({\"location_timestamp_local\": \"count\"})\n        .reset_index()\n        .rename(columns={\"location_timestamp_local\": new_col_title})\n    )\n\n    return count_vp\n\ndef total_counts(result: dd.DataFrame):\n\n    # Find the total number of vps for each route\n    total_vp_df = total_vp_counts_by_trip(result, \"total_vp\")\n\n    # Find the total number of vps that actually fall within the  route shape\n    subset = [\"trip_instance_key\", \"location_timestamp_local\"]\n    result2 = result.loc[result.is_within == True].reset_index(drop=True)[subset]\n\n    vps_in_shape = total_vp_counts_by_trip(result2, \"vp_in_shape\")\n\n    # Count total vps for the trip\n    count_df = pd.merge(total_vp_df, vps_in_shape, on=\"trip_instance_key\", how=\"left\")\n\n    count_df = count_df.assign(\n        vp_in_shape=count_df.vp_in_shape.fillna(0).astype(\"int32\"),\n        total_vp=count_df.total_vp.fillna(0).astype(\"int32\"),\n    )\n\n    return count_df\n\n# Complete\ndef vp_usable_metrics(analysis_date:str) -> pd.DataFrame:\n    start = datetime.datetime.now()\n    print(f\"Started running script at {start} for {analysis_date} metrics\")\n    \n    \"\"\"\n    Keep for testing temporarily\n    operator = \"Bay Area 511 Muni VehiclePositions\"\n    \n    gtfs_key = \"7cc0cb1871dfd558f11a2885c145d144\"\n    \n    vp_usable = (vp_usable.loc\n    [vp_usable.schedule_gtfs_dataset_key ==gtfs_key]\n    .reset_index(drop=True))\n    \n    \"\"\"\n    vp_usable = load_vp_usable(analysis_date)\n    \n    ## Find total rt service minutes ##\n    rt_service_df = total_trip_time(vp_usable)\n\n    time1 = datetime.datetime.now()\n    logger.info(f\"Rt service min: {time1-start}\")\n    \n    ## Update Completeness ##\n    # Find median pings per min, total min with\n    # GTFS and total min with at least 2 pings per min.\n    trips_by_one_min_df = vp_usable.map_partitions(\n    trips_by_one_min,\n    meta={\n        \"trip_instance_key\": \"object\",\n        \"number_of_pings_per_minute\": \"int64\",\n        \"min_w_atleast2_trip_updates\": \"int64\",\n    },\n    align_dataframes=False\n    ).persist()\n    time2 = datetime.datetime.now()\n    logger.info(f\"Grouping by each minute: {time2-time1}\")\n    \n    # Final function\n    pings_trip_time_df = update_completeness(trips_by_one_min_df)\n    time3 = datetime.datetime.now()\n    logger.info(f\"Spatial accuracy metric: {time3-time2}\")\n    \n    ## Spatial accuracy  ##\n    # Determine which trips have shapes associated with them\n    trips_with_shapes_df = grab_shape_keys_in_vp(vp_usable, analysis_date)\n\n    # Buffer the shapes \n    buffered_shapes_df = buffer_shapes(trips_with_shapes_df, analysis_date, 35)\n    time4 = datetime.datetime.now()\n    logger.info(f\"Buffering: {time4-time3}\")\n    \n    # Find the vps that fall into buffered shapes\n    in_shape_df = vp_usable.map_partitions(\n    vp_in_shape,\n    buffered_shapes_df,\n    meta={\n        \"trip_instance_key\": \"object\",\n        \"location_timestamp_local\": \"datetime64[ns]\",\n        \"is_within\":\"bool\",\n    },align_dataframes=False).persist()\n    time5 = datetime.datetime.now()\n    logger.info(f\"Find vps that fall into shapes: {time5-time4}\")\n    \n    # Compare total vps for a trip versus total vps that \n    # fell in the recorded shape\n    spatial_accuracy_df = in_shape_df.map_partitions(\n        total_counts,\n        meta={\n            \"trip_instance_key\": \"object\", \n            \"total_vp\": \"int32\", \n            \"vp_in_shape\": \"int32\"},\n    align_dataframes=False).persist()\n    time6 = datetime.datetime.now()\n    logger.info(f\"Spatial accuracy grouping metric: {time6-time5}\")\n    \n    ## Merges ##\n    # Load trip speeds\n    trip_speeds_df = load_trip_speeds(analysis_date)\n    rt_service_df = rt_service_df.compute()\n    pings_trip_time_df = pings_trip_time_df.compute()\n    spatial_accuracy_df = spatial_accuracy_df.compute()\n    \n    m1 = (rt_service_df.merge(pings_trip_time_df, on = \"trip_instance_key\", how = \"outer\")\n         .merge(spatial_accuracy_df, on =\"trip_instance_key\", how = \"outer\")\n         .merge(trip_speeds_df, on =\"trip_instance_key\", how = \"outer\"))\n    \n    # Some metrics\n    m1['pings_per_min'] = (m1.total_pings_for_trip / m1.rt_service_min)\n    m1['spatial_accuracy_pct'] = (m1.vp_in_shape/m1.total_vp) * 100\n    m1['rt_triptime_w_gtfs_pct'] = (m1.total_min_w_gtfs / m1.rt_service_min) * 100\n    m1['rt_v_scheduled_trip_time_pct'] = (m1.rt_service_min / m1.service_minutes - 1) * 100\n    \n    # Save\n    m1.to_parquet(f\"{GCS_FILE_PATH}rt_vs_schedule/trip_level_metrics/{analysis_date}_metrics.parquet\")\n    \n    time7 = datetime.datetime.now()\n    logger.info(f\"Total run time for metrics on {analysis_date}: {time7-start}\")\n\nif __name__ == \"__main__\":\n    LOG_FILE = \"../logs/rt_v_scheduled_trip_metrics.log\"\n    logger.add(LOG_FILE, retention=\"3 months\")\n    logger.add(sys.stderr, \n               format=\"{time:YYYY-MM-DD at HH:mm:ss} | {level} | {message}\", \n               level=\"INFO\")\n    for date in update_vars2.analysis_date_list:\n        vp_usable_metrics(date)\n        print('Done')","repo_name":"cal-itp/data-analyses","sub_path":"rt_scheduled_v_ran/scripts/rt_v_scheduled_trip.py","file_name":"rt_v_scheduled_trip.py","file_ext":"py","file_size_in_byte":11721,"program_lang":"python","lang":"en","doc_type":"code","stars":19,"dataset":"github-code","pt":"81"}
+{"seq_id":"40245482140","text":"import os\nimport glob\nimport nibabel as nib\nimport re\nimport numpy as np\nimport pydicom\nimport SimpleITK as sitk\nfrom skimage import draw\n\n# Internal libraries/scripts\nfrom miscnn.data_loading.interfaces.abstract_io import Abstract_IO\n\n#-----------------------------------------------------#\n#                 DICOM I/O Interface                 #\n#-----------------------------------------------------#\n\"\"\" This class provides a Data I/O Interface that can be used to load images and structure sets\n    that are provided in the Digital Imaging and Communications in Medicine (DICOM) format\n\n    The DICOM Viewer was designed for the Lung CT Segmentation Challenge 2017.\n    https://wiki.cancerimagingarchive.net/display/Public/Lung+CT+Segmentation+Challenge+2017#cb38430390714dbbad13f267f39a33eb\n\"\"\"\n\nclass DICOM_interface(Abstract_IO):\n    # Class variable initialization\n    def __init__(self, channels=1, classes=2, three_dim=True, mask_background=0,\n                 structure_dict={}, annotation_tag=None):\n\n        \"\"\"\n        Args:\n            channels (int): Number of channels of the input images.\n            classes (int): Number of segmentation classes.\n            three_dim (bool): 3D volume if True.\n            mask_background (int): Determines the value of the background pixels.\n            structure_dict (dict): Dictionary containg ROI names.\n            annotation_tag (string): String to identify annotation series.\n        Returns:\n            None\n        \"\"\"\n\n        self.data_directory = None\n        self.channels = channels\n        self.classes = classes\n        self.three_dim = three_dim\n        self.annotation_tag = annotation_tag\n        assert isinstance(mask_background,int), \"mask_background value should be an integer\"\n        self.mask_background = mask_background\n        self.structure_dict = structure_dict\n\n    #---------------------------------------------#\n    #                  initialize                 #\n    #---------------------------------------------#\n    # Initialize the interface and return number of samples\n    def initialize(self, input_path):\n\n        \"\"\"\n        Args:\n            input_path (str): Path to the dataset.\n        Returns:\n            sample_list (list): List of all cases found in the provided path.\n        \"\"\"\n\n        # Resolve location where imaging data should be living\n        if not os.path.exists(input_path):\n            raise IOError(\n                \"Data path, {}, could not be resolved\".format(str(input_path))\n            )\n        # Cache data directory\n        self.data_directory = input_path\n        # Identify samples\n        sample_list = os.listdir(input_path)\n        # Return sample list\n        return sample_list\n\n    #---------------------------------------------#\n    #                  load_image                 #\n    #---------------------------------------------#\n    # Read a volume DICOM file from the data directory\n    def load_image(self, index):\n\n        \"\"\"\n        Args:\n            index (str): Description of the sample that should be opened.\n        Returns:\n            dcm (array): CT 3D volume.\n        \"\"\"\n\n        dicom_images_path = None\n\n        img_path = os.path.join(self.data_directory, index)\n\n        # search folders and find a DICOM image of the whole scan to get\n        # the path of the DICOM images\n        dicom_images_path = None\n        for root, _, files in os.walk(img_path):\n            for _ in filter(lambda x: re.findall('.*001.dcm', x), files):\n                dicom_images_path = root\n                break\n\n        # Make sure that the image file exists in the data set directory\n        if not dicom_images_path or not os.path.exists(dicom_images_path):\n            raise ValueError(\n                \"No DICOM scans could not be found \\\"{}\\\"\".format(img_path)\n            )\n\n        #create a ImageSeriesReader Object\n        dicom_reader = sitk.ImageSeriesReader()\n\n        #read all DICOM files contained in the provided folder\n        dicom_file_names = dicom_reader.GetGDCMSeriesFileNames(\n                                                         str(dicom_images_path))\n\n        #set file names\n        dicom_reader.SetFileNames(dicom_file_names)\n\n        #read dicom files\n        dcm = dicom_reader.Execute()\n\n        # Read one of the images to get DICOM Pixelspacing and SliceThickness\n        # paramters\n        dcm_temp = pydicom.read_file(dicom_file_names[0])\n        spacing = np.array([dcm_temp.SliceThickness,\n                            dcm_temp.PixelSpacing[0],\n                            dcm_temp.PixelSpacing[1]],\n                            dtype=np.float32)\n        # Calculate absolute values for voxel spacing\n\n        #return both the sitk image and all images as a numpy array\n        return sitk.GetArrayFromImage(dcm), {\"type\":\"dicom\", \"spacing\":np.absolute(spacing)}\n\n\n    def load_image_sitk(self, index):\n\n        \"\"\"\n        Args:\n            index (str): Description of the sample that should be opened.\n        Returns:\n            dcm (sitkImage Object): Image in Sitk format (needed for other subfunctions of this class)\n        \"\"\"\n\n        dicom_images_path = None\n\n        # Make sure that the image file exists in the data set directory\n        img_path = os.path.join(self.data_directory, index)\n\n        # search folders and find a DICOM image of the whole scan to get\n        # the path of the DICOM images\n        dicom_images_path = None\n        for root, _, files in os.walk(img_path):\n            for _ in filter(lambda x: re.findall('.*001.dcm', x), files):\n                dicom_images_path = root\n                break\n\n        if not dicom_images_path or not os.path.exists(dicom_images_path):\n            raise ValueError(\n                \"No DICOM scans could not be found \\\"{}\\\"\".format(img_path)\n            )\n\n        #create a ImageSeriesReader Object\n        dicom_reader = sitk.ImageSeriesReader()\n\n        #read all DICOM files contained in the provided folder\n        dicom_file_names = dicom_reader.GetGDCMSeriesFileNames(\n                                                         str(dicom_images_path))\n\n        #set file names\n        dicom_reader.SetFileNames(dicom_file_names)\n\n        #read dicom files\n        dcm = dicom_reader.Execute()\n\n        #Read one of the images to get DICOM Pixelspacing and SliceThickness\n        #paramters\n        dcm_temp = pydicom.read_file(dicom_file_names[0])\n        spacing = np.array([dcm_temp.SliceThickness,\n                            dcm_temp.PixelSpacing[0],\n                            dcm_temp.PixelSpacing[1]],\n                            dtype=np.float32)\n\n        #return both the sitk image and all images as a numpy array\n        return dcm, {\"type\":\"dicom\", \"spacing\":np.absolute(spacing)}\n\n\n    #---------------------------------------------#\n    #              load_segmentation              #\n    #---------------------------------------------#\n    # Read a RT structure file from the data directory\n    def load_segmentation(self, index):\n\n        \"\"\"\n        Args:\n            index (str): Description of the sample that should be opened.\n        Returns:\n            segmentations (array): 3D volume containing segmentations of all ROIs.\n        \"\"\"\n\n        rtStruct_path = self.get_rtStruct_file(index)\n\n        #get names, IDs and sequences of all ROIs contained in the provided structure file\n        contours = self.get_ROI_data(rtStruct_path)\n\n        #Load CT images in sitk format\n        images, image_data = self.load_image_sitk(index)\n\n        #get segmentations\n        segmentations = self.convert(contours, images)\n\n        # Return segmentations\n        return segmentations\n\n\n    #Gets Name, ID and sequence from a provided structure file\n    def get_ROI_data(self, rtstruct_file = None):\n\n        \"\"\"\n        Args:\n            rtstruct_file (str): Path to an RT structure DICOM file.\n        Returns:\n            contour_data (dict): Dictionary containing ROI name, ID and Sequence.\n        \"\"\"\n\n        ss_file = pydicom.read_file(rtstruct_file)\n\n        contours = []\n\n        for sequence, metadata in zip(ss_file.ROIContourSequence,\n                                      ss_file.StructureSetROISequence):\n\n            contour_data = {}\n\n            contour_data['ROI_ID'] = metadata.ROINumber\n            contour_data['ROI_Name'] = metadata.ROIName\n            contour_data['ROI_Sequence'] = []\n            for contour in sequence.ContourSequence:\n                contour_data['ROI_Sequence'].append({\n                    'type': (contour.ContourGeometricType if hasattr(contour, 'ContourGeometricType') else 'No_type'),\n                    'points': {\n                        'x': ([contour.ContourData[i] for i in range(0, len(contour.ContourData), 3)]),\n                        'y': ([contour.ContourData[i + 1] for i in range(0, len(contour.ContourData), 3)]),\n                        'z': ([contour.ContourData[i + 2] for i in range(0, len(contour.ContourData), 3)])\n                    }\n                })\n\n            contours.append(contour_data)\n\n        return contours\n\n    #gets the path of a RT structure file\n    def get_rtStruct_file(self, index):\n\n        \"\"\"\n        Args:\n            index (str): Description of the sample that should be opened.\n        Returns:\n            rtStruct_path (str): path to a RT structure file.\n        \"\"\"\n\n\n        path = os.path.join(self.data_directory, index)\n\n        # Check if annotation tag is available\n        if not self.annotation_tag:\n            raise ValueError(\"Please provide an annotation tag\")\n\n        # search folder structure for the segmentation by using the\n        # annotation tag\n        rtStruct_path = None\n        pattern = \".*\" + self.annotation_tag + \".*\"\n        for root, dirs, files in os.walk(path):\n            annot_dir = list(filter(lambda x: re.match(pattern, x), dirs))\n            if len(annot_dir) >= 1:\n                annot_dir = os.path.join(root, annot_dir[0])\n                files = os.listdir(annot_dir)\n                if len(files) > 1 or len(files) == 0:\n                    raise ValueError(\"More than one or no segmentation file found\",\n                                     index, annot_dir)\n                rtStruct_path = os.path.join(annot_dir, files[0])\n                break\n\n        # Make sure that the segmentation file exists in the data set directory\n        if not rtStruct_path or not os.path.exists(rtStruct_path):\n            raise ValueError(\n                \"Structure file could not be found \\\"{}\\\"\".format(rtStruct_path)\n            )\n\n        return rtStruct_path\n\n    #---------------------------------------------#\n    #         convert structure sequences         #\n    #---------------------------------------------#\n    #converts structure sequences to filled poly masks\n    def convert(self, rtstruct_contours, dicom_image):\n\n        \"\"\"\n        Args:\n            rtstruct_contours (dict): dictionary containing structure name, ID and Sequence.\n            dicom_image (SitkImage Object): SITK image object.\n        Returns:\n            np_mask (array): segmentations as a numpy array.\n        \"\"\"\n\n        shape = dicom_image.GetSize()\n\n        mask = sitk.Image(shape, sitk.sitkUInt8)\n        mask.CopyInformation(dicom_image)\n\n        np_mask = sitk.GetArrayFromImage(mask)\n        np_mask.fill(self.mask_background)\n\n\n        for rtstruct in rtstruct_contours:\n\n\n            if rtstruct['ROI_Name'] in self.structure_dict:\n\n                rtstruct_seq = rtstruct['ROI_Sequence']\n\n                for seq in rtstruct_seq:\n\n                    coordinates = seq['points']\n\n                    pts = np.zeros([len(coordinates['x']), 3])\n\n                    for index in range(0, len(coordinates['x'])):\n                        # convert world coordinates to voxel coordinates\n                        voxel_coords = dicom_image.TransformPhysicalPointToIndex((coordinates['x'][index], coordinates['y'][index], coordinates['z'][index]))\n                        pts[index, 0] = voxel_coords[0]\n                        pts[index, 1] = voxel_coords[1]\n                        pts[index, 2] = voxel_coords[2]\n\n                    z = int(pts[0, 2])\n\n\n                    filled_poly = self._poly2mask(pts[:, 0], pts[:, 1], [shape[0], shape[1]])\n                    np_mask[z, filled_poly] = int(self.structure_dict[rtstruct['ROI_Name']]) # sitk is xyz, numpy is zyx\n\n        return np_mask\n\n    #converts polygon to mask\n    def _poly2mask(self, coords_x, coords_y, shape):\n\n        \"\"\"\n        Args:\n            coords_x (array): Array with x coordinates of a structure sequence.\n            coords_y (array): Array with y coordinates of a structure sequence.\n            shape (list): segmentation image shape.\n        Returns:\n            mask (array): filled 2D segmentation mask.\n        \"\"\"\n\n        poly_coords_x, poly_coords_y = draw.polygon(coords_x, coords_y, shape)\n        #create an empty mask\n        mask = np.zeros(shape, dtype=np.bool)\n        #Observe that sitk uses the xyz convention, wheras numpy use zyx\n        mask[poly_coords_y, poly_coords_x] = True\n\n        return mask\n\n\n    #---------------------------------------------#\n    #              load_ROI_names                 #\n    #---------------------------------------------#\n    #gets all ROIs contained in a DICOM structure file\n    def get_ROI_names(self, index):\n\n        \"\"\"\n        Args:\n            index (str): Description of the sample that should be opened.\n        Returns:\n            structure_names (list): List containing all ROI names and IDs found in a structure file.\n        \"\"\"\n\n        rtStruct_path = self.get_rtStruct_file(index)\n\n        contours = self.get_ROI_data(rtStruct_path)\n\n        structure_names = [[cont['ROI_ID'], cont['ROI_Name']] for cont in contours]\n\n        return structure_names\n\n\n    #---------------------------------------------#\n    #               load_prediction               #\n    #---------------------------------------------#\n    # Read a prediction NIFTI file from the MIScnn output directory\n    def load_prediction(self, index, output_path):\n        # Resolve location where data should be living\n        if not os.path.exists(output_path):\n            raise IOError(\n                \"Data path, {}, could not be resolved\".format(str(output_path))\n            )\n        # Parse the provided index to the prediction file name\n        pred_file = str(index) + \".nii.gz\"\n        pred_path = os.path.join(output_path, pred_file)\n        # Make sure that prediction file exists under the prediction directory\n        if not os.path.exists(pred_path):\n            raise ValueError(\n                \"Prediction could not be found \\\"{}\\\"\".format(pred_path)\n            )\n        # Load prediction from NIFTI file\n        pred = nib.load(pred_path)\n        # Transform NIFTI object to numpy array\n        pred_data = pred.get_fdata()\n        # Return prediction\n        return pred_data\n\n    #---------------------------------------------#\n    #               save_prediction               #\n    #---------------------------------------------#\n    # Write a segmentation prediction into in the NIFTI file format\n    def save_prediction(self, sample, output_path):\n        # Resolve location where data should be written\n        if not os.path.exists(output_path):\n            raise IOError(\n                \"Data path, {}, could not be resolved\".format(output_path)\n            )\n        # Convert numpy array to NIFTI\n        nifti = nib.Nifti1Image(sample.pred_data, sample.get_extended_data()[\"spacing\"])\n        #nifti.get_data_dtype() == pred.dtype\n        # Save segmentation to disk\n        pred_file = str(index) + \".nii.gz\"\n        nib.save(nifti, os.path.join(output_path, pred_file))\n\n    #---------------------------------------------#\n    #           check_file_termination            #\n    #---------------------------------------------#\n    @staticmethod\n    def check_file_termination(termination):\n        return termination in [\".dcm\"]","repo_name":"frankkramer-lab/MIScnn","sub_path":"miscnn/data_loading/interfaces/dicom_io.py","file_name":"dicom_io.py","file_ext":"py","file_size_in_byte":16006,"program_lang":"python","lang":"en","doc_type":"code","stars":382,"dataset":"github-code","pt":"81"}
+{"seq_id":"18226282793","text":"import sys\nsys.stdin = open(\"input.txt\",\"r\")\n\nn,k = map(int,input().split())\nmoney = []\nresult = []\nfor _ in range(n):\n    money.append(int(input()))\n\nfor i in money[::-1]:\n    if k>=i:\n        cnt=0\n        while k>=i:\n            k-=i\n            cnt+=1\n        result.append(cnt)\n\nprint(sum(result))","repo_name":"HorangApple/TIL","sub_path":"Algorithm/SWEA/23일차/11047_동전 0.py","file_name":"11047_동전 0.py","file_ext":"py","file_size_in_byte":302,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"35141842156","text":"#! /usr/bin/env python3 \n\n\"\"\" Therefore, we have the final expression for the partition function of ideal gas\"\"\" \n\nimport numpy as np \nimport matplotlib.pyplot as plt \n\ndifferent_x = 0.001\n# range\nx_range = np.arange(0, 10, different_x)\ny_range = x_range ** ((0.5) * np.exp(-x_range))\n\n# plotting\nplt.plot(x_range, y_range, color='k')\nplt.xlabel('x')\nplt.savefig('figure_for_integral.eps')\nplt.show()\n\n# under the curve\nprint(f'integral -> {8.7(np.sum(y_range) * different_x)}')\n","repo_name":"golanghack/ai_only","sub_path":"practic/phisics/module_6/partition_function.py","file_name":"partition_function.py","file_ext":"py","file_size_in_byte":479,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"28775010274","text":"import numpy as np\n\nN, M = [int(i) for i in input().split()]\nA = np.cumsum(np.array(input().split(), dtype=int)) % M\nans = 0\n\nremainder = {}\nfor i in A:\n    if i in remainder:\n        remainder[i] += 1\n    else:\n        remainder[i] = 1\n\nfor key in remainder:\n    ans += remainder[key] * (remainder[key] - 1) / 2\n\nans += remainder[0] if 0 in remainder else 0\n\nprint(int(ans))\n\n'''\nN, M = [int(i) for i in input().split()]\nA = np.cumsum(np.array(input().split(), dtype=int))\nans = 0\n\nfor first in range(N):\n    for last in range(first, N):\n        if first == last:\n            if A[first] % M == 0:\n                ans += 1\n        else:\n            if (A[last] - A[first]) % M == 0:\n                ans += 1\n\nprint(ans)\n'''\n","repo_name":"yasunariston/atCoder","sub_path":"ABC/ABC105/D.py","file_name":"D.py","file_ext":"py","file_size_in_byte":725,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"39694096876","text":"class Solution:\n    def setZeroes(self, matrix: List[List[int]]) -> None:\n        \"\"\"\n        Do not return anything, modify matrix in-place instead.\n        \"\"\"\n        # run through the matrix once, and keep track of where the zeroes are\n            # use a row set and column set\n        # then run through it againn, and if see the row or column corresponds to a row or column we found a zero in, then we can set it to zero\n\n        rows = set()\n        cols = set()\n\n        for r in range(len(matrix)):\n            for c in range (len(matrix[0])):\n                if matrix[r][c] == 0:\n                    rows.add(r)\n                    cols.add(c)\n        \n        for r in range(len(matrix)):\n            for c in range(len(matrix[0])):\n                if r in rows or c in cols:\n                    matrix[r][c] = 0\n\n#time = O(rc)\n# space complexity O(r+c)\n    #if you were making a copy of the array, it would be O(rc)","repo_name":"taystacksattack/leetCode","sub_path":"73-set-matrix-zeroes/set-matrix-zeroes.py","file_name":"set-matrix-zeroes.py","file_ext":"py","file_size_in_byte":929,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"71178324425","text":"#!/usr/bin/env python\n# coding: utf-8\n#\n# Author: Kazuto Nakashima\n# URL:    https://kazuto1011.github.io\n# Date:   28 January 2019\n\nimport torch\nimport torch.nn as nn\nimport matplotlib.pyplot as plt\n\nfrom libs.models.vgg import CircularConv2d\n\n\ndef init_weights(model):\n    for m in model.modules():\n        if isinstance(m, nn.Conv2d):\n            nn.init.constant_(m.weight, 1)\n            if m.bias is not None:\n                nn.init.constant_(m.bias, 0)\n\n\nif __name__ == \"__main__\":\n\n    input = torch.ones(1, 1, 16, 16).requires_grad_()\n    kwargs = {\"kernel_size\": 3, \"stride\": 1, \"bias\": False}\n\n    conv1 = nn.Sequential(\n        nn.Conv2d(1, 1, padding=1, **kwargs),\n        nn.Conv2d(1, 1, padding=1, **kwargs),\n        nn.Conv2d(1, 1, padding=1, **kwargs),\n        nn.Conv2d(1, 1, padding=1, **kwargs),\n        nn.Conv2d(1, 1, padding=1, **kwargs),\n    )\n    init_weights(conv1)\n\n    conv2 = nn.Sequential(\n        CircularConv2d(1, 1, **kwargs),\n        CircularConv2d(1, 1, **kwargs),\n        CircularConv2d(1, 1, **kwargs),\n        CircularConv2d(1, 1, **kwargs),\n        CircularConv2d(1, 1, **kwargs),\n    )\n    init_weights(conv2)\n\n    # Normal convolution\n    conv1(input).sum().backward()\n    plt.figure()\n    plt.imshow(input.grad.cpu().numpy()[0][0])\n\n    input.grad.zero_()\n\n    # Horizontal circular convolution\n    conv2(input).sum().backward()\n    plt.figure()\n    plt.imshow(input.grad.cpu().numpy()[0][0])\n\n    plt.show()\n","repo_name":"kazuto1011/circular-conv-pytorch","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1452,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"81"}
+{"seq_id":"43258605026","text":"from turtle import Turtle\nWIDTH = 5\nLENGTH = 1\n\n\nclass Paddle(Turtle):\n    def __init__(self, pos):\n        super().__init__()\n        self.pos = pos\n        self.create_paddle()\n\n    def create_paddle(self):\n        self.shape(\"square\")\n        self.penup()\n        self.shapesize(stretch_wid=WIDTH, stretch_len=LENGTH)\n        self.color(\"white\")\n        self.goto(self.pos)\n\n    def up(self):\n        y_pos = self.ycor() + 20\n        self.goto(self.xcor(), y_pos)\n\n    def down(self):\n        y_pos = self.ycor() - 20\n        self.goto(self.xcor(), y_pos)\n","repo_name":"mukeshmk7/Ping_Pong","sub_path":"paddle.py","file_name":"paddle.py","file_ext":"py","file_size_in_byte":559,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"21060173498","text":"from functools import reduce\r\n# def is_even(n):\r\n#     if n%2 == 0:\r\n#         return n\r\n\r\n\r\n# my_list = [1,2,3,4,5,6,7,8,9,10]\r\n# evens = list(filter(is_even,my_list)) #filter takes 2 paramters a function and a list\r\n# print(evens) \r\n\r\n#instead of having a function is_even we can have a lamba fn\r\n\r\nmy_list = [1,2,3,4,5,6,7,8,9,10]\r\nevens = list(filter(lambda n: n%2==0 ,my_list)) #to filter out from a list\r\nprint(evens)\r\n\r\ndoubles = list(map(lambda n: n*2,evens)) #to perform func on each of the iterable obj in list\r\nprint(doubles)\r\n\r\nsum_all = reduce(lambda a,b: a+b,doubles)\r\nprint(sum_all)\r\n\r\n\r\n","repo_name":"MidhaTahir/-Python-","sub_path":"filter,map,reduce,lambda.py","file_name":"filter,map,reduce,lambda.py","file_ext":"py","file_size_in_byte":603,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"81"}
+{"seq_id":"11670731355","text":"import sys\nfrom pathlib import Path\nsys.path.append(str(Path(__file__).resolve().parents[3]))  # 将父级目录加入执行目录列表\n\nfrom model.gen_dla import *\n\nif __name__ == \"__main__\":\n  tiling_test = gen_sxdla_code(\n    \"conv_nhwc_tiling_maxpool_112\",\n    \"weight\",\n    0x1000000,\n    0x100000,\n    112,\n    112,\n    56,\n    56,\n    2,\n    3,\n    3,\n    [\"conv3\", \"activate\",\"quantize\",\"maxpool\"]\n  )\n\n  conv_activate = gen_sxdla_code(\n    \"conv_nhwc_maxpool_63\",\n    \"weight\",\n    0x1000000,\n    0x100000,\n    63,\n    63,\n    63,\n    63,\n    1,\n    3,\n    3,\n    [\"conv3\", \"activate\",\"quantize\",\"maxpool\"]\n  )\n","repo_name":"SabreSu/sxdla","sub_path":"op_test/test_conv_nhwc_maxpool/operators/gen_ops.py","file_name":"gen_ops.py","file_ext":"py","file_size_in_byte":621,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"13197478515","text":"\nimport csv \nimport sys\nimport pandas\nimport random\nimport math \nimport copy\n\n#Euclidean distance between p1 and p2, ONLY USES ATTRIBUTRES PRESENT IN P1\ndef Euclidean(p1, p2):\n\tdistance = 0\n\n\tfor key in p1.keys():\n\t\tval1 = p1[key]\n\t\tval2 = p2[key]\n\t\tdistance += abs(val1-val2)**2\n\tdistance = math.sqrt(distance)\n\t\n\treturn distance\n\ndef kmeans(k, reader):\n\t#create initialization points\n\n\t#column_header->(min, max) of each attribute \n\tlimits_and_means = find_attribute_limits_and_mean(reader);\n\n\tattribute_limits = limits_and_means[0]\n\t#Get means of all attributes and convert to single dictionary\n\tattribute_means = limits_and_means[1][0]\n\tfor i in range (1, len(limits_and_means[1])):\n\t\tattribute_means.update(limits_and_means[1][i])\n\t#print(\"means:\", attribute_means)\n\n\tcentroids = [] #list of dictionaries, [{attribute_name: value}]\n\tfor i in range(0, k):\n\t\tpoint = {}\n\t\tfor header, limits in attribute_limits.items():\n\t\t\tpoint[header] = random.uniform(limits[0], limits[1]) \n\n\t\tcentroids.append(point)\n\n\ttimes_unchanged = 0\n\tattributes = centroids[0].keys();\n\n\treader['guessed_cluster'] = -1\n\n\twhile (times_unchanged <1): \n\t\tfor row in reader.iterrows(): \n\t\t\tpoint = row[1] #dictionary of attributes to values, includes extra\n\t\t\tid = row[0]\n\t\t\tmin_distance = 100\n\t\t\tcluster = 0;\n\t\t\tfor centroid in centroids:\n\t\t\t\tdistance = Euclidean(centroid, point)\n\t\t\t\tif(distance<min_distance):\n\t\t\t\t\tmin_distance = distance;\n\t\t\t\t\treader.set_value(id,'guessed_cluster', cluster)\n\t\t\t\tcluster += 1;\n\n\t\tprevious_centroids = copy.deepcopy(centroids)\n\t\t#print('centroids ', centroids)\n\t\t#make centroids zero\n\t\tfor centroid in centroids:\n\t\t\tfor attribute in attributes:\n\t\t\t\tcentroid[attribute] = 0 \n\n\t\tcluster_totals = []\n\t\tfor i in range(0,k):\n\t\t\tcluster_totals.append(0)\n\n\t\tfor row in reader.iterrows():\n\t\t\tpoint = row[1]\n\t\t\tfor i in range (0,k):\n\t\t\t\tif point['guessed_cluster'] == i:\n\t\t\t\t\tcluster_totals[i] += 1\n\t\t\t\t\tfor attribute in attributes:\n\t\t\t\t\t\tcentroids[i][attribute] += point[attribute]\n\n\t\tfor i in range(0,k):\n\t\t\tcentroid = centroids[i]\n\t\t\tfor attribute in attributes:\n\t\t\t\tif(cluster_totals[i] ==0):\n\t\t\t\t\tsys.exit()\n\t\t\t\t\n\t\t\t\tcentroid[attribute] /= cluster_totals[i]\n\n\t\tunchanged = True\n\n\t\tfor centroid, previous_centroid in zip(centroids, previous_centroids):\n\t\t\tif centroid != previous_centroid:\n\t\t\t\tunchanged = False\n\t\t\t\tif times_unchanged!= 0:\n\t\t\t\t\ttimes_unchanged = 0\n\n\t\tif(unchanged):\n\t\t\ttimes_unchanged+=1\n\n\tcluster_column = reader['guessed_cluster']\t\n\tcluster_totals = []\n\tfor i in range(0,k):\n\t\tcluster_totals.append(0)\n\t\tfor cluster in cluster_column:\n\t\t\tif cluster == i:\n\t\t\t\tcluster_totals[i] += 1\n\n\twith open('./output.csv', 'w') as output_file:\n\t\treader.to_csv(output_file, columns = ['ID', 'guessed_cluster'])\n\t#centroids= list of dicts [{attibute:value}], attribute means= dict {attribute: mean}, cluster_totals = list of totals, corresponding with the index of the centroid\n\treturn (centroids, attribute_means, cluster_totals)\n\n\ndef find_attribute_limits_and_mean(reader):\n\t#dictionary of attribute ids -> (max, min) of the attribute values\n\tlimits = {}\n\n\t#list of dictionaries [{attribute:mean}]\n\tmeans = []\n\tfor header in reader.columns.values:\n\t\tif header != 'ID' and header!= 'class' and header != 'cluster' and header != 'quality':\n\t\t\tattribute_values = reader[header].tolist()\n\t\t\tlimits[header] = (min(attribute_values), max(attribute_values))\n\t\t\tmeans.append({header: (sum(attribute_values)/float(len(attribute_values)))})\n\n\treturn (limits, means)\n\ndef get_true_centroids(key, reader):\n\ttrue_reader = reader.drop('guessed_cluster', 1)\n\tclusters = {}\n\tfor x in list(set(true_reader[key].values)):\n\t\tclusters[x] = true_reader.loc[true_reader[key] == x]\n\tcluster_centroids = {}\n\tfor cluster in clusters:\n\t\tcentroids, attribute_means, cluster_totals = kmeans(1,clusters[cluster])\n\t\tcluster_centroids[cluster] = centroids[0]\n\treturn cluster_centroids, cluster_totals\t\n\ndef sse(reader, centroids):\n\tSSE = 0\n\tcluster_SSE = {}\n\ttrue_SSE = 0\n\ttrue_cluster_SSE = {}\n\tif 'cluster' in reader.columns:\n\t\tkey = 'cluster'\n\telse:\n\t\tkey = 'class'\n\ttrue_centroids, totals = get_true_centroids(key, reader)\n\tfor row in reader.iterrows():\n\t\tpoint = row[1]\n\t\t\n\t\tguessed_cluster = int(point['guessed_cluster'])\n\t\tcentroid = centroids[guessed_cluster]\n\t\tdistance = Euclidean(centroid, point)\n\t\tse = math.pow(distance, 2)\n\t\tSSE += se\n\n\t\tif guessed_cluster in cluster_SSE:\n\t\t\tcluster_SSE[guessed_cluster] += se\n\t\telse:\n\t\t\tcluster_SSE[guessed_cluster] = se\n\n\t\tcluster = point[key]\n\t\tcentroid = true_centroids[cluster]\n\t\tdistance = Euclidean(centroid, point)\n\t\tse = math.pow(distance, 2)\n\t\ttrue_SSE += se\n\n\t\tif cluster in true_cluster_SSE:\n\t\t\ttrue_cluster_SSE[cluster] += se\n\t\telse:\n\t\t\ttrue_cluster_SSE[cluster] = se\n\treturn SSE\n\ndef ssb(centroids, cluster_totals, attribute_means, reader):\n\tSSB = 0\n\tfor i in range(0, len(centroids)):\n\t\tcluster_size = cluster_totals[i]\n\t\tcentroid = centroids[i]\n\t\tdistance = Euclidean(centroid, attribute_means)\n\t\tSSB += cluster_size*(math.pow(distance,2))\n\n\ttrue_SSB = 0\n\tif 'cluster' in reader.columns:\n\t\tkey = 'cluster'\n\telse:\n\t\tkey = 'class'\n\ttrue_centroids, totals = get_true_centroids(key, reader)\n\tfor cluster in true_centroids:\n\t\tcentroid = true_centroids[cluster]\n\t\tdistance = Euclidean(centroid, attribute_means)\n\t\ttrue_SSB += cluster_size*(math.pow(distance,2))\n\n\treturn SSB\n\n\n#expects arguments of k, then 1 for easy, 2 for hard, and 3 for wine\ndef main():\n\tfile_name = ''\n\tif(sys.argv[2] == '1'):\n\t\tfile_name = './TwoDimEasyNormalized.csv'\n\telif(sys.argv[2] == '2'):\n\t\tfile_name = './TwoDimHardNormalized.csv'\n\telif(sys.argv[2] == '3'):\n\t\tfile_name = './wine_normalized.csv'\n\telse:\n\t\tprint('invalid arguments\\n')\n\t\treturn\n\n\twith open(file_name) as data:\n\t\treader = pandas.read_csv(data)\n\n\tcentroids, attribute_means, cluster_totals = kmeans(int(sys.argv[1]), reader)\n\n\tSSE = sse(reader, centroids)\n\tSSB = ssb(centroids, cluster_totals, attribute_means, reader)\n\nif __name__ == \"__main__\":main()\n","repo_name":"moeller142/KNearestNeighbors","sub_path":"clustering/k-means.py","file_name":"k-means.py","file_ext":"py","file_size_in_byte":5936,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"7208416695","text":"from odoo import models, fields, api\n\n\nclass ResPartner(models.Model):\n    _inherit = \"res.partner\"\n\n    birthday = fields.Date(string=\"Birthday\")\n    age = fields.Integer(string=\"Age\", compute=\"_compute_age\")\n    branch_id = fields.Many2one(\n        comodel_name=\"res.users\",\n        string=\"Branch\",\n        default=lambda self: self.env.user.id,\n    )\n    hmo = fields.Selection(\n        selection=[\n            (\"national\", \"National\"),\n            (\"clalit\", \"Clalit\"),\n            (\"maccabi\", \"Maccabi\"),\n            (\"united\", \"United\"),\n            (\"private\", \"Private\"),\n        ],\n        string=\"Health Maintenance Organization\"\n    )\n\n    @api.depends(\"birthday\")\n    def _compute_age(self):\n        today = fields.Date.today()\n        for rec in self:\n            birthday = rec.birthday\n            rec.age = (\n                today.year - birthday.year - ((today.month, today.day) < (birthday.month, birthday.day))\n                if rec.birthday and today >= birthday\n                else 0\n            )\n","repo_name":"yediel/plobe_customer_card_design","sub_path":"plobe_customer_card/models/res_partner.py","file_name":"res_partner.py","file_ext":"py","file_size_in_byte":1022,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"24298621625","text":"import numpy as np\nfrom scipy import signal, stats\nimport matplotlib.pyplot as plt\nfrom pathlib import Path\nrepoPath = Path(__file__).absolute().parent\nfrom mantid.simpleapi import Load\nfrom scipy import ndimage\nimport time\nfrom iminuit import cost, Minuit\n\n\ndef main():\n    resPath = repoPath / \"wsResSmall.nxs\"\n    joyPath = repoPath / \"wsJOYsmall.nxs\"\n    wsRes = Load(str(resPath), OutputWorkspace=\"wsRes\")\n    wsJOY = Load(str(joyPath), OutputWorkspace=\"wsJOY\")\n\n    fitProfileMinuit(wsJOY, wsRes)\n\n\ndef oddConvolution(x, y, res):\n    # assert np.min(x) == -np.max(x), \"Resolution needs to be in symetric range!\"\n    # assert x.size == res.size, \" Resolution needs to have the same no of points as spectrum!\"\n    \n    if x.size % 2 == 0:\n        rangeRes = x.size+1  # If even change to odd\n    else:\n        rangeRes = x.size    # If odd, keep being odd\n\n    xInterp = np.linspace(np.min(x), np.max(x), rangeRes)\n    xDelta = xInterp[1] - xInterp[0]\n    resInterp = np.interp(xInterp, x, res)\n\n    conv = signal.convolve(y, resInterp, mode=\"same\") * xDelta\n    return conv \n\n\ndef fitProfileMinuit(wsYSpaceSym, wsRes):\n\n    dataY = wsYSpaceSym.extractY()[0]\n    dataX = wsYSpaceSym.extractX()[0]\n    dataE = wsYSpaceSym.extractE()[0]\n\n\n    resY = wsRes.extractY()[0]\n    resX = wsRes. extractX()[0]\n\n    assert np.min(resX) == -np.max(resX), \"Resolution needs to be in symetric range!\"\n    assert resX == dataX, \"Resolution range needs to be equal to dataX\"\n\n\n    def convolvedModel(x, A, x0, sigma1, c4, c6):\n        gramCharlier =  A * np.exp(-(x-x0)**2/2/sigma1**2) / (np.sqrt(2*3.1415*sigma1**2)) \\\n                *(1 + c4/32*(16*((x-x0)/np.sqrt(2)/sigma1)**4 \\\n                -48*((x-x0)/np.sqrt(2)/sigma1)**2+12) \\\n                +c6/384*(64*((x-x0)/np.sqrt(2)/sigma1)**6 \\\n                -480*((x-x0)/np.sqrt(2)/sigma1)**4 + 720*((x-x0)/np.sqrt(2)/sigma1)**2 - 120))\n        return oddConvolution(x, gramCharlier, resY)\n        # return ndimage.convolve1d(gramCharlier, resolution, mode=\"constant\") * histWidths0\n\n    def constrFunc(*pars):\n        return convolvedModel(dataX, *pars)\n\n    # Fit with Minuit\n    costFun = cost.LeastSquares(dataX, dataY, dataE, convolvedModel)\n    m = Minuit(costFun, A=1, x0=0, sigma1=4, c4=0, c6=0)\n    m.limits[\"A\"] = (0, None)\n    m.limits[\"c4\"] = (0, None)      # c4 always positive\n    m.simplex()\n    m.scipy(constraints=optimize.NonlinearConstraint(constrFunc, 0, np.inf))\n\n    # Explicit calculation of Hessian after the fit\n    m.hesse()\n    m.minos()\n    print(m)","repo_name":"guijmp/scatt_scripts","sub_path":"vesuvio_analysis/scribles/recent_scribles/minuit_GC_fit.py","file_name":"minuit_GC_fit.py","file_ext":"py","file_size_in_byte":2523,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"71127887944","text":"#Jacob Smith Brandeis University Makerlab 6/18/2019\n#A library of functions useful in interacting with a RasberryPi\n#Along with a main method to send a tweet when a button is pressed\n\n#sends a tweet with a picture when a button ispressed\ndef main():\n    import random\n    #messages to selectfrom when tweeting\n    messages=[\"HiFolks! Come check out the makerlab!\",\"Look at what's going on over here!\",\"#OurPublicityDepartmentIsAComputer\",\"Automation is our motto, cooperation is our dream\",\"#Collaboration\"]\n    hashtag=\"\\nMeet us on first floor of Farber in the library\\n#BrandeisMakerlab\"\n\n    #when the button is pressed,a randomly selected message will be tweeted with a picture\n    twitter=importKeys()\n    while(True):\n        if isButtonPressed():\n            print(\"Taking Picture for Tweet\")\n            #generate the message for the tweet\n            message=random.choice(messages)+hashtag\n            tweet(twitter,message)\n            print(\"Tweeted:%s\"%message)\n    return\n\n\n#imports a twitter account's keys from auth.py file\n#see tweetfunction for link\ndef importKeys():\n    from twython import Twython\n\n    from auth import(\n    consumer_key,\n    consumer_secret,\n    access_token,\n    access_token_secret\n    )\n\n    twitter=Twython(\n    consumer_key,\n    consumer_secret,\n    access_token,\n    access_token_secret\n    )\n    return twitter\n\n#takes a picture and tweets it with a given message\n#needs the twitter object and the message\n#https://www.raspberrypi.org/documentation/usage/webcams/\n#https://projects.raspberrypi.org/en/projects/getting-started-with-the-twitter-api\ndef tweet(twitter,message):\n    import subprocess\n    subprocess.call([\"fswebcam\",\"/home/pi/toSend.jpg\"]);\n    image=open('/home/pi/toSend.jpg','rb')\n    response=twitter.upload_media(media=image)\n    media_id=[response['media_id']]\n    twitter.update_status(status=message,media_ids=media_id)\n    return\n\n#returns true if a button wired to ports 18 and ground is pressed\n#http://razzpisampler.oreilly.com/ch07.html\ndef isButtonPressed():\n    import RPi.GPIO as GPIO\n    import time\n\n    GPIO.setmode(GPIO.BCM)\n\n    GPIO.setup(18,GPIO.IN,pull_up_down=GPIO.PUD_UP)\n\n    input_state=GPIO.input(18)\n    #delay for 200 milliseconds so multiple button presses aren't detected\n    time.sleep(.2)\n    return not input_state\n\n#actually call the main method to run the program\nmain()","repo_name":"BrandeisMakerLab/Raspi_Digital_Window_Project","sub_path":"twitter.py","file_name":"twitter.py","file_ext":"py","file_size_in_byte":2367,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"13382225025","text":"# Program that rewards based on greeting\n\ndef main():\n\n    greeting = input(\"Greeting: \").strip().lower()\n\n    # Checking response\n    if greeting.startswith(\"hello\"):\n        print(\"$0\")\n    elif (greeting.startswith(\"h\")) and (greeting != \"hello\"):\n        print(\"$20\")\n    else:\n        print(\"$100\")\n\n\nmain()\n","repo_name":"rubxcube/cs50code","sub_path":"bank/bank.py","file_name":"bank.py","file_ext":"py","file_size_in_byte":313,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"74015754824","text":"from selenium import webdriver\nimport os\n\n\nclass WebApp:\n\n    driver: webdriver = None\n\n    @classmethod\n    def start(cls, url='https://app.pageloot.com/auth/login'):\n        \"\"\"Creates instance of browser\"\"\"\n\n        executable_path = os.path.join(os.path.dirname(__file__), '../../drivers/chromedriver')\n\n        cls.driver = webdriver.Chrome(executable_path)\n        cls.driver.maximize_window()\n        cls.driver.get(url)\n\n        return cls.driver\n\n    # Quit app\n    @classmethod\n    def quit(cls):\n        cls.driver.quit()\n","repo_name":"NikolaS93/pageloot_autotests","sub_path":"Pageloot/base/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":533,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"33732276779","text":"#Creado por Saenz Barragan Ricardo\n\n#Interfaz Grafica\nfrom tkinter import *\nfrom tkinter import messagebox\n#Solucion recursiva del problema\nfrom class_problem import sudoku\nimport os\n\n#Funcion que recibe el nombre de un archivo para abrir, si el archivo no \n#existe regresa un None, de lo contratio abre el archivo y lo guarda en una lista\ndef Open_Sudoku(file_name, size):\n\tif not os.path.isfile(file_name):\n\t\treturn None\n\n\tpuzzle_file = open(file_name, \"r\")\n\tpuzzle_list = puzzle_file.read().splitlines()\n\tpuzzle_file.close()\n\n\tpuzzle = []\n\t#Separa cada elemento de la lista en sub listas que luego va añadiendo \n\t#Esto para que quede en la forma adecuada para ser solucionado\n\tfor i in range(size):\n\t\tpuzzle.append([])\n\t\tfor number in puzzle_list[i]:\n\t\t\tpuzzle[i].append(int(number))\n\t#Regresa una lista de listas con los elemnetos ordenados\n\treturn puzzle\n\n#Clase que se encarga de crear la ventana y lo que contiene \n# Ya sea las funciones que se realizan dentro de ella y los graficos que aparecen\nclass sudoku_Frame(Frame):\n\t#inicialisa creando por predeterminado un tamaño 9 que se puede ajustar para que sea\n\t#de tamaño n, pero por propositos de grafico solo sera de 9 x 9\n\tdef __init__(self, master = None):\n\t\tFrame.__init__(self, master)\n\t\tself.grid()\n\t\tself.size = 9\n\t\tself.sizePuzzle = self.size * self.size\n\t\tself.puzzle = None\n\t\tself.create_window()\n\n\t#Crea la ventana que se estara actualizando, un diccionario que guarda las casillas para \n\t#escribir y el tamaño del tablero\n\tdef create_window(self):\n\t\tself.entries = {}\n\t\tself.tableheight = self.size\n\t\tself.tablewidth = self.size\n\t\t#Botones que se encargan de empezar las funciones\n\n\t\t#Resuleve lo escrito en el tablero\n\t\tself.pack(fill=BOTH, expand= 1)\n\t\tsolve = Button(self, text= 'Solve', width = 6, height = 2, command=self.SolvePuzzle)\n\t\tsolve.place(x=45, y=300)\n\t\t#Abre un archivo para solucionar\n\t\tread = Button(self, text= 'Open', width = 6, height = 2, command=self.open_sudo)\n\t\tread.place(x=100, y=300)\n\t\t#Limpia la pantalla y lo que se haya guardado\n\t\trestart = Button(self, text= 'Restart', width = 6, height = 2, command=self.clear_text)\n\t\trestart.place(x=155, y=300)\n\t\t#Termina el programa\n\t\texit = Button(self, text= 'Exit', width = 6, height = 2, command=self.exitProgram)\n\t\texit.place(x=210, y=300)\n\n\t\t#Crea el tablero de 9 x 9 usando cajones para escribir\n\t\t#contador que sera la llave de  diccionario para los cajones\n\t\tcounter = 0\n\t\tfor row in range(self.tableheight):\n\t\t\tfor column in range(self.tablewidth):\n\t\t\t\t#Nos dicen el sector donde se encuentra para saber que color llevaran\n\t\t\t\tsectorRow = 3 * (row//3)\n\t\t\t\tsectorCol = 3 * (column//3)\n\t\t\t\t#Si se encuentran en los sectores, 2, 4, 6, 8 Seran de color gris\n\t\t\t\tif (sectorRow == 3 and (sectorCol == 0 or sectorCol == 6)) or ((sectorRow == 0 or sectorRow == 6) and  sectorCol == 3):\n\t\t\t\t\tself.entries[counter] = Text(self, width=2, height=1, bg=\"grey\", font=(\"Helvetica\",22))\n\t\t\t\telse : \n\t\t\t\t#De lo contraio seran blancos\n\t\t\t\t\tself.entries[counter] = Text(self, width=2, height=1, font=(\"Helvetica\",22))\n\t\t\t\t#Decimos donde se va a colocar el cajon en las celda \n\t\t\t\tself.entries[counter].grid(row=row, column=column)\n\t\t\t\tcounter += 1\n\n\tdef clear_text(self, type_ = \"All\"):\n\t\t#Limpia los cajones de texto uno por uno\n\t\tfor counter in range(self.sizePuzzle):\n\t\t\tself.entries[counter].delete(1.0, END)\n\t\t#Si se quiere eliminar todo rasto se hara y se sustituira por un None\n\t\tif self.puzzle != None and type_ == \"All\":\n\t\t\tdel self.puzzle\n\t\t\tself.puzzle = None\n\n\tdef open_sudo(self):\n\t\t#limpia los cajones por completo junto con la memoria\n\t\tself.clear_text()\n\t\t#Abre el archivo con el sudoku a resolver\n\t\tself.puzzle = Open_Sudoku(\"sudoku.txt\", 9)\n\t\t#Si no se abrio nada manda un error y termina\n\t\tif self.puzzle == None:\n\t\t\tmessagebox.showwarning(\"Error\", \"No se encontro el archivo: sudoku.txt\")\n\t\t\treturn\n\t\t#Si encuentra algo rellena el tablero con los numeros\n\t\tself.fill_Suoku()\n\n\tdef fill_Suoku(self):\n\t\t#limpia el tablero sin borrar la lista guardada\n\t\tself.clear_text(\"Normal\")\n\t\t#Comienza a rellenar los espacios de las celdas con los datos de las listas\n\t\tcounter = 0\n\t\tfor rows in self.puzzle:\n\t\t\tfor number in rows:\n\t\t\t\tif number != 0 :\n\t\t\t\t\tself.entries[counter].insert('1.0', number)\n\t\t\t\tcounter += 1\n\n\tdef read_Puzzle(self):\n\t\t#Si no existe un sudoku crea uno \n\t\tif self.puzzle == None :\n\t\t\tself.puzzle = []\n\t\t\tcounter = 0\n\t\t\t#Lee lo que se encuentra en las celdas \n\t\t\tfor i in range(self.size):\n\t\t\t\tself.puzzle.append([])\n\t\t\t\tfor number in range(self.size):\n\t\t\t\t\t#Guarda lo encontrado en listas de listas solo tomando el primer numero\n\t\t\t\t\t#E ignorando los saltos de lineas sustituyendo por 0\n\t\t\t\t\tif self.entries[counter].get(1.0) != '\\n' :\n\t\t\t\t\t\tself.puzzle[i].append(int(self.entries[counter].get(1.0)))\n\t\t\t\t\telse :\n\t\t\t\t\t\tself.puzzle[i].append(0)\n\t\t\t\t\tcounter += 1\n\t\t#Si existe un archivo no lee el tablero y soluciona directo\n\n\tdef SolvePuzzle(self):\n\t\t#Crea un sudoku o verifica que algo exista\n\t\tself.read_Puzzle()\n\t\t#Soluciona el sudoku por recusrividad\n\t\tsolve = sudoku(self.puzzle)\n\t\tsolve.solveSudoku()\n\t\t#regrea una lista resuelta\n\t\tself.puzzle = solve.puzzle\n\t\t#Rellena las celdas con la lsita resuleta\n\t\tself.fill_Suoku()\n\n\t# Termina el programa y cierra la ventana\n\tdef exitProgram(self):\n\t\texit()\n\n#Funcion que se encarga de crear el tamaño de la ventana, nombre de la misma y\n# el loop que actualiza lo que sucede dentro de ella.\ndef Window_sudoku():\n\troot = Tk()\n\troot.geometry(\"310x340\")\n\t\n\tprogram = sudoku_Frame(root)\n\t\n\troot.title('Sudoku')\n\troot.mainloop()","repo_name":"saenzzzup/Sudoku_and_8_Queens","sub_path":"GUI_sudoku.py","file_name":"GUI_sudoku.py","file_ext":"py","file_size_in_byte":5541,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"27566121499","text":"n = int(input())\nlst1 = list(map(int,input().split()))\nmod = 10**9+7\n\nimport fractions as math\n#リスト l の最大公約数\ndef lcmlist(l):\n    a = l[0]\n    for i in range(len(l)):\n        #change\n        a = (a*l[i]) //math.gcd(a,l[i])\n    return a\n\ng = lcmlist(lst1)\nans = 0\nfor i in range(n):\n    ans += g//lst1[i]%mod\n\nprint(ans%mod)\n\n\n","repo_name":"Yuta123456/AtCoder","sub_path":"python/AtCoder Beginner Contest 152/kohe.py","file_name":"kohe.py","file_ext":"py","file_size_in_byte":343,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"15614397610","text":"import torch\r\nfrom torch import nn\r\nfrom torch.autograd import Function\r\nimport torch.nn.functional as F\r\nimport math\r\nimport numpy as np\r\nimport random\r\n\r\nclass EcgClassifier(nn.Module):\r\n    \"\"\"Feature classifier class for MNIST -> MNIST-M experiment in ATDA.\"\"\"\r\n\r\n    def __init__(self, dropout_keep=None, num_classes=5):\r\n        \"\"\"Init classifier.\"\"\"\r\n        super(EcgClassifier, self).__init__()\r\n\r\n        self.dropout_keep = dropout_keep\r\n\r\n        self.classifier = nn.Sequential(\r\n            nn.Linear(256 * 10, 256 * 5),\r\n            # nn.Linear(256 *5, 256 * 1),\r\n            # nn.BatchNorm1d(256 * 5),\r\n            nn.ReLU(inplace=True),\r\n            nn.Dropout(self.dropout_keep),  # 0.5\r\n            nn.Linear(256 * 5, num_classes),\r\n\r\n        )\r\n\r\n\r\n    def forward(self, x):\r\n        \"\"\"Forward classifier.\"\"\"\r\n        out = self.classifier(x)\r\n        return out\r\n\r\nclass AlexNetforEcg_Single_Model(nn.Module):\r\n    '''input tensor size:(None,1,3,128)'''\r\n    def __init__(self):\r\n        super(AlexNetforEcg_Single_Model, self).__init__()\r\n        self.features = nn.Sequential(\r\n            nn.Conv2d(1, 64, kernel_size=(3, 5), padding=(0, 0)),\r\n            # nn.BatchNorm2d(64),\r\n            nn.ReLU(inplace=True),\r\n            nn.MaxPool2d(kernel_size=(1, 2), stride=(1, 2)),#（N,64,1,62)\r\n\r\n            nn.Conv2d(64, 192, kernel_size=(1, 5), padding=(0, 2)),\r\n            # nn.BatchNorm2d(192),\r\n            nn.ReLU(inplace=True),\r\n            nn.MaxPool2d(kernel_size=(1, 2), stride=(1, 2)),#(N,192,1,30)\r\n\r\n            nn.Conv2d(192, 256, kernel_size=(1, 5), padding=(0, 2)),#(N,_,1,30)\r\n            # nn.BatchNorm2d(256),\r\n            nn.ReLU(inplace=True),\r\n            nn.MaxPool2d(kernel_size=(1, 2), stride=(1, 2)),\r\n\r\n        )\r\n\r\n        self.fc = nn.Sequential(\r\n            nn.Linear(256 * 15, 256 * 10),\r\n            # nn.Linear(256 * 14, 256 * 10),\r\n            nn.ReLU(inplace=True),\r\n            nn.Dropout(0.5),#0.3\r\n            nn.BatchNorm1d(256 * 10),\r\n        )\r\n\r\n        self.classifier = nn.Sequential(\r\n            nn.Linear(256 * 10, 256 * 5),\r\n            nn.ReLU(inplace=True),\r\n            nn.Dropout(0.5),  # 0.5\r\n            nn.Linear(256 * 5, 4)\r\n\r\n        )\r\n\r\n    def forward(self, x):\r\n        x = self.features(x)\r\n        # print(\"feature size:\", x.size())\r\n        # x = x.view(x.size(0), self.num_flat_features(x))\r\n        # print(x.size())\r\n        x = x.view(x.size(0), -1)\r\n        x = self.fc(x)\r\n        y = self.classifier(x)\r\n        return x, y\r\n\r\n\r\nclass ResClassifier(nn.Module):\r\n    def __init__(self, num_classes=5, dropout_keep=0.5):\r\n        super(ResClassifier, self).__init__()\r\n        self.fc1 = nn.Sequential(\r\n            nn.Linear(256 * 10, 256 * 5),\r\n            # nn.BatchNorm1d(1000, affine=True),\r\n            nn.ReLU(inplace=True),\r\n            nn.Dropout(p=dropout_keep)\r\n            )\r\n        self.fc2 = nn.Linear(256 * 5, num_classes)\r\n        # self.extract = extract\r\n        self.dropout_p = dropout_keep\r\n\r\n    def forward(self, x, extract=False):\r\n        fc1_emb = self.fc1(x)\r\n        if self.training:\r\n            fc1_emb.mul_(math.sqrt(1 - self.dropout_p))\r\n        logit = self.fc2(fc1_emb)\r\n\r\n        if extract:\r\n            return fc1_emb, logit\r\n        return logit\r\n\r\nclass AlexNetforEcg_DS1_to_DS2(nn.Module):\r\n    '''input tensor size:(None,1,3,128)'''\r\n    def __init__(self):\r\n        super(AlexNetforEcg_DS1_to_DS2, self).__init__()\r\n        self.features = nn.Sequential(\r\n            nn.Conv2d(1, 64, kernel_size=(3, 5), padding=(0, 0)),\r\n            nn.ReLU(inplace=True),\r\n            # nn.BatchNorm2d(96),\r\n            nn.MaxPool2d(kernel_size=(1, 2), stride=(1, 2)),#（N,64,1,62)\r\n\r\n\r\n            nn.Conv2d(64, 192, kernel_size=(1, 5), padding=(0, 2)),\r\n            nn.ReLU(inplace=True),\r\n            # nn.BatchNorm2d(256),\r\n            nn.MaxPool2d(kernel_size=(1, 2), stride=(1, 2)),#(N,192,1,30)\r\n\r\n\r\n\r\n            nn.Conv2d(192, 256, kernel_size=(1, 5), padding=(0, 2)),#(N,_,1,30)\r\n            # nn.Conv2d(192, 256, kernel_size=(1, 3), padding=(0, 1)),\r\n            # # nn.BatchNorm2d(384),\r\n            # nn.LeakyReLU(0.2, inplace=True),\r\n            nn.ReLU(inplace=True),\r\n            # nn.Conv2d(384, 384, kernel_size=(1, 1), padding=(0, 0)),\r\n            # nn.ReLU(inplace=True),\r\n            # nn.Conv2d(384, 256, kernel_size=(1, 1), padding=(0, 0)),\r\n            # nn.ReLU(inplace=True),\r\n\r\n            # nn.Conv2d(384, 256, kernel_size=(1, 1), padding=(0, 0)),#(N,256,1,32)\r\n\r\n            # nn.BatchNorm2d(256),\r\n\r\n            # nn.ReLU(inplace=True),\r\n\r\n            nn.MaxPool2d(kernel_size=(1, 2), stride=(1, 2)),\r\n\r\n\r\n        )\r\n\r\n        self.fc = nn.Sequential(\r\n            nn.Linear(256 * 15, 256 * 10),\r\n            # nn.Linear(256 * 15, 256 * 5),\r\n            # nn.Linear(256 * 14, 256 * 10),\r\n            nn.ReLU(inplace=True),\r\n            nn.Dropout(0.3),#0.3\r\n            nn.BatchNorm1d(256 * 10),\r\n        )\r\n\r\n    def forward(self, x):\r\n        x = self.features(x)\r\n        # print(\"feature size:\", x.size())\r\n        # x = x.view(x.size(0), self.num_flat_features(x))\r\n        # print(x.size())\r\n        x = x.view(x.size(0), -1)\r\n        x = self.fc(x)\r\n        return x\r\n\r\nclass AlexNetforEcg_DS1_to_DS2_each_patient(nn.Module):\r\n    '''input tensor size:(None,1,3,128)'''\r\n    def __init__(self):\r\n        super(AlexNetforEcg_DS1_to_DS2_each_patient, self).__init__()\r\n        self.features = nn.Sequential(\r\n            nn.Conv2d(1, 64, kernel_size=(3, 5), padding=(0, 0)),\r\n            # nn.BatchNorm2d(64),\r\n            nn.ReLU(inplace=True),\r\n            nn.MaxPool2d(kernel_size=(1, 2), stride=(1, 2)),#（N,64,1,62)\r\n\r\n            nn.Conv2d(64, 192, kernel_size=(1, 5), padding=(0, 2)),\r\n            # nn.BatchNorm2d(192),\r\n            nn.ReLU(inplace=True),\r\n            nn.MaxPool2d(kernel_size=(1, 2), stride=(1, 2)),#(N,192,1,30)\r\n\r\n            nn.Conv2d(192, 256, kernel_size=(1, 5), padding=(0, 2)),#(N,_,1,30)\r\n            # nn.BatchNorm2d(256),\r\n            nn.ReLU(inplace=True),\r\n            nn.MaxPool2d(kernel_size=(1, 2), stride=(1, 2)),\r\n\r\n        )\r\n\r\n        self.fc = nn.Sequential(\r\n            nn.Linear(256 * 15, 256 * 10),\r\n            # nn.Linear(256 * 14, 256 * 10),\r\n            nn.ReLU(inplace=True),\r\n            nn.Dropout(0.5),#0.3\r\n            nn.BatchNorm1d(256 * 10),\r\n        )\r\n\r\n    def forward(self, x):\r\n        x = self.features(x)\r\n        # print(\"feature size:\", x.size())\r\n        # x = x.view(x.size(0), self.num_flat_features(x))\r\n        # print(x.size())\r\n        x = x.view(x.size(0), -1)\r\n        x = self.fc(x)\r\n        return x\r\n\r\nclass AlexNetforEcg_mitdb_to_incart(nn.Module):\r\n    '''input tensor size:(None,1,3,128)'''\r\n    def __init__(self):\r\n        super(AlexNetforEcg_mitdb_to_incart, self).__init__()\r\n        self.features = nn.Sequential(\r\n            nn.Conv2d(1, 64, kernel_size=(3, 5), padding=(0, 0)),\r\n            nn.ReLU(inplace=True),\r\n            nn.MaxPool2d(kernel_size=(1, 2), stride=(1, 2)),#（N,64,1,62)\r\n\r\n\r\n            nn.Conv2d(64, 192, kernel_size=(1, 5), padding=(0, 2)),\r\n            nn.ReLU(inplace=True),\r\n            nn.MaxPool2d(kernel_size=(1, 2), stride=(1, 2)),#(N,192,1,30)\r\n\r\n            nn.Conv2d(192, 256, kernel_size=(1, 5), padding=(0, 2)),#(N,_,1,30)\r\n            nn.ReLU(inplace=True),\r\n            nn.MaxPool2d(kernel_size=(1, 2), stride=(1, 2)),\r\n\r\n            ####DS1->SVDB、DS1->INCARTDB实验多加了一个卷积层\r\n            nn.Conv2d(256, 256, kernel_size=(1, 5), padding=(0, 2)),\r\n            nn.ReLU(inplace=True),\r\n            nn.Conv2d(256, 256, kernel_size=(1, 5), padding=(0, 2)),\r\n            nn.ReLU(inplace=True),\r\n\r\n\r\n        )\r\n\r\n        self.fc = nn.Sequential(\r\n            nn.Linear(256 * 15, 256 * 10),\r\n            # nn.Linear(256 * 14, 256 * 10),\r\n            nn.ReLU(inplace=True),\r\n            nn.Dropout(0.3),#0.3\r\n            nn.BatchNorm1d(256 * 10),\r\n        )\r\n\r\n    def forward(self, x):\r\n        x = self.features(x)\r\n        # print(\"feature size:\", x.size())\r\n        # x = x.view(x.size(0), self.num_flat_features(x))\r\n        # print(x.size())\r\n        x = x.view(x.size(0), -1)\r\n        x = self.fc(x)\r\n        return x\r\n\r\n\r\nclass AlexNetforEcg_mitdb_to_svdb(nn.Module):\r\n    '''input tensor size:(None,1,3,128)'''\r\n\r\n    def __init__(self):\r\n        super(AlexNetforEcg_mitdb_to_svdb, self).__init__()\r\n        self.features = nn.Sequential(\r\n            nn.Conv2d(1, 64, kernel_size=(3, 5), padding=(0, 0)),\r\n            nn.ReLU(inplace=True),\r\n            nn.MaxPool2d(kernel_size=(1, 2), stride=(1, 2)),  # （N,64,1,62)\r\n\r\n            nn.Conv2d(64, 192, kernel_size=(1, 5), padding=(0, 2)),\r\n            nn.ReLU(inplace=True),\r\n            nn.MaxPool2d(kernel_size=(1, 2), stride=(1, 2)),  # (N,192,1,30)\r\n\r\n            nn.Conv2d(192, 256, kernel_size=(1, 5), padding=(0, 2)),  # (N,_,1,30)\r\n            nn.ReLU(inplace=True),\r\n            nn.MaxPool2d(kernel_size=(1, 2), stride=(1, 2)),\r\n\r\n            ####DS1->SVDB、DS1->INCARTDB实验多加了一个卷积层\r\n            nn.Conv2d(256, 256, kernel_size=(1, 5), padding=(0, 2)),\r\n            nn.ReLU(inplace=True),\r\n\r\n\r\n        )\r\n\r\n        self.fc = nn.Sequential(\r\n            nn.Linear(256 * 15, 256 * 10),\r\n            # nn.Linear(256 * 14, 256 * 10),\r\n            nn.ReLU(inplace=True),\r\n            nn.Dropout(0.3),  # 0.3\r\n            nn.BatchNorm1d(256 * 10),\r\n        )\r\n\r\n    def forward(self, x):\r\n        x = self.features(x)\r\n        # print(\"feature size:\", x.size())\r\n        # x = x.view(x.size(0), self.num_flat_features(x))\r\n        # print(x.size())\r\n        x = x.view(x.size(0), -1)\r\n        x = self.fc(x)\r\n        return x\r\n\r\nclass GradReverse(Function):\r\n    def __init__(self, lambd):\r\n        self.lambd = lambd\r\n    def forward(self, x):\r\n        return x.view_as(x)\r\n\r\n    def backward(self, grad_output):\r\n        return (grad_output * -self.lambd)\r\n\r\ndef grad_reverse(x, lambd=1.0):\r\n    return GradReverse(lambd)(x)\r\n\r\n\r\nclass AdversarialLayer(torch.autograd.Function):\r\n    def __init__(self, high_value=1.0, max_iter=10000):\r\n        self.iter_num = 0\r\n        self.alpha = 10\r\n        self.low = 0.0\r\n        self.high = high_value\r\n        self.max_iter = 10000.0\r\n\r\n    def forward(self, input):\r\n        self.iter_num += 1\r\n        output = input * 1.0\r\n        return output\r\n\r\n    def backward(self, gradOutput):\r\n\r\n        self.coeff = np.float(\r\n            2.0 * (self.high - self.low) / (1.0 + np.exp(-self.alpha * self.iter_num / self.max_iter)) - (\r\n                        self.high - self.low) + self.low)\r\n        # self.coeff = 1\r\n        return -self.coeff * gradOutput\r\n\r\nclass AdversarialNetwork(nn.Module):\r\n  def __init__(self):\r\n    super(AdversarialNetwork, self).__init__()\r\n    self.ad_layer1 = nn.Linear(2560, 1024)\r\n    self.ad_layer2 = nn.Linear(1024,1024)\r\n    self.ad_layer3 = nn.Linear(1024, 1)\r\n    # self.ad_layer1.weight.data.normal_(0, 0.01)\r\n    # self.ad_layer2.weight.data.normal_(0, 0.01)\r\n    # self.ad_layer3.weight.data.normal_(0, 0.3)\r\n    # self.ad_layer1.bias.data.fill_(0.0)\r\n    # self.ad_layer2.bias.data.fill_(0.0)\r\n    # self.ad_layer3.bias.data.fill_(0.0)\r\n    self.relu1 = nn.ReLU()\r\n    self.relu2 = nn.ReLU()\r\n    self.dropout1 = nn.Dropout(0.5)\r\n    self.dropout2 = nn.Dropout(0.5)\r\n    self.sigmoid = nn.Sigmoid()\r\n\r\n  def forward(self, x):\r\n    x = self.ad_layer1(x)\r\n    x = self.relu1(x)\r\n    x = self.dropout1(x)\r\n    x = self.ad_layer2(x)\r\n    x = self.relu2(x)\r\n    x = self.dropout2(x)\r\n    x = self.ad_layer3(x)\r\n    x = self.sigmoid(x)\r\n    return x\r\n\r\n  def output_num(self):\r\n    return 1\r\n\r\nclass DomainClassifier(nn.Module):\r\n    \"\"\"Feature classifier class for MNIST -> MNIST-M experiment in ATDA.\"\"\"\r\n    def __init__(self, num_classes=5):\r\n        \"\"\"Init classifier.\"\"\"\r\n        super(DomainClassifier, self).__init__()\r\n\r\n        self.classifier_d = nn.Sequential(\r\n            nn.Linear(256 * 10, 256 * 2),\r\n            nn.ReLU(inplace=True),\r\n            nn.Linear(256 * 2, 256 * 2),\r\n            nn.ReLU(inplace=True),\r\n            nn.Linear(256 * 2, num_classes),\r\n        )\r\n\r\n    def forward(self, x):\r\n        \"\"\"Forward classifier.\"\"\"\r\n        # x = grad_reverse(x)\r\n        out = self.classifier_d(x)\r\n        return out\r\n\r\n\r\nclass Generator(nn.Module):\r\n\r\n    '''Dense Convolutional Networks With Focal Loss and Image Generation for\r\n       Electrocardiogram Classification\r\n       Convert beat to image'''\r\n\r\n    def __init__(self):\r\n        super(Generator, self).__init__()\r\n        self.fc = nn.Sequential(\r\n            nn.Linear(128, 1024),\r\n            nn.BatchNorm1d(1024),\r\n            nn.ReLU(inplace=True),\r\n            nn.Dropout(0.5),\r\n            nn.Linear(1024, 1568),\r\n\r\n        )\r\n\r\n        self.upsample = nn.Sequential(\r\n            nn.BatchNorm2d(32),\r\n            nn.ReLU(inplace=True),\r\n            nn.Dropout(0.5),\r\n\r\n            nn.Upsample(scale_factor=2, mode='nearest'),\r\n            nn.Conv2d(32, 64, kernel_size=(1, 1), padding=(0, 0)),\r\n            nn.BatchNorm2d(64),\r\n            nn.ReLU(inplace=True),\r\n            nn.Dropout(0.5),\r\n\r\n            nn.Upsample(scale_factor=2, mode='nearest'),\r\n            nn.Conv2d(64, 64, kernel_size=(1, 1), padding=(0, 0)),\r\n            nn.BatchNorm2d(64),\r\n            nn.ReLU(inplace=True),\r\n            nn.Dropout(0.5),\r\n            nn.Conv2d(64, 3, kernel_size=(1, 1), padding=(0, 0)),\r\n        )\r\n\r\n\r\n    def forward(self, x):\r\n        x = self.fc(x)\r\n        # print(\"feature size:\", x.size())\r\n        x = x.view(-1, 32, 7, 7)\r\n        # print(x.size())\r\n        # x = x.view(x.size(0), -1)\r\n        x = self.upsample(x)\r\n        return x\r\n\r\n\r\nclass Bottleneck(nn.Module):\r\n    '''\r\n        the above mentioned bottleneck, including two conv layer, one's kernel size is 1×1, another's is 3×3\r\n        in_planes可以理解成channel\r\n        after non-linear operation, concatenate the input to the output\r\n        DenseNet的非线性变换H采用了Bottleneck结构BN-ReLU-Conv(1×1)-BN-ReLU-Conv(3×3)，1×1的卷积用于降低维度，将channels数降\r\n        低至4 * Growth_rate\r\n        Bottleneck是这样一种网络，其输入输出channel差距较大，就像一个瓶颈一样，上窄下宽亦或上宽下窄，\r\n        特征图的大小会因为最后一步的cat从N×in_planes×H×W变成N×(in_planes+growth_rate)×H×W\r\n    '''\r\n\r\n    def __init__(self, in_planes, growth_rate):\r\n        super(Bottleneck, self).__init__()\r\n        self.bn1 = nn.BatchNorm2d(in_planes)\r\n        self.conv1 = nn.Conv2d(in_planes, 4 * growth_rate, kernel_size=1, bias=False)\r\n        self.bn2 = nn.BatchNorm2d(4 * growth_rate)\r\n        self.conv2 = nn.Conv2d(4 * growth_rate, growth_rate, kernel_size=3, padding=1, bias=False)\r\n        # self.conv2 = nn.Conv2d(in_planes, growth_rate, kernel_size=3, padding=1, bias=False)\r\n        self.dropout = nn.Dropout(0.5)\r\n\r\n    def forward(self, x):\r\n        out = self.conv1(F.relu(self.bn1(x)))\r\n        out = self.conv2(F.relu(self.bn2(out)))\r\n        # out = self.conv2(F.relu(self.bn1(out)))\r\n        out = self.dropout(out)\r\n        # input and output are concatenated here\r\n        out = torch.cat([out, x], 1)\r\n        return out\r\n\r\n\r\nclass Transition(nn.Module):\r\n    '''\r\n        transition layer is used for down sampling the feature\r\n\r\n        when compress rate is 0.5, out_planes is a half of in_planes\r\n    '''\r\n\r\n    def __init__(self, in_planes, out_planes):\r\n        super(Transition, self).__init__()\r\n        self.bn = nn.BatchNorm2d(in_planes)\r\n        self.conv = nn.Conv2d(in_planes, out_planes, kernel_size=1, bias=False)\r\n\r\n    def forward(self, x):\r\n        out = self.conv(F.relu(self.bn(x)))\r\n        # use average pooling change the size of feature map here\r\n\r\n        out = F.avg_pool2d(out, 2)\r\n        return out\r\n\r\n\r\nclass DenseNet(nn.Module):\r\n    def __init__(self, block, nblocks, growth_rate=8, reduction=1, num_classes=5):\r\n        super(DenseNet, self).__init__()\r\n        '''\r\n        Args:\r\n            block: bottleneck\r\n            nblock: a list, the elements is number of bottleneck in each denseblock\r\n            growth_rate: channel size of bottleneck's output\r\n            reduction: \r\n        '''\r\n        self.generator = Generator()\r\n        self.growth_rate = growth_rate\r\n\r\n        # num_planes = 2 * growth_rate\r\n        num_planes = 16\r\n        self.conv1 = nn.Conv2d(3, num_planes, kernel_size=32, padding=15, bias=False)\r\n\r\n        # a DenseBlock and a transition layer\r\n        self.dense1 = self._make_dense_layers(block, num_planes, nblocks[0])\r\n        num_planes += nblocks[0] * growth_rate\r\n        # the channel size is superposed, mutiply by reduction to cut it down here, the reduction is also known as compress rate\r\n        out_planes = int(math.floor(num_planes * reduction))\r\n        self.trans1 = Transition(num_planes, out_planes)\r\n        num_planes = out_planes\r\n\r\n        # a DenseBlock and a transition layer\r\n        self.dense2 = self._make_dense_layers(block, num_planes, nblocks[1])\r\n        num_planes += nblocks[1] * growth_rate\r\n        # the channel size is superposed, mutiply by reduction to cut it down here, the reduction is also known as compress rate\r\n        out_planes = int(math.floor(num_planes * reduction))\r\n        self.trans2 = Transition(num_planes, out_planes)\r\n        num_planes = out_planes\r\n\r\n        # a DenseBlock and a transition layer\r\n        self.dense3 = self._make_dense_layers(block, num_planes, nblocks[2])\r\n        num_planes += nblocks[2] * growth_rate\r\n        # the channel size is superposed, mutiply by reduction to cut it down here, the reduction is also known as compress rate\r\n        # out_planes = int(math.floor(num_planes * reduction))\r\n        # self.trans3 = Transition(num_planes, out_planes)\r\n        # num_planes = out_planes\r\n\r\n        # only one DenseBlock\r\n        # self.dense4 = self._make_dense_layers(block, num_planes, nblocks[3])\r\n        # num_planes += nblocks[3] * growth_rate\r\n\r\n        # the last part is a linear layer as a classifier\r\n        self.bn = nn.BatchNorm2d(num_planes)\r\n        self.linear = nn.Linear(num_planes, num_classes)\r\n\r\n    def _make_dense_layers(self, block, in_planes, nblock):\r\n        layers = []\r\n\r\n        # number of non-linear transformations in one DenseBlock depends on the parameter you set\r\n        for i in range(nblock):\r\n            layers.append(block(in_planes, self.growth_rate))\r\n            in_planes += self.growth_rate\r\n        return nn.Sequential(*layers)\r\n\r\n    def forward(self, x):\r\n        img = self.generator(x)\r\n        out = self.conv1(img)\r\n        out = self.trans1(self.dense1(out))\r\n        out = self.trans2(self.dense2(out))\r\n        out = self.dense3(out)\r\n        out = nn.AdaptiveAvgPool2d((1,1))(F.relu(self.bn(out)))\r\n        out = out.view(out.size(0), -1)\r\n        # out = self.linear(out)\r\n        return out\r\n\r\n\r\ndef densenet():\r\n    return DenseNet(Bottleneck, [2, 2, 2])\r\n\r\nclass AlexNet(nn.Module):\r\n    def __init__(self,num_classes=5):\r\n        super(AlexNet,self).__init__()\r\n\r\n        self.generator = Generator()\r\n\r\n        self.layer1 = nn.Sequential(  # 输入1*28*28\r\n            nn.Conv2d(3, 32, kernel_size=3, padding=1),  # 32*28*28\r\n            nn.MaxPool2d(kernel_size=2, stride=2),  # 32*14*14\r\n            nn.ReLU(inplace=True),\r\n        )\r\n        self.layer2 = nn.Sequential(\r\n            nn.Conv2d(32, 64, kernel_size=3, padding=1),  # 64*14*14\r\n            nn.MaxPool2d(kernel_size=2, stride=2),  # 64*7*7\r\n            nn.ReLU(inplace=True),\r\n        )\r\n        self.layer3 = nn.Sequential(\r\n            nn.Conv2d(64, 128, kernel_size=3, padding=1),  # 128*7*7\r\n        )\r\n        self.layer4 = nn.Sequential(\r\n            nn.Conv2d(128, 256, kernel_size=3, padding=1),  # 256*7*7\r\n        )\r\n\r\n        self.layer5 = nn.Sequential(\r\n            nn.Conv2d(256, 256, kernel_size=3, padding=1),  # 256*7*7\r\n            nn.MaxPool2d(kernel_size=3, stride=2),  # 256*3*3\r\n            nn.ReLU(inplace=True),\r\n        )\r\n\r\n        self.classifier = nn.Sequential(\r\n            nn.Linear(256 * 9, 256 * 4),\r\n            # nn.Linear(256 * 14, 256 * 10),\r\n            nn.ReLU(inplace=True),\r\n            nn.Dropout(0.3),  # 0.3\r\n            nn.BatchNorm1d(256 * 4),\r\n        )\r\n        # self.fc1 = nn.Linear(256 * 3 * 3, 1024)\r\n        # self.fc2 = nn.Linear(1024, 512)\r\n        # self.fc3 = nn.Linear(512, 10)\r\n\r\n\r\n    def forward(self, x):\r\n        x = self.generator(x)\r\n        x = self.layer1(x)\r\n        x = self.layer2(x)\r\n        x = self.layer3(x)\r\n        x = self.layer4(x)\r\n        x = self.layer5(x)\r\n        x = x.view(-1, 256 * 3 * 3)\r\n        x = self.classifier(x)\r\n        return x\r\n\r\n\r\n\r\n","repo_name":"banchuangliangyue/MIAT_ECG","sub_path":"block_network.py","file_name":"block_network.py","file_ext":"py","file_size_in_byte":20746,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"81"}
+{"seq_id":"20051800180","text":"T = int(input())\nA = [1,2,3,4,5,6,7,8,9,10,11,12]\n\ndef total(list):\n    sum = 0\n    for i in list:\n        sum += i\n    return sum\n\nfor tc in range(1, T+1):\n    N, K = map(int, input().split())\n    \n    cnt=0\n    for i in range(1<<len(A)):\n        lst=[] #\n        for j in range(len(A)):\n            if i&(1<<j):\n                lst.append(A[j])\n        if len(lst)==N and total(lst)==K:\n            cnt+=1\n    \n    print(f\"#{T} {cnt}\")\n            \n\n\n    \n            \n        ","repo_name":"junchicode/TIL","sub_path":"Algo_week_SWEA&Class/week1/0811/SWEA_4837.py","file_name":"SWEA_4837.py","file_ext":"py","file_size_in_byte":479,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"20107082454","text":"from rest_framework import serializers\n\nfrom .models import User, UserStatus\n\n\nclass UserStatusSerializer(serializers.ModelSerializer):\n    class Meta:\n        model = UserStatus\n        fields = ('id', 'status', 'color')\n\n\nclass UserSerializer(serializers.ModelSerializer):\n    status = UserStatusSerializer()\n\n    class Meta:\n        model = User\n        fields = ('id', 'username', 'status')\n","repo_name":"BGrigorovich/user_statuses","sub_path":"core/serializers.py","file_name":"serializers.py","file_ext":"py","file_size_in_byte":395,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"25837659227","text":"\"\"\"\nThis module implements general workflow of requested operation.\nAll interfaces implemented on 'Pure Python Objects'.\nThis module does not handle errors. All handling should be implemented\nby caller. All exceptions goes to outer scope.\n\"\"\"\nimport json\n\nfrom typing import (\n    Tuple, List, Dict,\n    Union, Any, Optional,\n    Iterable, MutableMapping\n)\nfrom uuid import uuid4\n\nfrom scripts import get_digest\nfrom db_adaptor import (\n    update_user, find_one_by_filter,\n    find_one_by_email, insert_user,\n    insert_users, count_tot_users, get_users\n)\n\n\ndef process_list_users() -> Dict[str, Any]:\n    \"\"\"Returns total users from Users collection. Counts result.\"\"\"\n    count_users = count_tot_users()\n    users_list = get_users()\n\n    users_collection = {\n        'usersCount': count_users,\n        'totalUsers': users_list,\n    }\n    return users_collection\n\n\ndef process_update_user(user_uuid: str,\n                        new_user: Dict[Union[str, int], Any],\n                        keys_to_digest: List[str]) \\\n                        -> Optional[Dict[Union[str, int], Any]]:\n    \"\"\"Updates user by given fields. Searches by uuid.\"\"\"\n    obj_to_digest = {key: value\n                     for key, value in new_user.items()\n                     if key in keys_to_digest}\n    new_digest: str = get_digest(json.dumps(obj_to_digest))\n\n    # Update a user and return\n    user_to_db = {key: value\n                  for key, value in new_user.items()\n                  if key in keys_to_digest}\n    user_to_db['uuid'] = user_uuid\n    user_to_db['digest'] = new_digest\n    updated_user = update_user(user_uuid, user_to_db)\n    return updated_user\n\n\ndef process_post_user(user_to_create: Dict[Union[str, int], Any],\n                      fields_to_digest: List[str]) \\\n        -> Tuple[bool, Union[str, Optional[Dict[Union[str, int], Any]]]]:\n    \"\"\"Adds only one user to collection\"\"\"\n    user_email = user_to_create.get('email')\n    exists = find_one_by_filter({'email': user_email})\n    if exists:\n        new: bool = False\n        return new, f\"User with email: {user_email}, is in the DB.\"\n\n    # If same digest in the DB, then return this record.\n    required_fields_digest = {key: value\n                              for key, value in\n                              user_to_create.items()\n                              if key in fields_to_digest}\n    digest: str = get_digest(json.dumps(required_fields_digest))\n\n    # Insert a record in DB then return user\n    required_fields_digest['uuid'] = 'USER-' + uuid4().hex.upper()\n    required_fields_digest['digest'] = digest\n    user_id: str = insert_user(required_fields_digest)\n    created_user = find_one_by_filter({'_id': user_id})\n    new = True\n    return new, created_user\n\n\ndef process_post_users(users_to_add: Iterable[Dict[Union[str, int], Any]],\n                       fields_to_digest: List[str]) \\\n                       -> Tuple[bool, Union[str, List[str]]]:\n    \"\"\" Adds new users to Users collection.\"\"\"\n    new_users_emails = [user['email'].lower() for user in users_to_add]\n    known_emails = [find_one_by_email({'email': user_email})\n                    for user_email in new_users_emails]\n    unknown_emails = set(new_users_emails) ^ set(known_emails)\n\n    if not unknown_emails:\n        new = False\n        return new, \"No new users, nothing to add.\"\n\n    # if we have new emails, - collect users\n    users_to_insert: List[MutableMapping[Any, Any]] = []\n    for user in users_to_add:\n        if user['email'] in unknown_emails:\n            # If same digest in the DB then return this record\n            required_fields_digest = {key: value\n                                      for key, value in user.items()\n                                      if key in fields_to_digest}\n            digest: str = get_digest(json.dumps(required_fields_digest))\n            # Insert a record in DB then return this record\n            required_fields_digest['uuid'] = 'USER-' + uuid4().hex.upper()\n            required_fields_digest['digest'] = digest\n            users_to_insert.append(required_fields_digest)\n    # if we have new users, - insert\n    result = insert_users(users_to_insert)\n    new = True\n    return new, result\n","repo_name":"w-e-ll/user-api","sub_path":"app/processors.py","file_name":"processors.py","file_ext":"py","file_size_in_byte":4188,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"29913754524","text":"import requests\r\nimport json\r\nimport pandas as pd\r\nfrom pprint import pprint\r\nfrom datetime import datetime\r\nfrom datetime import date, timedelta \r\npd.set_option('display.max_columns', 500)\r\npd.set_option('display.width', 1500)\r\nstartTime = datetime.now()\r\nimport pandas.io.sql as psql\r\nimport psycopg2 as pg\r\nfrom sqlalchemy import create_engine, MetaData, Table\r\nimport time\r\n\r\n#CREATE YOUR ENGINE \r\nengine = create_engine('postgresql+psycopg2://postgres:PASSWORD@HOST:PORT/DATABASE_NAME')\r\n\r\n#CREATE A TEMPORARY CONNECTION\r\nconn_1 = pg.connect(\"dbname=DATABASE_NAME user=USER_NAME host=HOST port=PORT password=PASSWORD\")\r\n\r\n#CREATE A TEMPORARY GRID WITH APPROPRIATE DISTANCES OVER THE CHOSEN POLYGON AREA\r\n# CHANGE okres_0 to your table\r\n# BE CAREFUL WIRH SRIDS\r\ndf2 = psql.read_sql(\"with base_grid as \\\r\n                        (SELECT I_Grid_Point_Distance(geom, 1000, 1000) as geom from okres_0 where idn3 in ('802', '803', '804', '805')) \\\r\n                    SELECT \\\r\n                        ST_X(ST_Transform(ST_SetSRID(ST_GeomFromText(ST_AsText( (ST_Dump(geom)).geom )), 102067), 4326)), \\\r\n                        ST_Y(ST_Transform(ST_SetSRID(ST_GeomFromText(ST_AsText( (ST_Dump(geom)).geom )), 102067), 4326)), \\\r\n                        ST_Transform(ST_SetSRID(ST_GeomFromText(ST_AsText( (ST_Dump(geom)).geom )), 102067), 4326) as geom \\\r\n                    from base_grid\", conn_1)\r\n\r\ndf2 = pd.DataFrame(df2)\r\nconn_1.close()\r\n\r\n#CREATE A LIST FROM THE EXTRACTED GRID POINTS\r\nc_or_lat_lon = [list(r) for r in df2[['st_y', 'st_x']].values]\r\n\r\n\r\n#CREATE PARTS OF URLS FOR THE API TO CALL\r\ntext_search_url = \"https://maps.googleapis.com/maps/api/place/textsearch/output?parameters\"\r\n\r\n#ADD YOUR API KEY\r\napi_key = 'API KEY'\r\n\r\nurl_base = 'https://maps.googleapis.com/maps/api/place/'\r\nsearch_type = 'nearbysearch/json?location='\r\nurl_and = '&'\r\ndist = 'radius='\r\ndistance_m = '1000'\r\ntypes = 'type='\r\n\r\n#ENTER THE DESIRED CATEGORIES OF PLACES\r\ntypes_n = 'lodging'\r\n#keywords = 'keyword='\r\n#keywords_n = 'burger'\r\n\r\nkeys = 'key='\r\nnext_p_url = \"https://maps.googleapis.com/maps/api/place/nearbysearch/json?\"\r\nnext_p = \"pagetoken=\"\r\n\r\n\r\n\r\nglobal list_of_base_urls\r\nlist_of_base_urls = []\r\n\r\nglobal list_of_next_pages\r\nlist_of_next_pages = []\r\n\r\nglobal dataframes_list\r\ndataframes_list = []\r\n\r\n# CREATE A SERACH URL FOR EACH POINT\r\ndef url_points():\r\n    #print(len(c_or_lat_lon))\r\n    x = 0\r\n    url = url = url_base + search_type + str(c_or_lat_lon[x])[1:-1] + url_and + dist + distance_m + url_and + types + types_n + url_and + keys + api_key\r\n    list_of_base_urls.append(url)\r\n    print(url)\r\n    while x < len(c_or_lat_lon) - 1:\r\n        x = x + 1\r\n        url = url = url_base + search_type + str(c_or_lat_lon[x])[1:-1] + url_and + dist + distance_m + url_and + types + types_n + url_and + keys + api_key\r\n        list_of_base_urls.append(url)\r\n        print(url)\r\n    \r\nurl_points()\r\n\r\n\r\n       \r\n#CREATE PRIMITIVE FUNC TO PROCESS THE CALLS\r\ndef url_kokocinka():\r\n    print(\"sleep 3 seconds\")\r\n    time.sleep(3)\r\n    #EXTRACT THE INITIAL PAGES' CONTENT\r\n    def page_content():\r\n        \r\n        x = 0\r\n        req = requests.get(list_of_base_urls[url_x])\r\n        global js\r\n        js = req.json()\r\n        global data\r\n        data = js['results']\r\n        \r\n        if data == []:\r\n            print(\"ZERO result in 1000m radius at url no. \" + str(url_x) +\" of \" + str(len(list_of_base_urls)) + \" at TIME \" + str(datetime.now() - startTime))\r\n            return\r\n        else:\r\n            limit = len(data)\r\n            dataf(x)\r\n        while x < limit - 1:\r\n            x = x +1\r\n            print(\"working on url no. \" + str(url_x) +\" of \" + str(len(list_of_base_urls)) + \" at TIME \" + str(datetime.now() - startTime))\r\n            dataf(x)\r\n        if 'next_page_token' in js:\r\n            next_p = js['next_page_token']\r\n            #print(next_p)\r\n            print(\"working on url no. \" + str(url_x) +\" of \" + str(len(list_of_base_urls)) + \" at TIME \" + str(datetime.now() - startTime))\r\n            next_pages()\r\n\r\n#EXTRACT DATA TO A TABLE\r\n    def dataf(x):\r\n        \r\n        def colls():\r\n            df.loc[:,'id'] = id\r\n            df.loc[:,'place_id'] = place_id\r\n            df.loc[:,'name'] = name\r\n            df.loc[:,'street'] = street\r\n            df.loc[:,'rating_avg'] = rating_avg\r\n            df.loc[:,'ratings_n'] = ratings_n\r\n            #df.loc[:,'types'] = str(types)\r\n            dataframes_list.append(df)\r\n        try:      \r\n            df = pd.DataFrame.from_dict(data[x]['geometry']['location'], orient='index')\r\n            df = df.T\r\n            id = data[x]['id']\r\n            place_id = data[x]['place_id']\r\n            name = data[x]['name']\r\n            street = data[x]['vicinity']\r\n        except(KeyError):\r\n            street = 'NaN'        \r\n        try:\r\n            rating_avg = data[x]['rating']\r\n        except(KeyError):\r\n            rating_avg = 'NaN'\r\n        try:\r\n            ratings_n = data[x]['user_ratings_total']\r\n        except(KeyError):\r\n            ratings_n = 'NaN'\r\n              \r\n        \r\n        colls()\r\n#EXTRACT DATA FROM NEXT PAGES\r\n       \r\n    def next_pages():\r\n        print(\"sleep 3 sec\")\r\n        time.sleep(3)\r\n        list_of_next_pages.append(js['next_page_token'])\r\n        new_url = next_p_url + keys + api_key + url_and + next_p + str(js['next_page_token'])\r\n        print(new_url)\r\n        req_paging = requests.get(str(new_url))\r\n        js_paging = req_paging.json()\r\n        global data\r\n        data = js_paging['results']\r\n        if data == []:\r\n            return\r\n        else:\r\n            x = 0\r\n            limit = len(data)\r\n            dataf(x)\r\n        while x < limit - 1:\r\n            x = x +1\r\n            dataf(x)\r\n            \r\n            \r\n        if 'next_page_token' in js_paging:\r\n            list_of_next_pages.append(js_paging['next_page_token'])\r\n            print(\"sleep 3 sec\")\r\n            time.sleep(3)\r\n\r\n            new_url = next_p_url + keys + api_key + url_and + next_p + js_paging['next_page_token']\r\n            req_paging = requests.get(new_url)\r\n                \r\n            data = js_paging['results']\r\n            if data == []:\r\n                return\r\n            else:\r\n                x = 0\r\n                limit = len(data)\r\n                dataf(x)\r\n            while x < limit - 1:\r\n                x = x +1\r\n                dataf(x)\r\n                \r\n            else:\r\n                return\r\n        \r\n    url_x = 0\r\n    page_content()\r\n    while url_x < len(list_of_base_urls) - 1:\r\n        url_x = url_x + 1\r\n        page_content()\r\n\r\n\r\nurl_kokocinka()\r\n\r\ndatas = pd.concat(dataframes_list)\r\ndatas.drop_duplicates(subset='id', keep=\"first\")\r\ndatas.reset_index(level=0, inplace=True)\r\n\r\n\r\n\r\n\r\nprint(datas)\r\n\r\n#DOWNLOAD THE EXTRACTED DATA TO YOUR DATABASE OR CSV\r\ndatas.to_sql('googleplacestest', engine, if_exists='append')\r\nprint( \"exported into database \" + str(datetime.now() - startTime))\r\n\r\nconn = engine.connect()\r\n\r\n#DELETE DUPLICATES\r\nstmt_delete_duplicates = \"DELETE FROM googleplacestest a USING googleplacestest b WHERE a.ctid < b.ctid AND a.id = b.id;\"\r\nresults = conn.execute(stmt_delete_duplicates)\r\n\r\n\r\n#Create the geographic variable as geom to facebookPlaceSearchAPI's export\r\n##BE AWARE OF THE SRIDS!!!!\r\nstmt_geom_column = \"alter table googleplacestest add column geom geometry;\"\r\nresults = conn.execute(stmt_geom_column)\r\nstmt_geom_variable = \"UPDATE googleplacestest SET geom = subquery.geoma FROM (SELECT *, ST_SetSRID(ST_MakePoint(lng, lat),4326) as geoma from googleplacestest) AS subquery WHERE googleplacestest.id=subquery.id;\"\r\nresults = conn.execute(stmt_geom_variable)\r\n\r\n#THIS PART is optional, it works for slovak adminisrative boundaries ID source: https://www.geoportal.sk/sk/zbgis_smd/na-stiahnutie/ just dowload the esri shp table and import to postgis\r\n# The aim is to add official IDs of administrative segmentation\r\n\r\nstmt = \"alter table googleplacestest add column district_id integer;\"\r\nresults = conn.execute(stmt)\r\nstmt = \"alter table googleplacestest add column region_id integer;\"\r\nresults = conn.execute(stmt)\r\nstmt = \"alter table googleplacestest add column municipality_id integer;\"\r\nresults = conn.execute(stmt)\r\nstmt = \"alter table googleplacestest add column district_name text;\"\r\nresults = conn.execute(stmt)\r\nstmt = \"alter table googleplacestest add column region_name text;\"\r\nresults = conn.execute(stmt)\r\nstmt = \"alter table googleplacestest add column municipality_name text;\"\r\nresults = conn.execute(stmt)\r\n\r\n#GET THE LIST OF ID FROM EXTRAXTED FB DATA\r\nconn_1 = pg.connect(\"dbname=DATABASE_NAME user=USER_NAME host=HOST port=PORT password=PASSWORD\")\r\n\r\nresults = psql.read_sql(\"select array_agg('''' || level_0 || '''') from googleplacestest;\", conn_1)\r\n\r\ngid = results['array_agg'].tolist()\r\n\r\n#UPDATE ADMINISTRATIVE SEGMENATION FOR EACH PLACE VIA ST_WITHIN\r\ndef update_admin():\r\n    x = 0\r\n    stmt = \"UPDATE googleplacestest SET district_id = subquery.idn3, district_name = subquery.nm3, region_id = subquery.idn2, region_name = subquery.nm2, municipality_id = subquery.idn4, municipality_name = subquery.nm4 FROM (SELECT * FROM  googleplacestest a JOIN obec_0 b ON ST_Within(ST_Transform(a.geom, 4326), ST_Transform(b.geom, 4326)) where a.level_0=\" +gid[0][x] + \") AS subquery WHERE googleplacestest.level_0=subquery.level_0;\"\r\n    results = conn.execute(stmt)\r\n    print( str(x) + \"is a success\")\r\n    while x < len(gid[0]) - 1:\r\n        x = x + 1\r\n        stmt = \"UPDATE googleplacestest SET district_id = subquery.idn3, district_name = subquery.nm3, region_id = subquery.idn2, region_name = subquery.nm2, municipality_id = subquery.idn4, municipality_name = subquery.nm4 FROM (SELECT * FROM  googleplacestest a JOIN obec_0 b ON ST_Within(ST_Transform(a.geom, 4326), ST_Transform(b.geom, 4326)) where a.level_0=\" +gid[0][x] + \") AS subquery WHERE googleplacestest.level_0=subquery.level_0;\"\r\n        results = conn.execute(stmt)\r\n        print( str(x) + \"is a success\")\r\nupdate_admin()\r\n\r\nconn_1.close()\r\nconn.close()\r\n#AFTERWARDS YOU SHOULD CLEAN THE DATASET IN POSTGRES OF NONSENSE(bad tags, duplicate object with differently formatted names, duplicate object registrations, etc)\r\n#the below example will help suspicious objects\r\n\r\n\r\n#with words as (\r\n#SELECT s.token, name, id\r\n#FROM   googleplacestest t, unnest(string_to_array(t.name, ' ')) s(token))\r\n#select token, count(*)\r\n\t#from words\r\n\t#group by token\r\n\t#order by token desc\r\n","repo_name":"csabasidor/occupancy_tax_payers_identifier","sub_path":"02_google_places_api_downloader.py","file_name":"02_google_places_api_downloader.py","file_ext":"py","file_size_in_byte":10464,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"25819641687","text":"class ListNode:\n    def __init__(self, val=0, next=None):\n        self.val = val\n        self.next = next\n\n    def __repr__(self):\n        return str(self.val)\n\n\ndef deleteDuplicates(head):  # Nice\n    dummy = ListNode(next=head)\n    curr, prev = head, dummy\n    while curr:\n        if curr.next and curr.val == curr.next.val:\n            while curr.next and curr.val == curr.next.val:\n                curr = curr.next\n            prev.next = curr.next\n        else:\n            prev = curr\n        curr = curr.next\n\n    return dummy.next\n\n\nhead = ListNode(1, next=ListNode(1, ListNode(1, ListNode(2, ListNode(3)))))\nhead11 = ListNode(1, next=ListNode(1))\ndeleteDuplicates(head)\n","repo_name":"miray-mustafov/LeetCode","sub_path":"08.Linked List/07.82. Remove Duplicates from Sorted List II.py","file_name":"07.82. Remove Duplicates from Sorted List II.py","file_ext":"py","file_size_in_byte":679,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"36830457203","text":"from django.conf.urls import url\n\nfrom . import views\n\nurlpatterns = [\n    url(r'^$', views.index, name='index'),\n    url(r'^login$', views.login, name='login'),\n    url(r'^auth$', views.auth, name='auth'),\n    url(r'^myCreation', views.my_creation, name='myCreation'),\n    url(r'^leaderBoard', views.leader_board, name='leaderBoard'),\n]","repo_name":"hungter007/Interview-code-practice-python","sub_path":"工具配置/nginx_uwsgi_django_channels/mysite/userlogin/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":337,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"21114942071","text":"import pandas as pd\r\nimport numpy as np\r\nimport matplotlib.pyplot as plot\r\nimport folium\r\nimport re\r\nfrom collections import defaultdict\r\n\r\n\r\ndef prepare_header(data):\r\n    with open(data, 'w') as wf:\r\n        wf.write('ID,NAME,YEAR,MONTH,LAT,LONG,MAX WIND,MIN PRESSURE,CATEGORY\\n')\r\n\r\n\r\ndef data_extraction(data, mode='atlantic'):\r\n    ORIGINAL_DATA = ''\r\n    if mode == 'atlantic':\r\n        ORIGINAL_DATA = 'data/atlantic.csv'\r\n    elif mode == 'pacific':\r\n        ORIGINAL_DATA = 'data/pacific.csv'\r\n\r\n    unnamed_counter = 0\r\n    hurricane_name = ''\r\n    hurricane_id = -1\r\n    with open(ORIGINAL_DATA, 'r') as f:\r\n        for line in f:\r\n            line = line.rstrip().split(',')    \r\n            year = 0\r\n            month = 0\r\n            lat = 0\r\n            long = 0\r\n            max_wind = 0\r\n            min_pressure = 0\r\n            cat = 0\r\n            # header line has length = 4\r\n            if len(line) == 4:\r\n                if line[1].replace(' ', '') == 'UNNAMED':\r\n                    hurricane_name = 'UNNAMED_' + str(unnamed_counter)\r\n                else:\r\n                    hurricane_name = line[1].replace(' ', '')\r\n                unnamed_counter += 1\r\n                hurricane_id += 1\r\n            else:\r\n                year = line[0][:4].replace(' ', '')\r\n                month = line[0][4:6].replace(' ', '')\r\n                lat = line[4].replace(' ', '')[:-1]\r\n                long = '-' + line[5].replace(' ', '')[:-1]\r\n                max_wind = int(line[6].replace(' ', ''))\r\n                if 74 <= max_wind <= 95:\r\n                    cat = 1\r\n                elif 96 <= max_wind <= 110:\r\n                    cat = 2\r\n                elif 111 <= max_wind <= 130:\r\n                    cat = 3\r\n                elif 131 <= max_wind <= 155:\r\n                    cat = 4\r\n                elif max_wind >= 155:\r\n                    cat = 5\r\n                else:\r\n                    cat = 0\r\n                min_pressure = int(line[7].replace(' ', ''))\r\n                if min_pressure == -999:\r\n                    min_pressure = 0\r\n                with open(data, 'a') as wf:\r\n                    wf.write(str(hurricane_id) + ',' + hurricane_name + ',' +  year + ',' + month +\r\n                            ',' + lat + ',' + long + ',' + str(max_wind) + ',' + str(min_pressure) +\r\n                            ',' + str(cat) + '\\n')\r\n\r\n\r\ndef get_CO2(data):\r\n    df = pd.read_csv(data)\r\n    df = df.drop('unc', axis=1)\r\n    return df\r\n\r\n\r\ndef graph_hurricane_category(hurricane_df, mode='atlantic'):\r\n    grouped = hurricane_df.groupby('CATEGORY')['ID'].nunique()\r\n    grouped.plot(kind='bar')\r\n    ax = plot.gca()\r\n    plot.xticks(rotation=0)\r\n    plot.ylabel('Counts')\r\n    plot.xlabel('Hurricane Category')\r\n    title = ''\r\n    if mode == 'atlantic':\r\n        title = 'Frequency of Hurricane Categories in the Atlantic Ocean'\r\n    elif mode == 'pacific':\r\n        title = 'Frequency of Hurricane Categories in the Northeast and North Central Pacific Oceans'\r\n    plot.title(title)\r\n    textstr = '\\n'.join(('Category 0: Winds less than 74 mph',\r\n                        'Category 1: Winds 74 to 95 mph',\r\n                        'Category 2: Winds 96 to 110 mph',\r\n                        'Category 3: Winds 111 to 130 mph',\r\n                        'Category 4: Winds 131 to 155 mph',\r\n                        'Category 5: Winds greater than 155 mph'))\r\n    props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)\r\n    ax.text(0.65, 0.98, textstr, transform=ax.transAxes, fontsize=14, verticalalignment='top', bbox=props)\r\n    for p in ax.patches:\r\n        ax.annotate(str(p.get_height()), (p.get_x() + 0.25, p.get_height() + 1.25), ha='center')\r\n    plot.show()\r\n\r\n\r\ndef graph_hurricane_month(hurricane_df, mode='atlantic'):\r\n    grouped = hurricane_df.groupby('MONTH')['ID'].nunique()\r\n    grouped.plot(kind='bar')\r\n    title = ''\r\n    if mode == 'atlantic':\r\n        title = 'Monthly Frequency of Hurricanes in the Atlantic Ocean'\r\n    elif mode == 'pacific':\r\n        title = 'Monthly Frequency of Hurricanes in the Northeast and North Central Pacific Oceans'\r\n    plot.xticks(rotation=0)\r\n    plot.ylabel('Counts')\r\n    plot.xlabel('Month')\r\n    plot.title(title)\r\n    plot.show()\r\n\r\n\r\ndef storm_track(hurricane_df, mode='atlantic'):\r\n    temp_df = hurricane_df[['ID', 'NAME', 'YEAR', 'CATEGORY']]\r\n    cat_0 = temp_df.loc[hurricane_df['CATEGORY'] == 0].sample(n=3)\r\n    cat_1 = temp_df.loc[hurricane_df['CATEGORY'] == 1].sample(n=3)\r\n    cat_2 = temp_df.loc[hurricane_df['CATEGORY'] == 2].sample(n=3)\r\n    cat_3 = temp_df.loc[hurricane_df['CATEGORY'] == 3].sample(n=3)\r\n    cat_4 = temp_df.loc[hurricane_df['CATEGORY'] == 4].sample(n=3)\r\n    cat_5 = temp_df.loc[hurricane_df['CATEGORY'] == 5]\r\n    hurricane_coor = cat_0.values.tolist()\r\n    hurricane_coor.extend(cat_1.values.tolist())\r\n    hurricane_coor.extend(cat_2.values.tolist())\r\n    hurricane_coor.extend(cat_3.values.tolist())\r\n    hurricane_coor.extend(cat_4.values.tolist())\r\n    hurricane_coor.extend(cat_5.values.tolist())\r\n    hurricane_list = []\r\n    for i in hurricane_coor:\r\n        id, name, year, category = i\r\n        coor = hurricane_df.loc[hurricane_df['ID'] == id][['LAT', 'LONG']].values.tolist()\r\n        hurricane_list.append((coor, name, year, category))\r\n\r\n    map = folium.Map(zoom_start=6)\r\n    for coord in hurricane_list:\r\n        coordinate, name, year, category = coord\r\n        color = ''\r\n        if category == 0:\r\n            color = 'gray'\r\n        elif category == 1:\r\n            color = 'blue'\r\n        elif category == 2:\r\n            color = 'orange'\r\n        elif category == 3:\r\n            color = 'lightred'\r\n        elif category == 4:\r\n            color = 'red'\r\n        elif category == 5:\r\n            color = 'darkred'\r\n        tooltip = name + ' in ' + str(year) + ' category ' + str(category)\r\n        my_PolyLine=folium.PolyLine(locations=coordinate, weight=3, color=color, tooltip=tooltip)\r\n        map.add_child(my_PolyLine)\r\n        folium.Marker(location=coordinate[0], tooltip=name + ' start', icon=folium.Icon(color=color)).add_to(map)\r\n        folium.Marker(location=coordinate[-1], tooltip=name + ' end', icon=folium.Icon(color=color)).add_to(map)\r\n    if mode == 'atlantic':\r\n        map.save('atlantic-map.html')\r\n    elif mode == 'pacific':\r\n        map.save('pacific-map.html')\r\n\r\n\r\ndef single_step_visualization(hurricane_df):\r\n    DATA_FILE = 'data/single.txt'\r\n    DATA_FILE_2 = 'data/single_2.txt'\r\n\r\n    id = ''\r\n    name = None\r\n    actual = []\r\n    predict = []\r\n    actual_pattern = re.compile('^   actual')\r\n    predict_pattern = re.compile('^   predict')\r\n    lat_long_pattern = re.compile('\\(.*?\\)')\r\n    name_pattern = re.compile('^Name')\r\n    id_pattern = re.compile('^ID')\r\n    map = folium.Map(zoom_start=6)\r\n    valid = ['EMA', 'FAUSTO', 'HENRIETTE', 'MARIE', 'WALAKA']\r\n    valid_2 = ['MARIO', 'TWENTYONE', 'HERNAN', 'NATE', 'ALBERTO', 'MICHAEL', 'HUMBERTO', 'CRISTOBAL', 'ISAIAS', 'OMAR',\r\n                'GAMMA', 'DELTA', 'ZETA']\r\n\r\n    with open(DATA_FILE_2, 'r') as f:\r\n        last_id = None\r\n        for line in f:\r\n            line = line.rstrip()\r\n            for match in re.finditer(id_pattern, line):\r\n                id = int(line.split(' ')[-1])\r\n                if id <= 48:\r\n                    id += 1121\r\n                else:\r\n                    id += 1799\r\n                if last_id != id and name in valid_2:\r\n                    actual = hurricane_df.loc[(hurricane_df['ID'] == int(last_id)) & (hurricane_df['NAME'] == name)][['LAT', 'LONG']].values.tolist()[0:12]\r\n                    predict_first_five = hurricane_df.loc[(hurricane_df['ID'] == int(last_id)) & (hurricane_df['NAME'] == name)][['LAT', 'LONG']].values.tolist()[0:5]\r\n                    predict_first_five.extend(predict)\r\n                    predict_PolyLine = folium.PolyLine(locations=predict_first_five, weight=3, color='red', tooltip='Predicted '+ name, id=name)\r\n                    map.add_child(predict_PolyLine)\r\n                    actual_PolyLine = folium.PolyLine(locations=actual, weight=3, color='blue', tooltip='Actual '+ name, id=name)\r\n                    map.add_child(actual_PolyLine)\r\n                    folium.Marker(location=actual[0], tooltip=name + ' start', icon=folium.Icon(color='green')).add_to(map)\r\n                    folium.Marker(location=predict_first_five[-1], tooltip=name + ' end predicted', icon=folium.Icon(color='red')).add_to(map)\r\n                    folium.Marker(location=actual[-1], tooltip=name + ' end actual', icon=folium.Icon(color='blue')).add_to(map)\r\n                actual = []\r\n                predict = []\r\n                last_id = id\r\n            for match in re.finditer(name_pattern, line):\r\n                name = line.split(' ')[-1]\r\n            for match in re.finditer(actual_pattern, line):\r\n                temp = lat_long_pattern.findall(line)[0][1:-1]\r\n                if temp:\r\n                    temp = temp.split(', ')\r\n                    lat = temp[0]\r\n                    long = temp[1]\r\n                    actual.append([float(lat), float(long)])\r\n            for match in re.finditer(predict_pattern, line):\r\n                temp = lat_long_pattern.findall(line)[0][1:-1]\r\n                if temp:\r\n                    temp = temp.split(', ')\r\n                    lat = temp[0]\r\n                    long = temp[1]\r\n                    predict.append([float(lat), float(long)])\r\n    map.save('prediction.html')\r\n\r\n\r\ndef multiple_step_visualization():\r\n    pass\r\n\r\n\r\nif __name__ == \"__main__\":\r\n    ATLANTIC_DATA = 'atlantic_cleanned_hurdat2.csv'\r\n    PACIFIC_DATA = 'pacific_cleanned_hurdat2.csv'\r\n\r\n    # getting the data\r\n    get_data = False\r\n    if get_data:\r\n        prepare_header(ATLANTIC_DATA)\r\n        hurricane_data = data_extraction(ATLANTIC_DATA)\r\n\r\n        prepare_header(PACIFIC_DATA)\r\n        hurricane_data = data_extraction(PACIFIC_DATA, 'pacific')\r\n\r\n    # load data\r\n    atlantic_hurricane_df = pd.read_csv(ATLANTIC_DATA, sep=\",\")\r\n    pacific_hurricane_df = pd.read_csv(PACIFIC_DATA, sep=\",\")\r\n\r\n    # print(atlantic_hurricane_df.head())\r\n    # print(pacific_hurricane_df.head())\r\n\r\n    # simple visualizations\r\n    # graph_hurricane_category(atlantic_hurricane_df)\r\n    # graph_hurricane_category(pacific_hurricane_df, mode='pacific')\r\n\r\n    # graph_hurricane_month(atlantic_hurricane_df)\r\n    # graph_hurricane_month(pacific_hurricane_df, mode='pacific')\r\n\r\n    # storm_track(atlantic_hurricane_df)\r\n    # storm_track(pacific_hurricane_df, 'pacific')\r\n    both_df = pd.concat([atlantic_hurricane_df, pacific_hurricane_df], axis=0)\r\n    single_step_visualization(both_df)\r\n","repo_name":"MinhDang98/lstm-data-preprocessing","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":10654,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"3598745962","text":"import json\nimport logging\nimport nltk\nimport re\nimport spacy\nimport en_core_web_sm\nimport datetime\n\ndef custom_stem(mapping, text):    \n    newtokens = []\n    for token in tokenize(text):\n        if token in mapping.keys():\n            newtokens.append(mapping[token])\n        else:\n            newtokens.append(token)\n    return \" \".join(newtokens)\n    \ndef create_mapping(sourcefile):\n    with open(sourcefile,\"r\") as infile: # data/out/stemmer[date].json\n        stemmer = json.loads(infile.read())\n    mapping = {}\n    for singular in stemmer.keys():\n        for variation in stemmer[singular]:\n            mapping[variation] = singular\n    return mapping\n\ndef tokenize(text):\n    return text.split()\n\ndef has_alpha(text):\n    return re.search(\"[A-Za-z]\", text)\n\ndef clean_and_stem(nlp, text, include_original = False):\n    lines = text.split(\"\\n\") #split on newline as not all sentences end with period\n    lines = [line.strip() for line in lines] # remove extra whitespace\n    # remove -\n    lines = [re.subn(\"-\",\"\",line)[0] for line in lines] # remove -\n    # tokenize into sentences\n    sentences = [sentence for line in lines for sentence in nltk.sent_tokenize(line)] # use nltk sentence tokenizer\n    if include_original:\n        return [(token.lemma_.lower(), token.text) for sentence in sentences for token in nlp(sentence)]\n    return \" \".join([token.lemma_.lower() for sentence in sentences for token in nlp(sentence) if has_alpha(token.lemma_) and token.lemma_ != \"-PRON-\"])\n\n# perfectly fine to use this one, but it's slow\ndef clean_and_custom_stem(text, stemmer_file):\n    mapping = create_mapping(stemmer_file)\n    nlp = en_core_web_sm.load()\n    return custom_stem(mapping, clean_and_stem(nlp, text.lower()))\n\n# faster implementation with resource preloading    \ndef clean_and_custom_stem_(text, mapping, nlp, include_original = False):\n    if include_original:\n        lemma_and_original_list = clean_and_stem(nlp, text, include_original=include_original)\n        return [(custom_stem(mapping, x[0]), x[1]) for x in lemma_and_original_list]\n    return custom_stem(mapping, clean_and_stem(nlp, text.lower()))\n    \n\ndef main(filename_library, book_filtering = False):\n    # load library.json\n    with open(filename_library,\"r\") as infile: # data/library.json\n        data = json.loads(infile.read())\n\n    if book_filtering:\n        # filter out double books\n        # use the longest version of each book\n        titles = set([x['title'] for x in data])\n        data_ = []\n        for title in titles:\n            versions = [book for book in data if book['title'] == title]\n            longest_version = [book for book in versions if len(book['text']) == max([len(book['text']) for book in versions])][0]\n            data_.append(longest_version)\n        data = data_\n        \n    nlp = en_core_web_sm.load() # load the small webcorpus from spacy\n    \n    stemmed_books = []\n    for book in data:\n        stemmed_books.append(clean_and_stem(nlp, book['text']))\n    \n    with open(\"data/out/stemmed_library_\" + datetime.datetime.strftime(datetime.datetime.now(),\"%Y%m%d%H%M\") + \".txt\", \"w\") as outfile:\n        for book in stemmed_books:\n            outfile.write(book)\n            outfile.write(\"\\n\")\n\n","repo_name":"jd7h/sentiment-lexicon-skyrim","sub_path":"src/clean_imperial_library.py","file_name":"clean_imperial_library.py","file_ext":"py","file_size_in_byte":3222,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"81"}
+{"seq_id":"39508578163","text":"import sys\nsys.setrecursionlimit(10**9)\n\ndef dfs(si):\n    global n,m,a,mr,visit\n\n    if visit[si]:\n        if visit[si]==-1: return True\n        return False\n\n    visit[si]=-1\n    for i in range(m):\n        mym=a[si][i]\n        if mym==1:\n            for j in mr[i][0]:\n                result=dfs(j)\n                if result: return True\n\n    visit[si]=1\n    return False\n\n\nn,m=map(int,input().split())\na=[]\nmr=[[[] for j in range(2)] for i in range(m)]\nvisit=[0]*1000\n\nfor i in range(n):\n    x=[*map(int,input().split())]\n    a.append(x)\n    for j in range(m):\n        mr[j][x[j]].append(i)\n\nfor i in range(n):\n    if dfs(i):\n        print(\"NO\")\n        exit(0)\n\nprint(\"YES\")","repo_name":"njw1204/BOJ-AC","sub_path":"problem/10000~19999/18248/18248.py3.py","file_name":"18248.py3.py","file_ext":"py","file_size_in_byte":677,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"81"}
+{"seq_id":"39460852183","text":"s=input()\nvolcount=0\nconstant=0\nvol=['a','e','i','o','u','A','E','I','O','U']\nfor i in range(len(s)):\n    if(s[i] in vol):\n        volcount+=1\n    else:\n        constant+=1\nprint(\"Vowels in string\",volcount)\nprint(\"Constant in String\",constant)\n","repo_name":"Thamizhselvan310/example","sub_path":"countVowel.py","file_name":"countVowel.py","file_ext":"py","file_size_in_byte":245,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"17329686879","text":"#2390.RemovingStarsFromaString.py\n# https://leetcode.com/problems/removing-stars-from-a-string/\nclass Solution:\n    def removeStars(self, s: str) -> str:\n        mystack = []\n        for i, val in enumerate(s):\n            if mystack and val == '*':\n                mystack.pop()\n            elif val != '*':\n                mystack.append(val)\n        return ''.join(mystack)","repo_name":"djparul/leetcode","sub_path":"python/2390.RemovingStarsFromaString.py","file_name":"2390.RemovingStarsFromaString.py","file_ext":"py","file_size_in_byte":376,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"2231322126","text":"import cPickle as pickle\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport glob as glob\nfrom scipy import integrate\n\ndef getfilerec(f):\n    return int(f.replace(\"disp.datto\",\"\"))\n\ndef read_disp(filebase=\"disp.datto\", step=300):\n    files =glob.glob(filebase + \"*\")\n    #k = [sortfn(f) for f in files]\n    files.sort(key=getfilerec)\n    nfiles = len(files)\n    d = np.genfromtxt(files[0])\n    nperfile = d[::step,:].shape[0] \n    nrecs = nperfile*nfiles\n    disp = np.zeros([nrecs, d.shape[1]])\n    for i, f in enumerate(files):\n        print(i, i*step, f)\n        d = np.genfromtxt(f)\n        startrec = getfilerec(f)\n        disp[nperfile*i:nperfile*(i+1),0] = startrec+d[::step,0]\n        disp[nperfile*i:nperfile*(i+1),1:] = d[::step,1:]\n\n    return disp\n\n#Set constants\nmu = 1.7\nrho = 0.8442\nU = 0.00025\nLmin = 0.3\nLmax = 5.3\nL = Lmax - Lmin\nRe = rho * U * L / mu\ngamma = 2.*U/L\nTinit = 1.0\ndt = 0.005\nLx = 30.\nLz = 30.\nA = Lx*Lz\n\n#Load data\n# alldata shape = [batches, trajectories, time, quantities]\n# The [batches] are batches of trajectories \n# [Trajectories] are pairs of mirrored child trajectories from a single\n#                mother trajectory run over a number of steps \n# The [time] is the time histroy of the child trajectory\n# The [quantities] are as follows:\n# step, temperature, MOP_lower, MOP_center, MOP_upper, Virial_Pxy, \n# liquid_solid_fij_lower, liquid_solid_fij_upper, \n# sum tether_Fx_lower, sum tether_Fx_upper\n# MOP_direct_lower MOP_direct_center MOP_direct_upper\n# pyy fij_direct_lower fij_direct_upper\nalldata = np.load(\"./outfile.npy\") \n\nt = np.linspace(0.,alldata.shape[2]*dt,alldata.shape[2])\n\n#Get average over all trajectories\ndata = np.mean(alldata,(0,1))\nT = data[:,1]\nMOPl = data[:,2]\nMOPc = data[:,3]\nMOPu = data[:,4]\nPxy = data[:,5]\nfijl = data[:,6]\nfiju = data[:,7]\ndispl = data[:,8]\ndispu = data[:,9]\n\n#Get error over all trajectories\nerror = np.std(alldata,(0,1))/np.sqrt(alldata.shape[0]*alldata.shape[1])\nTe = error[:,1]\nMOPle = error[:,2]\nMOPce = error[:,3]\nMOPue = error[:,4]\nPxye = error[:,5]\nfijle = error[:,6]\nfijue = error[:,7]\ndisple = error[:,8]\ndispue = error[:,9]\n\ndF = (alldata[:,:,:,7]-alldata[:,:,:,6])*L/A\n\n#Split mirror and original\nmp = alldata.reshape([alldata.shape[0],alldata.shape[1]/2,2,\n                      alldata.shape[2],alldata.shape[3]])\ndel alldata\n\n#Extract quantities\nT = mp[:,:,:,:,1]\nbeta = 1./T\nMOP_lower = mp[:,:,:,:,2]\nMOP_center = mp[:,:,:,:,3]\nMOP_upper = mp[:,:,:,:,4]\nVirial_Pxy = mp[:,:,:,:,5]\nliquid_solid_fij_lower = mp[:,:,:,:,6]/A\nliquid_solid_fij_upper = mp[:,:,:,:,7]/A\nliquid_solid_fij = liquid_solid_fij_upper - liquid_solid_fij_lower\ndisp_lower = -0.5*beta*gamma*L*mp[:,:,:,:,8]\ndisp_upper = 0.5*beta*gamma*L*mp[:,:,:,:,9]\ndisp = disp_upper+disp_lower\ndisp0 = disp[:,:,:,0]\n\n# Get ttcf integrand, sum over batches [0], child trajectories [1] and mirror pairs [2]\nintegrand = np.einsum('ijm,ijmkl->kl', disp0, mp)/(mp.shape[0]*mp.shape[1]*mp.shape[2])\n\n# Statistics improved by using\n# <disp(0)*B(s)> - <disp(0)>*<B(s)>\nintegrand -= np.mean(disp0)*np.mean(mp,(0,1,2))\n\n#Dissipation function used can be corresponding one for upper and lower surface if not average quantity\n# Lower surface\nfor i in [2,6,8]: \n    integrand[:,i] = np.einsum('ijm,ijmk->k', disp_lower[:,:,:,0], mp[:,:,:,:,i])/(mp.shape[0]*mp.shape[1]*mp.shape[2])\n    integrand[:,i] -= np.mean(disp_lower[:,:,:,0])*np.mean(mp[:,:,:,:,i],(0,1,2))\n# Upper surface\nfor i in [4,7,9]:\n    integrand[:,i] = np.einsum('ijm,ijmk->k', disp_upper[:,:,:,0], mp[:,:,:,:,i])/(mp.shape[0]*mp.shape[1]*mp.shape[2])\n    integrand[:,i] -= np.mean(disp_upper[:,:,:,0])*np.mean(mp[:,:,:,:,i],(0,1,2))\n\n#Get TTCF by integrating function\nTTCF = np.empty(integrand.shape)\nfor i in range(integrand.shape[1]):\n    TTCF[:-1,i] = integrate.cumtrapz(integrand[:,i], dx=dt)\n\ndel integrand\n\n#Gives pretty much the same as a looped simpsons integral\n#TTCFs = np.empty(integrand.shape)\n#for i in range(integrand.shape[1]):\n#    for j in range(1,integrand.shape[0]):\n#        TTCFs[j,i] = integrate.simps(integrand[:j,i], dx=dt)\n\n#Add mean of initial value (skip this as it just adds noise)\n#TTCF += np.mean(mp[:,:,:,0,:],(0,1,2))\n\n\n#Keep all trajectories\nintegrand_alldata = np.einsum('ijm,ijmkl->ijmkl', disp0, mp)\nTTCF_alldata = np.empty(integrand_alldata.shape)\nfor i in range(integrand_alldata.shape[-1]):\n    TTCF_alldata[:,:,:,:-1,i] = integrate.cumtrapz(integrand_alldata[:,:,:,:,i], dx=dt)\n\ndel integrand_alldata\n\n#Save TTCF quantities with names\nTTCF_data = np.mean(TTCF_alldata,(0,1,2))\nTTTCF = TTCF_data[:,1]\nMOPlTTCF = TTCF_data[:,2]\nMOPcTTCF = TTCF_data[:,3]\nMOPuTTCF = TTCF_data[:,4]\nPxyTTCF = TTCF_data[:,5]\nfijlTTCF = TTCF_data[:,6]\nfijuTTCF = TTCF_data[:,7]\ndisplTTCF = TTCF_data[:,8]\ndispuTTCF = TTCF_data[:,9]\n\n#Get TTCF error over all trajectories\nTTCF_error = np.std(TTCF_alldata,(0,1,2))/np.sqrt(TTCF_alldata.shape[0]*TTCF_alldata.shape[1]*TTCF_alldata.shape[2])\nTTTCFe = TTCF_error[:,1]\nMOPlTTCFe = TTCF_error[:,2]\nMOPcTTCFe = TTCF_error[:,3]\nMOPuTTCFe = TTCF_error[:,4]\nPxyTTCFe = TTCF_error[:,5]\nfijlTTCFe = TTCF_error[:,6]\nfijuTTCFe = TTCF_error[:,7]\ndisplTTCFe = TTCF_error[:,8]\ndispuTTCFe = TTCF_error[:,9]\n\ndel TTCF_alldata\n\n#Plot virials\nfig, ax = plt.subplots(1,1)\nax.errorbar(t, Pxy, Pxye, errorevery=50, color=\"b\", ecolor='k', label=\"Virial DAV\")\nax.errorbar(t, PxyTTCF, PxyTTCFe, errorevery=50, color=\"r\", ecolor='k', label=\"Virial TTCF\")\nax.legend()\nplt.show()\n\n#Plot data\nfig, ax = plt.subplots(2,1)\nax[0].plot(t, MOPl, color='k', label=\"MOP lower\")\nax[0].plot(t, MOPc, color='b', label=\"MOP centre\")\nax[0].plot(t, MOPu, color='r', label=\"MOP upper\")\nax[0].plot(t, Pxy, color='g', label=\"Pxy Virial\")\n\nax[1].plot(t, MOPlTTCF, 'k--', label=\"TTCF MOP lower\")\nax[1].plot(t, MOPcTTCF, 'b-', label=\"TTCFMOP centre\")\nax[1].plot(t, MOPuTTCF, 'r-', label=\"TTCFMOP upper\")\nax[1].plot(t, PxyTTCF, 'g-', label=\"TTCFPxy Virial\")\n\nax[0].legend()\nax[1].legend()\nplt.show()\n\nfig, ax = plt.subplots(2,1)\nax[0].plot(t, fijl/A - fiju/A, 'k-', label=\"fij wall\")\nax[0].plot(t, fijlTTCF/A - fijuTTCF/A, 'r-', label=\"TTCF fij wall\")\nax[1].plot(t, displ/A - dispu/A, 'k-', label=\"disp\")\nax[1].plot(t, displTTCF/A - dispuTTCF/A, 'r-', label=\"TTCF disp\")\n\n#ax[0].plot(t, fijl/A, 'k-', label=\"fijl\")\n#ax[0].plot(t, fiju/A, 'b-', label=\"fiju\")\n#ax[0].plot(t, fijlTTCF/A, 'r-', label=\"TTCF fijl\")\n#ax[0].plot(t, fijuTTCF/A, 'g-', label=\"TTCF fiju\")\n\n#ax[1].plot(t, displ/A, 'k-', label=\"displ\")\n#ax[1].plot(t, dispu/A, 'b-', label=\"dispu\")\n#ax[1].plot(t, displTTCF/A, 'r-', label=\"TTCF displ\")\n#ax[1].plot(t, dispuTTCF/A, 'g-', label=\"TTCF dispu\")\n\nax[0].legend()\nax[1].legend()\nplt.show()\n\n# Get dissipation function of mother trajectory\n# and correlate with initial value\n#disptb = read_disp()\n#ad = np.reshape(alldata,(disptb.shape[0],alldata.shape[-2],alldata.shape[-1]))\n#assert disptb.shape[0] == alldata.shape[0]\n#TTCF = np.zeros(ad.shape)\n#for i in range(disptb.shape[0]):\n#    #disp = disptb[i,1] + disptb[i,2]\n#    TTCF[i,:,:] = ad[i,:,:]*disptb[i,1]\n\n\n#Bernadi style plot of Direct AVeraging (DAV) data\nfig, ax = plt.subplots(1,1)\nd = np.mean(Virial_Pxy,(0,1,2))\ns = np.std(Virial_Pxy,(0,1,2))/np.sqrt(Virial_Pxy.shape[0]*Virial_Pxy.shape[1])\nplt.errorbar(t, d[:], s[:], errorevery=50, ecolor=\"k\", capsize=2)\n\n#dF is difference between force over top and bottom\n# used for TTCF in \n# Delhommelle and Cummings (2005) PHYSICAL REVIEW B 72, 172201\n#ax2 = ax.twinx()\na_dF0dF = np.einsum('ij,ijk->k', dF[:,:,0], dF[:,:,:])/(dF.shape[0]*dF.shape[1])\na_dF0 = np.mean(dF[:,:,0])\na_dF = np.mean(dF[:,:,:], (0,1))\ninint = a_dF0dF - a_dF0*a_dF\nax.plot(t[:-1], -np.array([integrate.simps(inint[:i], dx=dt) for i in range(1,inint.shape[0])]),'r-')\nplt.show()\n\n\n######################################\n\n# Attempt to plot analytical solution for stress as\n# a function of time to check MOP output\n\ndef stress_at_loc(y, H=1., nmodes=20000):\n\n    tau = 1.\n    dt = 0.005\n    t = 0. #dt*np.arange(1000)\n    n = np.arange(nmodes)\n    lam = n*np.pi*y/H \n    tau = np.sum(2* (-1)**n * np.exp(-mu*lam*t/rho)*np.cos(lam))\n    tau = -(mu*U/H)*tau \n\n\nfrom CouetteAnalytical import CouetteAnalytical\n\nCA = CouetteAnalytical(Re, U, Lmin, Lmax, nmodes=20000)\ntautime = []\ntrng = 0.05*np.arange(60)\nplot = True\n\nif plot:\n    fig, ax = plt.subplots(1,1)\n    plt.ion()\n\nfor t in trng:\n\n    print(t)\n    #Get velocity\n    y, u = CA.get_vprofile(t)\n    y, uf = CA.get_vprofile(t, flip=True)\n    u -= uf\n\n    #Stress\n    tau = mu*np.gradient(u, y)\n    dy = np.gradient(y)[3]\n    tautime.append(tau)\n\n    if plot:\n        ax.plot(y, u, 'k-')\n        ax.plot(y, tau, 'r')\n        plt.pause(0.001)\n        plt.cla()\n\nplt.clf()\nplt.ioff()\nfig, ax = plt.subplots(1,1)\ntautime = np.array(tautime)\n[ax.plot(trng, tautime[:,i], label=\"y=\"+ str(1.+i*dy)) for i in range(0,10,2)]\nplt.legend()\nplt.show()\n\nscale = 3.\nfig, ax = plt.subplots(1,1)\nax.plot(np.linspace(0, scale*trng.max(), MOPl.shape[0]),MOPl, label=\"MOP lower\")\nax.plot(np.linspace(0, scale*trng.max(), MOPc.shape[0]),MOPc, label=\"MOP centre\")\nax.plot(np.linspace(0, scale*trng.max(), MOPu.shape[0]), MOPu, label=\"MOP upper\")\nfor i in [3,10,16]:\n    ax.plot(trng, -tautime[:,i], label=\"y=\"+ str(1.+i*dy))\nplt.legend()\nax.set_xlim(0., scale*trng.max())\nplt.show()\n#import scipy\n#scale = tautime.shape[0]/float(MOPl.shape[0])\n#ax.plot(trng, scipy.ndimage.zoom(MOPl, scale), label=\"MOP lower\")\n#ax.plot(trng, scipy.ndimage.zoom(MOPl, scale), label=\"MOP centre\")\n#ax.plot(trng, scipy.ndimage.zoom(MOPl, scale), label=\"MOP upper\")\n\n","repo_name":"edwardsmith999/TTCF","sub_path":"superseeded/plot_summary.py","file_name":"plot_summary.py","file_ext":"py","file_size_in_byte":9502,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"81"}
+{"seq_id":"40068953667","text":"#!/usr/bin/env python\n\"\"\" This module will is a helper to safe_uploader and should not be called\n    directly.\n\"\"\"\nimport argparse\nimport os\nimport re\nimport subprocess\nimport sys\nimport time\n\nimport safe_uploader\n\n\nBLOCK_SIZE = 262144\nINIT_PID = 1\nSLEEP_TIME = .25\nSTDIN = 0\nSTDOUT = 1\n\n\ndef main():\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\"terminate_path\",\n                        help='When a file appears at this path, exit')\n    args = parser.parse_args()\n    repeater(args.terminate_path)\n\n\ndef repeater(terminate_path):\n    \"\"\" Repeat data from stdin to stdout until no more data is present comes\n    from stdin AND a stop file is populated with a magic value.\n\n    Args:\n    terminate_path - The path of the stop file\n    \"\"\"\n    while True:\n        if os.getppid() == INIT_PID:\n            kill_stdout_reader()\n            raise Exception('Safe uploader proc is now somehow the child of '\n                            'proc 1. This means that that parent of the '\n                            'repeater process no longer is no longer in '\n                            'control. Lacking any good option, the repeater '\n                            'process will terminate.')\n\n        data = sys.stdin.read(BLOCK_SIZE)\n        if len(data) == 0:\n            time.sleep(SLEEP_TIME)\n\n            # write empty data to detect broken pipes\n            sys.stdout.write(data)\n\n            if os.path.exists(terminate_path):\n                if check_term_file(terminate_path):\n                    sys.exit(0)\n        else:\n            sys.stdout.write(data)\n\n        if len(data) < BLOCK_SIZE:\n            sys.stdout.flush()\n\n\ndef check_term_file(term_path):\n    \"\"\" Check to see if a term file has been populated with a magic string\n        meaning that the repeater code can terminate\n\n    Returns\n    True if the file has been populated, false otherwise\n    \"\"\"\n    with open(term_path, 'r') as term_handle:\n        contents = term_handle.read(len(safe_uploader.TERM_STRING))\n    return contents == safe_uploader.TERM_STRING\n\n\ndef kill_stdout_reader():\n    \"\"\" Kill whatever is on the otherside of stdout \"\"\"\n    std_out_fd = '/proc/{pid}/fd/{stdout}'.format(pid=os.getpid(),\n                                                  stdout=STDOUT)\n    readlink = os.readlink(std_out_fd)\n    pipe_node = re.match('pipe:\\[([0-9]+)]', readlink).groups()[0]\n    cmd = (\"lsof | \"\n           \"awk '{{if($4 == \\\"{stdin}r\\\" && $8 == {pipe_node}) print $2}}'\"\n           \"\".format(stdin=str(STDIN),\n                     pipe_node=pipe_node))\n    lsof = subprocess.Popen(cmd, shell=True,\n                            stdout=subprocess.PIPE)\n    lsof.wait()\n    stdout_reader_pid = int(lsof.stdout.read())\n    try:\n        os.kill(stdout_reader_pid, 9)\n    except:\n        pass\n        # Nothing really to be done here, it is probalby hopeless to try\n        # to do anything more.\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"pinterest/mysql_utils","sub_path":"safe_uploader_repeater.py","file_name":"safe_uploader_repeater.py","file_ext":"py","file_size_in_byte":2926,"program_lang":"python","lang":"en","doc_type":"code","stars":877,"dataset":"github-code","pt":"81"}
+{"seq_id":"42132191651","text":"class Solution:\n    def detectCycleNaive(self, head):\n\n        visited = set()\n        curr = head\n\n        while curr:\n            if curr in visited:\n                return curr\n            visited.add(curr)\n            curr = curr.next\n        return None\n\n    def detectCycle(self, head):\n\n        def findIntersect():\n            fast, slow = head\n\n            while fast and fast.next:\n\n                fast = fast.next.next\n                slow = slow.next\n                if slow == fast:\n                    return slow\n            return None\n\n        if not head:\n            return None\n\n        intersect = findIntersect()\n        if not intersect:\n            return None\n        p1 = head\n        p2 = intersect\n\n        while p1 != p2:\n            p1 = p1.next\n            p2 = p2.next\n        return p1\n","repo_name":"mdiallo98/python-dataStructures-Algos","sub_path":"LeetcodeQuestions/linked_list/linkedListCycleII.py","file_name":"linkedListCycleII.py","file_ext":"py","file_size_in_byte":820,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"81"}
+{"seq_id":"30007683856","text":"from django.urls import path, include\nfrom . import views\n\nurlpatterns = [\n    path('about/', views.about, name= 'about'),\n    path('', views.home, name='home'),\n    path('teams/', views.team, name ='team'),\n    path('news/', views.news, name ='news'),\n    path('contact/', views.contact, name ='contact'),\n    path('appointment/', views.appointment, name = 'appointment'),\n    path('join/', views.join, name = 'add-doctor'),\n    path('register/', views.register_request, name = 'register'),\n    path('logout/', views.logout_request, name = 'logout'),\n    path('login/', views.login_request, name = 'login'),\n]","repo_name":"sparshsingh21/mhealth","sub_path":"website/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":610,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"81"}
+{"seq_id":"44755143573","text":"class ReadableInformationWriter:\r\n\tdef __init__(self, path):\r\n\t\t#Set the global PATH variable to path\r\n\t\tself.PATH = path\r\n\tdef write(self, key, value):\r\n\t\t#Write the key and value to self.PATH\r\n\t\tfile = open(self.PATH, \"w\")\r\n\t\tfile.write(\"[\"+key+\"] => \"+value+\"\\n\")\r\n\t\tfile.flush()\r\n\t\tfile.close()\r\n\r\nclass ReadableInformationReader:\r\n\tdef __init__(self, path):\r\n\t\t#Set the global PATH variable to path\r\n\t\tself.PATH = path\r\n\r\n\tdef get(self, key):\r\n\t\t#Get the value of the key from self.PATH\r\n\t\tfile = open(self.PATH, \"r\")\r\n\t\tcontent = file.readline();\r\n\t\tchar = \"\"\r\n\t\tfor text in list(content):\r\n\t\t\tchar += text\r\n\r\n\t\tif((\"[\"+key+\"] => \") in char):\r\n\t\t\treturn char.replace(\"[\"+key+\"] => \", \"\")\r\n\t\telse:\r\n\t\t\treturn \"Key not found\"\r\n","repo_name":"indie-dev/zipify","sub_path":"ReadableInformationFile.py","file_name":"ReadableInformationFile.py","file_ext":"py","file_size_in_byte":731,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"36262350807","text":"import pandas as pd\nimport numpy as np\n\n# Matplot\nimport seaborn as sns\nimport matplotlib.pyplot as plt\n\nfrom sklearn.metrics import confusion_matrix\n\nfrom keras.models import Sequential\nfrom keras.layers import LSTM, Dense, Embedding, Bidirectional, MaxPooling1D\nfrom sklearn.model_selection import train_test_split\n\nfrom preprocess import X, d2v_model, embedding_matrix, df, X_train, X_test, Y_train, Y_test\n\n\n\nmodel = Sequential([    \n    Embedding(len(d2v_model.wv)+1,20,input_length=X.shape[1],weights=[embedding_matrix],trainable=True),\n    MaxPooling1D(pool_size=2),\n    Bidirectional(LSTM(50)),\n    Dense(3, activation='sigmoid')\n\n])\n\n\nmodel.summary()\nmodel.compile(optimizer=\"adam\",loss=\"binary_crossentropy\",metrics=['acc'])\n\nY = pd.get_dummies(df['sentiment']).values\n\n\n\nhistory=model.fit(X_train, Y_train, epochs = 50, batch_size=32, verbose = 2)\n\nmodel.save(\"./saved_models\")\n\nplt.plot(history.history['acc'])\nplt.title('model accuracy')\nplt.ylabel('acc')\nplt.xlabel('epochs')\nplt.legend(['train', 'test'], loc='upper left')\nplt.show()\nplt.savefig('model_accuracy.png')\n\n# summarize history for loss\nplt.plot(history.history['loss'])\nplt.title('model loss')\nplt.ylabel('loss')\nplt.xlabel('epochs')\nplt.legend(['train', 'test'], loc='upper left')\nplt.show()\nplt.savefig('model_loss.png')\n\n\n# evaluate the model\n_, train_acc = model.evaluate(X_train, Y_train, verbose=2)\n_, test_acc = model.evaluate(X_test, Y_test, verbose=2)\nprint('Train: %.3f, Test: %.4f' % (train_acc, test_acc))\n\n\n\n# predict probabilities for test set\nyhat_probs = model.predict(X_test, verbose=0)\n\n# predict crisp classes for test set\npredict_x=model.predict(X_test) \nyhat_classes=np.argmax(predict_x,axis=1)\n\n# reduce to 1d array\nrounded_labels=np.argmax(Y_test, axis=1)\n\n\ncm = confusion_matrix(rounded_labels, yhat_classes)\n\n\nlstm_val = confusion_matrix(rounded_labels, yhat_classes)\n_, ax = plt.subplots(figsize=(5,5))\nsns.heatmap(lstm_val, annot=True, linewidth=0.7, linecolor='cyan', fmt='g', ax=ax, cmap=\"BuPu\")\nplt.title('LSTM Classification Confusion Matrix')\nplt.xlabel('Y predict')\nplt.ylabel('Y test')\nplt.show()\n\n","repo_name":"evanmoore0/Financial-News","sub_path":"model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":2109,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"30849161673","text":"\"\"\"Add users.\n\nRevision ID: 1ce7873ac4ced2\nRevises: \nCreate Date: 2023-06-18 15:00:32.799885\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\n\n\n# revision identifiers, used by Alembic.\nrevision = '1ce7873ac4ced2'\ndown_revision = '1b45aaa4b20d'\nbranch_labels = None\ndepends_on = '1b45aaa4b20d'\n\n\ndef upgrade():\n    op.execute(\"\"\"INSERT INTO \"user\" VALUES (1, 'admin', '2d58b0ac72f929ca9ad3238ade9eab69', 'admin');\"\"\")\n    op.execute(\"\"\"INSERT INTO \"user\" VALUES (2, 'julius', '4024fb06e1423da90b80f0274e8e4476', 'user');\"\"\")\n\n\ndef downgrade():\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.drop_table('user')\n    # ### end Alembic commands ###\n","repo_name":"LeftarCode/sekurak-academy-python-hacking","sub_path":"python-dashboard/migrations/versions/1ce7873ac4ced2_add_users.py","file_name":"1ce7873ac4ced2_add_users.py","file_ext":"py","file_size_in_byte":673,"program_lang":"python","lang":"en","doc_type":"code","stars":36,"dataset":"github-code","pt":"81"}
+{"seq_id":"44076553326","text":"from sys import stdin\n\n\"\"\"\n각 나무의 길이가 주어져있고, 나무를 일정 높이에서 잘라서 그 위의 것만 가져가려고 한다.\n그 때, 환경보호를 위하여 일정 길이 이상을 자르고, 목표에 최대한 가깝게 자르려고 한다.\n이럴 때 최대 높이를 구하는 것이 문제.\n\"\"\"\ndef cut_trees():\n    n, m = map(int, stdin.readline().split())\n    a = list(map(int, stdin.readline().split()))\n\n    left, right, ans = 0, max(a), 0\n    while left <= right:\n        mid = (left + right) // 2\n\n        tree = 0     # 잘리는 나무의 길이를 계산\n        for i in range(n):\n            if mid < a[i]:\n                tree += a[i] - mid\n\n        if tree >= m:     # 목표보다 많으면 높이를 늘리고(어쨌든 문제는 해결이니까 답은 저장해놓는다)\n            ans = mid\n            left = mid + 1\n        elif tree < m:    # 목표보다 적으면 높이를 낮춘다\n            right = mid - 1\n\n    print(ans)\n\n\nif __name__ == \"__main__\":\n    cut_trees()","repo_name":"appleg1226/python-project","sub_path":"Binary-Search/cut_tree.py","file_name":"cut_tree.py","file_ext":"py","file_size_in_byte":1029,"program_lang":"python","lang":"ko","doc_type":"code","stars":1,"dataset":"github-code","pt":"81"}
+{"seq_id":"13389571249","text":"\"\"\"\nCreates new files from templates with {{moustache}} syntax\n\"\"\"\n\nfrom os import path\nimport regex as re\n\n\nclass Templator:\n    _default_template = r\"fxpq/templates/{{object}}.fxpq\"\n\n    def __init__(self, package_manager):\n        self.package_manager = package_manager\n        self.Object = package_manager.get_class(\"fxpq.core\", \"Object\")\n        self.objects = self.Object.__subclasses__()\n\n        self.templates = package_manager.get_files_in(\"templates\", self.Object)\n\n    def new(self, obj_type, input_values=None):\n        if not input_values:\n            input_values = {}\n\n        typename = obj_type.__name__.lower()\n        filename = next((f for f in self.templates if typename in self._find_moustaches(path.basename(f))), None)\n        if not filename:\n            filename = self.package_manager.get_path(self._default_template)\n            input_values['object'] = typename\n\n        with open(filename) as f:\n            text = f.read()\n            result_text = self._replace_moustaches(text, input_values)\n            result_title = self._format_title(filename, typename, input_values)\n            return result_title, result_text\n\n    def _format_title(self, filename, typename, input_values):\n        if not input_values.get(\"name\", \"\"):\n            return \"untitled {}\".format(typename)\n\n        replacement = {}\n        replacement[typename] = input_values[\"name\"]\n        return self._replace_moustaches(path.basename(filename), replacement)\n\n    def _find_moustaches(self, string):\n        return re.findall(r'{{([a-zA-Z_][\\w_.-]*)}}', string)\n\n    def _replace_moustaches(self, string, dictionary):\n        for moustache in self._find_moustaches(string):\n            string = string.replace(r'{{' + moustache + r'}}', dictionary.get(moustache, \"\"))\n        return string\n","repo_name":"euhmeuh/fxpq","sub_path":"core/templator.py","file_name":"templator.py","file_ext":"py","file_size_in_byte":1799,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"32281204700","text":"#----------------------------------------------------------------------------#\n# Imports\n#----------------------------------------------------------------------------#\n\nimport json\nimport dateutil.parser\nimport babel\nfrom flask import Flask, render_template, request, Response, flash, redirect, url_for\nfrom flask_moment import Moment\nfrom flask_sqlalchemy import SQLAlchemy\nimport logging\nfrom logging import Formatter, FileHandler\nfrom flask_wtf import Form\nfrom forms import *\nfrom flask_migrate import Migrate\nfrom sqlalchemy import literal,func,or_\nfrom datetime import datetime\nfrom sqlalchemy.sql import label,case\n\n#----------------------------------------------------------------------------#\n# App Config.\n#----------------------------------------------------------------------------#\n\napp = Flask(__name__)\nmoment = Moment(app)\napp.config.from_object('config')\ndb = SQLAlchemy(app)\n\nmigrate = Migrate(app,db)\n\n# TODO: connect to a local postgresql database\n\n#----------------------------------------------------------------------------#\n# Models.\n#----------------------------------------------------------------------------#\n\n\n\n\nclass Venue(db.Model):\n    __tablename__ = 'venues'\n\n    id = db.Column(db.Integer, primary_key=True)\n    name = db.Column(db.String,nullable=False)\n    city = db.Column(db.String(120),nullable=False)\n    state = db.Column(db.String(120),nullable=False)\n    address = db.Column(db.String(120),nullable=False)\n    phone = db.Column(db.String(120),nullable=False)\n    image_link = db.Column(db.String(500),nullable=False)\n    facebook_link = db.Column(db.String(120),nullable=False)\n    \n\n    # TODO: implement any missing fields, as a database migration using Flask-Migrate\n    # artists = db.relationship('Artist',secondary = \"shows\",backref=\"venues\")\n    genres = db.Column(db.String(120),nullable=False)\n    seeking_talent = db.Column(db.Boolean,default=False)\n    seeking_description =  db.Column(db.String(500),default=\"\")\n    show = db.relationship(\"Show\",cascade=\"all, delete-orphan\")\n\n    \n\n\nclass Artist(db.Model):\n    __tablename__ = 'artists'\n\n    id = db.Column(db.Integer, primary_key=True)\n    name = db.Column(db.String)\n    city = db.Column(db.String(120),nullable=False)\n    state = db.Column(db.String(120),nullable=False)\n    phone = db.Column(db.String(120),nullable=False)\n    genres = db.Column(db.String(120),nullable=False)\n    image_link = db.Column(db.String(500),nullable=False)\n    facebook_link = db.Column(db.String(120),nullable=False)\n\n\n    # TODO: implement any missing fields, as a database migration using Flask-Migrate\n    # venues = db.relationship('Venue',secondary = \"show\")\n    seeking_venue = db.Column(db.Boolean,default=False)\n    seeking_description =  db.Column(db.String(500),default=\"\")\n    show = db.relationship(\"Show\",cascade=\"all, delete-orphan\")\n\n# TODO Implement Show and Artist models, and complete all model relationships and properties, as a database migration.\n\n\n# i made the show as class extends db.model because i have extra field 'date' to add with the 2 foreignkeys'  \n\n#-------------\n# Update (Review-1)\n#--------------\n# - change table names to be plural \n# - change the cascade=\"all, delete-orphan\" to be in the main class (Artist,venue) not the intermediate (Show) which fix \n#   the problem of deletion \n# - remove the relationship from venue to artist and this make me now unable to write artist.venues.append(venue) but i wouldn't write \n#   this any way because every relationship between venue and artist need 'date' field so it's easier to add them by creaing\n#   show instance  (i know removing this relationship  seems as if i am just not using many to many relationship at all)\n#   but i am using it i just made the refrence from the show to (Artist,venue) and from (Artist,venue) to the show so know i have 2 tables and a third one\n#   that have 2 foreign keys pointing to them (which is exactly the many to many relationship ) just with custom implementation\n#   to allow me to add 'date' field , and allow relationship between the same artist and venue with different dates with no      \n#   error on deletion .\n#   i get the idea from https://stackoverflow.com/questions/7417906/sqlalchemy-manytomany-secondary-table-with-additional-fields\n#----------------\n\n\nclass Show(db.Model):  # show represent the show (many to many relationship between venus and artists)\n    __tablename__ = \"shows\"\n\n    id = db.Column(db.Integer,primary_key=True)\n    artist_id = db.Column(db.Integer, db.ForeignKey('artists.id') )\n    venue_id = db.Column(db.Integer, db.ForeignKey('venues.id'))\n    date = db.Column(db.DateTime,default=datetime.utcnow())\n\n    venue = db.relationship(Venue)\n    artist = db.relationship(Artist)\n\n\n\n#----------------------------------------------------------------------------#\n# Filters.\n#----------------------------------------------------------------------------#\n\ndef format_datetime(value, format='medium'):\n  # date = dateutil.parser.parse(value)\n  # i removed the previous line because value type is already datetime  \n  if format == 'full':\n      format=\"EEEE MMMM, d, y 'at' h:mma\"\n  elif format == 'medium':\n      format=\"EE MM, dd, y h:mma\"\n  return babel.dates.format_datetime(value, format)\n\napp.jinja_env.filters['datetime'] = format_datetime\n# SELECT venue.id AS venue_id, count(show.id) AS count_1 FROM venue JOIN show ON venue.id = show.venue_id GROUP BY venue.id\n# SELECT venue.name AS venue_name, count(show.id) AS count_1 FROM venue JOIN show ON venue.id = show.venue_id GROUP BY venue.name\n# ans = db.session.query(Venue, label(\"counter\",func.count(Show.id))).join(Show,Show.venue_id==Venue.id).group_by(Venue).order_by('city').all()\n# SELECT venue.name AS venue_name, count(show.id) AS counter FROM venue JOIN show ON show.venue_id = venue.id GROUP BY venue.name\n# ans = db.session.query(Venue, label(\"counter\",func.count(Show.id))).outerjoin(Show,Show.venue_id==Venue.id).group_by(Venue).order_by('city').all()\n\n\n#----------------------------------------------------------------------------#\n# Controllers.\n#----------------------------------------------------------------------------#\n\n\n@app.route('/')\ndef index():\n  artists = db.session.query(Artist).order_by(Artist.id.desc()).limit(10).all();\n  venues = db.session.query(Venue).order_by(Venue.id.desc()).limit(10).all();    \n  return render_template('pages/home.html',venues=venues,artists=artists)\n\n\n\n# Venues\n#  -----------------------------------------------------------------\n\n@app.route('/venues')\ndef venues():\n\n\n  # TODO: replace with real venues data.\n  # num_shows should be aggregated based on number of upcoming shows per venue.\n\n  \n  data = []\n  \n  # getting all the venues with number of upcoming shows in one query\n  venues = db.session.query(Venue, label(\"counter\",func.count(case([(Show.date > datetime.now(), 1)],else_=None)))).outerjoin(Show,Show.venue_id==Venue.id).group_by(Venue).order_by('city').all()\n\n\n  # grouping all the venues by the city \n  for venue,counter in venues:\n    if (len(data)>0 and venue.city == data[-1]['city']):\n      data[-1]['venues'].append({\n        'id':venue.id,\n        'name':venue.name,\n        'num_upcoming_shows':counter,\n      })\n    else:\n      data.append({'city':venue.city,\n      'state':venue.state,\n      'venues':[{\n         'id':venue.id,\n        'name':venue.name,\n        'num_upcoming_shows':counter,\n      }],\n      })\n\n  return render_template('pages/venues.html', areas=data);\n\n@app.route('/venues/search', methods=['POST'])\ndef search_venues():\n  # TODO: implement search on artists with partial string search. Ensure it is case-insensitive.\n  # seach for Hop should return \"The Musical Hop\".\n  # search for \"Music\" should return \"The Musical Hop\" and \"Park Square Live Music & Coffee\"\n  \n  #-------------\n  # Update (Review-1)\n  #--------------\n  # using ilike in the search instead of converting  them to lower\n  #----------------\n\n\n  search_for = request.form.get('search_term', '')\n\n\n\n  # matching with names,city,\n  match_all = Venue.query.filter( \n    or_(\n      Venue.name.ilike(f'%{search_for}%'), # f'{val}' == '{}'.format(val)\n      or_(\n        Venue.city.ilike(f'%{search_for}%'),\n        Venue.state.ilike(f'%{search_for}%'))\n       ) ).all()\n    \n  count = len(match_all)\n\n  response = {'count':count,\"data\":match_all}\n  return render_template('pages/search_venues.html', results=response, search_term=search_for)\n\n\n@app.route('/venues/<int:venue_id>')\ndef show_venue(venue_id):\n  # shows the venue page with the given venue_id\n  # TODO: replace with real venue data from the venues table, using venue_id\n\n  #-------------\n  # Update (Review-1)\n  #--------------\n  # using join between shows and Artist insted of artist.shows\n  #----------------\n\n  data = Venue.query.get_or_404(venue_id);\n  data.upcoming_shows = [];\n  data.past_shows = [];\n\n  shows = db.session.query(\n    label(\"artist_id\",Artist.id),\n    label(\"artist_name\",Artist.name),\n    label(\"artist_image_link\",Artist.image_link),\n    label(\"start_time\",Show.date),\n    label(\"venue_id\",Show.venue_id),\n    ).select_from(Show).filter_by(venue_id=venue_id).join(Artist).all()\n\n  # i get  all the shows and seperate them in python instead of making 2 quiries one to get the previous shows and one\n  # to get the upcoming \n  for show in shows:\n    if(show.start_time > datetime.now()):\n      data.upcoming_shows.append(show)\n    else:\n      data.past_shows.append(show)\n  data.upcoming_shows_count = len(data.upcoming_shows);\n  data.past_shows_count = len(data.past_shows);\n\n\n  return render_template('pages/show_venue.html', venue=data)\n\n#  Create Venue\n#  ----------------------------------------------------------------\n\n@app.route('/venues/create', methods=['GET'])\ndef create_venue_form():\n  form = VenueForm()\n  return render_template('forms/new_venue.html', form=form,status=\"create\")\n\n\n@app.route('/venues/create', methods=['POST'])\ndef create_venue_submission():\n  # TODO: insert form data as a new Venue record in the db, instead\n  # TODO: modify data to be the data object returned from db insertion\n\n  # on successful db insert, flash success\n  \n  # TODO: on unsuccessful db insert, flash an error instead.\n  # e.g., flash('An error occurred. Venue ' + data.name + ' could not be listed.')\n  # see: http://flask.pocoo.org/docs/1.0/patterns/flashing/\n\n\n  form = VenueForm()\n\n  if form.validate_on_submit():\n    \n    venue = Venue()\n    venue.name = form.name.data\n    venue.city = form.city.data\n    venue.state = form.state.data\n    venue.address = form.address.data\n    venue.phone = form.phone.data\n    venue.image_link = form.image_link.data\n    venue.facebook_link = form.facebook_link.data\n    venue.genres = \",\".join(form.genres.data) # genres stored as string but should be sent as list\n    venue.seeking_description = form.seeking_description.data\n    venue.seeking_talent = form.seeking_talent.data\n\n    db.session.add(venue)\n    db.session.commit()\n\n    flash('Venue ' + form.name.data + ' was successfully listed!')\n    \n    return redirect(url_for('show_venue', venue_id=venue.id))\n  else:\n    return render_template('forms/new_venue.html', form=form,status=\"create\")\n\n  return redirect(url_for('index'))\n\n\n\n#  Update Venue\n#  ----------------------------------------------------------------\n\n\n@app.route('/venues/<int:venue_id>/edit', methods=['GET'])\ndef edit_venue(venue_id):\n  \n  # TODO: populate form with values from venue with ID <venue_id>\n\n  form = VenueForm()\n  venue = Venue.query.get_or_404(venue_id);\n  if(venue.genres):\n    venue.genres = venue.genres.split(\",\");\n  # passing the objecr value directly to the form and sending the form itself with the previous data\n  form = VenueForm(obj = venue)\n\n  return render_template('forms/new_venue.html', form=form ,status=\"update\")\n\n@app.route('/venues/<int:venue_id>/edit', methods=['POST'])\ndef edit_venue_submission(venue_id):\n  # TODO: take values from the form submitted, and update existing\n  # venue record with ID <venue_id> using the new attributes\n\n  form = VenueForm()\n  venue = Venue.query.get_or_404(venue_id)\n  if form.validate_on_submit():\n    venue.name = form.name.data\n    venue.city = form.city.data\n    venue.state = form.state.data\n    venue.address = form.address.data\n    venue.phone = form.phone.data\n    venue.image_link = form.image_link.data\n    venue.facebook_link = form.facebook_link.data\n    venue.genres = \",\".join(form.genres.data)\n    venue.seeking_talent = form.seeking_talent.data\n    venue.seeking_description = form.seeking_description.data\n\n    db.session.add(venue)\n    db.session.commit()\n\n    flash('Venue ' + form.name.data + ' was successfully updated!')\n    \n    return render_template('pages/show_venue.html',venue=venue)\n  else:\n    return render_template('forms/new_venue.html', form=form,status=\"update\")\n\n\n\n  return redirect(url_for('show_venue', venue_id=venue_id))\n\n\n\n#  Delete Venue\n#  ----------------------------------------------------------------\n\n@app.route('/venues/<venue_id>/delete', methods=['GET'])\ndef delete_venue(venue_id):\n  # TODO: Complete this endpoint for taking a venue_id, and using\n  # SQLAlchemy ORM to delete a record. Handle cases where the session commit could fail.\n  \n  \n  #-------------\n  # Update (Review-1)\n  #--------------\n  # change delete function\n  #----------------\n  \n  try:\n\n    venue = Venue.query.get_or_404(venue_id)\n    db.session.delete(venue)\n    db.session.commit()\n    flash(\"deleted successfully\");\n\n  except :\n    flash(\"Error happen\")\n\n  return redirect(url_for('index'))\n\n\n\n#  Artists\n#  ----------------------------------------------------------------\n@app.route('/artists')\ndef artists():\n  # TODO: replace with real data returned from querying the database\n\n  data = db.session.query(Artist.id,Artist.name).all()\n  return render_template('pages/artists.html', artists=data)\n\n@app.route('/artists/search', methods=['POST'])\ndef search_artists():\n  # TODO: implement search on artists with partial string search. Ensure it is case-insensitive.\n  # seach for \"A\" should return \"Guns N Petals\", \"Matt Quevado\", and \"The Wild Sax Band\".\n  # search for \"band\" should return \"The Wild Sax Band\".\n  \n  #-------------\n  # Update (Review-1)\n  #--------------\n  # using ilike in the search instead of converting  them to lower\n  #----------------\n\n  search_for = request.form.get('search_term', '')\n\n  # matching with names,city,\n  match_all = Artist.query.filter( \n    or_(\n      Artist.name.ilike(f'%{search_for}%'),\n      or_(\n        Artist.city.ilike(f'%{search_for}%'),\n        Artist.state.ilike(f'%{search_for}%'))\n       ) ).all()\n    \n  count = len(match_all)\n  \n  response = {'count':count,\"data\":match_all}\n  return render_template('pages/search_artists.html', results=response, search_term=request.form.get('search_term', ''))\n\n\n\n@app.route('/artists/<int:artist_id>')\ndef show_artist(artist_id):\n  # shows the venue page with the given venue_id\n  # TODO: replace with real venue data from the venues table, using venue_id\n  \n\n  #-------------\n  # Update (Review-1)\n  #--------------\n  # using join between shows and Venues insted of venue.shows\n  #----------------\n\n  data = Artist.query.get_or_404(artist_id);\n  data.upcoming_shows = [];\n  data.past_shows = [];\n  shows =   db.session.query(\n    label(\"venue_id\",Venue.id),\n    label(\"venue_name\",Venue.name),\n    label(\"venue_image_link\",Venue.image_link),\n    label(\"start_time\",Show.date),\n    label(\"artist_id\",Show.artist_id),\n    ).select_from(Show).filter_by(artist_id=artist_id).join(Venue).all()\n\n  for show in shows:\n    if(show.start_time > datetime.now()):\n      data.upcoming_shows.append(show)\n    else:\n      data.past_shows.append(show)\n  data.upcoming_shows_count = len(data.upcoming_shows);\n  data.past_shows_count = len(data.past_shows);\n  \n  \n  return render_template('pages/show_artist.html', artist=data)\n\n\n\n\n#  Create Artist\n#  ----------------------------------------------------------------\n\n@app.route('/artists/create', methods=['GET'])\ndef create_artist_form():\n  form = ArtistForm()\n  return render_template('forms/new_artist.html', form=form,status=\"create\")\n\n\n@app.route('/artists/create', methods=['POST'])\ndef create_artist_submission():\n  # called upon submitting the new artist listing form\n  # TODO: insert form data as a new Venue record in the db, instead\n  # TODO: modify data to be the data object returned from db insertion\n\n\n  form = ArtistForm()\n  if (form.validate_on_submit()):\n    artist = Artist()\n    artist.name = form.name.data\n    artist.city = form.city.data\n    artist.state = form.state.data\n    artist.phone = form.phone.data\n    artist.image_link = form.image_link.data\n    artist.facebook_link = form.facebook_link.data\n    artist.genres = \",\".join(form.genres.data)\n    artist.seeking_venue = form.seeking_venue.data\n    artist.seeking_description = form.seeking_description.data\n\n    db.session.add(artist)\n    db.session.commit()\n\n    flash('Artist ' + form.name.data + ' was successfully listed!')\n    return redirect(url_for('show_artist', artist_id=artist.id))\n  else:\n    return render_template('forms/new_artist.html', form=form, status=\"create\")\n\n\n\n\n#  Update\n#  ----------------------------------------------------------------\n\n@app.route('/artists/<int:artist_id>/edit', methods=['GET'])\ndef edit_artist(artist_id):\n  # TODO: populate form with fields from artist with ID <artist_id>\n\n  artist = Artist.query.get_or_404(artist_id);\n  if(artist.genres):\n    artist.genres = artist.genres.split(\",\");\n  # passing the objecr value directly to the form and sending the form itself with the previous data\n  form = ArtistForm(obj = artist)\n\n  return render_template('forms/new_artist.html', form=form, status=\"update\")\n\n@app.route('/artists/<int:artist_id>/edit', methods=['POST'])\ndef edit_artist_submission(artist_id):\n  # TODO: take values from the form submitted, and update existing\n  # artist record with ID <artist_id> using the new attributes\n\n\n  form = ArtistForm()\n  artist = Artist.query.get_or_404(artist_id)\n  if (form.validate_on_submit()):\n    artist.name = form.name.data\n    artist.city = form.city.data\n    artist.state = form.state.data\n    artist.phone = form.phone.data\n    artist.image_link = form.image_link.data\n    artist.facebook_link = form.facebook_link.data\n    artist.genres = \",\".join(form.genres.data)\n    artist.seeking_venue = form.seeking_venue.data\n    artist.seeking_description = form.seeking_description.data\n   \n    db.session.add(artist)\n    db.session.commit()\n\n    flash(\"done\")\n    return redirect(url_for('show_artist', artist_id=artist_id))\n  else:\n    return render_template('forms/new_artist.html', form=form, status=\"update\")\n\n\n  \n\n\n\n# Delete artists\n#  ----------------------------------------------------------------\n\n@app.route('/artists/<artist_id>/delete', methods=['GET'])\ndef delete_artist(artist_id):\n  \n  #-------------\n  # Update (Review-1)\n  #--------------\n  # change delete function\n  #----------------\n\n  try:\n    artist = Artist.query.get_or_404(artist_id)\n    db.session.delete(artist)\n    db.session.commit()\n    flash(\"deleted successfully\");\n  except :\n    flash(\"Error happen\")\n\n  return redirect(url_for('index'))\n\n\n\n\n#  Shows\n#  ----------------------------------------------------------------\n\n@app.route('/shows')\ndef shows():\n  # displays list of shows at /shows\n  # TODO: replace with real venues data.\n  #       num_shows should be aggregated based on number of upcoming shows per venue.\n  \n\n  data = db.session.query(\n    label(\"venue_id\",Venue.id),\n    label(\"venue_name\",Venue.name),\n    label(\"artist_id\",Artist.id),\n    label(\"artist_name\",Artist.name),\n    label(\"artist_image_link\",Artist.image_link),\n    label(\"start_time\",Show.date)\n  ).select_from(Show).join(Artist).join(Venue).all()\n\n\n  return render_template('pages/shows.html', shows=data)\n\n@app.route('/shows/create',methods=['GET'])\ndef create_shows():\n  # renders form. do not touch.\n  form = ShowForm()\n  return render_template('forms/new_show.html', form=form)\n\n@app.route('/shows/create', methods=['POST'])\ndef create_show_submission():\n  # called to create new shows in the db, upon submitting new show listing form\n  # TODO: insert form data as a new Show record in the db, instead\n\n  form = ShowForm()\n  \n  if (form.validate_on_submit()):\n    try:\n        \n      show = Show()\n      show.venue_id = form.venue_id.data\n      show.artist_id = form.artist_id.data\n      show.date = form.start_time.data\n      \n      db.session.add(show)\n      db.session.commit()\n\n      flash(\"created successfully\")\n      return redirect(url_for('index'))\n    except :\n      flash(\"The id of artist or venue is wrong \");\n      return render_template('forms/new_show.html', form=form)\n\n  else:\n    return render_template('forms/new_show.html', form=form)\n\n\n  \n\n@app.errorhandler(404)\ndef not_found_error(error):\n    return render_template('errors/404.html'), 404\n\n@app.errorhandler(500)\ndef server_error(error):\n    return render_template('errors/500.html'), 500\n\n\nif not app.debug:\n    file_handler = FileHandler('error.log')\n    file_handler.setFormatter(\n        Formatter('%(asctime)s %(levelname)s: %(message)s [in %(pathname)s:%(lineno)d]')\n    )\n    app.logger.setLevel(logging.INFO)\n    file_handler.setLevel(logging.INFO)\n    app.logger.addHandler(file_handler)\n    app.logger.info('errors')\n\n#----------------------------------------------------------------------------#\n# Launch.\n#----------------------------------------------------------------------------#\n\n# Default port:\n\n\nif __name__ == '__main__':\n    app.run()\n\n\n# Or specify port manually:\n\n'''\nif __name__ == '__main__':\n    # port = int(os.environ.get('PORT', 5000))\n    app.run( port='5000')\n'''","repo_name":"mena18/01_fyyur","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":21653,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"1405295931","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Jun 11 09:51:33 2015\n\n@author: jzhu0922\n\"\"\"\n\nimport numpy \nimport matplotlib.pyplot as plt\nimport pandas as pd \nfrom pandas import DataFrame \nfrom pandas import Series\nfrom datetime import datetime\n\ndata = pd.read_table('BradfordVictoria_May.csv', sep=',')\ndata.columns = ['time', 'power']\n\ndata['time'] = data['time'].map(lambda x : pd.to_datetime(x,dayfirst=True))\n#data['date'] = data['time'].map(lambda x: x.date())\n\n#Bradford Victoria, Weekday 7:00-23:00, Sunday 10:00-16:00, hard code now\ndef isopenat(x):\n    if x.weekday() == 6:\n        return 10 <= x.hour & x.hour < 16\n    else:\n        return 7 <= x.hour & x.hour < 23\n\ndef ecm(x):\n    return (x.open==False) & (x.power > 50.0)\n\ndata['open'] = data['time'].map(isopenat)\ndata['ecm'] = data.apply(ecm,axis=1)\ndata = data.set_index('time')\n\necms = {\n    'start': [],\n    'end':[],\n    'saving':[]\n}\n# loop through \ni = 0\nwhile i < len(data):\n    if data.ecm[i]:\n        j = i\n        save = 0\n        while data.ecm[i]:\n            i = i+1\n            save = save + (data.power[i] - 25.0) * 5 / 60\n        ecms['start'].append(data.index[j])\n        ecms['end'].append(data.index[i])\n        ecms['saving'].append(save)\n        #print ('start: ' + str(data.time[j]) + '; end: ' + str(data.time[i]) + '; saving: ' + str(save))\n    else:\n        i= i + 1\n        \ndf_ecm = DataFrame(ecms, columns=['start','end','saving'])\n\n#data.ix['20150430 8:00':'20150430 10:00','power']\n#df_morning = data[data.index.hour == 8]\n\ndata.ix['20150503':'20150508','power'].plot()\nfor i in range(len(ecms['start'])):\n    plt.axvspan(ecms['start'][i], ecms['end'][i], facecolor='r', alpha=0.4)\n\n","repo_name":"xszneil/Spyder_WorkSpace","sub_path":"Elutions/lighting_override.py","file_name":"lighting_override.py","file_ext":"py","file_size_in_byte":1676,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"75036203465","text":"# Reto 5\n#\ndef leerArchivo():\n    with open('data.csv', 'r') as datafile:\n        linea= datafile.readline()\n        encabezado= linea.rstrip('\\n').split(',')\n        matriz= []\n        sucursales= [' ']*32\n        linea = datafile.readline()\n        while linea:\n            fila = linea.rstrip('\\n').split(',')\n            # Concatena city_name y department_name en indice id_branch-1\n            sucursales[int(fila[5])-1]= fila[3]+ \" \" + fila[4]\n            matriz.append(fila)\n            linea = datafile.readline()\n    return matriz, sucursales, encabezado\n#\ndef main():\n    pacientes, centrales, columnas= leerArchivo()\n\n    # Obtiene los indices de las columnas utilizadas en el proceso\n    i_gender= columnas.index('gender')\n    i_branch= columnas.index('id_branch')\n    i_ps= columnas.index('systolic_pressure')\n    i_pd= columnas.index('diastolic_pressure')\n    i_mq= columnas.index('medicine_quantity')\n    hombres= 0\n    mujeres= 0\n    len_pacientes= len(pacientes)\n    cant_med= 0\n    # Capturar Entrada\n    input_central= int(input())\n    for i in range(len_pacientes):\n        if int(pacientes[i][i_branch])== input_central:\n            entrega= False\n            sis, dia = int(pacientes[i][i_ps]), int(pacientes[i][i_pd])\n            if sis < 91 and dia < 63:\n                entrega= True\n            elif 162 <= sis < 188 and 105 <= dia < 119:\n                entrega= True\n            elif 188 <= sis < 201 and 119 <= dia < 126:\n                entrega= True\n            elif 201 <= sis < 214 and 126 <= dia < 146:\n                entrega= True\n            elif sis >= 214 and dia >= 146:\n                entrega= True\n            elif sis >= 152 and dia < 77:\n                entrega= True\n            else:\n                entrega= False\n\n            if entrega:\n                if pacientes[i][i_gender]=='m':\n                    hombres += 1\n                else :\n                    mujeres += 1\n                # Acumula Cantidad Total de Medicamentos\n                cant_med += int(pacientes[i][i_mq])\n\n    # Salidas esperadas\n    print(f\"{input_central} {centrales[input_central-1]}\")\n    print(\"scheduled patients\")\n    print(f'male {hombres}')\n    print(f'female {mujeres}')\n    print(f'total {hombres + mujeres}')\n    print('scheduled medicine quantity')\n    print('mean {:.2f}'.format(cant_med/(hombres + mujeres)))\n    print('total {:}'.format(cant_med))\n\nif __name__=='__main__':\n    main()","repo_name":"SebastianMorales1/mintic-2022-curso","sub_path":"Python ciclo 1/Retos python/reto 5/reto5.py","file_name":"reto5.py","file_ext":"py","file_size_in_byte":2428,"program_lang":"python","lang":"es","doc_type":"code","stars":1,"dataset":"github-code","pt":"81"}
+{"seq_id":"1318392695","text":"import warnings\nwarnings.filterwarnings('ignore')\nimport pandas as pd\nimport numpy as np\nfrom itertools import combinations\nfrom itertools import permutations\nfrom Tools import Tools\n\n\n\nclass Picking:\n    def __init__(self, pko, df):\n        self.df = df   # data of items to be picked as (pd.DataFrame)\n        self.df_cls = None\n\n        self.pko = pko # object created from (Class: PickingLogic), \n                       # which store and organize some useful infos\n\n        self.Routes = []\n        self.routes = None\n        self.travelDistance = 0\n\n    # main function\n    def picking(self):\n        df = self.df\n        pko = self.pko\n        for cls in range(int(max(pko.posCls)+1)):\n            # (1) Filter positions in the same cls(class or section)\n            self.df_cls = df[df['Class']==cls]\n\n            # (2) Take away positions that contains more than 5 layers\n            # e.g. position 'LAY414' has 8 layers to be picked, then this step will take away 5 layers from 'LAY414'\n            self.routes = self._pickLayersMoreThan5()\n\n            # (3) Delete positions that all have been picked\n            # and use a dictionary to store the leftover items\n            # {'Position': number of layers}\n            self.df_cls = self.df_cls[self.df_cls['layers']>0]\n            self.dict_cls = {}\n            for idx, row in self.df_cls.iterrows():\n                self.dict_cls[row['Location']] = row['layers']\n\n            # (4) Pair picking\n            # (4.1) Filter pairs(from pko.posPairs) that contains positions having layers to be picked\n            usefulPosPairs = []\n            for p in list(permutations(self.dict_cls.keys(), 2)):\n                for pair in self.pko.posPairs[cls]:\n                    if list(p) in pair:\n                        usefulPosPairs.append(pair)\n\n            # (4.2) Sort pairs by distance\n            if len(usefulPosPairs) > 0:\n                df_pairs = pd.DataFrame(np.array(usefulPosPairs), columns=['pairs', 'route type', 'distance'])\n                df_pairs.sort_values(by=['distance'], ascending=True, inplace=True)\n                df_pairs['l1'] = np.array(df_pairs['pairs'].to_list())[:,0]\n                df_pairs['l2'] = np.array(df_pairs['pairs'].to_list())[:,1]\n                self.df_pairs = df_pairs\n\n            # (4.3) Start pair picking\n            # until there is at most one position that has not been picked\n            while sum(np.array(list(self.dict_cls.values())) > 0) > 1:\n                # (4.3.1) Picing the first pair with shortest distance\n                totalLayers, lastRouteType = self._pickFirstPair()\n                if totalLayers == 0:\n                    break\n                # (4.3.2) If the total layers picked <= 5, continue picking\n                self._continuePicking(totalLayers, lastRouteType)\n\n            # (4.4) Finally, pick the left layers if exists\n            for key in self.dict_cls.keys():\n                if self.dict_cls[key] > 0:\n                    self.routes.append([[key]])\n                    self.dict_cls[key] = 0\n            self.Routes.append(self.routes)\n\n        # Finally, calculate the total travel distance\n        for i in sum(sum(sum(self.Routes, []), []), []):\n            try:\n                if i > 1: self.travelDistance += i\n            except:\n                pass\n        \n\n    def _pickLayersMoreThan5(self):\n        df_cls = self.df_cls\n\n        routes = []\n        for i in df_cls.index:\n            layers = df_cls.loc[i,'layers']\n            times = layers // 5\n            for t in range(times):\n                routes.append([[df_cls.loc[i,'Location']]])\n            self.df_cls.loc[i,'layers'] = layers % 5\n        return routes\n\n    def _pickFirstPair(self):\n        df_pairs = self.df_pairs\n        dict_cls = self.dict_cls\n\n        ifPick = False\n        idx = 0\n        totalLayers = 0\n        lastRouteType = -1\n        for idx in range(len(df_pairs)):\n            loc1, loc2 = df_pairs.loc[df_pairs.index[idx], 'pairs']\n            lastRouteType = df_pairs.loc[df_pairs.index[idx], 'route type']\n            distance = df_pairs.loc[df_pairs.index[idx], 'distance']\n            lyr1 = dict_cls[loc1]\n            lyr2 = dict_cls[loc2]\n            totalLayers = lyr1 + lyr2\n            if (lyr1>0) and (lyr2>0) and (totalLayers<=5):\n                self.dict_cls[loc1] = 0\n                self.dict_cls[loc2] = 0\n                self.locs = [loc1, loc2]\n                self.route = [[loc1,loc2,lastRouteType,distance]]\n                return totalLayers, lastRouteType    \n        return 0,0\n\n    def _continuePicking(self, totalLayers, lastRouteType):\n        df_pairs = self.df_pairs\n        dict_cls = self.dict_cls\n        locs     = self.locs\n        route    = self.route\n\n        iters = 0\n        while totalLayers < 5 and iters < len(df_pairs):\n            iters += 1\n            for idx in df_pairs.index:\n                # Try a pair of positions: (loc3, loc4)\n                loc3, loc4 = df_pairs.loc[idx, 'pairs']\n                distance = df_pairs.loc[idx, 'distance']\n                # (1) First exclude pairs that don't need any pick\n                if dict_cls[loc3] == 0 and dict_cls[loc4] == 0: \n                    continue\n\n                # (2) Then test that list(locs) could only contain exactly one of loc3 and loc4\n                # which could make sure to build a 'route'\n                if3In = loc3 in locs\n                if4In = loc4 in locs\n                if not Tools.xor(if3In, if4In):\n                    continue\n                loc = loc4 if if3In else loc3\n\n                # (3) Finally exclude pairs that no on the same side(if applicable)\n                count = 0\n                for r in route:\n                    if loc in r:\n                        count += 1\n                if count >= 2:\n                    continue\n                \n                for r in route:\n                    if loc in r:\n                        lastRouteType = r[-1]\n                routeType = df_pairs.loc[idx, 'route type']\n                if lastRouteType == 0 and routeType == 1: continue\n                if lastRouteType == 1 and routeType == 0: continue\n                \n                if totalLayers + dict_cls[loc] <= 5:\n                    totalLayers += dict_cls[loc]\n                    dict_cls[loc] = 0\n                    locs.append(loc)\n                    lastRouteType = routeType\n                    route.append([loc3, loc4, lastRouteType, distance])\n                    break\n        self.routes.append(route)","repo_name":"ZhekaiLi/ISYE6335-Proj-Layer-Picking-Optimization","sub_path":"Picking.py","file_name":"Picking.py","file_ext":"py","file_size_in_byte":6525,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"72155689546","text":"# -*- coding: utf-8 -*-\n\"\"\"\n크롬 드라이버 설치 이후 driver에 설치 경로 입력\n꼭!!☆현재 크롬 버전☆에 맞게 설치해주셔야 합니다!\n크롬 버전이 91.0.4472.19인 경우(크롬 최신 업데이트(21/6/14 기준))\nhttps://chromedriver.storage.googleapis.com/index.html?path=90.0.4430.24/\n\n크롬 버전이 91.0.4472.19가 아닌 경우,\nhttps://sites.google.com/a/chromium.org/chromedriver/downloads\n에서 맞는 버전을 찾아 chromedriver다운로드\n* 이 프로젝트 버전 : 91.0.4472.19\n--------------------------------------------------\n\n★ pip install gensim==3.8.1\n- Gensim package version을 3.8.1로 해주어야 에러가 발생하지 않는다.\n참고 사이트 : https://stackoverflow.com/questions/66952438/attributeerror-cant-get-attribute-vocab-on-module-gensim-models-word2vec\n\n---------------------------------------------------\n\npre-trained word vector 모델 ko.bin 다운(https://github.com/Kyubyong/wordvectors)\nhttps://drive.google.com/file/d/0B0ZXk88koS2KbDhXdWg1Q2RydlU/view\n\n---------------------------------------------------\n라이브러리 설치 \n> pip install selenium\n> pip install bs4\n> pip install openpyxl\n> pip install lxml\n> pip install gensim==3.8.1\n\n\"\"\"\n#경로 입력해주세요.\nchrome_driver_PATH = 'chromedriver91.exe'\nstopwords_file_PATH = 'stopwords.txt' \nmodel_PATH = './ko.bin'\n\nimport konlpy, time\nimport re\nimport pandas as pd\nfrom bs4 import BeautifulSoup\nfrom selenium import webdriver as wd\nfrom gensim.models import Word2Vec\nfrom collections import Counter\n\ndef youtube_loading(chrome_driver_PATH, url):\n    #크롬 창만 안띄우기\n    chrome_options = wd.ChromeOptions()\n    chrome_options.add_argument('headless')\n    chrome_options.add_argument('--disable-gpu')\n    chrome_options.add_argument('lang=ko_KR')\n    \n    print(\"유튜브 링크에 접속 중 입니다. 잠시만 기다려 주세요...\")\n    driver = wd.Chrome(executable_path = chrome_driver_PATH, options= chrome_options)\n\n    print(\"댓글을 수집 중 입니다. 잠시만 기다려 주세요...\")\n    \n    driver.get(url)\n    \n    last_page_height = driver.execute_script(\"return document.documentElement.scrollHeight\")\n    n = 0\n    while True:\n        n+=1\n        if n%5==0:\n            print(\"댓글을 수집 중 입니다. 잠시만 기다려 주세요...\")\n        driver.execute_script(\"window.scrollTo(0, document.documentElement.scrollHeight);\")\n        time.sleep(1.0)       # 인터발 1이상으로 줘야 데이터 취득가능(롤링시 데이터 로딩 시간 때문)\n        new_page_height = driver.execute_script(\"return document.documentElement.scrollHeight\")\n    \n        if new_page_height == last_page_height:\n            break\n        last_page_height = new_page_height\n    \n    html_source = driver.page_source\n    driver.close()\n    return html_source\n\ndef tag_crawling(html_source):\n    # HTML 태크 크롤링 작업\n    soup = BeautifulSoup(html_source, \"lxml\")\n    \n    youtube_comments = soup.select(\"div#content > yt-formatted-string#content-text\") # A#B(A는 태그 값, B는 id 값)\n    youtube_comments_counts = soup.select(\"h2#count > yt-formatted-string > span\" )\n    youtube_title = soup.select(\"div#container > h1 > yt-formatted-string\") # title style-scope ytd-video-primary-info-renderer\n    youtube_title = youtube_title[0].text\n    return youtube_comments, youtube_comments_counts, youtube_title\n  \ndef processing_comments():\n    str_youtube_comments_all = []  # 전체 댓글 내용 배열\n    for i in range(len(youtube_comments)):\n        str_tmp = str(youtube_comments[i].text)    \n        str_tmp = re.sub('\\n', '', str_tmp)\n        str_tmp = re.sub('\\t', '', str_tmp)\n        str_tmp = re.sub('   ', '', str_tmp)\n        str_youtube_comments_all.append(str_tmp)\n    return str_youtube_comments_all\n    \ndef korean_comment_extract():\n    str_youtube_comments_ko = [] # 한글 댓글 내용 배열\n    for i in range(len(str_youtube_comments_all)):\n        if(re.search('[가-힣ㄱ-ㅎㅏ-ㅣ]+',  str_youtube_comments_all[i])):\n            str_youtube_comments_ko.append( str_youtube_comments_all[i])\n    return str_youtube_comments_ko\n\ndef sub_other_language():\n    #한글만 골라내기\n    comments_word = []\n    for i in range(len(str_youtube_comments_ko)): \n            #ㄱ-ㅎ이나 ㅏ-ㅣ같은 모음이나 자음으로만 이루어 진 것도 제\n            comments_word.append(re.sub('[^가-힣 ]', '', str_youtube_comments_ko[i]))\n    return comments_word\n\ndef POS_tagging():           \n    # POS tagging\n    pos_tagging = []\n    for i in range(len(comments_word)):\n        pos_tagging.append(Okt.pos(comments_word[i]))   \n    return pos_tagging  \n\n#명사만 추출(불용어 제거)\ndef noun_extract():  \n    noun_list = []\n    # list에 명사인건 추가, 불용어는 제거\n    for i in range(len(pos_tagging)):\n        for word,tag in pos_tagging[i]:\n            if tag in ['Noun'] and word not in stopwords:\n                noun_list.append(word)\n    return noun_list\n\ndef get_similarity():\n    similarity = []\n    for i in range(len(Common_words)):\n        for j in range(len(vocabs)):\n            try:\n                similarity.append((Common_words[i][0], vocabs[j], word_vectors.similarity(Common_words[i][0], vocabs[j])))\n            except: #예외 처리\n                continue \n    #유사도 순으로 정렬\n    similarity.sort(key = lambda x:-x[2])\n    return similarity\n\ndef delete_high_similarity(similarity):\n    similarity_high = [] #유사도 0.9 이상인 단어 모을 리스트 \n    for i in similarity: \n            if i[2] > 0.9: #유사도가 0.9이상인 단어 추가\n                similarity_high.append(i)            \n    #유사도 0.9 이상인 단어는 지운다.(거의 같은 단어에 가깝기때문)\n    for i in similarity_high:\n        similarity.remove(i)\n    return similarity\n\n \n# INPUT YOUTUBE URL\nurl = input(\"유튜브 링크를 입력해주세요 : \") # 댓글 수집할 Youtube URL입력\n\n#유튜브 접속\nhtml_source = youtube_loading(chrome_driver_PATH, url)\n#댓글 수집\nyoutube_comments, youtube_comments_counts, youtube_title = tag_crawling(html_source)\n\n#총 댓글 개수 출력\nprint(\"총 \", end = \"\")\nfor i in range(len(youtube_comments_counts)):\n    print(youtube_comments_counts[i].text, end = \" \")   \nprint()\n\n#불필요한 텍스트 제거(\\n,\\t 등)\nstr_youtube_comments_all = processing_comments()\n\n#한글 댓글만 추출\nstr_youtube_comments_ko = korean_comment_extract() \n\nprint(\"수집된 총 한글 댓글 개수 :\" , len(str_youtube_comments_ko), \"개\")\n\n'''\n#한글댓글 출력\nfor i in range(len(str_youtube_comments_ko)): \n    print(str_youtube_comments_ko[i])\n'''\n#한글 댓글에 섞여있는 다른언어 제거\ncomments_word = sub_other_language()\n\n#토큰화\nOkt = konlpy.tag.Okt()\nstopwords = open(stopwords_file_PATH, 'rt', encoding='UTF8').read() # 불용어 모음 파일 불러오기\nstopwords = stopwords.split(' ')\n\npos_tagging = POS_tagging()\nnoun_list = noun_extract()\n            \n#카운트 순 정렬, 상위 20개 정도만 뽑음\n#상위 20개 단어와 높은 유사도를 가진 단어 50개를 추출한다.\ncounts = Counter(noun_list)\nCommon_words = counts.most_common(30)\n\nmodel = Word2Vec.load(model_PATH)\nword_vectors = model.wv #단어 벡터\nvocabs = list(word_vectors.vocab.keys()) #로드한 모델에서 단어만 뽑아서 리스트로 만든다.\n\nsimilarity = delete_high_similarity(get_similarity())\n\nword1_to_excel = [] #엑셀 파일에 넣을 리스트, 빈도수 TOP30\nword2_to_excel = [] #엑셀 파일에 넣을 리스트, 유사도 TOP100 \nprint('\\n----------------------------------------------------------\\n')\nprint(\"빈도수 순 상위 30개 단어 출력 : \")\nfor i in Common_words:\n    print(i[0] , end = ' ')\n    word1_to_excel.append(i[0])\n    \nprint(\"\\n\\n위 30개 단어와 유사한 단어 100개 :\")\nfor i in similarity[:100]:\n    print(i[1], end = ' ') \n    word2_to_excel.append(i[1])     \n    \n#엑셀 파일 만들기\nkorean_comment =  pd.DataFrame({'한글 댓글' : str_youtube_comments_ko}) #리스트 자료형으로 생성\ncountTOP30 = pd.DataFrame({'단어 빈도순': word1_to_excel})\nsimTOP100 = pd.DataFrame({'유사도 높은 단어' : word2_to_excel})\n#korean_comment = pd.DataFrame(korean_comment) #데이터 프레임으로 전환\nyoutube_title = re.sub(\"[\\/:*?\\\"<>|]\", \" \", youtube_title)\nexcel_name = './output/' + str(youtube_title)+' 한글댓글 모음.xlsx'\n\nwith pd.ExcelWriter(excel_name) as writer:\n    korean_comment.to_excel(writer, sheet_name = '한글 댓글') #엑셀로 저장\n    countTOP30.to_excel(writer, sheet_name = '빈도 TOP30 단어')\n    simTOP100.to_excel(writer, sheet_name = '유사도 TOP100')","repo_name":"Eunji-new/YouTube_Korean_comments_count-similarity","sub_path":"youtube_comments_SourceCode.py","file_name":"youtube_comments_SourceCode.py","file_ext":"py","file_size_in_byte":8718,"program_lang":"python","lang":"ko","doc_type":"code","stars":1,"dataset":"github-code","pt":"81"}
+{"seq_id":"16965439438","text":"import sys\r\nimport socket\r\n\r\n# Check if the user entered the correct number of arguments\r\nif len(sys.argv) != 3:\r\n    sys.exit(\"syntax for command line: python client.py <IP> <Port>\")\r\n    \r\n# Save the IP address and port number of the server\r\nserver_ip_address = sys.argv[1]\r\nserver_port_no = int(sys.argv[2])\r\n# Create Socket\r\nclient_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\r\n# Set socket timeout value to 10 seconds\r\nclient_socket.settimeout(5)\r\n# Connect to Server\r\ntry:\r\n    client_socket.connect((server_ip_address, server_port_no))\r\n    data = client_socket.recv(1024)\r\n    print(\"{}\".format(data.decode()))\r\nexcept Exception as e:\r\n    sys.exit(\"unable to connect! try again. \\nError: {}\".format(e))\r\n    \r\n# Send Data\r\ntry:\r\n    while True:\r\n        input_message = input(\"Enter Message : \")\r\n        \r\n        # Send Request\r\n        client_socket.send(input_message.encode())\r\n        # receive the encoded data from the socket\r\n        encoded_data = client_socket.recv(1024)\r\n        print(\"Received: {}\".format(encoded_data.decode()))\r\n# handling the Ctrl C to exit the program\r\nexcept KeyboardInterrupt:\r\n    print(\"\\n Bye; nice talking to the server\")\r\n\r\n# for closing the connection of client\r\nclient_socket.close()\r\n","repo_name":"Tarun2021/2001CS76_CS359","sub_path":"tut03/client.py","file_name":"client.py","file_ext":"py","file_size_in_byte":1252,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"11179324846","text":"import dataclasses\nfrom typing import List, Mapping, Optional\nimport torch\n\nimport core.utils.occupancy_flow_data as occupancy_flow_data\n\nfrom waymo_open_dataset.protos import scenario_pb2\n\n_ObjectType = scenario_pb2.Track.ObjectType\n\n\n\n# Holds num_waypoints occupancy and flow tensors for one agent class.\n@dataclasses.dataclass\nclass _WaypointGridsOneType:\n  \"\"\"Sequence of num_waypoints occupancy and flow tensors for one agent type.\"\"\"\n  # num_waypoints tensors shaped [batch_size, height, width, 1]\n  observed_occupancy: List[torch.Tensor] = dataclasses.field(default_factory=list)\n  # num_waypoints tensors shaped [batch_size, height, width, 1]\n  occluded_occupancy: List[torch.Tensor] = dataclasses.field(default_factory=list)\n  # num_waypoints tensors shaped [batch_size, height, width, 2]\n  flow: List[torch.Tensor] = dataclasses.field(default_factory=list)\n  # The origin occupancy for each flow waypoint.  Notice that a flow field\n  # transforms some origin occupancy into some destination occupancy.\n  # Flow-origin occupancies are the base occupancies for each flow field.\n  # num_waypoints tensors shaped [batch_size, height, width, 1]\n  flow_origin_occupancy: List[torch.Tensor] = dataclasses.field(\n      default_factory=list)\n\n\n# Holds num_waypoints occupancy and flow tensors for all agent clases.  This is\n# used to store both ground-truth and predicted topdowns.\n@dataclasses.dataclass\nclass WaypointGrids:\n  \"\"\"Occupancy and flow sequences for vehicles, pedestrians, cyclists.\"\"\"\n  vehicles: _WaypointGridsOneType = dataclasses.field(\n      default_factory=_WaypointGridsOneType)\n  pedestrians: _WaypointGridsOneType = dataclasses.field(\n      default_factory=_WaypointGridsOneType)\n  cyclists: _WaypointGridsOneType = dataclasses.field(\n      default_factory=_WaypointGridsOneType)\n\n  def view(self, agent_type: str) -> _WaypointGridsOneType:\n    \"\"\"Retrieve occupancy and flow sequences for given agent type.\"\"\"\n    if agent_type == _ObjectType.TYPE_VEHICLE:\n      return self.vehicles\n    elif agent_type == _ObjectType.TYPE_PEDESTRIAN:\n      return self.pedestrians\n    elif agent_type == _ObjectType.TYPE_CYCLIST:\n      return self.cyclists\n    else:\n      raise ValueError(f'Unknown agent type:{agent_type}.')\n\n  def get_observed_occupancy_at_waypoint(\n      self, k: int) -> occupancy_flow_data.AgentGrids:\n    \"\"\"Returns occupancies of currently-observed agents at waypoint k.\"\"\"\n    agent_grids = occupancy_flow_data.AgentGrids()\n    if self.vehicles.observed_occupancy:\n      agent_grids.vehicles = self.vehicles.observed_occupancy[k]\n    if self.pedestrians.observed_occupancy:\n      agent_grids.pedestrians = self.pedestrians.observed_occupancy[k]\n    if self.cyclists.observed_occupancy:\n      agent_grids.cyclists = self.cyclists.observed_occupancy[k]\n    return agent_grids\n\n  def get_occluded_occupancy_at_waypoint(\n      self, k: int) -> occupancy_flow_data.AgentGrids:\n    \"\"\"Returns occupancies of currently-occluded agents at waypoint k.\"\"\"\n    agent_grids = occupancy_flow_data.AgentGrids()\n    if self.vehicles.occluded_occupancy:\n      agent_grids.vehicles = self.vehicles.occluded_occupancy[k]\n    if self.pedestrians.occluded_occupancy:\n      agent_grids.pedestrians = self.pedestrians.occluded_occupancy[k]\n    if self.cyclists.occluded_occupancy:\n      agent_grids.cyclists = self.cyclists.occluded_occupancy[k]\n    return agent_grids\n\n  def get_flow_at_waypoint(self, k: int) -> occupancy_flow_data.AgentGrids:\n    \"\"\"Returns flow fields of all agents at waypoint k.\"\"\"\n    agent_grids = occupancy_flow_data.AgentGrids()\n    if self.vehicles.flow:\n      agent_grids.vehicles = self.vehicles.flow[k]\n    if self.pedestrians.flow:\n      agent_grids.pedestrians = self.pedestrians.flow[k]\n    if self.cyclists.flow:\n      agent_grids.cyclists = self.cyclists.flow[k]\n    return agent_grids","repo_name":"YoushaaMurhij/OFMPNet","sub_path":"core/utils/occupancy_flow_grids.py","file_name":"occupancy_flow_grids.py","file_ext":"py","file_size_in_byte":3835,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"81"}
+{"seq_id":"28562640158","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Sep 24 15:30:14 2019\n\n@author: kjh1\n\"\"\"\n\nn,k = map(int,input().split())\n\nmat = []\n\nfor i in range(n):\n    mat.append(int(input()))\n    \nmat.sort()\n\n","repo_name":"kjh000/Algorithm","sub_path":"algo/백준2294__.py","file_name":"백준2294__.py","file_ext":"py","file_size_in_byte":191,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"9045726960","text":"import numpy as np\nimport torch\n\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\nfrom deconv.gmm.plotting import plot_covariance\nfrom deconv.gmm.sgd_deconv_gmm import SGDDeconvGMM\nfrom deconv.gmm.data import DeconvDataset\n\nfrom data import generate_data\n\ndef check_sgd_deconv_gmm(D, K, N, plot=False, verbose=False, device=None):\n\n    if not device:\n        device = torch.device('cpu')\n\n    data, params = generate_data(D, K, N)\n    X_train, nc_train, X_test, nc_test = data\n    means, covars = params\n\n    train_data = DeconvDataset(\n        torch.Tensor(X_train.reshape(-1, D).astype(np.float32)),\n        torch.Tensor(\n            nc_train.reshape(-1, D, D).astype(np.float32)\n        )\n    )\n\n    test_data = DeconvDataset(\n        torch.Tensor(X_test.reshape(-1, D).astype(np.float32)),\n        torch.Tensor(\n            nc_test.reshape(-1, D, D).astype(np.float32)\n        )\n    )\n\n    gmm = SGDDeconvGMM(\n        K,\n        D,\n        device=device,\n        batch_size=250,\n        epochs=200,\n        lr=1e-1\n    )\n    gmm.fit(train_data, val_data=test_data, verbose=verbose)\n    train_score = gmm.score_batch(train_data)\n    test_score = gmm.score_batch(test_data)\n\n    print('Training score: {}'.format(train_score))\n    print('Test score: {}'.format(test_score))\n\n    if plot:\n        fig, ax = plt.subplots()\n\n        ax.plot(gmm.train_loss_curve, label='Training Loss')\n        ax.plot(gmm.val_loss_curve, label='Validation Loss')\n\n        fig, ax = plt.subplots()\n\n        for i in range(K):\n            sc = ax.scatter(\n                X_train[:, i, 0],\n                X_train[:, i, 1],\n                alpha=0.2,\n                marker='x',\n                label='Cluster {}'.format(i)\n            )\n            plot_covariance(\n                means[i, :],\n                covars[i, :, :],\n                ax,\n                color=sc.get_facecolor()[0]\n            )\n\n        sc = ax.scatter(\n            gmm.means[:, 0],\n            gmm.means[:, 1],\n            marker='+',\n            label='Fitted Gaussians'\n        )\n\n        for i in range(K):\n            plot_covariance(\n                gmm.means[i, :],\n                gmm.covars[i, :, :],\n                ax,\n                color=sc.get_facecolor()[0]\n            )\n\n        ax.legend()\n        plt.show()\n\n\nif __name__ == '__main__':\n    sns.set()\n    D = 2\n    K = 3\n    N = 500\n    check_sgd_deconv_gmm(D, K, N, verbose=True, plot=True)\n","repo_name":"bayesiains/density-deconvolution","sub_path":"experiments/gmm/checks/check_sgd_deconv_gmm.py","file_name":"check_sgd_deconv_gmm.py","file_ext":"py","file_size_in_byte":2428,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"81"}
+{"seq_id":"25219031936","text":"from django.shortcuts import render\nfrom django.contrib.sites.shortcuts import get_current_site\nfrom django.shortcuts import render,redirect,reverse\nfrom django.http import HttpResponse, JsonResponse, HttpResponseBadRequest,HttpResponseRedirect\nimport Photo_Booth.tesseract\nfrom django.core.files.storage import FileSystemStorage\nfrom .forms import ImageUploadForm\nfrom fpdf import FPDF\nfrom django.views.decorators.csrf import csrf_exempt\nfrom Photo_Booth import settings\nimport mimetypes\n# Create your views here.\ndef HomeView(request):\n    return render(request,'home.html')\ndef Image_to_Text_View(request):\n    if request.method == 'POST':\n        print(request.FILES)\n        form = ImageUploadForm(request.POST,request.FILES)\n        if form.is_valid():\n            if form.validate_size():\n                if form.validate_file_type():\n                    files = form.cleaned_data['files']\n                    fs = FileSystemStorage()\n                    fs.save(files.name,files)\n                    try:\n                        text = Photo_Booth.tesseract.transcript(files)\n                    except Exception:\n                        response = JsonResponse({'message':\"File could not be read.\",\"success\":False})\n                        response.status_code = 200\n                        return response\n                    #print(text)\n                    pdf = FPDF()\n                    pdf.add_page()\n                    pdf.set_font('Helvetica',style='')\n                    filename = files.name.split('.')[0]\n                    path = \"Photo_Booth/static/results/{}.pdf\".format(filename)\n                    print(\"path = \",path)\n                    text.encode('utf-8').strip()\n                    try:\n                        pdf.multi_cell(0,5,text)\n                        pdf.output(path,'F').encode(\"utf-8\")\n                        print(\"file outputed\")\n                    except UnicodeEncodeError:\n                        response = JsonResponse({'message':\"Text could not be converted to pdf\",'success':True,'text':text})\n                        response.status_code = 200\n                        return response          \n                    response = JsonResponse({'message':\"File converted successfully\",'success':True,'text':text,'path':path})\n                else:\n                    print('1')\n                    response = JsonResponse({'message':\"Invalid file type.\",'success':False})\n            else:\n                print('2')\n                response = JsonResponse({'message':\"File size limit exceeds.\",'success':False})\n        else:\n            response = JsonResponse({'message':\"facing validation errors\",'success':False})\n            print('3')\n        response.status_code = 200\n        return response \n    return render(request,'image_to_text.html')\n@csrf_exempt\ndef downloadFile(request,filename):\n    path = 'Photo_Booth/static/results/' + filename\n    with open(path,'rb') as f:\n        response = HttpResponse(f)\n        response['Content-Type'] = 'application/pdf'\n        response['Content-disposition'] = 'attachment ; filename = {}'.format(filename)\n        return response\n\n@csrf_exempt\ndef clearFilesView(request):\n    pass\ndef Text_to_speech_View(request):\n    pass","repo_name":"invincible-coder/Photo_Booth","sub_path":"Image_to_Speech/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":3232,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"37027887145","text":"# ##### BEGIN GPL LICENSE BLOCK #####\n#\n#  This program is free software; you can redistribute it and/or\n#  modify it under the terms of the GNU General Public License\n#  as published by the Free Software Foundation; either version 2\n#  of the License, or (at your option) any later version.\n#\n#  This program is distributed in the hope that it will be useful,\n#  but WITHOUT ANY WARRANTY; without even the implied warranty of\n#  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\n#  GNU 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 Foundation,\n#  Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.\n#\n# ##### END GPL LICENSE BLOCK #####\n\n# <pep8 compliant>\n\n\"\"\"\nOpenGL Drawing code for viewing the orbit and other stuff in the 3DView.\ndraw_3d_callback is the function used as SpaceView3D draw handler.\n\"\"\"\n\nfrom math import degrees, cos\n\nimport blf\nimport gpu\nfrom gpu_extras.batch import batch_for_shader\nfrom mathutils import Vector, Euler\nfrom bpy_extras.view3d_utils import location_3d_to_region_2d\n\nfrom .calculation import eval_planet_orbit, orbit_orientation\n\nshader = None\nscn = None  # scene\nvl = None  # view layer\n\n# number of points used to draw the orbit and an arc\nNUM_ORBIT = 128\nNUM_ARC = 32\n\n# elements that should be drawn on the 2D screen (not the 3D view) go here\nDRAW_2D = []\n\n# Only one function (to_screen_coord) need these variables, I don't wanted\n# to use them as parameter for a lot of these functions, so I made them global\n# they are set when we call draw_3d_callback\nregion = None\nrv3d = None\n\n\ndef draw_3d_callback(self, context):\n    \"\"\"Draw inside the 3D Viewport\"\"\"\n    # set global variables\n    global scn, region, rv3d, vl, shader\n    scn = context.scene\n    vl = context.view_layer\n    region = context.region\n    rv3d = context.space_data.region_3d\n\n    # drawing shader\n    shader = gpu.shader.from_builtin('3D_UNIFORM_COLOR')\n    shader.bind()\n    gpu.state.blend_set(\"ALPHA\")\n\n    # default settings\n    default_point_size = 1  # there is not gpu.state.point_size_get()\n    default_line_width = gpu.state.line_width_get()\n\n    # draw info for every sssim object\n    for obj in vl.objects:\n        if obj.sssim_obj.use_sssim and obj.visible_get():\n            if obj.sssim_obj.object_type == 'PLANET':\n                show_orbit(obj)\n            if obj.sssim_obj.object_type == 'CENTER':\n                show_center_info(obj)\n\n    # restore opengl defaults\n    gpu.state.point_size_set(default_point_size)\n    gpu.state.line_width_set(default_line_width)\n    gpu.state.blend_set(\"NONE\")\n\n\ndef draw_2d_callback(self, context):\n    \"\"\"Draw on the 2D screen of the 3D Viewport\"\"\"\n    while DRAW_2D:\n        fct, args = DRAW_2D.pop()\n        fct(*args)\n\n\n# =============================================================================\n# These functions say what should be drawn\n# =============================================================================\n\ndef show_orbit(obj):\n    # BLENDER PYTHON AND THE HOLY OPENGL [PART I]\n    #\n    # GALAHAD: There it is!\n    # ARTHUR: The show_orbit function!\n    # ROBIN: Oh, great.\n\n    simorbit = obj.sssim_orbit\n    center = simorbit.center_object\n    center_loc = real_location(center)\n\n    # we want to draw more for the selected active object\n    is_active = obj.select_get() and vl.objects.active == obj\n\n    # mark the current position with a point and the object's name\n    pos = real_location(obj)\n    draw_point(pos)\n    draw_text(obj.name, pos)\n\n    # draw the orbit for every planet\n    points = []\n    period = simorbit.orbital_period\n    for i in range(NUM_ORBIT):\n        # get position relative to center\n        pos = orbit_position(obj, time=period * i / NUM_ORBIT)\n        points.append(center_loc + pos)\n\n    # color of the orbit, active orbit is draw orange\n    if is_active:\n        # that is the color active objects are usually shown in\n        col = (1.0, 0.66, 0.25, 1.0)\n    else:\n        col = (0.5, 0.5, 0.7, 1.0)\n\n    # [PART II]\n    # ARTHUR: Look! There's the old draw_line function from scene 24!\n    # BEDEVERE: What is he doing here?\n    # ARTHUR: He is the keeper of the Loop Drawing.\n    #         He asks each traveller five parameters--\n    # GALAHAD: Three parameters.\n    # ARTHUR: Three parameters. He who answers the five parameters--\n    # GALAHAD: Three parameters.\n    # ARTHUR: Three parameters may draw in safety.\n    # ROBIN: What if you get a parameter wrong?\n    # ARTHUR: Then you are cast into the Gorge of Eternal Artifacts.\n    # ROBIN: Oh, I won't go.\n    # GALAHAD: Who's going to answer the parameters?\n    draw_line(points, width=2, color=col, closed=True)\n\n    if is_active:\n        show_orbit_extra_info(obj)\n        show_rotation_angles(obj)\n\n\ndef show_orbit_extra_info(obj):\n    simorbit = obj.sssim_orbit\n    center = simorbit.center_object\n    center_loc = real_location(center)\n    period = simorbit.orbital_period\n\n    # compensate for the time offset\n    periapsis_time = -simorbit.time_offset * period\n\n    if period != 0:\n        show_true_anomaly(obj, center, periapsis_time, period)\n\n    if simorbit.eccentricity > 0:\n        # show periapsis with distance\n        peri_pos = center_loc + orbit_position(obj, periapsis_time)\n        draw_point(peri_pos, size=10)\n        draw_line((center_loc, peri_pos), color=(1.0, 1.0, 0.5, 1.0))\n        text = \"Periapsis: {:,} km\".format(round(simorbit.periapsis))\n        draw_text(text, peri_pos)\n\n        # show apoapsis with distance\n        time = periapsis_time + period / 2\n        apo_pos = center_loc + orbit_position(obj, time)\n        draw_point(apo_pos, size=10)\n        draw_line((center_loc, apo_pos), color=(1.0, 0.8, 0.5, 1.0))\n        text = \"Apoapsis: {:,} km\".format(round(simorbit.apoapsis))\n        draw_text(text, apo_pos)\n    else:\n        # draw radius text between center and starting position\n        pos = orbit_position(obj, time=0)\n        draw_line((center_loc, center_loc + pos))\n        text = \"Radius: {:,} km\".format(round(simorbit.semi_major_axis))\n        draw_text(text, center_loc + 0.8 * pos)\n\n    axis_bu = simorbit.semi_major_axis / scn.sssim_scn.length_mult\n    angle_radius = axis_bu / 3\n    show_orbit_angles(simorbit, angle_radius, center_loc)\n\n\ndef show_true_anomaly(obj, center, periapsis_time, period):\n    \"\"\"Draw the true anomaly = the angle from the periapsis\n\n    Note: This is not perfect, we get some overlap when time_offset > 0\n    \"\"\"\n    simorbit = obj.sssim_orbit\n    center_loc = real_location(center)\n\n    points = []\n    # time of this period = current time - time for full periods\n    time_since_periapsis = (scn.sssim_scn.time - periapsis_time) % period\n    for i in range(NUM_ORBIT + 1):\n        time = periapsis_time + time_since_periapsis * i / NUM_ORBIT\n        position = orbit_position(obj, time)\n        points.append(center_loc + position)\n\n    draw_sector(center_loc, points, color=(0.0, 0.3, 1.0, 0.2))\n\n    if simorbit.true_anomaly != 0:\n        # draw text in the circular sector\n        angle = degrees(simorbit.true_anomaly)\n        text = \"True anomaly: {}°\".format(round(angle, 2))\n        # the text position should be inside the arc\n        # between the center and the middle point of the arc\n        midarc = orbit_position(obj, periapsis_time + time_since_periapsis / 2)\n        text_pos = center_loc + midarc / 2\n\n        draw_text(text, text_pos, align=\"CENTER\")\n\n\ndef show_orbit_angles(simorbit, radius, center_loc):\n    \"\"\"Draw the Inclination, Ascending Node, Argument of Periapsis\"\"\"\n    inc = simorbit.inclination\n    asc_node = simorbit.asc_node\n    arg_peri = simorbit.arg_periapsis\n\n    if inc != 0:\n        col = (1.0, 1.0, 0.0, 0.5)\n        text = \"Inclination: {}°\"\n\n        def inc_rotate(angle):\n            vec = Vector((0, 1, 0))\n            vec.rotate(Euler((angle, 0, 0)))\n            vec.rotate(Euler((0, 0, asc_node)))\n            return vec\n\n        show_angle(inc, radius, inc_rotate, center_loc, col, text)\n\n    if asc_node != 0:\n        col = (0.0, 1.0, 0.5, 0.5)\n        text = \"Ascending Node: {}°\"\n\n        def asc_rotate(angle):\n            vec = Vector((1, 0, 0))\n            vec.rotate(Euler((0, 0, angle)))\n            return vec\n\n        show_angle(asc_node, radius, asc_rotate, center_loc, col, text)\n\n    if arg_peri != 0:\n        col = (0.0, 0.7, 1.0, 0.5)\n        text = \"Argument of Periapsis: {}°\"\n\n        def peri_rotate(angle):\n            vec = Vector((1, 0, 0))\n            vec.rotate(Euler((0, 0, angle)))\n            vec.rotate(Euler((inc, 0, 0)))\n            vec.rotate(Euler((0, 0, asc_node)))\n            return vec\n\n        show_angle(arg_peri, radius, peri_rotate, center_loc, col, text)\n\n\ndef show_rotation_angles(obj):\n    simorbit = obj.sssim_orbit\n    simrot = obj.sssim_rotation\n\n    if obj.sssim_obj.object_type == 'PLANET':\n        # when using planets, better scale with distance to center\n        axis_bu = simorbit.semi_major_axis / scn.sssim_scn.length_mult\n        radius = axis_bu / 5\n    else:\n        radius = 1\n\n    # the center of the angle is the object location\n    obj_loc = real_location(obj)\n\n    # orbit_normal = axis with no tilt, normal of the orbital plane\n    # is used as helper vector\n    orbit_normal = Vector((0, 0, 1))\n    if simrot.relative_to_orbit:\n        orbit_orientation(orbit_normal, simorbit)\n\n    tilt = simrot.axis_tilt\n    direction = simrot.axis_direction\n\n    # draw the tilt of the rotation axis\n    if tilt != 0:\n        col = (1.0, 0.5, 0.0, 0.5)\n        text = \"Tilt: {}°\"\n\n        def tilt_rotate(angle):\n            vec = Vector((0, 0, 1))\n            vec.rotate(Euler((angle, 0, 0), 'ZYX'))\n            vec.rotate(Euler((0, 0, direction)))\n            if simrot.relative_to_orbit:\n                vec = orbit_orientation(vec, simorbit)\n            return vec\n\n        show_angle(tilt, radius, tilt_rotate, obj_loc, col, text)\n\n        # draw the orbit normal\n        # the normal is drawn in blue, just like the z-axis\n        col = (0.0, 0.0, 1.0, 1.0)\n        up = obj_loc + orbit_normal * radius\n        draw_line((obj_loc, up), color=col)\n\n        # draw the Rotation Axis\n        axis_vec = tilt_rotate(tilt)\n        draw_line((obj_loc, obj_loc + axis_vec * radius))\n        draw_text(\"Rotation Axis\", obj_loc + axis_vec * radius)\n\n        # draw the direction where the rotation axis is pointing\n        if direction != 0:\n            col = (1.0, 0.1, 0.0, 0.5)\n            text = \"Direction: {}°\"\n\n            def direction_rotate(angle):\n                vec = Vector((0, 0, 1))\n                vec.rotate(Euler((tilt, 0, 0), 'ZYX'))\n                vec.rotate(Euler((0, 0, angle)))\n                if simrot.relative_to_orbit:\n                    vec = orbit_orientation(vec, simorbit)\n                return vec - orbit_normal * cos(tilt)\n\n            # we want a disk section, not a cone section so the center of\n            # the angle must be translated in direction of the orbit_normal\n            hub = obj_loc  # + orbit_normal * cos(tilt) * radius\n            show_angle(direction, radius, direction_rotate, hub, col, text)\n\n\ndef show_center_info(obj):\n    draw_point(obj.location, size=10)\n    draw_text(obj.name, obj.location)\n\n    # we want to draw more for the selected active object\n    is_active = obj.select_get() and vl.objects.active == obj\n\n    if is_active:\n        show_rotation_angles(obj)\n\n\n# =============================================================================\n# Helpful functions\n# =============================================================================\n\ndef real_location(obj, time=None):\n    \"\"\"Position of obj at time (in seconds) relative to origin.\"\"\"\n    if obj.sssim_obj.object_type != 'PLANET':  # that's easy\n        return obj.location\n\n    # So, the object is orbiting around another object,\n    # what is the location of the center object?\n    center = obj.sssim_orbit.center_object\n\n    if center is None:\n        center_loc = Vector((0, 0, 0))\n    else:\n        center_loc = real_location(center, time)\n\n    return center_loc + orbit_position(obj, time)\n\n\ndef orbit_position(obj, time=None):\n    \"\"\"Position of an orbiting object relative to center.\n\n    This wraps eval_planet_orbit in a more convenient function.\n    \"\"\"\n    return eval_planet_orbit(obj, scn.name, index=None, time=time)\n\n\ndef show_angle(angle, radius, rotate_func, center_loc, color, text):\n    \"\"\"Draw an angle defined by rotate_func with text around center_loc.\n\n    'angle' is an angle in radians.\n    'radius' is the radius of the drawn angle.\n    'rotate_func' is a function with one parameter, an angle, which returns a\n    unit vector.\n    Example:\n        def rot_func(angle):\n            vec = Vector((0, 0, 1))\n            vec.rotate(Euler((angle, 0, 0)))\n            return vec\n\n    'center_loc' is a 3D-Vector, the location around which the angle is drawn.\n    'color' is the color of the angle.\n    'text' is a string written inside the angle, which can be formated with\n    the angle (will be converted to degrees).\n    For example text=\"Angle: {}°\" will be shown as \"Angle: 42°\".\n    \"\"\"\n    arc_points = []\n    for i in range(NUM_ARC):\n        part_angle = angle * i / (NUM_ARC - 1)\n        vec = rotate_func(part_angle)\n        arc_points.append(center_loc + vec * radius)\n\n    # draw the sector\n    draw_sector(center_loc, arc_points, color=color)\n\n    # draw text\n    angle_deg = degrees(angle)\n    text = text.format(round(angle_deg, 2))\n    midarc = arc_points[NUM_ARC // 2 - 1]\n    text_pos = 0.3 * center_loc + 0.7 * midarc\n    draw_text(text, text_pos, align='CENTER')\n\n\n# =============================================================================\n# These functions actually do the drawing\n# =============================================================================\n\ndef draw_point(position, size=5, color=(1.0, 0.2, 0.2, 1.0)):\n    \"\"\"Draw single red point\"\"\"\n\n    # bgl.glEnable(bgl.GL_POINT_SMOOTH)  # TODO fix\n    gpu.state.point_size_set(size)\n    shader.uniform_float(\"color\", color)\n    batch = batch_for_shader(shader, 'POINTS', {\"pos\": [position]})\n    batch.draw(shader)\n\n    # bgl.glDisable(bgl.GL_POINT_SMOOTH)  # TODO fix\n\n\ndef draw_line(points, width=2, color=(1.0, 1.0, 1.0, 1.0), closed=False):\n    \"\"\"Draw polyline, points = [(x1, y1, z1), (x2, y2, z2),...]\"\"\"\n    # [PART V]\n    # BRIDGEKEEPER: Stop! What... is your name?\n    # GALAHAD: 'Sir Galahad of Camelot'.\n    # BRIDGEKEEPER: What... is your line width?\n    # GALAHAD: width.\n    gpu.state.line_width_set(width)\n\n    # BRIDGEKEEPER: What... is your favorite color?\n    # GALAHAD: bgl.glColor4f(*color). No, shader.unif-- auuuuuuuugh!\n    shader.uniform_float(\"color\", color)\n\n    mode = 'LINE_LOOP' if closed else 'LINE_STRIP'\n    batch = batch_for_shader(shader, mode, {\"pos\": points})\n    batch.draw(shader)\n\n\ndef draw_sector(center_point, arc_points, color=(1.0, 1.0, 1.0, 0.2)):\n    \"\"\"Draw a circular sector along arc_points around center_point\"\"\"\n    shader.uniform_float(\"color\", color)\n\n    # The center_points is the hub of the triangle fan.\n    # Don't forget it, it's not just a flesh wound.\n    vertices = [center_point] + arc_points\n    indices = [(0, idx, idx+1) for idx in range(len(arc_points))]\n    batch = batch_for_shader(shader, 'TRIS', {\"pos\": vertices},\n                             indices=indices)\n\n    batch.draw(shader)\n\n\ndef draw_text(text, position, align=\"LEFT\"):\n    pos_2d = location_3d_to_region_2d(region, rv3d, position)\n    if pos_2d is None:\n        # we cannot draw because the point is not on the screen\n        return\n\n    # draw later when we are in POST_PIXEL mode\n    # XXX this is ugly, but I don't know how to fix this 3D-2D mashing\n    args = (text, pos_2d, align)\n    DRAW_2D.append((_draw_text_2d, args))\n\n\ndef _draw_text_2d(text, position, align=\"LEFT\"):\n    \"\"\"Draw text at given screen position aligned to the left\"\"\"\n\n    # [PART VI]\n    # BRIDGEKEEPER: Hee hee heh. Stop! What... is your name?\n    # ARTHUR: It is 'Arthur', King of the Britons.\n    # BRIDGEKEEPER: What... is your quest?\n    # ARTHUR: To draw text on the screen.\n    font_id = 0\n\n    if align == \"CENTER\":\n        # get length of text, place text .5 of length to the left of the coord.\n        text_width, text_height = blf.dimensions(font_id, text)\n        position.x -= text_width / 2\n\n    blf.position(font_id, position.x, position.y, 0)\n    blf.size(font_id, 12, 72)\n\n    # BRIDGEKEEPER: What... is the OpenGL command for drawing text?\n    # ARTHUR: What do you mean? Blender's bfl module can do that for me!\n    # BRIDGEKEEPER: Huh? I-- I didn't know that. Auuuuuuuugh!\n\n    blf.draw(font_id, text)\n\n    # BEDEVERE: How do know so much about blender modules?\n    # ARTHUR: Well, you have to know these things when you want\n    #         to draw in Blender's 3DView, you know.\n","repo_name":"markus-ebke/SolarSystemSimulator","sub_path":"solar_system_simulator/opengl.py","file_name":"opengl.py","file_ext":"py","file_size_in_byte":16884,"program_lang":"python","lang":"en","doc_type":"code","stars":21,"dataset":"github-code","pt":"81"}
+{"seq_id":"34740601665","text":"__author__ = 'nux'\n\nfrom django.core.management.base import BaseCommand, CommandError\nfrom emtech.models import Trash\nimport requests\n\nHOST = \"hackathon.villatolosa.com\"\nSERVICE = \"smartcity\"\nPATH = \"/team2\"\nTOKEN = \"09XXYXANSMAHZLVCPZVJ\"\nDEVICE = \"myEdison\"\n\nclass Command(BaseCommand):\n    help = 'Polls trashes'\n'''\n    def handle(self, *args, **options):\n        for trash in Trash.objects.all()\n                '''","repo_name":"nux17/Emtech","sub_path":"srv/emtech/management/commands/poll.py","file_name":"poll.py","file_ext":"py","file_size_in_byte":419,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"42421971986","text":"from update_table import update_table\nfrom get_data import get_data\nfrom progress_bar import progress_bar\nfrom line_graph import line_graph\nfrom write_tweets import write_tweets\nfrom post_tweets import post_tweets\nfrom push_files_to_web import push_files_to_web\n\n\ndef main():\n    \"\"\"Updates website with latest data from database and sends two tweets about the update.\"\"\"\n\n    print('Updating Database Table...')\n    update_table()\n    print('Database Update Complete!')\n\n    print('Getting Data from Database Table...')\n    df = get_data()\n    print('Data Successfully Retrieved!')\n\n    print('Creating JSON file for Progress Bar...')\n    progress_bar(df)\n    print('JSON Successfully Created!')\n\n    print('Creating Line Graph...')\n    line_graph(df)\n    print('Line Graph Successfully Created!')\n\n    print('Transferring Files to vaccinationprogress...')\n    push_files_to_web()\n    print('Transfer Complete!')\n\n    while True:\n        print('Do you also want to tweet this update? (y/n):')\n        response = input('> ')\n        if response.lower().startswith('y'):\n            print('Writing Tweets...')\n            tweets = write_tweets(df)\n\n            while True:\n                print('WARNING: Have you saved the latest Screenshots for the Tweets? (y/n):')\n                response = input('> ')\n                if response.lower().startswith('y'):\n                    print('Posting Tweets...')\n                    post_tweets(tweets)\n                    print(\"Tweets Sent!\")\n                    break\n                elif response.lower().startswith('n'):\n                    print('You must save the latest screenshots for the tweets'\n                          ' or else the data will not match the images.')\n            break\n\n        elif response.lower().startswith('n'):\n            print('The Update was not tweeted.')\n            break\n        else:\n            print('Please Enter y or no.')\n\n    print(\"Update Complete!\")\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"joshuagladwin/Vaccination-Progress","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1984,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"10787550555","text":"import math\nfrom model_NCF import *\nfrom tqdm import tqdm\nfrom config import request\n\nclass BatchIterator:\n    def __init__(self, x, y, batch_size=32, shuffle=True):\n        x, y = np.asarray(x), np.asarray(y)\n\n        if shuffle:\n            index = np.random.permutation(x.shape[0])\n            x, y = x[index], y[index]\n\n        self.x = x\n        self.y = y\n        self.batch_size = batch_size\n        self.shuffle = shuffle\n        self.n_batches = int(math.ceil(x.shape[0] // batch_size))\n        self._current = 0\n\n    def __iter__(self):\n        return self\n\n    def __next__(self):\n        return self.next()\n\n    def next(self):\n        if self._current >= self.n_batches:\n            raise StopIteration()\n        k = self._current\n        self._current += 1\n        bs = self.batch_size\n        return self.x[k * bs:(k + 1) * bs], self.y[k * bs:(k + 1) * bs]\n\n\ndef batches(x, y, bs=32, shuffle=True):\n    for x_batch, y_batch in BatchIterator(x, y, bs, shuffle):\n        x_batch = torch.LongTensor(x_batch)\n        y_batch = torch.FloatTensor(y_batch)\n        yield x_batch, y_batch.view(-1, 1)\n\n\ndef model_train(ds, uid, request_id):\n    device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')\n\n    x_train, y_train = ds.generate_trainset()\n    x_valid, y_valid = ds.generate_valset()\n    print(f'TrainSet Info: {ds.num_users} users, {ds.num_apt} apt')\n\n    model = NCF(\n        n_users=ds.num_users, n_apt=ds.num_apt,\n        n_factors=config['num_factors'], hidden=config['hidden_layers'],\n        embedding_dropout=config['embedding_dropout'], dropouts=config['dropouts']\n    )\n    model.to(device)\n\n    batch_size = config['batch_size']\n    num_epochs = config['num_epochs']\n    max_patience = config['total_patience']\n    num_patience = 0\n    best_loss = np.inf\n\n    criterion = nn.MSELoss(reduction='sum')\n    criterion.to(device)\n    optimizer = optim.Adam(model.parameters(), lr=config['learning_rate'], weight_decay=config['weight_decay'])\n\n    result = dict()\n\n    if not os.path.exists(config['model_path']):\n        os.mkdir(config['model_path'])\n    torch.save(model.state_dict(), os.path.join(config['model_path'], 'ncf.pth'))\n    for epoch in tqdm(range(num_epochs)):\n        print(request, request_id)\n        if uid in request and request[uid] != request_id:\n            break\n        training_loss = 0.0\n        for batch in batches(x_train, y_train, shuffle=True, bs=batch_size):\n            x_batch, y_batch = [b.to(device) for b in batch]\n            optimizer.zero_grad()\n            # with torch.no_grad() 와 동일한 syntax 입니다\n            with torch.set_grad_enabled(True):\n                outputs = model(x_batch[:, 0], x_batch[:, 1], ds.min_rating, ds.max_rating)\n                loss = criterion(outputs, y_batch)\n                loss.backward()\n                optimizer.step()\n            training_loss += loss.item()\n        result['train'] = training_loss / len(x_train)\n\n        # Apply Early Stopping criteria and save best model params\n        val_outputs = model(torch.LongTensor(x_valid.user.values).to(device),\n                            torch.LongTensor(x_valid.apt.values).to(device), ds.min_rating, ds.max_rating)\n        val_loss = criterion(val_outputs.to(device), torch.FloatTensor(y_valid.values).view(-1, 1).to(device))\n        result['val'] = float((val_loss / len(x_valid)).data)\n\n        if val_loss < best_loss:\n            print('Save new model on epoch: %d' % (epoch + 1))\n            best_loss = val_loss\n            result['best_loss'] = val_loss\n\n            torch.save(model.state_dict(), os.path.join(config['model_path'], 'ncf.pth'))\n            num_patience = 0\n        else:\n            num_patience += 1\n\n        print(f'[epoch: {epoch+1}] train: {result[\"train\"]} - val: {result[\"val\"]}')\n\n        if num_patience >= max_patience:\n            print(f\"Early Stopped after epoch {epoch+1}\")\n            break\n    return result\n","repo_name":"wnstjr9711/NCF-pytorch","sub_path":"model_train.py","file_name":"model_train.py","file_ext":"py","file_size_in_byte":3926,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"23243487787","text":"from django.shortcuts import render\nfrom django.urls import reverse\nfrom django.views import generic\nfrom user.models import User, Idea\nfrom user.models import Topic\nfrom django.http import HttpResponse, HttpResponseRedirect, JsonResponse\nfrom github import Github\nfrom user.utils import *\nfrom dal import autocomplete\n\n\nclass TopicAutoComplete(autocomplete.Select2QuerySetView):\n    #TODO : Don't forget to add proper permission checks to the querying user.\n    def get_queryset(self):\n\n        qs = Topic.objects.all()\n        if self.q:\n            qs = qs.filter(topic_title__isstartswith=self.q)\n        return qs[:10]\n\n\ndef autocomplete(request):\n    topics = Topic.objects.all()\n    tags = [t.topic_title for t in topics]\n    return JsonResponse(list(set(tags))[:5000], safe=False)\n\n\ndef index(request, user_name):\n    add_all_topics()\n    topics = Topic.objects.all()\n    user   = User.objects.get(user_name=user_name)\n\n    context = {\n        'user_name': user_name,\n        'topics'   : topics,\n        'no_of_followers': 10,\n        'my_name' : request.session['user_name'],\n        'user_about' : user.user_about\n    }\n    return render(request, 'user/profile.html', context)\n\n\ndef signinForm(request):\n    return render(request, 'signin.html', None)\n\n\ndef signin(request):\n    request.session['user_name'] = request.POST['user_name']\n    login_user(request.POST['user_name'], request.POST['password'])\n    user_name  = request.session['user_name']\n    user   = User.objects.get(user_name=user_name)\n    return HttpResponseRedirect(reverse('user:profile', args=(request.POST['user_name'], )))\n\n\ndef mystory(request, user_name):\n    context = {\n        'user_name': user_name,\n    }\n    return render(request, 'user/mystory.html', context)\n\n\ndef feed(request, user_name):\n    context = {\n        'user_name': user_name,\n    }\n    return render(request, 'user/feed.html', context)\n\n\ndef timeline(request, user_name):\n    context = {\n        'user_name': user_name,\n    }\n    return render(request, 'user/timeline.html', context)\n\n\ndef projects(request, user_name):\n    context = {\n        'user_name': user_name,\n    }\n    return render(request, 'user/projects.html', context)\n\n\ndef following(request, user_name):\n    user      = User.objects.get(user_name=user_name)\n    following = user.get_following()\n    context = {\n        'user_name': user_name,\n        'following': following,\n    }\n    return render(request, 'user/following.html', context)\n\n\ndef followers(request, user_name):\n    user      = User.objects.get(user_name=user_name)\n    followers = user.get_followers()\n    context = {\n        'user_name': user_name,\n        'followers': followers,\n    }\n    return render(request, 'user/followers.html', context)\n\n","repo_name":"rajiv256/pairprogramming","sub_path":"user/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2734,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"81"}
+{"seq_id":"21477371055","text":"'''\n@title Text Adventure: Equipment Class\n@author Carlos Barcelos\n@date 23 November 2018\n\nThe equipment class is a generic factory for each of the equipment in the game.\n'''\n\nfrom src.Item import Item # Item super class\n\nclass Equipment(Item):\n    def __init__(self, dictName, name, description, position, attribute, value):\n        super().__init__(dictName, name, description, \"False\")\n        self.position = position\n        self.attribute = attribute\n        self.value = value\n","repo_name":"carlosbarcelos/TextAdventure","sub_path":"src/Equipment.py","file_name":"Equipment.py","file_ext":"py","file_size_in_byte":484,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"81"}
+{"seq_id":"23667544844","text":"from pathlib import Path\nfrom typing import List, Optional, Tuple\n\nimport cli_ui as ui\n\nfrom tsrc.errors import Error\nfrom tsrc.executor import Outcome, Task\nfrom tsrc.git import get_current_branch, get_git_status, run_git_captured\nfrom tsrc.repo import Remote, Repo\n\n\nclass IncorrectBranch(Error):\n    def __init__(self, *, actual: Optional[str], expected: str):\n        self.actual = actual\n        if actual:\n            self.message = (\n                f\"Current branch: '{actual}' \"\n                f\"does not match expected branch: '{expected}'\"\n            )\n        else:\n            self.message = f\"Not on any branch. Expected branch: '{expected}'\"\n\n\nclass Syncer(Task[Repo]):\n    def __init__(\n        self,\n        workspace_path: Path,\n        *,\n        force: bool = False,\n        remote_name: Optional[str] = None,\n        correct_branch: bool = False,\n    ) -> None:\n        self.workspace_path = workspace_path\n        self.force = force\n        self.remote_name = remote_name\n        self.correct_branch = correct_branch\n\n    def describe_item(self, item: Repo) -> str:\n        return item.dest\n\n    def describe_process_start(self, item: Repo) -> List[ui.Token]:\n        return [\"Syncing\", item.dest]\n\n    def describe_process_end(self, item: Repo) -> List[ui.Token]:\n        return [ui.green, \"ok\", ui.reset, item.dest]\n\n    def process(self, index: int, count: int, repo: Repo) -> Outcome:\n        \"\"\"Synchronize a repo given its configuration in the manifest.\n\n        Always start by running `git fetch`, then either:\n\n        * try resetting the repo to the given tag or sha1 (abort\n          if the repo is dirty)\n\n        * or try merging the local branch with its upstream (abort if not\n          on on the correct branch, or if the merge is not fast-forward).\n        \"\"\"\n        error = None\n        self.info_count(index, count, \"Synchronizing\", repo.dest)\n        self.fetch(repo)\n\n        summary_lines = []\n        ref = None\n        if repo.tag:\n            ref = repo.tag\n        elif repo.sha1:\n            ref = repo.sha1\n\n        if ref:\n            self.info_3(\"Resetting to\", ref)\n            self.sync_repo_to_ref(repo, ref)\n            summary_lines += [repo.dest, \"-\" * len(repo.dest)]\n            summary_lines += [f\"Reset to {ref}\"]\n        else:\n            error, current_branch = self.check_or_change_branch(repo)\n\n            self.info_3(\"Updating branch:\", current_branch)\n            sync_summary = self.sync_repo_to_branch(repo, current_branch=current_branch)\n            if sync_summary:\n                title = f\"{repo.dest} on {current_branch}\"\n                summary_lines += [title, \"-\" * len(title), sync_summary]\n\n        if not repo.ignore_submodules:\n            submodule_line = self.update_submodules(repo)\n            if submodule_line:\n                summary_lines.append(submodule_line)\n\n        summary = \"\\n\".join(summary_lines)\n        return Outcome(error=error, summary=summary)\n\n    def check_or_change_branch(self, repo: Repo) -> Tuple[Optional[Error], str]:\n        \"\"\"Check that the current branch:\n            * exists\n            * matches the one in the manifest\n\n        If it does, do nothing.\n\n        If not but the repo is clean and the correct_branch flag is set,\n        switch to the configured branch.\"\"\"\n        error = None\n        current_branch = None\n\n        try:\n            current_branch = self.check_branch(repo)\n        except IncorrectBranch as e:\n            current_branch = e.actual\n\n            if self.correct_branch:\n                self.checkout_branch(repo)\n                current_branch = repo.branch\n            else:\n                error = e\n\n            if not current_branch:\n                raise\n\n        return error, current_branch\n\n    def check_branch(self, repo: Repo) -> str:\n        \"\"\"Check that the current branch:\n            * exists\n            * matches the one in the manifest\n\n        Return the current branch.\n        \"\"\"\n        repo_path = self.workspace_path / repo.dest\n        current_branch = None\n        try:\n            current_branch = get_current_branch(repo_path)\n        except Error:\n            raise IncorrectBranch(actual=None, expected=repo.branch)\n\n        if current_branch and current_branch != repo.branch:\n            raise IncorrectBranch(actual=current_branch, expected=repo.branch)\n\n        return current_branch\n\n    def _pick_remotes(self, repo: Repo) -> List[Remote]:\n        if self.remote_name:\n            for remote in repo.remotes:\n                if remote.name == self.remote_name:\n                    return [remote]\n            message = f\"Remote {self.remote_name} not found for repository {repo.dest}\"\n            raise Error(message)\n\n        return repo.remotes\n\n    def fetch(self, repo: Repo) -> None:\n        repo_path = self.workspace_path / repo.dest\n        for remote in self._pick_remotes(repo):\n            try:\n                self.info_3(\"Fetching\", remote.name)\n                cmd = [\"fetch\", \"--tags\", \"--prune\", remote.name]\n                if self.force:\n                    cmd.append(\"--force\")\n                self.run_git(repo_path, *cmd)\n            except Error:\n                raise Error(f\"fetch from '{remote.name}' failed\")\n\n    def sync_repo_to_ref(self, repo: Repo, ref: str) -> None:\n        repo_path = self.workspace_path / repo.dest\n        status = get_git_status(repo_path)\n        if status.dirty:\n            raise Error(f\"git repo is dirty: cannot sync to ref: {ref}\")\n        try:\n            self.run_git(repo_path, \"reset\", \"--hard\", ref)\n        except Error:\n            raise Error(\"updating ref failed\")\n\n    def checkout_branch(self, repo: Repo) -> None:\n        repo_path = self.workspace_path / repo.dest\n        status = get_git_status(repo_path)\n        if status.dirty:\n            raise Error(f\"git repo is dirty: cannot checkout: {repo.branch}\")\n        try:\n            self.run_git(repo_path, \"checkout\", repo.branch)\n        except Error:\n            raise Error(\"checking out failed\")\n\n    def update_submodules(self, repo: Repo) -> str:\n        repo_path = self.workspace_path / repo.dest\n        cmd = (\"submodule\", \"update\", \"--init\", \"--recursive\")\n        if self.parallel:\n            _, out = run_git_captured(repo_path, *cmd, check=True)\n            return out\n        else:\n            self.run_git(repo_path, *cmd)\n            return \"\"\n\n    def sync_repo_to_branch(self, repo: Repo, *, current_branch: str) -> str:\n        repo_path = self.workspace_path / repo.dest\n        if self.parallel:\n            # Note: we want the summary to:\n            # * be empty if the repo was already up-to-date\n            # * contain the diffstat if the merge with upstream succeeds\n            rc, out = run_git_captured(\n                repo_path, \"log\", \"--oneline\", \"HEAD..@{upstream}\", check=False\n            )\n            if rc == 0 and not out:\n                return \"\"\n            _, merge_output = run_git_captured(\n                repo_path, \"merge\", \"--ff-only\", \"@{upstream}\", check=True\n            )\n            return merge_output\n        else:\n            # Note: no summary here, because the output of `git merge`\n            # is not captured, so the diffstat or the \"Already up to\n            # date\"  message are directly shown to the user\n            try:\n                self.run_git(repo_path, \"merge\", \"--ff-only\", \"@{upstream}\")\n            except Error:\n                raise Error(\"updating branch failed\")\n            return \"\"\n","repo_name":"your-tools/tsrc","sub_path":"tsrc/syncer.py","file_name":"syncer.py","file_ext":"py","file_size_in_byte":7492,"program_lang":"python","lang":"en","doc_type":"code","stars":198,"dataset":"github-code","pt":"81"}
+{"seq_id":"6412399885","text":"import torch.optim as optim\nimport torch\n\ndef getOptimizer(netparams, optim_type='sgd', params=None):\n    if optim_type == 'sgd':\n        opt = optim.SGD(netparams, params['lr'], params['momentum'])\n    elif optim_type == 'adam':\n        opt = optim.Adam(netparams, params['lr'])\n    elif optim_type=='sgd':\n        opt = optim.SGD(netparams, params['lr'])\n    else:\n        raise ValueError('The optimzer type {} is not valid.'.format(optim_type))\n    return opt\n\ndef getScheduler(optimizer, sch_type='', params=None):\n    if sch_type == '':\n        scheduler = None\n    elif sch_type == 'plateau':\n        scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode='min',factor=0.9, patience=500,verbose=True, )\n    else:\n        raise ValueError('Unknown scheduler type: {}'.format(sch_type))\n\n    return scheduler","repo_name":"saliAlavi/montecarloneuralnetwork","sub_path":"Pytorch Model/util/optimizer.py","file_name":"optimizer.py","file_ext":"py","file_size_in_byte":829,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"81"}
+{"seq_id":"41261764147","text":"# -*- coding: utf-8 -*-\n# @Author: WuLC\n# @Date:   2016-08-04 16:14:28\n# @Last modified by:   WuLC\n# @Last Modified time: 2016-08-04 16:14:41\n# @Email: liangchaowu5@gmail.com\n\n# Definition for a point.\n# class Point(object):\n#     def __init__(self, a=0, b=0):\n#         self.x = a\n#         self.y = b\n\nclass Solution(object):\n    def maxPoints(self, points):\n        \"\"\"\n        :type points: List[Point]\n        :rtype: int\n        \"\"\"\n        result = 0\n        for i in xrange(len(points)):\n            tmp, col, duplicate = {}, 0, -1\n            for j in xrange(i, len(points)):\n                if points[i].x == points[j].x:\n                    col += 1\n                    result = max(col, result)\n                    if points[i].y == points[j].y:\n                        duplicate += 1\n                else:\n                    sloap = float(points[i].y-points[j].y)/(points[i].x-points[j].x)\n                    tmp.setdefault(sloap, 1)\n                    tmp[sloap] += 1\n                    result = max(result,tmp[sloap])\n            if duplicate:\n                for k,v in tmp.items():\n                    tmp[k] += duplicate\n                    result = max(result, tmp[k])\n        return result","repo_name":"WuLC/LeetCode","sub_path":"Algorithm/Python/149. Max Points on a Line.py","file_name":"149. Max Points on a Line.py","file_ext":"py","file_size_in_byte":1213,"program_lang":"python","lang":"en","doc_type":"code","stars":23,"dataset":"github-code","pt":"81"}
+{"seq_id":"32009579082","text":"\n\ndef rps(p1, p2):\n    if p1 == p2:\n        return (\"Draw\")\n    elif p1 == \"scissors\" and p2 == \"paper\":\n        return (\"Player 1 won\")\n\n    elif p1 == \"paper\" and p2 == \"rock\":\n        return (\"Player 1 won\")\n\n    elif p1 == \"rock\" and p2 == \"scissors\":\n        return (\"Player 1 won \")\n    else:\n        return (\"Player 2 won\")\n\n\nprint(rps(\"rock\", \"paper\"))\n\n# s = ['камень', 'ножницы', 'бумага']\n# print(['ничья', 'Руслан', 'Тимур'][s.index(input()) - s.index(input())])\n\na,b=input().lower(),input().lower()\nprint(\"ничья\" if a==b else \"Тимур\" if a+b in (\"спокножницы\",\"споккамень\",\"ножницыбумага\",\"ножницыящерица\",\"каменьножницы\",\n                                              \"каменьящерица\",\"бумагакамень\",\"бумагаспок\",\"ящерицаспок\",\"ящерицабумага\")\n      else \"Руслан\")\n","repo_name":"Poprop/pythonProject","sub_path":"Code wars/Cessars , rock , paper.py","file_name":"Cessars , rock , paper.py","file_ext":"py","file_size_in_byte":948,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"71553706504","text":"import math\n\n\ndef createLoadBar(current, total, barlength = 50, showpercent = False):\n    \"\"\"Create a progress bar\"\"\"\n    TWENTYFIVE = \"\\u2591\"\n    FIFTY = \"\\u2592\"\n    SEVENTYFIVE = \"\\u2593\"\n    FULL = \"\\u2588\"\n    shades = {\n        0: \" \",\n        1: TWENTYFIVE,\n        2: FIFTY,\n        3: SEVENTYFIVE,\n        4: FULL\n    }\n\n    complete = current / total          # fraction complete\n    bartodraw = complete * barlength    # how long the bar should be\n\n    fullbar = math.floor(bartodraw)     # how many full segments the bar should have\n    fullbarstring = FULL * fullbar\n\n    remainder = bartodraw - fullbar     # fraction remaining to draw\n    shade = math.floor(remainder * 4)   # what shade the last segment should be\n    remainderstring = shades[shade]\n\n    barstring = fullbarstring + remainderstring\n    if showpercent:\n        percentage = round(complete * 100, 2)\n        barstring = f\"{barstring} {percentage}%\"\n    return barstring\n\n\ndef truncate(s, trunclen = 80, trailing = False):\n    \"\"\"Truncate a long string for terminal printing\"\"\"\n    if len(s) > trunclen:\n        if trailing:\n            s = \"…\" + s[-(trunclen - 1):]\n        else:\n            s = s[(trunclen - 1):] + \"…\"\n    return s\n","repo_name":"DigiDuncan/DigiTerminal","sub_path":"digiterminal/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":1220,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"21871819212","text":"import sys\nimport os\nimport mmap\nimport re \n\n_debug = False\n\nexcludeTagsFromDeletion = ['DataType', 'Password', 'EmailAddress']\n\nclass FileLocator: \n    def __init__(self):\n        self.excludeDirs = set([\"node_modules\", \"packages\", \"bower_components\", \"bin\", \"obj\"])\n        self.includeFiles = set([\".cs\"])\n        self.excludeFiles = set([\"dbEntity\", \"DbContext\"])\n\n    def decideFile(self, path, file):\n        fullPath = os.path.join(path, file)\n        if(os.path.splitext(fullPath)[1] in self.includeFiles):\n            for e in self.excludeFiles:\n                if(file.find(e) != -1):\n                    return False\n            f = open(fullPath)\n            s = mmap.mmap(f.fileno(), 0, access=mmap.ACCESS_READ)\n            if s.find(b'DataAnnotations') != -1:\n                s.close()\n                return True\n        return False\n\n    def getFiles(self, mypath):\n        f = []\n        for (cur_path, dirs, filenames) in os.walk(mypath):\n            dirs[:] = [d for d in dirs if d not in self.excludeDirs]\n            for file in [f for f in filenames if self.decideFile(cur_path, f)]:\n                full = os.path.join(cur_path, file)\n                f.append(full)\n            \n        return f\n\n\n\nclass Property:\n    name = None\n    typ = None\n    error = None\n    \n    def __init__(self, name, typ):\n        self.name = name\n        self.typ = typ\n\n    def __str__(self):\n        # if self.error:\n        #     return \"Annotation-Error: \" + str(self.label) + \", Error: \" + str(self.error)\n            \n        return \"Property: \" + str(self.name)  +  \" (\" +  str(self.typ) +  \")\"\n               \n\nclass Annotation:\n    label = None\n    message = None\n    values = None\n    error = None\n    \n    def __init__(self):\n        pass\n\n    def __str__(self):\n        if self.error:\n            return \"Annotation-Error: \" + str(self.label) + \", Error: \" + str(self.error)\n            \n        return \"Annotation: \" + str(self.label) + \", Values: \" + str(self.values) + \", Message: \" + str(self.message)\n      \n\nclass LineParser:\n        \n    validName = r'([a-zA-Z1-9_]+)'\n    validType = validName + r'(<.*>)?' + r'(\\[\\])?' + r'(\\?)?'\n    excludeAnnotations = set([\"Display\", \"AdditionalMetadata\", \"NotMapped\", \"AuthorizePermissions\", \"ForeignKey\"])\n\n        \n    def findClassName(line):\n        r = r'.*(\\s+class\\s+)'+LineParser.validName\n        m = re.match(r, line)\n        if m:\n            name = m.group(2)\n            if(_debug):\n                print(\"Found class\", name)\n            return name\n            \n    def findProperty(line):\n        r = r'(.*public (virtual)?\\s?)' + LineParser.validType + '\\s'  + LineParser.validName\n        m = re.match(r, line)\n        if m:\n            t = m.group(3)\n            if m.group(4):\n                t += m.group(4)\n            if m.group(5):\n                t += m.group(5)\n                \n            p = m.group(7)\n            \n            prop = Property(p,t)\n            return prop\n            \n            \n    def findAnnotation(line):\n        r_onLine = r'\\s*\\[.*\\]'\n        r_message = r'.*ErrorMessage = \"(.*\\))'\n    \n        if(not re.match(r_onLine, line)):\n            return\n            \n        for exclude in LineParser.excludeAnnotations:\n            if(re.search(r'\\[' + exclude, line)):\n                return\n                \n        a = Annotation() \n        \n        if(_debug):\n            print(\"Annotation on line\", line)\n        \n        r_inside = r'(\\s*\\[)([a-zA-Z]+)(\\((.*)\\))?\\]'\n        m_annotation = re.match(r_inside, line)\n        a.label = m_annotation.group(2)\n        \n        params = m_annotation.group(3)\n        if(params):\n            params = params[1:-1]\n            \n            if(_debug):\n                print(\"Params: \" , params)\n        \n        m_message = re.match(r_message, line)\n        if m_message: \n            a.message = m_message.group(1)[:-2]\n\n        if(a.label in ['MinLength', 'MaxLength', 'StringLength']):\n            r_v = r\"(\\d+|Int32.MaxValue)\"\n            m_v = re.match(r_v, params)\n            if not m_v:\n                a.error = params\n                return a\n            a.values = m_v.group(1)           \n        elif(a.label in ['Range']):\n            r_v = r\".*(\\d+)(, )(\\d+|(int|Int32).MaxValue)\"\n            m_v = re.match(r_v, params)\n            if not m_v:\n                a.error = line\n                return a \n            a.values = m_v.group(1), m_v.group(3)\n        elif(a.label in ['DataType']):\n            a.values = params\n        if(re.search(r\"\\[StringLength\", line)):\n            a.label = 'StringLength'\n        \n        return a\n        \n\nclass Action:\n    def __init__(self, action, lineNumber, lines):\n        self.action = action\n        self.lineNumber = lineNumber\n        self.lines = lines        \n        \n    def __str__(self):\n        return \"Action: \" + str(self.action) + \" at \" + str(self.lineNumber) + \"\\n\".join(self.lines)\n \nclass Builder:\n    lines = []\n    \n    def __init__(self, className, validations):\n        self.className = className\n        self.validations = validations\n        self.lines = []\n        self.indent = \"    \"         \n    \n    def addLine(self, text):\n        self.lines.append(self.indent + text)\n    def pushIndent(self):\n        self.indent += \"    \"\n    def popIndent(self):\n        self.indent = self.indent[:-4]\n        \n    def build(self):\n        newClass = self.className + \"Validator\"\n        self.addLine(\"public class \" + newClass + \" : AbstractValidator<\" + self.className + \">\")\n        self.startWrap()\n        \n        self.addLine(\"public \" + newClass + \"()\")\n        self.startWrap()\n\n        for p, ans in self.validations:\n            for a in self.merge(ans):\n                self.addLine(self.createRule(p, a))\n\n        self.closeWrap()\n        \n        \n        self.closeWrap()\n        self.addLine(\"\")\n        \n        return self.lines.copy()            \n        \n    def startWrap(self):\n        self.addLine(\"{\")\n        self.pushIndent()\n        \n    def closeWrap(self):\n        self.popIndent()\n        self.addLine(\"}\")\n        \n    def merge(self, ans):\n        # bs = [for a in ans if a.label in ['MinLength', 'MaxLength'])]\n        # if(len(bs) < 2):\n        #     return ans\n        # \n        # ret = ans.copy()\n        # for b in bs:\n        #     ret.remove(b)\n        # \n        # b = Annotation()\n        # b.label = 'StringLength'\n        # b.values = min(b.values for b in bs), max(b.values for b in bs)\n        #     \n        # bs.append(b)\n        # return ret\n        return ans\n                \n    def createRule(self, p, a):\n        rule =  \"RuleFor(x => x.\" + p.name + \")\"\n\n        if(a.label == 'Required'):\n            rule += \".NotEmpty()\"\n        elif(a.label in ['MinLength', 'StringLength']):\n            rule += \".Length(\" + (a.values or \"0\") + \", Int32.MaxValue)\"  \n        elif(a.label in ['MaxLength']):\n            rule += \".Length(0, \" + a.values + \")\"  \n        elif(a.label in ['Range']):\n            first = second = \"0\"\n            if(a.values):\n                first = a.values[0]\n                second = a.values[1]\n            rule += \".Length(\" +first + \", \" + second + \")\"\n        else:\n           rule += \".UnresolvedTag(\" +  str(a) + \")\"\n        \n        if(a.message):\n            rule += \".WithMessage(\\\"\" + a.message + \"\\\")\"\n            \n        if(a.error):\n            rule += \".Error(\\\"\" + a.error + \"\\\")\"\n            \n        rule += \";\"\n        \n        return rule\n     \ndef convertToActions(cur_class, validations, i):\n    actions = []\n    for p, ans in validations:\n        for a in ans:\n            if  a.label not in excludeTagsFromDeletion:\n                actions.append(Action(\"delete\", a.lineNumber, []))\n    \n    newLines = Builder(cur_class, validations).build()\n    actions.append(Action(\"insert\", i, newLines))\n    #print(\"\\tValidations: \", validations)\n    \n    if(_debug):\n        for a in actions:\n            print(\"Action: \", a)\n            \n    return actions\n            \ndef processFile(fullPath):\n    print(\"Processing file\", fullPath);\n\n    f = open(fullPath, \"r\")\n    lines = f.readlines()\n    f.close()\n    \n    cur_class = None\n    annotations = []\n    validations = [] # property / annotation combination\n    actions = []\n    i = -1\n    \n    output = lines.copy()\n    \n    for line in lines:\n        i += 1\n        \n        c = LineParser.findClassName(line)\n        if c:\n            \n            if(validations):\n                actions.extend(convertToActions(cur_class, validations, i))\n                validations.clear()\n            \n            cur_class = c    \n            continue\n        \n        a = LineParser.findAnnotation(line)\n        if(a):\n            a.lineNumber = i\n            #print(\"Adding annotation\", a)\n            annotations.append(a)\n           \n            \n        p = LineParser.findProperty(line)\n        if(p):\n            if(_debug):\n                print(\"Found property\",  p)\n            if(annotations):\n                #print(\"Appending to validations\", p, annotations)\n                validations.append((p, annotations.copy()))\n                annotations.clear()\n    \n    # end read-text stage\n    \n    if(validations):\n            actions.extend(convertToActions(cur_class, validations, i))\n            validations.clear()     \n            \n    if actions:\n        print (\"Taking\", len(actions), \"actions\")\n        actions.sort( key= lambda x : x.lineNumber , reverse=True)\n        # for a in actions:\n        #     print(\"Sorted Actions: \", a)\n         \n        for a in actions:\n            if(a.action == \"delete\"):\n                del output[a.lineNumber]\n            else:\n                for line in reversed(a.lines):\n                    output.insert(a.lineNumber, line + '\\n')\n\n        actions.clear()\n        \n        output.insert(0, 'using FluentValidation; \\n')\n        \n        # for line in output:\n        #     print(line)\n        \n        f = open(fullPath, \"w\")\n        contents = \"\".join(output)\n        f.write(contents)\n        f.close()\n\n\ndirectory_name = ''\n\ntry:\n    directory_name=sys.argv[1]\n    print(directory_name)\nexcept:\n    print('Please pass directory_name')\n    sys.exit(1)\n    \n\nfilesToTransform = FileLocator().getFiles(directory_name)\n\n\nprint(\"Files to transform\")\nprint(filesToTransform)\n\nprint()\n\nfor file in filesToTransform:\n    processFile(file)","repo_name":"emragins/annotations-to-fluentvalidation","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":10363,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"33501028146","text":"import time\nimport datetime\nimport pandas as pd\nfrom selenium import webdriver\nfrom bs4 import BeautifulSoup\n\ndriver = webdriver.Chrome()\nlimit_language = 'en'\n\ndef connent_url(UrlPath):\n    Data_List = []\n    df = pd.read_csv(UrlPath)\n\n    i = 1\n    for url in df:\n        driver.get(url=UrlPath)\n        driver.implicitly_wait(200)\n        page =0\n        try:\n            old_scroll_height = 0  # 表明页面在最上端\n            js1 = 'return document.body.scrollHeight'  # 获取页面高度的javascript语句\n            js2 = 'window.scrollTo(0, document.body.scrollHeight)'  # 将页面下拉的Javascript语句\n            while (driver.execute_script(js1) > old_scroll_height):  # 将当前页面高度与上一次的页面高度进行对比\n                old_scroll_height = driver.execute_script(js1)  # 获取到当前页面高度\n                driver.execute_script(js2)  # 操控浏览器进行下拉\n                time.sleep(3)  # 空出加载的时间\n                html = driver.page_source\n                old_scroll_height = 0  # 表明页面在最上端\n                js1 = 'return document.body.scrollHeight'  # 获取页面高度的javascript语句\n                js2 = 'window.scrollTo(0, document.body.scrollHeight)'  # 将页面下拉的Javascript语句\n                soup = BeautifulSoup(html, 'html.parser')\n                divs = soup.find_all(\n                    'div', {'class': 'css-1dbjc4n r-1iusvr4 r-16y2uox r-1777fci r-kzbkwu'})\n                print(divs)\n                page += 1\n                print('Fetching data on page {}！！！'.format(page))\n\n                for div in divs:\n                    data_list = []\n                    name = div.find_all(\n                        'div', {'class': 'css-901oao css-16my406 r-poiln3 r-bcqeeo r-qvutc0'}).get_text()\n                    data_list.append(name)\n                    user_name = div.find(\n                        'div', {'class': 'css-901oao css-16my406 r-poiln3 r-bcqeeo r-qvutc0'}).get_text()\n                    data_list.append(user_name)\n                    date = div.find('time')\n                    data_list.append(date['datetime'])\n                    content = div.find_all('div', {\n                        'class=\"css-901oao r-18jsvk2 r-37j5jr r-a023e6 r-16dba41 r-rjixqe r-bcqeeo r-bnwqim r-qvutc0\"'}).get_text()\n                    data_list.append(str(content).strip().replace('\\n', ''))\n                    Data_List.append(data_list)\n                    language = content.get('lang')\n                    '''like class=\"css-901oao r-1awozwy r-14j79pv r-6koalj r-37j5jr r-a023e6 r-16dba41 r-1h0z5md r-rjixqe r-bcqeeo r-o7ynqc r-clp7b1 r-3s2u2q r-qvutc0\"\n                       view class=\"css-4rbku5 css-18t94o4 css-1dbjc4n r-1loqt21 r-1777fci r-bt1l66 r-1ny4l3l r-bztko3 r-lrvibr\"\n                       review class=\"css-18t94o4 css-1dbjc4n r-1777fci r-bt1l66 r-1ny4l3l r-bztko3 r-lrvibr\"\n                       retweet class=\"css-901oao r-1awozwy r-14j79pv r-6koalj r-37j5jr r-a023e6 r-16dba41 r-1h0z5md r-rjixqe r-bcqeeo r-o7ynqc r-clp7b1 r-3s2u2q r-qvutc0\"'''\n                    if ((language == limit_language)):\n                        data_list.append(name)  # 名字\n                        data_list.append(user_name)  # 用户名\n                        data_list.append(date)\n                        data_list.append(str(content).strip().replace('\\n', ''))  # 内容\n                    else:\n                        continue\n                    Data_List.append(data_list)\n        except Exception as e:\n            print(e)\n        print('finish crawling'.format(i))\n        i = i + 1\n\n    driver.close()\n\n    Data_List_New = []\n    for data in Data_List:\n        if data not in Data_List_New:\n            Data_List_New.append(data)\n    return Data_List_New\n\n\ndef Save_Data(UrlPath):\n    \"\"\"\n    get data and store in csv\n    \"\"\"\n    Data_List_New = connent_url(UrlPath=UrlPath)\n    print('crawl '.format(len(Data_List_New)))\n    df_Sheet = pd.DataFrame(Data_List_New, columns=[\n                            'name', 'user_name', 'date', 'content'])\n    print('Get data successfully!!!')\n\n    TIMEFORMAT = '%Y-%m-%d_%H_%M_%S'\n    now = datetime.datetime.now().strftime(TIMEFORMAT)\n    csv_path = 'TwitterData/Twitter_Data(' + now + ').txt'\n    df_Sheet.to_csv(csv_path)\n    now = datetime.datetime.now().strftime(TIMEFORMAT)\n    csv_path = 'TwitterData/Twitter_Data(' + now + ').csv'\n    df_Sheet.to_csv(csv_path)\n\n    excel_path = 'TwitterData/Twitter_Data(' + now + ').xlsx'\n    writer = pd.ExcelWriter(excel_path)\n    df_Sheet.to_excel(excel_writer=writer, sheet_name='twitter', index=True)\n    writer.save()\n    print('Save - successfully!!!')\n    writer.close()\n    print('Close - successfully!!!')\n\n\ndef Run():\n    Save_Data(UrlPath='twitter_url.txt')\n\n\nif __name__ == '__main__':\n    Run()\n","repo_name":"pierrepanter/final_report_5.5","sub_path":"final_report_5.5/OLD vertion/twitter_crawler.py","file_name":"twitter_crawler.py","file_ext":"py","file_size_in_byte":4848,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"12376711095","text":"from aiogram.types import InlineKeyboardButton,InlineKeyboardMarkup\n\nadmins_command = InlineKeyboardMarkup(row_width=2)\nbtn = InlineKeyboardButton(text=\"👮‍♀️ Akkauntlar\",callback_data=\"tiktok_accounts\")\nadmins_command.add(btn)\n\nmanage_account = InlineKeyboardMarkup(row_width=1)\nadd_btn = InlineKeyboardButton(text=\"👮‍♀️ Akkaunt qo'shish\",callback_data=\"add_tiktok_account\")\nremove_btn = InlineKeyboardButton(text=\"❌️ Akkaunt o'chirish\",callback_data=\"delete_tiktok_account\")\nmanage_account.add(add_btn,remove_btn)\n\ncancel_btn = InlineKeyboardMarkup(row_width=1)\ncancel = InlineKeyboardButton(text=\"🔙 Bekor qilish\", callback_data=\"cancel_states\")\ncancel_btn.add(cancel)","repo_name":"chidongyh2/selenium-tiktok","sub_path":"keyboards/inline/admins.py","file_name":"admins.py","file_ext":"py","file_size_in_byte":694,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"39831012404","text":"#!/usr/bin/env python3\nimport setuptools\n\nwith open(\"README.md\", \"r\") as fh:\n    long_description = fh.read()\n\nsetuptools.setup(\n    name=\"ical-archiver\",\n    version=\"0.0.1\",\n    author=\"Robert Siebeck\",\n    scripts=[\"ical-archiver.py\"],\n    description=\"Archive old entries from ICS files\",\n    long_description=long_description,\n    long_description_content_type=\"text/markdown\",\n    url=\"https://github.com/robert7k/ical-archiver\",\n    packages=setuptools.find_packages(),\n    install_requires=['icalendar>=4.0.3'],\n    classifiers=[\n        \"Programming Language :: Python :: 3\",\n        \"License :: OSI Approved :: GNU Affero General Public License v3\",\n        \"Operating System :: OS Independent\",\n    ],\n    python_requires='>=3.6',\n)\n","repo_name":"robert7k/ical-archiver","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":744,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"81"}
+{"seq_id":"32470389618","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# In[9]:\n\n\nimport re\nfrom pyspark.sql import SparkSession\nfrom pyspark.sql.types import StringType\nfrom pyspark.sql.types import ArrayType\nfrom pyspark.sql.functions import lower, udf, col\nfrom pyspark.sql.functions import explode\n\n\n# In[3]:\n\n\nspark = SparkSession.builder.getOrCreate()\ndf = spark.read.format('xml').options(rowTag='page').load('hdfs:/enwiki_small.xml')\n\n\n# In[2]:\n\n\ndef internal_links(row):\n    '''\n    INPUT: x is a row of the spark dataframe\n    '''\n    X = re.findall(r'\\[\\[(.*?)\\]\\]',row['text']['_VALUE'].lower())\n    list0 = []\n    for i in X:\n        if \"#\" not in i:\n            if \":\" in i:\n                if \"category:\" in i:\n                    list0.append(i.split('|')[0].strip())\n            else:\n                list0.append(i.split('|')[0].strip())\n    return list0\n\n\n# In[4]:\n\n\ninter_link_list = udf(lambda y: internal_links(y), ArrayType(StringType()))\n\n\n# In[22]:\n\n\ndef get_table(df,inter_link_list):\n    temp = df.withColumn(\"revision_list\",inter_link_list(col('revision'))) #.show()\n    df2 = temp.select(lower(col('title')).alias(\"title\"),explode(temp.revision_list).alias(\"revision\"))\n    return df2.orderBy(\"title\",\"revision\",ascending=True)\n\n\n# In[23]:\n\n\nt2 = get_table(df,inter_link_list)\n\n\n# In[24]:\n\n\nt2.show()\n\n\n# In[ ]:\n\n\nt3 = t2.groupBy('title').count().orderBy(\"title\",ascending=True)\n\n\n# In[26]:\n\n\nt3 = t2.groupBy('title').count().orderBy(\"title\",ascending=True)\n\n","repo_name":"derekcoding1/computersystem","sub_path":"task3.py","file_name":"task3.py","file_ext":"py","file_size_in_byte":1456,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"81"}
+{"seq_id":"16638268280","text":"import json\nimport asyncio\nimport discord\nimport os\nimport config\nimport requests\nimport aiohttp\n\n# kgs room id\nROOM=788560\n\n# kgs bot account name\nkgs_bot = \"LooselyBot\"\n\n# kgs json api to read data\nkgs_url = \"http://www.gokgs.com/json/access\"\n\n# bold wrapper\ndef bold(fn):\n    def wrapper(user):\n        # insert some bold before and after text\n        return \"**\" + fn(user) + \"**\"\n    return wrapper\n\nasync def login(session, status):\n        if status == 200:\n            return\n\n        # need to put your account name/password\n        message = {\n            \"type\": \"LOGIN\",\n            \"name\": kgs_bot,\n            \"password\": \"\",\n            \"locale\": \"en_US\",\n        }\n\n        formatted_message = json.dumps(message)\n        await session.post(kgs_url, data=formatted_message)\n\nasync def logout(session):\n    session.post(kgs_url, json.dumps({\"type\": \"LOGOUT\"}))\n\n@bold\ndef formatted_username(user):\n    \"\"\"\n    user: KGS player dict\n    yes_status: boolean to dicate whether to contain status of user\n\n    Given a KGS player dict user, return a string containing user information\n    \"\"\"\n    rank = \"\"\n    if \"g\" in user[\"flags\"]:\n        rank = \"Guest\"\n    elif \"rank\" in user:\n        rank = user[\"rank\"]\n    else:\n        rank = \"-\"\n    return   user['name'] + \" [\" +  rank + \"]\"\n\ndef formatted_user_status(user):\n    if \"flags\" not in user:\n        return \"\"\n    for k, v in config.kgs_user_flags_status.items():\n        if k in user[\"flags\"]:\n            return v\n\ndef formatted_name(player):\n    \"\"\"\n    Given a KGS player dict, return a str: username(rank)\n    https://www.gokgs.com/json/dataTypes.html#user\n    \"\"\"\n    text = \"**\" + player['name'] + \"**\"\n    if 'rank' in player:\n        text += \" (\" + player['rank'] + \")\"\n    return\n\ndef result(score):\n    \"\"\"\n    Returns a string indicating the result of a game\n    https://www.gokgs.com/json/dataTypes.html#score\n    \"\"\"\n    if type(score) == float:\n        if score > 0:\n            out = \"Black + \" + str(score)\n        else:\n            out = \"White + \" + str(-score)\n    else:\n        out = score\n    return out\n\nasync def get_kgs_players(m, session, bot, channel):\n    if m[\"type\"] == \"ROOM_JOIN\" and m[\"channelId\"] == ROOM and \"users\" in m:\n        text = \"\"\n        for u in  m[\"users\"]:\n            text += \"\\n\" + formatted_username(u)+ \": \"\n            text += formatted_user_status(u)\n        await send_discord_message(bot, channel, text)\n        await send_kgs_messages(session, text)\n\nasync def handle_message(session, bot, channel, data, op):\n    if data is None or not \"messages\" in data:\n        #print(\"not in message\")\n        return\n    if \"messages\" in data:\n        for m in data[\"messages\"]:\n            if \"channelId\" in m and \"type\" in m:\n                if op == 1:\n                    await get_kgs_players(m, session, bot, channel)\n                    #text = \"\"\n                    #for u in  m[\"users\"]:\n                    #    text += \"\\n\" + formatted_username(u)+ \": \"\n                    #    text += formatted_user_status(u)\n                    #await send_discord_message(bot, channel, text)\n                   # await send_kgs_messages(session, text)\n                if op == 0 and m['type'] == 'CHAT' and m['channelId'] == ROOM:\n                    if m['user']['name'] != kgs_bot:\n\n                        text = formatted_username(m[\"user\"]) + \": \"\n                        #if (m[\"text\"].startswith(\"/\") and m[\"text\"] in config.clyde_command)\n                        if m[\"text\"] in config.clyde_command:\n                            text += config.clyde_command[m[\"text\"]]\n                            await send_kgs_messages(session,\n                                              [config.clyde_command[m[\"text\"]]])\n\n                        elif m[\"text\"] in config.addon_command:\n                            text += config.addon_command[m[\"text\"]]\n                            await send_kgs_messages(session,\n                                              [config.addon_command[m[\"text\"]]])\n                        else:\n                            text += m[\"text\"]\n\n                        #    await send_discord_message(bot, channel, m[\"text\"])\n                        #text = formatted_username(m[\"user\"]) + \": \" + m[\"text\"]\n                        #print(\"text is \" , text)\n                        await send_discord_message(bot, channel, text)\n            if m['type'] == 'LOGOUT':\n                await login(session, 0)\n\nasync def get_messages(session, bot, channel, op):\n   async with session.get(kgs_url) as r:\n       data = await r.json()\n       await handle_message(session, bot, channel, await r.json(), op)\n\n       #print(data)\n       #print(json.dumps(data))\n#       if \"messages\" in data:\n#           for m in data[\"messages\"]:\n#               if m[\"type\"] == \"CHAT\" and m[\"channelId\"] == ROOM:\n#                   text = m[\"user\"][\"name\"] + \" [\" +  m[\"user\"][\"rank\"] + \"]: \" + m[\"text\"]\n#                   print(text)\n#                   await send_discord_message(text, bot)\n\nasync def send_discord_message(bot, channel, message):\n    await bot.send_message(channel, message)\n\nasync def send_kgs_messages(session, messages):\n    for text in messages:\n        message = {\n            \"type\": \"CHAT\",\n            \"text\": text,\n            \"channelId\": ROOM\n        }\n        formatted_message = json.dumps(message)\n        await session.post(kgs_url, data=formatted_message)\n","repo_name":"alvinctk/Loosely-Bot","sub_path":"kgs.py","file_name":"kgs.py","file_ext":"py","file_size_in_byte":5408,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"34417209953","text":"import socket\n\nserver_ip = \"13.58.181.109\"  # Cambia esto a la dirección IP del servidor en la nube\nserver_port = 8080\n\ntry:\n    # Crear un socket de cliente y conectar al servidor\n    client_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n    client_socket.connect((server_ip, server_port))\n    print(\"Conectado al servidor Pong.\")\n\n    # Lógica de comunicación con el servidor\n    while True:\n        # Leer entrada del usuario\n        message = input(\"Ingrese un mensaje: \")\n\n        if message.startswith(\"BEST_SCORE:\"):\n            print(\"Mejor puntuación: \" + message[11:])\n        else:\n            # Enviar el mensaje al servidor\n            client_socket.send(message.encode())\n\n            # Recibir respuesta del servidor\n            response = client_socket.recv(1024).decode()\n            print(\"Respuesta del servidor:\", response)\n\nexcept ConnectionRefusedError:\n    print(\"No se pudo conectar al servidor. Asegúrate de que el servidor esté en ejecución.\")\nexcept Exception as e:\n    print(\"Error de comunicación con el servidor:\", str(e))\nfinally:\n    client_socket.close()\n","repo_name":"cortegons/Telepong-Telematica","sub_path":"Cliente en Python/PongClient.py","file_name":"PongClient.py","file_ext":"py","file_size_in_byte":1108,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"41895278150","text":"import json\n\n\n# This is a prototype function to determine the values for incomming messages\n# The closer the message is to the input the more it will affect the values\n# This will require a tuple of dict and percentage of similarity\n\ndef calc(a, b, c) -> None:\n    print(((a * 60 / 100) + (b * 45 / 100) + (c * 80 / 100)) / ((60 / 100 + 45 / 100 + 80 / 100)))\n\n\ndef getValues(prompt: str, messages: list) -> dict:\n    pass\n\n\ndef replace() -> None:\n    with open(\"Data/Data.json\", \"r\") as f:\n        data = json.load(f)\n    finalData: list = []\n    for d in data[\"messages\"]:\n        question = d[\"Question\"]\n        answers = d[\"Answers\"]\n        context = d[\"Context\"]\n        d.clear()\n        d.update({\"Question\": question})\n        d.update({\"Answers\": answers})\n        d.update({\"Context\": context})\n        d.update({\"Confidence\": 1})\n        finalData.append(d)\n        with open(\"Data/Data.json\", \"w\") as f:\n            data = json.dump({\"messages\": finalData}, f, indent=4)\n\n\nreplace()\n# calc(10,10,10)\n","repo_name":"TortoiseShell04/ChatBot","sub_path":"ChatBot-main/Data/sd.py","file_name":"sd.py","file_ext":"py","file_size_in_byte":1014,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"10889789390","text":"\nclass QueryCreator:\n    def createQuery(self,content):\n        pass\nclass QueryTruncator(QueryCreator):\n    def __init__(self, lineModified=None, linesBelow=None, linesAbove=None, truncation=None):\n        self.linesAbove = self.linesBelow = 10\n        if (lineModified is None):\n            self.lineModified= \"\"\n        else :\n            self.lineModified = lineModified\n        if (linesBelow is not None):\n            self.linesBelow = linesBelow\n        if (linesAbove is not None):\n            self.linesAbove = linesAbove\n        if (truncation is None):\n            self.truncation = False\n        else: \n            self.truncation = truncation\n    def setLineModified(self,lineModified):\n        self.lineModified = lineModified\n    def setLinesBelow(self,linesBelow):\n        self.linesBelow = linesBelow\n    def setLinesAbove(self,linesAbove):\n        self.linesAbove= linesAbove\n    def setTruncation(self, truncation):\n        self.truncation = truncation\n    def truncate(self,content,pointModified,linesBelow,linesAbove):\n        startLine = max(0,pointModified-linesAbove-1)\n        finishLine = pointModified + linesBelow\n        return content[startLine:finishLine]\n    def createQuery(self, content):\n        return self.truncate(content, self.lineModified,self.linesBelow,self.linesAbove)\n\n\nclass OAIStandardQuery(QueryCreator):\n    def addOAILabels(self,content):\n        \n        #Add at file start the necessary content\n        content.insert(0,\"##### Fix bugs in the below function  \\n\")\n        content.insert(1,\"\\n\")\n        content.insert(2,\"### Buggy Python \\n\")\n\n        #Add at the end the necesary content\n        content.append(\"\\n\")\n        content.append(\"### Fixed Python \\n\")\n        content.append(\"\\n\")\n        \n    def createQuery(self,content):\n        self.addOAILabels(content)\n        return content\n        \nclass OAIHintQuery(OAIStandardQuery):\n    hint = \"Buggy\"\n    linesAddHint= []\n\n    def setHint(self,hint):\n        self.hint = hint\n    def setLinesAddHint(self,linesAdd):\n        self.linesAddHint = linesAdd\n    def includeHint(self,content,pointModified,hint):\n        for point in pointModified:\n            line = content[point]\n            content[point] = line[:len(line)-1] +\" #\" + hint + \"\\n\"\n            \n    def createQuery(self, content): #before calling createQuery we have to set the lines\n        self.includeHint(content,self.linesAddHint,self.hint)\n        self.addOAILabels(content)\n        return content\n\n\n\nclass HFStandardQuery(QueryTruncator):\n    def __init__(self, placeholder=None, linesAddPlaceholder=None):\n        self.linesAbove = self.linesBelow = 10\n        if (placeholder is None):\n            self.placeholder = \"\"\n        else :\n            self.placeholder = placeholder\n        if (linesAddPlaceholder is None):\n            self.linesAddPlaceholder = []\n        else:\n            self.linesAddPlaceholder = linesAddPlaceholder\n    def setPlaceholder(self,placeholder):\n        self.placeholder = placeholder\n    def setLinesAddPlaceholder(self,linesAdd):\n        self.linesAddPlaceholder = linesAdd\n    def deleteBuggyLines(self,content,pointModified):\n        for point in pointModified:\n            content_start = content[:point-1]\n            content_end = content[point:]\n            content = (content_start + content_end)\n        return content\n    def includePlaceholder(self,content,pointModified,placeholder):\n        for point in pointModified:\n            buggyline = content[point-1]\n            indentation = buggyline[:len(buggyline)-len(buggyline.strip())-1]\n            content.insert(point,indentation + placeholder) #line behind\n        \n    def createQuery(self, content): #before calling createQuery we have to set the lines \n        self.includePlaceholder(content,self.linesAddPlaceholder,self.placeholder)\n        content = self.deleteBuggyLines(content,self.linesAddPlaceholder) \n        if self.truncation:\n            content = self.truncate(content, self.linesAddPlaceholder[0],self.linesBelow,self.linesAbove)\n        return content\n    \nclass HFHintQuery(HFStandardQuery):\n    def __init__(self, placeholder=None, linesAddPlaceholder=None, hint=None, linesAddHint=None):\n        self.linesAbove = self.linesBelow = 10\n        if (placeholder is None):\n            self.placeholder = \"\"\n        else :\n            self.placeholder = placeholder\n        if (linesAddPlaceholder is None):\n            self.linesAddPlaceholder = []\n        else:\n            self.linesAddPlaceholder = linesAddPlaceholder\n        if (hint is None):\n            self.hint = \"buggy line:\"\n        else :\n            self.hint = hint\n        if (linesAddHint is None):\n            self.linesAddHint = []\n        else:\n            self.linesAddHint = linesAddHint\n    def setHint(self,hint):\n        self.hint = hint\n    def setLinesAddHint(self,linesAdd):\n        self.linesAddHint = linesAdd\n    def includeHint(self,content,pointModified,hint):\n        for point in pointModified:\n            print(\"poimt\",point)\n            line = content[point-1]\n            \n            indentation = line[:len(line)-len(line.strip())-1]\n            content[point-1] = indentation + \"#\" + hint + line\n        return content   \n    def createQuery(self, content): #before calling createQuery we have to set the lines   \n        self.includePlaceholder(content,self.linesAddPlaceholder,self.placeholder) \n        self.includeHint(content,self.linesAddHint,self.hint)\n        if self.truncation:\n            content = self.truncate(content, self.linesAddPlaceholder[0],self.linesBelow,self.linesAbove)\n        \n        return content\n\nclass HFPlBartStandardQuery(HFStandardQuery, QueryTruncator):\n    def addSpecialTokens(self,content):\n        content[0] = \"<s> \" + content[0]\n        content.append(\"</s> Python\")\n    def createQuery(self, content): #before calling createQuery we have to set the lines  \n        self.includePlaceholder(content,self.linesAddPlaceholder,self.placeholder)\n        content = self.deleteBuggyLines(content,self.linesAddPlaceholder) \n        if self.truncation:\n            content = self.truncate(content, self.linesAddPlaceholder[0],self.linesBelow,self.linesAbove)\n        self.addSpecialTokens(content)\n        return content\nclass HFPlBartHintQuery(HFPlBartStandardQuery,HFHintQuery):\n    def __init__(self, placeholder=None, linesAddPlaceholder=None, hint=None, linesAddHint=None):\n        self.linesAbove = self.linesBelow = 10\n        if (placeholder is None):\n            self.placeholder = \"\"\n        else :\n            self.placeholder = placeholder\n        if (linesAddPlaceholder is None):\n            self.linesAddPlaceholder = []\n        else:\n            self.linesAddPlaceholder = linesAddPlaceholder\n        if (hint is None):\n            self.hint = \"buggy line:\"\n        else :\n            self.hint = hint\n        if (linesAddHint is None):\n            self.linesAddHint = []\n        else:\n            self.linesAddHint = linesAddHint\n    def createQuery(self, content): #before calling createQuery we have to set the lines   \n        self.includePlaceholder(content,self.linesAddPlaceholder,self.placeholder) \n        self.includeHint(content,self.linesAddHint,self.hint)\n        if self.truncation:\n            content = self.truncate(content, self.linesAddPlaceholder[0],self.linesBelow,self.linesAbove)\n        self.addSpecialTokens(content)\n        return content\n    \nclass HFCodegenStandardQuery(QueryTruncator):\n    def __init__(self, lineChanged=None):\n        self.linesAbove = self.linesBelow = 10\n        if (lineChanged is None):\n            self.lineChanged = 0\n        else :\n            self.lineChanged = lineChanged\n    def setLineChanged(self,lineChanged):\n        self.lineChanged = lineChanged\n      \n    def removeTail(self,content,point):\n        if (point):\n            if point >0 : \n                content = content[:point-1]\n        return content\n        \n    def createQuery(self, content): #before calling createQuery we have to set the lines  \n        content = self.removeTail(content, self.lineChanged)\n        if self.truncation:\n            content = self.truncate(content, self.lineChanged,self.linesBelow,self.linesAbove)\n        return content\n    \nclass HFCodegenHintQuery(HFCodegenStandardQuery,HFHintQuery):\n    def __init__(self, lineChanged=None, hint=None):\n        self.linesAbove = self.linesBelow = 10\n        if (lineChanged is None):\n            self.lineChanged = 1\n        else :\n            self.lineChanged = lineChanged\n        if (hint is None):\n            self.hint = \"buggy line:\"\n        else :\n            self.hint = hint  \n    def createQuery(self, content): #before calling createQuery we have to set the lines  \n        if (self.lineChanged >0):\n            self.includeHint(content,[self.lineChanged],self.hint)\n        content = self.removeTail(content, self.lineChanged + 1)\n        if self.truncation:\n            content = self.truncate(content, self.lineChanged,self.linesBelow,self.linesAbove)\n        return content\n\n\n\n","repo_name":"tipeXwins/PipeLLMine","sub_path":"Pipeline/Query_Creation.py","file_name":"Query_Creation.py","file_ext":"py","file_size_in_byte":9028,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"6134447649","text":"from osgeo import gdalnumeric\nimport numpy\n\n# Precalculate an array for elevations from 0 to 250 meters by interpolating \n# the rest of the values linearly\ndef calculate_wind_speeds():\n    wind_speeds = []\n    \n    step = (WIND_50 - WIND_0) / 50.0\n    for i in range(0, 50):\n        wind_speeds.append(WIND_0 + (i+1) * step)\n    \n    step = (WIND_100 - WIND_50) / 50.0\n    for i in range(50, 100):\n        wind_speeds.append(WIND_50 + (i+1) * step)\n    \n    step = (WIND_200 - WIND_100) / 100.0\n    for i in range(100, 250):\n        wind_speeds.append(WIND_100 + (i+1) * step)\n   \n    for i in range(0, 250):\n        wind_speeds[i] = round(wind_speeds[i], 2)\n    \n    return wind_speeds\n\n# Mean temperature and presipication at 07/2006\nMEAN_TEMP = 16.5 # celcius\nMEAN_PREC = 16 # mm\n\n# value of burning pixel\nBURNING_PIXEL = 1\n\n# Array which tells with tuple (x, y) how much you move in x, y coordinates in each direction\n# 701 from pixel P, for example direction 0 you move (0, -1). Directions are positive in down-right\n# 6P2 direction\n# 543\nMOVE_TO_DIRECTION = [\n    (0, -1),\n    (1, -1),\n    (1, 0),\n    (1, 1),\n    (0, 1),\n    (-1, 1),\n    (-1, 0),\n    (-1, -1),\n]\n\n# pixel values that are not suitable for new burning\nUNELIGIBLE_TYPES = [BURNING_PIXEL, 7, 42]\n\n# Width and height of the area\nWIDTH = 307\nHEIGHT = 409\n\n# Wind speeds at different altitudes in meters per second\nWIND_0 = 3.0 # estimate\nWIND_50 = 4.5 \nWIND_100 = 5.5\nWIND_200 = 6.5\n\nWIND_SPEEDS = calculate_wind_speeds()\n\n# The percentage of speed of the wind that baseline forest \n# burns in the direction of the wind (see documentation for reasoning)\nWIND_TO_FIRE_SPEED_PERCENTAGE = 0.11\n\n# Relative burning speed rates to the baseline (Pine in mineral soil).\n# More details in documentation about the reasoning behind the rates\nBURNING_SPEED_RATE = {\n    13 : 1.50,\n    20 : 0.50,\n    46 : 1.00,\n    47 : 1.10,\n    49 : 1.20,\n    51 : 0.50,\n    52 : 0.60,\n    56 : 1.00,\n    57 : 1.10,\n    59 : 1.20,\n    67 : 0.75,\n    69 : 0.85,\n    71 : 0.95,\n    78 : 1.00, # baseline\n    79 : 1.10,\n    81 : 1.20,\n    89 : 1.25,\n    90 : 1.35,\n    92 : 1.45,\n    100 : 0.75,\n    101 : 0.80,\n    103 : 0.85,\n    116 : 1.25,\n    117 : 1.35,\n    119 : 1.45,\n    132 : 0.75,\n}\n\n# Precalculate array that tells how quickly the baseline \n# forest burns n steps from the direction of the wind\ndef calculate_wind_speed_coefficients():\n    wind_speed_coefficients = []\n\n    # if the direction is 0 steps away, the fire burns at the optimal speed in this direction\n    wind_speed_coefficients.append(WIND_TO_FIRE_SPEED_PERCENTAGE)\n    \n    # if the direction is 1 steps (45 degrees) away, the fire burns at 60% of the speed of the wind coefficient\n    wind_speed_coefficients.append(WIND_TO_FIRE_SPEED_PERCENTAGE * 0.6)\n    \n    # rest of the steps with same kind of logic\n    wind_speed_coefficients.append(WIND_TO_FIRE_SPEED_PERCENTAGE * 0.35)\n    wind_speed_coefficients.append(WIND_TO_FIRE_SPEED_PERCENTAGE * 0.25)\n    wind_speed_coefficients.append(WIND_TO_FIRE_SPEED_PERCENTAGE * 0.15)\n    return wind_speed_coefficients\n\n# Validate that these coordinates are in the area\ndef validate_coordinates(coordinate_tuple):\n    x = coordinate_tuple[0]\n    y = coordinate_tuple[1]\n    if x >= 0 and x < WIDTH and y >= 0 and y < HEIGHT:\n        return True\n    else:\n        return False\n\ndef get_next_pixels(burn_dict, t):\n    next_pixels = []\n    for pixel, burn_time in burn_dict.iteritems():\n        if burn_time <= t:\n            next_pixels.append(pixel)\n    return next_pixels\n\n# marks the pixel burning in the forest\ndef mark_pixel_burning(forest, pixel):\n    #print \"Burning pixel: \", pixel\n    forest[pixel[1]][pixel[0]] = BURNING_PIXEL\n\n# returns list of tuples for eligible neighbours, as (direction from pixel, coordinates, land_type)\ndef get_eligible_neighbours(forest, pixel):\n    eligible_neighbours = []\n    for i in range(0, 8):\n        move = MOVE_TO_DIRECTION[i]\n        neighbour_coordinates = (pixel[0] + move[0], pixel[1] + move[1])\n        if validate_coordinates(neighbour_coordinates):\n            neighbour = forest[neighbour_coordinates[1], neighbour_coordinates[0]]\n            if neighbour not in UNELIGIBLE_TYPES:\n                eligible_neighbours.append((i, neighbour_coordinates, neighbour))\n    return eligible_neighbours\n\n# get wind speed for this pixel\ndef get_wind_speed(elevation, pixel):\n    return WIND_SPEEDS[get_elevation(elevation, pixel)]\n\n# get slope for this pixel, transform the percent value to fraction form\ndef get_slope(slope, pixel):\n    return float(slope[pixel[1]][pixel[0]]) / 100.0\n\n# get elevation for this pixel\ndef get_elevation(elevation, pixel):\n    return elevation[pixel[1]][pixel[0]]\n\n# calculate the effect the slope has on the speed. Check if we are going uphill\n# or downhill. Uphill accelerates and downhill decelerates the spread.\ndef calculate_slope_coefficient(slope_value, elevation, source_pixel, target_pixel):\n    source_elevation = get_elevation(elevation, source_pixel)\n    target_elevation = get_elevation(elevation, target_pixel)\n\n    if source_elevation < target_elevation:\n        # going uphill\n        return 1.00 + slope_value\n    else:\n        # going downhill\n        return 1.00 - slope_value\n\n# calculate the difference between directions\ndef calculate_direction_difference(direction1, direction2):\n    difference = abs(direction1 - direction2)\n    if difference <= 4:\n        return difference\n    else:\n        return 8 - difference\n\ndef load_forest_colormap():\n    colormap = {}\n    colorfile = open('forest.tif.clr', 'r')\n    for line in colorfile:\n        splitted = line.split(' ')\n        colormap[splitted[0]] = (splitted[1], splitted[2], splitted[3].strip())\n\n    return colormap\n\n# Pixels are the type of the terrain\ndef load_forest_map():\n    return gdalnumeric.LoadFile('forest.tif')\n\n# Load test map that has only one type of forest to study the wind coefficients\ndef load_test_map():\n    return gdalnumeric.LoadFile('test.tif')\n\n# Pixels are the rates in which the slope affects the speed of the fire\ndef load_slope_map():\n    return gdalnumeric.LoadFile('slope.tif')\n\n# Pixels are elevation in meters\ndef load_elevation_map():\n    array = gdalnumeric.LoadFile('elevation.tif')\n    for i in range(0, len(array)):\n        for j in range(0, len(array[i])):\n            array[i][j] = array[i][j] * 0.1\n    return array\n\n# draw output raster, use colormap to make the pixels colorful again!\ndef draw_color_tiff(array, colormap, filename):\n    original = gdalnumeric.LoadFile('originals/forest.tif')\n    for i in range(0, len(array)):\n        for j in range(0, len(array[i])):\n            value = array[i][j]\n            color = colormap[str(value)]\n            original[0][i][j] = numpy.uint8(color[0])\n            original[1][i][j] = numpy.uint8(color[1])\n            original[2][i][j] = numpy.uint8(color[2])\n\n    write_obj = gdalnumeric.SaveArray(original, filename)\n\n    # Flush cache many many times to actually get the picture out!\n    write_obj.FlushCache()\n    write_obj.FlushCache()\n    write_obj.FlushCache()\n    write_obj.FlushCache()\n    write_obj.FlushCache()\n","repo_name":"PGHM/forest_fire_simulator","sub_path":"util.py","file_name":"util.py","file_ext":"py","file_size_in_byte":7119,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"17153110066","text":"import numpy as np\nimport pyfftw\nimport random\n\n\nn_samples = 15000 # random samples\ndnty = np.load('rho_2_sm.npy')\ndnty = np.log(dnty) # change for log or no log\n\ndelta = (dnty - np.average(dnty))/np.average(dnty)\nresol = np.shape(dnty)[0]\n\n\n# SET UP K-SPACE FOR RHO (REAL)\nrkvec = 2 * np.pi * np.fft.fftfreq(resol, gridlength / float(resol))\nkrealvec = 2 * np.pi * np.fft.rfftfreq(resol, gridlength / float(resol))\nrkxarray, rkyarray, rkzarray = np.meshgrid(rkvec, rkvec, krealvec, sparse=True, indexing='ij')\nrkarray2 = rkxarray**2+rkyarray**2+rkzarray**2\n\n# FIND FOURIER SPACE OVERDENSITY\nrfft_delta = pyfftw.builders.rfftn(delta, axes=(0, 1, 2)) #Doesn't ever refer to real space coords\ndelta_k = rfft_delta(delta)\nphi_k = delta_k*(rkarray2**-1)\nphi_k[0,0,0] = 0.\nTidal_k = dict([])\nk_vals = dict([(0,rkxarray), (1,rkyarray), (2, rkzarray)])\nfor i in range(3):\n    for j in range(3):\n        Tidal_k['{}{}{}'.format('Tk_', i, j)] = k_vals[i]*k_vals[j]*phi_k\n\n# MAKE IT TRACELESS\nfor i in range(3):\n    for j in range(3):\n        if i == j:\n            Tidal_k['{}{}{}'.format('Tk_', i, j)] = Tidal_k['{}{}{}'.format('Tk_', i, j)] - delta_k/3.\n        else:\n            Tidal_k['{}{}{}'.format('Tk_', i, j)] = Tidal_k['{}{}{}'.format('Tk_', i, j)]\n\nTidal = dict([])\nirfft_funct = pyfftw.builders.irfftn(rkarray2, axes=(0, 1, 2))\n\ncnt = -1\nlst = []\nfor i in range(3):\n    for j in range(3):\n        cnt = cnt + 1\n        lst.append(irfft_funct(Tidal_k['{}{}{}'.format('Tk_', i, j)]).copy())\n\nind_list = []\nfor i in range(resol):\n    for j in range(resol):\n        for k in range(resol):\n            ind_list.append([i,j,k])\n\nsample_list = random.sample(ind_list, n_samples)\nresults = []\nfor a in sample_list:\n    matrx = np.zeros((3, 3))\n    cnt = -1\n    for i in range(3):\n        for j in range(3):\n            cnt = cnt + 1\n            arr = lst[cnt]\n            matrx[i][j] = np.real(arr[a[0], a[1], a[2]])\n    diag = np.linalg.eig(matrx)\n    new = np.real(diag[0]).tolist()\n    if new[0] == 0 and new[1] == 0 and new[2] == 0:\n        print('All eigenvalues zero')\n    else:\n        sml = new.index(min(new))\n        lge = new.index(max(new))\n        ord = [0,1,2]\n        ord.remove(sml)\n        ord.remove(lge)\n        mid = ord[0]\n        new = [[diag[0][lge],diag[1][lge]], [diag[0][mid],diag[1][mid]], [diag[0][sml],diag[1][sml]], a, delta[a[0], a[1], a[2]]]\n        results.append(new)\nresults.append(resol)\nnp.save('TTT_sm.npy', results)\n\n","repo_name":"erckendall/TTT_eigenvalues","sub_path":"Tidal_Tensor.py","file_name":"Tidal_Tensor.py","file_ext":"py","file_size_in_byte":2452,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"35948382081","text":"from __future__ import print_function\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom collections import Counter, defaultdict\nfrom IPython.display import clear_output\n\nfrom catboost import CatBoostClassifier\nfrom sklearn.metrics import accuracy_score\nfrom sklearn.model_selection import KFold\n\n\ndef read_file(filename):\n    with open(filename) as handler:\n        return [line.strip().split(\", \") for line in handler]\n\ndef split_one_hot(values, splits):\n    result = np.zeros((len(values), len(splits) - 1))\n    for i, value in enumerate(values):\n        for index, (min_bound, max_bound) in enumerate(zip(splits[:-1], splits[1:])):\n            if (value >= min_bound) and (value < max_bound):\n                result[i][index] = 1\n                break\n    return result\n\ndef split_one_hot_uniform(values, batches):\n    minimum = np.min(values)\n    maximum = np.max(values)\n    splits = np.linspace(minimum, maximum, num=batches + 1)\n    return split_one_hot(values, splits)\n\ndef equal_one_hot(values, positive_value):\n    result = np.array([1, 0]).reshape([1, -1]).repeat(len(values), axis=0)\n    positive_indices = (np.array(values) == positive_value)\n    result[positive_indices, 0] = 0\n    result[positive_indices, 1] = 1\n    return result\n\ndef around_one_hot(values, middle_value):\n    result = np.zeros((len(values), 3))\n    middle_indices = (np.array(values) == middle_value)\n    more_indices = (np.array(values) > middle_value)\n    less_indices = (np.array(values) < middle_value)\n    result[middle_indices, 1] = 1\n    result[more_indices, 2] = 1\n    result[less_indices, 0] = 1\n    return result\n\ndef make_map(values):\n    values_set = set(values)\n    return {\n        value: i\n        for i, value in enumerate(values_set)\n    }\n\ndef map_one_hot(values, map_to_label):\n    max_label = len(map_to_label)\n    result = np.zeros((len(values), max_label))\n    for i, value in enumerate(values):\n        result[i][map_to_label[value]] = 1\n    return result\n\ndef map_one_hot_le(values, map_to_label):\n    max_label = len(map_to_label)\n    result = np.zeros((len(values), max_label))\n    for i, value in enumerate(values):\n        for j in range(i + 1):\n            result[j][map_to_label[value]] = 1\n    return result\n\ndef function_one_hot(values, function):\n    labels = list(map(function, values))\n    max_label = len(map_to_label)\n    result = np.zeros((len(values), max_label))\n    for i, label in enumerate(labels):\n        result[i][label] = 1\n    return result\n    \ndef make_dataset(raw_data, average_positive_by_contry):\n    columns = [\n        [line[i] for line in raw_data]\n        for i in range(len(raw_data[0]))\n    ]\n\n    # map one_hot\n    one_hot_indices = [1, 5, 6, 7, 8, 9]\n    maps = [\n        make_map(columns[index])\n        for index in one_hot_indices\n    ]\n    features = []\n    for index, map_to_labels in zip(one_hot_indices, maps):\n        features.append(map_one_hot(columns[index], map_to_labels))\n    \n    # le one_hot\n    education_num_map = make_map(columns[4])\n    features.append(map_one_hot(columns[4], education_num_map))\n    \n    # intervel one_hot\n    batches_indices = [0]\n    batches = [5]\n    for index, batch in zip(batches_indices, batches):\n        values = list(map(int, columns[index]))\n        features.append(split_one_hot_uniform(values, batches=batch))\n\n    # zero or not\n    for index in [10, 11]:\n        values = list(map(int, columns[index]))\n        features.append(equal_one_hot(values, 0))\n    function_indices = []\n    \n    # more, less or equal\n    values = list(map(int, columns[12]))\n    features.append(around_one_hot(values, 40))\n    \n    # contry\n\n    def featrue_by_contry(country):\n        if country == 'United-States':\n            return [0, 1, 0]\n        elif average_positive_by_contry[country] < average_positive_by_contry['United-States']:\n            return [1, 0, 0]\n        else:\n            return [0, 0, 1]\n\n    contry_features = np.array([\n        featrue_by_contry(country)\n        for country in columns[13]\n    ])\n    features.append(contry_features)\n    \n    targets = np.array([\n        0 if target == '<=50K' else 1\n        for target in columns[-1]\n    ])\n    \n    return np.concatenate(features, axis=1), targets\n\n\ndef get_average_positive_target_rate(train):\n    countries = set(line[13] for line in train)\n    average_positive_target_rate = {}\n    countries_positives = defaultdict(lambda: 0)\n    countries_negatives = defaultdict(lambda: 0)\n    for line in train:\n        if line[-1] == '<=50K':\n            countries_negatives[line[13]] += 1\n        else:\n            countries_positives[line[13]] += 1\n    for country in countries:\n        support = countries_positives[country] + countries_negatives[country]\n        rate = float(countries_positives[country]) / support\n        average_positive_target_rate[country] = rate\n    return average_positive_target_rate\n\n\ndef get_catboost_acc(train_x, train_y, test_x, test_y):\n    catboost_model = CatBoostClassifier(iterations=15)\n    catboost_model.fit(train_x, train_y, verbose=0)\n    catboost_predictions = catboost_model.predict(test_x)\n    return accuracy_score(catboost_predictions, test_y)\n\n\ndef k_fold(X, Y, accuracy_calculator, folds=5):\n    assert len(X) == len(Y)\n    kf = KFold(n_splits=folds)\n    evals = []\n    for train_index, test_index in kf.split(X):\n        train_x, test_x = X[train_index], X[test_index]\n        train_y, test_y = Y[train_index], Y[test_index]\n        evals.append(accuracy_calculator(train_x, train_y, test_x, test_y))\n    return np.array(evals)\n\n\ndef unique_dataset(X, Y):\n    unique_X_Y = set(map(tuple, np.concatenate([X, Y.reshape([-1, 1])], axis=1)))\n    saved = set()\n    bad_x = set()\n    for x in map(tuple, unique_X_Y):\n        x = x[:-1]\n        if x in saved:\n            bad_x.add(x)\n        else:\n            saved.add(x)\n    unique_X_Y = np.array(list(unique_X_Y))\n    X = unique_X_Y[:, :-1]\n    Y = unique_X_Y[:, -1]\n    indices = np.array([tuple(x) not in bad_x for x in X])\n    return X[indices], Y[indices]\n\n\ndef to_set(features):\n    return set(i for i, feature in enumerate(features) if feature == 1)\n\n\ndef get_stats(positives,\n              negatives,\n              x,\n              pure_positive_intersections,\n              positive_supports):\n    lens = []\n    supports = []\n    pure_positive_supports = []\n    positive_intersections = [positive & x for positive in positives]\n    negative_intersections = [negative & x for negative in negatives]\n\n    for intersection in positive_intersections:\n        lens.append(len(intersection))\n\n    for intersection in positive_intersections + negative_intersections:\n        intersection_tuple = tuple(sorted(intersection))\n\n        if intersection_tuple in positive_supports:\n            supports.append(positive_supports[intersection_tuple])\n        else:\n            support = 0\n            for positive in positives:\n                if intersection <= positive:\n                    support += 1\n            supports.append(support)\n            positive_supports[intersection_tuple] = support\n\n        if intersection in pure_positive_intersections:\n            pure_positive_supports.append(supports[-1])\n        else:\n            for negative in negatives:\n                if intersection <= negative:\n                    break\n            else:\n                pure_positive_supports.append(supports[-1])\n                pure_positive_intersections.add(intersection_tuple)\n    return [\n        np.mean(lens),\n        np.mean(supports) / len(positives),\n        float(len(pure_positive_supports)) / len(positives),\n        np.mean(pure_positive_supports) / len(positives) if len(pure_positive_supports) != 0 else 0\n    ]\n\n\ndef lattices_stats(train_x, train_y, test_x):\n    positives = [\n        to_set(x)\n        for x, y in zip(train_x, train_y)\n        if y == 1\n    ]\n    negatives = [\n        to_set(x)\n        for x, y in zip(train_x, train_y)\n        if y == 0\n    ]\n    test_x = [to_set(x) for x in test_x]\n\n    pos_stats = []\n    neg_stats = []\n\n    pure_positive_intersections = set()\n    pure_negative_intersections = set()\n    positive_supports = {}\n    negative_supports = {}\n\n    for i, x in enumerate(test_x):\n        if i % 20 == 0:\n            clear_output()\n            print(i)\n\n        pos_stats.append(get_stats(\n            positives,\n            negatives,\n            x,\n            pure_positive_intersections,\n            positive_supports\n        ))\n        neg_stats.append(get_stats(\n            negatives,\n            positives,\n            x,\n            pure_negative_intersections,\n            negative_supports\n        ))\n\n    return np.array(pos_stats), np.array(neg_stats)\n","repo_name":"PKoshkin/lattices","sub_path":"code.py","file_name":"code.py","file_ext":"py","file_size_in_byte":8665,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"18506841715","text":"# logout.py\n# Author: Richard Gibson\n#\n# Logs the user out and redirects them back to where they came from as\n# specified by the 'from' query parameter.\n#\n\nimport handler\n\nclass Logout( handler.Handler ):\n    def get( self ):\n        return_url = self.request.get( 'from' )\n        if not return_url:\n            return_url = \"/\"\n        # Clear login cookie \n        cookie = 'user_id=;Path=/'\n        self.response.headers.add_header( 'Set-Cookie', cookie )\n        self.redirect( return_url )\n","repo_name":"rggibson/pb-tracker","sub_path":"logout.py","file_name":"logout.py","file_ext":"py","file_size_in_byte":496,"program_lang":"python","lang":"en","doc_type":"code","stars":18,"dataset":"github-code","pt":"81"}
+{"seq_id":"41471936710","text":"class Node:\r\n    def __init__(self, data):\r\n       self.data = data\r\n       self.next = None\r\n \r\nclass LinkedList:\r\n    def __init__(self):\r\n        self.head = None\r\n        self.last_node = None\r\n \r\n    def append(self, data):\r\n        if self.last_node is None:\r\n            self.head = Node(data)\r\n            self.last_node = self.head\r\n        else:\r\n            self.last_node.next = Node(data)\r\n            self.last_node = self.last_node.next\r\n    def display(self):\r\n        prints = self.head\r\n        while prints is not None:\r\n            print(prints.data, end = ' ')\r\n            prints = prints.next\r\nlistahan = LinkedList()\r\nn = int(input('How many elements would you like to add? '))\r\nlistahann = []\r\nfor i in range(n):\r\n    data = int(input('Enter data item: '))\r\n    listahan.append(data)\r\n    listahann.append(data)\r\nprint('The linked list: \\n', end = '')\r\nlistahan.display()\r\nlistahann.sort()\r\nprint(\"\\nSorted: \")\r\nfor items in listahann:\r\n    print(end = \"\")\r\n    print(items, end = \" \")\r\n\r\n\r\n\r\n","repo_name":"Gdstrings/School_Projs","sub_path":"ACT6_P2_DizonG.py","file_name":"ACT6_P2_DizonG.py","file_ext":"py","file_size_in_byte":1018,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"2413696059","text":"\"\"\"djangoProject URL Configuration\n\nThe `urlpatterns` list routes URLs to views. For more information please see:\n    https://docs.djangoproject.com/en/3.1/topics/http/urls/\nExamples:\nFunction views\n    1. Add an import:  from my_app import views\n    2. Add a URL to urlpatterns:  path('', views.home, name='home')\nClass-based views\n    1. Add an import:  from other_app.views import Home\n    2. Add a URL to urlpatterns:  path('', Home.as_view(), name='home')\nIncluding another URLconf\n    1. Import the include() function: from django.urls import include, path\n    2. Add a URL to urlpatterns:  path('blog/', include('blog.urls'))\n\"\"\"\nfrom django.conf.urls.i18n import i18n_patterns\nfrom django.contrib import admin\nfrom django.urls import path\n\nfrom app.views.index import IndexView\nfrom app.views.log import AppLoginView, AppLogoutView\nfrom app.views.recipe import RecipeListView, RecipeByIngredientListView, RecipeDetailView, RecipeCreateView, \\\n    RecipeUpdateView, RecipeDeleteView\n\nurlpatterns = []\nurlpatterns += i18n_patterns(\n    path(\"admin/\", admin.site.urls),\n    path(\"\", IndexView.as_view(), name=\"index\"),\n    path(\"recipe/list\", RecipeListView.as_view(), name=\"recipe_list\"),\n    path(\n        \"recipe/by-ingredient/<str:ingredient>\",\n        RecipeByIngredientListView.as_view(),\n        name=\"recipe_by_ingredient_list\",\n    ),\n    path(\n        \"recipe/detail<int:pk>\",\n        RecipeDetailView.as_view(),\n        name=\"recipe_detail\",\n    ),\n    path(\"recipe/create\", RecipeCreateView.as_view(), name=\"recipe_create\"),\n    path(\n        \"recipe/update<int:pk>\",\n        RecipeUpdateView.as_view(),\n        name=\"recipe_update\",\n    ),\n    path(\n        \"recipe/delete<int:pk>\",\n        RecipeDeleteView.as_view(),\n        name=\"recipe_delete\",\n    ),\n    path(\"login\", AppLoginView.as_view(), name=\"login\"),\n    path(\"logout\", AppLogoutView.as_view(), name=\"logout\"),\n)\n","repo_name":"FGiordanengo/recipes","sub_path":"djangoProject/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1893,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"41193467905","text":"import pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport math\nimport datetime\n\n### Setup ###\ndf = pd.read_csv('EA_trimmed.csv')\ndatasetName = \"EA\"\n\n# print(df.info())\n# print(df.describe())\n\ndf[\"Date\"] = pd.to_datetime(df[\"Date\"])\ndf.set_index(\"Date\", inplace=True)\n# print(df.head())\n\nplotName = [\"Close_Price\", \"Volume\", \"Nasdaq_100\", \"SP_500\", \"Ten_Year_Treasury_Rate\", \"PE_Ratio\", \"ROA\", \"GDP\", \"High_Price\", \"Low_Price\", \"Open_Price\", \"news\", \"Days_After_Last_News\"]\n\n### Date vs. Features Plot ###\n# for i in plotName:\n#     plt.figure(figsize=(11, 8)) # resizing the plot\n#     df[i].plot()\n#     name = i + \" History\"\n#     plt.title(name) # title\n#     plt.xlabel(\"Date\") # x-label\n#     plt.ylabel(i) # y-label\n#     # f = plt.figure()\n#     path = \"plot/\" + datasetName + \"/single/\" + name + \".jpg\"\n#     # f.savefig(path)\n#     plt.savefig(path)\n\n\n\n### Feature Boxplot ###\n# for i in plotName:\n#     plt.figure(figsize=(11, 8)) # resizing the plot\n#     df.boxplot(column=[i])\n#     name = i + \" Boxplot\"\n#     plt.title(name)\n#     path = \"plot/\" + datasetName + \"/boxplot/\" + name + \".jpg\"\n#     plt.savefig(path)\n\n\n\n### Take a look on GDP ###\n# print(df[df[\"GDP\"] < 0]) # 187 rows hmm\n\n### Generate a bar diagram for years ###\n# print(df.loc[\"2010-01-04\"])\n# year_dic = {}\n# for year in df.index.year.tolist():\n#     year_dic[year] = year_dic.get(year, 0) + 1\n# # print(year_dic)\n# years = list(year_dic.keys())\n# counts = list(year_dic.values())\n\n# plt.figure(figsize=(11, 8)) # resizing the plot\n\n# plt.bar(years, counts, color=\"blue\", width =0.4)\n# plt.xlabel(\"Years\")\n# plt.xticks(years)\n# plt.ylabel(\"No. information\")\n# plt.title(\"No. of information in different year\")\n# name = \"plot/\" + datasetName + \"/barplot/Year_Bar.jpg\"\n# plt.savefig(name)\n\n\n\n### Generate a bar graph for news ###\n# year_dic = {}\n# print(df[df[\"Days_After_Last_News\"] == 0].head())\n# for year in df[df[\"Days_After_Last_News\"] == 0].index.year.tolist():\n#     year_dic[year] = year_dic.get(year, 0) + 1\n# # print(year_dic)\n# years = list(year_dic.keys())\n# counts = list(year_dic.values())\n\n# plt.figure(figsize=(11, 8)) # resizing the plot\n\n# plt.bar(years, counts, color=\"blue\", width =0.4)\n# plt.xlabel(\"Years\")\n# plt.xticks(np.arange(2010,2020))\n# plt.ylabel(\"No. news\")\n# plt.title(\"No. of news in different year\")\n# name = \"plot/\" + datasetName + \"/barplot/Num_News_Bar.jpg\"\n# plt.savefig(name)\n\n\n\n### Take a look on stock price ###\n# cols = ['Open_Price', 'Close_Price', 'Volume', 'High_Price', 'Low_Price']\n# axes = df[cols].plot(figsize=(11, 9), subplots = True)\n# name = \"plot/\" + datasetName + \"/Stock_Price.jpg\"\n# plt.savefig(name)\n\n\n\n### Correlation Graph ###\n# print(df.corr())\n\n# define the mask to set the values in the upper triangle to True\n# plt.figure(figsize=(16, 6))\n# mask = np.triu(np.ones_like(df.corr(), dtype=np.bool_))\n# heatmap = sns.heatmap(df.corr(), mask=mask, vmin=-1, vmax=1, annot=True, cmap='BrBG')\n# heatmap.set_title('Triangle Correlation Heatmap', fontdict={'fontsize':18}, pad=16);\n# # save heatmap as .png file\n# # dpi - sets the resolution of the saved image in dots/inches\n# # bbox_inches - when set to 'tight' - does not allow the labels to be cropped\n# name = \"plot/\" + datasetName + '/correlation.png'\n# plt.savefig(name, dpi=300, bbox_inches='tight')\n\n\n### histogram ###\ncolumns = df.columns\nfor col in columns:\n    plt.figure(figsize=(11, 8)) # resizing the plot\n    print(\"col:\", col)\n    df[col].hist()\n    name = col + \" Histogram\"\n    plt.title(name)\n    path = \"../plot/\" + datasetName + '/histplot/' + col + \".jpg\"\n    plt.savefig(path)\n\n\n### generate EA_selected dataset ###\n'''\nFor all of our models, \nResponse: Close_Price\nFeatures: Volume, Nasdaq_100, SP_500, Ten_Year_Treasury_Rate, PE_Ratio, ROA, GDP, news\n'''\n# df = df[[\"Close_Price\",\"Volume\", \"Nasdaq_100\", \"SP_500\", \"Ten_Year_Treasury_Rate\", \"PE_Ratio\", \"ROA\", \"GDP\", \"news\"]]\n# print(df.head())\ndf.to_csv(\"../dataset/EA_dataset.csv\", index=True)\n\n\n\n\n# plt.show()\n\n","repo_name":"YimingZhao-2000/CISC499","sub_path":"process_data/understand_EA_dataset.py","file_name":"understand_EA_dataset.py","file_ext":"py","file_size_in_byte":4016,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"81"}
+{"seq_id":"39341591122","text":"import asyncio\nfrom random import randint\n\nimport click\nimport logging\nimport os\nimport subprocess\n\nfrom aiogram.types import ParseMode\nfrom aiogram import Bot, types, executor\nfrom aiogram.contrib.fsm_storage.memory import MemoryStorage\nfrom aiogram.dispatcher import Dispatcher\nfrom aiogram.utils.exceptions import Throttled\n\nfrom fundbot.coingecko import eth_price, fund_price\nfrom fundbot.uniswap import uniswap_data\nfrom fundbot.etherscan import total_supply\nfrom fundbot.utils import get_secret\n\nlog = logging.getLogger(__name__)\n\nemojis = \"🦄️,🌿️,😇,🙃,😁,🤑,🤥,🤮,😎,😊,😈,👻,☀️\".split(\",\")\n\nsecrets = {\n    'fundbot': get_secret('fundbot'),\n    'etherscan': get_secret('etherscan'),\n}\n\nbot = Bot(token=secrets['fundbot'])\n\nstorage = MemoryStorage()\ndp = Dispatcher(bot, storage=storage)\n\n\n@dp.message_handler(commands=['help'])\nasync def send_welcome(message: types.Message):\n    \"\"\"\n    This handler will be called when user sends `/start` or `/help` command\n    \"\"\"\n    attribution = '<a href=\"https://etherscan.io/apis\">Powered by ' \\\n                  'Etherscan.io APIs</a>'\n    lines = [\n        f\"Hi! I only know the /fund and /xfund command so far\",\n        f'{attribution}, Uniswap APIv2 and CoinGecko'\n    ]\n    msg = \"\\n\".join(lines)\n    await message.reply(msg, parse_mode=ParseMode.HTML)\n\n\n@dp.message_handler(commands=['fund', 'xfund'])\nasync def fund(message: types.Message):\n    try:\n        # Execute throttling manager with rate-limit equal to 2 seconds\n        await dp.throttle('start', rate=2)\n    except Throttled:\n        # If request is throttled, the `Throttled` exception will be raised\n        await message.reply('Too many requests!')\n    else:\n        log.info(f\"/fund /xfund called\")\n        msg = await render_pool()\n        await message.answer(msg, parse_mode=ParseMode.HTML)\n\n\n@dp.message_handler(commands=['version'])\nasync def version(message: types.Message):\n    label = subprocess.check_output(\n        [\"/bin/git\", \"rev-parse\", \"--short\", \"HEAD\"]).strip()\n    await message.answer(f\"{label}\")\n\n\n@click.group()\ndef main():\n    format = '%(asctime)s - %(name)s - %(levelname)s - %(message)s'\n    logging.basicConfig(level=os.environ.get(\"LOGLEVEL\", \"DEBUG\"), format=format)\n\n\n@main.command()\ndef run():\n    log.info(f\"Starting Telegram Bot\")\n    executor.start_polling(dp, skip_updates=True)\n\n\n@main.command()\ndef check():\n    log.info(f\"Checking queries\")\n    loop = asyncio.get_event_loop()\n    msg = loop.run_until_complete(render_pool())\n    print(msg)\n\n\nasync def render_pool():\n    ret = await asyncio.gather(\n        eth_price(),\n        total_supply(secrets['etherscan']),\n        uniswap_data(),\n        fund_price()\n    )\n    e_price = ret[0]\n    supply = ret[1]\n    pooled_eth, pooled_xfund, last_price = ret[2]\n    usd, usd_market_cap, usd_24h_vol, usd_24h_change = ret[3]\n\n    xfund_usd_price = last_price * e_price\n    market_cap = supply * xfund_usd_price\n    xfund_uni_liq = (pooled_xfund * xfund_usd_price) + (pooled_eth * e_price)\n\n    rando_emoji = emojis[randint(0, len(emojis) - 1)]\n\n    lines = [\n        f\"<b>xFUND</b>\",\n        f\"Price [USD]: ${xfund_usd_price:,.2f}\",\n        f\"Price [ETH]: {last_price:.4f} Ξ\",\n        f\"MarketCap: ${market_cap:,.0f}\",\n        f\"Uniswap Liquidity: ${xfund_uni_liq:,.0f}\",\n        f\"Uniswap Pool: {pooled_eth:.2f} Ξ / {pooled_xfund:.2f} xFUND\",\n        f\"Total supply: {supply:,.0f} xFUND\",\n        f\"\",\n        f\"<b>FUND</b>\",\n        f\"Price [USD]: ${usd:.4f}\",\n        f\"MarketCap: ${usd_market_cap:,.0f}\",\n        f\"Volume: ${usd_24h_vol:,.0f}\",\n        f\"24h: {usd_24h_change:.2f}%\",\n    ]\n    msg = \"\\n\".join(lines)\n    return msg\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"unification-com/fund-bot","sub_path":"src3/fundbot/cli.py","file_name":"cli.py","file_ext":"py","file_size_in_byte":3717,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"8997533009","text":"import puzIO\nimport sys\nimport numpy as np\n\ndef compute_puz_partOBJ(puzMat, partID):\n\n    try:\n        [Nx, Ny, Nz] = list(puzMat.shape)\n    except:\n        print(\"Error: the shape of the voxel matrix is wrong\")\n\n    v2i = lambda v: v[0] + v[1] * (Nx + 1) + v[2] * (Nx + 1) * (Ny + 1)\n\n    part_voxel = []\n    for iz in range(Nz):\n        for iy in range(Ny):\n            for ix in range(Nx):\n                if(puzMat[ix][iy][iz] == partID):\n                    part_voxel.append([ix, iy, iz])\n\n    cube_vertice = [\n        [0, 0, 0],\n        [1, 0, 0],\n        [0, 1, 0],\n        [1, 1, 0],\n        [0, 0, 1],\n        [1, 0, 1],\n        [0, 1, 1],\n        [1, 1, 1]\n    ]\n    cube_vertice = [np.array(v) for v in cube_vertice]\n\n    dV =[\n        [1, 0, 0],\n        [-1, 0, 0],\n        [0, 1, 0],\n        [0, -1, 0],\n        [0, 0, 1],\n        [0, 0, -1]\n    ]\n    dV = [np.array(dv) for dv in dV]\n\n    cube_face = [\n        [1, 3, 7, 5], # +x\n        [0, 4, 6, 2], # -x\n        [2, 6, 7, 3], # +y\n        [0, 1, 5, 4], # -y\n        [4, 5, 7, 6], # +z\n        [0, 2, 3, 1], # -z\n    ]\n\n    size = np.array([Nx, Ny, Nz])\n    Nmax = max(size) + 1\n    faces = []\n    vertices = []\n    vmap = {}\n    for voxel in part_voxel:\n        pos = np.array(voxel)\n        for i, dv in enumerate(dV):\n            npos = pos + dv\n            #face is between two voxels in the same part\n            if npos.min() >= 0 and (size - npos).min() > 0:\n                if puzMat.item(tuple(npos)) == puzMat.item(tuple(pos)):\n                    continue\n            face = [list(cube_vertice[vid] + pos) for vid in cube_face[i]]\n            for fv in face:\n                if v2i(fv) not in vmap:\n                    vmap[v2i(fv)] = len(vmap)\n                    #vertices.append([fv[0] / (Nx + 1) - 0.5, fv[1] / (Ny + 1) - 0.5, fv[2] / (Nz + 1) - 0.5])\n                    vertices.append([fv[0] / Nmax - 0.5, fv[2] / Nmax - 0.5, -(fv[1] / Nmax - 0.5)])\n            faces.append([vmap[v2i(fv)] for fv in face])\n\n    return {\"vertices\" : vertices, \"faces\" : faces}\n\ndef main():\n    if len(sys.argv) < 2:\n        print(\"Error: no input .puz file is indicated.\")\n        exit();\n\n    if len(sys.argv) == 2:\n        puzMat = puzIO.read_puzfile_into_numpy(sys.argv[1])\n        print(compute_puz_partOBJ(puzMat, partID = 1))\n    #plt.show()\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"EPFL-LGG/TopoLite","sub_path":"python/example/puzOBJ.py","file_name":"puzOBJ.py","file_ext":"py","file_size_in_byte":2355,"program_lang":"python","lang":"en","doc_type":"code","stars":29,"dataset":"github-code","pt":"81"}
+{"seq_id":"33244474868","text":"\"\"\"\r\nImplements the RunQueueTreeView class that is used to display a RunQueue in a Qt-Treeview\r\n\"\"\"\r\nimport logging\r\nimport typing\r\n\r\nfrom PySide6 import QtCore, QtWidgets\r\nfrom pyside6_utils.models import ExtendedSortFilterProxyModel\r\n\r\nfrom configurun.classes.run_queue import (RunQueue, RunQueueItemActions,\r\n                                          RunQueueItemStatus)\r\nfrom configurun.app.models.run_queue_table_model import RunQueueTableModel\r\n\r\nlog = logging.getLogger(__name__)\r\n\r\nclass RunQueueTreeView(QtWidgets.QTreeView):\r\n\t\"\"\"\r\n\tTreeview-equivalent used for run-queue items (MLQueueItems)\r\n\t\"\"\"\r\n\r\n\tdef __init__(self, parent: typing.Optional[QtWidgets.QWidget] = None) -> None:\r\n\t\tsuper().__init__(parent)\r\n\r\n\t\t# self.setModel(model)\r\n\r\n\t\t#===========Create menu ============\r\n\t\tself._rc_menu = QtWidgets.QMenu(self)\r\n\t\tself._delete_action = self._rc_menu.addAction(\"Delete\")\r\n\t\tself._cancel_action = self._rc_menu.addAction(\"Cancel\")\r\n\t\tself._move_up_action = self._rc_menu.addAction(\"Move Up\")\r\n\t\tself._move_down_action = self._rc_menu.addAction(\"Move Down\")\r\n\t\tself._move_top_action = self._rc_menu.addAction(\"Move to Top\")\r\n\t\tself._stop_action = self._rc_menu.addAction(\"Stop\")\r\n\t\tself._start_action = self._rc_menu.addAction(\"Run Item\")\r\n\r\n\t\tself.setContextMenuPolicy(QtCore.Qt.ContextMenuPolicy.CustomContextMenu)\r\n\t\tself.customContextMenuRequested.connect(self.custom_menu_requested)\r\n\r\n\t\tself.setSortingEnabled(True)\r\n\r\n\t\tself._action_dict = {\r\n\t\t\tRunQueueItemActions.DELETE: self._delete_action,\r\n\t\t\tRunQueueItemActions.CANCEL: self._cancel_action,\r\n\t\t\tRunQueueItemActions.MOVEUP: self._move_up_action,\r\n\t\t\tRunQueueItemActions.MOVEDOWN: self._move_down_action,\r\n\t\t\tRunQueueItemActions.MOVETOP: self._move_top_action,\r\n\t\t\tRunQueueItemActions.STOP: self._stop_action,\r\n\t\t\tRunQueueItemActions.START: self._start_action,\r\n\t\t}\r\n\r\n\t\t# =========== Link actions ===========\r\n\t\tfor key in self._action_dict: # pylint: disable=consider-using-dict-items\r\n\t\t\tif key != RunQueueItemActions.STOP:\r\n\t\t\t\tself._action_dict[key].triggered.connect(lambda *_, action=key : self.do_action_on_selection(action))\r\n\r\n\t\t#Stop is a special case, ask for confirmation\r\n\t\tself._action_dict[RunQueueItemActions.STOP].triggered.connect(lambda *_: self.confirm_stop_current_index())\r\n\r\n\t\tself.proxy_model = ExtendedSortFilterProxyModel(self)\r\n\t\tself.proxy_model.setDynamicSortFilter(True)\r\n\t\tself.proxy_model.set_filter_function(\"status_filter\", self.check_if_current_filter_accepts_row)\r\n\t\t#Make sure the dataChanged signal is emitted when the source model changes\r\n\r\n\t\tsuper().setModel(self.proxy_model)\r\n\r\n\r\n\t\t#Connect double-click\r\n\t\tself._double_click_connection = self.doubleClicked.connect(self._default_on_double_click)\r\n\t\tself._hide_status_list = []\r\n\r\n\tdef check_if_current_filter_accepts_row(self,\r\n\t\t\t\tsource_row: int,\r\n\t\t\t\tsource_parent: QtCore.QModelIndex | QtCore.QPersistentModelIndex,\r\n\t\t\t\tsource_model: QtCore.QAbstractItemModel\r\n\t\t\t) -> bool:\r\n\t\t\"\"\"Check if the passed row should be visible or not\r\n\r\n\t\tArgs:\r\n\t\t\tsource_row (int): The row to check\r\n\t\t\tsource_parent (QtCore.QModelIndex): The parent of the row to check\r\n\r\n\t\tReturns:\r\n\t\t\tbool: True if the row should be visible, False otherwise\r\n\t\t\"\"\"\r\n\t\t#Status column:\r\n\t\tindex = source_model.index(source_row, 0, source_parent)\r\n\t\t# data_roles = [role for role in RunQueueTableModel.CustomDataRoles]\r\n\t\tstatus = index.data(RunQueueTableModel.CustomDataRoles.StatusRole)[0]\r\n\t\tif status in self._hide_status_list:\r\n\t\t\treturn False\r\n\t\treturn True\r\n\r\n\tdef set_item_status_filter(self, hide_status_list : list[RunQueueItemStatus] | None = None) -> None:\r\n\t\t\"\"\"Sets the list of statuses that should be filtered (hidden)\r\n\r\n\t\tArgs:\r\n\t\t\thide_status_list (list[RunQueueItemStatus], optional): A list of statuses that should be filtered (hidden).\r\n\t\t\t\tDefaults to None.\r\n\t\t\"\"\"\r\n\t\tif hide_status_list is None:\r\n\t\t\thide_status_list = []\r\n\t\tself._hide_status_list = hide_status_list\r\n\t\tself.proxy_model.invalidateFilter()\r\n\r\n\tdef set_whether_status_filtered(self, status : RunQueueItemStatus,  hide_item : bool) -> None:\r\n\t\t\"\"\"Sets whether the passed status should be filtered (hidden) or not\r\n\r\n\t\tArgs:\r\n\t\t\tstatus (RunQueueItemStatus): The status to hide or show\r\n\t\t\thide_item (bool): Whether to hide the passed status or not\r\n\t\t\"\"\"\r\n\t\tif hide_item:\r\n\t\t\tif status not in self._hide_status_list:\r\n\t\t\t\tself._hide_status_list.append(status)\r\n\t\t\t\tself.proxy_model.invalidateFilter()\r\n\t\telse:\r\n\t\t\tif status in self._hide_status_list:\r\n\t\t\t\tself._hide_status_list.remove(status)\r\n\t\t\t\tself.proxy_model.invalidateFilter()\r\n\r\n\r\n\tdef get_item_status_filter(self) -> list[RunQueueItemStatus]:\r\n\t\t\"\"\"Returns a list of all statuses that are filtered (hidden)\r\n\r\n\t\tReturns:\r\n\t\t\tlist[RunQueueItemStatus]: A list of all statuses that are filtered (hidden)\r\n\t\t\"\"\"\r\n\t\treturn self._hide_status_list\r\n\r\n\r\n\tdef set_double_click_callback(self,\r\n\t\t\t\tfunction : typing.Callable[[QtCore.QModelIndex], None]\r\n\t\t\t) -> None:\r\n\t\t\"\"\"Used to overwrite the current callback when double-clicking on an item.\r\n\t\tE.g. if we first want to try to load the clicked-item, and on fail, do nothing\r\n\r\n\t\tNOTE: disconnects any existing connections\r\n\r\n\t\tArgs:\r\n\t\t\tfunction (typing.Callable[[QtCore.QModelIndex], None]): The function to call when double-clicking on an item\r\n\t\t\t\tshould take a single argument, the index of the double-clicked item\r\n\t\t\"\"\"\r\n\t\tif self._double_click_connection is not None:\r\n\t\t\tself.doubleClicked.disconnect(self._double_click_connection)\r\n\t\tself._double_click_connection = self.doubleClicked.connect(function)\r\n\r\n\tdef _default_on_double_click(self, index: QtCore.QModelIndex) -> None:\r\n\t\t\"\"\" Default action when double-clicking on an item in the treeview.\r\n\t\tSet the hightlight-item when double-clicking on an item\r\n\r\n\t\tArgs:\r\n\t\t\tindex (QtCore.QModelIndex): The double-clicked item\r\n\t\t\"\"\"\r\n\t\tcur_id = index.data(RunQueueTableModel.CustomDataRoles.IDRole)\r\n\t\tlog.debug(f\"Double clicked on index {index.row()} with id {cur_id}\")\r\n\t\tcur_model = self.proxy_model.sourceModel()\r\n\t\tif isinstance(cur_model, RunQueueTableModel):\r\n\t\t\tcur_model.set_highligh_by_id(cur_id)\r\n\r\n\t# def model(self):\r\n\t# \t\"\"\"Return the model for this view\r\n\t# \t\"\"\"\r\n\t# \treturn self.proxy_model\r\n\r\n\r\n\tdef setModel(self, new_model: RunQueueTableModel) -> None:\r\n\t\t\"\"\"Set the model for this view\r\n\r\n\t\tNOTE: this treeview uses a proxy model, so the passed model is set as the source model of the proxy model\r\n\t\t\t<this>.getModel() will return the proxy model, not this passed model\r\n\t\t\"\"\"\r\n\t\tret = self.proxy_model.setSourceModel(new_model)\r\n\t\t#Make sure the dataChanged signal is emitted when the source model changes:\r\n\t\tnew_model.dataChanged.connect(self.proxy_model.dataChanged) #NOTE: if we don't this, the proxy model won't emit\r\n\t\t\t# dataChanged signals. This seems to be a bug in Qt, though I could not find a mention of it on 25-06-2023\r\n\r\n\t\treturn ret\r\n\tdef confirm_stop_index(self, index : QtCore.QModelIndex) -> None:\r\n\t\t\"\"\" Ask user for confirmation to stop the item at the passed index\r\n\t\tArgs:\r\n\t\t\tindex (QtCore.QModelIndex): The index to confirm the stop action on\r\n\t\t\"\"\"\r\n\t\tmsg = QtWidgets.QMessageBox()\r\n\t\tmsg.setIcon(QtWidgets.QMessageBox.Icon.Warning)\r\n\t\tmsg.setText(\"Are you sure you want to stop this item?\")\r\n\t\tmsg.setInformativeText(\"This will force stop the item process - any unsaved progress of this run will be lost.\")\r\n\t\tmsg.setWindowTitle(\"Confirm stop\")\r\n\t\tmsg.setStandardButtons(QtWidgets.QMessageBox.StandardButton.Yes | QtWidgets.QMessageBox.StandardButton.No)\r\n\t\tmsg.setDefaultButton(QtWidgets.QMessageBox.StandardButton.No)\r\n\t\tret = msg.exec()\r\n\t\tif ret == QtWidgets.QMessageBox.StandardButton.Yes:\r\n\t\t\treturn self.do_action_on_index(RunQueueItemActions.STOP, index)\r\n\r\n\r\n\tdef confirm_stop_current_index(self) -> None:\r\n\t\t\"\"\" Ask user for confirmation to stop the currently selected item\r\n\t\t\"\"\"\r\n\t\tindex = self.currentIndex()\r\n\t\treturn self.confirm_stop_index(index)\r\n\r\n\r\n\r\n\tdef custom_menu_requested(self, pos : QtCore.QPoint) -> None:\r\n\t\t\"\"\"Create a context menu for the possible actions on the item under the mouse\r\n\r\n\t\tArgs:\r\n\t\t\tpos (QtCore.QPoint): The position of the mouse\r\n\t\t\"\"\"\r\n\r\n\t\t#Get the index of the item under the mouse\r\n\t\tindex = self.indexAt(pos)\r\n\t\t#If the index is valid, create a menu\r\n\t\tif not index.isValid():\r\n\t\t\treturn\r\n\r\n\t\tpossible_actions = index.data(RunQueueTableModel.CustomDataRoles.ActionRole)\r\n\t\tfor action in self._action_dict: # pylint: disable=consider-using-dict-items\r\n\t\t\tif action in possible_actions:\r\n\t\t\t\tself._action_dict[action].setVisible(True)\r\n\t\t\telse:\r\n\t\t\t\tself._action_dict[action].setVisible(False)\r\n\t\tself._rc_menu.popup(self.mapToGlobal(pos))\r\n\r\n\tdef do_action_on_index(self, action: RunQueueItemActions, index : QtCore.QModelIndex) -> None:\r\n\t\t\"\"\"Do the passed action on the passed index\r\n\r\n\t\tNOTE: no safety checks are performed, calling stop on an item will force stop it if it's running\r\n\t\twithout asking for user confirmation\r\n\r\n\t\tArgs:\r\n\t\t\taction (RunQueueItemActions): The action to perform\r\n\t\t\tindex (QtCore.QModelIndex): The index to perform the action on\r\n\t\t\"\"\"\r\n\t\tlog.debug(f\"Trying to perform action {str(action)} on index {index.row()}\")\r\n\t\t# cur_model = self.model()\r\n\t\tif not index.isValid():\r\n\t\t\tlog.info(f\"Could not perform action {str(action)} on index {index.row()} - index is invalid\")\r\n\t\t\treturn\r\n\t\ttry:\r\n\t\t\tself.model().setData(index, action, RunQueueTableModel.CustomDataRoles.ActionRole)\r\n\t\texcept Exception as exception: # pylint: disable=broad-exception-caught\r\n\t\t\tlog.error(f\"Failed to perform action {str(action)}: {exception}\")\r\n\t\t\t#Create qt message box with this notification\r\n\t\t\tmsg = QtWidgets.QMessageBox()\r\n\t\t\tmsg.setIcon(QtWidgets.QMessageBox.Icon.Critical)\r\n\t\t\tmsg.setText(f\"Failed to perform action {str(action)} on selected index ({index.row()})\")\r\n\t\t\tmsg.setInformativeText(f\"{type(exception).__name__}: {exception}\")\r\n\t\t\tmsg.setWindowTitle(\"Error\")\r\n\t\t\tmsg.exec_()\r\n\r\n\tdef do_action_on_selection(self, action: RunQueueItemActions) -> None:\r\n\t\t\"\"\"Do the passed action on the currently selected item (singular)\r\n\r\n\t\tArgs:\r\n\t\t\taction (RunQueueItemActions): The action to perform\r\n\t\t\"\"\"\r\n\t\tlog.debug(f\"Trying to perform action {str(action)} on selection ({self.currentIndex().row()})\")\r\n\t\tindex = self.currentIndex()\r\n\t\tif not index.isValid():\r\n\t\t\treturn\r\n\t\tself.do_action_on_index(action, index)\r\n\t\tlog.debug(\"Done performing action on selection\")\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\ndef run_example_app():\r\n\t\"\"\" Run an example of the run_queue tree view\"\"\"\r\n\t#pylint: disable=import-outside-toplevel\r\n\timport sys\r\n\r\n\tfrom configurun.examples.example_target_function import example_target_function\r\n\tapp = QtWidgets.QApplication([])\r\n\twindow = QtWidgets.QMainWindow()\r\n\twindow.resize(800, 600)\r\n\tcentral_widget = QtWidgets.QWidget()\r\n\twindow.setCentralWidget(central_widget)\r\n\tlayout = QtWidgets.QVBoxLayout()\r\n\tcentral_widget.setLayout(layout)\r\n\r\n\tqueue_view = RunQueueTreeView()\r\n\trun_queue = RunQueue(\r\n\t\ttarget_function=example_target_function\r\n\t)\r\n\tqueue_model = RunQueueTableModel(run_queue=run_queue)\r\n\r\n\tqueue_view.setModel(queue_model)\r\n\tlayout.addWidget(queue_view)\r\n\r\n\tfor i in range(10):\r\n\t\trun_queue.add_to_queue(\r\n\t\t\tf\"Testitem {i}\",\r\n\t\t\tconfig={\"test\": i}, #nonsense config #type: ignore\r\n\t\t)\r\n\r\n\twindow.show()\r\n\tres = app.exec()\r\n\tlog.info(\"Done!\")\r\n\tsys.exit(res)\r\n\r\n\r\nif __name__ == \"__main__\":\r\n\tlogging.getLogger('matplotlib').setLevel(logging.INFO)\r\n\tlogging.getLogger('PySide6').setLevel(logging.DEBUG)\r\n\tformatter = logging.Formatter(\"[{pathname:>90s}:{lineno:<4}] {levelname:<7s}   {message}\", style='{')\r\n\thandler = logging.StreamHandler()\r\n\thandler.setFormatter(formatter)\r\n\tlogging.basicConfig(\r\n\t\thandlers=[handler],\r\n\t\tlevel=logging.DEBUG\r\n\t) #Without time\r\n\troot = logging.getLogger()\r\n\troot.handlers = [handler]\r\n\r\n\trun_example_app()\r\n","repo_name":"Woutah/configurun","sub_path":"configurun/app/views/run_queue_tree_view.py","file_name":"run_queue_tree_view.py","file_ext":"py","file_size_in_byte":11769,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"81"}
+{"seq_id":"29599462911","text":"# coding: utf-8\n\nimport chainer\nimport varit.random_variables as rv\n\n\nclass VAE(chainer.Chain):\n    def __init__(self, qzgx, pxgz):\n        super().__init__(\n            qzgx=qzgx,\n            pxgz=pxgz,\n        )\n\n    def reconstract(self, x, sample=False):\n        pz = self.qzgx(x)\n        if sample:\n            z = pz.sample()\n        else:\n            z = pz.mu\n        px = self.pxgz(z)\n        return px\n\n    def lower_bound(self, x, C=1.0, sample=1):\n        # loss function\n        batchsize = len(x.data)\n        qz = self.qzgx(x)\n        llf = rv.LogLikelihood(self.pxgz, x)\n        rec_loss = rv.expectation(qz, llf, sample) / batchsize\n        kl_loss = rv.gaussian_kl_standard(qz) / batchsize\n        loss = -(rec_loss - C * kl_loss)\n        return loss\n","repo_name":"KojiOchiai/varit","sub_path":"varit/models/vae.py","file_name":"vae.py","file_ext":"py","file_size_in_byte":769,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"29787459026","text":"def mySort(arr):\n    array = arr\n    sorted = []\n    index = 0\n\n    while True: \n        for i in array:\n            if i == index:\n                sorted.append(i)\n\n        index += 1\n\n        if len(sorted) == len(array):\n            break\n\n    return sorted\n\narray = [1,5,3,6,7,4,3,1,6,8,10]\n\nprint(f'raw-array: {array}')\nprint(f'sorted-array: {mySort(array)}')","repo_name":"sakku116/challange","sub_path":"selection_shorting.py","file_name":"selection_shorting.py","file_ext":"py","file_size_in_byte":364,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"9878056088","text":"\"\"\"add unique contraint on tweet url\n\nRevision ID: 564c09eaecc6\nRevises: 1c89b3661a0\nCreate Date: 2014-12-13 13:38:43.325792\n\n\"\"\"\n\n# revision identifiers, used by Alembic.\nrevision = '564c09eaecc6'\ndown_revision = '1c89b3661a0'\nbranch_labels = None\ndepends_on = None\n\nfrom alembic import op\nimport sqlalchemy as sa\n\n\ndef upgrade():\n    op.create_unique_constraint(\"uq_tweets_url\", \"tweets\", [\"url\"])\n\n\ndef downgrade():\n    op.drop_contraint(\"uq_tweets_url\",\n                      \"tweets\",\n                       \"unique\")\n","repo_name":"roinir237/FollowMe","sub_path":"app/alembic/versions/564c09eaecc6_add_unique_contraint_on_tweet_url.py","file_name":"564c09eaecc6_add_unique_contraint_on_tweet_url.py","file_ext":"py","file_size_in_byte":523,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"28868519972","text":"import os, sys\r\nfrom os import listdir, getcwd, path\r\n\r\nEP = sys.argv[1]\r\n\r\ndef listToString(s): \r\n    # initialize an empty string\r\n    str1 = \" \"     \r\n    # return string  \r\n    return (str1.join(s))\r\n\r\nmypath = path.abspath(os.getcwd())\r\ntimestamps = list()\r\ntextonly = list()\r\nsubs_dir = mypath + f\"/subs/EP.A.{EP}/\"\r\nfilename = mypath + f\"/subs/\" + f\"subs_file_{EP}.txt\"\r\nprint(subs_dir)\r\nif not os.path.isdir(subs_dir):\r\n    os.mkdir(subs_dir)\r\nlines = list()\r\nwith open(filename) as f:\r\n    lines = f.readlines()\r\nprint(len(lines))\r\nfor line in lines:\r\n    line = line.split(\" \")\r\n    #print(line)\r\n    with open(subs_dir  + \"timestamps.txt\", \"a+\") as ti:\r\n        ti.write(line[0] + \"\\n\")\r\n        #print(line[0])\r\n    with open(subs_dir + \"textonly.txt\", \"a+\") as te:\r\n        te.write(listToString(line[1:]))\r\n","repo_name":"krviolent/subtitles_extract","sub_path":"scripts/divide_timestamp_and_text.py","file_name":"divide_timestamp_and_text.py","file_ext":"py","file_size_in_byte":821,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"81"}
+{"seq_id":"20248312660","text":"#Allison Fenichel \n#Homework3\n#EECS6892\n\n\nimport scipy\nfrom scipy import io, stats\nimport pandas as pd\nimport numpy as np\nimport math\nimport matplotlib.pyplot as plt\nimport sys\nimport os\n\n\n\n\ndef vars(n):\n\texec('dat=scipy.io.loadmat(\\'data%s.mat\\')' % n)\n\texec('X=dat[\\'X\\'].T')\n\texec('y=dat[\\'y\\']')\n\texec('z=dat[\\'z\\']')\n\treturn X, y, z\n\ndef iterations(X, y, T):\n\td,n=X.shape\n\te0=1.0\n\tf0=1.0\n\ta0=np.full(d, 10e-16)\n\tb0=np.full(d, 10e-16)\n\tmu0=np.ones((d,1))\n\tsigma0=np.ones((d,d))\n\tL=np.zeros(T)\n\tEln_qw=np.zeros(T)\n\tEln_qlambda=np.zeros(T)\n\tEln_qalpha=np.zeros(T)\n\tEln_pw=np.zeros(T)\n\tEln_plambda=np.zeros(T)\n\tEln_palpha=np.zeros(T)\n\tEln_py=np.zeros(T)\n\tfor t in range(T):\n\t\ta,b,e,f,mu,sigma=updates(X,y,a0,b0,e0,f0,mu0,sigma0)\n\t\tL[t]=obj_function(X,y,a,b,e,f,mu,sigma) \n\t\tif t<T:\n\t\t\ta0=a\n\t\t\tb0=b\n\t\t\te0=e\n\t\t\tf0=f\n\t\t\tmu0=mu\n\t\t\tsigma0=sigma\n\treturn a, b, e, f, L, mu\n\n\ndef updates(X,y,a0,b0,e0,f0,mu0,sigma0):\n\td,n=X.shape\n\txTsigmax=0\n\txxT=0\n\tyminusxTmu=0\n\tfor i in range(n):\n\t\txTsigmax=xTsigmax+np.dot(np.dot(X[:,i].reshape(1,d), sigma0),X[:,i].reshape(d,1))\n\t\txxT=xxT+np.dot(X[:,i].reshape(d,1), X[:,i].reshape(1,d))\n\t\tyminusxTmu=yminusxTmu+np.power(y[i]-X[:,i].T.dot(mu0),2)\n\ta=a0+0.5\n\tb=np.zeros(d)\n\tfor k in range(d):\n\t\tb[k]=b0[k]+0.5*(sigma0[k][k]+mu0[k]**2)\n\te=e0+float(n)/2.0\n\tf=f0+0.5*(yminusxTmu+xTsigmax)\n\tsigma=np.linalg.inv((e/f[0][0])*xxT+np.diag(a/b))\n\tmu=np.dot(sigma, e/f[0][0]*np.sum(np.multiply(y.reshape(1,n),X),1))\n\treturn a,b,e,f,mu,sigma\n\ndef obj_function(X,y,a,b,e,f,mu,sigma): \n\td,n=X.shape\n\te0=1.0\n\tf0=1.0\n\ta0=np.full(d, 10e-16)\n\tb0=np.full(d, 10e-16)\n\t\n\tEln_qw=0.5*np.log(np.linalg.det(sigma))\n\tEln_qlambda=e-np.log(f)+scipy.special.gammaln(e)+np.multiply(1.0-e,scipy.special.psi(e))\n\tEln_qalpha=sum(a-np.log(b)+scipy.special.gammaln(a)+np.multiply(1.0-a,scipy.special.psi(a)))\n\tdiag_ab=np.diag(a/b)\n\tEln_pw=0.5*sum(scipy.special.psi(a)-np.log(b))-0.5*np.trace(np.dot(diag_ab, sigma))-0.5*np.dot(np.dot(mu.T, diag_ab),mu) \n\tEln_plambda=(e0-1.0)*(scipy.special.psi(e)-np.log(f))-np.multiply(f0,e/f[0][0])\n\tEln_palpha=sum((a0-1.0)*(scipy.special.psi(a)-np.log(b))-np.multiply(b0,np.divide(a,b)))\n\txTsigmax=0\n\tyminusxTmu=0\n\tfor i in range(n):\n\t\txTsigmax=xTsigmax+np.dot(np.dot(X[:,i].reshape(1,d), sigma),X[:,i].reshape(d,1))\n\t\tyminusxTmu=yminusxTmu+np.power(y[i]-X[:,i].T.dot(mu),2)\n\tEln_py=float(n)/2*(scipy.special.psi(e)-np.log(f))-0.5*e/f*(yminusxTmu+xTsigmax)\n\tL=Eln_plambda+Eln_pw+Eln_palpha+Eln_py-Eln_qlambda-Eln_qw-Eln_qalpha\n\treturn L\n\t\ndef plotf(v, p):\n\tfig1=plt.figure()\n\tfig1.suptitle('Problem 2a: Data Set %s' % p)\n\tplt.plot(v)\n\tplt.xlabel('Iteration t')\n\tplt.ylabel('L')\n\tfig1.savefig('Problem2a_data%s' % p)\n\ndef stemplotf(v, p):\n\tfig2=plt.figure()\n\tfig2.suptitle('Problem 2b: Data Set %s' % p)\n\tplt.stem(v)\n\tplt.xlabel('k')\n\tplt.ylabel('1/E[alpha_k]')\n\tfig2.savefig('Problem2b_data%s' % p)\n\n\n\ndef distributionplot(X,mu,y,z,p):\n\tyhat = X.T.dot(mu)\n\tz_n = np.linspace(-5,5,len(y))\n\tfz_n = 10.0*np.sinc(z_n)\n\tfig3=plt.figure()\n\tfig3.suptitle('Problem 2d: Data Set %s' % p)\n\tplt.plot(z,yhat,color='orange',label='Predicted values y_hat')\n\tplt.scatter(z,y,color='grey',label='Actual values y')\n\tplt.plot(z_n,fz_n,color='blue',label='True values 10*sinc(z)')\n\tplt.legend()\n\tfig3.savefig('Problem2d_data%s' % p)\n\n\ndef main():\n\tk=3\n\te_p={}\n\tf_p={}\n\tfor i in range(k):\n\t\tp=i+1\n\t\tX, y, z=vars(p)\n\t\ta, b, e, f, L, mu=iterations(X, y, 500)\n\t\tplotf(L, p)\n\t\tstemplotf(b/a, p)\n\t\tdistributionplot(X,mu,y,z,p)\n\t\te_p[p]=e\n\t\tf_p[p]=f[0][0]\n\t\t\n\tf=open('homework3.txt', 'w')\n\tf.writelines('2a: \\nImages of the objective function have been saved as \\'Problem2a_data1.png\\', \\'Problem2a_data2.png\\', and \\'Problem2a_data3.png\\' for each respective data set\\n\\n')\n\tf.writelines('2b: \\nImages of 1/E[alpha_k] as a function of k have been saved as \\'Problem2b_data1.png\\', \\'Problem2b_data2.png\\', and \\'Problem2b_data3.png\\' for each respective data set\\n\\n')\n\tf.writelines('2c:\\n')\n\tfor i in range(k):\n\t \tp=i+1\n\t \tf.write('For data set %s, the final value for 1/E[lambda] is %s\\n' % (p, f_p[p]/e_p[p]))\n\tf.writelines('\\n2d: \\nPlot images have been saved as \\'Problem2d_data1.png\\', \\'Problem2d_data2.png\\', and \\'Problem2d_data3.png\\' for each respective data set\\n\\n')\n\tf.close()\n\t\n\nif __name__ == '__main__':\n\tmain()\t","repo_name":"wyfwdn/BayesianModelingMachineLearning","sub_path":"Assignment3/data_matlab/hw3.py","file_name":"hw3.py","file_ext":"py","file_size_in_byte":4237,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"23866604761","text":"import math\nimport sets as set\n\n#Smarter way of computing factors, much smaller search\ndef get_proper_divisors(n):\n    factors = []\n    factors.append(1)\n    # loop to square root as any higher factors can be determined from lower factors\n    for j in range(2, int(math.sqrt(n))+1):\n        if n % j == 0:\n            factors.append(j) # factor\n            factors.append(n/j) # find it's partner\n    #factors.append(n)\n    return set.Set(factors) # sorted unique terms\n\n\ndef main():\n\tmax = 28123\n\n\tsum_of_abundant_numbers = []\n\n\tabundant = []\n\tfor i in xrange(1,max+1):\n\t\tproper_divisors = get_proper_divisors(i)\n\t\tif sum(proper_divisors)>i:\n\t\t\tabundant.append(i)\n\n\tfor a in abundant:\n\t\tfor b in abundant:\n\t\t\tif a+b<=max:\n\t\t\t\tsum_of_abundant_numbers.append(a+b)\n\n\n\tabundant_sum = set.Set(sum_of_abundant_numbers)\n\tall_ints = set.Set(range(1,max+1))\n\tdeficient_sum = all_ints - abundant_sum\n\tprint(sum(deficient_sum))\n\n\nif __name__ == ('__main__'):\n    main()","repo_name":"murphsp1/ProjectEuler_Python","sub_path":"p23.py","file_name":"p23.py","file_ext":"py","file_size_in_byte":959,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"44201842767","text":"# flux conversion factor is handled here, not in reproduce_figs\n\n# should allow more things to be passed from cmd line\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom astropy.io import fits\nimport sys  # for command line args\nfrom math import floor  # for binning range\n\n'''\nCOMMAND LINE OPTIONS:\n\nboss: 1 to use boss data, 0 to use sdss-II data\nsave: enter savekey. figures for the paper will get filenames based on the key. otherwise figures are not saved.\nbootstrap: 1 to plot with bootstrap errors (and sometimes envelopes)\nloadkey: alphas will be loaded based on this key\n'''\n\n# command line options\nif len(sys.argv) != 6:\n    print(\"Usage: generate_plots.py [boss: 0, 1] [save: 0, savekey] [bootstrap: 0, 1] [loadkey] [binned: 0, 1] \")\n    exit(0)\nboss = int(sys.argv[1])\nsave = sys.argv[2]\nbootstrap = int(sys.argv[3])\nloadkey = sys.argv[4]\nbinned = int(sys.argv[5])\n\nalpha_direc = '../alphas_and_stds/'\nalpha_direc_boot = '../data/'  # '../alphas_and_stds'\nalpha_direc_boot = '../alphas_and_stds'\nhdulist_direc = '../data/'  # '/Users/blakechellew/Documents/DustProject/BrandtFiles/'\nhdulist_direc = '/Users/blakechellew/Documents/DustProject/BrandtFiles/'\n\n# load wavelengths\nwavelength_boss = np.load('../alphas_and_stds/wavelength_boss.npy')\nhdulist = fits.open(hdulist_direc + 'SDSS_allskyspec.fits')\nwavelength_sdss = np.array(hdulist[1].data)\nif boss:\n    wavelength = wavelength_boss\nelse:\n    wavelength = wavelength_sdss\n\nsdss_fluxfactor = 1.38\nboss_fluxfactor = 1.38\n\n# load in npy files\n# original, tao, tao AND iris, iris\n\n# boss alphas:\nalphas = [\n    np.load('../alphas_and_stds/partial_jacknife_alphas_boss_iris_1d_012720_30.npy')[0],\n    np.load('../alphas_and_stds/partial_jacknife_alphas_boss_iris_1d_012720_30.npy')[1],\n    np.load('../alphas_and_stds/partial_jacknife_alphas_boss_iris_1d_012720_30.npy')[2],\n    np.load('../alphas_and_stds/partial_jacknife_alphas_boss_iris_1d_012720_30.npy')[3],\n    np.load('../alphas_and_stds/partial_jacknife_alphas_boss_iris_1d_012720_30.npy')[4]\n    # np.load(alpha_direc + 'alphas_092220.npy'),\n    # np.load(alpha_direc + 'alphas_above_50_092220.npy'),\n    # np.load(alpha_direc + 'alphas_above_50_A_092220.npy')\n    # np.load(alpha_direc + 'alphas_above_50_B_092220.npy'),\n    # np.load(alpha_direc + 'alphas_above_50_C_092220.npy'),\n]\nalpha_stds = [\n    np.load(alpha_direc + 'alpha_stds_boss_91119_10.npy'),\n    np.load(alpha_direc + 'alpha_stds_boss_91119_10.npy'),\n    np.load(alpha_direc + 'alpha_stds_boss_91119_10.npy'),\n    np.load(alpha_direc + 'alpha_stds_boss_91119_10.npy'),\n    np.load(alpha_direc + 'alpha_stds_boss_91119_10.npy')\n    # np.load(alpha_direc + 'alpha_stds_092220.npy'),\n    # np.load(alpha_direc + 'alpha_stds_above_50_092220.npy'),\n    # np.load(alpha_direc + 'alpha_stds_above_50_A_092220.npy')\n    # np.load(alpha_direc + 'alpha_stds_above_50_B_092220.npy'),\n    # np.load(alpha_direc + 'alpha_stds_above_50_C_092220.npy'),\n]\n\n# flux conversion factor:\nif boss:\n    alphas = [a / boss_fluxfactor for a in alphas]\n    alpha_stds = [a / boss_fluxfactor for a in alpha_stds]\nelse:\n    alphas = [a / sdss_fluxfactor for a in alphas]\n    alpha_stds = [a / sdss_fluxfactor for a in alpha_stds]\n\nnum_arrays = len(alphas)\n\n\n# plot unbinned spectra (wavelength ranges: 4830-5040 and 6530-6770)\ndef plot_emissions(alpha_indices, labels, colors):\n    plt.figure(figsize=(12, 5))\n\n    # plot 4830 - 5040\n    plt.subplot(1, 2, 1)\n    for i, idx in enumerate(alpha_indices):\n        plt.plot(wavelength, alphas[idx], c=colors[i], drawstyle='steps', label=labels[i])\n        if bootstrap:\n            # plt.fill_between(wavelength, bootstrap_lower[idx], bootstrap_upper[idx], linewidth=0.0, color=colors[i], alpha=0.5, step='pre')\n            plt.plot(wavelength, bootstrap_stds[idx], c=colors[i], drawstyle='steps', linestyle='--')\n        else:\n            plt.plot(wavelength, alpha_stds[idx], c=colors[i], drawstyle='steps', linestyle='--')\n\n    plt.xlabel(r\"Wavelength ($\\AA$)\")\n    plt.ylabel(r\"$\\alpha_\\lambda$\")\n    plt.legend(loc='upper center', frameon=False)\n    plt.xlim(4830, 5040)\n    plt.ylim(0, 1)\n    xcoords = [4863, 4960, 5008]\n    for xc in xcoords:\n        plt.axvline(x=xc, color='k', linewidth=1, linestyle='--')\n    plt.text(4853, 0.4, r\"H$\\beta$\")\n    plt.text(4943, 0.4, \"O[III]\")\n    plt.text(4991, 0.4, \"O[III]\")\n\n    # line from 03 continuum::\n    # plt.axhline(y=0.14898818311840933, color='r', linewidth=1, linestyle='--')\n    # actual continuum for NII:\n    # plt.axhline(y=0.17930096676470586, color='r', linewidth=1, linestyle='--')\n\n    # plot 6530 - 6770 (original vs tao)\n    plt.subplot(1, 2, 2)\n    for i, idx in enumerate(alpha_indices):\n        plt.plot(wavelength, alphas[idx], c=colors[i], drawstyle='steps', label=labels[i])\n        if bootstrap:\n            # plt.fill_between(wavelength, bootstrap_lower[idx], bootstrap_upper[idx], linewidth=0.0, color=colors[i], alpha=0.5, step='pre')\n            plt.plot(wavelength, bootstrap_stds[idx], c=colors[i], drawstyle='steps', linestyle='--')\n        else:\n            plt.plot(wavelength, alpha_stds[idx], c=colors[i], drawstyle='steps', linestyle='--')\n\n    plt.xlabel(r\"Wavelength ($\\AA$)\")\n    plt.ylabel(r\"$\\alpha_\\lambda$\")\n    plt.legend(loc='upper center', frameon=False)\n    plt.xlim(6530, 6770)\n    plt.ylim(0, 1)\n    xcoords = [6550, 6565, 6585, 6718, 6733]\n    for xc in xcoords:\n        plt.axvline(x=xc, color='k', linewidth=1, linestyle='--')\n    plt.text(6535, 0.4, \"N[II]\")\n    plt.text(6568, 0.9, r\"H$\\alpha$\")\n    plt.text(6590, 0.6, \"N[II]\")\n    plt.text(6700, 0.6, \"S[II]\")\n    plt.text(6738, 0.5, \"S[II]\")\n\n    # line from 03 continuum::\n    # plt.axhline(y=0.14898818311840933, color='r', linewidth=1, linestyle='--')\n    # actual continuum for NII:\n    # plt.axhline(y=0.17930096676470586, color='r', linewidth=1, linestyle='--')\n\n    plt.tight_layout()\n\n'''\n#unbinned plots, SFD 1d vs 3 others\nplot_emissions([0, 1], [\"SFD\", r\"With $\\tau$ Correction\"], ['k', 'r'])\nplt.show()\nplot_emissions([0, 3], [\"SFD\", \"With IRIS data\"], ['k', 'r'])\nplt.show()\nplot_emissions([0, 2], [\"SFD\", r\"With $\\tau$ and IRIS\"], ['k', 'r'])\nplt.show()\n'''\n\ndef generate_binned_alphas(alphas, alpha_stds, wavelength_all, wavelength=None):\n    # plot binned alpha vs wavelength (original)\n    # wavelength_all is the one that determines the binned lambdas\n\n    if wavelength is None:\n        wavelength = wavelength_all\n\n    lambda_range = wavelength_all[-1] - wavelength_all[0]\n    left_over = lambda_range - 50 * floor(lambda_range / 50)\n    binned_lambdas = np.arange(wavelength_all[1] + left_over / 2, wavelength_all[-1],\n                               50)  # [1] to avoid going over left edge\n    binned_alphas = []\n    binned_stds = []\n\n    # mask emission lines\n    emission_line_mask = np.zeros(len(wavelength), dtype=int)\n    emission_lines = [4863, 4960, 5008, 5877, 6550, 6565, 6585, 6718, 6733]\n    if boss:\n        emission_lines.insert(0, 3727)\n    for line in emission_lines:\n        peak_idx = np.argmin(np.abs(wavelength - line))\n        emission_line_mask[peak_idx - 3:peak_idx + 4] = 1\n\n    for i in range(len(alphas)):\n\n        binned_alpha_arr = np.zeros(binned_lambdas.shape)\n        binned_std_arr = np.zeros(binned_lambdas.shape)\n        if alphas[i].ndim > 1:\n            binned_alpha_arr = np.zeros((alphas[i].shape[0], binned_lambdas.shape[0]))\n            binned_std_arr = np.zeros((alphas[i].shape[0], binned_lambdas.shape[0]))\n        for j, lmda in enumerate(binned_lambdas):\n            indices = np.where((wavelength > lmda - 50) & (wavelength < lmda) & np.logical_not(emission_line_mask))[\n                0]  # test\n            if alphas[i].ndim > 1:\n                relevant_alphas = alphas[i][:, indices]\n                relevant_stds = alpha_stds[i][:, indices]\n            else:\n                relevant_alphas = alphas[i][indices]\n                relevant_stds = alpha_stds[i][indices]\n\n            # weighted average:\n            variance = np.power(relevant_stds, 2)\n            if alphas[i].ndim > 1:\n                numerator = np.sum(np.divide(relevant_alphas, variance), axis=1)\n                denominator = np.sum(np.divide(1, variance), axis=1)\n            else:\n                numerator = np.sum(np.divide(relevant_alphas, variance))\n                denominator = np.sum(np.divide(1, variance))\n            avg1 = numerator / denominator\n            avg2 = 1 / denominator\n            if alphas[i].ndim > 1:\n                binned_alpha_arr[:, j] = avg1\n                binned_std_arr[:, j] = np.sqrt(avg2)\n            else:\n                binned_alpha_arr[j] = avg1\n                binned_std_arr[j] = np.sqrt(avg2)\n\n        binned_alphas.append(binned_alpha_arr)\n        binned_stds.append(binned_std_arr)\n\n    return binned_lambdas, binned_alphas, binned_stds\n\n#bin alphas\nif binned:\n    binned_lambdas, binned_alphas, binned_stds = generate_binned_alphas(alphas, alpha_stds, wavelength)\n\nif boss:\n    y_max = 0.3\n    x_min = 3700\n    x_max = 10000\nelse:\n    y_max = 0.3\n    x_min = 3850\n    x_max = 9200\n\n# plot binned alphas\n# takes alphas already binned. use generate_binned_alphas\ndef plot_binned(alpha_indices, colors, labels, envelope=False):\n    for i, idx in enumerate(alpha_indices):\n        plt.plot(binned_lambdas, binned_alphas[idx], c=colors[i], drawstyle='steps', label=labels[i])\n        if bootstrap:\n            plt.plot(binned_lambdas, bootstrap_binned_stds[idx], c=colors[i], drawstyle='steps', linestyle='--')\n        else:\n            plt.plot(binned_lambdas, binned_stds[idx], c=colors[i], drawstyle='steps', linestyle='--')\n        if envelope:\n            plt.fill_between(binned_lambdas, bootstrap_binned_lower[idx], bootstrap_binned_upper[idx], linewidth=0.0,\n                             color=colors[i], alpha=0.2, step='pre')\n\n    y_max = 0.35 #why manually adjusting this?\n    plt.xlabel(r\"Wavelength ($\\AA$)\")\n    plt.ylabel(r\"$\\alpha_\\lambda$\")\n    plt.xlim(x_min, x_max)\n    plt.ylim(0, y_max)\n    plt.legend(frameon=False)\n    plt.show()\n    y_max = 0.3\n\n\nindices = [0, 1, 2, 3, 4]\nlabels = ['2389', '2390', '2384', '2395', '2386']\ncolors = ['k', 'r', 'g', 'b', 'pink']\n\n# plot north and south on same plot (boss)\nif binned:\n    plot_binned(indices, colors, labels)\n    if save != '0':\n        plt.savefig('../paper_figures/' + save + '.pdf')\n    else:\n        plt.show()\nelse:\n    plot_emissions(indices, labels, colors)\n    if save != '0':\n        plt.savefig('../paper_figures/' + save + '.pdf')\n    else:\n        plt.show()\n","repo_name":"bchellew15/DustProject","sub_path":"python_scripts/generate_one_plot.py","file_name":"generate_one_plot.py","file_ext":"py","file_size_in_byte":10505,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"1374705072","text":"import csv\nimport sqlite3\nimport pandas as pd\nfrom datetime import timedelta, date\n\ndef daterange(start_date, end_date):\n    for n in range(int ((end_date - start_date).days)):\n        yield start_date + timedelta(n)\n\nstart_date = date(2017, 5, 2)\nend_date = date(2017, 5, 3)\nfor single_date in daterange(start_date, end_date):\n    curr_date=single_date.strftime(\"%Y-%m-%d\")\n    #Read from csv\n    df = pd.read_csv(filepath_or_buffer='/home/nishanksb/Downloads/bmfeq_ftp_data/OFER_VDA_20170502_FILTERED_nospace.TXT'\\\n                    , delimiter=';', header=None, usecols=[1,2,3,5,6,8,9,10,11,13], \\\n                    names=[\"symbol\", \"buy_sell\", \"seq_o_no\", \"exec_type\", \"time\",\"price\", \"total_qty\", \"traded_qty\", \"date\", \"order_status\"],\\\n                    dtype = {\"symbol\": str, \"buy_sell\":int, \"seq_o_no\":int, \"exec_type\":int, \"time\": str,\"price\":float, \"total_qty\":int, \"traded_qty\":int, \"order_status\": str}, parse_dates=[7]);\n\n    #Filter Trades (Consider date for current trade date, filter only certain combinations)\n    # Execution Type  Order Status\n    # processing these for now\n    # 001 0\n    # 002 5\n    # 003 4\n    # 004 1\n    # 004 2\n\n    # ignoring iceberg orders\n    # 005 0\n    # 005 1\n\n    # ignoring these\n    # 011 C\n    df = df[(df.date == curr_date) & (((df.exec_type==1) & (df.order_status=='0')) | ((df.exec_type==2) & (df.order_status=='5')) | \\\n            ((df.exec_type==3) & (df.order_status=='4')) | ((df.exec_type==4) & (df.order_status=='1')) | \\\n            ((df.exec_type==4) & (df.order_status=='2')))]\n\n    product_list = df.symbol.unique()\n    for sym in product_list:\n        prod_wise_df = df[(df.symbol==sym)]\n        order_no_list = prod_wise_df.seq_o_no.unique()\n        for curr_son in order_no_list:\n            min_time_idx=prod_wise_df[(prod_wise_df.seq_o_no == curr_son)].time.idxmin()\n            print(prod_wise_df.iloc[[min_time_idx]].exec_type)\n","repo_name":"nishankbisht/bmf_ftp","sub_path":"order_anomaly.py","file_name":"order_anomaly.py","file_ext":"py","file_size_in_byte":1908,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"12128738656","text":"from pynput.keyboard import Key, Listener, Controller\nfrom Racer import Racer\nimport os\n\n# path info of files:\nprint(os.path.dirname(__file__))\n\nprint('Hello, I am Python bot and I will try to play old RoadFighter game on NES emulator :)')\nprint('Press q to start, set focus on the emulator window, and start the game :)')\n\ndef on_press(key):\n    if str(key)==\"'q'\":\n        racer = Racer()\n        racer.playGame()\n    if key == Key.esc:\n        return False\n\nwith Listener(on_press=on_press) as listener:\n    listener.join()\n","repo_name":"tgilvonas/game-bots","sub_path":"roadfighter/roadfighter.py","file_name":"roadfighter.py","file_ext":"py","file_size_in_byte":527,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"5543815023","text":"import json\r\nimport time\r\nrecord ={100:{'Name':'Amul_Milk    1L','Category':'Dairy','Price':80,'Expire_Date':'10-09-2021','In_Stock':30},\r\n         101:{'Name':'Amul_Paneer  500g','Category':'Dairy','Price':200,'Expire_Date':'1-09-2022','In_Stock':10},\r\n         102:{'Name':'Amul_Cheese      ','Category':'Dairy','Price':30,'Expire_Date':'12-09-2021','In_Stock':10},\r\n         103:{'Name':'Amul_Butter      ','Category':'Dairy','Price':50,'Expire_Date':'13-09-2021','In_Stock':12},\r\n         104:{'Name':'Thumbs Up 1L     ','Category':'Drinks','Price':90,'Expire_Date':'10-09-2021','In_Stock':30},\r\n         105:{'Name':'Thumbs Up  750ml ','Category':'Drinks','Price':50,'Expire_Date':'30-08-2023','In_Stock':30},\r\n         106:{'Name':'Marie Gold  60g  ','Category':'Biscuit','Price':20,'Expire_Date':'1-12-2021','In_Stock':20},\r\n         107:{'Name':'Good Day  60g    ','Category':'Biscuit','Price':20,'Expire_Date':'1-12-2021','In_Stock':20},\r\n         108:{'Name':'Vita Marie Gold 60g','Category':'Biscuit','Price':20,'Expire_Date':'1-12-2021','In_Stock':20},\r\n         109:{'Name':'Parle G  60g   ','Category':'Biscuit','Price':20,'Expire_Date':'1-12-2021','In_Stock':20},\r\n         110:{'Name':'Bourbon  60g   ','Category':'Biscuit','Price':20,'Expire_Date':'11-12-2021','In_Stock':20},\r\n         111:{'Name':'Crack Jack  60g','Category':'Biscuit','Price':20,'Expire_Date':'1-12-2021','In_Stock':20},\r\n         112:{'Name':'Maggie          ','Category':'Noodles','Price':20,'Expire_Date':'10-12-2021','In_Stock':30},\r\n         113:{'Name':'Top Ramen       ','Category':'Noodles','Price':20,'Expire_Date':'22-12-2021','In_Stock':30},\r\n         114:{'Name':'Colgate  100g   ','Category':'ToothPaste','Price':54,'Expire_Date':'10-04-2022','In_Stock':15},\r\n         115:{'Name':'dettol  75g     ','Category':'Soap','Price':40,'Expire_Date':'10-12-2021','In_Stock':20},\r\n         116:{'Name':'Lux  75g        ','Category':'Soap','Price':40,'Expire_Date':'10-12-2021','In_Stock':20},\r\n         117:{'Name':'5 star 20g      ','Category':'Chocolate','Price':20,'Expire_Date':'20-03-2022','In_Stock':20},118:{'Name':'Dairy Milk 20g  ','Category':'Chocolate','Price':20,'Expire_Date':'20-03-2022','In_Stock':20},119:{'Name':'KitKat 20g      ','Category':'Chocolate','Price':20,'Expire_Date':'21-04-2022','In_Stock':20},120:{'Name':'Kurkure         ','Category':'Snaks','Price':30,'Expire_Date':'10-04-2022','In_Stock':20},\r\n         121:{'Name':'Lays            ','Category':'Snacks','Price':30,'Expire_Date':'20-04-2022','In_Stock':20},\r\n         122:{'Name':'Chettos         ','Category':'Snacks','Price':30,'Expire_Date':'20-04-2022','In_Stock':20},\r\n         123:{'Name':'NoteBook 200pages','Category':'stationary','Price':60,'Expire_Date':'NA       ','In_Stock':20},\r\n         124:{'Name':'Cello Gripper pen','Category':'stationary','Price':8,'Expire_Date':'NA       ','In_Stock':20},\r\n         125:{'Name':'Gatsby HairWax 100g ','Category':'Grooming','Price':200,'Expire_Date':'1-12-2023','In_Stock':10},\r\n         126:{'Name':'Nevia FaceWash  ','Category':'Grooming','Price':200,'Expire_Date':'10-12-2023','In_Stock':20},\r\n         127:{'Name':'Bread 100g      ','Category':'Bread','Price':40,'Expire_Date':'10-09-2021','In_Stock':20},\r\n         128:{'Name':'Kissan JAM 250g ','Category':'Jam','Price':100,'Expire_Date':'10-05-2021','In_Stock':10},\r\n         129:{'Name':'Tomato Ketchup  ','Category':'Ketchup','Price':40,'Expire_Date':'10-12-2021','In_Stock':20},\r\n         130:{'Name':'Penaut Butter 1kg','Category':'Dairy','Price':500,'Expire_Date':'10-12-2021','In_Stock':10}}\r\n# js = json.dumps(record)\r\n# fd = open(\"records.json\", 'w')\r\n# fd.write(js)\r\n# fd.close()\r\n\r\n#------ log in as Admin | customer |  suppiler --------------#\r\nprint(\"**********************************LOG IN AS ************************************** \\n\")\r\nprint(\" \\t 1. Admin\\t\\t 2.Customer\\t\\t 3.Suppiler\")\r\nprint(\"\\t\\tNOTE - *Please! Enter Number of Your Mode *\")\r\n\r\n#----------------------------------For ADMIN ------------------------------------#\r\nlogin = input(\"Enter Mode  :\")\r\nif(login == '1'):\r\n    passw = str(input(\"Enter Password : \"))\r\n    if(passw == '12345678'):\r\n        #_________________________________________Adding Data to  records.json file ____________________________#\r\n        rd= open(\"records.json\",'r')\r\n        r=rd.read()\r\n        rd.close()\r\n        record=json.loads(r)\r\n        #print(record.keys())\r\n        s= 1\r\n        i=100\r\n        print(\"__________________OUR INVENTORY ______________\\n\")\r\n        print(\"\\t Product_id \\t\\t Name \\t\\t  Price(Rs) \\t\\t Exipre_Date \\t\\t Stock \\t\\t Category\")\r\n        while(s<=len(record)):\r\n                print(\"\\t\",i,'\\t\\t',record[str(i)]['Name'],'\\t\\t',record[str(i)]['Price'],'\\t\\t',record[str(i)]['Expire_Date'],'\\t\\t',record[str(i)]['In_Stock'],'\\t\\t',record[str(i)]['Category'])\r\n                i=i+1\r\n                s=s+1\r\n        \r\n        print(\"\\t You Can Add or Update The Data...\\n\")\r\n        p_id = input(\"Enter Product Id :\")\r\n        \r\n        if(p_id not in record.keys()):\r\n                print(\"\\tYou Are Adding New Data With Id\"+p_id)\r\n\r\n\r\n                name = str(input(\"Enter Product Name :\"))\r\n                price = int(input(\"Price :\"))\r\n                Ex_date = str(input(\"Expire Date: \"))\r\n                istock = int(input(\"Enter Quantity:\"))\r\n                cat = str(input(\"Enter Category\"))\r\n                record[p_id] ={'Name': name, 'Price': price,'Expire_Date': Ex_date, 'In_Stock': istock,'Category':cat}\r\n#------------------------------Dumping into records.json ----------------#\r\n                js = json.dumps(record)\r\n                fd=open('records.json','w')\r\n                fd.write(js)\r\n                fd.close()\r\n                print(\"\\tRECORD ADDED\")\r\n#--------------------------------------------------------------------------------------------#\r\n        elif(p_id  in record.keys()):\r\n                print(\"\\tYou Are Updating Data of Product With ID\"+p_id)\r\n                iname = str(input(\"Enter Product Name :\"))\r\n                iprice = int(input(\"Price :\"))\r\n                iEx_date = str(input(\"Expire Date: \"))\r\n                iistock = int(input(\"Enter New Quantity:\"))\r\n                updated= record[p_id]['In_Stock']+iistock\r\n                record[p_id].update({'In_Stock':updated})\r\n                ircc= record[p_id]['In_Stock']\r\n                icat = str(input(\"Enter Category\")) \r\n                record[p_id] ={'Name': iname, 'Price': iprice,'Expire_Date': iEx_date,'In_Stock':updated,'Category':icat}\r\n# #------------------------------Dumping into records.json ----------------#\r\n                js = json.dumps(record)\r\n                fd=open('records.json','w')\r\n                fd.write(js)\r\n                fd.close()\r\n                print(\"\\RECORD UPDATED\")\r\n\r\n#___________________________________________ For CUSTOMER _______________________________________________-#\r\nelif(login == '2'):\r\n    print(\"\\t_____________________________________Welcome To SHREE KRISHNA Super Market___________________________\\n \")\r\n    fd = open(\"records.json\", \"r\")\r\n    r = fd.read()\r\n    fd.close()\r\n    data= json.loads(r)\r\n    s= 1\r\n    i=100\r\n    print(\"\\t Product_id \\t\\t Name \\t\\t  Price(Rs) \\t\\t Exipre_Date \\t\\t Stock \\t\\t Category\")\r\n    while(s<=len(data)):\r\n        print(\"\\t\",i,'\\t\\t',data[str(i)]['Name'],'\\t\\t',data[str(i)]['Price'],'\\t\\t',data[str(i)]['Expire_Date'],'\\t\\t',data[str(i)]['In_Stock'],'\\t\\t',data[str(i)]['Category'])\r\n        i=i+1\r\n        s=s+1   \r\n    id_prod  = str(input(\"\\n\\tEnter the product_Id: \"))\r\n    if(id_prod not in data.keys()):\r\n            print(\"\\tNo Such Time With These Id Enter Correct Id\")\r\n    \r\n    ui_quant = int(input(\"\\tEnter the quantity:\"))\r\n    if(ui_quant >data[id_prod]['In_Stock']):         \r\n            print(\"\\t SORRY ! Not Enough Quantity In Stock \")\r\n    \r\n    elif(ui_quant<=data[id_prod]['In_Stock'] or id_prod in data.keys()):\r\n            \r\n            u_name= input((\"\\tPlease Enter Your Name: \"))\r\n            print(\"\\t\\t\\n******************************* BILL *******************************\\t\\t\")\r\n            print(\"\\t\\t Name:\",u_name)\r\n            print(\"\\t\\t Bill No:\" ,str(len(data)),\"\\t\\t\\t\",\"Time:\",time.ctime())\r\n            print(\"\\t\\t\\tProduct Id :\",id_prod)\r\n            print(\"\\t\\t\\tProduct: \",data[id_prod]['Name'])\r\n            print(\"\\t\\t\\tPrice: \",data[id_prod]['Price'])\r\n            print(\"\\t\\t\\tQuantity :\",ui_quant)\r\n            # print(\"\\t\\t\\tTime:\",time.ctime())\r\n            amount=data[id_prod]['Price'] * (ui_quant)\r\n            print(\"\\t\\t---------------------------------------------------------------------\\n\")\r\n            print(\"\\t\\t\\tBilling Amount: \", \"'\",amount,\"Rs'\", \"-Only\")\r\n            print(\"\\t\\t---------------------------------------------------------------------\\n\")\r\n            print(\"\\t\\t_____________THANK YOU VISIT AGIAN 😊  _______________\")\r\n            print(\"\\n**********************************************************************\\t\\t\")\r\n            data[id_prod]['In_Stock']=data[id_prod]['In_Stock']-ui_quant\r\n#----------------Updating records.json -------------------------------------#\r\n            uf= json.dumps(data)\r\n            fd=open('records.json','w')\r\n            fd.write(uf)\r\n            fd.close()\r\n\r\n#____________________________________________________  for Sales.json file _______________________________________________#\r\n        #     sales ={0000:{'Customer_Name':'Dummy','Item_Purchased':'Dummy','Quantity':'Dummy','Price':'Dummy','Total':'Dummy'}}\r\n        #     js = json.dumps(sales)\r\n        #     fd = open(\"sales.json\", 'w')\r\n        #     fd.write(js)\r\n        #     fd.close()\r\n\r\n            rd= open(\"sales.json\",'r')\r\n            s=rd.read()\r\n            rd.close()\r\n            sales=json.loads(s)\r\n            sales[str(len(sales))]={'Customer_Name':u_name,'Item_Purchased':data[id_prod]['Name'],'Quantity':ui_quant,'Price':data[id_prod]['Price'],'Total':amount}\r\n            sl= json.dumps(sales)\r\n            sd= open('Sales.json','w')\r\n            sd.write(sl)\r\n            sd.close()\r\n#------------------------------------------------------------------------------------------------#\r\n    else:\r\n            print(\"Enter Valid Information \")\r\n\r\n# # _______________________________________ for  SUPPILER _____________________________#\r\n\r\nelif(login=='3'):\r\n        #_________________________________________Adding Data to  records.json file ____________________________#\r\n        rd= open(\"records.json\",'r')\r\n        r=rd.read()\r\n        rd.close()\r\n        record=json.loads(r)\r\n        print(\"__________________OUR INVENTORY ______________\\n\")\r\n        s=1\r\n        i=100\r\n        print(\"\\t Product_id \\t\\t Name \\t\\t  Price(Rs) \\t\\t Exipre_Date \\t\\t Stock \\t\\t Category\")\r\n        while(s<=len(record)):\r\n                print(\"\\t\",i,'\\t\\t',record[str(i)]['Name'],'\\t\\t',record[str(i)]['Price'],'\\t\\t',record[str(i)]['Expire_Date'],'\\t\\t',record[str(i)]['In_Stock'],'\\t\\t',record[str(i)]['Category'])\r\n                i=i+1\r\n                s=s+1\r\n        \r\n        print(\"\\t You Can Add or Update The Data...\\n\")\r\n        \r\n        p_id = input(\"Enter Product Id :\")\r\n        if(p_id not in record.keys()):\r\n                print(\"\\tYou Are Adding New Data With Id \"+p_id)\r\n\r\n\r\n                name = str(input(\"Enter Product Name :\"))\r\n                price = int(input(\"Price :\"))\r\n                Ex_date = str(input(\"Expire Date: \"))\r\n                istock = int(input(\"Enter Quantiy :\"))\r\n                cat = str(input(\"Enter Category\"))\r\n                record[p_id] ={'Name': name, 'Price': price,'Expire_Date': Ex_date, 'In_Stock': istock,'Category':cat}\r\n#------------------------------Dumping into records.json ----------------#\r\n                js = json.dumps(record)\r\n                fd=open('records.json','w')\r\n                fd.write(js)\r\n                fd.close()\r\n                print(\"\\tRECORD ADDED\")\r\n#--------------------------------------------------------------------------------------------#\r\n        elif(p_id  in record.keys()):\r\n                print(\"\\tYou Are Updating Data of Product With ID \"+p_id)\r\n                iname = str(input(\"Enter Product Name :\"))\r\n                iprice = int(input(\"Price :\"))\r\n                iEx_date = str(input(\"Expire Date: \"))\r\n                iistock = int(input(\"Enter Quantity :\"))\r\n                updated= record[p_id]['In_Stock']+iistock\r\n                record[p_id].update({'In_Stock':updated})\r\n                ircc= record[p_id]['In_Stock']\r\n                icat = str(input(\"Enter Category\")) \r\n                record[p_id] ={'Name': iname, 'Price': iprice,'Expire_Date': iEx_date,'In_Stock':updated,'Category':icat}\r\n# #------------------------------Dumping into records.json ----------------#\r\n                js = json.dumps(record)\r\n                fd=open('records.json','w')\r\n                fd.write(js)\r\n                fd.close()\r\n                print(\"\\RECORD UPDATED\")\r\nelse:\r\n         print(\"Enter Correct Option\")\r\n\r\n\r\n# #-------------------- END --------------------------#\r\n","repo_name":"Mohil224/ETF_IMS_USING_JSON","sub_path":"INVENTORY_MANAGMENT_USING_JSON.py","file_name":"INVENTORY_MANAGMENT_USING_JSON.py","file_ext":"py","file_size_in_byte":13078,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"81"}
+{"seq_id":"29950838863","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Dec 15 17:54:19 2020\n\n@author: benpg\n\"\"\"\n# External\nimport autograd.numpy as np\nfrom autograd.numpy.random import multivariate_normal\nfrom autograd import grad\nimport matplotlib.pyplot as plt\nplt.close('all')\nfrom numpy.linalg import inv, cholesky\n\n# Misc\nfrom utils import samples, draw_ellipse, plot_GMM, calculate_ELBO, HiddenPrints, cols\n\nimport pickle\n\n# playing with samples\n\n# Define GMMs for generation\ncentres = [np.array([0.,3.]), np.array([2.,0.])]\ncovs = [np.eye(2), np.array([[0.6,0.4],\n                             [0.4,0.6]])]\n\nK = 2\n\nN_total = 1000  # total number of datapoints wanted\nX1 = multivariate_normal(mean=centres[0],\n                         cov=covs[0],\n                         size=int(N_total/2))\nX2 = multivariate_normal(mean=centres[1],\n                         cov=covs[1],\n                         size=int(N_total/2))\nX = np.concatenate((X1,X2))\n\na = np.ones(2)*(10**0.5)  # large alpha means pi values are ~=\nb = np.ones(2)*(1000**0.5)  # large beta keeps Gaussian from which mu is drawn small\nV = [inv(cholesky(covs[k]))/(1000**0.5) for k in range(K)]\nm = centres\nu = np.ones(2)*(1000) - 2\n\nalpha, beta, nu = a**2, b**2, abs(u)+2\nW = [np.dot(V[k].T,V[k]) for k in range(K)]\n\n# alpha = np.ones(2)*10  # large alpha means pi values are ~=\n# beta = np.ones(2)*1000  # large beta keeps Gaussian from which mu is drawn small\n# W = [inv(covs[k])/1000 for k in range(K)]\n# m = centres\n# nu = np.ones(2)*1000\n\ninformative_priors = [a,b,V,m,u]\nwith open('informative_priors2.pkl', 'wb') as f:\n    pickle.dump(informative_priors, f)\n\"\"\"\nnu = d is the least informative prior, and the smallest, so  a large nu\nis more informative. On its own this makes the lam samples huge, so\nwe also give W a small prior, the scale - intuitively saying that we are \nsampling lam from a small space.\n\n\"the prior mean of Wishart(W,nu) is nu*W, so a reasonable choice for W would \nbe a prior guess for the precision / nu\"\nThis is what we have done here, divided W by nu. Nice!\n\"\"\"\n\n# the trick to informative priors - \n# alpha doesnt much matter, apparently\n\n\ntitle = ''\nfor i in range(10):\n    title = '%d'%i\n    Z, pi, mu, lam, r_nk = samples(alpha, beta, W, m, nu, X[0], K) \n    plot_GMM(X, mu, lam, pi, centres, covs, K, title, cols=['r', 'b'])\n\n    inv_lam_beta = [inv(beta[k]*lam[k]) for k in range(K)]\n    ccols = ['r--', 'b--']\n    for k in range(K):\n        x,y = draw_ellipse(m[k],inv_lam_beta[k])\n        plt.plot(x,y,ccols[k])","repo_name":"BenGutteridge/4YP","sub_path":"GMM/informative_priors.py","file_name":"informative_priors.py","file_ext":"py","file_size_in_byte":2491,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"34463504683","text":"#!/usr/bin/env python\nimport sys\nfrom numpy.random import shuffle\n\n\ndef split(lines, fn1, fn2):\n    shuffle(lines)\n    h = int( len(lines) // (100 / 30))\n\n    with open(fn1, 'w') as f:\n        for ln in lines[h:]:\n            f.write(ln)\n\n    with open(fn2, 'w') as f:\n        for ln in lines[0:h]:\n            f.write(ln)\n\n\nif __name__ == '__main__':\n\n    if len(sys.argv) < 3:\n        print('Splits dataset into train and test data')\n        print('usage: split.py [stdin] <train> <test>')\n        exit()\n\n    lines = [ln for ln in sys.stdin]\n    split(lines, sys.argv[1], sys.argv[2])\n","repo_name":"Smartling/ml-text","sub_path":"tools/scripts/split.py","file_name":"split.py","file_ext":"py","file_size_in_byte":588,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"81"}
+{"seq_id":"539600036","text":"from linepy import *\nfrom bs4 import BeautifulSoup\nimport requests\nimport traceback\nimport random , re\nfrom urllib.request import urlopen\n\n#=====================================================================\ntolg = LINE(\"EMAIL\",\"PASSWORD\",appName=\"DESKTOPMAC\\t6.5.2\\tMac OS X\\t10.16.5\")\n\npoll = OEPoll(tolg)\n\nweb = {\n    'data': { \n        'download-data': { \n          'loading-data': { \n            'process-data': True,\n        }\n      }\n   }\n}\n\ndef tratSnoitarepo(laylay):\n    try:\n        # LEAVE ROOM\n        if laylay.type in [22,24]:\n              tolg.leaveRoom(laylay.param1)\n        # LEAVE ROOM\n        \n        \n        # Just invite to group for sticker link\n        if laylay.type == 13 or laylay.type == 124:\n            hey1 = laylay.param1\n            hey2 = laylay.param2\n            hey3 = laylay.param3\n            if tolg.elifORPteg().mid in hey3:\n              G = tolg.PuORGtCApMOCTeG(hey1)\n              if web[\"data\"][\"download-data\"][\"loading-data\"][\"process-data\"] == True:\n                  tolg.ETIVNIPUORGREBYCRKTpeccA(hey1)\n              while True:\n                  try:\n                      num = 0\n                      lineurl = ['https://store.line.me/stickershop/showcase/top_creators/en?page=1',\n                                  'https://store.line.me/stickershop/showcase/top/en?page=2',\n                                  'https://store.line.me/stickershop/showcase/top/en?page=3',\n                                  'https://store.line.me/stickershop/showcase/top/en?page=4',\n                                  'https://store.line.me/stickershop/showcase/top/en?page=4',\n                                  'https://store.line.me/stickershop/showcase/top/en?page=5',\n                                  'https://store.line.me/stickershop/showcase/top/en?page=6',\n                                  'https://store.line.me/stickershop/showcase/top/en?page=7',\n                                  'https://store.line.me/stickershop/showcase/top/en?page=8',\n                                  'https://store.line.me/stickershop/showcase/top/en?page=9',\n                                  'https://store.line.me/stickershop/showcase/top/en?page=10']\n                      urls = random.choice(lineurl)\n                      tolgwebscrapping = requests.get(urls).text\n                      tolgkrs = BeautifulSoup(tolgwebscrapping, \"html.parser\")\n                      CyberTKR = tolgkrs.find('ul', {'class': 'mdCMN02Ul'})\n\n                      for linkler in CyberTKR.find_all('li'):\n                          for link in linkler.find_all('a', href=True):\n                                num += 1\n                                tolg.ResurEDNogegasSEMktreByc(hey1,'{}.https://store.line.me{}'.format(num, link['href']))\n                                time.sleep(10)\n                  except:\n                      pass\n    except Exception as error:\n        print(error)\n        traceback.print_tb(error.__traceback__)\n\ndef tolgstickeruns():\n    while True:\n        try:\n            ops = poll.singleTrace(count=1)\n            if ops != None:\n                for laylay in ops:\n                    tratSnoitarepo(laylay)\n                    poll.setRevision(laylay.revision)\n        except Exception as e:\n            print(e)\n\nif __name__ == \"__main__\":\n    tolgstickeruns()\n","repo_name":"CyberTKR/Line-Zodiac-StickerLink","sub_path":"sticker.py","file_name":"sticker.py","file_ext":"py","file_size_in_byte":3308,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"81"}
+{"seq_id":"4819452000","text":"import csv #https://stackoverflow.com/questions/62139040/python-csv-module-vs-pandas\nimport re\n\nclass redcap_template:\n    \n    infolder = './templates/'\n    outfolder = './instruments/'\n\n    def __init__(self, template, subject, subject_idx=1, start_pos='<#', end_pos='#>'):\n        self.template = template\n        self.placeholder = start_pos + '[^(' + start_pos + ')]*' + end_pos\n        self.subject = subject\n        self.subject_idx = subject_idx\n        self.subjectnowhite = re.sub(r\"\\s+\", \"\", subject)\n\n    def modify_template(self):\n        # csv file name\n        filename_r = self.infolder + self.template + '.csv'\n        filename_w = self.outfolder + self.template + '_' + self.subjectnowhite + '.csv'\n        \n        # initializing the titles and rows list\n        cols = []\n        rows = []\n        \n        # open one csv file in read mode and another in write mode\n        with open(filename_r, 'r') as infile, open(filename_w, 'w', newline='') as outfile:\n            # create a csv reader and a writer object\n            r = csv.reader(infile, delimiter = ',')\n            w = csv.writer(outfile, delimiter = ',')\n\n            # extract field names through first row\n            cols = next(r)\n            # write new header to outfile\n            w.writerow(cols)\n             \n            # Iterate over each row in the csv using reader object\n            for row in r:\n                # update parametrized string portion everywhere\n                row_update = [re.sub(self.placeholder, self.subject, str(row_item)) for row_item in row]\n                # modify Variable/Field Name\n                row_update[0] = row[0] + '_' + str(self.subject_idx)\n                # modify Branching Logic corresponding to updated Variable/Field name\n                row_update[11] = re.sub('\\]', '_' + str(self.subject_idx) + ']', row[11])\n                # write to output file\n                w.writerow(row_update)\n\n\n","repo_name":"sxinger/REDCap-Param-DataDict","sub_path":"src/util.py","file_name":"util.py","file_ext":"py","file_size_in_byte":1934,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"40053841160","text":"import json\n\nclass ComplianceType:\n    NOT_APPLICABLE = \"NOT_APPLICABLE\"\n    COMPLIANT = \"COMPLIANT\"\n    NON_COMPLIANT = \"NON_COMPLIANT\"\n\n    @staticmethod\n    def get_valid_compliances():\n        return [ComplianceType.NOT_APPLICABLE, ComplianceType.COMPLIANT, ComplianceType.NON_COMPLIANT]\n\nclass Evaluation:\n    annotation = \"\"\n    complianceResourceType = None\n    complianceType = None\n    complianceResourceId = None\n    orderingTimestamp = None\n\n    def __init__(self, complianceType, resourceId=None, resourceType=None, annotation=\"\"):\n        self.annotation = build_annotation(annotation)\n        self.complianceResourceId = resourceId\n        self.complianceResourceType = resourceType\n        if not complianceType in ComplianceType.get_valid_compliances():\n            print('The complianceType is not valid. Valid values include: ComplianceType.COMPLIANT, ComplianceType.COMPLIANT and ComplianceType.NOT_APPLICABLE')\n            raise Exception('The complianceType is not valid. Valid values include: ComplianceType.COMPLIANT, ComplianceType.COMPLIANT and ComplianceType.NOT_APPLICABLE')\n        self.complianceType = complianceType\n\n    def __repr__(self):\n        return f\"Evaluation(annotation='{self.annotation}', complianceResourceId='{self.complianceResourceId}', complianceResourceType='{self.complianceResourceType}', complianceType='{self.complianceType}')\"\n\n    def __eq__(self, other):\n        return (\n            (\n                (self.annotation is None and other.annotation is None) or\n                (self.annotation == other.annotation)\n            ) and\n            self.complianceResourceType == other.complianceResourceType and\n            self.complianceType == other.complianceType and\n            self.complianceResourceId == other.complianceResourceId and\n            self.orderingTimestamp == other.orderingTimestamp)\n\n    # This generate an evaluation for config\n    def import_fields_from_periodic_event(self, event):\n        self.orderingTimestamp = str(json.loads(event['invokingEvent'])['notificationCreationTime'])\n\n    def import_fields_from_configuration_item(self, configuration_item):\n        self.orderingTimestamp = configuration_item['configurationItemCaptureTime']\n        if not self.complianceResourceId:\n            self.complianceResourceId = configuration_item['resourceId']\n        if not self.complianceResourceType:\n            self.complianceResourceType = configuration_item['resourceType']\n\n    # Check that an evaluation is well-formed\n    def is_valid(self):\n        if not self.complianceType:\n            print('Missing complianceType from an evaluation result.')\n            raise Exception('Missing complianceType from an evaluation result.')\n\n        if not self.complianceResourceId:\n            print('Missing complianceResourceId from an evaluation result.')\n            raise Exception('Missing complianceResourceId from an evaluation result.')\n\n        if not self.complianceResourceType:\n            print('Missing complianceResourceType from an evaluation result.')\n            raise Exception('Missing complianceResourceType from an evaluation result.')\n\n        if not self.orderingTimestamp:\n            print('Missing orderingTimestamp from an evaluation result.')\n            raise Exception('Missing orderingTimestamp from an evaluation result.')\n\n        return True\n\n    def get_json(self):\n        output = {\n            \"ComplianceResourceId\": self.complianceResourceId,\n            \"ComplianceResourceType\": self.complianceResourceType,\n            \"ComplianceType\": self.complianceType,\n            \"OrderingTimestamp\": self.orderingTimestamp\n        }\n\n        if self.annotation:\n            output[\"Annotation\"] = self.annotation\n\n        return output\n\n# Build annotation within Service constraints\ndef build_annotation(annotation_string):\n    if len(annotation_string) > 256:\n        return annotation_string[:244] + \" [truncated]\"\n    return annotation_string\n","repo_name":"awslabs/aws-config-rdklib","sub_path":"rdklib/evaluation.py","file_name":"evaluation.py","file_ext":"py","file_size_in_byte":3958,"program_lang":"python","lang":"en","doc_type":"code","stars":89,"dataset":"github-code","pt":"81"}
+{"seq_id":"21605498452","text":"import argparse\nfrom itertools import groupby\nfrom datetime import datetime, timedelta\nfrom pathlib import Path\n\nfrom Bio import SeqIO\n\nperiods = {\n    \"day\": \"%Y-%m-%d\",\n    \"week\": \"%Y-%W\",\n    \"month\": \"%Y-%m\",\n}\n\ndef get_date_period(_id: str, period='day', format=\"%Y-%m-%d\", delimiter=\"|\", loc=-1):\n    date_string = _id.split(delimiter)[loc]\n    if format == 'decimal':\n        dec_year = float(_id.split('|')[-1])\n        year = int(dec_year)\n        rem = dec_year - year\n        base = datetime(year, 1, 1)\n        date: datetime = base + timedelta(seconds=(base.replace(year=base.year + 1) - base).total_seconds() * rem)\n    else:\n        date = datetime.strptime(date_string, format)\n    return date.strftime(periods[period])\n\n\nif __name__ == \"__main__\":\n    parser = argparse.ArgumentParser(description='Group Fasta by time period.')\n    parser.add_argument('FASTA', type=Path)\n    parser.add_argument('--period', default='day', choices=['day', 'week', 'month'])\n    parser.add_argument('--date-format', default='%Y-%m-%d')\n    parser.add_argument('--delimiter', default='|')\n    parser.add_argument('--location', default=-1, type=int)\n    parser.add_argument('--cumulative', default=False, action='store_true')\n\n    args = parser.parse_args()\n    records = list(SeqIO.parse(args.FASTA, \"fasta\"))\n\n    data = {}\n    for k, g in groupby(records, lambda rec:get_date_period(\n            rec.id, \n            period=args.period, \n            format=args.date_format, \n            delimiter=args.delimiter, \n            loc=args.location\n        )):\n        if not data.get(k, None):\n            data[k] = list(g)\n        else:\n            data[k] = data[k] + list(g)\n\n    dates = sorted(list(data.keys()))\n\n    if args.cumulative:\n        for i, k in enumerate(dates):\n            l = []\n            for date in dates[:i+1]:\n                l += data[date]\n        SeqIO.write(l, f\"{date}.fasta\", \"fasta\")\n    else:\n        for date in dates:\n            SeqIO.write(data[date], f\"{date}.fasta\", \"fasta\")\n    \n","repo_name":"mccoy-devs/mccoy-data","sub_path":"group.py","file_name":"group.py","file_ext":"py","file_size_in_byte":2019,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"6374783012","text":"import requests\n\nURL = \"https://translate.yandex.net/api/v1.5/tr.json/translate\" \n# Your api key\nKEY = \"trnsl.1.1.20190429T065522Z.18f4830c20ee2f4b.b940037513ea0a3e99dd7129948c0a456985e3d6b\"\n\ntext_input = r'1.txt'\ntext_output = r'2.txt'\ntext_ru = r'3.txt'\n\ndef rep_pdf_to_txt(text_in, text_out):\n  with open(text_input, 'r') as f1, open(text_output, 'w') as f2:\n    lines = f1.readlines()\n    for line in lines:\n        if(line.endswith(\".\\n\")):\n            f2.write(line + \"\\n\")\n        else:\n            f2.write(line.replace(\"\\n\", \" \") + \" \")\n\ndef split_text(text_out):\n  with open(text_output, 'r') as f2:\n    tmp_text = f2.read()\n    tmp = tmp_text.split(\".\")\n    n = 0\n    tmp_str = \"\"\n    tmp_list = []\n    for str in tmp:\n      n += len(str)\n      tmp_str += str + \".\"\n      if(n > 9000):  \n        tmp_list.append(tmp_str)\n        tmp_str = \"\"\n        n = 0\n    return tmp_list\n\ndef translate(my_text):\n  params = {\n    \"key\": KEY,\n    \"text\": my_text,    \n    \"lang\": 'en-ru'\n  }\n  response = requests.get(URL, params = params)\n  json_response = response.json()\n  return json_response\n\n# input parameters\n'''\nmy_text = \"Test params for my text input for translate\"\njson = translate(my_text)\nprint(''.join(json[\"text\"]))\n'''\nrep_pdf_to_txt(text_input, text_output)\nt_list = split_text(text_output)\nwith open(text_ru, 'w') as f3:\n  for i in t_list:\n    f3.write(''.join(translate(i)[\"text\"]))\n    f3.write(\"\\n Переведено сервисом «Яндекс.Переводчик»  http://translate.yandex.ru/ \\n\")\n","repo_name":"shardakov/Python_3_scripts","sub_path":"python3_yndex_translate_text/translate.py","file_name":"translate.py","file_ext":"py","file_size_in_byte":1528,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"24976630655","text":"from django.urls import path,include\n\nfrom . import views\nurlpatterns = [\n    path('movielist/',views.WatchListAv.as_view(),name='movie_list'),\n    path('<int:pkey>/',views.watchlistDetail.as_view(),name='get_by_id'),\n    path('stream/',views.streamplateformAv.as_view(),name='stream'),\n  path('stream/<int:pk>/',views.streamplateformDetail.as_view(),name='streamplateformlist'),\n  path('review/',views.reviewlistav.as_view(),name='review'),\n  path('review/<int:pk>/',views.reviewdetails.as_view(),name='reviewdetails')\n]\n","repo_name":"ankush18609/watchmate","sub_path":"watchlist_app/api/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":522,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"2983136855","text":"# imports\n\nimport urllib3\n\nfrom authentication.akeyless_api import set_credentials_to_keyring\nfrom models.google_api import upload_files_to_drive\nfrom models.os_functions import get_number_of_files_in_dir, get_files_names, pickle_in, pickle_out, \\\n    remove_unnecessary_files\nfrom models.workbooks import get_unmodified_workbooks_above_x_weeks, remove_extract_schedulers, download_workbooks, \\\n    get_workbooks_to_archive, delete_workbooks_from_server\n\n\n# ----------------------------------------------------------------------------------------------------------------------\n# step 1 - getting unmodified workbooks and remove schedulers\ndef step_1(x_weeks_ago):\n    print('starting step 1 - getting unmodified workbooks and remove schedulers')\n    # get the unmodified workbooks from dev server that exceeded x weeks\n    unmodified_workbooks = get_unmodified_workbooks_above_x_weeks(x_weeks_ago)\n    if len(unmodified_workbooks) != 0:\n        # remove extract schedulers for those workbooks\n        remove_extract_schedulers(unmodified_workbooks)\n    print(\"step 1 finished\")\n\n\n# ----------------------------------------------------------------------------------------------------------------------\n# step 2 - getting and downloading unmodified workbooks for week after\ndef step_2(x_weeks_ago):\n    print('starting step 2 - getting and downloading unmodified workbooks for week after')\n    archived_workbooks = get_workbooks_to_archive(x_weeks_ago + 1)\n    if len(archived_workbooks) != 0:\n        # download workbooks\n        download_workbooks(archived_workbooks)\n        # check if all files were downloaded\n        if get_number_of_files_in_dir('storage') == len(archived_workbooks):\n            print(\"step 2 finished\")\n            return True\n    print(\"step 2 finished\")\n    return False\n\n\n# ----------------------------------------------------------------------------------------------------------------------\n# step 3 - archive workbooks to google drive\ndef step_3():\n    files_names = get_files_names('storage')\n    upload_files_to_drive(files_names)\n    print(\"step 3 finished\")\n\n\n# ----------------------------------------------------------------------------------------------------------------------\ndef step_4():\n    archived_workbooks = pickle_out('unmodified_workbooks')\n    delete_workbooks_from_server(archived_workbooks)\n    print(\"step 4 finished\")\n\n\n# ----------------------------------------------------------------------------------------------------------------------\n# the main process for dev server cleanup process\ndef main():\n    x_weeks_ago = 3\n    urllib3.disable_warnings()\n    # connect to akeyless to get passwords\n    set_credentials_to_keyring()\n    print(\"credentials set\")\n    # step 1 - getting unmodified workbooks and remove schedulers\n    step_1(x_weeks_ago)\n    # step 2 - getting and downloading unmodified workbooks for week after\n    if step_2(x_weeks_ago):\n        # archive workbooks to google drive\n        step_3()\n        # delete workbook and notify the user\n        step_4()\n        remove_unnecessary_files('storage')\n    else:\n        print(\"there was no workbook to download and archive\")\n    print(\"program ends\")\n# ----------------------------------------------------------------------------------------------------------------------\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"omerl2322/TableauDevCleanUpProcess","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3333,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"40849814939","text":"import pygame #Allows for use of pygame \nfrom pygame_info import  *\nimport threading\n#Pip install: \"pip install python-vlc\"\nimport vlc\nfrom time import sleep\nfrom Map import Map\nfrom Player import Player\nfrom Menu import Menu\n\n\n#from \n\ninit() #Initilizes pygame\nmenu = Menu()\n\ngameState = MENUSTATE\nmx = my = 0\nbutton = 0\n\ndef game():\n    global gameState\n    \n    #Initilize Objects Player\n    player = Player()\n    enemies = []\n    bullets = []\n    \n    game_map = Map(player, bullets, enemies)\n\n    player.setPos(width/2, ground) #initial position\n    \n    running = True\n    while running:\n        pygame.draw.rect(screen, GREY, (0,0,width,height)) #Reset screen\n\n        #Set new player positions here (jumping, and direction)\n        player.update()\n\n\t\t#Starts multithreading for enemies update\n        enemiesThread = threading.Thread(target=game_map.updateEnemies)\n        enemiesThread.start()\n\n\t\t#Starts multithreading for bullets update\n        bulletsThread = threading.Thread(target=game_map.updateBullets)\n        bulletsThread.start()\n\n\t\t#Starts multithreading for collision update\n        collisionThread = threading.Thread(target=game_map.updateCollisions)\n        collisionThread.start()\n\n\t\t#Join the threads\n        enemiesThread.join()\n        bulletsThread.join()\n        collisionThread.join()\n\n        game_map.draw() #BIG DRAWING MOMENT\n\n        if player.getHealth() == 0:\n            pygame.time.wait(300)\n            running = False\n            gameState = MENUSTATE\n    \n        for event in pygame.event.get():\n            if event.type == KEYDOWN and event.key == K_ESCAPE:\n                    running = False\n                    gameState = MENUSTATE       \n\n            elif event.type == KEYDOWN and event.key == K_RIGHT:\n                    player.setMoving(True)\n                    player.setDirection(1)\n                    \n            elif event.type == KEYUP and event.key == K_RIGHT:\n                if player.getDirection() == 1: # must check direction here to avoid making player stop if user is holding down other direction but lets go of this one\n                    player.setMoving(False)        \n                    player.setFrame(0)\n\n            elif event.type == KEYDOWN and event.key == K_LEFT:\n                    player.setMoving(True)\n                    player.setDirection(-1)\n\n            elif event.type == KEYUP and event.key == K_LEFT:\n                if player.getDirection() == -1: # must check direction here to avoid making player stop if user is holding down other direction but lets go of this one\n                    player.setMoving(False)\n                    player.setFrame(0)\n\n            elif event.type == KEYDOWN and event.key == K_UP:\n                    if player.canJump():\n                        player.setJumping(True)\n\n            elif event.type == KEYDOWN and event.key == K_SPACE:\n                player.setShooting(True)\n\n            elif event.type == KEYUP and event.key == K_SPACE:\n                player.setShooting(False)\n\n        myClock.tick(60)\n\ndef music():\n    i = 0\n    playlist = [\"CoconutMall.mp3\", \"SparkMusic.mp3\", \"MegaMusic.mp3\"]\n    Instance = vlc.Instance()\n    player = Instance.media_player_new()\n    Media = Instance.media_new(playlist[i])\n    Media.get_mrl()\n    player.set_media(Media)\n    player.play()\n    sleep(4)\n    # While the game is running\n    while gameState == PLAYSTATE:\n        # If the song ends, queue up next song in playlist. Loop playlist.\n        if player.get_state() == 6:\n            i = i + 1\n            player.set_media(Instance.media_new(playlist[i % 3]))\n            player.play()\n        sleep(1)\n\n\n\n\n\ndef main():\n    global gameState\n\t # Creates new thread for music\n    t1 = threading.Thread(target=music)\n    t1.start()\n    print(\"No. of active threads: \" + str(threading.active_count()))\n    while gameState != QUITSTATE:\n        if gameState == PLAYSTATE:\n            game()\n\t\t# After game is launched initiate multithread of music\n        t1.join()\t\n        for evnt in pygame.event.get():  \n            if evnt.type == MOUSEMOTION:\n                mx, my = evnt.pos\n            if evnt.type == MOUSEBUTTONDOWN:\n                button = evnt.button\n                mx, my = evnt.pos\n                #Button has been clicked, check for what happens\n                if gameState == MENUSTATE:\n                    gameState = menu.checkButtonMenu(button, mx, my)\n        menu.paintMenu(mx, my)\n        display.flip()\n\n    pygame.display.quit() #Quits program\n\n\nif __name__ == \"__main__\":\n  main()\n","repo_name":"ansonmau/MegamanShooter","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":4539,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"74551526343","text":"q = int(input())        #Nhập số testcase\r\na = []      #Tạo 1 list a để đưa ra giá bán đồng giá tối thiểu    \r\n\r\nwhile q > 0:    \r\n    n = int(input())    #Nhập số sản phẩm\r\n    b = []      #Tạo 1 list để nhập giá gốc của các sản phẩm\r\n    b = list(map(int,input().split()))[:n]     #Nhập giá gốc của n sản phẩm\r\n    if (sum(b) % n == 0):       #Tìm giá bán đồng giá tối thiểu cho từng sản phẩm\r\n        a.append(int(sum(b) / n))    #Thêm giá bán vừa tìm được vào list a\r\n    else:\r\n        a.append(int(sum(b) // n + 1))\r\n    q -= 1      #Giảm 1 testcase sau mỗi lần chạy vòng while\r\nfor i in a:\r\n    print(i)        #In giá bán đồng giá tối thiểu ra màn hình","repo_name":"azqpbao/CS114.M11.KHCL","sub_path":"WeCode/ass2/ban hang.py","file_name":"ban hang.py","file_ext":"py","file_size_in_byte":766,"program_lang":"python","lang":"vi","doc_type":"code","stars":2,"dataset":"github-code","pt":"81"}
+{"seq_id":"5063865396","text":"import cv2\nimport sys\nimport logging as log\nimport datetime as dt\nfrom time import sleep\nfrom regionofinterest import regionOfInterest\nfrom tkinter import *\nimport tkinter.messagebox\n\n\n\ndef capturef():\n    cascPath = \"haarcascade_frontalface_default.xml\"\n    faceCascade = cv2.CascadeClassifier(cascPath)\n    log.basicConfig(filename='webcam.log', level=log.INFO)\n\n    video_capture = cv2.VideoCapture(0)\n    anterior = 0\n\n    while True:\n        if not video_capture.isOpened():\n            print('Unable to load camera.')\n            sleep(5)\n            pass\n\n        # Capture frame-by-frame\n        ret, frame = video_capture.read()\n\n        if cv2.waitKey(1) & 0xFF == ord('s'):\n           cv2.imwrite(\"image.jpg\",frame)\n           #root=Tk()\n           tkinter.messagebox.showinfo('Photo Capturation','Photo has been captured!')\n\n           #root.mainloop()\n           break\n        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n\n        faces = faceCascade.detectMultiScale(\n            gray,\n            scaleFactor=1.1,\n            minNeighbors=5,\n            minSize=(30, 30)\n        )\n\n\n        # Display the resulting frame\n        cv2.imshow('Video', frame)\n\n        if cv2.waitKey(1) & 0xFF == ord('q'):\n            break\n\n        # Display the resulting frame\n        cv2.imshow('Video', frame)\n\n    # When everything is done, release the capture\n    cv2.waitKey(0)\n    video_capture.release()\n    cv2.destroyAllWindows()\n\n\n","repo_name":"gokayay/Face-Detection-with-OpenCV","sub_path":"Capture.py","file_name":"Capture.py","file_ext":"py","file_size_in_byte":1441,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"38749769599","text":"# from sortedcontainers import *\n\nfrom bisect import *\nfrom collections import *\nfrom functools import *\nfrom heapq import *\nimport itertools\nfrom string import whitespace, ascii_lowercase, ascii_uppercase, ascii_letters, digits, hexdigits, octdigits, punctuation, printable\n\nfrom util import *\n\n\"\"\"\necho \"=== sample ===\" ; python3 day15.py < sample.in\necho \"=== real ===\" ; python3 day15.py < day15.in\necho \"=== sample ===\" ; python3 day15.py < sample.in ; echo \"=== real ===\" ; python3 day15.py < day15.in\n\"\"\"\n\nA = sys.stdin.read().strip().splitlines()\n# A = sys.stdin.read().strip().split('\\n\\n')\n\nN = len(A)\n\nsensors = []\n\nfor line in A:\n    l, r = line.split(': ')\n    ll, lr = l.split(', ')\n    sx = int(ll.split(' ')[-1].split('=')[1])\n    sy = int(lr.split('=')[1])\n    rl, rr = r.split(', ')\n    bx = int(rl.split(' ')[-1].split('=')[1])\n    by = int(rr.split('=')[1])\n    sensors.append((sx, sy, bx, by))\n\ndef solve(y_target):\n    beacons = set()\n    m = {}\n    for sx, sy, bx, by in sensors:\n        d = abs(sx - bx) + abs(sy - by)\n        m[(sx, sy)] = d\n        beacons.add((bx, by))\n    res = 0\n\n    for x in range(int(-5e6), int(5e6)):\n        good = True\n        for sx, sy, _, _ in sensors:\n            my_dist = abs(sx - x) + abs(sy - y_target)\n            if my_dist <= m[(sx, sy)]:\n                good = False\n                break\n        if not good:\n            if (x, y_target) not in beacons:\n                res += 1\n    return res\n\nfrom z3 import *\n\ndef solve2(bound):\n    beacons = set()\n    m = {}\n    for sx, sy, bx, by in sensors:\n        d = abs(sx - bx) + abs(sy - by)\n        m[(sx, sy)] = d\n        beacons.add((bx, by))\n    \n    s = Solver()\n    x, y = Int('x'), Int('y')\n    s.add(x >= 0)\n    s.add(x <= bound)\n    s.add(y >= 0)\n    s.add(y <= bound)\n    for sx, sy, bx, by in sensors:\n        my_dist = Abs(sx - x) + Abs(sy - y)\n        s.add(my_dist > m[(sx, sy)])\n        s.add(And(x != bx, y != by))\n\n    \n    assert s.check() == sat\n    m = s.model()\n    return m[x].as_long(), m[y].as_long()\n\n# print(solve(2000000))\n\nx, y = solve2(4000000)\nprint(x, y, x * 4000000 + y)","repo_name":"blin00/advent-of-code","sub_path":"2022/day15.py","file_name":"day15.py","file_ext":"py","file_size_in_byte":2113,"program_lang":"python","lang":"en","doc_type":"code","stars":14,"dataset":"github-code","pt":"81"}
+{"seq_id":"40589156625","text":"import os\nfrom flask import Flask, request, abort, jsonify\nfrom flask_sqlalchemy import SQLAlchemy\nfrom flask_cors import CORS\nimport random\n\nfrom models import setup_db, Question, Category\n\nQUESTIONS_PER_PAGE = 10\n\n#----------------------------------------------------------------------------#\n# App Setup\n#----------------------------------------------------------------------------#\n\ndef create_app(test_config=None):\n    # creating and configuring the app\n    app = Flask(__name__)\n    # To run Server, executing from backend directory:\n    # Only one Time:\n    # export FLASK_APP=flaskr\n    # export FLASK_ENV=development\n    # flask run\n    setup_db(app)\n    CORS(app, resources={r\"*\": {\"origins\": \"*\"}})\n\n    @app.after_request\n    def after_request(response):\n        response.headers.add('Access-Control-Allow-Headers',\n                             'Content-Type, Authorization')\n        response.headers.add('Access-Control-Allow-Methods',\n                             'GET, POST, DELETE, OPTIONS')\n        response.headers.add('Access-Control-Allow-Credentials', 'true')\n        return response\n\n    @app.route(\"/categories\", methods=[\"GET\"])\n    def category():\n\n        try:\n            categories_list = Category.query.all()\n\n            response = {\n                'categories': {},\n                \"success\": True\n            }\n\n            for category in categories_list:\n                response['categories'][str(category.id)] = category.type\n\n            return jsonify(response)\n\n        except Exception as e:\n            print(e)\n            return abort(500, 'Something went wrong')\n    \n    @app.route(\"/questions\", methods=[\"GET\"])\n    def question():\n        try:\n            page_no = int(request.args['page'])\n        except Exception as e:\n            return abort(400, 'Server could not understand the made request')\n\n        start = (page_no-1) * QUESTIONS_PER_PAGE\n        end = start + QUESTIONS_PER_PAGE\n\n        response = {\n            'questions': [],\n            'total_questions': -1,\n            'categories': {},\n            'current_category': \"\",\n            'success': True\n        }\n\n        all_questions = []\n        filtered_questions = []\n\n        try:\n            for question in Question.query.all():\n                all_questions.append(question.format())\n\n            for category in Category.query.all():\n                response['categories'][str(category.id)] = category.type\n        except Exception as e:\n            print(e)\n            return abort(500, 'Something went wrong')\n\n        filtered_questions = all_questions[start:end]\n\n        if len(filtered_questions) == 0:\n            return abort(404)\n\n        response['questions'] = filtered_questions\n        response['total_questions'] = len(all_questions)\n        response['current_category'] = response['categories'][str(\n            filtered_questions[0]['category'])]\n\n        return jsonify(response)\n    \n    @app.route(\"/questions/<int:question_id>\", methods=[\"DELETE\"])\n    def delete(question_id):\n        question = Question.query.filter(\n            Question.id == question_id).one_or_none()\n\n        if question is None:\n            abort(400)\n\n        else:\n            question.delete()\n            return jsonify({'success': True})\n    \n    @app.route(\"/questions\", methods=[\"POST\"])\n    def post():\n\n        # Validations\n        try:\n            category_check = Category.query.filter(\n                Category.id == int(request.json['category'])).one_or_none()\n\n            if category_check is None:\n                return abort(400)\n        except:\n            return abort(400)\n\n        try:\n            new_question = Question(\n                question=request.json['question'],\n                answer=request.json['answer'],\n                difficulty=int(request.json['difficulty']),\n                category=int(request.json['category'])\n            )\n        except:\n            return abort(400)\n\n        try:\n            new_question.insert()\n            return jsonify({'success': True})\n        except:\n            return abort(500)\n    \n    @app.route(\"/questions/search\", methods=[\"POST\"])\n    def search():\n\n        questions = Question.query.filter(Question.question.ilike(\n            \"%{}%\".format(request.json['searchTerm']))).all()\n\n        if len(questions) == 0:\n            return abort(404)\n\n        response = {\n            'questions': [],\n            'total_questions': -1,\n            'current_category': \"\",\n            'success': True\n        }\n\n        all_questions = []\n\n        try:\n            for question in questions:\n                all_questions.append(question.format())\n\n        except Exception as e:\n            print(e)\n            return abort(500)\n\n        if len(all_questions) == 0:\n            return jsonify()\n\n        current_category = Category.query.filter(Category.id == all_questions[0]['category']).one()\n\n        response['questions'] = all_questions\n        response['total_questions'] = len(all_questions)\n        response['current_category'] = current_category.type\n\n        return jsonify(response)\n    \n    @app.route(\"/categories/<int:category_id>/questions\")\n    def category_wise_questions(category_id):\n\n        questions = Question.query.filter(Question.category == category_id).all()\n\n        if len(questions) == 0:\n            return abort(404)\n\n        response = {\n            'questions': [],\n            'total_questions': -1,\n            'current_category': \"\",\n            'success': True\n        }\n\n        all_questions = []\n\n        try:\n            for question in questions:\n                all_questions.append(question.format())\n\n        except Exception as e:\n            print(e)\n            return abort(500)\n\n        if len(all_questions) == 0:\n            return abort(404)\n\n        current_category = Category.query.filter(Category.id == category_id).one()\n\n        response['questions'] = all_questions\n        response['total_questions'] = len(all_questions)\n        response['current_category'] = current_category.type\n\n        return jsonify(response)\n    \n    @app.route(\"/quizzes\", methods=[\"POST\"])\n    def quiz():\n\n        try:\n            quiz_category_id = int(request.json['quiz_category']['id'])\n            previous_questions = request.json['previous_questions']\n        except:\n            return abort(400)\n\n        if(quiz_category_id != 0):\n            available_questions_query = Question.query.filter(Question.category == quiz_category_id).filter(~Question.id.in_(previous_questions))\n        if(quiz_category_id == 0):\n            available_questions_query = Question.query.filter(~Question.id.in_(previous_questions))\n\n        available_questions = available_questions_query.all()\n\n        if len(available_questions) == 0:\n            return jsonify({\"question\":False, \"success\": True})\n\n        next_question = available_questions[randrange(len(available_questions))]\n\n        return jsonify({\"question\":next_question.format(), \"success\": True})\n    \n    @app.errorhandler(400)\n    def bad_request(error):\n        return jsonify({\n            \"success\": False,\n            \"error\": 400,\n            \"message\": \"This is a Bad Request\"\n        }), 400\n    \n    @app.errorhandler(404)\n    def not_found(error):\n        return jsonify({\n            \"success\": False,\n            \"error\": 404,\n            \"message\": \"That's an error, Page Not found\"\n        }), 404\n    \n    @app.errorhandler(500)\n    def internal_error(error):\n        return jsonify({\n            \"success\": False,\n            \"error\": 500,\n            \"message\": \"Internal Server Error\"\n        }), 500\n\n\n    \n\n    return app\n\n","repo_name":"samarthasm/mytriviaapi","sub_path":"backend/flaskr/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":7644,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"3994353585","text":"# Leia uma lista de nomes e exiba a sua ordem\n\nimport random\n\nnome_01 = input('Nome 01:')\nnome_02 = input('Nome 02:')\nnome_03 = input('Nome 03:')\nnome_04 = input('Nome 04:')\n\nlista_de_alunos = [nome_01, nome_02, nome_03, nome_04]\nrandom.shuffle(lista_de_alunos)\n\nprint('A ordem de apresentação é')\nprint(lista_de_alunos)","repo_name":"esthefaniagapito/PythonExercicios","sub_path":"exercises/ex017.py","file_name":"ex017.py","file_ext":"py","file_size_in_byte":323,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"4381407017","text":"from infogan.misc.distributions import Product, Distribution, Gaussian, Categorical, Bernoulli\nimport prettytensor as pt\nimport tensorflow as tf\nimport infogan.misc.custom_ops\nimport infogan.models.generative_networks as G\nimport infogan.models.discriminative_networks as D\n\n\nclass RegularizedGAN(object):\n    def __init__(self, output_dist, latent_spec, is_reg, batch_size, image_shape, network_type):\n        \"\"\"\n        :type output_dist: Distribution\n        :type latent_spec: list[(Distribution, bool)]\n        :type batch_size: int\n        :type network_type: string\n        \"\"\"\n        self.output_dist = output_dist\n        self.latent_spec = latent_spec\n        self.is_reg = is_reg\n        self.latent_dist = Product([x for x, _ in latent_spec])\n        self.reg_latent_dist = Product([x for x, reg in latent_spec if reg])\n        self.nonreg_latent_dist = Product([x for x, reg in latent_spec if not reg])\n        self.batch_size = batch_size\n        self.network_type = network_type\n        self.image_shape = image_shape\n        self.keys = ['prob', 'logits', 'features']\n\n        if self.is_reg:\n            self.encoder_dim=self.reg_latent_dist.dist_flat_dim\n            self.keys = self.keys + ['reg_dist_info']\n        else:\n            self.encoder_dim=None\n\n        assert all(isinstance(x, (Gaussian, Categorical, Bernoulli)) for x in self.reg_latent_dist.dists)\n\n        self.reg_cont_latent_dist = Product([x for x in self.reg_latent_dist.dists if isinstance(x, Gaussian)])\n        self.reg_disc_latent_dist = Product([x for x in self.reg_latent_dist.dists if isinstance(x, (Categorical, Bernoulli))])\n\n        self.set_D_net()\n        self.set_G_net()\n\n    def set_D_net(self):\n        with tf.variable_scope(\"d_net\"):\n            if self.network_type == \"mnist\":\n                self.D_model = D.InfoGAN_MNIST_net(image_shape=self.image_shape,is_reg=self.is_reg,encoder_dim=self.encoder_dim)\n                shared_template = self.D_model.shared_template\n                self.discriminator_template = shared_template.custom_fully_connected(1)\n                self.encoder_template = self.D_model.encoder_template\n            else:\n                if self.network_type == 'dcgan':\n                    self.D_model = D.dcgan_net(image_shape=self.image_shape,is_reg=self.is_reg,encoder_dim=self.encoder_dim)\n                elif self.network_type == 'deeper_dcgan':\n                    self.D_model = D.deeper_dcgan_net(image_shape=self.image_shape,is_reg=self.is_reg,encoder_dim=self.encoder_dim)\n                else:\n                    raise NotImplementedError\n                self.shared_template = self.D_model.shared_template\n                self.discriminator_template = self.shared_template.custom_fully_connected(1)\n                self.encoder_template = self.D_model.encoder_template\n\n    def set_G_net(self):\n        with tf.variable_scope(\"g_net\"):\n            if self.network_type == 'mnist':\n                self.gen_model = G.InfoGAN_mnist_net()\n                self.generator_template = self.gen_model.infoGAN_mnist_net(self.image_shape)\n            else:\n                if self.network_type == 'dcgan':\n                    self.gen_model = G.dcgan_net()\n                elif self.network_type == 'deeper_dcgan':\n                    self.gen_model = G.deeper_dcgan_net()\n                else:\n                    raise NotImplementedError\n                self.generator_template = self.gen_model.gen_net(self.image_shape)\n\n    def discriminate(self, x_var):\n        d_features = self.shared_template.construct(input=x_var)\n        d_logits = self.discriminator_template.construct(input=x_var)[:,0]\n        d_prob = tf.nn.sigmoid(d_logits)\n\n        d_dict = dict.fromkeys(self.keys)\n\n        d_dict['features'] = d_features\n        d_dict['logits'] = d_logits\n        d_dict['prob'] = d_prob\n\n        if self.is_reg:\n            reg_dist_flat = self.encoder_template.construct(input=x_var)\n            reg_dist_info = self.reg_latent_dist.activate_dist(reg_dist_flat)\n            d_dict['reg_dist_info'] = reg_dist_info\n\n        return d_dict\n\n    def generate(self, z_var):\n        x_dist_flat = self.generator_template.construct(input=z_var)\n        if self.network_type==\"mnist\":\n            x_dist_info = self.output_dist.activate_dist(x_dist_flat)\n            return self.output_dist.sample(x_dist_info), x_dist_info\n        else:\n            return x_dist_flat, None\n\n    def disc_reg_z(self, reg_z_var):\n        ret = []\n        for dist_i, z_i in zip(self.reg_latent_dist.dists, self.reg_latent_dist.split_var(reg_z_var)):\n            if isinstance(dist_i, (Categorical, Bernoulli)):\n                ret.append(z_i)\n        return self.reg_disc_latent_dist.join_vars(ret)\n\n    def cont_reg_z(self, reg_z_var):\n        ret = []\n        for dist_i, z_i in zip(self.reg_latent_dist.dists, self.reg_latent_dist.split_var(reg_z_var)):\n            if isinstance(dist_i, Gaussian):\n                ret.append(z_i)\n        return self.reg_cont_latent_dist.join_vars(ret)\n\n    def disc_reg_dist_info(self, reg_dist_info):\n        ret = []\n        for dist_i, dist_info_i in zip(self.reg_latent_dist.dists, self.reg_latent_dist.split_dist_info(reg_dist_info)):\n            if isinstance(dist_i, (Categorical, Bernoulli)):\n                ret.append(dist_info_i)\n        return self.reg_disc_latent_dist.join_dist_infos(ret)\n\n    def cont_reg_dist_info(self, reg_dist_info):\n        ret = []\n        for dist_i, dist_info_i in zip(self.reg_latent_dist.dists, self.reg_latent_dist.split_dist_info(reg_dist_info)):\n            if isinstance(dist_i, Gaussian):\n                ret.append(dist_info_i)\n        return self.reg_cont_latent_dist.join_dist_infos(ret)\n\n    def reg_z(self, z_var):\n        ret = []\n        for (_, reg_i), z_i in zip(self.latent_spec, self.latent_dist.split_var(z_var)):\n            if reg_i:\n                ret.append(z_i)\n        return self.reg_latent_dist.join_vars(ret)\n\n    def nonreg_z(self, z_var):\n        ret = []\n        for (_, reg_i), z_i in zip(self.latent_spec, self.latent_dist.split_var(z_var)):\n            if not reg_i:\n                ret.append(z_i)\n        return self.nonreg_latent_dist.join_vars(ret)\n\n    def reg_dist_info(self, dist_info):\n        ret = []\n        for (_, reg_i), dist_info_i in zip(self.latent_spec, self.latent_dist.split_dist_info(dist_info)):\n            if reg_i:\n                ret.append(dist_info_i)\n        return self.reg_latent_dist.join_dist_infos(ret)\n\n    def nonreg_dist_info(self, dist_info):\n        ret = []\n        for (_, reg_i), dist_info_i in zip(self.latent_spec, self.latent_dist.split_dist_info(dist_info)):\n            if not reg_i:\n                ret.append(dist_info_i)\n        return self.nonreg_latent_dist.join_dist_infos(ret)\n\n    def combine_reg_nonreg_z(self, reg_z_var, nonreg_z_var):\n        reg_z_vars = self.reg_latent_dist.split_var(reg_z_var)\n        reg_idx = 0\n        nonreg_z_vars = self.nonreg_latent_dist.split_var(nonreg_z_var)\n        nonreg_idx = 0\n        ret = []\n        for idx, (dist_i, reg_i) in enumerate(self.latent_spec):\n            if reg_i:\n                ret.append(reg_z_vars[reg_idx])\n                reg_idx += 1\n            else:\n                ret.append(nonreg_z_vars[nonreg_idx])\n                nonreg_idx += 1\n        return self.latent_dist.join_vars(ret)\n\n    def combine_reg_nonreg_dist_info(self, reg_dist_info, nonreg_dist_info):\n        reg_dist_infos = self.reg_latent_dist.split_dist_info(reg_dist_info)\n        reg_idx = 0\n        nonreg_dist_infos = self.nonreg_latent_dist.split_dist_info(nonreg_dist_info)\n        nonreg_idx = 0\n        ret = []\n        for idx, (dist_i, reg_i) in enumerate(self.latent_spec):\n            if reg_i:\n                ret.append(reg_dist_infos[reg_idx])\n                reg_idx += 1\n            else:\n                ret.append(nonreg_dist_infos[nonreg_idx])\n                nonreg_idx += 1\n        return self.latent_dist.join_dist_infos(ret)\n","repo_name":"VittalP/UnsupGAN","sub_path":"infogan/models/regularized_gan.py","file_name":"regularized_gan.py","file_ext":"py","file_size_in_byte":7968,"program_lang":"python","lang":"en","doc_type":"code","stars":15,"dataset":"github-code","pt":"81"}
+{"seq_id":"2386368516","text":"#!/usr/bin/env python\n# encoding: utf-8\n# 作者：Ksar4\n# 日期：2020/12/1 13:17\n# 工具：PyCharm\n# Python版本：3.7.0\n\n\"\"\"\"\"\"\n\n\n\"\"\"\n我们可以用2*1的小矩形横着或者竖着去覆盖更大的矩形。请问用n个2*1的小矩形无重叠地覆盖一个2*n的大矩形，总共有多少种方法？\n比如n=3时，2*3的矩形块有3种覆盖方法：\n\n\"\"\"\n\n\"\"\"\nin:4\n\nout:5\n\n\"\"\"\n\n\nclass Solution():\n    def rectCover(self, n):\n        ## 非递归方法换一种写法\n        if n == 0:\n            return n\n        a,b = 0,1\n        # i有n个数,每个数都是前两项之和\n        for i in range(n):\n            a,b = b, a+b\n        return b\n\n    def rectCover2(self, number):\n        res = [0, 1, 2]\n        for i in range(number):\n            res.append(res[-1] + res[-2])\n        return res[number]\n\n    def rectCover3(self, number):\n            res = [0,1,2]\n            while len(res)<=number:\n                res.append(res[-1]+res[-2])\n            return res[number]\n\n\nif __name__ == \"__main__\":\n\n    s= Solution()\n\n    print(s.rectCover3(4))","repo_name":"niuyb/code","sub_path":"algorithm/11_矩形覆盖.py","file_name":"11_矩形覆盖.py","file_ext":"py","file_size_in_byte":1070,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"25085801615","text":"\"\"\"\n================================\n08. Time-frequency decomposition\n================================\n\nThe epoched data is transformed to time-frequency domain using morlet wavelets.\n\"\"\"\n\nimport os.path as op\nimport os\nimport sys\nimport numpy as np\n# import pandas as pd\nimport matplotlib.pyplot as plt\n\nimport mne\n\nfrom config import site_id, subject_id, file_names, out_path\nfrom config import no_eeg_sbj\n#from config import study_path, out_path, site_id, no_eeg_sbj_exp1#, n_run\nfrom config import baseline_w, freq_band, factor, conditions\n\n\ndef run_time_frequency(make_plot = True):\n\n    stdout_obj = sys.stdout                 # store original stdout\n    sys.stdout = open(op.join(out_path,     # open log file\n                              \"08-time_frequency_%s.txt\" % (site_id+subject_id)),'w')\n    # Load epoched data\n    print(\"Processing subject: %s\" % subject_id)\n    fname_epo = op.join(out_path,\n                        file_names[0][0:13] + 'ALL_epo.fif')\n    epochs = mne.read_epochs(fname_epo,\n                             preload=True,\n                             verbose=True)\n    \n    ### Run time-frequency decomposition\n    # TFR of low frequencoes (< 30 Hz)\n    if freq_band in ['low', 'both']:\n        tfrs_lofr = dict()\n        # Params\n        freqs = np.arange(2, 30, 1)\n        n_cycles = freqs / 2.\n        time_bandwidth = 2.\n        # Run over each condition\n        for condition in conditions:\n            power = mne.time_frequency.tfr_multitaper(\n                epochs['%s == \"%s\"' % (factor, condition)],\n                freqs=freqs, \n                n_cycles=n_cycles, \n                use_fft=True,\n                return_itc=False,\n                average = True,\n                decim=2, ###\n                picks='grad', ###\n                time_bandwidth=time_bandwidth,\n                verbose=True)\n            tfrs_lofr[str(condition)] = power\n        # Save tfrs as .h5\n        tfr_lofr_fname_out = op.join(out_path,\n                        site_id + subject_id + '_tfr_lofr_%s.h5' % factor)\n        mne.time_frequency.write_tfrs(tfr_lofr_fname_out, \n                                      list(tfrs_lofr.values()), \n                                      overwrite=True)\n        # Plots\n        if make_plot:\n            for key, tfr in tfrs_lofr.items():\n                # Apply baseline correction\n                tfr.apply_baseline(baseline_w, \n                                   mode=\"percent\")\n                # Show TFRs in topography\n                fig = tfr.plot_topo(baseline=baseline_w,\n                                    mode='mean',\n                                    vmin=-1.,\n                                    vmax=1.,\n                                    fig_facecolor='w',\n                                    font_color='k',\n                                    title='TFR of %s' % key)\n                # Save figure\n                fname_fig = op.join(out_path, \n                                    site_id + subject_id + \"_tfr_lofr_%s\" % key + '.png')\n                fig.savefig(fname_fig)\n                plt.close(fig)\n    \n    # TFR of high frequencies (> 30 Hz)\n    if freq_band in ['high', 'both']:\n        tfrs_hifr = dict()\n        # Params\n        freqs = np.arange(30, 100, 2)\n        n_cycles = freqs / 4 \n        time_bandwidth = 4.0\n        # Run over each condition\n        for condition in conditions:\n            power = mne.time_frequency.tfr_multitaper(\n                epochs['%s == \"%s\"' % (factor, condition)],\n                freqs=freqs, \n                n_cycles=n_cycles, \n                use_fft=True,\n                return_itc=False,\n                average = True,\n                decim=2, ###\n                picks='grad', ###\n                time_bandwidth=time_bandwidth,\n                verbose=True)\n            tfrs_hifr[str(condition)] = power\n        # Save tfrs\n        tfr_hifr_fname_out = op.join(out_path,\n                        site_id + subject_id + '_tfr_hifr_%s.h5' % factor)\n        mne.time_frequency.write_tfrs(tfr_hifr_fname_out, \n                                      list(tfrs_hifr.values()), \n                                      overwrite=True)\n        # Plot\n        if make_plot:\n            for key, tfr in tfrs_hifr.items():\n                # Apply baseline correction\n                tfr.apply_baseline(baseline_w, \n                                   mode=\"percent\")\n                # Make topoplot\n                fig = tfr.plot_topo(baseline=baseline_w,\n                                    mode='mean',\n                                    vmin=-1.,vmax=1.,\n                                    fig_facecolor='w',\n                                    font_color='k',\n                                    title='TFR of %s' % key)\n                # Save figure\n                fname_fig = op.join(out_path, \n                                    site_id + subject_id + \"_tfr_hifr_%s\" % key + '.png')\n                fig.savefig(fname_fig)\n                plt.close(fig)    \n    \n    sys.stdout.close()      # close log file\n    sys.stdout = stdout_obj # restore command prompt\n\n\n# =============================================================================\n# PARAMETERS\n# =============================================================================\n\nif subject_id in no_eeg_sbj:\n    EEG = False\nelse:\n    EEG = True\n    \nmake_plot = True\n\n\n# main()\nrun_time_frequency(make_plot = make_plot)\n","repo_name":"qiaolililili/WM_GRID","sub_path":"B01-time_frequency.py","file_name":"B01-time_frequency.py","file_ext":"py","file_size_in_byte":5416,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"18263359148","text":"\r\nimport numpy as np\r\nimport pandas as pd\r\nimport seaborn as sn\r\nimport matplotlib.pyplot as plt\r\nimport streamlit as st\r\n\r\ndef filtering(df_dum, target, threshold=0.67):\r\n    corrMatrix=df_dum.corr().round(2)\r\n    correlated_features=set()\r\n    for i in range(len(corrMatrix.columns)):\r\n        for j in range(i):\r\n            if abs(corrMatrix.iloc[j,i]) > threshold:\r\n                if (abs(corrMatrix.iloc[j,-1])>abs(corrMatrix.iloc[i,-1])):\r\n                    colname = corrMatrix.columns[i]\r\n                else:\r\n                    colname = corrMatrix.columns[j]\r\n                correlated_features.add(colname)\r\n    new_columns=list(set(df_dum.columns.tolist())-correlated_features)\r\n    new_columns.remove(target)\r\n    new_columns.sort()\r\n    new_columns.append(target)\r\n    corrMatrix=df_dum[new_columns].corr().round(2)\r\n    fig=plt.figure(figsize=(10,10))\r\n    #sn.set(rc={'figure.figsize':(13,13)})\r\n    sn.heatmap(corrMatrix, annot=True, cmap='YlGnBu')\r\n\r\n    st.pyplot(fig)\r\n    df_dum=df_dum[new_columns]\r\n    \r\n    return df_dum\r\n","repo_name":"afanasiev-d/custom_scoring_model_test_version","sub_path":"correlation.py","file_name":"correlation.py","file_ext":"py","file_size_in_byte":1054,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"21087891431","text":"from socketio import Server\n\nimport application.usecase.room as room_usecase\nimport application.usecase.player as player_usecase\nimport application.usecase.game as game_usecase\nimport application.utils.player as player_util\nimport application.utils.room as room_util\n\n\ndef set(socket_io: Server):\n    @socket_io.on(\"connect\")\n    def on_connect(socket_id, environ):\n        room_usecase.connect(socket_io, socket_id)\n\n    @socket_io.on(\"create_room\")\n    def on_create_room(socket_id: str, player_name: str):\n        player = player_usecase.register_player(player_name, socket_id)\n        room = room_usecase.create_room(player.id)\n        room_usecase.enter_room(socket_io, room.number, [player], player)\n\n    @socket_io.on(\"enter_room\")\n    def on_enter_room(socket_id: str, player_name: str, room_number_str: str):\n        room_number = int(room_number_str)\n        player = player_usecase.register_player(player_name, socket_id)\n        players = player_util.get_players_in_room(room_number)\n\n        room_usecase.enter_room(socket_io, room_number, players, player)\n\n    @socket_io.on(\"leave_room\")\n    def on_leave_room(socket_id: str):\n        player = player_util.get_player_by_socket_id(socket_id)\n        room = room_util.get_room_by_player_id(player.id)\n        players = player_util.get_players_in_room(room.number)\n\n        room_usecase.leave_room(socket_io, room.id, players, player)\n\n    @socket_io.on(\"connect_waiting_room\")\n    def on_connect_waiting_room(new_socket_id: str, old_socket_id: str):\n        player = player_util.get_player_by_socket_id(old_socket_id)\n        room = room_util.get_room_by_player_id(player.id)\n        players = player_util.get_players_in_room(room.number)\n\n        room_usecase.reconnect(socket_io,\n                               new_socket_id, old_socket_id, players)\n\n    @socket_io.on(\"ready_game\")\n    def on_ready_game(socket_id: str):\n        player = player_util.get_player_by_socket_id(socket_id)\n        room = room_util.get_room_by_player_id(player.id)\n        players = player_util.get_players_in_room(room.number)\n\n        room_usecase.ready(socket_io, players, room.id, player)\n\n    @socket_io.on(\"unready_game\")\n    def on_unready_game(socket_id: str):\n        player = player_util.get_player_by_socket_id(socket_id)\n        room = room_util.get_room_by_player_id(player.id)\n        players = player_util.get_players_in_room(room.number)\n\n        room_usecase.unready(socket_io, players, room.id, player)\n\n    @socket_io.on(\"start_game\")\n    def on_start_game(socket_id: str):\n        player = player_util.get_player_by_socket_id(socket_id)\n        room = room_util.get_room_by_player_id(player.id)\n        players = player_util.get_players_in_room(room.number)\n\n        game_usecase.setup_game(socket_io, players, room.id)\n","repo_name":"KITA-DS12/koi-jan","sub_path":"server/interfaces/controller/room.py","file_name":"room.py","file_ext":"py","file_size_in_byte":2784,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"13222129590","text":"\"\"\"Module for retrieving application icon information\"\"\"\n\nload(\"@bazel_skylib//lib:paths.bzl\", \"paths\")\n\ndef _get_resource_set_name(path, suffix):\n    suffix_idx = path.find(suffix)\n    if suffix_idx == -1:\n        return None, None\n    suffix_end_idx = suffix_idx + len(suffix)\n    prev_delimiter_idx = path[:suffix_idx].rfind(\"/\")\n    if prev_delimiter_idx == -1:\n        name_start_idx = 0\n    else:\n        name_start_idx = prev_delimiter_idx + 1\n    return path[name_start_idx:suffix_idx], path[:suffix_end_idx]\n\n_RESOURCE_SET_SUFFIXES = [\".appiconset\", \".brandassets\"]\n\ndef _find_resource_set(app_icon_files):\n    for file in app_icon_files:\n        for suffix in _RESOURCE_SET_SUFFIXES:\n            set_name, set_path = _get_resource_set_name(file.short_path, suffix)\n            if not set_name:\n                continue\n            return set_name, set_path\n\n    return None, None\n\ndef _should_find_default_icon_path(ctx):\n    return ctx.attr._build_mode != \"xcode\"\n\n_IMAGE_EXTS = {\n    \".jpeg\": None,\n    \".jpg\": None,\n    \".png\": None,\n}\n\ndef _find_default_icon_path(set_path, app_icon_files):\n    # GH949: Update the file selection logic to use the contents of the resource\n    # set's `Contents.json`.\n    for file in app_icon_files:\n        file_path = file.short_path\n        if not file_path.startswith(set_path):\n            continue\n        _, ext = paths.split_extension(file_path)\n        if ext not in _IMAGE_EXTS:\n            continue\n        return file_path\n\n    return None\n\ndef _get_app_icon_info(ctx, automatic_target_info, rule_attr):\n    \"\"\"Attempts to find the application icon name.\n\n    Args:\n        ctx: The aspect context.\n        automatic_target_info: The `XcodeProjAutomaticTargetProcessingInfo` for\n            `target`.\n        rule_attr: `ctx.rule.attr`.\n\n    Returns:\n        The application icon name, if found. Otherwise, `None`.\n    \"\"\"\n    if not automatic_target_info.app_icons:\n        return None\n\n    app_icons = getattr(rule_attr, automatic_target_info.app_icons, None)\n    if not app_icons:\n        return None\n\n    app_icon_files = [\n        file\n        for target in app_icons\n        for file in target.files.to_list()\n    ]\n\n    set_name, set_path = _find_resource_set(app_icon_files)\n    if not set_name:\n        return None\n\n    if _should_find_default_icon_path(ctx):\n        default_icon_path = _find_default_icon_path(set_path, app_icon_files)\n    else:\n        default_icon_path = None\n\n    return _create(\n        set_name = set_name,\n        set_path = set_path,\n        default_icon_path = default_icon_path,\n    )\n\ndef _create(set_name, set_path, default_icon_path):\n    \"\"\"Creates a `struct` representing information about a target's application \\\n    icons.\n\n    Args:\n      set_name: The name of the resource set as a `string`.\n      set_path: The path of the resource set as a `string`.\n      default_icon_path: If a default icon should be identified, the path to\n          the icon file will be set as a `string`.\n\n    Returns:\n        A `struct` representing application icon information.\n    \"\"\"\n    return struct(\n        set_name = set_name,\n        set_path = set_path,\n        default_icon_path = default_icon_path,\n    )\n\napp_icons = struct(\n    create = _create,\n    get_info = _get_app_icon_info,\n)\n","repo_name":"MobileNativeFoundation/rules_xcodeproj","sub_path":"xcodeproj/internal/app_icons.bzl","file_name":"app_icons.bzl","file_ext":"bzl","file_size_in_byte":3280,"program_lang":"python","lang":"en","doc_type":"code","stars":419,"dataset":"github-code","pt":"81"}
+{"seq_id":"33412137186","text":"\ndef advanceReader(x):\n    return x + 4\n\ndef add(inst, prog):\n    prog[inst[2]] = prog[inst[0]] + prog[inst[1]]    \n\ndef multiply(inst, prog):\n    prog[inst[2]] = prog[inst[0]] * prog[inst[1]]    \n\ndef processProgram(prog):\n    reader = 0\n    while True:\n        instruction = prog[reader]\n        if instruction == 99:\n            break\n        elif instruction == 1:\n            add(prog[reader+1:reader+4], prog)\n        elif instruction == 2:\n            multiply(prog[reader+1:reader+4], prog)\n        \n        reader = advanceReader(reader)\n\n    return prog[0]\n\n\ndef main():\n    with open('input1.txt', 'r') as file:\n        content = file.read()\n        initial_prog = [int(x)for x in content.strip().split(',')]\n\n    target_output = 19690720\n\n    for a in range(100):\n        for b in range(100):\n            test_prog = initial_prog[:]\n            test_prog[1] = a\n            test_prog[2] = b\n            \n            test_output = processProgram(test_prog)\n\n            if test_output == target_output:\n                print(f\"The solution is: {a * 100 + b}\")\n                break\n\n\n\nif __name__ == \"__main__\":\n    main()","repo_name":"dneff/adventofcode","sub_path":"python/2019/02/solution2.py","file_name":"solution2.py","file_ext":"py","file_size_in_byte":1133,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"9002490674","text":"#coding:utf8\n\n'''\ngoogle authenticator 两步认证\n原理：\n    基于hotp算法，按照时间同样一个字符串与时间相结合在不同服务器上会算出相同的数\n    利用这相同的数去验证，服务器上只要保存明文即可。\n'''\nimport hmac, base64, struct, hashlib, time\nimport random\n\ndef get_hotp_token(secret, intervals_no):\n    key = base64.b32decode(secret, True)\n    msg = struct.pack(\">Q\", intervals_no)\n    h = hmac.new(key, msg, hashlib.sha1).digest()\n    o = ord(h[19]) & 15\n    h = (struct.unpack(\">I\", h[o:o+4])[0] & 0x7fffffff) % 1000000\n    return h\n\ndef get_totp_token(secret):\n    return get_hotp_token(secret, intervals_no=int(time.time())//30)\n\ndef shutff_str(): #生成随机字符串大字16位\n    src = \"abcdefghijklmnopqrstuvwxyz\".upper()\n    secret= \"\".join(random.sample(src,16)).replace(' ','')\n    return secret\n\nsecret = shutff_str()\n\nprint(secret) # goole authenticator手机上 输入进行比对\nfor i in range(1,100):\n    print(get_totp_token(secret))\n    time.sleep(3)\n","repo_name":"zwhset/pythons","sub_path":"Google_Authenticator.py","file_name":"Google_Authenticator.py","file_ext":"py","file_size_in_byte":1034,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"41695561694","text":"#!/usr/bin/python3\n\"\"\"\nWe have a two dimensional matrix A where each value is 0 or 1.\n\nA move consists of choosing any row or column, and toggling each value in that\nrow or column: changing all 0s to 1s, and all 1s to 0s.\n\nAfter making any number of moves, every row of this matrix is interpreted as a\nbinary number, and the score of the matrix is the sum of these numbers.\n\nReturn the highest possible score.\n\nExample 1:\n\nInput: [[0,0,1,1],[1,0,1,0],[1,1,0,0]]\nOutput: 39\nExplanation:\nToggled to [[1,1,1,1],[1,0,0,1],[1,1,1,1]].\n0b1111 + 0b1001 + 0b1111 = 15 + 9 + 15 = 39\n\nNote:\n\n1 <= A.length <= 20\n1 <= A[0].length <= 20\nA[i][j] is 0 or 1.\n\"\"\"\nfrom typing import List\n\n\nclass Solution:\n    def matrixScore(self, A: List[List[int]]) -> int:\n        \"\"\"\n        MSB > sum of remaining digit\n        => Toggle rows to set MSB to 1\n        Then we cannot toggle row anymore\n        Toggle the col if #0's < #1's\n        \"\"\"\n        m, n = len(A), len(A[0])\n        ret = 0\n        ret += (1 << (n-1)) * m  # all rows with MSB being 1\n        for j in range(1, n):\n            cnt = 0\n            for i in range(m):\n                if A[i][j] == A[i][0]:\n                    cnt += 1  #  number of 1's\n\n            # toggle \n            cnt = max(cnt, m-cnt)\n            ret += (1 << (n-1-j)) * cnt\n\n        return ret\n","repo_name":"algorhythms/LeetCode","sub_path":"861 Score After Flipping Matrix.py","file_name":"861 Score After Flipping Matrix.py","file_ext":"py","file_size_in_byte":1318,"program_lang":"python","lang":"en","doc_type":"code","stars":843,"dataset":"github-code","pt":"81"}
+{"seq_id":"6902967284","text":"\"\"\"Covid-19 Dashboard webapp program that uses the flask module.\nAlso uses modules made by me for retrieving UK Covid-19 data\nand related news articles\"\"\"\n\nimport time\nimport os\nimport logging\nfrom flask import Flask, render_template, request, send_from_directory\nfrom covid_data_handler import update_covid_data, schedule_covid_updates\nfrom covid_data_handler import updates, covid_data, scheduler\nfrom covid_news_handling import update_news, remove_article, news_data, removed_articles\n\nlogging.basicConfig(filename=\"pysys.log\", level=logging.INFO)\n\napp = Flask(__name__)\n\n#List of scheduled update widgets on the left hand of the screen\ngui_updates = []\n\ndef remove_update(title:str):\n    \"\"\"Called when the user closes an update widget\n    removes it from the list of updates so it is not rendered\"\"\"\n\n    if gui_updates:\n        #Finds the item in the list of updates that has the given title\n        to_remove = next((item for item in gui_updates if item[\"title\"] == title), None)\n        if to_remove:\n            #Removes this item from the list\n            gui_updates.remove(to_remove)\n\ndef get_title(update_title:str) -> str:\n    \"\"\"Method for making sure all updates have unique identifiers by reassigning taken titles\"\"\"\n    if update_title in updates and not update_title[-1].isnumeric():\n        #Adds a \"1\" to the end of an update title if it is taken\n        #and the last character is not already a digit\n        update_title += \"1\"\n    i = 0\n    while update_title in updates:\n        #Increments the digit while the title is still taken\n        i += 1\n        update_title = update_title[:-1] + str(i)\n    return update_title\n\ndef schedule_update(update:dict, desc:str):\n    \"\"\"Uses sched to schedule updates that the user has specified\"\"\"\n    #Time in seconds until the update should occur is calculated using current time\n    time_now = time.gmtime()\n    hours = int(update[\"time\"][:2]) - time_now.tm_hour\n    mins = int(update[\"time\"][3:]) - time_now.tm_min + hours*60 - 1\n    secs = (mins*60 + (60 - time_now.tm_sec)) % 86400 #Seconds in a day\n\n    #A new item is added to the dictionary of updates\n    #with the update title as the key and an empty list as the value\n    #This list will hold all the scheduled functions relating to this update event\n    updates[update[\"title\"]] = []\n\n    #Updates are scheduled for covid and news data respectively, if specified\n    if update[\"covid\"]:\n        schedule_covid_updates(secs, update[\"title\"])\n    if update[\"news\"]:\n        updates[update[\"title\"]].append(scheduler.enter(secs,0,update_news))\n\n    #Removes the update widget when the time has elapsed and the update has occurred\n    updates[update[\"title\"]].append(scheduler.enter(secs,1,remove_update,(update[\"title\"],)))\n    #Means that the update item gets cleared from the dictionary when it is complete\n    updates[update[\"title\"]].append(scheduler.enter(secs,1,updates.pop,(update[\"title\"],)))\n\n    #If the update is set to repeat, an event is scheduled\n    #that reschedules the same update again when it is complete\n    if update[\"repeats\"]:\n        #A new update widget also has to be added because the old one will be removed once complete\n        gui_update = {\"title\": update[\"title\"], \"content\": desc}\n        updates[update[\"title\"]].append(scheduler.enter(secs,2,gui_updates.append,(gui_update,)))\n        updates[update[\"title\"]].append(scheduler.enter(secs,2,schedule_update,(update,desc,)))\n\n    logging.info(\"Scheduled update: %s\", update[\"title\"])\n    logging.info(desc)\n\ndef deschedule_update(title:str):\n    \"\"\"Uses sched to cancel a scheduled update\"\"\"\n    if updates:\n        if title in updates:\n            for event in updates[title]:\n                #Cancels all scheduled events relating to the update\n                scheduler.cancel(event)\n            #Update is removed from the dictionary\n            updates.pop(title)\n            logging.info(\"Cancelled update: %s\", title)\n        else:\n            logging.error(\"%s is not a scheduled update\", title)\n    else:\n        logging.error(\"No updates to deschedule\")\n\n@app.route(\"/index\")\ndef main() -> str:\n    \"\"\"Main program script. Runs when /index page is loaded\"\"\"\n    #If user tries to remove an article, finds this article\n    #and removes it using remove_article method in covid_news_handling\n    to_remove = request.args.get(\"notif\")\n    if to_remove:\n        for item in news_data:\n            if item[\"title\"] == to_remove:\n                remove_article(item)\n\n    #If user enters an update to schedule, retrieves this information and adds an update to the list\n    update_time = request.args.get(\"update\")\n    if update_time:\n        update_title = get_title(request.args.get(\"two\"))\n        repeat_update = request.args.get(\"repeat\") is not None\n        do_update_covid_data = request.args.get(\"covid-data\") is not None\n        do_update_news_data = request.args.get(\"news\") is not None\n        if do_update_covid_data and do_update_news_data:\n            content = \"Updating COVID-19 data and news articles at \" + update_time\n        elif do_update_covid_data:\n            content = \"Updating COVID-19 data at \" + update_time\n        elif do_update_news_data:\n            content = \"Updating news articles at \" + update_time\n        else:\n            content = \"Updating at \" + update_time\n        if repeat_update:\n            content += \" with repeats\"\n\n        if do_update_covid_data or do_update_news_data:\n            gui_updates.append({\"title\": update_title, \"content\": content})\n            update = {\"title\": update_title,\n                \"time\": update_time,\n                \"repeats\": repeat_update,\n                \"covid\": do_update_covid_data,\n                \"news\": do_update_news_data}\n            schedule_update(update, content)\n        else:\n            logging.error(\"Empty update request\")\n\n    #If user tries to remove an update, finds this update and removes it from the list\n    update_to_remove = request.args.get(\"update_item\")\n    if update_to_remove and gui_updates:\n        deschedule_update(update_to_remove)\n        remove = next((item for item in gui_updates if item[\"title\"] == update_to_remove), None)\n        if remove:\n            gui_updates.remove(remove)\n\n    #Makes sure none of the removed articles are rendered\n    news_to_render = []\n    for item in news_data:\n        if item[\"url\"] not in removed_articles:\n            news_to_render.append(item)\n\n    #blocking=False means program does not wait for scheduled events before rendering the page\n    scheduler.run(blocking=False)\n\n    #Uses flask to render index.html with the following variables assigned\n    return render_template(\"index.html\",\n        title=\"COVID-19 Dashboard\",\n        favicon=\"favicon.ico\",\n        image=\"image.png\",\n        location=covid_data[\"location\"],\n        nation_location=covid_data[\"nation\"],\n        local_7day_infections=covid_data[\"local_cases\"],\n        national_7day_infections=covid_data[\"national_cases\"],\n        hospital_cases=covid_data[\"hospital_cases\"],\n        deaths_total=covid_data[\"deaths\"],\n        news_articles=news_to_render,\n        updates=gui_updates)\n\n@app.route('/favicon.ico')\ndef favicon():\n    \"\"\"Runs when the favicon is requested\"\"\"\n    return send_from_directory(os.path.join(app.root_path, 'static'),\n        'favicon.ico', mimetype='image/vnd.microsoft.icon')\n\nif __name__ == \"__main__\":\n    update_news()\n    update_covid_data()\n    app.run()\n","repo_name":"alexcairns02/covid-dashboard","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":7427,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"1272111339","text":"import urllib.parse\nfrom typing import Any\n\nimport allure\nfrom playwright.async_api import Page\n\n\nclass PhotoWallPage:\n    BROKEN_IMAGE: str = \"[alt='😼 #zatschi #whoneedsfourlegs']\"\n    JS_SET_ATTRIBUTE_COMMAND: str = 'document.querySelector(\"{element}\").setAttribute(\"{attribute}\", \"{value}\");'\n\n    def __init__(self, page: Page) -> None:\n        self.page: Page = page\n\n    @allure.step\n    async def fix_broken_image(self) -> None:\n        # Get broken img src\n        image_src: Any = await self.page.locator(self.BROKEN_IMAGE).get_attribute(\"src\")\n        decoded_src: str = urllib.parse.quote(image_src)\n        await self.page.evaluate(\n            self.JS_SET_ATTRIBUTE_COMMAND.format(element=self.BROKEN_IMAGE, attribute=\"src\", value=decoded_src)\n        )\n","repo_name":"aki0007/async_playwright_juice_shop","sub_path":"src/pom/photo_wall.py","file_name":"photo_wall.py","file_ext":"py","file_size_in_byte":770,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"18106744655","text":"#!E:/anaconda env python\n# -*- coding: utf-8 -*-\n# @Date    : 2018-07-21 13:09:08\n# @Author  : 1nsane SUN\n# @Email   : 771499532@qq.com\n# @Link    : pass\n# @Version : $Id$\n\nimport wx\n\nclass ListBoxFrame(wx.Frame):\n\t\"\"\"docstring for ClassName\"\"\"\n\tdef __init__(self):\n\t\twx.Frame.__init__(self,None,-1,\n\t\t\tsize=(250,200))\n\t\tpanel = wx.Panel(self,-1)\n\t\tsampleList = ['1','2','3','4','5']\n\t\tlistBox = wx.ListBox(panel,-1,(20,20),(80,120),sampleList,\n\t\t\twx.LB_SINGLE)\n\t\tlistBox.SetSelection(1)\n\n\nif __name__ == '__main__':\n\tapp = wx.PySimpleApp()\n\tListBoxFrame().Show()\n\tapp.MainLoop()\n\t\t\n","repo_name":"EriliSun/pyProject","sub_path":"ListBoxFrame.py","file_name":"ListBoxFrame.py","file_ext":"py","file_size_in_byte":583,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"41263074177","text":"# -*- coding: utf-8 -*-\n# Created on Mon Apr 30 2018 23:55:32\n# Author: WuLC\n# EMail: liangchaowu5@gmail.com\n\n# pay attention that same difficulty may have different profit\nclass Solution(object):\n    def maxProfitAssignment(self, difficulty, profit, worker):\n        \"\"\"\n        :type difficulty: List[int]\n        :type profit: List[int]\n        :type worker: List[int]\n        :rtype: int\n        \"\"\"\n        p = [0]*100001\n        for i in xrange(len(difficulty)):\n            p[difficulty[i]] = max(profit[i], p[difficulty[i]])  # same difficulty may have different profit\n        curr_max = 0\n        for i in xrange(1, len(p)):\n            curr_max = max(curr_max, p[i])\n            p[i] = curr_max\n        return sum(p[worker[i]] for i in xrange(len(worker))) ","repo_name":"WuLC/LeetCode","sub_path":"Algorithm/Python/826. Most Profit Assigning Work.py","file_name":"826. Most Profit Assigning Work.py","file_ext":"py","file_size_in_byte":768,"program_lang":"python","lang":"en","doc_type":"code","stars":23,"dataset":"github-code","pt":"81"}
+{"seq_id":"43960208142","text":"\"\"\"\nVersion: Python3.5\nAuthor: OniOn\nSite: http://www.cnblogs.com/TM0831/\nTime: 2019/2/9 16:46\n\"\"\"\nimport re\nimport json\nimport time\nimport requests\nfrom fake_useragent import UserAgent\n\n\nua = UserAgent()\ncity_dict = dict()\n\n\n# 获取城市-编码字典\ndef get_city_dict():\n    url = \"http://map.weather.com.cn/static_data/101.js\"\n    headers = {\n        \"User-Agent\": ua.random\n    }\n    res = requests.get(url, headers=headers)\n    js = json.loads(res.text.lstrip('var map_config_101='))\n    province_list = []\n    for i in js['text']['inner']:\n        province_list.append((i['data-name'], i['data-id']))\n    for i in province_list:\n        time.sleep(1)\n        href = \"http://map.weather.com.cn/static_data/{}.js\".format(i[1])\n        res = requests.get(href, headers=headers)\n        js = json.loads(res.text.lstrip('var map_config_{}='.format(i[1])))\n        for j in js['text']['inner']:\n            if i[0] in ['北京', '上海', '天津', '重庆']:\n                city_dict[i[0] + \"-\" + j['data-name']] = j['data-id']\n            else:\n                city_dict[i[0] + \"-\" + j['data-name']] = j['data-id'] + \"01\"\n    # print(.city_dict)\n\n\n# 获取城市对应的编码\ndef get_city_id(city_name):\n    result = [(k, city_dict[k]) for k in city_dict.keys() if city_name in k]\n    city_id = \"\"\n    if len(result) == 0:  # 如果字典中没有相关数据就报错\n        print(\"Error!\")\n        exit()\n    elif len(result) == 1:\n        city_id = result[0][1]\n    else:\n        for i in range(len(result)):  # 说明地名有相同的\n            print(i, result[i])\n        city_id = result[int(input(\"请确定您的城市的序号：\"))][1]\n    return city_id\n\n\n# 爬取天气信息\ndef get_weather(city_name):\n    get_city_dict()\n    city_id = get_city_id(city_name)\n    url = \"http://d1.weather.com.cn/sk_2d/{}.html?_=1544842784069\".format(city_id)\n    # 如果User-Agent被识别出来了，就再重新随机一个UA\n    while 1:\n        headers = {\n            \"Referer\": \"http://www.weather.com.cn/weather1d/{}.shtml\".format(city_id),\n            \"User-Agent\": ua.random\n        }\n        res = requests.get(url, headers=headers)\n        if not re.search('FlashVars', res.text):\n            break\n    res.encoding = \"utf-8\"\n    js = json.loads(res.text.lstrip('var dataSK = '))\n    weather_info = {\n        \"日期\": js['date'],\n        \"天气\": js['weather'],\n        \"温度\": js['temp'] + '℃',\n        \"PM2.5\": js['aqi_pm25'],\n        \"相对湿度\": js['SD'],\n    }\n    return weather_info\n","repo_name":"TM0831/Spiders","sub_path":"AutoWeather/get_wather.py","file_name":"get_wather.py","file_ext":"py","file_size_in_byte":2519,"program_lang":"python","lang":"en","doc_type":"code","stars":223,"dataset":"github-code","pt":"81"}
+{"seq_id":"27646019796","text":"\"\"\"\n\n.. autoclass:: StatusSink\n   :members:\n   :undoc-members:\n\n.. autoclass:: TreeStatusSink\n   :members:\n   :undoc-members:\n\n.. autoclass:: MongoStatusSink\n   :members:\n   :undoc-members:\n\n.. autoclass:: StdOutStatusSink\n   :members:\n   :undoc-members:\n\n\"\"\"\n\nfrom pymongo import MongoClient\nfrom up.util import NAME_SEPARATOR, colors\nfrom up.moods import moods\n\n\nclass StatusSink(object):\n    \"\"\"\n    A base class for a sink. Sinks are used for outputting the the statuses\n    gathered by sources.\n    \"\"\"\n\n    def process_status(self, status, deep=0, surname=''):\n        \"\"\"\n        Implementing classes should implement this hook to output an individual\n        status.\n\n        :param status: a status object to be output.\n        :param deep: an int of the number of `StatusTreeSource` that have been traversed down.\n        :param surname: The names of all of the parent sources concatenated with a '/'.\n        \"\"\"\n        raise NotImplemented\n\n    def add_annotation(self, note):\n        \"\"\"\n        Add an annotation to the output.\n        \"\"\"\n        raise NotImplemented\n\n    def set_mood(self, mood):\n        \"\"\"\n        The mood of any messaging.\n        \"\"\"\n        self.mood = mood\n\n    def set_verbosity(self, verbosity):\n        \"\"\"\n        If the sink outputs to stdout, use this value to control the amount of\n        output.\n        \"\"\"\n        self.verbosity = verbosity\n\n    def set_status(self, source, deep=0, surname=''):\n        self.process_status(source, deep, surname)\n\n        # duck type a StatusTreeSource, not to be confused with TreeStatusSink\n        if hasattr(source, 'children'):\n            for status in source.children:\n                self.set_status(status, deep + 1, surname + NAME_SEPARATOR + source.name)\n\n\nclass TreeStatusSink(StatusSink):\n    \"\"\"\n    Output a source to multiple sinks.\n    \"\"\"\n    def __init__(self, children):\n        super(TreeStatusSink, self).__init__()\n        self.children = children\n\n    def add_annotation(self, note):\n        for child in self.children:\n            child.add_annotation(note)\n\n    def set_mood(self, mood):\n        for child in self.children:\n            child.set_mood(mood)\n\n    def set_verbosity(self, verbosity):\n        for child in self.children:\n            child.set_verbosity(verbosity)\n\n    def set_status(self, source):\n        for child in self.children:\n            child.set_status(source)\n\n\nclass MongoStatusSink(StatusSink):\n    \"\"\"\n    Output a source to MongoDB. Each status is stored as a separate document.\n\n    Up-web requires statuses to be stored with this sink.\n    \"\"\"\n    DB_NAME = 'up'\n\n    def __init__(self, domain, port):\n        super(MongoStatusSink, self).__init__()\n        client = MongoClient(domain, port)\n        self.db = client[self.DB_NAME]\n\n    def add_annotation(self, note):\n        self.db.annotations.insert(note.__dict__)\n\n    def process_status(self, status, deep=0, surname=''):\n        self.db.statuses.insert({\n            'path': surname + NAME_SEPARATOR + status.name,\n            'up': status.status,\n            'date': status.date,\n            'duration': status.duration\n        })\n\n\nclass StdOutStatusSink(StatusSink):\n    \"\"\"\n    Output a source to stdout with fancy colors for easy skimming.\n    \"\"\"\n\n    messages = {\n        moods.REALIST: [\n            colors.FAIL + 'DOWN' + colors.END,\n            colors.FAIL + 'MOSTLY DOWN' + colors.END,\n            colors.WARNING + 'HALF UP' + colors.END,\n            colors.WARNING + 'MOSTLY UP' + colors.END,\n            colors.OK + 'UP' + colors.END\n        ],\n        moods.OPTIMIST: [\n            colors.FAIL + 'HANG IN THERE!' + colors.END,\n            colors.FAIL + 'SLIGHTLY UP' + colors.END,\n            colors.WARNING + 'HALF UP' + colors.END,\n            colors.WARNING + 'MOSTLY UP' + colors.END,\n            colors.OK + 'UP' + colors.END\n        ],\n        moods.PESSIMIST: [\n            colors.FAIL + 'GET TO WORK!' + colors.END,\n            colors.FAIL + 'MOSTLY DOWN' + colors.END,\n            colors.WARNING + 'HALF DOWN' + colors.END,\n            colors.WARNING + 'SLIGHTLY DOWN' + colors.END,\n            colors.OK + 'WHATEVER' + colors.END\n        ]\n    }\n\n    def add_annotation(self, note):\n        print(colors.PARENTHETICAL + note.message + colors.END + '\\n')\n\n    def process_status(self, status, deep=0, surname=''):\n        # stop outputting if we've passed the verbosity setting.\n        if deep > self.verbosity:\n            return\n\n        messages = self.messages[self.mood]\n        message = int(status.status * (len(messages) - 1))\n\n        print(('  ' * deep) + status.name + ': ', end='')\n        #print(message, end='')\n        print(messages[message], end='')\n\n        if status.status < 1 and status.status > 0:\n            print(colors.PARENTHETICAL + ' ({percent:.4}%)'.format(percent=float(status.status) * 100) + colors.END, end='')\n\n        if status.date is not None and status.duration > 1E-4:\n            print(colors.PARENTHETICAL + ' ({duration:.4}s)'.format(duration=status.duration) + colors.END)\n        else:\n            print()\n","repo_name":"Kataki-Takanashi/Halal-Stock-Organizer","sub_path":"venv/lib/python3.9/site-packages/up/sink.py","file_name":"sink.py","file_ext":"py","file_size_in_byte":5076,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"81"}
+{"seq_id":"10567718208","text":"import falcon\nimport json\n\n\nclass RequireJSON(object):\n\n    def process_request(self, request, response):\n        if not request.client_accepts_json:\n            raise falcon.HTTPNotAcceptable('Response encoded as JSON')\n\n        if request.method in ('POST'):\n            if 'application/json' not in request.content_type:\n                raise falcon.HTTPUnsupportedMediaType('Requests encoded as JSON')\n\n\nclass JSONTranslator(object):\n    def process_request(self, request, response):\n\n        if request.content_length in (None, 0):\n            return\n\n        body = request.stream.read()\n\n        if not body:\n            raise falcon.HTTPBadRequest('Empty request body')\n        try:\n            request.context['body'] = json.loads(body.decode('utf-8'))\n\n        except (ValueError, UnicodeDecodeError):\n            raise falcon.HTTPError(falcon.HTTP_753, 'Incorrect JSON')\n\n    def process_response(self, request, response, resource):\n        if 'body' not in response.context:\n            return\n\n        response.body = json.dumps(response.context['body'])\n","repo_name":"masachi/driver-data-backend","sub_path":"middleware/validate.py","file_name":"validate.py","file_ext":"py","file_size_in_byte":1068,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"81"}
+{"seq_id":"11502627702","text":"from __future__ import annotations\n\nfrom abc import ABC, abstractmethod\nfrom typing import Any, Dict, Type, Union\n\nfrom sanic.app import Sanic\nfrom sanic.exceptions import SanicException\n\nfrom sanic_ext.config import Config\nfrom sanic_ext.exceptions import InitError\n\n\nclass NoDuplicateDict(dict):  # type: ignore\n    def __setitem__(self, key: Any, value: Any) -> None:\n        if key in self:\n            raise KeyError(f\"Duplicate key: {key}\")\n        return super().__setitem__(key, value)\n\n\nclass Extension(ABC):\n    _name_registry: Dict[str, Type[Extension]] = NoDuplicateDict()\n    _started: bool\n    name: str\n    app: Sanic\n    config: Config\n\n    def __init_subclass__(cls):\n        if not getattr(cls, \"name\", None) or not cls.name.isalpha():\n            raise InitError(\n                \"Extensions must be named, and may only contain \"\n                \"alphabetic characters\"\n            )\n\n        if cls.name in cls._name_registry:\n            raise InitError(f'Extension \"{cls.name}\" already exists')\n\n        cls._name_registry[cls.name] = cls\n\n    def _startup(self, bootstrap):\n        if self._started:\n            raise SanicException(\n                \"Extension already started. Cannot start \"\n                f\"Extension:{self.name} multiple times.\"\n            )\n        self.startup(bootstrap)\n        self._started = True\n\n    @abstractmethod\n    def startup(self, bootstrap) -> None:\n        ...\n\n    def label(self):\n        return \"\"\n\n    def render_label(self):\n        if not self.included:\n            return \"~~disabled~~\"\n        label = self.label()\n        if not label:\n            return \"\"\n        return f\"[{label}]\"\n\n    def included(self):\n        return True\n\n    @classmethod\n    def create(\n        cls,\n        extension: Union[Type[Extension], Extension],\n        app: Sanic,\n        config: Config,\n    ) -> Extension:\n        instance = (\n            extension if isinstance(extension, Extension) else extension()\n        )\n        instance.app = app\n        instance.config = config\n        instance._started = False\n        return instance\n\n    @classmethod\n    def reset(cls) -> None:\n        cls._name_registry.clear()\n","repo_name":"sanic-org/sanic-ext","sub_path":"sanic_ext/extensions/base.py","file_name":"base.py","file_ext":"py","file_size_in_byte":2172,"program_lang":"python","lang":"en","doc_type":"code","stars":43,"dataset":"github-code","pt":"81"}
+{"seq_id":"71276522185","text":"#!/usr/bin/env python3\nlines = ''\nwith open(\"log1.txt\") as fobj1:\n    line1 = fobj1.read() \nwith open(\"log2.txt\") as fobj2:\n    line2 = fobj2.read()\nlines = line1 + line2\nresult = {}\nfor line in lines.splitlines():\n    if line[11] == '<':\n        i = line[12:].split('>')[0]\n        result[i] = result.get(i, 0) + 1\n       \nfor i in result.keys():\n    j = i + '\\t' + str(result[i])\n    print(j)\n","repo_name":"gobindaakhuli/dgplugproblems","sub_path":"logcheck.py","file_name":"logcheck.py","file_ext":"py","file_size_in_byte":395,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"17895227091","text":"import csv\nimport json\nimport os\nimport shutil\nimport time\nfrom collections import OrderedDict, defaultdict\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport torch\nimport torch.optim as optim\nfrom tqdm import trange\n\nfrom r2d2.data_loading.data_loader import create_train_test_data_loader\nfrom r2d2.training.models.policy_network import ImagePolicy\n\n\ndef exp_launcher(variant, run_id, exp_id):\n    variant[\"exp_name\"] = os.path.join(variant[\"exp_name\"], \"run{0}/id{1}/\".format(run_id, exp_id))\n\n    # Set Random Seeds #\n    torch.manual_seed(variant[\"seed\"])\n    np.random.seed(variant[\"seed\"])\n\n    # Set Compute Mode #\n    use_gpu = variant.get(\"use_gpu\", False)\n    torch.device(\"cuda:0\" if use_gpu else \"cpu\")\n\n    # Prepare Dataset Generators #\n    data_loader_kwargs = variant.get(\"data_loader_kwargs\", {})\n    data_processing_kwargs = variant.get(\"data_processing_kwargs\", {})\n    camera_kwargs = variant.get(\"camera_kwargs\", {})\n    train_dataloader, test_dataloader = create_train_test_data_loader(\n        data_loader_kwargs=data_loader_kwargs, data_processing_kwargs=data_processing_kwargs, camera_kwargs=camera_kwargs\n    )\n\n    # Create Model #\n    model = ImagePolicy(**variant.get(\"model_kwargs\", {}))\n    if use_gpu:\n        model.cuda()\n\n    # Create Trainer #\n    trainer = ModelTrainer(\n        model=model,\n        train_dataloader=train_dataloader,\n        test_dataloader=test_dataloader,\n        exp_name=variant[\"exp_name\"],\n        variant=variant,\n        **variant.get(\"training_kwargs\", {}),\n    )\n\n    # Launch Experiment #\n    trainer.train()\n\n\nclass ModelTrainer:\n    def __init__(\n        self,\n        model,\n        train_dataloader,\n        test_dataloader,\n        exp_name,\n        variant,\n        num_epochs=25,\n        weight_decay=0.0,\n        lr=1e-3,\n        grad_steps_per_epoch=1000,\n    ):\n        self.model = model\n        self.train_dataloader = iter(train_dataloader)\n        self.test_dataloader = iter(test_dataloader)\n        self.optimizer = None\n        self.grad_steps_per_epoch = grad_steps_per_epoch\n        self.weight_decay = weight_decay\n        self.num_epochs = num_epochs\n        self.lr = lr\n\n        self.persistent_statistics = defaultdict(list)\n        self.eval_statistics = defaultdict(list)\n        self.variant = variant\n\n        dir_path = os.path.dirname(os.path.realpath(__file__))\n        self.log_dir = os.path.join(dir_path, \"../../training_logs\", exp_name)\n\n    def compute_loss(self, batch, test=False):\n        prefix = \"test-\" if test else \"train-\"\n        loss = self.model.compute_loss(batch)\n        self.eval_statistics[prefix + \"Loss\"].append(loss.item())\n        return loss\n\n    def save_policy(self, epoch):\n        path = os.path.join(self.log_dir, \"models\", str(epoch) + \".pt\")\n        torch.save(self.model, path)\n\n    def train_batch(self, batch):\n        if self.optimizer is None:\n            self.compute_loss(batch)\n            params = list(self.model.parameters())\n            self.optimizer = optim.Adam(params, lr=self.lr, weight_decay=self.weight_decay)\n\n        self.optimizer.zero_grad()\n        loss = self.compute_loss(batch)\n\n        loss.backward()\n        self.optimizer.step()\n\n    def test_batch(self, batch):\n        self.compute_loss(batch, test=True)\n\n    def train_epoch(self, epoch):\n        start_time = time.time()\n        self.model.train()\n\n        # Train On Batches #\n        training_procedure = trange(self.grad_steps_per_epoch)\n        training_procedure.set_description(\"Epoch {0}: Training\".format(epoch))\n\n        for _i in training_procedure:\n            batch = next(self.train_dataloader)\n            self.train_batch(batch)\n\n        self.eval_statistics[\"train-epoch_duration\"].append(time.time() - start_time)\n\n    def test_epoch(self, epoch, test_batches=100):\n        start_time = time.time()\n        self.model.eval()\n\n        # Test On Batches #\n        testing_procedure = trange(test_batches)\n        testing_procedure.set_description(\"Epoch {0}: Testing\".format(epoch))\n\n        for _i in testing_procedure:\n            batch = next(self.test_dataloader)\n            self.test_batch(batch)\n\n        self.eval_statistics[\"test-epoch_duration\"].append(time.time() - start_time)\n\n    def prepare_logdir(self):\n        if os.path.exists(self.log_dir):\n            response = input(\"Directory Exists - Enter 'overwrite' to continue\\n\")\n            if response == \"overwrite\":\n                shutil.rmtree(self.log_dir)\n            else:\n                raise RuntimeError\n\n        os.makedirs(self.log_dir)\n        os.makedirs(self.log_dir + \"graphs/\")\n        os.makedirs(self.log_dir + \"models/\")\n\n        with open(self.log_dir + \"variant.json\", \"w\") as outfile:\n            json.dump(self.variant, outfile)\n\n    def output_diagnostics(self, epoch):\n        stats = OrderedDict()\n        for k in sorted(self.eval_statistics.keys()):\n            stats[k] = np.mean(self.eval_statistics[k])\n            self.persistent_statistics[k + \"/mean\"].append(stats[k])\n            self.persistent_statistics[k + \"/std\"].append(np.std(self.eval_statistics[k]))\n\n        self.update_plots(epoch)\n\n        if epoch == 0:\n            with open(self.log_dir + \"progress.csv\", \"w\", newline=\"\") as f:\n                writer = csv.DictWriter(f, fieldnames=stats.keys())\n                writer.writeheader()\n\n        with open(self.log_dir + \"progress.csv\", \"a\", newline=\"\") as f:\n            writer = csv.DictWriter(f, fieldnames=stats.keys())\n            writer.writerow(stats)\n\n        self.eval_statistics = defaultdict(list)\n        np.save(self.log_dir + \"logs.npy\", self.persistent_statistics)\n\n        print(\"\\nEPOCH: \", epoch)\n        for k, v in stats.items():\n            spacing = \":\" + \" \" * (30 - len(k))\n            print(k + spacing + str(round(v, 5)))\n\n    def update_plots(self, epoch):\n        x_axis = np.arange(epoch + 1)\n        for k in sorted(self.eval_statistics.keys()):\n            plt.clf()\n            mean = np.array(self.persistent_statistics[k + \"/mean\"])\n            std = np.array(self.persistent_statistics[k + \"/std\"])\n            plt.plot(x_axis, mean, color=\"blue\")\n            plt.fill_between(x_axis, mean - std, mean + std, facecolor=\"blue\", alpha=0.5)\n            plt.title(k)\n            plt.savefig(self.log_dir + \"graphs/{0}.png\".format(k))\n\n    def train(self):\n        self.prepare_logdir()\n\n        for epoch in range(self.num_epochs):\n            self.test_epoch(epoch)\n            self.train_epoch(epoch)\n            self.save_policy(epoch)\n            self.output_diagnostics(epoch)\n","repo_name":"AlexanderKhazatsky/R2D2","sub_path":"r2d2/training/model_trainer.py","file_name":"model_trainer.py","file_ext":"py","file_size_in_byte":6564,"program_lang":"python","lang":"en","doc_type":"code","stars":15,"dataset":"github-code","pt":"81"}
+{"seq_id":"43592536246","text":"from collections import Counter\n\nclass Solution:\n    def topKFrequent(self, words: List[str], k: int) -> List[str]:\n        words.sort()\n        c = Counter(words)\n        d = sorted(c.items(), key=lambda x: x[1], reverse=True)\n        \n        return [d[i][0] for i in range(k)]\n        ","repo_name":"shcDE/LeetCode","sub_path":"0692-top-k-frequent-words/0692-top-k-frequent-words.py","file_name":"0692-top-k-frequent-words.py","file_ext":"py","file_size_in_byte":288,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"27728503260","text":"'''\nAPS-2020\nProblem Description, Input, Output : https://www.codechef.com/problems/SALARY\nCode by : Sagar Kulkarni\n'''\n\nimport math\nT=int(input())\n\nfor _ in range(0,T):\n    a=int(input())\n    list1=[int(x) for x in input().split()]\n\n    sum=0\n    for elem in list1:\n        sum+=elem\n\n    print(sum-a*min(list1))\n","repo_name":"SagarKulk539/APS_Lib_2020","sub_path":"CodeChef/SALARY.py","file_name":"SALARY.py","file_ext":"py","file_size_in_byte":314,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"27097867736","text":"# Jogo da Forca\n# POO\n\n# Import\nimport random\n\n# Definindo especificacoes e metodos\n# tabuleiro\nboard = ['''\n\n>>>>>>>>>>Hangman<<<<<<<<<<\n\n    +---+\n    |   |   \n    |\t    \n    |\t    \n    |\t    \n    |\t    \n=================''', '''\n\n    +---+\n    |   |   \n    |   O   \n    |  \t    \n    |\t    \n    |\t    \n=================''', '''\n\n    +---+\n    |   |   \n    |   O   \n    |   |   \n    |\t    \n    |\t    \n=================''', '''\n\n     +---+\n     |   |   \n     |   O   \n     |  /|   \n     |       \n     |\t     \n==================''', '''\n\n     +---+\n     |   |   \n     |   O   \n     |  /|/  \n     |\t     \n     |\t     \n==================''', '''\n\n     +---+\n     |   |\t \n     |   O\t \n     |  /|/  \n     |  /    \n     |\t     \n==================''', '''\n\n     +---+\n     |   |   \n     |   O   \n     |  /|/  \n     |  / /  \n     |       \n==================''']\n\n\n# Classe\nclass Forca:\n\n\t# Metodo para construtir\n\tdef __init__(self, palavra):\n\t\t# Palavra alvo\n\t\tself.palavra = palavra\n\t\t# lista para letras erradas\n\t\tself.perdidas_letras = []\n\t\t# lista para letras certas\n\t\tself.corretas_letras = []\n\n\t# Metodo para adivinhar a letra. O que fazer com a letra inserida?\n\tdef guess(self, letra):\n\t\t# Se a letra estiver dentro da palavra e nao estiver dentro da lista de letras corretas\n\t\tif letra in self.palavra and letra not in self.corretas_letras:\n\t\t\t# Vou inserir na lista certas\n\t\t\tself.corretas_letras.append(letra)\n\t\t# Se a letra nao estiver na palavra e a letra nao estiver na lista de palavras perdidas\n\t\telif letra not in self.palavra and letra not in self.perdidas_letras:\n\t\t\t# Vou inserir na lista perdidas\n\t\t\tself.perdidas_letras.append(letra)\n\t\telse:\n\t\t\treturn False\n\t\treturn True\n\t\t\n\t# Metodo para verificar se o jogo terminou\n\tdef forca_over(self):\n\t\t# Ou chamaremos o fim vencedor ou teremos 6 vidas\n\t\treturn self.forca_won() or (len(self.perdidas_letras) == 6)\n\t\t\n\t# Metodo para verificar se o jogador venceu\n\tdef forca_won(self):\n\t\t# Se nao tiver underline, vence o jogo (True). Se tiver underline perde o jogo (False).\n\t\tif '_' not in self.hide_palavra():\n\t\t\treturn True\n\t\treturn False\n\n\t# Metodo para não mostrar a letra no board\n\tdef hide_palavra(self):\n\t\trtn = ''\n\t\t# Para cada letra dentro da palavra alvo\n\t\tfor letra in self.palavra:\n\t\t\t# Se a letra nao estiver dentro das corretas retorna underline\n\t\t\tif letra not in self.corretas_letras:\n\t\t\t\trtn += '_'\n\t\t\t# Se estiver, preencha com a letra\n\t\t\telse:\n\t\t\t\trtn += letra\n\t\treturn rtn\n\n\t# Metodo para checar o status do game e imprimir o board na tela\n\tdef print_game_status(self):\n\t\t# Usando o fatiamento para trazer cada elemento da lista pela posicao no board [0, 1, 2, 3, 4, 5]\n\t\tprint(board[len(self.perdidas_letras)])\n\t\tprint('\\nPalavra: ' + self.hide_palavra())\n\t\tprint('\\nLetras erradas: ',)\n\t\tfor letra in self.perdidas_letras:\n\t\t\tprint(letra,)\n\t\tprint()\n\t\tprint('Letras corretas: ',)\n\t\tfor letra in self.corretas_letras:\n\t\t\tprint(letra,)\n\t\tprint()\n\n# Metodo para ler uma palavra de forma aleatória do banco de palavras\ndef rand_palavra():\n\t\twith open(\"palavras.txt\", \"rt\") as f:\n\t\t\t\tbank = f.readlines()\n\t\t# Buscando de maneira aleatoria a palavra\n\t\treturn bank[random.randint(0, len(bank))].strip()\n\n\n# Metodo Main - Execucao do Programa\ndef main():\n\n\t# Objeto\n\tgame = Forca(rand_palavra())\n\n\t# Enquanto o jogo não tiver terminado, print do status, solicita uma letra e faz a leitura do caracter\n\twhile not game.forca_over():\n\t\tgame.print_game_status()\n\t\tuser_input = input('\\nDigite uma letra: ')\n\t\tgame.guess(user_input)\n\n\t# Verifica o status do jogo\n\tgame.print_game_status()\n\n\t# De acordo com o status, imprime mensagem na tela para o usuario\n\tif game.forca_won():\n\t\tprint('\\nParabéns! Você venceu!!')\n\telse:\n\t\tprint('\\nGame over! Você perdeu.')\n\t\tprint('A palavra era ' + game.palavra)\n\t\t\n\tprint('\\nJogue novamente!\\n')\n\n# Executa o programa\nif __name__ == \"__main__\":\n\tmain()\n","repo_name":"goudardroberto/Python","sub_path":"m2_Forca.py","file_name":"m2_Forca.py","file_ext":"py","file_size_in_byte":3863,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"1161643623","text":"import numpy as np\r\nfrom flask import Flask, request, render_template\r\nimport pickle\r\n\r\napp = Flask(__name_,template_folder='template')\r\n\r\nmodel = pickle.load(open(r\"F:\\Model_AIMl\\id3_random_forest.pkl\", 'rb'))\r\n\r\n\r\n@app.route('/')\r\ndef home():\r\n    return render_template('tennis.html')\r\n\r\n\r\n@app.route('/predict', methods=['POST'])\r\ndef predict():\r\n    int_features = [int(x) for x in request.form.values()]\r\n    features = [np.array(int_features)]\r\n    prediction = model.predict(features)\r\n    mapping = {'Outlook': {'Overcast': 0, 'Rain': 1, 'Sunny': 2},\r\n               'Humidity': {'High': 0, 'Normal': 1},\r\n               'Wind': {'Strong': 0, 'Weak': 1},\r\n               'Play Tennis': {'No': 0, 'Yes': 1}}\r\n    value = {i for i in mapping['Play Tennis'] if mapping['Play Tennis'][i] == prediction}\r\n    value = str(value)\r\n    return render_template('tennis.html', prediction_text='Will he go to play Tennis : {}'.format(value))\r\n\r\n\r\nif __name__ == \"__main__\":\r\n    app.run()","repo_name":"AlokiChaturvedi/PYTHON-PROJECT","sub_path":"web.py","file_name":"web.py","file_ext":"py","file_size_in_byte":985,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"74201106183","text":"from pathlib import Path\r\nimport sys\r\nfrom typing import Tuple\r\n\r\nroot_dir = (Path(__file__).parent / \"../\").resolve()\r\nsys.path.append(str(root_dir))\r\n\r\nfrom web_scraper.configure import ChromeConnection\r\nfrom web_scraper.login import Login\r\n\r\nfrom web_scraper.change_date import DateChanger\r\nfrom web_scraper.extract_links import LinkExtractor\r\nfrom web_scraper.popup import close_popup\r\nfrom web_scraper.extract_content import ContentExtractor\r\nfrom web_scraper.data_classes import ContentKeys\r\n\r\nfrom doc_generator.generate_doc import DocumentGenerator\r\n\r\n\r\nclass JWatchScraper:\r\n    def __init__(self, start_url: str, desired_dates: Tuple[str, str, str, str]):\r\n        \"\"\"\r\n        Arguments\r\n        ---------\r\n        start_url : str\r\n        desired_dates : (str, str, str, str)\r\n            start_month\r\n            start_year\r\n            end_month\r\n            end_year\r\n        \"\"\"\r\n        self.start_url = start_url\r\n        self.desired_dates = desired_dates\r\n\r\n    def scrape(self) -> None:\r\n        \"\"\"\r\n        Execute functions to extract content and save in docx\r\n        \"\"\"\r\n        # connect and log in\r\n        self.connect()\r\n        self.login()\r\n\r\n        # set date range\r\n        self.driver.get(self.start_url)\r\n        DateChanger(self.driver).change_date(self.desired_dates)\r\n\r\n        # extract all article links\r\n        self.article_links = LinkExtractor(self.driver).get_links()\r\n\r\n        # extract content from articles and create documents\r\n        self.get_paper_content()\r\n\r\n    def connect(self) -> None:\r\n        \"\"\"\r\n        Navigate to start URL.\r\n        \"\"\"\r\n        self.driver = ChromeConnection().driver\r\n        self.driver.get(self.start_url)\r\n\r\n    def login(self):\r\n        \"\"\" \"\r\n        Login to jwatch.org\r\n        \"\"\"\r\n        Login(self.driver).login()\r\n\r\n    def get_paper_content(self) -> None:\r\n        \"\"\"\r\n        Open article links and save content to document.\r\n        \"\"\"\r\n        close_popup(self.driver)\r\n\r\n        # initialise document\r\n        doc_generator = DocumentGenerator()\r\n\r\n        for link in self.article_links:\r\n            try:\r\n                # open article\r\n                self.open_article(link)\r\n\r\n                # extract content\r\n                content_extractor = ContentExtractor(self.driver)\r\n                content = content_extractor.extract_all_content()\r\n\r\n                # add content to document\r\n                doc_generator.add_page(content)\r\n\r\n                print(f\"✓ {content[ContentKeys.TITLE].text}\")\r\n\r\n            except:\r\n                print(f\"Unable to extract article: {content[ContentKeys.TITLE].text}\")\r\n\r\n        # save document\r\n        archive = self.start_url.split(\"/\")[-1]\r\n        start_month, start_year, end_month, end_year = self.desired_dates\r\n        save_name = f\"{archive}_{start_month}-{start_year}_to_{end_month}-{end_year}\"\r\n        doc_generator.document.save(f\"{save_name}.docx\")\r\n\r\n    def open_article(self, link: str) -> None:\r\n        \"\"\"\r\n        Arguments\r\n        ---------\r\n        link : str\r\n            navigate link to article\r\n        \"\"\"\r\n        close_popup(self.driver)\r\n\r\n        self.driver.get(link)\r\n","repo_name":"joekitsmith/web-scrape-j-watch","sub_path":"src/web_scraper/jwatch.py","file_name":"jwatch.py","file_ext":"py","file_size_in_byte":3166,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"35480421694","text":"import torch\nfrom torch.utils.data import Dataset, DataLoader\nimport numpy as np\nfrom skimage import io\nimport os\nimport time\nimport copy\nimport matplotlib\nmatplotlib.use('Agg')\nimport matplotlib.pyplot as plt\nfrom my_dataset import MyDataset\nimport torchvision\nfrom torchvision import datasets, models, transforms\nimport torch.nn as nn\nimport torch.optim as optim\nimport torch.nn.functional as F\nfrom torch.optim import lr_scheduler\nfrom datetime import datetime\nfrom tqdm import tqdm\nfrom sklearn.metrics import accuracy_score, confusion_matrix\nimport seaborn as sn\n\ndef LOG_INFO(msg):\n    now = datetime.now()\n    display_now = str(now).split(' ')[1][:-3]\n    print(display_now + ' ' + msg)\n\n\nTRAIN_BATCH_SIZE = 64\nTEST_BATCH_SIZE = 64\nLOG_INTERVAL = 50\nEPOCHS = 50\n\nLR = 0.001\nMM = 0.9\nWD = 0.0001\n\nkwargs = {'num_workers': 1, 'pin_memory': True}\n\n\ndata_transforms = {\n    'train': transforms.Compose([\n        transforms.RandomResizedCrop(224),\n        transforms.RandomHorizontalFlip(),\n        transforms.ToTensor(),\n        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n    ]),\n    'val': transforms.Compose([\n        transforms.Resize(256),\n        transforms.CenterCrop(224),\n        transforms.ToTensor(),\n        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n    ]),\n}\n\ntrain_loader = DataLoader(\n    MyDataset('../256_ObjectCategories/', '../data_additional/train.txt', data_transforms['train']),\n    batch_size=TRAIN_BATCH_SIZE, shuffle=True, **kwargs\n)\ntest_loader = DataLoader(\n    MyDataset('../256_ObjectCategories/', '../data_additional/test.txt', data_transforms['val']),\n    batch_size=TEST_BATCH_SIZE, shuffle=False, **kwargs\n)\n\ndevice = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n\n# DenseNet121\nmodel_ft = models.densenet121(pretrained=True)\nfor param in model_ft.parameters():\n    param.requires_grad = False\nnum_ftrs = model_ft.classifier.in_features\nmodel_ft.classifier = nn.Linear(num_ftrs, 257)\nmodel = model_ft.to(device)\n\n# ResNet18\n# model_ft = models.resnet18(pretrained=True)\n# for param in model_ft.parameters():\n#     param.requires_grad = False\n# num_ftrs = model_ft.fc.in_features\n# model_ft.fc = nn.Linear(num_ftrs, 257)\n# model = model_ft.to(device)\n\n# SGD\n# optimizer = optim.SGD(model.parameters(), lr=LR, momentum=MM, weight_decay=WD)\n# Adam\noptimizer = optim.Adam(model.parameters(), lr=LR, betas=(0.9, 0.999))\nexp_lr_scheduler = lr_scheduler.StepLR(optimizer, step_size=7, gamma=0.1)\n\ndef train(model, device, train_loader, optimizer, epoch, exp_lr_scheduler):\n    model = model.train()\n    exp_lr_scheduler.step()\n    loss_list = []\n    acc_list = []\n\n    train_loss_list = []\n    train_acc_list = []\n\n    for batch_idx, (data, target) in enumerate(train_loader):\n        data, target = data.to(device), target.to(device)\n        optimizer.zero_grad()\n        output = model(data)\n        loss = F.cross_entropy(output, target)\n        loss.backward()\n        optimizer.step()\n        loss_list.append(loss.item())\n        train_loss_list.append(loss.item())\n        pred = output.argmax(dim=1, keepdim=True)\n        acc = pred.eq(target.view_as(pred)).float().mean()\n        acc_list.append(acc.item())\n        train_acc_list.append(acc.item())\n        if batch_idx % LOG_INTERVAL == 0:\n            msg = 'Train Epoch: {} [{}/{} ({:.0f}%)]\\tAvg Loss: {:.4f}\\tAvg Acc: {:.4f}'.format(\n                epoch, batch_idx * len(data), len(train_loader.dataset),\n                100. * batch_idx / len(train_loader), np.mean(loss_list), np.mean(acc_list))\n            LOG_INFO(msg)\n            loss_list.clear()\n            acc_list.clear()\n    return np.mean(np.array(train_loss_list)), np.mean(np.array(train_acc_list))\n\n\ndef test(model, device, test_loader):\n    model.eval()\n    test_loss = 0\n    correct = 0\n\n    with torch.no_grad():\n        for data, target in tqdm(test_loader):\n            data, target = data.to(device), target.to(device)\n            output = model(data)\n            test_loss += F.cross_entropy(output, target).item()\n            pred = output.argmax(dim=1, keepdim=True)\n            correct += pred.eq(target.view_as(pred)).sum().item()\n\n    test_loss /= len(test_loader.dataset)\n    print('\\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.2f}%)\\n'.format(\n        test_loss, correct, len(test_loader.dataset),\n        100. * correct / len(test_loader.dataset)))\n\n    return test_loss, correct / len(test_loader.dataset)\n\nif __name__ == '__main__':\n    test_loss, test_acc = [], []\n    train_loss, train_acc = [], []\n    for epoch in range(1, EPOCHS + 1):\n        testloss, testacc = test(model, device, test_loader)\n        test_loss.append(testloss)\n        test_acc.append(testacc)\n\n        trainloss, trainacc = train(model, device, train_loader, optimizer, epoch, exp_lr_scheduler)\n        train_loss.append(trainloss)\n        train_acc.append(trainacc)\n\n    plt.figure()\n    plt.plot(train_loss, label='train loss vs. iterations', color='green')\n    plt.xlabel('iteration(s)')\n    plt.ylabel(\"train loss\")\n    plt.title(\"train loss vs. iterations\")\n    plt.savefig('trainloss_den_adam1.png')\n    plt.figure()\n    plt.plot(train_acc, label='train accuracy vs. iterations', color='r')\n    plt.xlabel('iteration(s)')\n    plt.ylabel(\"train accuracy\")\n    plt.title(\"train accuracy vs. iterations\")\n    plt.savefig('trainacc_den_adam1.png')\n    \n    plt.figure()\n    plt.plot(test_loss, label='test loss vs. epochs', color='green')\n    plt.xlabel('epoch(s)')\n    plt.ylabel(\"test loss\")\n    plt.title(\"test loss vs. epochs\")\n    plt.savefig('testloss_den_adam1.png')\n    plt.figure()\n    plt.plot(test_acc, label='test accuracy vs. epochs', color='r')\n    plt.xlabel('epoch(s)')\n    plt.ylabel(\"test accuracy\")\n    plt.title(\"test accuracy vs. epochs\")\n    plt.savefig('testacc_den_adam1.png')\n\n    test(model, device, test_loader)\n","repo_name":"luoyt14/Caltech256_classifictaion","sub_path":"code_additional/run_cnn.py","file_name":"run_cnn.py","file_ext":"py","file_size_in_byte":5869,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"81"}
+{"seq_id":"72479726666","text":"import pytest\nimport allure\n\nfrom base.base_test import BaseTest\nfrom config.locators_pytest_param import ParametrizationData\n\n\n@allure.feature(\"Women page functionality\")\nclass TestWomenCatalogFeatures(BaseTest):\n    PRICE = \"price\"\n\n    @pytest.fixture()\n    def _setup_page(self, browser):\n        self.women_page.open()\n        self.women_page.is_opened(self.women_page.PAGE_TITLE)\n        self.women_page.click_on_category(self.women_page.WOMEN_TOPS)\n\n    @allure.title(\"Women category page is opened\")\n    def test_women_category_is_opened(self, browser, with_expect, _setup_page):\n        with_expect(browser.locator(self.women_page.PAGE_TITLE)).to_be_visible()\n\n    @allure.title(\"Shopping category options are visible and correct\")\n    @pytest.mark.parametrize('options_titles', [pytest.param(ParametrizationData.CLOTH_OPTIONS_TITLE,\n                                                             id=\"Shopping Titles\")])\n    def test_shopping_options_on_tops(self, browser, options_titles, _setup_page):\n        shopping_options = self.women_page.get_shopping_options_titles(self.base_locators.SHOP_OPTIONS_CATEGORY)\n        assert options_titles == shopping_options, \"'Shopping Options' are not matched!\"\n\n    @allure.title(\"The item is added to the cart\")\n    def test_add_item_to_cart(self, browser, _setup_page):\n        self.women_page.choose_random_cloth()\n        self.women_page.check_success_alert(self.base_locators.ALERT_SUCCESS)\n\n    @allure.title(\"Items are sorted by price\")\n    def test_sort_items_by_price(self, browser, _setup_page):\n        products_not_sorted = self.women_page.get_products_details()\n        self.women_page.sort_products_by(self.PRICE)\n        product_sorted_by_price = self.women_page.get_products_details()\n        assert products_not_sorted != product_sorted_by_price\n","repo_name":"TqcxTqc/Automation_Portfolio","sub_path":"tests/test_women_page.py","file_name":"test_women_page.py","file_ext":"py","file_size_in_byte":1815,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"5426006562","text":"#!/usr/bin/env python\n\"\"\"Set up package.\"\"\"\nfrom pathlib import Path\n\nfrom setuptools import find_packages, setup\n\nPROJECT_DIR = Path(__file__).parent.resolve()\nVERSION = (PROJECT_DIR / \"leicacam\" / \"VERSION\").read_text(encoding=\"utf-8\").strip()\n\nREADME_FILE = PROJECT_DIR / \"README.md\"\nLONG_DESCRIPTION = README_FILE.read_text(encoding=\"utf-8\")\n\n\nsetup(\n    name=\"leicacam\",\n    version=VERSION,\n    description=\"Control Leica microscopes with python\",\n    long_description=LONG_DESCRIPTION,\n    long_description_content_type=\"text/markdown\",\n    author=\"Arve Seljebu\",\n    author_email=\"arve.seljebu@gmail.com\",\n    url=\"https://github.com/MartinHjelmare/leicacam\",\n    packages=find_packages(exclude=[\"test\", \"test.*\"]),\n    include_package_data=True,\n    python_requires=\">=3.8\",\n    install_requires=[\"async_timeout\", \"pydebug\"],\n    license=\"MIT\",\n    zip_safe=False,\n    keywords=\"leicacam\",\n    classifiers=[\n        \"Development Status :: 4 - Beta\",\n        \"Intended Audience :: Developers\",\n        \"License :: OSI Approved :: MIT License\",\n        \"Natural Language :: English\",\n        \"Programming Language :: Python :: 3\",\n        \"Programming Language :: Python :: 3.8\",\n        \"Programming Language :: Python :: 3.9\",\n        \"Programming Language :: Python :: 3.10\",\n    ],\n)\n","repo_name":"MartinHjelmare/leicacam","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1295,"program_lang":"python","lang":"en","doc_type":"code","stars":16,"dataset":"github-code","pt":"81"}
+{"seq_id":"2963096278","text":"\"\"\"\n30. НОП с восстановлением ответа\n\"\"\"\nn = int(input())\na = tuple(map(int, input().split()))\nm = int(input())\nb = tuple(map(int, input().split()))\n\nn_dp = n + 1\nm_dp = m + 1\ndp = [[0]*m_dp for _ in range(n_dp)]\nfor i in range(1, n_dp):\n\tfor j in range(1, m_dp):\n\t\tif a[i-1] == b[j-1]:\n\t\t\tdp[i][j] = dp[i-1][j-1] + 1\n\t\telse:\n\t\t\tdp[i][j] = max(dp[i-1][j], dp[i][j-1])\n\n# for row in dp:\n# \tprint(*row)\n# print(dp[n][m])\n\n#---find ans\nlcs = []\ni, j = n, m\nwhile i > 0 and j > 0:\n\tif a[i-1] == b[j-1]:\n\t\tlcs.append(a[i-1])\n\t\ti -= 1\n\t\tj -= 1\n\telif dp[i-1][j] == dp[i][j]:\n\t\ti -= 1\n\telse:\n\t\tj -= 1\n\nlcs.reverse()\nprint(*lcs)\n\n\"\"\"\n1) ДП\nTime O(n*m + (n+m))=O(n*m)\n\t(n+m) для восстановления ответа, т.к. двигаемся в худшем случаем на шаг по i/j\nMemory O(n*m)\n\ndp[i,j] длина НОП на первом префиксе до i, втором до j\nдобавим рамку из 0-лей (нет общих подпосл-ей)\n\nсравнимаем крайние на префиксе элементы\nесли равны, то dp[i,j] = dp[i-1,j-1] + 1\nиначе dp[i,j] = max(dp[i-1,j], dp[i,j-1])\n\"\"\"","repo_name":"Alex-Norden/algo_train_yandex_3","sub_path":"hw4/h_30.py","file_name":"h_30.py","file_ext":"py","file_size_in_byte":1182,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"28031053032","text":"import unittest\n\nfrom katacheckout2 import Item, Basket, Order, CheckOut, SpecialOffer\n\n\nclass CheckoutTestCase(unittest.TestCase):\n    def setUp(self) -> None:\n        return super().setUp()\n\n    def tearDown(self) -> None:\n        return super().tearDown()\n\n    def test_one_a_cost_30(self):\n        basket = Basket()\n        basket.add(Item(\"A\", 30.0, 1))\n\n        checkout = CheckOut()\n\n        result = checkout.calculate(basket)\n\n        self.assertEqual(result.total, 30)\n\n    def test_add_item_in_basket(self):\n        basket = Basket()\n        basket.add(Item(\"A\", 50.0, 1))\n        basket.add(Item(\"A\", 50.0, 1))\n\n        checkout = CheckOut()\n\n        result = checkout.calculate(basket)\n\n        self.assertEqual(result.total, 100)\n\n    def test_two_items_in_basket(self):\n        basket = Basket()\n        basket.add(Item(\"A\", 50.0, 1))\n        basket.add(Item(\"B\", 30.0, 1))\n\n        checkout = CheckOut()\n\n        result = checkout.calculate(basket)\n\n        self.assertEqual(result.total, 80)\n\n    def test_three_items_in_basket(self):\n        basket = Basket()\n        basket.add(Item(\"A\", 50.0, 1))\n        basket.add(Item(\"B\", 30.0, 1))\n        basket.add(Item(\"C\", 10.0, 1))\n\n        checkout = CheckOut()\n\n        result = checkout.calculate(basket)\n\n        self.assertEqual(result.total, 90)\n\n    def test_multi_item_in_the_basket(self):\n        basket = Basket()\n        basket.add(Item(\"A\", 50.0, 2))\n        basket.add(Item(\"A\", 50.0, 1))\n        basket.add(Item(\"B\", 30.0, 1))\n        basket.add(Item(\"C\", 10.0, 1))\n\n        checkout = CheckOut()\n\n        result = checkout.calculate(basket)\n\n        self.assertEqual(result.total, 190)\n        self.assertEqual(result.get_partials(\"A\"), 150)\n        self.assertEqual(result.get_partials(\"B\"), 30)\n        self.assertEqual(result.get_partials(\"C\"), 10)\n\n    def test_special_offer(self):\n        basket = Basket()\n        basket.add(Item(\"A\", 50.0, 1))\n        basket.add(Item(\"A\", 50.0, 1))\n        basket.add(Item(\"A\", 50.0, 1))\n        basket.add(Item(\"B\", 30.0, 1))\n        basket.add(Item(\"C\", 10.0, 1))\n\n        checkout = CheckOut()\n\n        checkout.register_offer(SpecialOffer(\"A\", 80, 2))\n\n        result = checkout.calculate(basket)\n\n        self.assertEqual(result.total, 170)\n        self.assertEqual(result.get_partials(\"A\"), 130)\n        self.assertEqual(result.get_partials(\"B\"), 30)\n        self.assertEqual(result.get_partials(\"C\"), 10)\n\n    def test_multiple_special_offer(self):\n        basket = Basket()\n        basket.add(Item(\"A\", 50.0, 1))\n        basket.add(Item(\"A\", 50.0, 1))\n        basket.add(Item(\"A\", 50.0, 1))\n        basket.add(Item(\"B\", 30.0, 1))\n        basket.add(Item(\"B\", 30.0, 1))\n\n        checkout = CheckOut()\n\n        checkout.register_offer(SpecialOffer(\"A\", 30, 2))\n        checkout.register_offer(SpecialOffer(\"B\", 30, 2))\n\n        result = checkout.calculate(basket)\n\n        self.assertEqual(result.total, 110)\n        self.assertEqual(result.get_partials(\"A\"), 80)\n        self.assertEqual(result.get_partials(\"B\"), 30)\n\n\nclass TestSpecialOffer(unittest.TestCase):\n    def test_special_offer_residuals_correct(self):\n\n        offer = SpecialOffer(\"A\", 10.0, 2, \"offer1\")\n\n        self.assertEqual(offer.residual(10), 0)\n        self.assertEqual(offer.residual(3), 1)\n        self.assertEqual(offer.residual(0), 0)\n\n    def test_special_offer_times(self):\n        offer = SpecialOffer(\"A\", 10.0, 2, \"offer1\")\n\n        self.assertEqual(offer.times(10), 5)\n        self.assertEqual(offer.times(3), 1)\n        self.assertEqual(offer.times(0), 0)\n\n    def test_special_offer_price(self):\n        offer = SpecialOffer(\"A\", 10.0, 2, \"offer1\")\n\n        self.assertEqual(offer.evaluate_price(10), 50)\n        self.assertEqual(offer.evaluate_price(3), 10)\n        self.assertEqual(offer.evaluate_price(0), 0)\n\n\nclass TestOrder(unittest.TestCase):\n    def test_offer_applied(self):\n        basket = Basket()\n        basket.add(Item(\"A\", 50.0, 2))\n        basket.add(Item(\"A\", 50.0, 1))\n        basket.add(Item(\"B\", 30.0, 1))\n        basket.add(Item(\"C\", 10.0, 1))\n\n        checkout = CheckOut()\n\n        offer = SpecialOffer(\"A\", 80, 2)\n        offer2 = SpecialOffer(\"D\", 10, 3)\n        checkout.register_offer(offer)\n        checkout.register_offer(offer2)\n\n        order = checkout.calculate(basket)\n\n        self.assertEqual(order.total, 170)\n\n        self.assertIn(offer, [a for a, _ in order.offers_applied])\n        for _, quantity in order.offers_applied:\n            self.assertEqual(quantity, 1)\n\n        self.assertNotIn(offer2, [a for a, _ in order.offers_applied])\n","repo_name":"IDepla/katacheckout2","sub_path":"test_katacheckout2.py","file_name":"test_katacheckout2.py","file_ext":"py","file_size_in_byte":4591,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"33442534185","text":"import itertools\n\nfrom models.GCAN.positional_encoding_layer import positional_encoding_layer\nfrom models.GCAN.GCA_module import GCA_module\nfrom src.feature_align import feature_align\nfrom src.utils.pad_tensor import pad_tensor\nfrom src.lap_solvers.sinkhorn import Sinkhorn\nfrom models.PCA.affinity_layer import Affinity\nfrom src.gconv import Siamese_Gconv\nfrom models.AFAT.k_pred_net import Encoder, TensorNetworkModule, DenseAttentionModule\nfrom torch_geometric import utils as geometric_util\nfrom models.AFAT.sinkhorn_topk import soft_topk, greedy_perm\nfrom src.lap_solvers.hungarian import hungarian\nfrom scipy.linalg import block_diag\nimport numpy as np\n\nfrom src.utils.config import cfg\n\nfrom src.backbone_gcan import *\nCNN = eval(cfg.BACKBONE)\n\ndef lexico_iter(lex):\n    return itertools.combinations(lex, 2)\n\n\ndef normalize_over_channels(x):\n    channel_norms = torch.norm(x, dim=1, keepdim=True)\n    return x / channel_norms\n\n\ndef concat_features(embeddings, num_vertices):\n    res = torch.cat([embedding[:, :num_v] for embedding, num_v in zip(embeddings, num_vertices)], dim=-1)\n    return res.transpose(0, 1)\n\ndef pair_split(node_features, ns):\n    batch_feature_src = []\n    batch_feature_tgt = []\n    partitions = []\n    idx = 0\n    for i in range(len(ns)):\n        partitions.append(node_features[idx:idx + ns[i], :])\n        idx = idx + ns[i]\n    for i in range(0, len(ns), 2):\n        batch_feature_src.append(partitions[i])\n        batch_feature_tgt.append(partitions[i + 1])\n    return batch_feature_src,  batch_feature_tgt\n\ndef get_graph_feature(batch_graphs_src, ns_src, batch_graphs_tgt, ns_tgt):\n    adjacency_matrixs_list = []\n    for idx in range(len(batch_graphs_src)):\n        adjacency_src = geometric_util.to_dense_adj(\n            batch_graphs_src[idx].edge_index, max_num_nodes=ns_src[idx]).squeeze().cpu()\n        adjacency_matrixs_list.append(np.array(adjacency_src))\n        adjacency_tgt = geometric_util.to_dense_adj(\n            batch_graphs_tgt[idx].edge_index, max_num_nodes=ns_tgt[idx]).squeeze().cpu()\n        adjacency_matrixs_list.append(np.array(adjacency_tgt))\n    adjacency_matrixs = block_diag(*adjacency_matrixs_list).astype('float32')\n\n    return torch.tensor(adjacency_matrixs).cuda()\n\n\nclass Net(CNN):\n    def __init__(self):\n        super(Net, self).__init__()\n        self.positional_encoding = positional_encoding_layer(input_node_dim=cfg.GCAN.FEATURE_CHANNEL * 2)\n        self.global_state_dim = cfg.GCAN.FEATURE_CHANNEL * 2\n        cross_parameters = [self.global_state_dim, self.positional_encoding.num_node_features]\n        self_parameters = [cfg.GCAN.NODE_HIDDEN_SIZE[-1]*2, int(cfg.GCAN.NODE_HIDDEN_SIZE[-1]/4), 4]\n        self.GCA_module1 = GCA_module(cross_parameters,self_parameters)\n        self.GCA_module2 = GCA_module(cross_parameters, self_parameters)\n\n        self.trainings = True\n        self.rescale = cfg.PROBLEM.RESCALE\n        self.tau = cfg.GCAN.SK_TAU\n        self.univ_size = cfg.AFA.UNIV_SIZE\n        self.k_factor = cfg.AFA.K_FACTOR\n        self.k_gnn_layer = cfg.AFA.K_GNN_LAYER\n        self.regression = cfg.AFA.REGRESSION\n        self.afau = cfg.AFA.AFAU\n        self.mean_k = cfg.AFA.MEAN_K\n\n        self.sinkhorn = Sinkhorn(max_iter=cfg.GCAN.SK_ITER_NUM, tau=self.tau, epsilon=cfg.GCAN.SK_EPSILON)\n\n        if self.regression:\n            self.k_params_id = []\n            if self.afau:\n                self.encoder_k = Encoder()\n                self.k_params_id += [id(item) for item in self.encoder_k.parameters()]\n\n                self.maxpool = nn.MaxPool1d(kernel_size=self.univ_size)\n                self.final_row = nn.Sequential(\n                    nn.Linear(self.univ_size, 8),\n                    nn.ReLU(),\n                    nn.Linear(8, 1),\n                    nn.Sigmoid()\n                )\n\n                self.final_col = nn.Sequential(\n                    nn.Linear(self.univ_size, 8),\n                    nn.ReLU(),\n                    nn.Linear(8, 1),\n                    nn.Sigmoid()\n                )\n\n                self.k_params_id += [id(item) for item in self.final_row.parameters()]\n                self.k_params_id += [id(item) for item in self.final_col.parameters()]\n\n                self.k_params = [\n                    {'params': self.encoder_k.parameters()},\n                    {'params': self.final_row.parameters()},\n                    {'params': self.final_col.parameters()}\n                ]\n            else:\n                self.k_params = []\n                for i in range(self.k_gnn_layer):\n                    if i == 0:\n                        gnn_layer_k = Siamese_Gconv(cfg.GCAN.FEATURE_CHANNEL * 2, cfg.AFA.REG_HIDDEN_FEAT)\n                    else:\n                        gnn_layer_k = Siamese_Gconv(cfg.AFA.REG_HIDDEN_FEAT, cfg.AFA.REG_HIDDEN_FEAT)\n                    self.add_module('_k_gnn_layer_{}'.format(i), gnn_layer_k)\n                    self.add_module('_affinity_{}'.format(i), Affinity(cfg.AFA.REG_HIDDEN_FEAT))\n\n                    self.k_params_id += [id(item) for item in eval('self._k_gnn_layer_{}'.format(i)).parameters()]\n                    self.k_params_id += [id(item) for item in eval('self._affinity_{}'.format(i)).parameters()]\n                    self.k_params.append({'params': eval('self._k_gnn_layer_{}'.format(i)).parameters()})\n                    self.k_params.append({'params': eval('self._affinity_{}'.format(i)).parameters()})\n\n                    if i == self.k_gnn_layer - 2:  # only second last layer will have cross-graph module\n                        self.add_module('_cross_graph_{}'.format(i),\n                                        nn.Linear(cfg.AFA.REG_HIDDEN_FEAT * 2, cfg.AFA.REG_HIDDEN_FEAT))\n                        self.k_params_id += [id(item) for item in eval('self._cross_graph_{}'.format(i)).parameters()]\n                        self.k_params.append({'params': eval('self._cross_graph_{}'.format(i)).parameters()})\n\n                self._attn_pool_1 = DenseAttentionModule(cfg.AFA.REG_HIDDEN_FEAT)\n                self._attn_pool_2 = DenseAttentionModule(cfg.AFA.REG_HIDDEN_FEAT)\n\n                self.k_params_id += [id(item) for item in self._attn_pool_1.parameters()]\n                self.k_params_id += [id(item) for item in self._attn_pool_2.parameters()]\n                self.k_params.append({'params': self._attn_pool_1.parameters()})\n                self.k_params.append({'params': self._attn_pool_2.parameters()})\n\n                self._ntnet = TensorNetworkModule(filters=cfg.AFA.REG_HIDDEN_FEAT, tensor_neurons=cfg.AFA.TN_NEURONS)\n\n                self.k_params_id += [id(item) for item in self._ntnet.parameters()]\n                self.k_params.append({'params': self._ntnet.parameters()})\n\n                self._final_reg = nn.Sequential(\n                    nn.Linear(cfg.AFA.TN_NEURONS, 8),\n                    nn.ReLU(),\n                    nn.Linear(8, 1),\n                    nn.Sigmoid()\n                )\n\n                self.k_params_id += [id(item) for item in self._final_reg.parameters()]\n                self.k_params.append({'params': self._final_reg.parameters()})\n\n\n    def forward(\n        self,\n        data_dict,\n    ):\n        images = data_dict['images']\n        points = data_dict['Ps']\n        n_points = data_dict['ns']\n        graphs = data_dict['pyg_graphs']\n        num_graphs = len(images)\n        batch_size = data_dict['batch_size']\n        A_src, A_tgt = data_dict['As']\n\n        global_avg_list = []\n        global_max_list = []\n        orig_graph_list = []\n        node_feature_list = []\n        for image, p, n_p, graph in zip(images, points, n_points, graphs):\n            # extract feature\n            nodes = self.node_layers(image)\n            edges = self.edge_layers(nodes)\n\n            global_avg_list.append(self.final_layers_avg(edges).reshape((nodes.shape[0], -1)))\n            global_max_list.append(self.final_layers_max(edges).reshape((nodes.shape[0], -1)))\n            nodes = normalize_over_channels(nodes)\n            edges = normalize_over_channels(edges)\n\n            # arrange features\n            U = concat_features(feature_align(nodes, p, n_p, self.rescale), n_p)\n            F = concat_features(feature_align(edges, p, n_p, self.rescale), n_p)\n            node_features = torch.cat((U, F), dim=1)\n            node_feature_list.append(node_features.detach())\n            graph.x = node_features\n\n            graph = self.positional_encoding(graph)\n            orig_graph = graph.to_data_list()\n            orig_graph_list.append(orig_graph)\n\n        ns_src, ns_tgt = n_points\n        P_src, P_tgt = points\n        global_avg_src, global_avg_tgt = global_avg_list\n        global_max_src, global_max_tgt = global_max_list\n        batch_graphs_src, batch_graphs_tgt = orig_graph_list\n        cross_attention_list = []\n        ### global weights\n        global_max_weights = torch.cat([global_max_src, global_max_tgt], axis=-1)\n        global_max_weights = normalize_over_channels(global_max_weights)\n        global_avg_weights = torch.cat([global_avg_src, global_avg_tgt], axis=-1)\n        global_avg_weights = normalize_over_channels(global_avg_weights)\n        ### src node features\n        batch_feature_src = [item.x for item in batch_graphs_src]\n        ### tgt node features\n        batch_feature_tgt = [item.x for item in batch_graphs_tgt]\n        ### adjacency\n        adjacency_matrixs = get_graph_feature(batch_graphs_src, ns_src, batch_graphs_tgt, ns_tgt)\n        ###GCAN\n        cross_attention, node_features, ns = self.GCA_module1(batch_feature_src, batch_feature_tgt, global_avg_weights, global_max_weights, ns_src, ns_tgt,adjacency_matrixs)\n        cross_attention_list = cross_attention_list + cross_attention\n        batch_feature_src, batch_feature_tgt = pair_split(node_features, ns)\n        cross_attention, node_features, ns = self.GCA_module2(batch_feature_src, batch_feature_tgt, global_avg_weights,\n                                                          global_max_weights, ns_src, ns_tgt, adjacency_matrixs)\n        cross_attention_list = [ori + 0.1*new for ori, new in zip(cross_attention_list, cross_attention)]\n\n        s_list, mgm_s_list, x_list, mgm_x_list, indices = [], [], [], [], []\n\n        for idx1, idx2 in lexico_iter(range(num_graphs)):\n            Kp = torch.stack(pad_tensor(cross_attention_list), dim=0)\n\n            s = Kp\n            ss = self.sinkhorn(s, n_points[idx1], n_points[idx2], dummy_row=True)\n\n            gt_ks = torch.tensor(\n                [torch.sum(data_dict['gt_perm_mat'][i]) for i in range(data_dict['gt_perm_mat'].shape[0])],\n                dtype=torch.float32, device=ss.device)\n\n            min_point_list = [int(min(n_points[0][b], n_points[1][b])) for b in range(data_dict['gt_perm_mat'].shape[0])]\n\n            min_point_tensor = torch.tensor(min_point_list, dtype=torch.float32, device=ss.device)\n\n            if self.regression:\n                dummy_row = self.univ_size - ss.shape[1]\n                dummy_col = self.univ_size - ss.shape[2]\n                assert dummy_row >= 0 and dummy_col >= 0\n\n                if not self.afau:\n\n                    emb1 = torch.zeros((batch_size, int(torch.max(n_points[idx1])), 2 * cfg.GCAN.FEATURE_CHANNEL),\n                                       dtype=torch.float32, device=ss.device)\n\n                    emb2 = torch.zeros((batch_size, int(torch.max(n_points[idx2])), 2 * cfg.GCAN.FEATURE_CHANNEL),\n                                       dtype=torch.float32, device=ss.device)\n\n                    mask1 = torch.zeros((batch_size, int(torch.max(n_points[idx1]))),\n                                        dtype=torch.float32, device=ss.device)\n                    mask2 = torch.zeros((batch_size, int(torch.max(n_points[idx2]))),\n                                        dtype=torch.float32, device=ss.device)\n\n                    total_nodes_1 = 0\n                    total_nodes_2 = 0\n                    for b in range(batch_size):\n                        emb1_one = node_feature_list[0][total_nodes_1: total_nodes_1 + n_points[idx1][b]]\n                        emb1[b, 0: n_points[idx1][b]] = emb1_one\n                        emb2_one = node_feature_list[1][total_nodes_2: total_nodes_2 + n_points[idx2][b]]\n                        emb2[b, 0: n_points[idx2][b]] = emb2_one\n                        total_nodes_1 += n_points[idx1][b]\n                        total_nodes_2 += n_points[idx2][b]\n                        mask1_one = torch.ones((n_points[idx1][b]),\n                                               dtype=torch.float32, device=ss.device)\n                        mask2_one = torch.ones((n_points[idx2][b]),\n                                               dtype=torch.float32, device=ss.device)\n                        mask1[b, 0: n_points[idx1][b]] = mask1_one\n                        mask2[b, 0: n_points[idx2][b]] = mask2_one\n\n                    for i in range(self.k_gnn_layer):\n                        gnn_layer = getattr(self, '_k_gnn_layer_{}'.format(i))\n                        emb1, emb2 = gnn_layer([A_src, emb1], [A_tgt, emb2])\n                        affinity = getattr(self, '_affinity_{}'.format(i))\n                        s = affinity(emb1, emb2)\n                        s = self.sinkhorn(s, n_points[idx1], n_points[idx2], dummy_row=True)\n\n                        if i == self.k_gnn_layer - 2:\n                            cross_graph = getattr(self, '_cross_graph_{}'.format(i))\n                            new_emb1 = cross_graph(torch.cat((emb1, torch.bmm(s, emb2)), dim=-1))\n                            new_emb2 = cross_graph(torch.cat((emb2, torch.bmm(s.transpose(1, 2), emb1)), dim=-1))\n                            emb1 = new_emb1\n                            emb2 = new_emb2\n\n                    global_emb1 = self._attn_pool_1(emb1, mask1)\n                    global_emb2 = self._attn_pool_2(emb2, mask2)\n\n                    sim = self._ntnet(global_emb1, global_emb2)\n                    ks = self._final_reg(sim).squeeze(-1)\n\n                else:\n                    init_row_emb = torch.zeros((batch_size, int(torch.max(n_points[idx1])), self.univ_size),\n                                               dtype=torch.float32, device=ss.device)\n\n                    init_col_emb = torch.zeros((batch_size, int(torch.max(n_points[idx2])), self.univ_size),\n                                               dtype=torch.float32, device=ss.device)\n\n                    for b in range(batch_size):\n                        index = torch.linspace(0, n_points[idx2][b].item() - 1, n_points[idx2][b].item(),\n                                               dtype=torch.long, device=ss.device).unsqueeze(1)\n                        init_col_emb_one = torch.zeros(int(torch.max(n_points[idx2])), self.univ_size,\n                                                       dtype=torch.float32, device=ss.device).scatter_(1, index, 1)\n                        init_col_emb[b] = init_col_emb_one\n\n                    out_emb_row, out_emb_col = self.encoder_k(init_row_emb, init_col_emb, ss.detach())\n                    out_emb_row = torch.nn.functional.pad(out_emb_row, (0, 0, 0, dummy_row),\n                                                          value=float('-inf')).permute(0, 2, 1)\n                    out_emb_col = torch.nn.functional.pad(out_emb_col, (0, 0, 0, dummy_col),\n                                                          value=float('-inf')).permute(0, 2, 1)\n                    global_row_emb = self.maxpool(out_emb_row).squeeze(-1)\n                    global_col_emb = self.maxpool(out_emb_col).squeeze(-1)\n                    k_row = self.final_row(global_row_emb).squeeze(-1)\n                    k_col = self.final_col(global_col_emb).squeeze(-1)\n                    if self.mean_k:\n                        ks = (k_row + k_col) / 2\n                    else:\n                        ks = k_row\n            else:\n                ks = gt_ks / min_point_tensor\n\n            if self.trainings:\n                _, ss = soft_topk(ss, gt_ks.view(-1), cfg.GCAN.SK_ITER_NUM, self.tau, n_points[idx1], n_points[idx2],\n                                  True)\n            else:\n                _, ss = soft_topk(ss, ks.view(-1) * min_point_tensor, cfg.GCAN.SK_ITER_NUM, self.tau, n_points[idx1],\n                                  n_points[idx2], True)\n\n            supervised_ks = gt_ks / min_point_tensor\n\n            if self.regression:\n                ks_loss = torch.nn.functional.mse_loss(ks, supervised_ks) * self.k_factor\n                ks_error = torch.nn.functional.l1_loss(ks * min_point_tensor, gt_ks)\n            else:\n                ks_loss = 0.\n                ks_error = 0.\n\n            x = hungarian(ss, n_points[idx1], n_points[idx2])\n            top_indices = torch.argsort(x.mul(ss).reshape(x.shape[0], -1), descending=True, dim=-1)\n            x = torch.zeros(ss.shape, device=ss.device)\n            x = greedy_perm(x, top_indices, ks.view(-1) * min_point_tensor)\n\n            s_list.append(ss)\n            x_list.append(x)\n            indices.append((idx1, idx2))\n        if cfg.PROBLEM.TYPE == '2GM':\n            data_dict.update({\n                'ds_mat': s_list[0],\n                'perm_mat': x_list[0],\n                'ks_loss': ks_loss,\n                'ks_error': ks_error\n            })\n\n        return data_dict\n","repo_name":"Thinklab-SJTU/ThinkMatch","sub_path":"models/GCAN/GCAN_model_topk.py","file_name":"GCAN_model_topk.py","file_ext":"py","file_size_in_byte":17244,"program_lang":"python","lang":"en","doc_type":"code","stars":759,"dataset":"github-code","pt":"81"}
+{"seq_id":"72461427464","text":"# -*- coding:utf-8 -*-\nfrom spiderManager import SpiderManager\n\nif __name__ == \"__main__\":\n\t'''\n\t爬取百度百科关于五险一金的相关词条100条\n\t环境：\n\t\tpython 3\n\t必备库：\n\t\trequests\n\t\tBeautifulSoup\n\t'''\n\troot_url = r'https://baike.baidu.com/item/%E4%BA%94%E9%99%A9%E4%B8%80%E9%87%91/637098?fr=aladdin'\n\tspider_manager = SpiderManager()\n\tspider_manager.crawl(root_url,100)","repo_name":"HanChengITer/PyStudy","sub_path":"crawl_demo/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":391,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"70703897864","text":"import logging\nfrom functools import partial\nfrom pathlib import Path\nfrom typing import Dict, Tuple\n\nimport numpy as np\nimport pandas as pd\nimport tensorflow as tf\nimport torch\nimport torch.nn\nimport tqdm\nimport wandb\nfrom einops.layers.torch import Reduce\nfrom matplotlib import pyplot as plt\nfrom omegaconf import DictConfig\nfrom sklearn.metrics import (\n    PrecisionRecallDisplay,\n    RocCurveDisplay,\n    average_precision_score,\n    roc_auc_score,\n)\nfrom torch import Tensor\n\nfrom osc.models.core_model import CoreModel\nfrom osc.utils import batches_per_epoch, check_num_samples\n\nlog = logging.getLogger(__name__)\n\n\ndef run_test_vqa(\n    cfg: DictConfig,\n    ds_vqa: tf.data.Dataset,\n    model: CoreModel,\n    step_counter: int,\n):\n    \"\"\"Train and evaluate linear probes for a VQA using both global and slot features.\n\n    Args:\n        cfg:\n        ds_vqa:\n        model:\n        step_counter:\n\n    Returns:\n\n    \"\"\"\n    f_slots, f_global, targets = extract_vqa_features(cfg, ds_vqa, model)\n    for feat_name, features in [(\"obj\", f_slots), (\"global\", f_global)]:\n        if features is None:\n            continue\n        wandb_metrics, wandb_imgs = train_vqa_linear_probe(cfg, features, targets)\n        log.info(\n            \"VQA %s: %s\",\n            feat_name,\n            \", \".join(f\"{k} {v:.2f}\" for k, v in wandb_metrics.items()),\n        )\n        wandb.log(\n            {\n                f\"vqa/{feat_name}/{k}\": v\n                for k, v in (wandb_metrics | wandb_imgs).items()\n            },\n            commit=False,\n            step=step_counter,\n        )\n\n\n@torch.no_grad()\ndef extract_vqa_features(\n    cfg: DictConfig,\n    ds_vqa: tf.data.Dataset,\n    model: CoreModel,\n) -> Tuple[Tensor, Tensor, Tensor]:\n    \"\"\"Extract VQA features, both per-slot and global, and one-hot targets.\n\n    Args:\n        cfg:\n        ds_vqa:\n        model:\n\n    Returns:\n        Three tensors, ``(f_slots, f_global, targets)``.\n    \"\"\"\n    f_slots = []\n    f_global = []\n    targets = []\n    device = cfg.other.device\n\n    model.eval()\n    bar = tqdm.tqdm(\n        total=cfg.data.test.vqa.max_samples,\n        desc=\"VQA feats\",\n        unit=\"img\",\n        mininterval=10,  # seconds\n        disable=not cfg.other.tqdm,\n        ncols=0,\n    )\n    for examples in ds_vqa.as_numpy_iterator():\n        images = torch.from_numpy(examples[\"image\"])\n        output = model(images.to(device))\n        if output.f_slots is not None:\n            f_slots.append(output.f_slots.cpu())\n        f_global.append(output.f_global.cpu())\n        targets.append(examples[\"vqa_target\"])\n        bar.update(images.shape[0])\n    bar.close()\n\n    f_slots = torch.cat(f_slots) if len(f_slots) > 0 else None\n    f_global = torch.cat(f_global)\n    targets = torch.from_numpy(np.concatenate(targets))\n    return f_slots, f_global, targets\n\n\ndef train_vqa_linear_probe(\n    cfg: DictConfig, features: Tensor, targets: Tensor\n) -> Tuple[Dict[str, float], Dict[str, wandb.Image]]:\n    wandb_imgs = {}\n    wandb_metrics = {}\n\n    device = cfg.other.device\n    split = int(cfg.data.test.vqa.split * len(features))\n    features_train = features[:split].to(device)\n    features_test = features[split:].to(device)\n    targets_train = targets[:split].float().to(device)\n    targets_test = targets[split:].float().to(device)\n\n    # For each True label there are 14 False labels\n    loss_fn = torch.nn.BCEWithLogitsLoss(\n        pos_weight=(\n            None\n            if cfg.data.test.vqa.pos_weight is None\n            else torch.tensor(cfg.data.test.vqa.pos_weight)\n        )\n    )\n    torch.manual_seed(cfg.data.test.vqa.seed)\n    probe = build_linear_probe(features.shape[1:], targets.shape[-1]).to(device)\n    optimizer = torch.optim.AdamW(probe.parameters())\n\n    # region Train\n    history = []\n    num_steps = cfg.data.test.vqa.num_steps\n    for i in tqdm.trange(\n        num_steps,\n        desc=\"VQA probe\",\n        unit=\"step\",\n        ncols=0,\n        mininterval=10,  # seconds\n        disable=not cfg.other.tqdm,\n    ):\n        with torch.no_grad():\n            preds = probe(features_test)\n            loss_test = loss_fn(preds.flatten(), targets_test.flatten())\n        optimizer.zero_grad()\n        preds = probe(features_train)\n        loss_train = loss_fn(preds.flatten(), targets_train.flatten())\n        loss_train.backward()\n        optimizer.step()\n        history.append((i, loss_train.item(), loss_test.item()))\n\n    fig, axs = plt.subplots(1, 2, figsize=(12, 4))\n    df = pd.DataFrame(history, columns=[\"step\", \"train\", \"val\"]).set_index(\"step\")\n    df.plot(ax=axs[0])\n    df[num_steps // 2 :].plot(ax=axs[1])\n    axs[0].set_ylabel(\"Loss\")\n    axs[0].set_title(\"Linear probe optimization\")\n    axs[0].fill_between(\n        np.arange(num_steps),\n        0,\n        1,\n        where=np.arange(num_steps) > (num_steps // 2),\n        color=\"gray\",\n        alpha=0.2,\n        transform=axs[0].get_xaxis_transform(),\n    )\n    fig.set_facecolor(\"white\")\n    wandb_imgs[\"linear_probe_opt\"] = wandb.Image(fig)\n    plt.close(fig)\n    # endregion\n\n    # region Predictions\n    with torch.no_grad():\n        preds = probe(features_test)\n\n    wandb_metrics[\"loss\"] = loss_fn(preds, targets_test).item()\n    targets_test = targets_test.cpu().numpy().astype(bool)\n    sample_weight = np.where(targets_test.flatten(), 14.0, 1.0)\n    preds = preds.sigmoid_().cpu().numpy()\n\n    for plot_name, plot_fn in [\n        (\"pr\", PrecisionRecallDisplay.from_predictions),\n        (\"auroc\", RocCurveDisplay.from_predictions),\n    ]:\n        fig, ax = plt.subplots(1, 1, figsize=(6, 6))\n        plot_fn(\n            y_true=targets_test.flatten(),\n            y_pred=preds.flatten(),\n            ax=ax,\n            name=\"Imbalanced\",\n        )\n        plot_fn(\n            y_true=targets_test.flatten(),\n            y_pred=preds.flatten(),\n            sample_weight=sample_weight,\n            ax=ax,\n            name=\"Balanced\",\n        )\n        fig.set_facecolor(\"white\")\n        wandb_imgs[plot_name] = wandb.Image(fig)\n        plt.close(fig)\n\n    for metric_name, metric_fn in [\n        (\"ap\", average_precision_score),\n        (\"auroc\", roc_auc_score),\n    ]:\n        for balanced in [False, True]:\n            metric_name_full = metric_name + [\"/imbalanced\", \"/balanced\"][balanced]\n            wandb_metrics[metric_name_full] = metric_fn(\n                y_true=targets_test.flatten(),\n                y_score=preds.flatten(),\n                sample_weight=sample_weight if balanced else None,\n            )\n    # endregion\n\n    return wandb_metrics, wandb_imgs\n\n\ndef build_linear_probe(\n    feat_shape: Tuple[int, ...], output_classes: int\n) -> torch.nn.Module:\n    if len(feat_shape) == 1:\n        # Global features [C]\n        return torch.nn.Linear(feat_shape[0], output_classes)\n\n    if len(feat_shape) == 2:\n        # Slot features [S, C]\n        return torch.nn.Sequential(\n            torch.nn.Linear(feat_shape[1], output_classes),\n            Reduce(\"B S C -> B C\", reduction=\"max\"),\n        )\n\n    raise ValueError(f\"Invalid feature shape {feat_shape}\")\n\n\ndef build_dataset_vqa(cfg: DictConfig) -> tf.data.Dataset:\n    \"\"\"Build VQA dataset for linear probing.\n\n    Args:\n        cfg: configuration\n\n    Returns:\n        VQA dataset.\n    \"\"\"\n    MAP_KW = {\"num_parallel_calls\": tf.data.AUTOTUNE, \"deterministic\": True}\n\n    if cfg.data.name == \"CLEVR10\":\n        import osc.data.clevr_with_masks\n\n        tfr_path = Path(cfg.data.root) / \"clevr_with_masks\" / \"imgs_test.tfrecords\"\n        check_num_samples(\n            cfg.data.test.vqa.max_samples, osc.data.clevr_with_masks.NUM_SAMPLES_TEST\n        )\n\n        ds = (\n            tf.data.TFRecordDataset(\n                tfr_path.expanduser().resolve().as_posix(), compression_type=\"GZIP\"\n            )\n            .take(cfg.data.test.vqa.max_samples)\n            .map(osc.data.clevr_with_masks.decode, **MAP_KW)\n            .map(osc.data.clevr_with_masks.fix_tf_dtypes, **MAP_KW)\n            .map(\n                partial(\n                    osc.data.clevr_with_masks.prepare_test_vqa,\n                    crop_size=tuple(cfg.data.crops.large.size),\n                    mean=tuple(cfg.data.normalize.mean),\n                    std=tuple(cfg.data.normalize.std),\n                ),\n                **MAP_KW,\n            )\n        )\n    else:\n        raise ValueError(cfg.data.name)\n\n    # Batch, prefetch, do not call .as_numpy_iterator()\n    ds = ds.batch(cfg.training.batch_size, drop_remainder=False)\n    ds = ds.prefetch(tf.data.AUTOTUNE)\n\n    log.info(\n        \"Dataset test VQA, %d samples, %d batch size, %d batches\",\n        cfg.data.test.vqa.max_samples,\n        cfg.training.batch_size,\n        batches_per_epoch(\n            cfg.data.test.vqa.max_samples, cfg.training.batch_size, drop_last=False\n        ),\n    )\n    return ds\n","repo_name":"baldassarreFe/iclr-osc-22","sub_path":"src/osc/vqa.py","file_name":"vqa.py","file_ext":"py","file_size_in_byte":8775,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"81"}
+{"seq_id":"25590561206","text":"# -*- coding: utf-8 -*-\n#!/usr/bin/env python\nimport os, sys\n\nsys.path.append('/home/l/lazerjtl_1805/.local/lib/python3.6/site-packages/bs4')\nsys.path.append('/home/l/lazerjtl_1805/.local/lib/python3.6/site-packages/requests')\n\nimport requests\nfrom bs4 import BeautifulSoup\nimport json\n\n\nnames = ['INTRR', 'RCOM', 'RG']\nurls = [\n    \"https://quote.rbc.ru/ticker/59272\",\n    \"https://quote.rbc.ru/ticker/59436\",\n    \"https://quote.rbc.ru/ticker/59268\"\n]\n\ncosts = []\n\nfor elem in urls:\n    response = requests.get(elem)\n    soup = BeautifulSoup(response.text, 'html.parser')\n    cost = soup.find('span', class_='chart__info__sum')\n    oldstr = str(cost.text)\n    costs.append(oldstr.replace(\"₽\", \"\"))\n\nwith open(\"./quotes21/public_html/costData.json\", \"w\") as f:\n    json.dump({names[i]: costs[i] for i in range(len(names))}, f, sort_keys=True, indent=4)\n\n","repo_name":"AVM1805/BuyingUpQuotes","sub_path":"cron/getCost.py","file_name":"getCost.py","file_ext":"py","file_size_in_byte":856,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"25026562434","text":"# 상어 중학교(https://www.acmicpc.net/problem/21609)\r\n\r\nfrom collections import deque\r\n\r\n\r\ndef bfs(y, x, color):\r\n    q = deque()\r\n    dy = [-1, 0, 1, 0]\r\n    dx = [0, 1, 0, -1]\r\n\r\n    q.append((y, x))\r\n    visited = [[False] * N for _ in range(N)]\r\n    visited[y][x] = True\r\n    block_cnt, rainbow_cnt = 1, 0  # 블록 갯수\r\n\r\n    blocks, rainbows = [[y, x]], []  # 블록 ��표 넣을리스트, 무지개 좌표 넣을 리스트\r\n\r\n    while q:\r\n        y, x = q.popleft()\r\n        for d in range(4):\r\n            ny = y + dy[d]\r\n            nx = x + dx[d]\r\n\r\n            # 범위 안이면서 방문 안한 일반 블록일 경우\r\n            if 0 <= nx < N and 0 <= ny < N and not visited[ny][nx] and arr[ny][nx] == color:\r\n                visited[ny][nx] = 1\r\n                q.append([ny, nx])\r\n                block_cnt += 1\r\n                blocks.append([ny, nx])\r\n            # 범위 안이면서 방문 안한 무지개 블록인 경우\r\n            if 0 <= nx < N and 0 <= ny < N and not visited[ny][nx] and arr[ny][nx] == 0:\r\n                visited[ny][nx] = 1\r\n                q.append([ny, nx])\r\n                block_cnt += 1\r\n                rainbow_cnt += 1\r\n                rainbows.append([ny, nx])\r\n    # 무지개블록은 방문 해제\r\n    for y, x in rainbows:\r\n        visited[y][x] = 0\r\n    return [block_cnt, rainbow_cnt, blocks+rainbows]\r\n\r\n# 블록 제거 함수\r\n\r\n\r\ndef remove(block):\r\n    for y, x in block:\r\n        arr[y][x] = -2\r\n\r\n\r\n# 중력 함수\r\ndef gravity(arr):\r\n    for i in range(N-2, -1, -1):  # 밑에서 부터 체크\r\n        for j in range(N):\r\n            if arr[i][j] > -1:  # -1이 아니면 아래로 다운\r\n                r = i\r\n                while True:\r\n                    # 다음행이 인덱스 범위 안이면서 -2 이면 아래로 다운\r\n                    if 0 <= r+1 < N and arr[r+1][j] == -2:\r\n                        arr[r+1][j] = arr[r][j]\r\n                        arr[r][j] = -2\r\n                        r += 1\r\n                    else:\r\n                        break\r\n# 반시계 회전 함수\r\n\r\n\r\ndef rot90(arr):\r\n    new_arr = [[0] * N for _ in range(N)]\r\n    for i in range(N):\r\n        for j in range(N):\r\n            new_arr[N-1-j][i] = arr[i][j]\r\n    return new_arr\r\n\r\n\r\nN, M = map(int, input().split())\r\n\r\narr = [list(map(int, input().split())) for _ in range(N)]\r\n\r\nscore = 0\r\n\r\nwhile True:\r\n    '''\r\n    오토플레이\r\n    1. 크기가 가장 큰 블록 찾기\r\n    2. 블록제거 + 점수더하기\r\n    3. 중력\r\n    4. 90도 회전\r\n    5. 중력\r\n    '''\r\n    visited = [[0] * N for _ in range(N)]  # 방문 체크\r\n    blocks = []  # 가능한 블록 그룹들 넣을 리스트\r\n    for i in range(N):\r\n        for j in range(N):\r\n            if arr[i][j] > 0 and not visited[i][j]:  # 일반블록 and 방문 x\r\n                visited[i][j] = 1  # 방문\r\n                block_info = bfs(i, j, arr[i][j])\r\n                if block_info[0] >= 2:\r\n                    blocks.append(block_info)\r\n    blocks.sort(reverse=True)\r\n\r\n    # 블록제거\r\n    if not blocks:\r\n        break\r\n    remove(blocks[0][2])\r\n    score += blocks[0][0] ** 2\r\n\r\n    gravity(arr)\r\n\r\n    arr = rot90(arr)\r\n    gravity(arr)\r\n\r\nprint(score)\r\n","repo_name":"parkchanghyup/algorithm","sub_path":"python/백준/[python]백준 - 상어 중학교.py","file_name":"[python]백준 - 상어 중학교.py","file_ext":"py","file_size_in_byte":3228,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"10598577886","text":"''' Documentation:\nboto3: https://boto3.amazonaws.com/v1/documentation/api/latest/index.html\npost_text format: https://docs.aws.amazon.com/lex/latest/dg/API_runtime_PostText.html\n'''\nimport boto3\nimport json\nimport logging\n\ndef lambda_handler(event, context):\n    client = boto3.client('lex-runtime')\n    message = event['messages'][0]['unstructured']['text']   # user's input text\n\n    response = client.post_text(\n        botName='latias_dining_chatbot_vone',\n        botAlias='chatbot_latias',\n        userId='user0',\n        inputText=message,\n    )\n\n    return {\n        'statusCode': 200,\n        'messages': [\n            {\n                \"type\": \"unstructured\", \n                \"unstructured\": {\n                    \"text\": json.dumps(response['message'])\n                }\n            }\n        ]\n    }\n","repo_name":"ReinaKousaka/COMS6998_Dining-Concierge-Assistant","sub_path":"Lambda/LF0.py","file_name":"LF0.py","file_ext":"py","file_size_in_byte":814,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"32123656777","text":"from collections import deque\nimport sys\ninput=sys.stdin.readline\nN=int(input())\nschedule=[]\nfor i in range(N):\n    time,money=map(int,input().split())\n    schedule.append((time,money))\n\nans=0\ndef dfs(day,money):\n    global ans\n    if day>N:\n        return\n    if day==N:\n        ans = max(ans, money)\n        return\n    ans=max(ans,money)\n    dfs(day+1,money)\n    dfs(day+schedule[day][0],money+schedule[day][1])\n\ndfs(0,0)\nprint(ans)","repo_name":"imdduoming/hitalgor","sub_path":"기출_삼성/백준14501.py","file_name":"백준14501.py","file_ext":"py","file_size_in_byte":434,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"18405947890","text":"import numpy as np\r\n\r\n\r\nimport os \r\n\r\n\r\n# #C TYPE LOAD LIBRARY\r\n# import ctypes as ct\r\n\r\n\r\n\r\n\r\nfrom commander.noise.geometry_noise import bummpy_noise\r\n\r\nclass FreqNoiseGenerator(object):\r\n    \"\"\"\r\n        Class HoleNoiseGenerator\r\n            this class is C/C++ dynamic Libary wrapper Class.\r\n\r\n            \r\n    \"\"\"\r\n\r\n    def __initialize_vars(self):\r\n        #NOISE CONFIGURATIONS\r\n        self.noise_type = None\r\n        self.noise_ratio = None\r\n\r\n        # FUNCTION HANDLING VARS\r\n        self.add_noise = None\r\n        self.calc_normal = None \r\n\r\n    def __init__(self, noise_ratio = 0.01, noise_type =\"gausian\", living_vertex_info = None):\r\n        \"\"\"\r\n            \r\n        \"\"\"\r\n        self.noise_ratio = noise_ratio\r\n        self.noise_type = noise_type\r\n        pass\r\n\r\n    \r\n\r\n    def compile(self):\r\n        \"\"\"\r\n            Method compile()\r\n            do nothing.\r\n        \"\"\"        \r\n        \r\n        return self\r\n\r\n    def __call__(self, vertex_list, face_list, **kwargs):\r\n        \"\"\"\r\n            Method __call__\r\n\r\n                -Args\r\n                    (3-dims or 2-dims : ndarray) vertex_list : vertex list \r\n                    (3-dim or 2-dims : ndarray) face_list : face list\r\n                -Return\r\n                    (v, f) generated_data\r\n        \"\"\"\r\n        return bummpy_noise(vertex_list, face_list, noise_ratio = 0.015, living_vertex_info = None), face_list\r\n        \r\n\r\n\r\n\r\nclass HoleNoiseGenerator():\r\n\r\n    \"\"\"\r\n        Class HoleNoiseGenerator\r\n            this class is C/C++ dynamic Libary wrapper Class.\r\n\r\n\r\n    \"\"\"\r\n    def __initialize_vars(self):\r\n        self.native_func_pkg = None\r\n        self.eye_vector = None\r\n        self.valid_angular = None\r\n        self.full_angle = 360\r\n\r\n\r\n        self.x_rotate = None\r\n        self.y_rotate = None\r\n        self.z_rotate = None\r\n\r\n        self.x_matrix = None\r\n        self.y_matrix = None\r\n        self.z_matrix = None\r\n\r\n        self.rotate_term = None\r\n        self.rotate_order = None\r\n\r\n        self.__iteration_num = 0\r\n\r\n\r\n    def __init__(self, \r\n                eye_vector, \r\n                valid_angular, \r\n                iterative_roation_size_x = 0, \r\n                iterative_roation_size_y = 0, \r\n                iterative_roation_size_z = 0,\r\n                rotate_order = [0, 1, 2],\r\n                rotate_term = [-1, -1, -1]\r\n                ):\r\n        \"\"\"\r\n        \"\"\"\r\n        self.__initialize_vars()\r\n        self.eye_vector = eye_vector\r\n        self.valid_angular = valid_angular\r\n        self.x_rotate = iterative_roation_size_x\r\n        self.y_rotate = iterative_roation_size_y\r\n        self.z_rotate = iterative_roation_size_z\r\n        \r\n        self.rotate_order = rotate_order\r\n        self.rotate_term = rotate_term\r\n        \r\n\r\n    def set_eye(self, eye_vector):\r\n        \"\"\"\r\n            eye_vector : 3-dims vector\r\n        \"\"\"\r\n        self.eye_vector = eye_vector\r\n        return self\r\n    \r\n    def set_angular(self, valid_angular):\r\n        \"\"\"\r\n            valid_angluar : scalar\r\n        \"\"\"\r\n        self.valid_angular = valid_angular\r\n        return self\r\n    \r\n    def set_roate( self, x_angle,\r\n                         y_angle, \r\n                         z_angle, \r\n                         rotate_order = [0, 1, 2], \r\n                         rotate_term = [-1, -1, -1]\r\n                         ):\r\n        self.x_rotate = x_angle\r\n        self.y_rotate = y_angle\r\n        self.z_rotate = z_angle\r\n        self.rotate_order = rotate_order\r\n        self.rotate_term = rotate_term\r\n\r\n\r\n    def __wrapper_function_initialize(self):\r\n        # path = __file__\r\n        # self.native_func_pkg = ct.cdll.LoadLibrary(os.path.join(path, \"noisecpp\", \"build\", \"Release\",\"noise.dll\"))\r\n        # self.addBackCullingNoise = self.native_func_pkg.addBackCullingNoise\r\n        pass\r\n    \r\n\r\n\r\n    def __default_inner_data_initialize(self):\r\n        self.__iteration_num = 0\r\n\r\n\r\n    def compile(self):\r\n        \"\"\"\r\n            Method compile()\r\n        \"\"\"\r\n\r\n        if type(self.eye_vector) != np.ndarray : \r\n            self.eye_vector = np.array(self.eye_vector)\r\n            \r\n        self.eye_vector = self.eye_vector.reshape(1, -1)\r\n        print(self.eye_vector, self.eye_vector.shape)\r\n        assert len(self.eye_vector.shape) == 2, \"it's not vector.\"\r\n        self.eye_vector = self.eye_vector / np.linalg.norm(self.eye_vector) # process UNIT Vector\r\n        self.valid_angular = self.valid_angular % self.full_angle\r\n        \r\n        \r\n        self.__default_inner_data_initialize()\r\n        \r\n        \r\n\r\n\r\n        #TODO\r\n        return self\r\n\r\n    def __get_normal_vector(self, vertice):\r\n        \"\"\"\r\n            vertice : triangle vertex, shape 3 by 3. assume it as column vector.\r\n        \"\"\"\r\n        \r\n        #Calcuating Edge(Vector)\r\n        \r\n        v1 = vertice[0] - vertice[1]\r\n        v2 = vertice[1] - vertice[2]\r\n\r\n        # Normal Vector\r\n        normal = np.cross(v1, v2)\r\n\r\n        assert len(normal.shape) == 1, \"wrong \"\r\n        return normal\r\n\r\n\r\n\r\n    def __build_rotate_matrix(self):\r\n        rot_x = self.__to_radian(self.x_rotate)\r\n        rot_y = self.__to_radian(self.y_rotate)\r\n        rot_z = self.__to_radian(self.z_rotate)\r\n        x_matrix = np.array([   [1,             0,              0],\r\n                                [0, np.cos(rot_x), -np.sin(rot_x)],\r\n                                [0, np.sin(rot_x), np.cos(rot_x)]\r\n                            ])\r\n        y_matrix = np.array([   [np.cos(rot_y), 0, np.sin(rot_y)],\r\n                                [0             , 1,            0],\r\n                                [-np.sin(rot_y), 0, np.cos(rot_y)]\r\n                            ])\r\n        z_matrix = np.array([  [np.cos(rot_z), -np.sin(rot_z), 0],\r\n                               [np.sin(rot_z), np.cos(rot_z), 0],\r\n                               [0            , 0            , 1]\r\n                            ])\r\n\r\n        matrix_collection = [x_matrix, y_matrix, z_matrix]\r\n        result_rotate = np.eye(x_matrix.shape[0]) # N x N shape identity matrix\r\n\r\n        assert len(self.rotate_order) == 3, \"out of size.\"\r\n        for idx in self.rotate_order : \r\n            if self.rotate_term[idx] != -1 and self.__iteration_num % self.rotate_term[idx] == 0 : \r\n                result_rotate = result_rotate.dot( matrix_collection[idx] ) \r\n\r\n        return result_rotate\r\n\r\n\r\n    def __to_radian(self, angle):\r\n        return angle * np.pi / self.full_angle / 2  # pi/180 * angle = radian\r\n\r\n\r\n\r\n    def __rotate_eye_vector(self) : \r\n        \r\n        self.__iteration_num += 1 \r\n        rotation = self.__build_rotate_matrix()\r\n        \r\n        result_eye = rotation.dot(self.eye_vector.T).T\r\n        self.eye_vector = result_eye\r\n\r\n    def __call__(self, vertex, face):\r\n        \"\"\"\r\n            vertex : shape of (N, 3)\r\n            face : shape of (M, 3)\r\n        \"\"\"\r\n        assert len(vertex.shape) == 2, \"vertex is not 2-dim matrix\"\r\n        assert len(face.shape) == 2, \"face is not 2-dim matrix\"\r\n\r\n        \r\n        vertex_size = vertex.shape[0]\r\n        vertex_dim = vertex.shape[-1]\r\n\r\n        face_size = face.shape[0]\r\n        face_dim = face.shape[-1]\r\n        #adj matrix\r\n        neighbor_face = [[] for _ in range(vertex_size)]\r\n        \r\n        # Initialize adj matrix TODO exists, calcuating neighbor that make Simply . \r\n        for face_idx, face_row in enumerate(face) : \r\n            for vertex_num in face_row :\r\n                neighbor_face[ vertex_num ].append( face_idx )\r\n                \r\n\r\n        #calculating Face Normal Vectors.\r\n        face_normal = np.zeros_like(face).astype(np.float32)\r\n        for idx, face_row in enumerate(face) : \r\n            normal = self.__get_normal_vector( vertex[face_row] ) \r\n            normal /= np.linalg.norm(normal, 2)\r\n            face_normal[ idx, : ] = normal\r\n        \r\n        # Normal Per each Vertex.\r\n        # TODO check libIGL. maybe exists, normal functions. now it is naive solution.\r\n        vertex_normal = np.zeros_like(vertex)\r\n        for idx in range(vertex_size) : \r\n            v_normal = np.mean( face_normal[neighbor_face[idx]], axis = 0 )\r\n            v_normal /= np.linalg.norm(v_normal, 2)\r\n            vertex_normal[ idx, : ] = v_normal\r\n\r\n\r\n\r\n        radian = np.cos(self.valid_angular * np.pi / (self.full_angle/2) )\r\n\r\n\r\n        vertex_angular = vertex_normal.dot(self.eye_vector.T)\r\n\r\n        mask = vertex_angular < radian\r\n        mask = mask.reshape(-1) #Shape [Vertex_size]\r\n        mask = mask != False # switch False to True, True to False\r\n\r\n\r\n        #tmporary for ... testing. if that vertex is hole info. it fills with 1.\r\n        vertex[mask] = np.array([1., 1., 1.])\r\n        # vertex[mask] = np.array([0., 0., 0.])\r\n\r\n        result_vertex = vertex\r\n        # result_vertex = vertex[mask]\r\n\r\n\r\n\r\n        #post processing \r\n        self.__rotate_eye_vector()\r\n        \r\n\r\n\r\n        return result_vertex, face\r\n\r\n\r\n            \r\n\r\n\r\n\r\n\r\n","repo_name":"fabyday/commander","sub_path":"noise/noisegenerator.py","file_name":"noisegenerator.py","file_ext":"py","file_size_in_byte":8955,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"81"}
+{"seq_id":"19724080088","text":"import os\n\n\ndef clea():\n    clear = lambda: os.system('cls')\n    clear()\n\ndef replaceAphos(stri):\n    code = [\"'\", \"&#39;\"]\n    stri = stri.replace(code[0], code[1])\n    return stri\n\n\ndef getInsiderInner(htmlStr, strStart, strEnd):\n    start = htmlStr.find(strStart, 0)\n    if start != -1:\n        htmlStr = htmlStr[start+len(strStart):]\n    else:\n        return \"\"\n    if strEnd != \"\":\n        return htmlStr[:htmlStr.find(strEnd, 0)]\n    else:\n        return htmlStr\n\ndef getInsider(htmlStr, strStart, strEnd):\n    strin = getInsiderInner(htmlStr, strStart, strEnd)\n    if len(strin) > 0:\n        if strin[0] == \"\\n\":\n            strin = strin[1:]\n    if len(strin) > 0:\n        if strin[len(strin)-1] == \"\\n\":\n            strin = strin[:len(strin)-1]\n    if len(strin) > 0:\n        if strin[0] == \"\\\"\":\n            strin = strin[1:]\n    if len(strin) > 0:\n        if strin[len(strin)-1] == \"\\\"\":\n            strin = strin[:len(strin)-1]\n    return strin\n\n\ndef all_occurences(strin, strStart, strEnd):\n    initial = 0\n    while True:\n        initial = strin.find(strStart, initial)\n        if initial == -1: return\n        yield initial\n        initial += len(strStart)\n        initial = strin.find(strEnd, initial)\n        if initial == -1: return\n        yield initial\n        initial += len(strEnd)\n\nimport sys\nsys.path.append(\"C:/Python34/Lib/site-packages\")\nimport goslate\n\ndef checkForNext(index, lang, hotel):\n    redo = True\n    gs = goslate.Goslate()\n    content=open(\"out.html\", encoding=\"UTF-8\").read()\n    #size = int(getInsider(content,\"<p class=\\\"page_showing\\\">\",\"</p>\")[len(\"Showing\\n\"):])\n    size = getInsider(content,\"<p class=\\\"page_showing\\\">\",\"</p>\").split(\"-\")\n    sizeSt = size[0]\n    sizeSt = sizeSt[len(\"Showing\\n\"):]\n    if len(sizeSt) > 1:\n        er = open(\"error.txt\", \"a\")\n        sizeSt = int(sizeSt[:len(sizeSt)-1])\n        sizeEnd = int(size[1][1:])\n        size = sizeEnd - sizeSt\n        sz = (size+1)/10\n        siz = size\n        gen = all_occurences(content, \"<li class=\\\"review_item clearfix\\\">\", \"</div>\\n</li>\")\n        outputtxt = ''\n        count = 0\n        while size != 0:\n            if(size % 10 == 0):\n                count = count + 1\n                count=count*(100/sz)\n                print(str(int(count)) + \"%\")\n                count = count/(100/sz)\n            stInd = next(gen)\n            try:\n                endInd = next(gen)\n            except StopIteration:\n                endInd = len(content)\n            newStr = content[stInd:endInd]\n            reviewer = getInsider(newStr,\"<div class=\\\"review_item_reviewer\\\">\",\"</div>\")\n            outputtxt = outputtxt + \"\\\"\" + getInsider(getInsider(reviewer,\"<h4\",\"</h4>\"), \">\\n\", \"\") + \"\\\",\" #name\n            outputtxt = outputtxt + \"\\\"\" + getInsider(getInsider(\n                getInsider(getInsider(newStr,\"<span class=\\\"reviewer_country\",\"</span>\\n<\"), \">\", \"\")\n                ,\"</span\",\"\"), \">\", \"\") + \"\\\",\" #country\n            outputtxt = outputtxt + \"\\\"\" + getInsider(getInsider(reviewer,\"<p class=\\\"reviewer_customer_type\",\"</p>\"),\">\", \"\") + \"\\\",\" #customerType\n            outputtxt = outputtxt + \"\\\"\" + getInsider(getInsider(newStr,\"<div class=\\\"review_item_review_score\",\"</div>\"),\">\",\"\") + \"\\\",\" #score\n            outputtxt = outputtxt + \"\\\"\" + getInsider(newStr,\"<div class=\\\"review_item_header_content\\\">\",\"</div>\") + \"\\\",\" #scoreMeaning\n            outputtxt = outputtxt + \"\\\"\" + getInsider(newStr,\"<div class=\\\"review_item_header_date\\\">\",\"</div>\") + \"\\\",\" #Date\n            prosCons = getInsider(newStr,\"<div class=\\\"review_item_review_content\",\"</div>\") + \"\\\",\" #ProsAndCons\n            pros = getInsider(getInsider(prosCons,\"<p class=\\\"review_pos\",\"</p>\"),\">\",\"\")\n            if redo and len(pros) > 0:\n                redo = False\n            if len(pros) > 0:\n                if pros[0] == \"-\":\n                    pros = pros[1:]\n            if lang != \"non\":\n                try:\n                    prosTr = gs.translate(pros, lang)\n                    outputtxt = outputtxt + \"\\\"\" + prosTr + \"\\\",\" #Pros Translated\n                except:\n                    outputtxt = outputtxt + \"\\\"\" + \"\" + \"\\\",\" #Pros Translated\n                    er.write(hotel + \" \" + str(int(index)*100 + (siz - size + 1)) + \" Pro\")\n            cons = getInsider(getInsider(prosCons,\"<p class=\\\"review_neg\",\"</p>\"),\">\",\"\")\n            if redo and len(cons) > 0:\n                redo = False\n            if len(cons) > 0:\n                if cons[0] == \"-\":\n                    cons = cons[1:]\n            if lang != \"non\":\n                try:\n                    consTr = gs.translate(cons, lang)\n                    outputtxt = outputtxt + \"\\\"\" + consTr + \"\\\",\" #Cons Translated\n                except:\n                    outputtxt = outputtxt + \"\\\"\" + \"\" + \"\\\",\" #Pros Translated\n                    er.write(hotel + \" \" + str(int(index)*100 + (siz - size + 1)) + \" Con\")\n            respon = getInsider(newStr,\"data-full-response=\\\"\",\"\\\">\")\n            if lang != \"non\":\n                try:\n                    responTr = gs.translate(respon, lang)\n                    outputtxt = outputtxt + \"\\\"\" + responTr + \"\\\",\" #Response Translated\n                except:\n                    outputtxt = outputtxt + \"\\\"\" + \"\" + \"\\\",\" #Pros Translated\n                    er.write(hotel + \" \" + str(int(index)*100 + (siz - size + 1)) + \" Rev\")\n            outputtxt = outputtxt + \"\\\"\" + pros + \"\\\",\" #Pros\n            outputtxt = outputtxt + \"\\\"\" + cons + \"\\\",\" #Cons\n            outputtxt = outputtxt + \"\\\"\" + respon + \"\\\",\" #Response\n            outputtxt = outputtxt + \"\\n\"\n            size = size - 1\n        #text_file = open(\"selective.html\", \"w\", encoding=\"UFT-8\")\n        #text_file.write(newStr)\n        #text_file.close()\n        if siz == 99:\n            print(str(int(index) + 1) + \" page(100 reviews per page) done!\")\n        else:\n            print(\"Done!\")\n        text_file1 = open(hotel + \"_\" + lang + \".csv\", \"a\", encoding=\"UTF-8\", newline='')\n        text_file1.write(outputtxt)\n        text_file1.close()\n        er.close()\n    else:\n        return -1\n    return redo\n\n\n#   while size != 0:\n#       stInd = next(gen)\n#       endInd = next(gen)\n#       newStr = newStr + content[stInd:endInd]\n#       size = size - 1\n\n\nimport urllib.request\nimport codecs\n\ndef mainFunc(hotel,startPos, lang):\n\n    initURL = \"http://www.booking.com/reviewlist.en-gb.html?sid=0;dcid=4;cc1=de;dist=1;pagename=\" + hotel + \";type=total&;offset=\" + str(startPos) + \";rows=100\"\n    req= urllib.request.Request(initURL,\n    headers={'User-Agent' : \"Mozilla/5.0 (Windows NT 5.1; rv:31.0) Gecko/20100101 Firefox/31.0\"})\n    html = urllib.request.urlopen(req).read()\n    html = html.decode(encoding='UTF-8')\n    text_file = open(\"out.html\", \"w\", encoding=\"UTF-8\")\n    text_file.write(html)\n    text_file.close()\n    #text_file_c = open(\"out\"+ str(startPos) + \".html\", \"w\", encoding=\"UTF-8\")\n    #text_file_c.write(html)\n    #text_file_c.close()\n    if checkForNext(startPos/100, lang, hotel) == -1:\n        return 0\n    return len(html)\n\nimport html\nimport time\nimport html.parser\ndef singleFile(url, lang):\n    start = time.time()\n    hotel = url.split('?')[0]\n    hotel = hotel.split('/')\n    hotel = hotel[len(hotel)-1].split('.')[0]\n    i=0\n    print(hotel)\n    f = open(hotel + \"_\" + lang + \".csv\", \"wb\")\n    f.write(codecs.BOM_UTF8)\n    if lang == \"non\":\n        st = \"\\\"Name\\\",\\\"Country\\\",\\\"Traveler Type\\\",\\\"Score\\\",\\\"Score Explanation\\\",\\\"Date\\\",\\\"Pros\\\",\\\"Cons\\\",\\\"Response\\\"\\n\"\n    else:\n        st = \"\\\"Name\\\",\\\"Country\\\",\\\"Traveler Type\\\",\\\"Score\\\",\\\"Score Explanation\\\",\\\"Date\\\",\\\"Pros\\\",\\\"Cons\\\",\\\"Response\\\",\\\"Pros Original\\\",\\\"Cons Original\\\",\\\"Response Original\\\"\\n\"\n    f.close()\n    f = open(hotel + \"_\" + lang + \".csv\", \"a\")\n    f.write(st)\n    f.close()\n    er = open(\"error.txt\", \"w\")\n    er.close()\n    lenOfHtml = mainFunc(hotel,i, lang)\n    while lenOfHtml>100:\n        i = i + 100\n        lenOfHtml = mainFunc(hotel,i,lang)\n    content=open(hotel + \"_\" + lang + \".csv\", encoding=\"UTF-8\").read()\n    #content = html.unescape(content)\n    content = html.parser.HTMLParser().unescape(content)\n    f1 = open(hotel + \"_\" + lang + \".csv\", \"w\", encoding=\"UTF-8\", newline='')\n    f1.write(content)\n    f1.close()\n    end = time.time()\n    print (end - start)\n    os.remove(\"out.html\")\n\nfileORSingle = input(\"enter F in order to get the info from txt file with list of links ( separated by enters ) or S for direct single input: \")\nif(fileORSingle == \"F\"):\n    lang = input(\"Please write he for Hebrew, en for English or ru for Russian or non (On all files): \")\n    fileLoc = input(\"Please enter file name: \")\n    listOfUrls=open(fileLoc).read()\n    listOfUrls = listOfUrls.split(\"\\n\")\n    for url in listOfUrls:\n        singleFile(url, lang)\nelse:\n    url = input(\"Please enter hotel link from booking.com: \")\n    lang = input(\"Please write he for Hebrew, en for English or ru for Russian or non: \")\n    singleFile(url, lang)\n\n#u = urllib.request.urlopen(urllib.request.Request(initURL, headers={'User-Agent': user_agent}))\n#raw_data = u.read()\n#with urllib.request.urlopen(initURL) as url:\n#    s = url.read()\n#I'm guessing this would output the html source code?\n#print(raw_data)","repo_name":"iamromand/bookingReviews","sub_path":"dataExtractor.py","file_name":"dataExtractor.py","file_ext":"py","file_size_in_byte":9203,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"4589516000","text":"\"\"\"\n次のスクリプトを実行して10を出力させたい。[ア]に入るものの説明として正しいものはどれか。\n\"\"\"\nimport pandas as pd\ndf = pd.DataFrame([[15, \"a\", True], [20, \"b\", False], [10, \"c\", False]])\ndf.index = [\"01\", \"02\", \"03\"]\ndf.columns = [\"A\", \"B\", \"C\"]\na = df.loc[\"03\", \"A\"]\nprint(a)\na = df.iloc[2, 0]\nprint(a)\n","repo_name":"devtitozzzzg/data_analysis","sub_path":"PRIME_STUDY/pandas_loc_iloc.py","file_name":"pandas_loc_iloc.py","file_ext":"py","file_size_in_byte":349,"program_lang":"python","lang":"ja","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"18204327097","text":"import collections\n\n\n\"\"\"\n# Definition for a Node.\nclass Node:\n    def __init__(self, val = 0, neighbors = None):\n        self.val = val\n        self.neighbors = neighbors if neighbors is not None else []\n\"\"\"\n\nclass Solution:\n    def cloneGraph(self, node: 'Node') -> 'Node':\n        # exception\n        if not node:\n            return node\n    \n        queue = collections.deque([node])\n        clone = {node.val: Node(node.val)}\n        \n        while queue:\n            cur_node = queue.popleft()\n            cur_clone = clone[cur_node.val]\n            \n            for neighbor in cur_node.neighbors:\n                if neighbor.val not in clone:\n                    clone[neighbor.val] = Node(neighbor.val)\n                    queue.append(neighbor)\n                \n                cur_clone.neighbors.append(clone[neighbor.val])\n                \n        return clone[node.val]\n","repo_name":"heejun32/Algorithm","sub_path":"LeetCode/133-clone-graph/133-clone-graph.py","file_name":"133-clone-graph.py","file_ext":"py","file_size_in_byte":883,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"41819853148","text":"\"\"\"\n\"\"\"\nimport pygame\n\nfrom . import container\nfrom . import layout\n\nclass _document_widget:\n    def __init__(self,w,align=None):\n        #w.rect.w,w.rect.h = w.resize()\n        #self.rect = w.rect\n        self.widget = w\n        if align != None: self.align = align\n\nclass Document(container.Container):\n    \"\"\"A document is a container that structures widgets in a left-to-right flow.\"\"\"\n\n    def __init__(self,**params):\n        params.setdefault('cls','document')\n        container.Container.__init__(self,**params)\n        self.layout =  layout.Layout(pygame.Rect(0,0,self.rect.w,self.rect.h))\n    \n    def add(self,e,align=None):\n        \"\"\"Add a widget to the document flow.\n\n        Arguments:\n            e -- widget\n            align -- alignment (None,-1,0,1)\n\n        \"\"\"\n        dw = _document_widget(e,align)\n        self.layout.add(dw)\n        e.container = self\n        e._c_dw = dw\n        self.widgets.append(e)\n        self.chsize()\n        \n    def remove(self,e):\n        self.layout._widgets.remove(e._c_dw)\n        self.widgets.remove(e)\n        self.chsize()\n        \n    \n    def block(self,align):\n        \"\"\"Start a new block given the alignment (-1, 0, 1)\"\"\"\n        self.layout.add(align)\n    \n    def space(self, size):\n        \"\"\"Add a spacer given the size.\"\"\"\n        self.layout.add(size)\n    \n    def br(self,height):\n        \"\"\"Add a line break, given the height.\"\"\"\n        self.layout.add((0, height))\n    \n    def resize(self,width=None,height=None):\n        if self.style.width: width = self.style.width\n        if self.style.height: height = self.style.height\n        \n        for w in self.widgets:\n            w.rect.w,w.rect.h = w.resize()\n            \n            if (width != None and w.rect.w > width) or (height != None and w.rect.h > height):\n                w.rect.w,w.rect.h = w.resize(width,height)\n            \n            dw = w._c_dw\n            dw.rect = pygame.Rect(0,0,w.rect.w,w.rect.h)\n        \n        if width == None: width = 65535\n        self.layout.rect = pygame.Rect(0,0,width,0)\n        self.layout.resize()\n        \n        _max_w = 0\n        \n        for w in self.widgets:\n            #xt,xl,xb,xr = w.getspacing()\n            dw = w._c_dw\n            w.style.x,w.style.y,w.rect.w,w.rect.h = dw.rect.x,dw.rect.y,dw.rect.w,dw.rect.h\n            #w.resize()\n            w.rect.x,w.rect.y = w.style.x,w.style.y\n            _max_w = max(_max_w,w.rect.right)\n        \n        #self.rect.w = _max_w #self.layout.rect.w\n        #self.rect.h = self.layout.rect.h\n        #print 'document',_max_w,self.layout.rect.h\n        return _max_w,self.layout.rect.h\n\n","repo_name":"pybox2d/pybox2d","sub_path":"library/Box2D/examples/pgu/gui/document.py","file_name":"document.py","file_ext":"py","file_size_in_byte":2620,"program_lang":"python","lang":"en","doc_type":"code","stars":455,"dataset":"github-code","pt":"81"}
+{"seq_id":"32247079180","text":"class Solution:\n\tdef sortColors(self, nums: List[int]) -> None:\n\t\t\"\"\"\n\t\tDo not return anything, modify nums in-place instead.\n\t\t\"\"\"\n\t\tmask = {0: 0, 1: 0, 2: 0}\n\t\tN = len(nums)\n\n\t\tfor i in nums:\n\t\t\tif i in mask.keys():\n\t\t\t\tmask[i] += 1\n\t\t\telse:\n\t\t\t\tprint(\"Unrecognized element!\")\n\n\t\tnums[0:mask[0]] = [0] * mask[0]\n\t\tnums[mask[0]:(mask[0]+mask[1])] = [1] * mask[1]\n\t\tnums[(mask[0]+mask[1]):N] = [2] * mask[2]","repo_name":"NeyoShinado/Learn_Leetcode","sub_path":"75_sort_Colors.py","file_name":"75_sort_Colors.py","file_ext":"py","file_size_in_byte":407,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"71527627785","text":"#! python3\r\n\"\"\"\r\nReally quick and dirty script to extract availability for one date from the HTML for the\r\nPitchero availability page.\r\n\"\"\"\r\n\r\nfrom collections import namedtuple\r\nimport re\r\nimport sys\r\n\r\n_Player = namedtuple(\"_Player\", \"surname firstname availability comment\")\r\n\r\n_NAME_RE = re.compile(r\"<strong>(?P<surname>[^<]*)</strong>,  (?P<firstname>.*)$\", re.M)\r\n_AVAIL_RE = re.compile(\r\n    r'/availability/(?P<date>[0-9-]*)\".* data-original-title=\"(?P<availability>[^\"]*)\"', re.M\r\n)\r\n_COMMENT_RE = re.compile(r\"- (?P<comment>.*) By\", re.M)\r\n_COMMENT_RE_2 = re.compile(r\"- (?P<comment>.*)$\", re.M)\r\n\r\n_AVAIL_TYPES = (\"Available\", \"Not sure\", \"Unavailable\", \"Not set\")\r\n\r\nwith open(sys.argv[1], encoding=\"utf8\") as players:\r\n    html = players.read()\r\n\r\nDATE = None\r\nplayers = []\r\n\r\nfor match in _NAME_RE.finditer(html):\r\n    availMatch = _AVAIL_RE.search(html, match.end())\r\n    assert availMatch\r\n    if DATE is None:\r\n        DATE = availMatch.group(\"date\")\r\n        print(\"Showing availability for\", DATE + \":\")\r\n    assert availMatch.group(\"date\") == DATE\r\n    AVAIL = availMatch.group(\"availability\")\r\n    for kind in _AVAIL_TYPES:\r\n        if AVAIL.startswith(kind):\r\n            commentMatch = _COMMENT_RE.search(AVAIL)\r\n            if commentMatch:\r\n                comment = commentMatch.group(\"comment\")\r\n            else:\r\n                commentMatch = _COMMENT_RE_2.search(AVAIL)\r\n                if commentMatch:\r\n                    comment = commentMatch.group(\"comment\")\r\n                else:\r\n                    comment = None\r\n            if comment is not None:\r\n                comment = comment.rstrip(\".\")\r\n            AVAIL = kind\r\n            break\r\n    else:\r\n        raise Exception(\"Unknown: \" + AVAIL)\r\n    player = _Player(match.group(\"surname\"), match.group(\"firstname\"), AVAIL, comment)\r\n    players.append(player)\r\n\r\nplayers.sort()\r\n\r\nfor kind in _AVAIL_TYPES:\r\n    print(\"\\n\" + kind + \":\")\r\n    for player in players:\r\n        if player.availability == kind:\r\n            comment = player.comment\r\n            if comment is None:\r\n                comment = \"\"\r\n            else:\r\n                comment = f\" ({comment})\"\r\n            print(\"   \", player.firstname, player.surname, comment)\r\n","repo_name":"CiderMan/pitchero-availability","sub_path":"availability.py","file_name":"availability.py","file_ext":"py","file_size_in_byte":2237,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"20349026261","text":"from colors import *\nfrom utils import *\nfrom .base import *\n\nclass Button(GuiObject):\n    def __init__(self, text = \"\", **params):\n        self.text = text\n        self.text_color = params.get(\"text_color\", BLACK)\n        font_size = params.get(\"font_size\", 20)\n        self.font = params.get(\"font\", pygame.font.Font(\"..\\\\data\\\\fonts\\\\corbelb.ttf\", font_size))\n        if not params.get(\"size\", None):\n            size = self.font.size(\"a\" * len(text))\n            params[\"size\"] = (size[0] + 10, size[1] + 10)\n        self.bg_color = params.get(\"bg_color\", (191, 161, 121))\n        self.border_color = params.get(\"border_color\", (249, 205, 87))\n        GuiObject.__init__(self, **params)\n        self.calls = []\n        #self.create_bg_image()\n        self.update_image()\n    def update_image(self):\n        bg_image = pygame.Surface((self.rect.width + 4, self.rect.height + 4))\n        bg_image.fill(BLACK)\n        bg_image.fill(self.border_color, pygame.Rect(2, 2, self.rect.width, self.rect.height))\n        bg_image.fill(self.bg_color, pygame.Rect(4, 4, self.rect.width - 4, self.rect.height - 4))\n        self.image = pygame.Surface(bg_image.get_rect().size, pygame.SRCALPHA)\n        self.image.blit(bg_image, (0,0))\n        self.image.blit(self.font.render(self.text, True, self.text_color), ((self.image.get_width() / 2) - (self.font.size(self.text)[0] / 2), 8))\n    def click(self, pos):\n        self.bg_color = (145, 112, 75)\n        self.update_image()\n    def release(self, pos):\n        self.bg_color = (191, 161, 121)\n        self.update_image()\n        if self.rect.collidepoint(pos):\n            self.activate()\n    def attach(self, func, *args, **params):\n        self.calls.append(Call(func, *args, **params))\n    def activate(self):\n        for call in self.calls:\n            call.call()\n\nclass ToggleButton(Button):\n    def __init__(self, text = \"\", **params):\n        Button(text, **params)\n        self.choices\n\nclass CheckBox(GuiObject):\n    def __init__(self, checked = False, **params):\n        self.checked = checked\n        size = params.get(\"size\", 20)\n##        self.text = params.get(\"text\", \"\")\n##        if self.text:\n##            pass\n    def update_image(self):\n        pass","repo_name":"Addgame/Test-Game","sub_path":"client/src/gui/button.py","file_name":"button.py","file_ext":"py","file_size_in_byte":2212,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"73034142345","text":"import os\nimport re\nfrom .visualizeSpecies import VisualizeSpecies\nfrom .visualizeFunction import VisualizeFunction\nfrom .visualizeHumann import VisualizeHumann\nfrom .pyutils.read import read_to_html_table, get_kingdom_ratio, format_file\nfrom .colorMap import ColorMap\nfrom .mapInfo import MapInfo\nfrom .reportConfig import *\n\n\nclass VisualizeAll(VisualizeSpecies):\n    \"\"\"\n    Sample usage:\n        from visualizeAll import VisualizeAll\n        VisualizeAll(\"/home/cheng/Projects/rll_testdir/mapping_file.txt\",\"Group1\").visualize()\n    \"\"\"\n\n    def __init__(self, mapping_file, categories=False, out_dir=False, filter_species=\"keep:Archaea;keep:Bacteria;keep:Fungi;keep:Viruses\"):\n        super(VisualizeAll, self).__init__(\"multi_table\", mapping_file,\n                                           categories, out_dir=out_dir)\n        root_dir = self.out_dir + 'Result/'\n        self.set_path(force=True,\n                      root_dir=root_dir,\n                      qc_dir=root_dir + '00-QCStats',\n                      )\n        self.set_path(force=False,\n                      FMAP_data=self.fmap_home + '/FMAP_data/',\n                      salmon_out=self.out_dir + \"salmon_out/\",\n                      )\n        self.filter_species = filter_species\n        self.mi = MapInfo()\n\n    def map_ko_annotation(self):\n        ko_file = self.function_dir + '1-KEGG/All.KO.abundance_unstratified.tsv'\n        # self.kegg_dir = os.path.dirname(ko_file) + '/'\n        self.system('''\n{perl_path} {base_dir}/ConvergeKO2Module.pl {ko_file} > {kegg_dir}/All.KEGG.Module.txt\n{perl_path} {base_dir}/ConvergeKO2Pathway.pl {ko_file} > {kegg_dir}/All.KEGG.Pathway.txt\n{perl_path} {base_dir}/ConvergePathway2Level1.pl {kegg_dir}/All.KEGG.Pathway.txt > {kegg_dir}/All.KEGG.Pathway.Level1.txt\n{perl_path} {base_dir}/ConvergePathway2Level2.pl {kegg_dir}/All.KEGG.Pathway.txt > {kegg_dir}/All.KEGG.Pathway.Level2.txt\n            ''', ko_file=ko_file)\n\n    @staticmethod\n    def extract_empper_cog(annotation):\n        return ['ENOG41' + c if not c.startswith('COG') else c for c in re.findall('([^,]+)@NOG', annotation)]\n\n    def map_func_definition(self):\n        FMAP_data = self.FMAP_data\n        map_level_map = self.base_dir + \"/FMAP_data/\"\n        FMAP = self.kegg_dir\n        mi = self.mi\n        asem = self.base_on_assembly\n        mi.mapping(FMAP + 'All.KEGG.Pathway.txt', [map_level_map + f for f in ['KEGG_Pathway2Level1.txt', 'KEGG_Pathway2Level2.txt', 'KEGG_pathway.txt']],\n                   out_file=FMAP + 'All.KEGG.Pathway.mapping.txt', mapped_headers=[\"Level1\", \"Level2\", \"Level3\"])\n        mi.mapping(FMAP + 'All.KO.abundance_unstratified.tsv', [FMAP_data + 'KEGG_orthology.txt'],\n                   out_file=FMAP + 'All.KO.mapping.txt', adjust_func=mi.ajust_ko_info, mapped_headers=['Gene_name\\tEnzyme_number\\tDefinition'])\n        mi.mapping(FMAP + 'All.KO.mapping.txt', [FMAP_data + f for f in [\n                   'KEGG_orthology2module.txt', 'KEGG_orthology2pathway.txt']], mapped_headers=[\"Module\", \"KEGG Pathway\"], add=True)\n\n        datas = [FMAP + f for f in [\"All.KO.abundance_unstratified.tsv\",\n                                    \"All.KEGG.Module.txt\",\n                                    \"All.KEGG.Pathway.txt\"]] + [self.function_dir + f for f in [\"5-EC/All.LEVEL4EC.abundance_unstratified.tsv\",\n                                                                                                \"{}/All.EGGNOG.abundance_unstratified.tsv\".format(\n                                                                                                    \"2-EggNOG\" if asem else\"3-EggNOG\"),\n                                                                                                \"{}/All.GO.abundance_unstratified.tsv\".format(\"3-GO\" if asem else \"4-GO\")]]\n\n        mapping_sources = [FMAP_data + f for f in [\"KEGG_orthology.txt\",\n                                                   \"KEGG_module.txt\",\n                                                   \"KEGG_pathway.txt\"]] + [self.path['humann2_utility_mapping'] + '/' + f for f in [\"map_level4ec_name.txt.gz\",\n                                                                                                                                    \"map_eggnog_name.txt.gz\",\n                                                                                                                                    \"map_go_name.txt.gz\"]]\n\n        for data, mapping_source in zip(datas, mapping_sources):\n            if os.path.exists(data):\n                mi.mapping(data, [mapping_source])\n\n        mi.mapping(self.cazy_dir + '/All.CAZY.abundance_unstratified.tsv',\n                   [self.FMAP_data + '/fam_description.txt'])\n\n    def init_out_dir(self, category):\n        asem = self.base_on_assembly\n        self.set_attr(\n            category=category\n        )\n        categroy_dir = \"{}/{}/\".format(self.root_dir, self.category)\n        self.set_path(force=True,\n                      categroy_dir=categroy_dir,\n                      taxa_dir=categroy_dir + '1-TaxaAundanceAnalysis/',\n                      function_dir=categroy_dir + '2-FuctionAnalysis/',\n                      amr_dir=categroy_dir + '3-AMRAnalysis/',\n                      report_dir=categroy_dir + 'FiguresTablesForReport/',\n                      )\n        self.set_path(force=True,\n                      kegg_dir=self.function_dir + '1-KEGG/',\n                      go_dir=self.function_dir + ('3-GO/' if asem else '4-GO/'),\n                      eggnog_dir=self.function_dir + ('2-EggNOG/' if asem else '3-EggNOG/'),\n                      cazy_dir=self.function_dir + ('4-CAZy/' if asem else '6-CAZy/'),\n                      )\n\n    def visualize(self, exclude='none', base_on_assembly=False, unify_colors=True, transcriptome=False, orders=[]):\n        self.base_on_assembly = base_on_assembly\n        # self.system(\"{bayegy_home}/piputils/write_colors_plan.py -i {mapping_file} -c {categories} -p {bayegy_home}/piputils/group_color.list -o {out_dir}colors_plan.json\")\n        # os.environ['COLORS_PLAN_PATH'] = self.out_dir + 'colors_plan.json'\n        if unify_colors:\n            self.set_colors()\n\n        if base_on_assembly:\n            self.set_path(force=True, asem_dir=self.root_dir + '01-Assembly')\n            spec = \"\"\"\nmkdir -p {asem_dir}/2-ORFPrediction/ {asem_dir}/1-Quast/\ncp {out_dir}Assembly_out/ORF* {asem_dir}/2-ORFPrediction/\ncp -r {out_dir}Assembly_out/quast_results/* {asem_dir}/1-Quast/\n# cp {out_dir}/salmon_out/All.genes.abundance.txt {out_dir}Assembly_out/NR.nucleotide.fa {asem_dir}/\n            \"\"\".format(**self.context)\n        else:\n            self.set_path(\n                force=True, table_dir=self.root_dir + '01-BaseTables')\n            self.system('''\n{humann2_home}/humann2_renorm_table --input {b}/RPK.All.UniRef90.genefamilies.tsv --units cpm -o {b}/All.UniRef90.genefamilies.tsv -s n\n{humann2_home}/humann2_renorm_table --input {b}/RPK.All.Metacyc.pathabundance.tsv --units cpm -o {b}/All.Metacyc.pathabundance.tsv -s n\n            ''', b=self.out_dir + 'Metagenome/Humann')\n            spec = \"\"\"\ncp {out_dir}Metagenome/Humann/All.UniRef90.genefamilies.tsv {table_dir}\ncp {out_dir}Metagenome/Metaphlan/All.Metaphlan2.profile.txt {table_dir}/All.Metaphlan2.taxa.txt\ncp {out_dir}Kraken2/All.Taxa.OTU.txt  {table_dir}/All.Kraken2.taxa.txt\n{humann2_home}/humann2_split_stratified_table -i {table_dir}/All.UniRef90.genefamilies.tsv -o {table_dir}\n            \"\"\".format(**self.context)\n\n        self.system(\"\"\"\nmkdir -p {qc_dir}/1-QC_report_Rawfastq/ {qc_dir}/2-QC_report_Filtered/\ncp {mapping_file} {root_dir}\ncp {out_dir}/fastqc_out/*/*html {qc_dir}/1-QC_report_Rawfastq/\ncp {out_dir}/kneaddata_out/*/fastqc/*kneaddata*html {qc_dir}/2-QC_report_Filtered/\ncp {out_dir}Report/reads_summary.txt {qc_dir}/\n{spec}\n            \"\"\", spec=spec)\n        for g in self.categories.split(','):\n            self.init_out_dir(g)\n\n            if base_on_assembly:\n                out_dir_dict = {\n                    \"KO\": self.kegg_dir,\n                    \"GO\": self.go_dir,\n                    \"EGGNOG\": self.eggnog_dir,\n                    \"CAZY\": self.cazy_dir,\n                }\n                for db, func_out in out_dir_dict.items():\n                    VisualizeFunction(self.salmon_out + 'All.{}.abundance_unstratified.tsv'.format(db),\n                                      self.mapping_file, self.category,\n                                      out_dir=func_out).visualize(exclude, orders=orders)\n\n            else:\n                for group, out_dir in zip(['ko', 'level4ec', 'go', 'eggnog', 'cazy'], ['1-KEGG', '5-EC', '4-GO', '3-EggNOG', '6-CAZy']):\n                    VisualizeHumann(self.out_dir + 'Metagenome/Humann/All.UniRef90.genefamilies.tsv', self.mapping_file,\n                                    self.category, custom_to=group, out_dir=self.function_dir + out_dir).visualize(exclude, orders=orders)\n                VisualizeHumann(self.out_dir + 'Metagenome/Humann/All.Metacyc.pathabundance.tsv',\n                                self.mapping_file, self.category, id_with_name=True, out_dir=self.function_dir + '2-Metacyc').visualize(exclude, orders=orders)\n\n            self.map_ko_annotation()\n\n            amr_home = self.salmon_out if base_on_assembly else self.out_dir + 'FMAP/'\n\n            self.mi.mapping(amr_home + 'All.AMR.abundance_unstratified.tsv', [self.FMAP_data + '/aro.csv'],\n                            pattern=r\"ARO[^\\|]+\",\n                            # first_pattern=\"[^\\|]+$\",\n                            first_pattern=r\"ARO[^\\|]+\",\n                            # add_sid_to_info=True,\n                            add_sid_to_info=False,\n                            map_column=-1)\n            VisualizeFunction(amr_home + 'All.AMR.abundance_unstratified.tsv',\n                              self.mapping_file, self.category, out_dir=self.amr_dir).visualize(exclude, orders=orders)\n\n            for abundance_table in [self.kegg_dir + f for f in (\n                # 'All.KO.abundance_unstratified.tsv',\n                'All.KEGG.Module.txt',\n                'All.KEGG.Pathway.txt',\n                'All.KEGG.Pathway.Level1.txt',\n                'All.KEGG.Pathway.Level2.txt'\n            )]:\n                VisualizeFunction(abundance_table, self.mapping_file, self.category,\n                                  out_dir=self.function_dir + '1-KEGG').visualize(exclude, orders=orders)\n\n            VisualizeSpecies(self.out_dir + 'Kraken2/All.Taxa.OTU.txt', self.mapping_file,\n                             self.category, out_dir=self.taxa_dir, tmp_dir=self.out_dir + '/TMP_DIR').visualize(exclude, orders=orders)\n            if self.filter_species:\n                for filter_object in self.filter_species.split(';'):\n                    if filter_object.startswith('exclude'):\n                        filter_object += ',Environmentalsamples'\n                    VisualizeSpecies(self.out_dir + 'Kraken2/All.Taxa.OTU.txt', self.mapping_file,\n                                     self.category, filter_species=filter_object,\n                                     out_dir=os.path.join(self.taxa_dir, re.sub(\n                                         '[,:]', '_', filter_object)),\n                                     tmp_dir=self.out_dir + '/TMP_DIR').visualize(exclude, orders=orders)\n\n            self.map_func_definition()\n            ko_lefse_lda = self.function_dir + '1-KEGG/4-SignificanceAnalysis/LEfSe/KO_{}_lefse_LDA2.LDA.txt'.format(g)\n            if os.path.exists(ko_lefse_lda):\n                ColorMap(ko_lefse_lda=ko_lefse_lda,\n                         ko_abundance_table=self.function_dir + '1-KEGG/All.KO.abundance_unstratified.tsv', mapping_file=self.mapping_file, category=g, out_dir=self.function_dir + '1-KEGG/5-ColoredMaps/').plot_all(map_level=self.function_dir + '1-KEGG/All.KEGG.Pathway.mapping.txt')\n\n            reads_spec = \"\"\"\ncp {function_dir}/2-Metacyc/1-Barplots/Metacyc_{category}_barplot.pdf  Figure5-5.pdf\ncpfirst \"{function_dir}/5-EC/4-SignificanceAnalysis/LEfSe/SignificantFeatures/.pdf\"   Figure5-8.pdf\ncpfirst \"{function_dir}/1-KEGG/4-SignificanceAnalysis/LEfSe/SignificantFeatures/.pdf\"   Figure5-4.pdf\n            \"\"\".format(**self.context)\n\n            asem_spec = \"\"\n\n            self.system(\"\"\"\ncd {report_dir}\ncp {taxa_dir}/1-AbundanceSummary/Classified_stat_relative.png Figure4-1.png\ncp {taxa_dir}/1-AbundanceSummary/2-Barplots/Taxa-bar-plots-top20/Phylum_{category}_barplot.pdf Figure4-2.pdf\ncp {taxa_dir}/1-AbundanceSummary/3-Heatmaps/Heatmap_top20_clustered/Phylum/{category}_heatmap.pdf  Figure4-3.pdf\ncp {taxa_dir}/2-AbundanceComparison/LEfSe/Genus/{category}_Genus_lefse_LDA2.pdf  Figure4-4.pdf\ncp {taxa_dir}/2-AbundanceComparison/VennAndFlower/{category}_Venn_plot.png Figure4-5.png\ncp {function_dir}/1-KEGG/1-Barplots/KEGG.Pathway.Level1_{category}_barplot.pdf Figure5-1.pdf\ncp {function_dir}/1-KEGG/4-SignificanceAnalysis/LEfSe/KEGG.Pathway_{category}_lefse_LDA2.pdf   Figure5-2.pdf\ncp {eggnog_dir}/2-Heatmaps/EGGNOG_{category}_clustered_heatmap.pdf   Figure5-6.pdf\ncp {go_dir}/3-Circos/GO_{category}_circos.png   Figure5-7.png\ncp {cazy_dir}/4-SignificanceAnalysis/LEfSe/CAZY_{category}_lefse_LDA2.pdf   Figure5-9.pdf\ncp {amr_dir}/1-Barplots/AMR_{category}_barplot.pdf Figure6-1.pdf\ncp {amr_dir}/2-Heatmaps/AMR_{category}_nocluster_heatmap.pdf Figure6-2.pdf\ncp {amr_dir}/3-Circos/AMR_{category}_circos.png Figure6-3.png\ncp {taxa_dir}/4-CorrelationAnalysis/RDA/Genus/{category}_RDA_features_location_plot.pdf Figure7-1.pdf\ncp {amr_dir}/5-CorrelationAnalysis/CorrelationHeatmap/AMR_Correlation_heatmap.pdf Figure7-2.pdf\nmv {kegg_dir}/5-CorrelationAnalysis {kegg_dir}6-CorrelationAnalysis\ncp -rp {base_dir}/Report/src {report_dir}\ncp {base_dir}/Report/结题报告.html {categroy_dir}\n{spec}\nif [ -f Figure4-2.pdf ];then echo \"Converting pdf to png\"; for pdfs in *.pdf; do echo $pdfs; base=$(basename $pdfs .pdf); convert  -density 300 -quality 80 $pdfs ${{base}}.png; rm $pdfs;done;fi;\n                \"\"\", spec=asem_spec if base_on_assembly else reads_spec)\n\n            # page = self.report_dir + \\\n            #     'src/pages/main_cleaned.html' if base_on_assembly else self.categroy_dir + \"结题报告.html\"\n\n            page = self.categroy_dir + \"结题报告.html\"\n            if base_on_assembly:\n                format_file(page, page, table1=read_to_html_table(\n                    self.out_dir + 'Report/reads_summary.txt', table_class=['table', 'table-striped', 'table-sm'], thead_class=['thead-dark']),\n                    species_ratio=get_kingdom_ratio(self.out_dir + 'Kraken2/All.Taxa.OTU.txt'), report_category=self.category,\n                    nav5=nav5_asem,\n                    nav3=nav3_asem,\n                    sum_steps=sum_steps_asem if not transcriptome else sum_steps_asem_trans,\n                    find_gene=find_gene_asem,\n                    func_steps=func_steps_asem,\n                    func_source=func_source_asem,\n                    amr_steps=amr_steps_asem,\n                    eggnog_od=\"2\",\n                    eggnog_fig=\"图5-4\",\n                    go_od=\"3\",\n                    go_fig=\"图5-5\",\n                    ec_doc=\"\",\n                    cazy_od=\"4\",\n                    cazy_fig=\"图5-6\",\n                    reads_type=\"宏转录组\" if transcriptome else \"宏基因组\",\n                    flowchart=\"2-2_trans\" if transcriptome else \"2-2\",\n                    summary=summary_trans if transcriptome else summary_gene,\n                    remove_rna=\"去rRNA污染（SortMeRNA软件），\" if transcriptome else \"\"\n                )\n            else:\n                format_file(page, page, table1=read_to_html_table(\n                    self.out_dir + 'Report/reads_summary.txt', table_class=['table', 'table-striped', 'table-sm'], thead_class=['thead-dark']),\n                    species_ratio=get_kingdom_ratio(self.out_dir + 'Kraken2/All.Taxa.OTU.txt'), report_category=self.category,\n                    nav3=\"\",\n                    nav5=nav5_reads,\n                    sum_steps=sum_steps_reads if not transcriptome else sum_steps_reads_trans,\n                    find_gene=\"\",\n                    func_steps=func_steps_reads,\n                    func_source=func_source_reads,\n                    amr_steps=amr_steps_reads,\n                    eggnog_od=\"3\",\n                    eggnog_fig=\"图5-6\",\n                    go_od=\"4\",\n                    go_fig=\"图5-7\",\n                    ec_doc=ec_doc_reads,\n                    cazy_od=\"6\",\n                    cazy_fig=\"图5-9\",\n                    reads_type=\"宏转录组\" if transcriptome else \"宏基因组\",\n                    flowchart=\"2-2_trans\" if transcriptome else \"2-2\",\n                    summary=summary_trans if transcriptome else summary_gene,\n                    remove_rna=\"去rRNA污染（SortMeRNA软件），\" if transcriptome else \"\"\n                )\n\n        self.system(\n            \"{python3_path} {base_dir}/change_suffix.py {root_dir} -o txt -s 'All.Taxa.OTU.taxa-bar-plots,bray_curtis_emperor' \")\n        self.system(\n            \"{python3_path} {base_dir}/change_suffix.py {root_dir} -o tsv -s 'All.Taxa.OTU.taxa-bar-plots,bray_curtis_emperor' \")\n        self.system(\n            \"echo visualize-function is finished\")\n        self.system(\n            \"date\")\n","repo_name":"bayegy/MetaGenome","sub_path":"visualizeAll.py","file_name":"visualizeAll.py","file_ext":"py","file_size_in_byte":17252,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"75136268425","text":"import bisect\n\n\nclass Solution:\n    def maxEnvelopes(self, envelopes):\n        envelopes.sort(key=lambda x: (x[0], -x[1]))\n        dp = list()\n        for width, height in envelopes:\n            point = bisect.bisect_left(dp, height)\n            if point == len(dp):\n                dp.append(height)\n            else:\n                dp[point] = height\n        return len(dp)\n","repo_name":"bamblebam/competitive-programming","sub_path":"2021/3_March_21/30-3-21/maxenvelopes.py","file_name":"maxenvelopes.py","file_ext":"py","file_size_in_byte":377,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"32763718069","text":"# python 3 \n\nfrom sklearn.model_selection import learning_curve\nfrom sklearn import preprocessing, model_selection, metrics, feature_selection\nfrom sklearn import grid_search\nfrom sklearn.model_selection import train_test_split,KFold\nfrom time import time\nfrom sklearn.grid_search import GridSearchCV, RandomizedSearchCV\nfrom operator import itemgetter\nimport pandas as pd\nimport numpy as np  \nimport datetime\n\n\n\nclass Class_Fit(object):\n    \n    def __init__(self, clf, params=None):\n        if params:            \n            self.clf = clf(**params)\n        else:\n            self.clf = clf()\n\n    def train(self, x_train, y_train):\n        self.clf.fit(x_train, y_train)\n\n    def predict(self, x):\n        return self.clf.predict(x)\n    \n    def grid_search(self, parameters, Kfold):\n        self.grid = GridSearchCV(estimator = self.clf, param_grid = parameters, cv = Kfold)\n        \n    def grid_fit(self, X, Y):\n        self.grid.fit(X, Y)\n        \n    def grid_predict(self, X, Y):\n        self.predictions = self.grid.predict(X)\n        print(\"Precision: {:.2f} % \".format(100*metrics.accuracy_score(Y, self.predictions)))\n\n\nclass Class_train(object):\n\n    def __init__(self, clf, params=None):\n        if params:            \n            self.clf = clf(**params)\n        else:\n            self.clf = clf()\n\n    def simple_train(self, clf, x_train,y_train):\n        self.clf.fit(x_train,y_train)\n\n    def k_fold_train(self, KFold_ ,x_train, y_train):\n        xtrain_ = x_train.values\n        ytrain_ = y_train.values\n        for i, (train_ind, val_ind) in enumerate(KFold(n_splits=KFold_, shuffle=True, \n                                        random_state=1989).split(xtrain_)):\n\n            print('----------------------')\n            print('Training model #%d' % i)\n            print('----------------------')\n            self.clf.fit(xtrain_[train_ind], ytrain_[train_ind])\n            print (self.clf.score(xtrain_[val_ind],ytrain_[val_ind]))\n            print (self.clf.predict(xtrain_[val_ind]))\n\n\n####################################################\n# credit \n# http://chrisstrelioff.ws/sandbox/2015/06/25/decision_trees_in_python_again_cross_validation.html\ndef report(grid_scores, n_top=3):\n    top_scores = sorted(grid_scores,\n                        key=itemgetter(1),\n                        reverse=True)[:n_top]\n    for i, score in enumerate(top_scores):\n        print(\"Model with rank: {0}\".format(i + 1))\n        print((\"Mean validation score: \"\n               \"{0:.3f} (std: {1:.3f})\").format(\n               score.mean_validation_score,\n               np.std(score.cv_validation_scores)))\n        print(\"Parameters: {0}\".format(score.parameters))\n        print(\"\")\n\n    return top_scores[0].parameters\n\n\ndef run_gridsearch(X, y, clf, cv=10):\n\n    param_grid = {\"criterion\": [\"gini\", \"entropy\"],\n                  \"min_samples_split\": [10,15,20,40],\n                  \"max_depth\": [10,15,30],\n                  \"min_samples_leaf\": [30,40,50,55,100],\n                  \"max_leaf_nodes\":  [35,50,60],\n                  \"min_samples_leaf\" : [15,20,30]}\n    grid_search = GridSearchCV(clf,\n                               param_grid=param_grid,\n                               cv=cv)\n    start = time()\n    grid_search.fit(X, y)\n    print((\"\\nGridSearchCV took {:.2f} \"\n           \"seconds for {:d} candidate \"\n           \"parameter settings.\").format(time() - start,\n                len(grid_search.grid_scores_)))\n    top_params = report(grid_search.grid_scores_, 3)\n    return  top_params\n\n\ndef run_randomsearch(X, y, clf, cv=5, n_iter_search=20):\n    param_dist = {\"criterion\": [\"gini\", \"entropy\"],\n                  \"min_samples_split\": range(15,40),\n                  \"max_depth\": range(8,30),\n                  \"min_samples_leaf\": range(10,60),\n                  \"max_leaf_nodes\": range(5,30)}\n    random_search = RandomizedSearchCV(clf,\n                                       param_distributions=param_dist,\n                                       n_iter=n_iter_search)\n    start = time()\n    random_search.fit(X, y)\n    print((\"\\nRandomizedSearchCV took {:.2f} seconds \"\n           \"for {:d} candidates parameter \"\n           \"settings.\").format((time() - start),\n                               n_iter_search))\n    top_params = report(random_search.grid_scores_, 3)\n    return  top_params\n\n\n\n####################################################\n\n\ndef kmean_evaluate(X):\n    # X = X_std \n    output = []\n    cluster_range = range(3, 15)\n    for n_cluster in cluster_range:\n        kmean = cluster.KMeans(n_clusters=n_cluster).fit(X)\n        label = kmean.labels_\n        # using silhouette_score evaluate kmeans model \n        # https://stackoverflow.com/questions/19197715/scikit-learn-k-means-elbow-criterion\n        sil_coeff = silhouette_score(X, label,  metric='euclidean')\n        #sil_coeff = silhouette_score(X, label,  metric='manhattan')\n        output.append(sil_coeff)\n        print(\"For n_clusters={}, The Silhouette Coefficient is {}\".format(n_cluster, sil_coeff))\n    plt.xlabel('# of culster (k)')\n    plt.ylabel('silhouette_score')\n    plt.title('kmeans model evaluate')\n    plt.plot(np.array(cluster_range),output)\n    plt.show()\n\n\n\ndef cluster_fit(clf , cluster_range_ , iter_range_):\n    output = []\n    cluster_range = range(cluster_range_ - 5 , cluster_range_)\n    max_iter = range(iter_range_ -5 ,iter_range_)\n    for n_cluster in cluster_range:\n        for iter_ in max_iter:\n            kmean = cluster.KMeans(n_clusters=n_cluster, max_iter=iter_, random_state=4000).fit(X)\n            label = kmean.labels_\n            sil_coeff = silhouette_score(X, label, metric='euclidean')\n            output.append(sil_coeff)\n    print(\"For n_clusters={}, The Silhouette Coefficient is {}\".format(n_cluster, sil_coeff))\n\n\n\ndef run_DT_model_2(df, criteria_col):\n    # run the tree for various 0,1 lebel (e.g. : high value or not..)\n    from sklearn.metrics import confusion_matrix\n    from sklearn.cross_validation import train_test_split\n    from sklearn.externals.six import StringIO\n    from IPython.display import Image  \n    import pydotplus\n    print ('criteria_col  =  ', criteria_col)\n    tree_col = [criteria_col,'Frequency', 'LTV', 'period_no_use','AverageTimeToOrder',\n          'late_by_collection', 'late_by_delivery', 'tickets', 'recleaned_orders',\n         'cancalled_orders', 'voucher_used']\n    df_train_ = df \n    #df_train_tree = df_train_[tree_col]\n    tree_data = df_train_[tree_col]\n    tree_data = tree_data.dropna()\n    tree_train, tree_test = train_test_split(tree_data,\n                                           test_size=0.2, \n                                           random_state=200,\n                                           stratify=tree_data[criteria_col])\n    clf = tree.DecisionTreeClassifier()\n    clf = clf.fit(tree_train.iloc[:,1:], tree_train[criteria_col])\n    print (clf.score(tree_test.iloc[:,1:], tree_test[criteria_col]))\n    # confusion matrix \n    print (confusion_matrix(tree_test[criteria_col], clf.predict(tree_test.iloc[:,1:])))\n    # visualize the tree \n    dot_data = StringIO()\n    tree.export_graphviz(clf,\n                       out_file=dot_data,\n                       feature_names=tree_col[1:],\n                       filled=True, \n                       rounded=True)\n    graph = pydotplus.graph_from_dot_data(dot_data.getvalue())\n    return Image(graph.create_png()), tree_train, tree_test\n\n\n\n####################################################\n\n\nnow = datetime.datetime.now()\ndate_ = now.strftime(\"%Y-%m-%d\")\n\n\nclass save_output(object):\n\n  def __init__(self, df):\n    self.df = df\n\n  def save_user_profile(self):\n    try:\n      self.df.to_csv('output/user_profile_.csv')\n      print ('Succefully save user profile to /output at {}'.format(date_))\n    except Exception as e:\n      print ('Save failed') \n      print (e)\n\n  def save_user_profile_DB(self):\n    try:\n      import sqlite3\n      conn = sqlite3.connect(\"output/user_classfication.db\")\n      self.df.to_sql(\"user_profile\", conn, if_exists=\"replace\")\n      print ('Succefully save user profile as sqlite db to /output at {}'.format(date_))\n    except:\n      print ('Save failed') \n\n  def model_IO(self):\n    pass \n\n\n\n\n\n####################################################\ndef save_user_profile(final_output):\n  try:\n    final_output.to_csv('output/user_profile_{}.csv'.format(date_))\n    print ('Succefully save user profile to /output at {}'.format(date_))\n  except:\n    print ('Save failed') \n\ndef save_user_profile_DB(final_output):\n  import sqlite3\n  conn = sqlite3.connect(\"output/user_classfication.db\")\n  try:\n    final_output.to_sql(\"user_profile\", conn, if_exists=\"replace\")\n    print ('Succefully save user profile as sqlite db to /output at {}'.format(date_))\n  except:\n    print ('Save failed') \n\n\n\n\n\n\n\n","repo_name":"yennanliu/analysis","sub_path":"archived/laundra_/utility_ML.py","file_name":"utility_ML.py","file_ext":"py","file_size_in_byte":8781,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"81"}
+{"seq_id":"7630684744","text":"#!/usr/bin/env python3\n\n\"\"\"\nDemo editor\n\"\"\"\n\nimport argparse\nfrom tui_editor import TuiEditor\n\n\ndef main():\n    \"\"\"main\"\"\"\n    arg_parser = argparse.ArgumentParser()\n    arg_parser.add_argument(\"file\", help=\"File content to edit (nothing will be written)\", default=__file__, nargs=\"?\")\n    arg_parser.add_argument(\"--height\", type=int, default=20)\n    args = arg_parser.parse_args()\n\n    with open(args.file) as f:\n        content = f.read().split('\\n')\n\n    print(\"Hello editor! Ctrl+S to quit.\")\n\n    e = TuiEditor()\n    e.set_text_lines(content)\n    e.height = args.height\n    e.show_line_numbers = True\n\n    e.on_cursor_pos_change = (\n        lambda: e.set_status_lines([\n            \"line: %d/%d\" % (e.cur_line_idx + 1, e.total_lines),\n            \"col: %d\" % e.col,\n            ]))\n\n    e.edit()\n\n    print(\"Good bye!\")\n\n\nif __name__ == \"__main__\":\n    try:\n        import better_exchook  # noqa\n        better_exchook.install()\n    except ImportError:\n        pass\n    try:\n        main()\n    except KeyboardInterrupt:\n        print(\"KeyboardInterrupt\")\n","repo_name":"albertz/py-tui-editor","sub_path":"demo-editor.py","file_name":"demo-editor.py","file_ext":"py","file_size_in_byte":1061,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"81"}
+{"seq_id":"32203269931","text":"#!/usr/local/bin/python\n\nimport pdb\nimport os\nimport shutil\nimport sys\nimport csv\nimport cv2\nimport numpy as np\nimport time\nimport hcv\n\nclass HackberryBenchmark:\n\n    def __init__(self, path, kp_a=[], des_a=[], image_ext=['jpg']):\n        self.hcv = hcv.ComputerVision()\n        self.h_matcher = hcv.Matcher()\n        self.image_ext = image_ext\n        self.path = path\n        self.kp_a = kp_a\n        self.des_a = des_a\n\n    def get_cv_kp_algorithims(self, types=False):\n        return self.kp_a\n\n    def get_cv_des_algorithims(self, types=False):\n        return self.des_a\n\n    def get_files(self):\n        files = []\n        for f in os.listdir(self.path):\n            ext = os.path.splitext(f)[1][1:].lower()\n            if ext in self.image_ext:\n                files.append(os.path.join(self.path, f)) \n        return files\n\n    def get_algorithim_name(self, a):\n        return a.__class__.__name__.lower()\n\n    def write_match_results_to_file(self, img1, img2, kp1, kp2, matches, f_name):\n\n        src_pts = np.float32([ kp1[m.queryIdx].pt for m in matches ])\n        dst_pts = np.float32([ kp2[m.trainIdx].pt for m in matches ])\n\n        if len(matches) >= 4:\n            (_, outlier_indices) = self.hcv.find_homography(dst_pts, src_pts)\n\n            src_pts = np.delete(src_pts, outlier_indices, 0)\n            dst_pts = np.delete(dst_pts, outlier_indices, 0)\n\n        match_img = self.hcv.draw_matches(img1, img2, src_pts, dst_pts)\n\n        result_path = os.path.join(self.path, 'matches')\n        if not os.path.isdir(result_path):\n            os.mkdir(result_path)\n\n        f_name = os.path.join(result_path, f_name+'.jpg')\n        cv2.imwrite(f_name+'.jpg', match_img)\n\n    def remove_outliers(self, kp1, kp2, matches):\n\n        if len(matches) < 4:\n            return (0,0)\n\n        src_pts = np.float32([ kp1[m.queryIdx].pt for m in matches ])\n        dst_pts = np.float32([ kp2[m.trainIdx].pt for m in matches ])\n\n        (_, outlier_indices) = self.hcv.find_homography(dst_pts, src_pts)\n\n        outliers = len(outlier_indices)\n\n        src_pts = np.delete(src_pts, outlier_indices, 0)\n        dst_pts = np.delete(dst_pts, outlier_indices, 0)\n\n        inlier = abs(len(matches)-outliers)\n\n        return (outliers, inlier)\n\n    def get_match_results(self, kp1, kp2, des1, des2, descriptor_name):\n\n        if descriptor_name == 'orb' or descriptor_name == 'brief':\n            bf_r = dict()\n            t = time.time()\n            bf_r['matches'] = self.h_matcher.binary_brute_force_match(des1, des2) \n            bf_r['time'] = round(time.time()-t, 6)\n            bf_r['outliers'], bf_r['inliers'] = self.remove_outliers(kp1, kp2, bf_r['matches'])\n\n            \n            knn_bf_r = dict()\n            t = time.time()\n            knn_bf_r['matches'] = self.h_matcher.binary_knn_brute_force_match(des1, des2) \n            knn_bf_r['time'] = round(time.time()-t, 6)\n            knn_bf_r['outliers'], knn_bf_r['inliers'] = self.remove_outliers(kp1, kp2, knn_bf_r['matches'])\n\n            flann_r = dict()\n            t = time.time()\n            flann_r['matches'] = self.h_matcher.binary_flann_match(des1, des2) \n            flann_r['time'] = round(time.time()-t, 6)\n            flann_r['outliers'], flann_r['inliers'] = self.remove_outliers(kp1, kp2, flann_r['matches'])\n        else:\n            bf_r = dict()\n            t = time.time()\n            bf_r['matches'] = self.h_matcher.brute_force_match(des1, des2) \n            bf_r['time'] = round(time.time()-t, 6)\n            bf_r['outliers'], bf_r['inliers'] = self.remove_outliers(kp1, kp2, bf_r['matches'])\n\n            \n            knn_bf_r = dict()\n            t = time.time()\n            knn_bf_r['matches'] = self.h_matcher.knn_brute_force_match(des1, des2) \n            knn_bf_r['time'] = round(time.time()-t, 6)\n            knn_bf_r['outliers'], knn_bf_r['inliers'] = self.remove_outliers(kp1, kp2, knn_bf_r['matches'])\n\n            flann_r = dict()\n            t = time.time()\n            flann_r['matches'] = self.h_matcher.flann_match(des1, des2) \n            flann_r['time'] = round(time.time()-t, 6)\n            flann_r['outliers'], flann_r['inliers'] = self.remove_outliers(kp1, kp2, flann_r['matches'])\n\n        return {\n            'bf'     : bf_r,\n            'knn_bf' : knn_bf_r, \n            'flann'  : flann_r, \n        }\n\n    def detect_and_compute(self, img, feature_detector, descriptor):\n\n        descriptor_name = descriptor[0]\n        feature_detector_name = feature_detector[0]\n\n        try:\n            kp, des = self.hcv.detect_and_compute(img, feature_detector[1], descriptor[1])\n        except cv2.error as e:\n            kp = feature_detector[1].detect(img, None)\n            des = np.empty([0,0])\n            print('Something happened when computing '\n                    + descriptor_name + '-'\n                    + feature_detector_name + ' discriptions')\n\n        return (kp, des)\n\n    def generate_matches(self, img1, img2, draw=False):\n\n        matches = dict()\n        for feature_detector in self.get_cv_kp_algorithims():\n            feature_detector_name = feature_detector[0]\n\n            for descriptor in self.get_cv_des_algorithims():\n                descriptor_name = descriptor[0]\n\n                print('-----------------------------------')\n                print('\\t'+feature_detector_name+'-'+descriptor_name)\n                print('-----------------------------------')\n                kp1, des1 = self.detect_and_compute(img1, feature_detector, descriptor)\n                kp2, des2 = self.detect_and_compute(img2, feature_detector, descriptor)\n                \n                if des1.size == 0 and des2.size == 0:\n                    continue\n\n                key = feature_detector_name+'-'+descriptor_name\n                matches[key] = self.get_match_results(kp1, kp2, des1, des2, descriptor_name)\n\n                for match in matches[key].keys():\n                    _m = matches[key][match]['matches']\n                    self.write_match_results_to_file(img1, img2, kp1, kp2, _m, key+'-'+match)\n\n        return matches\n\n    def generate_descriptions(self, kps):\n\n\n        for kp_row in kps:\n\n            img = os.path.join(self.path, kp_row['image'])\n            frame = cv2.imread(img, 0)\n\n            for descriptor in self.get_cv_des_algorithims():\n\n                d_name = descriptor[0] + '_descriptions'\n                des_name = descriptor[0] + '_d'\n\n                try:\n                    t_start = time.time()\n                    des = descriptor[1].compute(frame, kp_row['kps'])[1]\n                    t_end = round(time.time() - t_start, 6)\n                except cv2.error as e:\n                    des = np.empty([0,0])\n                    kp_row['time_'+des_name] = 'na'\n                    kp_row['time_per_'+des_name] = 'na'\n                    kp_row[d_name] = 'na'\n                    kp_row[des_name] = des\n                    print('Something happened when computing '\n                            + kp_row['algorithim'] + '/'\n                            + d_name + ' discriptions for ' + img)\n                    continue\n\n                kp_row['time_'+des_name] = t_end\n                kp_row['time_per_'+des_name] = t_end/des.shape[0]\n                kp_row[d_name] = str(des.shape)\n                kp_row[des_name] = des\n\n        return kps\n\n    def generate_keypoints(self):\n\n        rows = []\n\n        for a in self.get_cv_kp_algorithims():\n\n            for img in self.get_files():\n\n                row = {}\n                row['algorithim'] = a[0]\n                row['image'] = os.path.basename(img)\n                frame = cv2.imread(img, 0)\n\n                t_start = time.time()\n                kp = a[1].detect(frame, None)\n                t_end = round(time.time() - t_start, 6)\n\n                row['kp_time'] = t_end\n                row['time_per_keypoint'] = t_end/len(kp)\n                row['keypoints'] = len(kp)\n                row['kps'] = kp\n\n                self.write_img(img, frame, kp, a[0])\n\n                rows.append(row)\n\n        return rows\n        \n    def write_img(self, img_file, frame, kp, a_name):\n        \n\n        img_name, img_ext = os.path.splitext(img_file)\n\n        img_name = os.path.basename(img_name)\n\n        img_name = img_name + '_' + a_name + img_ext\n\n        img = cv2.drawKeypoints(frame, kp, None, (255,0,0), 4)\n\n        kp_path = os.path.join(self.path, 'keypoints')\n\n        if not os.path.isdir(kp_path):\n            os.mkdir(kp_path)\n\n        cv2.imwrite(os.path.join(kp_path, img_name), img)\n      \n    \n    def csv_match_stats(self, img1, img2):\n\n        matches = self.generate_matches(img1, img2)\n\n        with open('matcher_stats.csv', 'w+') as f:\n            csvwriter = csv.writer(f)\n\n            csv_header = [\n                'FeatureExtractor/Descriptor', \n                'BF Matches', \n                'BF Outlier', \n                'BF Inliers', \n                'BF Time', \n                'KNN BF Matches', \n                'KNN BF Outlier', \n                'KNN BF Inlier', \n                'KNN BF Time', \n                'FLANN Matches', \n                'FLANN Outlier', \n                'FLANN Inlier', \n                'FLANN Time', \n            ]\n\n            csvwriter.writerow(csv_header)\n            for m in matches:\n                \n                csvwriter.writerow([\n                    m,\n                    len(matches[m]['bf']['matches']),\n                    matches[m]['bf']['outliers'],\n                    matches[m]['bf']['inliers'],\n                    matches[m]['bf']['time'],\n                    len(matches[m]['knn_bf']['matches']),\n                    matches[m]['knn_bf']['outliers'],\n                    matches[m]['knn_bf']['inliers'],\n                    matches[m]['knn_bf']['time'],\n                    len(matches[m]['flann']['matches']),\n                    matches[m]['flann']['outliers'],\n                    matches[m]['flann']['inliers'],\n                    matches[m]['flann']['time'],\n                    ])\n            \nif __name__ == \"__main__\":\n\n    if sys.argv < 2:\n        exit(0)\n\n    path = sys.argv[1]\n    kp_algorithims = [\n                ('sift', cv2.SIFT()),\n                ('surf', cv2.SURF()),\n                ('fast', cv2.FastFeatureDetector()),\n                ('orb', cv2.ORB()),\n            ]\n\n    des_algorithims = [\n                ('sift', cv2.SIFT()),\n                ('surf', cv2.SURF()),\n                ('orb', cv2.ORB()),\n                #('brief', cv2.DescriptorExtractor_create(\"BRIEF\")),\n            ]\n\n    benchmark = HackberryBenchmark(\n            path, \n            kp_algorithims,\n            des_algorithims, \n            ['jpg']\n        )\n\n    img1 = cv2.imread(os.path.join(path, 'f-0.jpg'), 0)\n    img2 = cv2.imread(os.path.join(path, 'f-10.jpg'), 0)\n\n            \n    benchmark.csv_match_stats(img1, img2)\n    #exit(0)\n\n    rows = benchmark.generate_keypoints()\n    rows = benchmark.generate_descriptions(rows)\n\n    with open('stats.csv', 'w+') as f:\n        csvwriter = csv.writer(f)\n\n        csv_header = [\n            'Algorithim', \n            'Image', \n            'Total Time', \n            'Time per Keypoint', \n            'Keypoints',\n            'SIFT_Descriptions',\n            'Description Total Time', \n            'Time per one Description', \n            'SURF_Descriptions',\n            'Description Total Time', \n            'Time per one Description', \n            #'BRIEF_Descriptions',\n            #'Description Total Time', \n            #'Time per one Description', \n            'ORB_Descriptions',\n            'Description Total Time', \n            'Time per one Description', \n        ]\n\n        csvwriter.writerow(csv_header)\n\n        for row in rows:\n            csvwriter.writerow([\n                row['algorithim'],\n                row['image'],\n                row['kp_time'],\n                row['time_per_keypoint'],\n                row['keypoints'],\n                row['sift_descriptions'],\n                row['time_sift_d'],\n                row['time_per_sift_d'],\n                row['surf_descriptions'],\n                row['time_surf_d'],\n                row['time_per_surf_d'],\n                #row['brief_descriptions'],\n                #row['time_brief_d'],\n                #row['time_per_brief_d'],\n                row['orb_descriptions'],\n                row['time_orb_d'],\n                row['time_per_orb_d'],\n            ])\n","repo_name":"falmusha/hackberry","sub_path":"hackberry/benchmark.py","file_name":"benchmark.py","file_ext":"py","file_size_in_byte":12387,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"1940595171","text":"#!/usr/bin/env python3\n\nimport logging\nimport os.path\nimport json  # Language\nimport tempfile  # Cache\nimport datetime  # Cache\nimport urllib.request  # Connect\nimport urllib.parse  # Location\nimport xmltodict\n\nlog = logging.getLogger(__name__)\n\n\nclass YrObject:\n\n    script_directory = os.path.dirname(os.path.abspath(__file__))  # directory of the script\n    encoding = 'utf-8'\n\n\nclass YrException(Exception):\n\n    def __init__(self, message):\n        log.error(message)\n        raise\n\n\nclass Language(YrObject):\n\n    directory = 'languages'\n    default_language_name = 'en'\n    extension = 'json'\n\n    def __init__(self, language_name=default_language_name):\n        self.language_name = language_name\n        self.filename = os.path.join(\n            self.script_directory,\n            self.directory,\n            '{language_name}.{extension}'.format(\n                language_name=self.language_name,\n                extension=self.extension,\n            ),  # basename of filename\n        )\n        self.dictionary = self.get_dictionary()\n\n    def get_dictionary(self):\n        try:\n            log.info('read language dictionary: {}'.format(self.filename))\n            with open(self.filename, mode='r', encoding=self.encoding) as f:\n                return json.load(f)\n        except Exception as e:\n            raise YrException(e)\n\n\nclass Location(YrObject):\n\n    base_url = 'http://www.yr.no/'\n    default_forecast_link = 'forecast'\n    forecast_links = [default_forecast_link, 'forecast_hour_by_hour']\n    extension = 'xml'\n\n    def __init__(self, location_name, forecast_link=default_forecast_link, language=False):\n        self.location_name = location_name\n        self.language = language if isinstance(language, Language) else Language()\n\n        if forecast_link in self.forecast_links:  # must be valid forecast_link\n            self.forecast_link = self.language.dictionary[forecast_link]\n        else:\n            self.forecast_link = self.default_forecast_link\n\n        self.url = self.get_url()\n        self.hash = self.get_hash()\n\n    def get_url(self):\n        return '{base_url}{place}/{location_name}/{forecast_link}.{extension}'.format(\n            base_url=self.base_url,\n            place=self.language.dictionary['place'],\n            location_name=urllib.parse.quote(self.location_name),\n            forecast_link=self.forecast_link,\n            extension=self.extension,\n        )\n\n    def get_hash(self):\n        return '{location_name}.{forecast_link}'.format(\n            location_name=self.location_name.replace('/', '-'),\n            forecast_link=self.forecast_link,\n        )\n\n\nclass API_Locationforecast(YrObject):\n\n    \"\"\"Class to use the API of api.met.no\"\"\"\n\n    base_url = 'https://api.met.no/weatherapi/locationforecast/1.9/?'\n    forecast_link = 'locationforecast'\n\n    def __init__(self, lat, lon, msl=0, language=False):\n        \"\"\"\n        :param double lat: latitude coordinate\n        :param double lon: longitude coordinate\n        :param double msl: altitude (meters above sea level)\n        :param language: a Language object\n        \"\"\"\n        self.coordinates = dict(lat=lat, lon=lon, msl=msl)\n        self.location_name = 'lat={lat};lon={lon};msl={msl}'.format(**self.coordinates)\n        # self.language = language if isinstance(language, Language) else Language()\n        self.url = self.get_url()\n        self.hash = self.get_hash()\n\n    def get_url(self):\n        \"\"\"Return the url of API service\"\"\"\n        return '{base_url}{location_name}'.format(\n            base_url=self.base_url,\n            location_name=self.location_name,\n        )\n\n    def get_hash(self):\n        \"\"\"Create an hash with the three coordinates\"\"\"\n        return '{location_name}.{forecast_link}'.format(\n            location_name=self.location_name,\n            forecast_link=self.forecast_link,\n        )\n\n\nclass LocationXYZ(API_Locationforecast):  # ~> Deprecated!!!\n\n    \"\"\"Class to use the API of yr.no\"\"\"\n\n    def __init__(self, x, y, z=0, language=False):\n        \"\"\"\n        :param double x: longitude coordinate\n        :param double y: latitude coordinate\n        :param double z: altitude (meters above sea level)\n        :param language: a Language object\n        \"\"\"\n        super().__init__(y, x, z, language)\n\n\nclass Connect(YrObject):\n\n    def __init__(self, location):\n        self.location = location\n\n    def read(self):\n        try:\n            log.info('weatherdata request: {}, forecast-link: {}'.format(\n                self.location.location_name,\n                self.location.forecast_link,\n            ))\n            cache = Cache(self.location)\n            if not cache.exists() or not cache.is_fresh():\n                log.info('read online: {}'.format(self.location.url))\n                response = urllib.request.urlopen(self.location.url)\n                if response.status != 200:\n                    raise\n                weatherdata = response.read().decode(self.encoding)\n                cache.dump(weatherdata)\n            else:\n                weatherdata = cache.load()\n            return weatherdata\n        except Exception as e:\n            raise YrException(e)\n\n\nclass Cache(YrObject):\n\n    directory = tempfile.gettempdir()\n    extension = 'xml'\n    timeout = 15  # cache timeout in minutes\n\n    def __init__(self, location):\n        self.location = location\n        self.filename = os.path.join(\n            self.directory,\n            '{location_hash}.{extension}'.format(\n                location_hash=self.location.hash,\n                extension=self.extension,\n            ),  # basename of filename\n        )\n\n    def dump(self, data):\n        log.info('writing cachefile: {}'.format(self.filename))\n        with open(self.filename, mode='w', encoding=self.encoding) as f:\n            f.write(data)\n\n    def valid_until_timestamp_from_file(self):\n        xmldata = self.load()\n        d = xmltodict.parse(xmldata)\n        meta = d['weatherdata']['meta']\n        if isinstance(self.location, API_Locationforecast):\n            if isinstance(meta['model'], dict):\n                next_update = meta['model']['@nextrun']\n            elif isinstance(meta['model'], list):\n                next_update = meta['model'][0]['@nextrun']\n            else:\n                return False\n            next_update = next_update.replace(\"Z\", \" +0000\")\n            date_format = \"%Y-%m-%dT%H:%M:%S %z\"\n            # Read the UTC timestamp, convert to local time and remove the timezone information.\n            valid_until = datetime.datetime.strptime(next_update,date_format)\n            valid_until = valid_until.replace(tzinfo=datetime.timezone.utc).astimezone(tz=None).replace(tzinfo=None)\n        else:\n            next_update = meta['nextupdate']\n            date_format = '%Y-%m-%dT%H:%M:%S'\n            valid_until = datetime.datetime.strptime(next_update, date_format)\n        # hotfix API_Locationforecast ++ @nextrun <<<\n        log.info('Cache is valid until {}'.format(valid_until))\n        return valid_until\n\n    def is_fresh(self):\n        log.info('Now is {}'.format(datetime.datetime.now()))\n        return datetime.datetime.now() <= self.valid_until_timestamp_from_file()\n\n    def exists(self):\n        return os.path.isfile(self.filename)\n\n    def load(self):\n        log.info('read from cachefile: {}'.format(self.filename))\n        with open(self.filename, mode='r', encoding=self.encoding) as f:\n            return f.read()\n\n    def remove(self):\n        if os.path.isfile(self.filename):\n            os.remove(self.filename)\n            log.info('removed cachefile: {}'.format(self.filename))\n\nif __name__ == '__main__':\n    logging.basicConfig(level=logging.DEBUG)\n    logging.info('starting __main__')\n\n    weatherdata = Connect(Location(\n        location_name='Czech_Republic/Prague/Prague',\n        forecast_link='forecast',\n        language=Language(language_name='en'),\n    )).read()\n    # print(weatherdata)\n\n    weatherdata = Connect(Location(\n        location_name='Czech_Republic/Prague/Prague',\n        forecast_link='forecast_hour_by_hour',\n        language=Language(language_name='en'),\n    )).read()\n    # print(weatherdata)\n\n    weatherdata = Connect(API_Locationforecast(\n        50.0596696,\n        14.4656239,\n        11,\n        language=Language(language_name='en'),\n    )).read()\n    # print(weatherdata)\n\n    logging.info('stopping __main__')\n","repo_name":"FatJorm/daikin_controller","sub_path":"module/yr/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":8361,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"26346319479","text":"from uib_inf100_graphics import *\r\n\r\ndef app_started(app):\r\n    # oppretter variabler som skal brukes for å lage stigen\r\n    # ber at bruker skriver antall steg i stigen\r\n    app.step = int(input(\"Amount of ladder steps: \"))\r\n    # bredden på stigen//avstanden mellom de vertikale stigebeina\r\n    app.ladder_width = 100\r\n    # bestemmer x- og y-koordinatene til stigebeina \r\n    app.x1 = app.width//2 - 40\r\n    app.y1 = 20\r\n    app.x2 = app.x1 + app.ladder_width\r\n    app.y2 = 380\r\n    # bestemmer avstanden mellom hvert steg gitt av antall steg i stigen\r\n    app.height = (abs(app.y1-app.y2)-20)//app.step\r\n\r\n# funksjon som tegner stigesteg basert på steg nummeret\r\ndef draw_step_on_ladder(app, canvas, step_number):\r\n    # y-koordinatet begynner på samme y som beina også legges til avstanden mellom stigestegene basert på stigesteget\r\n    y_coor = app.y1 + (app.height*step_number) + app.height\r\n    # tegner stigesteget\r\n    canvas.create_line(app.x1, y_coor, app.x2, y_coor, width = 3, fill=\"red\")\r\n\r\ndef redraw_all(app, canvas):\r\n    # tegner først de to vertikale bena for stigen\r\n    canvas.create_line(app.x1, app.y1, app.x1, app.y2, width = 5, fill=\"red\")\r\n    canvas.create_line(app.x2, app.y1, app.x2, app.y2, width = 5, fill = \"red\")\r\n    # også tegnes alle stige stegene ved hjelp av hjelppfunksjon draw_step_on_ladder som blir kalt på x antall stigesteg\r\n    for i in range(0,app.step+1):\r\n        draw_step_on_ladder(app,canvas,i)\r\n\r\nrun_app(width=300, height=400, title=\"Ladder\")\r\n","repo_name":"amandaskaugerud/INF100","sub_path":"prøveeksamen/oppgave_13.py","file_name":"oppgave_13.py","file_ext":"py","file_size_in_byte":1507,"program_lang":"python","lang":"no","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"71150379786","text":"#! /usr/bin/env python3\n# https://github.com/Karmenzind/\n# -----------------------------------------------------------------------------\n# This script is no longer recommended.\n# Use symlink.py instead.\n# -----------------------------------------------------------------------------\n\n# 这个脚本写的很烂，后续有空重写\n\nimport warnings\n\nwarnings.warn(\"This script is no longer recommended. Use symlink.py instead.\")\n\nimport collections\nimport os\nimport re\nimport shutil\nimport sys\nfrom pprint import pprint\n\nREPO_DIR = os.path.dirname(os.path.abspath(__file__))\nHOME_DIR = os.path.expanduser('~')\nTAB = ' ' * 4\nYN = ('y', 'n')\nYNI = ('y', 'n', 'i')\nLINE = '\\n' + '-' * 44 + '\\n'\n\nprompt = \"\"\"\nUSAGE\n    python3 %s [update|apply]\n\nOPTIONS\n    apply       copy configure files and scripts to your system\n    update      copy your own files to this repo, which only works\n                if there is already a file with the same name in this repo\n\n\"\"\" % __file__\n\n\nos.chdir(REPO_DIR)\n\npath_map = {\n    \"home_k\": HOME_DIR,\n    \"local_bin\": \"/usr/local/bin\",\n}\n\ningored_patterns = (\n    re.compile(\".*__pycache__.*\"),\n    re.compile(\"home_k/Pictures\"),\n    re.compile(\"README\"),\n)\n\n\nclass Status:\n    \"\"\" as global counter\"\"\"\n    copied = []\n    not_copied = []\n\n    def display(self):\n        if self.copied:\n            print(len(self.copied), 'files have been directly copied.')\n            for _f, _t in self.copied:\n                print('\\t', _f, '-->', _t)\n        # if self.not_copied:\n        #     print(len(self.copied), 'files remained.')\n        #     for _f, _t in self.copied:\n        #         print('\\t', _f, '-->', _t)\n\n\nnot_sync = collections.defaultdict(list)  # {reason: files}\nsync_queue = []\n\n\ndef ask(choices, msg='Continue?'):\n    ans = None\n    msg += ' (%s) ' % '/'.join(choices)\n    while ans not in choices:\n        ans = input(msg)\n    return ans\n\n\ndef sync(mode):\n    for _f, _t in sync_queue:\n        if mode == 'i':\n            msg = 'Copy %s to %s?' % (_f, _t)\n            ans = ask(YN, msg)\n            if ans == 'y':\n                copy(_f, _t)\n        else:\n            copy(_f, _t)\n\n\ndef is_ignored(path):\n    for p in ingored_patterns:\n        if p.search(path):\n            return True\n    return False\n\n\ndef copy(_f, _t, interactive=False):\n    ok = True\n    if interactive:\n        msg = 'Copy %s to %s?' % (_f, _t)\n        ans = ask(YN, msg)\n        if ans != 'y':\n            ok = False\n    if ok:\n        create_parent_dir(_t)\n        if _t.startswith('/home'):\n            shutil.copyfile(_f, _t)\n        else:\n            os.system('sudo cp %s %s' % (_f, _t))\n        print('Copied %s to %s' % (_f, _t))\n\n        Status.copied.append((_f, _t))\n    else:\n        Status.not_copied.append((_f, _t))\n\n\ndef create_parent_dir(path):\n    \"\"\" before copy\"\"\"\n    par = os.path.dirname(path)\n    if not os.path.exists(par):\n        print('Creating', par)\n        if par.startswith('/home'):\n            os.makedirs(par)\n        else:\n            os.system('sudo mkdir -p %s' % par)\n\n\ndef cmp(f1, f2):\n    \"\"\" compare 2 files literally\"\"\"\n    with open(f1) as f1, open(f2) as f2:\n        return f1.read() == f2.read()\n\n\nclass Item:\n\n    def __init__(self,\n                 repo_path=None,\n                 sys_path=None,\n                 action=None):\n        self.repo_path = repo_path\n        self.sys_path = sys_path\n        self.action = action\n        self.both_exists = bool(\n            os.path.exists(self.repo_path) and os.path.exists(self.sys_path)\n        )\n\n        if self.action == 'update':\n            _f, _t = self.sys_path, self.repo_path\n        elif self.action == 'apply':\n            _f, _t = self.repo_path, self.sys_path\n        self._f, self._t = _f, _t\n\n    def get_sync_tuple(self):\n        return self._f, self._t\n\n    def is_valid(self):\n        if self.action == 'update':\n            if not os.path.exists(self.sys_path):\n                not_sync[\"Doesn't Exist\"].append(self.sys_path)\n                return False\n\n        if self.both_exists:\n            if cmp(self.repo_path, self.sys_path):\n                not_sync[\"The same\"].append((self._f, self._t))\n                return False\n\n            if os.path.exists(self.sys_path):\n                if not os.path.isfile(self.sys_path):\n                    not_sync[\"Different Type\"].append((self._f, self._t))\n                    return False\n\n        if self.both_exists:\n            f_stat = os.stat(self._f)\n            t_stat = os.stat(self._t)\n            if f_stat.st_mtime < t_stat.st_mtime:\n                not_sync[\"Override the newer\"].append((self._f, self._t))\n                return False\n\n        return True\n\n\ndef recurse(action):\n    for r, s in path_map.items():\n        par_d = os.path.join(REPO_DIR, r)\n\n        os.chdir(par_d)\n        for dname, _, sub_files in os.walk('.'):\n            if dname == '.':\n                dname = ''\n            if dname.startswith('./'):\n                dname = re.match(r'\\./(.*)', dname).group(1)\n\n            for f in sub_files:\n                repo_path = os.path.join(par_d, dname, f)\n                sys_path = os.path.join(s, dname, f)\n\n                if is_ignored(repo_path):\n                    not_sync['Ignored Pattern'].append(repo_path)\n                    continue\n                item = Item(repo_path, sys_path, action)\n                if item.is_valid():\n                    t = item.get_sync_tuple()\n                    if t:\n                        sync_queue.append(t)\n\n        os.chdir(REPO_DIR)\n\n\ndef main(action):\n    recurse(action)\n    # print('============================================')\n    print('\\nNot to be synchronized:')\n    for t, es in not_sync.items():\n        print('%s:' % (TAB + t))\n        print(TAB, len(t) * '-')\n        for e in es:\n            if isinstance(e, (tuple, list)):\n                e = '\\t'.join(e)\n            print(TAB * 2, e)\n\n    if sync_queue:\n        print('\\nTo be synchronized:')\n        for _f, _t in sync_queue:\n            print(TAB, _f, '-->', _t)\n        ans = None\n        print(\"\\n\\ty: copy all files\"\n              \"\\n\\ti: ask each file before copy it\"\n              \"\\n\\tn: cancel\")\n        ans = ask(YNI)\n        if ans in ('y', 'i'):\n            sync(ans)\n        print(LINE)\n        Status().display()\n\n    if not_sync['Override the newer']:\n        tool_ok = not os.system('command -v vimdiff > /dev/null 2>&1')\n        if tool_ok:\n            print('There are some files are ignored '\n                  'becaulse of stat comparing.')\n            ans = ask(YN, 'Edit these files with vimdiff?')\n            if ans == 'y':\n                for _f, _t in not_sync['Override the newer']:\n                    msg = 'Edit %s and %s?' % (_f, _t)\n                    ans = ask(YN, msg)\n                    if ans == 'y':\n                        os.system('vimdiff %s %s' % (_f, _t))\n\n\nif __name__ == '__main__':\n    if len(sys.argv) > 1:\n        arg = sys.argv[1]\n        assert arg in ('update', 'apply')\n        main(arg)\n    else:\n        print(prompt)\n    print('DONE :)')\n","repo_name":"Karmenzind/dotfiles-and-scripts","sub_path":"scripts/others/do_synch.py","file_name":"do_synch.py","file_ext":"py","file_size_in_byte":7033,"program_lang":"python","lang":"en","doc_type":"code","stars":163,"dataset":"github-code","pt":"81"}
+{"seq_id":"16508941060","text":"# coding: utf-8\n\n\"\"\"\n    CyberSource Merged Spec\n\n    All CyberSource API specs merged together. These are available at https://developer.cybersource.com/api/reference/api-reference.html\n\n    OpenAPI spec version: 0.0.1\n    \n    Generated by: https://github.com/swagger-api/swagger-codegen.git\n\"\"\"\n\n\nfrom pprint import pformat\nfrom six import iteritems\nimport re\n\n\nclass TssV2TransactionsGet200ResponsePointOfSaleInformation(object):\n    \"\"\"\n    NOTE: This class is auto generated by the swagger code generator program.\n    Do not edit the class manually.\n    \"\"\"\n\n\n    \"\"\"\n    Attributes:\n      swagger_types (dict): The key is attribute name\n                            and the value is attribute type.\n      attribute_map (dict): The key is attribute name\n                            and the value is json key in definition.\n    \"\"\"\n    swagger_types = {\n        'entry_mode': 'str',\n        'terminal_capability': 'int'\n    }\n\n    attribute_map = {\n        'entry_mode': 'entryMode',\n        'terminal_capability': 'terminalCapability'\n    }\n\n    def __init__(self, entry_mode=None, terminal_capability=None):\n        \"\"\"\n        TssV2TransactionsGet200ResponsePointOfSaleInformation - a model defined in Swagger\n        \"\"\"\n\n        self._entry_mode = None\n        self._terminal_capability = None\n\n        if entry_mode is not None:\n          self.entry_mode = entry_mode\n        if terminal_capability is not None:\n          self.terminal_capability = terminal_capability\n\n    @property\n    def entry_mode(self):\n        \"\"\"\n        Gets the entry_mode of this TssV2TransactionsGet200ResponsePointOfSaleInformation.\n        Method of entering credit card information into the POS terminal. Possible values:   - `contact`: Read from direct contact with chip card.  - `contactless`: Read from a contactless interface using chip data.  - `keyed`: Manually keyed into POS terminal.  - `msd`: Read from a contactless interface using magnetic stripe data (MSD).  - `swiped`: Read from credit card magnetic stripe.  The contact, contactless, and msd values are supported only for EMV transactions.  For details, see the `pos_entry_mode` field description in [Card-Present Processing Using the SCMP API.](https://apps.cybersource.com/library/documentation/dev_guides/Retail_SCMP_API/html/) \n\n        :return: The entry_mode of this TssV2TransactionsGet200ResponsePointOfSaleInformation.\n        :rtype: str\n        \"\"\"\n        return self._entry_mode\n\n    @entry_mode.setter\n    def entry_mode(self, entry_mode):\n        \"\"\"\n        Sets the entry_mode of this TssV2TransactionsGet200ResponsePointOfSaleInformation.\n        Method of entering credit card information into the POS terminal. Possible values:   - `contact`: Read from direct contact with chip card.  - `contactless`: Read from a contactless interface using chip data.  - `keyed`: Manually keyed into POS terminal.  - `msd`: Read from a contactless interface using magnetic stripe data (MSD).  - `swiped`: Read from credit card magnetic stripe.  The contact, contactless, and msd values are supported only for EMV transactions.  For details, see the `pos_entry_mode` field description in [Card-Present Processing Using the SCMP API.](https://apps.cybersource.com/library/documentation/dev_guides/Retail_SCMP_API/html/) \n\n        :param entry_mode: The entry_mode of this TssV2TransactionsGet200ResponsePointOfSaleInformation.\n        :type: str\n        \"\"\"\n        if entry_mode is not None and len(entry_mode) > 11:\n            raise ValueError(\"Invalid value for `entry_mode`, length must be less than or equal to `11`\")\n\n        self._entry_mode = entry_mode\n\n    @property\n    def terminal_capability(self):\n        \"\"\"\n        Gets the terminal_capability of this TssV2TransactionsGet200ResponsePointOfSaleInformation.\n        POS terminal’s capability. Possible values:   - `1`: Terminal has a magnetic stripe reader only.  - `2`: Terminal has a magnetic stripe reader and manual entry capability.  - `3`: Terminal has manual entry capability only.  - `4`: Terminal can read chip cards.  - `5`: Terminal can read contactless chip cards.  The values of 4 and 5 are supported only for EMV transactions. * Applicable only for CTV for Payouts.  For processor-specific details, see the `terminal_capability` field description in [Card-Present Processing Using the SCMP API.](https://apps.cybersource.com/library/documentation/dev_guides/Retail_SCMP_API/html/) \n\n        :return: The terminal_capability of this TssV2TransactionsGet200ResponsePointOfSaleInformation.\n        :rtype: int\n        \"\"\"\n        return self._terminal_capability\n\n    @terminal_capability.setter\n    def terminal_capability(self, terminal_capability):\n        \"\"\"\n        Sets the terminal_capability of this TssV2TransactionsGet200ResponsePointOfSaleInformation.\n        POS terminal’s capability. Possible values:   - `1`: Terminal has a magnetic stripe reader only.  - `2`: Terminal has a magnetic stripe reader and manual entry capability.  - `3`: Terminal has manual entry capability only.  - `4`: Terminal can read chip cards.  - `5`: Terminal can read contactless chip cards.  The values of 4 and 5 are supported only for EMV transactions. * Applicable only for CTV for Payouts.  For processor-specific details, see the `terminal_capability` field description in [Card-Present Processing Using the SCMP API.](https://apps.cybersource.com/library/documentation/dev_guides/Retail_SCMP_API/html/) \n\n        :param terminal_capability: The terminal_capability of this TssV2TransactionsGet200ResponsePointOfSaleInformation.\n        :type: int\n        \"\"\"\n        if terminal_capability is not None and terminal_capability > 5:\n            raise ValueError(\"Invalid value for `terminal_capability`, must be a value less than or equal to `5`\")\n        if terminal_capability is not None and terminal_capability < 1:\n            raise ValueError(\"Invalid value for `terminal_capability`, must be a value greater than or equal to `1`\")\n\n        self._terminal_capability = terminal_capability\n\n    def to_dict(self):\n        \"\"\"\n        Returns the model properties as a dict\n        \"\"\"\n        result = {}\n\n        for attr, _ in iteritems(self.swagger_types):\n            value = getattr(self, attr)\n            if isinstance(value, list):\n                result[attr] = list(map(\n                    lambda x: x.to_dict() if hasattr(x, \"to_dict\") else x,\n                    value\n                ))\n            elif hasattr(value, \"to_dict\"):\n                result[attr] = value.to_dict()\n            elif isinstance(value, dict):\n                result[attr] = dict(map(\n                    lambda item: (item[0], item[1].to_dict())\n                    if hasattr(item[1], \"to_dict\") else item,\n                    value.items()\n                ))\n            else:\n                result[attr] = value\n\n        return result\n\n    def to_str(self):\n        \"\"\"\n        Returns the string representation of the model\n        \"\"\"\n        return pformat(self.to_dict())\n\n    def __repr__(self):\n        \"\"\"\n        For `print` and `pprint`\n        \"\"\"\n        return self.to_str()\n\n    def __eq__(self, other):\n        \"\"\"\n        Returns true if both objects are equal\n        \"\"\"\n        if not isinstance(other, TssV2TransactionsGet200ResponsePointOfSaleInformation):\n            return False\n\n        return self.__dict__ == other.__dict__\n\n    def __ne__(self, other):\n        \"\"\"\n        Returns true if both objects are not equal\n        \"\"\"\n        return not self == other\n","repo_name":"mohosos/cybersource-rest-client-python","sub_path":"CyberSource/models/tss_v2_transactions_get200_response_point_of_sale_information.py","file_name":"tss_v2_transactions_get200_response_point_of_sale_information.py","file_ext":"py","file_size_in_byte":7527,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"81"}
+{"seq_id":"3847245478","text":"import numpy as np\nimport multiprocessing as mp\n\nimport bib_dijkstra as dij\n\ndef produit(*Matrices):#produit matriciel\n    P = np.identity(4)\n    for M in Matrices:\n        P = np.dot(P,M)\n    return P\n\nclass Embarcation:\n    def __init__(self, echelle, longitude_min, latitude_min):\n        self._longitude_min = longitude_min\n        self._latitude_min = latitude_min\n        self._echelle = echelle\n    def _convertir(self, longitude, latitude):#traduit (longitude, latitude) en coordonnées dans la matrice de risque\n        xm = round((longitude - self._longitude_min)*self._echelle)\n        ym = round((latitude - self._latitude_min)*self._echelle)\n        return xm, ym\n\n\nclass Bateau(Embarcation):\n    class Kalman:\n        def __init__(self, Q, R, vecteur_kalman):#vecteur_kalman = [latitude, longitude, vitesse_latitudinale, vitesse_longitudinale]\n            #constantes\n            self._Q = Q\n            self._R = R\n            #variables\n            self._X = vecteur_kalman\n            self._P = np.identity(4)#matrice de covariance de l'état estimé, arbitrairement grande au départ\n        def _predire(self, delta_t):#estime la position du bateau dans delta_t_pred secondes\n            X = self._X\n            P = self._P\n            Q = self._Q\n            F = np.array([[1,0,delta_t,0],[0,1,0,delta_t],[0,0,1,0],[0,0,0,1]]) #matrice représentant le modèle physique\n\n            X_prime = produit(F, X)\n            P_prime = produit(F, P, F.T) + Q\n            return X_prime, P_prime\n        def filtre(self, vecteurs):\n            #\"prédiction\"\n            t1 = vecteurs[0][0]\n            t2 = vecteurs[1][0]\n            delta_t = t2-t1\n            X_prime, P_prime = self._predire(delta_t)\n\n            #\"mise à jour\"\n            R = self._R\n            I = np.identity(4)\n            Y = vecteurs[1][1:]#mesures reçues\n\n            K = produit(P_prime, np.linalg.inv(P_prime + R))#gain de Kalman optimal\n\n            self._X = X_prime + produit(K, Y - X_prime)\n            self._P = produit(I-K, P_prime, I-K.T) + produit(K, R, K.T)\n        def recuperer_etat(self):\n            return self._X, self._P\n\n    def __init__(self, mmsi, vecteur, echelle, longitude_min, latitude_min):#vecteur = [instant, longitude, latitude, vitesse_longitudinale, vitesse_latitudinale]\n        super().__init__(echelle, longitude_min, latitude_min)\n        Q = 1e3*np.array([[1e-6,0,0,0],[0,1e-6,0,0],[0,0,1e-11,0],[0,0,0,1e-11]])#Q\n        R = np.array([[1e-6,0,0,0],[0,1e-6,0,0],[0,0,1e-11,0],[0,0,0,1e-11]])#R\n\n        self._mmsi = mmsi\n        self._r_risque = 30#rayon sur lequel on devra calculer le risque autour d'une position donnée\n\n        self._kalman = Bateau.Kalman(Q, R, vecteur[1:])\n        self._vecteurs =[vecteur]#cette liste contiendra les deux derniers vecteurs ajoutés, ce sera utile pour le filtre.\n        self._Dernier_risque = []#[mini, maxi, MAT]\n    def _calculer_delta_t(self, mon_bateau):\n        vitesse = mon_bateau.vitesse()\n        vecteur = self._vecteurs[-1]\n        position1 = mon_bateau.position()\n        lon0, lat0 = vecteur[1], vecteur[2]\n        lon1, lat1 = position1[0], position1[1]\n        distance = np.sqrt((lon1-lon0)**2 + (lat1-lat0)**2)#distance euclidienne\n        delta_t = round(distance/vitesse)\n        return delta_t\n    def _maj_risque(self, MAT_risque, kalman, mon_bateau):\n        def changer_repere(res, r, theta):\n            #création du repere\n            x = y = np.linspace(-res, res, num=res)\n            X, Y = np.meshgrid(x, y)\n            #rotation\n            U, V = X*np.cos(theta) - Y*np.sin(theta), X*np.sin(theta) + Y*np.cos(theta)\n            #translation\n            U = U - r/2\n            return U, V\n        def Risque(X, Y, sigx, sigy, sigx_prime, sigy_prime):#crée une matrice avec une répartition normale\n            T = np.linspace(0,1,len(X))\n            Sx = (1-T)*sigx + T*sigx_prime\n            Sy = (1-T)*sigy + T*sigy_prime\n            if not ((0 in Sx) or (0 in Sy)):\n                R = 1e10*np.exp(-X**2/(2*Sx**2) - Y**2/(2*Sy**2))\n            else:\n                R = 1e10*np.exp(-X**2/2 - Y**2/2)\n            return R\n\n        X, P = kalman.recuperer_etat()\n\n        delta_t_pred = self._calculer_delta_t(mon_bateau)\n        X_prime, P_prime = kalman._predire(delta_t_pred)\n\n        posx, posy = self._convertir(X[0], X[1])\n        kalx, kaly = self._convertir(X_prime[0], X_prime[1])\n        xmin, xmax, ymin, ymax = min(posx, kalx), max(posx, kalx), min(posy, kaly), max(posy, kaly)\n        mini, maxi = min(xmin, ymin), max(xmax, ymax)\n\n        x, y = kalx-posx, kaly-posy\n        r = np.sqrt(x**2 + y**2)\n        if not x+r == 0:\n             theta = 2*np.arctan(y/(x+r))\n        else:\n             theta = 0\n        sig = r/4\n\n        varx, vary, varx_prime, vary_prime = P[0,0], P[1,1], P_prime[0,0], P_prime[1,1]\n        sigx, sigy, sigx_prime, sigy_prime = np.sqrt(varx), np.sqrt(vary), np.sqrt(varx_prime), np.sqrt(vary_prime)\n        #sigx_prime, sigy_prime = sigx_prime*5, sigy_prime*5\n        sigu = np.cos(theta)*sigx + np.sin(theta)*sigy\n        sigv = -np.sin(theta)*sigx + np.cos(theta)*sigy\n        sigu_prime = np.cos(theta)*sigx_prime + np.sin(theta)*sigy_prime\n        sigv_prime = -np.sin(theta)*sigx_prime + np.cos(theta)*sigy_prime\n\n        R = self._r_risque\n        U, V = changer_repere(maxi-mini+2*R, r, theta)\n\n        G = Risque(U, V, sigu+sig, sigv, sigu_prime, sigv_prime)\n\n        def tronquer(G, min, max, taille, taille_tot):\n            try:\n                taille = taille[0]\n                if min < 0:\n                    G = G[-taille, -taille]\n                elif max >= taille_tot:\n                    G = G[taille, taille]\n            except IndexError:\n                G = np.zeros(taille)\n            return G\n\n        G = tronquer(G, mini-R, maxi+R, MAT_risque[mini-R:maxi+R, mini-R:maxi+R].shape, MAT_risque.shape[0])\n        D = self._Dernier_risque\n        if D:#si il y a le précedent risque de ce bateau à effacer, alors\n            MAT_risque[D[0]-R:D[1]+R, D[0]-R:D[1]+R] = MAT_risque[D[0]-R:D[1]+R, D[0]-R:D[1]+R] - D[2]\n\n        MAT_risque[mini-R:maxi+R, mini-R:maxi+R] = MAT_risque[mini-R:maxi+R, mini-R:maxi+R] + G\n        self._Dernier_risque=[mini, maxi, G]\n\n\n    def append(self, vecteur, MAT_risque, mon_bateau):#méthode appelée pour ajouter une mesure au bateau\n        if len(self._vecteurs) == 2:#kalman utilise les instants t et t+1\n            self._vecteurs[0], self._vecteurs[1] = self._vecteurs[1], vecteur\n        else:#executé au premier appel de append()\n            self._vecteurs.append(vecteur)\n\n        kalman = self._kalman\n        kalman.filtre(self._vecteurs)\n        self._maj_risque(MAT_risque, kalman, mon_bateau)\n\nclass MonBateau(Embarcation):\n    def __init__(self, n, echelle, longitude_min, latitude_min):\n        super().__init__(echelle, longitude_min, latitude_min)\n\n        self._Graphe = dij.graphe(n) #Le graphe pour Dijkstra\n        self._depart, self._arrivee = (-68.75,44.1),(-69.2,42.3)#(-66,46), (-70,42)#(-70.5,41.5), (-65.5,46.5)\n        self._vitesse = 1e-6\n\n        manager = mp.Manager()\n        self._return_dict = manager.dict()\n        self._return_dict[\"LON\"] = self._return_dict[\"LAT\"] = self._return_dict[\"chemin\"] = []\n        self._dij_proc = mp.Process(target=None)\n    def _dijkstra(self, M):#trouve le plus court chemin dans la matrice M\n        depart, arrivee = self._convertir(self._depart[0], self._depart[1]), self._convertir(self._arrivee[0], self._arrivee[1])\n        chemin = dij.trajet(self._Graphe, M, depart, arrivee)[1]\n        LON, LAT = dij.preparer_trajet(chemin, self._echelle, self._longitude_min, self._latitude_min)\n        self._return_dict[\"LON\"], self._return_dict[\"LAT\"], self._return_dict[\"chemin\"] = LON, LAT, chemin\n    def position(self):\n        return self._depart\n    def vitesse(self):\n        return self._vitesse\n    def calculer_plus_court_chemin(self, M):\n        fausse_vitesse = 10\n        if not self._dij_proc.is_alive():#On relance le calcul seulement si le précédent est fini\n            self._dij_proc = mp.Process(target=self._dijkstra, args=[M])\n            self._dij_proc.start()\n            chemin = self._return_dict[\"chemin\"]\n            LON, LAT = self._return_dict[\"LON\"], self._return_dict[\"LAT\"]\n            if len(LON):#len(LON)=len(LAT)\n                self._depart = (LON[fausse_vitesse], LAT[fausse_vitesse])#on fait avancer le bateau suivi\n            dij.affiche_trajet(LON, LAT)\n\n    # def calculer_plus_court_chemin(self, M):\n    #     self._dijkstra(M)\n    #     dij.affiche_trajet(self._return_dict[\"LON\"], self._return_dict[\"LAT\"])\n    #     LON, LAT = self._return_dict[\"LON\"], self._return_dict[\"LAT\"]\n    #     if len(LON):\n    #         vitesse_bateau_suivi = 10\n    #         self._depart = (LON[vitesse_bateau_suivi], LAT[vitesse_bateau_suivi])\n","repo_name":"brieuc-praud/TIPE","sub_path":"bib_bateau.py","file_name":"bib_bateau.py","file_ext":"py","file_size_in_byte":8847,"program_lang":"python","lang":"fr","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"29558284477","text":"# Plot a sensor with features\n\nimport matplotlib.pyplot as plt\nimport matplotlib\nimport pandas as pd\nimport numpy as np\n\n\nmatplotlib.style.use('ggplot')\n\ndataset_path = '/home/william/Documents/__Dev/PredictiveCarMaintenance/Ressources/Generated_files/Datasets/'\n\n\n#-------------------------#\n\ndataset_name = 'vasile'\nnormality = 'normal'\nfilename = '1.csv'\nsensor = 'SPEED'\nfeatures = ['ma_200_SPEED', 'std_w200_SPEED', 'd1_SPEED', 'd2_SPEED']\n\n#-------------------------#\n\n\n# Init filepath \nfile = dataset_path + '/' + dataset_name + '/' + normality + '/not_normalized/' + filename\nraw_file = dataset_path + '/' + dataset_name + '/' + normality + '/raw/' + filename\n\n# Read data\ndf = pd.read_csv(file)\nraw_df = pd.read_csv(raw_file)\nprint(df.shape)\nprint(raw_df.shape)\n\t\n\n# Store time series \nsensor = 'raw_' + sensor\nfeatures.append(sensor)\n\n# Plot curves\ndf['Time'] = pd.Series(list(range(len(df))))\nraw_df['Time'] = pd.Series(list(range(len(raw_df))))\n\n#df[100:350].plot(x='Time', y=features, subplots=True)\n'''\n# Moving average\nfig, axes = plt.subplots(nrows=2, ncols=1)\ndf['raw_SPEED'].plot(ax=axes[0], cmap=\"jet\")\naxes[0].set_title('Vitesse interpolée')\naxes[0].set_xlabel('Temps ($s$)')\naxes[0].set_ylabel('Vitesse ($km.h^{-1}$)')\ndf['ma_60_SPEED'].plot(ax=axes[1])\naxes[1].set_title('Moyenne glissante (60 secondes)')\naxes[1].set_xlabel('Temps ($s$)')\naxes[1].set_ylabel('Vitesse ($km.h^{-1}$)')\nplt.show()\n\n# STD\nfig, axes = plt.subplots(nrows=2, ncols=1)\ndf['raw_SPEED'].plot(ax=axes[0], cmap=\"jet\")\naxes[0].set_title('Vitesse interpolée')\naxes[0].set_xlabel('Temps ($s$)')\naxes[0].set_ylabel('Vitesse ($km.h^{-1}$)')\ndf['std_w60_SPEED'].plot(ax=axes[1])\naxes[1].set_title('Ecart-type (60 secondes)')\naxes[1].set_xlabel('Temps ($s$)')\naxes[1].set_ylabel('Vitesse ($km.h^{-1}$)')\nplt.show()\n\n# Derivatives\nfig, axes = plt.subplots(nrows=3, ncols=1)\ndf['raw_SPEED'].plot(ax=axes[0], cmap=\"jet\")\naxes[0].set_title('Vitesse interpolée')\naxes[0].set_xlabel('Temps ($s$)')\naxes[0].set_ylabel('Vitesse ($km.h^{-1}$)')\n\ndf['d1_SPEED'].plot(ax=axes[1])\naxes[1].set_title('Dérivée première instantanée')\naxes[1].set_xlabel('Temps ($s$)')\naxes[1].set_ylabel('Accélération ($km.h^{-2}$)')\n\ndf['d2_SPEED'].plot(ax=axes[2])\naxes[2].set_title('Dérivée seconde instantanée')\naxes[2].set_xlabel('Temps ($s$)')\naxes[2].set_ylabel(r\"$\\frac{dAcceleration}{dt}$ ($km.h^{-3}$)\" )\nplt.show()\n'''\n# RAW\ntime = raw_df['# Time'].values\npoints = raw_df['raw_SPEED'].values\n\nplt.scatter(time, points, label='Données vitesse brute')\ndf['raw_SPEED'].plot(label='Vitesse interpolée')\nplt.xlabel('Temps ($s$)')\nplt.ylabel('Vitesse ($km.h^{-1}$)')\nplt.legend(loc='best')\nplt.show()","repo_name":"WilliamDiakite/Stage","sub_path":"DataProcessing/trunk/plot_sensor.py","file_name":"plot_sensor.py","file_ext":"py","file_size_in_byte":2674,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"81"}
+{"seq_id":"39181135309","text":"import streamlit as st \nfrom PIL import Image\nfrom classify import predict\n\ndef converter(value):\n    if value == 0:\n        return \"infected\"\n    else:\n        return \"uninfected\"\n\nst.title(\"Upload + MALARIA IMAGE\")\nst.text(\"NAME : OMONIYI TEMIDAYO ANDREW\")\nst.text(\"TEAM : TEAM WAKANDA\")\nst.text(\"EMAIL: omoniyiandrewai@gmail.com, kiddojazz@gmail.com\")\nst.text(\"NO:    +2348166220117\")\nhtml_temp=\"\"\"\n<div style=\"background-color:gray;padding:15px;\">\n<h2>RGB IMAGE OF BLOOD SAMPLE</h2>\n</div>\n\"\"\"\nst.markdown(html_temp,unsafe_allow_html=True)\n\nuploaded_file = st.file_uploader(\"Choose an image...\", type=(\"jpeg\",\"jpg\",\"png\"))\nif uploaded_file is not None:\n    image = Image.open(uploaded_file)\n    st.image(image, caption='Uploaded Image.', use_column_width=True)\n    st.write(\"\")\n    st.write(\"Classifying...\")\n    converter = predict(uploaded_file)\n    st.write('I THINK:', converter)\n\n\n\n\n","repo_name":"SULEEE-AI/Streamlit-Heroku-Malaria-Classification","sub_path":"upload.py","file_name":"upload.py","file_ext":"py","file_size_in_byte":892,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"40567776422","text":"\"\"\"\nAuthor: Alex Walter\nDate: Aug 29, 2018\n\nThis code is for controlling/dithering the Newport Conex-AG-M100D stage in the MEC fore-optics\n\n\"\"\"\nfrom __future__ import print_function\n\nimport time\nfrom threading import RLock, Thread\n\nimport numpy as np\nimport requests\nimport serial\nfrom flask_restful import Api, Resource, reqparse\n\nimport mkidcore\nfrom mkidcore.corelog import create_log, getLogger\n\nTIMEOUT = 2.0  # timeout for request post\nCONEX_COM_PORT = \"COM9\"\nCONEX_SERVER_PORT = 50001\n\n\nclass Conex():\n    \"\"\"\n    This class actually talks with the conex mount over the pyserial connection\n    \"\"\"\n\n    def __init__(self, port=\"COM9\", baudrate=912600, bytesize=serial.EIGHTBITS,\n                 stopbits=serial.STOPBITS_ONE, timeout=1., xonxoff=True, controllerNum=1):\n\n        self._rlock = RLock()\n        # Generally 1. This is for when you daisy chain multiple conex stages together\n        self.ctrlN = controllerNum\n\n        self.port = port\n        self.baudrate = baudrate\n        self.bytesize = bytesize\n        self.stopbits = stopbits\n        self.timeout = timeout\n        self.xonxoff = xonxoff\n        self.u_lowerLimit = -np.inf\n        self.v_lowerLimit = -np.inf\n        self.u_upperLimit = np.inf\n        self.v_upperLimit = np.inf\n\n        self._started = 0\n        try:\n            self._device = serial.Serial(port=self.port, baudrate=self.baudrate, bytesize=self.bytesize,\n                                         stopbits=self.stopbits, timeout=self.timeout, xonxoff=self.xonxoff)\n            # self._started = 1\n            time.sleep(0.1)  # wait for port to open\n\n            q = [self.query(q) for q in ('SLU?', 'SLV?', 'SRU?', 'SRV?')]\n            f = lambda x: float(x[4:-2])\n            self.u_lowerLimit = f(q[0])\n            self.v_lowerLimit = f(q[1])\n            self.u_upperLimit = f(q[2])\n            self.v_upperLimit = f(q[3])\n        except serial.SerialException:\n            getLogger('conex').debug(\"Could not connect to Conex Mount\", exc_info=True)\n            raise\n\n        getLogger('conex').debug(\"Connected to Conex Mount\")\n\n    def close(self):\n        try:\n            getLogger('conex').debug(\"Disconnecting Serial port\")\n            self._device.close()\n        except:\n            pass\n\n    @property\n    def status(self):\n        \"\"\"\n        Get the status\n        syntax: \"xxTSabcdef\\r\\n\"\n                xx - the controller number\n                TS - the command\n                abcd - error code\n                ef - status\n\n        \"\"\"\n        status = self.query('TS')\n        getLogger('conex').debug(\"Status: \" + status[7:-2])\n        getLogger('conex').debug(\"Err State: \" + status[3:-4])\n        return status.strip()\n\n    def write(self, command):\n        \"\"\"\n        Send an ascii command to the conex controller\n        Format is: xxCCnn\\r\\n\n                   xx is the controller number (usually 1 unless you daisy chain multiple conex stages)\n                   CC is the command\n                   nn is the option input for the command\n                   \\r\\n is the terminator character letting Conex know you've finished sending the command\n\n        This is a blocking function. It will try to write to the conex until it\n        succeeds or the write_timeout is reached\n\n        INPUT:\n            command - The two letter ascii command (CC above)\n        \"\"\"\n        with self._rlock:\n            self._device.write('{}{}\\r\\n'.format(self.ctrlN, command).encode())\n\n    def read(self, bufferSize=1):\n        \"\"\"\n        read from the port buffer\n\n        This is a blocking function. It will try to read until\n        it sees the end line characters \\r\\n or it times out.\n\n        INPUT:\n            bufferSize - size of return string in bytes\n                         If you don't know the exact size, leave as 1.\n                         It'll keep adding onto the buffer until it reaches the \\r\\n characters\n        \"\"\"\n        with self._rlock:\n            bufferSize = abs(bufferSize)\n            if not bufferSize:\n                return ''\n            ret = self._device.read(\n                bufferSize).decode()  # blocks until at least buffersize bytes in receive buffer (or timeout)\n            if not ret:\n                return ret\n            while ret[-2:] != '\\r\\n':  # reads until end of line\n                # getLogger('conex').debug('Got \"{}\"'.format(new))\n                new = self._device.read(self._device.in_waiting).decode()\n                ret += new\n            return ret\n\n    def query(self, command, bufferSize=1):\n        \"\"\"\n        Send an ascii command to the conex controller\n        Read the output\n\n        INPUTS:\n            command - see write()\n            bufferSize - see read()\n        OUTPUTS:\n            the return buffer\n        \"\"\"\n        with self._rlock:\n            self._device.reset_output_buffer()  # abandon any command it's currently sending\n            self._device.reset_input_buffer()  # clear the input buffer\n            self.write(command)\n            self._device.flush()  # wait for command to finish writing\n            return self.read(bufferSize)\n\n    @property\n    def limits(self):\n        \"\"\"\n        The hardware limit for u, v in degrees\n\n        OUTPUT:\n            dictionary with keys (umin, umax, vmin, vmax)\n        \"\"\"\n        return dict(umin=self.u_lowerLimit, vmin=self.v_lowerLimit,\n                    umax=self.u_upperLimit, vmax=self.v_upperLimit)\n\n    def ready(self):\n        \"\"\"\n        Checks the status of the conex\n\n        returns True if it's ready for another command (like move)\n        returns false if not ready\n        If there was an error it raises an IOError\n        \"\"\"\n        with self._rlock:\n            self._device.flush()  # wait for last command to finish sending\n            self._device.reset_input_buffer()  # clear any return values in the buffer\n            self.write('TS')\n            # self.conex.flush()\n            status = self._device.read(11)  # blocking read until 11 bytes are returned\n\n            err = status[3:7]  # hex error code\n            state = status[7:9]  # decimal state\n            if err == '0020':\n                raise IOError('Motion Time out')\n            elif int(err, 16) > 0:\n                raise IOError('Unknown Err - ' + err)\n\n            return int(state) in (32, 33, 34, 35, 36)\n\n    def move(self, pos, blocking=False, timeout=5.):\n        \"\"\"\n        Move mirror to new position\n\n        INPUTS:\n            pos - [U, V] tuple position in degrees (Conex truncates this at 3 decimal places)\n            blocking - If True then don't return until move is complete\n            timeout - error out if it takes too long to complete move\n                      ignored if not blocking\n                      For some reason it needs a long time even though the move itself is fast\n        \"\"\"\n        with self._rlock:\n            if not self.inBounds(pos):\n                raise IOError('Target position outside of limits. Aborted move.')\n            self.write('PAU' + str(pos[0]))\n            self._device.flush()  # wait until write command finishes sending\n            self.write('PAV' + str(pos[1]))  # Conex is capable of moving both axes at once!\n            if blocking: self._device.flush()\n        if blocking:  # don't lock the resource while waiting for move to finish. This allows the stop() command\n            t = time.time()\n            while not self.ready():\n                if time.time() - t > timeout:\n                    status = self.query('TS')\n                    raise IOError(\"Move Abs timed out. Status: \" + status[:-2])\n                time.sleep(0.0001)\n\n    def stop(self):\n        \"\"\"\n        Aborts connex mount move\n        \"\"\"\n        with self._rlock:\n            self.write('ST')\n\n    def inBounds(self, target):\n        \"\"\"\n        Check that the target is within the movement limits\n\n        INPUT:\n            target - [U,V] tuple position in degrees\n        \"\"\"\n        return (self.u_lowerLimit <= target[0] <= self.u_upperLimit and\n                self.v_lowerLimit <= target[1] <= self.v_upperLimit)\n\n    def home(self, blocking=False):\n        self.move((0, 0), blocking=blocking)\n\n    def position(self):\n        \"\"\" Returns the pos [U,V] in degrees \"\"\"\n        return float(self.query('TPU')[4:-2]), float(self.query('TPV')[4:-2])\n\n\nclass ConexManager():\n    \"\"\"\n    This class manages the Conex() object\n\n    It implements a thread safe dither routine among other things\n\n    The self.state attribute is a tuple\n        self.state[0] was the last state\n        self.state[1] is the current state\n\n    Posible states:\n        'Unknown', 'Offline', 'Idle', 'Stopped', 'Moving ...', 'Dither ...', 'Error ...'\n    \"\"\"\n\n    def __init__(self, port):\n        self.conex = Conex(port=port)\n        self._completed_dithers = []  # list of completed dithers\n        self._movement_thread = None  # thread for moving/dithering\n        self._halt_dither = True\n        self._rlock = RLock()\n        self._startedMove = 0  # number of times start_move was called (not dither). Reset in queryMove and start_dither\n        self._completedMoves = 0  # number of moves completed (not dither)\n\n        self.state = ('Unknown', 'Unknown')\n        try:\n            if self.conex.ready(): self._updateState('Idle')\n        except:\n            pass\n        self.cur_status = self.status()\n\n    def queryMove(self):\n        \"\"\"\n        Checks to see if move completed\n\n        It should be thread safe. Even if you hit the move button several times really fast\n\n        OUTPUTS:\n            dictionary {'completed':True/False, 'status':self.cur_status}\n        \"\"\"\n        if self._completedMoves > 0:  # don't lock if no moves completed. Reading is thread safe\n            with self._rlock:  # if at least one move completed then lock\n                if self._completedMoves > 0:  # need to check again for thread safety (maybe started two moves but only 1 completed)\n                    self._completedMoves -= 1\n                    self._startedMove -= 1\n                    self._startedMove = max(0, self._startedMove)\n                    return {'completed': True, 'status': self.cur_status}\n        return {'completed': False, 'status': self.cur_status}\n\n    def queryDither(self):\n        \"\"\"\n        returns the dictionary containing information about an ongoing or completed dither\n\n        keys:\n            status - The current status of the conex manager\n            estTime - ?? Not implemented right now\n            dither - A dictionary containing the oldest completed dither that hasn't been popped yet\n                     see dither() output\n                     If no completed dithers then None\n            completed - True or False\n        \"\"\"\n        dith = None\n        estTime = 0\n        completed = False\n        if len(self._completed_dithers) > 0:  # Reading is thread safe\n            with self._rlock:  # only lock if at least one dither completed\n                try:\n                    dith = self._completed_dithers.pop(0)\n                    completed = True\n                except IndexError:\n                    pass\n        if dith is None:  # check if a dither was popped\n            estTime = time.time() + 1  # estimated unix time of when dither will complete\n        return {'status': self.cur_status, 'estTime': estTime, 'dither': dith, 'completed': completed}\n\n    def _updateState(self, newState):\n        with self._rlock:\n            self.state = (self.state[1], newState)\n\n    def status(self):\n        pos = (np.NaN, np.NaN)\n        status = ''\n        try:\n            status = self.conex.status\n            pos = self.conex.position()\n            # getLogger('ConexManager').debug(\"Conex: {} @ pos {}\".format(status,pos))\n        except (IOError, serial.SerialException):\n            getLogger('ConexManager').error('Unable to get conex status', exc_info=True)\n            self._halt_dither = True\n            self._updateState('Offline')\n        return {'state': self.state, 'pos': pos, 'conexstatus': status, 'limits': self.conex.limits}\n\n    def stop(self, wait=False):\n        \"\"\"\n        stops the current movement or dither\n\n        if wait is False then it forcibly writes to the conex to tell it to stop motion\n\n        after that it waits for the movement thread to finish\n        \"\"\"\n        getLogger('ConexManager').debug('stopping conex')\n\n        if self._movement_thread is not None and self._movement_thread.is_alive():\n            with self._rlock:\n                self._halt_dither = True\n                if not wait:\n                    self.conex.stop()  # puts conex in ready state so that _movement thread will finish\n                self._updateState('Stopped')\n                self.cur_status = self.status()\n            self._movement_thread.join()  # not in rlock\n            with self._rlock:\n                self.cur_status = self.status()  # could change in other thread\n        else:\n            with self._rlock:\n                self.cur_status = self.status()\n        return self.cur_status\n\n    def start_dither(self, dither_dict):\n        \"\"\"\n        Starts dither in a new thread\n        \"\"\"\n        getLogger('ConexManager').debug('starting dither')\n        self.stop(wait=False)  # stop whatever we were doing before (including a previous dither)\n        with self._rlock:\n            self.cur_status = self.status()\n            if self.cur_status['state'] == 'Offline': return False\n            self._halt_dither = False\n            self._startedMove = 0\n        self._preDitherPos = self.cur_status['pos']\n        self._movement_thread = Thread(target=self.dither_two_point, args=(dither_dict,), name=\"Dithering thread\")\n        self._movement_thread.daemon = True\n        self._movement_thread.start()\n        return True\n\n    def dither_two_point(self, dither_dict):\n        \"\"\"\n        INPUTS:\n            dither_dict - dictionary with keys:\n                        startx: (float) start x loc in conex degrees\n                        endx: (float) end x loc\n                        starty: (float) start y loc\n                        endy: (float) end y loc\n                        n: (int) number of steps in square grid\n                        t: (float) dwell time in seconds\n                        subStep: (float) degrees to offset for subgrid pattern\n                        subT: (float) dwell time for subgrid\n\n                        subStep and subT are optional\n\n        appends a dictionary to the self._completed_dithers attribute\n            keys - same as dither_dict but additionally\n                   it has keys (xlocs, ylocs, startTimes, endTimes)\n\n        \"\"\"\n        points = dither_two_point_positions(dither_dict['startx'], dither_dict['starty'], dither_dict['stopx'],\n                                            dither_dict['stopy'], dither_dict['n'])\n\n        subDither = 'subStep' in dither_dict.keys() and dither_dict['subStep'] > 0 and \\\n                    'subT' in dither_dict.keys() and dither_dict['subT'] > 0\n\n        x_locs = []\n        y_locs = []\n        startTimes = []\n        endTimes = []\n        for p in points:\n            startTime, endTime = self._dither_move(p[0], p[1], dither_dict['t'])\n            if startTime is not None:\n                x_locs.append(self.cur_status['pos'][0])\n                y_locs.append(self.cur_status['pos'][1])\n                startTimes.append(startTime)\n                endTimes.append(endTime)\n            if self._halt_dither: break\n\n            # do sub dither if neccessary\n            if subDither:\n                x_sub = [-dither_dict['subStep'], 0, dither_dict['subStep'], 0]\n                y_sub = [0, dither_dict['subStep'], 0, -dither_dict['subStep']]\n                for i in range(len(x_sub)):\n                    if self.conex.inBounds((p[0] + x_sub[i], p[1] + y_sub[i])):\n                        startTime, endTime = self._dither_move(p[0] + x_sub[i], p[1] + y_sub[i], dither_dict['subT'])\n                        if startTime is not None:\n                            x_locs.append(self.cur_status['pos'][0])\n                            y_locs.append(self.cur_status['pos'][1])\n                            startTimes.append(startTime)\n                            endTimes.append(endTime)\n                    if self._halt_dither: break\n            if self._halt_dither: break\n\n        # Dither has completed (or was stopped prematurely)\n        if not self._halt_dither:  # no errors and not stopped\n            self.move(*self._preDitherPos)\n            with self._rlock:\n                if not self._halt_dither:  # still no errors nor stopped\n                    self._updateState(\"Idle\")\n                self.cur_status = self.status()\n\n        dith = dither_dict.copy()\n        dith['xlocs'] = x_locs  # could be empty if errored out or stopped too soon\n        dith['ylocs'] = y_locs\n        dith['startTimes'] = startTimes\n        dith['endTimes'] = endTimes\n        with self._rlock:\n            self._completed_dithers.append(dith)\n\n\n    def dither(self, dither_dict):\n        \"\"\"\n        INPUTS:\n            dither_dict - dictionary with keys:\n                        startx: (float) start x loc in conex degrees\n                        endx: (float) end x loc\n                        starty: (float) start y loc\n                        endy: (float) end y loc\n                        n: (int) number of steps in square grid\n                        t: (float) dwell time in seconds\n                        subStep: (float) degrees to offset for subgrid pattern\n                        subT: (float) dwell time for subgrid\n\n                        subStep and subT are optional\n\n        appends a dictionary to the self._completed_dithers attribute\n            keys - same as dither_dict but additionally\n                   it has keys (xlocs, ylocs, startTimes, endTimes)\n\n        \"\"\"\n        # Ennabling single direction sweeps\n        if dither_dict['startx'] == dither_dict['endx']:\n            x_list = np.linspace(dither_dict['startx'], dither_dict['endx'], 1)\n        else:\n            x_list = np.linspace(dither_dict['startx'], dither_dict['endx'], dither_dict['n'])\n        if dither_dict['starty'] == dither_dict['endy']:\n            y_list = np.linspace(dither_dict['starty'], dither_dict['endy'], 1)\n        else:\n            y_list = np.linspace(dither_dict['starty'], dither_dict['endy'], dither_dict['n'])\n\n        subDither = 'subStep' in dither_dict.keys() and dither_dict['subStep'] > 0 and \\\n                    'subT' in dither_dict.keys() and dither_dict['subT'] > 0\n\n        x_locs = []\n        y_locs = []\n        startTimes = []\n        endTimes = []\n        for x in x_list:\n            for y in y_list:\n                startTime, endTime = self._dither_move(x, y, dither_dict['t'])\n                if startTime is not None:\n                    x_locs.append(self.cur_status['pos'][0])\n                    y_locs.append(self.cur_status['pos'][1])\n                    startTimes.append(startTime)\n                    endTimes.append(endTime)\n                if self._halt_dither: break\n\n                # do sub dither if neccessary\n                if subDither:\n                    x_sub = [-dither_dict['subStep'], 0, dither_dict['subStep'], 0]\n                    y_sub = [0, dither_dict['subStep'], 0, -dither_dict['subStep']]\n                    for i in range(len(x_sub)):\n                        if self.conex.inBounds((x + x_sub[i], y + y_sub[i])):\n                            startTime, endTime = self._dither_move(x + x_sub[i], y + y_sub[i], dither_dict['subT'])\n                            if startTime is not None:\n                                x_locs.append(self.cur_status['pos'][0])\n                                y_locs.append(self.cur_status['pos'][1])\n                                startTimes.append(startTime)\n                                endTimes.append(endTime)\n                        if self._halt_dither: break\n                if self._halt_dither: break\n            if self._halt_dither: break\n\n        # Dither has completed (or was stopped prematurely)\n        if not self._halt_dither:  # no errors and not stopped\n            self.move(*self._preDitherPos)\n            with self._rlock:\n                if not self._halt_dither:  # still no errors nor stopped\n                    self._updateState(\"Idle\")\n                self.cur_status = self.status()\n\n        dith = dither_dict.copy()\n        dith['xlocs'] = x_locs  # could be empty if errored out or stopped too soon\n        dith['ylocs'] = y_locs\n        dith['startTimes'] = startTimes\n        dith['endTimes'] = endTimes\n        with self._rlock:\n            self._completed_dithers.append(dith)\n\n    def _dither_move(self, x, y, t):\n        \"\"\"\n            Helper function for dither()\n\n            The state after this function call will be one of:\n                \"error: ...\" - If there there was an error during the move\n                \"processing\" - If everything worked\n        \"\"\"\n        polltime = 0.1  # wait for dwell time but have to check if stop was pressed periodically\n        self.move(x, y)\n        if self._halt_dither:\n            return None, None  # Stopped or error during move\n        self._updateState(\"Dither dwell for {:.1f} seconds\".format(t))\n        # dwell at position\n        startTime = time.time()\n        dwell_until = startTime + t\n        endTime = time.time()\n        with self._rlock:\n            self.cur_status = self.status()\n        while not self._halt_dither and endTime < dwell_until:\n            sleep = min(polltime, dwell_until - endTime)\n            time.sleep(max(sleep, 0))\n            endTime = time.time()\n        return startTime, endTime\n\n    def start_move(self, x, y):\n        \"\"\"\n        Starts move in new thread\n        \"\"\"\n        self.stop(\n            wait=False)  # If the user wants to move, then forcibly stop whatever we were doing before (indcluding dithers)\n        with self._rlock:\n            self.cur_status = self.status()\n            if self.cur_status['state'] == 'Offline': return False\n            self._startedMove += 1\n        # getLogger('ConexManager').error(\"Starting move to {:.2f}, {:.2f}\".format(x,y))\n        self._movement_thread = Thread(target=self.move, args=(x, y,),\n                                       name='Move to ({}, {})'.format(x, y))\n        self._movement_thread.daemon = True\n        self._movement_thread.start()\n\n        return True\n\n    def move(self, x, y):\n        \"\"\"\n        Tells conex to move and collects errors\n        \"\"\"\n        self._updateState('Moving to {:.2f}, {:.2f}'.format(x, y))\n        try:\n            self.conex.move((x, y), blocking=True)  # block until conex is done moving (or stopped)\n            if self._startedMove > 0:\n                self._updateState('Idle')\n            getLogger('ConexManager').debug('moved to ({}, {})'.format(x, y))\n        except (IOError, serial.SerialException) as e:  # on timeout it raise IOError\n            self._updateState('Error: move to {:.2f}, {:.2f} failed'.format(x, y))\n            self._halt_dither = True\n            getLogger('ConexManager').error('Error on move', exc_info=True)\n        except:  # I dont think this should happen??\n            self._updateState('Error: move to {:.2f}, {:.2f} failed'.format(x, y))\n            self._halt_dither = True\n            getLogger('ConexManager').error('Unexpected error on move', exc_info=True)\n        if self._startedMove > 0:\n            with self._rlock:\n                self.cur_status = self.status()\n                self._completedMoves += 1\n\n\nclass ConexAPI(Resource):\n    \"\"\"\n    class that defines the possible commands from the POST request\n    \"\"\"\n\n    def post(self):\n        \"\"\"\n        argument data is sent in a json blob dictionary\n\n        Arguments need to include the command type:\n            ie. status', 'move', 'dither', 'stop', 'queryMove', 'queryDither'\n        Additional arguments are passed to the function as required\n        \"\"\"\n        parser = reqparse.RequestParser()\n        choices = ('status', 'move', 'dither', 'stop', 'queryMove', 'queryDither')\n        parser.add_argument('command', type=str, required=True, choices=choices,\n                            help='Action', location='json')\n        args = parser.parse_args()\n\n        if args.command == 'stop':\n            getLogger('ConexManager').info('Stopping')\n            ret = conex_manager.stop()\n        elif args.command == 'status':\n            getLogger('ConexManager').info('Status')\n            ret = conex_manager.status()\n        elif args.command == 'move':\n            getLogger('ConexManager').info('Moving')\n            parser.add_argument('x', type=float, required=True,\n                                help='X angle', location='json')\n            parser.add_argument('y', type=float, required=True,\n                                help='Y angle', location='json')\n            args = parser.parse_args()\n            ret = conex_manager.start_move(args.x, args.y)\n        elif args.command == 'dither':\n            parser.add_argument('dither_dict', type=dict, required=True,\n                                help='dither dict', location='json')\n            args = parser.parse_args()\n            getLogger('ConexManager').info('Dithering')\n            ret = conex_manager.start_dither(args.dither_dict)\n        elif args.command == 'queryDither':\n            # getLogger('ConexManager').info('Query Dither')\n            ret = conex_manager.queryDither()\n        elif args.command == 'queryMove':\n            # getLogger('ConexManager').info('Query Move')\n            ret = conex_manager.queryMove()\n        if args.command in choices:\n            return ret, 200\n        else:\n            getLogger('ConexManager').error('Unknown command: ' + str(args.command))\n            return None, 400\n\n\ndef dither(dither_dict, address='http://localhost:50001', timeout=TIMEOUT):\n    \"\"\"\n    Client side function: Tells conex mount to start a dither\n\n    INPUTS:\n        dither_dict - see conex_manager.dither()\n\n    Returns:\n        HTTP status code\n    \"\"\"\n    req = {'command': 'dither', 'dither_dict': dither_dict}\n    r = requests.post(address + '/conex', json=req, timeout=timeout)\n    return r.json()\n\n\ndef move(x, y, address='http://localhost:50001', timeout=TIMEOUT):\n    \"\"\"\n    Client side function: Tells conex mount to move to position x,y (in degrees)\n\n    Returns:\n        HTTP status code\n    \"\"\"\n    req = {'command': 'move', 'x': x, 'y': y}\n    r = requests.post(address + '/conex', json=req, timeout=timeout)\n    return r.json()\n\n\ndef stop(address='http://localhost:50001', timeout=TIMEOUT):\n    \"\"\"\n    Client side function: Tells conex mount to stop all movement\n\n    Returns:\n        dictionary: {'state':self.state, 'pos':pos, 'conexstatus':status, 'limits':self.conex.limits}\n        HTTP status code\n    \"\"\"\n    r = requests.post(address + '/conex', json={'command': 'stop'}, timeout=timeout)\n    return r.json()\n\n\ndef status(address='http://localhost:50001', timeout=TIMEOUT):\n    \"\"\"\n    Client side function: Gets current status of connex mount\n\n    Returns:\n        dictionary: {'state':self.state, 'pos':pos, 'conexstatus':status, 'limits':self.conex.limits}\n        HTTP status code\n    \"\"\"\n    try:\n        r = requests.post(address + '/conex', json={'command': 'status'}, timeout=timeout)\n        return r.json()\n    except requests.exceptions.ConnectionError:\n        return {'state': 'Offline: HTTPConnectionError', 'pos': (0, 0), 'conexstatus': None, 'limits': None}\n\n\ndef queryDither(address='http://localhost:50001', timeout=TIMEOUT):\n    \"\"\"\n    Client side function: Checks if the latest dither has completed\n\n    Returns:\n        dictionary: {'completed':True/False, 'status':self.cur_status, 'estTime': estTime, 'dither':dith}\n                    dith is a dictionary containing the last dither info (or None if not completed yet)\n                    status is a dictionary like {'state':self.state, 'pos':pos, 'conexstatus':status, 'limits':self.conex.limits}\n        HTTP status code\n    \"\"\"\n    r = requests.post(address + '/conex', json={'command': 'queryDither'}, timeout=timeout)\n    return r.json()\n\n\ndef queryMove(address='http://localhost:50001', timeout=TIMEOUT):\n    \"\"\"\n    Client side function: Checks if the latest move has completed\n\n    Returns:\n        dictionary: {'completed': True/False, 'status':self.cur_status}\n                    status is a dictionary like {'state':self.state, 'pos':pos, 'conexstatus':status, 'limits':self.conex.limits}\n        HTTP status code\n    \"\"\"\n    r = requests.post(address + '/conex', json={'command': 'queryMove'}, timeout=timeout)\n    return r.json()\n\n\ndef dither_two_point_positions(start_x, start_y, stop_x, stop_y, user_n_steps, single_pixel_move=0.015):\n    if user_n_steps == 1:\n        getLogger(__name__).error('Number of steps must be greater than one!')\n        return\n    if stop_y == start_y and stop_x == start_x:\n        getLogger(__name__).error('No movement specified in x or y')\n        return\n\n    n_steps = user_n_steps - 1\n    points = []\n    interval_x = ((stop_x - (0.5 * single_pixel_move)) - start_x) / (n_steps / 2)\n    interval_y = ((stop_y - (0.5 * single_pixel_move)) - start_y) / (n_steps / 2)\n\n    x_list = np.arange(start_x, stop_x, interval_x)\n    y_list = np.arange(start_y, stop_y, interval_y)\n    if start_x == stop_x:\n        x_list = np.zeros(n_steps)\n    if start_y == stop_y:\n        y_list = np.zeros(n_steps)\n\n    offset_x = np.round(x_list + (interval_x / 2 + 0.5 * single_pixel_move), 3)\n    offset_y = np.round(y_list + (interval_y / 2 + 0.5 * single_pixel_move), 3)\n    x_grid = np.round(np.sort(np.concatenate((x_list, offset_x[offset_x <= round(stop_x, 4)]))), 3)\n    y_grid = np.round(np.sort(np.concatenate((y_list, offset_y[offset_y <= round(stop_y, 4)]))), 3)\n    cycle = 1\n\n    if not (np.all(x_grid == 0) or np.all(y_grid == 0)):\n        points.append((start_x, start_y))\n\n    while cycle <= n_steps:\n        for i in range(cycle + 1):\n            rev = cycle - i\n            points.append((x_grid[i], y_grid[rev]))\n        cycle += 1\n    cycle = 1\n    second_points = []\n    while cycle <= (n_steps - 1):\n        for i in range(cycle + 1):\n            rev = cycle - i\n            second_points.insert(0, (x_grid[-1 - i], y_grid[-1 - rev]))\n        cycle += 1\n    points = points + second_points\n    if not (np.all(x_grid == 0) or np.all(y_grid == 0)):\n        points.append((x_grid[-1], y_grid[-1]))\n    return points\n\nif __name__ == '__main__':\n    from flask import Flask\n\n    create_log('ConexManager',\n               console=True, mpsafe=True, propagate=False,\n               fmt='%(asctime)s %(levelname)s %(message)s',\n               level=mkidcore.corelog.DEBUG)\n    create_log('conex',\n               console=True, mpsafe=True, propagate=False,\n               fmt='%(asctime)s %(levelname)s %(message)s',\n               level=mkidcore.corelog.DEBUG)\n\n    app = Flask(__name__, static_url_path=\"\")\n    flasklog = getLogger('werkzeug')\n    flasklog.setLevel(mkidcore.corelog.ERROR)\n    api = Api(app)\n    api.add_resource(ConexAPI, '/conex', endpoint='conex')\n\n    conex_manager = ConexManager(port=CONEX_COM_PORT)\n    app.run(host='0.0.0.0', debug=False, port=CONEX_SERVER_PORT)\n    conex_manager.conex.close()\n    time.sleep(0.1)  # wait for conex to close\n","repo_name":"MazinLab/MKIDReadout","sub_path":"mkidreadout/hardware/conex.py","file_name":"conex.py","file_ext":"py","file_size_in_byte":31321,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"81"}
+{"seq_id":"32436665086","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Thu May 14 10:28:45 2020\r\n\r\n@author: 98kes\r\n\"\"\"\r\n\r\nimport networkx as nx\r\nG = nx.DiGraph()\r\n\r\nGene = [1,2,3,4]\r\nG.add_nodes_from(Gene)\r\nG.add_edges_from([(1,3)],signal='i')\r\nG.add_edges_from([(2,3),(3,4)],signal='a')\r\nnx.write_gml(G, \"test.gml\")\r\n\r\nGeneDict={1:None,2:[\"3P\",\"6P\",\"2P\",\"3P-6P-2P\"],3:[\"3P\",\"5P\",\"3P-4P\"],4:None}\r\n\r\nremoveNodeList=[]\r\n\r\nfor key in GeneDict.keys():\r\n    if GeneDict[key] != None:\r\n        removeNodeList.append(key)\r\n\r\n#for rm in removeNodeList:\r\n#    for value in GeneDict[rm]:\r\n#        if '-' not in value:\r\n#            for start in G.predecessors(rm):\r\n#                edge1=G.get_edge_data(start,rm)[\"signal\"]\r\n#                if edge1==\"i\":\r\n#                    G.add_edge(start,value,signal=\"i\")\r\n#                else:\r\n#                    G.add_edge(start,value,signal=\"a\")\r\n#            for finish in G.successors(rm):\r\n#                edge2=G.get_edge_data(rm,finish)[\"signal\"]\r\n#                if edge2==\"i\":\r\n#                    G.add_edge(value,finish,signal=\"i\")\r\n#                else:\r\n#                    G.add_edge(value,finish,signal=\"a\")\r\n\r\n\r\n\r\n\r\nfor rm in removeNodeList:\r\n    addNodeList=[]\r\n    for value in GeneDict[rm]:\r\n        if '-' in value:\r\n            flag = True\r\n            for element in value.split('-'):\r\n                if element not in GeneDict[rm]:\r\n                    flag= False\r\n                    break\r\n            if flag:\r\n                addNodeList.append(value)\r\n    if addNodeList != []:\r\n        for add in addNodeList:\r\n            for element in add.split('-'):\r\n                G.add_edge(element,add,signal=\"a\")\r\n                for start in G.predecessors(rm):\r\n                    edge1=G.get_edge_data(start,rm)[\"signal\"]\r\n                    if edge1==\"i\":\r\n                        G.add_edge(start,element,signal=\"i\")\r\n                    else:\r\n                        G.add_edge(start,element,signal=\"a\")\r\n            for finish in G.successors(rm):\r\n                edge2=G.get_edge_data(rm,finish)[\"signal\"]\r\n                if edge2==\"i\":\r\n                    G.add_edge(add,finish,signal=\"i\")\r\n                else:\r\n                    G.add_edge(add,finish,signal=\"a\")\r\n    else:\r\n        for value in GeneDict[rm]:\r\n            if value not in addNodeList:\r\n                for start in G.predecessors(rm):\r\n                    edge1=G.get_edge_data(start,rm)[\"signal\"]\r\n                    if edge1==\"i\":\r\n                        G.add_edge(start,value,signal=\"i\")\r\n                    else:\r\n                        G.add_edge(start,value,signal=\"a\")\r\n                for finish in G.successors(rm):\r\n                    edge2=G.get_edge_data(rm,finish)[\"signal\"]\r\n                    if edge2==\"i\":\r\n                        G.add_edge(value,finish,signal=\"i\")\r\n                    else:\r\n                        G.add_edge(value,finish,signal=\"a\")\r\n            \r\n\r\nG.remove_nodes_from(removeNodeList)\r\n\r\nnx.write_gml(G, \"test1.gml\")\r\n                        \r\n#for rm in removeNodeList:\r\n#\tfor start in G.predecessors(rm):\r\n#\t\tedge1=G.get_edge_data(start,rm)['signal']\r\n#\t\tif edge1=='i':\r\n#\t\t\tfor element in rm.split('-'):\r\n#\t\t\t\tG.add_edge(start,element,signal='i')\r\n#\t\telse:\r\n#\t\t\tfor element in rm.split('-'):\r\n#\t\t\t\tG.add_edge(start,element,signal='a')\r\n#                \r\n#\tfor finish in G.successors(rm):\r\n#\t\tedge2=G.get_edge_data(rm,finish)['signal']\t\t\r\n#\t\tif edge2=='i':\r\n#\t\t\tfor element in rm.split('-'):\r\n#\t\t\t\tG.add_edge(element,finish,signal='i')\r\n#\t\telse:\r\n#\t\t\tfor element in rm.split('-'):\r\n#\t\t\t\tG.add_edge(element,finish,signal='a')\r\n#\tG.remove_node(rm)","repo_name":"Swapnil-Keshari/test","sub_path":"rewiring.py","file_name":"rewiring.py","file_ext":"py","file_size_in_byte":3621,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"18880506929","text":"import json\nimport logging\nfrom uuid import uuid4\n\nfrom game.game import Game, InvalidTurn\nfrom game.manager import Player\n\n\nlog = logging.getLogger(__file__)\n\n\nclass RoomError(Exception):\n    pass\n\n\nclass Room(object):\n    def __init__(self, manager):\n        self.manager = manager\n        self.uuid = str(uuid4())\n        self.game = Game()\n        self.players = [None, None]\n\n    async def connect(self, player: Player):\n        \"\"\"Connect a new player to the room\"\"\"\n        if self.is_running:\n            return \n\n        for index, p in enumerate(self.players):\n            if p is None:\n                self.players[index] = player\n                await self.send(player, 'game_enter', {\n                    'index': index, \n                    'game_uuid': self.uuid\n                })\n                break\n        \n        await self.manager.update_room(self)\n\n        await self.chat(f'{player.username} is connected!')\n\n        if self.is_running:\n            log.debug(f'Game started: {self.uuid}')\n            self.game.reset()            \n\n            await self.broadcast('game_start', self.to_dict())\n            await self.send_state()\n            await self.chat('Game started!')\n\n        await self.cycle(player)\n\n    @property\n    def is_running(self):\n        \"\"\"Check the game is running\"\"\"\n        return self.players[0] is not None and self.players[1] is not None\n\n    @property\n    def is_empty(self):\n        \"\"\"Check is the room is empty\"\"\"\n        return self.players[0] is None and self.players[1] is None\n\n    def to_dict(self):\n        \"\"\"Convert a room to the dict\"\"\"\n        players = {player.uuid: player.to_dict() for player in self.players if player is not None}\n        return {\n            'uuid': self.uuid,\n            'players': players,\n            'is_running': self.is_running,\n        }\n\n    async def send(self, player: Player, message: str, payload: dict):\n        \"\"\"Send a message to the user\"\"\"\n        try:\n            await player.websocket.send(json.dumps({\n                \"message\": message,\n                \"payload\": payload\n            }))\n        except Exception as E:\n            log.error(f'Send error: {E}')\n            await self.disconnect(player)\n\n    async def broadcast(self, message: str, payload: dict):\n        \"\"\"Broadcast a message to all players\"\"\"\n        for p in self.players:\n            if p is not None:\n                await self.send(p, message, payload)\n\n    async def send_state(self):\n        \"\"\"Send a state of the game to all users\"\"\"\n        board = []\n        for x, board_line in enumerate(self.game.board):\n            line = {\n                'line': x,\n                'values': []\n            }\n            for y, value in enumerate(board_line):\n                line['values'].append({\n                    'index': x + y,\n                    'x': x,\n                    'y': y,\n                    'value': value\n                })\n            board.append(line)\n\n        await self.broadcast('game_state', {\n                'board': board,\n                'score': self.game.score,\n                'order': self.game.order\n            })\n\n    async def turn(self, payload, player):\n        \"\"\"Process a turn\"\"\"\n        x = payload['x']\n        y = payload['y']\n        try:\n            self.game.turn(x, y, self.players.index(player))\n        except InvalidTurn:\n            await self.send(player, 'error', {\n                'type': 'invalid_turn',\n                'args': payload\n            })\n        else:\n            await self.send_state()\n\n    async def chat(self, text, player=None):\n        \"\"\"Send a message to the chat\"\"\"\n        await self.broadcast('game_chat', {\n            'author': player.username if player else None,\n            'text': text\n        })\n\n    async def cycle(self, player):\n        \"\"\"Main game cycle\"\"\"\n        try:\n            while True:\n                data = await player.websocket.recv()\n                try:\n                    message = json.loads(data)\n                except Exception as E:\n                    log.error(f'Invalid game message: {E}, {data}')\n                    continue\n                log.debug(f'Process message: {message} from {player.username}')\n\n                if message['method'] == 'chat':\n                    await self.chat(message['payload']['text'], player)\n                if message['method'] == 'turn' and self.is_running:\n                    await self.turn(message['payload'], player)\n                if message['method'] == 'exit':\n                    break\n        except Exception as E:\n            log.error(f'Room error: {E}')\n        finally:\n            await self.disconnect(player)\n\n    async def disconnect(self, player):\n        \"\"\"Disconnect a player\"\"\"\n        self.players[self.players.index(player)] = None\n        await self.chat(f'{player.username} disconnected')\n        await self.broadcast('game_stop', self.to_dict())\n        await self.manager.update_room(self)\n","repo_name":"EvilX/reversi","sub_path":"backend/game/room.py","file_name":"room.py","file_ext":"py","file_size_in_byte":4964,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"13031545866","text":"from random import sample\n\nfrom django.http import Http404\nfrom django.http import HttpResponseNotFound, HttpResponseServerError\nfrom django.shortcuts import render\n\nimport tours.data as mock_data\n\n\ndef get_min_max_value(sequence_of_dict, key):\n    def get_key(item):\n        return item[key]\n\n    return min(sequence_of_dict, key=get_key)[key], max(sequence_of_dict, key=get_key)[key]\n\n\ndef main_view(request):\n    context = {\n        'subtitle': mock_data.subtitle,\n        'description': mock_data.description,\n        'tours': dict(sample(mock_data.tours.items(), min(6, len(mock_data.tours)))),\n    }\n\n    return render(request, 'tours/index.html', context=context)\n\n\ndef departure_view(request, departure):\n    context = {}\n    try:\n        context['current_departure_humanize'] = mock_data.departures[departure]\n    except KeyError:\n        raise Http404\n    context['tours'] = {pk: tour for pk, tour in mock_data.tours.items() if tour['departure'] == departure}\n\n    context['tours_length'] = len(context['tours'])\n    context['min_price'], context['max_price'] = get_min_max_value(context['tours'].values(), 'price')\n    context['min_night'], context['max_night'] = get_min_max_value(context['tours'].values(), 'nights')\n\n    return render(request, 'tours/departure.html', context=context)\n\n\ndef tour_view(request, pk):\n    context = {}\n    try:\n        context['tour'] = mock_data.tours[pk]\n    except KeyError:\n        raise Http404\n\n    if 'departure_humanize' not in context['tour']:\n        context['tour']['departure_humanize'] = mock_data.departures[context['tour']['departure']]\n\n    return render(request, 'tours/tour.html', context=context)\n\n\ndef custom_handler404(request, exception):\n    return HttpResponseNotFound(\"404 Страница не найдена\")\n\n\ndef custom_handler500(request):\n    return HttpResponseServerError(\"500 Server Error\")\n","repo_name":"TitovRoman/stepik_tours_week1_week2","sub_path":"tours/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1870,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"18827481646","text":"import os\nfrom lib.XLog import XLog\nfrom lib.XQBittorrent import XQBittorrentApi\nfrom lib.XTorrent import XTorrent\n\n\n# 递归获取所有文件\ndef get_all_files(path):\n    files = []\n    os.chdir(path)\n    file_list = os.listdir()\n    for file in file_list:\n        file_path = os.path.join(path, file)\n        files.append(file_path)\n        if os.path.isdir(file_path):\n            try:\n                files.extend(get_all_files(file_path))\n            except PermissionError:\n                pass\n    return files\n\n\n# 获取第一层文件夹所有文件\ndef get_first_dir_files(path):\n    files = []\n    os.chdir(path)\n    file_list = os.listdir()\n    for file in file_list:\n        file_path = os.path.join(path, file)\n        files.append(file_path)\n    return files\n\n\nif __name__ == '__main__':\n    from config import qbtorrentcfg\n\n    logger = XLog()\n    logger.info(\"------------------开始运行------------------\")\n    torrent_path = qbtorrentcfg[\"torrent_path\"]\n    search_path = qbtorrentcfg[\"search_path\"]\n    torrents = os.listdir(torrent_path)\n    path_list = get_first_dir_files(search_path)\n\n    qbtorrent = XQBittorrentApi(qbtorrentcfg[\"host\"], qbtorrentcfg[\"username\"], qbtorrentcfg[\"password\"],\n                                qbtorrentcfg[\"tag\"])\n\n    for torrent in torrents:\n        if not os.path.isdir(torrent) and torrent.endswith('torrent'):\n            torrens_file = os.path.join(torrent_path, torrent)\n            x_torrent = XTorrent(torrens_file)\n            filename = x_torrent.get_path_name()\n            torrent_hash = x_torrent.get_hash()\n            for path in path_list:\n                path_name = path.split('\\\\')[-1]\n                if filename == path_name:\n                    try:\n                        save_path = '\\\\'.join(path.split('\\\\')[:-1])\n                        qbtorrent.add_torrent_to_run(torrens_file, save_path=save_path)\n                        qbtorrent.add_tag_to_torrent(torrent_hash)\n                        logger.info(f\"成功添加 {torrens_file}\")\n                    except Exception as e:\n                        logger.error(f\"{torrent} 任务已存在\")\n    logger.info(\"------------------运行结束------------------\")\n","repo_name":"JishuLiu6/PTEasy","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2201,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"81"}
+{"seq_id":"35541567918","text":"import plotly.express as px\r\nimport pandas as pd\r\n\r\nfrom dash import Dash, html, dcc\r\nfrom dash.dependencies import Input, Output, State\r\n\r\ndf = pd.read_csv('Datasets/data_reports_monthly.csv')\r\n\r\ndf['Trips Per Day'] = (df['Trips Per Day'].str.strip()).apply(lambda x: float(x.replace(',', '')))\r\n\r\ngraph_config = dict(\r\n    {\r\n        \"scrollZoom\": True,\r\n        \"displayModeBar\": True,\r\n        \"displaylogo\": False,\r\n        \"modeBarButtonsToRemove\": [\"zoom\", \"zoomin\", \"zoomout\"],\r\n    }\r\n)\r\n\r\napp = Dash(__name__)\r\n\r\napp.layout = html.Div([\r\n    dcc.Graph(\r\n        id='graph1', config=graph_config\r\n    ),\r\n    html.Div(\r\n        dcc.Slider(2010, 2020, 1,\r\n                   value=2010,\r\n                   id='slider',\r\n                   marks={i: '{}'.format(i) for i in range(2010, 2021)},\r\n\r\n                   ), style={\"width\": \"40%\"}\r\n    ),\r\n\r\n    dcc.Graph(\r\n        id='graph2', config=graph_config\r\n    ),\r\n])\r\n\r\n\r\n@app.callback(\r\n    Output('graph2', 'clickData'),\r\n    Output('graph1', 'figure'),\r\n    Output('graph2', 'figure'),\r\n    Input('slider', 'value'),\r\n    Input('graph2', 'clickData'),\r\n)\r\ndef update_scatter(value, clickData):\r\n    dff = df[pd.to_datetime(df['Month/Year']).dt.year == value]\r\n    dff1 = dff\r\n    if clickData:\r\n        class_filted = clickData['points'][0]['y']\r\n        dff1 = df[(pd.to_datetime(df['Month/Year']).dt.year == value) & (df['License Class'] == class_filted)]\r\n    # if clickPoint:\r\n    #     trace_number = clickPoint['points'][0]['curveNumber']\r\n    #     print(dff1[(dff1['Avg Minutes Per Trip']==clickPoint['points'][0]['x'])&(dff1['Trips Per Day']==clickPoint['points'][0]['y'])]['Month/Year'].values[0])\r\n    fig = px.scatter(dff1, x=\"Avg Minutes Per Trip\", y=\"Trips Per Day\", color=\"License Class\",\r\n                     hover_data=[\"Month/Year\"])\r\n    fig.update_traces(marker=dict(size=12))\r\n    grouped = dff.groupby('License Class')['Trips Per Day'].mean()\r\n    dff2 = pd.DataFrame({'License Class': grouped.index, 'Average Trips Per Day': grouped.values})\r\n    horizontal_bar = px.bar(dff2, y=\"License Class\", x=\"Average Trips Per Day\", color=\"License Class\")\r\n\r\n    return None, fig, horizontal_bar\r\n\r\n\r\nif __name__ == '__main__':\r\n    app.run_server(host='127.0.0.1', port=8080,debug=True)\r\n","repo_name":"SakibAmin/CSC474-FInal-Project","sub_path":"FHV indicators.py","file_name":"FHV indicators.py","file_ext":"py","file_size_in_byte":2269,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"36675370634","text":"# Zadanie 25. Tablica t[N] jest wypełniona liczbami naturalnymi. Skok z pola i-tego można wykonać na pola\n# o indeksach i+k, gdzie k jest czynnikiem pierwszym liczby t[i] (mniejszym od niej samej). Napisz funkcję,\n# która sprawdza, czy da się przejść z pola 0 do N-1 – jeśli się da, zwraca ilość skoków, jeśli się nie da, zwraca\n# -1.\n\n\ndef div(num):\n    i = 2\n    c_num = num\n    arr = []\n    while num > 1 and i < c_num:\n        if num % i == 0:\n            arr.append(i)\n            while num % i == 0:\n                num //= i\n        i += 1\n    \n    return arr\n\n\ndef rek(t, index = 0, jumps = 0):\n    if index >= len(t)-1:\n        if index == len(t)-1:\n            return jumps\n        return -1\n\n    divs = div(t[index])\n\n    for divisor in divs:\n        res = rek(t, index+divisor, jumps+1)\n        if res >= 0:\n            return res\n    \n    return -1\n\narr = [4,4,6,1,1,3,1]\n\nprint(rek(arr))","repo_name":"Wisien999/WDI2020","sub_path":"zestaw6/zad25.py","file_name":"zad25.py","file_ext":"py","file_size_in_byte":918,"program_lang":"python","lang":"pl","doc_type":"code","stars":5,"dataset":"github-code","pt":"81"}
+{"seq_id":"30748630826","text":"PUBLIC_IPS = 'public_ip_addresses'\nPRIVATE_IPS = 'private_ip_addresses'\nSECGROUPS = 'security_groups'\nSTACK_SECGROUPS = 'stack_security_groups'\nEXTRA_SECGROUPS = 'extra_security_groups'\nTAGS = 'tags'\nNAME = 'name'\nID = 'id'\nSCOPES = 'config_scopes'\nVARS = 'hostvars'\nGROUPS = 'groups'\nREGION = 'region_name'\nAZ = 'availability_zone'\nLAUNCH_TIMEOUT = 'launch_timeout_s'\nPOST_DELAY = 'post_launch_delay_s'\nSSH_KEY = 'ssh_key_name'\nPROVIDER = 'provider'\n\n# these are ansible magic vars\nINV_NAME = 'inventory_hostname'\nINV_NAME_SHORT = 'inventory_hostname_short'\nGROUP_NAMES = 'group_names'\nPRIVATE_IP = 'private_ip'\n\n#: Provides the server definition from the Bang config as a fact available to\n#: the playbooks.  E.g. in order to get access to the ``disk_image_id`` in a\n#: playbook:\n#:\n#: .. code-block:: jinja\n#:\n#:     {{bang_server_attributes.disk_image_id}}\n#:\nBANG_ATTRS = 'bang_server_attributes'\n","repo_name":"fr33jc/bang","sub_path":"bang/attributes/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":902,"program_lang":"python","lang":"en","doc_type":"code","stars":22,"dataset":"github-code","pt":"81"}
+{"seq_id":"73544401224","text":"# PCF8591 ADC/DAC Driver\n\nimport wiringpi\n\ndef readData(address, channel) :\n   i2c = wiringpi.I2C()\n   file = i2c.setupInterface(\"/dev/i2c-1\", address)\n   return i2c.readReg8(file, 0x40 + channel)\n\ndef writeData(address, value) :\n   i2c = wiringpi.I2C()\n   file = i2c.setupInterface(\"/dev/i2c-1\", address)\n   i2c.writeReg8(file, 0x40, value)\n   return\n","repo_name":"siddhesh-patkar/Proteus","sub_path":"VSM Studio/drivers/RaspberryPi/pcf8591.py","file_name":"pcf8591.py","file_ext":"py","file_size_in_byte":352,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"8634681690","text":"from django.db import models\nfrom django.utils import timezone\n\nclass local(models.Model):\n    rua = models.CharField(max_length=300, verbose_name='Rua', null=True, blank=True)\n    numero = models.PositiveIntegerField(verbose_name='Número', null=True, blank=True)\n    bairro = models.CharField(max_length=250, verbose_name='Bairro', null=True, blank=True)\n    cidade = models.CharField(max_length=250, verbose_name='Cidade', null=True, blank=True)\n    estado = models.CharField(max_length=50, verbose_name='Estado', null=True, blank=True)\n    cep = models.CharField(max_length=10, verbose_name='CEP', null=True, blank=True)\n    pais = models.CharField(max_length=100, verbose_name='País', null=True, blank=True)\n    lat = models.CharField(max_length=30, verbose_name='Latitude', null=True, blank=True)\n    lon = models.CharField(max_length=30, verbose_name='Longitude', null=True, blank=True)\n    tipo = models.ManyToManyField('esporte', verbose_name='Tipo', blank=True)\n     \n    @property\n    def place_id(self):\n        return 0\n    \n    class Meta:\n        verbose_name_plural = \"Locais\"\nESPORTE_CHOICES = (\n    (u\"Futebol\",u\"Futebol\"),\n    (u\"Futebol G. Sintetica\",u\"Futebol G. Sintetica\"),\n    (u\"Futebol Campo\",u\"Futebol Campo\"),\n    (u\"Volei Quadra\",u\"Voleu Quadra\"),\n)\nclass esporte(models.Model):    \n    tipo = models.CharField(max_length=30, verbose_name='Tipo', choices=ESPORTE_CHOICES, blank=True)\n    cor = models.CharField(max_length=10, verbose_name='Cor', null=True, blank=True)\n    icon_url = models.ImageField(verbose_name='Icone', upload_to=\"media/leaflet/images/\", max_length=100, default=\"media/leaflet/images/marker-icon.png\")\n\n    class Meta:\n        verbose_name_plural = \"Esportes\"  \n          \nclass agendamento(models.Model):\n    local = models.ForeignKey('Local', on_delete=models.CASCADE, null=True, blank=True)\n    data = models.DateField(verbose_name='Data', null=True, blank=True)\n    hora = models.TimeField(max_length=10, verbose_name='Hora', null=True, blank=True)\n    duracao = models.CharField(max_length=10, verbose_name='Duração', null=True, blank=True)\n    \n    class Meta:\n        verbose_name_plural = \"Agendamentos\"\n\n\n","repo_name":"MichaelAC13/EsporteRandom","sub_path":"app/controles/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":2167,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"81"}
+{"seq_id":"6850143757","text":"from flask import Flask, render_template, request, session, send_from_directory\nimport os\nimport myfitnesspal\nfrom mfp_tom import make_chart\n\napplication = Flask(__name__)\n\n@application.route('/')\ndef showMain():\n\treturn render_template(\"main.html\")\n\n@application.route('/login', methods=['POST'])\ndef loginAndCalculate():\n\t# print(request.form['username'])\n\t\n\ttry:\n\t\tclient = myfitnesspal.Client(request.form['username'], request.form['password'])\n\texcept ValueError:\n\t\treturn showMain()\n\n\tsession['logged_in'] = True\n\tnumDays = 7\n\tmake_chart(client, numDays)\n\tsession['chart_made'] = True\n\treturn showChart()\n\n@application.route('/chart')\ndef showChart():\n\treturn send_from_directory('.', 'calories.html')\n\nif __name__ == '__main__':\n\tapplication.secret_key = os.urandom(12)\n\tapplication.run(host='0.0.0.0')","repo_name":"tjohnsen8/mfp-data","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":809,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"37752632414","text":"from constants import Constants\n\n\nclass DotaPlayer(Constants):\n    def __init__(self, player_data):\n        super().__init__(type=\"dota\")\n        self.raw_player_data = player_data\n        self.id = player_data[\"account_id\"]\n        self.name = player_data[\"name\"]\n        self.hero_id = player_data[\"hero_id\"]\n        self.hero_name = self.dota_hero_ids[str(self.hero_id)][\"localized_name\"]\n\n    def update_data(self, data):\n        self.raw_update_data = data\n        if self.hero_id != data[\"hero_id\"]:\n            print(\"WARNING WRONG HERO ID \\n\" * 100)\n\n        self.player_slot = data[\"player_slot\"]\n        self.kills = data[\"kills\"]\n        self.deaths = data[\"death\"]\n        self.assists = data[\"assists\"]\n        self.last_hits = data[\"last_hits\"]\n        self.denies = data[\"denies\"]\n        self.gold = data[\"gold\"]\n        self.level = data[\"level\"]\n        self.gpm = data[\"gold_per_min\"]\n        self.xpm = data[\"xp_per_min\"]\n        self.ultimate_state = data[\"ultimate_state\"]\n        self.ultimate_cooldown = data[\"ultimate_cooldown\"]\n        self.item_list = [data[f\"item{i}\"] for i in range(6)]\n        self.respawn_timer = data[\"respawn_timer\"]\n        self.position_x = data[\"position_x\"]\n        self.position_y = data[\"position_y\"]\n        self.net_worth = data[\"net_worth\"]\n\n","repo_name":"Kichma91/discord_esport_tracker","sub_path":"player_objects/dota_player.py","file_name":"dota_player.py","file_ext":"py","file_size_in_byte":1301,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"25301986244","text":"\"\"\"\nUtility module for parsing command-line arguments\n\"\"\"\n\nfrom argparse import ArgumentParser\nfrom logging import INFO\n\nfrom yamlfix.config import Config\n\n\ndef parse_arguments(*args: str) -> Config:\n    \"\"\"\n    Parse arguments and configures what needs to be.\n    \"\"\"\n    argument_parser = ArgumentParser(\"yamlfix\", description=\"Format YAML files\")\n    argument_parser.add_argument(\n        \"paths\",\n        metavar=\"path\",\n        type=str,\n        nargs=\"*\",\n        help=\"Path to file or folder to format\",\n    )\n    argument_parser.add_argument(\n        \"-c\",\n        \"--check\",\n        action=\"store_true\",\n        default=False,\n        help=\"Do not reformat files, only check need for reformatting. \"\n        \"Returns an error code if a file needs to be reformatted.\",\n    )\n    logging_arg_group = argument_parser.add_mutually_exclusive_group()\n    logging_arg_group.add_argument(\n        \"-v\", \"--verbose\", action=\"store_true\", help=\"Enable debug logging\"\n    )\n    logging_arg_group.add_argument(\n        \"-q\",\n        \"--quiet\",\n        action=\"store_true\",\n        help=\"Don't emit non-error messages to stderr. Errors are still emitted; silence those with 2>/dev/null.\",\n    )\n    logging_arg_group.add_argument(\n        \"-l\",\n        \"--log_level\",\n        type=int,\n        default=INFO // 10,\n        help=\"Enable a specific level of logging (1: DEBUG, 5: CRITICAL, default: INFO)\",\n    )\n\n    return Config(argument_parser.parse_args(args).__dict__)\n","repo_name":"xavierhardy/yamlfix","sub_path":"yamlfix/config_parser.py","file_name":"config_parser.py","file_ext":"py","file_size_in_byte":1468,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"81"}
+{"seq_id":"6514835260","text":"import mysql.connector\n\nconn = mysql.connector.connect(\n\n    host=\"localhost\",\n    user=\"root\",\n    password=\"adm123\",\n    database=\"maria\"\n\n)\n\ncursor = conn.cursor()\n\ncreate_table_query = \"\"\"\nCREATE TABLE IF NOT EXISTS funcionarios (\n    id INT AUTO_INCREMENT PRIMARY KEY,\n    nome VARCHAR(255),\n    cargo VARCHAR(255)\n)\n\"\"\"\ncursor.execute(create_table_query)\nconn.commit()\n\ninsr = \"INSERT INTO funcionarios (nome, cargo) VALUES (%s, %s)\"\ndt = (\"Ismael\", \"Developer\")\ncursor.execute(insr, dt)\nconn.commit()\n\nconsulta_query = \"SELECT * FROM funcionarios\"\ncursor.execute(consulta_query)\n\n\nfor (x, nome, cargo) in cursor:\n    print(f\"ID: {x}, Nome: {nome}, Cargo: {cargo}\")\n\natualizar_dados = \"UPDATE funcionarios SET cargo = %s WHERE nome = %s\"\ncargoN = (\"Gerente\", \"Ismael\")\ncursor.execute(atualizar_dados, cargoN)\nconn.commit()\n\ndelete = \"DELETE FROM funcionarios WHERE id = %s AND nome = %s\"\nidtodel = (13, \"Ismael\")\nidtodel = tuple(idtodel)\n\ncursor.execute(delete, idtodel)\nconn.commit()\n\ncursor.close()\nconn.close()\n","repo_name":"IsmaelRK/Databases-Learning","sub_path":"Mariadb/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1020,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"39958252098","text":"#!/usr/bin/python\n\nimport os, sys\nimport json\nimport pprint\n\n\nclass add_race(object):\n\tdef __init__(self):\n\t\tself.languages = [\n\t\t\t'common',\n\t\t\t'elvish',\n\t\t\t'halfling',\n\t\t\t'giant',\n\t\t\t'orc',\n\t\t\t'goblin',\n\t\t\t'auran',\n\t\t\t'draconic',\n\t\t\t'kor silent speech',\n\t\t\t'merfolk',\n\t\t\t'arakocra',\n\t\t\t'drwarf',\n\t\t\t'drow hand signs',\n\t\t\t'abyssal',\n\t\t\t'infernal',\n\t\t\t'primordial',\n\t\t\t'vampire'\n\t\t\t]\n\t\tself.stats = [\n\t\t\t'strength',\n\t\t\t'dexterity',\n\t\t\t'constitution',\n\t\t\t'intelligence',\n\t\t\t'wisdom',\n\t\t\t'charisma'\n\t\t\t]\n\t\tself.size = [\n\t\t\t'small',\n\t\t\t'medium'\n\t\t\t]\n\n\tdef error_message(self, msg):\n\t\tprint(\"error:\\t {}\".format(msg))\n\n\tdef clear_dnd_file(self):\n\t\tfd = open('dnd_races', 'w')\n\t\tfd.write('')\n\t\tfd.close()\n\n\tdef load_races(self):\n\t\tcontents = self._get_dnd_race_file()\n\t\tpprint.pprint(contents)\n\n\tdef _get_dnd_race_file(self):\n\t\ttry:\n\t\t\twith open('dnd_races') as fd:\n\t\t\t\t# http://stackoverflow.com/questions/2507808/python-how-to-check-file-empty-or-not\n\t\t\t\t# ensure contents weren't deleted\n\t\t\t\tfd.seek(0)\n\t\t\t\tfirst_char = fd.read(1)\n\t\t\t\tif not first_char:\n\t\t\t\t\tcontents = {}\n\t\t\t\telse:\n\t\t\t\t\tfd.seek(0)\n\t\t\t\t\tcontents = json.load(fd)\n\t\t\tfd.close()\n\t\texcept IOError:\n\t\t\tcontents = {}\n\n\t\treturn contents\n\n\tdef _update_dnd_race_file(self, contents):\n\t\tfiledesc = open('dnd_races', 'w')\n\t\tfiledesc.write(json.dumps(contents))\n\t\tfiledesc.write('\\n')\n\t\tfiledesc.close()\n\n\tdef add_new_race(self, race):\n\t\t# open and place json file as dict. Else, make new dict\n\t\tcontents = self._get_dnd_race_file()\n\n\t\t# add the new race\n\t\tnew_race = {}\n\t\tnew_race[race] = {}\n\t\tprofile = new_race[race]\n\t\tprofile['stats'] = {}\n\t\tprofile['languages'] = []\n\t\tprofile['extra'] = []\n\t\tprofile['speed'] = None\n\t\tprofile['size'] = None\n\n\t\tif race not in contents:\n\t\t\tcontents[race] = profile\n\t\telse:\n\t\t\tself.error_message(\"Race already exists\")\n\t\t\n\t\tself._update_dnd_race_file(contents)\n\n\tdef add_new_speed(self, race, speed):\n\t\tcontents = self._get_dnd_race_file()\n\n\t\tif race not in contents:\n\t\t\tself.error_message(\"Race not found! Creating new race \\\"{}\\\"...\".format(race))\n\t\t\tself.add_new_race(race)\n\n\t\tcontents[race]['speed'] = speed\n\n\t\tself._update_dnd_race_file(contents)\n\n\tdef add_new_stat(self, race, stat, value):\n\t\t#print(\"adding new stat\")\n\n\t\tcontents = self._get_dnd_race_file()\n\t\tstat_found = False\n\n\t\tif race not in contents:\n\t\t\tself.error_message(\"Race not found! Creating new race {}...\".format(race))\n\t\t\tself.add_new_race(race)\n\n\t\tif stat not in self.stats:\n\t\t\tself.error_message(\"Stat name {} not found in stat list\".format(stat))\n\t\telse:\n\t\t\tstat_found = True\n\n\t\tif stat_found:\n\t\t\tif contents[race]['stats'][stat]:\n\t\t\t\tcontents[race]['stats'][stat] += value\n\t\t\telse:\n\t\t\t\tcontents[race]['stats'][stat] = value\n\n\t\tself._update_dnd_race_file(contents)\n\n\tdef add_new_language(self, race, language):\n\t\t#print(\"adding new language\")\n\n\t\tcontents = self._get_dnd_race_file()\n\t\tlanguage_found = False\n\n\t\tif race not in contents:\n\t\t\tself.error_message(\"Race not found! Creating new race...\")\n\t\t\tself.add_new_race(race)\n\n\t\tif language not in self.languages:\n\t\t\tself.error_message(\"Langauage name: {} not found in language list\".format(language))\t\t\t\n\t\telse:\n\t\t\tlanguage_found = True\n\n\t\tif language_found and language not in contents[race]['languages']:\n\t\t\tcontents[race]['languages'].append(language)\n\t\telse:\n\t\t\tself.error_message(\"{} already has language: {}\".format(race, language))\n\n\t\tself._update_dnd_race_file(contents)\n\n\tdef add_new_size(self, race, size):\n\t\tcontents = self._get_dnd_race_file()\n\t\tsize_found = False\n\n\t\tif race not in contents:\n\t\t\tself.error_message(\"Race not found! Creating new race...\")\n\t\t\tself.add_new_race(race)\n\n\t\tif size not in self.size:\n\t\t\tself.error_message(\"Size: {} not found in size list\".format(size))\n\t\telif contents[race]['size'] is not None:\n\t\t\tself.error_message(\"{} size already set\".format(race))\n\t\telse:\n\t\t\tsize_found = True\n\n\t\t#print(\"Attempting to add new size: {}, found: {}\".format(size, size_found))\n\t\tif size_found and contents[race]['size'] is None:\n\t\t\tcontents[race]['size'] = size\n\n\t\tself._update_dnd_race_file(contents)\n\n\n\tdef add_new_extra(self, race, extra):\n\t\t#print(\"adding new extra\")\n\n\t\tcontents = self._get_dnd_race_file()\n\n\t\tif race not in contents:\n\t\t\tself.error_message(\"Race not found! Creating new race...\")\n\t\t\tself.add_new_race(race)\n\n\t\tif extra not in contents[race]['extra']:\n\t\t\tcontents[race]['extra'].append(extra)\n\t\telse:\n\t\t\tself.error_message(\"{} already has extra {}\".format(race, extra))\n\n\t\tself._update_dnd_race_file(contents)\n\n\n\n","repo_name":"csDaniel/dndCharacterMaker","sub_path":"dnd/add_race.py","file_name":"add_race.py","file_ext":"py","file_size_in_byte":4475,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"12345324088","text":"import json\nimport boto3\nfrom boto3.dynamodb.conditions import Key\nfrom decimal import Decimal\n\ndynamodb = boto3.resource('dynamodb')\nuserTable = dynamodb.Table('rpt_user')\nuserHourTable = dynamodb.Table('rpt_user_hour')\nuserMinuteTable = dynamodb.Table('rpt_user_minute')\nuserAdTypeTable = dynamodb.Table('rpt_user_ad_type')\nuserAdvertizerTable = dynamodb.Table('rpt_user_advertizer')\nuserAdvertizerCategoryTable = dynamodb.Table('rpt_user_advertizer_category')\n\ndef lambda_handler(event, context):\n    for record in event['Records']:\n        print (record)\n        if record['eventName'] == 'INSERT':\n            userId = record['dynamodb']['Keys']['user_id']['S']\n            year = record['dynamodb']['NewImage']['time_stamp']['S'][2:4]\n            month = record['dynamodb']['NewImage']['time_stamp']['S'][4:6]\n            day = record['dynamodb']['NewImage']['time_stamp']['S'][6:8]\n            hour = record['dynamodb']['NewImage']['time_stamp']['S'][9:11]\n            minute = record['dynamodb']['NewImage']['time_stamp']['S'][12:14]\n            hourString = '%s/%s/%s %s:00' % (month, day, year, hour)\n            minuteString = '%s/%s/%s %s:%s' % (month, day, year, hour, minute)\n            advertizer = record['dynamodb']['NewImage']['advertizer']['S']\n            adType = record['dynamodb']['NewImage']['ad_type']['S']\n            brandCategory = record['dynamodb']['NewImage']['brand_category']['S']\n            update_user_hour(userId, hourString)\n            update_user_minute(userId, minuteString)\n            update_user_advertizer_info(userId, advertizer, brandCategory)\n            update_user_ad_type(userId, adType)\n            \n    return {\n        'statusCode': 200,\n        'body': json.dumps('Hello from Lambda!')\n    }\n\ndef update_user_hour(userId, hourString):\n    userHourTable.update_item(\n        Key={'user_id': userId, 'hour_string': hourString},\n        UpdateExpression='ADD impressions :val1',\n        ExpressionAttributeValues={':val1': 1}\n    )\n    \ndef update_user_minute(userId, minuteString):\n    userMinuteTable.update_item(\n        Key={'user_id': userId, 'minute_string': minuteString},\n        UpdateExpression='ADD impressions :val1',\n        ExpressionAttributeValues={':val1': 1}\n    )\n\ndef update_user_ad_type(userId, adType):\n    userAdTypeTable.update_item(\n        Key={'user_id': userId, 'ad_type': adType},\n        UpdateExpression='ADD impressions :val1',\n        ExpressionAttributeValues={':val1': 1}\n    )\n    \ndef update_user_advertizer_info(userId, advertizer, brandCategory):\n    combo = advertizer + '|' + brandCategory\n    userAdvertizerCategoryTable.update_item(\n        Key={'user_id': userId, 'combo': combo},\n        UpdateExpression='SET advertizer = :val1, brand_category = :val2 ADD impressions :val3',\n        ExpressionAttributeValues={':val1':advertizer, ':val2': brandCategory, ':val3': 1}\n    )\n    \n    response = userAdvertizerTable.query(\n        KeyConditionExpression = 'user_id= :val1 AND advertizer = :val2',\n        ExpressionAttributeValues={':val1': userId, ':val2': advertizer}\n    )\n #   print(response)\n    if response['Count'] == 0:\n        moreAdvertizer = 1\n    else:\n        moreAdvertizer = 0\n        \n    userAdvertizerTable.update_item(\n        Key={'user_id': userId, 'advertizer': advertizer},\n        UpdateExpression='ADD impressions :val1',\n        ExpressionAttributeValues={':val1': 1}\n    )\n\n    response = userTable.query(KeyConditionExpression=Key('user_id').eq(userId))\n#    print(response)\n    if response['Count'] == 0:\n        impressions = 1\n        advertizers = 1\n    else:\n        item = response['Items'][0]\n        impressions = item['impressions'] + 1\n        advertizers = item['advertizers'] + moreAdvertizer\n        \n    avgImpressions = Decimal(impressions / advertizers)\n    \n    userTable.update_item(\n        Key={'user_id': userId},\n        UpdateExpression='SET impressions=:val1, advertizers=:val2, average_advertizer_impressions=:val3',\n        ExpressionAttributeValues={':val1': impressions, ':val2': advertizers, ':val3': avgImpressions}\n    )\n        \n   \n    ","repo_name":"GoneWithTheWin/TinFoil","sub_path":"dynamodb/triggers/trgAdEvent.py","file_name":"trgAdEvent.py","file_ext":"py","file_size_in_byte":4093,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"20951750524","text":"#!/usr/bin/env python3\n\"\"\" Freezing layers to avoid unfix pretrained layers \"\"\"\n\nfrom tensorflow.keras.models import Model, load_model\nfrom tensorflow.keras.layers import (\n        Input, Dense, Flatten,\n        Conv2D, MaxPooling2D)\n\n# Froze specific layers\ninputs = Input(shape=(8, 8, 1), name='input_layer')\nh = Conv2D(\n        16,\n        3,\n        activation='relu',\n        name='conv2d_layer',\n        trainable=False)(inputs)  # <- Here\n\nh = MaxPooling2D(3, name='max_pooling2d_layer')(h)\nh = Flatten(name='flatten_layer')(h)\noutputs = Dense(10, activation='softmax', name='softmax_layer')(h)\n\nmodel = Model(inputs=inputs, outputs=outputs)\n\nmodel.get_layer('conv2d_layer').trainable = False  # <- Here also\n\nmodel.compile(loss='sparse_categorical_crossentropy')\n\n# Froze the entire model\nmodel = load_model('some_model.h5')\nmodel.trainable = False\n\nflatten_out = model.get_layer('flatten_layer').output\nnew_out = Dense(5, activation='softmax', name='new_out')(flatten_out)\nnew_model = Model(inputs=model.input, outputs=new_out)\nnew_model.compile(loss='sparse_categorical_crossentropy')\n# sample\nX_train, y_train = [], []\nnew_model.fit(X_train, y_train, epochs=10)\n","repo_name":"Erick-INCS/Exotic-Tensorflow","sub_path":"freezing_layers/freezing.py","file_name":"freezing.py","file_ext":"py","file_size_in_byte":1173,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"24935982065","text":"import random\n# Description:\n#\n#         Write a Python program that simulates the \"Rock, Paper, Scissors\" game.\n#\n#         The game should ask the user to enter an option (either \"Rock\", \"Paper\", or \"Scissors\").\n#\n#         The player should play against the computer, which will select a random option.\n#\n#         The computer's selection will be compared against the player's selection to determine who wins.\n#\n#         A descriptive message should be displayed indicating if the player won, lost, or if the game ended in a tie.\n\n\n\n# ====== Welcome to the game ======\n# Please enter Rock, Paper, or Scissors below:\ndef game():\n    while True:\n        player = input('Please enter Rock, Paper, or Scissors below:').lower()\n        pc = random.randint(0,2)\n        pc_dict = ['Rock','Paper','Scissors']\n        pc_choose = pc_dict[pc]\n        print('pc_choose:',pc_choose)\n        print('player:',player)\n        result = ''\n        if player == 'rock':\n            if pc_choose == 'Paper':\n                result = 'lose'\n            elif pc_choose == 'Scissors':\n                result = 'win'\n            else :\n                result = 'tie'\n            # print(result)\n\n        elif player == 'paper':\n            if pc_choose == 'Paper':\n                result = 'tie'\n            elif pc_choose == 'Scissors':\n                result = 'lose'\n            else :\n                result = 'win'\n            # print(result)\n\n        elif player == 'scissors':\n            if pc_choose == 'Paper':\n                result = 'win'\n            elif pc_choose == 'Scissors':\n                result = 'tie'\n            else :\n                result = 'lose'\n            # print(result)\n        else:\n            print('you quit')\n            break\n        # print(result)\n        if result == 'lose':\n            print ('You lose! Your opponent chose {}'.format(pc_choose))\n        elif result == 'win':\n            print ('You win! Your opponent chose {}'.format(pc_choose))\n        elif result == 'tie':\n            print ('It\\'s a tie! Try again.')\n\ngame()\n\n\n\n\n\n\n\n","repo_name":"moryaziz/my-python-code","sub_path":"extra execise-101/program with conditions_level2/exercise4.py","file_name":"exercise4.py","file_ext":"py","file_size_in_byte":2068,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"9014149139","text":"from monitorTopology.models import Latency\r\nimport numpy\r\n\r\n### @function get_lat_stat(latencies)\r\n#   @descrip\r\n#   @params:\r\n#       latencies : the queryset of latencies\r\n#   @return: lat_mn ---- the mean value of latencies\r\n#            lat_std ---- the standard deviation of latencies\r\ndef get_lat_stat(latencies):\r\n    lat_list = []\r\n    for lat in latencies.all().order_by('-timestamp'):\r\n        cur_lat = float(lat.latency)\r\n        if cur_lat < 500:\r\n            lat_list.append(float(lat.latency))\r\n\r\n    if len(lat_list) > 0:\r\n        lat_mn = numpy.mean(lat_list)\r\n        lat_std = numpy.std(lat_list)\r\n    else:\r\n        lat_mn = -1\r\n        lat_std = -1\r\n\r\n    return lat_mn, lat_std\r\n\r\n","repo_name":"ephemeral2eternity/monitor-cmu-agens","sub_path":"monitorTopology/lat_utils.py","file_name":"lat_utils.py","file_ext":"py","file_size_in_byte":703,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"81"}
+{"seq_id":"41071965812","text":"from typing import Dict\nimport argparse\nfrom pathlib import Path\nimport datetime\nimport os\nfrom selenium import webdriver\nfrom webdriver_manager.chrome import ChromeDriverManager\nfrom contextlib import redirect_stdout\nimport time\nimport imageio\nimport io\n\nimport folium\nimport pandas as pd\nimport geonamescache # for countries population\ngeo_countries = geonamescache.GeonamesCache().get_countries_by_names()\n\n\ndef generate_corona_map(datasets: dict, date: str, colours: dict, norm_to_population=False):\n    \"\"\"Generate a single folium map from datasets for a given date.\n\n    :param datasets: timeseries datasets for confirmed and/or deaths\n    :param date: date string with a foramt mm/dd/yy\n    :param colours: dataset-name / colour to visualize\n    :param norm_to_population: normalize the number of cases to the country population\n    :return: folium map\n    \"\"\"\n    # Make an empty map optimized for Europe\n    m = folium.Map(location=[47, 12], zoom_start=5)\n    # iterate over the datasets\n    for name, dataset in datasets.items():\n        if name not in colours:\n            raise ValueError(f'Colour for {name} is not provided.')\n        # iterate through the countries\n        for i in range(0, len(dataset)):\n            data = dataset.iloc[i]\n            # add marker to the map\n            _add_marker(m, data, date=date, colour=colours[name], norm_to_population=norm_to_population)\n    return m\n\n\ndef _add_marker(m, data: pd.Series, date: str, colour: str, norm_to_population: bool = False) -> None:\n    \"\"\"Add individual marker/circle to the folium map.\n\n    :param m: folium map\n    :param data: timeseries with cases\n    :param date: str with date for which execute\n    :param colour: colour of the marker\n    :param norm_to_population: normalize to the population or not\n    :return:\n    \"\"\"\n    value = data[date]\n    country = data['Country/Region']\n    # multiplication factor for visualization\n    visual_factor = 10\n    # skip countries with 0 affected and Cruise Ship\n    if value > 0 and country not in ['Cruise Ship', 'San Marino', 'Holy See']:\n        # apply normalisation\n        if norm_to_population:\n            value /= country_population(country)\n            visual_factor = 1.5e8\n        marker = folium.Circle(\n              location=(data['Lat'], data['Long']),\n              popup=country+' '+value.astype(str),\n              radius=float(value*visual_factor),\n              color=colour,\n              fill=True,\n              fill_color=colour\n           )\n        # add marker to the map\n        marker.add_to(m)\n\n\ndef add_text_to_map(m, text: str, font_size: int, position: tuple = (39.7339, 24.4858)) -> None:\n    \"\"\"Add text to the map.\n\n    :param m: folium map\n    :param text: text to write on the map\n    :param font_size: font size of the text\n    :param position: Longitude and Lattitude of the text\n    :return:\n    \"\"\"\n    folium.map.Marker(\n        position,\n        icon=folium.features.DivIcon(\n            icon_size=(150, 36),\n            icon_anchor=(0, 0),\n            html=f'<div style=\"font-size: {font_size}pt\">{text}</div>',\n            )\n    ).add_to(m)\n\n\ndef country_population(country_name: str) -> int:\n    \"\"\"Lookup the country population from external resources.\n\n    :param country_name: name of the country\n    :return: population of the country\n    \"\"\"\n    country = country_name\n    if country == 'US': country = 'United States'\n    if country == 'Holy See': country = 'Vatican'\n    if country == 'Korea, South': country = 'South Korea'\n    if country == 'Taiwan*': country = 'Taiwan'\n    if country == 'Congo (Kinshasa)': country = 'Democratic Republic of the Congo'\n    if country == \"Cote d'Ivoire\": country = 'Ivory Coast'\n    if country == 'occupied Palestinian territory': country = 'Palestinian Territory'\n    if country == 'Congo (Brazzaville)': country = 'Republic of the Congo'\n    if country == 'The Bahamas' or country == 'Bahamas, The': country = 'Bahamas'\n    if country == 'The Gambia' or country == 'Gambia, The': country = 'Gambia'\n    return int(geo_countries[country]['population'])\n\n\ndef load_datasets(data_path: str, confirmed: bool, deaths: bool) -> Dict[str, pd.DataFrame]:\n    \"\"\"Load timeseries datasets for confirmed and/or deaths.\n\n    :param data_path: path to the novel-corona-virus-2019-dataset\n    :param confirmed: whether load confirmed cases data\n    :param deaths: whether load deaths data\n    :return dict with name:pd.DataFrame entries\n    \"\"\"\n    # sanity check\n    if not confirmed and not deaths:\n        raise ValueError('Neither confirmed nor deaths have been selected. At least one of the datasets have to be picked')\n    # load the datasets\n    keys = []\n    if confirmed: keys.append('confirmed')\n    if deaths: keys.append('deaths')\n    dataset = {name: pd.read_csv(f'{data_path}/time_series_covid_19_{name}.csv') for name in keys}\n    return dataset\n\n\ndef save_map_png(m, filename: str) -> str:\n    \"\"\"Hack to save folium map as png.\n\n    :param m: folium map\n    :param filename: name of the output file without extension\n    :return name of the .png file\n    \"\"\"\n    # save as html\n    tmpurl='file://{path}.html'.format(path=Path(filename).absolute())\n    m.save(f'{filename}.html')\n    out_file = f'{filename}.png'\n    screenshot_browser(tmpurl, filename=out_file)\n    return out_file\n\n\ndef screenshot_browser(url, filename, delay=5) -> None:\n    \"\"\"Run Chrome browser with selenium, load the url, take a screenshot.\n\n    :param url: url to load in Chrome\n    :param filename: filename of the screenshot\n    :param delay: seconds of the delay\n    :return:\n    \"\"\"\n    out = io.StringIO()\n    with redirect_stdout(out):\n        # run selenium\n        browser = webdriver.Chrome(ChromeDriverManager().install())\n        browser.set_window_position(0, 0)\n        browser.set_window_size(800, 800)\n        browser.get(url)\n        # Give the map tiles some time to load\n        time.sleep(delay)\n        # save the screenshot\n        browser.save_screenshot(filename)\n        browser.quit()\n\n\ndef get_list_of_dates(start_date, datasets):\n    \"\"\"Exract the list of dates to make a gif for.\n\n    :param start_date: A date to start iterating from\n    :param datasets: timeseries datasets for confirmed and/or deaths\n    :return:\n    \"\"\"\n    dates = [x for x in datasets.get('confirmed', 'deaths').columns[4:]]\n    start_index = dates.index(start_date)\n    return dates[start_index:]\n\ndef parse_cmd_args():\n    \"\"\"Parse cmd user input\n\n    :return: the namespace object\n    \"\"\"\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\"--output_dir\", type=str, default='./output')\n    parser.add_argument(\"--input_dir\", type=str, default='./novel-corona-virus-2019-dataset')\n    parser.add_argument('--plot_confirmed', type=bool, default=True)\n    parser.add_argument('--plot_deaths', type=bool, default=True)\n    parser.add_argument('--start_date', type=str, default='2/23/20', help='mm/dd/yy')\n    parser.add_argument('--normalize_to_population', type=bool, default=False, help='Normalize the number of cases to the country population')\n    args = parser.parse_args()\n    return args\n\n\ndef validate_cmd_args(args: argparse.Namespace) -> Dict:\n    \"\"\"Validate the cmd input\n\n    :param args: cmd inputs\n    :return:\n    \"\"\"\n    arg_dict = vars(args)\n    # validate the output dir\n    try:\n        Path(arg_dict['output_dir']).mkdir(parents=True, exist_ok=True)\n    except Exception as e:\n        raise e\n    # validate the input dir\n    in_p = Path(arg_dict['input_dir'])\n    if not in_p.exists():\n        raise ValueError(f'Input directory: {arg_dict[\"input_dir\"]} does not exist.')\n    # validate whether dir contains time_series datasets\n    for dtype in ['confirmed', 'deaths']:\n        filename = f'time_series_covid_19_{dtype}.csv'\n        if not (in_p / filename).exists() and arg_dict[f'plot_{dtype}'] == True:\n            raise ValueError(f'File {filename} is not found in {arg_dict[\"input_dir\"]}.')\n    # validate start_date\n    try:\n        datetime.datetime.strptime(arg_dict['start_date'], '%m/%d/%y')\n    except ValueError:\n        raise ValueError(\"Incorrect data format, should be mm/dd/yy\")\n    return arg_dict\n\n\ndef create_corona_spread_gif(args: dict):\n    date = args['start_date']\n    # load the input data\n    datasets = load_datasets(args['input_dir'], confirmed=args['plot_confirmed'], deaths=args['plot_deaths'])\n    # assign colours\n    colours = {'deaths': 'black', 'confirmed': 'crimson'}\n    # extract available dates:\n    dates = get_list_of_dates(date, datasets)\n    # post_fix to the file names\n    filename_postfix = ''\n    if args['normalize_to_population']:\n        filename_postfix += '_norm_to_population'\n    screenshots = []\n    # iterate through the dates\n    for date in dates:\n        # check if map already exist\n        filename = 'map_'+date.replace('/', '_')\n        filename = f'{args[\"output_dir\"]}/{filename}{filename_postfix}'\n        screenshot = filename+'.png'\n        # create a screenshot if doesn't exist\n        if not os.path.exists(screenshot):\n            # generate a single map\n            corona_map = generate_corona_map(datasets, date, colours=colours, norm_to_population=args['normalize_to_population'])\n            # add author name\n            add_text_to_map(corona_map, '@R.Shevchenko', font_size=13, position=(55.7, 23.4858))\n            # add date to the bottom\n            add_text_to_map(corona_map, date, font_size=24, position=(37.7339, 24.4858))\n            # save as png\n            screenshot = save_map_png(corona_map, filename=filename)\n            print(f'Date {date} has been processed.')\n        # append screenshot for the gif\n        screenshots.append(imageio.imread(screenshot))\n    # generate the gif\n    gif_name = f'{args[\"output_dir\"]}/corona{filename_postfix}'\n    imageio.mimsave(f'{gif_name}.gif', screenshots)\n\n\nif __name__ == '__main__':\n    # read cmd arguments\n    args = parse_cmd_args()\n    # validate user input\n    args = validate_cmd_args(args)\n    # create a gif\n    create_corona_spread_gif(args)","repo_name":"ShevchenkoRostyslav/covid19","sub_path":"corona_spread_gif.py","file_name":"corona_spread_gif.py","file_ext":"py","file_size_in_byte":10025,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"45460187106","text":"from libqtile import qtile\nfrom modules.widgets_custom import volumes, tasklist\n\n\ncolors = [\n    [\"#282c34\", \"#282c34\"],  # panel background\n    [\"#3d3f4b\", \"#434758\"],  # background for current screen tab\n    [\"#ffffff\", \"#ffffff\"],  # font color for group names\n    [\"#ff5555\", \"#ff5555\"],  # border line color for current tab\n    [\"#74438f\", \"#74438f\"],  # border line color for 'other tabs' and color for 'odd widgets'\n    [\"#4f76c7\", \"#4f76c7\"],  # color for the 'even widgets'\n    [\"#e1acff\", \"#e1acff\"],  # window name\n    [\"#ecbbfb\", \"#ecbbfb\"]  # backbround for inactive screens\n]\n\nwidget_defaults = dict(\n    font='Terminus', # Cantarell\n    fontsize=12,\n    padding=3,\n)\nextension_defaults = widget_defaults.copy()\n\n\nvolume = volumes.MyVolume(\n    fontsize=18,\n    # Font Awesome 5 Free\n    font='Terminus',\n    foreground=colors[4],\n    background='#2f343f',\n    mouse_callbacks={'Button1': lambda: qtile.cmd_spawn(\"pavucontrol\")}\n)\n\ntasklist = tasklist.TaskList()","repo_name":"UN-9BOT/qtile-config","sub_path":"modules/widgets.py","file_name":"widgets.py","file_ext":"py","file_size_in_byte":976,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"3598439479","text":"\r\n\"\"\"\r\nCreated on Sat Feb  2 16:38:36 2019\r\n\r\n@author: Henry\r\n\"\"\"\r\nimport numpy as np\r\nimport pandas as pd\r\nfrom collections import Counter\r\nimport matplotlib.pyplot as plt\r\n\r\nlinkedin = pd.read_csv('temp_datalab_records_linkedin_company.csv')\r\nlinkedin = np.array(linkedin)\r\ndate = linkedin[:,1]\r\ncompany_name = linkedin[:,2]\r\nnum = len(company_name)\r\n#followers_count = linkedin[:,3]\r\nemployees_on_platform = linkedin[:,4]\r\nindustry = linkedin[:,6]\r\n\r\nBanking_Names = []\r\nBanking_employees = []\r\nBanking_date = []\r\n\r\nfor i in range(num):\r\n    if industry[i] == 'Banking':\r\n        Banking_Names.append(company_name[i])\r\n        Banking_employees.append(employees_on_platform[i])\r\n        Banking_date.append(date[i])\r\n#print(len(Banking_Names))\r\nnum_banking = len(Banking_Names)\r\n\r\nclass company():\r\n    def __init__(self, linkedin,name_str):\r\n        self.name = name_str\r\n        num_all = len(linkedin[:,2])\r\n        self.as_of_date = []\r\n        self.employees = []\r\n        self.followers = []\r\n        self.date_updated = []\r\n        \r\n        for i in range(num_all):\r\n            if linkedin[i,2] == self.name:\r\n                self.industry = linkedin[i,6]\r\n                break\r\n        for i in range(num_all):\r\n            if linkedin[i,2] == self.name:\r\n                self.as_of_date.append(linkedin[i,1])\r\n                self.followers.append(linkedin[i,3])\r\n                self.employees.append(linkedin[i,4])\r\n                self.date_updated.append(linkedin[i,8])\r\n\r\ndef name_depuplication(bank_names, bank_employees):\r\n    names_simplified = []\r\n    employees_simplified = []\r\n    d = {}\r\n    for i in range(len(bank_names)-1,-1,-1):\r\n\r\n        if bank_names[i] not in d:\r\n            d[bank_names[i]] = 1\r\n            names_simplified.append(bank_names[i])\r\n            employees_simplified.append(bank_employees[i])\r\n    names_simplified.reverse()\r\n    employees_simplified.reverse()  \r\n    return names_simplified, employees_simplified\r\n\r\nRecently_banking_names,Recently_banking_employees = name_depuplication(Banking_Names, Banking_employees)\r\n\r\ndef find_order_max(List,order):\r\n    list_max = np.max(List)\r\n    index_max = List.index(list_max)\r\n    list_order = [list_max]\r\n    list_order_index = [index_max] \r\n    for j in range(order-1):\r\n        a = 0\r\n        for i in range(len(List)):\r\n            if List[i] < list_order[j] and List[i] > a:\r\n                a = List[i]\r\n        list_order.append(a)\r\n        b = List.index(a)\r\n        list_order_index.append(b)\r\n    return list_order, list_order_index\r\n    \r\nemployees, index = find_order_max(Recently_banking_employees,3)\r\n\r\ncount_recent = Counter(Recently_banking_names)\r\n\r\nprint(Recently_banking_names[index[0]])\r\nprint(Recently_banking_names[index[1]])\r\nprint(Recently_banking_names[index[2]])\r\n\r\n###############################################################################\r\n#results: Bank of America(BOA), Banco Santander(BS),RBS\r\n\r\nBOA = company(linkedin, 'Bank of America')\r\nBS = company(linkedin, 'Banco Santander')\r\nRBS= company(linkedin, 'RBS')\r\n\r\nplt.figure(1)\r\nplt.scatter(BOA.as_of_date, BOA.employees)\r\nplt.show()\r\n             \r\nrate = []\r\nfor i in range(len(BOA.employees)-1) :\r\n    rate.append(BOA.employees[i+1] - BOA.employees[i])\r\nplt.figure(2)\r\nBOA.as_of_date.pop()\r\nplt.scatter(BOA.as_of_date, rate)\r\nplt.show()\r\n###############################################################################\r\nindex_list = []\r\nfor i in range(num_banking):\r\n    if  Banking_Names[i] == 'Bank of America' :\r\n        index_list.append(i)\r\nRate_BOA = []\r\nx = []\r\ndate_share = []\r\nfor k in range(1,len(index_list)):\r\n    #print(k)\r\n    x.append(k)\r\n    i = index_list[k]\r\n    date_share.append(Banking_date[i])\r\n    Temp_names,temp_employees = name_depuplication(Banking_Names[0:i], Banking_employees[0:i])\r\n    BOA_num = Banking_employees[i]\r\n    sum_num = np.sum(temp_employees)\r\n    Rate_BOA.append(BOA_num/sum_num)\r\n\r\nplt.figure(3)\r\nplt.scatter(date_share, Rate_BOA)\r\nplt.show()\r\n\r\n\r\n\r\n","repo_name":"Henry3g/Incubator","sub_path":"Section_3.py","file_name":"Section_3.py","file_ext":"py","file_size_in_byte":3984,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"40085967917","text":"import wx\nimport metrics\n\nclass StoryMetadataFrame(wx.Frame):\n    \"\"\"\n    Changes automatically update as the user makes them;\n    \"\"\"\n\n    def __init__(self, app, parent = None):\n        self.app = app\n        self.parent = parent\n        wx.Frame.__init__(self, parent, wx.ID_ANY, title = parent.title + ' Metadata', \\\n                          style = wx.MINIMIZE_BOX | wx.CLOSE_BOX | wx.CAPTION | wx.SYSTEM_MENU)\n\n        panel = wx.Panel(parent = self, id = wx.ID_ANY)\n        borderSizer = wx.BoxSizer(wx.VERTICAL)\n        panel.SetSizer(borderSizer)\n        panelSizer = wx.FlexGridSizer(8, 1, metrics.size('relatedControls'), metrics.size('relatedControls'))\n        borderSizer.Add(panelSizer, flag = wx.ALL, border = metrics.size('windowBorder'))\n\n        for name, desc in [\n              (\"identity\", (\"What your work identifies as:\",\n                            \"Is it a game, a story, a poem, or something else?\\n(This is used for dialogs and error messages only.)\",\n                            False)),\n              (\"description\", (\"A short description of your work:\",\n                               \"This is inserted in the HTML file's <meta> description tag, used by\\nsearch engines and other automated tools.\",\n                               True))\n            ]:\n            textlabel = wx.StaticText(panel, label = desc[0])\n            if desc[2]:\n                textctrl = wx.TextCtrl(panel, size=(200,60), style=wx.TE_MULTILINE)\n            else:\n                textctrl = wx.TextCtrl(panel, size=(200,-1))\n            textctrl.SetValue(parent.metadata.get(name, ''))\n            textctrl.Bind(wx.EVT_TEXT, lambda e, name=name, textctrl=textctrl:\n                              self.saveSetting(name,textctrl.GetValue()))\n\n            hSizer = wx.BoxSizer(wx.HORIZONTAL)\n            hSizer.Add(textlabel,1,wx.ALIGN_LEFT|wx.ALIGN_TOP)\n            hSizer.Add(textctrl,1,wx.EXPAND)\n            panelSizer.Add(hSizer,flag=wx.ALL|wx.EXPAND)\n            panelSizer.Add(wx.StaticText(panel, label = desc[1]))\n            panelSizer.Add((1,2))\n\n        panelSizer.Fit(self)\n        borderSizer.Fit(self)\n        self.SetIcon(self.app.icon)\n        self.Show()\n        self.panelSizer = panelSizer\n        self.borderSizer = borderSizer\n\n    def saveSetting(self, name, value):\n        self.parent.metadata[name]=value\n        self.parent.setDirty(True)\n","repo_name":"tweecode/twine","sub_path":"storymetadataframe.py","file_name":"storymetadataframe.py","file_ext":"py","file_size_in_byte":2370,"program_lang":"python","lang":"en","doc_type":"code","stars":648,"dataset":"github-code","pt":"81"}
+{"seq_id":"27283612404","text":"import os\nimport sys\nimport pandas as pd\nimport numpy as np\nfrom chemblmltools import chembl_activity_target\n\n\nabspath = os.path.abspath(__file__)\nsys.path.append(abspath)\nfrom default import CHEMBL_PWD, CHEMBL_USR, DATAPATH\n\npathogens = sys.argv[1] #csv file indicating the pathogens to process, see example in ../config/pathogens.csv\n\n\nclass PathogenGetter():\n    \"\"\"\n    This class obtains the full data related to the specified pathogens and creates the directories to save it\n    \"\"\"\n    def __init__(self, patho_search):\n        self.db_user = CHEMBL_USR\n        self.db_pwd = CHEMBL_PWD\n        self.patho_search = patho_search\n\n    def create_datasets_pathogen(self):\n        df = chembl_activity_target(\n                db_user= self.db_user,\n                db_password=self.db_pwd,\n                organism_contains=self.patho_search,\n                max_heavy_atoms=100)\n        return df\n    \n\n#Obtain desired pathogen data  \ndf_pathogens = pd.read_csv(pathogens)\nlist_pathogen_codes = df_pathogens.pathogen_code\nlist_pathogen_search_text = df_pathogens.search_text\nfor i, patho_code in enumerate(list_pathogen_codes):\n    print('------------------------------------------------------------')\n    print(f'Creating data for pathogen {patho_code} ({list_pathogen_search_text[i]})')\n    print('------------------------------------------------------------')\n    patho_getter = PathogenGetter(list_pathogen_search_text[i])\n    df = patho_getter.create_datasets_pathogen()\n\n    if not os.path.exists(os.path.join(DATAPATH, patho_code)):\n        os.makedirs(os.path.join(DATAPATH, patho_code))\n    df.to_csv(os.path.join(DATAPATH, patho_code, \"{}_original.csv\".format(patho_code)), index=False)\n","repo_name":"ersilia-os/antimicrobial-ml-tasks","sub_path":"code/pathogens.py","file_name":"pathogens.py","file_ext":"py","file_size_in_byte":1701,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"81"}
+{"seq_id":"6711952993","text":"import types\nimport re\nimport logging\nimport traceback\nimport sys\n\n\nfrom db_commands.constants import CLUSTER_ALREADY_PRESENT, BUCKET_NAME_ALREADY_EXIST, MULTIPLE_VDB_ERROR, \\\n    SHUTDOWN_FAILED, ALREADY_CLUSTER_INIT, ALREADY_CLUSTER_FOR_BUCKET\nfrom controller import helper_lib\nfrom internal_exceptions.base_exceptions import GenericUserError\nfrom internal_exceptions.plugin_exceptions import ERR_RESPONSE_DATA\nfrom dlpx.virtualization.platform.exceptions import UserError\n\nlogger = logging.getLogger(__name__)\n\n\n# This is meta class which decorates the each functions of child class with below things:\n# Ignore common exceptions\n# Enable logging in more intuitive way\n\n\nclass DatabaseExceptionHandlerMeta(type):\n\n    def __new__(mcs, caller_name, caller_base_name, attributes_in_caller):\n        \"\"\"\n        :param caller_name:\n        :type caller_name: Class type\n        :param caller_base_name:\n        :type caller_base_name: Any base class of caller_name\n        :param attributes_in_caller:\n        :type attributes_in_caller: functions and parameters of class\n        :return:\n        \"\"\"\n\n        # iteration for each method of a caller class\n        for attribute_name, attribute_value in attributes_in_caller.iteritems():\n            if isinstance(attribute_value, types.FunctionType):\n                if attribute_name == \"__init__\" or attribute_name == \"status\" or attribute_name == \"check_attribute_error\":\n                    continue\n                attributes_in_caller[attribute_name] = mcs.handle_exception_decorator(attribute_value)\n        try:\n            return super(DatabaseExceptionHandlerMeta, mcs).__new__(mcs, caller_name, caller_base_name,\n                                                                    attributes_in_caller)\n        except Exception as err:\n            logger.debug(\"Exception occurred in metaclass: {}\".format(str(err)))\n            raise\n\n    @classmethod\n    def _exception_generator_factory(mcs, err_string):\n        \"\"\"\n        :param err_string:\n        :raises: Exceptions based on the output. It matches the error string with predefined strings.\n               In some cases we need to kill the program and in some cases it is not. This is distinguished by the\n               error string.\n        \"\"\"\n        if (re.search(CLUSTER_ALREADY_PRESENT, err_string) or\n                re.search(BUCKET_NAME_ALREADY_EXIST, err_string) or\n                re.search(MULTIPLE_VDB_ERROR, err_string) or\n                re.search(SHUTDOWN_FAILED, err_string) or\n                re.search(ALREADY_CLUSTER_FOR_BUCKET, err_string) or\n                re.search(ALREADY_CLUSTER_INIT, err_string)):\n            logger.debug(\"Gracefully accepted the last exception\")\n            return\n        logger.debug(\"Searching predefined exception for this error\")\n        err_code = get_err_code(err_string)\n        raise GenericUserError(ERR_RESPONSE_DATA[err_code]['MESSAGE'], ERR_RESPONSE_DATA[err_code]['ACTION'], err_string)\n\n    @classmethod\n    def handle_exception_decorator(mcs, function_name):\n        \"\"\"\n        Decorating function with exception handling. Also we can control the output of each couchbase\n        command at single place.\n        :param function_name: Method of a class which is not static and class\n        :type : function\n        :return : None\n         \"\"\"\n\n        def wrapper_function(*args, **kwargs):\n            try:\n                output_list = function_name(*args, **kwargs)\n                return output_list\n\n            except UserError as ue:\n                logger.debug(\"User Error found\")\n                ttype, value, traceb = sys.exc_info()\n                logger.debug(\"type: {}, value: {}\".format(ttype, value))\n                logger.debug(\"trackback\")\n                logger.debug(traceback.format_exc())            \n                raise\n\n            except Exception as error:\n                logger.debug(\"Caught Exception : {}\".format(str(error)))\n                logger.debug(\"pioro\")\n                ttype, value, traceb = sys.exc_info()\n                logger.debug(\"type: {}, value: {}\".format(ttype, value))\n                logger.debug(\"trackback\")\n                logger.debug(traceback.format_exc())   \n                mcs._exception_generator_factory(str(error))\n\n        return wrapper_function\n\n\n# Return error code for given string if it is defined in RESPONSE_DATA\ndef get_err_code(error_string):\n    logger.debug(\"error_string is {}\".format(error_string))\n    all_error_codes = ERR_RESPONSE_DATA.keys()\n    for each_err_code in all_error_codes:\n        search_string = ERR_RESPONSE_DATA[each_err_code][\"ERR_STRING\"]\n        if re.search(search_string, error_string):\n            return each_err_code\n    return 'DEFAULT_ERR'\n","repo_name":"delphix/couchbase-plugin","sub_path":"src/controller/db_exception_handler.py","file_name":"db_exception_handler.py","file_ext":"py","file_size_in_byte":4734,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"13811234294","text":"\nimport os\nimport pendulum\nfrom airflow import DAG\nfrom airflow.decorators import task\nimport requests\nimport feedparser\nfrom datetime import datetime\nfrom pymongo.mongo_client import MongoClient\nfrom pymongo.server_api import ServerApi\n\nwith DAG(\n    dag_id=\"hearing_news\",\n    catchup=False,\n    start_date=pendulum.datetime(2021, 1, 1, tz=\"UTC\"),\n    schedule=\"@daily\",\n    tags=[\"crawl_data\"],\n) as dag:\n    \n    uri = \"mongodb+srv://admin:admin123@cluster0.ymqhm3k.mongodb.net/?retryWrites=true&w=majority\"\n    client = MongoClient(uri, server_api=ServerApi('1'))\n    db = client[\"db\"]\n   \n    @task\n    def get_source_urls():\n        \"\"\"\n        input: None\n        Get rss feed urls from mongodb\n        Output: return all urls \n        \"\"\"\n        print(uri)\n        links = []\n        rss_collection = db[\"scholarship_links\"]\n        for document in list(rss_collection.find({}, {\"status\": \"New\"})):\n            links.append({\"title\":document[\"title\"], \"link\":document[\"link\"]})\n        return links\n\n    @task \n    def check_new_posts(url: str):\n        \"\"\"\n            input: a url \n            output: all new schoolarship today url\n        \"\"\"\n        response = requests.get(url, verify=False)\n        feed = feedparser.parse(response.text)\n        collection = db[\"scholarship_links\"]\n        if feed.bozo == 1:\n            print(\"some error\")\n        for entry in feed.entries:\n            if not collection.find_one({\"title\": entry.title}) and not collection.find_one({\"link\": entry.link}) and \"học bổng\" in entry.title.lower():\n                new_post = {\n                    \"title\": entry.title,\n                    \"link\": entry.link,\n                    \"published\": datetime.strptime(entry.published, \"%a, %d %b %Y %H:%M:%S %z\"),\n                    \"status\": \"New\"\n                }\n                collection.insert_one(new_post)\n\n\n    url = get_source_urls()\n    check_new_posts.expand(url=url)\n\n\n    ","repo_name":"lits-06/IScholarship","sub_path":"dags/hearing_news_dag.py","file_name":"hearing_news_dag.py","file_ext":"py","file_size_in_byte":1932,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"37237881994","text":"import pandas as pd\nimport numpy as np\n\ncolor_maps = {'descriptor': '#ff6a00', \n             'fingerprint':'#a700ff' }\n\n\ndf = pd.read_pickle('feature_scale.cfg')\n\n\nbitsinfo = pd.DataFrame(df.index,columns = ['IDs'])\nsubtypes =  ['fingerprint' for i in range(len(df) - 27)]\nsubtypes.extend(['descriptor' for i in range(27)])\n\n\n\nbitsinfo['Subtypes'] = subtypes\nbitsinfo['colors'] = bitsinfo['Subtypes'].map(color_maps)\n\nbitsinfo.to_pickle('feature_bitsinfo.cfg', compression = 'gzip')","repo_name":"shenwanxiang/biendata","sub_path":"molmaps/config/02_def_bitsinfo.py","file_name":"02_def_bitsinfo.py","file_ext":"py","file_size_in_byte":482,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"81"}
+{"seq_id":"25698059066","text":"# This Source Code Form is subject to the terms of the Mozilla Public\n# License, v. 2.0. If a copy of the MPL was not distributed with this\n# file, You can obtain one at https://mozilla.org/MPL/2.0/.\n\nimport numpy as np\nimport math\nimport cv2\nfrom tools import ransac\nfrom tools import visualization as vis\nfrom scipy.optimize import least_squares, root\n\n\ndef extract_SIFT_feature(img, mask_range=None):\n    '''\n    Function:\n            extract SIFT features from input image\n    Input:\n            img = input image\n            mask_range = a list with length 2, which describes the region of interest of img,\n                         containing coordinates of the top-left and the down-right points,\n                         None by default\n    Output:\n            kp = list of keypoints\n            des = list of descriptors\n    '''\n\n    sift = cv2.xfeatures2d.SIFT_create()\n\n    if mask_range == None:\n        mask = None\n    else:\n        mask = np.zeros(img.shape, dtype=img.dtype)\n        cv2.rectangle(mask, mask_range[0], mask_range[1], (255), thickness = -1)\n\n    kp, des = sift.detectAndCompute(img, mask)\n    return kp, des\n\n\ndef matching_feature(kp1, kp2, des1, des2, method=1, ratio=0.7):\n    '''\n    Function:\n            matching features that are extracted in two images\n    Input:\n            kp1,kp2,des1,des2: = keypoints and their descriptors in two images\n            method = 1: FLANN Matcher (default)\n                     0: Bruto-Force Matcher\n            ratio = threshold for ratio of similartiy measure between the best match\n                    and the second best match, only for FLANN Matcher, 0.7 by default\n    Output:\n            pts1 = pixel coordinates of corresponding features in img1\n            pts2 = pixel coordinates of corresponding features in img2,\n                   which has the same size as pts1\n            matches = the Matcher object\n            matchesMask = index of good matches, only for FLANN Matcher\n    '''\n\n    pts1 = []\n    pts2 = []\n\n    if method:\n        # FLANN Matcher\n        FLANN_INDEX_KDTREE = 0\n        index_params = dict(algorithm = FLANN_INDEX_KDTREE, trees = 5)\n        search_params = dict(checks=50)\n        flann = cv2.FlannBasedMatcher(index_params,search_params)\n        matches = flann.knnMatch(des1,des2,k=2)\n\n        # record good matches\n        matchesMask = [[0,0] for i in range(len(matches))]\n\n        # ratio test as per Lowe's paper\n        for i,(m,n) in enumerate(matches):\n            if m.distance < ratio*n.distance:\n                pts1.append(kp1[m.queryIdx].pt)\n                pts2.append(kp2[m.trainIdx].pt)\n                matchesMask[i]=[1,0]\n        \n        return pts1, pts2, matches, matchesMask\n\n    else:\n        # Brute Force Matching\n        bf = cv2.BFMatcher(crossCheck=True)\n        matches = bf.match(des1,des2)\n\n        # Use every matches\n        for m in matches:\n            pts1.append(kp1[m.queryIdx].pt)\n            pts2.append(kp2[m.trainIdx].pt)\n        return pts1, pts2, matches\n\n\ndef computeFundamentalMat(pts1, pts2, method=cv2.FM_RANSAC, error=3, inliers=True):\n    '''\n    Function:\n            compute fundamental matrix given correspondences (at least 8)\n    Input:\n            pts1, pts2 = list of pixel coordinates of corresponding features\n            method = cv2.FM_RANSAC: Using RANSAC algorithm (default)\n                     cv2.FM_LMEDS: Using least-median algorithm\n                     cv2.FM_8POINT: Using 8 points algorithm\n            error = reprojection threshold that describes maximal distance from a \n                    point to a epipolar line\n            inlier = True: return F and the mask for inliers\n                     False: only reture F\n    Output:\n            F = Fundamental matrix with size 3*3\n            mask = index for inlier correspondences (optional)\n    '''\n\n    pts1 = pts1[:2].T\n    pts2 = pts2[:2].T\n\n    F, mask = cv2.findFundamentalMat(pts1,pts2,method,error)\n\n    if inliers:\n        return F, mask.reshape(-1,)\n    else:\n        return F\n\n\ndef computeEssentialMat(x1,x2,K1,K2,error=0.001):\n    pts1 = np.dot(np.linalg.inv(K1),x1)\n    pts2 = np.dot(np.linalg.inv(K2),x2)\n\n    E, mask = cv2.findEssentialMat(pts1[:2].T,pts2[:2].T,np.diag((1,1,1)),threshold=error)\n    return E, mask.reshape(-1,)\n\n\n\ndef normalize_2d_points(x):\n    '''\n    Function:\n            normalize input points such that mean = 0 and distance to center = sqrt(2)\n    Input:\n            x = 2D points in numpy array\n    Output:\n            x_n = normalized 2D points in form of 3*N\n            T = 3x3 normalization matrix\n                (s.t. x_n=T*x when x is in homogenous coords)\n    '''\n\n    # Make sure x has the form of 3*N\n    if x.shape[0]==2:\n        x = np.vstack((x,np.ones(x.shape[1])))\n    elif x.shape[1] == 2:\n        x = np.hstack((x,np.ones(x.shape[0]).reshape(-1,1))).T\n    elif x.shape[1] == 3:\n        x = x.T\n    \n    # Calculate mean and scale\n    x_mean = np.mean(x[:2],axis=1)\n    x_scale = np.sqrt(2) / np.std(x[:2])\n\n    # Create normalization matrix T\n    T = np.array([[x_scale,0,-x_scale*x_mean[0]],[0,x_scale,-x_scale*x_mean[1]],[0,0,1]])\n    x_n = np.dot(T,x)\n\n    return x_n, T\n\n\ndef compute_fundamental(x1,x2):\n    '''\n    Compute fundamental matrix from 2d points in image coordinates.\n\n    Input points do not need to be normalized in advance.\n    '''\n\n    # Check that x1,x2 have same number of points\n    num = x1.shape[1]\n    if x2.shape[1] != num:\n        raise ValueError(\"Number of points do not match!\")\n    elif num < 8:\n        raise ValueError(\"At least 8 points needed!\")\n\n    # Normalize input points\n    x1, T1 = normalize_2d_points(x1)\n    x2, T2 = normalize_2d_points(x2)\n\n    # Design matrix A\n    A = np.array([x1[0]*x2[0],x1[0]*x2[1],x1[0],\n                  x1[1]*x2[0],x1[1]*x2[1],x1[1],\n                  x2[0],x2[1],np.ones(x1.shape[1])]).T\n    \n    # Solve F by SVD\n    U,S,V = np.linalg.svd(A)\n    F = V[-1].reshape(3,3)\n\n    # Constrain of det(F)=0\n    U,S,V = np.linalg.svd(F)\n    S[2] = 0\n    F = np.dot(np.dot(U,np.diag(S)),V)\n\n    # Denormalization\n    F = np.dot(np.dot(T1.T,F),T2)\n\n    return F.T/F[2,2]\n\n\ndef compute_fundamental_Ransac(x1,x2,threshold=10e-4,maxiter=500,verbose=False,loRansac=False):\n    \n    def model_function(data,param=None):\n        s1 = data[:3]\n        s2 = data[3:]\n        F = compute_fundamental(s1,s2)\n        return np.ravel(F)\n\n    def error_function(M,data,param=None):\n        s1 = data[:3]\n        s2 = data[3:]\n        F = M.reshape((3,3))\n        return Sampson_error(s1,s2,F)\n\n    data = np.append(x1,x2,axis=0)\n    if loRansac:\n        return ransac.loRansacSimple(model_function,error_function,data,8,threshold,maxiter,verbose=verbose)\n    else:\n        return ransac.vanillaRansac(model_function,error_function,data,8,threshold,maxiter,verbose=verbose)\n\n\ndef compute_essential(x1,x2):\n    '''\n    Compute essential matrix from 2d points correspondences, \n    \n    which have to be normalized by calibration matrix K in advance.\n    '''\n\n    # Check that x1,x2 have same number of points\n    num = x1.shape[1]\n    if x2.shape[1] != num:\n        raise ValueError(\"Number of points do not match!\")\n    elif num < 8:\n        raise ValueError(\"At least 8 points needed!\")\n\n    # Normalize input points\n    x1, T1 = normalize_2d_points(x1)\n    x2, T2 = normalize_2d_points(x2)\n\n    # Design matrix A\n    A = np.array([x1[0]*x2[0],x1[0]*x2[1],x1[0],\n                  x1[1]*x2[0],x1[1]*x2[1],x1[1],\n                  x2[0],x2[1],np.ones(x1.shape[1])]).T\n    \n    # Solve F by SVD\n    U,S,V = np.linalg.svd(A)\n    E = V[-1].reshape(3,3)\n\n    # Constrain of det(E)=0 and first two singular values are equal (set to 1)\n    U,S,V = np.linalg.svd(E)\n    S[0], S[1], S[2] = 1, 1, 0\n    E = np.dot(np.dot(U,np.diag(S)),V)\n\n    # Denormalization\n    E = np.dot(np.dot(T1.T,E),T2)\n\n    return E.T/E[2,2]\n\n\ndef Sampson_error(x1,x2,F):\n    Fx1 = np.dot(F,x1)\n    Fx2 = np.dot(F.T,x2)\n\n    w = Fx1[0]**2 + Fx1[1]**2 + Fx2[0]**2 + Fx2[1]**2\n    error = np.sum(x2*np.dot(F,x1),axis=0)**2 / w\n\n    return error\n\n\ndef skew(a):\n    return np.array([[0,-a[2],a[1]],[a[2],0,-a[0]],[-a[1],a[0],0]])\n\n\ndef compute_epipole_from_F(F,left=False):\n    '''\n    Compute the epipole given the fundamental matrix, by default return the right epipole\n    '''\n\n    if left:\n        F = F.T\n    \n    U,S,V = np.linalg.svd(F)\n    e = V[-1]\n    return e/e[2]\n\n\ndef compute_P_from_F(F):\n    '''\n    Compute P2 from the fundamental matrix, assuming P1 = [I 0]\n    '''\n\n    # Compute the left epipole\n    e = compute_epipole_from_F(F,left=True)\n\n    Te = skew(e)\n    P = np.vstack((np.dot(Te,F.T).T,e)).T\n    return P\n\n\ndef solve_PnP(x,X):\n\n    n = x.shape[1]\n    M = np.zeros((3*n,12+n))\n    for i in range(n):\n        M[3*i,0:4] = X[:,i]\n        M[3*i+1,4:8] = X[:,i]\n        M[3*i+2,8:12] = X[:,i]\n        M[3*i:3*i+3,i+12] = -x[:,i]\n    U,S,V = np.linalg.svd(M)\n    return V[-1,:12].reshape((3,4))\n\n\ndef PnP(x,X):\n    x_homo, X_homo = x,X\n    x,X = x[:2], X[:3]\n    num = x.shape[1]\n\n    x_mean = np.mean(x,axis=1)\n    X_mean = np.mean(X,axis=1)\n    x_scale = np.mean(np.sqrt(sum((x-x_mean.reshape(-1,1))**2))) / np.sqrt(2)\n    X_scale = np.mean(np.sqrt(sum((x-x_mean.reshape(-1,1))**2))) / np.sqrt(3)\n\n    T = np.linalg.inv(np.array([[x_scale,0,x_mean[0]],[0,x_scale,x_mean[1]],[0,0,1]]))\n    U = np.linalg.inv(np.array([[X_scale,0,0,X_mean[0]],[0,X_scale,0,X_mean[1]],[0,0,X_scale,X_mean[2]],[0,0,0,1]]))\n    x_n = np.dot(T,x_homo)\n    X_n = np.dot(U,X_homo)\n\n    A = np.vstack((np.concatenate((X_n.T,np.zeros((num,4)),(X_n*-x_n[0]).T),axis=1),\n                   np.concatenate((np.zeros((num,4)),-X_n.T,(X_n*x_n[1]).T),axis=1)))\n\n    u,s,v = np.linalg.svd(A)\n    P = np.dot(np.dot(np.linalg.inv(T),v[-1].reshape((3,4))),U)\n    return P\n\n\ndef solve_PnP_Ransac(x,X,threshold=10):\n\n    def PnP_handle(data,*param):\n        x = data[:3]\n        X = data[3:]\n        P = solve_PnP(x,X)\n        # P = PnP(x,X)\n        return np.ravel(P)\n\n    def PnP_error(model,data,*param):\n        x_true = data[:3]\n        X = data[3:]\n        P = model.reshape((3,4))\n\n        x_cal = np.dot(P,X)\n        x_cal /= x_cal[-1]\n        return reprojection_error(x_true,x_cal)\n\n    data = np.vstack((x,X))\n    result = ransac.loRansacSimple(PnP_handle,PnP_error,data,6,threshold=threshold,maxIter=500)\n\n    return result['model'].reshape((3,4)), result['inliers']\n\n\ndef focal_length_from_F(F):\n    e1 = compute_epipole_from_F(F)\n    e2 = compute_epipole_from_F(F.T)\n\n    e1_rot = np.array([np.sqrt(e1[0]**2+e1[1]**2), 0, e1[2]])\n    e2_rot = np.array([np.sqrt(e2[0]**2+e2[1]**2), 0, e2[2]])\n\n    phi_1 = math.acos(np.dot(e1[:2],e1_rot[:2]) / (e1[0]**2+e1[1]**2)) * ((e1[1]<0)*2-1)\n    phi_2 = math.acos(np.dot(e2[:2],e2_rot[:2]) / (e2[0]**2+e2[1]**2)) * ((e2[1]<0)*2-1)\n\n    T1 = np.array([[math.cos(phi_1), -math.sin(phi_1), 0], [math.sin(phi_1), math.cos(phi_1), 0], [0,0,1]])\n    T2 = np.array([[math.cos(phi_2), -math.sin(phi_2), 0], [math.sin(phi_2), math.cos(phi_2), 0], [0,0,1]])\n\n    F_new = np.dot(np.dot(np.linalg.inv(T2).T, F), np.linalg.inv(T1))\n    D_1 = np.diag(np.array([e1_rot[2],1,-e1_rot[0]]))\n    D_2 = np.diag(np.array([e2_rot[2],1,-e2_rot[0]]))\n\n    conic = np.dot(np.dot(np.linalg.inv(D_2), F_new), np.linalg.inv(D_1))\n\n    a,b,c,d = conic[0,0],conic[0,1],conic[1,0],conic[1,1]\n    k1 = np.sqrt(-a*c*e1_rot[0]**2 / (a*c*e1_rot[2]**2+b*d))\n    k2 = np.sqrt(-a*b*e2_rot[0]**2 / (a*b*e2_rot[2]**2+c*d))\n    \n    # print('a={}, b={}, c={}, d={}\\n'.format(a,b,c,d))\n    return k1,k2\n\n\ndef focal_length_from_F_and_P(F,p1,p2):\n    '''\n    This function computes the focal length corresponding to the principle point p1\n\n    To get the other focal length, interchange the parameter into (F.T, p2, p1)\n    '''\n\n    I = np.diag([1,1,0])\n    e2 = compute_epipole_from_F(F,left=True)\n\n    num = np.linalg.multi_dot([p2,skew(e2),I,F,p1]) * np.linalg.multi_dot([p1,F.T,p2])\n    denom = np.linalg.multi_dot([p2,skew(e2),I,F,I,F.T,p2])\n\n    return -num/denom\n\n\ndef focal_length_iter(x1,x2,p1,p2,f1,f2):\n\n    # Ensure F has rank 2\n    def F_rank2(x1,x2,p1,p2,f1,f2):\n        F, mask = computeFundamentalMat(x1,x2,error=5)\n        K1 = np.array([[f1,0,p1[0]],[0,f1,p1[1]],[0,0,1]])\n        K2 = np.array([[f2,0,p2[0]],[0,f2,p2[1]],[0,0,1]])\n\n        E = np.dot(np.dot(K2.T,F),K1)\n        U,S,V = np.linalg.svd(E)\n        S[0], S[1], S[2] = 1, 1, 0\n        E = np.dot(np.dot(U,np.diag(S)),V)\n        F = np.dot(np.dot(np.linalg.inv(K2).T,E),np.linalg.inv(K1))\n        return F\n\n\n    # Define cost function\n    def focal_length_cost(M,data,guess):\n        # Decompose model, data and guess\n        F, p1, p2 = M[:9].reshape((3,3)), np.append(M[9:11],1), np.append(M[11:],1)\n        x1, x2 = data[:3], data[3:]\n        p1_g, p2_g, f1_g, f2_g = guess[:3], guess[3:6], guess[6]**2, guess[7]**2\n\n        # Cost 1\n        c1 = np.sum(Sampson_error(x1,x2,F))\n\n        # Cost 2\n        w2 = 0.01\n        c2_1 = (p1[0]-p1_g[0])**2 + (p1[1]-p1_g[1])**2\n        c2_2 = (p2[0]-p2_g[0])**2 + (p2[1]-p2_g[1])**2\n\n        # Cost 3\n        w3 = 0\n        f1 = focal_length_from_F_and_P(F,p1,p2)\n        f2 = focal_length_from_F_and_P(F.T,p2,p1)\n        c3_1 = (f1-f1_g)**2\n        c3_2 = (f2-f2_g)**2\n\n        # Cost dependent\n        w4 = 0.01\n        f_min = 100*2\n        d1 = f_min > f1\n        d2 = f_min > f2\n        c4_1 = (f1-f_min)**2\n        c4_2 = (f2-f_min)**2\n\n        cost = c1 + w2**2*(c2_1+c2_2) + w3**2*(c3_1+c3_2) + w3**2*(c4_1*d1+c4_2*d2)\n        return cost\n\n\n    # Initial values\n    # F_ini = F_rank2(x1,x2,p1,p2,f1,f2)\n    # F_ini /= F_ini[2,2]\n    F_ini, mask = computeFundamentalMat(x1,x2,error=5)\n    M_ini = np.concatenate((np.ravel(F_ini),p1[:2],p2[:2]),axis=0)\n    guess = np.concatenate((p1,p2,np.array([f1,f2])),axis=0)\n\n    # Least square optimization\n    data = np.vstack((x1,x2))\n    fn = lambda x: focal_length_cost(x,data,guess)\n    res = least_squares(fn,M_ini)\n    M_o = res[\"x\"]\n\n    F_o, p1_o, p2_o = M_o[:9].reshape((3,3)), np.append(M_o[9:11],1), np.append(M_o[11:],1)\n    f1_o = focal_length_from_F_and_P(F_o, p1_o, p2_o)\n    f2_o = focal_length_from_F_and_P(F_o.T, p2_o, p1_o)\n\n    return f1_o, f2_o\n\n\ndef triangulate_point(x1,x2,P1,P2):\n    '''\n    Triangulate a single point using least square (SVD)\n    '''\n\n    M = np.zeros((6,6))\n    M[:3,:4] = P1\n    M[3:,:4] = P2\n    M[:3,4] = -x1\n    M[3:,5] = -x2\n\n    U,S,V = np.linalg.svd(M)\n    X = V[-1,:4]\n\n    return X / X[3]\n\n\ndef triangulate(x1,x2,P1,P2):\n    '''\n    Triangulate multiple points, x1 and x2 in form of (3*N)\n    '''\n\n    X = [triangulate_point(x1[:,i],x2[:,i],P1,P2) for i in range(x1.shape[1])]\n    return np.array(X).T\n\n\ndef triangulate_matlab(x1,x2,P1,P2):\n\n    X = np.zeros((4,x1.shape[1]))\n    for i in range(x1.shape[1]):\n        r1 = x1[0,i]*P1[2] - P1[0]\n        r2 = x1[1,i]*P1[2] - P1[1]\n        r3 = x2[0,i]*P2[2] - P2[0]\n        r4 = x2[1,i]*P2[2] - P2[1]\n\n        A = np.array([r1,r2,r3,r4])\n        U,S,V = np.linalg.svd(A)\n        X[:,i] = V[-1]/V[-1,-1]\n    \n    return X\n    \n\ndef compute_Rt_from_E(E):\n    '''\n    Compute the camera matrix P2, where P1=[I 0] assumed\n\n    Return 4 possible combinations of R and t as a list\n    '''\n\n    U,S,V = np.linalg.svd(E)\n    if np.linalg.det(np.dot(U,V))<0:\n        V = -V\n    E = np.dot(U,np.dot(np.diag([1,1,0]),V))\n\n    W = np.array([[0,-1,0],[1,0,0],[0,0,1]])\n    R1 = np.dot(U,np.dot(W,V))\n    R2 = np.dot(U,np.dot(W.T,V))\n    R1 = R1 * np.linalg.det(R1)\n    R2 = R2 * np.linalg.det(R2)\n    t1 = U[:,2].reshape((-1,1))\n    t2 = -U[:,2].reshape((-1,1))\n\n    Rt = [np.hstack((R1,t1)),np.hstack((R1,t2)),np.hstack((R2,t1)),np.hstack((R2,t2))]    \n\n    # P2 = [np.vstack((np.dot(U,np.dot(W,V)).T,U[:,2])).T,\n    #       np.vstack((np.dot(U,np.dot(W,V)).T,-U[:,2])).T,\n    #       np.vstack((np.dot(U,np.dot(W.T,V)).T,U[:,2])).T,\n    #       np.vstack((np.dot(U,np.dot(W.T,V)).T,-U[:,2])).T]\n    return Rt\n\n\ndef triangulate_from_E_old(E,K1,K2,x1,x2):\n    '''\n    Not correct !! \n\n    Use \"triangulate_from_E\" instead\n    '''\n\n    infront_max = 0\n\n    Rt = compute_Rt_from_E(E)\n    P1 = np.dot(K1,np.array([[1,0,0,0],[0,1,0,0],[0,0,1,0]]))\n    for i in range(4):\n        P2_temp = np.dot(K2,Rt[i])\n        X = triangulate_matlab(x1,x2,P1,P2_temp)\n        d1 = np.dot(P1,X)[2]\n        d2 = np.dot(P2_temp,X)[2]\n\n        if sum(d1>0)+sum(d2>0) > infront_max:\n            infront_max = sum(d1>0)+sum(d2>0)\n            infront = (d1>0) & (d2>0)\n            P2 = P2_temp\n    \n    X = triangulate_matlab(x1,x2,P1,P2) \n    return X[:,:], P2\n\n\ndef triangulate_from_E(E,K1,K2,x1,x2):\n    x1n = np.dot(np.linalg.inv(K1),x1)\n    x2n = np.dot(np.linalg.inv(K2),x2)\n\n    infront_max = 0\n\n    Rt = compute_Rt_from_E(E)\n    P1 = np.array([[1,0,0,0],[0,1,0,0],[0,0,1,0]])\n    for i in range(4):\n        P2_temp = Rt[i]\n        X = triangulate_matlab(x1n,x2n,P1,P2_temp)\n        d1 = np.dot(P1,X)[2]\n        d2 = np.dot(P2_temp,X)[2]\n\n        if sum(d1>0)+sum(d2>0) > infront_max:\n            infront_max = sum(d1>0)+sum(d2>0)\n            infront = (d1>0) & (d2>0)\n            P2 = P2_temp\n    \n    X = triangulate_matlab(x1n,x2n,P1,P2) \n    return X[:,:], P2\n\n\ndef triangulate_cv(E,K1,K2,x1,x2):\n    '''\n    Triangulation with OpenCV functions\n    '''\n\n    num, R, t, mask = cv2.recoverPose(E, x1[:2].T, x2[:2].T, K1)\n    P1 = np.dot(K1,np.array([[1,0,0,0],[0,1,0,0],[0,0,1,0]]))\n    P2 = np.dot(K2,np.hstack((R,t)))\n\n    # x1 = np.dot(np.linalg.inv(K1),x1)\n    # x2 = np.dot(np.linalg.inv(K2),x2)\n    # P1 = np.array([[1,0,0,0],[0,1,0,0],[0,0,1,0]])\n    # P2 = np.hstack((R,t))\n\n    pts1 = x1[:2].astype(np.float64)\n    pts2 = x2[:2].astype(np.float64)\n    X = cv2.triangulatePoints(P1,P2,pts1,pts2)\n    return X/X[-1], np.hstack((R,t))\n\n\ndef undistort(x_homo,coeff):\n\n    # Method 1\n    # x_dist = x_homo[:2]\n\n    # def dist_model(x):\n    #     u,v = np.split(x,2)\n    #     r = u**2 + v**2\n    #     res_1 = u + u*coeff[0]*r + u*coeff[1]*r**2 - x_dist[0]\n    #     res_2 = v + v*coeff[0]*r + v*coeff[1]*r**2 - x_dist[1]\n    #     return np.concatenate((res_1,res_2))\n\n    # sol = root(dist_model,x_dist.ravel())\n    # x_homo[:2] = sol.x.reshape((2,-1))\n\n    # Method 2\n    def dist_model(x,*arg):\n        c1, c2, x_dist, y_dist = arg[0], arg[1], arg[2], arg[3]\n        r = x[0]**2 + x[1]**2\n        return [x[0]*(1+c1*r+c2*r**2)-x_dist,x[1]*(1+c1*r+c2*r**2)-y_dist]\n\n    for i in range(x_homo.shape[1]):\n        sol = root(dist_model,[x_homo[0,i],x_homo[1,i]],args=(coeff[0],coeff[1],x_homo[0,i],x_homo[1,i]))\n        x_homo[:2,i] = sol.x\n\n    return x_homo\n\n\ndef reprojection_error(x,x_p):\n    return np.sqrt((x[0]-x_p[0])**2 + (x[1]-x_p[1])**2)\n\n\nif __name__ == \"__main__\":\n\n    ''' Test '''\n    img1 = cv2.imread('C:/Users/tong2/MyStudy/ETH/2018HS/ComputerVision/lab/lab04/cv_lab04_model_fitting/src/epipolar_geometry/images/pumpkin1.jpg',0)\n    img2 = cv2.imread('C:/Users/tong2/MyStudy/ETH/2018HS/ComputerVision/lab/lab04/cv_lab04_model_fitting/src/epipolar_geometry/images/pumpkin2.jpg',0)\n\n    # Extract SIFT features\n    kp1, des1 = extract_SIFT_feature(img1)\n    kp2, des2 = extract_SIFT_feature(img2)\n\n    # Match features\n    pts1, pts2, matches, matchesMask = matching_feature(kp1, kp2, des1, des2, ratio=0.8)\n\n    # Compute fundametal matrix F1\n    F1, mask = computeFundamentalMat(pts1, pts2)\n    pts1 = np.int32(pts1)[mask.ravel()==1]\n    pts2 = np.int32(pts2)[mask.ravel()==1]\n\n    # Compute fundametal matrix F2 using inliers\n    x1 = np.vstack((pts1.T,np.ones(pts1.shape[0])))\n    x2 = np.vstack((pts2.T,np.ones(pts2.shape[0])))\n    F2 = compute_fundamental(x1,x2)\n\n    # # Draw epipolar lines\n    vis.plotEpiline(img1, img2, pts1, pts2, F1)\n\n    # Draw epipolar lines\n    vis.plot_epipolar_line(img1,img2,F2,x1,x2)\n","repo_name":"CenekAlbl/mvus","sub_path":"multiviewunsynch/reconstruction/epipolar.py","file_name":"epipolar.py","file_ext":"py","file_size_in_byte":19679,"program_lang":"python","lang":"en","doc_type":"code","stars":62,"dataset":"github-code","pt":"81"}
+{"seq_id":"9583463345","text":"import sys\nimport ast\nimport re\nimport signal\nimport binascii\nimport telnetlib\nimport cs_go_weapons\nfrom time import sleep\nfrom enum import IntEnum, unique\nfrom ff_event import FeedbackEvent, FeedbackEventType, FeedbackEventDirection, FeedbackEventLocation\n\n# Config\nTN_PORT = \"2121\"\nTN_HOST = \"127.0.0.1\"\n\n@unique\nclass HitGroup(IntEnum):\n    Generic  = 0\n    Head     = 1\n    Chest    = 2\n    Stomach  = 3\n    LeftArm  = 4\n    RightArm = 5\n    LeftLeg  = 6\n    RightLeg = 7\n    Lethal   = 8\n\nWEAPON_HAND = FeedbackEventDirection.Right\n\nclass PlayerDamageEventParser:\n    def __init__(self, damage_str: str):\n        damage_dict = dict_from_ts_package(damage_str)\n        self.health = int(damage_dict[\"health\"])\n        self.armor = int(damage_dict[\"armor\"])\n        self.weapon = str(damage_dict[\"weapon\"])\n        self.dmg_health = int(damage_dict[\"dmg_health\"])\n        self.dmg_armor = int(damage_dict[\"dmg_armor\"])\n        self.hit_group = HitGroup(int(damage_dict[\"hitgroup\"]))\n        self.impact_height = float(damage_dict[\"imp_height\"])\n        self.impact_angle = float(damage_dict[\"imp_angle\"])\n\n    def __str__(self):\n        return \"health: \" + str(self.health) + \", armor: \" + str(self.armor) + \", weapon: \" + str(self.weapon) + \\\n            \", dmg_health: \" + str(self.dmg_health) + \", dmg_armor: \" + str(self.dmg_armor) + \", hit_group: \" + str(self.hit_group)\n\n    @staticmethod\n    def get_keyword():\n        return b\"TsDamage\"\n\nclass WeaponFireEventParser:\n    def __init__(self, damage_str: str):\n        fire_event_dict = dict_from_ts_package(damage_str)\n        self.weapon = str(fire_event_dict[\"weapon\"])\n        self.is_silenced = bool(ast.literal_eval(fire_event_dict[\"is_silenced\"].lower().capitalize()))\n\n    def __str__(self):\n        return \"weapon: \" + str(self.weapon) + \", is_silenced: \" + str(self.is_silenced)\n\n    @staticmethod\n    def get_keyword():\n        return b\"TsWeaponFire\"\n\n\nclass TsGoDamageListener:\n    def __init__(self):\n        self.host = TN_HOST\n        self.port = TN_PORT\n\n    def init(self):\n        while True:\n            if self.try_connect():\n                print(\"CS:GO game connected.\")\n                break;\n            else:\n                print(\"Wait for connection to CS:GO game...\")\n                sleep(1)\n\n    def set_event_callback(self, callback):\n        self.event_callback = callback\n\n    def read_handedness(self):\n        cl_righthand = b\"cl_righthand\"\n        handedness_str = self.get_var(cl_righthand)\n        if handedness_str == None:\n            return WEAPON_HAND\n        handedness_val = int(handedness_str)\n        if handedness_val > 0:\n            return FeedbackEventDirection.Right\n        else:\n            return FeedbackEventDirection.Left\n\n    def get_var(self, command: bytes):\n        command_unknown_str = (\"Unknown command \\\"\" + command.decode(\"utf-8\") + \"\\\"\")\n        cmd_unkn_str_b = command_unknown_str.encode(\"utf-8\")\n        self.tn.write(command + b\"\\n\")\n        line = b\"\"\n        response_received = False\n        while not(response_received):\n            line = self.tn.read_until(b\"\\n\")\n            if cmd_unkn_str_b in line:\n                response_received = True\n                return None\n            if command in line:\n                response_received = True\n                expr = re.compile(b'\"([^\"]*)\"')\n                res = expr.findall(line)\n                return res[1].decode(\"utf-8\")\n        return None\n\n\n    def try_connect(self):\n        try:\n            self.tn = telnetlib.Telnet(self.host, self.port)\n        except ConnectionRefusedError:\n            print(\"Connection refused. Make sure you have the following launch option set in game launch arguments:\")\n            print(\"  -netconport \" + str(self.port))\n            return False\n        self.tn.write(b\"echo TS CS:GO Damage Handler Activated\\n\")\n        WEAPON_HAND = self.read_handedness()\n        return True\n\n    def process(self):\n        line = b\"\"\n        package_received = False\n        haptic_event = None\n        while not(package_received):\n            line = self.tn.read_until(b\"\\n\")\n            if WeaponFireEventParser.get_keyword() in line:\n                package_received = True\n                fire_event = WeaponFireEventParser(line.decode(\"utf-8\"))\n                print(fire_event)\n                haptic_event = self.convert_weapon_fire_to_haptic_event(fire_event)\n            if PlayerDamageEventParser.get_keyword() in line:\n                package_received = True\n                damage_event = PlayerDamageEventParser(line.decode(\"utf-8\"))\n                print(damage_event)\n                haptic_event = self.convert_damage_to_haptic_event(damage_event)\n\n        if self.event_callback != None:\n            self.event_callback({haptic_event})\n        else:\n            print(\"CS GO client: no client subscribed to events\")\n\n    def convert_damage_to_haptic_event(self, damage_event):\n        dmg_percent = normalize(damage_event.dmg_health, float(0), float(100))\n        return FeedbackEvent(type=FeedbackEventType.Hit,\n            direction=convert_angle_to_direction(damage_event.impact_angle),\n            location=FeedbackEventLocation(damage_event.hit_group.value + 1),\n            intensity_percent=percent_to_intensity(dmg_percent, float(0.3), float(1)))\n\n    def convert_weapon_fire_to_haptic_event(self, weapon_fire_event):\n        return FeedbackEvent(type=FeedbackEventType.Recoil,\n            direction=WEAPON_HAND,\n            location=FeedbackEventLocation(WEAPON_HAND.value + 1),\n            intensity_percent=get_weapon_feedback_percent(weapon_fire_event.weapon),\n            weapon_type=get_weapon_type(weapon_fire_event.weapon))\n\ndef get_weapon_feedback_percent(code):\n    if code in cs_go_weapons.WEAPON_FIRE_FEEDBACK:\n            return cs_go_weapons.WEAPON_FIRE_FEEDBACK[code]\n    return 0\n\ndef get_weapon_type(code):\n    if code in cs_go_weapons.WEAPON_TYPES:\n            return cs_go_weapons.WEAPON_TYPES[code]\n    return FeedbackEventWeaponType.Undefined\n\ndef convert_angle_to_direction(angle):\n    if (angle < 90):\n        return FeedbackEventDirection.FrontLeft\n    elif (angle < 180):\n        return FeedbackEventDirection.BackLeft\n    elif (angle < 270):\n        return FeedbackEventDirection.BackRight\n    elif (angle < 360):\n        return FeedbackEventDirection.FrontRight\n    return FeedbackEventDirection.Front\n\n\ndef normalize(value, min, max):\n    if (value > max):\n        return float(1)\n    elif (value < min):\n        return float(0)\n    else:\n        return float(value - min) / float(max - min)\n\ndef percent_to_intensity(value, min, max):\n    if value == 0:\n        return 0\n    return float(max - min) * float(value) + float(min)\n\ndef dict_from_ts_package(ts_package_str: str):\n    package_dict = {}\n    ts_package_str = ts_package_str.strip().replace(\" \", \"\")\n    fields = ts_package_str[ts_package_str.find(\"{\") + 1 : ts_package_str.find(\"}\")].split(\",\")\n    for row in fields:\n        kv = row.split(\":\")\n        package_dict[kv[0]] = kv[1]\n    return package_dict\n","repo_name":"teslasuit/cs-go-teslasuit-experience","sub_path":"cs_go_client.py","file_name":"cs_go_client.py","file_ext":"py","file_size_in_byte":7033,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"81"}
+{"seq_id":"5231032582","text":"'''\n24. Swap Nodes in Pairs\nMedium\n\nShare\nGiven a linked list, swap every two adjacent nodes and return its head.\n\nExample:\n\nGiven 1->2->3->4, you should return the list as 2->1->4->3.\nNote:\n\nYour algorithm should use only constant extra space.\nYou may not modify the values in the list's nodes, only nodes itself may be changed.\n'''\n\n# Definition for singly-linked list.\nclass ListNode:\n    def __init__(self, x):\n        self.val = x\n        self.next = None\n\nclass Solution:\n    def swapPairs(self, head):\n        \"\"\"\n        :type head: ListNode\n        :rtype: ListNode\n        \"\"\"\n        dummy = ListNode(None)\n        nodedummy = dummy\n        nodehead = head\n\n        if nodehead == None or nodehead.next == None:\n          return nodehead\n        \n        while nodehead and nodehead.next:\n          second = nodehead\n          first = nodehead.next\n          second.next = first.next\n          nodedummy.next = first\n          first.next = second\n\n          nodehead = second.next\n          nodedummy = nodedummy.next.next\n\n        return dummy.next","repo_name":"yiyi1026/projecteuler_ruby","sub_path":"leetcode/pythonSolutions/24.swap_nodes_in_pairs.py","file_name":"24.swap_nodes_in_pairs.py","file_ext":"py","file_size_in_byte":1060,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"81"}
+{"seq_id":"3786542938","text":"'''\nA palindromic number reads the same both ways. The largest palindrome made from the product of two 2-digit numbers is 9009 = 91 × 99.\n\nFind the largest palindrome made from the product of two 3-digit numbers.\n'''\n\ndef is_palin(n):\n    str_reverse = \"\"\n    str_n = str(n)\n    length = len(str_n)\n    \n    if(length % 2 == 1):\n        str_first = str_n[0:int(length/2)+1]\n    else:\n        str_first = str_n[0:int(length/2)]\n\n    for i in range(length,int(length/2),-1):\n        str_reverse += str_n[i-1]\n\n    if str_first == str_reverse: return True\n    return False\n    \ndef largest_palindrome():\n    n = 998001\n    final = 1000\n    while n > 10000:\n        if(is_palin(n)):\n            \n            a = 100\n            while a < 1000:\n                if (n % a == 0):\n                    if len(str(int(n/a))) == 3:\n                        return n\n                a += 1\n        n -= 1\n                    \nprint(largest_palindrome())\nminx = 100*100\nmaxx = 999*999\n#print(minx)\n#print(maxx)\n","repo_name":"stanwong86/ProjectEuler","sub_path":"1_25/p4.py","file_name":"p4.py","file_ext":"py","file_size_in_byte":998,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"21354763466","text":"\nfrom django.core.validators import  MaxValueValidator\n\nclass SizeValidator(MaxValueValidator):\n    #Rewritting the MaxValueValidator from django in order to adapted to the current situation\n    \n    def __call__(self, value):\n        cleaned = self.clean(value)\n        limit_value = self.limit_value() if callable(self.limit_value) else self.limit_value\n        if self.compare(cleaned.size, limit_value):\n            raise ValidationError(f\"Ensure this file '{cleaned.name}' is less or equal than {limit_value/1000000} MB\", code=self.code)","repo_name":"daironirdp/PostingSocialApp","sub_path":"MainContent/Post/serializers/validators.py","file_name":"validators.py","file_ext":"py","file_size_in_byte":542,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"20425667053","text":"# uvozimo ustrezne podatke za povezavo\nimport auth as auth\n\n# uvozimo psycopg2\nimport psycopg2, psycopg2.extensions, psycopg2.extras\npsycopg2.extensions.register_type(psycopg2.extensions.UNICODE) # se znebimo problemov s šumniki\n\nimport csv\n\n\nconn = psycopg2.connect(database=auth.db, host=auth.host, user=auth.user, password=auth.password)\ncur = conn.cursor(cursor_factory=psycopg2.extras.DictCursor) \n\n##################################################################################################################################################\n#LASTNIKI\n\ndef ustvari_tabelo_lastniki():\n    cur.execute(\"\"\"\n        CREATE TABLE lastniki (\n            ekipa TEXT REFERENCES ekipe_osnovni_podatki(ekipa),\n            ime_ekipe TEXT NOT NULL,\n            lastniki TEXT PRIMARY KEY NOT NULL,\n            upravljalska_entiteta TEXT NOT NULL,\n            nakupna_cena TEXT NOT NULL,\n            leto_nakupa INTEGER NOT NULL\n            \n            );\n    \"\"\")\n    conn.commit()\n\ndef pobrisi_tabelo_lastniki():\n    cur.execute(\"\"\"\n        DROP TABLE lastniki;\n    \"\"\")\n    conn.commit()\n\ndef uvozi_podatke_lastniki():\n    with open(\"podatki/lastniki.csv\", encoding='utf8',errors='ignore') as f:\n        rd = csv.reader(f)\n        next(rd) # izpusti naslovno vrstico\n        for r in rd:\n            cur.execute(\"\"\"\n                INSERT INTO lastniki\n                (ekipa, ime_ekipe, lastniki, upravljalska_entiteta, nakupna_cena, leto_nakupa)\n                VALUES (%s, %s, %s, %s, %s, %s)\n                \"\"\", r)\n    conn.commit()\n\npobrisi_tabelo_lastniki()\nustvari_tabelo_lastniki()\nuvozi_podatke_lastniki()","repo_name":"gapo8/NBA","sub_path":"tabele/lastniki.py","file_name":"lastniki.py","file_ext":"py","file_size_in_byte":1615,"program_lang":"python","lang":"sl","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"74696474826","text":"import pandas as pd\nfrom sklearn.preprocessing import StandardScaler, LabelEncoder\nfrom sklearn.model_selection import StratifiedShuffleSplit\nfrom xgboost import XGBClassifier\nfrom imblearn.over_sampling import RandomOverSampler\n\ndef predict_likelihood(df, label):\n    # input data as a dataframe\n    # label = label column name in the data to predict\n        \n    # encode the label and one-hot code categorical variables\n    label_encoder = LabelEncoder()\n    df[label] = label_encoder.fit_transform(df[label])\n    df = pd.get_dummies(df, drop_first=True)\n    \n    features = ['age', 'euribor3m', 'campaign', 'housing_yes', 'pdays', 'loan_yes',\n       'poutcome_success', 'cons.price.idx', 'marital_married',\n       'education_high.school', 'education_university.degree', 'previous',\n       'job_technician', 'marital_single', 'day_of_week_mon',\n       'day_of_week_wed', 'day_of_week_tue', 'day_of_week_thu',\n       'contact_telephone', 'default_unknown', 'job_blue-collar',\n       'education_professional.course', 'education_basic.9y', 'job_management',\n       'poutcome_nonexistent', 'job_services', 'job_retired',\n       'education_unknown', 'education_basic.6y', 'job_self-employed',\n       'job_entrepreneur', 'job_unemployed', 'month_oct', 'job_student',\n       'job_housemaid', 'month_mar', 'month_may']\n    \n    # Lets split the data in training and test sets with stratification to keep the proportion of y classes even\n    stratifier = StratifiedShuffleSplit(n_splits=1, test_size=0.2, random_state=10)\n    for train_index, test_index in stratifier.split(df, df['y']):\n        train_data = df.loc[train_index]\n        test_data = df.loc[test_index]\n       \n    # feature select and scale the data\n    ytrain = train_data[label]\n    ytest = test_data[label]\n    sc = StandardScaler()\n    xtrain = sc.fit_transform(train_data.loc[:, features])\n    xtest = sc.transform(test_data.loc[:, features])\n    \n    # over-sample training data\n    os = RandomOverSampler(random_state=10)\n    xtrain, ytrain = os.fit_sample(xtrain, ytrain)\n    \n    # train and predict\n    clf = XGBClassifier(booster='gbtree', random_state=10)\n    clf.fit(xtrain, ytrain)\n    y_pred = clf.predict(xtest)\n    y_prob = clf.predict_proba(xtest)[:, 1]\n    return y_prob\n    \nif __name__ == '__main__':\n    df = pd.read_csv('../../Data/bank-direct-marketing.csv', sep=';')\n    print(predict_likelihood(df, 'y'))\n   \n\n      \n","repo_name":"danyzix/Binary-Classification","sub_path":"predict.py","file_name":"predict.py","file_ext":"py","file_size_in_byte":2403,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"72725351625","text":"from __future__ import division\nimport numpy as np\nfrom scipy.optimize import minimize\nfrom scipy.io import loadmat\nfrom math import sqrt\nimport time\nfrom sklearn.svm import SVC\nimport sys\nimport pickle\n\ndef preprocess():\n    \"\"\" \n     Input:\n     Although this function doesn't have any input, you are required to load\n     the MNIST data set from file 'mnist_all.mat'.\n\n     Output:\n     train_data: matrix of training set. Each row of train_data contains \n       feature vector of a image\n     train_label: vector of label corresponding to each image in the training\n       set\n     validation_data: matrix of training set. Each row of validation_data \n       contains feature vector of a image\n     validation_label: vector of label corresponding to each image in the \n       training set\n     test_data: matrix of training set. Each row of test_data contains \n       feature vector of a image\n     test_label: vector of label corresponding to each image in the testing\n       set\n\n     Some suggestions for preprocessing step:\n     - divide the original data set to training, validation and testing set\n           with corresponding labels\n     - convert original data set from integer to double by using double()\n           function\n     - normalize the data to [0, 1]\n     - feature selection\n    \"\"\"\n    \n    mat = loadmat('mnist_all.mat'); #loads the MAT object as a Dictionary\n    \n    n_feature = mat.get(\"train1\").shape[1];\n    n_sample = 0;\n    for i in range(10):\n        n_sample = n_sample + mat.get(\"train\"+str(i)).shape[0];\n    n_validation = 1000;\n    n_train = n_sample - 10*n_validation;\n    \n    # Construct validation data\n    validation_data = np.zeros((10*n_validation,n_feature));\n    for i in range(10):\n        validation_data[i*n_validation:(i+1)*n_validation,:] = mat.get(\"train\"+str(i))[0:n_validation,:];\n        \n    # Construct validation label\n    validation_label = np.ones((10*n_validation,1));\n    for i in range(10):\n        validation_label[i*n_validation:(i+1)*n_validation,:] = i*np.ones((n_validation,1));\n    \n    # Construct training data and label\n    train_data = np.zeros((n_train,n_feature));\n    train_label = np.zeros((n_train,1));\n    temp = 0;\n    for i in range(10):\n        size_i = mat.get(\"train\"+str(i)).shape[0];\n        train_data[temp:temp+size_i-n_validation,:] = mat.get(\"train\"+str(i))[n_validation:size_i,:];\n        train_label[temp:temp+size_i-n_validation,:] = i*np.ones((size_i-n_validation,1));\n        temp = temp+size_i-n_validation;\n        \n    # Construct test data and label\n    n_test = 0;\n    for i in range(10):\n        n_test = n_test + mat.get(\"test\"+str(i)).shape[0];\n    test_data = np.zeros((n_test,n_feature));\n    test_label = np.zeros((n_test,1));\n    temp = 0;\n    for i in range(10):\n        size_i = mat.get(\"test\"+str(i)).shape[0];\n        test_data[temp:temp+size_i,:] = mat.get(\"test\"+str(i));\n        test_label[temp:temp+size_i,:] = i*np.ones((size_i,1));\n        temp = temp + size_i;\n    \n    # Delete features which don't provide any useful information for classifiers\n    sigma = np.std(train_data, axis = 0);\n    index = np.array([]);\n    for i in range(n_feature):\n        if(sigma[i] > 0.001):\n            index = np.append(index, [i]);\n    train_data = train_data[:,index.astype(int)];\n    validation_data = validation_data[:,index.astype(int)];\n    test_data = test_data[:,index.astype(int)];\n\n    # Scale data to 0 and 1\n    train_data = train_data/255.0;\n    validation_data = validation_data/255.0;\n    test_data = test_data/255.0;\n    \n    return train_data, train_label, validation_data, validation_label, test_data, test_label\n\ndef sigmoid(z):\n    return 1.0/(1.0 + np.exp(-z));\n    \ndef blrObjFunction(params, *args):\n    \"\"\"\n    blrObjFunction computes 2-class Logistic Regression error function and\n    its gradient.\n\n    Input:\n        initialWeights: the weight vector of size (D + 1) x 1 \n        train_data: the data matrix of size N x D\n        labeli: the label vector of size N x 1 where each entry can be either 0 or 1\n                representing the label of corresponding feature vector\n\n    Output: \n        error: the scalar value of error function of 2-class logistic regression\n        error_grad: the vector of size (D+1) x 1 representing the gradient of\n                    error function\n    \"\"\"\n    n_data = train_data.shape[0];\n    n_feature = train_data.shape[1];\n    error = 0;\n    error_grad = np.zeros((n_feature+1,1));\n    \n    ##################\n    # YOUR CODE HERE #\n    ##################\n    print ('\\n---blrObjFunction---')\n    params = params.reshape(n_feature+1,1)\n    \n    X = np.hstack((train_data, np.ones((n_data, 1))))\n    \n    res = np.dot(X, params)\n    y = sigmoid(res)\n    \n    error = float(0.0)\n    error = float(-(np.dot(labeli.T, np.log(y)) + np.dot((1-labeli).T, np.log(1-y))))\n    print ('error', error)\n    \n    error_grad = (np.dot((y - labeli).T, X)).flatten()\n    print ('error grad', error_grad.shape)\n    \n    return error, error_grad\n    \ndef blrPredict(W, data):\n    \"\"\"\n     blrObjFunction predicts the label of data given the data and parameter W \n     of Logistic Regression\n     \n     Input:\n         W: the matrix of weight of size (D + 1) x 10. Each column is the weight \n         vector of a Logistic Regression classifier.\n         X: the data matrix of size N x D\n         \n     Output: \n         label: vector of size N x 1 representing the predicted label of \n         corresponding feature vector given in data matrix\n\n    \"\"\"\n    label = np.zeros((data.shape[0],1));\n    \n    ##################\n    # YOUR CODE HERE #\n    ##################\n    print ('\\n---blrPredict---')\n    \n    X = np.hstack((data, np.ones((data.shape[0],1))))\n    \n    result = np.dot(X, W)\n    y = sigmoid(result)\n    \n    for i in range(label.shape[0]):\n        label[i,0] = np.argmax(y[i])\n\n    return label\n\n\"\"\"\nScript for Logistic Regression\n\"\"\"\nts = time.time()\n\ntrain_data, train_label, validation_data,validation_label, test_data, test_label = preprocess();\n\n# number of classes\nn_class = 10;\n\n# number of training samples\nn_train = train_data.shape[0];\n\n# number of features\nn_feature = train_data.shape[1];\n\nT = np.zeros((n_train, n_class));\nfor i in range(n_class):\n    T[:,i] = (train_label == i).astype(int).ravel();\n    \n# Logistic Regression with Gradient Descent\nW = np.zeros((n_feature+1, n_class));\ninitialWeights = np.zeros((n_feature+1,1));\nopts = {'maxiter' : 50};\nfor i in range(n_class):\n    labeli = T[:,i].reshape(n_train,1);\n    args = (train_data, labeli);\n    nn_params = minimize(blrObjFunction, initialWeights, jac=True, args=args,method='CG', options=opts)\n    W[:,i] = nn_params.x.reshape((n_feature+1,));\n\n# Find the accuracy on Training Dataset\npredicted_label = blrPredict(W, train_data);\nprint('\\n Training set Accuracy:' + str(100*np.mean((predicted_label == train_label).astype(float))) + '%')\n\n# Find the accuracy on Validation Dataset\npredicted_label = blrPredict(W, validation_data);\nprint('\\n Validation set Accuracy:' + str(100*np.mean((predicted_label == validation_label).astype(float))) + '%')\n\n# Find the accuracy on Testing Dataset\npredicted_label = blrPredict(W, test_data);\nprint('\\n Testing set Accuracy:' + str(100*np.mean((predicted_label == test_label).astype(float))) + '%')\n\nsys.stdout.flush()\n\n#Data dump\nprint ('Writing to pickle file')\npickle.dump( [W], open( \"params.pickle\", \"wb\" ))\nprint ('Writing to pickle file done')\n\nts2 = time.time()\nprint ('Logistic Regression part took ', (ts2-ts), ' seconds to execute')\n\n\n\"\"\"\nScript for Support Vector Machine\n\n\"\"\"\n\nprint('\\n\\n--------------SVM-------------------\\n\\n')\n##################\n# YOUR CODE HERE #\n##################\n\n#linear kernel\nprint ('Linear Kernel')\nclf1 = SVC(kernel='linear')\nflat_train_label = train_label.flatten()\nclf1.fit(train_data, flat_train_label)\n\ntrain_acc_1 = clf1.score(train_data, train_label) *100\nprint('\\n Training set Accuracy:' + str(train_acc_1.astype(float)) + '%')\n\nvalidation_acc_1 = clf1.score(validation_data, validation_label) * 100\nprint('\\n Validation set Accuracy:' + str(validation_acc_1.astype(float)) + '%')\n\ntest_acc_1 = clf1.score(test_data, test_label) * 100\nprint('\\n Test set Accuracy:' + str(test_acc_1.astype(float)) + '%')\n\nsys.stdout.flush()\nts3 = time.time()\nprint ('SVM - Linear Kernel part took', (ts3-ts2), 'seconds to execute')\n\n\n#rbf with gamma 1\nprint ('RBF with gamma 1')\nclf2 = SVC(gamma=1.0)\nflat_train_label = train_label.flatten()\nclf2.fit(train_data, flat_train_label)\n\ntrain_acc_2 = clf2.score(train_data, train_label) *100\nprint('\\n Training set Accuracy:' + str(train_acc_2.astype(float)) + '%')\n\nvalidation_acc_2 = clf2.score(validation_data, validation_label) * 100\nprint('\\n Validation set Accuracy:' + str(validation_acc_2.astype(float)) + '%')\n\ntest_acc_2 = clf2.score(test_data, test_label) * 100\nprint('\\n Test set Accuracy:' + str(test_acc_2.astype(float)) + '%')\n\nsys.stdout.flush()\nts4 = time.time()\nprint ('SVM - RBF Kernel with gamma 1 part took', (ts4-ts3), 'seconds to execute')\n\n\n#rbf and default\nprint ('RBF and default')\nclf3 = SVC()\nflat_train_label = train_label.flatten()\nclf3.fit(train_data, flat_train_label)\n\ntrain_acc_3 = clf3.score(train_data, train_label) *100\nprint('\\n Training set Accuracy:' + str(train_acc_3.astype(float)) + '%')\n\nvalidation_acc_3 = clf3.score(validation_data, validation_label) * 100\nprint('\\n Validation set Accuracy:' + str(validation_acc_3.astype(float)) + '%')\n\ntest_acc_3 = clf3.score(test_data, test_label) * 100\nprint('\\n Test set Accuracy:' + str(test_acc_3.astype(float)) + '%')\n\nsys.stdout.flush()\nts5 = time.time()\nprint ('SVM - RBF Kernel with default part took', (ts5-ts4), 'seconds to execute')\n\n\n#rbf and varying C\nprint ('RBF with varying C')\n\nclf4 = SVC(C=1.0)\nflat_train_label = train_label.flatten()\nclf4.fit(train_data, flat_train_label)\n\ntrain_acc = clf4.score(train_data, train_label) *100\nprint('\\n Training set Accuracy:' + str(train_acc.astype(float)) + '%')\n\nvalidation_acc = clf4.score(validation_data, validation_label) * 100\nprint('\\n Validation set Accuracy:' + str(validation_acc.astype(float)) + '%')\n\ntest_acc = clf4.score(test_data, test_label) * 100\nprint('\\n Test set Accuracy:' + str(test_acc.astype(float)) + '%')\n\nfor i in range (10, 110, 10):\n    print ('\\n C :' + str(float(i)))\n    clf4 = SVC(C=float(i))\n    flat_train_label = train_label.flatten()\n    clf4.fit(train_data, flat_train_label)\n    \n    train_acc = clf4.score(train_data, train_label) *100\n    print('\\n Training set Accuracy:' + str(train_acc.astype(float)) + '%')\n    \n    validation_acc = clf4.score(validation_data, validation_label) * 100\n    print('\\n Validation set Accuracy:' + str(validation_acc.astype(float)) + '%')\n    \n    test_acc = clf4.score(test_data, test_label) * 100\n    print('\\n Test set Accuracy:' + str(test_acc.astype(float)) + '%')\n    \n    sys.stdout.flush()\n\n\nts6 = time.time()\nprint ('SVM - RBF Kernel with varying C part took', (ts6-ts5), 'seconds to execute')\n\n#time for SVM code\nprint ('SVM part took', (ts6-ts2), 'seconds to execute')\n\n#Time for full code\nprint ('Full code took', (ts6-ts), 'seconds to execute')\n","repo_name":"gauravk1001/regression-and-classification","sub_path":"script.py","file_name":"script.py","file_ext":"py","file_size_in_byte":11137,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"16518334451","text":"from django.contrib import admin\n\nfrom users.models import Users, Files\n\n\nclass FileInLine(admin.StackedInline):\n    model = Files\n    classes = ['collapse']\n    fk_name = \"user\"\n    fields = ('file', 'user', )\n    max_num = 20\n\n\nclass UsersAdmin(admin.ModelAdmin):\n    inlines = [FileInLine]\n\n\nadmin.site.register(Users, UsersAdmin)\n","repo_name":"nicolebarroos/propertyAcquisition","sub_path":"users/admin.py","file_name":"admin.py","file_ext":"py","file_size_in_byte":334,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"81"}
+{"seq_id":"12614829728","text":"#-*-coding:utf-8-*-\nfrom pandas import DataFrame,Series\nimport numpy as np\nimport pandas as pd\nclose_px=pd.read_csv('/home/jethro/文档/pydata-book-master/data/stock_px.csv'\n                     ,parse_dates=True,index_col=0  )\na=close_px[-4:]\n#print(a)\nrets=close_px.pct_change().dropna()\nspx_corr=lambda x: x.corrwith(x['SPX'])\nby_year=rets.groupby(lambda x:x.year)\nb=by_year.apply(spx_corr)\n#print(b)\n#苹果和微软的年度相关系数\nc=by_year.apply(lambda g:g['AAPL'].corr(g['MSFT']))\nprint(c)\n","repo_name":"JH95-ai/Python-for-data-analysis","sub_path":"数据聚合与分组运算/分组加权平均数和相关系数2.py","file_name":"分组加权平均数和相关系数2.py","file_ext":"py","file_size_in_byte":504,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"35659204304","text":"import os\nimport sys\nimport pytest\nimport logging\nimport subprocess\n\nfrom _pytest.runner import runtestprotocol\n\nPROJECT_ROOT = os.path.join(os.path.dirname(__file__), '..')\nPIO_EXECUTABLE = os.environ.get('PIO_EXECUTABLE', 'pio')\nARDUINO_PORT = os.environ.get('ARDUINO_PORT')\nLOGGER = logging.getLogger(__name__)\n\nos.environ['PYTHONUNBUFFERED'] = '1'\nsys.path.append(PROJECT_ROOT)  # Add project to PYTHONPATH\n\nfrom .lib.api import APIClient\nfrom .lib.api.arduino import ArduinoConnection\n\n\ndef pytest_runtest_protocol(item, nextitem):\n    reports = runtestprotocol(item, nextitem=nextitem)\n    for report in reports:\n        if report.when == 'call':\n            print('\\n%s --- %s' % (item.name, report.outcome))\n    return True\n\n\n@pytest.fixture(scope='session')\ndef upload_test_environment():\n    process = subprocess.Popen([PIO_EXECUTABLE, 'run', '-t', 'upload', '-e', 'test'], cwd=PROJECT_ROOT)\n    process.wait()\n\n    assert process.returncode == 0, 'Failed to upload test environment to Arduino'\n\n@pytest.fixture(autouse=True)\nasync def check_memory_leak(api_client: APIClient):\n    await api_client.reset()\n    await api_client.clear_io()\n    await api_client.reset_clock()\n    free_memory = await api_client.get_free_memory()\n    LOGGER.debug(f'Free memory before starting the test: {free_memory} bytes!')\n    yield\n    free_memory = await api_client.get_free_memory()\n    LOGGER.debug(f'Free memory after finishing the test: {free_memory} bytes!')\n    await api_client.reset()\n    await api_client.clear_io()\n    await api_client.reset_clock()\n\n\n@pytest.fixture\nasync def clear_eeprom(api_client: APIClient):\n    yield\n    await api_client.reset()\n    await api_client.save()\n\n\n@pytest.fixture(scope='session')\ndef arduino_connection():\n    yield ArduinoConnection(port=ARDUINO_PORT)\n\n\n@pytest.fixture\ndef api_client(arduino_connection, event_loop):\n    client = APIClient(arduino_connection, event_loop=event_loop)\n    yield client\n    client.close()\n\n\n@pytest.fixture\nasync def reset_api_timeout(api_client: APIClient):\n    api_client.set_timeout(APIClient.DEFAULT_REQUEST_TIMEOUT)\n","repo_name":"Igorxp5/water-manager","sub_path":"tests/conftest.py","file_name":"conftest.py","file_ext":"py","file_size_in_byte":2096,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"81"}
+{"seq_id":"17197080714","text":"import random\nfrom typing import List\n\nimport pandas as pd\n\nfrom Neurons.NeuronLayers import NeuronLayer, OutputNeuronLayer\nfrom Neurons.Neurons import Neuron, IConnection\n\n\nclass Connection(IConnection):\n\n    def __init__(self, input_neuron: Neuron, output_neuron: Neuron,\n                 initial_weight: float = 0.1, learning_rate: float = 0.1, show_logs: bool = False):\n        self.input_neuron: Neuron = input_neuron\n        input_neuron.add_output_connection(self)\n        self.output_neuron = output_neuron\n        output_neuron.add_input_connection(self)\n        self.weight = initial_weight\n        self.learning_rate = learning_rate\n        self.delta_w = 0\n        self.show_logs = show_logs\n\n    def feed_to_next(self, value: float):\n        self.output_neuron.feed_value(value * self.weight)\n\n    def update_delta(self, delta):\n        self.delta_w += self.learning_rate * self.input_neuron.output * delta\n        self.input_neuron.feed_delta_in(self.weight * delta)\n\n    def update_weight(self):\n        if self.show_logs:\n            print(f'before=> layer = {self.input_neuron.layer_index+1}: W_{self.input_neuron.index+1},{self.output_neuron.index+1} ='\n                  f' {self.weight}, delta: {\"%.3f\" % self.delta_w}')\n        self.weight -= self.delta_w\n        if self.show_logs:\n            print(f'after => layer = {self.input_neuron.layer_index+1}: W_{self.input_neuron.index+1},{self.output_neuron.index+1} ='\n                  f' {\"%.3f\"%self.weight}')\n        self.delta_w = 0\n\n        # if abs(self.weight < 0.02):\n        #     self.input_neuron.drop_output_connection(self)\n        #     self.output_neuron.drop_input_connection(self)\n\n\nclass ConnectionLayer:\n\n    def __init__(self, input_layer: NeuronLayer, output_layer: NeuronLayer, learning_rate=-1,\n                 initial_weights: pd.DataFrame = None, show_logs: bool = False):\n        self.connections: List[Connection] = []\n        self.dropped_connections: List[Connection] = []\n        self.input_layer = input_layer\n        self.output_layer = output_layer\n        out_size = output_layer.size\n        if not isinstance(self.output_layer, OutputNeuronLayer):\n            out_size = output_layer.size - 1\n        if learning_rate == -1:\n            learning_rate = 0.2 / input_layer.size\n        for i in range(input_layer.size):\n            for j in range(out_size):\n                wij = random.uniform(0.05, 0.25) if initial_weights is None else initial_weights.iloc[i, j]\n                self.connections.append(Connection(input_layer.neurons[i], output_layer.neurons[j], wij, learning_rate,\n                                                   show_logs))\n\n    def update_weights(self):\n        for i in range(len(self.connections)):\n            self.connections[i].update_weight()\n            # if self.connections[i].weight < 0.02:\n            #     self.dropped_connections.append(self.connections[i])\n\n        # for connection in self.dropped_connections:\n        #     if connection in self.connections:\n        #         self.connections.remove(connection)\n    #\n    # def drop_connection(self, connection: Connection):\n    #     if connection in self.connections:\n    #         self.dropped_connections.append(connection)\n    #         self.connections.remove(connection)\n\n\n\n","repo_name":"BahonarUniversity/MultiLayerPerceptron","sub_path":"Connections.py","file_name":"Connections.py","file_ext":"py","file_size_in_byte":3280,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"33025745583","text":"from Common import *\nfrom Id import *\nfrom GlobalSettings import *\nimport sys\n\n\ndef term_count(ids, tc):\n  max = len(tc)\n  for id in ids:\n    foo = id.split\n    l = len(foo)\n    if l >= max:\n        l = max-1\n    tc[l] = tc[l] + 1\n\n  return tc\n     \n\nif len(sys.argv) > 1:\n  tc = [0,0, 0,0,0,0,0,0,0,0,0,0]\n  p = [0,0, 0,0,0,0,0,0,0,0,0,0]\n  sys.argv = sys.argv[1:]\n  for name in sys.argv:\n    f = open(name, 'r')   \n    identifiers = read_ids(f, InputChar, DIGITS, False)\n    print(\"read\", len(identifiers), \"identifiers from\", name)\n    tc = term_count(identifiers, tc)\n  \n  sum = 0\n  for v in tc:\n    sum += v\n  \n  for i in range(0,len(tc)):\n    p[i] = tc[i] / sum * 100\n  \n  print(tc)\n  for v in p:\n    print(\"%4.2f%%  \" % v, end=\"\")\n  print(\"\")\n  exit (0)\n\n\n# hard code answer from counting\ntc = [0, 42349, 238173, 332481, 229867, 122816, 51149, 17646, 5452, 1661, 518, 559]\np = [0.00,  4.06,  22.84,  31.89,  22.05,  11.78,  4.91,  1.69,  0.52,  0.16,  0.05,  0.05]\n\n# \"generate\" data for thresholds array ...\nprint(\"x/x 0.01\") # cross validation runs better with this present?\n#for i in range(1, 7):\n  #for j in range(1,i+1):\n    #print(\"%d/%d %f\" % (j, i, (float(j)/float(i))))\n\n\n\n","repo_name":"secureIT-project/FAVD","sub_path":"dangerous-words/term-stats.py","file_name":"term-stats.py","file_ext":"py","file_size_in_byte":1189,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"81"}
+{"seq_id":"31880133677","text":"import re\nfrom collections import defaultdict\nfrom collections import namedtuple\nfrom collections import OrderedDict\n\nTASK_DURATION_CONSTANT = 0\n\nclass Step:\n\tdef __init__(self, name):\n\t\tself.name = name\n\t\tself.before = []\n\t\tself.after = []\n\t\tself.duration = ord(self.name) - 64 + TASK_DURATION_CONSTANT\n\n\tdef __str__(self):\n\t\treturn \"Step({}, before={}, after={})\".format(self.name, [b.name for b in self.before], [a.name for a in self.after])\n\n\tdef start(self, time):\n\t\tself.start = time\n\n\tdef done(self, time):\n\t\treturn self.start + self.duration <= time\n\n\nclass Steps(defaultdict):\n\tdef __missing__(self, key):\n\t\titem = Step(key)\n\t\tself.__setitem__(key, item)\n\t\treturn item\n\n\ndef read_input(filename):\n\tsteps = Steps()\n\twith open(filename, 'r') as f:\n\t\tfor l in f:\n\t\t\tm = re.match('Step (.) must be finished before step (.) can begin.', l)\n\t\t\ta, b = m.groups()\n\t\t\tsteps[a].after.append(steps[b])\n\t\t\tsteps[b].before.append(steps[a])\n\tstart = {}\n\tfor n, s in steps.items():\n\t\tif not s.before:\n\t\t\tstart[s.name] = s\n\treturn start\n\n\ndef pop_next_step(steps):\n\tnext_name = sorted(steps.keys())[0]\n\tnext_step = steps[next_name]\n\tdel steps[next_name]\n\treturn next_step\n\n\ndef immediate_execution(start):\n\trunnable = start\n\torder = []\n\tran = set()\n\twhile runnable:\n\t\ts = pop_next_step(runnable)\n\t\tran.add(s.name)\n\t\t#print(\"running \", s)\n\t\torder.append(s)\n\t\tfor a in s.after:\n\t\t\t#print(\"considering \", a.name)\n\t\t\tif all(b.name in ran for b in a.before):\n\t\t\t\t#print(\"taken\")\n\t\t\t\trunnable[a.name] = a\n\treturn order\n\n\ndef timed_execution(start, workers):\n\tidle_workers = workers\n\trunnable = start\n\tdone = set()\n\trunning = {}\n\ttime = 0\n\twhile runnable or (idle_workers < workers):\n\t\tfor s in list(running.values()):\n\t\t\tif s.done(time):\n\t\t\t\tidle_workers += 1\n\t\t\t\tdone.add(s.name)\n\t\t\t\tdel running[s.name]\n\t\t\t\tfor a in s.after:\n\t\t\t\t\tif all(b.name in done for b in a.before):\n\t\t\t\t\t\trunnable[a.name] = a\n\t\twhile idle_workers and runnable:\n\t\t\ts = pop_next_step(runnable)\n\t\t\ts.start(time)\n\t\t\tidle_workers -= 1\n\t\t\trunning[s.name] = s\n\t\ttime += 1\n\t\t#print(\"second {} in work {} done {}\".format(time, running.keys(), done))\n\treturn time - 1\n\n\ndef print_order(order):\n\tprint(''.join(s.name for s in order))\n\n\nprint_order(immediate_execution(read_input('test_input')))\nprint_order(immediate_execution(read_input('input')))\n\nprint(\"test execution takes {}s\".format(timed_execution(read_input('test_input'), 2)))\nTASK_DURATION_CONSTANT = 60\nprint(\"real execution takes {}s\".format(timed_execution(read_input('input'), 5)))\n","repo_name":"honggoff/advent","sub_path":"2018/07/07.py","file_name":"07.py","file_ext":"py","file_size_in_byte":2498,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"13565131404","text":"import numpy as np\nimport torch\n\n\ndef query_topk(node, x, M, k):\n    '''\n    :param node: SOM node of BxCxM tensor\n    :param x: input data BxCxN tensor\n    :param M: number of SOM nodes\n    :param k: topk\n    :return: mask: Nxnode_num\n    '''\n    # ensure x, and other stored tensors are in the same device\n    device = x.device\n    node = node.to(x.device)\n    node_idx_list = torch.from_numpy(np.arange(M).astype(np.int64)).to(device)  # node_num LongTensor\n\n    # expand as BxCxNxnode_num\n    node = node.unsqueeze(2).expand(x.size(0), x.size(1), x.size(2), M)\n    x_expanded = x.unsqueeze(3).expand_as(node)\n\n    # calcuate difference between x and each node\n    diff = x_expanded - node  # BxCxNxnode_num\n    diff_norm = (diff ** 2).sum(dim=1)  # BxNxnode_num\n\n    # find the nearest neighbor\n    _, min_idx = torch.topk(diff_norm, k=k, dim=2, largest=False, sorted=False)  # BxNxk\n    min_idx_expanded = min_idx.unsqueeze(2).expand(min_idx.size()[0], min_idx.size()[1], M, k)  # BxNxnode_numxk\n\n    node_idx_list = node_idx_list.unsqueeze(0).unsqueeze(0).unsqueeze(3).expand_as(\n        min_idx_expanded).long()  # BxNxnode_numxk\n    mask = torch.eq(min_idx_expanded, node_idx_list).int()  # BxNxnode_numxk\n    # mask = torch.sum(mask, dim=3)  # BxNxnode_num\n\n    # debug\n    B, N, M = mask.size()[0], mask.size()[1], mask.size()[2]\n    mask = mask.permute(0, 2, 3, 1).contiguous().view(B, M, k*N).permute(0, 2, 1).contiguous()  # BxMxkxN -> BxMxkN -> BxkNxM\n    min_idx = min_idx.permute(0, 2, 1).contiguous().view(B, k*N)\n\n    mask_row_max, _ = torch.max(mask, dim=1)  # Bxnode_num, this indicates whether the node has nearby x\n\n    return mask, mask_row_max, min_idx\n\n","repo_name":"lijx10/DeepI2P","sub_path":"util/som.py","file_name":"som.py","file_ext":"py","file_size_in_byte":1678,"program_lang":"python","lang":"en","doc_type":"code","stars":172,"dataset":"github-code","pt":"81"}
+{"seq_id":"16948632320","text":"#%%\nfrom cProfile import label\nfrom cv2 import normalize, threshold\nimport numpy as np\nimport pandas as pd\nfrom sklearn import metrics\nfrom sklearn.metrics import classification_report, precision_recall_curve,auc,confusion_matrix, roc_auc_score \nimport matplotlib.pyplot as plt\nimport json\n\n# ##### FIRST DATA #####\ndf1 = pd.read_csv(\"DTD/resize_resnet_texture.csv\", names= [\"values\"],sep=\" \")\ndf1['classification'] = 0\ndf1=df1[['classification','values']]\n\n# print(\"df1:\",df1.shape)\n# print(df1.head())\n\n##### Imagenet #####\n# with open('imagenet/imagenet_resize.json','r') as f:\n#     json_data=json.load(f)\n\n# imagenetmax =[]\n# for i in range(len(json_data['max'])):\n#     imagenetmax.append(json_data['max'][i])\n\n# # df2 = np.random.choice(imagenetmax, 2000)\n# df2 = pd.DataFrame({\"values\":imagenetmax})\n# print(df2.shape)\ndf2 = pd.read_csv(\"imagenet/resize_resnet_imagenet.csv\", names =[\"values\"],sep=\" \")\ndf2['classification']= 1\ndf2 =df2[['classification','values']]\n\n# print(\"df2: \",df2.size)\n# print(df2.head())\n\n##### concat df1,df2 #####\ndf3 = pd.concat([df1,df2])\n# print(df3.head())\n# print(df3.tail())\nprint(df3.shape)\n\n########################################################\n\n#df1 = imagenet o \n#df2 = imagenet \n#df3 = imagenet o + imagenet\n\n########################################################\n\n##### Precision-Recall Curve #####\n\ny_true = np.array(df3[\"classification\"])\n# print(y_true)\ny_scores = np.array(df3[\"values\"])\n# print(y_scores)\n\nprecision, recall, thresholds = precision_recall_curve(y_true,y_scores)\n\n# print(precision)\n# print(recall)\n# print (thresholds)\n##################################\n\n# plt.plot(recall[:-1],precision[:-1])]\nplt.title(\"ImageNet - O (Resize n Crop) Entropy\")\nplt.plot(recall, precision)\nplt.xlabel(\"recall\")\nplt.ylabel(\"Precision\")\nplt.show()\n\nfpr, tpr, thresholds = metrics.roc_curve(y_true, y_scores)\n\n#### Calculate AUC #####\nAUPR_score = auc(recall, precision)\nprint(\"AUPR\",AUPR_score)\n\nAUROC_score = roc_auc_score(y_true,y_scores)\nprint(\"AUROC\",AUROC_score)\n\n\n# %%\n","repo_name":"purplepatch-everyday/GradCAM","sub_path":"OOD.py","file_name":"OOD.py","file_ext":"py","file_size_in_byte":2029,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"71395677705","text":"import os\nfrom array import array\nfrom random import random\nfrom sys import path\n\nfloats = array('d', (random() for i in range(10**7)))\n\n\nprint(floats[-1])\n\nif os.path.exists('./floats.bin') == False:\n    fp = open('floats.bin', 'wb')\n    floats.tofile(fp)\n    fp.close()\n    print(\"created\")\nelse:\n    print(\"file exists\")\n\nfloats2 = array('d')\nfp = open('floats.bin', 'rb')\nfloats2.fromfile(fp, 10**7)   # read 10**7 numbers from fp file\nfp.close()\nprint(floats[-1])\n\n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\n\nfedotovdmitriy14ghp_aAb0","repo_name":"fedotovdmitriy14/python_tasks","sub_path":"fluent_python/arrays.py","file_name":"arrays.py","file_ext":"py","file_size_in_byte":536,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"23815443655","text":"\"\"\"\nThis script copies the latest pickle file, the latestscore.txt value, and the ingestfiles.txt file into the deployment directory.\n\"\"\"\n\nimport os\nimport json\nimport shutil\n\n\nwith open(\"config.json\", \"r\") as f:\n    config = json.load(f)\n\ndataset_csv_path = os.path.join(config[\"output_folder_path\"])\nprod_deployment_path = os.path.join(config[\"prod_deployment_path\"])\nmodel_path = os.path.join(config[\"output_model_path\"])\n\n\nif not os.path.exists(dataset_csv_path):\n    os.mkdir(dataset_csv_path)\n\nif not os.path.exists(prod_deployment_path):\n    os.mkdir(prod_deployment_path)\n\nif not os.path.exists(model_path):\n    os.mkdir(model_path)\n\n\ndef store_model_into_pickle():\n    # copy the latest pickle file, the latestscore.txt value, and the ingestfiles.txt file into the deployment directory\n\n    latestscore = model_path + \"/latestscore.txt\"\n    model = model_path + \"/trainedmodel.pkl\"\n    ingestfiles = dataset_csv_path + \"/ingestedfiles.txt\"\n    files = [latestscore, model, ingestfiles]\n\n    if not os.path.exists(prod_deployment_path):\n        os.mkdir(prod_deployment_path)\n\n    for file in files:\n        shutil.copy2(file, prod_deployment_path)\n\n\nif __name__ == \"__main__\":\n    store_model_into_pickle()\n","repo_name":"khalid4294/Udacity-Risk-Assessment","sub_path":"deployment.py","file_name":"deployment.py","file_ext":"py","file_size_in_byte":1216,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"3149591367","text":"import os\r\nfrom SB_Agent import Agent\r\nfrom SB_environment import Sokoban\r\n\r\n    \r\n\r\nif __name__==\"__main__\":\r\n    board=[\r\n\t[' ',' ','#','#','#','#','#',' '],           \r\n    ['#','#','#',' ',' ',' ','#',' '],  \r\n    ['#','.','@','$',' ',' ','#',' '],    \r\n    ['#','#','#',' ','$','.','#',' '],  \r\n    ['#','.','#','#','$',' ','#',' '],  \r\n    ['#',' ','#',' ','.',' ','#','#'],  \r\n    ['#','$',' ','*','$','$','.','#'],\r\n    ['#',' ',' ',' ','.',' ',' ','#'],  \r\n    ['#','#','#','#','#','#','#','#']]\r\n\r\n    workerPosX=None\r\n    workerPosY=None\r\n    boxPos =[]\r\n    goalPos= []\r\n    \r\n    for x in range(len(board)):\r\n        for y in range(len(board[x])):\r\n            if board[x][y] == '@':\r\n                workerPosX=x\r\n                workerPosY=y\r\n                board[x][y]=' '\r\n            if board[x][y] == '$':\r\n                boxPos.append((x,y))\r\n                board[x][y]=' '\r\n            if board[x][y] =='.':\r\n                goalPos.append((x,y))\r\n                board[x][y]=' '\r\n            if board[x][y] == '+':\r\n                workerPosX=x\r\n                workerPosY=y\r\n                goalPos.append((x,y))\r\n                board[x][y]=' '\r\n            if board[x][y] == '*':\r\n                boxPos.append((x,y))\r\n                goalPos.append((x,y))\r\n                board[x][y]=' '\r\n\r\n    workerPos=(workerPosX,workerPosY)\r\n\r\n    SBobj=Sokoban(board,boxPos,goalPos,workerPos)        \r\n\r\n    agnt=Agent(SBobj)\r\n\r\n    result,counter=agnt.DFS()\r\n\r\n    try:\r\n        os.remove(\"path.txt\")\r\n    except:\r\n        pass\r\n    path=agnt.printPath(result,\"path.txt\")\r\n\r\n    agnt.main(path)\r\n\r\n    print(\"Nodes explored: \",counter)\r\n    print(\"Path length: \",result.level)","repo_name":"neel195/Sokoban-Solver","sub_path":"SB_Driver.py","file_name":"SB_Driver.py","file_ext":"py","file_size_in_byte":1696,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"28949227953","text":"from django.db import models\n\nfrom account.models import User\n\n\nclass Article(models.Model):\n    description = models.TextField(\"内容\",max_length =2000)\n    count = models.IntegerField(\"浏览量\",default=0)\n    comment_count = models.IntegerField(\"评论量\", default=0)\n    images_count = models.IntegerField(\"图片数量\", default=0)\n    create_time = models.DateTimeField(auto_now_add=True)\n    update_time = models.DateTimeField(auto_now=True)\n    author = models.ForeignKey(User, on_delete=models.CASCADE)\n\n\nclass Comment(models.Model):\n    to_comment = models.ForeignKey('self', on_delete=models.CASCADE, default=None, null=True)\n    to_small_comment = models.IntegerField('面向小评论', default=0)\n    description = models.TextField(\"评论内容\",max_length =148)\n    create_time = models.DateTimeField(auto_now_add=True)\n    author = models.ForeignKey(User, on_delete=models.CASCADE)\n    article = models.ForeignKey(Article, on_delete=models.CASCADE)","repo_name":"315335718/QUST_OJ","sub_path":"comment/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":967,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"25077960356","text":"class Settings:\n    \"\"\"Una clase para guardar todas las configuraciones éstaticas (que no varian) del juego\"\"\"\n\n    def __init__(self):\n        \"\"\"Inicializa la configuración del juego\"\"\"\n        # Configuración de las dimensiones de la pantalla\n        self.screen_width = 1000\n        self.screen_height = 600\n        # Configura el color del fondo\n        # En Pygame se usa RGB.\n        self.bg_color = (230, 230, 230)\n\n        # Número de vidas\n        self.ship_limit = 2  # Son 3 vidas 0,1,2\n\n        # Configuración de las balas\n        self.bullet_width = 3\n        self.bullet_height = 15\n        self.bullet_color = (60, 60, 60)\n        # Muchos juegos de naves limitan el número de balas que puede tener el\n        # jugador en la pantalla.\n        self.bullet_allowed = 4\n\n        # Configuración del alien\n        self.fleet_drop_speed = 10\n        # Velocidad de bajada de los aliens al llegar al borde de la pantalla.\n\n        # Rapidez con la que se acelera el juego cuando destruimos cada flota.\n        self.speedup_scale = 1.1  # 1.1\n\n        # Lo rapido que aumenta el valor en puntos de los aliens al avanzar el juego\n        self.score_scale = 1.5\n\n        self.initialize_dynamic_settings()\n\n    def initialize_dynamic_settings(self):\n        \"\"\"Inicializa las configuracines que cambian durante el juego.\"\"\"\n        self.ship_speed = 1.5\n        self.bullet_speed = 3.0\n        self.alien_speed = 1.0\n\n        # fleet_direction = 1 es movimiento hacia la derecha y fleet_direction = -1\n        # movimiento hacia la izquierda\n        self.fleet_direction = 1\n\n        # Puntuación por destrución de aliens\n        self.alien_point = 50\n\n    def increase_speed(self):\n        \"\"\"Incrementa las configuracines de velocidad y los valores en puntos de los aliens.\"\"\"\n        self.ship_speed *= self.speedup_scale\n        self.bullet_speed *= self.speedup_scale\n        self.alien_speed *= self.speedup_scale\n\n        self.alien_point = int(self.alien_point * self.score_scale)\n","repo_name":"chema-hg/InvasionAlienigena","sub_path":"settings.py","file_name":"settings.py","file_ext":"py","file_size_in_byte":2007,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"75136318025","text":"class Solution:\n    def numSubmat(self, mat) -> int:\n        rows=len(mat)\n        cols=len(mat[0])\n        ans=0\n        \n        for i in range(rows):\n            for j in range(cols):\n                if i>0 and mat[i][j]!=0:\n                    mat[i][j]+=mat[i-1][j]\n        \n        for i in range(rows):\n            stack=list()\n            count=0\n            for j in range(cols):\n                while stack and mat[i][stack[-1]]>mat[i][j]:\n                    start=stack.pop()\n                    end=stack[-1] if stack else -1\n                    count-=(mat[i][start]-mat[i][j])*(start-end)\n                count+=mat[i][j]\n                ans+=count\n                stack.append(j)\n        \n        return ans","repo_name":"bamblebam/competitive-programming","sub_path":"2021/7_july_21/28-7-21/countsubmatriceswithallones.py","file_name":"countsubmatriceswithallones.py","file_ext":"py","file_size_in_byte":723,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"72651177866","text":"from math import ceil, floor\n\nG=int(input())\nresult=[]\nfor a in range(1,100000):\n    if G < a**2:\n        break\n    target=(G-a**2)/(a*2)\n    ub=ceil(target)\n    lb=floor(target)\n    if ub == lb and target != 0:\n        result.append(ub + a)\n\nprint(-1 if len(result) == 0 else \"\\n\".join(map(str, list(sorted(set(result))))))\n","repo_name":"malkoG/polyglot-cp","sub_path":"BOJ/Mathematics/Python/1484.py","file_name":"1484.py","file_ext":"py","file_size_in_byte":325,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"81"}
+{"seq_id":"41466774504","text":"from unittest.mock import patch\nimport numpy as np\nimport cv2\nfrom skimage.transform import resize\nfrom utils.geometry_utils import extend_array_to_homogeneous\nfrom utils.ocg_utils import compute_uv_bins, project_xyz_to_uv\nfrom src.data_structure import layout\nfrom scipy import stats\n\n\nclass OCGPatches:\n    \"\"\"\n    This class handles multiples Patches (defined from every Layout) by \n    aggregating, combining, and pruning them out properly.\n    \"\"\"\n    @property\n    def u_bins(self):\n        return self.__u_bins\n\n    @u_bins.setter\n    def u_bins(self, value):\n        if value is None:\n            return\n        self.__u_bins = value\n        self.W = int(self.__u_bins.size - 1)\n        self.uv_ref[0] = self.__u_bins[0]\n\n    @property\n    def v_bins(self):\n        return self.__v_bins\n\n    @v_bins.setter\n    def v_bins(self, value):\n        if value is None:\n            return\n        self.__v_bins = value\n        self.H = int(self.v_bins.size - 1)\n        self.uv_ref[1] = self.__v_bins[0]\n\n    def __init__(self, data_manager):\n        self.dt = data_manager\n        self.v_bins = None\n        self.u_bins = None\n        self.ocg_map = None\n        self.list_patches = []\n        self.dynamic_bins = True\n        self.is_initialized = False\n        self.H, self.W = None, None\n        self.uv_ref = [None, None]\n        self.grid_size = self.dt.cfg['room_id.grid_size']\n\n    def project_xyz_to_uv(self, xyz_points):\n\n        x_cell_u = [np.argmin(abs(p - self.u_bins-self.grid_size*0.5)) % self.get_shape()[1]\n                    for p in xyz_points[0, :]]\n\n        if xyz_points.shape[0] == 2:\n            # xyz_points: (2, N)\n            z_cell_v = [np.argmin(abs(p - self.v_bins-self.grid_size*0.5)) % self.get_shape()[0]\n                        for p in xyz_points[1, :]]\n        else:\n            # xyz_points: (3, N)\n            z_cell_v = [np.argmin(abs(p - self.v_bins-self.grid_size*0.5)) % self.get_shape()[0]\n                        for p in xyz_points[2, :]]\n        # ! this potentially can change the order of the points\n        # if unique:\n        #     return np.unique(np.vstack((x_cell_u, z_cell_v)), axis=0), True\n        return np.vstack((x_cell_u, z_cell_v))\n\n    def project_uv_to_xyz(self, uv_points):\n        if uv_points.dtype != np.int32:\n            uv_points = uv_points.astype(np.int32)\n        v_size, u_size = self.get_shape()\n        assert np.all(uv_points[0, :] <= u_size), f\"{np.max(uv_points[0, :])}, {u_size}\"\n        assert np.all(uv_points[1, :] <= v_size), f\"{np.max(uv_points[1, :])}, {v_size}\"\n        xs = self.u_bins[uv_points[0, :]] + self.grid_size * 0.5\n        ys = np.zeros_like(xs)\n        zs = self.v_bins[uv_points[1, :]] + self.grid_size * 0.5\n        return np.vstack([xs, ys, zs])\n\n    def project_xyz_points_to_hist(self, xyz_points):\n        x = xyz_points[0, :]\n        if xyz_points.shape[0] == 2:\n            # (x, z)\n            z = xyz_points[1, :]\n        else:\n            # (x, y, z)\n            z = xyz_points[2, :]\n        grid, _, _ = np.histogram2d(z, x, bins=(self.v_bins, self.u_bins))\n\n        return grid\n\n    def initialize(self, patch):\n        \"\"\"\n        Initializes the OCGPatch class by using the passed patch\n        \"\"\"\n        assert patch.is_initialized, \"Passed patch mst be initialized first...\"\n\n        # self.ocg_map = np.expand_dims(patch.ocg_map, 2)\n        self.ocg_map = np.copy(patch.ocg_map)\n        self.list_patches.append(patch)\n\n        self.u_bins, self.v_bins = patch.u_bins, patch.v_bins\n        self.is_initialized = True\n        return self.is_initialized\n\n    def update_bins(self):\n        \"\"\"\n        Updates bins based on the patches registered in list_patches\n        \"\"\"\n        bins = np.vstack([(patch.u_bins[0], patch.v_bins[0],\n                           patch.u_bins[-1], patch.v_bins[-1])\n                          for patch in self.list_patches\n                          ])\n\n        min_points = np.min(bins, axis=0)[0:2]\n        max_points = np.max(bins, axis=0)[2:4]\n        self.u_bins = np.mgrid[min_points[0]:max_points[0]+self.grid_size: self.grid_size]\n        self.v_bins = np.mgrid[min_points[1]:max_points[1]+self.grid_size: self.grid_size]\n        return self.u_bins, self.v_bins\n\n    def update_ocg_map(self, binary_map=True):\n        \"\"\"\n        Updates the OCG map by aggregating layers of registered Patches (layers, H, W)\n        \"\"\"\n        H, W = self.get_shape()\n        self.ocg_map = np.zeros((self.list_patches.__len__(), H, W))\n        for idx, patch in enumerate(self.list_patches):\n            h, w = patch.H, patch.W\n            uv = project_xyz_to_uv(\n                xyz_points=np.array((patch.uv_ref[0], 0, patch.uv_ref[1])).reshape((3, 1)),\n                u_bins=self.u_bins,\n                v_bins=self.v_bins\n            ).squeeze()\n\n            self.ocg_map[idx, uv[1]:uv[1]+h, uv[0]:uv[0]+w] = patch.ocg_map\n\n        if binary_map:\n            self.ocg_map[self.ocg_map < self.dt.cfg.get(\"room_id.ocg_threshold\")] = 0\n            self.ocg_map[self.ocg_map > self.dt.cfg.get(\"room_id.ocg_threshold\")] = 1\n            self.ocg_map = self.ocg_map.astype(np.int)\n\n    def patch_size_weighting(self):\n        \"\"\"\n        Creates a weighting vector for every patch based on the size of the closest neighboor patches.\n        \"\"\"\n        ly_sigam_ratio = [p.layout.sigma_ratio for p in self.list_patches]\n        weights = [np.exp(-np.abs(p-np.median(ly_sigam_ratio))) for p in ly_sigam_ratio]\n        return weights\n\n    def gaussian_weighting(self):\n        \"\"\"\n        Creates a corresponded weighted values for each registered self.list_patch using a gaussian function \n        centered at the median index.\n        \"\"\"\n        med_idx = self.list_patches.__len__() // 2\n        weights = stats.norm.pdf(\n            range(self.list_patches.__len__()),\n            med_idx,\n            self.dt.cfg[\"room_id.temporal_weighting_sigma\"]\n        )\n        return weights/stats.norm.pdf(med_idx, med_idx, self.dt.cfg[\"room_id.temporal_weighting_sigma\"])\n\n    def update_ocg_map2(self):\n        \"\"\"\n        Updates the OCG map by aggregating Patches using a temporal weight constraint\n        \"\"\"\n        H, W = self.get_shape()\n        self.ocg_map = np.zeros((H, W))\n        # temporal_weight = np.linspace(1, 0, self.list_patches.__len__())\n        temporal_weight = self.gaussian_weighting()\n        # temporal_weight = self.patch_size_weighting()\n\n        for idx, patch in enumerate(self.list_patches):\n            h, w = patch.H, patch.W\n            uv = project_xyz_to_uv(\n                xyz_points=np.array((patch.uv_ref[0], 0, patch.uv_ref[1])).reshape((3, 1)),\n                u_bins=self.u_bins,\n                v_bins=self.v_bins\n            ).squeeze()\n\n            if self.dt.cfg.get(\"room_id.temporal_weighting\", True):\n                self.ocg_map[uv[1]:uv[1]+h, uv[0]:uv[0]+w] += patch.ocg_map * temporal_weight[idx]\n            else:\n                self.ocg_map[uv[1]:uv[1]+h, uv[0]:uv[0]+w] += patch.ocg_map\n\n            # # ! Adding non-isotropic Normalization\n            # if self.dt.cfg.get(\"room_id.non_isotropic_normalization\", False):\n            #     self.ocg_map = self.ocg_map/self.ocg_map.max()\n\n        self.ocg_map = self.ocg_map/self.ocg_map.max()\n        # TODO: combine/ add update_ocg_map() as binary map option\n\n    def add_patch(self, patch):\n        \"\"\"\n        Adds a new Patch and Updated the bins definitions\n        \"\"\"\n        self.list_patches.append(patch)\n        self.update_bins()\n\n    def get_shape(self):\n        return (self.v_bins.size-1, self.u_bins.size-1)\n\n    def resize(self, scale):\n        # NOTE: This resize function only effects \"bins\" and \"ocg_map\".\n        # This will NOT maintain the correctness of instances under list_patches\n        v_bins = self.v_bins\n        u_bins = self.u_bins\n        v_bins = self.resize_bins(self.v_bins, scale)\n        u_bins = self.resize_bins(self.u_bins, scale)\n\n        height, width = self.get_shape()\n        height = round(height * scale)\n        width = round(width * scale)\n\n        self.v_bins = v_bins\n        self.u_bins = u_bins\n        self.ocg_map = resize(self.ocg_map, (height, width))\n        self.grid_size = u_bins[1] - u_bins[0]\n\n    def resize_bins(self, bins, scale):\n        num = round((len(bins) - 1) * scale) + 1\n        new_bins = np.linspace(bins[0], bins[-1], num)\n        return new_bins\n\n\nclass Patch:\n    \"\"\"\n    This Class handles the patch represtation for a Layout. \n    Layout information projected in 2D\n    \"\"\"\n    @property\n    def u_bins(self):\n        return self.__u_bins\n\n    @u_bins.setter\n    def u_bins(self, value):\n        if value is None:\n            return\n        self.__u_bins = value\n        self.W = int(self.__u_bins.size - 1)\n        self.uv_ref[0] = self.__u_bins[0]\n\n    @property\n    def v_bins(self):\n        return self.__v_bins\n\n    @v_bins.setter\n    def v_bins(self, value):\n        if value is None:\n            return\n        self.__v_bins = value\n        self.H = int(self.v_bins.size - 1)\n        self.uv_ref[1] = self.__v_bins[0]\n\n    def __init__(self, dt):\n        self.layout = None\n        self.dt = dt\n        self.ocg_map = None\n        self.uv_boundary = None\n        self.u_bins, self.v_bins = None, None\n        self.is_initialized = False\n        self.H, self.W = None, None\n        self.uv_ref = [None, None]\n\n    def initialize(self):\n        \"\"\"\n        Initialize the current Patch\n        Note each patch is linked to a unique Layout\n        \"\"\"\n        self.is_initialized = False\n\n        self.u_bins, self.v_bins = compute_uv_bins(\n            pcl=self.layout.boundary[:, self.layout.cam2boundary_mask],\n            grid_size=self.dt.cfg[\"room_id.grid_size\"],\n            padding=self.dt.cfg[\"room_id.grid_padding\"]\n        )\n\n        clipped_boundary = self.layout.get_clipped_boundary()\n        uv = project_xyz_to_uv(\n            xyz_points=clipped_boundary,\n            u_bins=self.u_bins,\n            v_bins=self.v_bins\n        )\n\n        self.ocg_map = np.uint8(np.zeros((self.H, self.W)))\n        cv2.fillPoly(self.ocg_map, [uv.T], color=(1, 1, 1))\n        self.uv_boundary = uv\n\n        self.is_initialized = True\n        return self.is_initialized\n","repo_name":"EnriqueSolarte/direct_360_FPE","sub_path":"src/data_structure/ocg_patch.py","file_name":"ocg_patch.py","file_ext":"py","file_size_in_byte":10204,"program_lang":"python","lang":"en","doc_type":"code","stars":35,"dataset":"github-code","pt":"81"}
+{"seq_id":"74060338504","text":"# ---------------------------------------------------------------------------- #\r\n# Title: Assignment 06\r\n# Description: Working with functions in a class,\r\n#              When the program starts, load each \"row\" of data\r\n#              in \"ToDoList.txt\" into a python Dictionary.\r\n#              Add the each dictionary \"row\" to a python list \"table\"\r\n# ChangeLog (Who,When,What):\r\n# RRoot,1.1.2030,Created started script\r\n# Jason Johanneck, 5/23/2022, Modified code to complete assignment 06\r\n# ---------------------------------------------------------------------------- #\r\n\r\n# Data ---------------------------------------------------------------------- #\r\n# Declare variables and constants\r\nstrFileName = \"ToDoList.txt\"  # The name of the data file\r\nfile_obj = None  # An object that represents a file\r\nrow_dic = {}  # A row of data separated into elements of a dictionary {Task,Rank}\r\nlstTable = []  # A list that acts as a 'table' of rows\r\nchoice_str = \"\"  # Captures the user option selection\r\n\r\n\r\n# Processing  --------------------------------------------------------------- #\r\nclass Processor:\r\n    \"\"\" Processes the data in a list of dictionaries to and from a text file \"\"\"\r\n\r\n    @staticmethod\r\n    def read_file_to_list_of_dictionaries(file_name, list_of_dictionary_rows):\r\n        list_of_dictionary_rows.clear()  # clear any old data before loading\r\n        file = open(file_name, \"r\")  # Causes ERROR if file does not exist!\r\n        for line in file:\r\n            item, value = line.split(\",\")\r\n            row = {\"Task\": item.strip(), \"Rank\": value.strip()}\r\n            list_of_dictionary_rows.append(row)\r\n        file.close()\r\n        return list_of_dictionary_rows, 'success'\r\n\r\n    @staticmethod\r\n    def remove_data_from_list_of_dictionaries(list_of_dictionary_rows, task_to_remove):\r\n        for row in list_of_dictionary_rows:\r\n            if row[\"Task\"].lower() == task_to_remove.lower():\r\n                lstTable.remove(row)\r\n                # print(\"row removed\")\r\n        return list_of_dictionary_rows, 'success'\r\n\r\n    @staticmethod\r\n    def add_data_to_list_of_dictionaries(list_of_dictionary_rows, item, value):\r\n        row = {\"Task\": str(item).strip(), \"Rank\": str(value).strip()}\r\n        list_of_dictionary_rows.append(row)\r\n        return list_of_dictionary_rows, 'success'\r\n\r\n    @staticmethod\r\n    def write_file_from_list_of_dictionaries(file_name, list_of_dictionary_rows):\r\n        file = open(file_name, \"w\")\r\n        for row in list_of_dictionary_rows:\r\n            file.write(row[\"Task\"] + \",\" + row[\"Rank\"] + \"\\n\")\r\n        file.close()\r\n        return list_of_dictionary_rows, 'success'\r\n\r\n# Presentation (Input/Output)  -------------------------------------------- #\r\nclass IO:\r\n    @staticmethod\r\n    def print_menu():\r\n        print('''\r\n        Menu of Options\r\n        1) Load Data from File\r\n        2) Add a new Task\r\n        3) Remove an existing Task\r\n        4) Save Data to File\r\n        5) Exit Program\r\n        ''')\r\n\r\n    @staticmethod\r\n    def input_menu_choice():\r\n        choice = str(input(\"Which option would you like to perform? [1 to 4] - \")).strip()\r\n        print()  # Add an extra line for looks\r\n        return choice\r\n\r\n    @staticmethod\r\n    def print_current_list_items(list_of_rows):\r\n        print(\"******* The Current Items Are: *******\")\r\n        for row in list_of_rows:\r\n            print(row[\"Task\"] + \" (\" + row[\"Rank\"] + \")\")\r\n        print(\"*******************************************\")\r\n        print()  # Add an extra line for looks\r\n\r\n    @staticmethod\r\n    def input_task_and_rank():\r\n        task = str(input(\"What is the Task? - \")).strip()\r\n        rank = str(input(\"What is the Rank? - \")).strip()\r\n        print()  # Add an extra line for looks\r\n        return task, rank\r\n\r\n    @staticmethod\r\n    def input_task_to_remove():\r\n        task = str(input(\"Remove which item? - \")).strip()\r\n        print()  # Add an extra line for looks\r\n        return task\r\n\r\n\r\n# Main Body of the Script  -------------------------------------------------- #\r\nwhile(True):\r\n    IO.print_current_list_items(lstTable)\r\n    IO.print_menu()   # Test menu\r\n    strChoice = IO.input_menu_choice()    # Test user choice\r\n\r\n    if (strChoice == \"1\"):  # 1) Load Data from File\r\n        lstTable, status = Processor.read_file_to_list_of_dictionaries(strFileName, lstTable)\r\n        if status == 'success':\r\n            print('Done!')\r\n    elif (strChoice == \"2\"):  # 2) Add a new item\r\n        strTask, strRank = IO.input_task_and_rank()\r\n        lstTable, status = Processor.add_data_to_list_of_dictionaries(lstTable, strTask,strRank)\r\n        if status == 'success':\r\n            print('Done!')\r\n    elif (strChoice == \"3\"):  # 3) Remove an existing item\r\n        strTask = IO.input_task_to_remove()\r\n        lstTable, status = Processor.remove_data_from_list_of_dictionaries(lstTable, strTask)\r\n        if status == 'success':\r\n            print('Done!')\r\n    elif (strChoice == \"4\"):  # 4) Save Data to File And Exit Program\r\n        lstTable, status = Processor.write_file_from_list_of_dictionaries(strFileName, lstTable)\r\n        if status == 'success':\r\n            print('Done!')\r\n    elif (strChoice == \"5\"):\r\n        print(\"Goodbye!\")\r\n        break\r\n","repo_name":"jjohanne514/IntroToProg_PythonAssignment06","sub_path":"Assignment06_Johanneck.py","file_name":"Assignment06_Johanneck.py","file_ext":"py","file_size_in_byte":5230,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"69927472906","text":"import cv2 as cv\n\nimg=cv.imread('photos/libg.jpg')\ncv.imshow('background image', img)\n\n#this works for images videos and live videos\ndef rescaleframe(frame,scale=0.5):\n    width= int(frame.shape[1] * scale)\n    height= int(frame.shape[0] * scale)\n    dimensions=(width,height)\n\n    return cv.resize(frame, dimensions, interpolation= cv.INTER_AREA)\n\nresized_image=rescaleframe(img)\ncv.imshow('Image',resized_image)\n\n#only works for live video\ndef changeres(width,height):\n    capture.set(3,width)\n    capture.set(4,height)\n\n\n\ncapture= cv.VideoCapture('videos/bb.mp4')# used to read video\n\nwhile True:\n    isTrue, frame= capture.read()#it reads what we stored in capture and assigns that to is true and frame\n    frame_resized= rescaleframe(frame)\n    cv.imshow('video',frame)#shows the video frame by frame as a video is a collection of frames\n    cv.imshow('video resized',frame_resized)\n    if cv.waitKey(20) & 0xFF==ord('d'): # this is the condition that stops the video and its said to make it last for 20 ms & if d is pressed break the video\n        break\n\ncapture.release()\ncv.destroyAllWindows() # its basically like freeing memory so we release \n\ncv.waitKey(0)","repo_name":"Aryansharma28/opencv_beginner","sub_path":"rescale.py","file_name":"rescale.py","file_ext":"py","file_size_in_byte":1167,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"10173412711","text":"#!/usr/bin/env python3\n\n\ndef getOccupiedNeighbours(i, j, seats):\n    count = 0\n    for ii in range(i - 1, i + 2):\n        if ii < 0 or ii >= len(seats):\n            continue\n        for jj in range(j - 1, j + 2):\n            if jj < 0 or jj >= len(seats[ii]):\n                continue\n            if ii == i and jj == j:\n                continue\n            if seats[ii][jj] == \"#\":\n                count += 1\n    return count\n\n\ndef UpdateSeating(seats, nos):\n    for i in range(0, len(seats)):\n        for j in range(0, len(seats[i])):\n            nos[i][j] = getOccupiedNeighbours(i, j, seats)\n    changed = False\n    for i in range(0, len(seats)):\n        for j in range(0, len(seats[i])):\n            if seats[i][j] == \"L\" and nos[i][j] == 0:\n                changed = True\n                seats[i][j] = \"#\"\n            elif seats[i][j] == \"#\" and nos[i][j] >= 4:\n                changed = True\n                seats[i][j] = \"L\"\n    return changed\n\n\ndef CountOccupied(seats):\n    count = 0\n    for row in seats:\n        for col in row:\n            if col == \"#\":\n                count += 1\n    return count\n\n\ndef printSeats(seats):\n    for line in seats:\n        print(line)\n\n\ndef main():\n    f = open(\"../input/day_11_input\")\n    seats = list()\n    for line in f:\n        line = line.strip().replace(\"\\r\", \"\").replace(\"\\n\", \"\")\n        seats.append([letter for letter in line])\n    nos = []\n    for i in range(0, len(seats)):\n        nos.append([0] * len(seats[i]))\n\n    changed = True\n    while changed:\n        changed = UpdateSeating(seats, nos)\n    count = CountOccupied(seats)\n    print(count)\n    return count\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"vss2sn/advent_of_code","sub_path":"2020/python/day_11a.py","file_name":"day_11a.py","file_ext":"py","file_size_in_byte":1661,"program_lang":"python","lang":"en","doc_type":"code","stars":22,"dataset":"github-code","pt":"81"}
+{"seq_id":"28213278924","text":"import os\nimport re\n\nfrom .scanner import DotScanner\n\nEOF = -1\nSKIP = -2\n\nID = 0\nSTR_ID = 1\nHTML_ID = 2\n\n\nclass Token:\n\n    def __init__(self, type, text, line, col):\n        self.type = type\n        self.text = text\n        self.line = line\n        self.col = col\n\n\nclass ParseError(Exception):\n\n    def __init__(self, msg=None, filename=None, line=None, col=None):\n        self.msg = msg\n        self.filename = filename\n        self.line = line\n        self.col = col\n\n    def __str__(self):\n        return ':'.join([str(part) for part in\n                         (self.filename, self.line, self.col, self.msg)\n                         if part is not None])\n\n\nclass Lexer:\n\n    # should be overriden by derived classes\n    scanner = None\n    tabsize = 8\n\n    newline_re = re.compile(br'\\r\\n?|\\n')\n\n    def __init__(self, buf=None, pos=0, filename=None, fp=None):\n        if fp is not None:\n            try:\n                fileno = fp.fileno()\n                length = os.path.getsize(fp.name)\n                import mmap\n            except:\n                # read whole file into memory\n                buf = fp.read()\n                pos = 0\n            else:\n                # map the whole file into memory\n                if length:\n                    # length must not be zero\n                    buf = mmap.mmap(fileno, length, access=mmap.ACCESS_READ)\n                    pos = os.lseek(fileno, 0, 1)\n                else:\n                    buf = b''\n                    pos = 0\n\n            if filename is None:\n                try:\n                    filename = fp.name\n                except AttributeError:\n                    filename = None\n\n        self.buf = buf\n        self.pos = pos\n        self.line = 1\n        self.col = 1\n        self.filename = filename\n\n    def __next__(self):\n        while True:\n            # save state\n            pos = self.pos\n            line = self.line\n            col = self.col\n\n            type, text, endpos = self.scanner.next(self.buf, pos)\n            assert isinstance(text, bytes)\n            assert pos + len(text) == endpos\n            self.consume(text)\n            type, text = self.filter(type, text)\n            self.pos = endpos\n\n            if type == SKIP:\n                continue\n            elif type is None:\n                msg = 'unexpected char %r' % (text,)\n                raise ParseError(msg, self.filename, line, col)\n            else:\n                break\n        return Token(type=type, text=text, line=line, col=col)\n\n    def consume(self, text):\n        # update line number\n        pos = 0\n        for mo in self.newline_re.finditer(text, pos):\n            self.line += 1\n            self.col = 1\n            pos = mo.end()\n\n        # update column number\n        while True:\n            tabpos = text.find(b'\\t', pos)\n            if tabpos == -1:\n                break\n            self.col += tabpos - pos\n            self.col = ((self.col - 1) // self.tabsize + 1) * self.tabsize + 1\n            pos = tabpos + 1\n        self.col += len(text) - pos\n\n\nclass DotLexer(Lexer):\n\n    scanner = DotScanner()\n\n    def filter(self, type, text):\n        # TODO: handle charset\n        if type == STR_ID:\n            text = text[1:-1]\n\n            # line continuations\n            text = text.replace(b'\\\\\\r\\n', b'')\n            text = text.replace(b'\\\\\\r', b'')\n            text = text.replace(b'\\\\\\n', b'')\n\n            # quotes\n            text = text.replace(b'\\\\\"', b'\"')\n\n            # layout engines recognize other escape codes (many non-standard)\n            # but we don't translate them here\n\n            type = ID\n\n        elif type == HTML_ID:\n            text = text[1:-1]\n            type = ID\n\n        return type, text\n","repo_name":"jrfonseca/xdot.py","sub_path":"xdot/dot/lexer.py","file_name":"lexer.py","file_ext":"py","file_size_in_byte":3722,"program_lang":"python","lang":"en","doc_type":"code","stars":816,"dataset":"github-code","pt":"81"}
+{"seq_id":"3116130370","text":"import datetime\n\n\ndef ts(timestamp: str) -> datetime.datetime:\n    return datetime.datetime.strptime(timestamp, \"%Y-%m-%d %H:%M:%S\")\n\n\ndef inventory_data(table_name):\n    return [{'meta_table': table_name,\n              'job': 'j1',\n              'job_identifier': 'j1_1',\n              'job_result_item': '{file: 1}',\n              'processing_start': ts('2023-01-01 10:00:00'),\n              'processing_end': ts('2023-01-01 11:00:00'),\n              'batch_id_start': 0,\n              'batch_id_end': 4,\n              'batch_weight': 8,\n              'batch_count': 5,\n              'attempt': 1,\n              'status': 'succeeded',\n              'config': {\"foo\": \"bar\"},\n              'logging': 'log1'},\n             {'meta_table': table_name,\n              'job': 'j2',\n              'job_identifier': 'j2_1',\n              'job_result_item': '{file: 2}',\n              'processing_start': ts('2023-01-01 12:00:00'),\n              'processing_end': ts('2023-01-01 13:00:00'),\n              'batch_id_start': 5,\n              'batch_id_end': 8,\n              'batch_weight': 23,\n              'batch_count': 4,\n              'attempt': 1,\n              'status': 'running',\n              'config': {\"bar\": \"foo\"},\n              'logging': 'log2'}]\n\nMETA = [{'uid': 0, 'item': 'f0', 'weight': 0},\n        {'uid': 1, 'item': 'f1', 'weight': 2},\n        {'uid': 2, 'item': 'f2', 'weight': 4},\n        {'uid': 3, 'item': 'f3', 'weight': 6},\n        {'uid': 4, 'item': 'f4', 'weight': 8},\n        {'uid': 5, 'item': 'f5', 'weight': 10},\n        {'uid': 6, 'item': 'f6', 'weight': 12},\n        {'uid': 7, 'item': 'f7', 'weight': 14},\n        {'uid': 8, 'item': 'f8', 'weight': 16},\n        {'uid': 9, 'item': 'f9', 'weight': 18}]\n","repo_name":"mansenfranzen/pybatchintory","sub_path":"tests/test_data.py","file_name":"test_data.py","file_ext":"py","file_size_in_byte":1731,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"81"}
+{"seq_id":"26732283371","text":"\n# My prediction: my Python code will run faster then my Spark code on a large dataset on Vocareum:\n#\n# Reason : The grading dataset is about 5GB and it is smaller than recent main memory size.\n# When I run code on python, it will store all the items into the main memory and sort them\n# However, when I run code with Spark, as the items are divided into multiple partitions, it has to do reshuffling process to sort the data.\n# This will make Python Code faster than Spark code.\n\nimport time\nimport json\nfrom pyspark import SparkContext\nimport sys\n\ndef main(review_filepath, business_filepath, output_filepath_question_a, output_filepath_question_b):\n\n\n    sc = SparkContext('local[*]', 'task3')\n    start_time = time.time()\n\n    sample1RDD = sc.textFile(review_filepath)\n    sample2RDD = sc.textFile(business_filepath)\n\n    tmp = sample1RDD.map(lambda line : (json.loads(line)[\"business_id\"],(json.loads(line)[\"stars\"])))\n    tmp2 = sample2RDD.map(lambda line : (json.loads(line)[\"business_id\"],(json.loads(line)[\"city\"])))\n\n    joined = tmp.join(tmp2)\n\n    joined = joined.map(lambda line :(line[1][1],(1,line[1][0])))\n\n    def ftn1(first,second):\n        return (first[0]+second[0], first[1]+second[1]) ##count, sum\n    joined = joined.reduceByKey(ftn1)\n\n    def ftn2(x): ##(avg_star, city)\n        return (x[1][1]/x[1][0], x[0])\n\n    question_a_tmp = joined.map(lambda x : ftn2(x)).groupByKey().sortByKey(False).mapValues(lambda v: sorted(v)).flatMap(lambda x : [(x[0], value) for value in x[1]]).collect()\n    time_m2 = (time.time() - start_time)\n\n    ### store result of a\n    with open(output_filepath_question_a, 'w') as writefile:\n        writefile.writelines(\"city,stars\\n\")\n        for i in question_a_tmp:\n            writefile.writelines(str(i[1]) + ',' + str(i[0]) + '\\n')\n\n\n\n    ######m2\n    start_time = time.time()\n\n    sample1RDD = sc.textFile(review_filepath)\n    sample2RDD = sc.textFile(business_filepath)\n\n    tmp = sample1RDD.map(lambda line: (json.loads(line)[\"business_id\"], (json.loads(line)[\"stars\"])))\n    tmp2 = sample2RDD.map(lambda line: (json.loads(line)[\"business_id\"], (json.loads(line)[\"city\"])))\n\n    joined = tmp.join(tmp2)\n    joined = joined.map(lambda line: (line[1][1], (1, line[1][0])))\n\n    def ftn1(first, second):\n        return (first[0] + second[0], first[1] + second[1])\n\n    joined = joined.reduceByKey(ftn1)\n\n    def ftn2(x):\n        return (x[1][1] / x[1][0], x[0])\n\n    def ftn2(x):\n        return (x[1][1] / x[1][0], x[0])\n\n    tmp = joined.map(lambda x: ftn2(x)).collect()\n\n    tmp = sorted(tmp, key=lambda x: (-x[0], x[1]))\n\n    if len(tmp) >= 10:\n        tmp = tmp[0:10]\n\n    time_m1 = (time.time() - start_time)\n\n    question_b = {}\n    question_b[\"m1\"] = time_m1\n    question_b[\"m2\"] = time_m2\n\n    with open(output_filepath_question_b, 'w') as json_file:\n        json.dump(question_b, json_file)\n\n\n\nreview_filepath = sys.argv[1]\nbusiness_filepath = sys.argv[2]\noutput_filepath_question_a = sys.argv[3]\noutput_filepath_question_b = sys.argv[4]\n\n# main(review_filepath, output_file_path, n_partition)\nmain(review_filepath, business_filepath, output_filepath_question_a, output_filepath_question_b)\n","repo_name":"jeayoung114/Data-Mining-DSCI553","sub_path":"1. MapReduce and Spark/python/task3.py","file_name":"task3.py","file_ext":"py","file_size_in_byte":3156,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"81"}
+{"seq_id":"72148473864","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nPython script to evaluate the performance of a given set of GP parameters on\r\nthe 9 acetone-based binary systems.\r\n\r\nSections:\r\n    . Imports\r\n    . Configuration\r\n    . Datasets Generation\r\n    . Main Loop\r\n\r\nLast edit: 2022-10-27\r\nAuthor: Dinis Abranches\r\n\"\"\"\r\n\r\n# =============================================================================\r\n# Imports\r\n# =============================================================================\r\n\r\n# Specific\r\nimport numpy\r\nfrom tqdm import tqdm\r\n\r\n# Local\r\nfrom lib import thermoAux as thermo\r\nfrom lib import mlAux as ml\r\n\r\n# =============================================================================\r\n# Configuration\r\n# =============================================================================\r\n\r\n# Systems definition\r\ncomp1='Acetone'\r\ncomp2s=['Methanol','Benzene','Chloroform','Cyclohexane','Hexane','Heptane',\r\n       'TCM','Toluene','Water']\r\n\r\n# Component of interest for gamma calculation\r\ntargetComp=2 # 1 or 2\r\n\r\n# Temperature range\r\nTmin=250\r\nTmax=550\r\n\r\n# Training Grid type\r\ntrainGridType=2 # 1 for 5x5, 2 for 4x5+100\r\n\r\n# GP Configuration\r\ngpConfig={'kernel':'ArcCosine_2',\r\n          'useWhiteKernel':False,\r\n          'doLogY':True,\r\n          'indepDim':False,\r\n          'trainLikelihood':False\r\n          }\r\n\r\n# =============================================================================\r\n# Datasets Generation\r\n# =============================================================================\r\n\r\n# Testing Set Range\r\nx1_range=numpy.linspace(0,1,101)\r\nT_range=numpy.linspace(Tmin,Tmax,101)\r\n# Build Training Grid\r\nX_Train=ml.build_X_Train_Binary(trainGridType,targetComp,Tmin,Tmax)\r\n\r\n# =============================================================================\r\n# Main Loop\r\n# =============================================================================\r\n\r\n# Initialize MPE\r\nMPE=[]\r\nfor comp2 in tqdm(comp2s,'Component: '):\r\n    # Get NRTL parameters\r\n    parameters=thermo.NRTL_Parameters(comp1,comp2)\r\n    # Define truth function\r\n    def F_Truth(x1,T):\r\n        gammas=thermo.NRTL_Binary_getGamma(parameters,x1,T)\r\n        return gammas[targetComp-1]\r\n    # Build Training Dataset\r\n    X_Train,Y_Train=ml.buildDataset_Binary(F_Truth,X=X_Train)\r\n    # Build Testing Dataset\r\n    X_Test,Y_Test=ml.buildDataset_Binary(F_Truth,x1_range=x1_range,\r\n                                         T_range=T_range)\r\n    # Define feature normalization\r\n    __,X_Scaler=ml.normalize(X_Test,method='MinMax')\r\n    # Build GP\r\n    model=ml.buildGP(X_Train,Y_Train,X_Scaler=X_Scaler,gpConfig=gpConfig)\r\n    # Perform predictions\r\n    Y_Pred,Y_STD=ml.gpPredict(model,X_Test,X_Scaler=X_Scaler,gpConfig=gpConfig)\r\n    # Evaluate GP\r\n    scores=ml.evaluateModel(Y_Pred,X_Test,Y_Test)    \r\n    # Append\r\n    MPE.append(scores['MPE'])\r\n# Print\r\nprint('Results:\\n\\n')\r\nfor entry in MPE:\r\n    print(entry)\r\n","repo_name":"MaginnGroup/ACAL","sub_path":"Code/03_GP_Binary_All.py","file_name":"03_GP_Binary_All.py","file_ext":"py","file_size_in_byte":2885,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"15533805219","text":"import base64\nimport os\nimport shutil\n\nimport Tribler.Core.Utilities.json_util as json\nfrom Tribler.Core.TorrentDef import TorrentDef\nfrom Tribler.Test.Core.Modules.RestApi.base_api_test import AbstractApiTest\nfrom Tribler.Test.common import TESTS_DATA_DIR\nfrom Tribler.Test.tools import trial_timeout\n\n\nclass TestMyChannelCreateTorrentEndpoint(AbstractApiTest):\n\n    def setUpPreSession(self):\n        super(TestMyChannelCreateTorrentEndpoint, self).setUpPreSession()\n        # Create temporary test directory with test files\n        self.files_path = os.path.join(self.session_base_dir, 'TestMyChannelCreateTorrentEndpoint')\n        if not os.path.exists(self.files_path):\n            os.mkdir(self.files_path)\n        shutil.copyfile(os.path.join(TESTS_DATA_DIR, 'video.avi'),\n                        os.path.join(self.files_path, 'video.avi'))\n        shutil.copyfile(os.path.join(TESTS_DATA_DIR, 'video.avi.torrent'),\n                        os.path.join(self.files_path, 'video.avi.torrent'))\n        self.config.set_libtorrent_enabled(True)\n\n    @trial_timeout(10)\n    def test_create_torrent(self):\n        \"\"\"\n        Testing whether the API returns a proper base64 encoded torrent\n        \"\"\"\n        torrent_path = os.path.join(self.files_path, \"video.avi.torrent\")\n        expected_tdef = TorrentDef.load(torrent_path)\n        export_dir = self.temporary_directory()\n\n        def verify_torrent(body):\n            response = json.twisted_loads(body)\n            torrent = base64.b64decode(response[\"torrent\"])\n            tdef = TorrentDef.load_from_memory(torrent)\n\n            # Copy expected creation date and created by (Tribler version) from actual result\n            creation_date = tdef.get_creation_date()\n            expected_tdef.metainfo[\"creation date\"] = creation_date\n            expected_tdef.metainfo[\"created by\"] = tdef.metainfo['created by']\n\n            self.assertEqual(dir(expected_tdef), dir(tdef))\n            self.assertTrue(os.path.exists(os.path.join(export_dir, \"test_torrent.torrent\")))\n\n        post_data = {\n            \"files\": [os.path.join(self.files_path, \"video.avi\"),\n                      os.path.join(self.files_path, \"video.avi.torrent\")],\n            \"description\": \"Video of my cat\",\n            \"trackers\": \"http://localhost/announce\",\n            \"name\": \"test_torrent\",\n            \"export_dir\": export_dir\n        }\n        self.should_check_equality = False\n        return self.do_request('createtorrent?download=1', 200, None, 'POST', post_data).addCallback(verify_torrent)\n\n    @trial_timeout(10)\n    def test_create_torrent_io_error(self):\n        \"\"\"\n        Testing whether the API returns a formatted 500 error if IOError is raised\n        \"\"\"\n\n        def verify_error_message(body):\n            error_response = json.twisted_loads(body)\n            expected_response = {\n                u\"error\": {\n                    u\"handled\": True,\n                    u\"code\": u\"IOError\",\n                    u\"message\": u\"Path does not exist: %s\" % post_data[\"files\"]\n                }\n            }\n            self.assertDictContainsSubset(expected_response[u\"error\"], error_response[u\"error\"])\n\n        post_data = {\n            \"files\": \"non_existing_file.avi\"\n        }\n        self.should_check_equality = False\n        return self.do_request('createtorrent', 500, None, 'POST', post_data).addCallback(verify_error_message)\n\n    @trial_timeout(10)\n    def test_create_torrent_missing_files_parameter(self):\n        expected_json = {\"error\": \"files parameter missing\"}\n        return self.do_request('createtorrent', 400, expected_json, 'POST')\n","repo_name":"morristech/tribler","sub_path":"Tribler/Test/Core/Modules/RestApi/test_create_torrent_endpoint.py","file_name":"test_create_torrent_endpoint.py","file_ext":"py","file_size_in_byte":3607,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"81"}
+{"seq_id":"32970479534","text":"from django.core.mail import send_mail\nfrom django_rq import job\n\n\n@job\ndef send_email_job(email):\n    print('Start email')\n    send_mail(\n        email['subject'],\n        email['plain_message'],\n        email['from_email'],\n        email['email_to'],\n        html_message=email['html_message']\n    )\n    print('Finish email')\n","repo_name":"TimBerk/university","sub_path":"contacts/tasks.py","file_name":"tasks.py","file_ext":"py","file_size_in_byte":328,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"957634195","text":"from machine import Pin, Timer, PWM, ADC\n\ncountTimer = Timer()\nbuzzTimer = Timer()\ncount_time = 3\n\nsegment_one = [\n    Pin(9, Pin.OUT),\n    Pin(10, Pin.OUT),\n    Pin(11, Pin.OUT),\n    Pin(18, Pin.OUT),\n    Pin(17, Pin.OUT),\n    Pin(16, Pin.OUT),\n    Pin(8, Pin.OUT)]\n\nsegment_two = [\n    Pin(13, Pin.OUT),\n    Pin(14, Pin.OUT),\n    Pin(15, Pin.OUT),\n    Pin(22, Pin.OUT),\n    Pin(26, Pin.OUT),\n    Pin(20, Pin.OUT),\n    Pin(12, Pin.OUT)]\n\nmax_time = 24\ncurrent_time = 0\n\nbuzzer = PWM(Pin(2))\nbuzz_on = False\nbuzzer.freq(800)\nbuzzer.duty_u16(0)\n\nbook_mark_sensor = ADC(Pin(28))\nbook_mark_threshold = 1000\n\ndef shut_off(segments):\n    for segment in segments:\n        segment.value(1)\n        \ndef turn_on(segments):\n    for segment in segments:\n        segment.value(0)\n        \ndef display_num(number, segments):\n    numbers = {\n        0: lambda segments : turn_on(segments[:6]),\n        1: lambda segments : turn_on(segments[2:4]),\n        2: lambda segments : turn_on(segments[1:3] + segments[4:]), \n        3: lambda segments : turn_on(segments[1:5] + [segments[6]] ),\n        4: lambda segments : turn_on([segments[0]] + segments[2:4] + [segments[6]]),\n        5: lambda segments : turn_on(segments[:2] + segments[3:5] + [segments[6]]),\n        6: lambda segments : turn_on(segments[:2] + segments[3:7]),\n        7: lambda segments : turn_on(segments[1:4]),\n        8: lambda segments : turn_on(segments),\n        9: lambda segments : turn_on((segments[:5] + [segments[6]]))\n    }\n    numbers[number](segments)\n    \ndef book_mark_in_use():\n    return book_mark_sensor.read_u16() <= book_mark_threshold\n\ndef time_is_up():\n    global max_time\n    global current_time\n    return current_time > max_time\n\ndef buzz(timer):\n    global buzz_on\n    if time_is_up() and book_mark_in_use():\n        if  buzz_on:\n            buzzer.duty_u16(0)\n        else:\n            buzzer.duty_u16(32512)\n            \n        buzz_on = not buzz_on\n    elif buzz_on and not time_is_up or not book_mark_in_use():\n        buzz_on = False\n        buzzer.duty_u16(0)\n    \ndef timer_counter(timer):\n    global current_time\n    if not time_is_up() and book_mark_in_use():\n        shut_off(segment_one)\n        shut_off(segment_two)\n        display_num(current_time//10, segment_one)\n        display_num(current_time%10, segment_two)\n        current_time += 1\n    elif not book_mark_in_use():\n        shut_off(segment_one)\n        shut_off(segment_two)\n        current_time = 0\n        \n#Init\nshut_off(segment_one)\nshut_off(segment_two)\ncountTimer.init(freq=count_time, mode=Timer.PERIODIC, callback=timer_counter)\nbuzzTimer.init(freq=1, mode=Timer.PERIODIC, callback=buzz)","repo_name":"rhipps/Annoying-Book-Mark","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2647,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"7186764889","text":"import sys\ninput = sys.stdin.readline\nm = int(input())\ngu_list = list(map(int,input().split()))\nk = int(input())\ns = sum(gu_list)\n\ndp = [[0 for _ in range(s+1)] for _ in range(s+1)]\n\nfor i in range(s+1):\n    dp[i][1] = i\n    dp[i][i] = 1\n    dp[i][0] = 1\n\n\nfor i in range(2, s+1):\n    for j in range(1,i):\n        dp[i][j] = dp[i-1][j] + dp[i-1][j-1]\n\ntmp = 0\nfor i in gu_list:\n    tmp += dp[i][k]\n\nprint(tmp/dp[s][k])","repo_name":"johnny9696/baekjoon_problem","sub_path":"10000~end/13000~13999/13251.py","file_name":"13251.py","file_ext":"py","file_size_in_byte":418,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"18744871748","text":"import torch\nimport scipy.optimize as opt\nimport numpy as np\n\n\ndef k_largest_index_argsort(a, k):\n    idx = np.argsort(a.ravel())[:k]\n    return np.column_stack(np.unravel_index(idx, a.shape))\n\n\ndef hungarian(s: torch.Tensor, n1=None, n2=None, Inlier_src_pre=None, Inlier_ref_pre=None, th=0.5):\n    \"\"\"\n    Solve optimal LAP permutation by hungarian algorithm.\n    :param s: input 3d tensor (first dimension represents batch)\n    :param n1: [num of objs in dim1] (against padding)\n    :param n2: [num of objs in dim2] (against padding)\n    :return: optimal permutation matrix\n    \"\"\"\n    device = s.device\n    batch_num = s.shape[0]\n\n    perm_mat = s.cpu().detach().numpy() * -1\n    for b in range(batch_num):\n        n1b = perm_mat.shape[1] if n1 is None else n1[b]\n        n2b = perm_mat.shape[2] if n2 is None else n2[b]\n        if Inlier_src_pre is not None and Inlier_ref_pre is not None:\n            perm_mat_bcopy = perm_mat[b].copy()\n            Inlier_src_pre_np = Inlier_src_pre[b].cpu().detach().numpy()\n            Inlier_ref_pre_np = Inlier_ref_pre[b].cpu().detach().numpy()\n            row = np.where(Inlier_src_pre_np > th)[0]\n            col = np.where(Inlier_ref_pre_np > th)[0]\n            perm_mat_bcopy = perm_mat_bcopy[row, :]\n            perm_mat_bcopy = perm_mat_bcopy[:, col]\n            row_ind, col_ind = opt.linear_sum_assignment(perm_mat_bcopy)\n            perm_mat[b] = np.zeros_like(perm_mat[b])\n            for i,j in zip(row_ind, col_ind):\n                perm_mat[b, row[i], col[j]] = 1\n            # if top_K is not None:\n            #     s_clone = s.clone()\n            #     topk_ind = torch.topk(s_clone[b].view(1, -1), 10)\n            #     row = (topk_ind[1] / s_clone[b].shape[1]).cpu().numpy()\n            #     col = (topk_ind[1] % s_clone[b].shape[1]).cpu().numpy()\n            #     perm_mat_1 = np.zeros_like(perm_mat[b])\n            #     for i, j in zip(row, col):\n            #         perm_mat_1[i, j] = 1\n            #     perm_mat[b] = perm_mat[b]*perm_mat_1\n        else:\n            row_ind, col_ind = opt.linear_sum_assignment(perm_mat[b, :n1b, :n2b])\n            perm_mat[b] = np.zeros_like(perm_mat[b])\n            perm_mat[b, row_ind, col_ind] = 1\n            # # 当外部未计算指派矩阵行和列和的情况下，使用如下计算方式。\n            # perm_mat_bcopy = perm_mat[b].copy()\n            # perm_mat_bcopyz = perm_mat[b].copy()*-1\n            # row = np.where(np.max(perm_mat_bcopyz, axis=1) > th)[0]\n            # col = np.where(np.max(perm_mat_bcopyz, axis=0) > th)[0]\n            # perm_mat_bcopy = perm_mat_bcopy[row, :]\n            # perm_mat_bcopy = perm_mat_bcopy[:, col]\n            # row_ind, col_ind = opt.linear_sum_assignment(perm_mat_bcopy)\n            # perm_mat[b] = np.zeros_like(perm_mat[b])\n            # for i,j in zip(row_ind, col_ind):\n            #     perm_mat[b, row[i], col[j]] = 1\n    perm_mat = torch.from_numpy(perm_mat).to(device)\n\n    return perm_mat\n","repo_name":"fukexue/RGM","sub_path":"utils/hungarian.py","file_name":"hungarian.py","file_ext":"py","file_size_in_byte":2961,"program_lang":"python","lang":"en","doc_type":"code","stars":101,"dataset":"github-code","pt":"81"}
+{"seq_id":"13373369548","text":"\"\"\"Taken from Teven and Leandro\"\"\"\nimport gzip\nimport os\nimport shutil\nimport time\nimport logging\nimport argparse\nimport datasets\n\nfrom datasets import load_dataset, Features\nfrom datasets.utils.logging import set_verbosity_info\n\n\nset_verbosity_info()\nlogger = logging.getLogger(__name__)\n\nnull = None\n# features = {\n#     \"HtmlPreprocessor_error\": {\"dtype\": \"int64\", \"id\": null, \"_type\": \"Value\"},\n#     \"HtmlPreprocessor_error_comment\": {\"dtype\": \"string\", \"id\": null, \"_type\": \"Value\"},\n#     \"content_languages\": {\"dtype\": \"string\", \"id\": null, \"_type\": \"Value\"},\n#     \"content_mime_detected\": {\"dtype\": \"string\", \"id\": null, \"_type\": \"Value\"},\n#     \"depth\": {\"dtype\": \"int16\", \"id\": null, \"_type\": \"Value\"},\n#     \"download_exception\": {\"dtype\": \"string\", \"id\": null, \"_type\": \"Value\"},\n#     \"external_urls\": [{\"dtype\": \"string\", \"id\": null, \"_type\": \"Value\"}],\n#     \"fetch_redirect\": {\"dtype\": \"string\", \"id\": null, \"_type\": \"Value\"},\n#     \"fetch_status\": {\"dtype\": \"int32\", \"id\": null, \"_type\": \"Value\"},\n#     \"fetch_time\": {\"dtype\": \"timestamp[ns]\", \"id\": null, \"_type\": \"Value\"},\n#     \"html_error\": {\"dtype\": \"string\", \"id\": null, \"_type\": \"Value\"},\n#     \"html_footer\": [{\"dtype\": \"string\", \"id\": null, \"_type\": \"Value\"}],\n#     \"html_head\": [{\"dtype\": \"string\", \"id\": null, \"_type\": \"Value\"}],\n#     \"html_str\": {\"dtype\": \"string\", \"id\": null, \"_type\": \"Value\"},\n#     \"html_title\": [{\"dtype\": \"string\", \"id\": null, \"_type\": \"Value\"}],\n#     \"metadata_html\": [\n#         {\n#             \"char_end_idx\": {\"dtype\": \"int64\", \"id\": null, \"_type\": \"Value\"},\n#             \"char_start_idx\": {\"dtype\": \"int64\", \"id\": null, \"_type\": \"Value\"},\n#             \"html_attrs\": {\n#                 \"attrs\": [{\"dtype\": \"string\", \"id\": null, \"_type\": \"Value\"}],\n#                 \"values\": [{\"dtype\": \"string\", \"id\": null, \"_type\": \"Value\"}],\n#             },\n#             \"key\": {\"dtype\": \"string\", \"id\": null, \"_type\": \"Value\"},\n#             \"relative_end_pos\": {\"dtype\": \"int64\", \"id\": null, \"_type\": \"Value\"},\n#             \"relative_start_pos\": {\"dtype\": \"int64\", \"id\": null, \"_type\": \"Value\"},\n#             \"type\": {\"dtype\": \"string\", \"id\": null, \"_type\": \"Value\"},\n#             \"value\": {\"dtype\": \"string\", \"id\": null, \"_type\": \"Value\"},\n#         }\n#     ],\n#     \"seed_id\": {\"dtype\": \"int32\", \"id\": null, \"_type\": \"Value\"},\n#     \"text\": {\"dtype\": \"string\", \"id\": null, \"_type\": \"Value\"},\n#     \"url\": {\"dtype\": \"string\", \"id\": null, \"_type\": \"Value\"},\n#     \"url_host_name\": {\"dtype\": \"string\", \"id\": null, \"_type\": \"Value\"},\n#     \"url_host_registered_domain\": {\"dtype\": \"string\", \"id\": null, \"_type\": \"Value\"},\n#     \"url_host_tld\": {\"dtype\": \"string\", \"id\": null, \"_type\": \"Value\"},\n#     \"url_surtkey\": {\"dtype\": \"string\", \"id\": null, \"_type\": \"Value\"},\n#     \"warc_filename\": {\"dtype\": \"string\", \"id\": null, \"_type\": \"Value\"},\n#     \"warc_record_length\": {\"dtype\": \"int32\", \"id\": null, \"_type\": \"Value\"},\n#     \"warc_record_offset\": {\"dtype\": \"int32\", \"id\": null, \"_type\": \"Value\"},\n# }\nfeatures = {\n    \"text\": {\"dtype\": \"string\", \"id\": null, \"_type\": \"Value\"},\n    \"meta\": {\n        \"content_languages\": {\"dtype\": \"string\", \"id\": null, \"_type\": \"Value\"},\n        \"seed_id\": {\"dtype\": \"int64\", \"id\": null, \"_type\": \"Value\"},\n        \"url\": {\"dtype\": \"string\", \"id\": null, \"_type\": \"Value\"},\n    },\n}\n\n\ndef convert_types(features):\n    if isinstance(features, dict) and \"_type\" in features:\n        return getattr(datasets, features[\"_type\"])(features[\"dtype\"])\n    elif isinstance(features, dict):\n        return {key: convert_types(value) for key, value in features.items()}\n    elif isinstance(features, list):\n        return [convert_types(value) for value in features]\n\n\nfinal_features = convert_types(features)\nfinal_features = Features(final_features)\nfinal_features\n\n\ndef get_hash(example):\n    \"\"\"Get hash of content field.\"\"\"\n    return {\"hash\": hash(example[\"text\"].replace(\" \", \"\"))}\n\n\ndef check_uniques(example, uniques):\n    \"\"\"Check if current hash is still in set of unique hashes and remove if true.\"\"\"\n    if example[\"hash\"] in uniques:\n        uniques.remove(example[\"hash\"])\n        return True\n    else:\n        return False\n\n\ndef preprocess(example):\n    \"\"\"Chain all preprocessing steps into one function to not fill cache.\"\"\"\n    results = dict()\n    results.update(get_hash(example))\n    return results\n\n\ndef filter(example, uniques, args):\n    \"\"\"Filter dataset with heuristics.\"\"\"\n    if not check_uniques(example, uniques):\n        return False\n    else:\n        return True\n\n\ndef compress_file(file_path):\n    \"\"\"Compress a file with g-zip.\"\"\"\n    with open(file_path, \"rb\") as f_in:\n        with gzip.open(file_path + \".gz\", \"wb\", compresslevel=6) as f_out:\n            shutil.copyfileobj(f_in, f_out)\n    os.unlink(file_path)\n\n\ndef main():\n    logging.basicConfig(\n        format=\"%(asctime)s - %(levelname)s - %(name)s - %(message)s\",\n        datefmt=\"%m/%d/%Y %H:%M:%S\",\n        level=logging.INFO,\n    )\n    parser = argparse.ArgumentParser(description=\"Load seed and upload to hub\")\n    parser.add_argument(\n        \"--seed-id\",\n        help=\"seed ID\",\n        required=True,\n        type=int,\n    )\n    parser.add_argument(\n        \"--save-dir\", required=True, type=str, help=\"Where to save the datasets.\"\n    )\n    parser.add_argument(\n        \"--pseudo_crawl_path\",\n        help=\"path to where the pseudocrawl is located\",\n        required=True,\n        type=str,\n    )\n    parser.add_argument(\n        \"--gzipped\",\n        help=\"Write file directly in jsonl.gz compressed format\",\n        action=\"store_true\",\n    )\n    parser.add_argument(\n        \"--save-batch-size\",\n        help=\"Batch size used for saving the dataset\",\n        required=True,\n        type=int,\n    )\n    parser.add_argument(\n        \"--batch-size\",\n        help=\"Batch size used for the mapping and saving of the dataset\",\n        required=True,\n        type=int,\n    )\n    parser.add_argument(\n        \"--num-proc\",\n        help=\"Number of processors used for the mapping and saving of the dataset\",\n        required=True,\n        type=int,\n    )\n    args = parser.parse_args()\n\n    # Load dataset\n    t_start = time.time()\n    ds = load_dataset(\n        \"json\",\n        # data_files=[f\"{args.pseudo_crawl_path}/seed_id={args.seed_id}/text__html/*.jsonl.gz\"],\n        data_files=[\n            f\"{args.pseudo_crawl_path}/lm_change_lang_id_seed_id_{args.seed_id}_pseudocrawl_change_name/*.jsonl\"\n        ],\n        features=final_features,\n        split=\"train\",\n    )\n    logger.info(f\"Time to load dataset: {time.time()-t_start:.2f}\")\n\n    # Run preprocessing\n    t_start = time.time()\n    ds = ds.map(preprocess, num_proc=args.num_proc)\n    logger.info(f\"Time to preprocess dataset: {time.time()-t_start:.2f}\")\n\n    # Deduplicate hashes\n    uniques = set(ds.unique(\"hash\"))\n    frac = len(uniques) / len(ds)\n    logger.info(f\"Fraction of duplicates: {1-frac:.2%}\")\n\n    # Deduplicate data and apply heuristics\n    t_start = time.time()\n    ds_filter = ds.filter(filter, fn_kwargs={\"uniques\": uniques, \"args\": args})\n    logger.info(f\"Time to filter dataset: {time.time()-t_start:.2f}\")\n    logger.info(f\"Size of filtered dataset: {len(ds_filter)}\")\n\n    # Save data\n    t_start = time.time()\n    if args.gzipped:\n        file_name = os.path.join(args.save_dir, f\"data.jsonl.gz\")\n        logger.info(f\"the dataset will be saved at {file_name}\")\n        ds_filter.to_json(\n            file_name,\n            num_proc=args.num_proc,\n            batch_size=args.save_batch_size,\n            compression=\"gzip\",\n        )\n    else:\n        file_name = os.path.join(args.save_dir, f\"data.jsonl\")\n        logger.info(f\"the dataset will be saved at {file_name}\")\n        ds_filter.to_json(\n            file_name,\n            num_proc=args.num_proc,\n            batch_size=args.save_batch_size,\n        )\n\n    logger.info(f\"Time to save dataset: {time.time()-t_start:.2f}\")\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"bigscience-workshop/data-preparation","sub_path":"sourcing/cc_pseudo_crawl/python_scripts/exact_deduplicates.py","file_name":"exact_deduplicates.py","file_ext":"py","file_size_in_byte":7938,"program_lang":"python","lang":"en","doc_type":"code","stars":239,"dataset":"github-code","pt":"81"}
+{"seq_id":"37891382619","text":"\nFRECUENCIA_MINUTOS=15\nMAIN_SLEEP_TIME= 60 * FRECUENCIA_MINUTOS\n\nSPLIT=10\nTIMEOUT= (FRECUENCIA_MINUTOS - 2) * 60 / SPLIT\nPriceOverlap=0.0015\n\nJUST_STARTED=0\n\nMinOrderAsset=0.1\nMinOrderCurr=0.0001\n\nTICK=0\n\n#Guayaba\napikey= '627eb3e8-0237-4bae-8394-56cfa19256c2'\nsecretkey= 'E5ED9F5C22E7A2EC0537AD4610B42224'\n\n\n# ////////////////////////////////////\n# CONSTANTES ZF4 ANG\n# ////////////////////////////////////\n\nBOT_NAME=\"OKCOIN ZF4 BTCCNY 15M B\"\nALGO=\"ZF4\"\n\nPAIR='btc_cny'\nPairAsset='BTC'\nPairCurr='CNY'\n\nCuenta = \"badolato\"\n\nA1\t=  10.27\nB1\t= -15.05\nC1\t=  45.15\nD1\t= -69.4\n\nLRp1\t=  380\nLRp2\t=  433\nLRp3\t=  131\nLRp4\t=   98\n\nSTDp1\t=  402\nSTDp2\t=  476\nSTDp3\t=  104\nSTDp4\t=  383\n\nFL1\t=  -9.63\nFL2\t=  -14\nFL3\t=  -5.43\nFL4\t=  -27.54\n\nTh1     =  44.386\nTh2     = -32.465\n\n\nFEEPCT = 0.016\n\n# ###############################\n\nNUMVELAS=5000\n\n\nrunning = True\n\nCheckCode=False\nDEBUG=False\nSHOWTABLE=False\n\n\nvZF4=[]\nTrendingUp=False\nTrendingDn=False\nsig=0\nticker=0\nprice=0\n\n\nbalance=0\nassets=0\ncurr=0\nnet=0\nprice=0\ntotal=0\n\nstbalance=0\nstassets=0\nstcurr=0\nstnet=0\nstprice=0\nstotal=0\n\nFirstTickError=0\n\n\n","repo_name":"aceri/portfolio","sub_path":"resources/finance/python_trading_bots/bots_python/Bot_Okcoin_ZF4_BTCCNY_15m_B/shared.py","file_name":"shared.py","file_ext":"py","file_size_in_byte":1088,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"17837252389","text":"#!/usr/bin/env python\n\nimport logging\nfrom twisted.internet import reactor\nfrom optparse import OptionParser\nimport sys\n\nimport ikarus.irc\n\ndef run():\n    logging.basicConfig(level=logging.DEBUG)\n    parser = OptionParser()\n    parser.add_option(\"-p\", \"--port\", type=\"int\", dest=\"port\",\n                      help=\"TCP port to listen on.  May only be specified once. (default: %default)\",\n                      default=6667)\n    (options, args) = parser.parse_args()\n    reactor.listenTCP(options.port, ikarus.irc.IRCFactory())\n    logging.info(\"Ikarus started.  Listening on port %i...\" % options.port)\n    reactor.run()\n\nif __name__ == '__main__':\n    run()\n","repo_name":"orospakr/ikarus","sub_path":"ikarus/ikarusd.py","file_name":"ikarusd.py","file_ext":"py","file_size_in_byte":660,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"18457550157","text":"\"\"\"\nRead file into texts and calls.\nIt's ok if you don't understand how to read files.\n\"\"\"\nimport csv\n\nwith open('texts.csv', 'r') as f:\n    reader = csv.reader(f)\n    texts = list(reader)\n\nwith open('calls.csv', 'r') as f:\n    reader = csv.reader(f)\n    calls = list(reader)\n\n\"\"\"\nTASK 4:\nThe telephone company want to identify numbers that might be doing\ntelephone marketing. Create a set of possible telemarketers:\nthese are numbers that make outgoing calls but never send texts,\nreceive texts or receive incoming calls.\n\nPrint a message:\n\"These numbers could be telemarketers: \"\n<list of numbers>\nThe list of numbers should be print out one per line in lexicographic order with no duplicates.\n\"\"\"\n\nN_text=len(texts)\nN_calls=len(calls)\n\ncandidates=set()\n#all number making calls:\nfor ii in range(N_calls):\n    calling=calls[ii][0]\n    candidates.add(calling)\n\n#print(candidates)\n#remove candidates that receiving calls:\nfor ii in range(N_calls):\n    receiving=calls[ii][1]\n    if receiving in candidates:\n        candidates.remove(receiving)\n\n# remove candidates that using texts:\nfor ii in range(N_text):\n    calling=texts[ii][0]\n    receiving=texts[ii][1]\n    if calling in candidates:\n        candidates.remove(calling)\n    if receiving in candidates:\n        candidates.remove(receiving)\n        \ncandidates=sorted(candidates)\nprint(\"These numbers could be telemarketers: \")\nfor num in candidates:\n    print(num)\n\n","repo_name":"Atropos007dy/Data-Structures-Algorithms","sub_path":"P1_Introduction/Task4.py","file_name":"Task4.py","file_ext":"py","file_size_in_byte":1420,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"29698325784","text":"pprompt =\"--> \"\n\n\ndef pyramid(length):\n    k = length\n    for i in range(0, length+1):\n        for j in range(0, k):\n            print(end=\" \")\n        k = k - 1\n        for j in range(0, i):\n            print(\"*\", end= \" \")\n        print(\"\\n\")\n\nflag = True\nnewFlag = \"\" \ndef take_input():\n    print (\"Enter number please\")\n    length = int(input(pprompt))\n    k = length - 1\n    pyramid(length)\n    \n\nwhile flag:\n    take_input()\n    print(\"Press 'Q' to exit.\")\n    newFlag = str.upper(input(newFlag)) \n    if newFlag =='Q':\n        flag = False\n    else:\n        print(\"Idiot option tho barabar dal\")    \n    newFlag=\" \"           ","repo_name":"azAZ09sdjk/python","sub_path":"pyramid.py","file_name":"pyramid.py","file_ext":"py","file_size_in_byte":633,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"10440750285","text":"# 7.\tFloat Rounding: Calculate the result of 10/3 as a float and then round it to two decimal places.\n\n\n\n#here we will use round() function .\n\n\na=10/3\nprint(a)\n\nb=10/3\nrounded_number=round(b)\nprint(rounded_number)\n\n\n#extra example just for understanding \n\n\nc=100/3\nprint(c)\n\n\n\nd=100/3\nrounded_num=round(d)\nprint(rounded_num)\n","repo_name":"shiixxam/python_in_30_days","sub_path":"python/DAY2/variables & data types/Q7.py","file_name":"Q7.py","file_ext":"py","file_size_in_byte":325,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"8812498754","text":"from setuptools import setup, find_packages\n\n__version__ = '1.0.1'\n\nsetup(\n    name='maildocker',\n    version=str(__version__),\n    packages=find_packages(),\n    description='Maildocker library for Python',\n    long_description=open('./README.rst').read(),\n    install_requires=['urllib2'],\n)\n","repo_name":"maildocker/maildocker-python","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":293,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"81"}
+{"seq_id":"25831630933","text":"import requests\nfrom bs4 import BeautifulSoup\nimport pandas as pd\nimport os \n\ndef shoes ():\n    print(\"---SCRAPPINGGG---\")\n    os.system(\"say scraping\")\n    url = \"https://www.murallasport.com/29-zapatillas-moda-mujer\"\n    html = requests.get(url)\n    soup = BeautifulSoup(html.content, \"html.parser\")\n\n    names_list = soup.find_all(\"div\", {\"class\":\"name_product_box\"})\n\n    names = [i.getText().strip() for i in names_list]\n    brand_list = soup.find_all(\"span\", {\"class\":\"marca-product-box\"})\n    brands = [i.getText().strip() for i in brand_list]\n    price_list = soup.find_all(\"div\", {\"class\": \"price_product_box\"})\n    prices = [i.getText().strip().split(\"\\n\")[0].replace(\"€\", \"\") for i in price_list]\n\n    list_links = soup.find_all(\"div\", {\"class\": \"name_product_box\"})\n    links = [f\"https://www.murallasport.com{i.span.a.get('href')}\".strip() for i in list_links]\n\n    all_together = {\n        \"names\": names,\n        \"brand\": brands,\n        \"price\": prices,\n        \"links\": links\n    }\n\n    return pd.DataFrame(all_together)\n\n\ndef get_description (url):\n    print(\"---GETTING DESCRIPTION---\")\n\n    html = requests.get(url)\n    soup = BeautifulSoup(html.content, \"html.parser\")\n    results = soup.find(\"div\", {\"id\": \"description\"})\n    return results.getText().strip()","repo_name":"Ironhack-data-bcn-april-2023/lectures","sub_path":"week-3/small-scraping/src/extraction.py","file_name":"extraction.py","file_ext":"py","file_size_in_byte":1282,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"41110785205","text":"from django.urls import path\n\nfrom . import views\n\nurlpatterns = [\n    path('meetups/', views.index, name='all-meetups'),  # our-domain.com/meetups\n    path('meetup/<slug:meetup_slug>/success', views.confirm_registration, name='confirm-registration'),\n    path('meetup/<slug:meetup_slug>', views.meetup_detail, name='meetup-detail'),  # our-domain.com/meetup/<dynamic-path-segment>\n\n    path('meetups/myfirstjson', views.my_first_json)  # our-domain.com/meetups/myfirstjson\n]\n","repo_name":"fabiosoaresf/Django-CI-CD","sub_path":"django_course_site/meetups/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":476,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"26051124254","text":"# October 7, 2019 Gilroy, California\n# Blake Southwood\n# the idea for this project stemmed from a request from\n# my mother a poet that wanted to check if she had used a phrase in her poems\n# collection previously. I decided to recreate the Poetry Database in Python\n# along with it's unique search characteristics.\n\n# Analyzing English focusing on poetry first\n# I decided to analyze hundreds of poems to master everything\n# with strings and analysis of English\n# I'm starting by analyzing 3 poems before searching through hundreds\n\n\n# Searches for substring phrase in multiple longstrings\n# this searches for whether or not a phrase(substring) exists in a poem(big string)\n# loop to search through each value in the dictionary based on the key\n\n# just added January 2020 searching for multiple words in a string in dictionary\n# this is essentially a boolean and where each word in a list must exist within\n# a string; in short this is searching for all substring words in a list inside of a dictionary\n# that has strings for the data; succinctly find matching strings that contain \n# all substrings in a string in a dictionary. For the example it's searching through\n# poems that all use a list of words\n\n\n'''\n\n count_word_match()        counts the number of times a word is used in one large string\n\n search_for_phrase()       searches through dictionary for a phrase in lots of large strings\n\n get_substring_location()  returns the location of first character of substring in string\n\n'''\n\n# module with all of the poems stored as strings\nimport poems\n\n\n# ==== dictionary ====\n\nsmalldictionary = {                     # each poem is a long string stored in module poems\n  \"cat\":           poems.catpoem,       # so each poem has a prefix of poems referencing the module.\n  \"homegardener\":  poems.homegardenerpoem,\n  \"theview\":       poems.theviewpoem,\n  \"december\":      poems.december,\n  \"treebites\":     poems.treebites,\n  \"oldpath\":       poems.oldpath,\n  \"holmes\":        poems.holmesappears,\n  \"nature\":        poems.nature_tankas,\n  \"bellyflower\":   poems.bellyflower_tanka_trio,\n  \"paintings\":     poems.painting,\n  \"snowballs\":     poems.snowballs,\n  \"pumpkins\":      poems.halloween,\n  \"shaky\":         poems.shaky,\n  \"honeylocust\":   poems.honeylocustspring,\n  \"einstein\":      poems.einstein,\n  \"stayhome\":      poems.pleasestayhome,\n  \"newyears\":      poems.newyearsevekiss,\n }\n\nmatching_list = []  #empty list\npeach = []   #empty list to have count inside of it\n\n\n\n\n\n\n# searches entire dictionary for a phrase(substring several words) that references large strings\n#####================================================\n##           search for phrase\n#####================================================\n'''\ninputs substring and string\nsearch for substring in strings from list in dictionary\nfind location of first substring in a string\ncount number of substring in each string\n'''\n# this searches for phrase in dname (dictionary with poems)\n\ndef search_for_phrase(dname,phrase):       # search for phrase in smalldictionary and get result\n    matching_list.clear()                  # need to empty list by default to start with an empty list\n    newphrase = \"'\" + phrase + \"'\"         # this adds a quote around the phrase\n    count = 0\n                                           # sets counter to 0\n    peach.clear()                          # start with empty peach list; peach holds\n    for key in dname:  #loop  if phrase in dictionary\n        if phrase.casefold() in dname.get(key).casefold():          #using casefold() for str1 == str2\n            get_substring_location(phrase,dname.get(key))           #gets first location of substring in string and prints it\n            apple =count_substring_in_string(phrase,dname.get(key)) #counts how many times the phrase occurs in string\n            peach.append(apple)                                     #this adds the count(apple) of substrings within a poem in list peach\n            matching_list.append(key)                               #this appends each match to end of list\n\n    thelist = len(matching_list)\n    if thelist > 0:                        #if there were matches print the list\n        if thelist == 1:\n            print(thelist,'match for',newphrase,'in poem')\n        else:\n            print(thelist,'matches for',newphrase,'in poems')\n\n        if len(matching_list) > 0:         #prints list of phrase locations in string of each poem\n            counter= 0\n            for item in matching_list:     #loop\n               if peach[counter] > 1:      #this is the count of the phrase occurence in this string(poem)\n                  print(matching_list[counter],\"at loc\",storage_list[counter], \", occurs\",peach[counter], \"times\")\n               else:\n                  print(matching_list[counter],\"at loc\",storage_list[counter])\n\n               count   += 1\n               counter += 1\n        storage_list.clear()               #clears out storage of locations of substring for poems\n    if count == 0: print(\"no matches for\",newphrase,\"in poems\")  #this tests if no results\n\n\n\n\n\n\n\n\n\n\n\n\n#version 1 searching thru strings for list of substrings(words)\n\nstorage_list = []  #empty list that stores loc of matches in each poem\n\nnew_list = ['eucalyptus', 'crocheted', 'fragrance']\n#make sure they are all lowercase for simplicity\n\nresult_list = []\ndef more_testing():    \n    small_counter = 0\n    print(\"length of dictionary = \", len(smalldictionary)) \n    print(\"new_list = \", new_list);\n \n    \n    #LOOP\n    for value in smalldictionary.values(): #look thru all of them\n        starbucks = value.lower();\n        i = 0;\n     \n        \n        result_list.clear()\n        while i < len(new_list):\n            if str(new_list[i]) in starbucks: #current poem value string\n                result_list.append('True');\n                i = i + 1\n               \n            else:\n                result_list.append('False');\n                i = i + 1\n                \n            \n            if len(result_list) == len(new_list) and \"False\" not in result_list: #means all true\n                print(\" ALL TRUE meaning all matches in this poem\")\n                print(\"this is poem \",small_counter)\n                print()\n            else:\n                pass\n        small_counter += 1; \n                 \n    \nmore_testing();\n\n\n\n\n\n\n\n\n\n## Thursday, January 16, 2020\n## version 2 seraching for strings that contain substrings from strings in a dictionary\n\n###========search for multiple words from list of words thru searching in dictionary\nnew_list = ['horrible', 'dinosaur', 'treats', 'trick', 'treat', 'princess', 'candy', 'adam', 'eve'];\ntotal_poems_matched = []\nnumber_in_list_of_strings_searched = []\nresult_list = []\n# find list of words in each string searching thru a dictionary of strings\n\n##========================================================================\n##   find_many_words_in_string  searches thru a dictionary of strings\n##========================================================================\n\n\ndef find_many_words_in_string(): #searches thru entire dictionary could do millions of strings\n    small_counter = 0\n    print(\"length of dictionary = \", len(smalldictionary)) \n    print(\"new_list = \", new_list);\n    for value in smalldictionary.values():    #loop thru each string and\n        starbucks = value.lower();            #check if all words in list are in current string\n        \n        i = 0;\n        result_list.clear() \n        while i < len(new_list):\n             if str(new_list[i]) in starbucks: #current poem value string\n                result_list.append('True');\n                i = i + 1\n             else:\n               result_list.append('False');\n               i = i + 1\n                                                      #means all true\n             if len(result_list) == len(new_list) and \"False\" not in result_list: \n                print(\" All matches in this poem \", small_counter)\n                number_in_list_of_strings_searched.append(small_counter)\n                total_poems_matched.append('True');\n                print()\n             else:\n                pass\n        small_counter += 1; \n        number_of_poems = len(total_poems_matched)\n    if len(total_poems_matched) > 0: #if one or more strings contained all of the substrings\n        print(\"total poems that matched the substring list \",number_of_poems) \n        print(\"poem(strings) numbers in db that matched = \", number_in_list_of_strings_searched) \n    else:                            #if there weren't any matches of list of words in strings in dictionary \n        print()\n        print(\"no matches in any strings in dictionary for all words in substring list\")\n\n\nfind_many_words_in_string()\n\n\n\n\n\n\n\n\n\n\n#find first occurence of substring in string\n#this is only called if there is a match for the phrase in this particular string (poem)\n#=========================================================\n#=====       get_substring_location                =======\n#=========================================================\n'''\n   this returns the location of the first occurence\n   of the phrase substring in the string(poem)\n   using the find method.\n\n'''\n#uses method find then appends it to storage.list\n\ndef get_substring_location(z,zen):          #string,phrase  THIS RETURNS THE LOCATION OF THE FIRST OCCURENCE only\n    x= zen.casefold().find(z.casefold())    #this returns the number location of the substring inside of string\n    storage_list.append(x);                 #puts x in storage_list[0] front far left of list\n    return x;                               #ignoring case using .casefold()\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n#=========================================================\n#=====       count_substring_in_string()           =======\n#=========================================================\n'''\n    this uses the count method to count the number of\n    occurences(times) that this phrase is in each string(poem)\n\n'''\ndef count_substring_in_string(z,zen):        #this COUNTS if multiple phrases  and returns the count\n    c= zen.casefold().count(z.casefold())     #in one string (poem)\n    return c;\n\n\n\n\n\n\n\n\n###=============================\nalpha = \"merry christmas\"\nbeta  = \"merry chritmas\"\ncharlie = \"scrooge\";\n\ndef compare_strings(x,y):\n    print(\"x =\",x, \"y =\",y)\n    if (x.casefold() == y.casefold()):\n       print('True');\n       return True;\n    else:\n       print('False');\n       return False;\n     #end if\n#end function\n\n\n\n\n\n\n\n\n\ndef test_compare(z,zz):\n    answer = compare_strings(z,zz)\n    print(\"answer = \",answer);\n\n\n\n\n\ntest_compare(alpha,beta); #called\n\n\n\n\n\n\n\n\n#testing searching for substring phrase in multiple strings\n\ndname = smalldictionary                 #dictionary name\na_phrase = \"grandfather clock ticks\"    #phrase looking for\nlength = len(smalldictionary)           #gets length of smalldictionary\n\n#these are words searching for matches in all poems\n#multiple word searches\nsearch_list =['content', 'ivy', 'English', 'porch']\nweasel =list(smalldictionary)[0]\nmoney = poems.catpoem\n#money2 = poems.homegardenerpoem\n\npoetry_list = [\"poems.catpoem\", \"poems.homegardenerpoem\", \n   \"poems.theviewpoem\", \"poems.december\",\"poems.treebites\",\n    \"poems.oldpath\",\"poems.holmesappears\",\"poems.nature_tankas\",\n    \"poems.bellyflower_tanka_trio\",\"poems.painting\", \"poems.snowballs\",\n    \"poems.halloween\",\"poems.shaky\", \"poems.honeylocustspring\", \"poems.einstein\",\n    \"poems.pleasestayhome\",\"poems.newyearsevekiss\"]\n  \n#if I can search through one I can use eval to iterate through more than one\n\n\n\n\n\n\n#january 15, 2020 new function\n# this was my initial one.\n\n# this example function only searches through one poem and not all of them\n\ndef search_for_many_words_in_string(po):\n   \n  \n    fill_list = []; #empty list\n    \n    #for value in smalldictionary.values(): #this should go through all of the poems\n    i = 0; j = 0;t =0;\n    while i < len(search_list):\n        for word in search_list:          #for loop for searching for one word\n            if word in money:   #only searches in one poem\n               \n                test1 = 'True'\n                j += 1;\n                print(\"word=\",word);\n                i += 1;\n                fill_list.append(test1)\n            else:\n                test1 = 'False'\n                i += 1;\n                fill_list.append(test1)\n\n\n        print(\"fill_list=\",fill_list);\n\n        if 'False' not in fill_list: #meaning they are all true with ands\n            print(\"they are all true\")\n            print(\"the words were \",search_list);\n            print(\"the number of words tested was \", len(fill_list));\n        if 'True' not in fill_list: #meaning they are all False\n            print(\"None of the words were found in the poem\")\n        if 'True' in fill_list:\n            print (\"there were \",j ,\" words matched in the poem\");\n\n \n\n\n\n\n\n# ====================#\n#  testing seaching for separate words within a poem like boolean word1 and word2\n#  and then I will search thru all of the poems\n#========\n\n\n\n\n\n\n# this works looking for phrase in strings in dictionary of poems\n# list of substring phrases to search for in dictionary of poems\nphraselist =[\"puddles of color\", \"cat\", \"trick or treat\",\n             \"ancient plum\", \"honey locust\", \"shriveling flower\",\n             \"deep breath\", \"trick or treat\", \"do not care\",\n             \"time is endless\", \"coleman light\", \"down the years\",\n             \"sighing leaves\", \"we stroll along\" ]\n\n\n\n\n\n\n\n\n\n#=========================================================\n#=====               look_in_list()                =======\n#=========================================================\n'''\n    this goes thru the phrase list in a loop and\n    calls search_for_phrase() to find the location\n    and number of occurences in each poem of the phrase\n    the list of phrases is just above called phraselist\n'''\n\ndef look_in_list():\n    print()\n    for item in phraselist:  #loop thru phraselist above of phrases\n        search_for_phrase(smalldictionary,item)\n        print()\n        #search_all_poems_for_words_in_list(dname,search_list);\n        print()\n\n\n\n\n\n#=========================================================\n#=====                   main()                    =======\n#=========================================================\n'''\n main() calls look_in_list() to do the search thru the list of poem strings\n for the matching and count of the substring phrases.\n'''\n\ndef main():\n    look_in_list();  #this calls the search\n    count_word_match(\"poems.nature_tankas\",\"mist\")\n    count_word_match(\"poems.honeylocustspring\",\"honey locust\")\n\n\n\n\n\n\n\n\n\n\n\n#####===================================\n##           count_word_match\n#####===================================\n'''\n Count number of times a word is used in a poem(big string)\n inputs are the poem(big string), and substring\n using a chain the methods lower() and count() are called\n this searches for a substring within a large string\n\n This function is designed to count in one string at a time\n and could easily be incorporated into a loop to be used in the dictionary\n'''\ndef count_word_match(apoem,substring):\n    print(apoem, substring)\n    answer =eval(\"\" + apoem + \".lower().count('\" + substring + \"')\")\n    print(answer , \"result for \" , substring, \"in\", apoem)\n\n#there has to be at least one match in the string(poem) and then\n#we should be able to figure out which poem it is.\n\n\n\n\n\n\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"blakesouthwood/poetry-analyzer-python","sub_path":"poetry_analyzer.py","file_name":"poetry_analyzer.py","file_ext":"py","file_size_in_byte":15420,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"4458440378","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Sun Apr 16 08:24:45 2023\r\n\r\n@author: ilmondovero \r\n\"\"\"\r\n\r\nfrom tiingo import TiingoClient\r\nimport pandas as pd\r\nconfig = {}\r\nconfig['session'] = True\r\nconfig['api_key'] = \"fbb82277db40c7a1924d0a5bcf1d563bd21323cd\"\r\nclient = TiingoClient(config)\r\ncapitale = 100000\r\nstrategy = {\r\n    \"All Weather\": {\"DBC\": 7.5, \"GLD\": 7.5, \"IEF\": 15, \"SPY\": 30, \"TLT\": 40},\r\n    \"Benchmark 60/40\": {\"SPY\": 60, \"IEF\": 40},\r\n    \"Golden Butterfly\": {\"GLD\": 20, \"IWN\": 20, \"SHY\": 20, \"SPY\": 20, \"TLT\": 20},\r\n}\r\nticker = set().union(*(d.keys() for d in strategy.values()))\r\ndf=client.get_dataframe(ticker,startDate=\"1980-01-01\",metric_name=\"adjClose\").dropna()\r\nrendimenti=df.pct_change().dropna()\r\nportafoglio_GB=rendimenti[[\"DBC\", \"GLD\", \"SHY\", \"SPY\", \"TLT\"]].sum(axis=1)/5\r\nequity_GB=(1+portafoglio_GB).cumprod()*capitale\r\nportafoglio_BK=0.6*rendimenti[\"SPY\"]+0.4*rendimenti[\"IEF\"]\r\nequity_BK=(1+portafoglio_BK).cumprod()*capitale\r\nportafoglio_AW=0.075*rendimenti[\"DBC\"]+0.075*rendimenti[\"GLD\"]+0.15*rendimenti[\"IEF\"]+0.3*rendimenti[\"SPY\"]+0.4*rendimenti[\"TLT\"]\r\nequity_AW=(1+portafoglio_AW).cumprod()*capitale\r\nequity=pd.concat([equity_GB,equity_BK,equity_AW],axis=1)\r\nequity.columns=[\"Golden Butterfly\",\"Benchmark 60/40\",\"All Weather\"]\r\nequity2=equity/equity.iloc[0]*100\r\nequity2.plot()\r\n","repo_name":"ilmondovero/sf","sub_path":"Lezione 4.py","file_name":"Lezione 4.py","file_ext":"py","file_size_in_byte":1313,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"2480098878","text":"import cv2\nimport sys\n\nimport matplotlib.pyplot as plt\n\ndef myImread(path, code):\n    src = cv2.imread(path, code)\n    if src is None:\n        print('--(!)Image load failed')\n        sys.exit()\n    return src\n\nsrc = myImread('./data/lenna.bmp', cv2.IMREAD_COLOR)\n# src_color = myImread('./data/lenna.bmp', cv2.IMREAD_COLOR)\n\ncolors = ['b','g','r']\nbgr_planes = cv2.split(src)\n\nfor p, c in zip(bgr_planes, colors):\n    hist = cv2.calcHist([p], [0], None, [256], [0,256])\n    plt.plot(hist, color = c)\n\n# one dimension matrix\n\ncv2.imshow('src', src)\nplt.show()\n\n\n\n\n","repo_name":"mth9406/deep-learning-reference-codes","sub_path":"reference-opencv/opencv-basics-brightness/histogram1.py","file_name":"histogram1.py","file_ext":"py","file_size_in_byte":563,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"408744990","text":"import asyncio\nimport aiohttp\nimport logging\nfrom . import BaseDownloader\n\n\nclass AioDown(BaseDownloader):\n    HEADERS = {'User-Agent': 'Chrome/80.0 (Windows NT 10.0; Win64; x64)'}\n\n    async def download(self) -> str:\n        logger = logging.getLogger(__name__)\n        try:\n            file = open(self.path, 'wb')\n            async with aiohttp.ClientSession(headers=self.HEADERS, timeout=aiohttp.ClientTimeout()) as session:\n                async with session.get(self.link) as response:\n                    while True:\n                        data = await response.content.read(1024**2)\n                        if not data:\n                            break\n                        file.write(data)\n\n            file.close()\n        except Exception as e:\n            logger.error(e)\n        return self.path\n\n    def start(self) -> str:\n        loop = asyncio.new_event_loop()\n        result = loop.run_until_complete(self.download())\n        loop.close()\n        return result\n","repo_name":"LiveDownload/LiveDownload","sub_path":"livedownload/downloader/aiodown.py","file_name":"aiodown.py","file_ext":"py","file_size_in_byte":985,"program_lang":"python","lang":"en","doc_type":"code","stars":15,"dataset":"github-code","pt":"81"}
+{"seq_id":"24817089447","text":"import logging\nimport os\n\nimport alembic.config\n\nlogger = logging.getLogger()\n\n\ndef run_migrations() -> None:\n    os.chdir(os.path.dirname(os.getcwd()))\n    logger.info('Running DB migrations')\n    alembic_args = [\n        '--raiseerr',\n        'upgrade', 'head',\n    ]\n    alembic.config.main(argv=alembic_args)\n\n\nif __name__ == '__main__':\n    run_migrations()\n","repo_name":"DobroAlex/laolab-nmap-test","sub_path":"migration/run_upgrade_head.py","file_name":"run_upgrade_head.py","file_ext":"py","file_size_in_byte":363,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"43057941701","text":"from Model.Car import Car\n\n\nclass CarSeat:\n\n    def __init__(self, serial_number, model):\n        self.serial_number = serial_number;\n        self.model = model\n        self.latitude = \"\"\n        self.longitude = \"\"\n        self.weight = 0.0\n        self.weight_unit = \"Lbs\"\n        self.temperature = 0.0\n        self.temperature_unit = \"Fahrenheit\"\n        self.car = Car()\n\n    def __delete__(self, instance):\n        del instance\n\n    def __get__(self, instance, owner):\n        return instance\n\n    def set_gps_location(self, latitude, longitude):\n        self.longitude = longitude\n        self.latitude = latitude\n\n    def set_weight(self, weight, unit):\n        self.weight = weight\n        self.weight_unit = unit\n\n    def set_temperature(self, temp, unit):\n        self.temperature = temp\n        self.temperature_unit = unit\n\n    def update_car_details(self, make, model, year, vin):\n        self.car.make = make\n        self.car.model = model\n        self.car.year = year\n        self.car.vin = vin\n\n    def print_car_seat(self):\n        print(self.serial_number)\n        print(self.latitude)\n        print(self.longitude)\n\n    def to_json(self):\n        car_seat = {\n            \"serial_number\": self.serial_number,\n            \"model\": self.model,\n            \"latitude\": self.latitude,\n            \"longitude\": self.longitude,\n            \"weight\": self.weight,\n            \"weight_unit\": self.weight_unit,\n            \"temperature\": self.temperature,\n            \"temperature_unit\": self.temperature_unit,\n            \"car\": self.car.to_json()\n        }\n        return car_seat\n","repo_name":"SWEN5236F19/EmergenSeat","sub_path":"Model/CarSeat.py","file_name":"CarSeat.py","file_ext":"py","file_size_in_byte":1594,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"81"}
+{"seq_id":"74319518665","text":"#Given an array of positive integers A and an integer B, find and return first continuous subarray which adds to B.\nclass Solution:\n    # @param A : list of integers\n    # @param B : integer\n    # @return a list of integers\n    def solve(self, A, B):\n\n        #array copy\n        array=A.copy()\n\n        #create a prefix sum\n        for index in range(1,len(A)):\n            A[index]=A[index-1]+A[index]\n        \n        #hashset\n        hashmap={}\n\n        #edge case\n        if B in A:\n            return array[:A.index(B)+1]\n\n        #answer variable\n        answer=[-1]\n        mini=len(A)\n\n        #iterate through all the values of A\n        for index,value in enumerate(A):\n            #check\n            if value in hashmap:\n                #check for mini\n                if hashmap[value]<mini:\n                    mini=hashmap[value]\n                    answer=array[hashmap[value]:index+1]\n            #check for the new value\n            if B+value in hashmap:\n                pass\n            else:\n                hashmap[B+value]=index+1\n        \n        #return the answer\n        return answer\n","repo_name":"SomilKSharma/AdvancedDSA","sub_path":"Hashing/firstsub.py","file_name":"firstsub.py","file_ext":"py","file_size_in_byte":1112,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"25382863611","text":"import numpy as np\nimport matplotlib.pyplot as plt\nfrom cov_spectrum_random_netw import *\nfrom numpy.random import randn\nfrom numpy.linalg import eigvals, eigvalsh\nfrom numpy import sqrt\nimport timeit\n\nplt.rcParams.update({'font.size': 18})\n\n# created by Yu Hu (mahy@ust.hk)\n\n# compare simulation and iid Gaussian theory\n# g_ls = [0.5]\ng_ls = [0.9]\n# N_ls = [100, 400, 800]\nN_ls = [100]\nifig = 0\nt0 = timeit.default_timer()\nfor g in g_ls:\n    for N in N_ls:\n        ifig += 1\n        J = g * randn(N,N)/sqrt(N)\n        C = J2C(J)\n        eig_C = eigvalsh(C)\n\n        eig_C = -np.sort(-eig_C)\n        x_ls = quantile_g(g,N)\n        rank_ls = np.arange(N)+1\n        fig = plt.figure(figsize=(7.5,6))\n        plt.plot(rank_ls, eig_C,'o',fillstyle='none',label='N='+str(N),markersize=5)\n        plt.plot(rank_ls, x_ls,'+',label='theory',markersize=5)\n        plt.xlabel('rank')\n        plt.ylabel('cov eigenvalue')\n        plt.legend()\n        plt.title('g='+str(g))\n        plt.tight_layout()\n        fig.savefig('./figure/rank_plot_'+str(ifig)+'.png', dpi=300)\n\n        fig = plt.figure(figsize=(7.5,6))\n        plt.loglog(rank_ls, eig_C,'o',fillstyle='none',label='N='+str(N),markersize=5)\n        plt.loglog(rank_ls, x_ls,'+',label='theory',markersize=5)\n        f_power = lambda p: (p*4*np.pi/3/sqrt(3))**(-3/2)\n        x_power_ls = f_power(rank_ls/N)\n        plt.loglog(rank_ls, x_power_ls,'r--',label='power law',linewidth=0.5)\n        plt.xlabel('rank')\n        plt.ylabel('cov eigenvalue')\n        plt.legend()\n        plt.title('g='+str(g))\n        plt.tight_layout()\n        fig.savefig('./figure/rank_plot_log_'+str(ifig)+'.png', dpi=300)\nt1 = timeit.default_timer()\nprint('computing time:', t1-t0)\n","repo_name":"huyu00/netw_cov_spectrum","sub_path":"rank_plot.py","file_name":"rank_plot.py","file_ext":"py","file_size_in_byte":1707,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"81"}
+{"seq_id":"35283535130","text":"from asgiref.sync import async_to_sync\nfrom channels.generic.websocket import JsonWebsocketConsumer\nfrom util import websocket\n\nfrom . import models\nfrom . import serializers\n\n\nclass CoreConsumer(JsonWebsocketConsumer):\n\n    def connect(self):\n        self.accept()\n        async_to_sync(self.channel_layer.group_add)(\"core\", self.channel_name)\n\n        self.send_json({\n            \"status\": \"Accepted\",\n            \"code\": 202,\n            \"channel_name\": self.channel_name,\n            \"group\": \"core\"\n        })\n\n    def server_data(self, event):\n        data = event.get(\"data\")\n        if data:\n            self.send_json(data)\n\n    def receive_json(self, content, **kwargs):\n        if content.get(\"method\") != \"get\":\n            self.send_json({\"status\": \"Method Not Allowed\", \"code\": 405})\n            return\n\n        if not content.get(\"model\"):\n            self.send_json({\"status\": \"Bad Request\", \"code\": 400})\n            return\n\n        model = content.get(\"model\")\n\n        if model == \"Notification\":\n            response = websocket.make_query(\n                models.Notification, content.get(\"query\"), serializers.NotificationSerializer)\n\n        elif model == \"Map\":\n            response = websocket.make_query(\n                models.Map, content.get(\"query\"), serializers.MapSerializer)\n\n        elif model == \"MapPool\":\n            response = websocket.make_query(\n                models.MapPool, content.get(\"query\"), serializers.MapPoolSerializer)\n\n        elif model == \"BombSpot\":\n            response = websocket.make_query(\n                models.BombSpot, content.get(\"query\"), serializers.BombSpotSerializer)\n\n        elif model == \"Operator\":\n            response = websocket.make_query(\n                models.Operator, content.get(\"query\"), serializers.OperatorSerializer)\n\n        else:\n            self.send_json({\"status\": \"Bad Request\", \"code\": 400})\n            return\n\n        self.send_json(websocket.parse_request(model, response))\n","repo_name":"sthorsten/CasterDashboard2","sub_path":"backend/src/core/channels.py","file_name":"channels.py","file_ext":"py","file_size_in_byte":1974,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"81"}
+{"seq_id":"41273871915","text":"from typing import Dict,AnyStr\nfrom net_tool.netIo import NetIo, UDP_IO\nfrom json import dumps\n\n\nclass SeniorNetIo:\n    def __init__(self, protocol: NetIo = UDP_IO):\n        self.__protocol = protocol\n        self.__cookie = None\n        self.__if_cookie = False\n\n    def set_cookie(self, cookie: bytes):\n        self.__cookie = cookie\n        self.__if_cookie = True\n\n    def remove_cookie(self):\n        self.__cookie = None\n        self.__if_cookie = False\n\n    def send_json(self, addr, args: Dict[AnyStr, AnyStr]) -> bool:\n        if self.__if_cookie:\n            args[\"cookie\"] = self.__cookie\n        j = dumps(args)\n        return self.__protocol.send(addr, j.encode(\"utf-8\"))\n\n\nUDP_SENIOR_IO = SeniorNetIo()\n","repo_name":"intmian/py_chatroom_udp","sub_path":"net_tool/SeniorNetIo.py","file_name":"SeniorNetIo.py","file_ext":"py","file_size_in_byte":717,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"81"}
+{"seq_id":"9616434673","text":"import numpy as np\r\nfrom utils import load_mnist, load_fashion_mnist, set_random_seed\r\nfrom models.LeNet_5 import LeNet_5\r\nfrom models.AlexNet import AlexNet\r\nfrom models.ResNet import ResNet\r\nimport torch\r\nimport random\r\n\r\nset_random_seed(123)\r\n\r\n# =============================== EDIT HERE ===============================\r\n\"\"\"\r\n    Build model Architecture and do experiment.\r\n\"\"\"\r\n# lenet / alexnet / resnet\r\nmodel_name = 'lenet'\r\n# mnist / fashion_mnist\r\ndataset = 'fashion_mnist'\r\n\r\n# Hyper-parameters to search\r\nnum_epochs_list = [1, 2, 3, 4, 5]\r\nlearning_rate_list = [0.1, 0.2, 0.3]\r\nreg_lambda_list = [0.1, 0.2, 0.3]\r\nbatch_size_list = [100]\r\nnum_search = 10\r\n\r\ntest_every = 1\r\nprint_every = 5000\r\n# =========================================================================\r\nassert dataset in ['mnist', 'fashion_mnist']\r\n\r\n# Dataset\r\nif dataset == 'mnist':\r\n    x_train, y_train, x_test, y_test = load_mnist('./data')\r\nelse:\r\n    x_train, y_train, x_test, y_test = load_fashion_mnist('./data')\r\n\r\ny_train, y_test = np.argmax(y_train, -1).astype(int), np.argmax(y_test, -1).astype(int)\r\n\r\n# Random 10% of train data as valid data\r\nnum_train = len(x_train)\r\nperm = np.random.permutation(num_train)\r\nnum_valid = int(len(x_train) * 0.1)\r\n\r\nvalid_idx = perm[:num_valid]\r\ntrain_idx = perm[num_valid:]\r\n\r\nx_valid, y_valid = x_train[valid_idx], y_train[valid_idx]\r\nx_train, y_train = x_train[train_idx], y_train[train_idx]\r\n\r\nnum_train, channel, height, width = x_train.shape\r\nnum_class = len(np.unique(y_test))\r\nprint('# of Training data : ', num_train)\r\ndevice = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\r\nprint('Device : ', device)\r\n\r\nprint('Search Starts...')\r\nbest_acc = -1\r\nbest_hyper_params = []\r\nfor search_cnt in range(num_search):\r\n    num_epochs = random.choice(num_epochs_list)\r\n    learning_rate = random.choice(learning_rate_list)\r\n    reg_lambda = random.choice(reg_lambda_list)\r\n    batch_size = random.choice(batch_size_list)\r\n\r\n    if model_name == 'lenet':\r\n        model = LeNet_5(x_train.shape[1], num_class, learning_rate, reg_lambda, device)\r\n    elif model_name == 'alexnet':\r\n        model = AlexNet(x_train.shape[1], num_class, learning_rate, reg_lambda, device)\r\n    elif model_name == 'resnet':\r\n        model = ResNet(x_train.shape[1], num_class, learning_rate, reg_lambda, device)\r\n    model = model.to(device)\r\n\r\n    cur_acc = model.train(x_train, y_train, x_valid, y_valid, num_epochs, batch_size, test_every, print_every)\r\n    if cur_acc > best_acc:\r\n        best_acc = cur_acc\r\n        best_hyper_params = [num_epochs, learning_rate, reg_lambda, batch_size]\r\n    print(f'search count: {search_cnt}, cur_valid_acc: {cur_acc},  best_valid_acc: {best_acc}')\r\nprint(f'best_valid_acc: {best_acc}, best_hyper_params: {best_hyper_params} (num_epochs, learning_rate, reg_lambda, batch_size)')\r\n","repo_name":"kojunseo/Introduction-of-Deep-Learning-Homework","sub_path":"DNN_HW_4_template/main_random_search.py","file_name":"main_random_search.py","file_ext":"py","file_size_in_byte":2839,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"81"}
+{"seq_id":"25178533990","text":"import sys\n\nfrom PyQt6 import uic, QtWidgets\n\nUi_MainWindow, QtBaseWindow = uic.loadUiType(\"team_editor.ui\")\n\nclass TeamEditor(QtBaseWindow, Ui_MainWindow):\n    def __init__(self, add=None, parent=None):\n        super().__init__(parent)\n        self.setupUi(self)\n        if add:\n            self.names_text_edit.setPlainText(add.name)\n\n    def update_add(self, add):\n        add.name = self.member_text_edit.toPlainText()\n\n    def delete_member_button_clicked(self):\n        dialog = QMessageBox(self)\n        dialog.setIcon(QMessageBox.Icon.Question)\n        dialog.setWindowTitle(\"Delete member\")\n        dialog.setText(\"Are you sure you want to delete this member?\")\n        no_way_button = dialog.addButton(\"No!\", QMessageBox.ButtonRole.RejectRole)\n        yes_button = dialog.addButton(\"Yes\", QMessageBox.ButtonRole.AcceptRole)\n\n        dialog.exec()\n        if dialog.clickedButton() == yes_button:\n            del self._add[self.address_list_widget.currentRow()]\n            self.update_ui()\n        else:\n            print(\"Not clicked\")\n\nif __name__ == '__main__':\n    app = QtWidgets.QApplication(sys.argv)\n    window = TeamEditor()\n    window.show()\n    sys.exit(app.exec())\n","repo_name":"Butler5/CPSC4970","sub_path":"ui/team_editor.py","file_name":"team_editor.py","file_ext":"py","file_size_in_byte":1187,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"3883218846","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# # Maquinas de soporte vectorial, (*Support vector machine SVM*)\n# \n# \n# \n# \n# \n# Las maquinas de soporte vectorial se pueden definir como:\n# \n# - Clasfiicar lineal de orden maximo \n# - Clasificador lineal en un espacio N-dimensional\n# \n# Entendamos la intuición detras de estos dos conceptos. \n# \n# Nuestra funcion de coste esta definda como sigue:\n# \n# \\begin{equation}\n# J(\\theta)=\\frac{1}{m}\\sum_{i=1}^{m} [-y^{(i)}\\log(h^{(i)}) + (1-y^{(i)})\\log(1-h^{(i)})]  +  \\frac{\\lambda}{2m} \\sum_{j=1}^{n}\\theta_j^2\n# \\end{equation}\n# \n# \n# Si suponemos que $y=1$ y $y=0$ tenemos para estos dos casos que:\n# \n# \n# \n# \n# \n\n# In[1]:\n\n\n#Cost function\nh = lambda z: 1/(1+np.exp(-z))\nJ1 = lambda z,y: -y*np.log(h(z))\nJ2 = lambda z,y: -(1-y)*np.log(1-h(z))\n\n# Ref metricas\nm1 = lambda z: z-1 \n\n\n\n#===================\nz1 = np.linspace(-2, 10)\nz2 = np.linspace(-10, 2)\nplt.plot(z1,J1(z1,1), label=\"y=1\")\n\nplt.plot(z2,J2(z2,0), label=\"y=0\")\nplt.legend()\nplt.xlabel(\"Z\")\nplt.ylabel(\"Cost function\")\n\n\n# - Para $y = 1$ se cumple que para $Z=\\theta^T X\\geq 0$ la clasificacion será tipo 1 \n# \n# - Para $y = 0$ se cumple que para $Z=\\theta^T X < 0 $ la clasificación será tipo 0\n# \n# Las métricas anteriores pueden ser definidas en términos de métricas que permitan clasificar en los siguiente intervalos, segun la curva roja y negra definida en la gráfica:\n# \n# - Para $y = 1$ se cumple que para $Z=\\theta^T X\\geq 1$ la clasificacion será tipo 1 \n# \n# - Para $y = 0$ se cumple que para $Z=\\theta^T X < 1 $ la clasificación será tipo 0\n# \n\n# Si interpretamos la funcion $J(\\theta)$ como:\n# \n# $J(\\theta)=A+\\lambda B$, podemos rescribir la anterior expresión como:\n# \n# $J(\\theta)=C A'+B'$ \n# \n# Siendo $C=\\frac{1}{\\lambda}$, el inverso del parametros de regularación descrito en las sesiones anteriores.\n# \n# \n# Nuestro objetivo sera mínimizar la función  $\\min [J(\\theta))] =\\min[ C A'+B']$, \n# \n# El termino B' de la anterior expresión puede ser expresado como sigue:\n#  \n#  $B' = \\frac{1}{2}\\sum_{j=1}^{n}\\theta_j^2=\\frac{1}{2} (\\theta_1^2 + \\theta_2^2+...+\\theta_n^2)=\\frac{1}{2}||\\theta||^2$\n# \n# ## Interpretacion geométrica\n# \n# Supongamos que tenemos dos caracteristicas, en nuestro sistema, de esta manera tenemos que:\n# \n# $\\theta^T X= [\\theta_1, \\theta_2]  [x1,x2]$\n# \n# Continuar con los aspectos teóricos...\n# \n# \n# \n# References\n# [1] https://www.cienciadedatos.net/documentos/34_maquinas_de_vector_soporte_support_vector_machines\n# \n\n# In[ ]:\n\n\nimport numpy as np\nimport matplotlib.pylab as plt\nfrom sklearn.datasets import make_moons\nfrom sklearn.datasets import make_circles\nfrom sklearn.datasets import make_blobs\nfrom sklearn.datasets import make_classification\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.svm import SVC\nfrom sklearn.model_selection import GridSearchCV\n\n# Libraries for draw contours\ndef make_meshgrid(x, y, h=0.02):\n    \"\"\"Create a mesh of points to plot in\n\n    Parameters\n    ----------\n    x: data to base x-axis meshgrid on\n    y: data to base y-axis meshgrid on\n    h: stepsize for meshgrid, optional\n\n    Returns\n    -------\n    xx, yy : ndarray\n    \"\"\"\n    x_min, x_max = x.min() - 1, x.max() + 1\n    y_min, y_max = y.min() - 1, y.max() + 1\n    xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))\n    return xx, yy\n\n\ndef plot_contours(ax, clf, xx, yy, **params):\n    \"\"\"Plot the decision boundaries for a classifier.\n\n    Parameters\n    ----------\n    ax: matplotlib axes object\n    clf: a classifier\n    xx: meshgrid ndarray\n    yy: meshgrid ndarray\n    params: dictionary of params to pass to contourf, optional\n    \"\"\"\n    Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n    Z = Z.reshape(xx.shape)\n    out = ax.contourf(xx, yy, Z, **params)\n    return out\n\n\ndef plot_contoursExact(ax, xx, yy, **params):\n    \"\"\"Plot the decision boundaries for a classifier.\n\n    Parameters\n    ----------\n    ax: matplotlib axes object\n    clf: a classifier\n    xx: meshgrid ndarray\n    yy: meshgrid ndarray\n    params: dictionary of params to pass to contourf, optional\n    \"\"\"\n    Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n    Z = Z.reshape(xx.shape)\n    out = ax.contourf(xx, yy, Z, **params)\n    return out\n\n\n# In[ ]:\n\n\n# Dataset Toys References\n# https://scikit-learn.org/stable/datasets/toy_dataset.html\n# https://matplotlib.org/stable/gallery/subplots_axes_and_figures/subplots_demo.html\n# Dataset sinteticos\nX0, y0 = make_classification(\n    n_features=2, n_redundant=0, n_informative=2, random_state=1, \n    n_clusters_per_class=1\n)\nX1, y1 = make_moons(n_samples=100, noise=0.15, shuffle=True,  random_state=1)\nX2, y2 = make_circles(n_samples=100, noise=0.05, shuffle=True,  random_state=1)\nX3, y3 = make_blobs(n_samples=500, centers=3, n_features=2,shuffle=True, \n                    random_state=10)\n\nfig, axs = plt.subplots(2,2)\n\naxs[0, 0].plot(X0[:,0][y0==0],X0[:,1][y0==0],\"ro\", alpha=0.5)\naxs[0, 0].plot(X0[:,0][y0==1],X0[:,1][y0==1],\"bo\", alpha=0.5)\n\n# Dataset a moons\naxs[0, 1].plot(X1[:,0][y1==0],X1[:,1][y1==0],\"ro\", alpha=0.5)\naxs[0, 1].plot(X1[:,0][y1==1],X1[:,1][y1==1],\"bo\", alpha=0.5)\n\n# Dataset circles\naxs[1, 0].plot(X2[:,0][y2==0],X2[:,1][y2==0],\"ro\", alpha=0.5)\naxs[1, 0].plot(X2[:,0][y2==1],X2[:,1][y2==1],\"bo\", alpha=0.5)\n\n# Dataset circles\naxs[1, 1].plot(X3[:,0][y3==0],X3[:,1][y3==0],\"ro\", alpha=0.5)\naxs[1, 1].plot(X3[:,0][y3==1],X3[:,1][y3==1],\"bo\", alpha=0.5)\naxs[1, 1].plot(X3[:,0][y3==2],X3[:,1][y3==2],\"go\", alpha=0.5)\n\n\n# In[ ]:\n\n\n# Based on :\n# https://rramosp.github.io/ai4eng.v1.20211.udea/content/NOTES%2003.03%20-%20SVM%20AND%20FEATURE%20TRANSFORMATION.html\n\n\n# In[ ]:\n\n\nX, y = make_circles(n_samples=200, noise=0.1, shuffle=True,  random_state=1)\n\nplt.plot(X[:,0][y==0],X[:,1][y==0],\"ro\", alpha=0.5)\nplt.plot(X[:,0][y==1],X[:,1][y==1],\"bo\", alpha=0.5)\n\n\n# In[ ]:\n\n\nclf = SVC(gamma = 0.10) #Complexity of algorithm \nclf.fit(X, y)\n\n#Countour plot\nfig, ax = plt.subplots()\nX0, X1 = X[:, 0], X[:, 1]\nxx, yy = make_meshgrid(X0, X1)\nplot_contours(ax, clf, xx, yy, cmap=plt.cm.coolwarm, alpha=0.8)\nplt.plot(X[y==0][:,0],X[y==0][:,1],\"bo\", alpha=1)\nplt.plot(X[y==1][:,0],X[y==1][:,1],\"ro\", alpha=1)\nprint(f\"Training error:{clf.score(X, y):.3f}\")\n\n\n# # Grid Search of parameter in SVM\n# \n# Grid-search is used to find the optimal hyperparameters of a model which results in the most ‘accurate’ predictions.\n# https://towardsdatascience.com/grid-search-for-model-tuning-3319b259367e\n\n# In[ ]:\n\n\n#https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html\nfrom sklearn.model_selection import GridSearchCV\n\n\nX, y = make_circles(n_samples=200, noise=0.1, shuffle=True,  random_state=1)\n\nplt.plot(X[:,0][y==0],X[:,1][y==0],\"ro\", alpha=0.5)\nplt.plot(X[:,0][y==1],X[:,1][y==1],\"bo\", alpha=0.5)\n\n\n# In[ ]:\n\n\nparameters = {'kernel':('linear', 'rbf'), 'C':[1, 10]}\nparameters = {'C': [0.1,1, 10, 100], 'gamma': [1,0.1,0.01,0.001],'kernel': ['rbf', 'poly', 'sigmoid']}\n\nclf = GridSearchCV(estimator=SVC(),\n             param_grid = parameters)\n\nclf.fit(X, y)\nsorted(clf.cv_results_.keys())\n\n\n# In[ ]:\n\n\nclf.cv_results_\n\n\n# In[ ]:\n\n\nclf.best_estimator_\n\n\n# In[ ]:\n\n\nclf.best_params_\n\n\n# In[ ]:\n\n\nclf.best_score_\n\n\n# In[ ]:\n\n\n#Countour plot\nfig, ax = plt.subplots()\nX0, X1 = X[:, 0], X[:, 1]\nxx, yy = make_meshgrid(X0, X1)\nplot_contours(ax, clf, xx, yy, cmap=plt.cm.coolwarm, alpha=0.8)\nplt.plot(X[y==0][:,0],X[y==0][:,1],\"bo\", alpha=1)\nplt.plot(X[y==1][:,0],X[y==1][:,1],\"ro\", alpha=1)\nprint(f\"Training error:{clf.score(X, y):.3f}\")\n\n\n# Tarea 11.1 \n# 1. Implementar un SVM para clasificar los siguientes datasets, para ello se deberá crear un grid search. \n# 2. Con los mejores párametros dibujar  las fronteras de clasificación. \n# ```\n# X0, y0 = make_classification(\n#     n_features=2, n_redundant=0, n_informative=2, random_state=1, \n#     n_clusters_per_class=1\n# )\n# X1, y1 = make_moons(n_samples=100, noise=0.15, shuffle=True,  random_state=1)\n# X3, y3 = make_blobs(n_samples=500, centers=3, n_features=2,shuffle=True, random_state=10)\n# ```\n\n# In[ ]:\n\n\n# Dataset Toys References\n# https://scikit-learn.org/stable/datasets/toy_dataset.html\n# https://matplotlib.org/stable/gallery/subplots_axes_and_figures/subplots_demo.html\n# Dataset sinteticos\nX0, y0 = make_classification(\n    n_features=2, n_redundant=0, n_informative=2, random_state=1, \n    n_clusters_per_class=1\n)\nX1, y1 = make_moons(n_samples=100, noise=0.15, shuffle=True,  random_state=1)\nX2, y2 = make_circles(n_samples=100, noise=0.05, shuffle=True,  random_state=1)\nX3, y3 = make_blobs(n_samples=500, centers=3, n_features=2,shuffle=True, \n                    random_state=10)\n\nfig, axs = plt.subplots(2,2)\n\naxs[0, 0].plot(X0[:,0][y0==0],X0[:,1][y0==0],\"ro\", alpha=0.5)\naxs[0, 0].plot(X0[:,0][y0==1],X0[:,1][y0==1],\"bo\", alpha=0.5)\n\n# Dataset a moons\naxs[0, 1].plot(X1[:,0][y1==0],X1[:,1][y1==0],\"ro\", alpha=0.5)\naxs[0, 1].plot(X1[:,0][y1==1],X1[:,1][y1==1],\"bo\", alpha=0.5)\n\n# Dataset circles\naxs[1, 1].plot(X3[:,0][y3==0],X3[:,1][y3==0],\"ro\", alpha=0.5)\naxs[1, 1].plot(X3[:,0][y3==1],X3[:,1][y3==1],\"bo\", alpha=0.5)\naxs[1, 1].plot(X3[:,0][y3==2],X3[:,1][y3==2],\"go\", alpha=0.5)\n\n\n# In[ ]:\n\n\n\n\n","repo_name":"hernansalinas/Curso_aprendizaje_estadistico","sub_path":"AprendizajeEstadisticoBook/_build/jupyter_execute/Sesiones/Sesion_11_SVM.py","file_name":"Sesion_11_SVM.py","file_ext":"py","file_size_in_byte":9152,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"81"}
+{"seq_id":"2810097146","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Apr 18 09:59:19 2016\n\n@author: yangzhou\n\"\"\"\nfrom numpy import *\nimport re\nimport pandas as pd\n#import operator\n#创建解析器\ndef Parser(filename,wordlist):\n#    filename=r'F:\\git2\\Algorithm-and-Data\\NaiveBayes\\email\\ham\\1.txt'\n    file=open(filename,'r',encoding='gbk')\n    line=re.split(r'\\W*',file.read())\n    for line1 in line:\n        wordlist[line1]=wordlist.get(line1,0)+1\n    return wordlist\n\n#统计测试样本中的词条数量\ndef Testwords(filename,wordslist):\n    testwords={}\n    testwords=Parser(filename,testwords)\n    testwordscount=sum(list(testwords.values()))\n    testlist=[]\n    for word in wordslist:\n        testlist.append(testwords.get(word,0))\n    return testlist,testwordscount\n\nif __name__=='__main__':\n    files=23  \n    hamwordlist={}\n    spamwordlist={}\n    wordlist={}\n#求出所有的词条\n    for i in arange(1,files+1):\n        hamfilename=r'F:\\git2\\Algorithm-and-Data\\NaiveBayes\\email\\ham\\%d.txt' %i\n        spamfilename=r'F:\\git2\\Algorithm-and-Data\\NaiveBayes\\email\\spam\\%d.txt' %i\n        hamwordlist=Parser(hamfilename,hamwordlist)\n        spamwordlist=Parser(spamfilename,spamwordlist)        \n        wordlist=Parser(hamfilename,wordlist)\n        wordlist=Parser(spamfilename,wordlist)\n##选出特征\n##    hamwordlist=sorted(hamwordlist.items(),key=operator.itemgetter(1))\n#    wordlist1=pd.DataFrame()\n#    wordlist1['words']=wordlist.keys()\n#    wordlist1['times']=wordlist.values()\n#    wordlist1=wordlist1.sort_values('times',ascending=False)\n#    wordlist2=list(wordlist1.head(100).words)\n#    \n#    wordlist3=[]\n#    wordsnumber=[]\n#    for i in arange(1,files+1):\n#        hamwordlist1=[]\n#        hamwordlist2={}\n#        hamfilename=r'F:\\git2\\Algorithm-and-Data\\NaiveBayes\\email\\ham\\%d.txt' %i\n#        hamwordlist2=Parser(hamfilename,hamwordlist2)\n#        wordsnumber.append(sum(list(hamwordlist2.values())))\n#        for word in wordlist2:\n#            hamwordlist1.append(int(hamwordlist2.get(word,0)))\n#        wordlist3.append(hamwordlist1)\n#    for i in arange(1,files+1):\n#        spamwordlist1=[]\n#        spamwordlist2={}\n#        spamfilename=r'F:\\git2\\Algorithm-and-Data\\NaiveBayes\\email\\spam\\%d.txt' %i\n#        spamwordlist2=Parser(spamfilename,spamwordlist2)\n#        wordsnumber.append(sum(list(spamwordlist2.values())))\n#        for word in wordlist2:\n#            spamwordlist1.append(int(spamwordlist2.get(word,0)))\n#        wordlist3.append(spamwordlist1)\n#    wordlist4=pd.DataFrame(wordlist3,columns=wordlist2)\n#    \n    number=100             #特征数                          \n    wordlist1=pd.DataFrame()\n    wordlist1['words']=wordlist.keys()\n    wordlist1['times']=wordlist.values()\n    wordlist1=wordlist1.sort_values('times',ascending=False)\n    hamwordcount=sum(list(hamwordlist.values()))\n    spamwordcount=sum(list(spamwordlist.values()))\n    wordlist1=wordlist1.head(number)\n    wordsdata=pd.DataFrame()\n    wordsdata['words']=list(wordlist1.words)\n    wordsdata['times']=list(wordlist1['times'])\n    hamword=[]\n    spamword=[]\n    for word in wordsdata.words:\n        hamword.append(hamwordlist.get(word,0))\n        spamword.append(spamwordlist.get(word,0))\n    wordsdata['hamwords']=hamword\n    wordsdata['spamwords']=spamword\n\n#输入测试数据\n    hamtestwords,hamtestwordscount=Testwords(r'F:\\git2\\Algorithm-and-Data\\NaiveBayes\\email\\spam\\16.txt',list(wordsdata.words))\n    wordsdata['hamtestwords']=hamtestwords/hamtestwordscount\n    wordsdata['hamwords']=(wordsdata['spamwords']+wordsdata['hamwords']+1)/(hamwordcount+1+spamwordcount)\n    wordsdata['spamwords']=(wordsdata['spamwords']+1)/(1+spamwordcount)\n    wordsdatatest1=wordsdata[wordsdata.hamtestwords!=0]\n    pspamwords=1\n    phamwords=1\n    j=0\n    for i in  wordsdatatest1.words:\n            pspamwords=list(wordsdatatest1.spamwords)[j]*pspamwords            \n            phamwords=list(wordsdatatest1.hamwords)[j]*phamwords\n            j+=1\n    print(0.5*pspamwords/phamwords)\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","repo_name":"sishenhei7/Algorithm-and-Data","sub_path":"3.NaiveBayes/git-naivebeyes.py","file_name":"git-naivebeyes.py","file_ext":"py","file_size_in_byte":4017,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"4947797192","text":"import logging\n\nfrom . import indexing\nfrom .collection import Collection\nfrom .indexing import (\n    BaseCrawler,\n    JSONCrawler,\n    MainCrawler,\n    MasterCrawler,\n    RegexFileCrawler,\n    SignacProjectCrawler,\n    export,\n    export_one,\n    export_pymongo,\n    export_to_mirror,\n    fetch,\n    fetched,\n    index,\n    index_files,\n)\nfrom .project import Project, TemporaryProject, get_job, get_project, init_project\n\nlogger = logging.getLogger(__name__)\n\n\n__all__ = [\n    \"indexing\",\n    \"Project\",\n    \"TemporaryProject\",\n    \"get_project\",\n    \"init_project\",\n    \"get_job\",\n    \"BaseCrawler\",\n    \"RegexFileCrawler\",\n    \"JSONCrawler\",\n    \"SignacProjectCrawler\",\n    \"MainCrawler\",\n    \"MasterCrawler\",\n    \"fetch\",\n    \"fetched\",\n    \"export_one\",\n    \"export\",\n    \"export_to_mirror\",\n    \"export_pymongo\",\n    \"index_files\",\n    \"index\",\n    \"Collection\",\n]\n\n\ntry:\n    import mpi4py  # noqa\nexcept ImportError:\n    logger.debug(\"Failed to import mpi4py. MPIPool will not be available.\")\nelse:\n    from .mpipool import MPIPool  # noqa\n\n    __all__.extend([\"MPIPool\"])\n","repo_name":"cntown2020/signac","sub_path":"signac/contrib/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":1081,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"81"}
+{"seq_id":"26520171407","text":"from systeme.FondMarin import *\n\nclass PosiJauge:\n    def __init__(self, longueur: int) -> None:\n        \"\"\"Permet de créer une jauge.\n\n        Args:\n            longueur (int): La longueur que doit mesurer la jauge à l'affichage.\n        \"\"\"\n        self.origine = None\n        self.hauteur = int(yf*0.01)\n        self.longueur = longueur\n        # Curseur\n        self.posMin = self.hauteur\n        self.posMax = self.longueur-self.hauteur\n        self.posCurseur = self.posMin\n        self.largeur = self.hauteur*2\n        self.defil = False\n\n    def dessine(self, x: int, y: int) -> None:\n        \"\"\"Permet de dessiner la jauge à l'écran.\n\n        Args:\n            x (int): La position des abcisses du point en haut à gauche de la jauge.\n            y (int): La position des ordonnées du point en haut à gauche de la jauge.\n        \"\"\"\n        h = int(self.hauteur/2)\n        self.origine = [x, y]\n        draw_rectangle_rounded([self.origine[0], self.origine[1], self.longueur, self.hauteur], 1, 30, \n                               GRAY)\n        if self.getContactCurseur() or self.defil:\n            couleur = WHITE\n        else:\n            couleur = BLUE\n        draw_rectangle_rounded([self.origine[0], self.origine[1], self.posCurseur, self.hauteur], 1, 30, \n                                BLUE)\n        draw_circle(int(self.origine[0]+self.posCurseur), self.origine[1]+h, self.largeur, couleur)\n        self.changePosCurseur()\n\n    def changePosCurseur(self) -> None:\n        \"\"\"Permet de déplacer le curseur sur la jauge.\n        \"\"\"\n        if not self.defil:\n            if self.getContactCurseur():\n                if is_mouse_button_down(0):\n                    self.defil = True\n        else:\n            self.AttribuerPosition()\n            if is_mouse_button_up(0):\n                self.defil = False\n        if not self.defil and self.getContact():\n            if is_mouse_button_pressed(0):\n                self.AttribuerPosition()\n\n    def AttribuerPosition(self) -> None:\n        \"\"\"Permet de modifier la position du curseur.\n        \"\"\"\n        x = get_mouse_x()\n        position = int(x-self.origine[0])\n        if position < self.posMin:\n            position = self.posMin\n        elif position > self.posMax:\n            position = self.posMax\n        self.posCurseur = position\n\n    def getContactCurseur(self) -> bool:\n        \"\"\"Vérifie si le pointeur de la souris est sur le curseur.\n\n        Returns:\n            bool: True si le pointeur est sur le curseur.\n        \"\"\"\n        rep = False\n        x = get_mouse_x()\n        y = get_mouse_y()\n        if y >= self.origine[1]-self.hauteur/2 and y <= self.origine[1]+self.hauteur*2:\n            if x >= self.origine[0]+self.posCurseur-self.hauteur and x <= self.origine[0]+self.posCurseur+self.hauteur:\n                rep = True\n        return rep\n\n    def getContact(self) -> bool:\n        \"\"\"Vérifie si le pointeur de la souris est sur la jauge.\n\n        Returns:\n            bool: True si le pointeur est sur la jauge.\n        \"\"\"\n        rep = False\n        x = get_mouse_x()\n        y = get_mouse_y()\n        if y >= self.origine[1] and y <= self.origine[1]+self.hauteur:\n            if x >= self.origine[0] and x <= self.origine[0]+self.longueur:\n                rep = True\n        return rep\n\n    def getDims(self) -> list:\n        \"\"\"Renvoie les dimensions de la jauge.\n\n        Returns:\n            list: [longueur de la jauge, hauteur de la jauge]\n        \"\"\"\n        return [self.longueur, self.hauteur]","repo_name":"engrenage13/Anovel","sub_path":"ui/Jauge.py","file_name":"Jauge.py","file_ext":"py","file_size_in_byte":3507,"program_lang":"python","lang":"fr","doc_type":"code","stars":2,"dataset":"github-code","pt":"81"}
+{"seq_id":"74319555785","text":"#Sort the given array in increasing order of number of distinct factors of each element\nclass Solution:\n    # @param A : list of integers\n    # @return a list of integers\n    def solve(self, A):\n\n        #get a modified sorting function\n        def sorting(number):\n            #count the factors\n            count=0\n            fact=1\n            while fact*fact<=number:\n                #check if divisible\n                if number%fact==0:\n                    count=count+2\n                fact=fact+1\n            #edge case\n            if number**0.5==int(number**0.5):\n                count=count-1\n            #return the value\n            return count,number\n        \n        #use the modfies sorting function to get the answer\n        A.sort(key=sorting)\n\n        #return the value\n        return A\n                    \n","repo_name":"SomilKSharma/AdvancedDSA","sub_path":"Sort/sortfactors.py","file_name":"sortfactors.py","file_ext":"py","file_size_in_byte":829,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"21477026735","text":"import pandas as pd\nimport re\nfrom datetime import date\n\n# Converts a given string into its seconds form\ndef convertToSeconds(i, str):\n    if 'sec' in str:\n        return i\n    if 'min' in str:\n        i *= 60\n    elif 'hour' in str:\n        i *= 3600\n    elif 'day' in str:\n        i *= 86400\n    elif 'week' in str:\n        i *= 604800\n    elif 'month' in str:\n        i *= 2592000\n    elif 'year' in str:\n        i *= 31556952\n    else:\n        return 0\n    return i\n\n# Read in dataset and create appropriate dataframe\ncsv = pd.read_csv(\"UFOSightings.csv\")\n\n# Drop rows with missing values\ncsv['Date / Time'] = pd.to_datetime(csv['Date / Time'], errors='coerce') # Convert incorrect datetime to NA\ncsv.dropna(inplace=True)\n\n# Separate 'Date/Time' into unique columns\ntemp = pd.DatetimeIndex(csv['Date / Time'])\ncsv['Date'] = temp.date\ncsv['Time'] = temp.time\ncsv.drop(columns=['Date / Time'], inplace=True)\n# Ignore sightings outside of range [January 1, 2005 AND September 22, 2016]\nminDate = date(2005, 1, 1)\nmaxDate = date(2016, 9, 22)\ncsv = csv[(csv['Date'] >= minDate) & (csv['Date'] <= maxDate)]\n\n# # Clean 'Duration' column\nfor index, row in csv.iterrows():\n    # 1) Remove text only durations\n    if row['Duration'].isalpha():\n        csv.drop(index, inplace=True)\n\nfor index, row in csv.iterrows():\n    # 2) Collect the upper bound of duration\n    try:\n        intList = [e for e in re.split(\"[^0-9]\", row['Duration']) if e != '']\n        intMax = max(map(int, intList))\n    except:\n        print('!!! ' + row['Duration'])\n    # 3) Convert to seconds\n    intMax = convertToSeconds(intMax, row['Duration'])\n    if intMax == 0:\n        csv.drop(index, inplace=True)\n        continue\n    # Finally, rewrite to the duration column\n    csv['Duration'][index] = str(intMax)\n\n# Write the cleaned result to CSV\nprint(list(csv))\ncsv.to_csv(\"../UFOSightings_CLEAN.csv\")\n","repo_name":"carlosbarcelos/DS3001","sub_path":"hw2/task1/cleanCSV.py","file_name":"cleanCSV.py","file_ext":"py","file_size_in_byte":1872,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"12094847344","text":"#!/bin/env python3\n\nfrom multiprocessing import Manager, Process\nimport time\n\n\nclass A:\n    def __init__(self):\n        self.manager = Manager()\n\n    def start(self):\n        print(\"started\")\n        time.sleep(5)\n\nif __name__ == \"__main__\":\n    a = A()\n    proc = Process(target=a.start)\n    print(f'{proc.start()}')\n    print(f'{proc.join()}')\n    proc.join()\n","repo_name":"blreay/test","sub_path":"python/manager.py","file_name":"manager.py","file_ext":"py","file_size_in_byte":362,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"81"}
+{"seq_id":"9648483332","text":"import numpy as np\nfrom scipy.optimize import fmin_l_bfgs_b\nfrom src.sketches.count_sketch import CountSketch\nfrom src.processing.parse_data import process_data\nimport os\nimport math\nfrom src.sketches.top_k import TopK, Node\nfrom guppy import hpy\nimport time\n\n\nclass MissionQuadreticLoss(object):\n    def __init__(self, top_k_size):\n        self.learning_rate = 0.2\n        self.cms = CountSketch(3, 1000)\n        # self.cms = CountSketch(3, int(np.log(self.D) ** 2 / 3))\n        self.top_k = TopK(top_k_size)\n        self.loss_val = 0\n\n    def train_with_sketch(self, feature_pos, features, label):\n        logit = 0\n        for i in range(len(feature_pos)):\n            val = self.top_k.get_value_for_key(feature_pos[i]) * features[i]\n            # calculating wTx\n            logit += val\n        # print(\"label {} wx {}\".format(label, logit))\n        gradient = (label - logit)\n        print(\"loss {}\".format(gradient))\n        if gradient != 0:\n            for i in range(len(feature_pos)):\n                updated_val = 2 * self.learning_rate * gradient * features[i]\n                value = self.cms.update(feature_pos[i], updated_val)\n                self.top_k.push(Node(feature_pos[i], value))\n        return gradient\n\n\nif __name__ == '__main__':\n    non_zero_count = 10\n    n = 100\n    d = 300\n    A = np.random.rand(n, d)\n    x = np.zeros((d, 1))\n    lgr = MissionQuadreticLoss(non_zero_count)\n    for j in range(non_zero_count):\n        pos = np.random.randint(d)\n        x[pos] = np.random.rand() * np.random.randint(10)\n    true_labels = np.dot(A, x)\n    start_time = time.time()\n    for epoch in range(4):\n        for i in range(A.shape[0]):\n            data_row = A[i, :]\n            true_label = true_labels[i]\n            lgr.train_with_sketch([i for i in range(d)], data_row, true_label)\n    non_zero_x, non_zero_y = np.where(x > 0)\n    print(\"non zero x pos {}\".format(non_zero_x))\n    non_zero_values = x[x > 0]\n    print(\"non zero values {}\".format(non_zero_values))\n    print(\"shape of true labels {}\".format(true_labels.shape))\n    lgr.top_k.print_heap()\n","repo_name":"neerajsharma9195/count-sketch-feature-selection","sub_path":"src/classifiers/logistic_regression_sparse_recovery.py","file_name":"logistic_regression_sparse_recovery.py","file_ext":"py","file_size_in_byte":2080,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"16518097911","text":"import unittest\nfrom io import StringIO\nfrom _io import TextIOWrapper\nfrom Alphabet import Alphabet\nfrom Permutation import Permutation\nfrom FixedRotor import FixedRotor\nfrom Utils import *\n\n\nclass MyTestCase(unittest.TestCase):\n\n    def test_alphabet(self):\n        alphabet = Alphabet(Default_Alphabet_String)\n        self.assertEqual(alphabet.size(), 26)\n        self.assertEqual(alphabet.toInt('A'), 0)\n        self.assertEqual(alphabet.toChar(0), 'A')\n        self.assertEqual(alphabet.toInt('Z'), 25)\n        self.assertEqual(alphabet.toChar(25), 'Z')\n\n    def test_permutation(self):\n        cycle = \"(AELTPHQXRU) (BKNW) (CMOY) (DFG) (IV) (JZ) (S)\"\n        alphabet = Alphabet(Default_Alphabet_String)\n        permutation = Permutation(cycle, alphabet)\n        self.assertEqual(permutation.permute('A'), 'E')\n        self.assertEqual(permutation.invert('E'), 'A')\n        self.assertEqual(permutation.permute('S'), 'S')\n        self.assertEqual(permutation.invert('S'), 'S')\n\n    def test_advance(self):\n        alphabet = Alphabet(\"ABCD\")\n        cycle = \"(ABCD)\"\n        permutation = Permutation(cycle, alphabet)\n        fix_rotor = FixedRotor(\"fix\", permutation)\n        self.assertEqual(fix_rotor.convertForward(0), 1)\n        self.assertEqual(fix_rotor.convertBackward(1), 0)\n\n\nif __name__ == '__main__':\n    unittest.main()\n","repo_name":"zhxfaann123/Enigma","sub_path":"Test.py","file_name":"Test.py","file_ext":"py","file_size_in_byte":1338,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"81"}
+{"seq_id":"39017933009","text":"\"\"\"\nStatus -> Solved by myself\nTime complexity -> O(N + M)\nSpace complexity -> O(N + M)\n\nSolution\n1. create dict to count how much each number occurs\n2. iterate by one of the dict\n3. if key exist in second dict -> add to result list of keys repeated minimum amount of times\n\"\"\"\n\nclass Solution:\n    def intersect(self, nums1: list[int], nums2: list[int]) -> list[int]:\n        def count_values(l):\n            counter = {}\n            for val in l:\n                counter[val] = counter.get(val, 0) + 1\n            return counter\n        \n        counter_1 = count_values(nums1)\n        counter_2 = count_values(nums2)\n        result = []\n        for key in counter_1:\n            if key in counter_2:\n                result.extend([key for amount in range(min(counter_1[key], counter_2[key]))])\n        return result\n\n\nclass Solution2:\n    def intersect(self, nums1: list[int], nums2: list[int]) -> list[int]:\n        def count_values(l):\n            counter = {}\n            for val in l:\n                counter[val] = counter.get(val, 0) + 1\n            return counter\n        \n        counter_1 = count_values(nums1)\n        counter_2 = count_values(nums2)\n        result = []\n        smaller_counter = counter_1 if len(nums1) < len(nums2) else counter_2\n        bigger_counter = counter_1 if len(nums1) > len(nums2) else counter_2\n        for key in smaller_counter:\n            if key in bigger_counter:\n                for i in range(min(counter_1[key], counter_2[key])):\n                    result.append(key)\n        return result\n\nif __name__ == '__main__':\n    n = [4,9,5]\n    m = [9,4,9,8,4]\n    sol = Solution2()\n    res = sol.intersect(n, m)\n    print(f'Result --> {res}')  ","repo_name":"mykhailov-my/leetcode","sub_path":"350_intersection_of_two_arrays.py","file_name":"350_intersection_of_two_arrays.py","file_ext":"py","file_size_in_byte":1690,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"6596662371","text":"\nimport gzip\nimport os\nimport io\nimport numpy as np\nfrom urllib.parse import urlparse\nimport yaml\nimport onnx\nimport onnxruntime\nimport onnxruntime.backend as backend\n\nfrom mltk.core.utils import get_mltk_logger\nfrom mltk.utils.archive_downloader import download_verify_extract\n\n\nonnxruntime.set_default_logger_severity(3)\n\n\n\n\nclass _DataList(list):\n    def __init__(self, params):\n        super().__init__()\n        self.columns = []\n        row = []\n        for key in sorted(params):\n            if key == 'self':\n                continue\n            row.append(params[key])\n            self.columns.append(key)\n        self.append(row)\n\n    def tonumpy(self) -> np.ndarray:\n        return np.asarray(self, dtype=np.int64)\n\n\nclass MetricBaseEstimator(object):\n    def __init__(self, onnx_model_file):\n        onnx_model = onnx.load(onnx_model_file)\n        self.onnx_model_backend = backend.prepare(onnx_model, 'CPU')\n        meta = onnx_model.metadata_props[0]\n        self.feature_names = meta.value.split(',')\n\n\n    def predict(self, **kwargs):\n        X = _DataList(kwargs)\n        y = self.onnx_model_backend.run(X.tonumpy())\n        return float(y[0])\n\n\n\ndef download_estimators() -> str:\n    curdir = os.path.dirname(os.path.abspath(__file__))\n    logger = get_mltk_logger()\n\n    try:\n        from mltk.cli import is_command_active\n        show_progress=is_command_active()\n    except:\n        show_progress = False\n\n    with open(f'{curdir}/estimators_url.yaml', 'r') as fp:\n        estimators_url_obj = yaml.load(fp, Loader=yaml.SafeLoader)\n\n    parsed_url = urlparse(estimators_url_obj['url'])\n    url_name, _ = os.path.splitext(os.path.basename(parsed_url.path))\n\n    # NOTE: If the estimators already exists then this doesn't do anything\n    dest_dir = download_verify_extract(\n        url=estimators_url_obj['url'],\n        dest_subdir=f'accelerators/mvp/estimators/{url_name}',\n        show_progress=show_progress,\n        file_hash=estimators_url_obj['sha1'],\n        file_hash_algorithm='sha1',\n        logger=logger\n    )\n\n    return dest_dir\n\n\ndef load_model(name:str, accelerator:str, metric:str) -> MetricBaseEstimator:\n    logger = get_mltk_logger()\n\n    estimator_name = f'{name}.{accelerator}.{metric}.onnx.gz'\n\n     # First see if the estimator archive exists in the local \"generated\" directory\n    curdir = os.path.dirname(os.path.abspath(__file__))\n    generated_dir = f'{curdir}/generated'\n    if os.path.exists(f'{generated_dir}/__init__.py'):\n        estimator_path = f'{generated_dir}/{estimator_name}'\n\n    else:\n        # Otherwise we need to download all the estimators\n        try:\n            estimators_dir = download_estimators()\n        except Exception as e:\n            logger.warning(f'Failed to download profiling estimators, err: {e}')\n            return None\n\n        estimator_path = f'{estimators_dir}/{estimator_name}'\n\n    try:\n        if os.path.exists(estimator_path):\n            with gzip.open(estimator_path, 'rb') as fp:\n                data = fp.read()\n\n            return MetricBaseEstimator(io.BytesIO(data))\n\n    except Exception as e:\n        logger.warning(f'Failed to load profiling estimator: {estimator_path}, err: {e}')\n\n    return None\n\n\ndef activation_to_int(activation:str) -> int:\n    activation = activation.lower()\n    if activation == 'none':\n        return 0\n    elif activation == 'relu':\n        return 1\n    elif activation == 'relu_n1_to_1':\n        return 2\n    elif activation == 'relu6':\n        return 3\n    elif activation == 'tanh':\n        return 4\n    elif activation == 'sign_bit':\n        return 5\n    else:\n        return -1\n\n\ndef use_activation(activation:str) -> int:\n    return bool_to_int(activation.lower() != 'none')\n\n\ndef padding_to_int(padding:str) -> int:\n    padding = padding.lower()\n    if padding == 'same':\n        return 1\n    elif padding == 'valid':\n        return 2\n    else:\n        return 0\n\ndef bool_to_int(value:bool) -> int:\n    return 1 if value else 0\n","repo_name":"SiliconLabs/mltk","sub_path":"mltk/core/tflite_micro/accelerators/mvp/estimator/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":3967,"program_lang":"python","lang":"en","doc_type":"code","stars":44,"dataset":"github-code","pt":"81"}
+{"seq_id":"28059074844","text":"#Paquete numpy - error, estadistica y matriz\r\n\r\nlista1=[1,2,3,4]\r\nlista2=[5,6,7]\r\n\r\nv1=numpy.array(lista1)\r\nv2=numpy.array(lista2)\r\n\r\nv1+v2 #distinto largo error\r\n\r\nprom1 = numpy.mean(v1)\r\nprom2 = numpy.mean(v2)\r\n\r\nv3 = v1[2:4]\r\nv4 = numpy.zeros(3)\r\nv5 = numpy.ones(4)\r\n\r\ntype(v5[1])\r\n\r\ndims = (3,4) #tupla\r\n\r\nmatriz = numpy.zeros(dims)\r\nmatriz.shape\r\n","repo_name":"FacuJulia/Diploma-in-Data-Science-with-R-and-Python","sub_path":"Projects/Python/Intro-Python/Primeras-clases/Ejercicio9.py","file_name":"Ejercicio9.py","file_ext":"py","file_size_in_byte":352,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"41707209396","text":"import openpyxl\nfrom openpyxl.styles import Font, Alignment, Border, Side\nfrom openpyxl.utils.dataframe import dataframe_to_rows\nimport xlsxwriter\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport os\n\n# global color values\nTABLE1_DARK = \"#4BACC6\"\nTABLE1_LIGHT = \"#DAEEF3\"\n\nTABLE2_DARK = \"#F79646\"\nTABLE2_LIGHT = \"#FDE9D9\"\n\n\ndef _style_range(ws, cell_range, border=Border(), fill=None, font=None, alignment=None):\n    \"\"\"\n    Apply styles to a range of cells as if they were a single cell.\n\n    Parameters\n    ----------\n    ws: Excel worksheet instance\n    cell_range: An excel range to style (e.g. A1:F20)\n    border: An openpyxl Border\n    fill: An openpyxl PatternFill or GradientFill\n    font: An openpyxl Font object\n\n    Note\n    ----\n    Originally from openpyxl documentation: http://openpyxl.readthedocs.io/en/default/styles.html\n    \"\"\"\n\n    top = Border(top=border.top)\n    left = Border(left=border.left)\n    right = Border(right=border.right)\n    bottom = Border(bottom=border.bottom)\n\n    first_cell = ws[cell_range.split(\":\")[0]]\n    if alignment:\n        ws.merge_cells(cell_range)\n        first_cell.alignment = alignment\n\n    rows = ws[cell_range]\n    if font:\n        first_cell.font = font\n\n    for cell in rows[0]:\n        cell.border += top\n    for cell in rows[-1]:\n        cell.border += bottom\n\n    for row in rows:\n        l = row[0]\n        r = row[-1]\n        l.border += left\n        r.border += right\n        if fill:\n            for c in row:\n                c.fill = fill\n\n\ndef _get_scale_draw_values(sample_dropna_values, values_four):\n    \"\"\"\n    Apply log10 scale on input values\n\n    Parameters\n    ----------\n    sample_dropna_values: array_like\n        Sample values without NaNs\n    values_four: array_like\n        Four values to treat separately\n\n    Returns\n    -------\n    draw_values: ndarray\n        The scaled values for sample_dropna_values\n    draw_value_4: ndarray\n        The scaled values for values_four\n    \"\"\"\n\n    signs = np.sign(sample_dropna_values)\n    draw_values = signs * np.log10(abs(sample_dropna_values) + 1)\n\n    draw_value_4 = []\n    for i in range(4):\n        draw_value_4.append(np.sign(values_four[i]) * np.log10(abs(values_four[i]) + 1))\n\n    return draw_values, draw_value_4\n\n\ndef _draw_texts(text_values, draw_value_4, mark, y_low, y_up, date_flag=False):\n    \"\"\"\n    Draw indicator texts for a distribution graph\n\n    Parameters\n    ----------\n    text_values: array_like\n        Text values\n    draw_value_4: array_like\n        X axis values for texts\n    mark: int\n        Mark for choosing between color scheme 1 or 2\n    y_low: float\n        The minimum value of y axis\n    y_up: float\n        The maximum value of y axis\n    date_flag: boolean\n        Whether it is drawing for date type feature\n    \"\"\"\n\n    color_dark = TABLE1_DARK if mark == 1 else TABLE2_DARK\n    color_light = TABLE1_LIGHT if mark == 1 else TABLE2_LIGHT\n    plt.axvline(x=draw_value_4[0], color=color_dark, linestyle='--', linewidth=1)\n    plt.axvline(x=draw_value_4[3], color=color_dark, linestyle='--', linewidth=1)\n\n    if date_flag:\n        plt.text(draw_value_4[0], y_low + (y_up - y_low) * 0.1 * mark, 'max:' + str(text_values[1]),\n            ha=\"center\", va=\"center\", bbox=dict(boxstyle=\"square\", facecolor=color_light, edgecolor='none'))\n        plt.text(draw_value_4[3], y_low + (y_up - y_low) * (0.6 + 0.1 * mark), 'min:' + str(text_values[0]),\n            ha=\"center\", va=\"center\", bbox=dict(boxstyle=\"square\", facecolor=color_light, edgecolor='none'))\n    else:\n        plt.axvline(x=draw_value_4[1], color=color_dark, linestyle='--', linewidth=1)\n        plt.axvline(x=draw_value_4[2], color=color_dark, linestyle='--', linewidth=1)\n\n        indicators = ['min', 'mean', 'median', 'max']\n        for i in range(4):\n            text = '%s:'%(indicators[i]) + str(round(text_values[i], 3))\n            plt.text(draw_value_4[i], y_low + (y_up - y_low) * (0.2 * i + 0.1 * mark), text,\n                ha=\"center\", va=\"center\", bbox=dict(boxstyle=\"square\", facecolor=color_light, edgecolor='none'))\n\n\ndef _adjust_ws(ws, row_height, row_heights=None, adjust_type=None):\n    \"\"\"\n    Adjust the height and width of columns for a worksheet\n\n    Parameters\n    ----------\n    ws: Excel worksheet instance\n    row_height: float\n        Height of the columns\n    row_heights: dict\n        Dictionary of (row_idx, row_height) for special style case\n    adjust_type: string or None\n    \"\"\"\n    col_widths = {}\n    for i, col in enumerate(ws.columns):\n        col_name = xlsxwriter.utility.xl_col_to_name(i)\n        col_widths[col_name] = 0\n        for cell in col:\n            cell.alignment = Alignment(horizontal='left', wrap_text=True)\n            if cell:\n                try:\n                    cell_length = len(str(cell.value))\n                except:\n                    cell_length = len(cell.value)\n                if cell_length > col_widths[col_name]:\n                    col_widths[col_name] = cell_length\n\n    for key in col_widths.keys():\n        col_widths[key] *= 1.5\n\n    for i, col in enumerate(range(ws.max_column)):\n        col_name = xlsxwriter.utility.xl_col_to_name(i)\n        ws.column_dimensions[col_name].width = np.min([np.max([15, col_widths[col_name]]), 80])\n\n    if adjust_type == 'str':\n        ws.column_dimensions['C'].width = col_widths['B']\n        for i in range(ws.max_row + 1):\n            try:\n                ws.row_dimensions[i].height = row_heights[i]\n            except:\n                ws.row_dimensions[i].height = row_height\n    else:\n        for i in range(ws.max_row + 1):\n            ws.row_dimensions[i].height = row_height\n\n\ndef _insert_df(result_df, ws, header=False, head_color=True, bold_first_column=True, head_style='Accent5'):\n    \"\"\"\n    Insert a pandas dataframe into a worksheet\n\n    Parameters\n    ----------\n    result_df: pandas Dataframe\n        The dataframe\n    ws: Excel worksheet instance\n    header: boolean\n        Whether to insert the dataframe header\n    head_color: boolean\n        Whether to color the dataframe header\n    head_style: Excel cell style\n        Cell style to apply on the header\n\n    Returns\n    -------\n    head_row: int\n        Row index of the header\n    \"\"\"\n\n    max_col = result_df.shape[1]\n    for r_idx, r in enumerate(dataframe_to_rows(result_df, index=False, header=header)):\n        ws.append(r)\n        for cell_idx, cell in enumerate(ws.iter_cols(max_col=max_col, min_row=ws.max_row, max_row=ws.max_row)):\n            cell = cell[0]\n            cell.font = Font(name='Calibri', size=11)\n\n            # apply cell style on header\n            # get the header row index\n            if r_idx == 0:\n                if head_color:\n                    cell.style = head_style\n                head_row = ws.max_row\n            else:\n                # bold the first column\n                if cell_idx == 0 and bold_first_column:\n                    cell.font = Font(bold=True)\n    return head_row\n\n\ndef _insert_numeric_results(numeric_results, ws, row_height, img_dir, date_flag=False):\n    \"\"\"\n    Insert results of a numeric feature into a worksheet\n\n    Parameters\n    ----------\n    numeric_results: dict\n        results to insert\n    ws: Excel worksheet instance\n    row_height: float\n        Height of the row\n    img_dir: string\n        Root directory for the generated images\n    date_flag: boolean\n        Whether the feature is date type\n    \"\"\"\n\n    # construct the thin border\n    thin = Side(border_style=\"thin\", color=\"000000\")\n    border = Border(top=thin, left=thin, right=thin, bottom=thin)\n\n    # loop and output the results\n    for result in numeric_results:\n        column = result['column']\n        if 'result_df' not in result.keys():\n            ws.append([column, result['error_msg']])\n            for col in ['A', 'B']:\n                ws['%s%d' %(col, ws.max_row)].style = 'Bad'\n            ws.append([''])\n            continue\n\n        result_df = result['result_df']\n        result_df = result_df[['feature', 'value', 'graph']]\n        head_row = _insert_df(result_df, ws)\n\n        # merge cells for the graph\n        ws.merge_cells('C%d:C%d' %(head_row+1, head_row+result_df.shape[0]-1))\n        _style_range(ws, 'A%d:C%d'%(head_row, head_row+result_df.shape[0]-1), border=border)\n        ws['C%d' %(head_row+1)].border = Border(top=None, left=None, right=thin, bottom=thin)\n\n        # add gap\n        ws.append([''])\n\n        # insert graph\n        try:\n            if date_flag:\n                graph_name = '%s_numeric' %(column)\n            else:\n                graph_name = column\n\n            if '/' in graph_name:\n                graph_name = graph_name.replace('/', '')\n\n            img = openpyxl.drawing.image.Image(os.path.join(img_dir, '%s.png' %(graph_name)))\n            ws.add_image(img, 'C%d' %(head_row+1))\n        except:\n            continue\n    # adjust worksheet\n    _adjust_ws(ws=ws, row_height=row_height)\n    ws.column_dimensions['C'].width = 90\n","repo_name":"SauceCat/pydqc","sub_path":"pydqc/dqc_utils.py","file_name":"dqc_utils.py","file_ext":"py","file_size_in_byte":8955,"program_lang":"python","lang":"en","doc_type":"code","stars":283,"dataset":"github-code","pt":"81"}
+{"seq_id":"39559187996","text":"import logging\nfrom asyncio import run\nfrom typing import Any, Optional, Union, Dict\n\nfrom df_engine.core import Context, Actor\nfrom df_db_connector import DBAbstractConnector\n\nfrom .group import ServiceGroup\nfrom .provider import AbsProvider, CLIProvider\nfrom .service import Service\nfrom .types import FrameworkKeys, ClearFunction\n\nlogger = logging.getLogger(__name__)\n\n\nclass Runner:\n    \"\"\"\n    Class, representing a runner for use in Pipelines.\n    Provider and connector are shared through the execution.\n    The services are executed sequentially with a shared context.\n    \"\"\"\n\n    def __init__(\n        self,\n        actor: Union[Actor, Service],\n        clear_function: Optional[ClearFunction] = None,\n        contex_db: Optional[Union[DBAbstractConnector, Dict]] = None,\n        provider: AbsProvider = CLIProvider(),\n        group: Optional[ServiceGroup] = None,\n    ):\n        self.contex_db = dict() if contex_db is None else contex_db\n        self.provider = provider\n        self.actor = actor\n        self._clear_function = clear_function\n        self._group = group\n\n    def start_sync(self) -> None:\n        \"\"\"\n        Method for starting a runner, sets up corresponding provider callback.\n        Since one runner always has only one provider, there is no need for thread management here.\n        Use this in async context, await will not work in sync.\n        \"\"\"\n\n        async def callback(request: Any) -> Context:\n            return await self._request_handler(request, self.provider.ctx_id)\n\n        run(self.provider.run(callback))\n\n    async def start_async(self) -> None:\n        \"\"\"\n        Method for starting a runner, sets up corresponding provider callback.\n        Since one runner always has only one provider, there is no need for thread management here.\n        Use this in sync context, asyncio.run() will produce error in async.\n        \"\"\"\n\n        async def callback(request: Any) -> Context:\n            return await self._request_handler(request, self.provider.ctx_id)\n\n        await self.provider.run(callback)\n\n    async def _request_handler(self, request: Any, ctx_id: Optional[Any] = None) -> Context:\n        ctx = self.contex_db.get(ctx_id)\n        if ctx is None:\n            ctx = Context()\n            ctx.framework_states[FrameworkKeys.RUNNER] = dict()\n            ctx.framework_states[FrameworkKeys.SERVICES] = dict()\n            ctx.framework_states[FrameworkKeys.SERVICES_META] = dict()\n\n        ctx.add_request(request)\n        ctx = await self._group(ctx, self.actor)\n\n        ctx.framework_states[FrameworkKeys.RUNNER] = dict()\n        if self._clear_function is not None:\n            services, meta = self._clear_function(\n                ctx.framework_states[FrameworkKeys.SERVICES], ctx.framework_states[FrameworkKeys.SERVICES_META]\n            )\n            ctx.framework_states[FrameworkKeys.SERVICES] = services\n            ctx.framework_states[FrameworkKeys.SERVICES_META] = meta\n        self.contex_db[ctx_id] = ctx\n\n        return ctx\n","repo_name":"deeppavlov/dialog_flow_runner","sub_path":"df_runner/runner.py","file_name":"runner.py","file_ext":"py","file_size_in_byte":3005,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"74945159305","text":"from Students.models import StudentActions, StudentActionsLoop, Student\nfrom Instructors.models import Courses\nimport json\nimport pprint\nfrom datetime import datetime\nfrom collections import defaultdict\n\ndef default_to_regular(d):\n    if isinstance(d, defaultdict):\n        d = {k: default_to_regular(v) for k, v in d.items()}\n    return d\n\npp = pprint.PrettyPrinter(indent=1)\n\njson_data = defaultdict()\nstudent_actions_all = StudentActions.objects.all().order_by('timestamp')\n# Timestamp Group (1st week) -> Course -> Student -> list 1hr data\n# Timestamp Group (2nd week) -> Course -> Student -> list 1hr data\nif student_actions_all:\n    current_date = student_actions_all.first().timestamp.date().strftime(\"%m/%d/%Y\")\n    current_course = student_actions_all.first().courseID.pk\n    current_student = student_actions_all.first().studentID.pk\n    course_dict = defaultdict()\n    student_dict = defaultdict()\n\n    for student_action in student_actions_all:\n        if current_date != student_action.timestamp.date().strftime(\"%m/%d/%Y\"):\n            current_date = student_action.timestamp.date().strftime(\"%m/%d/%Y\")\n        if current_course != student_action.courseID.pk:\n            current_course = student_action.courseID.pk\n        if current_student != student_action.studentID.pk:\n            current_student = student_action.studentID.pk\n\n        if not current_date in json_data:\n            json_data[current_date] = defaultdict()\n        if not current_course in json_data[current_date]:\n            json_data[current_date][current_course] = defaultdict()\n        if not current_student in json_data[current_date][current_course]:\n            json_data[current_date][current_course][current_student] = []\n\n        loops = StudentActionsLoop.objects.filter(studentActionsID=student_action.pk)\n        for loop in loops:\n            data = defaultdict()\n            data['warmups_attempted'] = loop.warmups_attempted\n            data['serious_attempted'] = loop.serious_attempted\n            data['duels_sent'] = loop.duels_sent\n            data['duels_accepted'] = loop.duels_accepted\n            data['callouts_sent'] = loop.callouts_sent\n            data['callouts_participated'] = loop.callouts_participated\n\n            data['high_score_challenges'] = loop.high_score_challenges\n            data['vc_earned'] = loop.vc_earned\n            data['badges_earned'] = loop.badges_earned\n            data['on_leaderboard'] = loop.on_leaderboard\n            data['duels_won'] = loop.duels_won\n            data['callouts_won'] = loop.callouts_won\n\n            data['low_score_challenges'] = loop.low_score_challenges\n            data['duels_lost'] = loop.duels_lost\n            data['callouts_lost'] = loop.callouts_lost\n\n            data['timestamp'] = loop.timestamp.strftime(\"%m/%d/%Y %H:%M:%S\")\n            json_data[current_date][current_course][current_student].append(data)\n\n\n\n\n\nd = dict(json_data)\nfor k, v in d.items():\n    d[k] = dict(v)\n    for a, b in d[k].items():\n        d[k][a] = dict(b)\n\n\nwith open(\"data.json\", \"w+\") as out:\n    json.dump(d, out, indent=4)\n\npp.pprint(d)\n","repo_name":"OneUp-Learning/oneUp","sub_path":"data-to-json.py","file_name":"data-to-json.py","file_ext":"py","file_size_in_byte":3096,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"81"}
+{"seq_id":"6194786952","text":"import requests\n\n# Define the base URL where your FastAPI server is running\nbase_url = \"http://localhost:8000\"  # Replace with the correct URL if different\n\n# Example 1: Retrieve the last 5 sensor readings for a specific sensor\nsensor_id = \"sensor001\"\nlast_n = 5\nresponse = requests.get(f\"{base_url}/sensor-readings/?sensor_id={sensor_id}&last_n={last_n}\")\n\nif response.status_code == 200:\n    sensor_data = response.json()\n    print(f\"Last {last_n} Sensor Readings for Sensor ID {sensor_id}:\")\n    for reading in sensor_data:\n        print(reading)\nelse:\n    print(f\"Error: {response.status_code}, {response.text}\")\n\n# Example 2: Retrieve sensor readings within a date range\nstart_range = \"2023-09-17T00:00:00\"\nend_range = \"2023-09-17T01:15:00\"\nresponse = requests.post(f\"{base_url}/sensor-readings/range/\", json={\"sensor_id\": sensor_id, \"start_timestamp\": start_range, \"end_timestamp\": end_range})\n\nif response.status_code == 200:\n    sensor_data = response.json()\n    print(f\"Sensor Readings within Range ({start_range} to {end_range}):\")\n    for reading in sensor_data:\n        print(reading)\nelse:\n    print(f\"Error: {response.status_code}, {response.text}\")\n","repo_name":"jibran1609/MQTT_Setup","sub_path":"request_sensor_data.py","file_name":"request_sensor_data.py","file_ext":"py","file_size_in_byte":1164,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"19161711377","text":"import asyncio\r\nimport base64\r\nimport io\r\nimport json\r\nimport logging\r\nimport os\r\n\r\nimport aiohttp\r\nimport discord\r\nimport openai\r\nimport tiktoken\r\nfrom colorama import Back, Fore, Style\r\nfrom discord.ext import commands\r\nfrom dotenv import load_dotenv\r\nfrom imaginepy import AsyncImagine\r\nfrom PyPDF2 import PdfReader\r\n\r\nlogging.basicConfig(filename='error_log.txt', level=logging.ERROR, format='%(asctime)s %(message)s', datefmt='%m/%d/%Y %I:%M:%S %p')\r\n\r\nload_dotenv()\r\nopenai.api_key = os.getenv(\"API_KEY\")\r\napi_key = openai.api_key\r\nopenai.api_base = os.getenv(\"API_URL\")\r\napi_base = openai.api_base\r\nignored_ids = os.getenv(\"IGNORED_IDS\").split(\",\")\r\nbot = commands.Bot(command_prefix='!', intents=discord.Intents.all())\r\nallow_commands = os.getenv(\"ALLOW_COMMANDS\").lower() == \"true\"\r\nadmin_id = os.getenv(\"ADMIN_IDS\").split(\",\")\r\nbehavior_name = os.getenv('DEFAULT_BEHAVIOR')\r\nimagine = AsyncImagine()\r\n\r\n\r\nchannel_messages = {}\r\nchannel_models = {}\r\nresponses = {}\r\ncommand_mode_flag = {}\r\nmessage_queue_locks = {}\r\nmodel_list_json = None\r\n\r\n\r\n\r\ndef allowed_command(user_id):\r\n    if str(user_id) in admin_id:\r\n        return True\r\n    elif allow_commands:\r\n        return True\r\n    else:\r\n        return False\r\n\r\n\r\ndef list_prompts():\r\n    \"\"\"Lists prompt filenames in \"./prompts\" directory with \".txt\" extension, removes extension, and returns modified names\"\"\"    \r\n    behavior_files = []\r\n    for filename in os.listdir(\"./prompts\"):\r\n        if filename.endswith(\".txt\"):\r\n            behavior_files.append(os.path.splitext(filename)[0])\r\n    return behavior_files\r\n\r\n\r\ndef load_prompt(filename):\r\n    \"\"\"Loads a prompt from a file, processes it, and returns JSON data as a Python object.\"\"\"   \r\n    if not filename.endswith(\".txt\"):\r\n        filename += \".txt\"\r\n    with open(f\"./prompts/{filename}\", encoding=\"utf-8\") as file:\r\n        lines = file.readlines()\r\n    return json.loads(build_convo(lines))\r\n\r\n\r\ndef save_convo(messages, filename, channel_id):\r\n    \"\"\"Saves the current memory to a text file\"\"\"\r\n    convo_str = de_json(messages)\r\n    with open(filename, \"w\", encoding=\"utf-8\") as file:\r\n        file.write(convo_str)\r\n        print(f\"{Fore.RED}Behavior Saved:\\n{Style.DIM}{Fore.GREEN}{Back.WHITE}{convo_str}{Style.RESET_ALL}\")\r\n    return f\"Behavior saved as `{os.path.splitext(os.path.basename(filename))[0]}`\"\r\n\r\n\r\ndef set_model(model_name, model_info, channel_id, message=None):\r\n    max_tokens = int(model_info[model_name] * 0.8)\r\n    channel_models[channel_id] = (model_name, max_tokens)\r\n\r\n\r\ndef de_json(convo):\r\n    \"\"\"Convert a conversation from JSON to human-readable text.\"\"\"\r\n    conversation = []\r\n    for message in convo:\r\n        role = message['role']\r\n        content = message['content']\r\n        conversation.append(f\"{role}: {content}\")\r\n    return \"\\n\".join(conversation)\r\n\r\n\r\ndef build_convo(lines):\r\n    \"\"\"Converts a conversation in text format to JSON format.\"\"\"\r\n    conversation = []\r\n    role = \"user\"\r\n    content = \"\"\r\n    for line in lines:\r\n        line = line.strip()\r\n        if not line:\r\n            continue\r\n        parts = line.split(':', maxsplit=1)\r\n        if len(parts) >= 2:\r\n            if content:\r\n                conversation.append({\"role\": role, \"content\": content})\r\n                content = \"\"\r\n            role = parts[0]\r\n            content = parts[1].strip()\r\n        else:\r\n            content += \"\\n\" + line if content else line\r\n    if content:\r\n        conversation.append({\"role\": role, \"content\": content})\r\n    return json.dumps(conversation)\r\n\r\n\r\ndef num_tokens_from_message(messages, model=\"gpt-4\"):\r\n    \"\"\"Returns the number of tokens used by a list of messages.\"\"\"\r\n    try:\r\n        encoding = tiktoken.encoding_for_model(model)\r\n    except KeyError:\r\n        encoding = tiktoken.get_encoding(\"cl100k_base\")\r\n    if model == model:  # note: future models may deviate from this\r\n        num_tokens = 0\r\n        for message in messages:\r\n            num_tokens += 4  # every message follows <im_start>{role/name}\\n{content}<im_end>\\n\r\n            for key, value in message.items():\r\n                num_tokens += len(encoding.encode(value))\r\n                if key == \"name\":  # if there's a name, the role is omitted\r\n                    num_tokens += -1  # role is always required and always 1 token\r\n        num_tokens += 2  # every reply is primed with <im_start>assistant\r\n        return num_tokens\r\n\r\n\r\nasync def get_tokens_api(api_key: str, model: str, messages: list):\r\n    headers = {'Authorization': f\"Bearer {api_key}\"}\r\n    json_data = {'model': model, 'messages': messages}\r\n    async with aiohttp.ClientSession(headers=headers) as session:\r\n        async with session.post(f\"{api_base}/chat/tokenizer\", json=json_data) as resp:            \r\n            response = await resp.json()\r\n            #print(response)\r\n            return response\r\n        \r\n\r\nasync def forget_mentions(user_channel_key): # todo: improve this\r\n    await asyncio.sleep(300)\r\n    if user_channel_key in channel_messages:\r\n        del channel_messages[user_channel_key]\r\n        print(f\"{Fore.RED}Forgetting side convo with user {user_channel_key}{Style.RESET_ALL}\")\r\n\r\n\r\nasync def get_models():\r\n    global model_list_json\r\n    models_path = 'models.json'\r\n    async with aiohttp.ClientSession() as session:\r\n        async with session.get(f\"{api_base}/models\") as response:\r\n            if response.status == 200:\r\n                all_models = await response.json()\r\n                with open(models_path, 'w') as outfile:\r\n                    json.dump(all_models, outfile, indent=4)\r\n                chat_models = [m for m in all_models.get(\"data\", []) if \"/api/v1/chat/completions\" in m.get(\"endpoints\", [])]\r\n                model_info = {}\r\n                for model in chat_models:\r\n                    model_id = model[\"id\"]\r\n                    if 'tokens' in model:\r\n                        tokens = model[\"tokens\"]\r\n                        model_info[model_id] = tokens\r\n                model_list_json = list(model_info.keys())\r\n                return model_info\r\n            else:\r\n                return []\r\n\r\n\r\nasync def get_chat_response(model, messages, max_tokens):   \r\n    response = await asyncio.to_thread(\r\n        openai.ChatCompletion.create,\r\n        model=model,\r\n        messages=messages,\r\n        max_tokens=max_tokens,\r\n        temperature=0.5\r\n    )\r\n    return response\r\n\r\n\r\nasync def chat_response(messages, channel_id): #todo: setup local tokenizer for llama\r\n    model, max_tokens = channel_models[channel_id]\r\n    #num_tokens_data = await get_tokens_api(api_key, model, messages)\r\n    #print(num_tokens_data)\r\n    #num_tokens = num_tokens_data['string']['count']\r\n    num_tokens = num_tokens_from_message(messages)\r\n    remaining_tokens = max_tokens - num_tokens\r\n\r\n    with open('channel_settings.json', 'r') as f:\r\n        channel_settings = json.load(f)\r\n\r\n    current_behavior = channel_settings.get(str(channel_id), {}).get(\"behavior\", \"default\")\r\n\r\n    if isinstance(current_behavior, list):\r\n        behavior_len = len(current_behavior)\r\n    else:\r\n        behavior_len = 1\r\n    start_index = behavior_len\r\n    while remaining_tokens / max_tokens < 0.2 and len(messages) > behavior_len:\r\n        #oldest_tokens_data = await get_tokens_api(api_key, model, messages[start_index:start_index+1])\r\n        #oldest_tokens = oldest_tokens_data['string']['count']\r\n        oldest_tokens = num_tokens_from_message(messages[start_index:start_index+1])\r\n        messages = messages[:start_index] + messages[start_index+1:]\r\n        num_tokens -= oldest_tokens\r\n        remaining_tokens = max_tokens - num_tokens\r\n    else:\r\n        if remaining_tokens / max_tokens >= 0.2:\r\n            response = await get_chat_response(model, messages, max_tokens)\r\n\r\n    return response, messages, remaining_tokens\r\n\r\n\r\nasync def discord_chunker(message, content):\r\n    \"\"\"(Almost) Seamless text and code splitter for Discord.\"\"\"\r\n    \r\n    async def send_chunk(chunk):\r\n        await message.channel.send(chunk)\r\n\r\n    def find_split_index(content, max_length, in_code_block):\r\n        if in_code_block:\r\n            indices = [\r\n            content.rfind('\\n', 0, max_length)  # Only consider the lines in the code blocks\r\n            ]\r\n        else:\r\n            indices = [\r\n                content.rfind('\\u200b', 0, max_length),\r\n                content.rfind('\\n', 0, max_length),\r\n                content.rfind('. ', 0, max_length),\r\n                content.rfind(' ', 0, max_length)\r\n            ]\r\n        return max(indices)\r\n\r\n    max_chunk_length = 1950\r\n    in_code_block = False\r\n    code_block_lang = \"\"\r\n\r\n    if len(content) <= max_chunk_length:\r\n        await message.channel.send(content)\r\n    else:\r\n        chunks = []\r\n        chunk = \"\"\r\n        for line in content.splitlines(True):\r\n            if line.startswith('```'):\r\n                if not in_code_block:\r\n                    code_block_lang = line[3:].strip()\r\n                else:\r\n                    code_block_lang = \"\"\r\n                in_code_block = not in_code_block\r\n\r\n            if len(chunk) + len(line) > max_chunk_length:\r\n                if in_code_block:\r\n                    split_index = find_split_index(chunk, max_chunk_length - 4, in_code_block)\r\n                    chunks.append(chunk[:split_index].rstrip() + '```')\r\n                    chunk = f'```{code_block_lang}\\n' + chunk[split_index:]\r\n                else:\r\n                    split_index = find_split_index(chunk, max_chunk_length, in_code_block)\r\n                    chunks.append(chunk[:split_index].rstrip())\r\n                    chunk = '\\u200b\\n' + chunk[split_index:].lstrip()\r\n            chunk += line\r\n        if chunk:\r\n            chunks.append(chunk)\r\n        for chunk in chunks:\r\n            await send_chunk(chunk)\r\n\r\n\r\nasync def handle_image(attachment, image_prompt=None):\r\n    if image_prompt is not None:\r\n        image_response = await image_question(image_prompt, attachment)\r\n    else:\r\n        image_response = await image_caption(attachment)    \r\n    return image_response\r\n\r\n\r\nasync def image_question(prompt, attachment):\r\n    image_data = await attachment.read()\r\n    caption = await imagine.interrogator(image_data)\r\n    print(f\"{Fore.YELLOW}Interrogator caption: {caption}{Style.RESET_ALL}\")\r\n    prompt_text = f\"A user is asking a question about an image. This is their question: {prompt}\\nThis is the raw output caption from an image to text model:\\n`{caption}`\\n Using this data returned from that model, attempt to answer the user's question in a complete sentence. Don't assume who made the image, and only mention 'in the style of' if artists are mentioned. The caption will be raw and contain multiple answers. The first block of information is the most accurate description:\"\r\n    response = await get_completion(prompt_text)\r\n    await imagine.close()\r\n    return response\r\n\r\n\r\nasync def image_caption(attachment):\r\n    image_data = await attachment.read()\r\n    caption = await imagine.interrogator(image_data)\r\n    print(f\"{Fore.YELLOW}Interrogator caption: {caption}{Style.RESET_ALL}\")\r\n    prompt_text = f\"The following text is the raw output from an image to text model. Using this text, attempt to describe the image it represents in a complete sentence. Don't assume who made the image, and only mention 'in the style of' if artists are mentioned. The first block of information is the most accurate description:\\n`{caption}`\"\r\n    response = await get_completion(prompt_text)\r\n    await imagine.close()\r\n    return response\r\n\r\n\r\nasync def get_completion(prompt):\r\n    max_retries = 3\r\n    backoff_factor = 2\r\n    for retry_attempt in range(max_retries):\r\n        try:\r\n            response = await asyncio.to_thread(\r\n                openai.Completion.create,\r\n                model=\"text-davinci-003\",\r\n                prompt=prompt,\r\n                max_tokens=256,\r\n                temperature=0.5,\r\n            )\r\n            content = response['choices'][0]['text']\r\n            return content.strip()\r\n        except aiohttp.ClientConnectorError:\r\n            if retry_attempt == max_retries - 1:\r\n                raise\r\n            sleep_time = (backoff_factor ** retry_attempt) + 1\r\n            print(f\"Rate limited. Retrying in {sleep_time} seconds...\")\r\n            await asyncio.sleep(sleep_time)\r\n\r\n\r\n\r\n# Command Functions\r\n\r\nasync def help(message):\r\n    bot_name = message.guild.me.nick or message.guild.me.name\r\n\r\n    title = f\"I'm {bot_name} the chat bot!\"\r\n    footer = f\"Made with ❤️ by Trypsky \"\r\n\r\n    embed = discord.Embed(\r\n        title=f\"{title}\", color=0x00ff00\r\n    )\r\n    embed.set_footer(text=footer)\r\n\r\n    embed.add_field(name=\"🗣️ Chat\", value=\" Send messages in an active channel to have a chat! Unless you reply to or @mention another user, then I'll keep quiet. 🤫 I also ignore messages that start with `!`\", inline=False)\r\n    embed.add_field(name=\"📣 Quick Mention\", value=f\"I'm always here for a quick question. Just call me out with a @mention in a non active channel! I will forget about what we talked about in a few minutes though.\\n\\u200b\", inline=False)\r\n\r\n    if allowed_command(message.author.id):\r\n        # Additional fields for admin users\r\n        embed.add_field(name=\"Commands (admin only)\", value=f\"Below are the available commands. To use them, just type the words, no need for symbols or slashes:\", inline=False)\r\n        embed.add_field(name=\"toggle active 🔄\", value=f\"Set a channel as active or inactive.\", inline=False)\r\n        embed.add_field(name=\"load model 🧠\", value=f\"Tired of my current conversation style? Choose from a list of chat models! Changing the model wipes my memory!\", inline=False)\r\n        embed.add_field(name=\"wipe memory 🧹\", value=f\"Clear my short-term memory.\", inline=False)\r\n        embed.add_field(name=\"new behavior 🎭\", value=f\"Write a new behaviour.\", inline=False)\r\n        embed.add_field(name=\"save behavior 💾\", value=f\"After sending your new behavior, type save behavior, followed by a name. You can then recall that behavior later!\", inline=False)\r\n        embed.add_field(name=\"load behavior 🔄\", value=f\"Type load behavior to see the full list of available behaviors. Then just respond with the name of the one you want! If you know the behavior name already you can type it all in one line like `load behavior default`.\", inline=False)\r\n        embed.add_field(name=\"reset 🔘\", value=f\"Wipe my memory and load the default behavior.\", inline=False)\r\n\r\n    await message.channel.send(embed=embed)\r\n    return\r\n\r\n\r\nasync def wipe_memory(message):\r\n\r\n    if not allowed_command(message.author.id):\r\n        await message.channel.send(\"You are not allowed to use this command.\")\r\n        return\r\n\r\n    global behavior_name\r\n    user_channel_key = message.channel.id\r\n    with open('channel_settings.json', 'r') as f:\r\n        channel_settings = json.load(f)\r\n\r\n    behavior_name = channel_settings[str(message.channel.id)][\"behavior\"]\r\n    behavior = load_prompt(behavior_name)\r\n\r\n    channel_messages[user_channel_key] = behavior\r\n    await message.channel.send(f\"Memory wiped! Current Behavior is `{os.path.splitext(os.path.basename(behavior_name))[0]}`\")\r\n    print(f\"{Fore.RED}Memory Wiped{Style.RESET_ALL}\")\r\n    return\r\n\r\n\r\nasync def reset(message):\r\n    if not allowed_command(message.author.id):\r\n        await message.channel.send(\"You are not allowed to use this command.\")\r\n        return\r\n\r\n    global behavior_name\r\n    user_channel_key = message.channel.id\r\n\r\n    behavior_name = os.getenv(\"DEFAULT_BEHAVIOR\")\r\n    default_model = os.getenv(\"DEFAULT_MODEL\")\r\n    behavior = load_prompt(behavior_name)\r\n\r\n    channel_messages[user_channel_key] = behavior\r\n\r\n    with open('channel_settings.json', 'r') as f:\r\n        channel_settings = json.load(f)\r\n\r\n    channel_settings[str(message.channel.id)][\"behavior\"] = os.getenv(\"DEFAULT_BEHAVIOR\")\r\n    channel_settings[str(message.channel.id)][\"model\"] = os.getenv(\"DEFAULT_MODEL\")\r\n\r\n    with open('channel_settings.json', 'w') as f:\r\n        json.dump(channel_settings, f, indent=4)\r\n\r\n    await message.channel.send(f\"Reset to defaults! Current Behavior is `{os.path.splitext(os.path.basename(behavior_name))[0]}` and Model is `{default_model}`\")\r\n    print(f\"{Fore.RED}Reset to defaults{Style.RESET_ALL}\")\r\n    return\r\n\r\n\r\nasync def new_behavior(message, check):\r\n    if not allowed_command(message.author.id):\r\n        await message.channel.send(\"You are not allowed to use this command.\")\r\n        return\r\n\r\n    command_mode_flag[message.channel.id] = True\r\n    channel_messages[message.channel.id] = []\r\n    embed = discord.Embed(title=f\"Write the new behavior\", description=(f\"Provide a new behavior. Can be a single prompt, or you can provide an example conversation in the following format:\\n\\nsystem: a system message\\nuser: user message 1\\nassistant: example response 1\\nuser: user message 2\\nassistant: example response 2\\n\\n\"), color=0x00ff00)        \r\n    embed.set_footer(text=(f\"If you wish to recall your new behavior later, don't forget to save it by typing `save behavior`\"))\r\n    await message.channel.send(embed=embed)\r\n    msg = await bot.wait_for(\"message\", check=check)\r\n\r\n    text_attachment_used = False\r\n\r\n    if msg.attachments:\r\n        if msg.attachments[0].filename.endswith('.txt'):\r\n            try:\r\n                behavior_text = await msg.attachments[0].read()\r\n                behavior_text = behavior_text.decode('utf-8')\r\n                text_attachment_used = True\r\n            except Exception as e:\r\n                embed = discord.Embed(title=\"Failed to read the attachment!\", description=str(e), color=0xff0000)\r\n                await message.channel.send(embed=embed)\r\n                return\r\n        else:\r\n            embed = discord.Embed(title=\"Unsupported file type!\", description=\"Please attach a .txt file.\", color=0xff0000)\r\n            await message.channel.send(embed=embed)\r\n            return\r\n    else:\r\n        behavior_text = msg.content.strip()\r\n\r\n    channel_messages[message.channel.id] = json.loads(build_convo(behavior_text.split('\\n')))\r\n\r\n    if not text_attachment_used:\r\n        async for m in message.channel.history(limit=1):\r\n            if m.author == message.author and m.content:\r\n                last_user_message = m.content\r\n                break\r\n        embed_description = last_user_message\r\n    else:\r\n        embed_description = \"New behavior Set! Type `save behavior` if you want to keep it!\"\r\n\r\n    embed = discord.Embed(title=\"New behavior Set! Type `save behavior` if you want to keep it!\", description=embed_description, color=0x00ff00)\r\n    await message.channel.send(embed=embed)\r\n    return\r\n\r\n\r\nasync def save_behavior(message, check):\r\n    if not allowed_command(message.author.id):\r\n        await message.channel.send(\"You are not allowed to use this command.\")\r\n        return\r\n    \r\n    command_mode_flag[message.channel.id] = True\r\n    await message.channel.send(\"Name your behavior:\")\r\n    msg = await bot.wait_for(\"message\", check=check)\r\n    filename = \"prompts/\" + msg.content.strip() + \".txt\"\r\n    messages = channel_messages[message.channel.id]\r\n    await message.channel.send(save_convo(messages, filename, message.channel.id))\r\n\r\n\r\nasync def load_behavior(message, check=None):\r\n    if not allowed_command(message.author.id):\r\n        await message.channel.send(\"You are not allowed to use this command.\")\r\n        return\r\n    \r\n    behavior_files = list_prompts()\r\n    if not behavior_files:\r\n        await message.channel.send(\"No behavior files found.\")\r\n        return\r\n    \r\n    words = message.content.lower().split()\r\n    if len(words) > 2:\r\n        filename = \" \".join(words[2:]).strip().lower()\r\n    elif check is not None:\r\n        command_mode_flag[message.channel.id] = True\r\n        behavior_files_str = \"\\n\".join(behavior_files)\r\n        embed = discord.Embed(title=\"Which behavior to load?\", description=behavior_files_str)\r\n        await message.channel.send(embed=embed)\r\n        msg = await bot.wait_for(\"message\", check=check)\r\n        filename = msg.content.strip().lower()\r\n    else:\r\n        await message.channel.send(\"No behavior name provided.\")\r\n        return\r\n    \r\n    if filename not in [f.lower() for f in behavior_files]:\r\n        await message.channel.send(f\"File not found: {filename}\")\r\n        return\r\n\r\n    with open('channel_settings.json', 'r') as f:\r\n        channel_settings = json.load(f)\r\n    \r\n\r\n    behavior = load_prompt(filename)\r\n    behavior_str = de_json(behavior)\r\n\r\n    channel_messages[message.channel.id] = behavior\r\n    behavior_name = filename\r\n\r\n    channel_settings[str(message.channel.id)][\"behavior\"] = filename\r\n    with open('channel_settings.json', 'w') as f:\r\n        json.dump(channel_settings, f, indent=4)\r\n\r\n    embed = discord.Embed(title=f\"Behavior loaded: {filename}\", description=\"\", color=0x00ff00)\r\n    await message.channel.send(embed=embed)\r\n    print(f\"{Fore.RED}Behavior Loaded:\\n{Style.DIM}{Fore.GREEN}{Back.WHITE}{behavior_str}{Style.RESET_ALL}\")\r\n\r\n\r\nasync def load_model(message, check):\r\n    if not allowed_command(message.author.id):\r\n        await message.channel.send(\"You are not allowed to use this command.\")\r\n        return\r\n          \r\n    try:\r\n        model_info = await get_models()\r\n        model_ids = list(model_info.keys())\r\n    except AttributeError:\r\n        await message.channel.send(\"Sorry, api is currently down, try again later!\")\r\n        return\r\n    \r\n    else:\r\n        command_mode_flag[message.channel.id] = True\r\n        model_ids_str = \"\\n\".join(model_ids)\r\n        embed = discord.Embed(title=\"Which model to load?\", description=model_ids_str)\r\n        await message.channel.send(embed=embed)\r\n        msg = await bot.wait_for(\"message\", check=check)\r\n        model_id = msg.content.strip().lower()\r\n        if model_id not in model_ids:\r\n            await message.channel.send(f\"Model not found: {model_id}\")\r\n            return\r\n        \r\n        else:\r\n            set_model(model_id, model_info, message.channel.id)\r\n            if message.channel.id in channel_messages:\r\n                channel_messages[message.channel.id].clear()\r\n\r\n            with open('channel_settings.json', 'r') as f:\r\n                channel_settings = json.load(f)\r\n\r\n            current_behavior = channel_settings.get(str(message.channel.id), {}).get(\"behavior\", \"default\")\r\n            channel_messages[message.channel.id] = load_prompt(current_behavior)\r\n            channel_settings[str(message.channel.id)][\"model\"] = model_id\r\n\r\n            with open('channel_settings.json', 'w') as f:\r\n                json.dump(channel_settings, f, indent=4)\r\n    print(f\"Model {Fore.YELLOW}{Style.BRIGHT}{model_id}{Style.RESET_ALL} loaded in channel {Fore.CYAN}{message.channel.name}{Style.RESET_ALL} in {Fore.GREEN}{message.guild.name}{Style.RESET_ALL}\")\r\n    embed = discord.Embed(title=f\"Model loaded: {model_id}\", description=f\"Max tokens: {model_info[model_id]}\", color=0x00ff00)\r\n    await message.channel.send(embed=embed)\r\n    return\r\n\r\n\r\nasync def toggle_active(message):\r\n    if not allowed_command(message.author.id):\r\n        await message.channel.send(\"You are not allowed to use this command.\")\r\n        return\r\n\r\n    with open('channel_settings.json', 'r') as f:\r\n        channel_settings = json.load(f)\r\n    if str(message.channel.id) not in channel_settings:\r\n        channel_settings[str(message.channel.id)] = {\r\n            \"model\": \"gpt-3.5-turbo\",\r\n            \"behavior\": \"default\",\r\n            \"active\": False\r\n        }\r\n    channel_settings[str(message.channel.id)][\"active\"] = not channel_settings[str(message.channel.id)][\"active\"]\r\n\r\n    with open('channel_settings.json', 'w') as f:\r\n        json.dump(channel_settings, f, indent=4)\r\n    print(f\"Channel {Fore.CYAN}{Style.BRIGHT}{message.channel.name}{Style.RESET_ALL} in {Fore.GREEN}{message.guild.name}{Style.RESET_ALL} is now {'active' if channel_settings[str(message.channel.id)]['active'] else 'inactive'}.\")\r\n    await message.channel.send(f\"This channel is now {'active' if channel_settings[str(message.channel.id)]['active'] else 'inactive'}.\")\r\n\r\n\r\nCOMMAND_HANDLERS = {\r\n    \"help\": help,\r\n    \"wipe memory\": wipe_memory,\r\n    \"new behavior\": new_behavior,\r\n    \"save behavior\": save_behavior,\r\n    \"load behavior\": load_behavior,\r\n    \"load model\": load_model,\r\n    \"reset\": reset\r\n}\r\n\r\n\r\n@bot.event\r\nasync def on_ready():\r\n    print(f\"{Fore.GREEN}Logged in as {bot.user}{Style.RESET_ALL}\")\r\n    invite_link = discord.utils.oauth_url(bot.user.id, permissions=discord.Permissions(), scopes=(\"bot\", \"applications.commands\"))\r\n    print(f\"{Fore.GREEN}Invite: {Style.BRIGHT}{invite_link}{Style.RESET_ALL}\")\r\n\r\n    if not os.path.exists('channel_settings.json'):\r\n        with open('channel_settings.json', 'w') as json_file:\r\n            json.dump({}, json_file)\r\n\r\n    models_path = 'models.json'\r\n    model_info = {}\r\n\r\n    try:\r\n        with open(models_path, 'r') as f:\r\n            all_models = json.load(f)\r\n            chat_models = [m for m in all_models.get(\"data\", []) if \"/api/v1/chat/completions\" in m.get(\"endpoints\", [])]\r\n            for model in chat_models:\r\n                model_id = model[\"id\"]\r\n                tokens = model[\"tokens\"]\r\n                model_info[model_id] = tokens\r\n            model_list_json = list(model_info.keys())\r\n    except:\r\n        model_info = await get_models()\r\n\r\n    with open('channel_settings.json', 'r') as f:\r\n        channel_settings_dict = json.load(f)\r\n    \r\n    for channel_id_str, channel_settings in channel_settings_dict.items():\r\n        channel_id = int(channel_id_str)\r\n        model_name = channel_settings[\"model\"]\r\n        if model_name not in model_info:\r\n            print(f\"Default model {model_name} for channel {channel_id} not found.\")\r\n        else:\r\n            set_model(model_name, model_info, channel_id)\r\n        filename = channel_settings[\"behavior\"]\r\n\r\n        try:\r\n            channel_messages[channel_id] = load_prompt(filename)\r\n\r\n        except FileNotFoundError:\r\n            print(f\"Prompt file {filename} not found for channel {channel_id}.\")\r\n        except Exception as e:\r\n            print(f\"Error loading prompt file {filename} for channel {channel_id}. Error: {e}\")\r\n\r\n    print(f\"{Fore.BLUE}{Style.BRIGHT}Defaults loaded.{Style.RESET_ALL}\")\r\n\r\n\r\n@bot.event\r\nasync def on_message(message):\r\n    global behavior_name\r\n\r\n    with open('channel_settings.json', 'r') as f:\r\n        channel_settings = json.load(f)\r\n\r\n    await asyncio.sleep(0.1)\r\n    if message.author.bot or message.author.id in ignored_ids:\r\n        return\r\n\r\n    if message.content.strip().lower().startswith(\"toggle active\"):\r\n        await toggle_active(message)\r\n        return\r\n\r\n    active = channel_settings.get(str(message.channel.id), {}).get(\"active\", False)\r\n\r\n    bot_mentioned_in_inactive_channel = not active and bot.user in message.mentions\r\n    other_users_mentioned = any(user.id != bot.user.id for user in message.mentions)\r\n\r\n    if not bot_mentioned_in_inactive_channel and other_users_mentioned:\r\n        return\r\n\r\n    if not bot_mentioned_in_inactive_channel and not active:\r\n        return\r\n    \r\n    message_content = message.content.replace(f\"{bot.user.mention}\", '').strip()\r\n\r\n    if (message.reference and not message.reference.resolved.attachments) or message.content.startswith('!'):\r\n        return\r\n\r\n    def check(msg):\r\n        return msg.author == message.author and msg.channel == message.channel\r\n\r\n    if bot_mentioned_in_inactive_channel:\r\n        user_channel_key = message.author.id\r\n    else:\r\n        user_channel_key = message.channel.id\r\n\r\n    messages = channel_messages.get(user_channel_key)\r\n    if messages is None:\r\n\r\n        messages = load_prompt(filename=os.getenv(\"DEFAULT_BEHAVIOR\"))\r\n        channel_messages[user_channel_key] = messages\r\n\r\n    attachment = message.attachments[0] if message.attachments else None\r\n\r\n    words = message_content.lower().split()\r\n    for idx in range(len(words)):\r\n        command = \" \".join(words[:idx + 1])\r\n        if command in COMMAND_HANDLERS:\r\n            handler = COMMAND_HANDLERS[command]\r\n            if handler.__code__.co_argcount > 1:\r\n                await handler(message, check)\r\n            else:\r\n                await handler(message)\r\n            return\r\n        \r\n    else:\r\n        if command_mode_flag.get(message.channel.id):\r\n            command_mode_flag[message.channel.id] = False\r\n            return\r\n        \r\n        if user_channel_key not in message_queue_locks:\r\n            message_queue_locks[user_channel_key] = asyncio.Lock()\r\n        image_prompt = message_content.strip() if attachment and message_content.strip() else None\r\n\r\n\r\n    async with message_queue_locks[user_channel_key]:\r\n        async with message.channel.typing():\r\n\r\n            _, max_tokens = channel_models[message.channel.id]\r\n            remaining_tokens = max_tokens\r\n\r\n            if message.author.nick is not None:\r\n                nickname = message.author.nick.split('#')[0].capitalize()\r\n            else:\r\n                nickname = message.author.name.split('#')[0].capitalize()\r\n\r\n            messages.append({\"role\": \"user\", \"content\": nickname + \": \" + message_content})\r\n\r\n            if message.reference:\r\n                original_message = await message.channel.fetch_message(message.reference.message_id)\r\n                attachment = original_message.attachments[0] if original_message.attachments else None\r\n\r\n            max_retries = 3\r\n            for i in range(max_retries):\r\n                try:\r\n\r\n                    if attachment:\r\n                        image_extensions = ('png', 'jpg', 'jpeg', 'gif')\r\n                        file_extension = attachment.filename.lower().split('.')[-1]\r\n                        if file_extension in image_extensions:\r\n                            content = await handle_image(attachment, image_prompt)\r\n\r\n                        else:\r\n                            # Handle text and PDF file attachment\r\n                            file_content = await attachment.read()\r\n                            if file_extension == 'pdf':\r\n                                file_io = io.BytesIO(file_content)\r\n                                pdf = PdfReader(file_io)\r\n                                extracted_text = []\r\n                                for page_num in range(len(pdf.pages)):\r\n                                    page = pdf.pages[page_num]\r\n                                    page_text = page.extract_text()\r\n                                    page_text_with_number = f'Page {page_num + 1}:\\n{page_text}'\r\n                                    extracted_text.append(page_text_with_number)\r\n                                file_content = \"\\n\".join(extracted_text)\r\n                                file_content = file_content.encode('utf-8')\r\n                            message_content_for_file = \"Document: \" + file_content.decode('utf-8')\r\n                            query_document = [{\"role\": \"user\", \"content\": message_content + \"\\n\\n\" + message_content_for_file}]\r\n\r\n                            # Get the token count of the file content\r\n                            model = channel_settings[str(message.channel.id)][\"model\"]\r\n                            #num_tokens_data = await get_tokens_api(api_key, model, query_document)\r\n                            #num_tokens_doc = num_tokens_data['string']['count']\r\n                            num_tokens_doc = num_tokens_from_message(query_document)\r\n                            print(f\"{Fore.YELLOW}Document loaded with token count of:{num_tokens_doc}{Style.RESET_ALL}\")\r\n\r\n                            # Check if the content exceeds the token limit\r\n                            max_content_length = remaining_tokens - 500\r\n                            if num_tokens_doc > max_content_length:\r\n                                print(f\"{Fore.YELLOW}Document too large for current model!{Style.RESET_ALL}\") # todo: use vectorstore                                \r\n                                await message.channel.send(\"Document too large for current model!\")\r\n                                return\r\n                               \r\n                            else:\r\n                                messages.append({\"role\": \"user\", \"content\": message_content + \"\\n\\n\" + message_content_for_file})\r\n                                response, messages, remaining_tokens = await chat_response(messages, message.channel.id)\r\n                                content = response['choices'][0]['message']['content']\r\n\r\n                    else:\r\n                        response, messages, remaining_tokens = await chat_response(messages, message.channel.id)\r\n                        if response is None:\r\n                            await message.channel.send(\"No model loaded for this channel. Please load a model using the `load model` command.\")\r\n                            return\r\n                        content = response['choices'][0]['message']['content']\r\n\r\n                    messages.append({\"role\": \"assistant\", \"content\": content})\r\n\r\n                    print(f\"{Style.DIM}{Fore.RED}Remaining tokens:{remaining_tokens}{Style.RESET_ALL}{Style.DIM}\\nCurrent Memory:{messages}{Style.RESET_ALL}\")\r\n                    print(f\"Channel: {message.channel.name}\\n{Style.DIM}{Fore.RED}{Back.WHITE}{message.author}: {Fore.BLACK}{message_content}{Style.RESET_ALL}\\n{Style.DIM}{Fore.GREEN}{Back.WHITE}{bot.user}: {Fore.BLACK}{content}{Style.RESET_ALL}\")\r\n                    \r\n                    responses[message.id] = content\r\n                    if message.id in responses:\r\n                        response_content = responses[message.id]\r\n                        await discord_chunker(message, response_content)\r\n                    if bot_mentioned_in_inactive_channel:\r\n                        asyncio.create_task(forget_mentions(user_channel_key))\r\n                    break\r\n\r\n                except openai.error.APIError as e:\r\n                    print(f\"Retry {i+1}:\", type(e), e)\r\n\r\n                except aiohttp.ContentTypeError as e:\r\n                    print(f\"Retry {i+1}:\", type(e), e)\r\n\r\n                except aiohttp.ClientConnectorError as e:\r\n                    print(f\"Retry {i+1}:\", type(e), e)\r\n\r\n                except Exception as e:\r\n                    print(type(e), e)\r\n\r\n            else:\r\n                logging.error(f\"{message.author.name}|{message.channel.name}\", exc_info=True)\r\n                await message.channel.send(\"I'm unable to respond right now, I'll be back!\")\r\n\r\n\r\nbot.run(os.getenv(\"DISCORD_TOKEN\"))\r\n","repo_name":"coalescentdivide/OmniChat-Discord","sub_path":"omni.py","file_name":"omni.py","file_ext":"py","file_size_in_byte":34617,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"21503238096","text":"def getRandomArray(n):\r\n    from random import random\r\n    d = {}\r\n    i = 0\r\n    while i < n:\r\n        rNum = int(random()*n)\r\n        if rNum not in d:\r\n            d[rNum] = 1\r\n            i = i + 1\r\n    return list(d.keys())\r\n\r\ndef getSortedArray(n):\r\n    lst = []\r\n    for i in range(n,0,-1):\r\n        lst.append(i)\r\n    return lst\r\n","repo_name":"GAstraeus/George-Diwan-CS-432-Project-1","sub_path":"part3.py","file_name":"part3.py","file_ext":"py","file_size_in_byte":338,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"41773278575","text":"if __name__ == '__main__':\n    print('List Comprehension 1 (*)\\n')\n\n    # Criação padrão de uma lista com o uso de colchetes\n    lista1 = []\n\n    # Preenchimento da lista com uso de for+range\n    for n in range(10):\n        lista1.append(n)\n    print('lista1 :\\n', lista1)\n\n\n    # Recria lista com valores de 0 a 9 com uso de list comprehension onde:\n    # n é a expressão (que tem a variável n apenas)\n    # for n in range(10) é o laço que dá valores para n\n    lista1 = [n for n in range(10)]\n    print('lista1*:\\n', lista1)\n\n    # Cria lista com valores pares de 0 a 20  com uso de for e range\n    lista2 = []\n    for n in range(10+1):\n        lista2.append(2*n)\n    print('lista2 :\\n', lista2)\n\n    # Recria lista com valores pares de 0 a 20 com uso de list comprehension onde:\n    # 2*n é a expressão (que tem a variável n apenas)\n    # for n in range(10+1) é o laço que dá valores para n\n    lista2 = [2*n for n in range(10+1)]\n    print('lista2*:\\n', lista2)\n","repo_name":"pjandl/opy2","sub_path":"U01/list_comprehension_1.py","file_name":"list_comprehension_1.py","file_ext":"py","file_size_in_byte":981,"program_lang":"python","lang":"pt","doc_type":"code","stars":5,"dataset":"github-code","pt":"81"}
+{"seq_id":"28625451658","text":"import random\r\nimport pandas as pd\r\nimport numpy as np\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\ndef assign(num):         #Function for assigning choice according to the choice no.\r\n    if num == 1:\r\n        choice = 'ROCK'\r\n    elif num == 2:\r\n\r\n        choice = 'PAPER'\r\n    elif num==3:\r\n        choice = 'SCISSOR'\r\n    return(choice)\r\n\r\ndef result(user,comp):   #Function for calculating result\r\n    if ((user == 1 and  comp== 2) or (user == 2 and comp == 1)):\r\n        print(\"paper wins => \", end=\"\")\r\n        return(2)\r\n\r\n    elif ((user == 1 and comp== 3) or (user == 3 and comp== 1)):\r\n        print(\"Rock wins =>\", end=\"\")\r\n        return(1)\r\n    else:\r\n        print(\"scissor wins =>\", end=\"\")\r\n        return(3)\r\n\r\n\r\nprint(\"Winning Rules of the Rock paper scissor game as follows: \\n\"\r\n                                +\"Rock vs paper->paper wins \\n\"\r\n                                + \"Rock vs scissor->Rock wins \\n\"\r\n                                +\"paper vs scissor->scissor wins \\n\")\r\n\r\n\r\nn=int(input(\"how many rounds?\\n\"))\r\ni=0\r\nround_score = []\r\nwhile(i!=n):\r\n    print(\"\\n\\n\\n*******ROUND-\"+str(i+1)+\" *******\\n\\n\\n\")  #For displaying which round is running.\r\n    print(\"Enter choice \\n 1. Rock \\n 2. paper \\n 3. scissor \\n\")\r\n    user_choice=int(input(\"User turn:-\\t\"))\r\n\r\n    while (user_choice > 3) or (user_choice < 1):\r\n        user_choice = int(input(\"enter valid input:-\\t \"))\r\n\r\n    comp_choice=random.randint(1,3)\r\n\r\n\r\n    while comp_choice==user_choice:\r\n        comp_choice = random.randint(1, 3)\r\n\r\n    user_choice_name=assign(user_choice)\r\n    comp_choice_name=assign(comp_choice)\r\n\r\n    print(\"\\n\\nuser choice is: \" + user_choice_name)\r\n    print(\"computer choice is: \" + comp_choice_name)\r\n\r\n    print(\"**** \"+user_choice_name+\" V/S \"+comp_choice_name+\" ****\")\r\n    winner= result(user_choice,comp_choice)\r\n\r\n    if(winner==user_choice):\r\n        print(\"*******YOU WIN*********\\n\\n\")\r\n        round_score.append([i+1,1,0])        #Score updating in list\r\n    else:\r\n        print(\"*******YOU LOSE*********\\n\\n\")\r\n        round_score.append([i+1,0,1])\r\n    i=i+1\r\nprint(\"#*#*#*#*#*#*#* SCORE-TABLE *#*#*#*#*#*#*#\")\r\ndf = pd.DataFrame(np.array(round_score), columns=['ROUND', 'USER_SCORE', 'COMP_SCORE'])  #Score table\r\nprint(df)\r\nprint(\"*$*$*$*$*$ TOTAL-SCORE $*$*$*$*$*$*\")\r\nprint(df[[\"USER_SCORE\",\"COMP_SCORE\"]].sum())\r\n","repo_name":"vishaldeswal/rock_paper_scissor","sub_path":"rock_paper_scissor.py","file_name":"rock_paper_scissor.py","file_ext":"py","file_size_in_byte":2329,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"8378464571","text":"\"\"\"PyPI Parker - Combatting Typosquatting\"\"\"\nimport os\nimport re\n\nfrom setuptools import find_packages, setup\n\nVERSION_RE = re.compile(r'''__version__ = ['\"]([0-9.]+)['\"]''')\nHERE = os.path.abspath(os.path.dirname(__file__))\n\n\ndef read(*args):\n    \"\"\"Read complete file contents.\"\"\"\n    return open(os.path.join(HERE, *args)).read()\n\n\ndef get_version():\n    \"\"\"Read the version from this module.\"\"\"\n    init = read('src', 'pypi_parker', '__init__.py')\n    return VERSION_RE.search(init).group(1)\n\n\ndef get_requirements():\n    \"\"\"Read the requirements file.\"\"\"\n    requirements = read('requirements.txt')\n    return [r for r in requirements.strip().splitlines()]\n\n\nsetup(\n    name='pypi-parker',\n    packages=find_packages('src'),\n    package_dir={'': 'src'},\n    version=get_version(),\n    author='Matt Bullock',\n    maintainer='Matt Bullock',\n    author_email='matt.s.b.42@gmail.com',\n    url='https://github.com/mattsb42/pypi-parker',\n    description='',\n    long_description=read('README.rst'),\n    keywords='pypi warehouse distutils typosquating',\n    license='Apache License 2.0',\n    install_requires=get_requirements(),\n    classifiers=[\n        # 'Development Status :: 5 - Production/Stable',\n        'Development Status :: 4 - Beta',\n        'Intended Audience :: Developers',\n        'Natural Language :: English',\n        'License :: OSI Approved :: Apache Software License',\n        'Programming Language :: Python',\n        'Programming Language :: Python :: 3',\n        'Programming Language :: Python :: 3.6',\n        'Programming Language :: Python :: Implementation :: CPython',\n        'Topic :: System :: Software Distribution',\n        'Topic :: Utilities'\n    ],\n    entry_points={\n        'distutils.commands': [\n            'park = pypi_parker:Park'\n        ]\n    }\n)\n","repo_name":"mattsb42/pypi-parker","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1792,"program_lang":"python","lang":"en","doc_type":"code","stars":15,"dataset":"github-code","pt":"81"}
+{"seq_id":"42137299960","text":"sample = open(\"sample.txt\", \"r\").read()\nfinal_input = open(\"input.txt\", \"r\").read()\n\ndef return_common(l1, l2):\n    for e1 in l1:\n        for e2 in l2:\n            if e1 == e2:\n                return e1\n\ndef calculate_sum_of_priority(l):\n    s = 0\n    for x in l:\n        if ord(x) >= 97:\n            s += ord(x) - 96\n        else:\n            s += ord(x) - 38\n    return s\n\ndef part1(Input):\n    common_items = []\n    rucksacks = list(filter(lambda x : x != '', Input.split('\\n')))\n    for rucksack in rucksacks:\n        comp1 = rucksack[0:(len(rucksack)//2)]\n        comp2 = rucksack[len(rucksack)//2:]\n        common_items.append(return_common(comp1,comp2))\n    return calculate_sum_of_priority(common_items)\n\ndef part2(Input):\n    badge = []\n    rucksacks = list(filter(lambda x : x != '', Input.split('\\n')))\n    for i in range(0,len(rucksacks),3):\n        for c in rucksacks[i]:\n            if c in rucksacks[i+1]:\n                if c in rucksacks[i+2]:\n                    badge.append(c)\n                    break\n    return calculate_sum_of_priority(badge)\n\nprint(\"part1 (sample input) :\",part1(sample))\nprint(\"part1 (final input)  :\",part1(final_input))\nprint(\"part2 (sample input) :\",part2(sample))\nprint(\"part2 (final input)  :\",part2(final_input))\n","repo_name":"Ghodawalaaman/aoc-2022","sub_path":"03/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1262,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"11564556887","text":"\nfrom numpy import zeros\n\nfrom sklearn.base import BaseEstimator, TransformerMixin\n\nfrom idepi.filters import null_filter\nfrom idepi.labeledmsa import LabeledMSA\n\n\n__all__ = ['SiteVectorizer']\n\n\nclass SiteVectorizer(BaseEstimator, TransformerMixin):\n\n    def __init__(self, encoder, filter=null_filter):\n        self.__alignment_length = 0\n        self.encoder = encoder\n        self.filter = filter\n        self.feature_names_ = []\n        self.vocabulary_ = {}\n\n    def fit(self, alignment):\n        if not isinstance(alignment, LabeledMSA):\n            raise ValueError(\"SiteVectorizers require a LabeledMSA\")\n\n        column_labels = list(alignment.labels)\n        vocab = {}\n        feature_names = []\n\n        k = 0\n        for i in range(alignment.get_alignment_length()):\n            letters = self.filter(alignment[:, i])\n\n            for ltr in letters:\n                try:\n                    j = self.encoder(ltr)\n                    feature_name = '{0:s}{1:s}'.format(column_labels[i], self.encoder[j])\n                    vocab[(i, j)] = k\n                    feature_names.append(feature_name)\n                    k += 1\n                except KeyError:\n                    pass\n\n        self.__alignment_length = alignment.get_alignment_length()\n        self.feature_names_ = feature_names\n        self.vocabulary_ = vocab\n\n        return self\n\n    def transform(self, alignment):\n        ncol = alignment.get_alignment_length()\n\n        if ncol != self.__alignment_length:\n            msg = 'alignment length ({0:d}) does not match the learned length ({1:d})'.format(\n                ncol,\n                self.__alignment_length)\n            raise ValueError(msg)\n\n        vocab = self.vocabulary_\n        data = zeros((len(alignment), len(vocab)), dtype=int)\n\n        if len(vocab) == 0:\n            return data\n\n        for i, seq in enumerate(alignment):\n            seq_ = ''.join(ltr.upper() for ltr in str(seq.seq))\n            for j, ltr in enumerate(seq_):\n                try:\n                    data[i, vocab[(j, self.encoder(ltr))]] = 1\n                except KeyError:\n                    pass\n\n        return data\n\n    def get_feature_names(self):\n        return self.feature_names_\n","repo_name":"nlhepler/idepi","sub_path":"idepi/feature_extraction/_sitevectorizer.py","file_name":"_sitevectorizer.py","file_ext":"py","file_size_in_byte":2216,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"81"}
+{"seq_id":"40116184777","text":"import numpy as np\nimport matplotlib.pyplot as plt\n\n# Определение области значений\nx1 = np.linspace(0, 30, 400)\nx2 = np.linspace(0, 30, 400)\nx1, x2 = np.meshgrid(x1, x2)\n\n# Определение функции\nf = 3*x1 + 4*x2\n\n# Определение ограничений\nc1 = x1 + x2 <= 20\nc2 = x1 >= 10\nc3 = x2 >= 5\nc4 = -x1 + 4*x2 <= 20\nc5 = np.logical_and(x1 > 0, x2 > 0)  # x1, x2 > 0\n\n# Объединение ограничений\nc = np.logical_and(np.logical_and(np.logical_and(c1, c2), np.logical_and(c3, c4)), c5)\n\nfig, ax = plt.subplots()\n\n# Отрисовка функции\ncf = ax.contourf(x1, x2, f, levels=50, alpha=0.5)\n\n# Отрисовка области допустимых значений\nax.imshow(c, extent=(0, 30, 0, 30), origin='lower', cmap='Greys', alpha=0.3)\n\n# Добавление подписей к осям\nax.set_xlabel('x1')\nax.set_ylabel('x2')\n\n# Добавление colorbar\nfig.colorbar(cf, ax=ax)\n\nplt.show()\n","repo_name":"OdilH/-","sub_path":"tpr/4_prac.py","file_name":"4_prac.py","file_ext":"py","file_size_in_byte":983,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"9637053525","text":"# Diseñe un algoritmo que muestre un menú: Amarillo = 1, azul = 2, rojo = 3.  Luego solicite al usuario digitar dos de estos números para descifrar la combinación. Ejemplo: 1 y 3, el resultado mostrado en pantalla (su combinación es naranja). Recuerde que el usuario puede colocar el mismo número dos veces y números fuera del rango.\n\nprint(f\"Agrega 2 numeros para hacer la combinacion de colores\\n\"\n      f\"Amarillo = 1 \"\n      f\"Azul = 2 \"\n      f\"Rojo = 3\")\n\nprimerNumero = int(input('Digita un numero del 1 al 3: '))\nsegundoNumero = int(input('Digita un numero del 1 al 3: '))\ncolores = [\"Verde\", \"Naranja\", \"Violeta\", \"Amarillo\", \"Azul\", \"Rojo\"]\n\nif primerNumero == 1 and segundoNumero == 2 or primerNumero == 2 and segundoNumero == 1:\n  print(f'La combinacion es {colores[0]}')\nelif primerNumero == 1 and segundoNumero == 3 or primerNumero == 3 and segundoNumero == 1:\n  print(f'La combinacion es {colores[1]}')\nelif primerNumero == 2 and segundoNumero == 3 or primerNumero == 3 and segundoNumero == 2:\n  print(f'La combinacion es {colores[2]}')\nelif primerNumero == 1 and segundoNumero == 1:\n  print(f'La combinacion es {colores[3]}')\nelif primerNumero == 2 and segundoNumero == 2:\n  print(f'La combinacion es {colores[4]}')\nelif primerNumero == 3 and segundoNumero == 3:\n  print(f'La combinacion es {colores[5]}')\nelse :\n  print('Datos erroneos')\n\n\n\n","repo_name":"Smendoza120/Notas-sena","sub_path":"Programacion/Talleres/3)Apropiación/Solucion/Python/7)Septimo punto/index.py","file_name":"index.py","file_ext":"py","file_size_in_byte":1365,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"75084113546","text":"import heapq\nINF = float('inf')\n\ndef dijkstra(start, N, distance,tree):\n    ans = [0, 0]\n    # N은 출발점\n    heap = []\n    visited = 1 << (N + 1)\n    distance[start] = 0\n    heapq.heappush(heap, [0, start])\n    while heap:\n        weight, Node = heapq.heappop(heap)\n        visited = visited | 1 << Node\n        for k, w in tree[Node].items():\n            if visited & 1 << k:\n                continue\n            via = weight + w\n            if distance[k] >= via:\n                distance[k] = via\n                heapq.heappush(heap, [via, k])\n\n\n\nresult = []\ntree = dict()\nreverse_tree = dict()\nN, M, X = map(int, input().split())\nfor i in range(M):\n    start, end, T = map(int, input().split())\n    if not start in tree:\n        tree[start] = dict()\n    if not end in reverse_tree:\n        reverse_tree[end] = dict()\n    tree[start][end] = T\n    reverse_tree[end][start] = T\n\ndistance1 = [INF for _ in range(N + 1)]\ndijkstra(X, N, distance1, tree)\ndistance2 = [INF for _ in range(N + 1)]\ndijkstra(X, N, distance2, reverse_tree)\n\nfor i in range(1,N+1):\n    result.append(distance1[i]+distance2[i])\nprint(max(result))\n","repo_name":"moonyeol/algorithm","sub_path":"python/acmicpc_1238.py","file_name":"acmicpc_1238.py","file_ext":"py","file_size_in_byte":1125,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"74067043465","text":"import torch.nn as nn\nfrom models.resnet import Resnet1D\n\nclass Encoder(nn.Module):\n    def __init__(self,\n                 input_emb_width = 3,\n                 output_emb_width = 512,\n                 down_t = 3,\n                 stride_t = 2,\n                 width = 512,\n                 depth = 3,\n                 dilation_growth_rate = 3,\n                 activation='relu',\n                 norm=None):\n        super().__init__()\n        \n        blocks = []\n        filter_t, pad_t = stride_t * 2, stride_t // 2\n        blocks.append(nn.Conv1d(input_emb_width, width, 3, 1, 1))\n        blocks.append(nn.ReLU())\n        \n        for i in range(down_t):\n            input_dim = width\n            block = nn.Sequential(\n                nn.Conv1d(input_dim, width, filter_t, stride_t, pad_t),\n                Resnet1D(width, depth, dilation_growth_rate, activation=activation, norm=norm),\n            )\n            blocks.append(block)\n        blocks.append(nn.Conv1d(width, output_emb_width, 3, 1, 1))\n        self.model = nn.Sequential(*blocks)\n\n    def forward(self, x):\n        return self.model(x)\n\nclass Decoder(nn.Module):\n    def __init__(self,\n                 input_emb_width = 3,\n                 output_emb_width = 512,\n                 down_t = 3,\n                 stride_t = 2,\n                 width = 512,\n                 depth = 3,\n                 dilation_growth_rate = 3, \n                 activation='relu',\n                 norm=None):\n        super().__init__()\n        blocks = []\n        \n        filter_t, pad_t = stride_t * 2, stride_t // 2\n        blocks.append(nn.Conv1d(output_emb_width, width, 3, 1, 1))\n        blocks.append(nn.ReLU())\n        for i in range(down_t):\n            out_dim = width\n            block = nn.Sequential(\n                Resnet1D(width, depth, dilation_growth_rate, reverse_dilation=True, activation=activation, norm=norm),\n                nn.Upsample(scale_factor=2, mode='nearest'),\n                nn.Conv1d(width, out_dim, 3, 1, 1)\n            )\n            blocks.append(block)\n        blocks.append(nn.Conv1d(width, width, 3, 1, 1))\n        blocks.append(nn.ReLU())\n        blocks.append(nn.Conv1d(width, input_emb_width, 3, 1, 1))\n        self.model = nn.Sequential(*blocks)\n\n    def forward(self, x):\n        return self.model(x)\n    \n","repo_name":"Mael-zys/T2M-GPT","sub_path":"models/encdec.py","file_name":"encdec.py","file_ext":"py","file_size_in_byte":2308,"program_lang":"python","lang":"en","doc_type":"code","stars":419,"dataset":"github-code","pt":"81"}
+{"seq_id":"36406583232","text":"import re\nfrom .. import lut, manip\nfrom . import helpers\n\nam_line_re = re.compile(r'^([A-Za-z]+)\\s+([A-Za-z]+)$')\nnelec_re = re.compile(r'^([a-z]+)\\s+nelec\\s+(\\d+)$', flags=re.IGNORECASE)\n\n\ndef _parse_electron_lines(basis_lines, bs_data):\n    ''' Parses lines representing all the electron shells for all elements\n\n    Resulting information is stored in bs_data\n    '''\n\n    # Remove 'end' from the lines (if they exist)\n    # They may exist when this is called from other readers\n    basis_lines = [x for x in basis_lines if x.lower() != 'end']\n\n    # Basis entry needs to start with 'basis'\n    if not basis_lines[0].lower().startswith('basis'):\n        raise RuntimeError(\"Basis entry must start with 'basis'\")\n\n    # Is the basis set spherical or cartesian?\n    am_type = 'cartesian' if basis_lines[0].lower().find('spherical') == -1 else 'spherical'\n\n    # Start at index 1 in order to strip of the first line ('BASIS AO PRINT' or something)\n    shell_blocks = helpers.partition_lines(basis_lines[1:], lambda x: x[0].isalpha(), min_size=2)\n\n    for sh_lines in shell_blocks:\n        element_sym, shell_am = helpers.parse_line_regex(am_line_re, sh_lines[0], \"Element sym, shell am\")\n        shell_am = lut.amchar_to_int(shell_am)\n\n        element_Z = lut.element_Z_from_sym(element_sym, as_str=True)\n        element_data = manip.create_element_data(bs_data, element_Z, 'electron_shells', key_exist_ok=True)\n\n        func_type = lut.function_type_from_am(shell_am, 'gto', am_type)\n\n        # How many columns of coefficients do we have?\n        # Only if this is a fused shell do we know\n        ngen = len(shell_am) if len(shell_am) > 1 else None\n\n        exponents, coefficients = helpers.parse_primitive_matrix(sh_lines[1:], ngen=ngen)\n\n        shell = {\n            'function_type': func_type,\n            'region': '',\n            'angular_momentum': shell_am,\n            'exponents': exponents,\n            'coefficients': coefficients\n        }\n\n        element_data['electron_shells'].append(shell)\n\n\ndef _parse_ecp_lines(basis_lines, bs_data):\n    ''' Parses lines representing all the ECP potentials for all elements\n\n    Resulting information is stored in bs_data\n    '''\n\n    # Remove 'end' from the lines (if they exist)\n    # They may exist when this is called from other readers\n    basis_lines = [x for x in basis_lines if x.lower() != 'end']\n\n    # Start at index 1 in order to strip of the first line ('ECP' or something)\n    # This splits out based on starting with an alpha character. A block can be either\n    #   a potential or the nelec line\n    ecp_blocks = helpers.partition_lines(basis_lines[1:], lambda x: x[0].isalpha())\n\n    for pot_lines in ecp_blocks:\n        # Check if this is the nelec line\n        if len(pot_lines) == 1:\n            # Check for this and give a better error message\n            if not nelec_re.match(pot_lines[0]):\n                raise RuntimeError(\"Unknown block with single line. Perhaps it's an empty block? Line: \" +\n                                   pot_lines[0])\n\n            element_sym, n_elec = helpers.parse_line_regex(nelec_re, pot_lines[0].lower(), \"ECP: Element sym, nelec\")\n\n            element_Z = lut.element_Z_from_sym(element_sym, as_str=True)\n            element_data = manip.create_element_data(bs_data, element_Z, 'ecp_electrons', create=int)\n            element_data['ecp_electrons'] = n_elec\n        else:\n            element_sym, pot_am = helpers.parse_line_regex(am_line_re, pot_lines[0], \"ECP: Element sym, pot AM\")\n\n            element_Z = lut.element_Z_from_sym(element_sym, as_str=True)\n            element_data = manip.create_element_data(bs_data, element_Z, 'ecp_potentials', key_exist_ok=True)\n\n            # See if this is the 'ul' angular momentum\n            if pot_am.lower() == 'ul':\n                pot_am = []  # Placeholder - leave for later\n            else:\n                pot_am = lut.amchar_to_int(pot_am)\n\n            ecp_data = helpers.parse_ecp_table(pot_lines[1:])\n            ecp_pot = {\n                'angular_momentum': pot_am,\n                'ecp_type': 'scalar_ecp',\n                'r_exponents': ecp_data['r_exp'],\n                'gaussian_exponents': ecp_data['g_exp'],\n                'coefficients': ecp_data['coeff']\n            }\n\n            element_data['ecp_potentials'].append(ecp_pot)\n\n    # Fix ecp angular momentum now that everything has been read\n    # Specifically, we can set the 'ul' potential to be the max am + 1\n    for el, v in bs_data.items():\n        if 'ecp_potentials' not in v:\n            continue\n\n        all_ecp_am = []\n        for x in v['ecp_potentials']:\n            all_ecp_am.extend(x['angular_momentum'])\n\n        max_ecp_am = max(all_ecp_am)\n\n        for s in v['ecp_potentials']:\n            if not s['angular_momentum']:\n                s['angular_momentum'] = [max_ecp_am + 1]\n\n    # Make sure the number of electrons replaced by the ECP was specified for all elements\n    for el, v in bs_data.items():\n        if 'ecp_potentials' in v and 'ecp_electrons' not in v:\n            raise RuntimeError(\"Number of ECP electrons not specified for element {}\".format(el))\n\n\ndef read_nwchem(basis_lines):\n    '''Reads NWChem-formatted file data and converts it to a dictionary with the\n       usual BSE fields\n\n       Note that the nwchem format does not store all the fields we\n       have, so some fields are left blank\n    '''\n\n    basis_lines = helpers.prune_lines(basis_lines, '#')\n\n    bs_data = {}\n    other_data = {}\n\n    # split into basis and ecp\n    basis_sections = helpers.partition_lines(basis_lines,\n                                             lambda x: x.lower() == 'end',\n                                             min_blocks=1,\n                                             max_blocks=2,\n                                             include_match=False)\n\n    for s in basis_sections:\n        if s[0].lower().startswith('basis'):\n            _parse_electron_lines(s, bs_data)\n        elif s[0].lower().startswith('ecp'):\n            _parse_ecp_lines(s, bs_data)\n        else:\n            raise RuntimeError(\"Unknown section: \" + s[0])\n\n    return bs_data, other_data\n","repo_name":"MolSSI-BSE/basis_set_exchange","sub_path":"basis_set_exchange/readers/nwchem.py","file_name":"nwchem.py","file_ext":"py","file_size_in_byte":6136,"program_lang":"python","lang":"en","doc_type":"code","stars":132,"dataset":"github-code","pt":"81"}
+{"seq_id":"21529834220","text":"#!/usr/bin/python\nimport sys\n# import os\n# import time\n# import getopt\n# import socket\n# import ConfigParser\nimport struct\n# import binascii\nfrom pymodbus.client.sync import ModbusSerialClient\n\nseradd = str(sys.argv[1])\nidadd = int(sys.argv[2])\n\nclient = ModbusSerialClient(method = \"rtu\", port=seradd, baudrate=9600, stopbits=1, bytesize=8, timeout=1)\n\nresp = client.read_input_registers(0x00,2, unit=idadd)\nvoltage = struct.unpack('>f',struct.pack('>HH',*resp.registers))[0]\nvoltage = float(\"%.1f\" % voltage)\nf = open('/var/www/html/openWB/ramdisk/llv1', 'w')\nf.write(str(voltage))\nf.close()\nresp = client.read_input_registers(0x06,2, unit=idadd)\nlla1 = float(struct.unpack('>f',struct.pack('>HH',*resp.registers))[0])\nlla1 = float(\"%.1f\" % lla1)\nf = open('/var/www/html/openWB/ramdisk/lla1', 'w')\nf.write(str(lla1))\nf.close()\nresp = client.read_input_registers(0x08,2, unit=idadd)\nlla2 = float(struct.unpack('>f',struct.pack('>HH',*resp.registers))[0])\nlla2 = float(\"%.1f\" % lla2)\nf = open('/var/www/html/openWB/ramdisk/llas11', 'w')\nf.write(str(lla2))\nf.close()\nresp = client.read_input_registers(0x0A,2, unit=idadd)\nlla3 = struct.unpack('>f',struct.pack('>HH',*resp.registers))[0]\nlla3 = float(\"%.1f\" % lla3)\nf = open('/var/www/html/openWB/ramdisk/llas21', 'w')\nf.write(str(lla3))\nf.close()\nresp = client.read_input_registers(0x0C,2, unit=idadd)\nllw1 = struct.unpack('>f',struct.pack('>HH',*resp.registers))[0]\nllw1 = int(llw1)\nif llw1 < 15:\n    llw1 = 0\nf = open('/var/www/html/openWB/ramdisk/llaktuell', 'w')\nf.write(str(llw1))\nf.close()\n\nresp = client.read_input_registers(0x015A,2, unit=idadd)\nllkwh = struct.unpack('>f',struct.pack('>HH',*resp.registers))[0]\nllkwh = float(\"%.3f\" % llkwh)\nf = open('/var/www/html/openWB/ramdisk/llkwh', 'w')\nf.write(str(llkwh))\nf.close()\nresp = client.read_input_registers(0x015C,2, unit=idadd)\nllkwh = struct.unpack('>f',struct.pack('>HH',*resp.registers))[0]\nllkwh = float(\"%.3f\" % llkwh)\nf = open('/var/www/html/openWB/ramdisk/llkwhs1', 'w')\nf.write(str(llkwh))\nf.close()\nresp = client.read_input_registers(0x015E,2, unit=idadd)\nllkwh = struct.unpack('>f',struct.pack('>HH',*resp.registers))[0]\nllkwh = float(\"%.3f\" % llkwh)\nf = open('/var/www/html/openWB/ramdisk/llkwhs2', 'w')\nf.write(str(llkwh))\nf.close()\nresp = client.read_input_registers(0x0E,2, unit=idadd)\nllw2 = struct.unpack('>f',struct.pack('>HH',*resp.registers))[0]\nllw2 = int(llw2)\nif llw2 < 15:\n    llw2 = 0\nf = open('/var/www/html/openWB/ramdisk/llaktuells1', 'w')\nf.write(str(llw2))\nf.close()\n\nresp = client.read_input_registers(0x10,2, unit=idadd)\nllw3 = struct.unpack('>f',struct.pack('>HH',*resp.registers))[0]\nllw3 = int(llw3)\nif llw3 < 15:\n    llw3 = 0\nf = open('/var/www/html/openWB/ramdisk/llaktuells2', 'w')\nf.write(str(llw3))\nf.close()\n\nresp = client.read_input_registers(0x02,2, unit=idadd)\nvoltage = struct.unpack('>f',struct.pack('>HH',*resp.registers))[0]\nvoltage = float(\"%.1f\" % voltage)\nf = open('/var/www/html/openWB/ramdisk/llvs11', 'w')\nf.write(str(voltage))\nf.close() \nresp = client.read_input_registers(0x04,2, unit=idadd)\nvoltage = struct.unpack('>f',struct.pack('>HH',*resp.registers))[0]\nvoltage = float(\"%.1f\" % voltage)\nf = open('/var/www/html/openWB/ramdisk/llvs21', 'w')\nf.write(str(voltage))\nf.close()\n","repo_name":"snaptec/openWB","sub_path":"modules/mpm3pmtripple/readmpm3pm.py","file_name":"readmpm3pm.py","file_ext":"py","file_size_in_byte":3244,"program_lang":"python","lang":"en","doc_type":"code","stars":322,"dataset":"github-code","pt":"81"}
+{"seq_id":"9946774827","text":"\"\"\"\nModels digunakan sebagai table database\n\"\"\"\nimport os\nimport enum\nfrom sqlalchemy import Boolean, Column, DateTime, Float, ForeignKey, Integer, String, func, Enum, BigInteger\nfrom sqlalchemy.orm import relationship, backref\n\nfrom database import Base\n\n\nclass User(Base):\n    __tablename__ = \"user\"\n\n    id = Column(BigInteger, primary_key=True, index=True, autoincrement=True)\n    email = Column(String(255), unique=True, index=True)\n    nama_lengkap = Column(String(255))\n    hashed_password =  Column(String(255))\n    profile_picture = Column(String(255), default='default.png')\n    time_created = Column(DateTime(timezone=True), server_default=func.now())\n    time_updated = Column(DateTime(timezone=True), onupdate=func.now())\n    is_active = Column(Boolean, default=False)\n    activation_code = Column(String(6))\n\n\n\nclass Mentor(User):\n    __tablename__ = \"mentor\"\n\n    uid = Column(BigInteger, ForeignKey(\"user.id\"), primary_key=True)\n    keahlian = Column(String(255))\n    Asal = Column(String(255))\n\n    user_data = relationship(User, backref='mentor')\n\nclass Pelajar(User):\n    __tablename__ = \"pelajar\"\n\n    uid = Column(BigInteger, ForeignKey(\"user.id\"), primary_key=True)\n    asal_sekolah = Column(String(255))\n    jurusan = Column(String(255))\n    is_member = Column(Boolean, default=False)\n\n    user_data = relationship(User, backref='pelajar')\n    histori_attemp = relationship(\"AttemptMengerjakanTugas\", backref=\"tugas_pembelajaran\")\n\n\nclass Admin(Base):\n    __tablename__ = \"admin\"\n\n    id = Column(String(255), primary_key=True, unique=True)\n    nama_lengkap = Column(String(255))\n    hashed_password =  Column(String(255))\n    time_created = Column(DateTime(timezone=True), server_default=func.now())\n    time_updated = Column(DateTime(timezone=True), onupdate=func.now())\n    created_by = Column(String(255), ForeignKey('admin.id'))\n\n    # One-to-Many relationship\n    children = relationship(\"Admin\", backref=backref('admin', remote_side=[id]))\n\n\nclass DaftarMapelSkolastik(enum.Enum):\n    kuantitatif = 1\n    penalaran_matematika = 2\n    literasi_inggris = 3\n    literasi_indonesia = 4\n    penalaran_umum = 5\n    membaca_dan_menulis = 6\n\nclass Materi(Base):\n    __tablename__ = \"materi_pembelajaran\"\n\n    id = Column(BigInteger, primary_key=True, index=True, autoincrement=True)\n    nama = Column(String(255), unique=True) \n    mapel = Column(Enum(DaftarMapelSkolastik))\n\nclass VideoPembelajaran(Base):\n    __tablename__ = \"video_pembelajaran\"\n\n    # Metadata\n    id = Column(BigInteger, primary_key=True, index=True, autoincrement=True)\n    creator_id = Column(BigInteger, ForeignKey(\"mentor.uid\"))\n    time_created = Column(DateTime(timezone=True), server_default=func.now())\n\n    # Content\n    judul = Column(String(127), nullable=False)\n    id_materi = Column(BigInteger, ForeignKey(\"materi_pembelajaran.id\"))\n    id_tugas = Column(BigInteger, ForeignKey(\"tugas_pembelajaran.id\"), unique=True)\n\n    s3_key = Column(String(255))\n\n    # Relation\n    creator = relationship(Mentor, backref=\"video_pembelajaran\")\n    materi = relationship(Materi, backref=\"video_pembelajaran\")\n    tugas = relationship(\"TugasPembelajaran\", backref=\"video_pembelajaran\", uselist=False, viewonly=True)\n\n\nclass TugasPembelajaran(Base):\n    __tablename__ = \"tugas_pembelajaran\"\n\n    id = Column(BigInteger, primary_key=True, index=True, autoincrement=True) # read: ada, post: tidak\n    time_created = Column(DateTime(timezone=True), server_default=func.now()) # read: ada, post: tidak\n    time_updated = Column(DateTime(timezone=True), onupdate=func.now()) # read: ada, post: tidak\n\n    judul = Column(String(255))\n    attempt_allowed = Column(Integer)\n\n    # Relation\n    video = relationship(VideoPembelajaran, backref=\"tugas_pembelajaran\", uselist=False, viewonly=True) # read tidak, post ada\n    soal = relationship(\"Soal\", backref=\"tugas_pembelajaran\")\n    attemp = relationship(\"AttemptMengerjakanTugas\")\n\nclass Soal(Base):\n    __tablename__ = \"soal\"\n\n    id = Column(BigInteger, primary_key=True, index=True, autoincrement=True)\n    pertanyaan = Column(String(512), nullable=False)\n    type = Column(String(32))\n\n    \n    id_tugas = Column(BigInteger, ForeignKey('tugas_pembelajaran.id'))\n    __mapper_args__ = {'polymorphic_on': type}\n\nclass SoalABC(Soal):\n    __mapper_args__ = {'polymorphic_identity': 'pilihan_ganda'}\n    __tablename__ = \"soal_pilihan_ganda\"\n\n    id_soal = Column(BigInteger, ForeignKey('soal.id'), primary_key=True)\n\n    kunci = Column(Integer) \n    pilihan = relationship(\"JawabanABC\", backref=\"soal_pilihan_ganda\")\n\nclass JawabanABC(Base):\n    __tablename__ = \"jawaban_pilihan_ganda\"\n\n    id = Column(BigInteger, primary_key=True, index=True, autoincrement=True)\n    id_soal = Column(BigInteger, ForeignKey('soal_pilihan_ganda.id_soal'))\n    jawaban = Column(String(127))\n\nclass SoalBenarSalah(Soal):\n    __mapper_args__ = {'polymorphic_identity': 'benar_salah'}\n    __tablename__ = \"soal_benar_salah\"\n\n    id_soal = Column(BigInteger, ForeignKey('soal.id'), primary_key=True)\n    benar = Column(String(32))\n    salah = Column(String(32))\n\n    pilihan = relationship(\"JawabanBenarSalah\", backref=\"soal_benar_salah\")\n\nclass JawabanBenarSalah(Base):\n    __tablename__ = \"jawaban_benar_salah\"\n\n    id = Column(BigInteger, primary_key=True, index=True, autoincrement=True)\n    id_soal = Column(BigInteger, ForeignKey('soal_benar_salah.id_soal'))\n    jawaban = Column(String(127))\n    kunci = Column(Boolean)\n\n\nclass SoalMultiPilih(Soal):\n    __mapper_args__ = {'polymorphic_identity': 'multi_pilih'}\n    __tablename__ = \"soal_multi_pilih\"\n\n    id_soal = Column(BigInteger, ForeignKey('soal.id'), primary_key=True)\n\n    pilihan = relationship(\"JawabanMultiPilih\", backref=\"soal_multi_pilih\")\n\n\nclass JawabanMultiPilih(Base):\n    __tablename__ = \"jawaban_multi_pilih\"\n\n    id = Column(BigInteger, primary_key=True, index=True, autoincrement=True)\n    id_soal = Column(BigInteger, ForeignKey('soal_multi_pilih.id_soal'))\n    jawaban = Column(String(127))\n    benar = Column(Boolean)\n\nclass AttemptMengerjakanTugas(Base):\n    __tablename__ = \"mengerjakan_tugas\"\n\n    id = Column(BigInteger, primary_key=True, index=True, autoincrement=True)\n    waktu_mulai = Column(DateTime(timezone=True))\n    waktu_selesai = Column(DateTime(timezone=True))\n    nilai = Column(Float)\n\n    id_pelajar = Column(BigInteger, ForeignKey(\"pelajar.uid\"))\n    id_tugas = Column(BigInteger, ForeignKey(\"tugas_pembelajaran.id\"))","repo_name":"Emot-Api-SE4403/SwiftBackend","sub_path":"models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":6431,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"22940615481","text":"import numpy as np\n\nclass LinearRegression(object):\n\n    def __init__(self):\n        pass\n    \n    # Q1: Carry out Linear Regression without transformation, i.e., with feature vector:(1,x1 ,x2),\n    # Run the experiments for 1000 times. What is the average E in over 1000 experiments?\n    \n    # generate target function: f(x1, x2) = sign(x1*x1 + x2*x2 - 0.6)\n    def target_function(self, X1, X2):\n        y = np.ones(X1.shape[0])\n        y[(X1*X1 + X2*X2 - 0.6) < 0] = -1\n    \n        return y\n    \n\n    # create train data with 10% flipping noise\n    def generate_data_1d(self, time_seed):\n        np.random.seed(time_seed)\n        X = np.zeros((1000, 3))  # 1000 train data\n        y = np.zeros((1000, 1))\n    \n        # X features\n        X[:, 0] = 1\n        X[:, 1] = np.random.uniform(-1, 1, 1000)\n        X[:, 2] = np.random.uniform(-1, 1, 1000)\n        # y label without noise\n        y = self.target_function(X[:, 1], X[:, 2])\n        # y label with  10% noise\n        y_noise = y * np.where(np.random.random(1000) < 0.1, -1, 1)\n    \n        return X, y_noise\n    \n\n    # calculate error rate\n    def cal_error(self, X, y, W):\n        # calculate scores\n        scores = np.dot(X, W) * y\n        # calculate all errors\n        error = np.sum(np.where(scores < 0, 1, 0))\n        error /= X.shape[0]\n    \n        return error\n    \n\n    # calculate linear regression closed form solution\n    def LinearR_closed_form(self, X, y):\n    \n        # linear regression closed form solution\n        return np.linalg.inv(X.T.dot(X)).dot(X.T).dot(y)\n\n\n    # calculate the average  Ein  by 1000 iteration\n    def cal_Ein_1d(self):\n        Ein_all = []  # store all Ein\n        N = 1000  # number of iteration\n        for i in range(1000):\n            X, y = self.generate_data_1d(i)  # generate data\n            W = self.LinearR_closed_form(X, y)  # calculate weights\n            Ein = self.cal_error(X, y, W)  # calculate Ein\n            Ein_all.append(Ein)\n\n        # mean of Ein\n        Ein = np.mean(Ein_all)\n        return Ein\n\n    \n    # Q2: Carry out Linear Regression without transformation, i.e., with feature vector:(1, x1, x2, x1x2, x1*x1, x2*x2),\n    # Run the experiments for 1000 times. What is the average E in over 1000 experiments?\n\n    # create train data with 10% flipping noise\n    def generate_data_2d(self, time_seed):\n\n        np.random.seed(time_seed)\n        X = np.zeros((1000, 6))  # 1000 train data\n        y = np.zeros((1000, 1))\n    \n        # X features\n        X[:, 0] = 1\n        X[:, 1] = np.random.uniform(-1, 1, 1000)\n        X[:, 2] = np.random.uniform(-1, 1, 1000)\n        X[:, 3] = X[:, 1] * X[:, 2]\n        X[:, 4] = X[:, 1] * X[:, 1]\n        X[:, 5] = X[:, 2] * X[:, 2]\n        # y label without noise\n        y = self.target_function(X[:, 1], X[:, 2])\n        # y label with  10% noise\n        y_noise = y * np.where(np.random.random(1000) < 0.1, -1, 1)\n    \n        return X, y_noise\n\n    \n    # calculate the average  Ein  by 1000 iteration\n    def cal_Ein_2d(self):\n        Ein_all = []  # store all Ein\n        N = 1000  # number of iteration\n        for i in range(1000):\n            X, y = self.generate_data_2d(i)  # generate data\n            W = self.LinearR_closed_form(X, y)  # calculate weights\n            Ein = self.cal_error(X, y, W)  # calculate Ein\n            Ein_all.append(Ein)\n\n        # mean of Ein\n        Ein = np.mean(Ein_all)\n        return Ein\n    \n\nclass LogisticRegression(object):\n\n    def __init__(self):\n        pass\n\n    # Implement the fixed learning rate gradient descent algorithm below for logistic regression, \n    # initialized with 0. Run the algorithm with η = 0.001 and T = 2000. What is the Eout from your algorithm, \n    # evaluated using the 0/1 error on the test set?\n    \n    # define load data function\n    def read_input_data(self, path):\n        x = []\n        y = []\n        for line in open(path).readlines():\n            items = line.strip().split(' ')\n            tmp_x = []\n            for i in range(0, len(items) - 1): \n                tmp_x.append(float(items[i]))\n            x.append(tmp_x)\n            y.append(float(items[-1]))\n        return np.array(x), np.array(y)\n\n    \n    # define sigmoid function\n    def sigmoid(self, x):\n        return 1.0 / (np.exp(-x) + 1)\n    \n\n    # Q1: Gradient Descent\n    # define gradient descent function\n    def gradient_descent(self, X, y):\n        y = y.reshape(-1, 1)  # reshape (1000,) to (1000,1)\n        m = X.shape[0]  # number of samples\n        n = X.shape[1]  # number of features\n        T = 2000  # number of iteration\n        learning_rate = 0.001  # learning rate\n        W = np.zeros((n, 1))  # initialize weights\n    \n        for i in range(T):\n            loss = 1/m * np.sum(np.log(1 + np.exp(-1 * X.dot(W) * y)))\n            dW = 1/m * np.sum(self.sigmoid(-X.dot(W) * y) * -y * X, axis=0).reshape(n, 1)\n            W = W - learning_rate * dW\n\n        return W\n    \n\n    # define predict error function by W\n    def predict(self, X, y, W):\n        y_hat = self.sigmoid(X.dot(W))  # predict probability [0,1]\n        y_hat[y_hat >= 0.5] = 1  # positive\n        y_hat[y_hat < 0.5] = -1  # negative\n    \n        y = y.reshape(-1,1)  # reshape 2D\n        Ein_mean = np.mean(y_hat != y)\n\n        return Ein_mean\n    \n\n    # Logistic regression by GD\n    def lr_gd(self, X_train, y_train, X_test, y_test):\n        # Gradient descent\n        W = self.gradient_descent(X_train, y_train)  # calculate weights\n\n        # calculate test data error\n        Ein_mean = self.predict(X_test, y_test, W)\n\n        return Ein_mean\n\n\n    # Q2: Stochastic Gradient Descent\n    # define stochastic gradient descent function\n    def stochastic_gradient_descent(self, X, y):\n        m = X.shape[0]  # number of samples\n        n = X.shape[1]  # number of features\n        T = 2000  # number of iteration\n        learning_rate = 0.001  # learning rate\n        W = np.zeros((n, 1))  # initialize weights\n    \n        for t in range(T):\n            loss = 0.0\n            for i in range(m):\n                loss += np.log(1 + np.exp(-1 * X[i,:].dot(W) * y[i]))\n                dW = (self.sigmoid(-X[i,:].dot(W) * y[i]) * -y[i] * X[i,:]).reshape(n, 1)\n                W = W - learning_rate * dW\n\n        return W\n\n    # Logistic regression by SGD\n    def lr_sgd(self, X_train, y_train, X_test, y_test):\n        # Gradient descent\n        W = self.stochastic_gradient_descent(X_train, y_train)  # calculate weights\n\n        # calculate test data error\n        Ein_mean = self.predict(X_test, y_test, W)\n\n        return Ein_mean\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","repo_name":"RedstoneWill/HsuanTienLin-ML-Camp","sub_path":"Week3/reference_hw3/LR.py","file_name":"LR.py","file_ext":"py","file_size_in_byte":6569,"program_lang":"python","lang":"en","doc_type":"code","stars":53,"dataset":"github-code","pt":"81"}
+{"seq_id":"74584033863","text":"from collections import defaultdict\n\nclass AoC():\n    def __init__(self):\n        self.data = None\n\n    def ParseInput(self, filename):\n        input_file = open(filename, \"r\")\n\n        input_data = list(map(int, [x for x in input_file.read().split(\"\\n\") if x]))\n\n        input_file.close()\n\n        self.data = input_data\n\n    def calculateDeltas(self) -> [int]:\n        inOrder = sorted(self.data)\n        inOrder = [0] + inOrder + [inOrder[-1] + 3]\n\n        deltas = [int]\n\n        pos = 0\n        while pos + 2 < len(inOrder):\n            pair = inOrder[pos:pos + 2]\n            deltas.append(pair[1] - pair[0])\n            pos += 1\n\n        deltas.append(inOrder[-1] - inOrder[-2])\n\n        return deltas\n\n    def Task1(self) -> int:\n        deltas = self.calculateDeltas()\n\n        delta1 = deltas.count(1)\n        delta3 = deltas.count(3)\n\n        return delta1 * delta3\n\n    def Task2(self) -> int:\n        inOrder = sorted(self.data)\n        inOrder = [0] + inOrder + [inOrder[-1] + 3]\n\n        paths = defaultdict(int)\n        paths[0] = 1\n\n        for adapter in inOrder:\n            for diff in range(1, 4):\n                next_adapter = adapter + diff\n                if next_adapter in inOrder:\n                    paths[next_adapter] += paths[adapter]\n\n        return paths[inOrder[-1]]\n\n    def Run(self, filename):\n        self.ParseInput(filename)\n        print(f\"Task1: {self.Task1()}\")\n        print(f\"Task2: {self.Task2()}\")\n\nif __name__ == \"__main__\":\n    day = AoC()\n    day.Run(\"Day 10/input\")\n","repo_name":"BasJ93/Advent-of-Code-2020","sub_path":"Day 10/solver.py","file_name":"solver.py","file_ext":"py","file_size_in_byte":1519,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"24879486777","text":"#!/usr/bin/env python\n\n\"\"\"\nWe have to finish the previous python file (0_preparation.py) to use this file.\nWe also have to finish the cases of the weights < k to run this for the weight = k.\n\nThis file makes dictionary datas for binary harmonic (stuffle) and shuffle products in pickle format. \nThe dictionary key is the pair of index (pnd) or the pair of word (prd).\nThe dictionary value is the linear relation in terms of the numbers corresponding to the binary MZSs that were created by the initial file (0_preparation.py).\n\nWe give some examples of dictionary datas:\n[weight 3] \n Harmonic products with pnd-key: {((1,), (2,)): frozenset({2,3,4}), ((1,), (1, 1)): frozenset({1,2,3})}\n Shuffle products with pnd-key:  {((1,), (2,)): frozenset({3}),     ((1,), (1, 1)): frozenset({1}) }\n Shuffle products with prd-key:  {((Y,), (X,Y)): frozenset({3}),    ((Y,), (Y, Y)): frozenset({1}) }\n Note that the numbers 1,2,3,4 in linear relations are equivalent to the formal MZVs Z(1,1,1),Z(2,1),Z(1,2),Z(3), respectively.\n\nThe created datas are named as pndKpdHs_xx.pkl_Bbb, pndKpdSs_xx.pkl_Bbb and prdKpdSs_xx.pkl_Bbb.\nHere \"xx\" is in {\"H0\",\"a1\",\"H9\"} and \"bb\" means the block number to divide files.\nNote that \"xx\" means a condition of pair of index (pnd) or pair of word (prd) such that\n    *  H0 -> the pair is for creating a finite double shuffle relation, or both indices (words) are in the Hoffman algebra usually denoted by H^0;\n    *  a1 -> there is an index (word) whose entries are all 1;\n    ** H9 -> no condition, so wrd that has no index expression is permitted.\nThe datas are stored in \"../Data/Wkk/PdX/\", where \"kk\" means a weight.\nThe datas of the mark (**) (or prdKpdSs_H9.pkl_Bbb) are created for only small weights; they are necessary to calculate shuffle products for large weights.\n\n[Command Line Examples]\n    $ python 1_product.py \n    $ python 1_product.py -w 2 19\n\"\"\"\n\n# import from python\nimport sys;     sys.path.append(\"../Work/\")\nimport argparse,os,pickle,psutil,time\n# import from Work\nfrom WS import WS\n\n# defalut parameter\nMDL_wrt  = \"make_1_pdX\"     # pd->product\nPID      = psutil.Process(os.getpid())\nWGTs_def = range(2,12)\nWGT_upp  = 22\n\n# ---------- private function\ndef load_prdKpdSs_don(ws, wgt_arg, FLD_data=None):      # don->done\n    wgt1     = 1 if wgt_arg < 10 else int(wgt_arg/2)\n    wgts_don = {wgt1,wgt_arg-wgt1}\n    # -- set\n    prdKpdSs_don = dict()\n    for wgt in wgts_don:\n        if wgt == 1:    continue\n        fld = FLD_data+\"W\"+str(wgt)+\"/\"\n        # - set\n        with open(fld+\"noKwrds.pkl\",\"rb\") as f:                  noKwrds  = pickle.load(f)\n        fles = ws.fles_grep(fld+\"PdX/\",\"^prdKpdSs.*pkl_B.*\")\n        # - load \n        for fle in fles:\n            # open and cv\n            with open(fle,\"rb\") as f:          prdKpdSs = pickle.load(f)\n            prdKpdSs = { prd:frozenset({ noKwrds[no] for no in pdS }) for prd,pdS in prdKpdSs.items() }\n            # update\n            prdKpdSs_don.update(prdKpdSs)\n    # - return\n    return (prdKpdSs_don,min(wgts_don))\n\ndef make9dump_pndKpdSs_xx(ws, prds_H0a1H9, xx, prdKpdSs_don, wgt_don, wrdKnos, fld=None, Prcs=None, onH9=False, onVB=False):\n    prds_H0a1 = { (wrd1,wrd2) for (wrd1,wrd2) in prds_H0a1H9 if wrd1[-1] == ws._nmY and wrd2[-1] == ws._nmY }\n    # - set prds\n    if   xx == \"H0\":\n        prds = { (wrd1,wrd2) for (wrd1,wrd2) in prds_H0a1 if wrd1[0] == ws._nmX and wrd2[0] == ws._nmX  }\n    elif xx == \"a1\":\n        prds = { (wrd1,wrd2) for (wrd1,wrd2) in prds_H0a1 if ws._nmX not in set(wrd1) or ws._nmX not in set(wrd2)  }\n    #K elif xx == \"d1\":\n    #K     prds = { (wrd1,wrd2) for (wrd1,wrd2) in prds_H0a1 if min(len([ 1 for nm in wrd1 if nm == ws._nmY ]),len([ 1 for nm in wrd2 if nm == ws._nmY ])) == 1 }\n    elif xx == \"H9\":\n        prds = prds_H0a1H9\n    else:\n        prds = set()\n    if onVB:    print(\" Prds =\", len(prds), end=\" \")\n    # - make and dump\n    ws.mk9dp_pndKpdSs(prds,xx,prdKpdSs_don,wgt_don,wrdKnos,fld=fld,Prcs=Prcs,onH9=onH9,onVB=onVB)\n    # - end\n    return True\n\ndef make9dump_pndKpdHs_xx(ws, pnds_H0a1, xx, indKnos, fld=None, Prcs=None, onVB=False):\n    # - set pnds\n    if   xx == \"H0\":\n        pnds = { (ind1,ind2) for (ind1,ind2) in pnds_H0a1 if ind1[0] >= 2 and ind2[0] >= 2 }\n    elif xx == \"a1\":\n        pnds = { (ind1,ind2) for (ind1,ind2) in pnds_H0a1 if len(ind1) == sum(ind1) or len(ind2) == sum(ind2) }\n    #K elif xx == \"d1\":\n    #K     pnds = { (ind1,ind2) for (ind1,ind2) in pnds_H0a1d1 if min(len(ind1),len(ind2)) == 1 }\n    else:\n        pnds = set()\n    if onVB:    print(\" Pnds =\", len(pnds), end=\" \")\n    # - make and dump\n    ws.mk9dp_pndKpdHs(pnds,xx,indKnos,fld=fld,Prcs=Prcs,onVB=onVB)\n    # - end\n    return True\n\n# ---------- main function\ndef main(arg_w,onVB):\n    # --- create ws (work space)\n    ws = WS()\n    onVB     = onVB if ws._keyKcfgs.get(\"onDebug\",\"False\") != \"True\" else True\n    FLD_data = ws._keyKcfgs.get(\"dataDir\",\"../Data/\")\n    # --- set wgts    \n    if   arg_w == None or len(arg_w) == 0:\n        wgts = list(WGTs_def)\n    elif len(arg_w) == 1:\n        wgts = [ int(stg) for stg in arg_w ]\n    else:\n        wgts = list(range(int(arg_w[0]),int(arg_w[1])+1,1))\n    wgts = [ wgt for wgt in wgts if wgt >= 2 and wgt <= WGT_upp ]\n    if onVB:    print(\"SS: wgts,onVB =\",wgts,onVB); tmeS = time.time()\n    # --- loop 1\n    if onVB:    print(\"@M: get pdXs ...\")\n    for wgt in wgts:\n        tmlS,rss = time.time(),PID.memory_info().rss\n        fld_W = FLD_data+\"W\"+str(wgt)+\"/\"\n        xxs   = [\"H0\",\"a1\"]    # d1\n        Prcs  = 8 if wgt <=10 else (16 if wgt <= 20 else 24)\n        onH9  = True     if wgt <= WGT_upp/2     else False\n        # -- get pnds_???,prds_???; pnd->pair of inds, prd->pair of wrds\n        pnds_H0a1,prds_H0a1H9 = ws.pnds9prds_H0a1H9(wgt,onH9=onH9)\n        if onVB:    print(\" SS: wgt,(Pnds_H0a1,Prds_H0a1H9) =\",wgt,(len(pnds_H0a1),len(prds_H0a1H9)))     \n        # -----\n        # -- set indKnos\n        with open(fld_W+\"noKinds.pkl\",\"rb\") as f:   noKinds = pickle.load(f)\n        indKnos = { ind:no for no,ind in noKinds.items() }\n        # -- for pdH\n        for xx in xxs:\n            if onVB:    print(\"  SS: pdH: xx =\", xx, end=\" \");  tmS = time.time()\n            make9dump_pndKpdHs_xx(ws,pnds_H0a1,xx,indKnos,fld=fld_W+\"PdX/\",Prcs=Prcs,onVB=onVB) \n            if onVB:    hms,rss = time.time()-tmS,ws.rss_peak(rss,PID);     print(\"  EE: (tm:\"+ws.stg_hms(hms)+\", \"+ws.stg_rss(rss)+\")\")\n        # -----\n        # -- set \n        prdKpdSs_don,wgt_don = load_prdKpdSs_don(ws,wgt,FLD_data=FLD_data)      # don->done\n        #:\n        with open(fld_W+\"noKwrds.pkl\",\"rb\") as f:   noKwrds = pickle.load(f)  \n        wrdKnos = { wrd:no  for no,wrd in noKwrds.items() }\n        # -- for pdS\n        xxs  = xxs if not onH9 else xxs + [\"H9\"]\n        for xx in xxs:\n            if onVB:    print(\"  SS: pdS: xx =\", xx, end=\" \");  tmS = time.time()\n            make9dump_pndKpdSs_xx(ws,prds_H0a1H9,xx,prdKpdSs_don,wgt_don,wrdKnos,fld=fld_W+\"PdX/\",Prcs=Prcs,onH9=onH9,onVB=onVB)\n            if onVB:    hms,rss = time.time()-tmS,ws.rss_peak(rss,PID);     print(\"  EE: (tm:\"+ws.stg_hms(hms)+\", \"+ws.stg_rss(rss)+\")\") \n        # - write \n        if onVB:    hms,rss = int(time.time()-tmlS),ws.rss_peak(rss,PID);     ws.rec_hms(hms,fld_W,MDL_wrt);      ws.rec_rss(rss,fld_W,MDL_wrt)\n        if onVB:    print(\" EE: (loop:\"+ws.stg_hms(hms)+\", \"+ws.stg_rss(rss)+\")(total:\"+ws.stg_hms(time.time()-tmeS)+\")\") \n    # - info\n    if onVB:    print(\"EE: (total:\"+ws.stg_hms(time.time()-tmeS)+\")\") \n\nif __name__ == \"__main__\":\n    # -- parse\n    parser = argparse.ArgumentParser(description=\"This file calculates binary harmonic (stuffle) and shuffle products, and makes their datas. \"\\\n                                                 + \"See the head of the file for details.\")\n    parser.add_argument(\"-v\", help=\"Verbose mode (default:False).\", action='store_true')\n    parser.add_argument(\"-w\", help=\"Target weights (wgts) for making. \"\\\n                                    + \"If no args, default target, from \" + str(WGTs_def[0]) + \" to \" + str(WGTs_def[-1]) + \"; \" \\\n                                    + \"If 1 arg (wgt1), only wgt1; \" \\\n                                    + \"If 2 args (wgt1,wgt2), from wgt1 to wgt2.\" \\\n                            , nargs=\"*\")\n    # -- get args\n    args = parser.parse_args()\n    # -- do main\n    main(args.w,args.v)\n    \n","repo_name":"machide-tomoyan/BMZS-calculator","sub_path":"Main_make/1_product.py","file_name":"1_product.py","file_ext":"py","file_size_in_byte":8357,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"21114371619","text":"import functools\nfrom typing import Callable, Optional, Tuple, TypeVar\n\nimport jax\nimport jax.numpy as jnp\nimport numpy as onp\n\n\ndef maybe_static_cond(pred, true_fn, false_fn, val):\n  \"\"\"Conditional that first checks if pred can be determined at compile time.\"\"\"\n  if isinstance(pred, (onp.ndarray, bool)):\n    return true_fn(val) if pred else false_fn(val)\n  else:\n    return jax.lax.cond(pred, true_fn, false_fn, val)\n\n\n@functools.lru_cache(None)\ndef cached_jit(fn, *args, **kwargs):\n  return jax.jit(fn, *args, **kwargs)\n\n\ndef maybe_do(pred, do_fn, operand):\n\n  def body_fn(_, operand):\n    return do_fn(operand)\n\n  return jax.lax.fori_loop(0, jnp.asarray(pred, jnp.int32), body_fn, operand)\n\n\ndef in_jit() -> bool:\n  \"\"\"Returns true if tracing jit.\"\"\"\n  return \"DynamicJaxprTrace\" in str(\n      jax.core.thread_local_state.trace_state.trace_stack\n  )\n\n\nCarry = TypeVar(\"Carry\")\nX = TypeVar(\"X\")\nY = TypeVar(\"Y\")\n\n\n@functools.partial(jax.jit, static_argnames=(\"reverse\",))\ndef _stack(ys, reverse=False):\n  maybe_reversed = reversed if reverse else lambda x: x\n  return jax.tree_util.tree_map(lambda *y: jnp.vstack(y), *maybe_reversed(ys))\n\n\ndef scan(f: Callable[[Carry, X], Tuple[Carry, Y]],\n         init: Carry,\n         xs: X,\n         length: Optional[int] = None,\n         reverse: bool = False,\n         unroll: int = 1) -> Tuple[Carry, Y]:\n  \"\"\"If not in jit, use a python for loop -- otherwise jax.lax.scan.\"\"\"\n  if in_jit():\n    return jax.lax.scan(f, init, xs, length, reverse, unroll)\n  else:\n    xs_flat, xs_tree = jax.tree_util.tree_flatten(xs)\n    if length is None:\n      length = jax.tree_util.tree_leaves(xs)[0].shape[0]\n    carry = init\n    ys = []\n    maybe_reversed = reversed if reverse else lambda x: x\n    for i in maybe_reversed(range(length)):\n      xs_slice = [x[i] for x in xs_flat]\n      carry, y = f(carry, jax.tree_util.tree_unflatten(xs_tree, xs_slice))\n      ys.append(y)\n    stacked_y = _stack(ys, reverse=reverse)\n    return carry, stacked_y\n\n\ndef _print_aval(a):\n  try:\n    aval = jnp.asarray(a).aval\n    return aval\n  except:  # pylint: disable=bare-except\n    return str(a)\n\n\ndef print_arg_shapes(fn):\n  \"\"\"Debug decorator to print abstract values of the function.\"\"\"\n\n  @functools.wraps(fn)\n  def f(*args, **kwargs):\n    print(\"Called:\", fn.__name__)\n    print(\"\\t Args:\")\n    for a in args:\n      print(\"\\t\\t\", jax.tree_util.tree_map(_print_aval, a))\n    for k, v in kwargs.values():\n      print(f\"\\t\\t {k}={jax.tree_util.tree_map(_print_aval, v)}\")\n    return fn(*args, **kwargs)\n\n  return f\n","repo_name":"google/learned_optimization","sub_path":"learned_optimization/jax_utils.py","file_name":"jax_utils.py","file_ext":"py","file_size_in_byte":2535,"program_lang":"python","lang":"en","doc_type":"code","stars":702,"dataset":"github-code","pt":"81"}
+{"seq_id":"34684504412","text":"import tensorflow as tf\nfrom flask import Flask, request, render_template\nfrom PIL import Image\nimport numpy as np\n\nclass_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',\n               'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Boutine']\n\napp = Flask(__name__)\nmodel = tf.keras.models.load_model('./static/model/Model.h5')\n\n\n@app.route('/', methods=['GET', 'POST'])\ndef predict():\n    if request.method == 'POST':\n        # return jsonify({'prediction': \"hello its post\"})\n        #print(\"hello\")\n        data = request.files['uploaded-file']\n        data.save(\"./static/uploads/\" + data.filename)\n        imagePath = \"./static/uploads/\" + data.filename\n        img = Image.open(imagePath)\n        img = img.resize((28, 28), Image.ANTIALIAS).convert(mode='L')\n        img_arr = np.array(img).reshape(1, 28, 28, 1)\n        img_arr = img_arr.reshape(1, 28, 28, 1).astype(\"float32\") / 255.0\n        pred = model.predict(img_arr)\n\n        index_label = np.argmax(pred[0], axis=-1)\n        print(pred)\n        return render_template(\"index.html\", data={\"image\": data.filename, \"label\": class_names[index_label]})\n\n    return render_template(\"index.html\")\n\n\napp.run()","repo_name":"nizarsbai/PythonProjectWithFlask","sub_path":"cross/cross/Lab3/Deploy_model/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1172,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"18678684301","text":"# i_C.MODEL.PRIORS = CN()\n# _C.MODEL.PRIORS.FEATURE_MAPS = [38, 19, 10, 5, 3, 1]\n# _C.MODEL.PRIORS.STRIDES = [8, 16, 32, 64, 100, 300]\n# _C.MODEL.PRIORS.MIN_SIZES = [30, 60, 111, 162, 213, 264]\n# _C.MODEL.PRIORS.MAX_SIZES = [60, 111, 162, 213, 264, 315]\n# _C.MODEL.PRIORS.ASPECT_RATIOS = [[2], [2, 3], [2, 3], [2, 3], [2], [2]]\n# # When has 1 aspect ratio, every location has 4 boxes, 2 ratio 6 boxes.\n# # #boxes = 2 + #ratio * 2\n# _C.MODEL.PRIORS.BOXES_PER_LOCATION = [\n#     4, 6, 6, 6, 4, 4\n# ]  # number of boxes per feature map location\n# _C.MODEL.PRIORS.CLIP = True\n\nfrom itertools import product\nimport torch\nfrom math import sqrt\n\n\nclass PriorBox:\n\n    def __init__(self):\n        self.image_size = 300\n        self.feature_maps = [38, 19, 10, 5, 3, 1]\n        self.min_sizes = [30, 60, 111, 162, 213, 264]\n        self.max_sizes = [60, 111, 162, 213, 264, 315]\n        self.strides = [8, 16, 32, 64, 100, 300]\n        self.aspect_ratios = [[2], [2, 3], [2, 3], [2, 3], [2], [2]]\n        self.clip = True\n        #PriorBox NUMBERS: 38*38*(1+1+2*1) + 19*19*(1+1+2*2) + 10*10*(1+1+2*2) + 5*5*(1+1+2*2) + 3*3*(1+1+2*1) + 1*1*(1+1+2*1) = 8732\n        #F * F *(MIN_BOX + BIG_BOX + RATIO_BOX(2) * RATIO_NUM)\n    def __call__(self):\n        \"\"\"Generate SSD Prior Boxes.\n            It returns the center, height and width of the priors. The values are relative to the image size\n            Returns:\n                priors (num_priors, 4): The prior boxes represented as [[center_x, center_y, w, h]]. All the values\n                    are relative to the image size.\n        \"\"\"\n        priors = []\n        for k, f in enumerate(self.feature_maps):\n            scale = self.image_size / self.strides[k]\n            for i, j in product(range(f), repeat=2):\n                # unit center x,y\n                cx = (j + 0.5) / scale\n                cy = (i + 0.5) / scale\n\n                # small sized square box\n                size = self.min_sizes[k]\n                h = w = size / self.image_size\n                priors.append([cx, cy, w, h])\n\n                # big sized square box\n                size = sqrt(self.min_sizes[k] * self.max_sizes[k])\n                h = w = size / self.image_size\n                priors.append([cx, cy, w, h])\n\n                # change h/w ratio of the small sized box\n                size = self.min_sizes[k]\n                h = w = size / self.image_size\n                for ratio in self.aspect_ratios[k]:\n                    ratio = sqrt(ratio)\n                    priors.append([cx, cy, w * ratio, h / ratio])\n                    priors.append([cx, cy, w / ratio, h * ratio])\n\n        priors = torch.tensor(priors)\n        if self.clip:\n            priors.clamp_(max=1, min=0)\n        return priors\n\n\nif __name__ == '__main__':\n    pb = PriorBox()\n    print(pb().shape)\n","repo_name":"shiwoya96/ssd","sub_path":"prior_box.py","file_name":"prior_box.py","file_ext":"py","file_size_in_byte":2811,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"13199431370","text":"import logging\n\nfrom pssh.clients.ssh import ParallelSSHClient\n\nimport medusa.utils\nfrom medusa.storage import divide_chunks\n\n\ndef display_output(host_outputs):\n    for host_out in host_outputs:\n        for line in host_out.stdout:\n            logging.info(\"{}-stdout: {}\".format(host_out.host, line))\n        for line in host_out.stderr:\n            logging.info(\"{}-stderr: {}\".format(host_out.host, line))\n\n\nclass Orchestration(object):\n    def __init__(self, config, pool_size=10):\n        self.pool_size = pool_size\n        self.config = config\n\n    def pssh_run(self, hosts, command, hosts_variables=None, ssh_client=None):\n        \"\"\"\n        Runs a command on hosts list using pssh under the hood\n        Return: True (success) or False (error)\n        \"\"\"\n        if ssh_client is None:\n            ssh_client = ParallelSSHClient\n        pssh_run_success = False\n        success = []\n        error = []\n        i = 1\n\n        username = self.config.ssh.username if self.config.ssh.username != '' else None\n        port = int(self.config.ssh.port)\n        pkey = self.config.ssh.key_file if self.config.ssh.key_file != '' else None\n        cert_file = self.config.ssh.cert_file if self.config.ssh.cert_file != '' else None\n\n        logging.info('Executing \"{command}\" on following nodes {hosts} with a parallelism/pool size of {pool_size}'\n                     .format(command=command, hosts=hosts, pool_size=self.pool_size))\n\n        for parallel_hosts in divide_chunks(hosts, self.pool_size):\n\n            client = ssh_client(parallel_hosts,\n                                forward_ssh_agent=True,\n                                pool_size=len(parallel_hosts),\n                                user=username,\n                                port=port,\n                                pkey=pkey,\n                                cert_file=cert_file)\n            logging.debug('Batch #{i}: Running \"{command}\" on nodes {hosts} parallelism of {pool_size}'\n                          .format(i=i, command=command, hosts=parallel_hosts, pool_size=len(parallel_hosts)))\n            output = client.run_command(command, host_args=hosts_variables,\n                                        sudo=medusa.utils.evaluate_boolean(self.config.cassandra.use_sudo))\n            client.join(output)\n\n            success = success + list(filter(lambda host_output: host_output.exit_code == 0, output))\n            error = error + list(filter(lambda host_output: host_output.exit_code != 0, output))\n\n        # Report on execution status\n        if len(success) == len(hosts):\n            logging.info('Job executing \"{}\" ran and finished Successfully on all nodes.'\n                         .format(command))\n            pssh_run_success = True\n        elif len(error) > 0:\n            logging.error('Job executing \"{}\" ran and finished with errors on following nodes: {}'\n                          .format(command, sorted(set(map(lambda host_output: host_output.host, error)))))\n            display_output(error)\n        else:\n            err_msg = 'Something unexpected happened while running pssh command'\n            logging.error(err_msg)\n            raise Exception(err_msg)\n\n        return pssh_run_success\n","repo_name":"thelastpickle/cassandra-medusa","sub_path":"medusa/orchestration.py","file_name":"orchestration.py","file_ext":"py","file_size_in_byte":3201,"program_lang":"python","lang":"en","doc_type":"code","stars":243,"dataset":"github-code","pt":"81"}
+{"seq_id":"24109775387","text":"import pytest\nimport torch\n\nimport theseus as th\n\nfrom tests.theseus_tests.decorators import run_if_baspacho\nfrom theseus.utils import numeric_grad\n\ntorch.manual_seed(0)\n\n\n@pytest.mark.parametrize(\n    \"linear_solver_cls\", [th.CholeskyDenseSolver, th.CholmodSparseSolver]\n)\ndef test_backwards_quad_fit(linear_solver_cls):\n    def generate_data(num_points=10, a=1.0, b=0.5, noise_factor=0.01):\n        data_x = torch.rand((1, num_points))\n        noise = torch.randn((1, num_points)) * noise_factor\n        data_y = a * data_x.square() + b + noise\n        return data_x, data_y\n\n    num_points = 10\n    data_x, data_y = generate_data(num_points)\n\n    x = th.Variable(data_x.requires_grad_(), name=\"x\")\n    y = th.Variable(data_y.requires_grad_(), name=\"y\")\n    a = th.Vector(1, name=\"a\")\n    b = th.Vector(1, name=\"b\")\n\n    def error_fn(optim_vars, aux_vars):\n        a, b = optim_vars\n        x, y = aux_vars\n        est = a.tensor * x.tensor.square() + b.tensor\n        err = y.tensor - est\n        return err\n\n    optim_vars = [a, b]\n    aux_vars = [x, y]\n    cost_function = th.AutoDiffCostFunction(\n        optim_vars,  # type: ignore\n        error_fn,\n        num_points,\n        aux_vars=aux_vars,\n        name=\"quadratic_cost_fn\",\n    )\n    objective = th.Objective()\n    objective.add(cost_function)\n    optimizer = th.GaussNewton(\n        objective,\n        max_iterations=15,\n        step_size=1.0,\n        linear_solver_cls=linear_solver_cls,\n    )\n\n    theseus_inputs = {\n        \"a\": 2 * torch.ones((1, 1)).requires_grad_(),\n        \"b\": torch.ones((1, 1)).requires_grad_(),\n        \"x\": data_x,\n        \"y\": data_y,\n    }\n    theseus_optim = th.TheseusLayer(optimizer)\n\n    # First we use torch.autograd.functional to numerically compute the gradient\n    # the optimal a w.r.t. the x part of the data\n    with torch.no_grad():\n\n        def fit_x(data_x_np):\n            theseus_inputs[\"x\"] = (\n                torch.from_numpy(data_x_np)\n                .float()\n                .clone()\n                .requires_grad_()\n                .unsqueeze(0)\n            )\n            updated_inputs, _ = theseus_optim.forward(\n                theseus_inputs,\n                optimizer_kwargs={\"track_best_solution\": True, \"verbose\": False},\n            )\n            return updated_inputs[\"a\"].item()\n\n        data_x_np = data_x.detach().clone().numpy().squeeze()\n        dfit_x = numeric_grad(fit_x, h=1e-4)\n        da_dx_numeric = torch.from_numpy(dfit_x(data_x_np)).float()\n\n    theseus_inputs[\"x\"] = data_x\n    updated_inputs, _ = theseus_optim.forward(\n        theseus_inputs,\n        optimizer_kwargs={\n            \"track_best_solution\": True,\n            \"verbose\": False,\n            \"backward_mode\": \"unroll\",\n        },\n    )\n    da_dx_unroll = torch.autograd.grad(updated_inputs[\"a\"], data_x, retain_graph=True)[\n        0\n    ].squeeze()\n    torch.testing.assert_close(da_dx_numeric, da_dx_unroll, atol=1e-3, rtol=1e-3)\n\n    updated_inputs, _ = theseus_optim.forward(\n        theseus_inputs,\n        optimizer_kwargs={\n            \"track_best_solution\": True,\n            \"verbose\": False,\n            \"backward_mode\": th.BackwardMode.IMPLICIT,\n        },\n    )\n    da_dx_implicit = torch.autograd.grad(\n        updated_inputs[\"a\"], data_x, retain_graph=True\n    )[0].squeeze()\n    torch.testing.assert_close(da_dx_numeric, da_dx_implicit, atol=1e-3, rtol=1e-3)\n\n    updated_inputs, _ = theseus_optim.forward(\n        theseus_inputs,\n        optimizer_kwargs={\n            \"track_best_solution\": True,\n            \"verbose\": False,\n            \"backward_mode\": \"TRUNCATED\",\n            \"backward_num_iterations\": 5,\n        },\n    )\n    da_dx_truncated = torch.autograd.grad(\n        updated_inputs[\"a\"], data_x, retain_graph=True\n    )[0].squeeze()\n    torch.testing.assert_close(da_dx_numeric, da_dx_truncated, atol=1e-3, rtol=1e-3)\n\n    updated_inputs, _ = theseus_optim.forward(\n        theseus_inputs,\n        optimizer_kwargs={\n            \"track_best_solution\": True,\n            \"verbose\": False,\n            \"backward_mode\": th.BackwardMode.DLM,\n            \"dlm_epsilon\": 0.001,\n        },\n    )\n    da_dx_dlm = torch.autograd.grad(updated_inputs[\"a\"], data_x, retain_graph=True)[\n        0\n    ].squeeze()\n    torch.testing.assert_close(da_dx_numeric, da_dx_dlm, atol=1e-1, rtol=1e-1)\n\n\n@run_if_baspacho()\n@pytest.mark.parametrize(\n    \"linear_solver_cls\",\n    [\n        th.CholeskyDenseSolver,\n        th.CholmodSparseSolver,\n        th.LUCudaSparseSolver,\n        th.BaspachoSparseSolver,\n    ],\n)\ndef test_backwards_quartic(linear_solver_cls):\n    device = \"cpu\"\n    if linear_solver_cls in [\n        th.LUCudaSparseSolver,\n        th.BaspachoSparseSolver,\n    ]:\n        if not torch.cuda.is_available():\n            return\n        device = \"cuda:0\"\n\n    def error_fn(optim_vars, aux_vars):\n        (a,) = optim_vars\n        (x,) = aux_vars\n        err = (a.tensor - x.tensor) ** 2\n        return err\n\n    a = th.Vector(1, name=\"a\")\n    x_th = torch.zeros([1, 1]).requires_grad_()\n    x = th.Variable(x_th, name=\"x\")\n    optim_vars = [a]\n    aux_vars = [x]\n    cost_function = th.AutoDiffCostFunction(\n        optim_vars,\n        error_fn,\n        1,\n        aux_vars=aux_vars,\n        name=\"cost_fn\",\n    )\n    objective = th.Objective()\n    objective.add(cost_function)\n    optimizer = th.GaussNewton(\n        objective,\n        max_iterations=15,\n        step_size=1.0,\n        linear_solver_cls=linear_solver_cls,\n    )\n\n    theseus_inputs = {\n        \"a\": torch.ones([1, 1]).requires_grad_().to(device),\n        \"x\": x_th.to(device),\n    }\n    theseus_optim = th.TheseusLayer(optimizer)\n    theseus_optim.to(device)\n\n    updated_inputs, _ = theseus_optim.forward(\n        theseus_inputs,\n        optimizer_kwargs={\n            \"track_best_solution\": True,\n            \"verbose\": False,\n            \"backward_mode\": \"implicit\",\n        },\n    )\n\n    da_dx = torch.autograd.grad(updated_inputs[\"a\"], x_th, retain_graph=True)[\n        0\n    ].squeeze()\n\n    # Equality should hold exactly even in floating point\n    # because of how the derivatives cancel\n    assert da_dx.item() == 1.5\n\n\ndef test_implicit_fallback_linear_solver():\n    # Create a singular system that can only be solved if damping added\n    x = th.Vector(2, name=\"x\")\n    t = th.Vector(2, name=\"t\")\n\n    o = th.Objective()\n    w = th.DiagonalCostWeight(torch.FloatTensor([1, 0]).view(1, 2))\n    o.add(th.Difference(x, t, w, name=\"cost\"))\n    opt = th.TheseusLayer(th.LevenbergMarquardt(o, max_iterations=5))\n\n    input_dict = {\"x\": torch.ones(1, 2), \"t\": torch.zeros(1, 2)}\n\n    # __strict_implicit_final_gn__ = True shows that this problem leads to errors\n    with pytest.raises(RuntimeError):\n        opt.forward(\n            input_dict,\n            optimizer_kwargs={\n                \"damping\": 0.1,\n                \"backward_mode\": \"implicit\",\n                \"__strict_implicit_final_gn__\": True,\n            },\n        )\n    # No error is raised by default\n    opt.forward(\n        input_dict,\n        optimizer_kwargs={\"damping\": 0.1, \"backward_mode\": \"implicit\"},\n    )\n","repo_name":"facebookresearch/theseus","sub_path":"tests/theseus_tests/optimizer/nonlinear/test_backwards.py","file_name":"test_backwards.py","file_ext":"py","file_size_in_byte":7083,"program_lang":"python","lang":"en","doc_type":"code","stars":1481,"dataset":"github-code","pt":"81"}
+{"seq_id":"13817534186","text":"# -*- coding: utf-8 -*-\r\n\"\"\"Input text file of sentences, output text file of terms (Chinese)\r\n\r\nOutput format: '/詞1/詞2/詞2\\n...'\r\n\"\"\"\r\n\r\nimport jieba\r\nimport jieba.posseg as pseg\r\nfrom udicOpenData.dictionary import *\r\nfrom udicOpenData.stopwords import rmsw\r\nimport os.path, sys\r\n\r\n\r\ndef PosTokenizer(BASE_DIR, inputData, output, mission, save=None, remove=None):\r\n    f2 = open(output, 'a')\r\n    if save != None and remove != None:\r\n        print('can\\'t set save and remove at once')\r\n    elif mission == 't':\r\n        for sentence in inputData:\r\n            if sentence != '\\n':\r\n                words = pseg.cut(sentence)\r\n                for word, flag in words:\r\n                    if word != '\\n':\r\n                        if remove != None and str(flag)[0] not in remove and str(flag) not in remove:\r\n                            f2.write(\"/\" + word + \" \" + flag)\r\n                        elif save != None and str(flag)[0] in save:\r\n                            f2.write(\"/\" + word + \" \" + flag)\r\n                        else:\r\n                            f2.write(\"/\" + word + \" \" + flag)\r\n                    else:\r\n                        f2.write('\\n')\r\n\r\n    elif mission == 'w' and (save != None or remove != None):\r\n        for sentence in inputData:\r\n            if sentence != '\\n':\r\n                words = pseg.cut(sentence)\r\n                for word, flag in words:\r\n                    if word != '\\n':\r\n                        if remove != None and str(flag)[0] not in remove and str(flag) not in remove:\r\n                            f2.write(\"/\" + word)\r\n                        elif save != None and (str(flag)[0] in save or flag in save):\r\n                            f2.write(\"/\" + word)\r\n                    else:\r\n                        f2.write('\\n')\r\n\r\n\r\n    elif mission == 'w' and save == None and remove == None:\r\n        for sentence in inputData:\r\n            if sentence != '\\n':\r\n                words = jieba.cut(sentence, cut_all=False)\r\n                for word in words:\r\n                    if word != '\\n':\r\n                        f2.write(word + ' ')\r\n                    else:\r\n                        f2.write('\\n')\r\n    elif mission == 'r':\r\n        # only remove stopwords\r\n        for sentence in inputData:\r\n            if sentence != '\\n':\r\n                words = rmsw(sentence)\r\n                for word in words:\r\n                    if word != '\\n':\r\n                        f2.write(word + ' ')\r\n                    else:\r\n                        f2.write('\\n')\r\n    f2.close()\r\n\r\n\r\nif __name__ == '__main__':\r\n    \"\"\"\r\n    prog : 程式名稱\r\n    formatter_class : help信息输出格式\r\n    description : -help 所顯示的文本\r\n    \"\"\"\r\n    parser = argparse.ArgumentParser(prog=os.path.basename(sys.argv[0]),\r\n                                     formatter_class=argparse.RawDescriptionHelpFormatter,\r\n                                     description=__doc__)\r\n\r\n    parser.add_argument(\"input\", help=\"input file\")\r\n\r\n    # add_argument_group 這個function是為了讓-h的時候顯示的參數可以依照組別去放\r\n    groupO = parser.add_argument_group('Output')\r\n    groupO.add_argument(\"-o\", \"--output\", default=\"output\", help=\"output file\")\r\n\r\n    groupP = parser.add_argument_group('Processing')\r\n    # w只存斷詞後結果，t是跟著詞性一起存\r\n    groupP.add_argument(\"-m\", \"--model\", default=\"w\", choices=[\"w\", \"t\"],\r\n                        help=\"w:only save word , t: save word along with Part of Speech\")\r\n    groupP.add_argument(\"-s\", \"--save\", nargs='+', help=\"only save specific POS. [Priority: Save > remove]\")\r\n    groupP.add_argument(\"-r\", \"--remove\", nargs='+', help=\"only remove specific POS. [Priority: Save > remove]\")\r\n\r\n    # 解析參數\r\n    args = parser.parse_args()\r\n    PosTokenizer(args.input, args.output, args.model, save=args.save, remove=args.remove)\r\n","repo_name":"UDICatNCHU/KCM","sub_path":"KCM/build/sentences_to_terms_cht.py","file_name":"sentences_to_terms_cht.py","file_ext":"py","file_size_in_byte":3874,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"81"}
+{"seq_id":"10503830706","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nSpyder Editor\r\n\r\nThis is a temporary script file.\r\n\"\"\"\r\n\r\nimport numpy as np\r\nimport pandas as pd\r\nnp.random.seed(1)\r\n\r\nfull_labels = pd.read_csv('data/raccoon_labels.csv') #clw note:将csv文件读取到一个DataFrame中，shape为(217, 8)\r\n                                                     #         虽然只有200张图片，但由于个别图片有多个raccoon，\r\n                                                     #         因此会有会有217条(>200)的记录； 8代表表格的columns\r\nprint(full_labels.head())\r\n\r\n\r\ngrouped = full_labels.groupby('filename')  \r\na=grouped.apply(lambda x: len(x))  #clw note:a是一个包含200条记录的Series，因为有200张图片\r\n                                   #        该函数的作用在于，统计含有同一'filename'的记录\r\n                                   #        因此如果某张图片含有2只raccoon，则该filename对应的值即为2\r\nb=a.value_counts()   # 统计含有n只raccoon的图片总数，这里1只raccoon有184张，2只有15张，3只1张，之前的1*184+2*15+3=217\r\nprint(b)\r\n\r\ngrouped_list = [grouped.get_group(x) for x in grouped.groups] #clw note:汇总成200个DataFrame，对于只有1只raccon的图\r\n                                                              #         该DataFrame只有1条记录，否则会有多余一条...\r\n                \r\n\r\ntrain_index = np.random.choice(len(grouped_list), size=160, replace=False) #clw note：size=160相当于2:8分测试集\r\ntest_index = np.setdiff1d(list(range(200)), train_index) #clw note:在len(grouped_list)中，除了train_index\r\n                                                         #         剩下的都给到中，除了test_index\r\n\r\nwith open('train.txt', 'w') as f:\r\n    for index in train_index:\r\n        #print('raccoon-' + str(index) + '.jpg')\r\n        f.write('raccoon-' + str(index+1)) #clw note:这里没加.jpg，只是文件名\r\n                                      #加1是因为index范围是0-199而实际xml是1-200\r\n        if not index == train_index[-1]:\r\n            f.write('\\n')\r\n\r\n        \r\nwith open('eval.txt', 'w') as f:  \r\n    for index in test_index:\r\n        #print('raccoon-' + str(index))\r\n        f.write('raccoon-' + str(index+1)) \r\n        if not index == test_index[-1]:\r\n            f.write('\\n')\r\n\r\n\r\n\r\n#train = pd.concat([grouped_list[i] for i in train_index])\r\n#test = pd.concat([grouped_list[i] for i in test_index])","repo_name":"witzou/Python_Practice","sub_path":"常用数据预处理脚本/训练集切分一部分到验证集/csv数据集切分/csv_to_txt.py","file_name":"csv_to_txt.py","file_ext":"py","file_size_in_byte":2457,"program_lang":"python","lang":"zh","doc_type":"code","stars":3,"dataset":"github-code","pt":"81"}
+{"seq_id":"28204552650","text":"#TASK ONE  Use the figures in the variable tuple and give results as follows:\n#'file__002:    123.46     1.00e+04    1.23e+04\ntuple = (2, 123.4567, 10000, 12345.67)\nprint (\"'file__{:0>3d}:\".format(tuple[0]), \"{:.2f}\".format(tuple[1]), \"{:.2e}\".format(tuple[2]), \"{:.2e}\".format(tuple[3]))\n\n#TASK TWO:  use a different formatting to re-do Task One\ntuple = (2, 123.4567, 10000, 12345.67)\ntuple1 = (\"file__{:0>3d}:\".format(tuple[0]))\ntuple2 = (\"{:.2f}\".format(tuple[1]))\ntuple3 = (\"{:.2e}\".format(tuple[2]))\ntuple4 = (\"{:.2e}\".format(tuple[3]))\nprint (f\"{tuple1, tuple2, tuple3, tuple4}\")\n\n#TASK THREE.  reprint a set up of a tuple with 3 values and fitting exactly into a sentence with 3 variables to allow for \n# arbitrary tuple values   \nnums = [9,103]  #assigns a valuable \"nums\" to an arbitrary number of numbers in a list. \nt = len(nums)   # assigns a variable \"t\" as the number of elements in the variable \"nums\"\n[\"{}\"]*t  #sets up a certain number of f strings to be put in place multiplied by the number of elements in the list\n\", \".join([\"{}\"]*t).format(*nums)  #joins by a comma, the f strings and in them will be the elements in nums\nprint ((\"There are {} numbers and they are \" + \", \".join([\"{}\"] * t)).format(t, *nums))  #puts it together, \n#sets up a string with an f string linking to the variable \"t\", and the above string of f strings, each filled by an element in nums \n\n#TASK FOUR.  given a 5 element tuple (4, 30, 2017, 2, 27), return this tuple:  (02, 27, 2017, 04, 30)\ntask_four= (4, 30, 2017, 2, 27)\nprint(\"{:0>2d}\".format((task_four[3])), task_four[4], task_four[2], \"{:0>2d}\".format((task_four[0])), task_four[1])\n\n#TASK FIVE PART I.  Given the following four element list:  [\"oranges\", 1.3, \"lemon\", 1.1], \n#write an f string that will display \"the weight of an orange is 1.3 and the weight of a lemon is 1.1\"\n \ndata = [\"orange\", 1.3, \"lemon\", 1.1]\nprint (f\"The weight of an {data[0]} is {data[1]} and the weight of a {data[2]} is {data[3]}.\")\n\n#TASK FIVE PART II:  Now see if you can change the f-string so that it displays the names of the fruit in \n# upper case, and the weight 20% higher (that is 1.2 times higher).\nprint (f\"The weight of an {data[0].upper()} is {data[1]*1.2} and the weight of a {data[2].upper()} is {data[3]*1.2}.\")\n\n#TASK SIX EXAMPLE write some code that alighs the below information with other information later on\nitems = ['First', '$99.01', 'Second', '$88.09']\nitem_number = len(items)\nprint (('{:<30}'*item_number).format(*items))\n#TASK SIX EXERCISE:  Print a table of several rows, each with a name, age, and cost in upwards of a large sum of money\n\nheader = ['NAME', 'AGE', 'COST']\nitem_number = len(header)\nprint (('{:<30}'*item_number).format(*header))\nitems = [\"Johnny Walker Blue\", \"20 years\", \"$250.00\"]\nprint (('{:<30}'*item_number).format(*items))\n\nt = (1,2,3,5,6)\nprint (t[-2:-1])","repo_name":"Kenpatner/Lesson03","sub_path":"strformat_lab.py","file_name":"strformat_lab.py","file_ext":"py","file_size_in_byte":2842,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"70855968586","text":"from request_api_handler import RequestApiCreator\n\n\ndef dict_sub_lister(dict_, begin, end):\n    dict_to_return = {}\n    for key in dict_.keys():\n        if type(dict_[key]) != list:\n            continue\n        sub_list = dict_[key][begin:end]\n        if len(sub_list) == 1:\n            sub_list = sub_list[0]\n        dict_to_return[key] = sub_list\n    return dict_to_return\n\n\nclass WeatherReportFabric:\n    def __init__(self, weather_data=None):\n        self.weather_data = weather_data\n        self.global_options = {}\n        self.reports = []\n\n    def produce(self):\n        # date = datetime()\n        # properties = {temperature_2m_max: 21.0, temperature_2m_min: 9.0 ...}\n        # properties_hourly = [{time: 00:00, temperature: 21,3} ...]\n        if self.weather_data is None:\n            raise Exception(\"weather_data was not initiated\")\n        self.reports = []\n        daily_index = 0\n        begin_index = 0  # included\n        end_index = 24  # excluded\n        # set global options\n        print(self.weather_data[\"current_weather\"])\n        for date in self.weather_data[\"daily\"][\"time\"]:\n            weather_rep = WeatherReport()\n            weather_rep.date = date  # format?\n            weather_rep.properties = dict_sub_lister(self.weather_data[\"daily\"], daily_index, daily_index + 1)\n            weather_rep.properties_hourly = dict_sub_lister(self.weather_data[\"hourly\"], begin_index, end_index)\n            self.reports.append(weather_rep)\n            # print(f\"{begin_index} - {end_index}\")\n            begin_index = end_index\n            end_index = end_index + 24\n            daily_index = daily_index + 1\n        self.global_options = {\"current_weather\": self.weather_data[\"current_weather\"]}\n\n\n\n    def return_reports(self):\n        if len(self.global_options) == 0 and len(self.reports) == 0:\n            return None, None\n        return self.global_options, self.reports\n\n\nclass WeatherReport:\n    def __init__(self):\n        # date = datetime()\n        # properties = {temperature_2m_max: 21.0, temperature_2m_min: 9.0 ...}\n        # properties_hourly = [{time: 00:00, temperature: 21,3} ...]\n        self.date = None\n        self.properties = None\n        self.properties_hourly = None\n        pass\n\n    def get_data_by_index(self, index):\n        return dict_sub_lister(self.properties_hourly, index, index + 1)\n\n    def __str__(self):\n        return f\"date=              {self.date}\\n\" \\\n               f\"properties=        {self.properties}\\n\" \\\n               f\"properties_hourly= {self.properties_hourly}\\n\" \\\n               f\"-------------------------------------------------\"\n\n\n# if __name__ == '__main__':\n#     print(\"aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa\")\n#     requester = RequestApiCreator()\n#     # req.requests[\"latitude\"] = 2\n#     # print(req.requests)\n#     # print(RequestApiCreator.get_temp())\n#     # requester.set_coord(51.1, 17.0333)\n#     requester.set_by_location(\"New York\")\n#\n#     print(requester.append_hour_day(\"hourly\", \"temperature_2m\"))\n#     requester.append_many_hour_day(\"hourly\", [\"temperature_2m\",\n#                                               \"relativehumidity_2m\",\n#                                               \"apparent_temperature\",\n#                                               \"pressure_msl\",\n#                                               \"cloudcover\",\n#                                               \"windspeed_10m\",\n#                                               \"winddirection_10m\",\n#                                               \"windgusts_10m\",\n#                                               \"precipitation\",\n#                                               \"weathercode\",\n#                                               ])\n#     # print(requester.append_hour_day(\"hourly\", \"pressure_msl\"))\n#     print(requester.append_hour_day(\"daily\", \"temperature_2m_max\"))\n#     requester.append_many_hour_day(\"daily\", [\"temperature_2m_max\",\n#                                              \"temperature_2m_min\",\n#                                              \"apparent_temperature_max\",\n#                                              \"apparent_temperature_min\",\n#                                              \"precipitation_sum\",\n#                                              \"precipitation_hours\",\n#                                              \"weathercode\",\n#                                              \"sunrise\",\n#                                              \"sunset\",\n#                                              \"windspeed_10m_max\",\n#                                              \"windgusts_10m_max\",\n#                                              \"winddirection_10m_dominant\"])\n#     requester.set_param(\"current_weather\", True)\n#     requester.create()\n#     fabric = WeatherReportFabric(requester.obtain_data())\n#     fabric.produce()\n#     for rep in fabric.reports:\n#         print(rep)\n#         print(rep.get_data_by_index(0))\n","repo_name":"commdeb/ProjectsFromCollege","sub_path":"Python/WeatherApp/weather_info_container.py","file_name":"weather_info_container.py","file_ext":"py","file_size_in_byte":4943,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"40101099081","text":"#-*- coding: UTF-8 -*-\n\nimport requests\n\npath = 'D://ab.jpg'\nurl = \"http://script.zxxk.com/flash/js1709203.jpg\"\nr = requests.get(url)\nr.status_code\nprint(r.status_code)\n\nwith open(path, 'wb') as f:\n\tf.write(r.content)\n\n","repo_name":"wh7033413/Pa_Chong","sub_path":"0003save_pic.py","file_name":"0003save_pic.py","file_ext":"py","file_size_in_byte":219,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"34530588079","text":"from PyQt5.QtWidgets import *\nfrom PyQt5.QtGui import QIcon, QPixmap, QImage, QPalette, QBrush\nfrom PyQt5.QtCore import QSize, Qt, QThread ,QTimer\nfrom PyQt5 import QtCore, QtGui, QtWidgets\nimport sys, random, time\nimport time, variables\nfrom playerstate import  State\n\nclass Charachter(QLabel):\n\n    def __init__(self, parent, broj):\n        QLabel.__init__(self, parent)\n        self.koji = broj\n        self.initChar()\n        self.jump = State(broj)\n        self.jump.start()\n        self.time = time.time()\n        self.isJumping = False\n\n\n\n    def initChar(self):\n        if(self.koji==0):\n            self.pix1 = QPixmap('Pictures/bbobble.png')\n            self.setPixmap(self.pix1.scaled(50,50))\n            self.setGeometry(370,552 , 50, 50)\n            self.setFixedSize(50, 50)\n            #self.setStyleSheet(\"background-image: url()\")\n            variables.x = 370\n            variables.y = 552\n            timer = QTimer(self)\n            timer.start(20)\n            timer.timeout.connect(self.changePosition)\n\n    def changePosition(self):\n        self.move(variables.x, variables.y)\n        if variables.charDead == True:\n            self.setVisible(False)\n        else:\n            self.setVisible(True)\n        if variables.left == True:\n            self.setPixmap(self.pix1.scaled(50,50).transformed((QtGui.QTransform().scale(-1, 1))))\n        else:\n            self.setPixmap(self.pix1.scaled(50, 50))\n\n    def __update_position__(self, key):\n        if self.koji == 0:\n            if variables.charDead == False:\n                if key == Qt.Key_Space:\n                    if self.bullet.shoot.ableToFire == True:\n                        self.bullet.shoot.char = self\n                        self.bullet.shoot.bullet = self.bullet\n                        self.bullet.shoot.ableToFire = False\n                        self.bullet.shoot.fireing = True\n                if key == Qt.Key_Right:\n                    self.jump.movingRight = True\n                    self.bullet.shoot.turnedLeft = False\n                if key == Qt.Key_Up:\n                    self.jump.isJumping = True\n                if key == Qt.Key_Left:\n                    self.jump.movingLeft = True\n                    self.bullet.shoot.turnedLeft = True\n\n","repo_name":"vcamagic/Bubble_Bobble","sub_path":"Pictures/Bubble_Bobble-master/character.py","file_name":"character.py","file_ext":"py","file_size_in_byte":2244,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"25739525511","text":"# -*- coding: utf-8 -*-\n\nfrom __future__ import unicode_literals, print_function, division\nimport time\nimport math\n\nimport torch \nimport torch.nn as nn\nfrom torch import optim\n\nfrom lm_model import LM2\nfrom text_data import TextIterator\n\nfrom mylib.utils import timeSince, ids2words, unbpe\nimport nmt_const as Const\nimport sys\n\nuse_cuda = torch.cuda.is_available()\ndevice=torch.device(\"cuda\" if use_cuda else \"cpu\")\n\ndef train(model, optimizer, data, mask, args):\n\n    loss, n_words, _ = model(data, mask) # a, b not used\n\n    model.zero_grad()\n    loss.backward()\n    if args.grad_clip > 0:\n        nn.utils.clip_grad_value_(model.parameters(), args.grad_clip)\n    optimizer.step()\n\n    return loss.item(), n_words\n\ndef eval_model(model, data_file, args):\n    model.eval() \n    torch.no_grad()\n    valid_iter = TextIterator(data_file, args.data_dict,\n                         batch_size=1, maxlen=args.max_length,\n                         ahead=1, resume_num=0, mask_pos=False, const_id=Const)\n    loss_total = 0.0\n    n_total = 0.0\n    for data, mask, cur_line, iloop in valid_iter:\n        loss, n_words, _ = model(data, mask)\n        loss_total += loss.item()\n        n_total += n_words\n\n    torch.set_grad_enabled(True)\n    return loss_total / n_total \n\ndef train_model(args, neptune):\n    # data loading\n    train_iter = TextIterator(args.train_data_file, args.data_dict,\n                         batch_size=args.batch_size, maxlen=args.max_length,\n                         ahead=10, resume_num=0, mask_pos=False, const_id=Const)\n \n    args.data_words_n = len(train_iter.data_dict2)\n    start = time.time()\n    loss_total = 0  # Reset every args.print_every\n    words_total = 0\n\n    # model \n    model = LM2(args=args).to(device)\n\n    # optimizer\n    optimizer = optim.Adam(model.parameters(), lr=args.learning_rate, betas=(0.0, 0.999))\n\n    file_name = args.save_dir + '/' + args.exp_id + '.pth'\n    bad_counter=0\n    best_loss=None\n    \n    try: \n        for data, mask, cur_line, iloop in train_iter:\n            loss, n_words = train(model, optimizer, data, mask, args)\n            loss_total += loss\n            words_total += n_words \n\n            if iloop % args.print_every == 0:\n                loss_avg = loss_total / words_total\n                loss_total = 0; words_total = 0\n                print('%s: %d iters - %.4f %s' % (args.name + args.tag, iloop, loss_avg, timeSince(start)))\n                neptune.log_metric('train loss', loss_avg)\n                neptune.log_metric('train ppl', math.exp(loss_avg)) \n\n            if iloop >= args.val_start and iloop % args.valid_every == 0:\n                val_loss = eval_model(model, args.valid_data_file, args)\n                model.train()\n\n                if iloop > args.val_start: \n                    if best_loss is None or val_loss <= best_loss: \n                        bad_counter = 0\n                        torch.save(model, file_name +'.best.pth')\n                        best_loss = val_loss\n                    else:\n                        bad_counter += 1\n                        print('bad_counter:', bad_counter)\n\n                    if bad_counter >= args.patience:\n                        flag = False\n                        for param_group in optimizer.param_groups:\n                            param_group['lr'] /= args.lr_decay\n                            \n                            if not flag:\n                               print('lr decayed to {:.4f}'.format(param_group['lr'])) \n                               flag = True\n\n                        bad_counter = 0\n\n                print ('valid loss', val_loss)\n                neptune.log_metric('valid loss', val_loss)\n                neptune.log_metric('valid ppl', math.exp(val_loss)) \n    \n    except KeyboardInterrupt:\n        test_loss = eval_model(model, args.test_data_file, args)\n        print ('test loss {:.4f} ppl: {:.4f}'.format(test_loss, math.exp(test_loss)))\n        neptune.log_metric('test loss', test_loss)\n        neptune.log_metric('test ppl', math.exp(test_loss)) \n","repo_name":"ChungJunn/LanguageModel","sub_path":"lm_main.py","file_name":"lm_main.py","file_ext":"py","file_size_in_byte":4035,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"41645379836","text":"import customtkinter\nfrom tkinter import *\nimport SM3\nimport time\nfrom tkinter import filedialog\nfrom xml.etree import ElementTree as ET\nfrom decimal import Decimal\nfrom tksheet import Sheet\n\ncustomtkinter.set_appearance_mode(\"System\")  # Modes: \"System\" (standard), \"Dark\", \"Light\"\ncustomtkinter.set_default_color_theme(\"blue\")  # Themes: \"blue\" (standard), \"green\", \"dark-blue\"\n\n\nclass App(customtkinter.CTk):\n    def __init__(self):\n        super().__init__()\n\n        # configure window\n        self.title(\"MuTools\")\n        self.geometry(f\"{1100}x{580}\")\n\n        # configure grid layout (2x1)\n        self.grid_columnconfigure(1, weight=1)\n        self.grid_rowconfigure(1, weight=1)\n\n        # create sidebar frame with widgets\n        self.sidebar_frame = customtkinter.CTkFrame(self, width=140, corner_radius=0)\n        self.sidebar_frame.grid(row=0, column=0, rowspan=4, sticky=\"nsew\")\n        self.sidebar_frame.grid_rowconfigure(4, weight=1)\n        self.logo_label = customtkinter.CTkLabel(self.sidebar_frame, text=\"工具箱\", font=customtkinter.CTkFont(size=20, weight=\"bold\"))\n        self.logo_label.grid(row=0, column=0, padx=20, pady=(20, 10))\n        self.sidebar_button_1 = customtkinter.CTkButton(self.sidebar_frame, text=\"性能提取\", command=self.init_showperf)\n        self.sidebar_button_1.grid(row=1, column=0, padx=20, pady=10)\n        self.sidebar_button_2 = customtkinter.CTkButton(self.sidebar_frame, text=\"SM3算法\", command=self.SM3)\n        self.sidebar_button_2.grid(row=2, column=0, padx=20, pady=10)\n        self.scaling_label = customtkinter.CTkLabel(self.sidebar_frame, text=\"UI Scaling:\", anchor=\"w\")\n        self.scaling_label.grid(row=7, column=0, padx=20, pady=(10, 0))\n        self.scaling_optionemenu = customtkinter.CTkOptionMenu(self.sidebar_frame, values=[\"80%\", \"90%\", \"100%\", \"110%\", \"120%\"],\n                                                               command=self.change_scaling_event)\n        self.scaling_optionemenu.grid(row=8, column=0, padx=20, pady=(10, 20))\n\n\n    def open_input_dialog_event(self):\n        dialog = customtkinter.CTkInputDialog(text=\"Type in a number:\", title=\"CTkInputDialog\")\n        print(\"CTkInputDialog:\", dialog.get_input())\n\n    def change_scaling_event(self, new_scaling: str):\n        new_scaling_float = int(new_scaling.replace(\"%\", \"\")) / 100\n        customtkinter.set_widget_scaling(new_scaling_float)\n\n    def init_showperf(self):\n        self.perf_frame = customtkinter.CTkFrame(self, corner_radius=0, fg_color='#EEEEEE')\n        self.perf_frame.grid(row=0, column=1, rowspan=3, columnspan=3, sticky=\"nsew\")\n        self.perf_name = customtkinter.CTkTextbox(self.perf_frame)\n        self.perf_name.grid(row=0, column=0, padx=(20, 0), pady=(20, 20), sticky=\"nsew\")\n        self.perf_source = customtkinter.CTkTextbox(self.perf_frame)\n        self.perf_source.grid(row=0, column=1, padx=(20, 0), pady=(20, 20), sticky=\"nsew\")\n        self.perf_result = customtkinter.CTkTextbox(self.perf_frame)\n        self.perf_result.grid(row=0, column=2, padx=(20, 0), pady=(20, 20), sticky=\"nsew\")\n        self.file_path = customtkinter.CTkTextbox(self, width=250, height=20)\n        self.file_path.grid(row=3, column=1, columnspan=2, padx=(20, 0), pady=(20, 20), sticky=\"nsew\")\n        self.main_button_1 = customtkinter.CTkButton(master=self, fg_color=\"transparent\", border_width=2,\n                                                     text_color=(\"gray10\", \"#DCE4EE\"), command=self.showperf, text='选择文件')\n        self.main_button_1.grid(row=3, column=3, padx=(20, 20), pady=(20, 20), sticky=\"nsew\")\n\n\n    def showperf(self):\n        path = filedialog.askopenfilename(filetypes=[(\"日志文件\", [\".xml\"])])\n        self.file_path.insert(END, path)\n        self.perf_name.delete(1.0, END)\n        self.perf_source.delete(1.0, END)\n        self.perf_result.delete(1.0, END)\n        if path:\n            try:\n                tree = ET.parse(path)\n                root = tree.getroot()\n                perf = []\n                for child in root:\n                    for son in child:\n                        if son.tag == 'cipherPerformance':\n                            for grchild in son:\n                                if 'SM3' in grchild.tag:\n                                    perf.append(grchild)\n                                else:\n                                    for grson in grchild:\n                                        perf.append(grson)\n                        if son.tag == 'devicePerformance':\n                            for grchild in son:\n                                if 'con' not in child.tag:\n                                    perf.append(grchild)\n                                else:\n                                    for grson in grchild:\n                                        perf.append(grson)\n                prename = ''\n                name = 'non'\n                for item in perf:\n                    prename = name[:3]\n                    res = '0'\n                    for re in item:\n                        if re.tag == 'performanceResult':\n                            res = re.text\n                        if (re.tag == 'usecaseName') & (type(re.text) == type('str')):\n                            name = re.text.split(': ')[1]\n                    if res != '0':\n                        if (prename != 'non') & (prename != name[:3]):\n                            self.perf_name.insert(END, '\\n')\n                            self.perf_source.insert(END, '\\n')\n                            self.perf_result.insert(END, '\\n')\n                        if name[2] not in ['1', '3', '4', '7']:\n                            if (\"加密\" in name) | (\"解密\" in name):\n                                res = res + \"Kbps\"\n                            if (\"签名\" in name) | (\"验签\" in name):\n                                res = res + \"次/秒\"\n                            if \"密钥\" in name:\n                                res = res + \"对/秒\"\n                            if name == \"随机数性能\":\n                                res = res + \"Mbps\"\n                            if name == \"对称密钥产生性能\":\n                                res = res + \"组/秒\"\n                            if name == \"非对称密钥产生性能\":\n                                res = res + \"对/秒\"\n                            if \"连接数\" in name:\n                                res = res + \"条\"\n                            self.perf_name.insert(END, name + '\\n')\n                            self.perf_source.insert(END, res + '\\n')\n                            self.perf_result.insert(END, res + '\\n')\n\n                        else:\n                            self.perf_name.insert(END, name + '\\n')\n                            self.perf_source.insert(END, res + 'Kbps' +'\\n')\n                            res = str(Decimal(str(float(res) / 1024)).quantize(Decimal(\"0.01\"),\n                                                                               rounding=\"ROUND_HALF_UP\")) + 'Mbps'\n                            self.perf_result.insert(END, res + '\\n')\n            except:\n                self.perf_name.delete(1.0, END)\n                self.perf_name.insert(1.0, \"export performance failed\")\n        else:\n            self.perf_name.delete(1.0, END)\n            self.perf_name.insert(1.0, \"ERROR:export performance failed\")\n\n\n\n    def SM3(self):\n        self.sm3_frame = customtkinter.CTkFrame(self, corner_radius=0, fg_color='#EEEEEE')\n        self.sm3_frame.grid(row=0, column=1, rowspan=4, columnspan=3, sticky=\"nsew\")\n        self.sm3source = customtkinter.CTkTextbox(self.sm3_frame, width=250)\n        print('SM3_Hash')\n\n\nif __name__ == \"__main__\":\n    app = App()\n    app.mainloop()","repo_name":"mf0715/guitool","sub_path":"tools.py","file_name":"tools.py","file_ext":"py","file_size_in_byte":7733,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"74358916106","text":"import model\r\nimport torch\r\nfrom torch.nn import functional as F\r\nfrom torch.utils.data import DataLoader\r\nfrom loss import loss_l2 as Loss\r\nimport cv2\r\nimport dataset\r\nimport os\r\n# to train refinenet \r\n\r\n\r\n\r\n\r\n\r\n###############\r\ndef train(model,train_loader,optimizer):\r\n    #train\r\n    # print(\"training...\")\r\n    model.train()\r\n    epoch_loss=0\r\n    # i=0\r\n    for input,label in train_loader:\r\n        # i+=1\r\n        # print(i)\r\n\r\n        input=input.cuda()\r\n        # print(input.shape)\r\n        label=label.cuda()\r\n\r\n        output=model(input)\r\n        loss=Loss(output,label)\r\n        epoch_loss+=loss\r\n\r\n        # compute gradient and do SGD step\r\n        optimizer.zero_grad()\r\n        loss.backward()\r\n        optimizer.step()\r\n\r\n \r\n    return model,epoch_loss/len(train_loader)\r\n\r\n\r\n\r\n\r\ndef test(model,val_loader):\r\n    val_loss=0\r\n    model.eval()\r\n    with torch.no_grad():\r\n        # print(len(val_loader))\r\n        # i=0\r\n        for input,label in val_loader: \r\n            # i+=1\r\n            # print(i)\r\n            input=input.cuda()\r\n            label=label.cuda()\r\n            output=model(input)\r\n\r\n            # print(output)\r\n            loss=Loss(output,label)\r\n            val_loss+=loss\r\n        \r\n\r\n        return val_loss/len(val_loader)\r\n\r\n\r\ndef main():\r\n\r\n    #Hyper parameters\r\n\r\n    initial_lr = 0.01\r\n    momentum = 0.9\r\n    weight_decay = 5e-4\r\n    num_epoch = 5000\r\n    use_gpu = True\r\n    gen_kp_gt = False\r\n    number_point = 8\r\n    modulating_factor = 1.0\r\n    in_channels=8\r\n    num_keypoints=8\r\n    batch_size=32\r\n    train_pred_file='./../tools/results/ape_101/refinement/train_8_101_pred.json'\r\n    train_gt_file='./../datasets/LINEMOD/ape/ape_val_gen.json'\r\n    val_pred_file='./../tools/results/ape_101/refinement/val_8_101_pred.json'\r\n    val_gt_file='./../datasets/LINEMOD/ape/ape_val_gen.json'\r\n\r\n    model_savepath='./../tools/results/ape_101/refinement/model_regression'\r\n\r\n    modelpath='./../tools/results/ape_101/refinement/model_regression/4999.pth'\r\n    #\r\n\r\n    print(\"train data loading...\")\r\n    train_data = dataset.CoordDataset(train_pred_file, train_gt_file)\r\n    train_loader = DataLoader(train_data, shuffle=True, batch_size=batch_size)\r\n\r\n    #\r\n    print(\"test data loading...\")\r\n    val_data=dataset.CoordDataset(val_pred_file, val_gt_file)\r\n    val_loader = DataLoader(val_data, shuffle=True, batch_size=batch_size)\r\n                \r\n    #\r\n    #     \r\n    # #build RefineNet\r\n    print(\"RefineNet building...\")\r\n    Feature_extractor=model.KeypointRCNNFeatureExtractor(in_channels)\r\n    Predictor=model.KeypointRCNNPredictor2(16,num_keypoints)\r\n    RefineNet=model.RefineNet(Feature_extractor,Predictor)\r\n    print(\"model build done \")     \r\n\r\n    RefineNet.load_state_dict(torch.load(modelpath))\r\n    #\r\n    optimizer = torch.optim.SGD(RefineNet.parameters(), lr=initial_lr, \r\n                                momentum=momentum, weight_decay=weight_decay)\r\n    scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, \r\n                                [int(0.5*num_epoch), int(0.75*num_epoch),\r\n                                    int(0.9*num_epoch)], gamma=0.1)\r\n    #gpu \r\n    RefineNet.cuda()\r\n            \r\n\r\n    # val_loss=test(RefineNet,val_loader)\r\n    print('training...')\r\n    for epoch in range(num_epoch):\r\n        #train\r\n        scheduler.step()\r\n        RefineNet,trian_loss=train(RefineNet,train_loader,optimizer)\r\n\r\n        # print(len(train_loader))\r\n        print(\"epoch:{}\".format(epoch)+\"=\"*50+\"train loss:{}\".format(trian_loss))\r\n        #test\r\n\r\n        val_loss=test(RefineNet,val_loader)\r\n        # print(val_loader)\r\n        print(\"epoch:{}\".format(epoch)+\"=\"*50+\"validate loss:{}\".format(val_loss))\r\n\r\n\r\n        #save model \r\n        modelname=str(epoch)+'.pth'\r\n        modelpath=os.path.join(model_savepath,modelname)\r\n        torch.save(RefineNet.state_dict(), modelpath)\r\n\r\n    print(\"train done\")\r\n\r\n        \r\n    return True\r\n\r\n\r\n\r\n# \r\nif __name__ == \"__main__\":\r\n    main()\r\n    ","repo_name":"CvHadesSun/E2P","sub_path":"refinenet/train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":3986,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"81"}
+{"seq_id":"1321617927","text":"#Import modules\nfrom Patient import Patient\nimport random\n\n#initialize global variable\nrandom.seed(5)  # random seed used for reproducibility\nLENGTH_DNA=20\n\n\n########################\n##### Functions ########\n########################\n\ndef display_menu():\n    \"\"\" Display all the options, no input, no output \"\"\"\n    print(\"\\n1-List; 2-Info; 3-Remove; 4-Insert; 5-Compare; 6-Compare all; 7-Analyze\")\n\n\ndef random_base():\n    \"\"\"select the letter A,C,G,T at random, output (String) \"\"\"\n    bases=[\"A\", \"C\", \"G\", \"T\"]\n    return random.choice(bases)\n\n\n### to complete\ndef random_strand():\n    \"\"\"returns a random str dna by calling random_base()\"\"\"\n    d = \"\"\n    for i in range(LENGTH_DNA):\n        d += random_base()\n    return d\n\ndef initialize():\n    \"\"\"Initializing the first 16 patients in the list\"\"\"\n    name = \"Andrea\"\n    age = \"37\"\n    dna = random_strand()\n    p1 = Patient(name, age, dna)\n\n    name = \"Bob\"\n    age = 28\n    dna = random_strand()\n    p2 = Patient(name, age, dna)\n\n    name = \"Brooke\"\n    age = 34\n    dna = random_strand()\n    p3 = Patient(name, age, dna)\n\n    name = \"Connor\"\n    age = 27\n    dna = random_strand()\n    p4 = Patient(name, age, dna)\n\n    name = \"James\"\n    age = 25\n    dna = random_strand()\n    p5 = Patient(name, age, dna)\n\n    name = \"Jenna\"\n    age = 44\n    dna = random_strand()\n    p6 = Patient(name, age, dna)\n\n    name = \"John\"\n    age = 45\n    dna = random_strand()\n    p7 = Patient(name, age, dna)\n\n    name = \"Julie\"\n    age = 37\n    dna = random_strand()\n    p8 = Patient(name, age, dna)\n\n    name = \"Kate\"\n    age = 48\n    dna = random_strand()\n    p9 = Patient(name, age, dna)\n\n    name = \"Keith\"\n    age = 28\n    dna = random_strand()\n    p10 = Patient(name, age, dna)\n\n    name = \"Kelly\"\n    age = 25\n    dna = random_strand()\n    p11 = Patient(name, age, dna)\n\n    name = \"Luke\"\n    age = 33\n    dna = random_strand()\n    p12 = Patient(name, age, dna)\n\n    name = \"Mark\"\n    age = 34\n    dna = random_strand()\n    p13 = Patient(name, age, dna)\n\n    name = \"Pat\"\n    age = 26\n    dna = random_strand()\n    p14 = Patient(name, age, dna)\n\n    name = \"Taylor\"\n    age = 30\n    dna = random_strand()\n    p15 = Patient(name, age, dna)\n\n    name = \"Tony\"\n    age = 55\n    dna = random_strand()\n    p16 = Patient(name, age, dna)\n\n    return [p1, p2, p3, p4, p5, p6, p7, p8, p9, p10, p11, p12, p13, p14, p15, p16]\n\n\n\ndef display(patients, condition):\n    \"\"\"displaying the list of patients in the list\"\"\"\n    j = 1\n    print(\"\\tName\\tage\\tDNA-strand (20 length)\\t\",condition)\n    print(\"----------------------------------------------------------------\")\n\n    if not condition:  # will not print out condition column if its empty\n        for i in patients:\n            print(str(j) + \"\\t\" + i.name + \"\\t\" + str(i.age) + \"\\t\" + i.strand)\n            j += 1\n    else:\n        for i in patients:\n            print(str(j)+\"\\t\"+i.name+\"\\t\"+str(i.age)+\"\\t\"+i.strand + \"\\t\" + str(i.has_condition))\n            j += 1\n\n\ndef info(patients, condition):\n    \"\"\"prints out the percentage of patients in each age gap\"\"\"\n    u20 = 0\n    twenty = 0\n    thirty = 0\n    fourty = 0\n    fifty = 0\n    o60 = 0\n    sum_age = 0\n    l = len(patients)\n    for i in patients:\n        sum_age += int(i.age)\n        if int(i.age) < 20:\n            u20 += 1\n        elif 20 <= int(i.age) < 30:\n            twenty += 1\n        elif 30 <= int(i.age) < 40:\n            thirty += 1\n        elif 40 <= int(i.age) < 50:\n            fourty += 1\n        elif 50 <= int(i.age) < 60:\n            fifty += 1\n        elif int(i.age) >= 60:\n            o60 += 1\n\n    #  Calculating the percentage of each age group\n    u20f = float((u20/l)*100)\n    twentyf = float((twenty/l) * 100)\n    thirtyf = float((thirty / l) * 100)\n    fourtyf = float((fourty / l) * 100)\n    fiftyf = float((fifty / l) * 100)\n    o60f = float((o60 / l) * 100)\n\n    #  printing the info\n    print(\"#Patients \" + str(len(patients)))\n    print(\"<20: \" + str(u20f) + \"%\")\n    print(\"20's: \" + str(twentyf) + \"%\")\n    print(\"30's: \" + str(thirtyf) + \"%\")\n    print(\"40's: \" + str(fourtyf) + \"%\")\n    print(\"50's: \" + str(fiftyf) + \"%\")\n    print(\">=60: \" + str(o60f) + \"%\\n\")\n    print(\"Age Mean: \" + str(float(sum_age/l)))\n\n    #  to get the number of patients with the condtions and print out the number\n    if condition:\n        count = 0\n        for i in patients:\n            if i.has_condition:\n                count += 1\n        print(condition + \":\", str(count/len(patients)*100), \"%\")\n\n\ndef add_new_patient(patients):\n    \"\"\"returns a new patient list with the new patient attributes\"\"\"\n    name = input(\"Enter Name: \")\n    while True:\n        try:\n            age = int(input(\"Enter Age:\"))\n        except ValueError:\n            continue\n        else:\n            break\n    count = 0\n\n    while True:\n        strand = input(\"DNA strand: \")\n        for i in strand:\n            if i == 'A' or i == 'G' or i == 'T' or i == 'C':\n                count += 1\n        if count == LENGTH_DNA:\n            break\n        else:\n            print(\"Bad input! -length must be 20\")\n    p_new = Patient(name, age, strand)\n\n    return patients+[p_new]\n\ndef compare(p1, p2):\n    \"\"\"Compares the two strands and returns a new strand\"\"\"\n    temp_strand = \"\"\n    p1_strand = p1.strand\n    p2_strand = p2.strand\n    for i in range(len(p1.strand)):\n        # Simply comparing the character at position i of one strand with the character at the same position in strand 2\n        if p1_strand[i] == p2_strand[i]:\n            temp_strand += p1_strand[i]\n        else:\n            temp_strand += 'x'\n\n    return temp_strand\n\ndef check_completeness(strand):\n    \"\"\"returns the similarity percentage of the new_strand. \"\"\"\n    count = 0\n    for i in strand:\n        if i == 'x':\n            count += 1\n\n    count = LENGTH_DNA - count  #  subtracting the number of x's from the actual length, which are the total number of equal bases\n    return float((count/len(strand)) * 100)\n\ndef compare_all(p):\n    \"\"\"Compares each patient with another and prints the similarity if <33%\"\"\"\n    for i in range(len(p)):\n        for j in range(i):\n            simi_strand = compare(p[i], p[j])  # stores the value of the compared string b/w patients at index i and j\n            comparision_percent = check_completeness(simi_strand)  # stores the percent b/w the two patients from the previous line\n            if comparision_percent > 33:\n                print(p[i].name, \"vs\", p[j].name, comparision_percent,\"%\")\n\ndef find_pattern(p_list, sequence):\n    \"\"\"Looks for the sequence in each strand, sets the has_attribute condition for Patient object and returns the count\"\"\"\n    count = 0\n    for i in range(len(p_list)):\n        for j in range(LENGTH_DNA):\n            if p_list[i].strand[j:j+3] == sequence:\n                p_list[i].has_condition = True\n                count += 1\n                break\n            else:\n                p_list[i].has_condition = False\n\n\n    return count\n\n\n##########################\n########## Main Function #  to uncomment step by step fo testing\n##########################\n                \ndef main():\n    print(\"TEST FOR OPTION 1\")\n    print(\"\\n****TEST the random_strand function****\")\n    print(random_strand())\n\n    #################### TEST FOR OPTION 1\n    print(\"\\n****TEST the class Patient****\")\n    patient=Patient(\"Tom\",20,random_strand())\n    print(patient.name,patient.age,patient.strand)\n    \n    #################### TEST FOR OPTION 1\n    print(\"\\n****TEST the display function****\")\n    patients=[patient,Patient()]\n    patients[1].name=\"Lucy\"\n    patients[1].age=25\n    patients[1].strand=\"TCTTGTAAACTCGGAAACTG\"\n    display(patients)\n\n    #################### TEST FOR OPTION 2\n    print(\"\\n****TEST the info function****\")\n    info(patients)\n\n    # ##################### TEST for OPTION 4\n    # print(\"\\n****TEST the add_new_patient function****\")\n    # patients=add_new_patient(patients)\n    # display(patients)\n    \n    ##################### TEST for OPTION 5\n    print(\"\\n****TEST the compare function****\")\n    common_strand=compare(patients[0],patients[1])\n    print(common_strand)\n\n    ##################### TEST for OPTION 5\n    print(\"\\n****TEST the check_completness function****\")\n    print(check_completeness(common_strand))\n\n    \n\n\nif __name__==\"__main__\":\n    main()\n    \n","repo_name":"anushr03/Fun-with-DNA","sub_path":"main/dna_tool.py","file_name":"dna_tool.py","file_ext":"py","file_size_in_byte":8287,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"39136470547","text":"import sys\r\nfrom PyQt5.QtWidgets import *\r\nfrom PyQt5.QtGui import *\r\nfrom PyQt5.QtCore import *\r\n\r\nfrom math import *\r\nfrom shutil import *\r\nimport numpy as np\r\nimport os\r\nfrom PIL import Image\r\n#from settings import *\r\n\r\n\r\nclass Example(QMainWindow):\r\n    def __init__(self):\r\n        super().__init__()\r\n        \r\n        self.setWindowTitle('Image Viewer')  \r\n        screenRect = QApplication.desktop().screenGeometry()\r\n        self.sh = screenRect.height()\r\n        self.sw = screenRect.width()\r\n\r\n        self.config = {'num_fold': 4, 'folders': ['',] * 4, 'img_list': [], 'num_img': 0, 'path': 'C:/Users/admin/Desktop/', 'rename': False, 'img_size': 300}\r\n        self.default_path = os.path.dirname(self.config['folders'][0]) if self.config['folders'] is not None else ''\r\n        \r\n        self.listview = None\r\n        self.lbls = []\r\n        self.open_btn = []\r\n        self.method_name = []\r\n        self.suffix = ['.png', '.jpg', '_HS.png', '_segmentation.png', '_sal_fuse.png', '_HS.png', '_RBD.png']\r\n        \r\n        self.initUI()\r\n        self.show()\r\n\r\n        \r\n    def initUI(self):\r\n        #self.row = self.config['row'] # floor(sqrt(self.config['row']))\r\n        #self.col = self.config['col'] # ceil(self.config['num_fold'] /self.row)\r\n        \r\n        self.row = floor(sqrt(self.config['num_fold']))\r\n        self.col = ceil(self.config['num_fold'] / 1. / self.row)\r\n        \r\n        self.img_size = self.config['img_size'] # 280\r\n        self.pad_x = 15\r\n        self.pad_y = self.pad_x + 20\r\n\r\n        # calculate window size\r\n        win_w = 260 + self.col * (self.img_size + self.pad_x)\r\n        win_h = 60 + self.row * (self.img_size + self.pad_y)\r\n        self.setGeometry((self.sw - win_w) // 2, (self.sh - win_h) // 2, win_w, win_h)\r\n        self.setFixedSize(win_w, win_h)\r\n        \r\n        self.statusBar().showMessage('Ready!!!')\r\n        \r\n        # main menu\r\n        #setAct = QAction('Settings', self)\r\n        #setAct.setStatusTip('Set number of folders')\r\n        #setAct.triggered.connect(lambda: self.child_show(Settings))\r\n        \r\n        #loadAct = QAction('Set folders', self)\r\n        #loadAct.setStatusTip('Select image folders')\r\n        #loadAct.triggered.connect(lambda: self.child_show(LoadWindow))\r\n        \r\n        #self.exsetAct = QAction('Export Settings', self)\r\n        #self.exsetAct.setStatusTip('Export the selected images to target folder')\r\n        #self.exsetAct.triggered.connect(lambda: self.child_show(ExportSetting))\r\n        #self.exsetAct.setEnabled(False)\r\n\r\n        self.exportAct = QAction('Export')\r\n        self.exportAct.setShortcut(\"S\")\r\n        self.exportAct.setStatusTip('Export the selected images to target folder')\r\n        self.exportAct.triggered.connect(self.export)\r\n        self.exportAct.setEnabled(False)\r\n\r\n        exitAct = QAction(QIcon('exit.png'), '&Exit', self)\r\n        #exitAct.setShortcut('Ctrl+Q')\r\n        exitAct.setStatusTip('Exit application')\r\n        exitAct.triggered.connect(qApp.quit)\r\n\r\n        menubar = self.menuBar()\r\n        menubar.clear()\r\n        fileMenu = menubar.addMenu('&File')\r\n        #fileMenu.addAction(setAct)\r\n        #fileMenu.addAction(loadAct)\r\n        #fileMenu.addAction(self.exsetAct)\r\n        fileMenu.addAction(self.exportAct)\r\n        fileMenu.addAction(exitAct)\r\n        \r\n        \r\n        lbl = QLabel(self)\r\n        lbl.setText('Blocks:')\r\n        lbl.setGeometry(40, 50, 100, 30)\r\n        \r\n        self.le_num = QSpinBox(self)\r\n        self.le_num.setValue(self.config['num_fold'])\r\n        self.le_num.setGeometry(110, 55, 50, 20)\r\n        \r\n        self.btn1 = QPushButton(self, text=\"Open\")\r\n        self.btn1.clicked.connect(self.set_save_path)\r\n        self.btn1.setGeometry(160, 50, 60, 30)\r\n        \r\n        \r\n        lbl1 = QLabel(self)\r\n        lbl1.setText('Save to:')\r\n        lbl1.setGeometry(40, 90, 100, 30)\r\n        self.qle = QLabel(self)\r\n        #self.qle.setText(self.default_path)\r\n        self.qle.setGeometry(105, 90, 200, 30)\r\n        \r\n        self.btn0 = QPushButton(self, text=\"Set\")\r\n        self.btn0.clicked.connect(self.change)\r\n        self.btn0.setGeometry(110, 130, 40, 30)\r\n        \r\n        self.list_lbl = QLabel(self)\r\n        self.list_lbl.setGeometry(40, 170, 180, 20)\r\n        self.list_lbl.setText('Image list:')\r\n        \r\n        if self.listview is None:\r\n            self.listview = QListWidget(self)\r\n            self.listview.clicked.connect(self.checkItem)\r\n        self.listview.setGeometry(40, 200, 180, self.row * (self.img_size + self.pad_y) - self.pad_y - 130)\r\n        #self.layout().addWidget(self.listview)\r\n        \r\n        for lbl, btn, f_name in zip(self.lbls, self.open_btn, self.method_name):\r\n            lbl.deleteLater()\r\n            btn.deleteLater()\r\n            f_name.deleteLater()\r\n            \r\n        self.lbls.clear()\r\n        self.open_btn.clear()\r\n        self.method_name.clear()\r\n        \r\n        for i in range(self.config['num_fold']):\r\n            x = i % self.col\r\n            y = i // self.col\r\n            \r\n            f_name = QLabel(self)\r\n            f_name.setText('Folder {}:'.format(i))\r\n            f_name.setGeometry(x * (self.img_size + self.pad_x) + 250, y * (self.img_size + self.pad_y) + 40, 100, 25)\r\n            \r\n            btn = QPushButton(self, text=\"Set\")\r\n            btn.tag = i\r\n            btn.clicked.connect(self.open_folder)\r\n            btn.setGeometry(x * (self.img_size + self.pad_x) + 470, y * (self.img_size + self.pad_y) + 40, 60, 25)\r\n            \r\n            lbl = QLabel(self)\r\n            lbl.setStyleSheet(\"QLabel{background:white;}\"\r\n                              \"QLabel{color:rgb(0,0,0,120);font-size:25px;font-weight:bold;font-family:宋体;}\"\r\n                             )\r\n            lbl.setAlignment(Qt.AlignCenter)\r\n            lbl.setText('Folder {}'.format(i))\r\n            #lbl.setMouseTracking(True)\r\n            lbl.mouseMoveEvent = self.mouseMove\r\n            #lbl.installEventFilter(self.mouseMoveEvent(self))\r\n            #lbl.moved.connect(self.mouseMoveEvent)\r\n            lbl.setGeometry(x * (self.img_size + self.pad_x) + 250, y * (self.img_size + self.pad_y) + 70, self.img_size, self.img_size)\r\n            \r\n            \r\n            self.method_name.append(f_name)\r\n            self.open_btn.append(btn)\r\n            self.lbls.append(lbl)\r\n            #print(len(self.open_btn))\r\n            \r\n            #self.layout().addWidget(f_name)\r\n            self.layout().addWidget(f_name)\r\n            self.layout().addWidget(btn)\r\n            self.layout().addWidget(lbl)\r\n                \r\n        self.show()\r\n        \r\n    def change(self):\r\n    \r\n        self.config['num_fold'] = int(self.le_num.text())\r\n        \r\n        self.row = floor(sqrt(self.config['num_fold']))\r\n        self.col = ceil(self.config['num_fold'] / 1. / self.row)\r\n        \r\n        win_w = 260 + self.col * (self.img_size + self.pad_x)\r\n        win_h = 60 + self.row * (self.img_size + self.pad_y)\r\n        #self.setGeometry((self.sw - win_w) // 2, (self.sh - win_h) // 2, win_w, win_h)\r\n        #print(self.config['num_fold'], win_w, win_h, self.col, self.row)\r\n        self.setFixedSize(win_w, win_h)\r\n        \r\n        self.listview.setGeometry(40, 200, 180, self.row * (self.img_size + self.pad_y) - self.pad_y - 130)\r\n        \r\n        self.statusBar().showMessage('Ready!!!')\r\n        \r\n        for lbl, btn, f_name in zip(self.lbls, self.open_btn, self.method_name):\r\n            lbl.deleteLater()\r\n            btn.deleteLater()\r\n            f_name.deleteLater()\r\n            \r\n        self.lbls.clear()\r\n        self.open_btn.clear()\r\n        self.method_name.clear()\r\n        \r\n        for i in range(self.config['num_fold']):\r\n            x = i % self.col\r\n            y = i // self.col\r\n            \r\n            f_name = QLabel(self)\r\n            f_name.setText('Folder {}:'.format(i))\r\n            f_name.setGeometry(x * (self.img_size + self.pad_x) + 250, y * (self.img_size + self.pad_y) + 40, 100, 25)\r\n            \r\n            btn = QPushButton(self, text=\"Set\")\r\n            btn.tag = i\r\n            btn.clicked.connect(self.open_folder)\r\n            btn.setGeometry(x * (self.img_size + self.pad_x) + 470, y * (self.img_size + self.pad_y) + 40, 60, 25)\r\n            \r\n            lbl = QLabel(self)\r\n            lbl.setStyleSheet(\"QLabel{background:white;}\"\r\n                              \"QLabel{color:rgb(0,0,0,120);font-size:25px;font-weight:bold;font-family:宋体;}\"\r\n                             )\r\n            lbl.setAlignment(Qt.AlignCenter)\r\n            lbl.setText('Folder {}'.format(i))\r\n            #lbl.setMouseTracking(True)\r\n            lbl.mouseMoveEvent = self.mouseMove\r\n            #lbl.installEventFilter(self.mouseMoveEvent(self))\r\n            #lbl.moved.connect(self.mouseMoveEvent)\r\n            lbl.setGeometry(x * (self.img_size + self.pad_x) + 250, y * (self.img_size + self.pad_y) + 70, self.img_size, self.img_size)\r\n            \r\n            \r\n            self.method_name.append(f_name)\r\n            self.open_btn.append(btn)\r\n            self.lbls.append(lbl)\r\n            #print(len(self.open_btn))\r\n            \r\n            #self.layout().addWidget(f_name)\r\n            self.layout().addWidget(f_name)\r\n            self.layout().addWidget(btn)\r\n            self.layout().addWidget(lbl)\r\n        \r\n    def checkItem(self, index):\r\n        #print(index.row(), index.column(), index.data())\r\n        self.show_img(index.row())\r\n\r\n    def set_save_path(self):\r\n        dir_path = QFileDialog.getExistingDirectory(self, \"choose directory\", self.config['path'])\r\n        self.qle.setText(dir_path)\r\n        self.config['path'] = dir_path\r\n        \r\n    def recursion(self, base_path, cur_path='.', img_list=[]):\r\n        sub_list = os.listdir(base_path)\r\n        for sub_name in sub_list:\r\n            sub_path = os.path.join(base_path, sub_name)\r\n            if cur_path is not None:\r\n                curp = os.path.join(cur_path, sub_name)\r\n            else:\r\n                curp = sub_name\r\n            if os.path.isdir(sub_path):\r\n                self.recursion(sub_path, curp, img_list)\r\n            elif sub_path.split('.')[-1] in ('png', 'jpg'):\r\n                img_list.append(curp)\r\n            \r\n    def open_folder(self):\r\n        idx = self.sender().tag\r\n        dir_path = QFileDialog.getExistingDirectory(self, \"choose directory\", \"C:/Users/admin/Desktop/debug1/debug\")\r\n        if not os.path.exists(dir_path):\r\n            return\r\n        self.method_name[idx].setText(dir_path.split('/')[-1])\r\n        self.config['folders'][idx] = dir_path\r\n        \r\n        img_list = []\r\n        self.recursion(dir_path, None, img_list) #os.listdir(dir_path)\r\n        #print(img_list[0])\r\n        \r\n        #print(idx)\r\n        if not idx:\r\n            self.config['img_list'] = []\r\n            for img_name in img_list:\r\n                suf = img_name.split('.')[-1]\r\n                #suf = img_name.split('.')[-1]\r\n                if '.' + suf in self.suffix:\r\n                    self.config['img_list'].append(img_name)\r\n                    #print(self.config['img_list'])\r\n                    #print(img_name)\r\n            self.config['num_img'] = len(self.config['img_list'])\r\n            self.list_lbl.setText('Total {} images: '.format(self.config['num_img']))\r\n            if self.config['path'] == '':\r\n                self.config['path'] = os.path.dirname(self.config['folders'][idx])\r\n            #img_list = self.config['img_list']\r\n            \r\n            self.show_img(0)\r\n            self.listview.clear()\r\n            self.listview.addItems(self.config['img_list'])\r\n            self.listview.setCurrentRow(0)\r\n            self.exportAct.setEnabled(True)\r\n        else:\r\n            sel_idx = self.listview.currentRow()\r\n            self.show_img(sel_idx)\r\n        \r\n        self.statusBar().showMessage('Load image complete!')\r\n\r\n    def show_img(self, idx):\r\n        name = self.config['img_list'][idx]\r\n    \r\n        assert len(self.config['folders']) == self.config['num_fold']\r\n        for i, fold in enumerate(self.config['folders']):\r\n            if fold == '':\r\n                continue\r\n            self.method_name[i].setText(fold.split('/')[-1] + ':')\r\n            \r\n            find = False\r\n            tag = name.rsplit('.', 1)[0]\r\n            tag = '.'.join(name.split('.')[:-1])\r\n            for suf in self.suffix:\r\n                new_name = tag + suf\r\n                path = os.path.join(fold, new_name)\r\n                if os.path.exists(path):\r\n                    find = True\r\n                    break\r\n            \r\n            #print(path)\r\n            #print(os.path.exists(path))\r\n            if not find:\r\n                self.lbls[i].setText('No Image!')\r\n            else:\r\n                #scaledPixmap = QPixmap(path).scaled(QSize(self.img_size, self.img_size))\r\n                scaledPixmap = QPixmap(path)\r\n                scaledPixmap = scaledPixmap.scaled(self.img_size, self.img_size)\r\n                self.lbls[i].setPixmap(scaledPixmap)\r\n\r\n    def keyPressEvent(self, event):\r\n        #print(event.key())\r\n        sel_idx = self.listview.currentRow()\r\n        if self.config['num_img'] < 1:\r\n            return\r\n        \r\n        # 1677723 4-7 左上右下\r\n        key = event.key()\r\n        #print(event)\r\n        if key in [16777234, 16777236]:\r\n            sel_idx = np.clip(key - 16777235 + sel_idx, 0, self.config['num_img'] - 1)\r\n            self.show_img(sel_idx)\r\n            self.listview.setCurrentRow(sel_idx)\r\n            \r\n            msg = 'total image: {}, now: {} .'.format(self.config['num_img'], sel_idx)\r\n            self.statusBar().showMessage(msg)\r\n\r\n        #if event.key() == 83:\r\n        #    # s\r\n        #    self.export()\r\n\r\n    def mouseMove(self, e):\r\n        if self.config['num_img'] < 1:\r\n            return\r\n\r\n        gx = e.globalPos().x()\r\n        gy = e.globalPos().y()\r\n        ind_x = (gx - 770) // (self.img_size + self.pad_x)\r\n        ind_y = (gy - 260) // (self.img_size + self.pad_x)\r\n        index = np.clip(self.col * ind_y + ind_x, 0, self.config['num_fold']-1)\r\n        \r\n        x = e.pos().x()\r\n        y = e.pos().y()\r\n        x = np.clip(x, 0, self.img_size - 1)\r\n        y = np.clip(y, 0, self.img_size - 1)\r\n\r\n        pix = self.lbls[index].pixmap()\r\n        if pix is None:\r\n            return\r\n        val = pix.toImage().pixel(x, y)\r\n        red, green, blue = qRed(val), qGreen(val), qBlue(val)\r\n        self.statusBar().showMessage('x: {}, y: {}, Value: {}, {}, {}.'.format(x, y, red, green, blue))\r\n\r\n        for i in range(self.config['num_fold']):\r\n            pix = self.lbls[i].pixmap()\r\n            if pix is None:\r\n                continue\r\n            im = pix.toImage()\r\n            value = qRgb(255, 255, 0)\r\n            \r\n            im.setPixel(x-1, y-1, value)\r\n            im.setPixel(x-1, y  , value)\r\n            im.setPixel(x-1, y+1, value)\r\n            im.setPixel(x  , y-1, value)\r\n            im.setPixel(x  , y  , value)\r\n            im.setPixel(x  , y+1, value)\r\n            im.setPixel(x+1, y-1, value)\r\n            im.setPixel(x+1, y  , value)\r\n            im.setPixel(x+1, y+1, value)\r\n            \r\n            self.lbls[i].setPixmap(QPixmap.fromImage(im))\r\n    \r\n    def export(self):\r\n        #print('??????????')\r\n        main_path = os.path.join(self.config['path'], 'comparison')\r\n        if not os.path.exists(main_path):\r\n            os.mkdir(main_path)\r\n\r\n        img_name = self.listview.selectedItems()[0].text()\r\n        for i, fold in enumerate(self.config['folders']):\r\n            if fold != '':\r\n                method = fold.split('/')[-1]\r\n                method_path = os.path.join(main_path, method)\r\n                if not os.path.exists(method_path):\r\n                    os.mkdir(method_path)\r\n                #print(method_path)\r\n                \r\n                fold_img = os.path.join(fold, img_name)\r\n                find = False\r\n                tag = fold_img.rsplit('.', 1)[0]\r\n                #tag = img_name.split('.')[0]\r\n                for suf in self.suffix:\r\n                    new_name = tag + suf #'.'.join([tag, suf])\r\n                    path = os.path.join(fold, new_name)\r\n                    if os.path.exists(path):\r\n                        find = True\r\n                        break\r\n                \r\n                #print('path:', tag, fold_img, find)\r\n                if find:\r\n                    src_path = path #os.path.join(fold, img_name)\r\n                    \r\n                    num_img = len(os.listdir(method_path))\r\n                    img_tag, suffix = img_name.rsplit('.', 1)\r\n                    img_tag = str(num_img) if self.config['rename'] else img_tag\r\n                    new_name = '{}.{}'.format(img_tag, suffix)\r\n                    tar_path = os.path.join(method_path, new_name)\r\n                    #print('save:', src_path, tar_path)\r\n                    \r\n                    tar_fold = tar_path.rsplit('\\\\', 1)[0]\r\n                    if not os.path.exists(tar_fold):\r\n                        os.makedirs(tar_fold)\r\n                    \r\n\r\n                    else:\r\n                        copyfile(src_path, tar_path)\r\n        \r\n        msg = 'Image {} has been saved in {}.'.format(img_name, main_path)\r\n        self.statusBar().showMessage(msg)\r\n\r\nif __name__ == '__main__':\r\n    app = QApplication(sys.argv)\r\n    app.setStyle('WindowsXP')\r\n    ex1 = Example()\r\n    sys.exit(app.exec_())","repo_name":"moothes/Image_Comparison_Tool","sub_path":"viewer.py","file_name":"viewer.py","file_ext":"py","file_size_in_byte":17447,"program_lang":"python","lang":"en","doc_type":"code","stars":10,"dataset":"github-code","pt":"81"}
+{"seq_id":"73828072266","text":"#-I-DS------------------------------------------------------------------------------------------\nimport pandas as pd\n#-P-Import--------------------------------------------------------------------------------------\nfrom Multi_Stage_Diamond_Heritage.DiamondDataClass import DiamondDataClass as ddc\n#-----------------------------------------------------------------------------------------------\n\n#-F-DataPreparationForVisualization--------------------------------------------------------------\ndef data_assembler() -> None:\n    \"\"\"\n    This function is used to assemble the data for the visualization.\n\n    Parameters\n    ----------\n    None\n\n    Returns\n    -------\n    None\n\n    \"\"\"\n\n    #get the data from xlsx by page\n    data_2022 = pd.read_excel(\n        \"data_visualisation/Population_par_commune.xlsx\", sheet_name=\"Population 2022\")\n\n    #get the data from xlsx by page\n    data_2021 = pd.read_excel(\n        \"data_visualisation/Population_par_commune.xlsx\", sheet_name=\"Population 2021\")\n\n    data_2022[\"Population variation\"]  = (data_2022[\"Total\"] - data_2021[\"Total\"]\n                                            )/data_2021[\"Total\"]\n    data_2022.dropna()\n    data_2022.to_csv(\"data_visualisation/Population_par_commune.csv\", index=False)\n\n    INS = pd.read_excel(\"data_visualisation/INS.xlsx\")\n    INS.to_csv(\"data_visualisation/INS.csv\", index=False)\n    \n    taxes = pd.read_excel(\"data_visualisation/taxes.xlsx\")\n    taxes.to_csv(\"data_visualisation/taxes.csv\", index=False)\n\n    data = ddc()\n\n    data.add_column(\"data_visualisation/code-postaux.csv\", \"zipcode\")\n    data.add_column(\"data_visualisation/INS.csv\", \"zipcode\")\n    data.add_column(\"data_visualisation/Population_par_commune.csv\", \"INS\")\n    data.add_column(\"data_visualisation/taxes.csv\", \"INS\")\n\n    data.drop_rows_by_columns(\"Price\")\n    data.drop_rows_by_columns(\"Living Area\")\n    data.drop_rows_by_column_value_range_and_bool(\n        \"To sell\" , \"Price\", 50_000 , 50_000_000, 200, 20_000)\n    data.drop_rows_by_column_value_range(\"Living Area\", 0, 20_000)\n    data.dropna(\"Number of rooms\")\n\n    data.new_by_div(\"Price\" , \"Living Area\" , \"Price by M**2\")\n\n    data.drop_rows_by_column_value_range_and_bool(\n        \"To sell\" , \"Price by M**2\", 200 , 20_000, 1, 1_000)\n\n\n    old_types = ['Ferme', 'Appartement', 'Logementétudiant', 'Penthouse', \n                 'Appartementdeservice', 'Chalet', 'Maison', 'Rez-de-chaussée', \n                 'Maisondemaître', 'Autresbiens', 'Maisondecampagne', 'Loft', \n                 'Maisonbel-étage', 'Pavillon', 'Duplex', 'Triplex', 'Studio', \n                 'Immeublemixte', 'Bienexceptionnel', 'Château', 'Immeuble', \n                 'Manoir', 'Bungalow', 'Villa']\n\n    new_types = ['Farm', 'Apartment', 'Student housing', 'Penthouse',\n                 'Service apartment', 'Chalet', 'House', 'Ground floor',\n                 'Master house', 'Other goods', 'Country house', 'Loft',\n                 'Bel-etage house', 'Pavilion', 'Duplex', 'Triplex', 'Studio',\n                 'Mixed building', 'Exceptional property', 'Castle', 'Building',\n                 'Manor', 'Bungalow', 'Villa']\n\n    appartments = ['Apartment', 'Student housing', 'Penthouse', 'Service apartment',\n                    'Studio', 'Duplex', 'Triplex']\n\n    houses = ['Farm', 'Chalet', 'House', 'Ground floor', 'Master house', 'Other goods',\n                'Country house', 'Loft', 'Bel-etage house', 'Pavilion', 'Mixed building',\n                'Exceptional property', 'Castle', 'Building', 'Manor', 'Bungalow', 'Villa']\n\n    data.Change_values_of_column(\"type\", old_types, new_types)\n\n    data.new_column_by_separation(\"type\", \"Appartment\", appartments, houses)\n\n    data.save_data()\n\n\n\n\n\nif __name__ == \"__main__\":\n    data_assembler()","repo_name":"Flotchet/real-estate-price-prediction","sub_path":"b_data_preparation/data_preparation_for_visualization.py","file_name":"data_preparation_for_visualization.py","file_ext":"py","file_size_in_byte":3748,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"81"}
+{"seq_id":"32393277994","text":"import glob\nimport json\nimport os\nimport xml.etree.ElementTree as ET\nfrom collections import defaultdict\n\nimport cv2\nfrom tqdm import tqdm\n\ndef concat_custom_json(json_paths, output_json, \n                       shuffle=False):\n\n\n    new_label_dict = {}    \n    for json_path in json_paths:\n        with open(json_path) as f:\n            label_dict = json.load(f)\n            new_label_dict.update(label_dict)\n        \n    with open(output_json, 'w', encoding='utf-8') as f_out:\n        json.dump(new_label_dict, f_out, ensure_ascii=False, indent=4)\n\nif __name__ == '__main__':\n    concat_custom_json(\n        json_paths=[\n            '/Users/rudy/Desktop/Development/dataset/license_plate/dataset/artoa/det_data/plate_detection_train.json',\n            '/Users/rudy/Desktop/Development/dataset/license_plate/dataset/plate_detection_train_ksf_02462.json',\n        ],\n        output_json='test.json'\n    )","repo_name":"rudyRyu/dataset-converter","sub_path":"utils/concat_custom_json.py","file_name":"concat_custom_json.py","file_ext":"py","file_size_in_byte":904,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"40895208777","text":"import operator\nimport sys\n\nopcodes = {\n    1: operator.add,\n    2: operator.mul,\n    99: lambda a, b: StopIteration()\n}\nwith open(\"input.txt\") as f:\n    original_data = [int(d) for d in f.read().split(',')]\n\ndef execute(pos1_val, pos2_val):\n    data = original_data.copy()\n\n    data[1] = pos1_val\n    data[2] = pos2_val\n    pc = 0\n\n    while pc <= len(data):\n        op = opcodes[data[pc]]\n        index1 = data[pc+1]\n        index2 = data[pc+2]\n        dest = data[pc+3]\n        result = op(data[index1], data[index2])\n        # Ugly, but it works\n        if type(result) == StopIteration:\n            break\n        data[dest] = result\n        pc += 4\n    \n    return data[0]\n\nprint(f\"Part 1: {execute(12, 2)}\")\n\n\nfor noun in range(100):\n    for verb in range(100):\n        if execute(noun, verb) == 19690720:\n            print(f\"Part 2: {100*noun + verb}\")\n            sys.exit(0)","repo_name":"mjpatter88/aoc-2019","sub_path":"day02/solve.py","file_name":"solve.py","file_ext":"py","file_size_in_byte":883,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"34238214670","text":"'''\n    파일복사\n    \n    원본 파일 -->변수 --> 복사본\n     (읽기)              (쓰기)\n    1.원본 파일 읽기\n    2.1024Byte(1KB) 씩 읽어온다\n    3.1024Byte(1KB) 씩 저장어온다\n    4.저장 왈료\n    \n    #파일(20GB) --복사--> [버퍼](1KB) -->  생성\n    #버퍼 : 임시저장공간\n    \n    1000MB          :1GB\n    20 * 1000MB     :20GB (20000MB)\n    20GB 처리하는 시간 :200초 (3분20초)\n    \n    #1초 10억 bit이상 (100MB~300MB)\n\n    \n    \n    with\n    :파일 입출력 시,자동으로 close() 함수를 호출한다.\n    \n    with open(파일경로,모드) as 파일 개체:\n        처리 코드\n    \n'''\n\n\nfrom shutil import copyfile\n\n\np = 'C:/c언어&파이썬/파이썬/sbs_python/파이썬/파이썬/'\nf = p + 'helo.txt'\nc = p + 'helo(복사).txt'\n\nbuffer_size = 1024      # 버퍼용량 : 1KB\n\n#f 원본 파일객 채체\n#c 복사 파일객 채체\nwith open (f,'rb')as s:\n    with open (c,'wb')as c:\n        while True:\n            #원본파일을 버퍼용량 만큼 일어와서 buffer 에저장\n            buffer = s.read(buffer_size)\n            if not buffer:\n                break\n            c.write(buffer)\nprint(c)\nprint('복사성공')\n            \n","repo_name":"rlaqjatjr8922/sbs_python","sub_path":"파이썬/파이썬/Dey10/ex06.py","file_name":"ex06.py","file_ext":"py","file_size_in_byte":1223,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"23377576300","text":"from __future__ import print_function\nimport os.path\nfrom googleapiclient.discovery import build\nfrom google_auth_oauthlib.flow import InstalledAppFlow\nfrom google.auth.transport.requests import Request\nfrom google.oauth2.credentials import Credentials\nimport tkinter as tk\nimport sys\n\nimport Email\nimport School\nimport Weather\n\n\n# If modifying these scopes, delete the file token.json.\nSCOPES = ['https://www.googleapis.com/auth/gmail.readonly']\n\ndef main():\n    \"\"\"Shows basic usage of the Gmail API.\n    Lists the user's Gmail labels.\n    \"\"\"\n    creds = None\n    # The file token.json stores the user's access and refresh tokens, and is\n    # created automatically when the authorization flow completes for the first\n    # time.\n    if os.path.exists('token.json'):\n        creds = Credentials.from_authorized_user_file('token.json', SCOPES)\n    # If there are no (valid) credentials available, let the user log in.\n    if not creds or not creds.valid:\n        if creds and creds.expired and creds.refresh_token:\n            creds.refresh(Request())\n        else:\n            flow = InstalledAppFlow.from_client_secrets_file(\n                'credentials.json', SCOPES)\n            creds = flow.run_local_server(port=0)\n        # Save the credentials for the next run\n        with open('token.json', 'w') as token:\n            token.write(creds.to_json())\n\n    service = build('gmail', 'v1', credentials=creds)\n\n    #Class calls\n    email = Email(service)\n    inbox = email.batchEmails() #list\n    \n    school = School()\n    assignments = school.getAssignments() #list \n\n    weather = Weather()\n    today = weather.getWeather() #list \n\n    # -----------------------------\n    # TKINTER CODE BELOW THIS POINT \n    # -----------------------------\n\n    window = tk.Tk()\n    #window.geometry('1280x1024')\n    window.attributes('-fullscreen', True)\n    window.configure(bg='black')\n\n    #Close on escape key\n    def close(event):\n        window.withdraw() # if you want to bring it back\n        sys.exit() # if you want to exit the entire thing\n\n    window.bind('<Escape>', close)\n    window.mainloop()\n\n\n","repo_name":"jakelantzman/random-projects","sub_path":"gmail/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2104,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"1228675816","text":"#!/usr/bin/env python\n\nimport os\nimport sys\nimport argparse\nimport logging\n\nimport nibabel as nib\n\nfrom macuto.atlas.atlas_group import AtlasGroup\nfrom macuto.scriptutils import whoami\n\n#logging config\nlogging.basicConfig(level=logging.DEBUG, filename='atlasquerpy.log',\n                    format=\"%(asctime)-15s %(message)s\")\nlog = logging.getLogger(__name__)\n\n'''\ncd ~/Dropbox/Documents/work/atlasquerpy\npython atlasquerpy -m /home/alexandre/Data/cobre/presels.nii.gz -a \"MNI Structural Atlas\"\npython atlasquerpy -m /home/alexandre/Dropbox/Documents/locals.nii.gz -a \"MNI Structural Atlas\"\n'''\n\n\n#from IPython.core.debugger import Tracer; debug_here = Tracer()\n#-------------------------------------------------------------------------------\ndef set_parser():\n    parser = argparse.ArgumentParser(description='Atlasquerpy')\n    parser.add_argument('-a', '--atlas', dest='atlas', required=True,\n                        help='name of atlas to use')\n    parser.add_argument('-V', '--verbose', dest='verbose', required=False,\n                        action='store_true', default=False,\n                        help='switch on diagnostic messages')\n    parser.add_argument('-m', '--mask', dest='mask', required=False, \n                        default = '',\n                        help='a mask image to use during structural lookups')\n    parser.add_argument('-b', '--binarise', dest='binarise', required=False, \n                        action='store_true', default = False,\n                        help='switch on binarization of the mask.')\n    parser.add_argument('-t', '--type', dest='type', required=False, \n                        choices=['avgprob', 'roiover'], default='avgprob',\n                        help='''Type of measure: average mask or coordinate \n                              Probability; or ROI overlapping percentage''')\n    parser.add_argument('-c', '--coords', dest='coords', required=False, \n                        help='''specify coordinates of the point of interest \n                             (as mm coordinates): <X>,<Y>,<Z>''')\n    parser.add_argument('-p', '--precision', dest='precision', required=False, \n                        default=4,\n                        help='''specify the precision of the floats that will \n                             be printed when using -m''')\n    parser.add_argument('--dumpatlases', dest='dumpatlases', required=False, \n                        action='store_true', default=False,\n                        help='Dump a list of the available atlases')\n\n    return parser\n#-------------------------------------------------------------------------------\n\n\ndef main(argv=None):\n\n    parser  = set_parser()\n\n    try:\n       args = parser.parse_args()\n    except argparse.ArgumentError as exc:\n       log.error(exc.message)\n       parser.error(str(exc.message))\n       return -1\n\n    log.info('atlasquer.py {0} {1} {2}'.format(os.path.basename(__file__),\n                                               whoami(),\n                                               locals()))\n\n    dumpatlases = args.dumpatlases\n    precision = args.precision\n    atlas_name = args.atlas\n    mask_file = args.mask\n    binarise = args.binarise\n    verbose = args.verbose\n    coords = args.coords\n    qtype = args.type\n\n    atlas_group = AtlasGroup()\n\n    if dumpatlases:\n        print(atlas_group.atlases.keys())\n        return 0\n\n    if verbose:\n        log.info('Using atlas: ' + atlas_name)\n\n    atlas = atlas_group.get_atlas_by_name(atlas_name)\n    if atlas is None:\n        log.error('Invalid atlas name. Try one of:')\n        print(atlas_group.atlases.keys())\n        return 1\n\n    if mask_file != '':\n        try:\n            mask_img = nib.load(mask_file)\n        except:\n            log.error('Problem reading mask file ' + mask_file)\n            return 1\n\n        if verbose:\n            log.info('Working with mask ' + mask_file)\n\n        if binarise:\n            mask_vol = mask_img.get_data()\n            mask_vol[mask_vol > 0] = 1\n\n        lidx = atlas.get_labels_ids()\n        for li in lidx:\n            value = 0.0\n            if qtype == 'avgprob':\n                value = atlas.get_avg_probability(mask_img, li)\n            elif qtype == 'roiover':\n                value = atlas.get_roi_overlap(mask_img, li)\n\n            struct_name = atlas.get_structure_name(li)\n\n            if verbose:\n                log.debug(str(li))\n\n            if value > 0:\n                val_text = \"%.*f\" % (precision, round(value, precision))\n                log.info(struct_name + ':' + val_text)\n\n    elif coords != '':\n        try:\n            k = coords.split(',')\n            x, y, z = float(k[0]), float(k[1]), float(k[2])\n        except:\n            log.error('Problem parsing given coordinates, try <x>,<y>,<z>')\n            return 1\n\n        if verbose:\n            log.debug('Working with coords: ' + str(x) + ',' + str(y) + ',' + str(z))\n\n        try:\n            print(atlas.get_description(x, y, z))\n        except Exception as exc:\n            log.error(exc)\n            log.error(sys.exc_info())\n            return 1\n\n    return 0\n\n#-------------------------------------------------------------------------------\nif __name__ == \"__main__\":\n    sys.exit(main())\n\n\n","repo_name":"Neurita/atlas","sub_path":"scripts/atlasquer.py","file_name":"atlasquer.py","file_ext":"py","file_size_in_byte":5215,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"25594964737","text":"#credits to https://www.geeksforgeeks.org/linear-regression-python-implementation/\n# for skeleton of code\nimport csv\nimport numpy as np\nfrom sklearn import linear_model\nimport os\nimport machine_learning.utils.algorithm_results_plotter as inhouse_plotter\nimport machine_learning.utils.math as inhouse_math\ndirname = os.path.dirname(__file__)\ndata_pairs_path = '../data/preprocessed_files/rbi/crop_data_pairs.csv'\nfilename = os.path.join(dirname, data_pairs_path)\nfile = open(filename)\ncsvreader = csv.reader(file)\nx_axis_vals = []\ny_axis_vals = []\nrows = []\n# here, you can choose which crop you want to use to perform linear regression upon.\n# The aim is to predict the value of the column \"Total Cereals\" as appears in the data set.\n# Consider the data-point for 2020-2021: Rice - 1223,Wheat - 1095,Course Cereals- 512,Total Cereals - 2829\n# Simple observation concludes that\n# Rice + wheat + course cereal = total cereal,\n# i.e. we want to attach a coefficient of 1 to each to get closer to the \"truth\"\n\nlist_of_possible_crops = ['Rice','Wheat','Coarse Cereals']\ncrop_for_analysis = 'Wheat'\nindex_to_search = list_of_possible_crops.index(crop_for_analysis)\nfor row in csvreader:\n    rows.append(row)\n    x_axis_vals.append(row[index_to_search])\n    y_axis_vals.append(row[-1])\nfile.close()\nX = np.array(x_axis_vals).astype(np.float)\ny = np.array(y_axis_vals).astype(np.float)\n\ndef find_deviations(axis_1, axis_2):\n    n = len(axis_1)\n\n    axis_1 = np.array(axis_1).astype(np.float)\n    axis_2 = np.array(axis_2).astype(np.float)\n    mean_1 = np.mean(axis_1)\n\n\n    mean_2 = np.mean(axis_2)\n\n    running_sum = 0\n    for i in range(0,n):\n        v1 = axis_1[i]\n        v2 = axis_2[i]\n\n        running_sum += (v1*v2)\n\n    return running_sum - (n * mean_1 * mean_2)\n\ndeviation_xx = find_deviations(axis_1=X, axis_2=X)\ndeviation_xy = find_deviations(axis_1= X, axis_2 = y)\n\n# in the equation y=b_0 + b_1x, we are writing the code to find values for beta manually.\n# below this, there is also a version where I fit a LinearRegression model and invoke the .coef_ property\nb_1 = deviation_xy/deviation_xx\nb_0 = np.mean(y) - (b_1 * np.mean(X))\n\nfrom sklearn.model_selection import train_test_split\n\n\nX_train, X_test, y_train, y_test = train_test_split(X.reshape(-1,1), y, test_size=0.4,\n\t\t\t\t\t\t\t\t\t\t\t\t\trandom_state=1)\n\nprint(X_train.shape)\n# create linear regression object\nreg = linear_model.LinearRegression()\n\n# train the model using the training sets\nreg.fit(X_train, y_train)\n\nprint('Coefficients: ', reg.coef_)\n\n\n# closeness score: 1 means perfect prediction\n# We want to find how close we are to the truth, so closer to the value of 1, better our prediction is.\n# We can change the crop we use for analysis and see how our performance changes.\n# In a summary, it was found that Rice as about 99% closeness score, wheat about 98% and course cereal about 80%\n# This is interesting because examining the values in\n#    2020-2021: Rice - 1223,Wheat - 1095,Course Cereals- 512,Total Cereals - 2829\n#    we can say that Rice and Wheat are clearly cultivated in larger quantities compared to total cereals,\n#    so the differences in performance make intuitive sense\nprint('Closeness score: {}'.format(reg.score(X_test, y_test)))\n\n\n# In the following graph, we will plot the values of predictions and the closeness score line\n# The code to auto-save the image to disc has been omitted but rather stored manually.\n# Please refer to the image univariate_linearregression.png.\ninhouse_plotter.plot_variance(X_train, y_train,X_test,y_test,reg)\n\n# In the next graph, we will plot the data itself and the regression line to see how well we fit the data.\n# Again, based on the crop we choose, we can see different graphs here.\n# Refer to univariate_regressionline.png\ninhouse_plotter.plot_regression_line(X, y, b_0,b_1,crop_for_analysis)","repo_name":"ShraddhaThumsi/ml_experiments","sub_path":"machine_learning/linear_regression/single_parameter_regression.py","file_name":"single_parameter_regression.py","file_ext":"py","file_size_in_byte":3820,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"70786327625","text":"from typing import List\nfrom models.dmxcversion import DMXCVersion\n\n\nclass Version:\n\n    def __init__(self, version: str, dmxcversion: List[DMXCVersion], url: str, hashval: str) -> None:\n        self.version = version\n        self.dmxcversion = dmxcversion\n        self.url = url\n        self.hash = hashval\n","repo_name":"DMXControl/DMXCPluginLibrary","sub_path":"scripts/models/version.py","file_name":"version.py","file_ext":"py","file_size_in_byte":308,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"20341951561","text":"class Solution:\n    def convert(self, s, numRows):\n        \"\"\"\n        :type s: str\n        :type numRows: int\n        :rtype: str\n        \"\"\"\n        if (numRows == 1): return s\n        outer_list = []\n        for rows in range(numRows): \n            outer_list.append([])\n        \n        def next_row(current_row, sign):\n            if (current_row == 0):\n                return 1, 1\n            elif (current_row == numRows - 1):\n                return current_row - 1, 0\n            elif (sign == 1):\n                return current_row + 1, 1\n            elif (sign == 0):\n                return current_row - 1, 0\n        \n        row, sign = 0, 1\n        \n        for each_character in s:\n            outer_list[row].append(each_character)\n            row, sign = next_row(row, sign)\n        \n        final_answer = \"\"\n        \n        for each_inner_list in outer_list:\n            final_answer += \"\".join(each_inner_list)\n        \n        print(outer_list, final_answer)\n        return final_answer","repo_name":"addherbs/LeetCode","sub_path":"Medium/6. ZigZag Conversion.py","file_name":"6. ZigZag Conversion.py","file_ext":"py","file_size_in_byte":1007,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"7543741337","text":"#!/bin/env python\nimport requests\nAPI_key = input('Paste Your CloudFlare API Key here:')\nzone_identifier = input('Paste Your CloudFlare Zone ID here:')\n\ndef get_dns_records(API_key, zone_identifier):\n    url = f\"https://api.cloudflare.com/client/v4/zones/{zone_identifier}/dns_records?per_page=1000\"\n    headers = {\n        'Content-Type': 'application/json',\n        'Authorization': f\"Bearer {API_key}\"\n    }\n    resp = requests.get(url, headers=headers)\n    nums = len(resp.json()['result'])\n    print(f\"have {nums} record(s)\")\n    i = 0\n    id_list = []\n    for i in range(nums):\n        id_list.append(resp.json()['result'][i]['id'])\n    return id_list\n\ndef delete_dns_records(API_key, zone_identifier, id_list):\n    headers = {\n        'Content-Type': 'application/json',\n        'Authorization': f\"Bearer {API_key}\"\n    }\n    for i in range(len(id_list)):\n        print(f'deleting {i+1} records')\n        url = f'https://api.cloudflare.com/client/v4/zones/{zone_identifier}/dns_records/{id_list[i]}'\n        requests.delete(url, headers=headers)\n    print('finished')\n\nid_list = get_dns_records(API_key, zone_identifier)\ndelete_dns_records(API_key, zone_identifier, id_list)","repo_name":"ours1505/CloudFlare-Delete-All-Record","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1181,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"81"}
+{"seq_id":"27097738616","text":"\"\"\"Crie um programa que leia o ano de nascimento de sete pessoas.\r\nNo final, mostre quantas pessoas ainda não atingiram a maioridade e quantas já são maiores.\"\"\"\r\n\r\nfrom datetime import date\r\natual = date.today().year\r\nmenor = 0\r\nmaior = 0\r\n\r\nfor i in range(1, 8):\r\n    nascimento = int(input(f\"Em que ano a {i} pessoa nasceu? \"))\r\n    idade = atual - nascimento\r\n    if idade >= 18:\r\n        maior += 1\r\n    else:\r\n        menor += 1\r\nprint(f\"Ao todo temos {maior} pessoas maiores de idade\")\r\nprint(f\"E também {menor} pessoas menores de idade\")\r\n","repo_name":"goudardroberto/Python","sub_path":"g8_grupo_da_Maioridade.py","file_name":"g8_grupo_da_Maioridade.py","file_ext":"py","file_size_in_byte":551,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"8811627197","text":"import datetime\nimport os\n\nfrom django.shortcuts import render, redirect\nfrom django.template.defaulttags import register\nfrom .models import Personnel, Expenses, Log, Notes\nfrom django.http import HttpResponseRedirect\n\n# ORM помощник (client side)\nfrom .forms import *\nfrom .filters import get_query\n\n# LogIn\nfrom django.contrib.auth.models import User\nfrom django.contrib.auth import authenticate, login, logout\nfrom django.contrib.auth import logout\n\n# ORM запросы\nfrom django.db.models import Q\n\n# Работа с изображениями\nfrom .crop_and_format_img import image_formatting\n\n# API TG, ALERTS\nfrom .sup_bot import send_support_message\n\n\n# Декораторы\n@register.filter\ndef image_url_miniature(path):\n    return f\"img/png/expenses/{str(path).split('/')[-1]}\"\n\n\n@register.filter\ndef image_url(path):\n    return f\"img/jpeg/expenses/{str(path).split('/')[-1].split('.')[0]}.jpeg\"\n\n\n@register.filter\ndef fist_second_name(fs_name):\n    second, first = str(fs_name).split(' ')\n    return f'{second} {first[0]}.'\n\n\n@register.filter\ndef time_logs(dt, mode='default'):\n    if mode == 'ym':\n        result = dt.strftime(\"%d %H:%M\")\n    else:  # mode == 'default'\n        result = dt.strftime(\"%d.%m.%y %H:%M\")\n    return result\n\n\n# Страница авторизации\ndef login_page(request):\n    if request.user.is_authenticated:\n        logout(request)\n    else:\n        if request.method == 'POST':\n            # Получаем данные авторизации с формы\n            user_login = str(request.POST['loginOfUser'])\n            user_password = str(request.POST['passwordOfUser'])\n\n            # try to get user in Db\n            user = authenticate(username=user_login, password=user_password)\n\n            # if user exists\n            if user is not None:\n                # authorize user\n                login(request, user)\n                return HttpResponseRedirect(\"/main\")\n            else:\n                # not correct login or password\n                return HttpResponseRedirect(\"/login_page\")\n\n    return render(request, 'login_page.html')\n\n\n# Страница с ошибкой\ndef error_page(request):\n    return render(request, 'error_page.html')\n\n\n# Начальная\\Главная страница\ndef main(request):\n    if request.user.is_authenticated:\n        err = None\n        if request.method == 'POST':\n            # Логика работы FORM\n            if str(request.POST['task']).isdigit() and len(str(request.POST['task'])) == 7:\n                one_expense = Expenses.objects.get(pk=request.POST['expense'])\n                count = int(one_expense.quantity) - int(request.POST['count'])\n                if count > 0:\n                    status = 'РАСХОД'\n                else:\n                    status = 'ИЗМЕНЕНИЕ'\n                id_creator = Personnel.objects.get(user=request.user)\n                log_line = Log.objects.create(\n                    creator_id=id_creator, id_expenses=one_expense,\n                    status=status, task_url=request.POST['task'],\n                    quantity=abs(count)\n                )\n                log_line.save()\n                one_expense.quantity = request.POST['count']\n                one_expense.save()\n            else:\n                err = \"Введите полностью номер задачи и повторите попытку!\"\n        expenses = Expenses.objects.all()\n\n        # формируем список для страницы\n        context = {'expenses': expenses, 'search': None, 'err': err}\n\n        return render(request, 'main.html', context)\n    else:\n        return redirect(\"/login_page\")\n\n\n# Страница добавления расходника (новой записи в БД)\ndef add_expenses(request):\n    if request.user.is_authenticated:\n        if request.method == 'GET':\n            # Форма\n            form = ExistsExpensesForm()\n        if request.method == 'POST':\n            # Заполняем\n            expense = Expenses.objects.create()\n            form = ExistsExpensesForm(request.POST, request.FILES, instance=expense)\n            if form.is_valid():\n                form_expense = form.save()\n                form_expense.image = image_formatting(POST_FILES=request.FILES['image'], pre_name=expense.short_name)\n                form_expense.save()\n                return redirect('/main')\n        # формируем список для страницы\n        context = {'form': form, 'err': None}\n\n        return render(request, 'add_expenses.html', context)\n    else:\n        return redirect(\"/login_page\")\n\n\n# Страница просмотра/изменения расходника (чтение/редактирование записи в БД)\ndef refactor_expenses(request, expenses_id=None):\n    if request.user.is_authenticated:\n        # GET запрос\n        if request.method == 'GET':\n            # Если expenses_id не пустой, вытаскиваем объект класса\n            if expenses_id is not None:\n                expense = Expenses.objects.get(pk=expenses_id)\n                form = ExistsExpensesForm(instance=expense)\n                meta = \"VIEW\"\n            # Если expenses_id пустой\n            else:\n                expense = None\n                form = ExpensesForm()\n                meta = \"CHOICE\"\n\n        # POST запрос\n        if request.method == 'POST':\n            # Если пост запрос со страницы /crm/expenses, то достаем из БД выбранную запись\n            if 'show' in request.POST:\n                expenses_id = request.POST['expenses']\n                return redirect(f'/crm/expenses-{expenses_id}')\n            # Если пост запрос на редактирование, то достаем из БД выбранную запись и заполняем ей форму\n            elif 'refactor' in request.POST:\n                expense = Expenses.objects.get(pk=expenses_id)\n\n                class form(object):\n                    form_image = RefactorExpensesImageForm\n                    form_data = RefactorExpensesDataForm(instance=expense)\n\n                meta = \"EDITING\"\n            # Изменение изображения в записи расходника\n            elif 'apply-image' in request.POST:\n                form = RefactorExpensesImageForm(request.POST, request.FILES)\n                if form.is_valid():\n                    expense = Expenses.objects.get(pk=expenses_id)\n                    # Обновляем фото в БД (почитать подробно можно в модуле crop_and_format_img в комментариях)\n                    expense.image = image_formatting(POST_FILES=request.FILES['file'], pre_name=expense.short_name)\n                    expense.save()\n                    # Смотрим изменения\n                    return redirect(f'/crm/expenses-{expenses_id}')\n                else:\n                    expense = None\n                    meta = \"EDITING\"\n            # Изменение текстовых данных в записи расходника\n            elif 'apply-data' in request.POST:\n                form = RefactorExpensesDataForm(request.POST)\n                if form.is_valid():\n                    expense = Expenses.objects.get(pk=expenses_id)\n                    expense.short_name = request.POST['short_name']\n                    expense.name = request.POST['name']\n                    expense.quantity = request.POST['quantity']\n                    expense.save()\n\n                    return redirect(f'/crm/expenses-{expenses_id}')\n                else:\n                    expense = None\n                    meta = \"EDITING\"\n            # Удаление записи из БД\n            elif 'delete' in request.POST:\n                expense = Expenses.objects.get(pk=expenses_id)\n                try:\n                    os.remove(f'{str(expense.image)}')\n                except:\n                    pass\n                expense.delete()\n                return redirect(f'/main')\n\n        # формируем список для страницы\n        context = {'expense': expense, 'form': form, 'meta': meta, 'err': None}\n\n        return render(request, 'refactor_expenses.html', context)\n    else:\n        return redirect('/login_page')\n\n\n# Страница просмотра/изменения аккаунта (чтение/редактирование записи в БД)\ndef account(request):\n    # Логика работы фильтров\n    # tasks, filters = crm_task_filter(request)\n\n    # формируем список для страницы\n    context = {'tasks': \"\", 'filters': \"\", 'err': None}\n\n    return render(request, 'account.html', context)\n\n\n# Журнал\ndef log(request):\n    if request.user.is_authenticated:\n        err = None\n\n        if request.method == \"POST\":\n            logs = None\n            mode = 'default'\n            # Изменили поле даты\n            if request.POST['date'] != '':\n                year, month = request.POST['date'].split('-')[0:2]\n                logs = Log.objects.filter(\n                    created_dt__year=year,\n                    created_dt__month=month\n                )\n                mode = 'ym'\n            # Изменили поле КАДР\n            if request.POST['person'] != '':\n                if logs is None:    # Поле даты не меняли\n                    year = datetime.datetime.now().strftime(\"%Y\")\n                    query_string = request.POST['person']\n                    search_fields = ['first_name', 'last_name']\n                    entry_query = get_query(query_string, search_fields)\n\n                    if entry_query is not None:     # Не пустой запрос получился\n                        persons = Personnel.objects.filter(entry_query)\n                        logs = Log.objects.filter(\n                            created_dt__year=year,\n                            creator_id__in=persons\n                        )\n                    else:   # Если запрос пустой, выдаем по дефолту\n                        logs = Log.objects.all()[:10]\n                    mode = 'default'\n                else:   # Поле даты меняли\n                    query_string = request.POST['person']\n                    search_fields = ['first_name', 'last_name']\n                    entry_query = get_query(query_string, search_fields)\n                    if entry_query is not None:     # Не пустой запрос накладывается на выборку по дате\n                        persons = Personnel.objects.filter(entry_query)\n\n                        logs = logs.filter(\n                            creator_id__in=persons\n                        )\n                    else:   # Если запрос пуст, пропускаем и идем дальше\n                        pass\n                    mode = 'ym'\n\n            # Изменили поле Ключевые Слова\n            if request.POST['keyword'] != '':\n                if logs is None:    # Поля до этого не меняли\n                    year = datetime.datetime.now().strftime(\"%Y\")\n                    query_string = request.POST['keyword']\n                    search_fields = ['status', 'task_url', 'quantity']\n                    entry_query = get_query(query_string, search_fields)\n                    if entry_query is not None:  # Не пустой запрос накладывается на выборку по дате\n                        logs = Log.objects.filter(\n                            created_dt__year=year\n                        )\n                        logs = logs.filter(entry_query)\n                    else:   # Если запрос пустой, выдаем по дефолту\n                        logs = Log.objects.all()[:10]\n                    mode = 'default'\n                else:   # Поля до этого меняли\n                    query_string = request.POST['keyword']\n                    search_fields = ['creator_id', 'status', 'description', 'id_expenses']\n                    entry_query = get_query(query_string, search_fields)\n                    if entry_query is not None:     # Не пустой запрос накладывается на выборку по дате\n                        logs = Log.objects.filter(\n                            created_dt__year=year\n                        )\n                        logs = logs.filter(entry_query)\n                    else:   # Если запрос пуст, пропускаем и идем дальше\n                        pass\n                    mode = 'ym'\n\n            if logs is None:\n                logs = Log.objects.all()[:10]\n\n        elif request.method == \"GET\":\n            # Получим первые 10 строк из таблицы\n            logs = Log.objects.all()[:10]\n            mode = 'default'\n\n        context = {'logs': logs, 'mode': mode, 'err': err}\n\n        return render(request, 'log.html', context)\n    else:\n        return redirect('/login_page')\n\n\n# Заметки\ndef notes(request):\n    if request.user.is_authenticated:  # Страница откроется только авторизованному пользователю\n        err = None\n        if request.method == \"POST\":  # Со стороны клиента было отправлено сообщение\n            if len(request.POST['send-message']) != 0:  # Если оно не пустое, обрабатываем\n                form = AddNoteForm(request.POST)\n                if form.is_valid():  # Если форма валидна, заполняем поля как следует\n                    id_creator = Personnel.objects.get(user=request.user)\n\n                    if request.POST['id_expenses'] == '':\n                        expense_id = None\n                    else:\n                        expense_id = Expenses.objects.get(pk=request.POST['id_expenses'])\n\n                    note = Notes.objects.create(\n                        creator_id=id_creator, status=request.POST['status'],\n                        description=request.POST['send-message'],\n                        id_expenses=expense_id\n                    )\n                    note.save()\n                else:\n                    pass\n            else:  # Если оно пустое, отправляем alert\n                err = \"Сообщение не должно быть пустым ;)\"\n\n        # Логика страницы: форма и данные\n        form = AddNoteForm()\n        notice = Notes.objects.all()[:10]\n        notice = notice[::-1]\n\n        if len(notice) == 0:  # Если заметок пока нет, показываем красивое предложение начать\n            notice = None\n\n        context = {'notes': notice, 'form': form, 'err': err}\n        return render(request, 'notes.html', context)\n    else:\n        return redirect('/login_page')\n\n\ndef settings(request):\n    if request.user.is_authenticated:\n        return render(request, 'settings.html')\n    else:\n        return redirect('/login_page')\n\n\ndef support(request):\n    if request.user.is_authenticated:\n        err = None\n        if request.method == \"POST\":\n            form = TechnicalSupportForm(request.POST)\n            if form.is_valid():\n                id_creator = Personnel.objects.get(user=request.user)\n                if 'attachments' in request.POST:\n                    attachment = None\n                else:\n                    attachment = request.FILES['attachments']\n                sup = TechnicalSupport.objects.create(\n                    creator_id=id_creator, theme=request.POST['theme'],\n                    description_of_the_problem=request.POST['description_of_the_problem'],\n                    logs=request.POST['logs'],\n                    attachments=attachment,\n                )\n                sup.save()\n\n                # Отправляем сообщение в бот ТГ\n                alert = TechnicalSupport.objects.all()[0]\n\n                #if attachment is not None:\n                #    with open(str(alert.attachments), \"rb\") as misc:\n                #        attachment = misc.read()\n                #else:\n                #    pass\n                \n                response = send_support_message(alert.theme, alert.description_of_the_problem, alert.logs,\n                                                alert.attachments)\n                if response == 200:\n                    err = \"Сообщение доставлено!\"\n                else:\n                    err = response\n\n                # Обнуляем форму\n                form = TechnicalSupportForm()\n        else:\n            form = TechnicalSupportForm()\n\n        context = {'form': form, 'err': err}\n\n        return render(request, 'support.html', context)\n    else:\n        return redirect('/login_page')\n","repo_name":"Samson-P/closet.vse-svoi.it","sub_path":"closet/web_interface/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":17213,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"32625040499","text":"import discord\nfrom discord.interactions import Interaction\nfrom db import db\n\n\nclass AddModal(discord.ui.Modal):\n    def __init__(self, msg: discord.Message):\n        super().__init__(title=\"Add Todo Item\")\n        self.add_item(discord.ui.InputText(label=\"Name\"))\n        self.add_item(discord.ui.InputText(label=\"Description\"))\n        self.msg = msg\n\n    async def callback(self, interaction: Interaction):\n        name_in_use = db.execute(\n            \"SELECT * FROM todo WHERE name = %s AND guild = %s\",\n            (self.children[0].value, interaction.guild.id),\n        ).fetchone()\n        if name_in_use is not None:\n            await interaction.response.send_message(\n                \"That name is already in use\", ephemeral=True\n            )\n            return\n        db.execute(\n            \"INSERT INTO todo (name, description, owner, guild) VALUES (%s, %s, %s, %s)\",\n            (\n                self.children[0].value,\n                self.children[1].value,\n                interaction.user.id,\n                interaction.guild.id,\n            ),\n        )\n        db.commit()\n        await interaction.response.send_message(\n            f\"Added todo item **{self.children[0].value}**\", ephemeral=True\n        )\n        self.msg.embeds[0].add_field(\n            name=self.children[0].value, value=self.children[1].value\n        )\n        await self.msg.edit(embed=self.msg.embeds[0])\n\n\nclass DeleteSelect(discord.ui.Select):\n    def __init__(self, userId: int, guildId: int, msg: discord.Message):\n        results = db.execute(\n            \"SELECT * FROM todo WHERE owner = %s AND guild = %s\", (userId, guildId)\n        ).fetchmany()\n        options = [\n            discord.SelectOption(label=row[1], value=str(row[0]), description=row[2])\n            for row in results\n        ]\n        super().__init__(placeholder=\"Please select a todo item\", options=options)\n        self.msg = msg\n\n    async def callback(self, interaction: Interaction):\n        deleted = db.execute(\n            \"DELETE FROM todo WHERE id = %s AND guild = %s RETURNING *\",\n            (self.values[0], interaction.guild.id),\n        ).fetchone()\n        await interaction.response.send_message(\n            f\"Deleted todo item **{deleted[1]}**\", ephemeral=True\n        )\n\n        for i, field in enumerate(self.msg.embeds[0].fields):\n            if field.name == deleted[1]:\n                del self.msg.embeds[0].fields[i]\n                break\n        await self.msg.edit(embed=self.msg.embeds[0])\n\n\nclass DeleteView(discord.ui.View):\n    def __init__(self, userId: int, guildId: int, msg: discord.Message):\n        super().__init__()\n        self.add_item(DeleteSelect(userId, guildId, msg))\n\n\nclass Todolist(discord.ui.View):\n    def __init__(self):\n        super().__init__(timeout=None)\n\n    @discord.ui.button(\n        label=\"Add\", style=discord.ButtonStyle.success, emoji=\"➕\", custom_id=\"todo-add\"\n    )\n    async def add_callback(\n        self, button: discord.ui.Button, interaction: discord.Interaction\n    ):\n        await interaction.response.send_modal(AddModal(self.message))\n\n    @discord.ui.button(\n        label=\"Delete\",\n        style=discord.ButtonStyle.danger,\n        emoji=\"🗑️\",\n        custom_id=\"todo-delete\",\n    )\n    async def delete_callback(\n        self, button: discord.ui.Button, interaction: discord.Interaction\n    ):\n        await interaction.response.send_message(\n            view=DeleteView(interaction.user.id, interaction.guild.id, self.message),\n            ephemeral=True,\n        )\n","repo_name":"codeman1o1/bert","sub_path":"ui/todolist.py","file_name":"todolist.py","file_ext":"py","file_size_in_byte":3529,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"81"}
+{"seq_id":"24126141521","text":"import re\n\ndef file_text():\n    filename = \"text.txt\"\n    with open(filename, 'r') as file:\n        text = file.read()\n    return text\n\n# визначаємо кількість слів\ndef word_counter(text):\n    words = re.findall(r'\\b\\w+\\b', text)\n    num_words = len(words)\n    return print(\"Кількість слів у файлі:\", num_words)\n\n# визначаємо кількість речень\n\ndef sentences_counter(text):\n    sentences = re.findall(r'[\\w\\s]*[\\.\\?!]+|[\\w\\s]*\\.{3}', text)\n    num_sentences = len(sentences)\n    return print(\"Кількість речень у файлі:\", num_sentences)\n\nword_counter(file_text())\nsentences_counter(file_text())","repo_name":"DanielNoshadha/Middle_Test","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":679,"program_lang":"python","lang":"uk","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"21137589180","text":"import numpy as np\n\nimport tensorflow as tf\nimport cv2\n\nif __name__ == \"__main__\":\n    mnist_model = tf.keras.models.load_model('./handwrite_digit_ocr/model-EMNIST-RCNN.h5')\n\n    mnist_out = np.load('E:/_dataset/EMNIST/EMNIST.npz')\n    x_train, y_train, x_test, y_test = mnist_out['x_train'], \\\n                                       mnist_out['y_train'], \\\n                                       mnist_out['x_test'], \\\n                                       mnist_out['y_test']\n\n    # loss_and_metrics = mnist_model.evaluate(x_test, y_test, verbose=2)\n\n    # print(\"Test Loss: {}\".format(loss_and_metrics[0]))\n    # print(\"Test Accuracy: {}%\".format(loss_and_metrics[1] * 100))\n\n\n    img = x_test[123]\n    cv2.imshow(\"est\", img)\n    cv2.waitKey(0)\n\n\n    img_data = img.reshape(-1, 28, 28, 3)\n    predict_result = mnist_model.predict(img_data)\n    print(np.argmax(predict_result), predict_result[0][np.argmax(predict_result)])\n\n\n\n\n    # predicted_classes = mnist_model.predict_classes(x_test)\n    #\n    # correct_indices = np.nonzero(predicted_classes == y_test)[0]\n    # incorrect_indices = np.nonzero(predicted_classes != y_test)[0]\n    # print(\"Classified correctly count: {}\".format(len(correct_indices)))\n    # print(\"Classified incorrectly count: {}\".format(len(incorrect_indices)))\n","repo_name":"victoriest/deep-learning-playgroud","sub_path":"character_ocr_test.py","file_name":"character_ocr_test.py","file_ext":"py","file_size_in_byte":1289,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"81"}
+{"seq_id":"2584336207","text":"# requests, bs4 모듈 import\nimport requests, bs4, re\n\n# download import\nfrom filedown import download\n\n# 증권 자료 사이트\nsite = 'MK'\n\n# 페이지 지정\npages = input(\"페이지 입력 :\")\n\n# 첫 페이지\ncurrent = 1\n\n# 크롤링\nwhile True:\n\n    if current > int(pages):\n        break\n\n    # 크롤링 URL 지정\n    crawling_url = f\"https://vip.mk.co.kr/newSt/news/news_list2.php?p_page={current}&sCode=13&termDatef=&search=\"\n\n    # 지정한 URL에 요청을 보내고, 받기\n    response = requests.get(crawling_url)\n\n    # HTML 파싱 & 한글 깨짐 현상 제거\n    result_html = bs4.BeautifulSoup(response.content.decode('euc-kr','replace'),'html.parser')\n\n    # 정보 찾기\n    stocks = result_html.find(\"table\",{\"class\":\"table_6\"}).find_all(\"tr\")\n\n    for stock_info in stocks[:]:\n\n        info = stock_info.find(\"td\",{\"class\":\"title\"})\n\n        if info != None:\n            # 링크\n            stock_url = \"https://vip.mk.co.kr/newSt/news/\" + info.find(\"a\")[\"href\"]\n            # 종목 \n            stock_sub = info.get_text().strip().split('(')[0]\n            # 제목\n            stock_title = info.get_text().strip().split(')')[1]\n            # 날짜\n            stock_date = stock_info.find(\"td\",{\"class\":\"t_11_brown\"}).get_text().split(' ')[0]\n\n            #--------------------------------------------------------------------------\n\n            # 기사 크롤링\n\n            # 지정한 article URL에 요청을 보내고, 받기\n            article_response = requests.get(stock_url)\n\n            # HTML 파싱 & 한글 깨짐 현상 제거\n            article_result_html = bs4.BeautifulSoup(article_response.content.decode('euc-kr','replace'),'html.parser')\n\n            # 기사 본문 내용찾기\n            article = article_result_html.find(\"div\",{\"class\":\"read1\"})\n            # <div> 태그 제거를 위한 변수 저장\n            div_str = '<div class=\"read1\" id=\"Conts\">'\n            # html 태그 제거\n            article = str(article).replace('<br/>','\\n').replace('<p>','\\n').replace('</p>','').replace(f'{div_str}','').replace('</div>','')\n\n            # 증권사 찾기\n            stock_com = article_result_html.find(\"table\").find_all(\"span\")\n            \n            for name in stock_com:\n                name = name.get_text().replace('\\n','').replace('\\xa0','').strip()\n                if '종목리서치' in name:\n                    name = name.split('|')[1].strip().split(' ')[0]\n                    stock_com = name\n\n            # #--------------------------------------------------------------------------\n\n            # 종목명 (stock_sub)\n            print('종목명 :',stock_sub)\n            \n            # 제목 (stock_title)\n            print('제목 :',stock_title)\n            \n            # 증권사 (stock_com)\n            print('증권사 :',stock_com)\n\n            # 작성일 (stock_date) \n            print('작성일 :',stock_date)\n\n            # 첨부 (stock_file)\n            print('URL :',stock_url)\n\n            # 텍스트 파일 저장 이름\n            file_name = stock_com + \"_\" + stock_sub + \"_\" + \"_\" + stock_date + \".txt\"\n\n            # 파일로 다운\n            download(site, article, file_name)\n\n            print('------------------------------\\n')\n\n    current = current + 1","repo_name":"syhan7516/Crawling_Kumoh_OpenSource","sub_path":"stock_mk.py","file_name":"stock_mk.py","file_ext":"py","file_size_in_byte":3289,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"13282458130","text":"PHI = (1 + sqrt(5)) / 2\n\n# spiral properties\na = 4\nb = (2 * log(PHI)) / PI\nc = (a / b) * sqrt(1 + b ** 2)\nsep = 10    # separation of points along spiral\nn = 300    # number of points\n\n\ndef setup():\n    size(800, 500)\n    background(241, 241, 241)\n    frameRate(30)\n    fill(255, 180, 2)\n    noStroke()\n    global origin\n    origin = (24 * width / 34, 15 * height / 21)\n\n    for i in xrange(n + 1):\n        t = theta(i)\n        r = a * exp(b*t)\n        x, y = cart(r, t, origin)\n        ellipse(x, y, 5, 5)\n\n\ndef draw():\n    # sine wave properties\n    k = TWO_PI / (7 * sep)\n    w = TWO_PI / 120.0\n    background(241, 241, 241)\n    for i in xrange(n + 1):\n        t = theta(i)\n        sine_val = sin((k * c * (exp(b*t) - 1)) - (w * frameCount))\n        amp_factor = (30 / exp(5*b*PI)) * exp(b*t)\n        r = (a * exp(b*t)) + (amp_factor * sine_val)\n        x, y = cart(r, t, origin)\n        size = ((5 / exp(5*b*PI)) * exp(b*t)) + 3\n        ellipse(x, y, size, size)\n\n\ndef cart(r, theta, origin):\n    x = origin[0] + (r * cos(theta))\n    y = origin[1] - (r * sin(theta))\n    return (x, y)\n\n\ndef theta(n):\n    t = 0\n    while n > 0:\n        t = (1.0 / b) * log((sep / c) + exp(b*t))\n        n -= 1\n    return t\n","repo_name":"whonut/Ginger-GIFs","sub_path":"sine_spiral/sine_spiral.pyde","file_name":"sine_spiral.pyde","file_ext":"pyde","file_size_in_byte":1210,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"25210682264","text":"from collections import namedtuple\nimport json\nimport logging\nimport flask\nfrom superdesk import get_resource_service\nimport superdesk\nfrom superdesk.errors import SuperdeskApiError\nfrom superdesk.metadata.item import not_analyzed, ITEM_STATE, PUBLISH_STATES\nfrom superdesk.metadata.utils import aggregations, get_elastic_highlight_query\nfrom superdesk.resource import Resource\nfrom superdesk.services import BaseService\nfrom superdesk.utc import utcnow\n\nfrom bson.objectid import ObjectId\nfrom eve.utils import ParsedRequest, config\nfrom flask import current_app as app, request\n\nfrom apps.archive.archive import SOURCE as ARCHIVE\nfrom apps.archive.common import handle_existing_data, item_schema\nfrom superdesk.publish.publish_queue import PUBLISHED_IN_PACKAGE\nfrom apps.content import push_content_notification\nfrom flask_babel import _\nfrom apps.archive.highlights_search_mixin import HighlightsSearchMixin\n\nlogger = logging.getLogger(__name__)\n\nPUBLISHED = \"published\"\nLAST_PUBLISHED_VERSION = \"last_published_version\"\nQUEUE_STATE = \"queue_state\"\nERROR_MESSAGE = \"error_message\"\nqueue_states = [\"pending\", \"in_progress\", \"queued\", \"queued_not_transmitted\", \"error\"]\nPUBLISH_STATE = namedtuple(\"PUBLISH_STATE\", [\"PENDING\", \"IN_PROGRESS\", \"QUEUED\", \"QUEUED_NOT_TRANSMITTED\", \"ERROR\"])(\n    *queue_states\n)\n\npublished_item_fields = {\n    \"item_id\": {\"type\": \"string\", \"mapping\": not_analyzed},\n    \"publish_state\": {\"type\": \"string\"},\n    # last_published_version field is set to true for last published version of the item in the published collection\n    # and for the older version is set to false. This field is used to display the last version of the digital copy\n    # in the published view.\n    LAST_PUBLISHED_VERSION: {\"type\": \"boolean\", \"default\": True},\n    QUEUE_STATE: {\n        \"type\": \"string\",\n        \"default\": \"pending\",\n        \"allowed\": queue_states,\n    },\n    ERROR_MESSAGE: {\"type\": \"string\"},\n    \"is_take_item\": {  # deprecated\n        \"type\": \"boolean\",\n        \"default\": False,\n    },\n    \"digital_item_id\": {\"type\": \"string\"},  # deprecated\n    \"publish_sequence_no\": {\"type\": \"integer\", \"readonly\": True},\n    \"last_queue_event\": {\"type\": \"datetime\"},\n    # item is moved to legal. True if moved else false.\n    \"moved_to_legal\": {\"type\": \"boolean\", \"default\": False},\n    # if item is published as part of a package this will\n    # be set to package id so transmitter can handle such\n    # items differently\n    PUBLISHED_IN_PACKAGE: {\n        \"type\": \"string\",\n    },\n    \"_current_version\": {  # must be set explicitly, there is no versioning\n        \"type\": \"integer\",\n    },\n}\n\n\ndef get_content_filter(req=None):\n    \"\"\"Filter out content of stages not visible to current user (if any).\n\n    :return:\n    \"\"\"\n    user = getattr(flask.g, \"user\", None)\n    if user:\n        if \"invisible_stages\" in user:\n            stages = user.get(\"invisible_stages\")\n        else:\n            stages = get_resource_service(\"users\").get_invisible_stages_ids(user.get(\"_id\"))\n\n        if stages:\n            return {\"bool\": {\"must_not\": {\"terms\": {\"task.stage\": stages}}}}\n\n\nclass PublishedItemResource(Resource):\n    datasource = {\n        \"search_backend\": \"elastic\",\n        \"aggregations\": aggregations,\n        \"es_highlight\": get_elastic_highlight_query,\n        \"default_sort\": [(\"_updated\", -1)],\n        \"elastic_filter_callback\": get_content_filter,\n        \"projection\": {\n            \"old_version\": 0,\n            \"last_version\": 0,\n        },\n    }\n\n    schema = item_schema(published_item_fields)\n    etag_ignore_fields = [config.ID_FIELD, \"highlights\", \"item_id\", LAST_PUBLISHED_VERSION, \"moved_to_legal\"]\n\n    privileges = {\"POST\": \"publish_queue\", \"PATCH\": \"publish_queue\"}\n    item_methods = [\"GET\", \"PATCH\"]\n    additional_lookup = {\"url\": 'regex(\"[\\w,.:-]+\")', \"field\": \"item_id\"}\n\n    mongo_indexes = {\n        \"guid_1\": ([(\"guid\", 1)], {\"background\": True}),\n        \"item_id_1\": ([(\"item_id\", 1)], {\"background\": True}),\n        \"translation_id_1\": ([(\"translation_id\", 1)], {\"background\": True}),\n    }\n\n\nclass PublishedItemService(BaseService, HighlightsSearchMixin):\n    \"\"\"\n    PublishedItemService class is the base class for ArchivedService.\n    \"\"\"\n\n    SEQ_KEY_NAME = \"published_item_sequence_no\"\n\n    def on_fetched(self, docs):\n        \"\"\"\n        Overriding this to enhance the published article with the one in archive collection\n        \"\"\"\n\n        self.enhance_with_archive_items(docs[config.ITEMS])\n\n    def on_fetched_item(self, doc):\n        \"\"\"\n        Overriding this to enhance the published article with the one in archive collection\n        \"\"\"\n\n        self.enhance_with_archive_items([doc])\n\n    def on_create(self, docs):\n        \"\"\"Runs on create.\n\n        An article can be published multiple times in its lifetime. So, it's necessary to preserve the _id which comes\n        from archive collection. Also, sets the expiry on the published item and removes the lock information.\n        \"\"\"\n\n        for doc in docs:\n            self.raise_if_not_marked_for_publication(doc)\n            doc[config.LAST_UPDATED] = doc[config.DATE_CREATED] = utcnow()\n            self.set_defaults(doc)\n\n    def on_update(self, updates, original):\n        if ITEM_STATE in updates:\n            self.raise_if_not_marked_for_publication(updates)\n\n    def on_updated(self, updates, original):\n        if \"marked_for_user\" in updates:\n            updated = original.copy()\n            updated.update(updates)\n            # Send notification on mark-for-user operation\n            get_resource_service(\"archive\").handle_mark_user_notifications(updates, original)\n            push_content_notification([updated, original])  # see SDBELGA-192\n\n    def raise_if_not_marked_for_publication(self, doc):\n        \"\"\"\n        Item should be one of the PUBLISH_STATES. If not raise error.\n        \"\"\"\n        if doc.get(ITEM_STATE) not in PUBLISH_STATES and doc.get(ITEM_STATE) != \"spiked\":\n            raise SuperdeskApiError.badRequestError(\n                _(\"Invalid state ({state}) for the Published item.\").format(state=doc.get(ITEM_STATE))\n            )\n\n    def set_defaults(self, doc):\n        doc[\"item_id\"] = doc[config.ID_FIELD]\n        doc[\"versioncreated\"] = utcnow()\n        doc[\"publish_sequence_no\"] = get_resource_service(\"sequences\").get_next_sequence_number(self.SEQ_KEY_NAME)\n        doc.pop(config.ID_FIELD, None)\n        doc.pop(\"lock_user\", None)\n        doc.pop(\"lock_time\", None)\n        doc.pop(\"lock_action\", None)\n        doc.pop(\"lock_session\", None)\n\n    def enhance_with_archive_items(self, items):\n        if items:\n            ids = list(set([item.get(\"item_id\") for item in items if item.get(\"item_id\")]))\n            archive_items = []\n            archive_lookup = {}\n            if ids:\n                query = {\"$and\": [{config.ID_FIELD: {\"$in\": ids}}]}\n                archive_req = ParsedRequest()\n                archive_req.max_results = len(ids)\n                # can't access published from elastic due filter on the archive resource hence going to mongo\n                archive_items = list(\n                    superdesk.get_resource_service(ARCHIVE).get_from_mongo(req=archive_req, lookup=query)\n                )\n\n                for item in archive_items:\n                    handle_existing_data(item)\n                    archive_lookup[item[config.ID_FIELD]] = item\n\n            for item in items:\n                archive_item = archive_lookup.get(item.get(\"item_id\"), {config.VERSION: item.get(config.VERSION, 1)})\n\n                updates = {\n                    config.ID_FIELD: item.get(\"item_id\"),\n                    \"item_id\": item.get(config.ID_FIELD),\n                    \"lock_user\": archive_item.get(\"lock_user\", None),\n                    \"lock_time\": archive_item.get(\"lock_time\", None),\n                    \"lock_action\": archive_item.get(\"lock_action\", None),\n                    \"lock_session\": archive_item.get(\"lock_session\", None),\n                    \"archive_item\": archive_item if archive_item else None,\n                }\n\n                if request and request.args.get(\"published_id\") == \"1\":\n                    updates.pop(config.ID_FIELD)\n                    updates.pop(\"item_id\")\n\n                item.update(updates)\n                handle_existing_data(item)\n\n    def on_delete(self, doc):\n        \"\"\"Deleting a published item has a workflow which is implemented in remove_expired().\n\n        Overriding to avoid other services from invoking this method accidentally.\n        \"\"\"\n\n        if app.testing:\n            super().on_delete(doc)\n        else:\n            raise NotImplementedError(\n                _(\"Deleting a published item has a workflow which is implemented in remove_expired().\")\n            )\n\n    def delete_action(self, lookup=None):\n        \"\"\"Deleting a published item has a workflow which is implemented in remove_expired().\n\n        Overriding to avoid other services from invoking this method accidentally.\n        \"\"\"\n\n        if app.testing:\n            super().delete_action(lookup)\n        else:\n            raise NotImplementedError(\n                _(\"Deleting a published item has a workflow which is implemented in remove_expired().\")\n            )\n\n    def on_deleted(self, doc):\n        \"\"\"Deleting a published item has a workflow which is implemented in remove_expired().\n\n        Overriding to avoid other services from invoking this method accidentally.\n        \"\"\"\n\n        if app.testing:\n            super().on_deleted(doc)\n        else:\n            raise NotImplementedError(\n                _(\"Deleting a published item has a workflow which is implemented in remove_expired().\")\n            )\n\n    def get_other_published_items(self, _id):\n        try:\n            query = {\"query\": {\"filtered\": {\"filter\": {\"term\": {\"item_id\": _id}}}}}\n            request = ParsedRequest()\n            request.args = {\"source\": json.dumps(query)}\n            return super().get(req=request, lookup=None)\n        except Exception:\n            return []\n\n    def get_last_published_version(self, _id):\n        \"\"\"Returns the last published entry for the passed item id\n\n        :param _id:\n        :return:\n        \"\"\"\n        try:\n            query = {\n                \"query\": {\n                    \"filtered\": {\n                        \"filter\": {\n                            \"bool\": {\"must\": [{\"term\": {\"item_id\": _id}}, {\"term\": {LAST_PUBLISHED_VERSION: True}}]}\n                        }\n                    }\n                }\n            }\n\n            request = ParsedRequest()\n            request.args = {\"source\": json.dumps(query), \"repo\": \"published\"}\n            items = list(self.get(req=request, lookup=None))\n            if items:\n                return items[0]\n        except Exception:\n            return None\n\n    def get_rewritten_items_by_event_story(self, event_id, rewrite_id, rewrite_field):\n        \"\"\"Returns all the rewritten stories from published and archive for a given event and rewrite_id.\n\n        :param str event_id: event id of the document\n        :param str rewrite_id: rewrite id of the document\n        :param str rewrite_field: field name 'rewrite_of' or 'rewritten_by'\n        \"\"\"\n        try:\n            query = {\n                \"query\": {\n                    \"filtered\": {\n                        \"filter\": {\n                            \"bool\": {\"must\": [{\"term\": {\"event_id\": event_id}}, {\"term\": {rewrite_field: rewrite_id}}]}\n                        }\n                    }\n                }\n            }\n\n            request = ParsedRequest()\n            request.args = {\"source\": json.dumps(query), \"repo\": \"archive,published\"}\n            return list(get_resource_service(\"search\").get(req=request, lookup=None))\n        except Exception:\n            return []\n\n    def is_rewritten_before(self, item_id):\n        \"\"\"Checks if the published item is rewritten before.\n\n        :param _id: item_id of the published item\n        :return: True is it is rewritten before\n        \"\"\"\n        doc = self.find_one(req=None, item_id=item_id)\n        return doc and \"rewritten_by\" in doc and doc[\"rewritten_by\"]\n\n    def update_published_items(self, _id, field, state):\n        items = self.get_other_published_items(_id)\n        for item in items:\n            try:\n                super().system_update(ObjectId(item[config.ID_FIELD]), {field: state}, item)\n            except Exception:\n                # This part is used in unit testing\n                super().system_update(item[config.ID_FIELD], {field: state}, item)\n\n    def delete_by_article_id(self, _id):\n        \"\"\"Removes the article from the published collection.\n\n        Removes published queue entries.\n\n        :param str _id: id of the document to be deleted. In mongo, it is the item_id\n        \"\"\"\n        lookup = {\"item_id\": _id}\n        self.delete(lookup=lookup)\n        get_resource_service(\"publish_queue\").delete_by_article_id(_id)\n\n    def find_one(self, req, **lookup):\n        item = super().find_one(req, **lookup)\n        handle_existing_data(item)\n\n        return item\n\n    def move_to_archived(self, _id):\n        published_items = list(self.get_from_mongo(req=None, lookup={\"item_id\": _id}))\n        if not published_items:\n            return\n        get_resource_service(\"archived\").post(published_items)\n        self.delete_by_article_id(_id)\n\n    def set_moved_to_legal(self, item_id, version, status):\n        \"\"\"Update the legal flag.\n\n        :param str item_id: id of the document\n        :param int version: version of the document\n        :param boolean status: True if the item is moved to legal else false\n        \"\"\"\n        items = self.get_other_published_items(item_id)\n\n        for item in items:\n            try:\n                if item.get(config.VERSION) <= version and not item.get(\"moved_to_legal\", False):\n                    super().system_update(ObjectId(item.get(config.ID_FIELD)), {\"moved_to_legal\": status}, item)\n            except Exception:\n                logger.exception(\n                    \"Failed to set the moved_to_legal flag \" \"for item {} and version {}\".format(item_id, version)\n                )\n\n    def get_published_items_by_moved_to_legal(self, item_ids, move_to_legal):\n        \"\"\"Get the pulished items where flag is moved.\n\n        :param list item_ids: List of item\n        :param bool move_to_legal: move_to_legal boolean flag\n        :return: list of published items\n        \"\"\"\n        if item_ids:\n            try:\n                query = {\n                    \"query\": {\n                        \"filtered\": {\n                            \"filter\": {\n                                \"and\": [{\"terms\": {\"item_id\": item_ids}}, {\"term\": {\"moved_to_legal\": move_to_legal}}]\n                            }\n                        }\n                    }\n                }\n\n                request = ParsedRequest()\n                request.args = {\"source\": json.dumps(query)}\n                return list(super().get(req=request, lookup=None))\n            except Exception:\n                logger.exception(\n                    \"Failed to get published items \" \"by moved to legal: {} -- ids: {}.\".format(move_to_legal, item_ids)\n                )\n\n        return []\n\n    def get(self, req, lookup):\n        req, lookup = self._get_highlight(req, lookup)\n        return super().get(req, lookup)\n","repo_name":"superdesk/superdesk-core","sub_path":"apps/publish/published_item.py","file_name":"published_item.py","file_ext":"py","file_size_in_byte":15319,"program_lang":"python","lang":"en","doc_type":"code","stars":31,"dataset":"github-code","pt":"81"}
+{"seq_id":"74096550344","text":"# -*- coding: utf-8 -*-\nimport json\nimport requests\n\n# http://map.amap.com/service/subway?_1592051499713&srhdata=3100_drw_shanghai.json\nwith open('subway.json') as f:\n  data = json.load(f)\n\noutput = open(\"home1.csv\", \"w\")\n\nfrom_data = [\n    # ('静安寺', \"BV10025485\"),\n    # ('陆家嘴', \"BV10029743\"),\n    # ('漕河泾开发区','BV10025213'),\n    ('张江高科', 'BV10039897'),\n]\n\n#reading file\nfor from_stop, from_id in from_data:\n    for line in data['l']:\n        print(line['ln'])\n        for stop in line['st']:\n            log, lat = stop['sl'].split(',')\n            # print (stop['n'], stop['sl'], stop['poiid'])\n            url = 'https://gaode.com/service/nav/bus'\n            payload = {\n                'night': 0,\n                'group': 1,\n                'pure_walk': 1,\n                'date': '2020-6-13',\n                'time': '19-00',\n                'eta': 1,\n            }\n            payload.update({\n                'poiid1': from_id,\n                'poiid2': stop['poiid']\n            })\n            r = requests.get(url, params=payload)\n            data = r.json()\n            print(data.keys())\n            if 'data' in data:\n                res = '{},{},{}\\n'.format(from_stop, stop['n'], data['data']['buslist'][0]['expensetime'])\n                output.write(res)\n                print(res)\n\noutput.close()","repo_name":"haojingliu/shanghai-railway-distance","sub_path":"calculate_commuting_time.py","file_name":"calculate_commuting_time.py","file_ext":"py","file_size_in_byte":1347,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"14215140489","text":"import re\nfrom lxml import etree\n\nNS_XML = 'http://www.w3.org/XML/1998/namespace'\nNS_RDF = 'http://www.w3.org/1999/02/22-rdf-syntax-ns#'\n\nclass Namespace(object):\n    def __init__(self, ns):\n        self.ns = ns\n\n    def __getattr__(self, el):\n        return self.ns + el\n\n    def __getitem__(self, el):\n        return self.ns + el\n\nclass NS(object):\n    rdf = Namespace('http://www.w3.org/1999/02/22-rdf-syntax-ns#')\n    rdfs = Namespace('http://www.w3.org/2000/01/rdf-schema#')\n    xsd = Namespace('http://www.w3.org/2001/XMLSchema#')\n    viveur = Namespace('http://vivo.eur.nl/individual/')\n    foaf = Namespace('http://xmlns.com/foaf/0.1/')\n    core = Namespace('http://vivoweb.org/ontology/core#')\n    vitro = Namespace('http://vitro.mannlib.cornell.edu/ns/vitro/0.7')\n    skos = Namespace('http://www.w3.org/2004/02/skos/core#')\n    local = Namespace('http://localhost/core/ontology/core-local#')\n    localVivo = Namespace('http://vivo.libr.tue.nl/ontology/')\n    tue = Namespace('http://vivo.libr.tue.nl/ontology/tue#')\n    eur = Namespace('http://vivo.eur.nl/ontology#')\n    public = Namespace('http://vitro.mannlib.cornell.edu/ns/vitro/public#')\n    event = Namespace('http://purl.org/NET/c4dm/event.owl#')\n    bibo = Namespace('http://purl.org/ontology/bibo/')\n    dcterms = Namespace('http://purl.org/dc/terms/')\n    dctype = Namespace('http://purl.org/dc/dcmitype/')\n    owl = Namespace('http://www.w3.org/2002/07/owl#')\n    score = Namespace('http://vivoweb.org/ontology/score#')\n    event = Namespace('http://purl.org/NET/c4dm/event.owl#')\n    eur = Namespace('http://vivo.eur.nl/ontology/eur-1.0#')\n\n    @classmethod\n    def uri_split(cls, uri):\n        for sep in ['#', '/', ':']:\n            parts = uri.rsplit(sep, 1)\n            if len(parts) == 2:\n                return parts[0] + sep, parts[1]\n        raise ValueError('Can not split uri: %s' % uri)\n\n\ndef uris(*values):\n    return [{'type': 'uri', 'value': value} for value in values]\n\ndef literals(*values, **args):\n    if 'datatype' in args:\n        return [{'type': 'literal',\n                 'value': v,\n                 'datatype': args['datatype']} for v in values]\n    elif 'lang' in args:\n        return [{'type': 'literal',\n                 'value': v,\n                 'lang': args['lang']} for v in values]\n    else:\n        return [{'type': 'literal',\n                 'value': v} for v in values]\n\ndef register_etree_prefix(prefix, ns):\n    if hasattr(etree, 'register_namespace'):\n        etree.register_namespace(prefix, ns)\n    else:\n        etree._namespace_map[ns] = prefix\n\ndef escape_invalid_unicode_range(value):\n    \"removes UTF-8 character ranges that are not allowed in XML\"\n    if not isinstance(value, unicode):\n        value = value.decode('utf8')\n    return re.sub(\n        u'[^\\x09\\x0A\\x0D\\u0020-\\uD7FF\\uE000-\\uFFFD]+', '', value)\n\n\ndef pyjson2rdfxml(data):\n    for prefix in dir(NS):\n        if isinstance(getattr(NS, prefix), Namespace):\n            register_etree_prefix(prefix, getattr(NS, prefix).ns)\n    doc = etree.Element('{%s}RDF' % NS_RDF)\n    for subject in data:\n        s_el = etree.SubElement(doc, '{%s}Description' % NS_RDF)\n        if subject.startswith('_:'):\n            s_el.attrib['{%s}nodeID' % NS_RDF] = subject[2:]\n        else:\n            s_el.attrib['{%s}about' % NS_RDF] = subject\n        for predicate in data[subject]:\n            for object in data[subject][predicate]:\n                object['value'] = escape_invalid_unicode_range(object['value'])\n                ns, tag = NS.uri_split(predicate)\n                p_el = etree.SubElement(s_el, '{%s}%s'  % (ns, tag))\n                if object['type'] == 'uri':\n                    p_el.attrib['{%s}resource' % NS_RDF] = object['value']\n                elif object['type'] == 'bnode':\n                    assert object['value'].startswith('_:'), (\n                        \"Blank node should start with '_:'\")\n                    p_el.attrib['{%s}nodeID' % NS_RDF] = object['value'][2:]\n                elif object['type'] == 'literal':\n                    p_el.text = object['value']\n                    lang = object.get('lang')\n                    datatype = object.get('datatype')\n                    if lang:\n                        p_el.attrib['{%s}lang' % NS_XML] = lang\n                    elif datatype:\n                        p_el.attrib['{%s}datatype' % NS_RDF] = datatype\n    return etree.tostring(doc, encoding='UTF-8')\n\ndef rdfxml2pyjson(data):\n    result = {}\n    if not data:\n        return result\n    doc = etree.fromstring(data)\n    for s_el in doc:\n        subject = s_el.attrib.get('{%s}about' % NS_RDF)\n        if not subject:\n            subject = '_:%s' % s_el.attrib['{%s}nodeID' % NS_RDF]\n        predicates = result.get(subject)\n        if predicates is None:\n            predicates = {}\n            result[subject.decode('utf8')] = predicates\n        for p_el in s_el:\n            ns, tag = p_el.tag[1:].split('}')\n            predicate = ns + tag\n            objects = predicates.get(predicate)\n            if objects is None:\n                objects = []\n                predicates[predicate.decode('utf8')] = objects\n            uri = p_el.attrib.get('{%s}resource' % NS_RDF)\n            if not uri is None:\n                objects.append(dict(value=uri.decode('utf8'), type='uri'))\n                continue\n            blank = p_el.attrib.get('{%s}nodeID' % NS_RDF)\n            if not blank is None:\n                objects.append(dict(value=u'_:%s' % blank, type='bnode'))\n                continue\n            literal = p_el.text\n            if isinstance(literal, str):\n                literal = literal.decode('utf8')\n            value = dict(value=literal, type='literal')\n            lang = p_el.attrib.get('{%s}lang' % NS_XML)\n            datatype = p_el.attrib.get('{%s}datatype' % NS_RDF)\n            if lang:\n                value['lang'] = lang\n            elif datatype:\n                value['datatype'] = datatype\n            objects.append(value)\n    return result\n","repo_name":"jascoul/viveur","sub_path":"viveur/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":5994,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"37209066718","text":"import logging as log\n\nimport cv2 as cv\nimport numpy as np\nfrom skimage import feature\n\n# from skimage.feature import local_binary_pattern\nfrom .config import GLOBAL_CONFIG\n\n\nclass Image(object):\n    \"\"\"Image: Image instance is a frame of video or a image\n\n    An instance represents a frame or an image that can be used to train a model or to identify.\n    \"\"\"\n    image = None\n\n    def __init__(self, target, name=\"Init\"):\n        self.name = name\n\n        if isinstance(target, str):\n            # if target is img dir\n            self.image = cv.imread(target)\n        else:\n            # if target is img\n            self.image = target\n\n    def resize(self, width, height):\n        try:\n            return cv.resize(src=self.image,\n                             dsize=(width, height),\n                             interpolation=cv.INTER_AREA)\n        except Exception as err:\n            log.info('%s resize failed. error: %s', self.name, err)\n            return None\n\n    def get_features(self):\n        return np.append(self.get_HOG(), self.get_LBP())\n\n    def get_HOG(self):\n        # get hog and save to self.descriptor\n        # 1. resize image to get copy\n\n        copy = self.resize(GLOBAL_CONFIG.hog_width, GLOBAL_CONFIG.hog_height)\n        # copy = self.image\n        if copy is None:\n            log.error('resize failed. get hog failed')\n            return\n        # 2. get hog descriptor\n        hog = cv.HOGDescriptor(\n            (GLOBAL_CONFIG.hog_width, GLOBAL_CONFIG.hog_height), (16, 16),\n            (8, 8), (8, 8), 9)\n        descriptor = hog.compute(copy)\n\n        if descriptor is None:\n            return np.array([])\n        else:\n            return descriptor\n\n    def get_LBP(self):\n        numPoints = 24\n        radius = 8\n        eps = 1e-7\n        # copy = self.image\n        copy = self.resize(GLOBAL_CONFIG.hog_width, GLOBAL_CONFIG.hog_height)\n        gray = cv.cvtColor(copy, cv.COLOR_BGR2GRAY)\n        lbp = feature.local_binary_pattern(gray,\n                                           numPoints,\n                                           radius,\n                                           method=\"uniform\")\n        (hist, _) = np.histogram(lbp.ravel(),\n                                 bins=np.arange(0, numPoints + 3),\n                                 range=(0, numPoints + 2))\n        hist = hist.astype('float')\n        hist /= (hist.sum() + eps)\n\n        return hist\n\n    def get_src(self):\n        # self.show()\n        return cv.cvtColor(self.image, cv.COLOR_BGR2RGB)\n\n    def show(self):\n        cv.imshow(self.name, self.image)\n        cv.waitKey(1)\n\n    def get_width(self):\n        return self.image.shape[1]\n\n    def get_height(self):\n        return self.image.shape[0]\n\n    def cut(self, x, y, px, py):\n        return Image(self.image[y:py, x:px])\n\n    def draw_rectangle(self, x, y, px, py):\n        cv.rectangle(self.image, (x, y), (px, py), (0, 255, 255), thickness=1)\n","repo_name":"chasezcz/smokeRecognize","sub_path":"src/img.py","file_name":"img.py","file_ext":"py","file_size_in_byte":2925,"program_lang":"python","lang":"en","doc_type":"code","stars":16,"dataset":"github-code","pt":"81"}
+{"seq_id":"74303338183","text":"from flask import Flask, render_template, request\r\nimport joblib\r\n\r\n\r\nmodel = joblib.load('modelos/modelo_petalos.pkl') \r\n\r\napp = Flask(__name__)\r\n\r\n@app.route('/')\r\ndef index():\r\n    return render_template('index.html')\r\n\r\n\r\n@app.route('/predict', methods=['POST'])\r\ndef predict():\r\n    w_sepal = float(request.form['W_sepal'])\r\n    l_sepal = float(request.form['L_sepal'])\r\n    w_petal = float(request.form['W_petal'])\r\n    l_petal = float(request.form['L_petal'])\r\n  \r\n    pred_probabilities = model.predict_proba([[l_sepal,w_sepal,l_petal, w_petal ]])\r\n\r\n    class_names = model.classes_\r\n\r\n    mensaje = \"\"\r\n    for i, class_name in enumerate(class_names):\r\n        prob = pred_probabilities[0, i] * 100\r\n        mensaje += f\"Probabilidad de {class_name}: {round(prob, 2)}% <br/>\"\r\n\r\n    return render_template('result.html', pred=mensaje)\r\n\r\n\r\nif __name__ == '__main__':\r\n    app.run()","repo_name":"DevRaki/Alvaro","sub_path":"regretion_model/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":891,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"38858647347","text":"from django.urls import path\nfrom .views import Login, Register, Logout, Dashboard\n\n\nurlpatterns = [\n    path('accounts/login/', Login.as_view(), name='login'),\n    path('accounts/register/', Register.as_view(), name='register'),\n    path('accounts/logout/', Logout.as_view(), name='logout'),\n    path('accounts/dashboard/', Dashboard.as_view(), name='dashboard'),\n]\n","repo_name":"sadegh-masoumi/house_pricing","sub_path":"app_user/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":367,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"9876756733","text":"#!/usr/bin/env python\r\n# -*- coding: utf-8 -*-\r\n\r\nimport http.client\r\nimport logging\r\nimport os\r\nimport re\r\nimport urllib\r\n\r\nimport speech_recognition as sr\r\nimport urllib3\r\nfrom lib.fritzcall import FritzCall\r\n\r\nlogger = logging.getLogger(__name__)\r\n\r\n\r\nclass FritzCallsDuringAbsense():\r\n\r\n\tdef __init__(self, connection, prefs):\r\n\t\tself.prefs = prefs\r\n\t\tself.areaCode = (connection.call_action('X_VoIP', 'GetVoIPCommonAreaCode'))['NewVoIPAreaCode']\r\n\t\tself.http = urllib3.PoolManager()\r\n\t\tself.callURLList = connection.call_action('X_AVM-DE_OnTel', 'GetCallList')\r\n\t\tentries = re.search(\"sid=(.*)$\", self.callURLList['NewCallListURL'])\r\n\t\tself.sid = entries.group(0)\r\n\t\tself.FC = FritzCall(fc=connection)\r\n\t\tself.unresolved_list = []\r\n\r\n\tdef get_sid(self):\r\n\t\treturn self.sid\r\n\r\n\tdef get_fullCode(self, code):\r\n\t\tif code == None or code[:1] == '0':\r\n\t\t\treturn code\r\n\t\telse:\r\n\t\t\treturn self.areaCode + code\r\n\r\n\tdef set_unresolved(self, caller):\r\n\t\tself.unresolved_list.append(caller)\r\n\r\n\tdef get_unresolved(self):\r\n\t\tfor caller in self.unresolved_list:\r\n#\t\t\tcalls = [\r\n#\t\t\t\tcall for call in self.FC.get_calls(update=True, days=5)\r\n#\t\t\t\tif call.Type == \"1\" and call.Port == \"40\" and call.Path and call.Caller in caller]\t\t\t\r\n\t\t\tcalls = [\r\n\t\t\t\tcall for call in self.FC.get_calls(update=True, days=5)\r\n\t\t\t\tif call.Type == \"1\" and call.Port == \"40\" and call.Caller in caller]\r\n\t\t\tcalls += [\r\n\t\t\t\tcall for call in self.FC.get_missed_calls(update=True, days=3)\r\n\t\t\t\tif call.Type == \"2\" and call.Caller in caller]\r\n\t\t\tcalls = sorted(calls, key=lambda x: x.Date, reverse=True)\r\n\t\t\tfor call in calls:\r\n\t\t\t\tself.process_notification(call)\r\n\t\t\t\tself.unresolved_list.remove(call.Caller)\r\n\t\t\t\tbreak\r\n\r\n\tdef process_notification(self, call):\r\n\t\tphone_message = self.get_phone_message(call)\r\n\t\tlogger.info(\"phone_message=\"+str(phone_message))\r\n\t\tself.pushover(self.get_message(call, phone_message))\r\n\t\treturn\r\n\r\n\tdef get_phone_message(self, call):\r\n\t\tphone_message = \"\"\r\n\t\t# if it is a phone message\r\n\t\tif call.Path:\r\n\t\t\ttry:\r\n\t\t\t\t# build download link for phone message\r\n\t\t\t\tentries = re.search(\"(path=)(.*)\", call.Path)\r\n\t\t\t\tdlpath = entries.group(2)\r\n\t\t\t\tdlfile = dlpath.split(\"/\")\r\n\t\t\t\tresponse = self.http.request(\r\n\t\t\t\t\t'GET',\r\n\t\t\t\t\t'{}/lua/photo.lua?{}&myabfile={}'.format(self.prefs['fritz_ip_address'], self.get_sid(), dlpath)\r\n\t\t\t\t)\r\n\t\t\t\twave = open(os.path.join(self.prefs['phone_msg_dir'], '{}.wav'.format(dlfile[-1])), 'wb')\r\n\t\t\t\twave.write(response.data)\r\n\t\t\t\twave.close()\r\n\t\t\t\tphone_message = self.speech_to_text(wave.name)\r\n\t\t\texcept Exception as e:\r\n\t\t\t\tself.pushover('Error in get_phone_message ' + str(e))\r\n\t\treturn phone_message\r\n\r\n\tdef pushover(self, message):\r\n\t\tif self.prefs['pushover_token'] and self.prefs['pushover_userkey']:\r\n\t\t\tconn = http.client.HTTPSConnection(\"api.pushover.net:443\")\r\n\t\t\tconn.request(\"POST\", \"/1/messages.json\",\r\n\t\t\t\t\t\t urllib.parse.urlencode({\r\n\t\t\t\t\t\t\t \"token\": self.prefs['pushover_token'],\r\n\t\t\t\t\t\t\t \"user\": self.prefs['pushover_userkey'],\r\n\t\t\t\t\t\t\t \"message\": message,\r\n\t\t\t\t\t\t }), {\"Content-type\": \"application/x-www-form-urlencoded\"})\r\n\t\t\tconn.getresponse()\r\n\r\n\tdef get_message(self, call, phone_message):\r\n\t\ttext = '{} {} {} {}'.format(\r\n\t\t\tcall.Date,\r\n\t\t\tcall.Name,\r\n\t\t\tcall.Caller,\r\n\t\t\tcall.Called,\r\n\t\t)\r\n\t\tif phone_message:\r\n\t\t\ttext += ' /Message: {}'.format(\r\n\t\t\t\tphone_message,\r\n\t\t\t)\r\n\t\treturn text\r\n\r\n\tdef speech_to_text(self, filename):\r\n\t\t# initialize the recognizer\r\n\t\tr = sr.Recognizer()\r\n\t\t# open the file\r\n\t\twith sr.AudioFile(filename) as source:\r\n\t\t\t# listen for the data (load audio to memory)\r\n\t\t\taudio_data = r.record(source)\r\n\t\t\t# recognize (convert from speech to text)\r\n\t\t\ttext = r.recognize_google(audio_data, language=\"de-DE\")\r\n\t\t\treturn text\r\n","repo_name":"williwacker/FritzBackwardSearch","sub_path":"fritzCallsDuringAbsense.py","file_name":"fritzCallsDuringAbsense.py","file_ext":"py","file_size_in_byte":3703,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"5196767446","text":"\"\"\"Altere o programa anterior permitindo ao usuário informar\nas populações e as taxas de crescimento iniciais.\nValide a entrada e permita repetir a operação.\"\"\"\n\n\npopA = float(input(\"Digite a população do país A: \"))\npopB = float(input(\"Digite a população do país B: \"))\nanos = 0\ntaxa_crescimentoA = float(input(\"informe a taxa de crescimento da população da cidade A: \"))\ntaxa_crescimentoB = float(input(\"informe a taxa de crescimento da população da cidade B: \"))\nwhile popA < popB:\n\tpopA += round((popA*taxa_crescimentoA)/100)\n\tpopB += round((popB*taxa_crescimentoB)/100)\n\tanos += 1\n\nprint(\"levará\",anos,\"anos para população da cidade A ser maior que a cidade B\")\nprint(\"populaçãoB:\",popB,\"habitantes\")\nprint(\"populaçãoA:\", popA,\"habitantes\")","repo_name":"CelestinoKalueyo/curso","sub_path":"subir/c_e_s_a_r/populacao.py","file_name":"populacao.py","file_ext":"py","file_size_in_byte":767,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"19185768221","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\n\nclass Solution:\n    def insert(self, intervals, newInterval):\n        \"\"\"\n        :type intervals: List[Interval]\n        :type newInterval: Interval\n        :rtype: List[Interval]\n        \"\"\"\n        results = list()\n\n        for inter in intervals:\n            if inter.end < newInterval.start or inter.start > newInterval.end:\n                results.append(inter)\n            else:\n                newInterval.start = min(newInterval.start, inter.start)\n                newInterval.end = max(newInterval.end, inter.end)\n\n        results.append(newInterval)\n        results.sort(key=lambda x: x.start)\n\n        return results\n\n    def insert_2(self, intervals, newInterval):\n        results = list()\n\n        i = 0\n        while i < len(intervals):\n            inter = intervals[i]\n\n            if inter.end >= newInterval.start:\n                break\n            results.append(inter)\n            i += 1\n\n        while i < len(intervals):\n            inter = intervals[i]\n\n            if inter.start > newInterval.end:\n                break\n\n            newInterval.start = min(newInterval.start, inter.start)\n            newInterval.end = max(newInterval.end, inter.end)\n            i += 1\n\n        results.append(newInterval)\n\n        while i < len(intervals):\n            results.append(intervals[i])\n            i += 1\n\n        return results\n\n","repo_name":"pololee/oj-leetcode","sub_path":"companies/square/p57/Solution.py","file_name":"Solution.py","file_ext":"py","file_size_in_byte":1486,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"41261522047","text":"#\n# @lc app=leetcode id=1471 lang=python3\n#\n# [1471] The k Strongest Values in an Array\n#\n\n# @lc code=start\n        \nclass Solution:\n    def getStrongest(self, arr: List[int], k: int) -> List[int]:\n        arr.sort()\n        n = len(arr)\n        mid = arr[int((n-1)/2)]\n        \n        left, right, result = 0, n-1, []\n        while k > 0:\n            if abs(arr[right] - mid) >= abs(arr[left] - mid):\n                result.append(arr[right])\n                right -= 1\n            else:\n                result.append(arr[left])\n                left += 1\n            k -= 1\n        return result\n    \n# @lc code=end\n\n","repo_name":"WuLC/LeetCode","sub_path":"Algorithm/Python/1000+/1471.the-k-strongest-values-in-an-array.py","file_name":"1471.the-k-strongest-values-in-an-array.py","file_ext":"py","file_size_in_byte":619,"program_lang":"python","lang":"en","doc_type":"code","stars":23,"dataset":"github-code","pt":"81"}
+{"seq_id":"14415566922","text":"import tkinter\r\nimport customtkinter\r\nfrom pytube import YouTube\r\nimport os\r\n\r\ndef startDownload():\r\n    try:\r\n        ytLink = link.get()\r\n        ytObject = YouTube(ytLink, on_progress_callback=on_progress)\r\n        if format.get() == \"mp3\":\r\n            file = ytObject.streams.get_audio_only()\r\n            file.download()\r\n            base = os.path.splitext(file)\r\n            new_file = base + '.mp3'\r\n            os.rename(file, new_file)\r\n        else:\r\n            file = ytObject.streams.get_highest_resolution()\r\n            file.download()\r\n        title.configure(text=ytObject.title)\r\n        finishLabel.configure(text=\"\")\r\n        finishLabel.configure(text=\"Donwload complete!\", text_color=\"green\")\r\n    except:\r\n        finishLabel.configure(text=\"Invalid link\", text_color=\"red\")\r\n\r\ndef on_progress(stream, chunk, bytes_remaining):\r\n    total_size = stream.filesize\r\n    bytes_downloaded = total_size - bytes_remaining\r\n    percent_completed = bytes_downloaded / total_size * 100\r\n    per = str(int(percent_completed))\r\n    progressPercentage.configure(text = per + \"%\")\r\n    progressPercentage.update()\r\n    progressBar.set(float(percent_completed) / 100)\r\n\r\ncustomtkinter.set_appearance_mode(\"System\")\r\ncustomtkinter.set_default_color_theme(\"blue\")\r\n\r\napp = customtkinter.CTk()\r\napp.geometry(\"720x480\")\r\napp.title(\"YouTube Downloader\")\r\n\r\ntitle = customtkinter.CTkLabel(app, text=\"Insert YouTube link:\")\r\ntitle.pack(padx=10, pady=10)\r\n\r\nurl_var = tkinter.StringVar()\r\nlink = customtkinter.CTkEntry(app, width=350, height=40,  textvariable=url_var)\r\nlink.pack()\r\n\r\nformat = tkinter.StringVar(value=\"mp3\")\r\nradiobutton_mp3 = customtkinter.CTkRadioButton(master=app, text=\"mp3\", variable=format, value=\"mp3\")\r\nradiobutton_mp3.pack(padx=20, pady=10)\r\nradiobutton_hd = customtkinter.CTkRadioButton(master=app, text=\"HD\", variable=format, value=\"HD\")\r\nradiobutton_hd.pack(padx=20, pady=10)\r\n\r\nfinishLabel = customtkinter.CTkLabel(app,  text=\"\")\r\nfinishLabel.pack()\r\n\r\ndownload = customtkinter.CTkButton(app, text=\"Download\", command=startDownload)\r\ndownload.pack(padx=10, pady=10)\r\n\r\nprogressPercentage = customtkinter.CTkLabel(app, text=\"0%\")\r\nprogressPercentage.pack()\r\n\r\nprogressBar = customtkinter.CTkProgressBar(app, width=400)\r\nprogressBar.set(0)\r\nprogressBar.pack(padx=10, pady=10)\r\n\r\napp.mainloop()","repo_name":"Saxar88/YouTube-downloader","sub_path":"youtube-downloader.py","file_name":"youtube-downloader.py","file_ext":"py","file_size_in_byte":2322,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"33188417953","text":"from typing import Any, List, Set\n\nimport numpy as np\n\nfrom .function import Function\n\n\nclass BatchNormalization(Function):\n    def __init__(self, inputs: List[bytes], outputs: List[bytes], **params: Any):\n        required_params = {\"eps\", \"avg_mean\", \"avg_var\", \"gamma\", \"beta\"}\n        optional_params: Set[str] = set()\n        super(BatchNormalization, self).__init__(inputs, outputs, params, required_params, optional_params)\n\n    def run(self, x):  # type: ignore\n        if self.params[\"gamma\"] is None:\n            self.params[\"gamma\"] = np.ones(self.params[\"avg_mean\"].size).astype(np.float32)\n        if self.params[\"beta\"] is None:\n            self.params[\"beta\"] = np.zeros(self.params[\"avg_mean\"].size).astype(np.float32)\n\n        shape = (1, self.params[\"gamma\"].size) + ((1,) * (len(x.shape) - 2))  # type: ignore\n        gamma = self.params[\"gamma\"].reshape(shape)  # type: ignore\n        beta = self.params[\"beta\"].reshape(shape)  # type: ignore\n        avg_mean = self.params[\"avg_mean\"].reshape(shape)\n        avg_var = self.params[\"avg_var\"].reshape(shape)\n        return gamma * (x - avg_mean) / np.sqrt(avg_var + self.params[\"eps\"]) + beta\n","repo_name":"Idein/nnoir","sub_path":"nnoir/nnoir/functions/batch_normalization.py","file_name":"batch_normalization.py","file_ext":"py","file_size_in_byte":1161,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"81"}
+{"seq_id":"43203540642","text":"from urllib.request import urlopen\nfrom bs4 import BeautifulSoup\n\nlinks=['']\n\ndef get_all_issue(url):\n    html=urlopen(url)\n    bso=BeautifulSoup(html,\"html.parser\")\n    for link in bso.findAll(\"body\"):\n\n            print(link.attrs[\"body\"])\n            links.append(link.attrs[\"body\"])\n\n\nget_all_issue(\"https://api.github.com/repos/tensorflow/tensorflow/issues\")\nprint(links)\nprint(links)\n","repo_name":"yzflee/cxsj","sub_path":"cxsj-cyn/lda模型分析tesorflow项目/issue获取整理分析/getdata.py","file_name":"getdata.py","file_ext":"py","file_size_in_byte":390,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"18595703265","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sat Mar 25 08:20:03 2017\n\n@author: liushuai\n\"\"\"\nimport matplotlib.pyplot as plt\nfrom sklearn import datasets\nfrom  sklearn.linear_model import LinearRegression\nloaded_data = datasets.load_boston()\ndata_X = loaded_data.data\ndata_y = loaded_data.target\nmodel = LinearRegression()\nmodel.fit(data_X,data_y)\nprint(model.predict(data_X[:4,:]))\nprint(data_y[:4])\nx,y = datasets.make_regression(n_samples=100,n_features=1,n_targets=1,noise=4)\nplt.plot(x,y,'o')","repo_name":"bleedingfight/tfnote","sub_path":"code/ski-learn.py","file_name":"ski-learn.py","file_ext":"py","file_size_in_byte":514,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"6126213560","text":"import os\nimport traceback\nfrom logger import log\nfrom utils.helpers import notification_client\n\nNOTIFY_CONTACT_EMAIL = os.environ.get(\"NOTIFY_CONTACT_EMAIL\", None)\nNOTIFY_CONTACT_TEMPLATE = os.environ.get(\"NOTIFY_CONTACT_TEMPLATE\", None)\n\n\ndef send_contact_email(\n    contact_email: str,\n    contact_subject: str,\n    contact_details: str,\n):\n    \"\"\"\n    Sends an email to the contact email address.\n    \"\"\"\n    try:\n        client = notification_client()\n        client.send_email_notification(\n            email_address=NOTIFY_CONTACT_EMAIL,\n            template_id=NOTIFY_CONTACT_TEMPLATE,\n            personalisation={\n                \"contact_email\": contact_email,\n                \"contact_subject\": contact_subject,\n                \"contact_details\": contact_details[:10000],\n            },\n        )\n    except Exception:\n        log.error(\"Failed to send contact email: %s\", traceback.format_exc())\n        return {\"error\": \"error_contact_send_failed\"}\n    return {\"success\": \"success_contact_sent\"}\n","repo_name":"cds-snc/url-shortener","sub_path":"api/utils/contact.py","file_name":"contact.py","file_ext":"py","file_size_in_byte":1010,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"81"}
+{"seq_id":"9428847604","text":"from django.urls import path, include\nfrom django.contrib import admin\nfrom cocktail.views import CocktailAPIView, CocktailDetailAPIView, CategoryAPIView, CategoryDetailAPIView, IngredientAPIView, IngredientDetailAPIView\nurlpatterns = [\n    path('', CocktailAPIView.as_view()),\n    path('<int:pk>/', CocktailDetailAPIView.as_view()),\n    path('category/', CategoryAPIView.as_view()),\n    path('category/<int:pk>/', CategoryDetailAPIView.as_view()),\n    path('ingredient/', IngredientAPIView.as_view()),\n    path('ingredient/<int:pk>/', IngredientDetailAPIView.as_view()),\n    \n]\n","repo_name":"kyj-lgh/Back-End","sub_path":"cocktail/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":579,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"18217182432","text":"from django.shortcuts import render\nfrom lxml import etree\nfrom bs4 import BeautifulSoup\nfrom edinet.models import CorporateOfficer\nfrom django.http.response import JsonResponse\nfrom django.views.decorators.csrf import ensure_csrf_cookie\n\nimport requests\nimport json\nimport zipfile\nimport io\nimport os\nimport re\nimport json\n\nlist_api = \"https://disclosure.edinet-fsa.go.jp/api/v1/documents.json?date={target}&type=2\"\nfile_api = \"https://disclosure.edinet-fsa.go.jp/api/v1/documents/{document_id}?type=1\"\nnumber_translate_dictionary = {\"０\":\"0\",\"１\":\"1\",\"２\":\"2\",\"３\":\"3\",\"４\":\"4\",\"５\":\"5\",\"６\":\"6\",\"７\":\"7\",\"８\":\"8\",\"９\":\"9\"}\ntrans_table = str.maketrans(number_translate_dictionary)\n\ndef index(request):\n    context = {'corporate_officer_list': list(), 'corporate_officer_list_count': 0}                               \n    return render(request, 'edinet/index.html', context)\n\ndef try_ajax(request):\n    data = {\"message\" : \"Success\"}\n    return JsonResponse(data)\n\ndef call_edinet_api(request):\n\n    for result in get_results(\"2018-06-19\"):\n        if result[\"edinetCode\"] == \"E01777\" and \"有価証券\" in result[\"docDescription\"]:\n            document_id = result[\"docID\"]\n            url = file_api.format(document_id=document_id)\n            r = requests.get(url)\n\n            zip = zipfile.ZipFile(io.BytesIO(r.content))\n            zip.extractall()\n\n            file = os.getcwd() + r\"\\XBRL\\PublicDoc\\jpcrp030000-asr-001_E01777-000_2018-03-31_01_2018-06-19.xbrl\"\n            data = get_information_about_officers_text(file)\n            soup = BeautifulSoup(data.text)\n\n            corporate_officer_list = get_corporate_officer_list(soup)          \n\n    context = {'corporate_officer_list': corporate_officer_list, 'corporate_officer_list_count': len(corporate_officer_list)}\n                               \n    return render(request, 'edinet/index.html', context)\n\ndef reload(request):\n\n    file = os.getcwd() + r\"\\XBRL\\PublicDoc\\jpcrp030000-asr-001_E01777-000_2018-03-31_01_2018-06-19.xbrl\"\n    data = get_information_about_officers_text(file)\n    soup = BeautifulSoup(data.text)\n    corporate_officer_list = get_corporate_officer_list(soup)  \n    \n    context = {'corporate_officer_list': corporate_officer_list, 'corporate_officer_list_count': len(corporate_officer_list)}\n                               \n    return render(request, 'edinet/index.html', context)\n\ndef get_corporate_officer_list(soup):\n\n    corporate_officer_list = list()\n\n    for table in soup.find_all(\"table\"):\n        for tableChild in table.children:\n\n            if tableChild.name == \"tbody\":\n                for tr in tableChild.children:\n\n                    if tr.name == \"tr\":\n                        column_value_list = list()\n\n                        if len(tr.find_all(\"td\")) > 7:\n                            for trChild in tr.children:\n                                if trChild.name == \"td\":\n                                    column_value_list.append(trChild.get_text().replace('\\n', '/'))\n\n                        if len(column_value_list) > 0:\n                            corporate_officer = create_corporate_officer(column_value_list)\n                            corporate_officer_list.append(corporate_officer)\n    \n    return corporate_officer_list\n\ndef create_corporate_officer(column_value_list):\n    corporate_officer = CorporateOfficer()\n\n    if len(column_value_list) > 0:\n        corporate_officer.position = process_item(column_value_list[0]).replace(\"/\",\"\")\n    \n    if len(column_value_list) > 1:\n        corporate_officer.job = process_item(column_value_list[1]).replace(\"/\",\"\")\n    \n    if len(column_value_list) > 2:\n        corporate_officer.name = process_item(column_value_list[2]).replace(\"/\",\"\")\n    \n    if len(column_value_list) > 3:\n        corporate_officer.birthday = process_item(column_value_list[3])\n    \n    if len(column_value_list) > 4:\n        corporate_officer.biography = get_biography_value(column_value_list)\n    \n    if len(column_value_list) > 5:\n        corporate_officer.term = process_item(column_value_list[5])\n    \n    if len(column_value_list) > 6:\n        corporate_officer.stock = process_item(column_value_list[6])\n    \n    return corporate_officer\n\ndef get_biography_value(column_value_list):\n    biography = process_item(column_value_list[4])\n    biography_list = biography.split(\"/\")\n\n    number = 1\n    value = \"\"\n    for target in biography_list:\n        if target.strip() != \"\":\n            if re.search(\"[0-9]\",target):\n                number = 1\n\n                if value.strip() != \"\":\n                    value += \"\\n\"\n\n            if number == 1:\n                value += target + \" \"\n                number += 1\n                continue\n\n            if number == 2:\n                value += target\n                continue\n    return value\n\ndef process_item(item):\n    return item.translate(trans_table).strip(\"/\")\n\ndef get_information_about_officers_text(file):\n    root = read_xml_lxml_etree(file)\n    namespace = root.nsmap['jpcrp_cor']\n    tag = 'InformationAboutOfficersTextBlock'\n    attr = 'contextRef'\n    value = 'FilingDateInstant'\n\n    xpath = './/{%s}%s[@%s=\"%s\"]' % (namespace, tag, attr, value)\n    data = root.find(xpath)\n\n    return data\n\ndef read_xml_lxml_etree(file):\n    with open(file, 'rb') as f:\n        return etree.fromstring(f.read())\n\ndef get_results(target):\n\n    url = list_api.format(target=target)\n    r = requests.get(url)\n    data = json.loads(r.text)\n    results = data[\"results\"]\n\n    return results\n","repo_name":"TsJazz27Sumin/djangoFirstProject","sub_path":"edinet/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":5500,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"38921569142","text":"# try-except with else statement\n\ntry:\n    num=int(input(\"Enter your number: \"))\n    assert num%2==0\nexcept:\n    print(\"Not an Even number\")\nelse:\n    reciprocal=1/num\n    print(\"The reciprocal is\",reciprocal)\n\n","repo_name":"Dhiraj123-star/Python-Practice","sub_path":"Exceptions_3.py","file_name":"Exceptions_3.py","file_ext":"py","file_size_in_byte":211,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"32198473575","text":"# -*- coding: utf-8 -*-\nimport os, sys\n\n# env\nsys.path.append('/usr/lib/python2.7/')\nsys.path.append('/usr/lib/python2.7/dist-packages/')\nsys.path.append('/usr/local/lib/python2.7/dist-packages/')\nsys.path.append('/data2/django_1.9/')\nsys.path.append('/data2/django_projects/')\nsys.path.append('/data2/django_third/')\nos.environ.setdefault(\"DJANGO_SETTINGS_MODULE\", \"djtito.settings\")\n\nimport argparse\nimport django\ndjango.setup()\n\nfrom django.conf import settings\n\nfrom djtito.utils import create_archive, fetch_news, send_newsletter\n\n\"\"\"\nShell script that sends out email to students/faculty/staff\nwith news and events from LiveWhale data\n\nCron delivery for testing at:\n\nMonday @ 7h\nWednesday @ 7h\nFriday @ 7h\n\"\"\"\n\n# set up command-line options\ndesc = \"\"\"\nAccepts as input y or n, to send email to everybody or to\ndefault, debug address.\n\"\"\"\n\nparser = argparse.ArgumentParser(description=desc)\n\nparser.add_argument(\n    \"-s\", \"--send\",\n    required=True,\n    help=\"Dry run or not? y/n.\",\n    dest=\"send\"\n)\nparser.add_argument(\n    \"-d\", \"--days\",\n    required=False,\n    help=\"Time frame in days.\",\n    dest=\"days\"\n)\n\ndef main():\n    # fetch our stories\n    data = fetch_news(days=days)\n    # prepare template for static URLs without Analytics tracking\n    data[\"static\"] = True\n    # create static file for archives\n    data = create_archive(data)\n    # send mail\n    data[\"static\"] = False\n    data = send_newsletter(send, data)\n\n######################\n# shell command line\n######################\n\nif __name__ == \"__main__\":\n\n    args = parser.parse_args()\n    send = args.send\n    days = args.days\n\n    sys.exit(main())\n","repo_name":"carthage-college/django-djtito","sub_path":"djtito/bin/bridge_mail.py","file_name":"bridge_mail.py","file_ext":"py","file_size_in_byte":1625,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"22882704985","text":"import pygame\nfrom pygame.locals import *\n\n\ndef main():\n    pygame.init()\n\n    # SURFACE 객체\n    SURFACE = pygame.display.set_mode((800, 600), 0, 0, 32)\n    pygame.display.set_caption('D   I   C   E   R   S')\n\n    # 변수 설정\n    GRAY = (180, 180, 180)\n    WHITE = (250, 250, 250)\n    RED = (200, 0, 0)\n    DICERSFONT = 'material\\\\NEXONFootballGothicL.ttf'\n\n\n    # 메인프레임\n    UPPERBOX = (30, 30, 740, 300)\n    LOWERBOX = (30, 400, 740, 150)\n\n    # 폰트\n    TITLEFONT = pygame.font.Font(DICERSFONT, 75)\n    MAINFONT = pygame.font.Font(DICERSFONT, 25)\n\n    # 타이틀 텍스트\n    TITLE_SURFACE = TITLEFONT.render('D    I    C    E    R    $', True, RED)\n    TITLE_RECT = TITLE_SURFACE.get_rect(topleft = [80, 150])\n\n\n    # 시작 텍스트\n    START_SURFACE = MAINFONT.render('start', True, GRAY)\n    START_RECT = START_SURFACE.get_rect(center=[400, 430])\n\n    # 사용법\n\n    HTP_SURFACE = MAINFONT.render('how to play', True, GRAY)\n    HTP_RECT = HTP_SURFACE.get_rect(center = [400, 460])\n\n    # 랭크 텍스트\n\n    RANK_SURFACE = MAINFONT.render('ranking', True, GRAY)\n    RANK_RECT = RANK_SURFACE.get_rect(center=[400, 490])\n\n    # 종료 텍스트\n    EXIT_SURFACE = MAINFONT.render('exit', True, GRAY)\n    EXIT_RECT = EXIT_SURFACE.get_rect(center=[400, 520])\n\n\n    # 메인 루프\n\n    while True:\n        SURFACE.fill(WHITE)\n        pygame.draw.rect(SURFACE, GRAY, UPPERBOX, 5)\n        pygame.draw.rect(SURFACE, GRAY, LOWERBOX, 5)\n\n        SURFACE.blit(START_SURFACE, START_RECT)\n        SURFACE.blit(HTP_SURFACE, HTP_RECT)\n\n        SURFACE.blit(TITLE_SURFACE, TITLE_RECT)\n\n        SURFACE.blit(RANK_SURFACE, RANK_RECT)\n        SURFACE.blit(EXIT_SURFACE, EXIT_RECT)\n\n        for event in pygame.event.get():\n            if event.type == QUIT:\n                pygame.quit()\n\n            if event.type == pygame.MOUSEMOTION:\n\n                if START_RECT.collidepoint(event.pos):\n                    START_SURFACE = MAINFONT.render('$ start $', True, RED)\n                    START_RECT = START_SURFACE.get_rect(center = [400, 430])\n                    SURFACE.blit(START_SURFACE, START_RECT)\n\n                elif HTP_RECT.collidepoint(event.pos):\n                    HTP_SURFACE = MAINFONT.render('$ how to play $', True, RED)\n                    HTP_RECT = HTP_SURFACE.get_rect(center = [400,460])\n\n\n                elif RANK_RECT.collidepoint(event.pos):\n                    RANK_SURFACE = MAINFONT.render('$ ranking $', True, RED)\n                    RANK_RECT = RANK_SURFACE.get_rect(center = [400, 490])\n                    SURFACE.blit(RANK_SURFACE, RANK_RECT)\n\n                elif EXIT_RECT.collidepoint(event.pos):\n                    EXIT_SURFACE = MAINFONT.render('$ exit $', True, RED)\n                    EXIT_RECT = EXIT_SURFACE.get_rect(center = [400, 520])\n                    SURFACE.blit(EXIT_SURFACE, EXIT_RECT)\n\n                else:\n                    START_SURFACE = MAINFONT.render('start', True, GRAY)\n                    START_RECT = START_SURFACE.get_rect(center=[400, 430])\n                    HTP_SURFACE = MAINFONT.render('how to play', True, GRAY)\n                    HTP_RECT = HTP_SURFACE.get_rect(center=[400, 460])\n                    RANK_SURFACE = MAINFONT.render('ranking', True, GRAY)\n                    RANK_RECT = RANK_SURFACE.get_rect(center=[400, 490])\n                    EXIT_SURFACE = MAINFONT.render('exit', True, GRAY)\n                    EXIT_RECT = EXIT_SURFACE.get_rect(center=[400, 520])\n\n            if event.type == pygame.MOUSEBUTTONDOWN:\n                if START_RECT.collidepoint(event.pos):\n                    import DICERS_game\n                    DICERS_game.game()\n\n                if HTP_RECT.collidepoint(event.pos):\n                    import DICERS_rule\n                    DICERS_rule.rule()\n\n                if RANK_RECT.collidepoint(event.pos):\n                    import DICERS_rank\n                    DICERS_rank.rank()\n\n                if EXIT_RECT.collidepoint(event.pos):\n                    pygame.quit()\n                else:\n                    continue\n\n        pygame.display.update()\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"javaclassorg/Kimgh-Dicers","sub_path":"1. GUI/DICERS/DICERS_main.py","file_name":"DICERS_main.py","file_ext":"py","file_size_in_byte":4136,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"81"}
+{"seq_id":"39051947974","text":"__author__ = 'Ahmet Tolga Erdönmez'\n__version__ = '1.0'\nfrom PyQt5 import QtWidgets,QtGui\nimport sys\nimport musiclist_v2\n\nclass MainWindow(QtWidgets.QMainWindow):\n    def __init__(self):\n        super().__init__()\n        self.setupUI()\n        self.setWindowIcon(QtGui.QIcon('music.ico'))\n        self.setWindowTitle('Music List')\n        \n        \n    def setupUI(self):\n        mainMenu = self.menuBar() \n        file_menu = mainMenu.addMenu('File')\n        #File\n        file_save = QtWidgets.QAction('Save TXT', self)\n        file_save.setShortcut('Ctrl+S')\n        \n        file_add_song = QtWidgets.QAction('Add Song', self)\n        file_add_song.setObjectName('file_add_song')\n        file_add_song.setShortcut('Ctrl+A')\n        \n        file_delete_list = QtWidgets.QAction('Delete List', self)\n        file_delete_list.setShortcut('Ctrl+D')\n\n        file_quit = QtWidgets.QAction('Close', self)\n        file_quit.setShortcut('Ctrl+Q')\n\n        file_menu.addAction(file_save)\n        file_menu.addAction(file_add_song)\n        file_menu.addAction(file_delete_list)\n        file_menu.addAction(file_quit)\n\n        app_musiclist = musiclist_v2.App_Musiclist()\n        self.setCentralWidget(app_musiclist)\n        \n        #Menu Events\n        file_save.triggered.connect(app_musiclist.event_save_list_to_txt)\n        file_add_song.triggered.connect(app_musiclist.event_add_song)\n        file_delete_list.triggered.connect(app_musiclist.event_delete_list)\n        file_quit.triggered.connect(self.close)\n            \n        \n        \n\n        self.setGeometry(750,100,500,500)\n        icon = QtGui.QIcon()\n        icon.addPixmap(QtGui.QPixmap(\"music.ico\"), QtGui.QIcon.Normal, QtGui.QIcon.Off)\n        self.show()\n        \n        \napp = QtWidgets.QApplication(sys.argv)\napp.setStyle(QtWidgets.QStyleFactory.create(\"fusion\"))\nwindow = MainWindow()\nsys.exit(app.exec_())\n","repo_name":"tolgaerdonmez/music-list-lastfm","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1878,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"81"}
+{"seq_id":"73589437706","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Apr 21 23:50:17 2022\n\n@author: byeonghwa\n\"\"\"\n\nfrom nltk.tokenize import sent_tokenize\nfrom nltk.tokenize import word_tokenize\nfrom nltk.corpus import stopwords\n\nraw_text = \"A barber is a person. a barber is good person. a barber is huge person. he Knew A Secret! The Secret He Kept is huge secret. Huge secret. His barber kept his word. a barber kept his word. His barber kept his secret. But keeping and keeping such a huge secret to himself was driving the barber crazy. the barber went up a huge mountain.\"\n\n# 문장 토큰화\nsentences = sent_tokenize(raw_text)\nprint(sentences)\n\nvocab = {}\npreprocessed_sentences = []\nstop_words = set(stopwords.words('english'))\n\nfor sentence in sentences:\n    # 단어 토큰화\n    tokenized_sentence = word_tokenize(sentence)\n    result = []\n\n    for word in tokenized_sentence: \n        word = word.lower() # 모든 단어를 소문자화하여 단어의 개수를 줄인다.\n        if word not in stop_words: # 단어 토큰화 된 결과에 대해서 불용어를 제거한다.\n            if len(word) > 2: # 단어 길이가 2이하인 경우에 대하여 추가로 단어를 제거한다.\n                result.append(word)\n                if word not in vocab:\n                    vocab[word] = 0 \n                vocab[word] += 1\n    preprocessed_sentences.append(result) \nprint(preprocessed_sentences)\n\nprint('단어 집합 :',vocab)\n\nvocab_sorted = sorted(vocab.items(), key = lambda x:x[1], reverse = True)\nprint(vocab_sorted)\n\nword_to_index = {}\ni = 0\nfor (word, frequency) in vocab_sorted :\n    if frequency > 1 : # 빈도수가 작은 단어는 제외.\n        i = i + 1\n        word_to_index[word] = i\n\nprint(word_to_index)\n\nvocab_size = 5\n\n# 인덱스가 5 초과인 단어 제거\nwords_frequency = [word for word, index in word_to_index.items() if index >= vocab_size + 1]\n\n# 해당 단어에 대한 인덱스 정보를 삭제\nfor w in words_frequency:\n    del word_to_index[w]\nprint(word_to_index)\n\nword_to_index['OOV'] = len(word_to_index) + 1\nprint(word_to_index)\n\nencoded_sentences = []\nfor sentence in preprocessed_sentences:\n    encoded_sentence = []\n    for word in sentence:\n        try:\n            # 단어 집합에 있는 단어라면 해당 단어의 정수를 리턴.\n            encoded_sentence.append(word_to_index[word])\n        except KeyError:\n            # 만약 단어 집합에 없는 단어라면 'OOV'의 정수를 리턴.\n            encoded_sentence.append(word_to_index['OOV'])\n    encoded_sentences.append(encoded_sentence)\nprint(encoded_sentences)\n\n# Counter 사용\n\nfrom collections import Counter\n\nprint(preprocessed_sentences)\n# words = np.hstack(preprocessed_sentences)으로도 수행 가능.\nall_words_list = sum(preprocessed_sentences, [])\nprint(all_words_list)\n\n# 파이썬의 Counter 모듈을 이용하여 단어의 빈도수 카운트\nvocab = Counter(all_words_list)\nprint(vocab)\n\nvocab_size = 5\nvocab = vocab.most_common(vocab_size) # 등장 빈도수가 높은 상위 5개의 단어만 저장\nvocab\n\nword_to_index = {}\ni = 0\nfor (word, frequency) in vocab :\n    i = i + 1\n    word_to_index[word] = i\n\nprint(word_to_index)\n\n# NLTK의 FreqDist 사용하기\nfrom nltk import FreqDist\nimport numpy as np\n\n# np.hstack으로 문장 구분을 제거\nvocab = FreqDist(np.hstack(preprocessed_sentences))\n\nvocab_size = 5\nvocab = vocab.most_common(vocab_size) # 등장 빈도수가 높은 상위 5개의 단어만 저장\nprint(vocab)\n\nword_to_index = {word[0] : index + 1 for index, word in enumerate(vocab)}\nprint(word_to_index)\n\n# enumerate 이해하기\n\ntest_input = ['a', 'b', 'c', 'd', 'e']\nfor index, value in enumerate(test_input): # 입력의 순서대로 0부터 인덱스를 부여함.\n  print(\"value : {}, index: {}\".format(value, index))\n  \nfrom tensorflow.keras.preprocessing.text import Tokenizer\n\npreprocessed_sentences = [['barber', 'person'], ['barber', 'good', 'person'], ['barber', 'huge', 'person'], ['knew', 'secret'], ['secret', 'kept', 'huge', 'secret'], ['huge', 'secret'], ['barber', 'kept', 'word'], ['barber', 'kept', 'word'], ['barber', 'kept', 'secret'], ['keeping', 'keeping', 'huge', 'secret', 'driving', 'barber', 'crazy'], ['barber', 'went', 'huge', 'mountain']]\n\ntokenizer = Tokenizer()\n\n# fit_on_texts()안에 코퍼스를 입력으로 하면 빈도수를 기준으로 단어 집합을 생성.\ntokenizer.fit_on_texts(preprocessed_sentences) \n\nprint(tokenizer.word_index)\nprint(tokenizer.word_counts)\n\nprint(tokenizer.texts_to_sequences(preprocessed_sentences))\n\nvocab_size = 5\ntokenizer = Tokenizer(num_words = vocab_size + 1) # 상위 5개 단어만 사용\ntokenizer.fit_on_texts(preprocessed_sentences)\n\nprint(tokenizer.texts_to_sequences(preprocessed_sentences))\n\ntokenizer = Tokenizer()\ntokenizer.fit_on_texts(preprocessed_sentences)\nvocab_size = 5\nwords_frequency = [word for word, index in tokenizer.word_index.items() if index >= vocab_size + 1] \n\n# 인덱스가 5 초과인 단어 제거\nfor word in words_frequency:\n    del tokenizer.word_index[word] # 해당 단어에 대한 인덱스 정보를 삭제\n    del tokenizer.word_counts[word] # 해당 단어에 대한 카운트 정보를 삭제\n\nprint(tokenizer.word_index)\nprint(tokenizer.word_counts)\nprint(tokenizer.texts_to_sequences(preprocessed_sentences))\n\n# 숫자 0과 OOV를 고려해서 단어 집합의 크기는 +2\nvocab_size = 5\ntokenizer = Tokenizer(num_words = vocab_size + 2, oov_token = 'OOV')\ntokenizer.fit_on_texts(preprocessed_sentences)\n\nprint(tokenizer.texts_to_sequences(preprocessed_sentences))","repo_name":"by-hwa/Hustar","sub_path":"딥러닝을 이용한 자연어처리/5.Integer_coding.py","file_name":"5.Integer_coding.py","file_ext":"py","file_size_in_byte":5548,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"74500391944","text":"from picamera import PiCamera\nfrom time import sleep\nfrom PIL import Image\nfrom PIL import ImageOps\nfrom io import BytesIO\nfrom sense_hat import SenseHat\nfrom orbit import ISS\nfrom skyfield.api import load\n\n# Initialize all the things\nsense = SenseHat()\ncamera = PiCamera()\ncamera.resolution = (2592, 1944)\n\n\nfor i in range(360):\n    # Capture magnetometer readings\n    raw = sense.get_compass_raw()\n    with open(\"magnetometerReadings.csv\", \"a\") as f:\n        f.write(str(raw[\"x\"]) + \", \" + str(raw[\"y\"]) + \",\" + str(raw[\"z\"]) + \"\\n\")\n    \n    \n    # Capture camera images\n    img = BytesIO()\n    camera.capture(img, format='jpeg')\n    img.seek(0)\n    img = Image.open(img)\n    img = ImageOps.grayscale(img)\n    img.save(f'image_{i:03d}.jpg')\n    \n    \n    # Capture current ISS location\n    time = load.timescale().now()\n    position = ISS.at(time)\n    location = position.subpoint()\n    with open(\"locations.txt\", \"a\") as locations:\n        locations.write(f\"{location}\\n\")\n    \n    sleep(30)\n","repo_name":"apollo-1845/2021-Team-B","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":996,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"13615049904","text":"# -*- coding:utf-8 -*-\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\nfrom __future__ import unicode_literals  # compatible with python3 unicode coding\n\nimport os\nfrom pathlib import Path\n\nimport numpy as np\nimport tensorflow as tf\nfrom PIL import Image\nfrom matplotlib import pyplot as plt\n\nfrom object_detection.utils import label_map_util\nfrom object_detection.utils import visualization_utils as vis_util\nfrom visual_caption.image_caption.data.data_config import ImageCaptionDataConfig\n\nhome = str(Path.home())  # home dir\nbase_data_dir = os.path.join(home, 'data')\nmodel_data_dir = os.path.join(base_data_dir, \"tf/models/object_detection\")\n\nMODEL_NAME = \"faster_rcnn_inception_resnet_v2_atrous_coco_2017_11_08\"\n\n# Path to frozen detection graph. This is the actual model that is used for the object detection.\nPATH_TO_CKPT = os.path.join(model_data_dir, MODEL_NAME + '/frozen_inference_graph.pb')\n\n# List of the strings that is used to add correct label for each box.\nPATH_TO_LABELS = os.path.join(model_data_dir, 'data' + '/mscoco_label_map.pbtxt')\n\nNUM_CLASSES = 90\n# Size, in inches, of the output images.\nIMAGE_SIZE = (12, 8)\n\n\nclass DetectorConfig():\n    model_ckpt = PATH_TO_CKPT\n    path_labels = PATH_TO_LABELS\n    num_classes = NUM_CLASSES\n    image_size = IMAGE_SIZE\n\n\nclass ImageObjectDetector(object):\n    def __init__(self, sess, detection_graph):\n        self.config = DetectorConfig()\n        self.sess = sess\n        self.detection_graph = detection_graph\n        self._build_inputs()\n        self._load_label_map()\n\n    def _build_inputs(self):\n        self.image_tensor = self.detection_graph.get_tensor_by_name('image_tensor:0')\n        # Each box represents a part of the image where a particular object was detected.\n        detection_boxes = self.detection_graph.get_tensor_by_name('detection_boxes:0')\n        # Each score represent how level of confidence for each of the objects.\n        # Score is shown on the result image, together with the class label.\n        detection_scores = self.detection_graph.get_tensor_by_name('detection_scores:0')\n        detection_classes = self.detection_graph.get_tensor_by_name('detection_classes:0')\n        num_detections = self.detection_graph.get_tensor_by_name('num_detections:0')\n        self.fetches = [detection_boxes, detection_scores, detection_classes, num_detections]\n\n    @staticmethod\n    def load_image_into_numpy_array(image):\n        (im_width, im_height) = image.size\n        return np.array(image.getdata()).reshape(\n            (im_height, im_width, 3)).astype(np.uint8)\n\n    def _load_label_map(self):\n        PATH_TO_LABELS = self.config.path_labels\n        NUM_CLASSES = self.config.num_classes\n        label_map = label_map_util.load_labelmap(PATH_TO_LABELS)\n        categories = label_map_util.convert_label_map_to_categories(\n            label_map, max_num_classes=NUM_CLASSES, use_display_name=True)\n        self.category_index = label_map_util.create_category_index(categories)\n\n    def detect(self, image_path):\n        image = Image.open(image_path)\n        # the array based representation of the image will be used later in order to prepare the\n        # result image with boxes and labels on it.\n        image_np = self.load_image_into_numpy_array(image)\n        # Expand dimensions since the model expects images to have shape: [1, None, None, 3]\n        image_np_expanded = np.expand_dims(image_np, axis=0)\n        # Actual detection.\n        result = self.sess.run(fetches=self.fetches,\n                               feed_dict={self.image_tensor: image_np_expanded})\n        (boxes, scores, classes, num) = result\n        return image_np, result\n\n    def show(self, result):\n        # Visualization of the results of a detection.\n\n        image_np, (boxes, scores, classes, num) = result\n        IMAGE_SIZE = self.config.image_size\n        vis_util.visualize_boxes_and_labels_on_image_array(\n            image_np,\n            np.squeeze(boxes),\n            np.squeeze(classes).astype(np.int32),\n            np.squeeze(scores),\n            self.category_index,\n            use_normalized_coordinates=True,\n            line_thickness=1)\n        plt.figure(figsize=IMAGE_SIZE)\n        plt.imshow(image_np)\n\n\nbatch_size = 40\n\n\ndef load_images():\n    # data_config = VisualGenomeDataConfig()\n    data_config = ImageCaptionDataConfig()\n    image_files = list()\n    for file_path in Path(data_config.train_image_dir).glob('**/*'):\n        image_files.append(file_path.absolute())\n        if len(image_files) == batch_size:\n            yield image_files\n            image_files = list()\n    if len(image_files) > 0:\n        yield image_files\n\n\ndef main(_):\n    gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.6)\n    sess_config = tf.ConfigProto(gpu_options=gpu_options,\n                                 allow_soft_placement=True,\n                                 log_device_placement=False)\n    image_gen = load_images()\n\n    detection_graph = tf.Graph()\n    with detection_graph.as_default():\n        od_graph_def = tf.GraphDef()\n        with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid:\n            serialized_graph = fid.read()\n            od_graph_def.ParseFromString(serialized_graph)\n            tf.import_graph_def(od_graph_def, name='')\n            with tf.Session(graph=detection_graph, config=sess_config) as sess:\n                detector = ImageObjectDetector(sess=sess,detection_graph=detection_graph)\n                for batch, image_batch in enumerate(image_gen):\n                    for idx, image_file in enumerate(image_batch):\n                        result = detector.detect(image_path=image_file)\n                        detector.show(result=result)\n\n\n\ndef restore():\n    summary_writer = tf.summary.FileWriter(\"./log/\")\n\n    gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.6)\n    sess_config = tf.ConfigProto(gpu_options=gpu_options,\n                                 allow_soft_placement=True,\n                                 log_device_placement=False)\n\n\n    with tf.Session(config=sess_config) as sess:\n        server = tf.train.Saver()\n        server.restore(sess=sess,save_path=PATH_TO_CKPT)\n        detection_graph = sess.graph\n        summary_writer.add_graph(detection_graph)\n\n\nif __name__ == '__main__':\n    restore()\n","repo_name":"changquanyou/visual_to_caption","sub_path":"visual_caption/image_caption/feature/object_detector.py","file_name":"object_detector.py","file_ext":"py","file_size_in_byte":6332,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"42857450704","text":"# -*- coding: utf-8 -*-\n\"\"\"\nSpyder Editor\n\nThis is a temporary script file.\n\"\"\"\nimport numpy as np\n\n#Waypoint learner\n\n#Generating a square from x,y=-1e3,-1e3 to x,y=1e3,1e3. This is assuemd to be travel grid.\n\nx_up=1e3\ny_up=1e3\nx_lo=-1e3\ny_lo=-1e3\n\n#Putting the starting position at the center\n\nx_start=0\ny_start=0\n\nnCases=1\n\nfor count in range(0,nCases-1):\n    #Generating velocity towards x direction of a random value from 0 to 100\n    \n    v_start=np.random.random_sample()*100\n    \n    #choosing the ending position at a distance of 500 units at a random angle\n    \n    theta=np.random.random_sample()*(np.pi)\n    x_end=500*np.cos(theta)\n    y_end=500*np.sin(theta)\n    \n    #generate random final velocity angle phi\n    \n    phi=np.random.random_sample()*(2*np.pi)\n    #with assumption that final velocity magnitude and starting velocity mmagnitude is same\n    vx_end=v_start*np.cos(phi)\n    vy_end=v_start*np.sin(phi)\n    \n    #generate obstacle position and radius. Gamma goes from 0 to 1, based on where it stands on the trajectory and radius is random from 20 to 50.\n    gamma=np.random.random_sample()\n    obs_rad=np.random.random_sample()*30+20# What should be the best radii for obstacle?\n    \n    \n    \n    #many independent samples for the trajectory and obstacles can be generated to gain experience.\n    #to avoid obstacle, a waypoint can be generated inside the 2000x2000 square outside the obstacle radius and trajectory to the waypoint from source and from the waypoint to...\n    #destination can be generated.","repo_name":"ut-amrl/WaypointLearning","sub_path":"Waypoint_learner_foundation.py","file_name":"Waypoint_learner_foundation.py","file_ext":"py","file_size_in_byte":1531,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"19641564421","text":"import numpy as np\nimport pandas as pd\nfrom sklearn.preprocessing import MinMaxScaler\nimport googleapiclient.discovery\nimport os\n\nnp.random.seed(42)\nfrom sklearn.preprocessing import StandardScaler\nfrom flask import Flask, render_template, redirect, url_for\n\napp = Flask(__name__)\napp.config['SECRET_KEY'] = 'hard to guess string'\n\nfrom flask_bootstrap import Bootstrap\n\nBootstrap(app)\n\nfrom flask_wtf import FlaskForm\nfrom wtforms import StringField, SubmitField\nfrom wtforms.validators import DataRequired\n\n\nclass LabForm(FlaskForm):\n    latitude = StringField('latitude.', validators=[DataRequired()])\n    longitude = StringField('longitude', validators=[DataRequired()])\n    month = StringField('month', validators=[DataRequired()])\n    day = StringField('day', validators=[DataRequired()])\n    avg = StringField('avg_temp', validators=[DataRequired()])\n    max = StringField('max_temp', validators=[DataRequired()])\n    wind_s = StringField('max_wind_speed', validators=[DataRequired()])\n    wind_avg = StringField('avg_wind', validators=[DataRequired()])\n    submit = SubmitField('Submit')\n\n\n@app.route('/')\n@app.route('/index')\ndef index():\n    return render_template('index.html')\n\n\n@app.route('/prediction', methods=['GET', 'POST'])\ndef lab():\n    form = LabForm()\n\n    if form.validate_on_submit():\n        X_test = np.array([[float(form.latitude.data),\n                            float(form.longitude.data),\n                            str(form.month.data),\n                            str(form.day.data),\n                            float(form.avg.data),\n                            float(form.max.data),\n                            float(form.wind_s.data),\n                            float(form.wind_avg.data)]])\n        print(X_test.shape)\n        fires = pd.read_csv('datasets/sanbul-5.csv', sep=',')\n        X_test = pd.DataFrame(X_test, columns=['latitude','longitude','month','day','avg_temp','max_temp','max_wind_speed','avg_wind'])\n        print(X_test)\n\n        from sklearn.model_selection import train_test_split\n        train_set, test_set = train_test_split(fires, test_size=0.2, random_state=42)\n        from sklearn.model_selection import StratifiedShuffleSplit\n        split = StratifiedShuffleSplit(n_splits=1, test_size=0.2, random_state=42)\n        for train_index, test_index in split.split(fires, fires[\"month\"]):\n            strat_train_set = fires.loc[train_index]\n            strat_test_set = fires.loc[test_index]\n\n        fires = strat_train_set.drop([\"burned_area\"], axis=1)  # drop labels for training set\n        fires_labels = strat_train_set[\"burned_area\"].copy()\n        fires_num = fires.drop([\"month\", \"day\"], axis=1)\n\n        from sklearn.preprocessing import OneHotEncoder\n        cat_encoder = OneHotEncoder()\n        fires_cat = fires[[\"month\"]]\n        fires_cat_1hot = cat_encoder.fit_transform(fires_cat)\n        cat_encoder = OneHotEncoder(sparse=False)\n        fires_cat_1hot = cat_encoder.fit_transform(fires_cat)\n\n        cat_encoder2 = OneHotEncoder()\n        fires_cat = fires[[\"day\"]]\n        fires_cat_1hot_2 = cat_encoder2.fit_transform(fires_cat)\n        cat_encoder2 = OneHotEncoder(sparse=False)\n        fires_cat_1hot_2 = cat_encoder2.fit_transform(fires_cat)\n\n        from sklearn.pipeline import Pipeline\n        from sklearn.preprocessing import StandardScaler\n\n        num_pipeline = Pipeline([\n            ('std_scaler', StandardScaler()),\n        ])\n        fires_num_tr = num_pipeline.fit_transform(fires_num)\n\n        from sklearn.compose import ColumnTransformer\n        num_attribs = list(fires_num)\n        cat_attribs = [\"month\", \"day\"]\n        full_pipeline = ColumnTransformer([\n            (\"num\", num_pipeline, num_attribs),\n            (\"cat\", OneHotEncoder(), cat_attribs),\n        ])\n        fires_prepared = full_pipeline.fit_transform(fires)\n        X_test = full_pipeline.transform(X_test)\n\n        MODEL_NAME = \"my_sanbul_model\"\n        os.environ[\"GOOGLE_APPLICATION_CREDENTIALS\"] = \"term-224506-9bc8286b5d7b.json\"\n        project_id = 'term-224506'\n        model_id = MODEL_NAME\n        model_path = \"projects/{}/models/{}\".format(project_id, model_id)\n        model_path += \"/versions/v0001/\"\n        ml_resource = googleapiclient.discovery.build(\"ml\", \"v1\").projects()\n\n        input_data_json = {\"signature_name\": \"serving_default\",\n                           \"instances\": X_test.tolist()}\n        request = ml_resource.predict(name=model_path, body=input_data_json)\n        response = request.execute()\n        print(\"\\nresponse:\\n\", response)\n\n        if \"error\" in response:\n            raise RuntimeError(response[\"error\"])\n\n        predD = np.array([pred['dense_1'] for pred in response[\"predictions\"]])\n        print(predD[0][0])\n        res = predD[0][0]\n        return render_template('result.html', res=res)\n\n    return render_template('prediction.html', form=form)\n\n\nif __name__ == '__main__':\n    app.run()","repo_name":"jmani0223/sanbul","sub_path":"sanbul_flask.py","file_name":"sanbul_flask.py","file_ext":"py","file_size_in_byte":4913,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"2132141995","text":"import argparse\nimport os\nfrom enum import Enum\n\nimport optuna\nimport pytorch_lightning as pl\nimport torch\nfrom optuna.integration import PyTorchLightningPruningCallback\nfrom pytorch_lightning import Callback\nfrom pytorch_lightning.callbacks import ModelCheckpoint\nfrom pytorch_lightning.loggers import TensorBoardLogger\n\nfrom trading.dataloader.sequential_dataset import SequentialDataset\nfrom trading.dataloader.stock_dataset import (\n    StockParams,\n    StockHistoryDataset,\n    StockHistoryUpdateMode,\n)\nfrom trading.dataloader.temperature_dataset import TemperatureDataset\nfrom trading.model.tpa_lstm_model import TPA_LSTM_Model\n\n\nclass DATASET(Enum):\n    TEMPERATURE = \"temperature\"\n    STOCK = \"stock\"\n\n\ndef load_dataset(dataset_to_load: DATASET) -> SequentialDataset:\n    # Create dataset\n    root_folder = os.path.abspath(os.getcwd())\n    data_folder = os.path.join(root_folder, \"data\")\n\n    if dataset_to_load == DATASET.TEMPERATURE:\n        dataset = TemperatureDataset(root_path=data_folder)\n    elif dataset_to_load == DATASET.STOCK:\n        stock_symbol = \"BAS.DE\"\n        period = \"max\"\n        stock_params = StockParams(symbol=stock_symbol, period=period)\n\n        root_folder = os.path.abspath(os.getcwd())\n        data_folder = os.path.join(root_folder, \"data\")\n\n        dataset = StockHistoryDataset(\n            root_path=data_folder,\n            stock_params=stock_params,\n            update_mode=StockHistoryUpdateMode.KEEP,\n        )\n    else:\n        raise NotImplementedError()\n\n    return dataset\n\n\ndef create_model(dataset, hparams) -> TPA_LSTM_Model:\n    model = TPA_LSTM_Model(hparams=hparams, sequential_dataset=dataset)\n    return model\n\n\ndef fit_model(model: TPA_LSTM_Model, log_dir: str):\n    os.makedirs(log_dir, exist_ok=True)\n\n    epochs = 50\n\n    # Creating a logging object\n    segmentation_nn_logger = TensorBoardLogger(\n        save_dir=log_dir, name=\"temperature_model\"\n    )\n\n    checkpoint_callback = ModelCheckpoint(\n        save_top_k=1,\n        save_last=True,\n        verbose=False,\n        monitor=\"loss\",\n        mode=\"min\",\n        prefix=\"best\",\n    )\n\n    trainer = pl.Trainer(\n        checkpoint_callback=checkpoint_callback,\n        default_root_dir=\"../models/\",\n        max_epochs=epochs,\n        logger=segmentation_nn_logger,\n        gpus=-1,\n    )\n    trainer.fit(model)\n    trainer.test()\n\n    model.eval()\n    model.freeze()\n\n\ndef load_model(chechpoint_path: str) -> TPA_LSTM_Model:\n    model = TPA_LSTM_Model.load_from_checkpoint(checkpoint_path=chechpoint_path)\n    return model\n\n\ndef plot_prediction(\n    input: SequentialDataset, ground_truth: SequentialDataset, model: TPA_LSTM_Model\n):\n    model.plot(input, ground_truth)\n\n\ndef main():\n    global args\n\n    def objective(trial):\n        class MetricsCallback(Callback):\n            \"\"\"PyTorch Lightning metric callback.\"\"\"\n\n            def __init__(self):\n                super().__init__()\n                self.metrics = []\n\n            def on_validation_end(self, trainer, pl_module):\n                self.metrics.append(trainer.callback_metrics)\n\n        # as explained above, we'll use this callback to collect the validation accuracies\n        metrics_callback = MetricsCallback()\n\n        log_dir = \"tpa_lstm_model_logs\"\n        log_name = \"tpa_lstm_model\"\n\n        # Creating a logging object\n        model_logger = TensorBoardLogger(save_dir=log_dir, name=log_name)\n\n        # create a trainer\n        trainer = pl.Trainer(\n            # train_percent_check=1.0,\n            # val_percent_check=1.0,\n            max_epochs=3,  # epochs\n            gpus=0 if torch.cuda.is_available() else None,  # #gpus\n            callbacks=[metrics_callback],  # save latest accuracy\n            default_root_dir=log_dir,\n            logger=model_logger,\n            early_stop_callback=PyTorchLightningPruningCallback(\n                trial, monitor=\"valid_acc\"\n            ),\n            # early stopping\n        )\n\n        # here we sample the hyper params, similar as in our old random search\n        # trial_hparams = {'output_sequence_length': 30,\n        #                  'num_of_batches': 64,\n        #                  'batch_size': trial.suggest_int(\"batch_size\", 8, 16),\n        #                  'input_sequence_length': trial.suggest_int(\"input_sequence_length\", 64, 128),\n        #                  'hidden_size': trial.suggest_int(\"hidden_size\", 8, 16),\n        #                  'n_layers': trial.suggest_int(\"n_layers\", 1, 2),\n        #                  'num_filters': trial.suggest_int(\"num_filters\", 8, 16),\n        #                  'input_size': len(StockHistoryDataset.FEATURES),\n        #                  'draw_params': {'input_kwargs': {'custom_openkwargs': {'label': None},\n        #                                                   'custom_closekwargs': {'label': None},\n        #                                                   'custom_rangekwargs': {'label': 'Input'}},\n        #                                  'ground_truth_kwargs': {'custom_openkwargs': {'color': 'blue', 'label': None},\n        #                                                          'custom_closekwargs': {'color': 'cyan', 'label': None},\n        #                                                          'custom_rangekwargs': {'color': 'lightblue',\n        #                                                                                 'label': 'GroundTruth'}}}}\n\n        trial_hparams = {\n            \"output_sequence_length\": 30,\n            \"num_of_batches\": 64,\n            \"batch_size\": trial.suggest_int(\"batch_size\", 8, 64),\n            \"input_sequence_length\": trial.suggest_int(\n                \"input_sequence_length\", 64, 256\n            ),\n            \"hidden_size\": trial.suggest_int(\"hidden_size\", 8, 64),\n            \"n_layers\": trial.suggest_int(\"n_layers\", 1, 3),\n            \"num_filters\": trial.suggest_int(\"num_filters\", 8, 64),\n            \"input_size\": len(StockHistoryDataset.FEATURES),\n            \"draw_params\": {\n                \"input_kwargs\": {\n                    \"custom_openkwargs\": {\"label\": None},\n                    \"custom_closekwargs\": {\"label\": None},\n                    \"custom_rangekwargs\": {\"label\": \"Input\"},\n                },\n                \"ground_truth_kwargs\": {\n                    \"custom_openkwargs\": {\"color\": \"blue\", \"label\": None},\n                    \"custom_closekwargs\": {\"color\": \"cyan\", \"label\": None},\n                    \"custom_rangekwargs\": {\n                        \"color\": \"lightblue\",\n                        \"label\": \"GroundTruth\",\n                    },\n                },\n            },\n        }\n\n        # create model from these hyper params and train it\n        dataset = load_dataset(DATASET.STOCK)\n        model = create_model(dataset, trial_hparams)\n        model.prepare_data()\n        trainer.fit(model)\n\n        valid_acc = metrics_callback.metrics[-1][\"valid_acc\"].cpu().item()\n\n        # del trial_hparams['draw_params']\n        # model_logger.experiment.add_hparams(trial_hparams, {\"validation_accuracy\": valid_acc})\n\n        # return validation accuracy from latest model, as that's what we want to minimize by our hyper param search\n        return valid_acc\n\n    pruner = optuna.pruners.NopPruner()\n    study = optuna.create_study(direction=\"maximize\", pruner=pruner)\n    study.optimize(objective, n_trials=20)\n\n    print(\"Number of finished trials: {}\".format(len(study.trials)))\n    print(\"Best trial:\")\n    best_trial = study.best_trial\n\n    print(\"  Value: {}\".format(best_trial.value))\n    print(\"  Params: \")\n    for key, value in best_trial.params.items():\n        print(\"    {}: {}\".format(key, value))\n\n    df = study.trials_dataframe()\n    print(df)\n\n    # df_records = df.to_dict(orient='records')\n    #\n    # # Creating a logging object\n    # writer = TensorBoardLogger(\n    #     save_dir='tpa_lstm_model_logs',\n    #     name='model_optimization'\n    # )\n    #\n    # for i in range(len(df_records)):\n    #     df_records[i]['datetime_start'] = str(df_records[i]['datetime_start'])\n    #     df_records[i]['datetime_complete'] = str(df_records[i]['datetime_complete'])\n    #     df_records[i]['duration'] = str(df_records[i]['duration'])\n    #     value = df_records[i].pop('value')\n    #     value_dict = {'validation_accuracy': value}\n    #     writer.experiment.add_hparams(df_records[i], value_dict)\n\n\nif __name__ == \"__main__\":\n    global args\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\n        \"--dataset\",\n        \"-d\",\n        type=DATASET,\n        default=DATASET.TEMPERATURE,\n        choices=list(DATASET),\n    )\n    args = parser.parse_args()\n\n    main()\n","repo_name":"Peter-Kocsis/sequential_nn","sub_path":"trading/hyperparam_optim.py","file_name":"hyperparam_optim.py","file_ext":"py","file_size_in_byte":8617,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"34238193150","text":"\nimport time as a \n\n#1970년 1월 1일 0시 0분 0.00000000초기준으로 경괴한시간 반환\nprint('time() : {}'.format(a.time()) )\n\nt = a.time()\nprint('ctime() : {}'.format(a.ctime(t)))\n\n'''\n    날짜 지시자\n    %y  :2자리연도(22)\n    %Y  :4자리냔도(2022)\n    %m  :2자리 월(01~12)\n    %b  :3자리 영문요 월(Jan~Dec)\n    %B  :전채 영문 월()\n    %d  :2자리 일\n    %a  :3자리 요일\n    %A  :전채영문요일\n    %i  :12시 시간(00~12)\n    %H  :24시 시간(00~23)\n    %M  :분(00~59)\n    %S  :초(00~59)\n\n'''\n\n\n\ns = a.strftime('%y/%m/%d (%a) %H:%M:%S')\nprint('str() : {}'.format(s) )\n\n\n#지정 한초만큼 일시중지\na.sleep(5)  #5초정지\n\n\nimport datetime \n\nniw = datetime.datetime.now ()\nprint('날자시간:{}'.format(niw))\n\nto = datetime.date(2022,1,13)\nprint('날시 시간:{}'.format)\n\nsp = datetime\n\n\n#사진1","repo_name":"rlaqjatjr8922/sbs_python","sub_path":"파이썬/파이썬/Dey09/ex05.py","file_name":"ex05.py","file_ext":"py","file_size_in_byte":859,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"17607596156","text":"import folium\nimport pandas\n\ndata = pandas.read_csv(\"volcanoes.txt\")\nlat = list(data[\"LAT\"])\nlon=list(data[\"LON\"])\nname  = list(data[\"NAME\"])\nelev = list(data[\"ELEVATION\"])\nhtml = \"\"\"<h4> Volcano information: </h4>\nHeight: %s m\n\"\"\"\ndef color_producer(elevation):\n    if elevation == \"-\":\n        return'purple'\n    else:\n        ele = float(elevation)\n        if ele < 1000.0:\n            return'darkblue'\n        elif ele < 2000.0:\n            return'orange'\n        else:\n            return'red'\n\ndef pop_return(name,elevation):\n    if elevation == \"-\":\n        return name\n    else:\n        return name + \" \"+ elevation+\" ft\"\n\nmap = folium.Map(location = [22.895985, 77.757801],zoom_start=6,tiles = \"Stamen Terrain\")\n\nfgv = folium.FeatureGroup(name=\"Volcanoes\")\n\nfor lt,ln,nm,el in zip(lat,lon,name,elev):\n    fgv.add_child(folium.Marker(location = [lt,ln],radius=8.0, tooltip=pop_return(nm,el),\n    icon=folium.Icon(color=color_producer(el))))\n\nfgp = folium.FeatureGroup(name=\"Population\")\nfgp.add_child(folium.GeoJson(data=(open('world.json','r',encoding='utf-8-sig').read()),\n    style_function=lambda x:{'fillColor':'yellow' if x['properties']['POP2005'] < 10000000\n    else 'orange' if 10000000 <= x['properties']['POP2005']< 20000000 else 'red'}))\n\n\nmap.add_child(fgv)\nmap.add_child(fgp)\nmap.add_child(folium.LayerControl())\n\n\nmap.save(\"India.html\")\n","repo_name":"lipakshi-2001/Mapping","sub_path":"map1.py","file_name":"map1.py","file_ext":"py","file_size_in_byte":1359,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"81"}
+{"seq_id":"72668283785","text":"# Eventually will move to DB.\nappTypes = [\n  {\n    'type': 'input-output',\n    'name': 'Simple I/O',\n  },\n  {\n    'type': 'hello-world',\n    'name': 'Hello World',\n  },\n  {\n    'type': 'fibonacci',\n    'name': 'Fibonacci',\n  },\n  {\n    'type': 'math',\n    'name': 'All math operations',\n  },\n]\n\ndef getAppTypesText():\n  \"\"\"\"\"\"\n\n  appTypeTextArr = ['Select a project:\\n']\n  for index, appType in enumerate(appTypes):\n    appTypeTextArr.append(str(index + 1) + '. ' + appType.get('name'))\n  return '\\n'.join(appTypeTextArr)\n","repo_name":"abhaystoic/autocoder","sub_path":"src/data/app_types.py","file_name":"app_types.py","file_ext":"py","file_size_in_byte":522,"program_lang":"python","lang":"ta","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"13785272937","text":"\"\"\"\r\n此小脚本，为我数据库中房源信息，按小区位置，接入高德地图API，获取经纬度，为后数据可视化做准备。\r\n数据量不大，此处未采用多进程，可以加入Poll多进程加速获取\r\n\"\"\"\r\nimport pymongo\r\nimport pandas as pd\r\nimport requests\r\nimport re\r\n\r\n#连接到数据库\r\nclient = pymongo.MongoClient(\"localhost\",27017)\r\nlianjia = client[\"ershoufang\"]\r\ninfo = lianjia[\"lianjia_info\"]\r\nlocation = lianjia['location_info']\r\n#可以不加请求头，这里以防万一加上了\r\nheaders = {\r\n\t\"User-Agent\" : \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/72.0.3626.121 Safari/537.36\"\r\n}\r\n#高德地图获取地理信息的api接口\r\n\r\ngaode_api_url = \"https://restapi.amap.com/v3/geocode/geo?address={}&output=XML&key=c9ac8c7e25cddfd71c8d58da50199181\"\r\n#获取分表内容转换为DataFrame格式\r\ndata = pd.DataFrame(list(info.find()))\r\n#删掉了一些列，这里可不做处理\r\ndata_now = data.drop(['_id', 'url', 'title', 'room_num', 'structure', 'decorate'], axis=\"columns\")\r\n#按小区名字进行聚合，降序排序，同时大概了解总小区的数量\r\nhouse_name = data_now.groupby([\"house_name\"]).size().sort_values(ascending=False)\r\n#输出查看小区总数\r\nprint(house_name.shape)\r\n\r\n#遍历小区名，请求api，利用正则解析数据\r\nfor loc in house_name.index[5900:]:  # 高德api限制每天请求不超过6000个\r\n\tnew_loc = \"成都市\"+loc\r\n\tparse_adress_url = gaode_api_url.format(new_loc)\r\n\tresponse = requests.get(parse_adress_url,headers=headers).text\r\n\t#加入判断防止空白信息返回\r\n\tif re.search(r\"<count>1</count>\", response, re.S):\r\n\t\t#提取api反馈的地理信息\r\n\t\tdetail_info = re.findall(r\"_address>(.*?)</.*?<district>(.*?)</district>.*?<location>(.*?)</location>\", response, re.S)[0]\r\n\r\n\t\tresult = {\r\n\t\t\t'house_name': loc,\r\n\t\t\t'adress': detail_info[0],\r\n\t\t\t'district': detail_info[1],\r\n\t\t\t'location': detail_info[2],\r\n\t\t\t'longitude': detail_info[2].split(\",\")[0],\r\n\t\t\t'latitude': detail_info[2].split(\",\")[1]\r\n\t\t}\r\n\t\t#插入数据库\r\n\t\tlocation.insert_one(result)\r\n\t\tprint(result)\r\n\telse:\r\n\t\tprint(\"Something Wrong！未获取到api信息！\")\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n","repo_name":"ayueaa/script_spider","sub_path":"脚本爬虫/链家二手房-成都/为数据库房源添加经纬度.py","file_name":"为数据库房源添加经纬度.py","file_ext":"py","file_size_in_byte":2212,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"22467430853","text":"import telebot\nfrom telebot import types\nfrom game import WordGame\nfrom tb_token import get_token\n\nuser_game = {}  # создание словаря с пользователями и персональными играми к ним\n\ntoken = get_token()\n\nbot = telebot.TeleBot(token)\n\n\n@bot.message_handler(commands=['start'])\ndef start(message):\n    markup = types.ReplyKeyboardMarkup(resize_keyboard=True)\n    game_btn = types.KeyboardButton('Игра в слова')\n    new_game_btn = types.KeyboardButton('Новая игра')\n    markup.add(game_btn, new_game_btn)\n    bot.send_message(message.chat.id, f'Привет {message.from_user.first_name}', reply_markup=markup)\n\n\n# @bot.message_handler(commands=['game'])\n# def start_game(message):\n#     bot.send_message(message.chat.id, 'Введите слово')\n#\n#\n# @bot.message_handler(commands=['new_game'])\n# def start_game(message):\n#     game.new_game()\n#     bot.send_message(message.chat.id, 'Введите слово')\n#\n\n@bot.message_handler(content_types=['text'])\ndef user_word(message):\n    if message.text == 'Игра в слова':\n        user_game[message.from_user.id] = WordGame() # добавление пользователя в словарь\n        bot.send_message(message.chat.id, 'Введите слово')\n    elif message.text == 'Новая игра':\n        user_game[message.from_user.id] = WordGame() # добавление пользователя в словарь, если он не нажал кнопку \"игра в слова\"\n        user_game[message.from_user.id].new_game() #новая игра\n        bot.send_message(message.chat.id, 'Введите слово')\n    else:\n        answer = user_game[message.from_user.id].progress(message.text)\n        bot.send_message(message.chat.id, answer)\n\n\nbot.infinity_polling()\n","repo_name":"KKM-developer/GameWordBot","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1835,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"42875586604","text":"'''\nGiven an array of integers, return indices of the two numbers such that they add up to a specific target.\n\nYou may assume that each input would have exactly one solution, and you may not use the same element twice.\n\nExample:\n\nGiven nums = [2, 7, 11, 15], target = 9,\n\nBecause nums[0] + nums[1] = 2 + 7 = 9,\nreturn [0, 1].\n'''\n\nclass Solution(object):\n    def twoSum(self, nums, target):\n        dict = {}\n        for i in range(len(nums)):\n            dict[nums[i]] = i\n\n        for i in range(len(nums)):\n            if target - nums[i] in dict and dict.get(target - nums[i]) != i:\n                return i, dict.get(target - nums[i])   \n            \n        ","repo_name":"prashantchanne12/Leetcode","sub_path":"two sum.py","file_name":"two sum.py","file_ext":"py","file_size_in_byte":664,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"81"}
+{"seq_id":"37138682866","text":"import sys\ninput = sys.stdin.readline\nN = int(input())\npaper = [list(map(int, input().split())) for _ in range(N)]\n\niswhite = 0\nisblue = 0\n\ndef solve(x, y, size):\n    global iswhite, isblue\n    for i in range(x, x + size):\n        for j in range(y, y + size):\n            if paper[x][y] != paper[i][j]:           # 하나라도 색이 다르다면 4등분 해줌\n                                                     # 4등분후 각각의 사분면에 대해서 재귀로 탐색\n                solve(x, y, size // 2)             # 1사분면\n                solve(x, y+size // 2, size // 2)      # 2사분면\n                solve(x + size // 2, y, size // 2)    # 3사분면\n                solve(x + size // 2, y + size // 2, size // 2)     # 4사분면\n                return\n\n    if paper[x][y] == 0:           # 여기 까지 왔으면 구역내 모든 종이가 색이 같은거   -> 탐색 시작 지점이 0이면 흰색 +1\n        iswhite += 1\n        return\n    else:\n        isblue += 1\n        return\n\nsolve(0, 0, N)\nprint(iswhite)\nprint(isblue)\n","repo_name":"wony5248/Daily_Study","sub_path":"Coding_Study/04-14/백준 2630 색종이 만들기.py","file_name":"백준 2630 색종이 만들기.py","file_ext":"py","file_size_in_byte":1059,"program_lang":"python","lang":"ko","doc_type":"code","stars":1,"dataset":"github-code","pt":"81"}
+{"seq_id":"33815252343","text":"from datetime import datetime\nimport models\nimport uuid\n\n\nclass BaseModel:\n    def __init__(self, *args, **kwargs):\n        if kwargs:\n            for key, value in kwargs.items():\n                if key == 'created_at' or key == 'updated_at':\n                    format_tm = datetime.strptime(value,\n                                                  \"%Y-%m-%dT%H:%M:%S.%f\")\n                    setattr(self, key, format_tm)\n                elif key != '__class__':\n                    setattr(self, key, value)\n        else:\n            self.id = str(uuid.uuid4())\n            self.created_at = datetime.now()\n            self.updated_at = datetime.now()\n            models.storage.new(self)\n\n    def __str__(self):\n        my_name = type(self).__name__\n        my_id = self.id\n        my_dict = self.__dict__\n        return (\"[{}] ({}) {}\".format(my_name,\n                                      my_id,\n                                      my_dict))\n\n    def save(self):\n        self.updated_at = datetime.now()\n        models.storage.save()\n\n    def to_dict(self):\n        my_dict = self.__dict__.copy()\n        my_dict[\"__class__\"] = type(self).__name__\n        my_dict[\"created_at\"] = self.created_at.isoformat()\n        my_dict[\"updated_at\"] = self.updated_at.isoformat()\n        return my_dict\n","repo_name":"Adrncalel/AirBnB_clone","sub_path":"models/base_model.py","file_name":"base_model.py","file_ext":"py","file_size_in_byte":1300,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"70134986184","text":"#!/usr/bin/env python\n\"\"\"\n# Hetzner Infrastructure Tool\n\nhttps://www.hetzner.com/cloud/#pricing\n\n## Purpose\nLow level Operations on the DO Cloud, using their REST API\nE.g. when terraform destroy fails, you can delete droplets using this.\n\n## Caution\n\nMost playbooks are tested against a Fedora host filesystem (the default image)\n\n## Requirements\n- API Token\n\n## Examples:\n\n### Listing\n- ops ihc\n- ops ihc --since 10h\n- ops ihc -m \"*foo*\"\n\n### Creation\n- ops ihc dc -n mydroplet                             # create a minimal droplet\n- ops ihc dc -n mydroplet -f k3s -S M                 # create a droplet with this size, name foo, with k3s feature\n- ops ihc cc --name=mycluster --nodes=master,n1,2     # cluster\n\n### Deletion\n- ops ihc dd --since 1h # delete all created within the last hour (-f skips confirm)\n\n## Misc\n\nSee Actions at -h\n\"\"\"\n\n\nfrom devapp.app import run_app, FLG, app\nfrom devapp.tools.infra import Provider, Api, Prov, fmt, rm\nfrom devapp.tools.infra.actions import Actions, Flags, conv2classmethods\nfrom operator import setitem\nfrom devapp.tools import write_file, os\n\n# Droplet list results are cached in tmp/droplets.json. We will read from here if recent.'\n# droplet_list_cache_max_age = int(os.environ.get('droplet_list_cache_max_age', 3))\n\n\nclass Actions(Actions):\n    class _cli(Actions._cli):\n        image = 'i', 'fedora-37'   # 'fedora-36-x64'\n        region = 'w', 'hel1', '*w*here to create the resource'\n        network_name = 'nn', '', 'name of subnet this cluster is within'\n\n    def prices(A):\n        return Api.get('pricing')\n\n    def placement_group_list(A, name='*'):\n        return HProv.list_simple(\n            name,\n            HProv.placement_group,\n            headers=['id', 'name', fmt.key_tags, 'since', fmt.key_droplets],\n        )\n\n    def placement_group_create(A, name, type='spread'):\n        HProv.assert_sane_name(name, True)\n        d = {'name': name, 'type': type}\n        Api.req(HProv.placement_group.endpoint, 'post', data=d)\n        return Actions.placement_group_list(name=name)\n\n    # TODO\n    # placement_group_delete = rm('placement_group')\n\n    def kube_add_ccm(A, network_name='default', cidr='10.0.0.8/24', version='1.12.1'):\n        k = HProv.kubectl\n        token = Api.secrets['hcloud_api_token']\n        k.add_secret('hcloud', 'kube-system', {'token': token, 'network': network_name})\n        s = TCCM.format(cidr=cidr, version=version)\n        k.apply('conf/ccm.yaml', s)\n\n\nconv2classmethods(Actions)\n\n\nclass Flags(Flags):\n    class hcloud_api_token:\n        d = 'cmd:pass show HCloud/token'\n\n\n_ = 'We will create default network, if not present yet'\n# Flags.Actions.droplet_create.private_network.n += _\n\n\ndef monthly(server_type, region='hel1', which='monthly'):\n    s = server_type\n    p = [l for l in s['prices'] if l['location'] == region]\n    if not p:\n        app.info(\n            'Not available in region',\n            name=s['name'],\n            region=region,\n        )\n        return -1\n    return round(float(p[0][f'price_{which}']['gross']), 2)\n\n\ndef fmt_region(key, d, into):\n    l = d['datacenter']['location'] if 'datacenter' in d else d['location']\n    into['region'] = l['name']\n\n\ndef fmt_tags(key, d, into):\n    return setitem(into, 'tags', [f'{k}:{v}' for k, v in d['labels'].items()])\n\n\ndef fmt_ip_ranges(key, d, into):\n    return setitem(into, 'iprange', [i['ip_range'] for i in d[key]])\n\n\ndef fmt_net(key, d, into):\n    return setitem(into, key, d['ip_range'])\n\n\ndef fmt_ips(key, d, into):\n    r = into\n    try:\n        r['ip_priv'] = d['private_net'][0]['ip']\n    except:\n        pass\n    for k, t in (['ip', 'public_net'],):\n        try:\n            r[k] = d[t]['ipv4']['ip']\n        except:\n            pass\n\n\ndef fmt_price(key, d, into):\n    if key == 'prices':\n        into[fmt.key_price_monthly] = monthly(d)\n    else:\n        st = d['server_type']\n        into[fmt.key_typ] = fmt.typ(st['cores'], int(st['memory']), st['disk'])\n        reg = d['datacenter']['location']['name']   # droplet\n        into[fmt.key_curncy] = monthly(st, reg)\n        fmt.price_total(key, d, into, monthly(st, reg, 'hourly'))\n\n\ndef fmt_target_to_servers(_, d, into):\n    t = d['targets']\n    if not t:\n        d['servers'] = []\n        return\n    # we've seen 2 formats of targets, once nested one direct, dependent no label sel presence\n    if 'server' in t[0]:\n        return [s['server']['id'] for s in t]\n    d['servers'] = [i['server']['id'] for k in range(len(t)) for i in t[k]['targets']]\n\n\nclass HProv(Provider):\n    name = 'Hetzner'\n    secrets = 'hcloud_api_token'\n    base_url = 'https://api.hetzner.cloud/v1'\n    vol_price_gig_month = 0.0476   # https://www.hetzner.com/cloud/#pricing\n    pgroup = None\n    Actions = Actions\n\n    # fmt:off\n    alias_sizes = [\n        ['XXS'      , 'cpx11'         ],\n        ['XS'       , 'cx31'          ],\n        ['S'        , 'ccx12'         ],\n        ['M'        , 'ccx32'         ],\n        ['L'        , 'ccx52'         ],\n        ['XL'       , 'ccx62'         ],\n    ]\n    # fmt:on\n\n    def normalize_pre(d, r, cls, headers):\n        if 'created' in d:\n            fmt.to_since('created', d, r)\n        if 'datacenter' in d or 'location' in d:\n            fmt_region('', d, r)\n        if 'labels' in d:\n            fmt_tags('labels', d, r)\n        if 'targets' in d:   # loadbalancer\n            fmt_target_to_servers('', d, r)\n        if 'servers' in d:\n            fmt.droplet_id_to_name('servers', d, r)\n        if 'server' in d:\n            fmt.droplet_id_to_name('server', d, r)\n        if 'public_net' in d:\n            fmt_ips('', d, r)\n\n    def rm_junk(api_response):\n        try:\n            [i['datacenter'].pop('server_types') for i in api_response]\n        except:\n            pass\n\n    class droplet:\n        # fmt:off\n        endpoint = 'servers'\n\n        normalize = [\n            ['volumes'     , fmt.volumes  ] ,\n            ['id'          , fmt_ips      ] ,\n            ['server_type' , fmt_price    ] ,\n        ]\n        # fmt:on\n\n        # def prepare_create(env):\n        #     if not FLG.range:\n        #         return app.info('Skipping placement group for single droplet')\n        #     A, cn = HProv.Actions, env['cluster_name']\n        #     def h(k):\n        #         i = [i for i in k['data'] if i['name'] == cn]\n        #         return i[0] if i else None\n        #     HProv.pgroup = _ = h(A.placement_group_list())\n        #     if _:\n        #         return\n        #     HProv.pgroup = h(A.placement_group_create(cn))\n\n        def create_data(d):\n            _ = {'image', 'name', 'ssh_keys'}\n            r = {k: v for k, v in d.items() if k in _}\n            if HProv.pgroup:\n                r['placement_group'] = HProv.pgroup['id']\n            pn = d.get('private_network')\n            if pn:\n                r['networks'] = [HProv.NETWORKS[pn]['id']]\n            r['automount'] = False\n            r['location'] = d.get('region')\n            t = d.get('tags')\n            if t:\n                r['labels'] = {'plays': ','.join([i.replace(':', '_') for i in t])}\n            r['server_type'] = d.get('size')\n            return r\n\n    class image:\n        # fmt:off\n        endpoint = 'images'\n        normalize = [\n            ['disk_size'    , fmt.key_disk_size ] ,\n            ['rapid_deploy' , 'rapid'           ] ,\n        ]\n        # fmt:on\n\n    class load_balancer:\n        endpoint = 'load_balancers'\n\n    class network:\n        endpoint = 'networks'\n\n        def default():\n            return 'default'\n\n        normalize = [['subnets', fmt_ip_ranges], ['net', fmt_net]]\n\n        def create_data(d):\n            r = {'name': d['name']}\n            i = d['ip_range']\n            if not i.startswith('10.'):\n                app.die('ip range must start with 10.', have=i)\n            r['ip_range'] = '10.0.0.0/8'\n            r['subnets'] = [\n                {\n                    'network_zone': 'eu-central',  # only option\n                    'ip_range': i,\n                    'type': 'server',  # not sure difference to 'cloud'\n                }\n            ]\n            t = d.pop('tags')\n            if t:\n                r['labels'] = {'l': ','.join([i.replace(':', '_') for i in t])}\n            return r\n\n    class placement_group:\n        endpoint = 'placement_groups'\n\n    class ssh_keys:\n        endpoint = 'ssh_keys'\n        normalize = [['public_key', fmt.ssh_pub_key]]\n\n    class sizes:\n        # fmt:off\n        endpoint = 'server_types?per_page=100'\n        normalize = [\n            ['name'        , fmt.size_name_and_alias ] ,\n            ['prices'      , fmt_price               ] ,\n            ['cores'       , 'CPU'                   ] ,\n            ['memory'      , fmt.to_ram              ] ,\n            ['disk'        , fmt.key_disk_size       ] ,\n            ['description' , 'Descr'                 ] ,\n        ]\n        # fmt:on\n\n    class volume:\n        # fmt:off\n        endpoint = 'volumes'\n        normalize = [\n            ['size'     , fmt.key_disk_size      ] ,\n            ['id'       , fmt.vol_price          ] ,\n        ]\n        # fmt:on\n\n        def create_data(d):\n            r = dict(d)\n            s, rg = d.pop('_attach_to', ''), d.pop('region')\n            if s:\n                r['server'] = s['server']['id']\n                # r['automount'] = True\n            else:\n                r['location'] = rg\n            t = r.pop('tags')\n            return r\n\n        def prepare_delete(d):\n            s = d.get('server')\n            if not s:\n                return\n            id = d['id']\n            app.warn('detaching', **d)\n            Api.req(f'volumes/{id}/actions/detach', 'post', data={})\n\n\ndef main():\n    return run_app(Actions, flags=Flags)\n\n\nTCCM = \"\"\"\n---\n# https://github.com/hetznercloud/hcloud-cloud-controller-manager\napiVersion: v1\nkind: ServiceAccount\nmetadata:\n  name: cloud-controller-manager\n  namespace: kube-system\n---\nkind: ClusterRoleBinding\napiVersion: rbac.authorization.k8s.io/v1\nmetadata:\n  name: system:cloud-controller-manager\nroleRef:\n  apiGroup: rbac.authorization.k8s.io\n  kind: ClusterRole\n  name: cluster-admin\nsubjects:\n  - kind: ServiceAccount\n    name: cloud-controller-manager\n    namespace: kube-system\n---\napiVersion: apps/v1\nkind: Deployment\nmetadata:\n  name: hcloud-cloud-controller-manager\n  namespace: kube-system\nspec:\n  replicas: 1\n  revisionHistoryLimit: 2\n  selector:\n    matchLabels:\n      app: hcloud-cloud-controller-manager\n  template:\n    metadata:\n      labels:\n        app: hcloud-cloud-controller-manager\n      annotations:\n        # TODO: yields warning\n        scheduler.alpha.kubernetes.io/critical-pod: ''\n    spec:\n      serviceAccountName: cloud-controller-manager\n      dnsPolicy: Default\n      tolerations:\n        # this taint is set by all kubelets running --cloud-provider=external\n        # so we should tolerate it to schedule the cloud controller manager\n        - key: \"node.cloudprovider.kubernetes.io/uninitialized\"\n          value: \"true\"\n          effect: \"NoSchedule\"\n        - key: \"CriticalAddonsOnly\"\n          operator: \"Exists\"\n        # cloud controller manages should be able to run on masters\n        - key: \"node-role.kubernetes.io/master\"\n          effect: NoSchedule\n          operator: Exists\n        - key: \"node-role.kubernetes.io/control-plane\"\n          effect: NoSchedule\n          operator: Exists\n        - key: \"node.kubernetes.io/not-ready\"\n          effect: \"NoSchedule\"\n      hostNetwork: true\n      containers:\n        # TODO FIXME: kustomize for private registry/version\n        # TODO FIXME: kustomize for pod CIDR\n        - image: hetznercloud/hcloud-cloud-controller-manager:v{version}\n          name: hcloud-cloud-controller-manager\n          command:\n            - \"/bin/hcloud-cloud-controller-manager\"\n            - \"--cloud-provider=hcloud\"\n            - \"--leader-elect=false\"\n            - \"--allow-untagged-cloud\"\n            - \"--allocate-node-cidrs=true\"\n            - \"--cluster-cidr={cidr}\"\n          resources:\n            requests:\n              cpu: 100m\n              memory: 50Mi\n          env:\n            # TODO: kustomize debug\n            - name: HCLOUD_DEBUG\n              value: \"true\"\n            - name: NODE_NAME\n              valueFrom:\n                fieldRef:\n                  fieldPath: spec.nodeName\n            - name: HCLOUD_TOKEN\n              valueFrom:\n                secretKeyRef:\n                  name: hcloud\n                  key: token\n            - name: HCLOUD_NETWORK\n              valueFrom:\n                secretKeyRef:\n                  name: hcloud\n                  key: network\n\n\n\"\"\"\nProv(init=HProv)\nFlags._pre_init(Flags, Actions)\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"AXGKl/devapps","sub_path":"src/devapp/plugins/ops_devapp/infra_hetzner_cloud/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":12668,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"71962802184","text":"# 手写数字识别 加载模型\nfrom model import *\nimport cv2\nimport torch\nimport torchvision\nimport matplotlib.pyplot as plt\nimport os\nos.environ['KMP_DUPLICATE_LIB_OK'] = 'TRUE'\n# 加载模型\ncnn_model = torch.load('data/cnn_model.pt',map_location='cpu')\ncnn_model.eval()\nprint(cnn_model)\n# 加载测试图片\nimage_path = \"test_img/4.png\"\nimg = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)\nimg = cv2.resize(img, dsize=(28, 28), fx=1, fy=1, interpolation=cv2.INTER_LINEAR)\n#print(img.shape)\nplt.imshow(img, 'gray'), plt.title('ORIGINAL')\nplt.show()\n#输入测试图片img\ntransform = torchvision.transforms.Compose([torchvision.transforms.ToTensor()])\nimg = transform(img)\nimg = torch.reshape(img,(1, 1, 28, 28))\n#print(img.shape)\noutput = cnn_model(img)\n_, prediction = torch.max(output.data, 1)\nprint(output)\nprint(\"prediction={}\".format(prediction[0]))\n","repo_name":"MasonMachine/cv_homework_lzs","sub_path":"刘镇松-U202010652-CNN作业/model-test.py","file_name":"model-test.py","file_ext":"py","file_size_in_byte":862,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"81"}
+{"seq_id":"4675488578","text":"import pandas as pd\nimport json, yaml\nimport requests\nimport logging\nimport math\nfrom entity_extractor.doccano.doccanoAPI import DoccanoClient as Client\n\ndef csv2text(input_dataset, output_dataset, column_name):\n    ##### Saves column from csv to txt file.\n\n    logging.info(f'Reading input dataset {input_dataset}.')\n    data = pd.read_csv(input_dataset, usecols = [column_name])\n\n    logging.info(f'Processing input dataset {input_dataset}.')\n    prod_desc = data.loc[:,column_name].unique()\n\n    num_lines = len(prod_desc)\n    logging.info(f'Processing output dataset {output_dataset} ({num_lines} unique lines).')\n\n    with open(output_dataset, \"w\") as txt_file:\n        for line in prod_desc:\n            txt_file.write(line + \"\\n\")\n\n    return\n\ndef labels2dict(labels_json):\n    ##### Converts a Doccano labels json to a dictionary\n\n    labels_dict = {}\n    for label in labels_json:\n        labels_dict[label['id']] = label['text']\n\n    return labels_dict\n\ndef get_annotations(labels_dict, labels):\n    ##### Converts the Doccano labels dictionary to a [onset, offset, label] format\n\n    annotations = []\n    for label in labels:\n        annotation = [label['start_offset'], label['end_offset'], labels_dict[label['label']]]\n        annotations.append(annotation)\n    return annotations\n\ndef convert_json(documents_file = None, labels_file = None):\n    ##### Converts a Docanno output json to a Doccano input json\n\n    logging.info(f'Reading documents from {documents_file}.')\n    with open(documents_file) as input_file:\n        document_input = json.load(input_file)\n\n    logging.info(f'Reading labels from {labels_file}.')\n    with open(labels_file) as input_file:\n        labels_input = json.load(input_file)\n\n    labels_dict = labels2dict(labels_input)\n\n    output_file = documents_file.lower().strip('.json') + '_annotated.json'\n    logging.info(f'Outputing annoted documents to {output_file}.')\n\n    document_output = {}\n\n    with open(output_file, 'w') as output:\n        for document in document_input[\"results\"]:\n            document_output['text'] = document['text']\n            document_output['labels'] = get_annotations(labels_dict, document['annotations'])\n            json.dump(document_output, output)\n            output.write('\\n')\n\n    return\n\ndef find_project(client, name):\n    ##### Finds a Doccano project by name\n\n    projectExists = False\n\n    project_list = client.get_project_list()\n\n    for project in project_list:\n        if project['name'] == name and project['project_type'] == project_type:\n            logging.info(f'Project {name} found!')\n            return project['id']\n\n    return False\n\ndef doccano_downloader(client, project_name, documents_file = None, labels_file = None):\n    ##### Downloads a Doccano annotated project\n\n    logging.info(f'Checking existing projects named {project_name}.')\n    project_id = find_project(client, project_name)\n\n    if project_id:\n        logging.info(f'Getting project {project_name} statistics.')\n        statistics = client.get_project_statistics(project_id = project_id)\n\n        logging.info(f'Fetching documents from {project_name}.')\n        documents = client.get_document_list(project_id = project_id,\n                                             url_parameters = {'limit': [statistics['total']], 'offset': [0]})\n\n        logging.info(f'Saving documents from {project_name} to {documents_file}.')\n        with open(documents_file, 'w') as output_file:\n            json.dump(documents, output_file)\n\n        logging.info(f'Fetching labels from {project_name}.')\n        labels = client.get_label_list(project_id=project_id)\n\n        logging.info(f'Saving labels from {project_name} to {labels_file}.')\n        with open(labels_file, 'w') as output_file:\n            json.dump(labels, output_file)\n\n    else:\n        logging.info(f'Project {project_name} not found.')\n\n    return\n\ndef doccano_uploader(client, project_name, input_dataset, batch = 1000):\n    ##### Uploads data to a Doccano project\n\n    logging.info(f'Checking existing projects named {project_name}.')\n    project_id = find_project(client, project_name)\n\n    if not project_id:\n        logging.info(f'Creating {project_name}.')\n        project = client.post_project(project_name = project_name,\n                                      project_desc = '',\n                                      project_type = config.doccano_project_type).json()\n        project_id = project['id']\n\n    logging.info(f'Uploading {input_dataset} to {project_name}.')\n    if '.json' in input_dataset:\n        file_format = 'json'\n    else:\n        file_format = 'plain'\n\n    num_lines = sum(1 for line in open(input_dataset))\n    if batch > 0:\n        num_batch = math.ceil(num_lines / batch)\n    else:\n        num_batch = 1\n        batch = num_lines\n    rf = open(input_dataset, 'r')\n    responses = []\n    for i in range(num_batch):\n        new_file = f'{input_dataset}.batch.{i}'\n        wf = open(new_file, 'w')\n        for i in range(batch):\n            wf.write(rf.readline())\n        wf.close()\n        logging.info(f'Uploading batch {i} of {num_batch}.')\n        responses.append(client.post_doc_upload(project_id=project_id,\n                                     file_format=file_format,\n                                     file_name=new_file))\n        if responses[-1].status_code >= 400:\n            if responses[-1].status_code == 413:\n                logging.error('File-size too large. Try a smaller batch size.')\n            else:\n                logging.error('Error uploading. Read response.')\n            return responses\n\n    rf.close()\n    return responses\n","repo_name":"ianbakst/custom-ner","sub_path":"entity_extractor/doccano/doccanoLabelMaker.py","file_name":"doccanoLabelMaker.py","file_ext":"py","file_size_in_byte":5609,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"12804614603","text":"from django.contrib.gis.utils import LayerMapping\n\nfrom .models import HealthFacility\n\nhealth_facility_mapping = {\n\t'name': 'F_NAME',\n\t'geom': 'POINT',\n\t'district': 'DIST',\n\t'location': 'LOCATION',\n\t'sub_location': 'SUB_LOCATI',\n\t'agency': 'AGENCY',\n\t'level': 'LEVEL',\n}\n\nhealth_facility_shp = '/home/oty/Desktop/Axis/Axis Innovate4Life/kisii_health/kisii_health.shp'\n\n\ndef load_health_facilities(verbose=True):\n\tlm = LayerMapping(\n\t\tHealthFacility, health_facility_shp, health_facility_mapping,\n\t\ttransform=False, encoding='iso-8859-1',\n\t)\n\tlm.save(strict=True, verbose=verbose)\n","repo_name":"edgespatial/innovate4life2017","sub_path":"health/load.py","file_name":"load.py","file_ext":"py","file_size_in_byte":580,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"81"}
+{"seq_id":"40979034466","text":"import io\nimport pytest\n\nfrom watchadoin.lexer import TaskPaperLexer\nfrom watchadoin.parser import TaskPaperParser\nfrom watchadoin.visitors.printer import PrintVisitor\n\n\ndef pytest_collect_file(parent, path):\n    if path.ext == \".data\" and path.basename.startswith(\"test\"):\n        return TaskPaperParserFile.from_parent(parent, fspath=path)\n\n\nclass TaskPaperParserFile(pytest.File):\n    def collect(self):\n        name = \"\"\n        input = \"\"\n        output_lexer = \"\"\n        output_printer = \"\"\n        section = \"name\"\n        lineno = 0\n        test_lineno = 0\n        with self.fspath.open() as f:\n            for line in f:\n                if line.startswith(\"#\"):\n                    pass\n                elif line.startswith(\"=== testcase\"):\n                    if section == \"output printer\":\n                        yield TaskPaperParserItem.from_parent(\n                            self,\n                            input=input,\n                            name=name,\n                            output_lexer=output_lexer,\n                            output_printer=output_printer,\n                            lineno=test_lineno,\n                        )\n                    section = \"name\"\n                    name = \"\"\n                    test_lineno = lineno\n                elif line.startswith(\"<-- input\"):\n                    if section == \"name\":\n                        section = \"input\"\n                        input = \"\"\n                elif line.startswith(\"--> output lexer\"):\n                    if section == \"input\":\n                        section = \"output lexer\"\n                        output_lexer = \"\"\n                elif line.startswith(\"--> output printer\"):\n                    if section == \"output lexer\":\n                        section = \"output printer\"\n                        output_printer = \"\"\n                elif line.startswith(\"=== eof\"):\n                    if section == \"output printer\":\n                        yield TaskPaperParserItem.from_parent(\n                            self,\n                            name=name,\n                            input=input,\n                            output_lexer=output_lexer,\n                            output_printer=output_printer,\n                            lineno=test_lineno,\n                        )\n                    return\n                else:\n                    if section == \"name\":\n                        name = line.strip().replace(\" \", \"_\")\n                    elif section == \"input\":\n                        input += line\n                    elif section == \"output lexer\":\n                        output_lexer += line\n                    elif section == \"output printer\":\n                        output_printer += line\n                lineno += 1\n\n\nclass TaskPaperParserItem(pytest.Item):\n    def __init__(self, name, parent, input, output_lexer, output_printer, lineno):\n        super().__init__(name, parent)\n        self.input = input\n        self.output_lexer = output_lexer\n        self.output_printer = output_printer\n        self.lineno = lineno\n\n    def runtest(self):\n        lexer = TaskPaperLexer()\n        tokens = list(lexer.tokenize_text(self.input, eof=True))\n        input_tokens = tokens[: -2 or None]\n        result = \"\\n\".join([repr(token) for token in input_tokens]) + \"\\n\"\n        if result != self.output_lexer:\n            raise TaskPaperLexerException(self, result, self.output_lexer)\n\n        parser = TaskPaperParser()\n        token_iter = iter(tokens)\n        doc = parser.parse(token_iter)\n        output = io.StringIO()\n        printer = PrintVisitor(f=output)\n        printer.visit(doc)\n        result = output.getvalue()\n        if result != self.output_printer:\n            # For alignment purposes we swap tabs for spaces.\n            # The actual conparison above was done with the original tabs.\n            raise TaskPaperParserException(self, result.replace(\"\\t\", \"  \"), self.output_printer.replace(\"\\t\", \"  \"))\n\n    def repr_failure(self, excinfo, style=None):\n        if isinstance(excinfo.value, TaskPaperLexerException):\n            result = excinfo.value.args[1].splitlines()\n            expected = excinfo.value.args[2].splitlines()\n\n            diff = len(result) - len(expected)\n            if diff < 0:\n                result.extend([\"\" for _ in range(abs(diff))])\n            elif diff > 0:\n                expected.extend([\"\" for _ in range(abs(diff))])\n\n            header = \"\\n\".join([\"Usecase execution failed\", \"{:<55}   {:<55}\".format(\"Result:\", \"Expected:\")])\n            report = \"\\n\".join(\n                [\n                    f\"{left:<55}   {right:<55}\" if left == right else f\"{left:<55} ! {right:<55}\"\n                    for (left, right) in zip(result, expected)\n                ]\n            )\n            return \"\\n\".join([header, report])\n        elif isinstance(excinfo.value, TaskPaperParserException):\n            result = excinfo.value.args[1].splitlines()\n            expected = excinfo.value.args[2].splitlines()\n\n            diff = len(result) - len(expected)\n            if diff < 0:\n                result.extend([\"\" for _ in range(abs(diff))])\n            elif diff > 0:\n                expected.extend([\"\" for _ in range(abs(diff))])\n\n            header = \"\\n\".join([\"Usecase execution failed\", \"{:<55}   {:<55}\".format(\"Result:\", \"Expected:\")])\n            report = \"\\n\".join(\n                [\n                    f\"{left:<55}   {right:<55}\" if left == right else f\"{left:<55} ! {right:<55}\"\n                    for (left, right) in zip(result, expected)\n                ]\n            )\n            return \"\\n\".join([header, report])\n        else:\n            return self._repr_failure_py(excinfo, style)\n\n    def reportinfo(self):\n        return self.fspath, self.lineno, f\"usecase: {self.name}\"\n\n\nclass TaskPaperLexerException(Exception):\n    \"\"\"Custom exception for error reporting after lexing.\"\"\"\n\n\nclass TaskPaperParserException(Exception):\n    \"\"\"Custom exception for error reporting after parsing.\"\"\"\n","repo_name":"oohlaf/watchadoin","sub_path":"tests/parser/conftest.py","file_name":"conftest.py","file_ext":"py","file_size_in_byte":6011,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"18607235205","text":"import os\r\nimport tempfile\r\n\r\nimport pytest\r\nimport json\r\nfrom flask_sqlalchemy import SQLAlchemy\r\n\r\nfrom backend import app as _app, db as _db, models, socketio\r\nfrom backend.models import Room, Player, RATING_MAX, RATING_MIN\r\nfrom config import TestConfig, TESTDB, TESTDB_PATH\r\nimport datetime\r\nimport random\r\nimport logging\r\nfrom worker import conn\r\nfrom redis import ConnectionError\r\n\r\nlogger = logging.getLogger()\r\n\r\n# PLAYERS_TO_GEN = 5\r\n\r\n# mock_players = {}\r\n\r\ndef is_redis_available(conn):\r\n    try:\r\n        conn.ping()\r\n        return True\r\n    except ConnectionError:\r\n        return False\r\n\r\n# pytestmark = pytest.mark.skipif(not is_redis_available(conn), reason=\"redis instance is not available\")\r\n\r\ndef test_player_json(client):\r\n    \"\"\"Test with single player.\"\"\"\r\n    response = client.get(\r\n        '/api/player/1'\r\n    )\r\n    print(response.get_data(as_text=True))\r\n    assert response.status_code == 200\r\n    player = json.loads(response.get_data(as_text=True))\r\n    assert response.status_code == 200\r\n    assert RATING_MIN <= player['rating']\r\n    assert player['id'] == 1\r\n    assert player['username'] == 'Randomplayer1'\r\n\r\n\r\ndef test_login(client):\r\n    \r\n    response = client.post(\r\n        '/api/auth/login',\r\n         data=json.dumps({'username': 'Randomplayer1', 'password': 'Rand1'}),\r\n        content_type='application/json'\r\n    )\r\n\r\n    data = json.loads(response.get_data(as_text=True))\r\n    print(data)\r\n    assert response.status_code == 200\r\n\r\n@pytest.mark.skipif(not is_redis_available(conn), reason=\"redis instance is not available\")\r\ndef test_room_create(client):\r\n    \"\"\"Create a new room with single participant\"\"\"\r\n    response = client.post(\r\n        '/api/room/create',\r\n        data=json.dumps({'players': [2],\r\n                         'lobby' : 1}),\r\n        content_type='application/json'\r\n    )\r\n    print(response.get_data(as_text=True))\r\n    room = json.loads(response.get_data(as_text=True))\r\n\r\n    assert response.status_code == 200\r\n    assert RATING_MIN <= room['rating'] <= RATING_MAX\r\n    assert [player['id'] for player in room['players']] == [2]\r\n\r\n@pytest.mark.skipif(not is_redis_available(conn), reason=\"redis instance is not available\")\r\ndef test_room_existed(client):\r\n    \"\"\"Create a new room with single participant already in a match\"\"\"\r\n    response = client.post(\r\n        '/api/room/create',\r\n        data=json.dumps({'players': [2],\r\n                         'lobby' : 1}),\r\n        content_type='application/json'\r\n    )\r\n    response = client.post(\r\n        '/api/room/create',\r\n        data=json.dumps({'players': [2],\r\n                         'lobby' : 1}),\r\n        content_type='application/json'\r\n    )\r\n    status = json.loads(response.get_data(as_text=True))\r\n    print(status)\r\n\r\n    assert status['status'] == 'Player 2 is already in a match'\r\n\r\n@pytest.mark.skipif(not is_redis_available(conn), reason=\"redis instance is not available\")\r\ndef test_room_json(client):\r\n    \"\"\"Test with a blank database.\"\"\"\r\n    response = client.get(\r\n        '/api/room/1',\r\n    )\r\n    print(response.get_data(as_text=True))\r\n    room = json.loads(response.get_data(as_text=True))\r\n\r\n    assert response.status_code == 200\r\n    assert RATING_MIN <= room['rating'] <= RATING_MAX\r\n    assert room['id'] == 1\r\n    assert [player['id'] for player in room['players']] == [2]\r\n\r\ndef test_players_list(client):\r\n    \"\"\"List all current player\"\"\"\r\n    response = client.get(\r\n        '/api/player/list',\r\n    )\r\n\r\n    data = json.loads(response.get_data(as_text=True))\r\n    print(data)\r\n    assert response.status_code == 200\r\n    assert [player['id'] for player in data] == [1, 2, 3, 4, 5]\r\n    for player in data:\r\n        assert RATING_MIN <= player['rating'] <= RATING_MAX\r\n    \r\n\r\n\r\ndef test_rating_update(client):\r\n    \"\"\"Match a current player to one room\"\"\"\r\n    response = client.post(\r\n        '/api/player/1/update-rating',\r\n        data=json.dumps({'toxic': 1, 'skill': 1}),\r\n        content_type='application/json'\r\n    )\r\n\r\n    player = json.loads(response.get_data(as_text=True))\r\n    assert response.status_code == 200\r\n    assert player['id'] == 1\r\n    assert RATING_MIN <= player['rating'] <= RATING_MAX\r\n    assert player['num_evals'] == 2\r\n\r\ndef test_player_create(client):\r\n    response = client.post(\r\n        '/api/player/create',\r\n         data=json.dumps({'username': 'Randomplayer9', 'password': 'Rand9', 'email': 'Rando9@somewhere.somehow'}),\r\n        content_type='application/json'\r\n    )\r\n    print(response.get_data(as_text=True))\r\n    assert response.status_code == 200\r\n    player = json.loads(response.get_data(as_text=True))\r\n    assert player['username'] == 'Randomplayer9'\r\n    assert player['email'] == 'Rando9@somewhere.somehow'\r\n    \r\ndef test_player_create_int(client):\r\n    response = client.post(\r\n        '/api/player/create',\r\n        data=json.dumps({'username': 22, 'password': 22, 'email': 22}),\r\n        content_type='application/json'\r\n    )\r\n    print(response.get_data(as_text=True))\r\n    assert response.status_code == 200\r\n    player = json.loads(response.get_data(as_text=True))\r\n    assert player['username'] == '22'\r\n    assert player['email'] == '22'\r\n\r\ndef test_lobby_list(client):\r\n    response = client.get(\r\n        '/api/lobby/list'\r\n    )\r\n    assert response.status_code == 200\r\n    lobbies = json.loads(response.get_data(as_text=True))\r\n    assert len(lobbies) == 1\r\n    assert lobbies[0]['id'] == 1","repo_name":"TStarshak/Gameroom","sub_path":"test/test_app.py","file_name":"test_app.py","file_ext":"py","file_size_in_byte":5427,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"34886630927","text":"# Task 6: Write a Python program to count the number of even and odd numbers in a series of numbers\n# Sample numbers : numbers = (1, 2, 3, 4, 5, 6, 7, 8, 9)\n# Expected Output :\n# Number of even numbers : 5\n# Number of odd numbers : 4\n\n\ndef odd_even(*args):\n    even_num = 0\n    odd_num = 0\n    for number in args:\n        if number % 2 == 0:\n            even_num += 1\n        else:\n            odd_num += 1\n    print(f\"Number of even numbers: {even_num}\\n\" f\"Number of odd numbers: {odd_num}\")\n\n\nodd_even(5, 6, 6, 6, 6, 5, 5)\n","repo_name":"T6nisValk/School","sub_path":"Basics/Class_4-5_13.05-14.05.23/Tasks 13.05.23/task_6.py","file_name":"task_6.py","file_ext":"py","file_size_in_byte":526,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"2574194900","text":"class Node(object):\n    def __init__(self, x):\n        self.val = x\n        self.next = None\n\n\nclass LinkedList:\n    def __init__(self):\n        self.head = None\n\n    def printList(self):\n        temp = self.head\n        while (temp):\n            print(temp.data)\n            temp = temp.next\n\n    def reverseList(self, head):\n        \"\"\"\n        :type head: ListNode\n        :rtype: ListNode\n        \"\"\"\n        #let curr point to start of the the LL\n        curr = head\n        #initialize prev and next to None\n        prev = None\n        next = None\n        # while curr is not None(i.e end)\n        while curr is not None:\n            #store next first,\n            # store prev in curr.next,\n            # update prev and next\n            next = curr.next\n            curr.next = prev\n            prev = curr\n            curr = next\n        return prev\n\n\n\nif __name__ == '__main__':\n    llist = LinkedList()\n\n    llist.head = Node(1)\n    second = Node(2)\n    third = Node(3)\n    llist.head.next = second\n    second.next = third\n\n    llist.middle()","repo_name":"mrunalhirve12/Interviews2","sub_path":"LinkedList/Reverse list.py","file_name":"Reverse list.py","file_ext":"py","file_size_in_byte":1053,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"23281204450","text":"import random\r\n\r\nfrom cryptography.hazmat.primitives.ciphers import Cipher, algorithms, modes\r\nfrom cryptography.hazmat.backends import default_backend\r\nimport base64\r\nimport datetime\r\n\r\nclass TripleDES:\r\n\r\n    def __init__(self,textData):\r\n        self.textData=textData\r\n        self.BS = DES3.block_size\r\n        while len(self.textData) % 8 != 0:\r\n            self.textData += b' '\r\n        self.pad = lambda s: s + (self.BS - len(s) % self.BS) * chr(self.BS - len(s) % self.BS).encode()\r\n        self.unpad = lambda s: s[0:-ord(s[-1])]\r\n        self.key =b\"1234567887654321\"\r\n        self.iv =b\"1234567337654321\"\r\n    def getCipher(self):\r\n        backend = default_backend()\r\n        cipher = Cipher(algorithms.TripleDES(self.key), modes.CBC(self.iv), backend=backend)\r\n        encryptor = cipher.encryptor()\r\n\r\n        # Encrypt the message\r\n        encrypted_message = encryptor.update(self.message) + encryptor.finalize()\r\n\r\n        return base64.b64encode(encrypted_message)\r\n\r\n    def toNormal(self):\r\n        # Encrypted message to be decrypted (in base64 encoding)\r\n        encrypted_message = base64.b64decode(self.textData)\r\n\r\n        # Create a 3DES cipher object\r\n        backend = default_backend()\r\n        cipher = Cipher(algorithms.TripleDES(self.key), modes.CBC(self.iv), backend=backend)\r\n        decryptor = cipher.decryptor()\r\n\r\n        # Decrypt the message\r\n        decrypted_message = decryptor.update(encrypted_message) + decryptor.finalize()\r\n\r\n        # Remove the padding from the decrypted message\r\n        decrypted_message = decrypted_message.rstrip()\r\n        return decrypted_message","repo_name":"harsha47n/django","sub_path":"source/TripleDES.py","file_name":"TripleDES.py","file_ext":"py","file_size_in_byte":1620,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"70044277386","text":"import logging\nfrom telegram.ext import Updater, CommandHandler, CallbackContext, MessageHandler\nfrom telegram.update import Update\nfrom telegram.ext.filters import Filters\nimport requests\nimport settings.local_settings as settings\n\nlogging.basicConfig(\n    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',\n    level=logging.DEBUG)\n\nupdater = Updater(token=settings.TELEGRAM_TOKEN)\n\n\ndef start(update: Update, context: CallbackContext):\n    update.message.reply_text('Salom')\n    context.bot.send_message(chat_id=update.message.chat_id, text='Assalomu alaykum! \\n'\n                                                                  'Wikipediya botiga xuh kelibsiz!\\n'\n                                                                  'Izlash uchun /search Amir temur')\n\n\ndef search(update: Update, context: CallbackContext):\n    args = context.args\n\n    if len(args) == 0:\n        update.message\\\n            .reply_text(\"Hech bo'lmasa nimadir kiriting. \"\n                        \"Misol uchun /search Amir Temur\")\n    else:\n        search_text = ' '.join(args)\n        response = requests.get('https://uz.wikipedia.org/w/api.php', {\n            'action': 'opensearch',\n            'search': search_text,\n            'limit': 1,\n            'namespace': 0,\n            'format': 'json',\n        })\n\n    result = response.json()\n    link = result[3]\n\n    if len(link):\n        update.message\\\n                .reply_text(\"Sizning sorovingiz bo‘yicha havola: \" + link[0])\n    else:\n        update.message\\\n            .reply_text(\"Sizning sorovingiz bo‘yicha havola yo'q:\")\n\n\ndispatcher = updater.dispatcher\ndispatcher.add_handler(CommandHandler('start', start))\ndispatcher.add_handler(CommandHandler('search', search))\ndispatcher.add_handler(MessageHandler(Filters.all, start))\n\nupdater.start_polling()\nupdater.idle()\n","repo_name":"bekzodergashev1/CLC_wiki_search_bot","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1833,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"22801698499","text":"from datetime import date, timedelta\nimport copy\nfrom datetime import datetime\nimport pandas as pd\n\nfrom django.shortcuts import get_object_or_404\nfrom ..courses.models import GeneralLedger, ChartOfAccounts\nfrom .utils import get_number, add_years_months_days\nfrom .sql import SQL\n\n\n# gl_entries = [(account, amount), ]\ndef post_gl(gl_entries):\n    sub_transaction_number = 1\n    source = 1\n    transaction_number = get_number(1)\n    for gle in gl_entries:\n        a = get_object_or_404(ChartOfAccounts, id=gle[0])\n        # print(gle)\n\n        glp = GeneralLedger.objects.create(transaction=transaction_number,\n                                           sub_transaction=sub_transaction_number,\n                                           account=a,\n                                           amount=gle[1],\n                                           target=gle[2],\n                                           )\n        sub_transaction_number += 1\n    return 1\n\n\n    # PERIOD_TYPES = (\n    #     (0, 'Other'),\n    #     (1, 'day'),\n    #     (2, 'month'),\n    #     (3, 'year'),\n    # )\n    #\n    # FLOW_TYPES = (\n    #     (0, 'Other'),\n    #     (1, 'flow'),\n    #     (2, 'cumulative'),\n    # )\n\n\ndef update_trial_balance(ll):\n    # print(from_date)\n    sql = SQL()\n    if '1' in ll and '2' in ll and '3' in ll:\n        sql.exc_sql(\"\"\"truncate table courses_trialbalance\"\"\", ())\n    else:\n        if '1' in ll:\n            sql.exc_sql(\"\"\"delete from courses_trialbalance where period_type = 1\"\"\", ())\n        elif '2' in ll:\n            sql.exc_sql(\"\"\"delete from courses_trialbalance where period_type = 2\"\"\", ())\n        elif '3' in ll:\n            sql.exc_sql(\"\"\"delete from courses_trialbalance where period_type = 3\"\"\", ())\n\n    from_date_ = GeneralLedger.objects.earliest('created').created\n    from_date = copy.copy(from_date_)\n    today_end = add_years_months_days(date.today(), years=0, months=0, days=1)\n    if '1' in ll:\n        while from_date < today_end:\n            print('000-------')\n            print(from_date_)\n            print(from_date)\n            print('000-------')\n            create_trial_balance_(period_type=1, for_beginning_date=from_date, from_date_=from_date_)\n            from_date = add_years_months_days(from_date, years=0, months=0, days=1)\n\n    if '2' in ll:\n        from_month = from_date.replace(day=1)\n        today_end_month = add_years_months_days(date.today().replace(day=1), years=0, months=1, days=0)\n        while from_month < today_end_month:\n            create_trial_balance_(period_type=2, for_beginning_date=from_month, from_date_=from_date_)\n            from_month = add_years_months_days(from_month, years=0, months=1, days=0)\n\n    if '3' in ll:\n        from_year = date(from_date.year, 1, 1)\n        today_end_year = add_years_months_days(date(date.today().year,1,1), years=1, months=0, days=0)\n        while from_year < today_end_year:\n            create_trial_balance_(period_type=3, for_beginning_date=from_year, from_date_=from_date_)\n            from_year = add_years_months_days(from_year, years=1, months=0, days=0)\n\n\n# period_type, flow_type, for_date, account, Amount\ndef create_trial_balance_(period_type, for_beginning_date, from_date_):\n    print('0001----------')\n    print(from_date_)\n    print(for_beginning_date)\n    print('0001----------')\n    for_end_date = None\n\n    if period_type == 1:\n        for_end_date = add_years_months_days(for_beginning_date, years=0, months=0, days=1)\n        pkey_date = for_beginning_date.year*10000 + for_beginning_date.month*100 + for_beginning_date.day\n\n        for_beginning_date_1 = add_years_months_days(for_beginning_date, years=0, months=0, days=-1)\n        pkey_date_1 = for_beginning_date_1.year*10000 + for_beginning_date_1.month*100 + for_beginning_date_1.day\n\n        pkey_from_date_ = from_date_.year*10000 + from_date_.month*100 + from_date_.day\n    elif period_type == 2:\n        for_end_date = add_years_months_days(for_beginning_date, years=0, months=1, days=0)\n        pkey_date = for_beginning_date.year*10000 + for_beginning_date.month*100\n\n        for_beginning_date_1 = add_years_months_days(for_beginning_date, years=0, months=-1, days=0)\n        pkey_date_1 = for_beginning_date_1.year*10000 + for_beginning_date_1.month*100\n\n        pkey_from_date_ = from_date_.year*10000 + from_date_.month*100\n    elif period_type == 3:\n        for_end_date = add_years_months_days(for_beginning_date, years=1, months=0, days=0)\n        pkey_date = for_beginning_date.year*10000\n\n        for_beginning_date_1 = add_years_months_days(for_beginning_date, years=-1, months=0, days=0)\n        pkey_date_1 = for_beginning_date_1.year*10000\n\n        pkey_from_date_ = from_date_.year*10000\n    # --- sum all records for the period\n\n\n    print('---pkey_from_date_-----')\n    print(pkey_from_date_)\n    print(pkey_date)\n    print(pkey_date_1)\n    print('--------')\n\n    sql = SQL()\n    ssql_ = \"\"\"\n    insert into courses_trialbalance (period_type, flow_type, pkey_date, account_id, amount)\n    select %s, %s, %s, a.account_id, a.amount\n    from\n    (Select g.account_id, sum(amount) as amount \n     from courses_generalledger as g\n     where created >= %s and created < %s \n     Group by account_id) a\n    \"\"\"\n    data_ = (period_type, 1, pkey_date, for_beginning_date, for_end_date)\n\n    print('1------------')\n    print(ssql_)\n    print(data_)\n    print('------------')\n    count = sql.exc_sql(ssql_, data_)\n    print(count)\n    print('-----------')\n\n    # - closing entry. not needed in BI dimension\n    ssql_ = \"\"\"\n    insert into courses_trialbalance (period_type, flow_type, pkey_date, account_id, amount)\n    select %s, %s, %s, 11, a.amount\n    from\n    (select sum(t.amount) as amount\n    from courses_trialbalance t, courses_chartofaccounts c\n    where t.pkey_date = %s and\n    t.account_id = c.id and\n    c.account_type = 3) a\n    \n    \"\"\"\n    data_ = (period_type, 1, pkey_date, pkey_date)\n\n    print('2.1------------')\n    print(ssql_)\n    print(data_)\n    print('------------')\n    count = sql.exc_sql(ssql_, data_)\n    print(count)\n    print('-----------')\n\n    ##-----\n    ssql_ = \"\"\"\n    insert into courses_trialbalance (period_type, flow_type, pkey_date, account_id, amount)\n    select %s, %s, %s, 12, a.amount\n    from\n    (select -sum(t.amount) as amount\n    from courses_trialbalance t, courses_chartofaccounts c\n    where t.pkey_date = %s and\n    t.account_id = c.id and\n    c.account_type = 3) a\n\n    \"\"\"\n    data_ = (period_type,1, pkey_date, pkey_date)\n\n    print('2.2------------')\n    print(ssql_)\n    print(data_)\n    print('------------')\n    count = sql.exc_sql(ssql_, data_)\n    print(count)\n    print('-----------')\n\n    # #-- cumulative calculation for beginning balance. notice, in the way I added the closing entry\n    # #    I can ignore the income statement accounts\n\n    print('000000')\n    print(str(pkey_date))\n    print(str(pkey_from_date_))\n    print('000000')\n\n    if pkey_date == pkey_from_date_:\n        ssql_ = \"\"\"\n        insert into courses_trialbalance (period_type, flow_type, pkey_date, account_id, amount)\n        Select t.period_type, %s as flow_type, t.pkey_date, t.account_id, t.amount \n        from courses_trialbalance as t, courses_chartofaccounts c\n        where t.account_id = c.id and\n        c.account_type = 2 and\n        t.pkey_date = %s\n        \"\"\"\n        print('0000001')\n        print(str(pkey_date))\n        print(str(pkey_from_date_))\n        print('0000001')\n\n        data_ = (2, pkey_from_date_)\n\n        print('3.1------------')\n        print(str(pkey_from_date_))\n        print(ssql_)\n        print(data_)\n        print('------------')\n        count = sql.exc_sql(ssql_, data_)\n        print(count)\n        print('-----------')\n    else:\n        ssql_ = \"\"\"\n        insert into courses_trialbalance (period_type, flow_type, pkey_date, account_id, amount)   \n        select a.period_type, a.flow_type, a.pkey_date, a.account_id, sum(a.amount) as amount\n        from\n        (Select t.period_type, %s as flow_type, %s as pkey_date, t.account_id, t.amount \n         from courses_trialbalance as t, courses_chartofaccounts c\n         where t.account_id = c.id and\n         ((t.pkey_date =  %s and t.flow_type = 1) or\n          (t.pkey_date =  %s and t.flow_type = 2 and c.account_type = 2))) a\n          group by a.period_type, a.flow_type, a.pkey_date, a.account_id\n        \"\"\"\n\n        data_ = (2, pkey_date, pkey_date, pkey_date_1)\n\n        print('3.2------------')\n        print(ssql_)\n        print(data_)\n        print('------------')\n        count = sql.exc_sql(ssql_, data_)\n        print(count)\n        print('-----------')\n\n    sql.exc_sql(\"\"\"delete from courses_trialbalance where abs(amount) < 0.001\"\"\", ())\n\n","repo_name":"amosbaranes/development","sub_path":"academycity/academycity/apps/core/accounting.py","file_name":"accounting.py","file_ext":"py","file_size_in_byte":8700,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"70020971786","text":"import matplotlib.pyplot as plt\nimport matplotlib.cm as cm\nimport numpy as np\nfrom scipy.optimize import curve_fit\n\ndef plot_snapshots(sim, i, var = 'c', colormap = 'Greys', ax = None):\n    \n    if var not in ['c', 'v', 'cgrad']:\n        print(\"allowed values of var: 'c' for concentration, 'v' for flow field, 'cgrad' gradient of concentration\")\n        return\n\n    if ax == None:\n        fig, ax = plt.subplots()\n    \n    x = sim.x\n   \n    #for sim containing multiple simulations with different random initial conditions\n    if hasattr(sim.sol_c[0][0], \"__len__\"):\n        if var in ['c', 'cgrad']:\n            y = sim.sol_c[0]\n        elif var == 'v':\n            y = sim.sol_v[0]\n    #for sim containing a single simulation\n    else:\n        if var in ['c', 'cgrad']:\n            y = sim.sol_c\n        elif var == 'v':\n            y = sim.sol_v\n\n    color = cm.get_cmap(colormap)\n    shades = [color(1./x) for x in range(i, 0, -1)]\n\n    #plot snapshots of the simulation with time intervals. \n    #small offset (len(y) - 5) to make sure 5*t_interval is less than len(y)\n    t_interval = int((1./i)*(len(y) - 5))\n    if var in ['c', 'v']:\n        for j in range(i):\n            ax.plot(x, y[t_interval*j + 5], label = '{:.0f}'.format(np.ceil(sim.t[t_interval*j + 5])), color = shades[j])\n    elif var == 'cgrad':\n        for j in range(i):\n            temp = [sim.Omega*(y[t_interval*j + 5, k] - y[t_interval*j + 5, k-1])/(x[k] - x[k-1]) for k in range(1, len(x))]\n            ax.plot(x[1:], temp, label = '{:.0f}'.format(np.ceil(sim.t[t_interval*j + 5])), color = shades[j], ls = '--')\n            \n    plt.xlabel(\"x\")\n    #make legend\n#     lgd = plt.legend(title = 't', bbox_to_anchor=(-0.2, 3), ncol=1)\n\ndef get_front_speed(sim, plot = False, ax = None):\n    \"\"\"\n    calculates the speed of the wavefront, defined as the point where c = some constant between 0 and 1.\n    NB: this function assumes that the speed is constant, it does not check whether\n    this is the case! Use plot = True to check graphically.\n    \"\"\"\n    tmin = int(len(sim.t) - 0.2*len(sim.t))\n    tmax = int(len(sim.t) - 0.1*len(sim.t))\n\n\n    #define the wavefront as the rightmost location where c = 1/2\n    front_loc = [sim.x[np.where(sim.sol_c[i] > 0.5)[0][-1]] for i in range(len(sim.t))]\n    if any([fl > (sim.x_max - 10)  for fl in front_loc]):\n        t_hit = sim.t[np.where(np.array(front_loc) > (sim.x_max - 10))[0][0]]\n        print(\"warning, front nearing end of box at time {:}\".format(t_hit))\n        if t_hit < sim.t[tmax]:\n            print(\"valid front speed cannot be calculated\")\n            return\n        else:\n            print(\"valid front speed can still be calculated; check fitted region of curve using plot=True\") \n  \n    z, pcov = curve_fit(lin_fxn, sim.t[tmin:tmax], front_loc[tmin:tmax])\n\n    if plot:\n        if ax == None:\n            fig, ax = plt.subplots()\n        ax.scatter(sim.t, front_loc)\n        ax.plot(sim.t[tmin:tmax], lin_fxn(sim.t[tmin:tmax], *z), c = 'red')\n        #ax.set_ylabel(\"front position\")\n        #ax.set_xlabel(\"t\")\n        #ax.set_title(title)\n\n    return z[0]\n\ndef lin_fxn(x, m, b):\n    return m*x + b\n","repo_name":"cdeljunco/rdam","sub_path":"active_rd_simulation/active_rd_simulation/analysis_and_plotting.py","file_name":"analysis_and_plotting.py","file_ext":"py","file_size_in_byte":3140,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"81"}
+{"seq_id":"9767112420","text":"# Assignment\n# File: \n# Date:    12/7/22\n# By:      Jacob Freidline\n# Section: 005\n# Team:    259\n# \n# ELECTRONIC SIGNATURE\n# Jacob Freidline\n#\n# The electronic signature above indicates the script\n# submitted for evaluation is my individual work, and I\n# have a general understanding of all aspects of its\n# development and execution.\n#\n# A BRIEF DESCRIPTION OF WHAT THE SCRIPT OR FUNCTION DOES\n# This script is a header template that will be used for \n# all your python files the rest of the semester.\n\ndef Algebra(a1,b1,c1,a2,b2,c2):\n    T = a1*b2-a2*b1\n    if T == 0:\n        x = \"No unique solution\"\n        y = \"No unique solution\"\n    else:\n        x = \"{:0.3f}\".format((c2-(b2*c1)/(b1))/(a2-(b2*a1)/(b1)))\n        y = \"{:0.3f}\".format((c1-(a1*x))/(b1))\n    return x,y","repo_name":"Ert850/Code-Trial","sub_path":"JF/NOTE/hehe.py","file_name":"hehe.py","file_ext":"py","file_size_in_byte":775,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"7187157629","text":"def BFS(point,n,m):\n    global maze\n    visited =[[1,1]]\n    depth = 1\n    cp = None\n    cp_list = [[[1, 1]]]\n    while True:\n        cp = cp_list.pop(0)\n        #print(cp)\n        nnp = []\n        for j in cp:\n            x=j[0]\n            y=j[1]\n            np = [[x-1,y],[x+1,y],[x,y-1],[x,y+1]]\n\n            for i in np:\n                if [i[0],i[1]] ==[n,m]:\n                    return depth+1\n                if maze[i[0]][i[1]] and [i[0],i[1]] not in visited:\n                    visited.append([i[0],i[1]])\n                    nnp.append([i[0],i[1]])\n        cp_list.append(nnp)\n        #print(visited)\n        depth +=1\n\n\nn,m = map(int,input().split())\nmaze = [[0 for _ in range(m+2)] for _ in range(n+2)]\nfor i in range(1,n+1):\n    num = input()\n    t_list = []\n    for j in num:\n        t_list.append(int(j))\n    maze[i][1:-1]= t_list\n#print(maze)\nprint(BFS([1,1],n,m))","repo_name":"johnny9696/baekjoon_problem","sub_path":"1~9999/2000~2999/2178.py","file_name":"2178.py","file_ext":"py","file_size_in_byte":882,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"8870616039","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\nimport logging\nimport argparse\nimport os\nimport sys\nimport codecs\nfrom flair.data import Dictionary\nfrom flair.models import LanguageModel\nfrom flair.trainers.language_model_trainer import LanguageModelTrainer, TextCorpus\n\n\"\"\"\nModule for XXX\n\n\"\"\"\n\n\nsys.stderr = codecs.getwriter('UTF-8')(sys.stderr.buffer)\nsys.stdout = codecs.getwriter('UTF-8')(sys.stdout.buffer)\nsys.stdin = codecs.getreader('UTF-8')(sys.stdin.buffer)\n\n\ndef process(options):\n    \"\"\"\n    Do the processing\n    \"\"\"\n\n    # are you training a forward or backward LM?\n    is_forward_lm =  not options.is_backward_lm\n\n    # load the default character dictionary\n    dictionary: Dictionary = Dictionary.load('chars')\n\n    # get your corpus, process forward and at the character level\n    corpus = TextCorpus(options.corpus_dir,\n                        dictionary,\n                        is_forward_lm,\n                        character_level=True)\n\n    # instantiate your language model, set hidden size and number of layers\n    language_model = LanguageModel(dictionary,\n                                   is_forward_lm,\n                                   hidden_size=2048,\n                                   nlayers=1,\n                                   embedding_size=100, # recommendations?\n                                   dropout=0) # dropout probs?\n\n    # train your language model\n    trainer = LanguageModelTrainer(language_model, corpus)\n\n    trainer.train(options.model_dir, # embeddings_in_memory=False: effect on 'RuntimeError: CUDA out of memory'?\n                  sequence_length=250,\n                  learning_rate=20,\n                  mini_batch_size=100,\n                  anneal_factor=0.25,\n                  patience=22, # 'patience' value of the learning rate scheduler: 1/2 training splits\n                  clip=0.25, # clipping gradients?\n                  max_epochs=75)\n\ndef main():\n    \"\"\"\n    Invoke this module as a script\n    \"\"\"\n    parser = argparse.ArgumentParser(\n        usage = '%(prog)s [OPTIONS] [ARGS...]',\n        description='Calculate something',\n        epilog='Contact simon.clematide@uzh.ch'\n        )\n    parser.add_argument('--version', action='version', version='0.99')\n    parser.add_argument('-l', '--logfile', dest='logfile',\n                      help='write log to FILE', metavar='FILE')\n    parser.add_argument('-q', '--quiet',\n                      action='store_true', dest='quiet', default=False,\n                      help='do not print status messages to stderr')\n    parser.add_argument('-d', '--debug',\n                      action='store_true', dest='debug', default=False,\n                      help='print debug information')\n    parser.add_argument('-c', '--corpus_dir',\n                      action='store', dest='corpus_dir', default='corpus',\n                      help='directory with corpus data %(default)')\n    parser.add_argument('-m', '--model_dir',\n                      action='store', dest='model_dir', default='model',\n                      help='directory with model data %(default)')\n    parser.add_argument('-B', '--is_backward_lm',\n                      action='store_true', dest='is_backward_lm', default=False,\n                      help='build backward model')\n    parser.add_argument('args', nargs='*')\n    options = parser.parse_args()\n    if options.logfile:\n        logging.basicConfig(filename=logfile)\n    if options.debug:\n        logging.basicConfig(level=logging.DEBUG)\n\n    process(options)\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"stbirc/quaero_ner","sub_path":"lib/train_cm.py","file_name":"train_cm.py","file_ext":"py","file_size_in_byte":3544,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"40614614877","text":"from __future__ import print_function\n\nimport os\nimport sys\nimport numpy as np\nimport torch\nimport random\nfrom torch.autograd import Variable\nfrom torch.nn.parameter import Parameter\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.optim as optim\nfrom tqdm import tqdm\nimport networkx as nx\n\nfrom cmd_args import cmd_args\nfrom graph_embedding import EmbedMeanField, EmbedLoopyBP\n\nsys.path.append('%s/../../pytorch_structure2vec/s2v_lib' % os.path.dirname(os.path.realpath(__file__)))\nfrom pytorch_util import weights_init\n\nclass MLPRegression(nn.Module):\n    def __init__(self, input_size, hidden_size):\n        super(MLPRegression, self).__init__()\n\n        self.h1_weights = nn.Linear(input_size, hidden_size)\n        self.h2_weights = nn.Linear(hidden_size, 1)\n\n        weights_init(self)\n\n    def forward(self, x, y = None):\n        h1 = self.h1_weights(x)\n        h1 = F.relu(h1)\n\n        pred = self.h2_weights(h1)\n        \n        if y is not None:\n            y = Variable(y)\n            mse = F.mse_loss(pred, y)\n            mae = F.l1_loss(pred, y)\n            return pred, mae, mse\n        else:\n            return pred\n\nclass MLPClassifier(nn.Module):\n    def __init__(self, input_size, hidden_size, num_class):\n        super(MLPClassifier, self).__init__()\n        self.hidden_size = hidden_size\n        if hidden_size > 0:\n            self.h1_weights = nn.Linear(input_size, hidden_size)\n            self.last_weights = nn.Linear(hidden_size, num_class)\n        else:\n            self.last_weights = nn.Linear(input_size, num_class)\n\n        weights_init(self)\n\n    def forward(self, x, y = None):\n        if self.hidden_size:\n            x = self.h1_weights(x)\n            x = F.relu(x)\n        \n        logits = self.last_weights(x)\n        logits = F.log_softmax(logits, dim=1)        \n\n        if y is not None:\n            y = Variable(y)\n            loss = F.nll_loss(logits, y)\n\n            pred = logits.data.max(1, keepdim=True)[1]\n            acc = pred.eq(y.data.view_as(pred)).cpu()\n            return logits, loss, acc\n        else:\n            return logits\n\nclass GraphClassifier(nn.Module):\n    def __init__(self, label_map, **kwargs):\n        super(GraphClassifier, self).__init__()\n        self.label_map = label_map\n        if kwargs['gm'] == 'mean_field':\n            model = EmbedMeanField\n        elif kwargs['gm'] == 'loopy_bp':\n            model = EmbedLoopyBP\n        else:\n            print('unknown gm %s' % kwargs['gm'])\n            sys.exit()\n        if 'feat_dim' in kwargs:\n            self.feat_dim = kwargs['feat_dim']\n        else:\n            self.feat_dim = 0\n        self.s2v = model(latent_dim=kwargs['latent_dim'], \n                        output_dim=kwargs['out_dim'],\n                        num_node_feats=kwargs['feat_dim'], \n                        num_edge_feats=0,\n                        max_lv=kwargs['max_lv'])\n        out_dim = kwargs['out_dim']\n        if out_dim == 0:\n            out_dim = kwargs['latent_dim']\n        self.mlp = MLPClassifier(input_size=out_dim, hidden_size=kwargs['hidden'], num_class=len(label_map))\n\n        self.projection_head = nn.Sequential(nn.Linear(kwargs['latent_dim'], kwargs['latent_dim']), nn.ReLU(inplace=True), nn.Linear(kwargs['latent_dim'], kwargs['latent_dim']))\n\n\n    def PrepareFeatureLabel(self, batch_graph):\n        labels = torch.LongTensor(len(batch_graph))\n        n_nodes = 0\n        concat_feat = []\n        for i in range(len(batch_graph)):\n            labels[i] = self.label_map[batch_graph[i].label]\n            n_nodes += batch_graph[i].num_nodes\n            if batch_graph[i].node_tags is not None:\n                concat_feat += batch_graph[i].node_tags\n        if len(concat_feat):\n            node_feat = torch.zeros(n_nodes, self.feat_dim)\n            concat_feat = torch.LongTensor(concat_feat).view(-1, 1)\n            node_feat.scatter_(1, concat_feat, 1)\n        else:\n            node_feat = torch.ones(n_nodes, 1)\n        if cmd_args.ctx == 'gpu':\n            node_feat = node_feat.cuda()\n        return node_feat, None, labels\n\n    def forward(self, batch_graph): \n        node_feat, edge_feat, labels = self.PrepareFeatureLabel(batch_graph)\n        if cmd_args.ctx == 'gpu':\n            node_feat = node_feat.cuda()\n            labels = labels.cuda()\n            \n        _, embed = self.s2v(batch_graph, node_feat, edge_feat, pool_global=True)\n\n        return self.mlp(embed, labels)\n\n\n    def forward_cl(self, batch_graph): \n        node_feat, edge_feat, labels = self.PrepareFeatureLabel(batch_graph)\n        if cmd_args.ctx == 'gpu':\n            node_feat = node_feat.cuda()\n            labels = labels.cuda()\n            \n        _, embed = self.s2v(batch_graph, node_feat, edge_feat, pool_global=True)\n        embed = self.projection_head(embed)\n\n        return embed\n\n    def loss_cl(self, x1, x2):\n        T = 0.5\n        batch_size, _ = x1.size()\n        x1_abs = x1.norm(dim=1)\n        x2_abs = x2.norm(dim=1)\n\n        sim_matrix = torch.matmul(x1, x2.t()) / torch.matmul(x1_abs.view(-1, 1), x2_abs.view(1, -1))\n        sim_matrix = torch.exp(sim_matrix / T)\n        pos_sim = sim_matrix[range(batch_size), range(batch_size)]\n        loss = pos_sim / (sim_matrix.sum(dim=1) - pos_sim)\n        loss = - torch.log(loss).mean()\n        return loss\n\n\n\n\n\n\n\n\n\n\n\n","repo_name":"Shen-Lab/GraphCL","sub_path":"adversarialRobustness_Component/code/common/dnn.py","file_name":"dnn.py","file_ext":"py","file_size_in_byte":5313,"program_lang":"python","lang":"en","doc_type":"code","stars":484,"dataset":"github-code","pt":"81"}
+{"seq_id":"69808105867","text":"import asyncio\nimport logging\n\nfrom .base import ConnectionClose, UDPBaseConnection\nfrom .packet import Auth, AuthOk, Close, Header, Ping, Pong, SESSION_ID\n\n\nasync def connect_client(session_id, host, port, loop):\n    transport, protocol = await loop.create_datagram_endpoint(\n        lambda: UDPClientProtocol(session_id, loop),\n        remote_addr=(host, port))\n    return transport, protocol\n\n\nclass UDPClientProtocol(UDPBaseConnection, asyncio.DatagramProtocol):\n\n    log = logging.getLogger(__name__)\n\n    def __init__(self, session_id, loop):\n        self.session_id = session_id\n        self.loop = loop\n        self.transport = None\n        self._status = None\n        self._background_task = None\n        self._channels = {}\n        # Clock sync veriables\n\n    @property\n    def status(self):\n        return self._status\n\n    @property\n    def channels(self):\n        return self._channels\n\n    def __repr__(self):\n        return \"UDPClientProtocol<{!r}, status={!r}>\".format(\n            self.session_id, self._status)\n\n    async def auth(self):\n        \"\"\" Perform authorization sequence, send/receive pings until connection\n            timeouts. Basically everything on channel 0x00. Something like:\n                -> AUTH    status: auth-sent\n                -> AUTH    status: auth-sent\n                <- AUTH_OK status: established\n                -> Ping    status: established\n                -> Ping    status: established\n                <- Pong    status: established\n                <- Close   status: closed\n        \"\"\"\n        channel = self.open_channel(0)\n        # TODO: make retries configurable\n        for _ in range(10):\n            sent = self.loop.time()\n            channel.send_packet(Auth())\n            self._status = \"auth-sent\"\n            try:\n                # TODO: make timeout configurable\n                auth_ok = await channel.wait_packet(AuthOk, timeout=0.05)\n            except asyncio.TimeoutError:\n                continue\n            # It will be calibrated further later, so we ignore the posibility\n            # that this auth_ok is not a response to prev auth.\n            self._calibrate(sent, self.loop.time(), auth_ok.timestamp)\n            break\n        else:\n            return self.close(\"Could not connect to server\")\n        self._status = \"established\"\n\n        # Ping server until timeout\n        # TODO: make timeout configurable\n        ping_interval = 0.5\n        disconnect_timeout = 0\n        last_pong = self.loop.time()\n        seq = 0\n        while True:\n            last_ping = self.loop.time()\n            seq = (seq + 1) % 256\n            channel.send_packet(Ping(seq))\n            # TODO: make timeout configurable\n            try:\n                pong = await channel.wait_packet(Pong, timeout=ping_interval)\n            except asyncio.TimeoutError:\n                if (self.loop.time() - last_pong) > disconnect_timeout:\n                    self.close(\"Connection timeout\")\n                    return\n            else:\n                last_pong = self.loop.time()\n                self._calibrate(last_ping, last_pong, pong.timestamp)\n            sleep_for = ping_interval - (self.loop.time() - last_ping)\n            if sleep_for > 0:\n                await asyncio.sleep(sleep_for, loop=self.loop)\n\n    def close(self, msg=\"\"):\n        # Last attempt to notify server about connection close\n        self.send_packet(0, Close())\n        if self._background_task is not None and \\\n                not self._background_task.done():\n            self._background_task.cancel()\n            self._background_task = None\n        super().close(msg)\n        self._status = \"closed\"\n        self.protocol = None\n        self.transport = None\n\n    def _calibrate(self, t_send, t_recv, server_timestamp):\n        \"\"\" Performs simple clock synchronization. It will remember last\n            N measurements, record average latency and set client-server time\n            delta.\n        \"\"\"\n        pass\n\n    # Protocol interface method implementation\n\n    def connection_made(self, transport):\n        self.transport = transport\n        self._background_task = self.loop.create_task(self.auth())\n\n    def datagram_received(self, data, addr):\n        self.log.debug('Received %r from %s' % (data, addr))\n        # We must have a proper header\n        if len(data) < Header.size:\n            return\n        # Fast check for session to be same\n        [session_id] = SESSION_ID.unpack_from(data)\n        if session_id != self.session_id:\n            self.log.info(\"Received datagram from another session \\n%r\", data)\n            return\n        # Ok, looks valid, lets pass to the proper channel\n        self.dispatch(data)\n\n    def error_received(self, exc):\n        self.log.warn(\"Datagram operation failed, exc=%r\", exc)\n\n    def connection_lost(self, exc):\n        self.log.info(\"Connection lost, exc=%r\", exc)\n\n    # Packet processing methods\n\n    def send_packet(self, channel_id, packet):\n        if self._status == \"closed\":\n            raise ConnectionClose(\"Connection closed\")\n        header = Header(self.session_id, channel_id,\n                        packet.op_code, packet.version, reserved=0)\n        data = bytearray(packet.size + header.size)\n        header.encode(data)\n        packet.encode(data, offset=Header.size)\n        self.transport.sendto(data)\n","repo_name":"tvoinarovskyi/battlegrounds","sub_path":"battlegrounds/server/net/client.py","file_name":"client.py","file_ext":"py","file_size_in_byte":5334,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"29973526238","text":"import json, gzip\n\nimport scrape_amedas\n\nimport boto3\ns3_client = boto3.client('s3')\n\ndef main():\n    time, geojson = scrape_amedas.scrape()\n    print(time)\n\n    upload_geojson(geojson, time)\n    update_amedas_json(time)\n\n    \ndef upload_geojson(geojson, time):\n    file = '/tmp/amedas-' + time + '.geojson.gz'\n    key = time[:8] + '/amedas-' + time + '.geojson.gz'\n\n\n    with gzip.open(file, 'w') as f:\n        f.write(json.dumps(geojson, ensure_ascii=False).encode('utf-8'))\n\n    print('upload: ' + key)\n    s3_client.upload_file(file, 'amedas', key, ExtraArgs={\n        'ContentType': 'application/json; charset=utf-8',\n        'ACL': 'public-read',\n        'ContentEncoding': 'gzip'})\n\n\ndef update_amedas_json(time):\n    key = 'amedas.json'\n    file = '/tmp/' + key\n\n    data = {\n        'time': time\n    }\n\n    with open(file, 'w') as f:\n        json.dump(data, f)\n\n    print('update amedas.json')\n    s3_client.upload_file(file, 'amedas', key, ExtraArgs={\n        'ContentType': 'application/json; charset=utf-8',\n        'ACL': 'public-read' })\n\n\n\n# called by aws lambda\ndef handler(event, context):\n    main()\n\n\nif __name__ == '__main__':\n    main()\n\n\n","repo_name":"weatherbox/amedas","sub_path":"data/functions/scrape-geojson/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1160,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"81"}
+{"seq_id":"19784753121","text":"#!/usr/bin/python3\n#-*-coding:utf-8-*-\n\n\nfrom threading import Thread\nimport pymysql\nfrom Sqlcore import Sqlcore\n\n\n'''\n入口功能\n'''\n\n'''vmode打印显示模式full/standard/simple'''\n\n#本机的photo相册\nconn = {\"hostname\":\"localhost\",\"username\":\"website\",\"password\":\"website123\",\"database\":\"photodb\",\"hostport\":3306}\ncfgs = {\"table\":\"sdb_photo\", \"column\":\"corver,gallery\", \"offset\":0, \"vmode\":\"simple\", \"limit\":5, \"sleep\": 0}\n\n\n\n\n'''开始执行核心工作'''\ndef runcore(uniq, conn, cfgs):\n    sc = Sqlcore(uniq, conn, cfgs)\n    sc.run()\n\n'''结果集总数'''\ndef totalcount(conn, table):\n    db = pymysql.connect(conn['hostname'], conn['username'], conn['password'], conn['database'], conn['hostport'])\n    cursor = db.cursor()\n    cursor.execute(\"SELECT count(*) FROM `{table}`\".format(table=table))\n    #cursor.fetchone()\n    data = cursor.fetchone()\n    #print(data[0])\n    return int(data[0])#type(data[0])\n\n    \n\nif __name__ == \"__main__\":\n    print(\"welcome\")\n    \n    total = totalcount(conn, cfgs[\"table\"])\n    print(\"发现数据库表\"+cfgs[\"table\"]+\"存在总数：\"+str(total)+\"条记录\")\n    \n    while True:\n        tnum = input(\"开启线程数，最少1个，最多10个，exit退出：\")\n        if tnum == \"exit\":\n            exit()\n        if tnum.isdigit():\n            tnum = int(tnum)\n            if tnum <1:\n                raise ValueError(\"输入的必须是正整数啊\")\n            elif tnum > 50:\n                print(\"线程数不能够超过50啊~\")\n            elif total < tnum:\n                print(\"结果集还没有线程多~\")\n            else:\n                break\n        else:\n            print(\"输入的必须是1以上的数字啊~\")\n\n    if tnum == 1:\n        #单线程\n        runcore(\"ONETHREAD\", conn, cfgs)\n    else:\n        #多线程\n        one = total//tnum #每个线程的开始游标\n        tlist = []\n        for i in range(tnum):\n            cfg = cfgs.copy()\n            cfg[\"offset\"] = one * i\n            #print(cfgs)\n            t = Thread(target=runcore, args=(\"#t\"+str(i), conn, cfg))\n            tlist.append(t)\n            t.start()\n            #print(\"\\n\" + t.getName())#获取线程名\n            \n        for i in tlist:\n            i.join()#阻塞主线程，当前执行完再执行下一步\n\n    \n    print(\"allDownloaded\")\n","repo_name":"backtent/spider","sub_path":"multiple.py","file_name":"multiple.py","file_ext":"py","file_size_in_byte":2314,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"81"}
+{"seq_id":"7367328445","text":"from typing import Tuple\n\nfrom flask import Flask, jsonify, request, Response\nimport mockdb.mockdb_interface as db\n\napp = Flask(__name__)\n\n\ndef create_response(\n    data: dict = None, status: int = 200, message: str = \"\"\n) -> Tuple[Response, int]:\n    \"\"\"Wraps response in a consistent format throughout the API.\n    \n    Format inspired by https://medium.com/@shazow/how-i-design-json-api-responses-71900f00f2db\n    Modifications included:\n    - make success a boolean since there's only 2 values\n    - make message a single string since we will only use one message per response\n    IMPORTANT: data must be a dictionary where:\n    - the key is the name of the type of data\n    - the value is the data itself\n\n    :param data <str> optional data\n    :param status <int> optional status code, defaults to 200\n    :param message <str> optional message\n    :returns tuple of Flask Response and int, which is what flask expects for a response\n    \"\"\"\n    if type(data) is not dict and data is not None:\n        raise TypeError(\"Data should be a dictionary 😞\")\n\n    response = {\n        \"code\": status,\n        \"success\": 200 <= status < 300,\n        \"message\": message,\n        \"result\": data,\n    }\n    return jsonify(response), status\n\n\n\"\"\"\n~~~~~~~~~~~~ API ~~~~~~~~~~~~\n\"\"\"\n\n\n@app.route(\"/\")\ndef hello_world():\n    return create_response({\"content\": \"hello world!\"})\n\n\n@app.route(\"/mirror/<name>\")\ndef mirror(name):\n    data = {\"name\": name}\n    return create_response(data)\n\n\n@app.route(\"/shows/<id>\", methods=['DELETE'])\ndef delete_show(id):\n    if db.getById('shows', int(id)) is None:\n        return create_response(status=404, message=\"No show with this id exists\")\n    db.deleteById('shows', int(id))\n    return create_response(message=\"Show deleted\")\n\n\n# TODO: Implement the rest of the API here!\n\n# Does the order matter?\n# Learned: <x> allows us to pass in a as an argument\n@app.route(\"/shows/<id>\", methods=['GET'])\ndef get_a_show(id):\n    if db.getById('shows', int(id)) is None:\n        return create_response(status=404, message=\"Sorry show with this ID Does not exist, yet.\")\n    else:\n        return create_response(db.getById('shows', int(id)))\n\n@app.route(\"/shows\", methods=['POST'])\ndef post_a_show():\n    # Error check\n    if not \"name\" in request.json:\n        return create_response(status=422, message=\"name not provided in POST request body\")\n    elif not \"episodes_seen\" in request.json:\n        return create_response(status=422, message=\"episodes_seen not provided in POST request body\")\n\n    request.json[\"episodes_seen\"] = int(request.json[\"episodes_seen\"])\n    \n    db_response = db.create(\"shows\", request.json)\n    return create_response(db_response, status=201, message=\"Successfully inputted show\")\n\n\n# I read partial updates should use PATCH\n@app.route(\"/shows/<id>\", methods=['PUT'])\ndef update_a_show(id):\n    # If id cannot be found\n    if db.getById('shows', int(id)) is None:\n        return create_response(status=404, message=\"show with provided id not found\")\n    if not \"name\" in request.json:\n        return create_response(status=422, message=\"name not provided in request body\")\n    elif not \"episodes_seen\" in request.json:\n        return create_response(status=422, message=\"episodes_seen not provided in request body\")\n\n    # Create a new json object with two values only\n    temp_json = {}\n    temp_json[\"name\"] = request.json[\"name\"]\n    temp_json[\"episodes_seen\"] = int(request.json[\"episodes_seen\"])\n    \n    db_response = db.updateById(\"shows\", int(id), temp_json)\n    return create_response(db_response, status=201, message=\"Successfully updated show\")\n   \n@app.route(\"/shows\", methods=['GET'])\ndef hello():\n    if request.args.get('minEpisodes') is not None:\n        return get_shows_with_minEpisodes()\n    else:\n        return get_all_shows()\n\ndef get_all_shows():\n    return create_response({\"shows\": db.get('shows')})\n\ndef get_shows_with_minEpisodes():\n    min_episodes = int(request.args.get('minEpisodes'))\n    all_shows = db.get(\"shows\")\n    temp_json = {}\n    temp_json[\"shows\"] = []\n\n    for show in all_shows:\n        if show[\"episodes_seen\"] >= min_episodes:\n            temp_json[\"shows\"].append(show)\n    return create_response(temp_json, status=201, message=\"Successfully retrieved shows\")\n\n\"\"\"\n~~~~~~~~~~~~ END API ~~~~~~~~~~~~\n\"\"\"\nif __name__ == \"__main__\":\n    app.run(port=8080, debug=True)\n","repo_name":"MAGNUSGAO/takehome-assignment-f21","sub_path":"backend/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":4366,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"81"}
+{"seq_id":"13065875554","text":"# -*- coding: utf-8 -*-\n\"\"\"\nModule containing logic for double cross hairs on plots\n\n\"\"\"\n\n__author__ = 'Samir Adrik'\n__email__ = 'samir.adrik@gmail.com'\n\nfrom numpy import percentile, ndarray, array, where, insert\n\nfrom pyqtgraph import TextItem, InfiniteLine, mkPen, PlotWidget, BarGraphItem\nfrom PyQt5.QtCore import Qt, QObject\n\nfrom source.domain import Amount\nfrom source.util import Assertor\n\n\nclass DoubleCrossHair(QObject):\n    \"\"\"\n    Double cross hair implementation\n\n    \"\"\"\n\n    def __init__(self, x_1: ndarray, y_1: ndarray, x_2: ndarray, y_2: ndarray,\n                 plot_widget_1: PlotWidget, plot_widget_2: PlotWidget, labels: tuple, units=None,\n                 precision=0, width=1, x_labels=None, display=int, highlight_bars=True):\n        \"\"\"\n        Constructor / Instantiation of class\n\n        Parameters\n        ----------\n        x_1               : np.ndarray\n                            x-values\n        y_1               : np.ndarray\n                            y-values\n        x_2               : np.ndarray\n                            x-values\n        y_2               : np.ndarray\n                            y-values\n        plot_widget_1     : PlotWidget\n                            graphics view to place chart\n        plot_widget_2     : PlotWidget\n                            graphics view to place chart\n        labels            : tuple\n                            labels for legend\n        precision         : int\n                            precision for rounding, default is zero\n        width             : int, float\n                            width of any bars\n        x_labels          : array-like\n                            array of dates, i.e. time-period or other labels\n        display           : type\n                            display dtype for labels\n        highlight_bars    : bool\n                            whether to highlight bars in plot\n\n        \"\"\"\n        super().__init__(parent=None)\n        Assertor.assert_data_types(\n            [x_1, y_1, x_2, y_2, plot_widget_1, plot_widget_2, labels, precision, width],\n            [ndarray, ndarray, ndarray, ndarray, PlotWidget,\n             PlotWidget, tuple, int, (int, float)])\n        self.x_1, self.y_1 = x_1, y_1\n        self.x_2, self.y_2 = x_2, y_2\n\n        self.plot_widget_1 = plot_widget_1\n        self.plot_widget_2 = plot_widget_2\n        self.precision = precision\n        self.width = width\n        self.x_time = x_labels\n        self.display = display\n\n        self.bar_item_1 = None\n        self.bar_item_2 = None\n        self.highlight_bars = highlight_bars\n\n        pen = mkPen(color=\"#4c96d7\", style=Qt.SolidLine, width=1)\n        self.vertical_line_1 = InfiniteLine(angle=90, movable=False, pen=pen)\n        self.vertical_line_2 = InfiniteLine(angle=90, movable=False, pen=pen)\n        self.label_1 = TextItem()\n        self.label_2 = TextItem()\n        self.labels = labels\n        self.units = units if units else (\"\" for _ in range(10))\n\n        if self.highlight_bars:\n            self.plot_widget_1.addItem(self.vertical_line_1)\n            self.plot_widget_2.addItem(self.vertical_line_2)\n\n        self.view_box_1 = self.plot_widget_1.getViewBox()\n        self.view_box_2 = self.plot_widget_2.getViewBox()\n        self.configure_cross_hair()\n\n    def configure_cross_hair(self):\n        \"\"\"\n        method for configuring cross hair\n\n        \"\"\"\n        place = percentile(insert(array(self.x_1), 0, 0), 2)\n\n        self.label_1.setPos(place, int(abs(max(self.y_1, key=abs)) * 1.5))\n        self.label_2.setPos(place, int(abs(max(self.y_2, key=abs)) * 1.5))\n        self.plot_widget_1.addItem(self.label_1)\n        self.plot_widget_2.addItem(self.label_2)\n\n    def move_vertical_lines(self, pos):\n        \"\"\"\n        method for moving the vertical lines on the plot\n\n        \"\"\"\n        mouse_point_1 = self.view_box_1.mapSceneToView(pos)\n        mouse_point_2 = self.view_box_2.mapSceneToView(pos)\n\n        x_val_1 = int(round(mouse_point_1.x(), self.precision))\n        x_val_2 = int(round(mouse_point_2.x(), self.precision))\n        x_idx_1 = where(self.x_1 == x_val_1)\n        x_idx_2 = where(self.x_2 == x_val_2)\n        y_val_1 = self.display(self.y_1[x_idx_1]) if self.y_1[x_idx_1] else 0\n        y_val_2 = self.display(self.y_2[x_idx_2]) if self.y_2[x_idx_2] else 0\n\n        self.vertical_line_1.setPos(x_val_1)\n        self.vertical_line_2.setPos(x_val_1)\n        limits = min(self.x_1) <= x_val_1 <= max(self.x_1)\n\n        if len(self.plot_widget_1.getViewBox().allChildren()) > 3 and limits \\\n                and self.highlight_bars:\n            self.highlight_bar_items(x_val_1, y_val_1, x_val_2, y_val_2)\n\n        return x_val_1, y_val_1, x_val_2, y_val_2, limits\n\n    def highlight_bar_items(self, x_val_1, y_val_1, x_val_2, y_val_2):\n        \"\"\"\n        method for highlighting bar items\n\n        \"\"\"\n        self.plot_widget_1.removeItem(self.bar_item_1)\n        self.plot_widget_2.removeItem(self.bar_item_2)\n        self.bar_item_1 = BarGraphItem(x=[x_val_1], height=y_val_1, width=self.width,\n                                       brush=\"#69a8de\")\n        self.bar_item_2 = BarGraphItem(x=[x_val_2], height=y_val_2, width=self.width,\n                                       brush=\"#69a8de\")\n        self.plot_widget_1.addItem(self.bar_item_1)\n        self.plot_widget_2.addItem(self.bar_item_2)\n\n    def mouse_moved(self, evt):\n        \"\"\"\n        method for moving the vertical lines based on mouse placement\n\n        \"\"\"\n        pos = evt[0]\n        if self.plot_widget_1.sceneBoundingRect().contains(pos) or \\\n                self.plot_widget_2.sceneBoundingRect().contains(pos):\n            x_val_1, y_val_1, x_val_2, y_val_2, limits = self.move_vertical_lines(pos)\n\n            x_label_idx = where(array(self.x_1) == x_val_1)[0]\n            x_label_1 = self.x_time[x_label_idx.item()] + self.units[0] if \\\n                self.x_time and x_label_idx.size != 0 else \\\n                Amount(str(x_val_1)).amount + self.units[0]\n            y_label_1 = Amount(str(y_val_1)).amount + self.units[1]\n\n            x_label_2 = self.x_time[x_label_idx.item()] + self.units[0] if \\\n                self.x_time and x_label_idx.size != 0 else \\\n                Amount(str(x_val_2)).amount + self.units[2]\n            y_label_2 = Amount(str(y_val_2)).amount + self.units[3]\n\n            if limits:\n                self.label_1.setHtml('<div style=\"text-align: center\">'\n                                     '<span style=\"font-size: 10pt\">{}</span><br>'\n                                     '<span style=\"font-size: 10pt\">{}</span><br>'\n                                     '<span style=\"font-size: 10pt\">({})</span>'\n                                     '</div>'.format(self.labels[0], x_label_1, y_label_1))\n                self.label_2.setHtml('<div style=\"text-align: center\">'\n                                     '<span style=\"font-size: 10pt\">{}</span><br>'\n                                     '<span style=\"font-size: 10pt\">{}</span><br>'\n                                     '<span style=\"font-size: 10pt\">({})</span>'\n                                     '</div>'.format(self.labels[1], x_label_2, y_label_2))\n","repo_name":"seemir/stressa","sub_path":"source/ui/graphics/double_cross_hair.py","file_name":"double_cross_hair.py","file_ext":"py","file_size_in_byte":7174,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"35111566031","text":"# -*- coding: utf-8 -*-\n# Author ：CYCSICS\nimport time\nimport tkinter\nimport os\nfrom tkinter import *\nfrom tkinter.filedialog import askdirectory\nfrom tkinter import messagebox\nfrom tkinter import ttk\nfrom tkinter import scrolledtext\nfrom tkinter import END\nfrom bs4 import BeautifulSoup\nimport datetime\nimport threading\nfrom selenium import webdriver\nfrom selenium.webdriver.common.keys import Keys\nfrom selenium.webdriver.support import expected_conditions as EC\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.support.ui import WebDriverWait\nfrom selenium.webdriver.support.select import Select\nfrom selenium.webdriver.common.action_chains import ActionChains\nfrom webdriver_manager.chrome import ChromeDriverManager\n\n\noptions = webdriver.ChromeOptions()\noptions.add_argument('--ignore-certificate-errors')\n# options.add_argument(\"--user-data-dir=\"+r\"C:\\\\Users\\\\Administrator\\\\AppData\\\\Local\\\\Google\\\\Chrome\\\\User Data\")\ndr = webdriver.Chrome(chrome_options=options)\n#ChromeDriverManager().install() 在windows环境下自动安装chromedriver\n# '/usr/lib/chromium-browser/chromedriver'   树莓派环境下chromium位置\n# dr = webdriver.Chrome('/usr/lib/chromium-browser/chromedriver')\n# dr = webdriver.Chrome(ChromeDriverManager().install())\n# dr.implicitly_wait(30)\nhandles = dr.window_handles\nroot = Tk()\nroot.geometry('150x450')\nroot.title(\"'楽天'处理器\")\nlistbox_input = tkinter.scrolledtext.ScrolledText(root,width = 100)\nlistbox_output = tkinter.scrolledtext.ScrolledText(root,width = 200)\nmessage_url = ''\npath = StringVar()\nlt_info_url = \"https://order-rp.rms.rakuten.co.jp/order-rb/individual-order-detail-sc/init?orderNumber=\"\nlt_list_url = 'https://order-rp.rms.rakuten.co.jp/order-rb/order-list-sc/init?&SEARCH_MODE=1&ORDER_PROGRESS=100'\nlt_list_check_url = \"https://order-rp.rms.rakuten.co.jp/order-rb/order-list-sc/\"\namazon_url = \"https://www.amazon.co.jp/gp/css/order-history?ref_=nav_orders_first\"\namazon_check_url = 'https://www.amazon.co.jp'\nrecords_names = []\nlt_account1 = \"bubu2016aA1P\"\npwd1 = \"benetech0927\"\nlt_account2 = \"957320820@qq.com\"\npwd2 = \"RoseMerry818\"\namazon_account = \"sato@bjgbf.com\"\namazon_pwd = \"91sf7ac2\"\nwait_time = 2\ncredit_card_number = \"4649882195642418\"\norder_infomation_id = ''\naddress_3line=[]\n\n##################################################################################################\n#\n#句柄以及窗口切换流程：\n#1、获取所有句柄（handles = dr.window_handles）\n#2、循环切换对应句柄的窗口（dr.switch_to.window(handle)）\n#3、获取当前窗口的url（dr.current_url）\n#4、判断当前url是哪个网址并储存进对应的变量（考虑直接每次匹配对应url进行切换，不保存变量）\n#5、切换对应变量储存的窗口，例（dr.switch_to.window(lt_list_handle)）\n#\n##################################################################################################\n\n\n#自动化操作\ndef autoctl():\n\tlt_login()\n\tAmazon_login()\n\t\n#操控浏览器\ndef lt_login():\n\t# set little time stop and big time stop for viewing changes\n\tdr.get(\"https://glogin.rms.rakuten.co.jp/?sp_id=1\")\n\tWebDriverWait(dr,20).until(EC.element_to_be_clickable((By.ID,\"rlogin-username-ja\")))\n\t#第一个登陆流程\n\tdr.find_element_by_id(\"rlogin-username-ja\").clear()\n\tdr.find_element_by_id(\"rlogin-password-ja\").clear()\n\tdr.find_element_by_id(\"rlogin-username-ja\").clear()\n\tdr.find_element_by_id(\"rlogin-username-ja\").send_keys(lt_account1)\n\tdr.find_element_by_id(\"rlogin-password-ja\").send_keys(pwd1)\n\ttime.sleep(3)\n\tdr.find_element_by_xpath(\"//button[@name = 'submit']\").click()\n\t# above = dr.find_element_by_xpath(\"/html/body/div[2]/main/div/section[1]/form/p[4]/button\")\n\t# ActionChains(dr).move_to_element(above).click()\n\t#  \n\tWebDriverWait(dr,15).until(EC.element_to_be_clickable((By.NAME,\"submit\")))\n#def second_login():\n\t#第二个登陆流程\n\tdr.find_element_by_id(\"rlogin-username-2-ja\").clear()\n\tdr.find_element_by_id(\"rlogin-password-2-ja\").clear()\n\tdr.find_element_by_id(\"rlogin-username-2-ja\").clear()\n\tdr.find_element_by_id(\"rlogin-username-2-ja\").send_keys(lt_account2)\n\tdr.find_element_by_id(\"rlogin-password-2-ja\").send_keys(pwd2)\n\tdr.find_element_by_xpath(\"//button[@name = 'submit']\").click()\n\t#  \n\tWebDriverWait(dr,15).until(EC.presence_of_element_located((By.NAME,\"submit\")))\n\tdr.find_element_by_xpath(\"//button[@name = 'submit']\").click()\n\t#  \n\tWebDriverWait(dr,15).until(EC.presence_of_element_located((By.XPATH,\"//*[@id='confirm']/p/button\")))\n\tdr.find_element_by_xpath(\"//button[@type = 'submit']\").click()\n\t# WebDriverWait(dr,15).until(EC.presence_of_element_located((By.XPATH,\"//*[@id='confirm']/p/button\")))\n\t# dr.find_element_by_xpath(\"//button[@type = 'submit']\").click()\n\t# WebDriverWait(dr,15).until(EC.presence_of_element_located((By.XPATH,\"//*[@id='confirm']/p/button\")))\n\t# dr.find_element_by_xpath(\"//button[@type = 'submit']\").click()\n\t# \n\t# WebDriverWait(dr,15).until(EC.presence_of_element_located((By.XPATH,\"//*[@id='mm_gg002_more002']\")))\n\t# 点击进入订单界面\n\tdr.get(\"https://order-rp.rms.rakuten.co.jp/order-rb/order-list-sc/init?&SEARCH_MODE=1&ORDER_PROGRESS=100\")\n\t# 点击进入待发送信件订单界面\n\tabove = dr.find_element_by_xpath('//*[@id=\"filter1\"]/ul/li[4]/a/h5/span')\n\tdr.execute_script(\"arguments[0].click();\", above)\n\t# dr.find_element_by_xpath(\"//*[@id='filter1']/ul/li[4]/a\").click()\n\t# dr.find_element_by_xpath(\"//*[@id='filter1']/ul/li[4]/a\").send_keys(Keys.ENTER)\n\ttime.sleep(3)\n\n#管理发送邮件\ndef send_message():\n\ttry:\n\t\t#句柄判断\n\t\tamazon_handle = ''\n\t\tlt_list_handle = ''\n\t\tlt_info_handle = ''\n\t\thandles = dr.window_handles\n\t\tfor handle in handles:\n\t\t\tdr.switch_to.window(handle)\n\t\t\tcurl = dr.current_url\n\t\t\t#判断当前url是否为亚马逊url\n\t\t\tif curl.find(amazon_check_url) != -1:\n\t\t\t\tamazon_handle = handle\n\t\t\t#判断当前url是否为乐天订单列表url\n\t\t\telif curl.find(lt_list_check_url) != -1:\n\t\t\t\tlt_list_handle = handle\n\t\t\t#判断当前url是否为乐天订单详情url\n\t\t\telif curl.find(lt_info_url) != -1:\n\t\t\t\tlt_info_handle = handle\n\t\t#切换到乐天订单列表详情\n\t\tdr.switch_to.window(lt_list_handle)\n\t\t#利用重新登录网址达成刷新目的(直接刷新窗口会有刷不出来新单的情况)\n\t\tdr.get(lt_list_url)\n\t\t#切换到待发送新建订单的窗口\n\t\tprint(\"点击待发送\")\n\t\tWebDriverWait(dr,10,0.5,ignored_exceptions=None).until(\n\t\t\tEC.element_to_be_clickable((By.XPATH,'//*[@id=\"filter1\"]/ul/li[3]/a/h5/span'))\n\t\t)\n\t\ttime.sleep(5)\n\t\tabove = dr.find_element_by_xpath('//*[@id=\"filter1\"]/ul/li[4]/a/h5/span')\n\t\tdr.execute_script(\"arguments[0].click();\", above)\n\t\t# above = \"$('div[id=filter1] ul li[3] h5 span').click()\"\n\t\t# dr.execute_script(above)\n\t\t# above = dr.find_element_by_xpath('//*[@id=\"filter1\"]/ul/li[3]/a/h5/span')\n\t\t# dr.find_element_by_xpath(\"//*[@id='filter1']/ul/li[4]/a\").click()\n\t\t# ActionChains(dr).click(above).perform()\n\t\t#解码网页\n\t\tsource = dr.page_source.encode('GBK', 'ignore')\n\t\tstrsource = source.decode('GBK','strict')\n\t\tsoup = BeautifulSoup(strsource,\"html.parser\")\n\t\t#定位有页码的列表\n\t\tul = soup.find('ul',class_ = 'pagination')\n\t\tlistbox_output.insert(END,'开始发信\\n')\n\t\t#执行发送欢迎信件的函数\n\t\tprint(\"开始发送欢迎信件\")\n\t\tsend_welcome_message()\n\t\t#执行发送发货信件的函数\n\t\tprint(\"开始发送发货信件\")\n\t\tsend_deliver_message()\n\t\t#发信完毕切换到订单列表的窗口\n\t\tdr.switch_to.window(lt_list_handle)\n\t\t#利用重新登录网址达成刷新目的(直接刷新窗口会有刷不出来新单的情况)\n\t\tdr.get(lt_list_url)\n\t\t#点击到待发送信件的订单窗口\n\t\tWebDriverWait(dr,10,0.5,ignored_exceptions=None).until(\n\t\t\tEC.element_to_be_clickable((By.XPATH,'//*[@id=\"filter1\"]/ul/li[4]/a/h5/span'))\n\t\t)\n\t\ttime.sleep(2)\n\t\t# above = \"$('div[id=filter1] ul li[4] h5 span').click()\"\n\t\t# dr.execute_script(above)\n\t\tabove = dr.find_element_by_xpath('//*[@id=\"filter1\"]/ul/li[4]/a/h5/span')\n\t\tdr.execute_script(\"arguments[0].click();\", above)\n\t\t# dr.find_element_by_xpath(\"//*[@id='filter1']/ul/li[4]/a\").click()\n\t\t# dr.find_element_by_xpath(\"//*[@id='filter1']/ul/li[4]/a\").send_keys(Keys.ENTER)\n\t\t# dr.find_element_by_xpath('//*[@id=\"filter1\"]/ul/li[4]/a/h5/span').click()\n\t\t# dr.find_element_by_xpath(\"//li[@data-order-list-filter-tab='発送待ち']\").click()\n\t\tSelect(dr.find_element_by_xpath('//*[@id=\"ddlDisplay\"]')).select_by_value(\"500\")\n\t\t# ActionChains(dr).click(above).perform()\n\texcept BaseException as e:\n\t\tprint(e)\n\t\tif str(e).find(\"no such window: window was already closed\"):\n\t\t\tha = dr.window_handles\n\t\t\tdr.switch_to.window(lt_list_handle)\t\n\n\n#发货信件\n#发货信件操作流程\n##################################################################################################\n#\n#1、没有发送过发货信件\n#判断流程：\n#找到属性为\"('span',attrs={\"data-original-title\": '発送のご案内'}))\"的字段并判断class中是否存在'disabled'字样\n#已发送发货信的class：\"rms-icon icon light rms-icon-square-mail-2-mini \"\n#未发送发货信的class：\"rms-icon icon light rms-icon-square-mail-2-mini disabled\"\n#\n##################################################################################################\n#\n#2、在订单列表界面有\"未入力\"字样\n#判断流程：\n#获取class为\"rms-text-alert rms-text-bold\"的标签并获取文本内容，判断文本内容是否为\"未入力\"\n#\n##################################################################################################\n#\n#整体流程：\n#1、订单列表查找符合条件1（没有发送过发货信件）的订单id\n#2、判断id是否满足条件2（在订单列表界面有\"未入力\"字样），满足则加入list\n#2、查找完毕以后点击进入订单（当前应获取所有句柄，并循环切换句柄对应窗口判断当前url来判断句柄对应的窗口是哪个）\n#3、获取句柄之后切换到（lt_info_handle）对应的窗口\n#4、不符合条件2时切换回（lt_list_handle）对应的窗口并重新进行循环\n#5、符合条件2进入发信url进行发信\n#\n##################################################################################################\n###\t\t\t发货邮件获取条件需要重写，句柄操作有bug\t\t###\ndef send_deliver_message():\n\t#重写\n\tamazon_handle = ''\n\tlt_list_handle = ''\n\tlt_info_handle = ''\n\thandles = dr.window_handles\n\tfor handle in handles:\n\t\tdr.switch_to.window(handle)\n\t\tcurl = dr.current_url\n\t\t#判断当前url是否为乐天订单列表url\n\t\tif curl.find(lt_list_check_url) != -1:\n\t\t\tlt_list_handle = handle\n\tdr.switch_to.window(lt_list_handle)\n\tWebDriverWait(dr,10,0.5,ignored_exceptions=None).until(\n\t\t\tEC.element_to_be_clickable((By.XPATH,'//*[@id=\"filter1\"]/ul/li[4]/a/h5/span'))\n\t\t)\n\ttime.sleep(5)\n\t# above = \"$('div[id=filter1] ul li[4] h5 span').click()\"\n\t# dr.execute_script(above)\n\t# above = dr.find_element_by_xpath('//*[@id=\"filter1\"]/ul/li[4]/a/h5/span')\n\t# dr.find_element_by_xpath('//*[@id=\"filter1\"]/ul/li[4]/a/h5/span').click()\n\t# ActionChains(dr).click(above).perform()\n\tabove = dr.find_element_by_xpath('//*[@id=\"filter1\"]/ul/li[4]/a/h5/span')\n\tdr.execute_script(\"arguments[0].click();\", above)\n\t# dr.find_element_by_xpath(\"//*[@id='filter1']/ul/li[4]/a\").click()\n\t# dr.find_element_by_xpath(\"//*[@id='filter1']/ul/li[4]/a\").send_keys(Keys.ENTER)\n\t# dr.find_element_by_xpath(\"//li[@data-order-list-filter-tab='発送待ち']\").click()\n\ttime.sleep(5)\n\tsource = dr.page_source.encode('GBK', 'ignore')\n\tstrsource = source.decode('GBK','strict')\n\tsoup = BeautifulSoup(strsource,\"html.parser\")\n\ttbodys = soup.find_all('tbody',class_ = 'rms-content-order-list-item rms-list-item rms-item-order-inprogress')\n\ttot = 0\n\tids = []\n\tprint(\"开始检查符合发信条件的订单\")\n\t# print(tbodys)\n\tfor tbody in tbodys:\n\t\tspan1 = str(tbody.find('span',attrs={\"data-original-title\": '発送のご案内'}))\n\t\tspan2 = str(tbody.find('span',attrs={'class','rms-text-alert rms-text-bold'}))\n\t\tprint('span:',span2)\n\t\tloc = -1\n\t\ttry:\n\t\t\tloc = span2.find('未入力')\n\t\t\t# print('未入力：Loc = ',loc)\n\t\texcept:\n\t\t\tloc = -1\n\t\t\tpass\n\t\t\t\n\t\tif span1.find('disabled') != -1:\n\t\t\tprint(\"disabled\")\n\t\t\tif loc == -1:\n\t\t\t\tprint('ID append')\n\t\t\t\tids.append(tbody.find('a',target = 'order_detail').get_text())\n\tt = 1\n\tif len(ids) == 0:\n\t\treturn\n\tfor i in reversed(ids):\n\t\tprint(\"当前正在发送发货邮件，订单号为%s\\n\"%i)\n\t\t# listbox_output.insert(END,'订单号为  %s  发货信件已发送\\n'%i)\n\t\turl1 = 'https://order-rp.rms.rakuten.co.jp/order-rb/individual-order-detail-sc/init?orderNumber='+i\n\t\tif t == 1:\n\t\t\tdr.switch_to.window(lt_list_handle)\n\t\t\ttime.sleep(2)\n\t\t\tabove = dr.find_element_by_link_text(i)\n\t\t\tdr.execute_script(\"arguments[0].click();\", above)\n\t\t\thandles = dr.window_handles\n\t\t\tfor handle in handles:\n\t\t\t\tdr.switch_to.window(handle)\n\t\t\t\tif dr.current_url.find(i) != -1:\n\t\t\t\t\tlt_info_handle = handle\n\t\t\t\t\tt += 1\n\t\t\tdr.switch_to.window(lt_info_handle)\n\t\telse:\n\t\t\tdr.switch_to.window(lt_info_handle)\n\t\t\tdr.get(url1)\n\t\tdr.switch_to.window(lt_info_handle)\n\t\turl2 = 'https://order-rp.rms.rakuten.co.jp/order-rb-mail/individual-mail-send-template-select-sc/init?orderNumber='+i\n\t\tdr.get(url2)\n\t\ttime.sleep(2)\n\t\tabove = dr.find_element_by_xpath('//*[@id=\"templateId3\"]')\n\t\tdr.execute_script(\"arguments[0].click();\", above)\n\t\ttime.sleep(2)\n\t\tabove = dr.find_element_by_xpath(\"//td[@align='center']/input[@class='submitControlButton']\")\n\t\tdr.execute_script(\"arguments[0].click();\", above)\n\t\ttime.sleep(2)\n\t\tabove = dr.find_element_by_xpath(\"//td[@align='center']/input[2][@class='doubleSubmitInherit submitControlButton']\")\n\t\tdr.execute_script(\"arguments[0].click();\", above)\t\t\n# print(3)\n#\t\tdr.switch_to.window(lt_list_handle)\n#\tdr.refresh()\n\n\n#欢迎邮件\n#发送欢迎邮件条件：\n##################################################################################################\n#\n#1.没有发送过欢迎信件\n#判断流程：\n#找到属性为\"('span',attrs={\"data-original-title\": 'サンクスメール'}))\"的字段并判断class中是否存在'disabled'字样\n#已发送欢迎信的class：\"rms-icon icon light rms-icon-square-mail-1-mini \"\n#未发送欢迎信的class：\"rms-icon icon light rms-icon-square-mail-1-mini disabled\"\n#\n##################################################################################################\n#\n#2.已填入担当者\n#判断流程：\n#先进入未发送欢迎信件的订单详情，获取id为\"orderDetailsFormPersonInCharge-1\"的input框的文本内容，并判断是否为空\n#\n##################################################################################################\n#\n#整体流程：\n#1、订单列表查找符合条件1的订单id并加入list\n#2、查找完毕以后点击进入订单（当前应获取所有句柄，并循环切换句柄对应窗口判断当前url来判断句柄对应的窗口是哪个）\n#3、获取句柄之后切换到（lt_info_handle）对应的窗口\n#4、判断是否符合条件2\n#5、不符合条件2时切换回（lt_list_handle）对应的窗口并重新进行循环\n#6、符合条件2进入发信url进行发信\n#\n##################################################################################################\n###\t\t欢迎信件需要重写获取条件过程，句柄操作有bug\t\t###\ndef send_welcome_message():\n\tamazon_handle = ''\n\tlt_list_handle = ''\n\tlt_info_handle = ''\n\thandles = dr.window_handles\n\tfor handle in handles:\n\t\tdr.switch_to.window(handle)\n\t\tcurl = dr.current_url\n\t\t#判断当前url是否为乐天订单列表url\n\t\tif curl.find(lt_list_check_url) != -1:\n\t\t\tlt_list_handle = handle\n\tdr.switch_to.window(lt_list_handle)\n\t\n\t# WebDriverWait(dr,10,0.5,ignored_exceptions=None).until(\n\t# \t\tEC.element_to_be_clickable((By.XPATH,'//*[@id=\"filter1\"]/ul/li[4]'))\n\t# \t)\n\ttime.sleep(2)\n\tabove = dr.find_element_by_xpath('//*[@id=\"filter1\"]/ul/li[4]/a/h5/span')\n\tdr.execute_script(\"arguments[0].click();\", above)\n\t# above = \"$('div[id=filter1] ul li[4] a span').click()\"\n\t# dr.execute_script(above)\n\t# above = dr.find_element_by_xpath('//*[@id=\"filter1\"]/ul/li[4]/a')\n\t# dr.find_element_by_xpath(\"//*[@id='filter1']/ul/li[4]/a\").click()\n\t# ActionChains(dr).click(above).perform()\n\t# time.sleep(2)\n\t# dr.find_element_by_xpath(\"//li[@data-order-list-filter-tab='発送待ち']\").click()\n\ttime.sleep(5)\n\tsource = dr.page_source.encode('GBK', 'ignore')\n\tstrsource = source.decode('GBK','strict')\n\tsoup = BeautifulSoup(strsource,\"html.parser\")\n\tprint(soup)\n\ttbodys = soup.find_all('tbody',class_ = 'rms-content-order-list-item rms-list-item rms-item-order-inprogress')\n\t# tbodys = soup.find_all('tbody',name = 'listBody')\n\ttot = 0\n\tids = []\n\t# print(tbodys)\n\tprint(\"开始检查符合发信条件的订单\")\n\tfor tbody in tbodys:\n\t\t# print(\"tbody\")\n\t\tspan1 = str(tbody.find('span',attrs={\"data-original-title\": 'サンクスメール'}))\n\t\tprint(span1)\n\t#\tprint(span)\n\t\tspan2 = tbody.find('span',attrs={\"data-sort-column\": 'deliveryDate'})\n\t\tprint(span2)\n\t\tif span1.find('disabled') != -1:\n\t\t\t# print(\"disabled\")\n\t\t\tdate = span2.get_text()\n\t\t\tif date != '':\n\t\t\t\tids.append(tbody.find('a',target = 'order_detail').get_text())\n\tt = 1\n\tif len(ids) == 0:\n\t\tprint(\"无欢迎信件可发\")\n\t\treturn\n\n\tprint(ids)\n\tfor i in reversed(ids):\n\t\tprint(\"当前正在发送欢迎邮件，订单号为%s\\n\"%i)\n\t\turl1 = 'https://order-rp.rms.rakuten.co.jp/order-rb/individual-order-detail-sc/init?orderNumber='+i\n\t\tif t == 1:\n\t\t\tdr.switch_to.window(lt_list_handle)\n\t\t\ttime.sleep(2)\n\t\t\tabove = dr.find_element_by_link_text(i)\n\t\t\tdr.execute_script(\"arguments[0].click();\", above)\n\t\t\thandles = dr.window_handles\n\t\t\tfor handle in handles:\n\t\t\t\tdr.switch_to.window(handle)\n\t\t\t\tif dr.current_url.find(i) != -1:\n\t\t\t\t\tprint(handle)\n\t\t\t\t\tprint(dr.current_url)\n\t\t\t\t\tlt_info_handle = handle\n\t\t\t\t\tt += 1\n\t\t\tdr.switch_to.window(lt_info_handle)\n\t\telse:\n\t\t\tdr.switch_to.window(lt_info_handle)\n\t\t\tdr.get(url1)\n\t\t \n\t\ttime.sleep(2)\n\t\tdr.switch_to.window(lt_info_handle)\n\t\tprint(dr.current_url)\n\t\tdetail_of_person = dr.find_element_by_id(\"orderDetailsFormPersonInCharge-1\").get_attribute(\"value\")\n\t\tprint(detail_of_person)\n\t\tif detail_of_person == '' or detail_of_person is None:\n\t\t\tprint(\"订单号为：{}，未填入担当者，不发送欢迎信件\\n\".format(id))\n\t\t\tlistbox_output.insert(END,\"订单号为：{0}，未填入担当者，不发送欢迎信件\\n\".format(id))\n#\t\t\tdr.switch_to.window(lt_list_handle)\n\t\t\tcontinue\n\t\telse:\n\t\t\turl2 = 'https://order-rp.rms.rakuten.co.jp/order-rb-mail/individual-mail-send-template-select-sc/init?orderNumber='+i\n\t\t\tdr.get(url2)\n\t\t\ttime.sleep(2)\n\t\t\tabove = dr.find_element_by_xpath(\"//td[@align='center']/input[@class='submitControlButton']\")\n\t\t\tdr.execute_script(\"arguments[0].click();\", above)\n\t\t\ttime.sleep(2)\n\t\t\tabove = dr.find_element_by_xpath(\"//td[@align='center']/input[2][@class='doubleSubmitInherit submitControlButton']\")\n\t\t\tdr.execute_script(\"arguments[0].click();\", above)\t\n\t\t#\t\tprint(3)\n\t#\t\tdr.switch_to.window(lt_list_handle)\n#\tdr.refresh()\n\n#取得乐天网址数据\ndef get_data():\n\tamazon_handle = ''\n\tlt_list_handle = ''\n\tlt_info_handle = ''\n\thandles = dr.window_handles\n\tfor handle in handles:\n\t\tdr.switch_to.window(handle)\n\t\tcurl = dr.current_url\n\t\tif curl.find(amazon_url) != -1:\n\t\t\tamazon_handle = handle\n\t\telif curl.find(lt_list_check_url) != -1:\n\t\t\tlt_list_handle = handle\n\t\telif curl.find(lt_info_url) != -1:\n\t\t\tlt_info_handle = handle\n\tdr.switch_to.window(lt_info_handle)\n\tinfo_id = dr.find_element_by_xpath(\"//*[@id='layoutContent']/form/div/div/div/div[2]/div/div[2]/ul[1]/li[1]/a\").text\n\t#收件人地址\n\taddress_info = dr.find_element_by_xpath(\"//*[@id='rms-content-order-details-block-destination-1-1-options']/div[1]/div[3]/span[1]\").text\n\t#收件人电话\n\tinfo_phone = dr.find_element_by_xpath(\"//*[@id='rms-content-order-details-block-destination-1-1-options']/div[1]/div[3]/span[2]\").text\n\t#收件人姓名   抓取（送付先情报）\n\tinfo_name = dr.find_element_by_xpath(\"//*[@id='rms-content-order-details-block-destination-1-1-options']/div[1]/div[2]/div[2]/span[2]\").text\n\t# //*[@id=\"rms-content-order-details-block-destination-1-1-options\"]/div[1]/div[2]/div[2]/span[2]\n\t\n\tinfo_address = address_info[10:]\n\tinfo_zipcode1 = address_info[1:4]\n\tinfo_zipcode2 = address_info[5:9]\n\tinfo_province = ''\n\tinfo_address1 = ''\n\tinfo_address2 = ''\n\tprovince_location = info_address.find('県')\n\t\n\tif province_location == 3:\n\t\tinfo_province = info_address[:4]\n\t\tinfo_address = info_address[4:]\n\telse:\n\t\tinfo_province = info_address[:3]\n\t\tinfo_address = info_address[3:]\n\tlenth = len(info_address)\n#\tlistbox_output.insert(END, \"当前地址长度为%d\\n\"%lenth)\n\tif lenth > 16:\n\t\tpass\n#\t\tlistbox_output.insert(END, \"地址长度超过16位\" + '\\n')\n#\t\tinfo_address2 = address_info[lenth-1]\n\t\t\n\tlistbox_output.insert(END, info_id + '\\n')\n\tlistbox_output.insert(END, info_name + '\\n')\n\tlistbox_output.insert(END, info_zipcode1+'-'+info_zipcode2 + '\\n')\n\tlistbox_output.insert(END, info_phone + '\\n')\n\tlistbox_output.insert(END, info_province + '\\n')\n\tlistbox_output.insert(END, info_address + '\\n')\n\n#管理物流信息\ndef deliver_Manage():\n\ttry:\n\t\t#句柄判断\n\t\tamazon_handle = ''\n\t\tlt_list_handle = ''\n\t\tlt_info_handle = ''\n\t\thandles = dr.window_handles\n\t\tfor handle in handles:\n\t\t\tdr.switch_to.window(handle)\n\t\t\tcurl = dr.current_url\n\t\t\t#判断当前url是否为亚马逊url\n\t\t\tif curl.find(amazon_check_url) != -1:\n\t\t\t\tamazon_handle = handle\n\t\t\t#判断当前url是否为乐天订单列表url\n\t\t\telif curl.find(lt_list_check_url) != -1:\n\t\t\t\tlt_list_handle = handle\n\t\t\t#判断当前url是否为乐天订单详情url\n\t\t\telif curl.find(lt_info_url) != -1:\n\t\t\t\tlt_info_handle = handle\n\t\t#切换到乐天订单列表详情\n\t\tdr.switch_to.window(lt_list_handle)\n\t\t#利用重新登录网址达成刷新目的(直接刷新窗口会有刷不出来新单的情况)\n\t\tdr.get(lt_list_url)\n\t\t \n\t\ttime.sleep(5)\n\t\t#切换到待发送新建订单的窗口\n\t\tabove = dr.find_element_by_xpath('//*[@id=\"filter1\"]/ul/li[4]/a/h5/span')\n\t\tdr.execute_script(\"arguments[0].click();\", above)\n\t\t# dr.find_element_by_xpath(\"//*[@id='filter1']/ul/li[4]/a\").click()\n\t\t# print(\"ActionChains\")\n\t\t# above = dr.find_element_by_xpath(\"//li[@data-order-list-filter-tab='発送待ち' and @data-order-progress='300']/a/span\")\n\t\t# ActionChains(dr).move_to_element(above).click(above).perform()\n\t\t# print(\"drfindxpath\")\n\t\t# above.send_keys(Keys.ENTER)\n\t\t# print(\"500\")\n\t\t# dr.execute_script(\"window.scrollTo(0, document.body.scrollHeight);\")\n\t\t# time.sleep(5)\n\t\t#解码网页\n\t\tsource = dr.page_source.encode('GBK', 'ignore')\n\t\tstrsource = source.decode('GBK','strict')\n\t\tsoup = BeautifulSoup(strsource,\"html.parser\")\n\t\tSelect(dr.find_element_by_xpath('//*[@id=\"ddlDisplay\"]')).select_by_value(\"500\")\n\t\ttime.sleep(3)\n\t\tabove = dr.find_element_by_xpath('//*[@id=\"filter1\"]/ul/li[4]/a/h5/span')\n\t\tdr.execute_script(\"arguments[0].click();\", above)\n\t\t# dr.find_element_by_xpath(\"//*[@id='filter1']/ul/li[4]/a\").click()\n\t\t# dr.find_element_by_xpath(\"//*[@id='filter1']/ul/li[4]/a\").click()\n\t\t# dr.find_element_by_xpath(\"//li[@data-order-list-filter-tab='発送待ち']/a\").click()\n\t\t#查询物流信息\n\t\tprint('开始查询物流信息')\n\t\tcheck_Deliver_info()\n\t\t \n\t\t#发信完毕切换到订单列表的窗口\n\t\tdr.switch_to.window(lt_list_handle)\n\t\t#利用重新登录网址达成刷新目的(直接刷新窗口会有刷不出来新单的情况)\n\t\tdr.get(lt_list_url)\n\t\t#点击到待发送信件的订单窗口\n\t\t# dr.find_element_by_xpath(\"//li[@data-order-list-filter-tab='発送待ち']/a/h5/span\").send_keys(Keys.ENTER)\n\texcept BaseException as e:\n\t\tprint(e)\n\t\tif str(e).find(\"no such window: window was already closed\"):\n\t\t\tha = dr.window_handles\n\t\t\tdr.switch_to.window(lt_list_handle)\t\n\n#检查发货进度\n#发货进度判断条件：\n##################################################################################################\n#Amazon改了新UI以后物流信息难判断，暂时总结规律\n#\n#\n#1.物流进程到达第二个判断框并出现勾，但是这只能初步判断可能出现物流信息，暂不用此方法判断\n#\n#2.出现“さらに表示”字样，则说明有详细物流信息，可以点击该字样或者伝票番号来获取详细信息\n#\n#3.有一种情况不出现“さらに表示”字样但是也能获取信息，即出现“再配達リクエストを受け付けました”字样，疑似出现配送问题，此时最好点击伝票番号来获取详细信息\n#\n#\n##################################################################################################\ndef check_Deliver_info():\n\ttry:\n\t\tamazon_handle = ''\n\t\tlt_list_handle = ''\n\t\tlt_info_handle = ''\n#\t\tprint(\"check_Deliver_info\")\n\t\t#检查句柄并确认对应网址的标签页的句柄\n\t\thandles = dr.window_handles\n\t\tfor handle in handles:\n\t\t\tdr.switch_to.window(handle)\n\t\t\tcurl = dr.current_url\n\t\t\t#判断当前url是否为亚马逊url\n\t\t\tif curl.find(amazon_check_url) != -1:\n\t\t\t\tamazon_handle = handle\n\t\t\t#判断当前url是否为乐天订单列表url\n\t\t\telif curl.find(lt_list_check_url) != -1:\n\t\t\t\tlt_list_handle = handle\n\t\t\t#判断当前url是否为乐天订单详情url\n\t\t\telif curl.find(lt_info_url) != -1:\n\t\t\t\tlt_info_handle = handle\n\t\t\t\t\n\t\t#切换到订单列表窗口\n\t\tdr.switch_to.window(lt_list_handle)\n#\t\t#利用重新登录网址达成刷新目的(直接刷新窗口会有刷不出来新单的情况)\n\t\tdr.get(lt_list_url)\n\t\ttime.sleep(3)\n#\t\t#切换到待发送新建的订单列表窗口\n\t\tabove = dr.find_element_by_xpath('//*[@id=\"filter1\"]/ul/li[4]/a/h5/span')\n\t\tdr.execute_script(\"arguments[0].click();\", above)\n\t\t# dr.find_element_by_xpath('//*[@id=\"filter1\"]/ul/li[4]/a/h5/span').click()\n\t\t# dr.find_element_by_xpath(\"//li[@data-order-list-filter-tab='発送待ち']/a\").send_keys(Keys.ENTER)\n\t\t# dr.find_element_by_xpath(\"//li[@data-order-list-filter-tab='発送待ち']\").click()\n\t\t# dr.find_element_by_xpath(\"//li[@data-order-list-filter-tab='発送待ち' and @class = 'filtertab']\").click()\n\t\t \n\t\ttime.sleep(2)\n\t\t#对网页进行解码\n\t\tsource = dr.page_source.encode('GBK', 'ignore')\n\t\tstrsource = source.decode('GBK','strict')\n\t\tsoup = BeautifulSoup(strsource,\"html.parser\")\n\t\ttbodys = soup.find_all('tbody',class_ = 'rms-content-order-list-item rms-list-item rms-item-order-inprogress')\n\t\t\n\t\t#定义list类型的ids列表，储存符合条件的id\n\t\tids = []\n\t\tfor tbody in tbodys:\n\t\t\t#获取订单生成时间\n\t\t\torder_time = str(tbody.find(\"span\",attrs = {'class','rms-content-order-list-item-order-datetime'}).get_text())\n\t\t\t#获取亚马逊订单id\n\t\t\tamazon_id = str(tbody.find(\"span\",attrs = {'class','rms-content-order-list-item-order-memo'}).get_text())\n\t\t\t#如果订单已经进货并生成亚马逊订单，就不会为空\n\t\t\tif amazon_id != '':\n\t\t\t\t#裁剪出生成时间的年月日\n\t\t\t\tday = order_time[0:10]\n\t\t\t\t#获取当前时间的年月日\n\t\t\t\tnow_day = time.strftime(\"%Y-%m-%d\")\n\t\t\t\t#如果是一天以前的订单，则查询物流进度，加入id列表\n\t\t\t\tif now_day > day:\n#\t\t\t\t\tprint(tbody.find('a',target = 'order_detail').get_text())\n\t\t\t\t\tids.append(tbody.find('a',target = 'order_detail').get_text())\n\t\tprint(ids)\n\t\t\n\t\tt = 1\n#\t\tprint(\"ids:\\n\",ids,'\\n')\n\t\tfor i in reversed(ids):\n\t\t\turl1 = 'https://order-rp.rms.rakuten.co.jp/order-rb/individual-order-detail-sc/init?orderNumber='+i\n\t\t\t#如果第一次则点击订单列表的订单生成新的窗口\n\t\t\tif lt_info_handle == '':\n\t\t\t\tprint('新建页面')\n\t\t\t\t# dr.find_element_by_link_text(i).click()\n\t\t\t\tnewwindow = 'window.open(\"%s\")' % url1\n\t\t\t\tdr.execute_script(newwindow)\n\t\t\t\t#生成新窗口以后获取新窗口的句柄\n\t\t\t\thandles = dr.window_handles\n\t\t\t\tfor handle in handles:\n\t\t\t\t\tdr.switch_to.window(handle)\n\t\t\t\t\tif dr.current_url.find(i) != -1:\n\t\t\t\t\t\tlt_info_handle = handle\n\t\t\t\t\t\tt += 1\n\t\t\t\t\t\tprint(\"t = \",t,\"\\n\")\n\t\t\t\tdr.switch_to.window(lt_info_handle)\n\t\t\t\tcontinue\n\t\t\t#如果不是第一次则切换到用以生��订单详情的窗口\n\t\t\telse:\n\t\t\t\tdr.switch_to.window(lt_info_handle)\n\t\t\t\tdr.get(url1)\n#\t\t\tdr.switch_to.window(lt_info_handle)\n\t\t\t \n\t\t\ttime.sleep(2)\n\t\t\t#如果抓取到的物流号长度不为零，即已经查询过物流信息，切换到列表进行下一次查询\n\t\t\tdeliver_id = dr.find_element_by_id(\"rms-content-order-details-block-destination-1-1-options-group-0-parcel-number\").get_attribute(\"value\")\n\t\t\t# //*[@id=\"rms-content-order-details-block-destination-1-1-options-group-0-parcel-number\"]\n\t\t\tif len(deliver_id) > 0:\n\t\t\t\tdr.switch_to.window(lt_list_handle)\n\t\t\t#如果抓取到物流号未填写，则进行查询\n\t\t\telif len(dr.find_element_by_id(\"rms-content-order-details-block-destination-1-1-options-group-0-parcel-number\").get_attribute(\"value\")) == 0:\n\t\t\t\t#获取亚马逊订单号\n\t\t\t\t# amazon_id1 = dr.find_element_by_id(\"orderDetailsFormMemoTextArea-1\").text\n\t\t\t\tamazon_id1 = dr.find_element_by_xpath(\"//*[@id='orderDetailsFormMemoTextArea-1']\").text\n\t\t\t\tdr.switch_to.window(amazon_handle)\n\t\t\t\tprint(\"AMAZON_ID = \"+amazon_id1+'\\n')\n\t\t#\t\tgetsecondurl()\n\t\t\t\t \n\t\t\t\t#进入搜索页面\n\t\t\t\tsearch_url = \"https://www.amazon.co.jp/gp/your-account/order-history/ref=oh_aui_search?opt=ab&__mk_ja_JP=%E3%82%AB%E3%82%BF%E3%82%AB%E3%83%8A&search=\"+amazon_id1\n\t\t\t\t# https://www.amazon.co.jp/progress-tracker/package/ref=ppx_yo_dt_b_track_package?_encoding=UTF8&itemId=mipgttnrnlkrt&orderId=249-9322521-6796603&packageIndex=0&shipmentId=D0whGsDfB&vt=\n\t\t\t\t# 249-9322521-6796603 \n\t\t\t\t# dr.get(search_url)\n\t\t\t\t#进入查询页面（因为直接进入查询页面也会被弹到搜索页面，所以循序进行）\n\t\t\t\tinfo_url = \"https://www.amazon.co.jp/progress-tracker/package/ref=ppx_yo_dt_b_track_package?_encoding=UTF8&itemId=mipgttnrnlkrt&orderId=\"+amazon_id1+\"&&packageIndex=0&shipmentId=D0whGsDfB&vt=\"\n\t\t\t\tprint(\"Info_url : \",info_url,\"\\n\")\n\t\t\t\tdr.get(info_url)\n\t\t\t\t#点击配送状况确认\n\t\t\t\t# dr.find_element_by_link_text(\"配送状況を確認\").click()\n\t\t\t\n\t\t\t\t# WebDriverWait(dr,15).until(EC.element_to_be_clickable((By.XPATH ,\"//*[@id='a-autoid-1']/span\")))\n\t\t\t\tprint(\"点击表示按钮\\n\")\n\t\t\t\tprint(dr.current_url)\n\t\t\t\t# dr.find_element_by_xpath(\"//*[@id='deliveredAddress']/div/span/a\").click()\n\t\t\t\t# dr.find_element_by_xpath(\"//*[@id='a-autoid-1']/span\").send_keys(Keys.ENTER)\n\t\t\t\t\n\t\t\t\t# dr.get(info_url)\n\t\t\t\tsign_url = \"https://www.amazon.co.jp/ap/signin?_\"\n\t\t\t\tif dr.current_url.find(sign_url) != -1:\n\t\t\t\t\tprint(\"sign\\n\")\n\t\t\t\t\tdr.find_element_by_id(\"ap_password\").send_keys(amazon_pwd)\n\t\t\t\t\tdr.find_element_by_id(\"signInSubmit\").click()\n\t\t\t\t\tdr.get(search_url)\n\t\t\t\t\tdr.find_element_by_link_text(\"配送状況を確認\").click()\n\t\t\t\tprint(\"Amazon_id = %s\\nOrderid = %s\\n\"%(amazon_id1,i))\n\t\t\t\tif dr.current_url.find(info_url) == -1:\n\t\t\t\t\tprint(\"break\\n\")\n\t\t\t\t\tcontinue\n\t\t\t\t#查询配送详情，如果内容匹配为“配送情報は準備が整いしだい更新されます”则说明没有物流信息\n\t\t\t\t# deliver_info = dr.find_element_by_xpath(\"//*[@id='a-page']/div[2]/div[6]/div[2]\").text\n\t\t\t\t#新UI判断\n\t\t\t\t#解码网页\n\t\t\t\t \n\t\t\t\tprint(\"开始解析网页\\n\")\n\t\t\t\tsource = dr.page_source.encode('GBK', 'ignore')\n\t\t\t\tstrsource = source.decode('GBK','strict')\n\t\t\t\tsoup = BeautifulSoup(strsource,\"html.parser\")\n#\t\t\t\tprint(str(soup))\n\t\t\t\t# print(\"text = \",dr.find_element_by_xpath(\"//*[@id='deliveredAddress-container'']\").text)\n\t\t\t\tprint(\"さらに表示 is \",str(soup).find(\"さらに表示\"),\"\\n\")\n\t\t\t\tprint(\"再配達リクエストを受け付けました is \",str(soup).find(\"再配達リクエストを受け付けました\"),\"\\n\")\n#\t\t\t\twhile(1):\n#\t\t\t\t\ta = input()\n#\t\t\t\t\tprint(a)\n#\t\t\t\t\tif a == \"1\":\n#\t\t\t\t\t\tbreak\n\t\t\t\tprint(\"さらに表示 = \",str(soup).find(\"さらに表示\"),'\\n')\n\t\t\t\tprint(\"再配達リクエストを受け付けました = \",str(soup).find(\"再配達リクエストを受け付けました\"),'\\n')\n\t\t\t\tprint(\"配達完了 = \",str(soup).find(\"配達完了\"),'\\n')\n\t\t\t\tprint(\"ご不在のため配達できませんでした = \",str(soup).find(\"ご不在のため配達できませんでした\"),'\\n')\n\t\t\t\tprint(\"明日到着予定 = \",str(soup).find(\"明日到着予定\"),'\\n')\n\t\t\t\tif not (str(soup).find(\"配達完了\") == -1):\n\t\t\t\t\t\n\t\t\t\t\t# dr.find_element_by_xpath(\"//*[@id='carrierRelatedInfo-container']/div/span/a\").click()\t\n\t\t\t\t\tprint(\"有物流信息，获取中...\")\t\n\t\t\t\t\t# above = dr.find_element_by_xpath(\"a[@class=a-link-normal tracking-events-modal-trigger tracker-seeDetailsLink]\")\n\t\t\t\t\tabove = dr.find_element_by_link_text('さらに表示')\n\t\t\t\t\tdr.execute_script(\"arguments[0].click();\", above)\n\t\t\t\t\t# if not (str(soup).find(\"本日到着予定\") == -1 and str(soup).find('明日到着予定') == -1 and str(soup).find('にお届け') == -1) :\n\t\t\t\t\t# \t# にお届け済み\n\t\t\t\t\t# \tdr.find_element_by_xpath('//*[@id=\"progressTracker-container\"]/div[2]/div/div[2]/div/div/div/div[2]/span/a').send_keys(Keys.ENTER)\n\t\t\t\t\t# else:\n\t\t\t\t\t# \tdr.find_element_by_xpath(\"//*[@id='deliveredAddress']/div/span/a\").send_keys(Keys.ENTER)\n\t\t\t\t\t \n\t\t\t\t\ttime.sleep(2)\n\t\t\t\t\tsource = dr.page_source.encode('GBK', 'ignore')\n\t\t\t\t\tstrsource = source.decode('GBK','strict')\n\t\t\t\t\tsoup = BeautifulSoup(strsource,\"html.parser\")\n\t\t\t\t\t# text = soup.find_all('tbody',class_ = 'rms-content-order-list-item rms-list-item rms-item-order-inprogress')\n#\t\t\t\t\tsource1 = dr.page_source.encode('GBK', 'ignore')\n#\t\t\t\t\tstrsource1 = source.decode('GBK','strict')\n#\t\t\t\t\tsoup1 = BeautifulSoup(strsource,\"html.parser\")\n#\t\t\t\t\tdeliverInfoDiv = soup1.find('div',id = 'tracking-events-container').get_text().replace('\\n','')\n#\t\t\t\t\tprint(soup.find_all(\"div\",class_ = \"a-container\"))\n\t\t\t\t\ttext = dr.find_element_by_xpath(\"//*[@id='tracking-events-container']/div\").text\n\t\t\t\t\t# //*[@id=\"tracking-events-container\"]/div\n\t\t\t\t\tcode = dr.find_element_by_xpath(\"//*[@id='tracking-events-container']/div/div[2]/h4\").text\n\t\t\t\t\tcode = code[5:]\n#\t\t\t\t\tprint(\"deliverInfoDiv = \",deliverInfoDiv,\"\\n\")\n\t\t\t\t\tprint(\"text = \",text,\"\\n\")\n\t\t\t\t\tprint(code)\n\t\t\t\t\thandles = dr.window_handles\n\t\t\t\t\tinfo_handle = ''\n#\t\t\t\t\tinput()\n#\t\t\t\t\tprint(url1,'\\n')\n\t\t\t\t\t#获取完信息后切换到乐天订单详情\n\t\t\t\t\tfor handle in handles:\n\t\t\t\t\t\tdr.switch_to.window(handle)\n#\t\t\t\t\t\tprint(dr.current_url)\n\t\t\t\t\t\tif dr.current_url.find(i)!=-1:\n#\t\t\t\t\t\t\tprint(\"now is current\\n\")\n\t\t\t\t\t\t\tinfo_handle = handle\n\t\t\t\t\tdr.switch_to.window(info_handle)\n\t\t\t\t\t#清除框内内容并输入\n\t\t\t\t\tdr.find_element_by_id(\"orderDetailsFormEmailStatement-1\").clear()\n\t\t\t\t\tdr.find_element_by_id(\"orderDetailsFormEmailStatement-1\").send_keys(\"配送状況の詳細\"+Keys.ENTER)\n\t\t\t\t\tdr.find_element_by_id(\"orderDetailsFormEmailStatement-1\").send_keys(text+Keys.ENTER)\n#\t\t\t\t\tdr.find_element_by_id(\"orderDetailsFormEmailStatement\").send_keys(text2+Keys.ENTER)\n#\t\t\t\t\tdr.find_element_by_id(\"orderDetailsFormEmailStatement\").send_keys(text3+Keys.ENTER)\n\t\t\t\t\t \n\t\t\t\t\t#匹配到亚马逊物流时点击亚马逊物流按钮\n\t\t\t\t\tif text.find(\"ヤマト\") == -1:\n\t\t\t\t\t\tdr.find_element_by_xpath(\"//*[@id='rms-content-order-details-block-destination-1-1-options-group-0-delivery-company']/option[2]\").click()\n\t\t\t\t\t# loc = text3.find(\"番号\")\n\t\t\t\t\t# code = text3[loc+3:]\n\t\t\t\t\t# loc = text3.find(\"（\")\n\t\t\t\t\t# code = code.split(\" \")[0]\n\t\t\t\t\tprint(code,'\\n')\n\t\t\t\t\t#裁剪出物流编号\n\t\t\t\t\tcode = code.split(':')[1]\n\t\t\t\t\tlistbox_output.insert(END,'订单号为  %s  物流编号为  %s  \\n'%(i,code))\n\t\t\t\t\t#输出物流ID\n\t\t\t\t\tprint(\"已输出物流ID\")\n\t\t\t\t\tdr.find_element_by_id(\"rms-content-order-details-block-destination-1-1-options-group-0-parcel-number\").clear()\n\t\t\t\t\tdr.find_element_by_id(\"rms-content-order-details-block-destination-1-1-options-group-0-parcel-number\").send_keys(code)\n\t\t\t\t\t \n\t\t\t\t\tprint(\"已点击保存按钮\")\n\t\t\t\t\t#保存乐天信息\n\t\t\t\t\ttime.sleep(2)\n\t\t\t\t\t# dr.find_element_by_xpath(\"/html/body\").send_keys(Keys.CONTROL+Keys.END)\n\t\t\t\t\tabove = dr.find_element_by_xpath('//*[@id=\"btnSave\"]')\n\t\t\t\t\tdr.execute_script(\"arguments[0].click();\", above)\n\t\t\t\t\t# dr.find_element_by_xpath(\"/html/body/div[1]/div[2]/div/div[2]/div/div/form/div/div/div/div[2]/div/div/div[4]/button\").click()\n\t\t\t\t\ttime.sleep(2)\n\t#\t\t\t\tdr.switch_to.window(lt_list_handle)\n\t\t\t\telse:\n\t\t\t\t\tlistbox_output.insert(END,'订单号为  %s  暂无物流信息\\n'%i)\n\t\t\t\t\tprint(\"物流未更新信息\\n\")\n\t\t\t\t\tdr.switch_to.window(lt_list_handle)\n\t\t\t\t\tcontinue\n\n\texcept BaseException as e:\n\t\tprint(e)\n\t\tpass\n\n\n#--------------------------------Star--------------------------------------------------\n#*****************************************************************************\n#* AddressProcess(address_str) 地址处理函数                                     * \n#* 输入参数：不限字符个数地址，省份已去掉                                          *  \n#* 返回：3行地址变量，同时存在全局变量 address_3line[]中， 对应填入到亚马逊的3行地址 *          *\n#* 如第3行地址为空，已填入'(Rakuten BUBU蔵の注文)'                                *\n#* Author: lion                                                                *    \n#  Date：2020-Oct-9                                                            *                               *\n#*******************************************************************************\n #全局变量，调用完地址处理函数后，返回3行地址，同时也存在这里\ndef Addressprocess(address_str):\n    global address_3line\n    address_3line.clear()                   #清空\n    \n    if len(address_str) <= 16:              #小于等于16个字符，    \n        address_3line.append(address_str)   #第1行不处理\n        address_3line.append('')            #第2行填''    \n        address_3line.append('(Rakuten BUBU蔵の注文)') #第3行加上广告词\n        return address_3line\n    else:                                    #大于16字符，\n        with open('addressbook.txt','a',encoding='utf-8') as fp: #收集地址数据，\n            fp.write(address_str+'\\n')\n        addresswindow=tkinter.Tk()                               #打开文本窗口处理      \n        addresswindow.title('乐天地址处理')\n        addresswindow.geometry('500x300')\n        \n        #统计窗口，显示统计字数 （下一步要实时统计）\n        addresslistbox=tkinter.Listbox(addresswindow, width=40)\n        addresslistbox.place(x=10, y=5, width=40, height=100)\n        \n        #文本窗口，地址参数载入\n        txtContent=tkinter.scrolledtext.ScrolledText(addresswindow, wrap=tkinter.WORD)\n        #txtContent.pack(fill=tkinter.BOTH, expand=tkinter.YES)  \n        txtContent.place(x=60, y=5, width=400, height=100)\n        \n        def Addressload(): #把地址装人文本窗口\n            txtContent.delete(0.0, tkinter.END)             #清空文本窗口\n            txtContent.insert(tkinter.INSERT, address_str)  #载入地址到文本窗口重排\n        def Statistics(): #统计每行字数\n            str=txtContent.get(0.0, tkinter.END)    #读取的地址文本 \n            line=str.split('\\n')                    #以换行分割                    \n            addresslistbox.delete(0, END)        #清空字数统计窗口\n            for i in range(len(line)-1, -1, -1): #把统计好的字数压进统计窗口，最后的行数先进。\n                addresslistbox.insert(0, len(line[i]))\n        Addressload() #把地址装人文本窗口\n        Statistics()  #统计每行字数\n\n        #统计，取消，确认 3个按键处理\n        def Statistics_button():    #统计按键\n            Statistics()            #统计每行字数\n        buttonStatistics=tkinter.Button(addresswindow, text='统计', width=50, command=Statistics_button)\n        buttonStatistics.place(x=20, y=180, width=50, height=20)\n\n        def Cancel(): #取消=重新载入数据\n            Addressload()   #把地址装人文本窗口\n            Statistics()    #统计每行字数\n        buttonCancel=tkinter.Button(addresswindow, text='取消', width=50, command=Cancel)\n        buttonCancel.place(x=120, y=180, width=50, height=20)\n\n        def Comfirm(): #确认按键，地址的3行数据存到全局变量中\n            global address_3line                    #定义address_3line全局变量\n            Statistics()                            #统计\n            str=txtContent.get(0.0, tkinter.END)    #读出文本窗口排版\n            \n            address_3line = str.split('\\n')         #\n            for j in range(0, 3, 1):  #检查地址行字数，>16个字符重新排\n                if len(address_3line[j]) > 16:\n                    tkinter.messagebox.showerror(title='地址错误', message='地址行字数多于16个\\n请重排')\n                    return\n            \n            n = len(address_3line)  #检查地址行数，多于4行的弹出错误窗口要求重排，第3行如空，填入(Rakuten BUBU蔵の注文)\n            if n == 2:\n                address_3line.append('(Rakuten BUBU蔵の注文)') \n            elif n == 3:\n                address_3line[2] = '(Rakuten BUBU蔵の注文)'\n            elif n == 4:\n                del address_3line[3]\n            else:\n                tkinter.messagebox.showerror(title='地址错误', message='地址行数多于3行\\n请重排')\n                return   \n            \n            #print(address_3line)       #调试时打开这句，关掉下面两句\n            addresswindow.quit()        #确认成功，退出地址处理窗口\n            addresswindow.destroy()     #关掉窗口\n        buttonComfirm=tkinter.Button(addresswindow, text='确认', width=50, command=Comfirm)\n        buttonComfirm.place(x=220, y=180, width=50, height=20)\n        print(address_3line)\n        addresswindow.mainloop()        #弹出地址处理窗口后循环等待，通过\"确认\"按键关闭跳出\n\t\t\n        return address_3line\n    #----------------------------地址处理函数 -----END-----  \n\t\n#操作两站\ndef lt_ama():\n\ttry:\n\t\tamazon_handle = ''\n\t\tlt_list_handle = ''\n\t\tlt_info_handle = ''\n\t\thandles = dr.window_handles\n\t\tfor handle in handles:\n\t\t\tdr.switch_to.window(handle)\n\t\t\tcurl = dr.current_url\n\t\t\tif curl.find(amazon_check_url) != -1:\n\t\t\t\tamazon_handle = handle\n\t\t\telif curl.find(lt_list_check_url) != -1:\n\t\t\t\tlt_list_handle = handle\n\t\t\telif curl.find(lt_info_url) != -1:\n\t\t\t\tlt_info_handle = handle\n\t\tdr.switch_to.window(lt_info_handle)\n\t\t# source = dr.page_source.encode('GBK', 'ignore')\n\t\t# strsource = source.decode('GBK','strict')\n\t\t# soup = BeautifulSoup(strsource,\"html.parser\")\n\t\t# data_id = soup.find('a', class_ = 'rms-status-order-nr')\n\t\t# info_id = data_id.get_text()\n\t\tinfo_id = dr.find_element_by_xpath(\"//*[@id='order-details-1']/div/div[6]/div[4]/div/div[1]/div[2]/table/tbody/tr[1]/td[2]\").text\n\t\taddress_info = dr.find_element_by_xpath(\"//*[@id='rms-content-order-details-block-destination-1-1-options']/div[1]/div[3]/span[1]\").text\n\t\tinfo_phone = dr.find_element_by_xpath('//*[@id=\"rms-content-order-details-block-destination-1-1-options\"]/div[1]/div[3]/span[2]').text\n\t\tinfo_name = dr.find_element_by_xpath(\"//*[@id='rms-content-order-details-block-destination-1-1-options']/div[1]/div[2]/div[2]/span[2]\").text\n\t\torder_infomation_id = info_id\n\t\tprint(\"order_info=\",order_infomation_id)\n\t\tprint(\"Phone : \",info_phone)\n\t\tinfo_address = address_info[10:]\n\t\tinfo_zipcode1 = address_info[1:4]\n\t\tinfo_zipcode2 = address_info[5:9]\n\t\tinfo_province = ''\n\t\tinfo_address1 = ''\n\t\tinfo_address2 = ''\n\t\tprovince_location = info_address.find('県')\n\t\tif province_location == 3:\n\t\t\tinfo_province = info_address[:4]\n\t\t\tinfo_address = info_address[4:]\n\t\telse:\n\t\t\tinfo_province = info_address[:3]\n\t\t\tinfo_address = info_address[3:]\n\t\tlenth = len(info_address)\n\t#\tlistbox_output.insert(END, \"当前地址长度为%d\\n\"%lenth)\n\t\t#切换到Amazon\t\t\t\n\t\tdr.switch_to.window(amazon_handle)\n\t\t#有可能出现长时间未操作需要输入密码重新登录的情况，\n\t\t#解析网页查找有无输入密码选项来判断是否出现此情况，\n\t\t#不能用dr....text，会出现无法查找到元素并跳出的状况\n\t\tsource = dr.page_source.encode('GBK', 'ignore')\n\t\tstrsource = source.decode('GBK','strict')\n\t\tsoup = BeautifulSoup(strsource,\"html.parser\")\n\t\tprint(info_address)\n\t\tprint(\"Shopping cart Current Url is : \",dr.current_url,'\\n')\n\t\tdr.get(\"https://www.amazon.co.jp/gp/cart/view.html?ref=nav_cart\")\n\t\t#  \n\t\tWebDriverWait(dr,15,ignored_exceptions=NONE).until(EC.element_to_be_clickable((By.NAME,\"proceedToRetailCheckout\")))\n\t#\tdr.find_element_by_name(\"proceedToCheckout\").click()\n\t\tdr.find_element_by_xpath(\"//*[@id='sc-buy-box-ptc-button']/span/input\").send_keys(Keys.ENTER)\n\t\tsign_url = \"https://www.amazon.co.jp/ap/signin?_\"\n\t\tif dr.current_url.find(sign_url) != -1:\n\t\t\tdr.find_element_by_id(\"ap_password\").send_keys(amazon_pwd)\n\t\t\tdr.find_element_by_id(\"signInSubmit\").click()\n\t\t\tdr.get(\"https://www.amazon.co.jp/gp/cart/view.html?ref=nav_cart\")\n\t\t\tdr.find_element_by_xpath(\"//input[@name = 'proceedToCheckout']\").click()\n\t\t \n\t\ttime.sleep(3)\n\t\tdr.find_element_by_xpath('//*[@id=\"address-ui-widgets-enterAddressFullName\"]').send_keys(info_name)\n\t\tdr.find_element_by_xpath('//*[@id=\"address-ui-widgets-enterAddressFullName\"]').send_keys(\"様\")\n\t\ttime.sleep(1)\n\t\tdr.find_element_by_xpath('//*[@id=\"address-ui-widgets-enterAddressPostalCodeOne\"]').send_keys(info_zipcode1)\n\t\tdr.find_element_by_xpath('//*[@id=\"address-ui-widgets-enterAddressPostalCodeTwo\"]').send_keys(info_zipcode2)\n\t\tdr.find_element_by_xpath('//*[@id=\"address-ui-widgets-enterAddressPhoneNumber\"]').send_keys(info_phone)\n\t\t# xpath1 = \"//option[@value='%s']\" % info_province\n\t\t# dr.find_element_by_xpath('//*[@id=\"address-ui-widgets-enterAddressStateOrRegion\"]/span/span').find_element_by_xpath(xpath1).click()\n\t\tdata_address = Addressprocess(info_address)\n\t\ttime.sleep(1)\n\t\tdr.find_element_by_xpath('//*[@id=\"address-ui-widgets-enterAddressLine3\"]').clear()\n\t\tdr.find_element_by_xpath('//*[@id=\"address-ui-widgets-enterAddressLine3\"]').send_keys(data_address[2])\n\t\tdr.find_element_by_xpath('//*[@id=\"address-ui-widgets-enterAddressLine1\"]').clear()\n\t\tdr.find_element_by_xpath('//*[@id=\"address-ui-widgets-enterAddressLine1\"]').send_keys(data_address[0])\n\t\tdr.find_element_by_xpath('//*[@id=\"address-ui-widgets-enterAddressLine2\"]').clear()\n\t\tdr.find_element_by_xpath('//*[@id=\"address-ui-widgets-enterAddressLine2\"]').send_keys(data_address[1])\n\t\ttime.sleep(1)\n#\t\tif info_address2 != ''\n\t\t\n\n#\t\t\tdr.find_element_by_id(\"enterAddressAddressLine2\").send_keys(info_address2)\n\t\ttime.sleep(1)\n\t\t\n\t\tlistbox_output.insert(END, \"订单编号为：%s\\n姓名：%s\\n电话：%s\\n邮编：%s-%s\\n地址：%s\\n\"%(info_id,info_name,info_phone,info_zipcode1,info_zipcode2,info_address))\n\t\tif len(info_address) <= 16:\n\t\t\tpass\n\t\telse:\n#\t\t\tlistbox_output.insert(END, \"订单编号为：%s\\n地址为:%s\\n\"%(info_id,info_address))\n\t\t\tlistbox_output.insert(END, \"地址长度超过16字符，请处理完后点击确认购物\\n\")\n\n\n\texcept BaseException as e:\n\t\tprint(e)\n\t\tpass\n\n#确认地址\ndef after_submit_address():\n\ttry:\n\t\t#判断句柄\n\t\tamazon_handle = ''\n\t\tlt_list_handle = ''\n\t\tlt_info_handle = ''\n\t\thandles = dr.window_handles\n\t\tfor handle in handles:\n\t\t\tdr.switch_to.window(handle)\n\t\t\tcurl = dr.current_url\n\t\t\tif curl.find(amazon_check_url) != -1:\n\t\t\t\tamazon_handle = handle\n\t\t\telif curl.find(lt_list_check_url) != -1:\n\t\t\t\tlt_list_handle = handle\n\t\t\telif curl.find(lt_info_url) != -1:\n\t\t\t\tsource = dr.page_source.encode('GBK', 'ignore')\n\t\t\t\tstrsource = source.decode('GBK','strict')\n\t\t\t\tsoup = BeautifulSoup(strsource,\"html.parser\")\n\t\t\t\tdata_id = soup.find('a', class_ = 'rms-status-order-nr')\n\t\t\t\tinfo_id = data_id.get_text()\n\t\t\t\tlt_info_handle = handle\n\t\tdr.switch_to.window(amazon_handle)\n\t\tdr.find_element_by_tag_name('body').send_keys(Keys.END)\n\t\t# 点击确认按钮\n\t\t#  \n\t\t# print(\"第一个按钮\")\n\t\t# dr.find_element_by_xpath('/html/body/div[5]/div[2]/div/div[3]/div[1]/div/div[1]/form/div/span/span/span/input').send_keys(Keys.ENTER)\n\t\t# dr.find_element_by_xpath('/html/body/div[5]/div[2]/div/div[3]/div[1]/div/div[1]/form/div/span/span/span/input').click()\n\t\tzhusuo = \"$('span[class=a-button-inner] input[aria-labelledby=address-ui-widgets-form-submit-button-announce]').click()\"\n\t\tdr.execute_script(zhusuo)\n\t\ttime.sleep(3)\n\t\tprint(\"第二个\")\n\t\t \n\t\tprint(\"3\")\n\t\tdr.find_element_by_xpath('//*[@id=\"shippingOptionFormId\"]/div[3]/div/div/span[1]/span/input').send_keys(Keys.ENTER)\n\t\t\n\t\t\n\t\t \n\t\ttime.sleep(2)\n\t\tprint(\"信用卡输入\")\n\t\tif not dr.find_element_by_xpath('/html/body/div[5]/div/div[2]/div[3]/div/div[2]/div[1]/form/div[1]/div/div/div[1]/div[2]/div/div/div/div/div/div[1]/span/div/label/input').is_selected():\n\t\t\twhile(1):\n\t\t\t\tif dr.find_element_by_xpath('/html/body/div[5]/div/div[2]/div[3]/div/div[2]/div[1]/form/div[1]/div/div/div[1]/div[2]/div/div/div/div/div/div[1]/span/div/label/input').is_selected():\n\t\t\t\t\tprint(\"信用卡已选中\")\n\t\t\t\t\tdr.find_element_by_xpath('/html/body/div[5]/div/div[2]/div[3]/div/div[2]/div[1]/form/div[1]/div/div/div[1]/div[2]/div/div/div/div/div/div[2]/div[2]/div/span/div[3]/input').send_keys('4649882195642418')\n\t\t\t\t\ttime.sleep(1)\n\t\t\t\t\txykbut = \"$('span[class=a-button a-button-primary pmts-button-input] span button').click()\"\n\t\t\t\t\tdr.execute_script(xykbut)\n\t\t\t\t\tbreak\n\t\t\t\telse:\n\t\t\t\t\tprint(\"信用卡未选中\")\n\t\t\t\t\ttime.sleep(2)\n\t\t\t\t\txyk = \"$('div:first\t[data-a-input-name = ppw-instrumentRowSelection] label input').click()\"\n\t\t\t\t\tdr.execute_script(xyk)\n\t\t\t\t\tcontinue\n\t\ttime.sleep(2)\n\t\tprint(\"CONTI\")\n\t\tconti = \"$('input:first [class=a-button-input a-button-text]').click()\"\n\t\tdr.execute_script(conti)\n\t\tprint('下一步')\n\t\ttime.sleep(2)\n\t\tdr.find_element_by_xpath('/html/body/div[5]/div[1]/div[2]/div[3]/div/div[2]/div[1]/form/div[2]/div/div/div/span/span/input').send_keys(Keys.ENTER)\n\t\t# dr.find_element_by_link_text('続行').send_keys(Keys.ENTER)\n\t\t\n\t\tprint(\"Before Switch Current Url is : \",dr.current_url,'\\n')\n\t\ttime.sleep(5)\n\t\t# dr.find_element_by_xpath(\"/a[@class = 'no-link a-button-text gift-popover-link a-declarative ']\").click()\n\t\t# gift_button = \"$(a[class=no-link a-button-text gift-popover-link a-declarative ]).click()\"\n\t\t# dr.execute_script(gift_button)\n\t\t#模拟鼠标点击礼物订单\n\t\t# above = dr.find_element_by_xpath('/html/body/div[5]/div[1]/div[2]/form/div/div/div/div[1]/div[5]/div/div/div/div[3]/div/div/div[2]/div[1]/div/div[2]/div[6]/div[1]/span/span/a')\n\t\t# ActionChains(dr).move_to_element(above).click(above).perform()\n\t\t# aboves = dr.find_elements_by_xpath('//span[@class=\"a-button a-button-small a-button-icon\"]/a[@class=\"no-link a-button-text gift-popover-link a-declarative\"]')\n\t\taboves = dr.find_elements_by_xpath('//span[@class=\"a-button a-button-small a-button-icon\"]/span/a[@class=\"no-link a-button-text gift-popover-link a-declarative \"]')\n\t\t# aboves = dr.find_elements_by_xpath('//*[@id=\"spc-orders\"]/div/div/div[3]/div/div/div[2]/div[1]/div/div[2]/div[6]/div[1]/span/span/a/b')\n\t\t# aboves = dr.find_elements_by_partial_link_text\n\t\tprint(aboves)\n\t\tt = 0\n\t\tfor above in aboves:\n\t\t\tprint(\"礼物设置\")\n\t\t\tdr.execute_script(\"arguments[0].click();\", above)\n\t\t\t# above.send_keys(Keys.ENTER)\n\t\t\ttime.sleep(2)\n\t\t\t#不显示金额\n\t\t\tprint(\"点击金额显示\")\n\t\t\tinMoney = dr.find_elements_by_xpath('/html/body/div[8]/div/div[2]/div/div[1]/ol/li[3]/div[2]/div[2]/div[4]/label/input')[t]\n\t\t\tdr.execute_script(\"arguments[0].click();\", inMoney)\n\t\t\ttime.sleep(4)\n\t\t\tprint(\"输入礼物信息\")\n\t\t\ttextarea = dr.find_elements_by_xpath(\"//textarea[@name='gift-message-text']\")[t]\n\t\t\tprint(textarea.is_displayed())\n\t\t\ttextarea.clear()\n\t\t\ttextarea.send_keys(\"受注番号 \t\")\n\t\t\ttextarea.send_keys(info_id)\n\t\t\ttextarea.send_keys(Keys.ENTER)\n\t\t\ttextarea.send_keys(\"Rakuten BUBU蔵\")\n\t\t\ttime.sleep(2)\n\t\t\tsubmit_button = dr.find_elements_by_xpath('//*[@id=\"a-popover-1\"]/div/div[2]/div/div[1]/ol/li[4]/span[1]/span/input')[t]\n\t\t\tdr.execute_script(\"arguments[0].click();\", submit_button)\n\t\t\tt += 1\n\t\t# dr.find_element_by_xpath('/html/body/div[5]/div[1]/div[2]/form/div/div/div/div[1]/div[5]/div/div/div/div[3]/div/div/div[2]/div[1]/div/div[2]/div[7]/div[1]/span/span/a').click()\n\t\t# dr.switch_to.window(lt_info_handle)\n\n\t\t# time.sleep(2)\n\t\t# source = dr.page_source.encode('GBK', 'ignore')\n\t\t# strsource = source.decode('GBK','strict')\n\t\t# soup = BeautifulSoup(strsource,\"html.parser\")\n\t\t# data_id = soup.find('a', class_ = 'rms-status-order-nr')\n\t\t# info_id = data_id.get_text()\n\t\t# dr.switch_to.window(amazon_handle)\n\t\t# time.sleep(2)\n\t\tprint(\"已点开设定\")\n\t\t\n\t\t# WebDriverWait(dr, 20, 0.5).until(EC.presence_of_element_located(above))\n\t\t# ActionChains(dr).click(above).perform()\n\t\t\n\t\ttime.sleep(2)\n\t\tprint(\"输入信息\")\n\t\t\n\t\ttime.sleep(1)\n\t\tprint('点击不显示金额')\n\t\t# dr.find_element_by_xpath(\"//input[@type = 'checkbox' and @class = 'a-declarative hide-prices-checkbox']\").click()\n\t\ttime.sleep(2)\n\t\t# money = '$(\"label:first[class=hide-prices-amazon-msg a-checkbox multi-line-checkbox] input\").click()'\n\t\t# money = dr.find_element_by_xpath(\"//label[@class = 'hide-prices-amazon-msg a-checkbox multi-line-checkbox']/input\")\n\t\t\n\t\t# if not dr.find_element_by_xpath('/html/body/div[8]/div/div[2]/div/div[1]/ol/li[3]/div[2]/div[2]/div[4]/label/input').is_selected():\n\t\t# \tdr.execute_script(money)\n\t\t# \t# dr.find_element_by_xpath('/html/body/div[8]/div/div[2]/div/div[1]/ol/li[3]/div[2]/div[2]/div[4]/label/input').send_keys(Keys.SPACE)\n\t\t# \t# dr.find_element_by_xpath('/html/body/div[8]/div/div[2]/div/div[1]/ol/li[3]/div[2]/div[2]/div[4]/label/input').click()\n\n\t\t# time.sleep(2)\n\t\t# # a-declarative hide-prices-checkbox\n\t\t# # Select()\n\t\t\n\t\t# while(1):\n\t\t# \tif dr.find_element_by_xpath(\"//label[@class = 'hide-prices-amazon-msg a-checkbox multi-line-checkbox']/input\").is_selected():\n\t\t# \t\tprint(\"显示金额已选中\")\n\t\t# \t\tbreak\n\t\t# \telse:\n\t\t# \t\tprint(\"显示金额未选中\")\n\t\t# \t\tdr.execute_script(money)\n\t\t# \t\tcontinue\n\t\t# \tprint('点击礼物设置保存')\n\t\t# # time.sleep(1)\n\t\t# if soup.find('giftForm') == -1:\n\t\t# \tabove = dr.find_element_by_xpath('//*[@id=\"a-popover-1\"]/div/div[2]/div/div[1]/ol/li[4]/span[1]/span/input')\n\t\t# \t# ActionChains(dr).click(above).perform()\n\t\t# \tdr.execute_script(\"arguments[0].click();\", above)\n\t\t# else:\n\t\t# \tabove = dr.find_element_by_xpath('//*[@id=\"giftForm\"]/div[1]/div[2]/div/span[1]/span/input')\n\t\t# \tdr.execute_script(\"arguments[0].click();\", above)\n\t\t# save = \"$('span[class=a-button a-button-primary a-spacing-mini set-gift-options-button] span input').click()\"\n\t\t# dr.execute_script(save)\n\t\t# dr.find_element_by_xpath('//*[@id=\"a-popover-1\"]/div/div[2]/div/div[1]/ol/li[4]/span[1]/span/input').send_keys(Keys.ENTER)\n\t\t# dr.find_element_by_xpath('/html/body/div[8]/div/div[2]/div/div[1]/ol/li[4]/span[1]/span/input').send_keys(Keys.ENTER)\n\t\t# \"納品書に金額を表示しない\"\n\t\t# dr.find_element_by_xpath('//input[@value = \"ギフトの設定を保存\"]').send_keys(Keys.ENTER)\n\texcept BaseException as e:\n\t\tprint(e)\n\t\tif str(e).find(\"no such window: window was already closed\"):\n\t\t\tdr.switch_to.window(lt_list_handle)\t\t\n\t\tpass\n#确认购买\ndef submit_order():\n\ttry:\n\t\tamazon_handle = ''\n\t\tlt_list_handle = ''\n\t\tlt_info_handle = ''\n\t\thandles = dr.window_handles\n\t\tfor handle in handles:\n\t\t\tdr.switch_to.window(handle)\n\t\t\tcurl = dr.current_url\n\t\t\tif curl.find(amazon_check_url) != -1:\n\t\t\t\tamazon_handle = handle\n\t\t\telif curl.find(lt_list_check_url) != -1:\n\t\t\t\tlt_list_handle = handle\n\t\t\telif curl.find(lt_info_url) != -1:\n\t\t\t\tlt_info_handle = handle\n\t\tdr.switch_to.window(amazon_handle)\n\t\tprint(\"Before Switch Current Url is : \",dr.current_url,'\\n')\n\t\t#  \n\t\t\n\n\n\t\tdr.find_element_by_xpath('//*[@id=\"placeYourOrder\"]/span/input').send_keys(Keys.ENTER)\n\t\t \n\t\tsource = dr.page_source.encode('GBK', 'ignore')\n\t\tstrsource = source.decode('GBK','strict')\n\t\tsoup = BeautifulSoup(strsource,\"html.parser\")\n\t\t# get_amazon_id = soup.find('span', class_ = 'a-text-bold')\n\t\t# amazon_id = str(get_amazon_id.get_text())\n\t\t#diliver_Date = dr.find_element_by_xpath(\"//*[@id='a-page']/div[2]/div[1]/div/div/div/div/div[1]/ul/li/span/b\").text\n\t\t# diliver_Date_parser = soup.find(attrs={'class':'a-list-item'})\n\t\t# diliver_Date = diliver_Date_parser.find(\"b\").get_text()\n\t\t\n\t\tinfo_data = dr.current_url\n\t\tprint('URL：',info_data,type(info_data),'\\n')\n\t\t# url = str(url)\n\t\tinfo_data = info_data.split('orderId=')[1]\n\t\tinfo_data = info_data.split('&')[0]\n\t\tprint('Split url',info_data,'\\n')\n\t\t# dr.find_element_by_link_text('')\n\t\t#购买完成点击注文详细\n\t\t# dr.find_element_by_xpath('//*[@id=\"a-page\"]/div[3]/div[1]/div/div/div/div/div[1]/div[2]/div/a[1]').send_keys(Keys.ENTER)\n\t\t# above = dr.find_element_by_partial_link_text('注文詳細')\n\t\t# ActionChains(dr).click(above).perform()\n\t\ttime.sleep(3)\n\t\tinfo_url = \"https://www.amazon.co.jp/gp/your-account/order-details/ref=ppx_yo_dt_b_order_details_o00?ie=UTF8&orderID=\"+info_data\n\t\tdr.get(info_url)\n\t\t# above = dr.find_element_by_xpath('//*[@id=\"a-page\"]/div[3]/div[1]/div[1]/div/div/div/div[1]/div[2]/div/span/a')\n\t\t# dr.execute_script(\"arguments[0].click();\", above)\n\t\t# date_xpath = '//*[@id=\"delivery-promise-'+url+'#itemGroupID0\"]/span[2]/span'\n\t\ttime.sleep(3)\n\t\t# dliy_date = dr.find_element_by_xpath('//*[@id=\"orderDetails\"]/div[5]/div/div[1]/div[1]/div[1]/span').text()\n\t\tsource = dr.page_source.encode('GBK', 'ignore')\n\t\tstrsource = source.decode('GBK','strict')\n\t\tsoup = BeautifulSoup(strsource,\"html.parser\")\n\t\tdili_date = soup.find('span', class_ = 'a-size-medium a-color-base a-text-bold').get_text()\n\t\tprint('current\\t',dr.current_url,'\\n')\n\t\t# orderid = dr.find_element_by_xpath('/html/body/div[1]/div[2]/div[2]/div[1]/div/span[2]/bdi').text()\n\t\t# dili_date = dr.find_element_by_xpath('/html/body/div[1]/div[2]/div[5]/div/div[1]/div[1]/div[1]/span').text()\n\t\tprint(\"dili_date = \\n\",dili_date,'\\n')\n\t\t# print(\"order id = \\n\",orderid,'\\n')\n\t\tdr.switch_to.window(lt_info_handle)\n#\t\tdr.find_element_by_xpath(\"//span[@class = 'a-text-bold']\").\n\t\t# dr.find_element_by_xpath('//*[@id=\"orderDetailsFormMemoTextArea-1\"]').send_keys(amazon_id.strip())\n\t\t\n\n\t\tdr.find_element_by_xpath('//*[@id=\"orderDetailsFormPersonInCharge-1\"]').send_keys(\"SAT\")\n\t\t# dr.find_element_by_xpath('//*[@id=\"rms-content-order-details-block-destination-1-1-options-group-0-shipment-date\"]').send_keys(time.strftime(\"%g%m%d\"))\n\t\tabove = dr.find_element_by_xpath('//*[@id=\"rms-content-order-details-block-destination-1-1-options-group-0\"]/div[3]/div[1]/ul/li[1]/span')\n\t\tdr.execute_script(\"arguments[0].click();\", above)\n\t\tdili_date = dili_date.strip()\n\t\tprint('dili_date',dili_date)\n\t\t# year = dili_date.split(\"年\")[0]\n\t\t# print(year)\n\t\t# dili_date = dili_date.split(\"年\")[1]\n\t\t# print(dili_date)\n\t\tmonth = dili_date.split(\"月\")[0]\n\t\tdili_date = dili_date.split(\"月\")[1]\n\t\tday = dili_date.split(\"日\")[0]\n\t\tprint(day)\n\t\tif len(month)<2:\n\t\t\tmonth = '0'+str(month)\n\t\tif len(day)<2:\n\t\t\tday = '0'+str(day)\n\t\tdr.find_element_by_xpath('//*[@id=\"orderDetailsFormDeliveryDate-1\"]').send_keys(\"%s-%s-%s\" % (2020,month,day))\n\t\tdr.find_element_by_xpath('//*[@id=\"orderDetailsFormMemoTextArea-1\"]').send_keys(info_data.strip())\n\t\tdr.find_element_by_xpath(\"/html/body\").send_keys(Keys.CONTROL+Keys.END)\n\t\t# above = dr.find_element_by_xpath(\"/html/body/div[1]/div[2]/div/div[2]/div/div/form/div/div/div/div[2]/div/div/div[4]/button\")\n\t\tabove = dr.find_element_by_xpath('//*[@id=\"btnSave\"]')\n\t\tdr.execute_script(\"arguments[0].click();\", above)\n\texcept BaseException as e:\n\t\tprint(e)\n\t\tpass\n\n#批量注文确认\ndef submit_text():\n\ttry:\n\t\t#判断句柄\n\t\tamazon_handle = ''\n\t\tlt_list_handle = ''\n\t\tlt_info_handle = ''\n\t\thandles = dr.window_handles\n\t\tfor handle in handles:\n\t\t\tdr.switch_to.window(handle)\n\t\t\tcurl = dr.current_url\n\t\t\tif curl.find(amazon_check_url) != -1:\n\t\t\t\tamazon_handle = handle\n\t\t\telif curl.find(lt_list_check_url) != -1:\n\t\t\t\tlt_list_handle = handle\n\t\t\telif curl.find(lt_info_url) != -1:\n\t\t\t\tlt_info_handle = handle\n\t\tdr.switch_to.window(lt_list_handle)\n\t\tprint(\"批量注文开始\")\n\t\tdr.get(\"https://order-rp.rms.rakuten.co.jp/order-rb/order-list-sc/init?&SEARCH_MODE=1&ORDER_PROGRESS=100\")\n\t\tdr.refresh()\n\t\telement = WebDriverWait(dr, 10).until(\n\t\t\tEC.presence_of_element_located((By.XPATH, '//*[@id=\"rms-checkbox-order-header-checkbox\"]'))\n\t\t)\n\t\t#点击全选按钮\n\t\tdr.find_element_by_xpath('//*[@id=\"rms-checkbox-order-header-checkbox\"]').click()\n\t\t# above = dr.find_element_by_xpath('//*[@id=\"rms-checkbox-order-header-checkbox\"]')\n\t\t# ActionChains(dr).click(above).perform()\n\t\ttime.sleep(1)\n\t\t#点击 “一括処理” 按钮触发下拉菜单\n\t\t# dr.find_element_by_xpath('//*[@id=\"rms-content-order-filter-final-order-btn\"]').click()\n\t\tabove = dr.find_element_by_xpath('//*[@id=\"filter2\"]/div[1]/div/a/span')\n\t\tActionChains(dr).move_to_element(above).click(above).perform()\n\t\ttime.sleep(1)\n\t\t\n\t\tabove = dr.find_element_by_xpath('//*[@id=\"rms-content-order-filter-final-order-btn\"]')\n\t\tActionChains(dr).click(above).perform()\n\t\ttime.sleep(1)\n\t\tabove = dr.find_element_by_xpath('//*[@id=\"orderConfirm\"]')\n\t\tActionChains(dr).click(above).perform()\n\t\t# dr.find_element_by_xpath('//*[@id=\"orderConfirm\"]').click()\t\n\t#\t\tdr.find_element_by_xpath(\"/html/body\").send_keys(Keys.CONTROL+Keys.END)\n\t\t\t#  \n\t#\t\tdr.find_element_by_id(\"allCheckBox\").click()\n\t#\t\tdr.find_element_by_xpath(\"//div[@id='orderListTable']/thead[1]/tr/th[1]/div/label/input\").click()\n\t\t#\tdr.find_element_by_xpath(\"//li[@data-order-list-filter-tab='楽天処理中' and @class = 'filtertab']\").click()\n\t\t\t\n\t\t\t# dr.find_element_by_xpath(\"//a[@class = 'rms-collapse-button collapsed' and @aria-controls = 'collapseOrderListFilterBatchProcessing']\").click()\n\t\t\t#  \n\t#\t\tdr.find_element_by_xpath(\"//div[@class = 'rms-content-order-details-block-form-title']\").click()\n\t\t\t#  \n\t\t\t# dr.find_element_by_id(\"rms-content-order-filter-final-order-btn\").click()\n\t\t\t#  \n\t\t\t# dr.find_element_by_id(\"orderConfirm\").click()\n\t\tlistbox_output.insert(END, \"批量注文确认完毕\\n\")\n\texcept BaseException as e:\n\t\tprint(e)\n\t\tpass\n\n#登陆Amazon.co.jp\ndef Amazon_login():\n\ttry:\n#\t\tdr.refresh()\n\t\t\n\t\tnewwindow = 'window.open(\"%s\")' % amazon_url\n\t\tdr.execute_script(newwindow)\n\t\thandles = dr.window_handles\n\t\t#判断句柄\n\t\thandles = dr.window_handles\n\t\tfor handle in handles:\n\t\t\tdr.switch_to.window(handle)\n\t\t\tcurl = dr.current_url\n\t\t\tif curl.find(amazon_check_url) != -1:\n\t\t\t\tamazon_handle = handle\n\t\t\telif curl.find(lt_list_check_url) != -1:\n\t\t\t\tlt_list_handle = handle\n\t\t\telif curl.find(lt_info_url) != -1:\n\t\t\t\t\tlt_info_handle = handle\n\t\t \n\t\tdr.switch_to.window(amazon_handle)\n\t\t \n#\t\tdr.find_element_by_xpath(\"//*[@id='nav-link-accountList']\").click()\n\t\tsource = dr.page_source.encode('GBK', 'ignore')\n\t\tstrsource = source.decode('GBK','strict')\n\t\tsoup = BeautifulSoup(strsource,\"html.parser\")\n\t\temail = soup.find(\"sato@bjgbf.com\")\n\t\t# print('sato is'str(soup.find(\"satoさん\")))\n\t\tif str(soup.find(\"satoさん\")) == -1:\n\t\t\tif str(soup).find(\"sato@bjgbf.com\") != -1:\n\t\t\t\tprint(\"find email account\\n\")\n\t\t\t\tdr.find_element_by_id(\"ap_password\").send_keys(amazon_pwd)\n\t\t\t\ttry:\n\t\t\t\t\tWebDriverWait(dr,15).until(EC.element_to_be_clickable((By.XPATH ,\"//*[@id='signInSubmit']\")))\n\t\t\t\tfinally:\n\t\t\t\t\tdr.find_element_by_xpath(\"//*[@id='signInSubmit']\").send_keys(Keys.ENTER)\n\t\t\t\t\tdr.find_element_by_xpath(\"//*[@id='signInSubmit']\").click()\n\t\t\t\t\t# dr.find_element_by_xpath(\"//*[@id='signInSubmit']\").click()\n\t\t\t\t\n\t\t\telse:\n\t\t\t\tdr.find_element_by_id(\"ap_email\").send_keys(amazon_account)\n\t\t\t\tdr.find_element_by_id(\"continue\").click()\n\t\t\t\tdr.find_element_by_id(\"ap_password\").send_keys(amazon_pwd)\n\t\t\t\tdr.find_element_by_xpath(\"//*[@id='signInSubmit']\").send_keys(Keys.ENTER)\n\texcept BaseException as e:\n\t\tprint(e)\n\t\tif str(e).find(\"no such window: window was already closed\"):\n\t\t\tha = dr.window_handles\n\t\t\tdr.switch_to.window(ha[0])\n\t\tpass\n\n#清除日志\ndef cleanlogs():\n\tlistbox_output.delete(1.0,END)\n\n#结束\ndef endofroot():\n\tdr.quit()\n\troot.destroy()\n\nwith open(\"record.txt\",'w+') as f:\n\trecords_names = f.readline().split(',')\n\tf.close()\n\nButton(root, text = \"登陆亚马逊\", command = Amazon_login).place(x = 25,y = 50, width = 100,height = 20)\nButton(root, text = \"开始购物\", command = lt_ama).place(x = 25,y = 100, width = 100,height = 20)\nButton(root, text = \"地址确认\", command = after_submit_address).place(x = 25,y = 150, width = 100,height = 20)\n#Button(root, text = \"提取信息\", command = get_data).place(x = 425,y = 250, width = 150,height = 20)\nButton(root, text = \"确认购物\", command = submit_order).place(x = 25,y = 200, width = 100,height = 20)\nButton(root, text = \"自动操作\", command = autoctl).place(x = 25,y = 250, width = 100,height = 20)\nButton(root, text = \"批量注文\", command = submit_text).place(x = 25,y = 300, width = 100,height = 20)\nButton(root, text = \"一键发信\", command = send_message).place(x = 25,y = 350, width = 100,height = 20)\nButton(root, text = \"物流信息\", command = deliver_Manage).place(x = 25,y = 400, width = 100,height = 20)\n#Button(root, text = \"获得数据\", command = get_data).place(x = 50,y = 250, width = 325,height = 20)\n# Button(root, text = \"清除输出\", command = cleanlogs).place(x = 50,y = 320, width = 325,height = 20)\n# Button(root, text = \"退出程序\", command = endofroot).place(x = 425,y = 320, width = 325,height = 20)\n\nlistbox_output.insert(END,lt_account1+'\\n')\nlistbox_output.insert(END,pwd1+'\\n')\nlistbox_output.insert(END,lt_account2+'\\n')\nlistbox_output.insert(END,pwd2+'\\n')\n\n\n#Canvas(root, width = 250, height = 50).grid(row = 7, column = 1)\n\nt1 = threading.Thread(target = autoctl)\nt1.start()\n#t2 = threading.Thread(target = Amazon_login)\n#t2.start()\nroot.mainloop()\n","repo_name":"Cycsics/Rakuten-Amazon","sub_path":"letian1(1).py","file_name":"letian1(1).py","file_ext":"py","file_size_in_byte":65314,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"14348688224","text":"from AoE2ScenarioParser.datasets.object_support import Civilization\nfrom AoE2ScenarioParser.objects.aoe2_object import AoE2Object\nfrom AoE2ScenarioParser.objects.data_objects.player.player import Player\nfrom AoE2ScenarioParser.sections.retrievers.retriever_object_link import RetrieverObjectLink\nfrom AoE2ScenarioParser.sections.retrievers.retriever_object_link_group import RetrieverObjectLinkGroup\nfrom AoE2ScenarioParser.sections.retrievers.support import Support\n\n\nclass PlayerMetaData(AoE2Object):\n    \"\"\"Object for handling a tile in the map.\"\"\"\n\n    _link_list = [\n        RetrieverObjectLinkGroup(\"DataHeader\", \"player_data_1[__index__]\", group=[\n            RetrieverObjectLink(\"active\"),\n            RetrieverObjectLink(\"human\"),\n            RetrieverObjectLink(\"civilization\"),\n            RetrieverObjectLink(\"architecture_set\", support=Support(since=1.40), destination_object=Player),\n        ])\n    ]\n\n    def __init__(self, active: int, human: int, civilization: int, architecture_set: int, **kwargs):\n        super().__init__(**kwargs)\n\n        self.active: bool = bool(active)\n        self.human: bool = bool(human)\n        self.civilization: Civilization = Civilization(civilization)\n        self.architecture_set: Civilization = Civilization(architecture_set) if architecture_set is not None else None\n","repo_name":"KSneijders/AoE2ScenarioParser","sub_path":"AoE2ScenarioParser/objects/data_objects/player/player_meta_data.py","file_name":"player_meta_data.py","file_ext":"py","file_size_in_byte":1321,"program_lang":"python","lang":"en","doc_type":"code","stars":70,"dataset":"github-code","pt":"81"}
+{"seq_id":"25757276645","text":"import requests\n\n\nheaders = {\n    \"User-Agent\": \"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_5) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/50.0.2661.102 Safari/537.36\"\n}\n\n\ndef get_page_txt(site_url):\n    \"\"\"Uses requests library to get text from the url\n    It also catches the most common errors and stores them in an error list to check which likns failed later\"\"\"\n    try:\n        response = requests.get(site_url, headers=headers)\n        response.raise_for_status()\n        # print(f\"statuscode:{response.status_code}\")\n        return response.text\n    except requests.exceptions.ConnectionError:\n        # wrong_links.append(site_url)\n        print(f\"Failed to load the following link: {site_url}\")\n    except requests.exceptions.HTTPError:\n        # wrong_links.append(site_url)\n        print(f\"Failed to load the following link: {site_url}\")\n    return None\n","repo_name":"ivanbaug/wn-scrape","sub_path":"project/shared_funcs.py","file_name":"shared_funcs.py","file_ext":"py","file_size_in_byte":870,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"37365936843","text":"import sys\nimport os\nsys.path.append(os.path.abspath(os.path.dirname(os.path.dirname(__file__))))\nfrom app.api.models import Energy, db\nimport pandas as pd\n\n\n# Create the database\ndb.drop_all()\ndb.create_all()\n\npath = basedir = os.path.abspath(os.path.dirname(__file__))\ncsv_file_path = os.path.join(path,'Monitoring_report.csv')\n\n# Read CSV with Pandas\nwith open(csv_file_path, 'r') as file:\n    df = pd.read_csv(file)\n\nengine = db.get_engine()\ndf.columns = Energy.__table__.columns.keys()\ndf=df[~df.date.duplicated()]\ndf.date = pd.to_datetime(df.date,format='%d %b %Y %H:%M:%S')\n\n# Insert to DB\ndf.to_sql('energia',\n          con=engine,\n          index=False,\n          if_exists='append')\n\ndb.session.commit()","repo_name":"robamemar94/Prueba_Devloper_Wellnes","sub_path":"app/build_database.py","file_name":"build_database.py","file_ext":"py","file_size_in_byte":713,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"27638520162","text":"#! /usr/bin/env python\n\n# waf imports\nfrom waflib.Configure import conf\n\ndef options(opt):\n    opt.load('compiler_cxx')\n\n    ins_gr = opt.get_option_group('Installation and uninstallation options')\n    bld_gr = opt.get_option_group('Build and installation options')\n    conf_gr = opt.get_option_group('Configuration options')\n \n    def_enable_compiler_warnings = False\n    conf_gr.add_option(\n            '--enable-compiler-warnings',\n            dest = 'ENABLE_COMPILER_WARNINGS',\n            default = def_enable_compiler_warnings,\n            action= \"store_true\",\n            help = \"Enable compiler warnings. (default: %s)\" % def_enable_compiler_warnings\n            )\n\n    def_werror = False\n    conf_gr.add_option(\n            '--werror',\n            dest = 'WERROR',\n            default = def_werror,\n            action= \"store_true\",\n            help = \"Consider warnings fatal. (default: %s)\" % def_werror\n            )\n\n    def_disable_compile_opt = False\n    conf_gr.add_option(\n            '--disable-compile-optimizations',\n            dest = 'DISABLE_CXXFLAGS_OPT',\n            default = def_disable_compile_opt,\n            action= \"store_true\",\n            help = \"Don't check/set compile optimization flags. (default: %s)\" % def_disable_compile_opt\n            )\n\n    def_disable_link_opt = False\n    conf_gr.add_option(\n            '--disable-link-optimizations',\n            dest = 'DISABLE_LINK_OPT',\n            default = def_disable_link_opt,\n            action= \"store_true\",\n            help = \"Don't check/set link optimization flags. (default: %s)\" % def_disable_link_opt\n            )\n\n    def_disable_lto = False\n    conf_gr.add_option(\n            '--disable-lto',\n            dest = 'DISABLE_LTO',\n            default = def_disable_lto,\n            action= \"store_true\",\n            help = \"Don't check/set lto flags. (default: %s)\" % def_disable_lto\n            )\n\n    def_enable_debug = False\n    conf_gr.add_option(\n            '--enable-debug',\n            dest = 'ENABLE_DEBUG',\n            default = def_enable_debug,\n            action= \"store_true\",\n            help = \"Set debug flags. (default: %s)\" % def_enable_debug\n            )\n\n    def_enable_fftw3 = False\n    conf_gr.add_option(\n            '--enable-fftw3',\n            dest = 'ENABLE_FFTW3',\n            default = def_enable_fftw3,\n            action= \"store_true\",\n            help = \"Compile and link against libfftw3. (default: %s)\" % def_enable_fftw3\n            )\n\n    def_fftw3_cxxflags = None # Use pkg-config or env\n    conf_gr.add_option(\n            '--fftw3-cxxflags',\n            dest = 'FFTW3_CXXFLAGS',\n            default = def_fftw3_cxxflags,\n            action= \"store\",\n            help = \"Skip pkg-config and set fftw3 cxxflags explicitly (default: %s)\" % def_fftw3_cxxflags\n            )\n\n    def_fftw3_libs = None # Use pkg-config or env\n    conf_gr.add_option(\n            '--fftw3-libs',\n            dest = 'FFTW3_LIBS',\n            default = def_fftw3_libs,\n            action= \"store\",\n            help = \"Skip pkg-config and set fftw3 libs explicitly (default: %s)\" % def_fftw3_libs\n            )\n\n#------------------------------------------------------------------------------\n\n@conf\ndef check_api(conf):\n    if conf.env['DEST_OS'] != 'win32':\n        print('Checking API support:')\n        conf.check_cxx(function_name='std::chrono::steady_clock::now', header_name=\"chrono\")\n        conf.check_cxx(function_name='setpriority', header_name=\"sys/resource.h\")\n        conf.check_cxx(function_name='stat', header_name=\"sys/stat.h\")\n        conf.check_cxx(function_name='chmod', header_name=\"sys/stat.h\")\n        conf.check_cxx(function_name='nanosleep', header_name=\"ctime\")\n        conf.check_cxx(function_name='sincos', header_name=\"math.h\", mandatory=False)\n\n@conf\ndef check_flags(conf):\n    # Load this before checking flags\n    conf.load('compiler_cxx')\n\n    # Set warning flags 1st so -Werror catches all warnings\n    if conf.options.ENABLE_COMPILER_WARNINGS:\n        check_warning_cxxflags(conf)\n\n    check_required_flags(conf)\n\n    if conf.options.ENABLE_DEBUG:\n        check_debug_cxxflags(conf)\n    else:\n        if not conf.options.DISABLE_CXXFLAGS_OPT:\n            check_opt_cxxflags(conf)\n        if not conf.options.DISABLE_LTO:\n            check_lto_flags(conf)\n\n    if not conf.options.DISABLE_LINK_OPT:\n        check_link_flags(conf)\n\n@conf\ndef check_required_flags(conf):\n    conf.check_cxx(cxxflags = '-std=c++11', uselib_store='LOSSYWAV_REQUIRED', mandatory=False)\n    conf.check_cxx(cxxflags = '-O2', uselib_store='LOSSYWAV_REQUIRED', mandatory=False)\n    conf.check_cxx(cxxflags = '-pipe', uselib_store='LOSSYWAV_REQUIRED', mandatory=False)\n\n    conf.check_cxx(cxxflags = '-fPIE', uselib_store='LOSSYWAV_REQUIRED', mandatory=False)\n    conf.check_cxx(linkflags = '-pie', uselib_store='LOSSYWAV_REQUIRED', mandatory=False)\n\n    if conf.env['CXXFLAGS_LOSSYWAV_REQUIRED']:\n        conf.env.append_value('CXXFLAGS', conf.env['CXXFLAGS_LOSSYWAV_REQUIRED'])\n\n@conf\ndef check_warning_cxxflags(conf):\n    print('Checking for warning CXXFLAGS support:')\n\n    warn_flags = [\n            ['-Wall'],\n            ['-Wextra'],\n            ['-Wmissing-format-attribute'],\n    ]\n\n    for w in warn_flags:\n        conf.check_cxx(cxxflags = w, uselib_store='LOSSYWAV_WARNING', mandatory=False)\n\n    # Set -Werror if requested\n    if conf.options.WERROR:\n        conf.check_cxx(cxxflags = ['-Werror'], uselib_store='LOSSYWAV_WARNING', mandatory=False)\n    else:\n        conf.check_cxx(cxxflags = ['-Wno-error'], uselib_store='LOSSYWAV_WARNING', mandatory=False)\n\n\n    if conf.env['CXXFLAGS_LOSSYWAV_WARNING']:\n        conf.env.append_value('CXXFLAGS', conf.env['CXXFLAGS_LOSSYWAV_WARNING'])\n    else:\n        conf.fatal('None of the warning CXXFLAGS are supported by the compiler!')\n\n@conf\ndef check_debug_cxxflags(conf):\n    print('Checking for debug CXXFLAGS support:')\n\n    debug_flags = [\n            ['-Og'],\n            ['-ggdb'],\n            ['-fvar-tracking-assignments'],\n            ['-fno-omit-frame-pointer'],\n            ['-fstack-protector-strong'],\n            ['-fstack-check']\n    ]\n\n    for d in debug_flags:\n        conf.check_cxx(cxxflags = d, uselib_store='LOSSYWAV_DEBUG', mandatory=False)\n\n    if conf.env['CXXFLAGS_LOSSYWAV_DEBUG']:\n        conf.env.append_value('CXXFLAGS', conf.env['CXXFLAGS_LOSSYWAV_DEBUG'])\n    else:\n        conf.fatal('None of the debug CXXFLAGS are supported by the compiler!')\n\n@conf\ndef check_opt_cxxflags(conf):\n    print('Checking for optimized CXXFLAGS support:')\n\n    conf.check_cxx(cxxflags = '-Ofast', uselib_store='LOSSYWAV_OPT', mandatory=False)\n    if not conf.env['CXXFLAGS_LOSSYWAV_OPT']:\n        conf.check_cxx(cxxflags = '-O3', uselib_store='LOSSYWAV_OPT', mandatory=False)\n        conf.check_cxx(cxxflags = '-ffast-math', uselib_store='LOSSYWAV_OPT', mandatory=False)\n\n    if conf.env['CXXFLAGS_LOSSYWAV_OPT']:\n        if '-O2' in conf.env['CXXFLAGS']:\n            conf.env['CXXFLAGS'].remove('-O2')\n        conf.env.append_value('CXXFLAGS', conf.env['CXXFLAGS_LOSSYWAV_OPT'])\n\n@conf\ndef check_link_flags(conf):\n    print('Checking for optimized LINKFLAGS support:')\n\n    linkflags = [\n            ['-Wl,-O1'],\n            ['-Wl,--sort-common'],\n            ['-Wl,--as-needed'],\n            ['-Wl,-z,relro'],\n            ['-Wl,-z,now'],\n    ]\n\n    for l in linkflags:\n        conf.check_cxx(linkflags = l, uselib_store='LOSSYWAV', mandatory=False)\n\n    if conf.env['LINKFLAGS_LOSSYWAV']:\n        conf.env.append_value('LINKFLAGS', conf.env['LINKFLAGS_LOSSYWAV'])\n\n@conf\ndef check_lto_flags(conf):\n    print('Checking for LTO CXXFLAGS/LINKFLAGS support:')\n    conf.check_cxx(cxxflags = '-flto', uselib_store='LOSSYWAV_LTO', mandatory=False)\n    conf.check_cxx(linkflags = '-flto', uselib_store='LOSSYWAV_LTO', mandatory=False)\n\n    if conf.env['CXXFLAGS_LOSSYWAV_LTO'] or conf.env['LINKFLAGS_LOSSYWAV_LTO']:\n        if conf.env['CXXFLAGS_LOSSYWAV_LTO']:\n            conf.env.append_value('CXXFLAGS', conf.env['CXXFLAGS_LOSSYWAV_LTO'])\n\n        if conf.env['LINKFLAGS_LOSSYWAV_LTO']:\n            conf.env.append_value('LINKFLAGS', conf.env['LINKFLAGS_LOSSYWAV_LTO'])\n    else:\n        print('lto flags not supported by the compiler!')\n\n\n@conf\ndef check_pkg_deps(conf):\n    print('Check dependencies:')\n\n    # Make sure these exist exist\n    for v in ['INCLUDES', 'RPATH', 'CXXFLAGS', 'LDFLAGS' 'LIB' 'LIBPATH']:\n        if not v in conf.env:\n            conf.env[v] = []\n\n    if conf.options.ENABLE_FFTW3:\n        check_fftw3(conf)\n\n@conf\ndef check_fftw3(conf):\n    pkg_name = 'fftw3'\n    check_args = ['--cflags', '--libs']\n    min_ver = '3.3.0'\n    check_pkg(conf, pkg_name, check_args, min_ver)\n\n@conf\ndef check_pkg(conf, pkg_name, check_args, min_ver):\n\n    conf_opts_dict = eval( str(conf.options) )\n\n    opt_cxxflags_var = pkg_name.upper() + '_CXXFLAGS'\n    opt_libs_var = pkg_name.upper() + '_LIBS'\n\n    opt_cxxflags = (opt_cxxflags_var in conf_opts_dict) and  conf_opts_dict[opt_cxxflags_var] != None\n    opt_libs = (opt_libs_var in conf_opts_dict) and conf_opts_dict[opt_libs_var] != None\n\n    conf.start_msg('Checking %s:' % pkg_name)\n\n    if opt_cxxflags and opt_libs:\n        conf.end_msg('user-provided')\n        conf.env['CXXFLAGS'] += conf_opts_dict[opt_cxxflags_var].split(' ')\n        conf.env['LDFLAGS'] += conf_opts_dict[opt_libs_var].split(' ')\n    elif opt_cxxflags or opt_libs:\n        conf.fatal('Either set both %s and %s or let pkg-config do the checking.' % (opt_cxxflags_var, opt_libs_var))\n    else:\n        conf.end_msg('pkg-config')\n        conf.check_cfg(package = pkg_name, variables = ['includedir', 'prefix'])\n        # Store without version\n        conf.check_cfg(package = pkg_name + ' >= ' + min_ver, args = check_args, uselib_store=pkg_name.upper())\n\n        defines_var = 'DEFINES_' + pkg_name.upper()\n        if conf.env[defines_var]:\n            conf.env.DEFINES += conf.env[defines_var]\n\n        includes_var = 'INCLUDES_' + pkg_name.upper()\n        if conf.env[includes_var]:\n            conf.env.INCLUDES += conf.env[includes_var]\n\n        if conf.env[pkg_name.upper() + '_includedir']:\n            conf.env.INCLUDES += [ conf.env[pkg_name.upper() + '_includedir'] ]\n\n        # NetBSD relies on RPATH instead of ldconfig\n        # This would only be set if -Wl,-R<dir> (or -Wl,-rpath<DIR>) is a part of Libs\n        rpath_var = 'RPATH_' + pkg_name.upper()\n        if conf.env[rpath_var]:\n            conf.env.RPATH += conf.env[rpath_var]\n\n        libpath_var = 'LIBPATH_' + pkg_name.upper()\n        if conf.env[libpath_var]:\n            conf.env.LIBPATH += conf.env[libpath_var]\n\n        cxxflags_var = 'CXXFLAGS_' + pkg_name.upper()\n        if conf.env[cxxflags_var]:\n            conf.env.CXXFLAGS += conf.env[cxxflags_var]\n\n        lib_var = 'LIB_' + pkg_name.upper()\n        if conf.env[lib_var]:\n            conf.env.LIB += conf.env[lib_var]\n\n#------------------------------------------------------------------------------\n\ndef configure(conf):\n    check_flags(conf)\n    check_api(conf)\n    check_pkg_deps(conf)\n\n#------------------------------------------------------------------------------\n\ndef build(bld):\n\n    bld.objects(\n            source = [\n                'units/fftw_interface.cpp',\n                'units/nCore.cpp',\n                'units/nFFT.cpp',\n                'units/nFillFFT.cpp',\n                'units/nInitialise.cpp',\n                'units/nOutput.cpp',\n                'units/nParameter.cpp',\n                'units/nProcess.cpp',\n                'units/nRemoveBits.cpp',\n                'units/nSGNS.cpp',\n                'units/nShiftBlocks.cpp',\n                'units/nSpreading.cpp',\n                'units/nWav.cpp',\n                ],\n            target = ['lossywav-objs']\n            )\n\n    bld.program(\n            use = ['lossywav-objs'],\n            source = ['lossyWAV.cpp'],\n            target = 'lossywav'\n            )\n\n#------------------------------------------------------------------------------\n","repo_name":"MoSal/lossywav-for-posix","sub_path":"wscript","file_name":"wscript","file_ext":"","file_size_in_byte":11981,"program_lang":"python","lang":"en","doc_type":"code","stars":13,"dataset":"github-code","pt":"81"}
+{"seq_id":"17600629056","text":"#English Threasus\n\nimport json #library\nfrom difflib import get_close_matches\n\ndata = json.load(open(\"data.json\"))\n\ndef meaning(w):\n    word = w.lower()\n\n    if word in data:\n        return(data[word])\n    elif word.title() in data:\n        return(data[word.title()])\n    elif word.upper() in data:\n        return(data[word.upper()])\n    elif len(get_close_matches(word,data.keys())) > 0:\n        ans = input(\"Did you mean %s instead? Enter Y if yes, or N for no: \" %get_close_matches(word,data.keys())[0])\n        if ans == \"Y\":\n            return(meaning(get_close_matches(word,data.keys())[0]))\n        elif ans ==\"N\":\n            return(\"The word doesnot exit . Please double check it.\")\n        else:\n            return(\"We didn't understand your entry.\")\n    else:\n        return \"The word doesnot exit . Please double check it.\"\n\nword = input(\"Enter a word: \")\n\noutput = meaning(word)\n\nif isinstance(output,list):\n    for item in output:\n        print(item)\nelse:\n    print(output)\n","repo_name":"lipakshi-2001/English-Dictionay","sub_path":"engdict.py","file_name":"engdict.py","file_ext":"py","file_size_in_byte":989,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"35129801545","text":"import numpy\nimport math\n\n\ndef iter(a1, n, e):\n    z = [1] * n\n    y1 = numpy.zeros(n)\n    l1 = numpy.zeros(30)\n    v = numpy.zeros(n)\n    y2 = numpy.zeros(n)\n    l2 = numpy.zeros(200)\n    eps = True\n    k1 = 0\n    while eps:\n        for i in range(n):\n            y1[i] = sum(a1[i][j] * z[j] for j in range(n))\n\n        l1[k1] = sum(y1[i] * z[i] for i in range(n))\n\n        for i in range(n):\n            z[i] = y1[i] / math.sqrt(sum(y1[i] * y1[i] for i in range(n)))\n\n        Eps = math.sqrt(\n            sum(\n                (y1[i] - l1[k1] * z[i]) * (y1[i] - l1[k1] * z[i])\n                for i in range(n)\n            )\n        )\n        if Eps < e:\n            L1 = l1[k1]\n            x1 = z\n            eps = False\n        k1 += 1\n\n    z = [1] * n\n    eps2 = True\n    k2 = 0\n    while eps2:\n        for i in range(n):\n            v[i] = sum(a1[i][j] * z[j] for j in range(n))\n\n        l2[k2] = sum(v[i] * z[i] for i in range(n))\n        p = sum(v[i] * x1[i] for i in range(n))\n        for i in range(n):\n            y2[i] = v[i] - p * x1[i]\n\n        for i in range(n):\n            z[i] = y2[i] / math.sqrt(sum(y2[i] * y2[i] for i in range(n)))\n\n        Eps = math.sqrt(\n            sum(\n                (y2[i] - l2[k2] * z[i]) * (y2[i] - l2[k2] * z[i])\n                for i in range(n)\n            )\n        )\n        if Eps < e:\n            L2 = l2[k2]\n            x2 = z\n            eps2 = False\n        k2 += 1\n    return L1, L2, x1, x2, k1, k2\n\n\ndef vrash(a, n, e):\n    global k3\n    k3 = 0\n    v = numpy.zeros((n, n))\n    h = numpy.zeros((n, n))\n    hh = numpy.zeros((n, n))\n    s1 = numpy.zeros((n, n))\n    s2 = numpy.zeros((n, n))\n    chek = True\n    while chek:\n        imax = 0\n        jmax = 1\n        max = a[0][1]\n        for i in range(n):\n            for j in range(n):\n                if i < j:\n                    if abs(max) < abs(a[i][j]):\n                        max = a[i][j]\n                        imax = i\n                        jmax = j\n        if a[imax][imax] == a[jmax][jmax]:\n            f = math.pi / 4\n        else:\n            f = 0.5 * (\n                math.atan(2 * a[imax][jmax] / (a[imax][imax] - a[jmax][jmax]))\n            )\n\n        for i in range(n):\n            for j in range(n):\n                if i != j:\n                    h[i, j] = 0\n                else:\n                    h[i, j] = 1\n        h[imax][imax] = math.cos(f)\n        h[jmax][jmax] = math.cos(f)\n        h[imax][jmax] = -math.sin(f)\n        h[jmax][imax] = math.sin(f)\n\n        if k3 == 0:\n            for i in range(n):\n                for j in range(n):\n                    v[i][j] = h[i][j]\n        else:\n            for i in range(n):\n                for l in range(n):\n                    s1[i][l] = sum(v[i][j] * h[j][l] for j in range(n))\n            for i in range(n):\n                for j in range(n):\n                    v[i][j] = s1[i][j]\n\n        for i in range(n):\n            for j in range(n):\n                hh[i][j] = h[j][i]\n\n        for i in range(n):\n            for l in range(n):\n                s2[i][l] = sum(hh[i, j] * a[j][l] for j in range(n))\n\n        for i in range(n):\n            for l in range(n):\n                a[i][l] = sum(s2[i][j] * h[j][l] for j in range(n))\n\n        if abs(max) < e:\n            chek = False\n        k3 += 1\n\n    for i in range(n):\n        v[i][n - 1] = v[i][n - 1] / math.sqrt(\n            sum(v[j][n - 1] * v[j][n - 1] for j in range(n))\n        )\n    return v, a, k3\n\n\nN = 5\nE = 0.000001\n\nwith open('input.txt') as file:\n    lst = list()\n    for line in file.readlines():\n        lst.extend(line.rstrip().split(', '))\na = numpy.zeros((N, N))\nk = 0\nfor i in range(N):\n    for j in range(N):\n        a[i][j] = lst[k]\n        k += 1\n\noutFile = open('out.txt', 'w')\noutFile.write(\"Метод итераций:\\n\")\noutFile.write(\n    \"Собственное значение L1 = {0}      Число итераций = {1}  \\n\".format(\n        iter(a, N, E)[1], iter(a, N, E)[5]\n    )\n)\noutFile.write(\"Собственный вектор x1:\\n\")\nfor i in range(N):\n    outFile.write(\" {:>13.10}  \".format(iter(a, N, E)[3][i]))\noutFile.write(\n    \"\\nСобственное значение L2 = {0}      Число итераций = {1}\\n\".format(\n        iter(a, N, E)[0], iter(a, N, E)[4]\n    )\n)\noutFile.write(\"Собственный вектор x2:\\n\")\nfor i in range(N):\n    outFile.write(\" {:>13.10}  \".format(iter(a, N, E)[2][i]))\n\noutFile.write(\"\\n\\nМетод вращений:\\n\")\nV = vrash(a, N, E)[0]\noutFile.write(\"Число итераций = {0} \\n\".format(k3))\nfor i in range(N):\n    outFile.write(\"Собственное значение L{0} = {1}\\n\".format(i + 1, a[i][i]))\n    outFile.write(\"Собственный вектор x{0}:\\n\".format(i + 1))\n    for j in range(N):\n        outFile.write(\" {:>13.10}   \".format(V[j][i]))\n    outFile.write(\"\\n\")\noutFile.close()\n","repo_name":"slayzerg01/NM","sub_path":"LAB3/main3.py","file_name":"main3.py","file_ext":"py","file_size_in_byte":4870,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"26243055678","text":"# -*- coding: utf-8 -*-\n\n#    This file is part of Gertrude.\n#\n#    Gertrude is free software; you can redistribute it and/or modify\n#    it under the terms of the GNU General Public License as published by\n#    the Free Software Foundation; either version 3 of the License, or\n#    (at your option) any later version.\n#\n#    Gertrude 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 Gertrude; if not, see <http://www.gnu.org/licenses/>.\n\nfrom __future__ import unicode_literals\nfrom facture import *\nfrom ooffice import *\n\ntemplate_total_lines_count = 19\ntemplate_first_line = 4\ntemplate_lines_count = 8\n\n\nclass ExportFacturationModifications(object):\n    def __init__(self, annee):\n        self.multi = False\n        self.template = 'Export facturation.ods'\n        self.default_output = \"Export facturation %d.ods\" % annee\n        self.annee = annee\n        self.site = None\n        self.errors = {}\n        self.email = None\n\n    def execute(self, filename, dom):\n        if filename == 'styles.xml':\n            ReplaceTextFields(dom, GetCrecheFields(database.creche))\n            return []\n\n        elif filename == 'content.xml':\n            spreadsheet = dom.getElementsByTagName('office:spreadsheet').item(0)\n            tables = spreadsheet.getElementsByTagName(\"table:table\")\n            template_table = tables.item(0)\n\n            for m in range(0, 12, 2):\n                debut = datetime.date(self.annee, m + 1, 1)\n                fin = GetNextMonthStart(debut)\n                if fin > datetime.date.today():\n                    break\n                fin = GetMonthEnd(fin)\n\n                table = template_table.cloneNode(1)\n                table.setAttribute(\"table:name\", \"%s-%s %d\" % (months[m], months[m + 1], self.annee))\n                spreadsheet.insertBefore(table, template_table)\n\n                lines = table.getElementsByTagName(\"table:table-row\")\n                template_line = lines[2]\n                total_line = lines[3]\n                total_cell = total_line.getElementsByTagName(\"table:table-cell\")[2]\n\n                inscrits = database.creche.select_inscrits(debut, fin)\n                index = 0\n                for i, inscrit in enumerate(inscrits):\n                    try:\n                        facture = Facture(inscrit, self.annee, m+1, NO_NUMERO)\n                        if facture.total > 0:\n                            facture2 = Facture(inscrit, self.annee, m+2, NO_NUMERO)\n                            facture.jours_realises += facture2.jours_realises\n                            for activity in database.creche.activites:\n                                label = activity.label\n                                facture.heures_supplement_activites[label] += facture2.heures_supplement_activites[label]\n                        else:\n                            facture = Facture(inscrit, self.annee, m+2, NO_NUMERO)\n                        if facture.total == 0:\n                            continue\n                        line = template_line.cloneNode(1)\n                        fields = GetInscritFields(inscrit) + GetFactureFields(facture)\n                    except CotisationException as e:\n                        self.errors[GetPrenomNom(inscrit)] = e.errors\n                        continue\n\n                    # print fields\n                    ReplaceFields(line, fields)\n                    IncrementFormulas(line, row=+index)\n                    table.insertBefore(line, template_line)\n                    index += 1\n\n                table.removeChild(template_line)\n                total_cell.setAttribute(\"table:formula\", \"of:=SUM([.R3:.R%d])\" % (2+index))\n\n            spreadsheet.removeChild(template_table)\n\n        return self.errors\n","repo_name":"studio1247/gertrude","sub_path":"generation/export_facturation.py","file_name":"export_facturation.py","file_ext":"py","file_size_in_byte":3984,"program_lang":"python","lang":"en","doc_type":"code","stars":14,"dataset":"github-code","pt":"81"}
+{"seq_id":"32308220193","text":"import wx\n\nfrom state.lineprocedure_states import (\n    NewLineProcedureListState,\n    NewLineProcedureListStateItem,\n)\nfrom ui.generics import AddBitmapBtn, DeleteBitMapBtn\nfrom ui.mainview_widgets.order_book.procedure_page import page\n\n\nclass AddProcedureButton(AddBitmapBtn):\n    def __init__(self, parent):\n        super().__init__(parent)\n        self.parent: page.ProcedurePage\n\n    def Add(self):\n        choice_idx: int = self.parent.procedure_picker.GetSelection()\n        if choice_idx != wx.NOT_FOUND:\n            pr_id = self.parent.procedure_picker.GetDBID()\n            new_pr = NewLineProcedureListStateItem(pr_id)\n            NewLineProcedureListState.append_state(self.parent.mv, new_pr)\n            self.parent.mv.price.FetchPrice()\n            self.parent.procedure_picker.SetSelection(wx.NOT_FOUND)\n\n\nclass DelProcedureButton(DeleteBitMapBtn):\n    def __init__(self, parent):\n        super().__init__(parent)\n        self.parent: page.ProcedurePage\n\n    def onClick(self, _):\n        pr_list_idx: int = self.parent.procedure_list.GetFirstSelected()\n        if pr_list_idx != -1:\n            pr_id = self.parent.procedure_picker.GetDBID()\n            new_pr = NewLineProcedureListStateItem(pr_id)\n            if new_pr in self.parent.mv.state.new_lineprocedure_list:\n                self.parent.mv.state.new_lineprocedure_list.remove(new_pr)\n            else:\n                for old_pr in self.parent.mv.state.old_lineprocedure_list:\n                    if old_pr.procedure_id == pr_id:\n                        self.parent.mv.state.to_delete_old_lineprocedure_list.append(\n                            old_pr\n                        )\n                        self.parent.mv.state.old_lineprocedure_list.remove(old_pr)\n                        break\n            self.parent.procedure_list.pop_ui(pr_list_idx)\n            self.parent.mv.price.FetchPrice()\n","repo_name":"vuongkienthanh/SimpleClinic","sub_path":"src/ui/mainview_widgets/order_book/procedure_page/buttons.py","file_name":"buttons.py","file_ext":"py","file_size_in_byte":1872,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"12481212048","text":"#  File: WordSearch.py\n\n#  Description:\n\n#  Student Name: Adam Alam\n\n#  Student UT EID: aba2288\n\n#  Partner Name: Edoardo Palazzi\n\n#  Partner UT EID: emp2587\n\n#  Course Name: CS 313E\n\n#  Unique Number: 50205\n\n#  Date Created: 12 Sep 2019\n\n#  Date Last Modified: 16 Sep 2019\n\n\ndef main():\n    words = open(\"hidden.txt.\", \"r\")\n    words_new = words.readlines()\n    for x in range(len(words_new)):\n        words_new[x] = words_new[x].replace(\"\\n\", \"\")\n    dimensions = words_new[0].split()\n    m = int(dimensions[0])\n    n = int(dimensions[1])\n    words.close()\n\n    grid = []\n    for x in range(2, 2 + m):\n        grid.append(words_new[x].split())\n\n    rows = []\n    rows_rev = []\n    for x in grid:\n        rows.append(''.join(x))\n    for x in range(len(grid)):\n        grid[x].reverse()\n        rows_rev.append(''.join(grid[x]))\n    org_grid = grid\n    columns = []\n    new_column = []\n    columns_rev = []\n    for x in range(m):\n        for y in range(n):\n            new_column.append(grid[y][x])\n        columns.append(''.join(new_column))\n        new_column.reverse()\n        columns_rev.append(''.join(new_column))\n        new_column = []\n    columns.reverse()\n    columns_rev.reverse()\n\n    org_grid = []\n    for x in range(2, 2 + m):\n        org_grid.append(words_new[x].split())\n\n    diag_TR = []\n\n    s_col = 1\n    s_row = 1\n\n    while s_row < m:\n        new_diag = []\n        to_mov = 0\n        while s_row - to_mov > 0 and s_col + to_mov <= m:\n            new_diag.append(org_grid[s_row-1-to_mov][s_col-1+to_mov])\n            to_mov += 1\n        diag_TR.append(new_diag)\n        s_row += 1\n    while s_col <= m:\n        new_diag = []\n        to_mov = 0\n        while s_row - to_mov > 0 and s_col + to_mov <= m:\n            new_diag.append(org_grid[s_row-1-to_mov][s_col-1+to_mov])\n            to_mov += 1\n        diag_TR.append(new_diag)\n        s_col += 1\n\n    diag_TL = []\n    s_row = m\n    s_col = 1\n    while s_col != m:\n        new_diag = []\n        to_mov = 0\n        while s_col-to_mov > 0 and s_row - to_mov > 0:\n            new_diag.append(org_grid[s_row-1-to_mov][s_col-1-to_mov])\n            to_mov += 1\n        diag_TL.append(new_diag)\n        s_col += 1\n    while s_row > 0:\n        new_diag = []\n        to_mov = 0\n        while s_col-to_mov > 0 and s_row - to_mov > 0:\n            new_diag.append(org_grid[s_row-1-to_mov][s_col-1-to_mov])\n            to_mov += 1\n        diag_TL.append(new_diag)\n        s_row -= 1\n\n    diag_DR = []\n    for x in diag_TL:\n        diag_DR.append(x[::-1])\n    diag_DL = []\n    for x in diag_TR:\n        diag_DL.append(x[::-1])\n\n    diag_TL_str = []\n    diag_DR_str = []\n    diag_TR_str = []\n    diag_DL_str = []\n    for x in diag_TL:\n        diag_TL_str.append(''.join(x))\n    for x in diag_DL:\n        diag_DL_str.append(''.join(x))\n    for x in diag_TR:\n        diag_TR_str.append(''.join(x))\n    for x in diag_DR:\n        diag_DR_str.append(''.join(x))\n\n    word_list = []\n    for x in range(m+4, m+4+int(words_new[m+3])):\n        word_list.append(words_new[x].replace(\"\\n\", \"\"))\n\n    # finds words if they are not diagonal\n    def find_straight(word):\n        found = False\n        check = word.upper()\n        for x in range(m):\n            if check in columns_rev[x]:\n                f_col = m-x\n                f_row = columns_rev[x].index(check)+1\n                found = True\n            elif check in columns[x]:\n                f_col = x+1\n                f_row = columns[x].index(check)+1\n                found = True\n            elif check in rows_rev[x]:\n                f_row = x+1\n                f_col = m - rows_rev[x].index(check)\n                found = True\n            elif check in rows[x]:\n                found = True\n                f_row = x+1\n                f_col = rows[x].index(check)+1\n        if found:\n            if f_row < 10:\n                return str(f_row) + \"   \" + str(f_col)\n            elif f_row < 100 and f_row >= 10:\n                return str(f_row) + \"  \" + str(f_col)\n            elif f_row > 99:\n                return str(f_row) + \" \" + str(f_col)\n\n    # finds words in diagonals\n    def find_diag(word):\n        found = False\n        check = word.upper()\n        for x in range(2*m-1):\n            if check in diag_DR_str[x]:\n                found = True\n                second_index = diag_DR_str[x].index(check)\n                if x < m:\n                    f_row = m-x+second_index\n                    f_col = second_index+1\n                elif x >= m:\n                    f_row = second_index+1\n                    f_col = x-m+2+second_index\n            elif check in diag_DL_str[x]:\n                found = True\n                second_index = diag_DL_str[x].index(check)\n                if x < m:\n                    f_col = x-second_index+1\n                    f_row = second_index + 1\n                elif x >= m:\n                    f_col = m - second_index\n                    f_row = x-m + second_index + 2\n            elif check in diag_TR_str[x]:\n                found = True\n                second_index = diag_TR_str[x].index(check)\n                if x < m:\n                    f_col = second_index + 1\n                    f_row = x - (second_index - 1)\n                elif x >= m:\n                    f_col = x - m + second_index + 2\n                    f_row = m - second_index\n            elif check in diag_TL_str[x]:\n                found = True\n                second_index = diag_TL_str[x].index(check)\n                if x < m:\n                    f_row = m - second_index\n                    f_col = x - second_index + 1\n                elif x >= m:\n                    f_row = m - (x-m) - second_index - 1\n                    f_col = m - second_index\n            if found:\n                if f_row < 10:\n                    return str(f_row) + \"   \" + str(f_col)\n                elif f_row < 100 and f_row >= 10:\n                    return str(f_row) + \"  \" + str(f_col)\n                elif f_row > 99:\n                    return str(f_row) + \" \" + str(f_col)\n\n    f_file = open(\"found.txt\", \"w+\")\n    max_width = 0\n    for x in word_list:\n        if len(x) > max_width:\n            max_width = len(x)\n    for x in word_list:\n        y = find_straight(x)\n        z = find_diag(x)\n        if y:\n            f_file.write(x.ljust(max_width+1) + \" \" + y+\"\\n\")\n        elif z:\n            f_file.write(x.ljust(max_width+1) + \" \" + z+\"\\n\")\n        elif y is None and z is None:\n            f_file.write(x.ljust(max_width+1) + \" 0   0\"+\"\\n\")\n\n    f_file.close()\n\n\nmain()\n","repo_name":"AdamAlam/CS313E","sub_path":"Assignment 4/WordSearch.py","file_name":"WordSearch.py","file_ext":"py","file_size_in_byte":6502,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"72292291784","text":"# This file is example for the handling the login with oauth authentication.\n# it has two endpoints \n# 1. /login : it is used for redirecting the user to a aunthentication_url(you can get this deatils from the mantifest file) of particular plugin.\n# 2. /callback : it is called automatically on success or failure response of the /login endpoint.\n# requirements.\n# pip install requests_oauthlib\n# run this file first.\n\n\nimport json\nfrom urllib.parse import urljoin\n\nfrom requests_oauthlib import OAuth2Session\nfrom flask import Flask, request, session, redirect\nimport ssl\n\nredirect_uri = 'https://localhost:5000/callback'\napp = Flask(__name__)\napp.secret_key = \"qazwsx\"\n# Update the SSL context with the certificate and key file paths\n\ncontext = ssl.create_default_context(ssl.Purpose.CLIENT_AUTH)\n# add the certificate and key from the pyopenssl or generate it.\ncontext.load_cert_chain(certfile='certificate.crt', keyfile='private.key')\nclient_id = \"client_id\"\nclient_secret = \"client_secret\"\nclient_url = \"client_url\" \ntoken_url = \"authorization_token_url\"\n\n@app.route('/login', methods=[\"GET\"])\ndef login():\n    try:\n        global oauth\n        oauth = OAuth2Session(client_id, redirect_uri=redirect_uri)\n        authorization_url, state = oauth.authorization_url(client_url)\n        session['oauth_state'] = state\n        return redirect(authorization_url)\n    except Exception as e:\n        print(e)\n\n\n@app.route('/callback')\ndef callback():\n    if 'error' in request.args:\n        return 'Error: ' + request.args['error']\n\n    token = oauth.fetch_token(\n        token_url,\n        client_secret=client_secret,\n        code=request.args.get(\"code\", \"\")\n    )\n\n    return token\n\n\nif __name__ == '__main__':\n    app.run(debug=True, ssl_context=context)\n","repo_name":"edreisMD/plugnplai","sub_path":"examples/oauth_example/oauth_server.py","file_name":"oauth_server.py","file_ext":"py","file_size_in_byte":1758,"program_lang":"python","lang":"en","doc_type":"code","stars":219,"dataset":"github-code","pt":"81"}
+{"seq_id":"85095039","text":"# 선수의 수는 100명이하, 경기수는 4500 -> 충분히 작다.\r\n# 플로이드워셜 jk\r\ndef solution(n, results):\r\n    answer = 0\r\n\r\n    game = [[0] * (n + 1) for _ in range(n + 1)]\r\n\r\n    for i in range(n):\r\n        game[i][i]=0\r\n\r\n    for j in results:\r\n        game[j[0]][j[1]] = 1 # 승리\r\n        game[j[1]][j[0]] = -1 # 패배\r\n\r\n    for i in range(1, n + 1):\r\n        for j in range(1, n + 1):\r\n            for k in range(1, n + 1):\r\n                if game[j][k]==0: # i와 j의 승패를 모를때. 플로이드워셜 순서 중요. jk!! 순서에따라 결과라 다를수있음\r\n                    if game[j][i]== 1 and game[i][k]==1: # i가 k를 이기고, k가 j를 이기면 i는 j를 이긴다.\r\n                        game[j][k]=1\r\n                    elif game[j][i]== -1 and game[i][k]== -1: # 지는경우\r\n                        game[j][k]=-1\r\n\r\n    for i in range(1, len(game)):\r\n        print(game[i][1:])\r\n        if game[i][1:].count(0) == 1:  # 0이 한개라는건 자기자신 외 다른 선수와의 승패결과가 모두 존재.\r\n            answer += 1\r\n\r\n    return answer\r\n\r\nn=5\r\nresult = [[1, 4], [4, 2], [2, 5], [5, 3]]\r\nsolution(n,result)","repo_name":"Areum0921/Abox","sub_path":"Programmers/ranking.py","file_name":"ranking.py","file_ext":"py","file_size_in_byte":1188,"program_lang":"python","lang":"ko","doc_type":"code","stars":1,"dataset":"github-code","pt":"81"}
+{"seq_id":"4157346073","text":"#coding: utf-8\nimport os\nimport glob\nimport pickle\n\n\nMAXSIZE = 30\n\n\ndef index_all(path, ftype, wd):\n    index = {}\n    def _index(p, ftype):\n        nonlocal index\n        for file in glob.glob(os.path.join(p, ftype)):\n            print(file, flush=True)\n            index[file] = open(file, encoding='utf-8').read()\n        for np in glob.glob(os.path.join(p, \"*\")):\n            _index(np, ftype)\n    _index(path, ftype)\n    c = 0\n    while len(index):\n        i = 0\n        new = {}\n        keys = list(index.keys())\n        for key in keys:\n            new[key] = index.pop(key)\n            i += 1\n            if i == MAXSIZE:\n                break\n        pickle.Pickler(open(os.path.join(wd, \"indexes\", os.path.basename(path) + str(c) + \".part\"), \"wb\")).dump(new)\n        c += 1\n    pickle.Pickler(open(os.path.join(wd, os.path.basename(path) + \".index\"), \"wb\")).dump([os.path.basename(path) + str(k) + \".part\" for k in range(c)])\n\n\ndef search(archive, kws):\n    full_index = pickle.Unpickler(open(archive, \"rb\")).load()\n    results = {}\n    for f in full_index:\n        temp = pickle.Unpickler(open(os.path.join(os.path.dirname(archive), f), \"rb\")).load()\n        for ntemp, ftemp in temp.items():\n            if kws in ftemp:\n                fv = ftemp.replace(\"\\r\\n\", \"\\n\").replace(\"\\r\", \"\\n\").split(\"\\n\")\n                enc = []\n                for i, line in enumerate(fv):\n                    if kws in line:\n                        enc.append(i - 4)\n                        enc.append(i + 4)\n                        break\n                results[ntemp] = [\"{:4}: {}\".format(i + enc[0], fv[enc[0]:enc[1]][i]) for i in range(7)]\n    display(results)\n\n\ndef display(r):\n    for k, v in r.items():\n        print(k, \"\\n\".join(v), sep=\"\\n\" + \"-\" * len(k) + \"\\n\")\n        if input() == \"q\":\n            break\n\n\ndef main():\n    wd = os.getcwd()\n    while True:\n        cmd, *args = input(os.getcwd() + \"$ \").split(\" \")\n        if cmd == \"cd\":\n            os.chdir(os.path.join(os.getcwd(), \" \".join(args)))\n        elif cmd == \"index\":\n            ftype = args[1] if args[1:2] else \"*.*\"\n            p = os.path.join(os.getcwd(), args[0])\n            print(\"This action is going to take a will. Registering files in {}\".format(os.path.join(p, ftype)), flush=True)\n            index_all(p, ftype, wd)\n        elif cmd == \"search\":\n            archive = os.path.join(wd, \"indexes\", args[0] + \".index\")\n            print(\"Searching in index at {}, will take a will\".format(archive), flush=True)\n            search(archive, \" \".join(args[1:]))\n        elif cmd == \"lib\":\n            for file in glob.glob(\"indexes/*.index\"):\n                print(os.path.basename(file)[:-6])\n        elif cmd == \"help\":\n            print(\"Commands :\")\n            print(\"   cd path to change your current directory to path\")\n            print(\"   index directory [file type] to index all the files matching file type, in the directory specified\")\n            print(\"   search directory (keywords) to search in the archive directory.index for the keywords specified\")\n            print(\"   lib to display all the available indexes\")\n        elif cmd == \"quit\":\n            break\n\n\nif __name__ == '__main__':\n    main()","repo_name":"SuperFola/sh","sub_path":"sh_index.py","file_name":"sh_index.py","file_ext":"py","file_size_in_byte":3201,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"81"}
+{"seq_id":"33517036255","text":"#!/usr/bin/env python\n\n\"\"\"\n\nLighthouses on an island have blink codes that let ships know which lighthouse they are seeing. Input should be strings of S and L. Here is a (Python) example of codes for 4 lighthouses as a list of strings:\n\n['SLS', 'SLL', 'SSS', 'LSL']\nwhere:\n\nS = short ON (1 sec)\nL = long ON (3 sec)\n\nLong and short ON's are both followed by 1 sec OFF, except the last in the code, which is followed by a 7-second LONG OFF pause to signify end of code. The code for the first lighthouse in the above example corresponds to this blink schedule in seconds :\n\n1 ON, 1 OFF, 3 ON, 1 OFF, 1 ON, 7 OFF [repeat]\n\nChallenge:\n\nn Lighthouses come on simultaneously, each at the start of its schedule. Write a program that takes a series of blink codes (strings in the above format) and outputs the total number of seconds that exactly zero, one, two, three, ... n lighthouses were on at the same time after the first hour of lighthouse operation (So the output numbers will always total 3600.)\n\nTest Cases:\n\nInput -> Output\n['SLS', 'SLL', 'SSS', 'LSL'] -> 1125, 890, 652, 590, 343\n['S', 'L', 'SS'] -> 2250, 630, 540, 180\n['SLSL', 'S'] -> 1850, 1450, 300\n['SLS'] -> 2314, 1286\n['SS', 'SS', 'L'] -> 2520, 360, 0, 720\nRules:\n\nThere can be one or more lighthouses (code strings).\n\nAll lighthouses come on simultaneously, each at the start of its code schedule.\n\nCodes can be any length >= 1 and don't have to be unique (however off-shore mayhem may ensue).\n\nJust for clarity, the 652 in the first example is the time in seconds that exactly 2 lighthouse (not 2 or more) were on.\n\nThe shortest code in bytes wins (code-golf).\n\n\"\"\"\n\n# Based on @SevC_10 solution\ndef calculate(lighthouses):\n    lighthouses = [x.replace('S', '10') for x in lighthouses]\n    lighthouses = [x.replace('L', '1110') for x in lighthouses]\n    \n    lighthouses = [x + '0'*6 for x in lighthouses]\n    lighthouses = [x * 450 for x in lighthouses]\n\n    total = [0] * (len(lighthouses) + 1)\n    for i in range(3600):\n        total[[c[i] for c in lighthouses].count('1')] += 1\n    return total\n\ndef main():\n    assert(calculate(['SLS', 'SLL', 'SSS', 'LSL']) == [1125, 890, 652, 590, 343])\n    assert(calculate(['S', 'L', 'SS']) == [2250, 630, 540, 180])\n    assert(calculate(['SLSL', 'S']) == [1850, 1450, 300])\n    assert(calculate(['SLS']) == [2314, 1286])\n    assert(calculate(['SS', 'SS', 'L']) == [2520, 360, 0, 720])\n\nmain()\n","repo_name":"qeedquan/challenges","sub_path":"codegolf/blinking-lighthouses.py","file_name":"blinking-lighthouses.py","file_ext":"py","file_size_in_byte":2400,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"70457928264","text":"def comb(n, k):\n    if n == k: return 1\n    if k == 0: return 1\n    if k == 1: return n\n    if k > n: return 0\n\n    fac = list()\n    fac.append(1)\n    fac.append(2)\n\n    for i in range(2, n):\n        fac.append(fac[i-1]*(i+1))\n\n    a = fac[n-1]/(fac[k-1]*fac[(n-k-1)])\n    return a\n\n\ndef main():\n    n = int(input())\n    l = input().split()\n    k = int(input())\n\n    a = l.count('a')\n\n    all_combi = comb(n, k)\n    div_combi = comb(n-a, k)\n\n    print('{:.3f}'.format((all_combi-div_combi)/all_combi))\n\n\nif __name__ == \"__main__\":\n    # execute only if run as a script\n    main()\n","repo_name":"DonCebsi/python","sub_path":"Hackerrank/Python/Medium/5.Itertools/Iterables and Iterators.py","file_name":"Iterables and Iterators.py","file_ext":"py","file_size_in_byte":580,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"8259496453","text":"import numpy as np\nfrom scipy.optimize import minimize_scalar\n\n\nfrom Population import Population\n\n\nnp.random.seed(1)\n\n\nlineList = [line.rstrip('\\n') for line in open(\"plasmid_8k.fasta\")]\nseq = ''.join(lineList[1:])\npop_size = 1000\nnb_gen = 20\n\n\ndef best_score_given_mutation_probability(alpha):\n    \"\"\"permet d'effectuer une optimisation sur la probabilité de mutation d'un individu\"\"\"\n    pop = Population(seq, pop_size)\n    pop.evolve(nb_gen, \"Tournoi\", alpha=alpha)\n    return pop._Get_Current_Best()[1]\n\n\n# Problème d'optimisation sous contrainte : 0<=alpha<=1 car c'est une\n# probabilité et minimisation de la fonction coût\nres = minimize_scalar(\n    best_score_given_mutation_probability, bounds=(\n        0, 1), method='bounded')\n\nif res.success:\n    print(\"Minimum trouvé !\\n\")\n    print(\"Valeur minimale obtenue de\", res.fun, \"pour alpha =\", res.x)\nelse:\n    print(\"Pas de minimum trouvé...\")\n","repo_name":"TanguyColleville/Genetic_Algorithm","sub_path":"Optimize_parameters.py","file_name":"Optimize_parameters.py","file_ext":"py","file_size_in_byte":909,"program_lang":"python","lang":"fr","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"25425957412","text":"\n# coding: utf-8\n\n# In[1]:\n\n\nfrom IPython.display import SVG\nfrom rdkit.Chem import AllChem as Chem \nfrom rdkit.Chem.Draw import rdMolDraw2D, MolToFile #IPythonConsole #Needed to show molecules\nfrom rdkit.Chem.Draw.MolDrawing import MolDrawing, DrawingOptions\nimport pandas as pd\nfrom cairosvg import svg2png\n\nfrom rdkit.Chem.Lipinski import RotatableBondSmarts\n# In[29]:\n\n\n# Generate images for all kinase inhibitors for both OE conjugation and RDKit conjugation\nrdkit_mols = Chem.SmilesMolSupplier('kinase_inhibitors.smi')\nfor mol in rdkit_mols:\n    # Write out image of resonance structure\n    Chem.Compute2DCoords(mol)\n    conjugated_bonds = [bond.GetIdx() for bond in mol.GetBonds() if bond.GetIsConjugated()]\n    drawer = rdMolDraw2D.MolDraw2DSVG(800,400)\n    drawer.DrawMolecule(mol, highlightAtoms=[], highlightBonds=conjugated_bonds)\n    drawer.FinishDrawing()\n    svg = drawer.GetDrawingText().replace('svg:','')\n    outfile = open(\"images/{}_rdkit_res.png\".format(mol.GetProp(\"_Name\")), 'wb')\n    svg2png(svg, write_to=outfile)\n    outfile.close()\n    \n    # Find rotatable bonds\n    atom_tuples = mol.GetSubstructMatches(RotatableBondSmarts)\n    rotatable_bonds = []\n    for a1, a2 in atom_tuples:\n        bond = mol.GetBondBetweenAtoms(a1, a2)\n        rotatable_bonds.append(bond.GetIdx())\n    drawer = rdMolDraw2D.MolDraw2DSVG(800,400)\n    drawer.DrawMolecule(mol, highlightAtoms=[], highlightBonds=rotatable_bonds)\n    drawer.FinishDrawing()\n    svg = drawer.GetDrawingText().replace('svg:','')\n    outfile = open(\"images/{}_rdkit_rotor.png\".format(mol.GetProp(\"_Name\")), 'wb')\n    svg2png(svg, write_to=outfile)\n    outfile.close()\n\n","repo_name":"choderalab/fragmenter_data","sub_path":"conjugation/RDKit_images.py","file_name":"RDKit_images.py","file_ext":"py","file_size_in_byte":1649,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"81"}
+{"seq_id":"4308085512","text":"import time\nimport signal\nimport asyncio\nfrom functools import wraps\nfrom multiprocessing import TimeoutError\nfrom multiprocessing.pool import ThreadPool\n\ndef exec_time(logger=None):\n    '''\n    Decorator for module execution time.\n    '''\n\n    def timeit(f):\n        @wraps(f)\n        def timed(*args, **kwargs):\n            ts = time.time()\n            result = f(*args, **kwargs)\n            te = time.time()\n            if logger:\n                logger.debug('[%r] execution time: %2.2f seconds' %\n                             (f.__name__, (te-ts)))\n            else:\n                print('[%r] execution time: %2.2f seconds' %\n                      (f.__name__, (te-ts)))\n            return result\n        return timed\n    return timeit\n\n\ndef retry(*exceptions, retries=4, delay=4, logger=None):\n    '''\n    Decorator for function retry\n    '''\n\n    def deco_retry(f):\n        @wraps(f)\n        def inner(*args, **kwargs):\n            mtries, mdelay = retries, delay\n            while mtries > 0:\n                try:\n                    return f(*args, **kwargs)\n                except exceptions as e:                    \n                    msg = '%s; %d tries left, Retrying in %d seconds...' % (str(e), mtries, mdelay)\n                    mtries -= 1\n\n                    if logger:\n                        logger.warning(msg)\n                    else:\n                        print(msg)\n\n                    time.sleep(mdelay)\n\n            return f(*args, **kwargs)\n        inner.__name__ = f.__name__\n        return inner\n    return deco_retry\n\n\ndef async_retry(*exceptions, retries=4, delay=4, logger=None):\n    '''\n    Decorator for async function retry.\n    '''\n\n    def deco_retry(f):\n        @wraps(f)\n        async def inner(*args, **kwargs):\n            mtries, mdelay = retries, delay\n\n            while mtries > 0:\n                try:\n                    return await f(*args, **kwargs)\n                except exceptions as e:\n                    msg = '{}; {} tries left, Retrying in {} seconds...'.format(str(e), mtries, mdelay)\n                    mtries -= 1\n\n                    if logger:\n                        logger.warning(msg)\n                    else:\n                        print(msg)\n\n                    await asyncio.sleep(mdelay)\n\n            return await f(*args, **kwargs)\n\n        return inner\n    return deco_retry\n\nclass DTCTimeoutException(Exception):\n    def __init__(self, seconds):\n        self.message = 'timeout after {} seconds'.format(seconds)\n        super().__init__(self.message)\n\ndef timeout(s, handler=None):\n    '''\n    Timeout decorator.\n    ref: https://anonbadger.wordpress.com/2018/12/15/python-signal-handlers-and-exceptions/\n    param: \n        s:  time in seconds the function run before terminated.\n        handler:  optional, for unix like system only, define custom function handling the timeout event.\n    '''\n\n    def deco_timeout(f):\n        if hasattr(signal, \"SIGALRM\"):\n            '''\n            for unix like system.\n            '''\n            @wraps(f)\n            def inner(*args, **kwargs):\n                _handler = lambda _s, _f: (_ for _ in ()).throw(DTCTimeoutException(s)) if handler == None else handler\n\n                signal.signal(signal.SIGALRM, _handler)\n                signal.alarm(s)\n                try:\n                    return f(*args, **kwargs)\n                except:\n                    raise\n                finally:\n                    signal.alarm(0)\n                \n            return inner\n\n        else:\n            '''\n            for others systems without SIGALRM, use multiprocessing instead.\n            '''\n\n            def wrapper(*args, **kwargs):\n                pool = ThreadPool(processes=1)\n                results = pool.apply_async(f, args, kwargs)\n                pool.close()\n                try:\n                    return results.get(s)\n                except TimeoutError:\n                    raise DTCTimeoutException(s)\n                finally:\n                    pool.terminate() ## thread seems still running. todo...\n\n            return wrapper\n\n    return deco_timeout","repo_name":"cctester/pydtc","sub_path":"pydtc/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":4110,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"1849565398","text":"# -*- coding: utf-8 -*-\n# @Author: JinHua\n# @Date:   2019-11-08 10:30:37\n# @Last Modified by:   JinHua\n# @Last Modified time: 2019-11-08 10:48:30\n\n\nimport os\nimport log\nimport flask\n\n\n\nlogger = log.get_logger('http_main', filePath='log/http_main.log')\n\nbase_folder = os.path.dirname(os.path.abspath(__file__))\nstatic_folder = os.path.join(base_folder, 'file')\nlogger.info('Static folder is {}'.format(static_folder))\n\napp = flask.Flask('http_server', static_url_path='/static', static_folder=static_folder)\n\n\ndef send_file(filename):\n    logger.info('Start to download file {}'.format(filename))\n    with open(os.path.join(static_folder,filename), 'rb') as f:\n        while 1:\n            data = f.read(10 * 1024 * 1024)\n            if not data:\n                break\n            yield data\n\n\n@app.route('/status')\ndef check_server_status():\n    logger.info('Statr to check server status')\n    return '200'\n\n@app.route('/download', methods=['GET'])\ndef download_file():\n    filename = flask.request.args.get('filename')\n    logger.info('Filename is {}'.format(filename))\n    response = flask.Response(send_file(filename), content_type='application/octet-stream')\n    response.headers[\"Content-disposition\"] = 'attachment; filename={}'.format(filename)\n    return  response\n\napp.run(host=\"0.0.0.0\", port=6000)","repo_name":"15871722713/http_server","sub_path":"http_main.py","file_name":"http_main.py","file_ext":"py","file_size_in_byte":1308,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"72174664585","text":"import glob\nimport json\nimport os\nimport subprocess\n\nfrom hatch_jupyter_builder import npm_builder\nfrom packaging.version import Version\n\n\ndef builder(target_name, version, *args, **kwargs):\n    # Allow building from sdist without node.\n    if target_name == \"wheel\" and not os.path.exists(\"dev_mode\"):\n        return\n\n    npm_builder(target_name, version, *args, **kwargs)\n\n    if version == \"editable\":\n        return\n\n    files = glob.glob(\"jupyterlab/static/*.js.map\")\n    for path in files:\n        os.remove(path)\n\n    target = glob.glob(\"jupyterlab/static/package.json\")[0]\n    with open(target) as fid:\n        npm_version = json.load(fid)[\"jupyterlab\"][\"version\"]\n\n    py_version = subprocess.check_output([\"hatchling\", \"version\"])  # noqa S603 S607\n    py_version = py_version.decode(\"utf-8\").strip()\n\n    if Version(npm_version) != Version(py_version):\n        msg = \"Version mismatch, please run `npm run prepare:python-release`\"\n        msg += f\"; NPM {npm_version} / Python {py_version}\"\n        raise ValueError(msg)\n","repo_name":"jupyterlab/jupyterlab","sub_path":"buildapi.py","file_name":"buildapi.py","file_ext":"py","file_size_in_byte":1032,"program_lang":"python","lang":"en","doc_type":"code","stars":13417,"dataset":"github-code","pt":"81"}
+{"seq_id":"71284806346","text":"import pandas as pd\nimport sys\n\nargs = sys.argv   \n\n# The function deletes rows with any null value/s because the file comes with several\n# corrupted rows\ndef transform_data(filepath='datos_data_engineer.tsv', output_file='datos_data_engineer_output_file.csv'):\n    \"\"\"The function takes a file_path, read the file, process it and then rewrite in the desired format\"\"\"\n    print(\"parameter filepath: \"+filepath+\", parameter output_file= \"+output_file)\n    data = pd.read_csv(filepath,sep='\\t',encoding='UTF-16LE').dropna(how='any')\n    data.to_csv(output_file,sep='|',encoding='UTF-8',index=False)\n\ntransform_data(filepath=args[1], output_file=args[2])","repo_name":"fedezimm/ejercicio_etermax","sub_path":"ejercicio2.py","file_name":"ejercicio2.py","file_ext":"py","file_size_in_byte":652,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"11744727881","text":" \n# -*- coding: iso-8859-1 -*-\n\"\"\"\nCreated on Sat May 24 17:13:35 2014\n\n@author: iury\n\"\"\"\n\nimport numpy as np\n\neo = 8.854e-12\nmuo = np.pi*4*e-7\n\nprint(\"\\n\")\nprint(\"------------CABO COAXIAL-------------\")\n\nraioI = float(input(\"Digite o raio interno (mm): \"))\nraioE = float(input(\"Digite o raio externo (mm): \"))\npermissividadeR = float(input(\"Digite a permissividade relativa: \"))\ncondutividadeD = float(input(\"Digite a conditividade do dieletrico (S/m): \"))\ncondutividadeC = float(input(\"Digite a conditividade do condutor (S/m): \"))\nfrequencia = float(input(\"Digite a frequencia (Hz): \"))\n\nomega = 2*np.pi*frequencia\n\nG = (2*np.pi*condutividadeD)/(np.log(raioE/raioI))\nC = (2*np.pi*permissividadeR*eo)/(np.log(raioE/raioI))\nL = muo*np.log(raioE/raioI)/(2*np.pi)\nRs = np.sqrt(np.pi*frequencia*muo/condutividadeC)\nR = (1000*((1/raioI) + (1/raioE))*Rs)/(2*np.pi)\ngamma = np.sqrt((R + 1j*omega*L)*(G + 1j*omega*C))\n\nprint(\"\\n\")\nprint(\"-------------RESULTADOS-------------\")\nprint(\"G = \" + str(G) + \" S/m\")\nprint(\"C = \" + str(C) + \" F/m\")\nprint(\"L = \" + str(L) +  \" H/m\")\nprint(\"R = \" + str(R) + \" ohm/m\")\nprint(\"Constante de Propagacao (Gamma) = \" + str(gamma))","repo_name":"icleveston/Python-Codes","sub_path":"Eletromagnetismo/Parte_3/parametrosDistribuidos.py","file_name":"parametrosDistribuidos.py","file_ext":"py","file_size_in_byte":1158,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"6778670280","text":"from __future__ import print_function\n\n\"\"\"This module implements a small database for tracking security bugs.\n\nNote that the database is always secondary to the text files.  The\ndatabase is only an implementation tool, and not used for maintaining\nthe data.\n\nThe data is kept in a SQLite 3 database.\n\nFIXME: Document the database schema once it is finished.\n\"\"\"\n\nfrom apt_pkg import version_compare\nimport apsw\nimport base64\nimport bugs\nfrom collections import namedtuple\nimport pickle\nimport glob\nimport itertools\nimport os\nimport os.path\nimport re\nimport sys\nimport zlib\n\nimport config\nimport debian_support\nfrom debian_support import PointUpdateParser\n\nfrom helpers import isstring\n\nclass InsertError(Exception):\n    \"\"\"Class for capturing insert errors.\n\n    The 'errors' member collects all error messages.\n    \"\"\"\n\n    def __init__(self, errors):\n        assert len(errors) > 0, errors\n        assert isinstance(errors, list), errors\n        self.errors = errors\n\n    def __str__(self):\n        return self.errors[0] + ' [more...]'\n\ndef mergeLists(a, b):\n    \"\"\"Merges two lists.\"\"\"\n    if isstring(a):\n        if a == \"\":\n            a = []\n        else:\n            a = a.split(',')\n    if isstring(b):\n        if b == \"\":\n            b = []\n        else:\n            b = b.split(',')\n    result = {}\n    for x in a:\n        result[x] = 1\n    for x in b:\n        result[x] = 1\n    result = list(result.keys())\n    result.sort()\n    return result\n\nclass NVDEntry:\n    \"\"\"A class for an entry in the nvd_data table.\n    Objects have the same fileds as the table.\"\"\"\n    def __init__(self, row, description):\n        for x in range(len(row)):\n            setattr(self, description[x][0], row[x])\n    def rangeString(self):\n        result = []\n        if self.range_local:\n            result.append(\"local\")\n        if self.range_remote:\n            result.append(\"remote\")\n        if self.range_user_init:\n            result.append(\"user-initiated\")\n        return \", \".join(result)\n\nclass SchemaMismatch(Exception):\n    \"\"\"Raised to indicate a schema mismatch.\n\n    The caller is expected to remove and regenerate the database.\"\"\"\n\n# Returned by getBugsForSourcePackage().\n# all/open/unimportant/resolved are sequences of BugForSourcePackage.\nBugsForSourcePackage = namedtuple(\n    \"BugsForSourcePackage\",\n    \"all_releases all open unimportant resolved\")\n\n# Returned by getBugsForSourcePackage().  releases is a sequence of\n# BugForSourcePackageRelease.  global_state is the aggregated state\n# across all releases (open/resolved/unimportant).\nBugForSourcePackage = namedtuple(\n    \"BugForSourcePackage\",\n    \"bug description global_state releases\")\n\n# Returned by getBugsForSourcePackage(). release, subrelease, version\n# come from the source_packages table.  vulnerable comes from\n# source_package_status.  state is open/no-dsa/resolved/unimportant\n# and inferred from vulnerable and package_notes_nodsa.\n# The reason field holds no-dsa substates, which can be ignored/postponed\nBugForSourcePackageRelease = namedtuple(\n    \"BugForSourcePackageRelease\",\n    \"release subrelease version vulnerable state comment reason\")\n\n# Internally used by getBugsForSourcePackage().\nBugsForSourcePackage_internal = namedtuple(\n    \"BugsForSourcePackage_internal\",\n    \"bug_name description release subrelease version vulnerable urgency\")\nBugsForSourcePackage_query = \\\n\"\"\"SELECT bugs.name AS bug_name, bugs.description AS description,\n    sp.release AS release, sp.subrelease AS subrelease, sp.version AS version,\n    st.vulnerable AS vulnerable, st.urgency AS urgency\n  FROM bugs\n  JOIN source_package_status st ON (bugs.name = st.bug_name)\n  JOIN source_packages sp ON (st.package = sp.rowid)\n  WHERE sp.name = ?\n  AND (bugs.name LIKE 'CVE-%' OR bugs.name LIKE 'TEMP-%')\n  ORDER BY bugs.name COLLATE version DESC, sp.release\"\"\"\n# Sort order is important for the groupby operation below.\n\ndef getBugsForSourcePackage(cursor, pkg):\n    data = [BugsForSourcePackage_internal(*row) for row in\n            cursor.execute(BugsForSourcePackage_query, (pkg,))]\n    # Filter out special releases such as backports.\n    data = [row for row in data\n            if debian_support.internRelease(row.release) is not None]\n    # Obtain the set of releases actually in used, by canonical order.\n    all_releases = tuple(sorted(set(row.release for row in data),\n                                   key = debian_support.internRelease))\n    # dict from (bug_name, release) to the no-dsa reason/comment string.\n    no_dsas = {}\n    for bug_name, release, reason, comment in cursor.execute(\n            \"\"\"SELECT bug_name, release, reason, comment FROM package_notes_nodsa\n            WHERE package = ?\"\"\", (pkg,)):\n        no_dsas[(bug_name, release)] = [reason, comment]\n\n    all_bugs = []\n    # Group by bug name.\n    for bug_name, data in itertools.groupby(data,\n                                            lambda row: row.bug_name):\n        data = tuple(data)\n        description = data[0].description\n        open_seen = False\n        unimportant_seen = False\n        releases = {}\n        # Group by release.\n        for release, data1 in itertools.groupby(data, lambda row: row.release):\n            data1 = tuple(data1)\n            # The best row is the row with the highest version number.\n            # If there is a tie, the empty subrelease row wins.\n            best_row = data1[0]\n            for row in data1[1:]:\n                cmpresult = version_compare(row.version, best_row.version)\n                if cmpresult > 0 \\\n                   or (cmpresult == 0 and row.subrelease == ''):\n                    best_row = row\n            reason = None\n            comment = None\n\n            # Compute state.  Update state-seen flags for global state\n            # determination.\n            if best_row.vulnerable:\n                if best_row.urgency == 'unimportant':\n                    state = 'unimportant'\n                    unimportant_seen = True\n                else:\n                    open_seen = True\n                    reason, comment = no_dsas.get((bug_name, best_row.release), [None, None])\n                    if comment is not None:\n                        state = 'no-dsa'\n                    else:\n                        state = 'open'\n            else:\n                state = 'resolved'\n\n            bug = BugForSourcePackageRelease(\n                best_row.release, best_row.subrelease, best_row.version,\n                best_row.vulnerable, state, comment, reason)\n            releases[best_row.release] = bug\n\n        # Compute global_state.\n        if open_seen:\n            global_state = 'open'\n        elif unimportant_seen:\n            global_state = 'unimportant'\n        else:\n            global_state = 'resolved'\n\n        all_bugs.append(BugForSourcePackage(bug_name, description,\n                                            global_state, releases))\n\n    # Split all_bugs into per-state sequences.\n    per_state = {'all_releases': all_releases,\n                 'all': all_bugs}\n    for state in (\"open\", \"unimportant\", \"resolved\"):\n        per_state[state] = tuple(bug for bug in all_bugs\n                                 if bug.global_state == state)\n\n    return BugsForSourcePackage(**per_state)\n\n# Returned by DB.getDSAsForSourcePackage().\nDSAsForSourcePackage = namedtuple(\n    \"DSAsForSourcePackage\",\n    \"bug description\")\n\nclass DB:\n    \"\"\"Access to the security database.\n\n    This is a wrapper around an SQLite database object (which is\n    accessible as the \"db\" member.\n\n    Most operations need a special cursor object, which can be created\n    with a cursor object.  The name \"cursor\" is somewhat of a\n    misnomer because these objects are quite versatile.\n    \"\"\"\n\n    def __init__(self, name, verbose=False):\n        self.name = name\n        self.db = apsw.Connection(name)\n        self.verbose = verbose\n        c = self.cursor()\n\n        # This gives us better performance (it's usually the file\n        # system block size).  This must come first to be effective.\n\n        c.execute(\"PRAGMA page_size = 4096\")\n\n        # Enable WAL.  This means that updates will not block readers.\n        c.execute(\"PRAGMA journal_mode = WAL\")\n\n        self.schema_version = 23\n        self._initFunctions()\n\n        for (v,) in c.execute(\"PRAGMA user_version\"):\n            if v == 0:\n                self.initSchema()\n            elif v == 20:\n                self._initSchema20()\n            elif v == 21:\n                # Remove legacy views.\n                for view in ('testing_status', 'stable_status',\n                             'oldstable_status'):\n                    try:\n                        c.execute('DROP VIEW ' + view)\n                    except apsw.SQLError:\n                        pass\n                c.execute(\"PRAGMA user_version = 22\")\n            elif v == 22:\n                self._initSchema22()\n            elif v != self.schema_version:\n                if self.verbose:\n                    print(\"DB: schema version mismatch: expected %d, got %d\"\n                          % (self.schema_version, v))\n                raise SchemaMismatch(repr(v))\n            self._initViews(c)\n            return\n        assert False\n\n    def __del__(self):\n        self.db.close()\n\n    def cursor(self):\n        \"\"\"Creates a new database cursor.\n\n        Also see the writeTxn method.\"\"\"\n        return self.db.cursor()\n\n    def writeTxn(self):\n        \"\"\"Creates a cursor for an exclusive transaction.\n\n        No other process may modify the database at the same time.\n        After finishing the work, you should invoke the commit or\n        rollback methods below.\n        \"\"\"\n        c = self.cursor()\n        c.execute(\"BEGIN TRANSACTION EXCLUSIVE\")\n        return c\n\n    def commit(self, cursor):\n        \"\"\"Makes the changes in the transaction permanent.\"\"\"\n        cursor.execute(\"COMMIT\")\n\n    def rollback(self, cursor):\n        \"\"\"Undos the changes in the transaction.\"\"\"\n        cursor.execute(\"ROLLBACK\")\n\n    def initSchema(self):\n        \"\"\"Creates the database schema.\"\"\"\n        cursor = self.cursor()\n\n        # Set the schema version to an invalid value which is\n        # different from zero.  We can use this to detect a partially\n        # created schema.\n\n        cursor.execute(\"PRAGMA user_version = 1\")\n\n        cursor.execute(\"\"\"CREATE TABLE inodeprints\n        (file TEXT NOT NULL PRIMARY KEY,\n         inodeprint TEXT NOT NULL,\n         parsed BLOB)\"\"\")\n\n        cursor.execute(\"\"\"CREATE TABLE version_linear_order\n        (id INTEGER NOT NULL PRIMARY KEY,\n         version TEXT NOT NULL UNIQUE)\"\"\")\n\n        cursor.execute(\n            \"\"\"CREATE TABLE source_packages\n            (name TEXT NOT NULL,\n            release TEXT NOT NULL,\n            subrelease TEXT NOT NULL,\n            archive TEXT NOT NULL,\n            version TEXT NOT NULL,\n            version_id INTEGER NOT NULL DEFAULT 0,\n            PRIMARY KEY (name, release, subrelease, archive))\"\"\")\n\n        cursor.execute(\n            \"\"\"CREATE TABLE binary_packages\n            (name TEXT NOT NULL,\n            release TEXT NOT NULL,\n            subrelease TEXT NOT NULL,\n            archive TEXT NOT NULL,\n            version TEXT NOT NULL,\n            source TEXT NOT NULL,\n            source_version TEXT NOT NULL,\n            archs TEXT NOT NULL,\n            PRIMARY KEY (name, release, subrelease, archive, version, source,\n            source_version))\"\"\")\n        cursor.execute(\n            \"\"\"CREATE INDEX binary_packages_source\n            ON binary_packages(source)\"\"\")\n\n        cursor.execute(\"\"\"CREATE TABLE package_notes\n        (id INTEGER NOT NULL PRIMARY KEY,\n         bug_name TEXT NOT NULL,\n         package TEXT NOT NULL,\n         fixed_version TEXT\n             CHECK (fixed_version IS NULL OR fixed_version <> ''),\n         fixed_version_id INTEGER NOT NULL DEFAULT 0,\n         release TEXT NOT NULL,\n         package_kind TEXT NOT NULL DEFAULT 'unknown',\n         urgency TEXT NOT NULL,\n         bug_origin TEXT NOT NULL DEFAULT '')\"\"\")\n        cursor.execute(\n            \"\"\"CREATE UNIQUE INDEX package_notes_bug\n            ON package_notes(bug_name, package, release)\"\"\")\n        cursor.execute(\n            \"\"\"CREATE INDEX package_notes_package\n            ON package_notes(package)\"\"\")\n\n        cursor.execute(\"\"\"CREATE TABLE debian_bugs\n        (bug INTEGER NOT NULL,\n         note INTEGER NOT NULL,\n         PRIMARY KEY (bug, note))\"\"\")\n\n        cursor.execute(\"\"\"CREATE TABLE bugs\n        (name TEXT NOT NULL PRIMARY KEY,\n         cve_status TEXT NOT NULL\n             CHECK (cve_status IN\n                    ('', 'CANDIDATE', 'ASSIGNED', 'RESERVED', 'REJECTED')),\n         not_for_us INTEGER NOT NULL CHECK (not_for_us IN (0, 1)),\n         description TEXT NOT NULL,\n         release_date TEXT NOT NULL,\n         source_file TEXT NOT NULL,\n         source_line INTEGER NOT NULL)\"\"\")\n\n        cursor.execute(\"\"\"CREATE TABLE bugs_notes\n        (bug_name TEXT NOT NULL CHECK (typ <> ''),\n         typ TEXT NOT NULL CHECK (typ IN ('TODO', 'NOTE')),\n         release TEXT NOT NULL DEFAULT '',\n         comment TEXT NOT NULL CHECK (comment <> ''))\"\"\")\n\n        cursor.execute(\"\"\"CREATE TABLE bugs_xref\n        (source TEXT NOT NULL,\n         target TEXT NOT NULL,\n         PRIMARY KEY (source, target))\"\"\")\n        cursor.execute(\"CREATE INDEX bugs_xref_target ON bugs_xref(target)\")\n\n        cursor.execute(\"\"\"CREATE TABLE bug_status\n        (bug_name TEXT NOT NULL,\n         release TEXT NOT NULL,\n         status TEXT NOT NULL\n             CHECK (status IN ('vulnerable', 'fixed', 'unknown', 'undetermined',\n                               'partially-fixed', 'todo')),\n         reason TEXT NOT NULL,\n         PRIMARY KEY (bug_name, release))\"\"\")\n\n        cursor.execute(\"\"\"CREATE TABLE source_package_status\n        (bug_name TEXT NOT NULL,\n         package INTEGER NOT NULL,\n         vulnerable INTEGER NOT NULL,\n         urgency TEXT NOT NULL,\n         PRIMARY KEY (bug_name, package))\"\"\")\n        cursor.execute(\n            \"\"\"CREATE INDEX source_package_status_package\n            ON source_package_status(package)\"\"\")\n\n        cursor.execute(\n            \"CREATE TABLE removed_packages (name TEXT NOT NULL PRIMARY KEY)\")\n\n        cursor.execute(\n            \"\"\"CREATE TABLE nvd_data\n            (cve_name TEXT NOT NULL PRIMARY KEY,\n            cve_desc TEXT NOT NULL,\n            discovered TEXT NOT NULL,\n            published TEXT NOT NULL,\n            severity TEXT NOT NULL,\n            range_local INTEGER,\n            range_remote INTEGER,\n            range_user_init INTEGER,\n            loss_avail INTEGER NOT NULL,\n            loss_conf INTEGER NOT NULL,\n            loss_int INTEGER NOT NULL,\n            loss_sec_prot_user INTEGER NOT NULL,\n            loss_sec_prot_admin INTEGER NOT NULL,\n            loss_sec_prot_other INTEGER NOT NULL)\"\"\")\n\n        cursor.execute(\n            \"\"\"CREATE TABLE debsecan_data\n            (name TEXT NOT NULL PRIMARY KEY,\n            data TEXT NOT NULL)\"\"\")\n\n        self._initNoDSA(cursor)\n\n        self._initNextPointRelease(cursor)\n\n        cursor.execute(\"PRAGMA user_version = %d\" % self.schema_version)\n\n    def _initSchema20(self):\n        cursor = self.db.cursor()\n\n        cursor.execute(\"PRAGMA user_version = 1\")\n        self._initNoDSA(cursor)\n        self._initViews(cursor)\n        cursor.execute(\"DELETE FROM inodeprints WHERE file ='data/CVE/list'\")\n        cursor.execute(\"PRAGMA user_version = %d\" % self.schema_version)\n\n    def _initNoDSA(self, cursor):\n        cursor.execute(\n            \"\"\"CREATE TABLE package_notes_nodsa\n            (bug_name TEXT NOT NULL,\n            package TEXT NOT NULL,\n            release TEXT NOT NULL,\n            reason TEXT NOT NULL,\n            comment TEXT NOT NULL,\n            PRIMARY KEY (bug_name, package, release))\n            \"\"\")\n\n    def _initSchema22(self):\n        cursor = self.db.cursor()\n\n        cursor.execute(\"PRAGMA user_version = 1\")\n        self._initNextPointRelease(cursor)\n        cursor.execute(\"PRAGMA user_version = %d\" % self.schema_version)\n\n    def _initNextPointRelease(self, cursor):\n        cursor.execute(\n            \"\"\"CREATE TABLE next_point_update\n            (cve_name TEXT NOT NULL,\n            release TEXT NOT NULL,\n            PRIMARY KEY (cve_name, release))\n            \"\"\")\n\n    def _initViews(self, cursor):\n        testing = config.get_release_codename('testing')\n        cursor.execute(\n            \"\"\"CREATE TEMPORARY VIEW testing_status AS\n            SELECT DISTINCT sp.name AS package, st.bug_name AS bug,\n            sp.archive AS section, st.urgency AS urgency,\n            st.vulnerable AS vulnerable,\n            (SELECT vulnerable\n            FROM source_packages AS sidp, source_package_status AS sidst\n            WHERE sidp.name = sp.name\n            AND sidp.release = 'sid' AND sidp.subrelease = ''\n            AND sidp.archive = sp.archive\n            AND sidst.bug_name = st.bug_name\n            AND sidst.package = sidp.rowid) AS unstable_vulnerable,\n            COALESCE((SELECT NOT vulnerable\n            FROM source_packages AS tsecp, source_package_status AS tsecst\n            WHERE tsecp.name = sp.name\n            AND tsecp.release = '%s' AND tsecp.subrelease = 'security'\n            AND tsecp.archive = sp.archive\n            AND tsecst.bug_name = st.bug_name\n            AND tsecst.package = tsecp.rowid), 0) AS testing_security_fixed,\n            (SELECT range_remote FROM nvd_data\n             WHERE cve_name = st.bug_name) AS remote,\n            (EXISTS (SELECT * FROM package_notes_nodsa AS pnd\n            WHERE pnd.bug_name = st.bug_name\n            AND pnd.package = sp.name\n            AND pnd.release = '%s')) AS no_dsa\n            FROM source_package_status AS st, source_packages AS sp\n            WHERE st.vulnerable > 0 AND sp.rowid = st.package\n            AND sp.release = '%s' AND sp.subrelease = ''\n            ORDER BY sp.name, st.urgency, st.bug_name\"\"\"\n            % (testing, testing, testing))\n\n        releases = config.get_supported_releases()\n        releases.remove(config.get_release_codename('testing'))\n        releases.remove('sid')\n\n        for release in releases:\n            alias = config.get_release_alias(release)\n            cursor.execute(\n                \"\"\"CREATE TEMPORARY VIEW %s_status AS\n                SELECT DISTINCT sp.name AS package, st.bug_name AS bug,\n                sp.archive AS section, st.urgency AS urgency,\n                st.vulnerable AS vulnerable,\n                (SELECT range_remote FROM nvd_data\n                 WHERE cve_name = st.bug_name) AS remote,\n                (SELECT comment FROM package_notes_nodsa AS pnd\n                 WHERE pnd.bug_name = st.bug_name\n                 AND pnd.package = sp.name\n                 AND pnd.release = '%s') AS no_dsa,\n                (SELECT reason FROM package_notes_nodsa AS pnd\n                 WHERE pnd.bug_name = st.bug_name\n                 AND pnd.package = sp.name\n                 AND pnd.release = '%s') AS no_dsa_reason\n                FROM source_package_status AS st, source_packages AS sp\n                WHERE st.vulnerable > 0 AND sp.rowid = st.package\n                AND sp.release = '%s' AND sp.subrelease = ''\n                AND NOT COALESCE((SELECT NOT vulnerable\n                FROM source_packages AS secp, source_package_status AS secst\n                WHERE secp.name = sp.name\n                AND secp.release = '%s' AND ( secp.subrelease = 'security' OR secp.subrelease = 'lts' )\n                AND secp.archive = sp.archive\n                AND secst.bug_name = st.bug_name\n                AND secst.package = secp.rowid), 0)\n                ORDER BY sp.name, urgency_to_number(urgency), st.bug_name\"\"\"\n                % (alias, release, release, release, release))\n\n        cursor.execute(\n            \"\"\"CREATE TEMPORARY VIEW debian_cve AS\n            SELECT debian_bugs.bug, st.bug_name\n            FROM package_notes, debian_bugs, source_package_status AS st\n            WHERE package_notes.bug_name = st.bug_name\n            AND debian_bugs.note = package_notes.id\"\"\")\n\n    def _initFunctions(self):\n        \"\"\"Registers user-defined SQLite functions.\"\"\"\n\n        def string_list_add(lst, *args):\n            for arg in args:\n                lst.append(arg)\n        def string_list_to_string(lst):\n            return ', '.join(lst)\n        def string_list_factory():\n            return ([], string_list_add, string_list_to_string)\n        self.db.createaggregatefunction(\"string_list\", string_list_factory)\n\n        def string_set_add(lst, *args):\n            for arg in args:\n                for arch in arg.split(','):\n                    lst[arch] = True\n        def string_set_to_archs(lst):\n            l = list(lst.keys())\n            l.sort()\n            return ','.join(l)\n        def string_set_factory():\n            return ({}, string_set_add, string_set_to_archs)\n        self.db.createaggregatefunction(\"string_set\", string_set_factory)\n\n        urgencies = ['high', 'medium', 'low', 'unimportant']\n        def urgency_to_number(u):\n            try:\n                return urgencies.index(u)\n            except ValueError:\n                return 999\n        self.db.createscalarfunction(\"urgency_to_number\", urgency_to_number, 1)\n\n        def releasepart_to_number(r):\n            # expects a string in the form \"codename (security)\"\n            try:\n                # split the (optional) subrelease\n                u=r.split()[0]\n                # split the (optional) component\n                u=u.split('/')[0]\n                return release_to_number(u)\n            except ValueError:\n                return -1\n        self.db.createscalarfunction(\"releasepart_to_number\", releasepart_to_number, 1)\n\n        def subreleasepart_to_number(r):\n            # expects a string in the form \"codename (security)\"\n            try:\n                if not \"(\" in r:\n                    return 0\n                u=r.split('(', 1)[1].split(')')[0]\n                return subrelease_to_number(u)\n            except ValueError:\n                return -1\n        self.db.createscalarfunction(\"subreleasepart_to_number\", subreleasepart_to_number, 1)\n\n        releases = config.get_all_releases()\n        def release_to_number(u):\n            try:\n                return releases.index(u)\n            except ValueError:\n                return -1\n        self.db.createscalarfunction(\"release_to_number\", release_to_number, 1)\n\n        subreleases = ['', 'security', 'lts']\n        def subrelease_to_number(u):\n            try:\n                return subreleases.index(u)\n            except ValueError:\n                return -1\n        self.db.createscalarfunction(\"subrelease_to_number\", subrelease_to_number, 1)\n\n        archives = ['main', 'contrib', 'non-free']\n        def archive_to_number(u):\n            try:\n                return archives.index(u)\n            except ValueError:\n                return -1\n        self.db.createscalarfunction(\"archive_to_number\", archive_to_number, 1)\n\n        def release_name(release, subrelease, archive):\n            if archive != 'main':\n                release = release + '/' + archive\n            if subrelease:\n                return \"%s (%s)\" % (release, subrelease)\n            else:\n                return release\n        self.db.createscalarfunction(\"release_name\", release_name, 3)\n\n        self.db.createcollation(\"version\", debian_support.version_compare)\n\n        def source_arch():\n            return \"source\"\n        self.db.createscalarfunction(\"source_arch\", source_arch, 0)\n\n    def filePrint(self, filename):\n        \"\"\"Returns a fingerprint string for filename.\"\"\"\n\n        st = os.stat(filename)\n        # The \"1\" is a version number which can be used to trigger a\n        # re-read if the code has changed in an incompatible way.\n        return repr((st.st_size, st.st_ino, st.st_mtime, 1))\n\n    def _parseFile(self, cursor, filename):\n        current_print = self.filePrint(filename)\n\n        def do_parse(packages):\n            if self.verbose:\n                print(\"    reading \" + repr(filename))\n\n            re_source = re.compile\\\n                (r'^([a-zA-Z0-9.+-]+)(?:\\s+\\(([a-zA-Z0-9.+:~-]+)\\))?$')\n\n            data = {}\n            for pkg in packages:\n                pkg_name = None\n                pkg_version = None\n                pkg_arch = None\n                pkg_source = None\n                pkg_source_version = None\n                pkg_extra_source_only = False\n                for (name, contents) in pkg:\n                    if name == \"Package\":\n                        pkg_name = contents\n                    elif name == \"Version\":\n                        pkg_version = contents\n                    elif name == \"Source\":\n                        match = re_source.match(contents)\n                        if match is None:\n                            raise SyntaxError(('package %s references '\n                                               + 'invalid source package %s') %\n                                              (pkg_name, repr(contents)))\n                        (pkg_source, pkg_source_version) = match.groups()\n                    elif name == \"Architecture\":\n                        pkg_arch = contents\n                    elif name == \"Extra-Source-Only\":\n                        pkg_extra_source_only = contents.strip() == \"yes\"\n                if pkg_name is None:\n                    raise SyntaxError\\\n                          (\"package record does not contain package name\")\n                if pkg_version is None:\n                    raise SyntaxError\\\n                          (\"package record for %s does not contain version\"\n                           % pkg_name)\n                if pkg_arch is None:\n                    raise SyntaxError\\\n                          (\"package record for %s lacks Architecture: field\"\n                           % pkg_name)\n                if pkg_extra_source_only:\n                    # Skip, sources are included only for GPL\n                    # compliance reasons.\n                    continue\n                if pkg_name in data:\n                    oversion = debian_support.Version(data[pkg_name][0])\n                    if oversion < debian_support.Version(pkg_version):\n                        data[pkg_name] = (pkg_version, pkg_arch,\n                                         pkg_source, pkg_source_version)\n                else:\n                    data[pkg_name] = (pkg_version, pkg_arch,\n                                     pkg_source, pkg_source_version)\n\n            return data\n\n        def toString(data):\n            return pickle.dumps(data)\n\n        for (old_print, contents) in cursor.execute(\n            \"SELECT inodeprint, parsed FROM inodeprints WHERE file = ?\",\n            (filename,)):\n            if old_print == current_print:\n                return (True, pickle.loads(contents))\n            result = do_parse(debian_support.PackageFile(filename))\n            cursor.execute(\"\"\"UPDATE inodeprints SET inodeprint = ?, parsed = ?\n            WHERE file = ?\"\"\", (current_print, toString(result), filename))\n            return (False, result)\n\n        # No inodeprints entry, load file and add one.\n        result = do_parse(debian_support.PackageFile(filename))\n        cursor.execute(\"\"\"INSERT INTO inodeprints (file, inodeprint, parsed)\n        VALUES (?, ?, ?)\"\"\", (filename, current_print, toString(result)))\n        return (False, result)\n\n    def readPackages(self, cursor, directory):\n        \"\"\"Reads a directory of package files.\"\"\"\n\n        if self.verbose:\n            print(\"readPackages:\")\n\n        self._readSourcePackages(cursor, directory)\n        self._readBinaryPackages(cursor, directory)\n\n        if self.verbose:\n            print(\"  finished\")\n\n    def _readSourcePackages(self, cursor, directory):\n        \"\"\"Reads from directory with source package files.\"\"\"\n\n        re_sources = re.compile(r'.*/([a-z-]+)_([a-z-]*)_([a-z-]+)_Sources$')\n\n        if self.verbose:\n            print(\"  reading source packages\")\n\n        for filename in glob.glob(directory + '/*_Sources'):\n            match = re_sources.match(filename)\n            if match is None:\n                raise ValueError(\"invalid file name: \" + repr(filename))\n\n            (release, subrelease, archive) = match.groups()\n            (unchanged, parsed) = self._parseFile(cursor, filename)\n            if unchanged:\n                continue\n\n            cursor.execute(\n                \"\"\"DELETE FROM source_packages\n                WHERE release = ? AND subrelease = ? AND archive = ?\"\"\",\n                (release, subrelease, archive))\n            self._clearVersions(cursor)\n\n            def gen():\n                for name in parsed.keys():\n                    (version, archs, source, source_version) = parsed[name]\n                    assert source is None\n                    assert source_version is None\n                    yield name, release, subrelease, archive, version\n            cursor.executemany(\n                \"\"\"INSERT INTO source_packages\n               (name, release, subrelease, archive, version)\n               VALUES (?, ?, ?, ?, ?)\"\"\",\n                gen())\n\n    def _readBinaryPackages(self, cursor, directory):\n        \"\"\"Reads from a directory with binary package files.\"\"\"\n\n        re_packages \\\n            = re.compile(\n            r'.*/([a-z-]+)_([a-z-]*)_([a-z-]+)_([a-z0-9-]+)_Packages$')\n\n        if self.verbose:\n            print(\"  reading binary packages\")\n\n        # First check for any changes.\n\n        filenames = glob.glob(directory + '/*_Packages')\n        filenames.sort()\n        changed = False\n        for filename in filenames:\n            changed = True\n            for (old_print,) in cursor.execute(\n                \"SELECT inodeprint FROM inodeprints WHERE file = ?\",\n                (filename,)):\n                if self.filePrint(filename) == old_print:\n                    changed = False\n            if changed:\n                break\n        if not changed:\n            if self.verbose:\n                print(\"    finished (no changes)\")\n            return\n\n        # Real import.  We have to re-read all Packages files even if\n        # only some of them have changed because the database only\n        # stores aggregated data, and there is no efficient way to\n        # handle updates of the records related to a single file.\n\n        packages = {}\n        unchanged = True\n        for filename in filenames:\n            match = re_packages.match(filename)\n            if match is None:\n                raise ValueError(\"invalid file name: \" + repr(filename))\n\n            (release, subrelease, archive, architecture) = match.groups()\n            (unch, parsed) = self._parseFile(cursor, filename)\n            unchanged = unchanged and unch\n            for name in parsed.keys():\n                (version, arch, source, source_version) = parsed[name]\n                if source is None:\n                    source = name\n                if source_version is None:\n                    source_version = version\n                if arch != 'all' and arch != architecture:\n                    raise ValueError(\"invalid architecture %s for package %s\"\n                                       % (arch, name))\n                key = (name, release, subrelease, archive, version,\n                       source, source_version)\n                if key in packages:\n                    packages[key][arch] = 1\n                else:\n                    packages[key] = {arch : 1}\n\n        if unchanged:\n            if self.verbose:\n                print(\"    finished (no changes)\")\n            return\n\n        if self.verbose:\n            print(\"    deleting old data\")\n        cursor.execute(\"DELETE FROM binary_packages\")\n        self._clearVersions(cursor)\n\n        l = list(packages.keys())\n\n        if len(l) == 0:\n            raise ValueError(\"no binary packages found\")\n\n        l.sort()\n        def gen():\n            for key in l:\n                archs = list(packages[key].keys())\n                archs.sort()\n                archs = ','.join(archs)\n                yield key + (archs,)\n\n        if self.verbose:\n            print(\"    storing binary package data\")\n\n        cursor.executemany(\n            \"\"\"INSERT INTO binary_packages\n            (name, release, subrelease, archive, version,\n            source, source_version, archs)\n            VALUES (?, ?, ?, ?, ?, ?, ?, ?)\"\"\",\n            gen())\n\n    def getSources(self):\n        config = debian_support.getconfig()\n        sources = config[\"sources\"]\n\n        return sources\n\n    def genDBAdvisoryString(self, field, dtsa=False):\n        sources = self.getSources()\n        advs = []\n\n        for src in sources:\n            name = src[\"name\"]\n            cls = src[\"class\"]\n            if cls == 'DSAFile':\n                advs.append(name)\n\n            if cls == 'DTSAFile' and dtsa:\n                advs.append(name)\n\n        advs = [\"{} LIKE '{}-%'\".format(field, adv) for adv in advs]\n        return \" OR \".join(advs)\n\n    def readBugs(self, cursor, path):\n        if self.verbose:\n            print(\"readBugs:\")\n\n        def clear_db(cleared=[False]):\n            # Avoid clearing the database multiple times.\n            if cleared[0]:\n                return\n            else:\n                cleared[0] = True\n\n            cursor.execute(\"DELETE FROM debian_bugs\")\n            cursor.execute(\"DELETE FROM bugs\")\n            cursor.execute(\"DELETE FROM package_notes\")\n            cursor.execute(\"DELETE FROM bugs_notes\")\n            cursor.execute(\"DELETE FROM bugs_xref\")\n            cursor.execute(\"DELETE FROM package_notes_nodsa\")\n            cursor.execute(\"DELETE FROM removed_packages\")\n            cursor.execute(\"DELETE FROM next_point_update\")\n\n            # The *_status tables are regenerated anyway, no need to\n            # delete them here.\n\n            self._clearVersions(cursor)\n\n        def do_parse(source, cleared=[False]):\n            errors = []\n\n            clear_db()\n\n            if self.verbose:\n                print(\"  reading \" + repr(source.name))\n\n            for bug in source:\n                try:\n                    bug.writeDB(cursor)\n                except ValueError as e:\n                    errors.append(\"%s: %d: error: %s\"\n                                  % (bug.source_file, bug.source_line, e))\n            if errors:\n                raise InsertError(errors)\n\n            cursor.executemany(\n                \"INSERT OR IGNORE INTO removed_packages (name) VALUES (?)\",\n                map(lambda x: (x,), source.removed_packages.keys()))\n\n        def has_changed(filename):\n            current_print = self.filePrint(filename)\n            for (old_print,) in cursor.execute(\n                \"SELECT inodeprint FROM inodeprints WHERE file = ?\",\n                (filename,)):\n                if old_print == current_print:\n                    return False\n                else:\n                    return True\n            return True\n\n        source_removed_packages = '/packages/removed-packages'\n        sources = self.getSources()\n        source_paths = [src[\"path\"] for src in sources]\n\n        unchanged = True\n        for filename in source_paths + [source_removed_packages]:\n            if has_changed(path + filename):\n                unchanged = False\n                break\n        if unchanged:\n            if self.verbose:\n                print(\"  finished (no changes)\")\n            return\n\n        clear_db()\n\n        def read_one(source):\n            filename = source.name\n            current_print = self.filePrint(filename)\n\n            do_parse(source)\n            cursor.execute(\n                \"\"\"INSERT OR REPLACE INTO inodeprints (inodeprint, file)\n                VALUES (?, ?)\"\"\", (current_print, filename))\n\n        for src in sources:\n            srcpath = src[\"path\"]\n            cls = src[\"class\"]\n            cls = getattr(bugs, cls)\n            read_one(cls(path + srcpath))\n\n        if self.verbose:\n            print(\"  update removed packages\")\n        self.readRemovedPackages(cursor, path + source_removed_packages)\n\n        errors = []\n\n        if self.verbose:\n            print(\"  check cross-references\")\n\n        for (bug,) in cursor.execute(\n            \"\"\"SELECT DISTINCT target FROM bugs_xref\n        EXCEPT SELECT name FROM bugs\"\"\"):\n            if bug[0:3] == \"VU#\":\n                continue\n            errors.append(\"reference to unknown bug \" + bug)\n\n        if self.verbose:\n            print(\"  copy notes\")\n\n        # Copy notes from DSA/DTSA/DLA to CVE.\n\n        old_source = ''\n        source_like = self.genDBAdvisoryString(\"source\", dtsa=True)\n        for source, target in list(cursor.execute(\n            \"\"\"SELECT source, target FROM bugs_xref\n            WHERE (\"\"\" + source_like +  \"\"\")\n            AND target LIKE 'CVE-%'\"\"\")):\n            if source != old_source:\n                source_bug = bugs.BugFromDB(cursor, source)\n                old_source = source\n            for n in source_bug.notes:\n                # We do not copy recursively.\n                assert not n.bug_origin\n\n                if n.release:\n                    rel = str(n.release)\n                else:\n                    rel = ''\n                present = False\n\n                for (version, note_id) in list(cursor.execute(\n                    \"\"\"SELECT fixed_version, id\n                    FROM package_notes\n                    WHERE bug_name = ? AND package = ? AND release = ?\"\"\",\n                    (target, n.package, rel))):\n                    if version is None:\n                        # The target is marked as unfixed.  Our\n                        # version cannot win.\n                        present = True\n                        continue\n\n                    if (n.fixed_version is None\n                        or n.fixed_version > debian_support.Version(version)):\n                        # If our version is larger, it is the definitive one.\n                        # Remove the existing entry in this case.\n                        cursor.execute(\n                            \"DELETE FROM debian_bugs WHERE note = ?\",\n                            (note_id,))\n                        cursor.execute(\n                            \"\"\"DELETE FROM package_notes\n                            WHERE bug_name = ? AND package = ?\n                            AND release = ?\"\"\",\n                            (target, n.package, rel))\n                    else:\n                        present = True\n                if not present:\n                    n.writeDB(cursor, target, bug_origin=source)\n\n        def insert_next_point_update(cve_names, code_name):\n            for cve_name in cve_names:\n                cursor.execute(\n                    \"\"\"INSERT OR REPLACE INTO next_point_update (cve_name, release)\n                    VALUES (?, ?)\"\"\", (cve_name, code_name))\n\n        def read_next_point_update():\n            if self.verbose:\n                print(\"    insert next-point-update.txt/next-oldstable-point-update.txt\")\n\n            insert_next_point_update(PointUpdateParser.parseNextPointUpdateStable(),\n                                     config.get_release_codename('stable'))\n\n            insert_next_point_update(PointUpdateParser.parseNextOldstablePointUpdate(),\n                                     config.get_release_codename('oldstable'))\n\n        read_next_point_update()\n\n        if errors:\n            raise InsertError(errors)\n\n        if self.verbose:\n            print(\"  finished\")\n\n    def availableReleases(self, cursor=None):\n        \"\"\"Returns a list of tuples (RELEASE, ARCHIVE,\n        SOURCES-PRESENT, ARCHITECTURE-LIST).\"\"\"\n        if cursor is None:\n            cursor = self.cursor()\n\n        result = []\n        result.append(('', '', '', False, []))\n        for (rel, subrel, archive, archs) in cursor.execute(\n            \"\"\"SELECT * FROM\n            (SELECT DISTINCT release, subrelease, archive, archs\n            FROM binary_packages\n            UNION SELECT DISTINCT release, subrelease, archive, source_arch() as archs\n            FROM source_packages)\n            ORDER BY release_to_number(release), subrelease_to_number(subrelease), archive_to_number(archive)\"\"\"):\n            if \"source\" in archs:\n                sources=True\n            else:\n                sources=False\n            (p_rel, p_subrel, p_archive, p_sources, p_archs) = result.pop()\n            if rel == p_rel and subrel == p_subrel and archive == p_archive:\n                sources = sources or p_sources\n                result.append((rel, subrel, archive, sources, mergeLists(p_archs, archs)))\n            else:\n                result.append((p_rel, p_subrel, p_archive, p_sources, mergeLists([], p_archs)))\n                result.append((rel, subrel, archive, sources, mergeLists([], archs)))\n        result.pop(0)\n\n        return result\n\n    def getFunnyPackageVersions(self):\n        \"\"\"Returns a list of (PACKAGE, RELEASE, ARCHIVE, VERSION,\n        SOURCE-VERSION) tuples such that PACKAGE is both a source and\n        binary package, but the associated version numbers are\n        different.\"\"\"\n\n        return list(self.db.cursor().execute(\n            \"\"\"SELECT DISTINCT name, release, archive, version, source_version\n            FROM binary_packages\n            WHERE name = source AND version <> source_version\n            ORDER BY name, release, archive\"\"\"))\n\n    def _clearVersions(self, cursor):\n        cursor.execute(\"DELETE FROM version_linear_order\")\n\n    def _updateVersions(self, cursor):\n        \"\"\"Updates the linear version table.\"\"\"\n\n        if self.verbose:\n            print(\"updateVersions:\")\n\n        for x in cursor.execute(\"SELECT * FROM version_linear_order LIMIT 1\"):\n            if self.verbose:\n                print(\"  finished (no changes)\")\n            return\n\n        if self.verbose:\n            print(\"  reading\")\n\n        versions = []\n        for (v,) in cursor.execute(\n            \"\"\"SELECT DISTINCT *\n            FROM (SELECT fixed_version FROM package_notes\n                WHERE fixed_version IS NOT NULL\n            UNION ALL SELECT version FROM source_packages)\"\"\"):\n            versions.append(debian_support.Version(v))\n\n        if self.verbose:\n            print(\"  calculating linear order\")\n        versions.sort()\n\n        if self.verbose:\n            print(\"  storing linear order\")\n        for v in versions:\n            cursor.execute(\n                \"INSERT INTO version_linear_order (version) VALUES (?)\",\n                (str(v),))\n\n        if self.verbose:\n            print(\"  updating package notes\")\n        cursor.execute(\n            \"\"\"UPDATE package_notes\n            SET fixed_version_id = (SELECT id FROM version_linear_order\n            WHERE version = package_notes.fixed_version)\n            WHERE fixed_version IS NOT NULL\"\"\")\n\n        if self.verbose:\n            print(\"  updating source packages\")\n        cursor.execute(\n            \"\"\"UPDATE source_packages\n            SET version_id = (SELECT id FROM version_linear_order\n            WHERE version = source_packages.version)\"\"\")\n\n        if self.verbose:\n            print(\"  finished\")\n\n    def calculateVulnerabilities(self, cursor):\n        \"\"\"Calculate vulnerable packages.\n\n        To each package note, a release-specific vulnerability status\n        is attached.  Currently, only testing is processed.\n\n        Returns a list strings describing inconsistencies.\n        \"\"\"\n\n        result = []\n\n        self._updateVersions(cursor)\n\n        if self.verbose:\n            print(\"calculateVulnerabilities:\")\n            print(\"  checking version consistency in package notes\")\n\n        # The following does not work because stable->security ->\n        # testing -> unstable propagation is no longer available.\n        if False:\n            # Ignore testing because stable issues may be\n            # fast-tracked into testing, bypassing unstable.\n            testing = config.get_release_codename('testing')\n            for (bug_name, pkg_name, rel, unstable_ver, rel_ver) \\\n                    in list(cursor.execute(\n            \"\"\"SELECT a.bug_name, a.package, b.release,\n            a.fixed_version, b.fixed_version\n            FROM package_notes a, package_notes b\n            WHERE a.bug_name = b.bug_name AND a.package = b.package\n            AND a.release = '' AND b.release NOT IN ('', '%s')\n            AND a.fixed_version IS NOT NULL\n            AND a.fixed_version_id < b.fixed_version_id\"\"\" % (testing,))):\n                b = bugs.BugFromDB(cursor, bug_name)\n                result.append(\"%s:%d: inconsistent versions for package %s\"\n                              % (b.source_file, b.source_line, pkg_name))\n                result.append(\"%s:%d: unstable: %s\"\n                              % (b.source_file, b.source_line, unstable_ver))\n                result.append(\"%s:%d: release %s: %s\"\n                              % (b.source_file, b.source_line, repr(rel), rel_ver))\n\n        if self.verbose:\n            print(\"  checking source packages\")\n        cursor.execute(\n            \"\"\"UPDATE package_notes SET package_kind = 'unknown'\n            WHERE package_kind IN ('source', 'binary')\"\"\")\n        cursor.execute(\n            \"\"\"UPDATE package_notes SET package_kind = 'source'\n            WHERE package_kind = 'unknown'\n            AND EXISTS (SELECT * FROM source_packages AS p\n                        WHERE p.name = package_notes.package)\"\"\")\n        cursor.execute(\n            \"\"\"UPDATE package_notes SET package_kind = 'source'\n            WHERE package_kind = 'unknown'\n            AND EXISTS (SELECT * FROM removed_packages AS p\n                        WHERE p.name = package_notes.package)\"\"\")\n\n        for (bug_name, package) in list(cursor.execute(\n            \"\"\"SELECT n.bug_name, n.package\n            FROM package_notes AS n\n            WHERE n.package_kind = 'itp'\n            AND ((EXISTS (SELECT * FROM source_packages\n                         WHERE name = n.package))\n                 OR (EXISTS (SELECT * FROM binary_packages\n                             WHERE name = n.package)))\"\"\")):\n            b = bugs.BugFromDB(cursor, bug_name)\n            result.append(\"%s:%d: ITPed package %s is in the archive\"\n                          % (b.source_file, b.source_line, package))\n\n        if result:\n            return result\n\n        if self.verbose:\n            print(\"  remove old status\")\n        cursor.execute(\"DELETE FROM source_package_status\")\n        cursor.execute(\"DELETE FROM bug_status\")\n\n        if self.verbose:\n            print(\"  calculate package status\")\n            print(\"    source packages (unqualified)\")\n\n        cursor.execute(\n            \"\"\"INSERT INTO source_package_status\n            SELECT n.bug_name, p.rowid,\n            CASE WHEN n.fixed_version == 'undetermined' THEN 2\n            ELSE CASE WHEN n.fixed_version IS NULL THEN 1\n            ELSE CASE WHEN p.version_id < n.fixed_version_id THEN 1\n            ELSE 0 END END END,\n            n.urgency\n            FROM package_notes AS n, source_packages AS p\n            WHERE n.release = '' AND p.name = n.package\"\"\")\n\n        # Release annotations always override previous results,\n        # therefore we use INSERT OR REPLACE.\n\n        if self.verbose:\n            print(\"    source packages (qualified)\")\n        cursor.execute(\n            \"\"\"INSERT OR REPLACE INTO source_package_status\n            SELECT n.bug_name, p.rowid,\n            CASE WHEN n.fixed_version == 'undetermined' THEN 2\n            ELSE CASE WHEN n.fixed_version IS NULL THEN 1\n            ELSE CASE WHEN p.version_id < n.fixed_version_id THEN 1\n            ELSE 0 END END END,\n            n.urgency\n            FROM package_notes AS n, source_packages AS p\n            WHERE p.name = n.package\n            AND p.release = n.release\"\"\")\n\n        # assign nvd urgencies to those that have not yet been assigned\n        if self.verbose:\n            print(\"    insert nvd urgencies\")\n        cursor.execute(\n            \"\"\"REPLACE INTO source_package_status\n            SELECT s.bug_name, s.package, s.vulnerable,\n            CASE WHEN n.severity == 'Medium' THEN 'medium**'\n            ELSE CASE WHEN n.severity == 'High' THEN 'high**'\n            ELSE CASE WHEN n.severity == 'Low' THEN 'low**'\n            ELSE 'not yet assigned' END END END\n            FROM nvd_data AS n, source_package_status AS s\n            WHERE s.bug_name == n.cve_name\n            AND s.urgency == 'not yet assigned'\"\"\")\n        cursor.execute(\n            \"\"\"REPLACE INTO package_notes\n            SELECT p.id, p.bug_name, p.package, p.fixed_version,\n            p.fixed_version_id, p.release, p.package_kind,\n            CASE WHEN n.severity == 'Medium' THEN 'medium'\n            ELSE CASE WHEN n.severity == 'High' THEN 'high'\n            ELSE CASE WHEN n.severity == 'Low' THEN 'low'\n            ELSE 'not yet assigned' END END END,\n            p.bug_origin\n            FROM nvd_data AS n, package_notes AS p\n            WHERE p.bug_name == n.cve_name\n            AND p.urgency == 'not yet assigned'\"\"\")\n\n        # Calculate the release-specific bug status.\n\n        if self.verbose:\n            print(\"  calculate release status\")\n\n        c = self.cursor()\n\n        for (bug_name,) in cursor.execute(\n            \"SELECT name FROM bugs WHERE NOT not_for_us\"):\n\n            self._calcUnstable(c, bug_name)\n\n            for release in config.get_supported_releases():\n                if release == 'sid':\n                    continue\n\n                alias = config.get_release_alias(release)\n                self._calcTesting(c, bug_name, alias, release)\n\n        return result\n\n    def _calcUnstable(self, cursor, bug_name):\n        \"\"\"Update bug_status with bug_name for unstable.\"\"\"\n\n        vulnerable_packages = []\n        undetermined_packages = []\n        unimportant_packages = []\n        have_something = False\n        for (package, vulnerable, urgency) in cursor.execute(\n            \"\"\"SELECT DISTINCT sp.name, st.vulnerable, n.urgency\n            FROM source_package_status AS st,\n            source_packages AS sp, package_notes AS n\n            WHERE st.bug_name = ? AND sp.rowid = st.package\n            AND sp.release = 'sid'\n            AND n.bug_name = st.bug_name AND n.package = sp.name\n            ORDER BY sp.name\"\"\",\n            (bug_name,)):\n            have_something = True\n            if vulnerable == 1:\n                if urgency == 'unimportant':\n                    unimportant_packages.append( package )\n                else:\n                    vulnerable_packages.append(package)\n            elif vulnerable == 2:\n                undetermined_packages.append(package)\n\n        if vulnerable_packages or undetermined_packages:\n            pkgs = \"\"\n            status = 'undetermined'\n            if vulnerable_packages:\n                status = 'vulnerable'\n                if len(vulnerable_packages) == 1:\n                    pkgs += \"package %s is vulnerable. \" % vulnerable_packages[0]\n                else:\n                    pkgs += (\"packages %s are vulnerable. \"\n                            % ', '.join(vulnerable_packages))\n            if undetermined_packages:\n                if len(undetermined_packages) == 1:\n                    pkgs += \"package %s may be vulnerable but needs to be checked.\" % undetermined_packages[0]\n                else:\n                    pkgs += (\"packages %s may be vulnerable but need to be checked.\"\n                             % ', '.join(undetermined_packages))\n            cursor.execute(\"\"\"INSERT INTO bug_status\n                (bug_name, release, status, reason)\n                VALUES (?, 'unstable', ?, ?)\"\"\", (bug_name, status, pkgs))\n        elif unimportant_packages:\n            if len(unimportant_packages) == 1:\n                pkgs = \"package %s is vulnerable; however, the security impact is unimportant.\" % unimportant_packages[0]\n            else:\n                pkgs = \"packages %s are vulnerable; however, the security impact is unimportant.\" % (', '.join(unimportant_packages))\n            cursor.execute(\"\"\"INSERT INTO bug_status\n                (bug_name, release, status, reason)\n                VALUES (?, 'unstable', 'fixed', ?)\"\"\", (bug_name, pkgs))\n        else:\n            if have_something:\n                status = \"not vulnerable.\"\n            else:\n                status = \"not known to be vulnerable.\"\n            cursor.execute(\"\"\"INSERT INTO bug_status\n                (bug_name, release, status, reason)\n                VALUES (?, 'unstable', 'fixed', ?)\"\"\",\n                      (bug_name, status))\n\n    def _calcTesting(self, cursor, bug_name, suite, nickname):\n        \"\"\"Update bug_status with bug_name for testing/stable.\"\"\"\n\n        # Note that there is at most one source package per\n        # note/release/subrelease triple, but we should check that\n        # here.\n\n        status = {'' : {}, 'security' : {}, 'lts' : {}}\n        for (package, note, subrelease, vulnerable, urgency) in cursor.execute(\n            \"\"\"SELECT DISTINCT sp.name, n.id, sp.subrelease,\n            st.vulnerable, n.urgency\n            FROM source_package_status AS st,\n            source_packages AS sp, package_notes AS n\n            WHERE st.bug_name = ? AND sp.rowid = st.package\n            AND sp.release = ? AND sp.subrelease IN ('', 'security', 'lts')\n            AND n.bug_name = st.bug_name AND n.package = sp.name\n            ORDER BY sp.name\"\"\",\n            (bug_name, nickname)):\n            status[subrelease][(package, note)] = (vulnerable,urgency)\n\n        # Check if any packages in plain testing are vulnerable, and\n        # if all of those have been fixed in the security archive.\n        fixed_in_security = True\n        unfixed_pkgs = {}\n        undet_pkgs = {}\n        unimp_pkgs = {}\n        for ((package, note), (vulnerable, urgency)) in status[''].items():\n            if vulnerable == 1:\n                if urgency == 'unimportant':\n                    unimp_pkgs[package] = True\n                else:\n                    unfixed_pkgs[package] = True\n                if status['security'].get((package, note), True):\n                    fixed_in_security = False\n                elif status['lts'].get((package, note), True):\n                    fixed_in_security = False\n            elif vulnerable == 2:\n                undet_pkgs[package] = True\n\n        unfixed_pkgs = list(unfixed_pkgs.keys())\n        unfixed_pkgs.sort()\n        undet_pkgs = list(undet_pkgs.keys())\n        undet_pkgs.sort()\n        unimp_pkgs = list(unimp_pkgs.keys())\n        unimp_pkgs.sort()\n\n        pkgs = \"\"\n        result = \"undetermined\"\n        if len(unfixed_pkgs) == 0 and len(undet_pkgs) == 0:\n            if len(status[''].keys()) == 0:\n                pkgs += \"not known to be vulnerable.\"\n            else:\n                pkgs += \"not vulnerable.\"\n            result = \"fixed\"\n        if len(unfixed_pkgs) > 0:\n            if len(unfixed_pkgs) == 1:\n                pkgs += \"package \" + unfixed_pkgs[0] + \" is \"\n            else:\n                pkgs += \"packages \" + \", \".join(unfixed_pkgs) + \" are \"\n            if fixed_in_security:\n                pkgs = \"%sfixed in %s-security. \" % (pkgs, suite)\n                if suite == \"stable\":\n                    result = \"fixed\"\n                else:\n                    result = \"partially-fixed\"\n            else:\n                pkgs += \"vulnerable. \"\n                result = \"vulnerable\"\n        if len(undet_pkgs) > 0:\n            if len(undet_pkgs) == 1:\n                pkgs += \"package \" + undet_pkgs[0] + \" may be vulnerable but needs to be checked.\"\n            else:\n                pkgs += \"packages \" + \", \".join(undet_pkgs) + \" may be vulnerable but need to be checked.\"\n        if len(unimp_pkgs) > 0 and len(undet_pkgs) == 0 and len(unfixed_pkgs) == 0:\n            result = \"fixed\"\n            if len(unimp_pkgs) == 1:\n                pkgs = \"package %s is vulnerable; however, the security impact is unimportant.\" % unimp_pkgs[0]\n            else:\n                pkgs = \"packages %s are vulnerable; however, the security impact is unimportant.\" % (', '.join(unimp_pkgs))\n\n        cursor.execute(\"\"\"INSERT INTO bug_status\n        (bug_name, release, status, reason)\n        VALUES (?, ?, ?, ?)\"\"\",\n              (bug_name, suite, result, pkgs))\n\n    def calculateDebsecan0(self, release):\n        \"\"\"Create data for the debsecan tool (VERSION 0 format).\"\"\"\n\n        c = self.cursor()\n\n        c.execute(\"\"\"CREATE TEMPORARY TABLE vulnlist (\n        name TEXT NOT NULL,\n        package TEXT NOT NULL,\n        note INTEGER NOT NULL,\n        PRIMARY KEY (name, package)\n        )\"\"\")\n\n        # Populate the table with the unstable vulnerabilities;\n        # override them with the release-specific status.\n\n        c.execute(\"\"\"INSERT INTO vulnlist\n        SELECT bug_name, package, id FROM package_notes WHERE release = ''\"\"\")\n\n        if release != 'sid':\n            c.execute(\"\"\"INSERT OR REPLACE INTO vulnlist\n            SELECT bug_name, package, id FROM package_notes\n            WHERE release = ?\"\"\", (release,))\n\n        urgency_to_flag = {'low' : 'L', 'medium' : 'M', 'high' : 'H',\n                           'not yet assigned' : ' '}\n\n        result = [\"VERSION 0\\n\"]\n        for (name, package, fixed_version, kind, urgency, remote, description,\n             note_id) in list(c.execute(\"\"\"SELECT\n                vulnlist.name, vulnlist.package,\n                COALESCE(n.fixed_version, ''),\n                n.package_kind, n.urgency,\n                (SELECT range_remote FROM nvd_data\n                 WHERE cve_name = vulnlist.name) AS remote,\n                bugs.description,\n                n.id\n                FROM vulnlist, bugs, package_notes AS n\n                WHERE bugs.name = vulnlist.name\n                AND n.id = vulnlist.note\n                ORDER BY vulnlist.package\"\"\")):\n            if fixed_version == '0' or urgency == 'unimportant' \\\n                    or urgency == 'end-of-life' \\\n                    or kind not in ('source', 'binary', 'unknown'):\n                continue\n\n            # Normalize FAKE-* names a bit.  The line number (which\n            # makes the name unique) is completely useless for the\n            # client.\n\n            if name[0:5] == 'TEMP-':\n                name = '-'.join(name.split('-')[0:2])\n\n            # Determine if a fix is available for the specific\n            # release.\n\n            fix_available = ' '\n            if kind == 'source':\n                fix_available_sql = \"\"\"SELECT st.vulnerable\n                    FROM source_packages AS p, source_package_status AS st\n                    WHERE p.name = ?\n                    AND p.release = ?\n                    AND p.subrelease IN ('', 'security', 'lts')\n                    AND st.bug_name = ?\n                    AND st.package = p.rowid\n                    ORDER BY p.version COLLATE version DESC\"\"\"\n            else:\n                fix_available_sql = ''\n\n            if fix_available_sql:\n                for (v,) in c.execute(fix_available_sql,\n                                      (package, release, name)):\n                    assert v is not None\n                    if not v:\n                        fix_available = 'F'\n                    break\n\n            if kind == 'source':\n                kind = 'S'\n            elif kind == 'binary':\n                kind = 'B'\n            else:\n                kind = ' '\n\n            if remote is None:\n                remote = '?'\n            elif remote:\n                remote = 'R'\n            else:\n                remote = ' '\n\n            result.append(\"%s,%c%c%c%c,%s,%s,%s\\n\"\n                          % (name,\n                             kind, urgency_to_flag[urgency], remote,\n                             fix_available,\n                             package, fixed_version, description))\n        result = base64.encodebytes(zlib.compress(''.join(result).encode('utf-8'), 9))\n\n        c.execute(\n            \"INSERT OR REPLACE INTO debsecan_data (name, data) VALUES (?, ?)\",\n            ('release/' + release, result))\n\n        c.execute(\"DROP TABLE vulnlist\")\n\n    def calculateDebsecan1(self):\n        \"\"\"Calculates debsecan data (release-independent, VERSION 1).\"\"\"\n\n        c = self.cursor()\n\n        result_start = ['VERSION 1']\n        bug_to_index = {}\n        bug_to_remote_flag = {}\n\n        def fill_bug_to_index():\n            index = 0\n            for (bug, desc, remote) in c.execute(\n                \"\"\"SELECT DISTINCT p.bug_name, b.description,\n                (SELECT range_remote FROM nvd_data\n                 WHERE cve_name = p.bug_name)\n                FROM package_notes AS p, bugs AS b\n                WHERE (p.bug_name LIKE 'CVE-%' OR p.bug_name LIKE 'TEMP-%')\n                AND p.urgency <> 'unimportant'\n                AND COALESCE(p.fixed_version, '') <> '0'\n                AND p.package_kind IN ('source', 'binary', 'unknown')\n                AND b.name = p.bug_name\n                ORDER BY p.bug_name\"\"\"):\n                if remote is None:\n                    remote = '?'\n                elif remote:\n                    remote = 'R'\n                else:\n                    remote = ' '\n\n                result_start.append(\"%s,,%s\" % (bug, desc))\n                bug_to_index[bug] = index\n                bug_to_remote_flag[bug] = remote\n                index += 1\n            result_start.append('')\n        fill_bug_to_index()\n\n        urgency_to_flag = {'low' : 'L', 'medium' : 'M', 'high' : 'H',\n                           'not yet assigned' : ' '}\n\n        vuln_list = []\n        source_packages = {}\n        def fill_vuln_list(source_packages=source_packages):\n            for (bug, package) in list(c.execute(\n                \"\"\"SELECT DISTINCT bug_name, package\n                FROM package_notes\n                WHERE (bug_name LIKE 'CVE-%' OR bug_name LIKE 'TEMP-%')\n                AND package_kind IN ('source', 'binary', 'unknown')\n                GROUP BY package, bug_name\n                ORDER BY package, bug_name\"\"\")):\n\n                # By default, unstable is unfixed even if there are\n                # only release-specific annotations available.  This\n                # is slightly at odds with the web front end (see\n                # data/latently-vulnerable) which does not normally\n                # report unstable versions as vulnerable in this case.\n                # However, in our tracking model, the main branch\n                # (sid) cannot be non-vulnerable, while the\n                # release-specific branches are.\n                unstable_fixed = ''\n\n                total_urgency = ''\n                other_versions = {}\n                is_binary = False\n                is_unknown = False\n                fixed_releases = {}\n                for (release, kind, urgency, version) in list(c.execute(\n                    \"\"\"SELECT release, package_kind, urgency, fixed_version\n                    FROM package_notes WHERE bug_name = ? AND package = ?\"\"\",\n                    (bug, package))):\n                    if not total_urgency:\n                        total_urgency = urgency\n                    elif total_urgency == 'unknown':\n                        if urgency != 'unimportant':\n                            total_urgency = urgency\n                    elif urgency == 'unknown':\n                        if total_urgency == 'unimportant':\n                            total_urgency = 'unknown'\n                    elif bugs.internUrgency(urgency) \\\n                             > bugs.internUrgency(total_urgency):\n                        total_urgency = urgency\n\n                    if kind == 'binary':\n                        is_binary = True\n                    elif kind == 'source':\n                        source_packages[package] = True\n                    else:\n                        is_unknown = True\n\n                    if release == '':\n                        unstable_fixed = version\n                        if version:\n                            v_ref = debian_support.Version(version)\n                            for (v,) in c.execute(\"\"\"SELECT version\n                            FROM source_packages WHERE name = ?\n                            AND release = 'sid' AND subrelease = ''\"\"\",\n                                                  (package,)):\n                                if debian_support.Version(v) >= v_ref:\n                                    fixed_releases['sid'] = True\n                                    break\n                    elif version is not None:\n                        fixed_releases[release] = True\n\n                        # Collect newer versions in the same release\n                        # (which are supposed to fix the same bug).\n\n                        v_ref = debian_support.Version(version)\n                        for (v,) in c.execute(\"\"\"SELECT fixed_version\n                        FROM package_notes\n                        WHERE package = ? AND release = ?\"\"\",\n                                              (package, release)):\n                            if v is None:\n                                continue\n                            if debian_support.Version(v) >= v_ref:\n                                other_versions[v] = True\n\n                        # The second part of this SELECT statement\n                        # covers binary-only NMUs.\n                        for (v,) in c.execute(\"\"\"SELECT version\n                        FROM source_packages WHERE name = ?1\n                        AND release = ?2 AND subrelease IN ('', 'security', 'lts')\n                        UNION ALL SELECT source_version\n                        FROM binary_packages WHERE source = ?1\n                        AND release = ?2 AND subrelease IN ('', 'security', 'lts')\"\"\",\n                                              (package, release)):\n                            if debian_support.Version(v) >= v_ref:\n                                other_versions[v] = True\n\n                if not total_urgency:\n                    total_urgency = 'unknown'\n\n                # Check if the issue does not actually mark any\n                # packages as vulnerable.  (If unstable_fixed == '0',\n                # release-specific annotations cannot create\n                # vulnerabilities, either.)\n                if total_urgency == 'unimportant' or unstable_fixed == '0' \\\n                        or total_urgency == 'end-of-life':\n                    continue\n\n                if unstable_fixed is None:\n                    unstable_fixed = ''\n                bs_flag = 'S'\n                if is_binary:\n                    assert not is_unknown\n                    bs_flag = 'B'\n                elif is_unknown:\n                    bs_flag = ' '\n\n                other_versions = list(other_versions.keys())\n                other_versions.sort()\n                other_versions = ' '.join(other_versions)\n\n                vuln_list.append((\"%s,%d,%c%c%c\"\n                                  % (package, bug_to_index[bug],\n                                     bs_flag, urgency_to_flag[total_urgency],\n                                     bug_to_remote_flag[bug]),\n                                  fixed_releases.keys(),\n                                  \",%s,%s\"\n                                  % (unstable_fixed, other_versions)))\n        fill_vuln_list()\n        source_packages = list(source_packages.keys())\n        source_packages.sort()\n\n        def store_value(name, value):\n            value = base64.encodebytes(zlib.compress(value.encode('utf-8'), 9))\n            c.execute(\"\"\"INSERT OR REPLACE INTO debsecan_data\n            VALUES (?, ?)\"\"\", (name, value))\n\n        def gen_release(release):\n            result = result_start[:]\n\n            for (prefix, releases, suffix) in vuln_list:\n                if release in releases:\n                    fixed = 'F'\n                else:\n                    fixed = ' '\n                result.append(prefix + fixed + suffix)\n            result.append('')\n\n            for sp in source_packages:\n                bp_list = []\n                for (bp,) in c.execute(\"\"\"SELECT name FROM binary_packages\n                WHERE source = ? AND release = ? AND subrelease = ''\n                ORDER BY name\"\"\",\n                                       (sp, release)):\n                    bp_list.append(bp)\n                if bp_list != [sp]:\n                    # We intentionally store the empty list, it means\n                    # that the source package is obsolete as a whole.\n                    result.append(\"%s,%s\" % (sp, ' '.join(bp_list)))\n            result.append('')\n\n            store_value('release/1/' + release, '\\n'.join(result))\n\n        for release in config.get_supported_releases():\n            gen_release(release)\n\n        result = result_start\n        for (prefix, release, suffix) in vuln_list:\n            result.append(prefix + ' ' + suffix)\n        result.append('')\n        result.append('')\n        result.append('')\n        store_value ('release/1/GENERIC', '\\n'.join(result))\n\n    def calculateDebsecan(self):\n        \"\"\"Calculate all debsecan data.\"\"\"\n        for release in config.get_supported_releases():\n            self.calculateDebsecan0(release)\n        self.calculateDebsecan1()\n\n    def getDebsecan(self, name):\n        \"\"\"Returns the debsecan data item NAME.\"\"\"\n        for (data,) in self.cursor().execute(\n            \"SELECT data FROM debsecan_data WHERE name = ?\", (name,)):\n            return base64.decodebytes(data)\n        else:\n            return None\n\n    def updateNVD(self, cursor, data, incremental):\n        \"\"\"Adds (and overwrites) NVD data stored in the database.  This\n        can be used for incremental updates if incremental is True.\"\"\"\n        if not incremental:\n            cursor.execute(\"DELETE FROM nvd_data\");\n        cursor.executemany(\"INSERT OR REPLACE INTO nvd_data VALUES (?\"\n                           + (\", ?\" * (len(data[0]) - 1))\n                           + \")\", data)\n\n    def getNVD(self, cursor, cve_name):\n        \"\"\"Returns a dictionary with NVD data corresponding to the CVE name,\n        or None.\"\"\"\n        for row in cursor.execute(\"SELECT * FROM nvd_data WHERE cve_name = ?\",\n                                  (cve_name,)):\n            return NVDEntry(row, cursor.getdescription())\n        return None\n\n    def getSourcePackageVersions(self, cursor, pkg):\n        \"\"\"A generator which returns tuples (RELEASE-LIST, VERSION),\n        the available versions of the source package pkg.\"\"\"\n\n        releases = config.get_supported_releases()\n        values = [pkg] + releases\n\n        for (release, version) in cursor.execute(\n            \"\"\"SELECT release_name(release, subrelease, archive)\n            AS release, version FROM source_packages\n            WHERE name = ?\n            AND release IN (\"\"\" + \",\".join(\"?\" * len(releases)) + \"\"\")\n            GROUP BY release, version\n            ORDER BY release_to_number(release), subrelease_to_number(subrelease), version COLLATE version\"\"\", values):\n            yield release, version\n\n    def getBinaryPackageVersions(self, cursor, pkg):\n        \"\"\"A generator which returns tuples (RELEASE-LIST,\n        SOURCE-PACKAGE, VERSION, ARCH-LIST), the available versions of\n        the binary package pkg.\"\"\"\n\n        for (releases, source, version, archs) in cursor.execute(\n            \"\"\"SELECT string_list(release) AS releases, source, version, archs\n            FROM (SELECT release, source, version, string_set(archs) AS archs\n            FROM binary_packages\n            WHERE name = ?\n            GROUP BY release, source, version\n            ORDER BY release_to_number(release))\n            GROUP BY source, version, archs\"\"\", (pkg,)):\n            yield releases.split(', '), source, version, archs.split(',')\n\n    def getBinaryPackagesForSource(self, cursor, pkg):\n        \"\"\"A generator which returns tuples (PACKAGES, RELEASE-LIST,\n        VERSION), the available binary packages built from the source\n        package pkg.\"\"\"\n\n        for (packages, releases, version, archs) in cursor.execute(\n            \"\"\"SELECT string_list(package) AS packages, releases, version,\n            archs\n            FROM (SELECT package, string_list(rel) AS releases, version, archs\n            FROM (SELECT name AS package,\n            release_name(release, subrelease, archive) AS rel,\n            version, string_set(archs) AS archs\n            FROM binary_packages\n            WHERE source = ?\n            GROUP BY name, release, subrelease, archive, version\n            ORDER BY release_to_number(release), subrelease_to_number(subrelease))\n            GROUP BY package, version, archs\n            ORDER BY package)\n            GROUP BY releases, version, archs\n            ORDER BY version COLLATE version\"\"\", (pkg,)):\n            yield (packages.split(', '), releases.split(', '),\n                   archs.split(','), version)\n\n    def getSourcePackages(self, cursor, bug):\n        \"\"\"A generator which returns tuples (SOURCE-PACKAGE,\n        RELEASE-LIST, VERSION, VULNERABLE-FLAG) of source packages\n        which are related to the given bug.\"\"\"\n\n        releases = config.get_supported_releases()\n        values = [bug] + releases\n\n        for (package, releases, version, vulnerable) in cursor.execute(\n            \"\"\"SELECT package, string_list(release), version, vulnerable\n            FROM (SELECT p.name AS package,\n            release_name(p.release, p.subrelease, p.archive) AS release,\n            p.version AS version, s.vulnerable AS vulnerable\n            FROM source_package_status AS s, source_packages AS p\n            WHERE s.bug_name = ? AND p.rowid = s.package\n            AND release in (\"\"\" + \",\".join(\"?\" * len(releases)) + \"\"\"))\n            GROUP BY package, version, vulnerable\n            ORDER BY package, releasepart_to_number(release), subreleasepart_to_number(release), version COLLATE version\"\"\",\n            values):\n            yield package, releases.split(', '), version, vulnerable\n\n    def getBugsFromDebianBug(self, cursor, number):\n        \"\"\"A generator which returns a list of tuples\n        (BUG-NAME, URGENCY, DESCRIPTION).\"\"\"\n\n        return cursor.execute(\n            \"\"\"SELECT DISTINCT bugs.name, package_notes.urgency,\n            bugs.description\n            FROM debian_bugs, package_notes, bugs\n            WHERE debian_bugs.bug = ? AND package_notes.id = debian_bugs.note\n            AND bugs.name = package_notes.bug_name\n            ORDER BY bug_name\"\"\", (number,))\n\n    def isSourcePackage(self, cursor, pkg):\n        \"\"\"Returns a true value if pkg is a source package.\"\"\"\n        ((flag,),) = cursor.execute(\n            \"SELECT EXISTS (SELECT * FROM source_packages WHERE name = ?)\",\n            (pkg,))\n        return flag\n\n    def isBinaryPackage(self, cursor, pkg):\n        \"\"\"Returns a true value if pkg is a binary package.\"\"\"\n        ((flag,),) = cursor.execute(\n            \"SELECT EXISTS (SELECT * FROM binary_packages WHERE name = ?)\",\n            (pkg,))\n        return flag\n\n    def getDSAsForSourcePackage(self, cursor, package):\n        bugs_like = self.genDBAdvisoryString(\"bugs.name\", dtsa=False)\n        for row in cursor.execute(\n            \"\"\"SELECT DISTINCT bugs.name, bugs.description\n            FROM bugs, package_notes as p\n            WHERE p.bug_name = bugs.name\n            AND ( \"\"\" + bugs_like + \"\"\" )\n            AND p.package = ?\n            ORDER BY bugs.release_date DESC\"\"\", (package,)):\n            yield DSAsForSourcePackage(*row)\n\n\n    def getTODOs(self, cursor=None, hide_check=False):\n        \"\"\"Returns a list of pairs (BUG-NAME, DESCRIPTION).\"\"\"\n        if cursor is None:\n            cursor = self.cursor()\n        if hide_check:\n            return cursor.execute(\n                \"\"\"SELECT DISTINCT bugs.name, bugs.description, bugs_notes.comment\n                FROM bugs_notes, bugs\n                WHERE bugs_notes.typ = 'TODO'\n                AND bugs_notes.comment <> 'check'\n                AND bugs.name = bugs_notes.bug_name\n                ORDER BY name COLLATE version\"\"\")\n        else:\n            return cursor.execute(\n                \"\"\"SELECT DISTINCT bugs.name, bugs.description, bugs_notes.comment\n                FROM bugs_notes, bugs\n                WHERE bugs_notes.typ = 'TODO'\n                AND bugs.name = bugs_notes.bug_name\n                ORDER BY name COLLATE version\"\"\")\n\n    def getBugXrefs(self, cursor, bug):\n        \"\"\"Returns a generator for a list of bug names.  The listed\n        bugs refer to the given bug, or the bug refers to them.\"\"\"\n\n        for (bug_name,) in cursor.execute(\n            \"\"\"SELECT DISTINCT bug\n            FROM (SELECT target AS bug\n            FROM bugs_xref WHERE source = ?\n            UNION ALL SELECT source AS bug\n            FROM bugs_xref WHERE target = ?\n            UNION ALL SELECT bug_origin AS bug FROM package_notes\n            WHERE bug_name = ? AND bug_origin <> '')\n            WHERE bug <> ?\n            ORDER BY bug\"\"\", (bug, bug, bug, bug)):\n            yield bug_name\n\n    def readRemovedPackages(self, cursor, filename):\n        \"\"\"Reads a file of removed packages and stores it in the database.\n        The original contents of the removed_packages table is preserved.\"\"\"\n\n        f = open(filename)\n\n        re_package = re.compile(r'^\\s*([a-z0-9]\\S+)\\s*$')\n\n        # Not very good error reporting, but changes to that file are\n        # rare.\n\n        def gen():\n            for line in f:\n                if line == '':\n                    break\n                if line[0] == '#' or line == '\\n':\n                    continue\n                match = re_package.match(line)\n                if match:\n                    yield match.groups()\n                else:\n                    raise ValueError(\"not a package: \" + repr(line))\n\n        cursor.executemany(\n            \"INSERT OR IGNORE INTO removed_packages (name) VALUES (?)\", gen())\n\n    def getUnknownPackages(self, cursor):\n        \"\"\"Returns a generator for a list of unknown packages.\n        Each entry has the form (PACKAGE, BUG-LIST).\"\"\"\n\n        old_package = ''\n        bugs = []\n        for (package, bug_name) in cursor.execute(\n            \"\"\"SELECT DISTINCT package, bug_name\n            FROM package_notes WHERE package_kind = 'unknown'\n            AND COALESCE (release, '') <> 'experimental'\n            AND NOT EXISTS (SELECT * FROM removed_packages\n                            WHERE name = package)\n            ORDER BY package, bug_name\"\"\"):\n            if package != old_package:\n                if old_package:\n                    yield (old_package, bugs)\n                old_package = package\n                bugs = []\n            bugs.append(bug_name)\n        if old_package:\n            yield (old_package, bugs)\n\n    def getFakeBugs(self, cursor=None, vulnerability=0):\n        \"\"\"Returns a list of pairs (BUG-NAME, DESCRIPTION).\"\"\"\n\n        if cursor is None:\n            cursor = self.cursor()\n\n        return list(cursor.execute(\n            \"\"\" SELECT DISTINCT  b.name, b.description\n                FROM bugs AS b,\n                source_package_status AS st\n                WHERE\n                b.name = st.bug_name AND\n                st.vulnerable=? AND\n                st.bug_name > 'TEMP-' AND st.bug_name LIKE 'TEMP-%'\n                ORDER BY st.bug_name\"\"\",(vulnerability,)))\n\n    def getUnreportedVulnerabilities(self, cursor=None):\n        \"\"\"Returns a list of pairs (BUG_NAME, DESCRIPTION)\n        of vulnerabilities which are unfixed in unstable and lack a filed bug.\n        \"\"\"\n        if cursor is None:\n            cursor = self.cursor()\n        last_bug = None\n        result = []\n        for bug, pkg in cursor.execute(\n\"\"\"SELECT DISTINCT source_package_status.bug_name, source_packages.name\n  FROM source_packages\n  JOIN source_package_status\n    ON source_packages.rowid = source_package_status.package\n  JOIN package_notes\n    ON source_packages.name = package_notes.package\n      AND package_notes.bug_name = source_package_status.bug_name\n      AND source_packages.release = 'sid'\n      AND package_notes.release = ''\n  WHERE source_package_status.bug_name LIKE 'CVE-%'\n  AND package_notes.urgency <> 'unimportant'\n  AND package_notes.rowid NOT IN (SELECT note FROM debian_bugs)\n  AND source_package_status.vulnerable\n  ORDER BY source_package_status.bug_name, source_packages.name\"\"\"):\n            if last_bug is None or last_bug != bug:\n                last_bug = bug\n                result.append((bug, []))\n            result[-1][1].append(pkg)\n        return result\n\n    def getITPs(self, cursor):\n        \"\"\"Returns a generator for a list of unknown packages.\n        Each entry has the form (PACKAGE, BUG-LIST, DEBIAN-BUG-LIST).\"\"\"\n\n        # The \"|| ''\" is required to convert the string_set argument\n        # to a string.\n        for (package, bugs, debian_bugs) in cursor.execute(\n            \"\"\"SELECT DISTINCT n.package, string_set(n.bug_name),\n            string_set(db.bug || '')\n            FROM package_notes AS n, debian_bugs AS db\n            WHERE package_kind = 'itp'\n            AND db.note = n.id\n            GROUP BY n.package\n            ORDER BY n.package\"\"\"):\n            yield (package, bugs.split(','), map(int, debian_bugs.split(',')))\n\n    def check(self, cursor=None):\n        \"\"\"Runs a simple consistency check and prints the results.\"\"\"\n\n        if cursor is None:\n            cursor = self.cursor()\n\n        for (package, release, archive, architecture, source) in\\\n            cursor.execute(\n            \"\"\"SELECT package, release, archive, architecture, source\n            FROM binary_packages\n            WHERE NOT EXISTS\n            (SELECT *\n                    FROM source_packages AS sp\n                    WHERE sp.package = binary_packages.source\n                    AND sp.release = binary_packages.release\n                    AND sp.archive = binary_packages.archive)\n            \"\"\"):\n            print(\"error: binary package without source package\")\n            print(\"  binary package:\", package)\n            print(\"  release:\", release)\n            if archive:\n                print(\"  archive:\", archive)\n            print(\"  architecture:\", architecture)\n            print(\"  missing source package:\", source)\n\n        for (package, release, archive, architecture, version,\n             source, source_version) \\\n            in cursor.execute(\"\"\"SELECT binary_packages.package,\n            binary_packages.release, binary_packages.archive,\n            binary_packages.architecture,binary_packages.version,\n            sp.package, sp.version\n            FROM binary_packages, source_packages AS sp\n            WHERE sp.package = binary_packages.source\n            AND sp.release = binary_packages.release\n            AND sp.archive = binary_packages.archive\n            AND sp.version <> binary_packages.source_version\"\"\"):\n            assert debian_support.Version(version) != debian_support.Version(source_version)\n            if debian_support.Version(version) <= debian_support.Version(source_version):\n                print(\"error: binary package is older than source package\")\n            else:\n                print(\"warning: binary package is newer than source package\")\n            print(\"  binary package: %s (%s)\" % (package, version))\n            print(\"  source package: %s (%s)\" % (source, source_version))\n            print(\"  release:\", release)\n            if archive:\n                print(\"  archive:\", archive)\n            print(\"  architecture:\", architecture)\n\ndef test():\n    assert mergeLists(u'',u'') == [], mergeLists(u'', u'')\n    assert mergeLists(u'', []) == []\n    assert mergeLists(u'a', u'a') == [u'a']\n    assert mergeLists(u'a', u'b') == [u'a', u'b']\n    assert mergeLists(u'a,c', u'b') == [u'a', u'b', 'c']\n    assert mergeLists(u'a,c', [u'b', u'de']) == [u'a', u'b', u'c', u'de']\n\n    import os\n    db_file = 'test_security.db'\n    try:\n        db = DB(db_file)\n    except SchemaMismatch:\n        os.unlink(db_file)\n        db = DB(db_file)\n\n    cursor = db.writeTxn()\n    db.readBugs(cursor, '../../data')\n    db.commit(cursor)\n\n    b = bugs.BugFromDB(cursor, 'CVE-2005-2491')\n    assert b.name == 'CVE-2005-2491', b.name\n    assert b.description == 'Integer overflow in pcre_compile.c in Perl Compatible Regular ...', b.description\n    assert len(b.xref) == 2, b.xref\n    assert not b.not_for_us\n    assert 'DSA-800-1' in b.xref, b.xref\n    assert 'DTSA-10-1' in b.xref, b.xref\n    assert 'DLA-23-1' in b.xref, b.xref\n    assert tuple(b.comments) == (('NOTE', 'gnumeric/goffice includes one as well; according to upstream not exploitable in gnumeric,'),\n                                 ('NOTE', 'new copy will be included any way')),\\\n                                 b.comments\n\n    assert len(b.notes) == 4, len(b.notes)\n\n    for n in b.notes:\n        assert n.release is None\n        if n.package == 'pcre3':\n            assert n.fixed_version == debian_support.Version('6.3-0.1etch1')\n            assert tuple(n.bugs) == (324531,), n.bugs\n            assert n.urgency == bugs.internUrgency('medium')\n        elif n.package == 'python2.1':\n            assert n.fixed_version == debian_support.Version('2.1.3dfsg-3')\n            assert len(n.bugs) == 0, n.bugs\n            assert n.urgency == bugs.internUrgency('medium')\n        elif n.package == 'python2.2':\n            assert n.fixed_version == debian_support.Version('2.2.3dfsg-4')\n            assert len(n.bugs) == 0, n.bugs\n            assert n.urgency == bugs.internUrgency('medium')\n        elif n.package == 'python2.3':\n            assert n.fixed_version == debian_support.Version('2.3.5-8')\n            assert len(n.bugs) == 0, n.bugs\n            assert n.urgency == bugs.internUrgency('medium')\n        else:\n            assert False\n\n    assert bugs.BugFromDB(cursor, 'DSA-311').isKernelOnly()\n\nif __name__ == \"__main__\":\n    test()\n","repo_name":"CVEDB/security-tracker","sub_path":"lib/python/security_db.py","file_name":"security_db.py","file_ext":"py","file_size_in_byte":85843,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"70628178825","text":"\nimport os\nos.environ[\"CUDA_VISIBLE_DEVICES\"] = \"\"\nimport tensorflow as tf\nimport tensorflow_quantum as tfq\nfrom tensorflow.keras import layers\nimport tensorflow_probability as tfp\nimport gym, cirq, sympy\nimport numpy as np\nfrom functools import reduce\nimport matplotlib.pyplot as plt\nfrom collections import deque, namedtuple ,defaultdict\nimport copy\nimport random\nimport pickle\n\n# gpu_options = tf.compat.v1.GPUOptions(per_process_gpu_memory_fraction=0.333)\n\n# sess = tf.compat.v1.Session(config=tf.compat.v1.ConfigProto(gpu_options=gpu_options))\n\ndef one_qubit_rotation(qubit, symbols):\n    \"\"\"\n    Returns Cirq gates that apply a rotation of the bloch sphere about the X,\n    Y and Z axis, specified by the values in `symbols`.\n    \"\"\"\n    return [cirq.rx(symbols[0])(qubit),\n            cirq.ry(symbols[1])(qubit),\n            cirq.rz(symbols[2])(qubit)]\n\ndef entangling_layer(qubits):\n    \"\"\"\n    Returns a layer of CZ entangling gates on `qubits` (arranged in a circular topology).\n    \"\"\"\n    cz_ops = [cirq.CZ(q0, q1) for q0, q1 in zip(qubits, qubits[1:])]\n    cz_ops += ([cirq.CZ(qubits[0], qubits[-1])] if len(qubits) != 2 else [])\n    return cz_ops\n\ndef generate_circuit(qubits, n_layers):\n\n    # Number of qubits\n    n_qubits = len(qubits)\n    \n    # Sympy symbols for variational angles\n    params = sympy.symbols(f'theta(0:{3*(n_layers+1)*n_qubits})')\n    params = np.asarray(params).reshape((n_layers + 1, n_qubits, 3))\n    \n    # Sympy symbols for encoding angles\n    inputs = sympy.symbols(f'x(0:{n_layers})'+f'_(0:{n_qubits})')\n    inputs = np.asarray(inputs).reshape((n_layers, n_qubits))\n    \n    # Define circuit\n    circuit = cirq.Circuit()\n    for l in range(n_layers):\n        # Variational layer\n        circuit += cirq.Circuit(one_qubit_rotation(q, params[l, i]) for i, q in enumerate(qubits))\n        circuit += entangling_layer(qubits)\n        # Encoding layer\n        circuit += cirq.Circuit(cirq.rx(inputs[l, i])(q) for i, q in enumerate(qubits))\n\n    # Last varitional layer\n    circuit += cirq.Circuit(one_qubit_rotation(q, params[n_layers, i]) for i,q in enumerate(qubits))\n    \n    return circuit, list(params.flat), list(inputs.flat)\n\nclass ReUploadingPQC(tf.keras.layers.Layer):\n\n    def __init__(self, qubits, n_layers, observables, activation=\"linear\", name=\"re-uploading_PQC\"):\n        super(ReUploadingPQC, self).__init__(name=name)\n        self.n_layers = n_layers\n        self.n_qubits = len(qubits)\n\n        circuit, theta_symbols, input_symbols = generate_circuit(qubits, n_layers)\n\n        theta_init = tf.random_uniform_initializer(minval=0.0, maxval=np.pi)\n        self.theta = tf.Variable(\n            initial_value=theta_init(shape=(1, len(theta_symbols)), dtype=\"float32\"),\n            trainable=True, name=\"thetas\"\n        )\n        # self.theta = tf.Variable(\n        #     initial_value=tf.zeros(shape=(1, len(theta_symbols)), dtype=\"float32\"),\n        #     trainable=True, name=\"thetas\"\n        # )\n        \n        lmbd_init = tf.ones(shape=(self.n_qubits * self.n_layers,))\n        self.lmbd = tf.Variable(\n            initial_value=lmbd_init, dtype=\"float32\", trainable=True, name=\"lambdas\"\n        )\n        \n        # Define explicit symbol order.\n        symbols = [str(symb) for symb in theta_symbols + input_symbols]\n        self.indices = tf.constant([symbols.index(a) for a in sorted(symbols)])\n        \n        self.activation = activation\n        self.empty_circuit = tfq.convert_to_tensor([cirq.Circuit()])\n        self.computation_layer = tfq.layers.ControlledPQC(circuit, observables)        \n\n    def call(self, inputs):\n        # inputs[0] = encoding data for the state.\n        # batch_dim = tf.gather(tf.shape(inputs[0]), 0)\n        batch_dim = tf.gather(tf.shape(inputs), 0)\n        tiled_up_circuits = tf.repeat(self.empty_circuit, repeats=batch_dim)\n        tiled_up_thetas = tf.tile(self.theta, multiples=[batch_dim, 1])\n        # tiled_up_inputs = tf.tile(inputs[0], multiples=[1, self.n_layers])\n        tiled_up_inputs = tf.tile(inputs, multiples=[1, self.n_layers])\n        scaled_inputs = tf.einsum(\"i,ji->ji\", self.lmbd, tiled_up_inputs)\n        squashed_inputs = tf.keras.layers.Activation(self.activation)(scaled_inputs)\n\n        joined_vars = tf.concat([tiled_up_thetas, squashed_inputs], axis=1)\n        joined_vars = tf.gather(joined_vars, self.indices, axis=1)\n        \n        return self.computation_layer([tiled_up_circuits, joined_vars])\n\nclass OUNoise:\n    \"\"\"Ornstein-Uhlenbeck process.\"\"\"\n\n    def __init__(self, size, mu=0., theta=0.15, sigma=0.2):\n        \"\"\"Initialize parameters and noise process.\"\"\"\n        self.mu = mu * np.ones(size)\n        self.theta = theta\n        self.sigma = sigma\n        self.reset()\n\n    def reset(self):\n        \"\"\"Reset the internal state (= noise) to mean (mu).\"\"\"\n        self.state = copy.copy(self.mu)\n\n    def sample(self):\n        \"\"\"Update internal state and return it as a noise sample.\"\"\"\n        x = self.state\n        dx = self.theta * (self.mu - x) + self.sigma * np.array([random.random() for i in range(len(x))])\n        self.state = x + dx\n        return self.state\n\nclass ReplayBuffer:\n\n    def __init__(self, buffer_size, batch_size):\n        \"\"\"Initialize a ReplayBuffer object.\n        Params\n        ======\n            buffer_size (int): maximum size of buffer\n            batch_size (int): size of each training batch\n            seed (int): random seed\n        \"\"\"\n        self.memory = deque(maxlen=buffer_size)  \n        self.batch_size = batch_size\n        self.experience = namedtuple(\"Experience\", field_names=[\"state\", \"action\", \"reward\", \"next_state\", \"done\"])\n\n\n        self.n_step_buffer = deque(maxlen=1)\n    \n    def add(self, state, action, reward, next_state, done):\n        e = self.experience(state, action, reward, next_state, done)\n        self.memory.append(e)\n    \n\n    def sample(self):\n        \"\"\"Randomly sample a batch of experiences from memory.\"\"\"\n        experiences = random.sample(self.memory, k=self.batch_size)\n\n        states = tf.convert_to_tensor(np.stack([e.state for e in experiences if e is not None]),dtype=tf.float32)\n        actions = tf.convert_to_tensor(np.vstack([e.action for e in experiences if e is not None]),dtype=tf.float32)\n        rewards = tf.convert_to_tensor(np.vstack([e.reward for e in experiences if e is not None]),dtype=tf.float32)\n        next_states = tf.convert_to_tensor(np.stack([e.next_state for e in experiences if e is not None]),dtype=tf.float32)\n        dones = tf.convert_to_tensor(np.vstack([e.done for e in experiences if e is not None]),dtype=tf.float32)\n  \n        return (states, actions, rewards, next_states, dones)\n\n    def __len__(self):\n        \"\"\"Return the current size of internal memory.\"\"\"\n        return len(self.memory)\n\nclass NAF(tf.keras.Model):\n  def __init__(self,qubits,n_layers,n_actions,observables):\n    super(NAF,self).__init__()\n    self.n_actions=n_actions\n    self.re_uploading_pqc = ReUploadingPQC(qubits, n_layers, observables, activation='tanh')\n    # self.re_uploading_pqc2 = ReUploadingPQC(qubits, n_layers, observables, activation='tanh')\n    # self.re_uploading_pqc3 = ReUploadingPQC(qubits, n_layers, observables, activation='tanh')\n    self.value=layers.Dense(1, activation=None,kernel_regularizer='l2')\n    self.action_values=layers.Dense(self.n_actions,activation=None,kernel_regularizer='l2')\n    # self.low_matrix_entries=layers.Dense(int(self.n_actions*(self.n_actions-1)/2),activation=None)\n    self.diag_matrix_entries=layers.Dense(int(self.n_actions),activation=None,kernel_regularizer='l2')\n    \n  def call(self,inputs,action=None,take_action=True):\n    x=self.re_uploading_pqc(inputs)\n    # x2=self.re_uploading_pqc2(inputs)\n    # x3=self.re_uploading_pqc3(inputs)\n    # x=tf.concat([x1,x2,x3],1)\n    action_value=2*tf.math.tanh(self.action_values(x))\n    # low_entries = tf.math.tanh(self.low_matrix_entries(x))\n    diga_entries = tf.math.exp(tf.math.tanh(self.diag_matrix_entries(x)))\n    V=self.value(x)\n    \n    action_value=tf.expand_dims(action_value,axis=-1)  \n\n    L = tf.zeros((inputs.shape[0],self.n_actions,self.n_actions),dtype=V.dtype)\n    # get lower triagular and diganal indices\n    # tril_indic=tf.transpose(tf.constant(np.tril_indices(n=self.n_actions,m=self.n_actions,k=-1)))\n    dig_ind= tf.transpose( tf.tile( tf.constant(np.arange(self.n_actions),dtype=tf.int32,shape=(1,self.n_actions)) ,(2,1) )  )\n    # t_ind= tf.concat([tril_indic,dig_ind],0)\n    # entris= tf.concat([low_entries,diga_entries],1)\n\n    L=tf.transpose(L,perm=(1,2,0))\n    # L=tf.tensor_scatter_nd_update(L,t_ind,tf.transpose(entris))\n    L=tf.tensor_scatter_nd_update(L,dig_ind,tf.transpose(diga_entries))\n    L=tf.transpose(L,perm=(2,0,1))\n\n    # calculate state-dependent, positive-definite square matrix\n    P=L@tf.transpose(L,perm=(0,2,1))\n\n    Q=None\n    \n    if action is not None:\n      #calculate Advantage:\n      A =tf.squeeze(-0.5 * tf.matmul( tf.matmul(tf.transpose(tf.expand_dims(action,axis=-1) - action_value, perm= (0,2,1)), P) , \n                            tf.expand_dims(action,axis=-1) - action_value ), axis=-1 )\n      Q= A+V\n    if take_action==True:\n        dist= tfp.distributions.MultivariateNormalLinearOperator(loc=tf.squeeze(action_value,axis=-1), scale=tf.linalg.LinearOperatorLowerTriangular(tf.linalg.inv(L)))\n        action = dist.sample()\n        action = tf.clip_by_value(action,-2,2)\n    action_value=tf.squeeze(action_value,axis=-1)\n    return action,Q,V, action_value\n\nclass DQN_Agent():\n    \"\"\"Interacts with and learns from the environment.\"\"\"\n\n    def __init__(self,\n                 observables,\n                 qubits,\n                 action_size,\n                 layer_size,\n                 BATCH_SIZE,\n                 BUFFER_SIZE,\n                 LR,\n                 LR2,\n                 TAU,\n                 GAMMA,\n                 UPDATE_EVERY,\n                 NUPDATES,\n                 ):\n\n\n        self.action_size = action_size\n        # self.seed = random.seed(seed)\n        self.TAU = TAU\n        self.GAMMA = GAMMA\n        self.UPDATE_EVERY = UPDATE_EVERY\n        self.NUPDATES = NUPDATES\n        self.BATCH_SIZE = BATCH_SIZE\n        \n\n        # Q-Network\n        self.qnetwork_local = NAF(qubits,layer_size, action_size,observables )#qubits,n_layers,n_actions,observables \n        self.qnetwork_target = copy.deepcopy(self.qnetwork_local)\n\n        self.optimizer =tf.keras.optimizers.Adam(learning_rate=LR, amsgrad=True)\n        self.optimizer2 =tf.keras.optimizers.Adam(learning_rate=LR2, amsgrad=True)\n\n        print(self.qnetwork_local)\n        \n        # Replay memory\n        self.memory = ReplayBuffer(BUFFER_SIZE, BATCH_SIZE)\n        \n        # Initialize time step (for updating every UPDATE_EVERY steps)\n        self.t_step = 0\n        \n        # Noise process\n        self.noise = OUNoise(action_size)\n    \n    def step(self, state, action, reward, next_state, done):\n        # Save experience in replay memory\n        self.memory.add(state, action, reward, next_state, done)\n        \n        # Learn every UPDATE_EVERY time steps.\n        self.t_step = (self.t_step + 1) % self.UPDATE_EVERY\n        if self.t_step == 0:\n            # If enough samples are available in memory, get random subset and learn\n            if len(self.memory) > self.BATCH_SIZE:\n                for _ in range(self.NUPDATES):\n                    experiences = self.memory.sample()\n                    loss = self.learn(experiences)\n                  \n          \n    def act(self, state):\n        \"\"\"Calculating the action\n        Params\n        ======\n            state (array_like): current state  \n        \"\"\"\n\n        state = tf.expand_dims(tf.convert_to_tensor((state),dtype=tf.float32),axis=0  )  \n        action, _, _,_ = self.qnetwork_local(state)\n        return tf.squeeze(action,axis=0).numpy()\n    \n    def act_without_noise(self, state):\n        state = tf.expand_dims(tf.convert_to_tensor((state),dtype=tf.float32),axis=0  )  \n        _, _, _,action = self.qnetwork_local(state)\n        return tf.squeeze(action,axis=0).numpy()\n\n\n    def learn(self, experiences):\n        \"\"\"Update value parameters using given batch of experience tuples.\n        Params\n        ======\n            experiences (Tuple[torch.Tensor]): tuple of (s, a, r, s', done) tuples \n        \"\"\"\n        states, actions, rewards, next_states, dones = experiences\n\n        # get the Value for the next state from target model\n   \n        _, _, V_,_ = self.qnetwork_target(next_states,take_action=False)\n\n        # Compute Q targets for current states \n        V_targets = rewards + (self.GAMMA * V_ * (1 - dones))\n        \n        with tf.GradientTape() as tape:\n        # Get expected Q values from local model\n          tape.watch(self.qnetwork_local.trainable_variables)\n          _,Q, _ ,_= self.qnetwork_local(states, actions,take_action=False)\n          loss = tf.keras.losses.MeanSquaredError()(V_targets, Q)\n      \n        grads = tape.gradient(loss,self.qnetwork_local.trainable_variables)\n       \n        self.optimizer.apply_gradients([(grads[0],self.qnetwork_local.trainable_variables[0])])\n        self.optimizer2.apply_gradients(zip(grads[1:],self.qnetwork_local.trainable_variables[1:]))\n        # ------------------- update target network ------------------- #\n        self.soft_update()\n        \n        # self.noise.reset()\n        #self.noise.reset()\n        return loss.numpy() \n\n\n\n    def soft_update(self):\n        \"\"\"Soft update model parameters.\n        θ_target = τ*θ_local + (1 - τ)*θ_target\n        Params\n        ======\n            local_model (PyTorch model): weights will be copied from\n            target_model (PyTorch model): weights will be copied to\n            tau (float): interpolation parameter \n        \"\"\"\n        # self.qnetwork_local.set_weights([self.TAU*i+(1.0-self.TAU)*j for i,j in zip(self.qnetwork_local.get_weights(),self.qnetwork_target.get_weights())])\n        self.qnetwork_target.set_weights([self.TAU*i+(1.0-self.TAU)*j for i,j in zip(self.qnetwork_local.get_weights(),self.qnetwork_target.get_weights())])\n\ndef evaluate(eval_runs):\n    scores2 = []\n    # test_env=gym.make('Pendulum-v0', g=9.81)\n    for i in range(eval_runs):\n        state = test_env.reset()    \n        score = 0\n        done = 0\n        while not done:\n            action = agent.act_without_noise(state)\n            state, reward, done, _ = test_env.step(action)\n            score += reward\n            if done:\n                scores2.append(score)\n                break\n    return np.mean(scores2)\n\nn_qubits = 3# Dimension of the state vectors in CartPole\nn_layers = 10 # Number of layers in the PQC\n\n\nqubits = cirq.GridQubit.rect(1, n_qubits)\nops = [cirq.Z(q) for q in qubits]\nobservables1 = reduce((lambda x, y: x * y), ops) # Z_0*Z_1*Z_2*Z_3\n\nops2 = [cirq.X(q) for q in qubits]\nobservables2 = reduce((lambda x, y: x * y), ops2) # Z_0*Z_1*Z_2*Z_3\n\nops3 = [cirq.Y(q) for q in qubits]\nobservables3 = reduce((lambda x, y: x * y), ops3) # Z_0*Z_1*Z_2*Z_3\n\n# observables= [observables1,observables2,observables3]\nobservables= [ops[0] ,ops[1],ops[2]]\nBUFFER_SIZE = 10000\nBATCH_SIZE = 64\nGAMMA = 0.9\nTAU = 0.01\nLR = 1e-3\nLR2=1e-2\nUPDATE_EVERY = 4\nNUPDATES = 1\nenv=gym.make('Pendulum-v0', g=9.81)\n#env=gym.make('Pendulum-v0', g=9.81)\ntest_env=gym.make('Pendulum-v0', g=9.81)\n\n\naction_size = 1\nfor i in range(3):\n    agent = DQN_Agent(observables,qubits,    \n                        action_size=action_size,\n                        layer_size=10,\n                        BATCH_SIZE=BATCH_SIZE, \n                        BUFFER_SIZE=BUFFER_SIZE, \n                        LR=LR, \n                        LR2=LR2,\n                        TAU=TAU, \n                        GAMMA=GAMMA, \n                        UPDATE_EVERY=UPDATE_EVERY,\n                        NUPDATES=NUPDATES)\n\n    # frames=1000000\n    scores = []  \n    evas=[]                      # list containing scores from each episode\n    # scores_window = deque(maxlen=100)  # last 100 scores\n    frame = 0\n    i_episode = 0\n    state = env.reset()\n    step=0\n    score = 0  \n    # agent.qnetwork_local.load_weights('./QUANTUM')\n    # agent.qnetwork_target.load_weights('./QUANTUM')\n    #1001\n    while i_episode <1001:\n        action = agent.act(state)\n        step+=1\n        next_state, reward, done, _ = env.step(np.array([action]))\n        next_state= np.reshape(next_state,3)# /np.array([1.0,1.0,8.0])\n        agent.step(state, action, reward, next_state, done)\n        state = next_state\n        score += reward\n        \n        \n            \n        if done:\n            # scores_window.append(score)       # save most recent score\n            # scores.append(score)              # save most recent score\n            print('episode=',i_episode)\n            # print('\\rEpisode {}\\tFrame {} \\tAverage Score: {:.2f}'.format(i_episode, frame, np.mean(scores_window)))\n            # agent.qnetwork_local.save_weights('./QUANTUM/')\n            if i_episode % 5 == 0:       \n                eva=evaluate(eval_runs=3)\n                evas.append(eva)\n                print('evaluate=',eva)\n                # agent.qnetwork_local.save_weights('./QUANTUM/qweight10')\n            # if i_episode % 100 == 0:\n            #     print('\\rEpisode {}\\tFrame {}\\tAverage Score: {:.2f}'.format(i_episode,frame, np.mean(scores_window)))\n            if i_episode % 100 == 0:    \n                with open(f'./QUANTUM/qevas10_{i+20}','wb') as f:\n                    pickle.dump(evas,f)\n        \n            i_episode +=1 \n            state = env.reset()\n            # state=state/np.array([1.0,1.0,8.0])\n            step=0\n            score = 0  \n    with open(f'./QUANTUM/qevas10_{i+20}','wb') as f:\n        pickle.dump(evas,f)\n","repo_name":"yliuls/quantum-reinforcement-learning","sub_path":"QNAF2.py","file_name":"QNAF2.py","file_ext":"py","file_size_in_byte":17695,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"5418173190","text":"# Definition for singly-linked list.\n# class ListNode(object):\n#     def __init__(self, val=0, next=None):\n#         self.val = val\n#         self.next = next\nclass Solution(object):\n    def addTwoNumbers(self, l1, l2):\n        \"\"\"\n        :type l1: ListNode\n        :type l2: ListNode\n        :rtype: ListNode\n        \"\"\"\n        n1, n2 = \"\", \"\"\n        while l1:\n            n1 += str(l1.val)\n            l1 = l1.next\n        while l2:\n            n2 += str(l2.val)\n            l2 = l2.next\n        n = int(n1[::-1]) + int(n2[::-1])\n\n        result = ListNode()\n        curr = result\n        \n        if n < 10:\n            return ListNode(n)\n        \n        print(n)\n        \n        while n >= 10:\n            curr.val = n % 10\n            n //= 10\n            if n < 10:\n                curr.next = ListNode(n)\n                break\n            else:\n                curr.next = ListNode()\n                curr = curr.next\n        return result\n","repo_name":"RussellDash332/leetcode","sub_path":"Add Two Numbers.py","file_name":"Add Two Numbers.py","file_ext":"py","file_size_in_byte":951,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"81"}
+{"seq_id":"19597034244","text":"from SearchingAPI import *\nfrom BuildingProcessing import *\nimport shutil\nimport os\nimport pandas as pd\nimport time\nfrom pyproj import CRS\nfrom pyproj import Transformer\n\n#2020-09-10. 이사님 작업보조로 인한 코드 수정의 건.\ndef get_address_original_codes(processed_address):\n    road_result = road_search(processed_address)\n    if len(road_result['results']['juso']) == 0:\n        raise Exception(\"잘못된 주소를 입력하였습니다. 주소값이 0입니다.\")\n\n    def get_road_code(index):\n        return road_result['results']['juso'][index]\n\n    rode_code = get_road_code(0)\n    # mncode : 건물관리번호\n    mncode = rode_code['bdMgtSn']\n\n    coord_result = coord_search(rode_code['admCd'], rode_code['rnMgtSn'], rode_code['udrtYn'], rode_code['buldMnnm'], rode_code['buldSlno'])\n    print(coord_result)\n    if len(coord_result['results']['juso']) == 0:\n        raise Exception(\"잘못된 코드를 입력하였습니다. 결과값이 0입니다.\")\n\n    def get_coord_code(index):\n        return coord_result['results']['juso'][index]\n\n    coord_code = get_coord_code(0)\n    #x좌표, y좌표\n    #x_coord_temp = coord_code['entX']\n    #y_coord_temp = coord_code['entY']\n\n\n    crs_5179 = CRS.from_epsg(5179)\n    crs_4326 = CRS.from_epsg(4326)\n    transformer = Transformer.from_crs(crs_5179, crs_4326, always_xy=True)\n    x_coord = transformer.transform(coord_code['entX'], coord_code['entY'])[0]\n    y_coord = transformer.transform(coord_code['entX'], coord_code['entY'])[1]\n\n\n\n\n    bldgname = coord_code['bdNm']\n\n    return mncode, bldgname, x_coord, y_coord\n\ndef make_excel_coord(all_infos, columns):\n    index_format = [index+1 for index, all_info in enumerate(all_infos)]\n    #columns = ['raw_address', 'processed_address', 'address', 'sggu_code', 'Result', 'Ref']\n\n    # DataFrame 초기화\n    values = pd.DataFrame(index=index_format, columns=columns)\n    row_numbers = values.shape[0]\n    index_row = index_format\n\n    for index in range(row_numbers):\n        values.iloc[index, 0] = index_row[index]\n        for column_index, column in enumerate(columns):\n            values.iloc[index, column_index] = all_infos[index].get(column)\n\n    # saves DataFrame(values) into an Excel file\n    values.to_excel('./weather_result.xlsx',\n                    sheet_name='Sheet1',\n                    columns=columns,\n                    header=True,\n                    index=index_format,\n                    startrow=1,\n                    startcol=0,\n                    engine=None,\n                    merge_cells=True,\n                    encoding=None,\n                    inf_rep='inf',\n                    verbose=True,\n                    freeze_panes=None)\n'''\nadmCd = 2914011800\nrnMgtSn = 291403160032\nudrtYn = 0\nbuldMnnm = 47\nbuldSlno = 0\nk = coord_search(str(admCd), str(rnMgtSn), str(udrtYn), str(buldMnnm), str(buldSlno))\nprint(k)\n'''\n\n#임시로 만든 것임 (2020-09-10)\n#파일 소스 읽는 기능은 자체적으로 넣기.\ndata = pd.read_csv('./cluster_20210911/8 clusters_good/original_data2.csv', header=0, encoding='ANSI')\nfiltered_data = data[['bjdCode']]\nfiltered_lists = filtered_data[\"bjdCode\"].tolist()\n\nall_coord_infos = []\n\nfor item in filtered_lists:\n    temp_coord_info = {}\n    temp_coord_info['bjdcode'] = item\n    sggcode = int(str(item)[0:5])\n    if sggcode < 20000:\n        temp_coord_info['Area'] = '서울경기'\n    elif sggcode < 27000:\n        temp_coord_info['Area'] = '경남'\n    elif sggcode < 28000:\n        temp_coord_info['Area'] = '경북'\n    elif sggcode < 29000:\n        temp_coord_info['Area'] = '서울경기'\n    elif sggcode < 30000:\n        temp_coord_info['Area'] = '전남'\n    elif sggcode < 31000:\n        temp_coord_info['Area'] = '충북'\n    elif sggcode < 36110:\n        temp_coord_info['Area'] = '경남'\n    elif sggcode < 41000:\n        temp_coord_info['Area'] = '충남'\n    elif sggcode < 42000:\n        temp_coord_info['Area'] = '서울경기'\n    elif sggcode < 42150:\n        temp_coord_info['Area'] = '강원영서'\n    elif sggcode < 42720:\n        temp_coord_info['Area'] = '강원영동'\n    elif sggcode < 42820:\n        temp_coord_info['Area'] = '강원영서'\n    elif sggcode < 43000:\n        temp_coord_info['Area'] = '강원영동'\n    elif sggcode < 44000:\n        temp_coord_info['Area'] = '충북'\n    elif sggcode < 45000:\n        temp_coord_info['Area'] = '충남'\n    elif sggcode < 46000:\n        temp_coord_info['Area'] = '전북'\n    elif sggcode < 47000:\n        temp_coord_info['Area'] = '전남'\n    elif sggcode < 48000:\n        temp_coord_info['Area'] = '경북'\n    elif sggcode < 50000:\n        temp_coord_info['Area'] = '경남'\n    elif sggcode >= 50000:\n        temp_coord_info['Area'] = '제주'\n\n    weather_data = pd.read_csv('./cluster_20210911/8 clusters_good/temperature_annual.csv', header=0, encoding='ANSI')\n    weather_arr = weather_data.values\n    #print(type(weather_arr))\n    #print(weather_arr)\n    for region in weather_arr:\n        if region[0] == temp_coord_info['Area']:\n            temp_coord_info['Temp_avg'] = region[1]\n            temp_coord_info['Temp_min'] = region[2]\n            temp_coord_info['Temp_max'] = region[3]\n\n    all_coord_infos.append(temp_coord_info)\nprint(all_coord_infos)\nrowlist_for_excel = all_coord_infos[0].keys()\nmake_excel_coord(all_coord_infos, rowlist_for_excel)\n","repo_name":"swellfish92/Apartment_Clustering","sub_path":"weather_link.py","file_name":"weather_link.py","file_ext":"py","file_size_in_byte":5347,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"16655731754","text":"from django.shortcuts import redirect, render\r\n\r\nfrom .forms import ClienteForm\r\nfrom .models import Cliente\r\n\r\n\r\ndef cliente_lista(request):\r\n    data = {}\r\n    search = request.GET.get('search')\r\n    if search:\r\n        data['db'] = Cliente.objects.filter(nome_cliente__icontains=search) | Cliente.objects.filter(\r\n            cod_cliente__icontains=search) | Cliente.objects.filter(cpf__icontains=search)\r\n    else:\r\n        data['db'] = Cliente.objects.all()\r\n    return render(request, 'clientes/lista.html', data)\r\n\r\n\r\ndef cliente_form(request):\r\n    data = {'form': ClienteForm()}\r\n    return render(request, 'clientes/form.html', data)\r\n\r\n\r\ndef cliente_create(request):\r\n    if request.method == 'POST':\r\n        post_data = request.POST\r\n        post_data._mutable = True\r\n        form = ClienteForm(request.POST or None)\r\n\r\n        if float(request.POST.get('renda')) > 4591:\r\n            post_data['classe'] = 'AB'\r\n        elif float(request.POST.get('renda')) > 1064:\r\n            post_data['classe'] = 'C'\r\n        elif float(request.POST.get('renda')) > 768:\r\n            post_data['classe'] = 'D'\r\n        else:\r\n            post_data['classe'] = 'E'\r\n\r\n        if form.is_valid():\r\n            form.save()\r\n            return redirect('cliente_form')\r\n\r\n\r\ndef cliente_edit(request, pk):\r\n    data = {'db': Cliente.objects.get(pk=pk)}\r\n    data['form'] = ClienteForm(instance=data['db'])\r\n    return render(request, 'clientes/form.html', data)\r\n\r\n\r\ndef cliente_update(request, pk):\r\n    data = {'db': Cliente.objects.get(pk=pk)}\r\n    post_data = request.POST\r\n    post_data._mutable = True\r\n    form = ClienteForm(request.POST or None, instance=data['db'])\r\n    if float(request.POST.get('renda')) > 4591:\r\n        post_data['classe'] = 'AB'\r\n    elif float(request.POST.get('renda')) > 1064:\r\n        post_data['classe'] = 'C'\r\n    elif float(request.POST.get('renda')) > 768:\r\n        post_data['classe'] = 'D'\r\n    else:\r\n        post_data['classe'] = 'E'\r\n\r\n    if form.is_valid():\r\n        form.save()\r\n        return redirect('cliente_form')\r\n\r\n\r\ndef cliente_delete(request, pk):\r\n    db = Cliente.objects.get(pk=pk)\r\n    db.delete()\r\n    return redirect('cliente_lista')\r\n","repo_name":"JoonMarion/sistema-vendas","sub_path":"apps/clientes/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2195,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"5992229958","text":"nu = 1420.405751786\nfreqs = np.asarray(data[freq]) + 1270 # add on the LO that reduces our signal\ndelta_nu = freqs - nu\nc = 3 * (10**10)\nv = -(delta_nu / freqs) * c\n\ndoppler_v = ugradio.get_projected_velocity(RA, DEC, JD) * 100\n\nfig = plt.figure(figsize = (7, 4))\nplt.plot(-np.fft.fftshift(v), np.fft.fftshift(smooth_data), label = 'Rest Velocity', color='orange')\nplt.plot(-np.fft.fftshift(v - doppler_v), np.fft.fftshift(smooth_data), label = 'Doppler Velocity', color='b')\nplt.xlim(-0.4e8,-0.1e8)\nplt.legend(loc='best')\nplt.xlabel('Velocity (cm/s)', fontsize=12)\nplt.ylabel('Temperature (K)', fontsize=12)\nplt.title('Signal Intensity', fontweight='bold', fontsize=15)\n","repo_name":"dozomatt/magellanicstream","sub_path":"doppler.py.py","file_name":"doppler.py.py","file_ext":"py","file_size_in_byte":671,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"72744189386","text":"import sys\nsys.setrecursionlimit(10**6)\na , b = map(int,sys.stdin.readline().split())\nnode = [[0]*(a+1)for _ in range(a+1)]\nvisit = [False]*(a+1)\ncount = 0\nfor _ in range(b):\n  c , d = map(int,sys.stdin.readline().split())\n  node[c][d] = 1\n  node[d][c] = 1\ndef dfs(start,visit,node):\n  visit[start] = True\n  for i in range(1, a+1):\n    if visit[i] == False and node[start][i]==1:\n      dfs(i,visit,node)\n      \nfor i in range(1,a+1):\n  if visit[i]==False:\n    dfs(i,visit,node)\n    count +=1\nprint(count)\n  ","repo_name":"kangjunyoung37/algorizm-python","sub_path":"탐색/11724번 연결 요소의 개수.py","file_name":"11724번 연결 요소의 개수.py","file_ext":"py","file_size_in_byte":507,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"81"}
+{"seq_id":"4285024232","text":"from django.contrib import admin\n\nfrom app.enquiries.models import Enquiry, Enquirer, Owner\n\n\ndef all_fields_of(model, exclude=()):\n    return [field.name for field in\n            model._meta.fields if field.name not in exclude]\n\n\n@admin.register(Enquirer)\nclass EnquirerAdmin(admin.ModelAdmin):\n    list_display = all_fields_of(Enquirer)\n\n\n@admin.register(Owner)\nclass OwnerAdmin(admin.ModelAdmin):\n    list_display = all_fields_of(Owner, (\"password\",))\n\n\ndef enquirer(obj):\n    return f\"{obj.enquirer.first_name} {obj.enquirer.last_name}\"\n\n\nenquirer.short_description = \"Enquirer\"\n\n\n@admin.register(Enquiry)\nclass EnquiryAdmin(admin.ModelAdmin):\n    list_display = (\"owner\", enquirer, \"region\", \"country\", \"enquiry_stage\",\n                    \"datahub_project_status\", \"date_received\",\n                    \"date_added_to_datahub\", \"primary_sector\", \"project_code\",\n                    \"project_name\")\n","repo_name":"uktrade/enquiry-mgmt-tool","sub_path":"app/enquiries/admin.py","file_name":"admin.py","file_ext":"py","file_size_in_byte":903,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"81"}
+{"seq_id":"25730129247","text":"###########################################################################################\n###########################################################################################\n###                                                                                     ###\n###                             ~~~Cool Code for Finding Flares~~~                      ###\n###                                       ~~(CCFF)~~                                    ###\n###                                                                                     ###\n###########################################################################################\n###########################################################################################\n\n# here we import the light curve data as well as import some functions and other misc.\nimport numpy as np\nimport numpy as numpy\nfrom scipy.signal import lombscargle\nimport matplotlib.pyplot as plt\nimport scipy.signal as signal\nfrom astropy.timeseries import LombScargle\nimport scipy\nfrom scipy.signal import find_peaks\nfrom numpy import sin\nfrom scipy import optimize\nfrom multiprocessing import Pool\nimport statistics\nimport sys\nimport math\nfrom scipy import interpolate\n\npi = 3.14159265358979323846\n\nplt.ion()\nfig, (ax1, ax2) = plt.subplots(2)\n\n\n\nxu1, yu1 = np.loadtxt(\"KIC_6791060_all_jump_removed.dat\", unpack = True, usecols = [0,2])\n\n\nfrequency, power = LombScargle(xu1, yu1).autopower()\n\nperiods = 1/frequency\n\n#        here we graph the data and the LombScarge (this will probbably be deleted later)\n#plt.plot(periods, power)\n#plt.plot(xu1, yu1)\n#plt.show()\n\n# nyquist_frequency is frequency/2\n#######################################################################################\n\n\n\n#######################################################################################\n#  here we calculate the sampling frequency and nyquist frequency and period\nsampling_frequency =  len(xu1)/(xu1[len(xu1)-1]-xu1[0])\nnyquist_frequency = sampling_frequency/2\nnyquist_period = 1/nyquist_frequency\n\n#print (\"nyquist_period:\", nyquist_period)\n#print (\"nyquist_frequency:\", nyquist_frequency)\n#######################################################################################\n\n\n\n#######################################################################################\n#program for finding peaks and making sure they are large enough to be significant\nall_peaks = []\nperiods_counter = 0\nfor periods_values in range(len(periods)-1):\n\tperiods_counter += 1\n\tif ((((power[periods_counter] - power[periods_counter - 1]) > 0.2*power[periods_counter-1] ) \\\n\tor ((power[periods_counter] - power[periods_counter - 1]) > 0.2*power[periods_counter-1] )) \\\n\tand ((power[periods_counter] > power[periods_counter - 1]) \\\n\tand (power[periods_counter] > power[periods_counter + 1])) \\\n\tand (power[periods_counter] > 0.01)):\n\t\tall_peaks.append(periods[periods_counter])\n#######################################################################################\n\n\n\n#######################################################################################\n#program to ensure peaks are far enough apart to be distinct periods\npeaks_counter = 0\ndistinct_peaks = []\nfor peaks in range(len(all_peaks)-2):\n\tpeaks_counter += 1\n\tif  (all_peaks[peaks_counter] - all_peaks[peaks_counter+1]) > int(0.005):\n\t\tdistinct_peaks.append(all_peaks[peaks_counter])\n\npeaks_counter2 = 0\ndistinct_peaks2 = []\nfor peaks in range(len(distinct_peaks)-2):\n\tpeaks_counter2 += 1\n\tif  (distinct_peaks[peaks_counter2] - distinct_peaks[peaks_counter2+1]) > int(0.005):\n\t\tdistinct_peaks2.append(distinct_peaks[peaks_counter2])\n#######################################################################################\n\n\n\n#######################################################################################\n# defining printing functions to check answers for error (printlist)\ndef printlist(lst):\n\tfor a in range(len(lst)-1):\n\t\tprint (lst[a])\n\ndef printlist2(lst , lst2):\n\tfor a in range(len(lst)-1):\n\t\tprint (lst[a], lst2[a])\n\ndef printlistnumbered(lst):\n\tn = 1\n\tfor a in range(len(lst)-1):\n\t\tprint (n, \")\" ,\" \" , lst[a], sep = '')\n\t\tn +=1\n\n#print (\"all peak:\")\n#print (printlist(all_peaks))\n#print (\" \")\n#print (\"distinct peaks:\")\n#print (printlist(distinct_peaks2))\n#######################################################################################\n\n\n\n#######################################################################################\n#function that takes the average value of a list ######################################\ndef average(lst):\n\ttotal = 0\n\tfor i in lst:\n\t\ttotal += i\n\taverage = total/ len(lst)\n\treturn average\n#######################################################################################\n\n\n\n#######################################################################################\n#program that stores that parameters of the function in a dynamic parameters dictionary\nparameters_guess = {}           #used to store the parameters of the sine function\n\n\nparameter_index = 0\n\nfor peak in distinct_peaks2:\n\tif (peak > nyquist_period):\n\t\tparameter_index = parameter_index + 1\n\t\t#using form An*sin(2*pi*an*x+bn)\n\t\tparameters_guess['A' + str(parameter_index)]='1'\n\t\tparameters_guess['a' + str(parameter_index)]= peak\n\t\tparameters_guess['b' + str(parameter_index)]='1'\n\n\nprint (\"parameters guess: \", parameters_guess)\n#######################################################################################\n\n\n\n#######################################################################################\n# function to calculate the standard deviation of a list of deviations\ndef standard_deviation(lst):\n\ttotal = 0\n\tfor p in lst:\n\t\ttotal += p**2\n\tif len(lst) > 0:\n\t\tsd = (total / len(lst))**(0.5)\n\t\t#print (\"total:\", total)\n\t\t#print (\"n=\",  len(lst))\n\n\t\treturn sd\n#######################################################################################\n\n\n\n#######################################################################################\n#fits sin equations to the data in sets of *length* data points and deviation and sd###\nlength = 40     #can be adjusted for different fittings,length of running_sd calculator\nx_value = 2650\ninitial = x_value #this is where the program starts in the data set\nrunning_sd = []\n\nfor x_values in range(len(xu1)-64800):\n\tax2.clear()\n\tax1.clear()\n\tdef test_func(x, A, b, A2, b2, Z):\n\n\t\t#y = parameters['A1']*sin(2*pi*parameters['a1']*x+paramters['b1']) + paramters['A2']*sin(2*pi*paramters['a2']*x+paramters['b2']) + Z\n\t\treturn A*sin(2*pi*(1/parameters_guess['a1'])*x+b) + A2*sin(2*pi*(1/parameters_guess['a2'])*x+b2) + Z\n\n\tparams, params_covariance = optimize.curve_fit(test_func, xu1[x_value:x_value+length], yu1[x_value:x_value+length],\n\t\t                                                       p0=[2, 2, 2, 2, 2], maxfev = 100000)\n\tax1.scatter(xu1, yu1, label='Data', color = 'green')\n\tax1.plot(xu1[x_value: x_value+length], test_func(xu1[x_value:x_value+length], params[0], params[1], params[2], params[3], params[4]), label='Fitted function', color = \"blue\")\n\tdeviation = []\n\tfor point in range(x_value, x_value+length):\n\t\tdeviation.append( yu1[point] - ( params[0]*sin(2*pi*(1/parameters_guess['a1'])*point+params[1]) + params[2]*sin(2*pi*(1/parameters_guess['a2'])*point+params[3]) + params[4] ) )\n\trunning_sd.append( standard_deviation(deviation) )\n\tx_value+=(1)\n\tax1.set_xlim([xu1[x_value]-0.5, xu1[x_value+length]+0.5])\n\t#print (x_value-2743, len(running_sd))\n\tax2.scatter(xu1[0:(x_value - initial)] , running_sd, 2, color = 'black', linewidth = 2)\n\tax2.axhline(y=statistics.median(running_sd))\n\trunning_sd_deviation = []\n\tfor i in running_sd:\n\t\trunning_sd_deviation.append(abs(i-average(running_sd)))\n\tax2.axhline(y=statistics.median(running_sd) + 2.5* standard_deviation(running_sd_deviation), color = 'red')\n\tax2.axhline(y=statistics.median(running_sd) - 2.5* standard_deviation(running_sd_deviation), color = 'red')\n\t#print (3* standard_deviation(running_sd))\n\tfig.canvas.draw()\n\tfig.canvas.flush_events()\n\n#######################################################################################\n\n\n\n#######################################################################################\n#running standard deviation dependant on *length* and flares###########################\n\nflares_not_3 = []\n#^list of points that pass the standard deviation test but\n#may not necesarrily be long enough to be a flare\nflares_starttime = []\nflares_starttime_index = []\nflares_endtime = []\nflares_endtime_index = []\nflare_lengths = []\nflares_dataPoints_x = [] #this is a list containing a list of all the data points in a flare\nflares_dataPoints_y = [] #this is a list containing a list of all the data points in a flare\ni=0\nfor k in range(len(running_sd)-length):\n\tif running_sd[i] > average(running_sd) + 2.5*standard_deviation(running_sd_deviation):\n\t\t#^this ensures that the flares are actually noteworthy (pass the 2.5sigma threshold)\n\t\tflares_not_3.append(xu1[i])\n\t\tfor p in range(20,0,-1): #here the code has found a point that meets the 2.5sigma threshold\n\t\t#then the code looks 20 points ahead to see if that point meets the threshold too\n\t\t#if that point also meets the threshold it records it as the flares_endtime\n\t\t#if +20 does not meet the threshold it will look at +19, then +18, so on\n\t\t\tif running_sd[i+length+p-5] > average(running_sd) + 2.5*standard_deviation(running_sd_deviation):\n\t\t\t\tflares_starttime.append(xu1[i+initial+length-3])\n\t\t\t\tflares_starttime_index.append(i+initial+length-3)\n\t\t\t\tflares_endtime.append(xu1[i+p+initial+length])\n\t\t\t\tflares_endtime_index.append(i+p+initial+length)\n\t\t\t\tflare_lengths.append(p)\n\t\t\t\tflares_dataPoints_x.append([])\n\t\t\t\tflares_dataPoints_y.append([])\n\t\t\t\tfor d in range(flares_starttime_index[len(flares_starttime_index)-1], flares_endtime_index[len(flares_endtime_index)-1]):\n\t\t\t\t#here we say \"take every data point between a flares_starttime and flares_endtime_index\"\n\t\t\t\t#and add them as a list to a list containing all the point that are in a flare\n\t\t\t\t\tflares_dataPoints_x[len(flares_dataPoints_x)-1].append(xu1[d])\n\t\t\t\t\tflares_dataPoints_y[len(flares_dataPoints_y)-1].append(yu1[d])\n\t\t\t\ti = i + length + p - 5\n\t\t\t\tbreak\n\n\ti = i+1\n\t#if running_sd[i] > average(running_sd) - 3*standard_deviation(running_sd):\n\t#\tflares_not_3.append(xu1[i])\nprint ('flares_not_3', flares_not_3)\nprint ('flares_starttime', flares_starttime)\nprint ('flares_starttime_index', flares_starttime_index)\nprint ('flares_endtime', flares_endtime)\nprint ('flares_endtime_index', flares_endtime_index)\nprint ('flare_lengths',flare_lengths)\n#######################################################################################\n\n\n\n#######################################################################################\n# here we will remove the flares from the data\nxu3 = xu1\nyu3 = yu1\nfor n in range(len(flares_starttime)):\n\tfor i in range(flares_starttime_index[n], flares_endtime_index[n]):\n\t\txu3 = np.delete(xu3, i)\n\t\tyu3 = np.delete(yu3, i)\n\nprint(\"xu1 Dimention = \",len(xu1))\nprint(\"xu3 Dimention = \",len(xu3))\n\n#######################################################################################\n\n\n'''THIS IS THE OLD METHOD: (I JUST LEFT IT HERE INCASE IT'S NEEDED LATER)\n(IT DOESNT WORK, I DONT KNOW WHY)\n#######################################################################################\n# here we will use the new flareless data and interpolate it\nf3 = interpolate.interp1d(xu3, yu3) #saves interpolated data as a function f3\n\nxu3_new = np.arange(xu3[0], xu3[len(xu3)-2], 0.0001) #creates a whole bunch of x-values very close together\n\n#gets y-values for those x-values to be used in reimann sum\nyu3_new = f3(xu3_new)   # use interpolation function returned by `interp1d`\n\n#ax1.plot(xu3, yu3, 'o', xu3_new, y_u3new, '-')\n\n#######################################################################################\n\n\n\n#######################################################################################\n# here we interpolate the original lightcurve data with the flares\nf1 = interpolate.interp1d(xu1, yu1)\n\nxu1_new = np.arange(xu1[0], xu1[len(xu1)-2], 0.0001)\n\nyu1_new = f1(xu1_new)\n\n\n#######################################################################################\n\n\n\n#######################################################################################\n# here we calculate the area between the flareless curve and the flarecurve\n\nenergy = [] #this is a list that contains the area (proportional to energy) of each flare\nfor q in range(len(flares_starttime)):\n\n\tsector = flares_endtime[q] - flares_starttime[q]\n\tlimit = 1000 #this is the number of dA's we are deviding the flare area into\n\tarea = 0\n\tfor p in range(limit): #the p is the counter varibale for the dA\n\t\t#print (f2(flares_starttime[q] + (p * sector) / limit))\n\t\t#print (f1(flares_starttime[q]+ (p * sector) / limit))\n\t\t#print ( f2(flares_starttime[q]+ (sector)/limit) - f1(flares_starttime[q]+ (sector)/ limit) )\n\t\tdA = (sector/limit)(f1[flares_starttime[q]+p]-f3[flares_starttime[q]+p])\n\t\tarea += dA\n\t\t#print(\"dA =\", dA)\n\tenergy.append(area)\nprint (\"energy\", energy)\n#######################################################################################\n'''\n\n\n#######################################################################################\n#this gets us a list of the data points around a flare so we can fit a curve to it\n#then using the fitted curve we can predict what the flareless behavior would be\npoints_around_flares_without_flares_x = []\npoints_around_flares_without_flares_y = []\nfor k in range(len(flares_starttime_index)):\n\tempty_list = []\n\tfor i in range(flares_starttime_index[k]-(2*length),flares_starttime_index[k]-2):\n\t\tempty_list.append(xu1[i])\n\tfor i in range(flares_endtime_index[k],flares_endtime_index[k]+(2*length)):\n\t\tempty_list.append(xu1[i])\n\tpoints_around_flares_without_flares_x.append(empty_list)\n\t\n\tempty_list = []\n\tfor i in range(flares_starttime_index[k]-(2*length),flares_starttime_index[k]-2):\n\t\tempty_list.append(yu1[i])\n\tfor i in range(flares_endtime_index[k],flares_endtime_index[k]+(2*length)):\n\t\tempty_list.append(yu1[i])\t\n\tpoints_around_flares_without_flares_y.append(empty_list)\n\t\n#######################################################################################\n\n\n\n#######################################################################################\n#defines a function that fits a sin graph to a curve, nothing fancy\ndef fit_sin(tt, yy):\n    '''Fit sin to the input time sequence, and return fitting parameters \"amp\", \"omega\", \"phase\", \"offset\", \"freq\", \"period\" and \"fitfunc\"'''\n    tt = numpy.array(tt)\n    yy = numpy.array(yy)\n    ff = numpy.fft.fftfreq(len(tt), (tt[1]-tt[0]))   # assume uniform spacing\n    Fyy = abs(numpy.fft.fft(yy))\n    guess_freq = abs(ff[numpy.argmax(Fyy[1:])+1])   # excluding the zero frequency \"peak\", which is related to offset\n    guess_amp = numpy.std(yy) * 2.**0.5\n    guess_offset = numpy.mean(yy)\n    guess = numpy.array([guess_amp, 2.*numpy.pi*guess_freq, 0., guess_offset])\n\n    def sinfunc(t, A, w, p, c):  return A * numpy.sin(w*t + p) + c\n    popt, pcov = scipy.optimize.curve_fit(sinfunc, tt, yy, p0=guess)\n    A, w, p, c = popt\n    f = w/(2.*numpy.pi)\n    fitfunc = lambda t: A * numpy.sin(w*t + p) + c\n    return {\"amp\": A, \"omega\": w, \"phase\": p, \"offset\": c, \"freq\": f, \"period\": 1./f, \"fitfunc\": fitfunc, \"maxcov\": numpy.max(pcov), \"rawres\": (guess,popt,pcov)}\n   \n#params[0]*sin(2*pi*(1/parameters_guess['a1'])*point+params[1]) + params[2]*sin(2*pi*(1/parameters_guess['a2'])*point+params[3]) + params[4]\n\n#######################################################################################\n\n#######################################################################################  \nenergy = [] \nflares_detrended_x = []\nflares_detrended_y = []\nflare_rises_dataset_x = []\nflare_decays_dataset_x = []\nflare_rises_dataset_y = []\nflare_decays_dataset_y = []\nflare_rises_dataset_y_detrended = []\nflare_decays_dataset_y_detrended = []\nfor k in range(len(flares_starttime_index)):\n\tactive_flare_dataset_x = flares_dataPoints_x[k]\n\tactive_flare_dataset_y = flares_dataPoints_y[k]\n\tactive_flareless_dataset_x = points_around_flares_without_flares_x[k]\n\tactive_flareless_dataset_y = points_around_flares_without_flares_y[k]\n\t#############################\n\t# this fits a curve to the points around the flareless neighborhood\n\tdef test_func(x, A, b, A2, b2, Z):\n\n\t\t#y = parameters['A1']*sin(2*pi*parameters['a1']*x+paramters['b1']) + paramters['A2']*sin(2*pi*paramters['a2']*x+paramters['b2']) + Z\n\t\treturn A*sin(2*pi*(1/parameters_guess['a1'])*x+b) + A2*sin(2*pi*(1/parameters_guess['a2'])*x+b2) + Z\n\n\tparams, params_covariance = optimize.curve_fit(test_func, active_flareless_dataset_x[0:len(active_flareless_dataset_x)], active_flareless_dataset_y[0:len(active_flareless_dataset_y)],\n\t\t                                                       p0=[2, 2, 2, 2, 2], maxfev = 100000)\n\t\n\t#############################\n\t#here we sum up dA to get the total area of each flare\n\tarea = 0\n\tfor t in range(len(active_flare_dataset_y)-1):\n\t\tdA = (   (active_flare_dataset_x[t+1]-active_flare_dataset_x[t])*( (active_flare_dataset_y[t]-test_func((t+1),params[0],params[1],params[2],params[3],params[4])) + (0.5)*(active_flareless_dataset_y[t+1]-active_flareless_dataset_y[t]) + (0.5)*(test_func((t+1),params[0],params[1],params[2],params[3],params[4])-test_func(t,params[0],params[1],params[2],params[3],params[4])) ) )\n\t\tarea += dA\n\tenergy.append(area)\n\t#############################\n\toneflare_detrended_x = []\n\toneflare_detrended_y = []\n\tfor t in range(flares_starttime_index[k]-(2*length),flares_endtime_index[k]+(2*length)):\n\t\tdetrend_value = test_func(xu1[t],params[0],params[1],params[2],params[3],params[4])\n\t\toneflare_detrended_y.append( yu1[t] -  detrend_value )\n\t\toneflare_detrended_x.append( xu1[t] )\n\tflares_detrended_x.append(oneflare_detrended_x)\n\tflares_detrended_y.append(oneflare_detrended_y)\n\t#############################\n\t#next we will split the flare into post peak and pre peak data \n\tone_flare_rises_dataset_x = []\n\tone_flare_decays_dataset_x = []\n\tone_flare_rises_dataset_y = []\n\tone_flare_decays_dataset_y = []\n\tindex = active_flare_dataset_y.index(max(active_flare_dataset_y))\n\tfor t in range(0,index+1):\n\t\tone_flare_rises_dataset_y.append(active_flare_dataset_y[t])\n\t\tone_flare_rises_dataset_x.append(active_flare_dataset_x[t])\n\tfor t in range(index, len(active_flare_dataset_y)):\n\t\tone_flare_decays_dataset_y.append(active_flare_dataset_y[t])\n\t\tone_flare_decays_dataset_x.append(active_flare_dataset_x[t])\n\tflare_rises_dataset_x.append(one_flare_rises_dataset_x)\n\tflare_decays_dataset_x.append(one_flare_decays_dataset_x)\n\tflare_rises_dataset_y.append(one_flare_rises_dataset_y)\n\tflare_decays_dataset_y.append(one_flare_decays_dataset_y)\n\t#############################\n\tone_flare_rises_dataset_y_detrended = []\n\tone_flare_decays_dataset_y_detrended = []\n\tfor t in range(len(one_flare_rises_dataset_y)):\n\t\tdetrend_value = test_func(xu1[t],params[0],params[1],params[2],params[3],params[4])\n\t\tone_flare_rises_dataset_y_detrended.append(one_flare_rises_dataset_y[t] - detrend_value)\n\tfor t in range(len(one_flare_decays_dataset_y)):\n\t\tdetrend_value = test_func(xu1[t],params[0],params[1],params[2],params[3],params[4])\n\t\tone_flare_decays_dataset_y_detrended.append(one_flare_decays_dataset_y[t] - detrend_value)\n\tflare_rises_dataset_y_detrended.append(one_flare_rises_dataset_y_detrended)\t\n\tflare_decays_dataset_y_detrended.append(one_flare_decays_dataset_y_detrended)\n#######################################################################################\nprint(flare_rises_dataset_y)\nprint(flare_decays_dataset_y)\n\nprint(flares_dataPoints_x[0])\nprint(flares_dataPoints_y[0])\n\n#then we will fit an exponential line to that data to parameterize t_r and t_d\n\n\n\n\n#original_stdout = sys.stdout # Save a reference to the original standard output\n\n#with open('filename.txt', 'w') as f:\n#    sys.stdout = f # Change the standard output to the file we created.\n#    print(printlist2(running_sd, xu1[2700:len(xu1)-62400]))\n#    sys.stdout = original_stdout # Reset the standard output to its original value\n\n#plt.savefig(\"flares.png\")\n\n#print (\"Running SD:\")\n#printlistnumbered(running_sd)\n\n#ax2.scatter(xu1[0:945] , running_sd, 2)\n","repo_name":"padln/flares","sub_path":"flarestest3 (2).py","file_name":"flarestest3 (2).py","file_ext":"py","file_size_in_byte":20187,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"19070125027","text":"import random\n\nid=100\nfhand=open('edges.ope','w')\nfor i in range(1,10):\n\t#print(\"CPF de id: \"+str(i))\n\tnumero_empresas=random.randint(1,7)\n\tfor j in range(1,numero_empresas):\n\t\tid=id+1\n\t\tempresa=random.randint(11,29)\n\t\t#print(\"socio/a da :\" +str(empresa))\n\t\tlinha=str(id)+\",\"+str(i)+\",\"+str(empresa)+\",socio,\"+\" \"+\",,,,\"\n\t\tfhand.write(linha+\"\\n\")\n\t\tprint(linha)\n\nfhand.close()","repo_name":"murilo-cerone/pypoc_utilities","sub_path":"bvs_gera_edges.py","file_name":"bvs_gera_edges.py","file_ext":"py","file_size_in_byte":376,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"72071776905","text":"# Definition for a binary tree node.\n# class TreeNode:\n#     def __init__(self, val=0, left=None, right=None):\n#         self.val = val\n#         self.left = left\n#         self.right = right\nclass Solution:\n    def rightSideView(self, root: TreeNode) -> List[int]:\n        if not root: return []\n        stack = [root]\n\n        res = []\n        # layer \n        while stack:\n            n = TreeNode()\n            for i in range(len(stack)):\n                n = stack.pop(0)\n                if n.left:\n                    stack.append(n.left)\n                if n.right:\n                    stack.append(n.right)\n            res.append(n.val)\n        \n        return res\n","repo_name":"CastleWhite/LeetCodeProblems","sub_path":"199.py","file_name":"199.py","file_ext":"py","file_size_in_byte":672,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"81"}
+{"seq_id":"74996321863","text":"import argparse\n\ndef create_mapping_for_encoding(english_alphabet, selected_alphabets, shift):\n    mapping = {}\n    for i, char in enumerate(english_alphabet):\n        mapped_char = selected_alphabets[(i + shift) % len(selected_alphabets)]\n        mapping[char] = mapped_char\n    return mapping\n\ndef create_mapping_for_decoding(english_alphabet, selected_alphabets, shift):\n    mapping = {}\n    shifted_alphabets = selected_alphabets[shift:] + selected_alphabets[:shift]\n    for i, char in enumerate(shifted_alphabets):\n        mapped_char = english_alphabet[i % len(english_alphabet)]\n        mapping[char] = mapped_char\n    return mapping\n\ndef custom_rot_cipher(input_str, shift, mode, alphabets, alphabets_dict, null_symbol=None):\n    english_alphabet = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz'\n    selected_alphabets = ''.join(alphabets_dict[char] for char in alphabets if char in alphabets_dict)\n\n    if mode == 'encode':\n        mapping = create_mapping_for_encoding(english_alphabet, selected_alphabets, shift)\n    elif mode == 'decode':\n        mapping = create_mapping_for_decoding(english_alphabet, selected_alphabets, shift)\n\n    result = ''\n\n    for char in input_str:\n        if char == null_symbol:\n            continue\n\n        result += mapping.get(char, char)\n\n    return result\n\ndef main():\n    parser = argparse.ArgumentParser(description='Custom ROT cipher with multiple selectable alphabets for encoding and decoding English text.')\n    parser.add_argument('mode', type=str, choices=['encode', 'decode'], help='Mode of operation: encode or decode.')\n    parser.add_argument('shift', type=int, help='Shift value for the cipher.')\n    parser.add_argument('message', type=str, help='Message to encode or decode.')\n    parser.add_argument('--alphabets', type=str, default='E', help='Alphabets to use: E for English, Z for Zodiac, G for Greek, A for Arabic, P for Planetary, J for Japanese, S for Scientific.')\n    parser.add_argument('--null_symbol', type=str, default=None, help='Optional null symbol to be ignored in the cipher.')\n\n    args = parser.parse_args()\n\n    # Define alphabets\n    alphabets_dict = {\n        'E': 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz',\n        'Z': '♈♉♊♋♌♍♎♏♐♑♒♓',  # Zodiac symbols\n        'G': 'ΑΒΓΔΕΖΗΘΙΚΛΜΝΞΟΠΡΣΤΥΦΧΨΩ',  # Greek characters\n        'A': 'ابتثجحخدذرزسشصضطظعغفقكلمنهوي',  # Arabic characters\n        'P': '☉☽☿♀♁♂♃♄♅♆♇',  # Planetary symbols\n        'J': 'あいうえおかきくけこさしすせそたちつてとなにぬねのはひふへほ',  # Japanese Hiragana characters\n        'S': '∀∂∃∅∇∈∉∋∏∑−∕∗∘√∝∞∠∧∨∩∪∫≈≠≡≤≥⊂⊃⊆⊇⊕⊗⊥⋅'  # Common scientific symbols\n    }\n\n    transformed_message = custom_rot_cipher(args.message, args.shift, args.mode, args.alphabets, alphabets_dict, args.null_symbol)\n    print(transformed_message)\n\nif __name__ == \"__main__\":\n    main()\n\n","repo_name":"kernelzeroday/ciphersuite","sub_path":"megazod.py","file_name":"megazod.py","file_ext":"py","file_size_in_byte":3034,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"81"}
+{"seq_id":"30448752489","text":"from datetime import datetime as dt\nimport dash\nimport dash_table\nimport dash_core_components as dcc\nimport dash_html_components as html\nfrom dash.dependencies import Input, Output\nimport plotly.express as px\nimport pandas as pd\nfrom qsr import que_sum\nfrom adherence import adherence\nfrom user_prod import acd\nimport dash_auth\n\n#external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']\napp = dash.Dash(__name__)#, external_stylesheets=external_stylesheets)\n\n# server = app.server\n\napp.config.suppress_callback_exceptions = True\n# Keep this out of source code repository - save in a file or a database\n# VALID_USERNAME_PASSWORD_PAIRS = [\n#   ['[user]', '[pword'],\n#  ]\n\n# auth = dash_auth.BasicAuth(\n#   app,\n#  VALID_USERNAME_PASSWORD_PAIRS\n# )\n\n\ndef generate_table(que_sum, max_rows=10):\n    return html.Table([\n        html.Thead(\n            html.Tr([html.Th(col) for col in que_sum.columns])\n        ),\n        html.Tbody([\n            html.Tr([\n                html.Td(que_sum.iloc[i][col]) for col in que_sum.columns\n            ]) for i in range(min(len(que_sum), max_rows))\n        ])\n    ])\n\n\n\nresults = pd.merge(acd, adherence, how='left', on=['userid'])\n\n#results.set_index('userid', inplace=True)\n\ncols = {'userid': 'Representative', 'totnAnsweredACD': 'Calls Taken', 'acw': 'ACW', 'aht': 'AHT', 'adherence':'ADH', 'goal': 'Goal'}\ncols2 = {'DisplaycName': 'Customer Service', 'nEnteredAcd': 'Inbound Calls', 'nAnsweredAcd': 'Calls Answered', 'nAbandonedAcd': 'Abandoned', 'acw': 'ACW', 'aht': 'AHT', 'acw_goal': 'ACW Target', 'aht_goal': 'AHT Target', 'abn_goal': 'ABN Target'}\n\nresults.rename(columns=cols, inplace=True)\nque_sum.rename(columns=cols2, inplace= True)\n\n\nfig1 = px.bar(que_sum, x= 'Customer Service', y= ['Inbound Calls', 'Calls Answered', 'Abandoned', 'ABN Target'], barmode = 'group', width= 500, height= 500)\nfig2 = px.bar(que_sum, x= 'Customer Service', y= ['ACW', 'ACW Target', 'AHT', 'AHT Target'], barmode = 'group', width= 500, height= 500)\n\n# print(results.columns)\n\n# userid, adherence, goal, totnAnsweredACD, acw, aht\n\n# fig = go.Figure(data=[go.Table(header=dict(values=['Agent', 'Calls Rec', 'ACW', 'AHT', 'ADH']),\n#                cells=dict(values=[col1, col2, col3, col4]))\n#                   ])\n\n# plotly.offline.plot(fig, filename='Customer Service.html',auto_open=False)\n\napp.layout = html.Div(children=[\n\n    html.H1(children=\"Customer Service Performance Dashboard\", style={'textAlign': 'left',\n                                                   'fontSize': '24px', 'display': 'inline-blocks'}),\n\n    dcc.DatePickerRange(\n        id=\"date_range_picker\",\n        calendar_orientation='horizontal',  # vertical or horizontal\n        day_size=39,  # size of calendar image. Default is 39\n        end_date_placeholder_text=\"Select a date\",  # text that appears when no end date chosen\n        with_portal=False,  # if True calendar will open in a full screen overlay portal\n        first_day_of_week=0,  # Display of calendar when open (0 = Sunday)\n        reopen_calendar_on_clear=True,\n        is_RTL=False,  # True or False for direction of calendar\n        clearable=True,  # whether or not the user can clear the dropdown\n        number_of_months_shown=1,  # number of months shown when calendar is open\n        min_date_allowed=dt(2018, 1, 1),  # minimum date allowed on the DatePickerRange component\n        max_date_allowed=dt(2020, 6, 20),  # maximum date allowed on the DatePickerRange component\n        initial_visible_month=dt(2020, 5, 1),  # the month initially presented when the user opens the calendar\n        start_date=dt(2018, 8, 7).date(),\n        end_date=dt(2020, 5, 15).date(),\n        display_format='MMM Do, YY',  # how selected dates are displayed in the DatePickerRange component.\n        month_format='MMMM, YYYY',  # how calendar headers are displayed when the calendar is opened.\n        minimum_nights=2,  # minimum number of days between start and end date\n        style={'display': 'inline-blocks', 'textAlign': 'left'},\n\n        persistence=True,\n        persisted_props=['start_date'],\n        persistence_type='session',  # session, local, or memory. Default is 'local'\n\n        updatemode='singledate'  # singledate or bothdates. Determines when callback is triggered\n    ),\n\n    html.H2(children=\"Grouped Performance\", style={'textAlign': 'left',\n                                                       'fontSize': '24px', 'display': 'inline-blocks'}),\n    generate_table(que_sum),\n\n    html.Div(children=[\n    dcc.Graph(id='graph1',figure=fig1,style={'display': 'inline-block'}),\n    dcc.Graph(id='graph2',figure=fig2,style={'display': 'inline-block'})\n    ]\n\n            ),\n    html.H3(\"Performance by Agent\",\n            style={'textAlign': 'left', 'fontSize': '24px', 'display': 'inline-blocks'}\n    ),\n\n    html.Div([dash_table.DataTable(\n        id='datatable-interactivity',\n        columns=[\n            {\"name\": i, \"id\": i, \"deletable\": False, \"selectable\": False, \"hideable\": True}\n            if i == \"iso_alpha3\" or i == \"year\" or i == \"id\"\n            else {\"name\": i, \"id\": i, \"deletable\": False, \"selectable\": False}\n            for i in results.columns\n        ],\n        data=results.to_dict('records'),  # the contents of the table\n        editable=False,  # allow editing of data inside all cells\n        filter_action=\"none\",  # allow filtering of data by user ('native') or not ('none')\n        sort_action='native',  # enables data to be sorted per-column by user or not ('none')\n        sort_mode=\"single\",  # sort across 'multi' or 'single' columns\n        style_table={'height': '200px'},\n        column_selectable=\"multi\",  # allow users to select 'multi' or 'single' columns\n        row_selectable=False,  # allow users to select 'multi' or 'single' rows\n        row_deletable=False,  # choose if user can delete a row (True) or not (False)\n        selected_columns=[],  # ids of columns that user selects\n        selected_rows=[],  # indices of rows that user selects\n        page_action=\"native\",  # all data is passed to the table up-front or not ('none')\n        page_current=0,  # page number that user is on\n        page_size=24,  # number of rows visible per page\n\n        style_cell={'padding': '5px',},\n        style_data={\n            'color': 'black',\n            'backgroundColor': 'RGB(238,213,210)',\n            'whitespace': 'normal',\n            'textAlign': 'left',\n            'height': 'auto',\n        },\n        #fill_width=False,\n\n        style_data_conditional=\n        [\n            {\n                'if': {'row_index': 'odd'},\n                'backgroundColor': 'RGB(104,131,139)',\n            }\n        ] + [\n            {\n                'if': {'row_index': 'odd'},\n                'black': 'white',\n            }\n        ],\n        style_header={\n            'backgroundColor': 'RGB(54,100,139)',\n            'color': 'white',\n            'fontWeight': 'bold',\n            'textAlign': 'center',\n            'font_size': '13px',\n            'height': 'auto',\n        },\n\n    )\n\n\n])\n    ])\n\n\n#@app.callback(\n#    dash.dependencies.Output(\"datatable-interactivity\", \"data\"),\n#    [\n#        dash.dependencies.Input(\"my-date-picker-range\", \"start_date\"),\n#        dash.dependencies.Input(\"my-date-picker-range\", \"end_date\"),\n#    ],\n#)\n\nif __name__ == '__main__':\n    app.run_server(debug=True)\n\n# fig.show()\n","repo_name":"pga-analyst/queue_dashboard","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":7356,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"17308091643","text":"from datetime import datetime\nfrom flask import Blueprint, jsonify, request, abort\nfrom flask_login import login_required, current_user\nfrom deck import find_deck_or_fail\nfrom util import error, message\nfrom models import db, Card, Review, ReviewCard\n\nblueprint = Blueprint('review', __name__)\n\n\ndef find_review_or_fail(review_id, deck_id):\n    find_deck_or_fail(deck_id)\n    review = Review.query.filter_by(id=review_id, deck_id=deck_id).first()\n    if not review:\n        abort(error(\"Review not found.\", 404))\n    return review\n\n\n@blueprint.route('/decks/<int:deck_id>/reviews')\n@login_required\ndef index(deck_id):\n    deck = find_deck_or_fail(deck_id)\n    deck.reviews.sort(key=lambda review: review.taken_at)\n    deck.reviews.reverse()\n    return jsonify({\n        'deck': deck,\n        'reviews': deck.reviews\n    })\n\n\n@blueprint.route('/decks/<int:deck_id>/reviews/<int:review_id>')\n@login_required\n\n# render the previous review for deck\ndef show(deck_id, review_id):\n    review = find_review_or_fail(review_id, deck_id)\n    review_cards = ReviewCard.query.filter_by(\n        review_id=review_id).order_by(ReviewCard.score.asc()).all()\n\n    return jsonify({\n        'deck': review.deck,\n        'review': review,\n        'review_cards': review_cards,\n    })\n\n\n@blueprint.route('/decks/<int:deck_id>/reviews', methods=['POST'])\n@login_required\n# SAve reviews for cards\ndef store(deck_id):\n    find_deck_or_fail(deck_id)\n\n    cards = request.form.getlist('cards[]')\n    difficulties = request.form.getlist('difficulties[]')\n\n    review = Review(user_id=current_user.id,\n                    deck_id=deck_id, taken_at=datetime.now())\n\n    for i in range(len(cards)):\n        card = Card.query.filter_by(id=cards[i]).first()\n        score = int(difficulties[i]) * int(card.points)\n        review.cards.append(ReviewCard(\n            card_id=cards[i], difficulty=difficulties[i], score=score))\n\n    db.session.add(review)\n    db.session.commit()\n\n    return jsonify({\n        'message': 'Review completed successfully.',\n        'review': review\n    })\n\n\n@blueprint.route('/decks/<int:deck_id>/reviews/<int:review_id>', methods=['DELETE'])\n@login_required\n\n#delete reviews\ndef destroy(deck_id, review_id):\n    review = find_review_or_fail(review_id, deck_id)\n\n    db.session.delete(review)\n    db.session.commit()\n\n    return message('Review deleted successfully.', 200)\n","repo_name":"maneesamasarat/quickflash","sub_path":"review.py","file_name":"review.py","file_ext":"py","file_size_in_byte":2372,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"72948880264","text":"import tensorflow as tf\nimport numpy as np\nimport pandas as pd\nimport random\nimport matplotlib.pyplot as plt\nimport os, codecs\nfrom tensorflow import keras\nfrom tensorflow.keras import layers\nfrom tensorflow.keras import callbacks\nfrom datetime import datetime\nfrom datetime import timedelta\nfrom dateutil.relativedelta import relativedelta\nfrom ydata_synthetic.synthesizers import ModelParameters\nfrom ydata_synthetic.synthesizers.timeseries import TimeGAN\nimport sklearn.preprocessing as preprocessing\nimport pickle\n\nclass FeatureLabelExplorer:\n    def __init__(self, dataFrom, dataTo=None):\n        self.dataFrom = dataFrom\n        self.dataTo = dataTo\n    \n    def __saveDict(self, dictionary, count, invalidFeatureDict):\n        with open(os.path.join(self.dataTo, 'featureDict.pkl'), 'wb') as fwb:\n            pickle.dump({\"dict\": dictionary, \"count\": count, \"invalidFeatureDict\": invalidFeatureDict}, fwb)\n    \n    def __showResults(self, featureDict, newColIndex, invalidFeatureDict):\n        print(\"\\nfeatureDict is built, encoded features number: %d\" % newColIndex)\n        for columnStr in featureDict.keys():\n            print(\"Columns: %s)\" % (columnStr))\n            for key,value in featureDict[columnStr].items():\n                print(\"\\t%s -> %d\" % (key, value))\n        print(\"invalidFeatureDict: %s\" % invalidFeatureDict)\n        print(\"FeatureDict loaded\")\n    \n    def loadfeatureDict(self):\n        dict_ = None\n        with open(os.path.join(self.dataFrom, 'featureDict.pkl'), 'rb') as frb:\n            dict_ = pickle.load(frb)\n        self.__showResults(dict_[\"dict\"], dict_[\"count\"], dict_[\"invalidFeatureDict\"])\n        return dict_[\"dict\"], dict_[\"count\"], dict_[\"invalidFeatureDict\"]\n        \n    #returned featureDict: {columnStr1: dict(value1: newColIndex1, value2: newColIndex2, ...), columnStr2: set(...), ...}\n    #returned invalidFeatureDict: {columnStr1: set(value1, value2, ...), ...)\n    #some labels can easily be reduced\n    def exploreLabel(self, threshold=1000):\n        print(\"FeatureLabelExplorer:\")\n        featureDict = {}\n        excludedfeatureDict=set([\"transaction_date\", \"CASHING_SUM\", \"SEX_CD\", \"HOME_PREFECTURE_CD\", \"WORK_PREF_SAME_AS_HOME_FLG\"])\n        for filePath in sorted(os.listdir(self.dataFrom)):\n            print(filePath)\n            with codecs.open(os.path.join(self.dataFrom, filePath), \"r\", \"Shift-JIS\", \"ignore\") as fp:\n                df = pd.read_csv(fp)\n                for columnStr in df.columns:\n                    if columnStr not in excludedfeatureDict:\n                        if columnStr not in featureDict.keys():\n                            featureDict[columnStr] = {}\n                        for i in range(len(df)):\n                            value = df[columnStr][i]\n                            if not pd.isna(value):\n                                value = str(value)\n                                featureDict[columnStr][value] = featureDict[columnStr].get(value, 0)+1\n        #filt bad labels\n        newColIndex = 0\n        invalidFeatureDict = {}\n        for columnStr in featureDict.keys():\n            for key,value in featureDict[columnStr].items():\n                if value < threshold:\n                    if columnStr not in invalidFeatureDict:\n                        invalidFeatureDict[columnStr] = set()\n                    invalidFeatureDict[columnStr].add(key)\n                else:\n                    featureDict[columnStr][key] = newColIndex\n                    newColIndex += 1\n        for columnStr in invalidFeatureDict.keys():\n            for key in invalidFeatureDict[columnStr]:\n                del featureDict[columnStr][key]\n                print(\"Invalid feature deleted: %s -> %s\" % (columnStr, key))\n        #create 3 references to the same empty dict, bad    \n        #featureDict[\"SEX_CD\"], featureDict[\"HOME_PREFECTURE_CD\"], featureDict[\"WORK_PREF_SAME_AS_HOME_FLG\"]  = [{}]*3    \n        featureDict[\"SEX_CD\"] = {}\n        for i in np.arange(1,3):\n            featureDict[\"SEX_CD\"][str(float(i))] = newColIndex\n            newColIndex += 1\n        featureDict[\"HOME_PREFECTURE_CD\"] = {}\n        for i in np.arange(1,48):\n            featureDict[\"HOME_PREFECTURE_CD\"][str(float(i))] = newColIndex\n            newColIndex += 1\n        featureDict[\"WORK_PREF_SAME_AS_HOME_FLG\"] = {}\n        for i in np.arange(0,2):\n            featureDict[\"WORK_PREF_SAME_AS_HOME_FLG\"][str(float(i))] = newColIndex\n            newColIndex += 1    \n            \n        self.__saveDict(featureDict, newColIndex, invalidFeatureDict)        \n        self.__showResults(featureDict, newColIndex, invalidFeatureDict)\n        return featureDict, newColIndex, invalidFeatureDict               \n\n# get 2D data from DataManager\nclass DataManager:\n    #featureDict: a three elements tuple. [dataMonthStart, dataMonthEnd)\n    def __init__(self, dataFrom, dataMonthStart=\"2020-02\", dataMonthEnd=\"2021-10\", isConvertData=False, featureDict=None, dataTo=None, isExcludeInvalidFeature=True):\n        # ndarray\n        self.featuresData = None\n        self.labelsData = None\n        self.isExcludeInvalidFeature = isExcludeInvalidFeature\n        self.featureDataMonthStartStr = \"2018-09\"\n        #\"2021-10\" is excluded\n        self.featureDataMonthEndStr = \"2021-10\"\n        self.dataMonthStartStr = dataMonthStart\n        self.dataMonthEndStr = dataMonthEnd\n        print(\"DataManager\")\n        if isConvertData:\n            featuresDataList = []\n            labelsDataList = []\n            self.featureDict = featureDict[0]\n            self.featureNum = featureDict[1]\n            self.invalidFeatureDict = featureDict[2] \n            for filePath in sorted(os.listdir(dataFrom)):\n                print(filePath)\n                with codecs.open(os.path.join(dataFrom, filePath), \"r\", \"Shift-JIS\", \"ignore\") as fp:\n                    df = pd.read_csv(fp)\n                    df.loc[:, [\"SEX_CD\", \"HOME_PREFECTURE_CD\", \"WORK_PREF_SAME_AS_HOME_FLG\"]] = df.loc[:, [\"SEX_CD\", \"HOME_PREFECTURE_CD\", \"WORK_PREF_SAME_AS_HOME_FLG\"]].astype(\"float\").round()  \n                    validDf = self.__removeNanValue(df)\n                    subFeaturesData, subLabelsData = self.__convertDataFrame(validDf, [\"transaction_date\"])\n                    \n                    #debug\n                    print(\"Converted subFeaturesData\")\n                    print(subFeaturesData)\n                    print(subFeaturesData.shape)\n                    print(\"Converted subLabelsData\")\n                    print(subLabelsData)\n                    print(subLabelsData.shape)\n                    \n                    featuresDataList.append(subFeaturesData)\n                    labelsDataList.append(subLabelsData)\n            self.featuresData = np.concatenate(featuresDataList, axis=0)\n            self.labelsData = np.concatenate(labelsDataList, axis=0)\n            np.save(os.path.join(dataTo, 'featuresData'), self.featuresData)\n            np.save(os.path.join(dataTo, 'labelsData'), self.labelsData)\n            print(\"Data saved\")\n            #debug\n            print(\"Aggregated featuresData\")\n            print(self.featuresData)\n            print(self.featuresData.shape)\n            print(\"Aggregated labelsData\")\n            print(self.labelsData)\n            print(self.labelsData.shape)\n            print(\"Debugging for DataManager is done\")\n        else:\n            self.featuresData = np.load(os.path.join(dataFrom, \"featuresData.npy\"))\n            self.labelsData = np.load(os.path.join(dataFrom, \"labelsData.npy\"))\n        print(\"Data loaded\")\n    \n    # some other works may be done here in the future, you may filling the NAN instead of dropping it    \n    def __removeNanValue(self, df):\n        deletedRows = set()\n        if self.isExcludeInvalidFeature:\n            for columnStr in self.invalidFeatureDict.keys():\n                for row in range(len(df)):\n                    value = str(df[columnStr][row])\n                    if value in self.invalidFeatureDict[columnStr]:\n                        deletedRows.add(row)\n            deletedRows = sorted(list(deletedRows))\n            for row in deletedRows:\n                print(\"row %d in DataFrame will be deleted.\" % row)\n                print(\"\\t%s\" % (df.iloc[row].to_numpy()))\n            df.drop(deletedRows)\n        return df.dropna()\n\n    def __convertDataFrame(self, df, excludedColumns):\n        featureDict = self.featureDict\n        # cashingSum for each time_step(day), the date info is dropped\n        newDfGb = df.groupby(['transaction_date'])\n        cashingSumArray = np.zeros(len(newDfGb.groups))\n        featureArray = np.zeros((len(cashingSumArray), self.featureNum))\n        dateList = []\n        for dateStr, rowList in newDfGb.groups.items():\n            dateList.append((dateStr, rowList))\n        dateList.sort(key=lambda x : x[0])\n        \n        for i in range(len(dateList)):\n            # in one time step(one day)\n            for dfCol in range(len(df.columns)):\n                columnStr = df.columns[dfCol]\n                if columnStr not in excludedColumns:\n                    if columnStr != \"CASHING_SUM\":\n                        for dfRow in range(len(dateList[i][1])):\n                            feature = str(df.iloc[dfRow, dfCol])\n                            #filt bad features\n                            if feature in featureDict[columnStr].keys():\n                                featureArray[i, featureDict[columnStr][feature]] += 1\n                    else:\n                        for dfRow in range(len(dateList[i][1])):\n                            cashingSumArray[i] += df.iloc[dfRow, dfCol]\n        return featureArray, cashingSumArray\n    \n    def __countDaysFromMonthPeriod(self, monthStartStr, monthEndStr):\n        monthStart = datetime.strptime(monthStartStr, \"%Y-%m\")\n        monthEnd = datetime.strptime(monthEndStr, \"%Y-%m\")\n        return (monthEnd - monthStart).days\n    \n    def __checkLoadedData(self):\n        print(\"featuresData: %s\\nlabelsData: %s\" % (self.featuresData.shape, self.labelsData.shape))\n        assert self.featuresData.shape[0] == self.labelsData.shape[0], \"Invalid loaded data\"\n    \n    def __dataPreprocess(self, featuresData_, labelsData_, labelDiv=5e9):\n        featureData = preprocessing.StandardScaler().fit_transform(featuresData_)\n        #featureData = preprocessing.MinMaxScaler().fit_transform(featuresData_)\n        labelsData = labelsData_ / labelDiv\n        return featureData, labelsData\n        \n    #[2020-02, 2021-10)\n    def __getData(self):\n        self.__checkLoadedData()\n        beginDays = self.__countDaysFromMonthPeriod(self.featureDataMonthStartStr, self.dataMonthStartStr)\n        days = self.__countDaysFromMonthPeriod(self.dataMonthStartStr, self.dataMonthEndStr)\n        print(\"total days: %d\\t, begin: %d\\t, end: %d\\t\" % (self.__countDaysFromMonthPeriod(self.featureDataMonthStartStr, self.featureDataMonthEndStr), beginDays, beginDays+days))\n        feturesData, labelsData = self.featuresData[beginDays:beginDays+days], self.labelsData[beginDays:beginDays+days]\n        return feturesData, labelsData\n    \n    def getData(self):\n        featuresData, labelsData = self.__getData()\n        return self.__dataPreprocess(featuresData, labelsData, labelDiv=1e9)\n    \n    def getWeeklyData(self):\n        featuresData, labelsData = self.__getData()\n        print(\"shape for labelsData: %s\" % str(labelsData.shape))\n        days, features = featuresData.shape\n        weeks = days//7\n        feturesDataWeekly = np.empty((weeks, features))\n        labelsDataWeekly = np.empty((weeks))\n        #print(\"WeeklyData processing...\")\n        for i in range(weeks):\n            dayIndexEnd = days - i*7\n            weekIndex = weeks - i - 1\n            feturesDataWeekly[weekIndex] = featuresData[dayIndexEnd-7:dayIndexEnd].sum(axis=0)\n            labelsDataWeekly[weekIndex] = labelsData[dayIndexEnd-7:dayIndexEnd].sum()\n            #print(\"One record: %s %s\" % (str(featuresData[dayIndexEnd-7:dayIndexEnd].sum(axis=0)), str(labelsData[dayIndexEnd-7:dayIndexEnd].sum())))\n        return self.__dataPreprocess(feturesDataWeekly, labelsDataWeekly, labelDiv=5e9)\n    \n    #Incooperate more features into self.featuresData, for future work\n    #You can call:\n    #   dataManager = DataManager(dataFrom=\"./data\")\n    #   dataManager.expendFeatures(featureList)\n    #   data = dataManager.getData()\n    #   #in this way, the extra feature will be fully used by model training without DimensionalityReduction\n    #   data = DataDimensionalityReduction().reduceDimension(data[:,:(len(data)-len(featureList))])\n    #featureList: [ndarray_1D, ndarray_1D_2, ndarray_1D_3, ...]\n    '''\n    def expandFeatures(self, featureList, startMonth=\"2020-02\", endMonth=\"2021-10\"):\n        oldFeatures = None\n        if len(featureList) == len(self.featuresData):\n            oldFeatures = self.featuresData\n        else:\n            startDayIndex = self.__countDaysFromMonthPeriod(self.dataMonthStartStr, startMonth)\n            days = self.__countDaysFromMonthPeriod(startMonth, endMonth)\n            for i in range(len(featureList)):\n                assert days == len(featureList[i]), \"Illegal parameters\"\n            oldFeatures = self.featuresData[startDayIndex:startDayIndex + days]\n            #oldFeatures = self.featuresData[startDayIndex:startDayIndex + days,:-1]\n            #if self.featuresData.shape[1] == 2:\n            #    oldFeatures = oldFeatures[:,None]\n        self.featuresData = np.concatenate(([oldFeatures] + [featureList[i][:,None] for i in range(len(featureList))]), axis=-1)\n        self.labelsData = self.labelsData[startDayIndex:startDayIndex+days]\n        self.__checkLoadedData()\n        return self.featuresData, self.labelsData\n    '''\n    \n    #featureList should contain data from 2018-09 to 2021-09\n    def expandFeatures(self, featureList):\n        if len(featureList) != 0:\n            for i in range(len(featureList)):\n                assert len(featureList[i]) == len(self.featuresData), \"illegal paramters\"\n            # /7.0 for weekly consumer confidence addition\n            self.featuresData = np.concatenate(([self.featuresData] + [(featureList[i][:,None].astype(float)) / 7.0 for i in range(len(featureList))]), axis=-1)\n            self.__checkLoadedData()\n        return self\n'''\n#for autoencoder    \nclass DenseTranspose(keras.layers.Layer):\n    def __init__(self, dense, activation=None, **kwargs):\n        self.dense = dense\n        self.activation = keras.activations.get(activation)\n        super().__init__(**kwargs)\n        \n    def build(self, batch_input_shape):\n        self.biases = self.add_weight(name=\"bias\", initializer=\"zeros\",\n        shape=[self.dense.input_shape[-1]])\n        super().build(batch_input_shape)\n        \n    def call(self, inputs):\n        z = tf.matmul(inputs, self.dense.weights[0], transpose_b=True)\n        return self.activation(z + self.biases)\n    \n    def get_config(self):\n        config = super().get_config()\n        config.update({\n            \"dense\": self.dense,\n            \"activation\": self.activation,\n        })\n        return config\n'''\nclass DataDimensionalityReduction:\n    # pass a trained encoder (path) or nothing\n    def __init__(self, featureNum=None, encoder=None, encoderPath='./', isEncoderTraining = False):\n        self.encoder = encoder\n        self.encoderPath = encoderPath\n        self.isEncoderTraining = isEncoderTraining\n        if not isEncoderTraining:\n            if encoder == None:\n                self.encoder = keras.models.load_model(os.path.join(encoderPath, \"autoencoder.h5\"))\n        else:\n            #build an default autoencoder model\n            assert featureNum != None, \"Illegal parameters\"\n            encoder = keras.models.Sequential([\n                keras.layers.Dense(featureNum, activation=\"selu\", input_shape=[featureNum]),\n                keras.layers.Dense(featureNum//2, activation=\"selu\"),\n                keras.layers.Dense(featureNum//4, activation=\"selu\")\n                #keras.layers.ActivityRegularization(l1=1e-3)\n            ])\n            decoder = keras.models.Sequential([\n                keras.layers.Dense(featureNum//2, activation=\"selu\", input_shape=[featureNum//4]),\n                keras.layers.Dense(featureNum, activation=\"selu\", input_shape=[featureNum//2]),\n                keras.layers.Dense(featureNum, activation=\"sigmoid\")\n            ])\n            autoEncoder = keras.models.Sequential([encoder, decoder])\n            autoEncoder.compile(loss=\"mse\", optimizer=keras.optimizers.Adam())\n            earlyStoppingCB = callbacks.EarlyStopping(monitor=\"val_loss\", patience=50, restore_best_weights=True)\n            #checkpointCB = callbacks.ModelCheckpoint(\"autoencoder.h5\", monitor=\"val_binary_crossentropy\", save_best_only=True)\n            self.callbacks = [earlyStoppingCB]\n            self.model = autoEncoder\n            self.encoder = encoder\n    # 2D featureData: (samples, features)\n    def reduceDimension(self, featureData):\n        if self.isEncoderTraining:\n            size = len(featureData)\n            indexes = np.array([i for i in range(size)])\n            random.shuffle(indexes)\n            split = int(size*0.8)\n            X_train = featureData[indexes[:split]]\n            X_valid = featureData[indexes[split:]]\n            self.model.fit(X_train, X_train, epochs=2000, validation_data=(X_valid, X_valid), callbacks=self.callbacks)\n            #save encoder, not model\n            self.encoder.save(os.path.join(self.encoderPath, \"autoencoder.h5\"))\n        return self.encoder.predict(featureData)\n\n# make the 2D data(ndarray, X) transferred to 3D time-series data(samples, timeStepPerSample, features) for X\n# 1. Dimension Transfer, 2. preprocess labelsArray(cashing_sum) (value: 0 ~ 1.0)\nclass DimensionTransfer:\n    #default: use 180 days(time steps) as a sample, for each time_step, predict the cashing_sum for next 60 days\n    def __init__(self, timeStepPerSample, nextTimeStepPerPrediction):\n        self.timeStepPerSample = timeStepPerSample\n        self.nextTimeStepPerPrediction = nextTimeStepPerPrediction\n    \n    def __checkData(self, X, y, XForLastPrediction):\n        print(\"shape for X: %s\\nshape for y: %s\\nshape for XForLastPrediction: %s\\n\" % (X.shape, y.shape, XForLastPrediction.shape))\n        print(\"Data transferred\\n\")\n        print(X)\n        print(y)\n    \n    #use transferData(featuresArray[-(testSize+nextTimeStepPerPrediction):], labelsArray[-(testSize+nextTimeStepPerPrediction):]) to generate X_test and y_test\n    #Or use transferData(featuresArray, labelsArray) to generate X and y without TimeGAN application\n    def transferData(self, featuresArray, labelsArray, testSize, validateRate):\n        totalTimeStep, featurePerTimeStep = featuresArray.shape\n        sampleSize = totalTimeStep-self.timeStepPerSample-self.nextTimeStepPerPrediction+1\n        newX = np.empty((sampleSize, self.timeStepPerSample, featurePerTimeStep+1))\n        newY = np.empty((sampleSize, self.timeStepPerSample, self.nextTimeStepPerPrediction))\n        for i in range(sampleSize):\n            for j in range(self.timeStepPerSample):\n                newX[i,j,:-1] = featuresArray[i+j,:]\n                newX[i,j,-1] = labelsArray[i+j]        \n                start = i+j+1\n                newY[i,j,:] = labelsArray[start:start+self.nextTimeStepPerPrediction]\n        # XForLastPrediction has no labels, the prediction from XForLastPrediction is for the future\n        # shape for XForLastPrediction should be (1, nextTimeStepPerPrediction, features+1) or (1, timeStepPerSample, features+1)\n        #XForLastPrediction = np.concatenate([featuresArray[-self.nextTimeStepPerPrediction:], labelsArray[-self.nextTimeStepPerPrediction:][:,None]], axis=-1)[None,:]    \n        XForLastPrediction_ = np.concatenate([featuresArray[-self.timeStepPerSample:], labelsArray[-self.timeStepPerSample:][:,None]], axis=-1)[None,:]    \n        print(featuresArray.dtype)\n        print(labelsArray.dtype)\n        print(\"XForLastPrediction data type\")\n        print(XForLastPrediction_.dtype)\n        XForLastPrediction = XForLastPrediction_.astype(newX.dtype)\n        #print(XForLastPrediction.dtype)\n        self.__checkData(newX, newY, XForLastPrediction)\n        X_train, y_train, X_valid, y_valid  = self.splitData(newX[:-testSize], newY[:-testSize], validateRate)\n        return X_train, y_train, X_valid, y_valid, newX[-testSize:], newY[-testSize:], XForLastPrediction\n    \n    #call transferToTimeSeries first to get timeSeriesData data for TimeGAN training, then convertTimeSeriesData to get X and y for model training\n    #methods for TimeGAN training and synthetic time series generation\n    #testing data is excluded\n    def transferToTimeSeries(self, featuresArray, labelsArray, testSize):\n        sampleTimeSteps = self.timeStepPerSample + self.nextTimeStepPerPrediction\n        totalTimeStep, featurePerTimeStep = featuresArray.shape\n        # minus nextTimeStepPerPrediction to exclude last 3 month data in training set for testing\n        sampleSize = totalTimeStep - sampleTimeSteps - self.nextTimeStepPerPrediction - testSize + 1\n        assert sampleSize > 0, \"illegal parameters\"\n        timeSeriesData = np.empty((sampleSize, sampleTimeSteps, featurePerTimeStep+1))\n        for i in range(sampleSize):\n            for j in range(sampleTimeSteps):\n                timeSeriesData[i,j,:-1] = featuresArray[i+j]\n                timeSeriesData[i,j,-1] = labelsArray[i+j]\n        print(\"TimeSeries Data transferred\")\n        print(timeSeriesData.shape)\n        return timeSeriesData\n    \n    #convert (samples, XtimeSteps + YtimeSteps, features) to X: (samples, XtimeSteps, features) and y: (samples, XtimeSteps, predictionForEachYtimeSteps)\n    #X, y used only for training\n    def convertTimeSeriesData(self, timeSeriesData, syntheticDataList):\n        X_all = np.concatenate(syntheticDataList + [timeSeriesData], axis=0)\n        sampleSize, totalTimeStep, featurePerTimeStep = X_all.shape\n        assert totalTimeStep == self.timeStepPerSample + self.nextTimeStepPerPrediction, \"illegal parameters\"\n        X = np.empty((sampleSize, self.timeStepPerSample, featurePerTimeStep))\n        y = np.empty((sampleSize, self.timeStepPerSample, self.nextTimeStepPerPrediction))\n        for i in range(sampleSize):\n            X[i] = X_all[i, :self.timeStepPerSample]\n            for j in range(self.timeStepPerSample):\n                y[i,j] = X_all[i,j+1:j+1+self.nextTimeStepPerPrediction,-1]\n        return X, y\n    \n    def splitData(self, X, y, valDataRate):\n        assert len(X) == len(y), \"illegal parameters\"\n        valDataSize = int(valDataRate * len(X))\n        indexes = np.random.permutation(len(X))\n        X_valid = X[indexes[:valDataSize]]\n        y_valid = y[indexes[:valDataSize]]\n        X_train = X[indexes[valDataSize:]]\n        y_train = y[indexes[valDataSize:]]\n        print(\"Data splited\")\n        print(\"X_train %s\\ty_train: %s\\nX_valid: %s\\ty_valid: %s\\n\" % (X_train.shape, y_train.shape, X_valid.shape, y_valid.shape))\n        return X_train, y_train, X_valid, y_valid \n    '''\n    #saveData(dataTo=\"./\", dataList=dataList, nameList=[\"X_test.npy\", \"y_test.npy\", \"XForLastPrediction.npy\", \"timeSeriesData.npy\"])\n    def saveData(self, dataTo, dataList, nameList):\n        assert len(dataList) == len(nameList), \"illegal parameters\"\n        for i in range(len(dataList)):\n            np.save(os.path.join(dataTo, nameList[i]), dataList[i])\n    # X_test, y_test, XForLastPrediction, timeSeriesData = loadData(dataFrom=\"./\", nameList=[\"X_test.npy\", \"y_test.npy\", \"XForLastPrediction.npy\", \"timeSeriesData.npy\"])\n    def loadData(self, dataFrom, nameList):\n        dataList = []\n        for i in range(len(dataList)):\n            dataList.append(np.load(os.path.join(dataFrom, nameList[i])))\n        return dataList\n    '''\n    \n# A naive model    \nclass CNNLSTM:\n    def __init__(self, featurePerTimeStep=None, outputNum=None, epochs=50, batchSize=32, extraCallbackList=[], isLoadModel=False, modelFrom=None):\n        self.batchSize = batchSize\n        self.earlyStoppingCB = callbacks.EarlyStopping(monitor=\"val_lastTimeStepMSEError\", patience=30, restore_best_weights=True)\n        self.checkpointCB = callbacks.ModelCheckpoint(\"CNNLSTM_only_covid_extra_feature.h5\", monitor=\"val_lastTimeStepMSEError\", save_best_only=True)\n        self.callbacks = [self.earlyStoppingCB, self.checkpointCB] + extraCallbackList\n        self.epochs = epochs\n        if isLoadModel:\n            self.model = self.__loadModel(os.path.join(modelFrom, \"CNNLSTM_only_covid_extra_feature.h5\"))\n        else:\n            assert (featurePerTimeStep!=None and outputNum!=None), \"illegal parameters\"\n            self.model = None\n            self.model = self.__buildModel(featurePerTimeStep, outputNum)\n    \n    def lastTimeStepMAEError(self, y_true, y_pred):\n        return keras.metrics.mae(y_true[:, -1], y_pred[:,-1])\n    \n    def lastTimeStepMSEError(self, y_true, y_pred):\n        return keras.metrics.mse(y_true[:, -1], y_pred[:,-1]) \n    \n    def lastTimeStepMAPEError(self, y_true, y_pred):\n        return keras.metrics.mape(y_true[:, -1], y_pred[:,-1]) \n    \n    def __buildModel(self, featurePerTimeStep, outputNum):\n        input_ = layers.Input(shape=[None, featurePerTimeStep])\n        prevLayer = input_\n        #for dilation in (1, 2, 4, 7, 14, 28):\n        for dilation in (1, 2, 4, 6, 8, 12, 16):\n            prevLayer = layers.Conv1D(filters=60, kernel_size=2, padding=\"causal\", \n                                      activation=\"relu\", \n                                      dilation_rate=dilation)(prevLayer)\n        lstm1 = layers.LSTM(120, return_sequences=True)(prevLayer)\n        lstm2 = layers.LSTM(120, return_sequences=True)(lstm1)\n        lstm3 = layers.LSTM(60, return_sequences=True)(lstm2)\n        outputLayer = layers.TimeDistributed(layers.Dense(outputNum))(lstm3)\n        model = keras.Model(inputs=[input_], outputs=[outputLayer])\n        #model.compile(loss=keras.losses.Huber(delta=2.5), optimizer=keras.optimizers.Adam(learning_rate=0.0005), metrics=[self.lastTimeStepMAEError, self.lastTimeStepMSEError, self.lastTimeStepMAPEError])\n        model.compile(loss=\"mse\", optimizer=\"adam\", metrics=[self.lastTimeStepMAEError, self.lastTimeStepMSEError, self.lastTimeStepMAPEError])\n        #model.compile(loss=keras.losses.Huber(delta=2.5), optimizer=\"adam\", metrics=[self.lastTimeStepError])\n        return model\n    \n    def __loadModel(self, modelPath):\n        #model = keras.models.load_model(modelPath, custom_objects={\"lastTimeStepError\": self.lastTimeStepError}, compile=False)\n        model = keras.models.load_model(modelPath, compile=False)\n        model.compile(loss=\"mse\", optimizer=\"adam\", metrics=[self.lastTimeStepMAEError, self.lastTimeStepMSEError, self.lastTimeStepMAPEError])\n        return model\n    \n    def getModel(self):\n        return self.model\n    \n    def fit(self, X_train, y_train, X_valid, y_valid):\n        \n        return self.model.fit(X_train, y_train, epochs=self.epochs, steps_per_epoch=X_train.shape[0]//self.batchSize+1, validation_data=(X_valid, y_valid), callbacks=self.callbacks)\n\n    # Once the training is stopped by accident, it can be recovered from file \"CNNLSTM.h5\"\n    # You can call: \n    #    cnnLSTM = CNNLSTM(isLoadModel=True, modelFrom=\"./model/\")\n    #    cnnLSTM.resumeTraining( X_train, y_train, X_valid, y_valid, leftEpochs) \n    # To recover the left model training\n    def resumeTraining(self, X_train, y_train, X_valid, y_valid):\n        return self.fit(X_train, y_train, X_valid, y_valid)\n        \n\n# For tuning hyperparameters, not finished yet\nclass WalkForwardValidation:\n    def __init__(self):\n        pass\n\nclass WeekToDaysDecoder:\n    # Train a new WeekToDaysDecoder\n    #weekToDaysDecoder = WeekToDaysDecoder()\n    #weekToDaysDecoder.train(weeklyX, weeklyY, weeks, dayEnd=\"2021-07-02\")\n    # or just loading: \n    #weekToDaysDecoder = WeekToDaysDecoder(isLoadModel=True)\n    #prediction = weekToDaysDecoder.predict(self, weeklyX, weeks, dayEnd=\"2021-10-01\"):\n    def __init__(self, isLoadModel=False, modelFrom='./', modelTo='./'):\n        self.featureNum = 1+12+6+4*7\n        self.earlyStoppingCB = callbacks.EarlyStopping(monitor=\"val_loss\", patience=50, restore_best_weights=True)\n        #From: https://www.timeanddate.com/holidays/japan/\n        self.holidays = {\"2018-09-17\", \"2018-09-23\", \"2018-09-24\", \"2018-10-08\", \"2018-11-03\",\n                         \"2018-11-15\", \"2018-11-23\", \"2018-12-22\", \"2018-12-23\", \"2018-12-24\",\n                         \"2018-12-25\", \"2018-12-31\", \"2019-01-01\", \"2019-01-02\", \"2019-01-03\",\n                         \"2019-01-14\", \"2019-02-11\", \"2019-02-14\", \"2019-03-03\", \"2019-03-21\",\n                         \"2019-04-29\", \"2019-04-30\", \"2019-05-01\", \"2019-05-02\", \"2019-05-03\",\n                         \"2019-05-04\", \"2019-05-05\", \"2019-05-06\", \"2019-06-22\", \"2019-07-07\",\n                         \"2019-07-15\", \"2019-08-06\", \"2019-08-09\", \"2019-08-11\", \"2019-08-12\",\n                         \"2019-09-16\", \"2019-09-23\", \"2019-10-14\", \"2019-10-22\", \"2019-11-03\",\n                         \"2019-11-04\", \"2019-11-15\", \"2019-11-23\", \"2019-12-22\", \"2019-12-25\",\n                         \"2019-12-31\", \"2020-01-01\", \"2020-01-02\", \"2020-01-03\", \"2020-01-13\",\n                         \"2020-02-11\", \"2020-02-14\", \"2020-02-23\", \"2020-02-24\", \"2020-03-03\",\n                         \"2020-03-20\", \"2020-04-29\", \"2020-05-03\", \"2020-05-04\", \"2020-05-05\", \n                         \"2020-05-06\", \"2020-06-21\", \"2020-07-07\", \"2020-07-23\", \"2020-07-24\", \n                         \"2020-08-06\", \"2020-08-09\", \"2020-08-10\", \"2020-09-21\", \"2020-09-22\", \n                         \"2020-11-03\", \"2020-11-15\", \"2020-11-23\", \"2020-12-21\", \"2020-12-25\",\n                         \"2020-12-31\", \"2021-01-01\", \"2021-01-02\", \"2021-01-03\", \"2021-01-11\",\n                         \"2021-01-14\", \"2021-02-23\", \"2021-03-03\", \"2021-03-20\", \"2021-04-29\", \n                         \"2021-05-03\", \"2021-05-04\", \"2021-05-05\", \"2021-06-21\", \"2021-07-07\", \n                         \"2021-07-22\", \"2021-07-23\", \"2021-08-06\", \"2021-08-08\", \"2021-08-09\",\n                         \"2021-09-20\", \"2021-09-23\"}\n        if not isLoadModel:\n            decoder = keras.models.Sequential([\n                keras.layers.Dense(self.featureNum//2, activation=\"selu\", input_shape=[self.featureNum]),\n                keras.layers.Dense(self.featureNum, activation=\"selu\"),\n                keras.layers.Dense(self.featureNum//2, activation=\"selu\"),\n                keras.layers.Dense(7)\n            ])\n            decoder.compile(loss=\"mse\", optimizer=keras.optimizers.Adam())\n            self.model = decoder\n        else:\n            self.model = keras.models.load_model(os.path.join(modelFrom, \"weekToDaysDecoder.h5\"))\n        \n    def __one_hot(self, total, index):\n        res = np.zeros((total,)) + 0.01\n        if index >= 0:\n            res[index] = 0.99\n        return res\n    \n    def __isHoliday(self, day):\n        dayStr = day.strftime(\"%Y-%m-%d\")\n        return dayStr in self.holidays\n    \n    def __isConsecutiveHoliday(self, day):\n        #day.weekday() start from 0\n        weekday = day.weekday()+1\n        if weekday == 5 or weekday == 1 or self.__isHoliday(day+relativedelta(days=1)) or self.__isHoliday(day-relativedelta(days=1)):\n            return True\n        return False\n    #return value: 0~5 \n    def __getWeekOfMonth(self, day):\n        year, month, d = day.year, day.month, 1\n        date = datetime.strptime(\"%d-%02d-%02d\" % (year,month,d), \"%Y-%m-%d\")\n        extraDays = 7 - date.weekday()\n        baseWeek = 1\n        if extraDays == 7:\n            extraDays = 0\n            baseWeek = 0\n        days = day.day\n        if days <= extraDays:\n            return 0\n        assert (days-extraDays-day.weekday()-1) % 7 == 0\n        return (days-extraDays-1) // 7 + baseWeek\n    \n    #dayEnd is excluded [startDay, endDay)\n    def __genTimeFeautures(self, weeklyX, weeks, dayEndStr):\n        featuresArray = np.empty((weeks, self.featureNum))\n        endDay = datetime.strptime(dayEndStr, \"%Y-%m-%d\")\n        #the first day for this week\n        crtDay = endDay - relativedelta(days=7)\n        crtWeek = weeks-1\n        while crtWeek >= 0:\n            featuresArray[crtWeek][0] = weeklyX[crtWeek]\n            #month\n            featuresArray[crtWeek][1:13] = self.__one_hot(12, crtDay.month-1)\n            #week of month\n            featuresArray[crtWeek][13:19] = self.__one_hot(6, self.__getWeekOfMonth(crtDay))\n            #holiday\n            tmpDay = crtDay\n            for i in range(1,8):\n                if (tmpDay.month == 12 and tmpDay.day <= 31 and tmpDay.day >=24) or (tmpDay.month == 1 and tmpDay.day <= 3 and tmpDay.day >=1):\n                    holiday_type = 0\n                elif (tmpDay.month == 4 and tmpDay.day <= 31 and tmpDay.day >=29) or (tmpDay.month == 5 and tmpDay.day <= 5 and tmpDay.day >=1):\n                    holiday_type = 1\n                elif self.__isHoliday(tmpDay):\n                    #holidays for 3 consecutive days including weekends\n                    if self.__isConsecutiveHoliday(tmpDay):\n                        holiday_type = 2\n                    else:\n                        holiday_type = 3\n                else:\n                    holiday_type = -1\n                featuresArray[crtWeek][19+(i-1)*4:19+i*4] = self.__one_hot(4, holiday_type)\n                tmpDay += relativedelta(days=1)\n            crtDay -= relativedelta(days=7)\n            crtWeek -= 1\n        return featuresArray;\n    \n    def __splitData(self, X, y, validRatio=0.2):\n        assert len(X) == len(y), \"illegal parameters\"\n        indexes = np.random.permutation(len(X))\n        splitIndex = int(validRatio*len(X))\n        X_train = X[indexes[:-splitIndex]]\n        y_train = y[indexes[:-splitIndex]]\n        X_valid = X[indexes[-splitIndex:]]\n        y_valid = y[indexes[-splitIndex:]]\n        return X_train, X_valid, y_train, y_valid\n    \n    #assume the factors effect each day equally.\n    def train(self, weeklyX, weeklyY, weeks, dayEnd=\"2021-07-02\"):\n        X = self.__genTimeFeautures(weeklyX, weeks, dayEnd)\n        print(\"genetated X in WeekToDaysDecoder\")\n        print(X)\n        X_train, X_valid, y_train, y_valid = self.__splitData(X, weeklyY, 0.2)\n        self.model.fit(X_train, y_train, epochs=5000, validation_data=(X_valid, y_valid), callbacks=[self.earlyStoppingCB])\n        \n    def predict(self, weeklyX, weeks, dayEnd=\"2021-10-01\"):\n        X = self.__genTimeFeautures(weeklyX, weeks, dayEnd)\n        return self.model.predict(X)   \n\n#------------------------------------------extra features------------------------------------------#\n#extract data during 2020-02 ~ 2021-11, 22 months, based on per month\n#however the training data is during 2020-02 ~ 2021-09, 20 months, based on per day\nclass ConsumerConfidenceExtractor:\n    def __init__(self, path, prev_month_start=39, prev_month_end=2 ,extractedColumnIndexes=[]):\n        self.month_data = []\n        df = pd.read_csv(path)\n        df = df.iloc[-prev_month_start:-prev_month_end]\n        if len(extractedColumnIndexes) > 0:\n            for index in extractedColumnIndexes:\n                self.month_data.append(df.loc[:, df.columns[index]].to_numpy())\n        else:\n            array = df.to_numpy()\n            self.month_data = [array[:,i] for i in array.shape[1]]\n            \n    # return a list with each element being an ndarray as a feature column       \n    def getData(self):\n        return self.month_data\n\nclass MonthlyFeatureToDailyFeature:\n    #[monthStartStr, monthEndStr]\n    def __init__(self, monthStartStr, monthEndStr, monthFeatureList):\n        self.monthStart = datetime.strptime(monthStartStr, \"%Y-%m\")\n        self.monthEnd = datetime.strptime(monthEndStr, \"%Y-%m\") + relativedelta(months=1)\n        self.monthFeatureList = monthFeatureList\n        \n    def convertData(self):\n        featureList = [[] for i in range(len(self.monthFeatureList))]\n        monthCrt = self.monthStart\n        monthIndex = 0\n        nextMonth = monthCrt + relativedelta(months=1)\n        while monthCrt != self.monthEnd:\n            days = (nextMonth - monthCrt).days\n            for featureIndex in range(len(self.monthFeatureList)):\n                for day in range(days):\n                    featureList[featureIndex].append(self.monthFeatureList[featureIndex][monthIndex])\n            monthIndex += 1\n            monthCrt = nextMonth\n            nextMonth = monthCrt + relativedelta(months=1)\n        assert (self.monthEnd - self.monthStart).days == len(featureList[0]), \"Illegal parameters\"\n        return [np.array(featureList[i]) for i in range(len(self.monthFeatureList))]\n'''    \nclass DateFeatureGenerator:\n    def __init__(self, startDateStr, endDateStr, formatStr=\"%Y-%m-%d\"):\n        self.startDate = datetime.strptime(startDateStr, formatStr)\n        self.endDate = datetime.strptime(endDateStr, formatStr) + relativedelta(days=1)\n        self.format = formatStr\n    \n    def __week_of_month(self, date_value):\n        value = (date_value.isocalendar()[1] - date_value.replace(day=1).isocalendar()[1] + 1)\n        if value < 0:\n            value += 52\n        return value\n    \n    def genDateFeatureList(self):\n        dates = []\n        crtDate = self.startDate\n        while crtDate != self.endDate:\n            dates.append(crtDate)\n            crtDate += relativedelta(days=1)\n        df = pd.DataFrame(np.array(dates)[:,None])\n        print(\"df\")\n        print(df)\n        df['date'] = df[0] # index: DatetimeIndex\n        df['day_of_week'] = df['date'].dt.dayofweek.values\n        df['quarter'] = df['date'].dt.quarter.values\n        df['month'] = df['date'].dt.month.values\n        #df['year'] = df['date'].dt.year.values\n        df['day_of_year'] = df['date'].dt.dayofyear.values\n        df['day_of_month'] = df['date'].dt.day.values\n        df['week_of_year'] = df['date'].dt.weekofyear.values\n        df['week_of_month'] = df.apply(lambda x: self.__week_of_month(x.date), axis = 1)\n        #X = df[['day_of_week', 'quarter', 'month', 'year', 'day_of_year', 'day_of_month', 'week_of_year','week_of_month']]\n        X = df[['day_of_week', 'quarter', 'month', 'day_of_year', 'day_of_month', 'week_of_year','week_of_month']]\n        print(\"X\")\n        print(X)\n        featureList = []\n        for columnStr in X.columns:\n             featureList.append(df[columnStr].to_numpy())\n        print(\"Date Feature List\")\n        print(featureList)\n        return featureList    \n'''  \n\nclass Main:\n    def __init__(self, dataFrom, extraFeatures=[], labelFrom=None, isCreatefeatureDict=False, isCreateData=False, isResumeTraining=False, isTimeGANTraining=False, isAutoEncoderTraining=False, isTimeGANEnabled=False, isLoadEncoder=False, leftEpochs=50, modelFrom=None, dataTo=None):\n        self.leftEpochs = leftEpochs\n        X_test, y_test, timeSeriesData, X, y, XForLastPrediction, model = [None]*7\n        #weekly\n        timeStepPerSampleWeekly = 39\n        nextTimeStepPerPredictionWeekly = 13\n        testSizeWeekly = 13\n        validationRate = 0.2\n        # assume timeStepPerSample >= nextTimeStepPerPrediction\n        timeStepPerSampleDaily = timeStepPerSampleWeekly*7\n        nextTimeStepPerPredictionDaily = nextTimeStepPerPredictionWeekly*7\n        testSizeDaily = nextTimeStepPerPredictionWeekly*7\n        \n        timeStepPerSample = timeStepPerSampleWeekly\n        nextTimeStepPerPrediction = nextTimeStepPerPredictionWeekly\n        testSize = testSizeWeekly\n        \n        dimensionTransferDaily = DimensionTransfer(timeStepPerSample, nextTimeStepPerPrediction)\n        dimensionTransferWeekly = DimensionTransfer(timeStepPerSampleWeekly, nextTimeStepPerPredictionWeekly)\n        dimensionTransfer = dimensionTransferWeekly\n\n        featureDict, invalidFeatureDict = None, None\n        if isCreatefeatureDict:\n            featureDict, featureNum, invalidFeatureDict = FeatureLabelExplorer(dataFrom, dataTo).exploreLabel()\n        else:\n            featureDict, featureNum,invalidFeatureDict = FeatureLabelExplorer(labelFrom).loadfeatureDict()\n        if isCreateData:\n            dataManager = DataManager(dataFrom, dataMonthStart=\"2018-09\", dataMonthEnd=\"2021-10\", isConvertData=True, featureDict=(featureDict, featureNum, invalidFeatureDict), dataTo=dataTo)\n        else:\n            dataManager = DataManager(dataFrom, dataMonthStart=\"2018-09\", dataMonthEnd=\"2021-10\", isConvertData=False)\n        #add more features\n        dataManager.expandFeatures(extraFeatures)\n        #featuresArrayDaily, labelsArrayDaily = dataManager.getData()\n        featuresArrayWeekly, labelsArrayWeekly = dataManager.getWeeklyData()\n\n        #training or loading autoencoder\n        #newFeaturesArray = DataDimensionalityReduction(featureNum=featuresArray.shape[1], isEncoderTraining=isAutoEncoderTraining).reduceDimension(featuresArray)\n\n        #weekly feature array\n        newFeaturesArray = featuresArrayWeekly\n        labelsArray = labelsArrayWeekly\n        #newFeatureSArray = DataDimensionalityReduction(featureNum=featuresArrayWeekly.shape[1], isEncoderTraining=isAutoEncoderTraining).reduceDimension(featuresArrayWeekly)\n        print(\"converted FeaturesArray shape:\")\n        print(newFeaturesArray.shape)\n        print(newFeaturesArray)\n        if isTimeGANEnabled:\n            #timeSeriesData excludes testing data \n            #generate only time series data after Covid-19(2020-01)\n            featuresArrayForTimeGAN, labelsArrayForTimeGAN = DataManager(dataFrom, dataMonthStart=\"2018-09\", dataMonthEnd=\"2021-10\", isConvertData=False).expandFeatures(extraFeatures).getWeeklyData()\n            timeSeriesData = dimensionTransfer.transferToTimeSeries(featuresArrayForTimeGAN, labelsArrayForTimeGAN, testSize)\n            X_train_real, y_train_real, X_valid_real, y_valid_real, X_test, y_test, XForLastPrediction = dimensionTransfer.transferData(newFeaturesArray, labelsArray, testSize, validationRate)\n            #parameters for TimeGAN\n            seq_len = timeStepPerSample + nextTimeStepPerPrediction\n            n_seq = timeSeriesData.shape[2]\n            hidden_dim=120\n            gamma=1\n            noise_dim = int(n_seq*4)\n            layers_dim = 128\n            batch_size = 14\n            learning_rate = 5e-4\n            gan_args = ModelParameters(batch_size=batch_size, lr=learning_rate, noise_dim=noise_dim, layers_dim=layers_dim)\n            # Loading or training TimeGAN\n            if not isTimeGANTraining:\n                timeGAN = TimeGAN.load(os.path.join(dataFrom, 'TimeGAN_100epoch_consumer_confidence.pkl'))\n            else:\n                timeGAN = TimeGAN(model_parameters=gan_args, hidden_dim=hidden_dim, seq_len=seq_len, n_seq=n_seq, gamma=gamma)\n                #shape for \"stock_data\": (samples, time_steps, features), time series data\n                timeGAN.train(timeSeriesData, train_steps=1000)\n                timeGAN.save(os.path.join(dataTo, 'TimeGAN.pkl'))\n            extraDataCount = 4\n            X_List = [timeGAN.sample(len(timeSeriesData)) for i in range(extraDataCount)] + [timeSeriesData]\n            print(\"synthetic data shape %s\" % str(X_List[0].shape))\n            print(\"real data shape %s\" % str(timeSeriesData.shape))\n            X, y = dimensionTransfer.convertTimeSeriesData(timeSeriesData, X_List)\n            X_train_gen, y_train_gen, X_valid_gen, y_valid_gen = dimensionTransfer.splitData(X, y, validationRate)\n            X_train = np.concatenate([X_train_real, X_train_gen], axis=0)\n            X_valid = np.concatenate([X_valid_real, X_valid_gen], axis=0)\n            y_train = np.concatenate([y_train_real, y_train_gen], axis=0)\n            y_valid = np.concatenate([y_valid_real, y_valid_gen], axis=0)\n            indexes_train = np.random.permutation(len(X_train))\n            indexes_valid = np.random.permutation(len(X_valid))\n            X_train = X_train[indexes_train]\n            X_valid = X_valid[indexes_valid]\n            y_train = y_train[indexes_train]\n            y_valid = y_valid[indexes_valid]\n            #dimensionTransfer.saveData(dataTo=dataTo, dataList=[X_train, y_train, X_valid, y_valid, X_test, y_test, XForLastPrediction, timeSeriesData], nameList=[\"X_train_t.npy\", \"y_train_t.npy\", \"X_valid_t.npy\", \"y_valid_t.npy\", \"X_test_t.npy\", \"y_test_t.npy\", \"XForLastPrediction_t.npy\", \"timeSeriesData.npy\"])\n        else:\n            X_train, y_train, X_valid, y_valid, X_test, y_test, XForLastPrediction = dimensionTransfer.transferData(newFeaturesArray, labelsArray, testSize, validationRate)\n            #dimensionTransfer.saveData(dataTo=dataTo, dataList=[X_train, y_train, X_valid, y_valid, X_test, y_test, XForLastPrediction], nameList=[\"X_train.npy\", \"y_train.npy\", \"X_valid\", \"y_valid\", \"X_test.npy\", \"y_test.npy\", \"XForLastPrediction.npy\"])\n\n        model,history = None,None\n        if isResumeTraining:\n            model = CNNLSTM(X_train.shape[2], nextTimeStepPerPrediction, isLoadModel=True, modelFrom=modelFrom, epochs=leftEpochs)\n            history = model.resumeTraining(X_train, y_train, X_valid, y_valid)\n        else:\n            model = CNNLSTM(X_train.shape[2], nextTimeStepPerPrediction, epochs=leftEpochs)\n            history = model.fit(X_train, y_train, X_valid, y_valid)\n        model = model.getModel()\n        print(model.summary())\n        model.evaluate(X_test, y_test)\n        #meanLoss, lastLoss = model.evaluate(X_test, y_test)\n        losses = model.evaluate(X_test, y_test)\n        print(\"losses\")\n        print(losses)\n        errorDict = {}\n        keys = [\"MAE\", \"MSE\", \"MAPE\"]\n        for i in range(len(keys)):\n            errorDict[keys[i]] = losses[i+1]\n        print(\"lastTimeStepError:\\nMAE: %.12f\\tMSE: %.12f\\tMAPE: %.12f\" % (losses[1], losses[2], losses[3]))\n        baseMAE, baseMSE, baseMAPE = self.__genBaselineError(X_test, y_test, nextTimeStepPerPrediction)\n        print(\"BaselineError:\\nMAE: %.12f\\tMSE: %.12f\\tMAPE: %.12f\" % (baseMAE, baseMSE, baseMAPE))\n\n        #make prediction for the following 3 months\n        predictionForFuture = model.predict(XForLastPrediction)\n        '''\n        print(\"\\npredictionForFuture\")\n        print(predictionForFuture)\n        '''\n        predictionForYTest = model.predict(X_test)\n        '''\n        print(\"\\npredictionForYTest\")\n        print(predictionForYTest)\n        print(predictionForYTest[-1,-1,:])\n        print(\"\\ny_test\")\n        print(y_test)\n        print(y_test[-1,-1,:])\n        '''\n        dataManager = DataManager(dataFrom, dataMonthStart=\"2018-09\", dataMonthEnd=\"2021-10\", isConvertData=False).expandFeatures(extraFeatures)\n        features_weekly, cashing_sum_weekly = dataManager.getWeeklyData()\n        features_daily, cashing_sum_daily = dataManager.getData()\n        weeklyX = cashing_sum_weekly\n        weeks = len(cashing_sum_weekly)\n        weeklyY = np.empty((weeks, 7))\n        for i in range(1, weeks+1):\n            weeklyY[-i] = cashing_sum_daily[len(cashing_sum_daily)-7*i:len(cashing_sum_daily)-7*(i-1)]\n        weekToDaysDecoder = WeekToDaysDecoder()\n        if not isLoadEncoder:\n            weekToDaysDecoder.train(weeklyX, weeklyY, weeks=weeks, dayEnd=\"2021-07-02\")\n        dailyPrediction_ = weekToDaysDecoder.predict(predictionForYTest[-1,-1,:], weeks=len(predictionForYTest[-1,-1,:]), dayEnd=\"2021-10-01\")\n        dailyPrediction = dailyPrediction_.flatten()\n        dailyReal = cashing_sum_daily[-91:]\n        self.__plotResults(history, predictionForYTest[-1,-1,:], y_test[-1,-1,:], predictionForFuture[-1,-1,:], errorDict) \n        #plot daily prediction\n        errorDict2 = {}\n        errorDict2[\"MAE\"] = keras.metrics.mae(dailyPrediction, dailyReal)\n        errorDict2[\"MSE\"] = keras.metrics.mse(dailyPrediction, dailyReal)\n        errorDict2[\"MAPE\"] = keras.metrics.mape(dailyPrediction, dailyReal)\n        self.__plotSingleImage([i for i in range(1, 92)], [dailyPrediction, dailyReal], labels=[\"Prediction\", \"Real value\"], title=\"Prediction Comparasion for Last 91days\", figSize=(18,6), errorDict=errorDict2, yLabel = 'daily Cashing sum(1e9)')\n    \n    def __genBaselineError(self, X_test, y_test, nextTimeStepPerPrediction):\n        # The cashing_sum for last 60(nextTimeStepPerPrediction) time_steps is used as the prediction for next 60 time_step\n        y_pred = X_test[:, -nextTimeStepPerPrediction:,-1].flatten()\n        error = [keras.metrics.mae(y_pred, y_test[:,-1].flatten()), keras.metrics.mse(y_pred, y_test[:,-1].flatten()), keras.metrics.mape(y_pred, y_test[:,-1].flatten())]\n        return error\n    \n    def __plotSingleImage(self, X, yList, labels, errorDict={}, title='', figSize=(12,6), yLabel=''):\n        plt.figure(figsize=figSize)\n        ax = plt.gca()\n        ax.set_title(title)\n        ax.set_xlabel('Day')\n        ax.set_ylabel(yLabel)\n        #ax.set_ylim(ymin=0.0, ymax=1.0)\n        for i in range(len(yList)):\n            plt.plot(X, yList[i], label=labels[i])\n        text = ''.join([\"%s: %.12f\\n\" % (key, value) for key, value in errorDict.items()])\n        ax.text(0.1, 0.1, text)\n        plt.legend(loc='best')\n        plt.grid(True)\n        plt.show()\n    \n    #y_pred_test: ndarray_1D, last_time_step_predictions\n    #errorDict: mae:..., mse:..., mape:...\n    def __plotResults(self, history, y_pred_test, y_test, y_pred_future, errorDict):\n        if self.leftEpochs == 0:\n            self.__history_half_shift_plot(history)\n        else:\n            self.__history_half_shift_plot(history, shifted_column_names=[\"val_loss\", \"val_lastTimeStepMAEError\"])\n        X = np.linspace(1, len(y_test), len(y_test))\n        self.__plotSingleImage(X, [y_pred_test, y_test], errorDict=errorDict, title='Prediction Comparasion for Last 13 weeks(91days, testing data)', labels=[\"Prediction\", \"Real value\"], yLabel=\"Weekly Cashing sum(5e9)\") \n        X_ = np.linspace(1, len(y_pred_future), len(y_pred_future))\n        self.__plotSingleImage(X_, [y_pred_future], title='Prediction for future 13 weeks(91days)', labels=[\"Prediction\"], yLabel='Weekly Cashing sum(5e9)')\n         \n    def __history_half_shift_plot(self, history, shifted_column_names=[], figSize=(8,5)):\n        ndf = pd.DataFrame(history.history)\n        plt.figure(figsize=figSize)\n        #shift by half epoch to the left for training curve\n        for column_name in shifted_column_names:\n            ndf[column_name].index -= 0.5\n        #index starts from 0, shift 1\n        for column_name in ndf.columns:\n            ndf[column_name].index += 1.0\n            ndf[column_name].plot(ax=plt.gca(), label=column_name)\n        plt.legend(loc='best')\n        plt.grid(True)\n        plt.gca().set_ylim(0,1)\n        plt.gca().set_xticks(np.arange(0, len(ndf), len(ndf) // figSize[0] + 1))\n        plt.gca().set_title(\"Loss during Training\")\n        plt.show()    \n\nconsumerFeatureList_ = ConsumerConfidenceExtractor(path=\"./shouhi2_simplified.csv\", extractedColumnIndexes=[4,6,8,10,12,14]).getData()\nconsumerFeatureDailyList = MonthlyFeatureToDailyFeature(monthStartStr=\"2018-09\", monthEndStr=\"2021-09\", monthFeatureList=consumerFeatureList_).convertData()\nprint(pd.DataFrame(np.array(consumerFeatureList_).T))\nprint(pd.DataFrame(np.array(consumerFeatureDailyList).T))\n\n#dateFeatureList = DateFeatureGenerator(startDateStr=\"2020-02-01\", endDateStr=\"2021-09-30\").genDateFeatureList()\n\nprint(\"Running\")\n#Main(dataFrom=\"../data/cashing_mesh/\", isCreatefeatureDict=True, isCreateData=True, dataTo=\"./\")\n#Main(dataFrom=\"../data/cashing_mesh/\", labelFrom=\"./\", isCreateData=True, isCreatefeatureDict=False, dataTo=\"./\")\n\n#restart training\n#train autoencoder\n#Main(dataFrom=\"./\", labelFrom=\"./\", isCreateData=False, isCreatefeatureDict=False, isTimeGANTraining=True, isTimeGANEnabled=True, isAutoEncoderTraining=True, dataTo=\"./\", leftEpochs=5000)\n#Main(dataFrom=\"./\", labelFrom=\"./\", extraFeatures=dateFeatureList+consumerFeatureDailyList, isCreateData=False, isCreatefeatureDict=False, isTimeGANTraining=True, isAutoEncoderTraining=True, dataTo=\"./\", leftEpochs=50)\n#train TimeGAN\n#Main(dataFrom=\"./\", labelFrom=\"./\", extraFeatures=dateFeatureList+consumerFeatureDailyList, isTimeGANEnabled=True, isCreateData=False, isCreatefeatureDict=False, isTimeGANTraining=True, dataTo=\"./\", leftEpochs=50)\n#without timeGAN Training\n#Main(dataFrom=\"./\", labelFrom=\"./\", extraFeatures=dateFeatureList+consumerFeatureDailyList, isTimeGANEnabled=True, isCreateData=False, isCreatefeatureDict=False, dataTo=\"./\", leftEpochs=50)\n#Main(dataFrom=\"./\", labelFrom=\"./\", extraFeatures=consumerFeatureDailyList, isTimeGANEnabled=True, isCreateData=False, isCreatefeatureDict=False, dataTo=\"./\", leftEpochs=500)\nMain(dataFrom=\"./\", labelFrom=\"./\", extraFeatures=consumerFeatureDailyList, isTimeGANEnabled=False, isCreateData=False, isCreatefeatureDict=False, dataTo=\"./\", leftEpochs=5000)\n#TimeGAN disabled\n#Main(dataFrom=\"./\", labelFrom=\"./\", extraFeatures=dateFeatureList+consumerFeatureDailyList, isCreateData=False, isCreatefeatureDict=False, dataTo=\"./\")\n\n#Resume training\n#Main(dataFrom=\"./\", isResumeTraining=True, modelFrom=\"./\", leftEpochs=50)\n#just evaluation, no training\n#Main(dataFrom=\"./\", isResumeTraining=True, modelFrom=\"./\", leftEpochs=0)","repo_name":"uiwjsxn/DSS-2021a","sub_path":"final_model(CNNLSTM + TimeGAN + Decoder).py","file_name":"final_model(CNNLSTM + TimeGAN + Decoder).py","file_ext":"py","file_size_in_byte":53043,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"39458458254","text":"import pandas as pd\nimport numpy as np\ndf = pd.read_csv(\"C:\\\\Users\\\\Yash\\\\AppData\\\\Local\\\\Programs\\\\Python\\\\Python36-32\\\\Machine Learning\\\\IPL\\\\matches.csv\")\ndef func(x):\n    a = x.win_by_runs.where(x.win_by_runs != 0).count()\n    b = x.win_by_wickets.where(x.win_by_wickets != 0).count()\n    res = pd.DataFrame(data = {\"Batting first\":a, \"chasing\":b}, index = [1])\n    return res\nresult = df.groupby(\"season\")[\"win_by_runs\", \"win_by_wickets\", \"match_id\"].apply(lambda x: func(x)).reset_index().drop(columns = \"level_1\", axis =1)\nresult = result.append(pd.DataFrame(data = {\"season\": \"total\", \"Batting first\": result[\"Batting first\"].sum(), \"chasing\": result[\"chasing\"].sum()}, index = [1]))\nprint(result)\n","repo_name":"forceyash/PythonProjects","sub_path":"IPL/Mixed records and analysis/batting-first-and-chansing-analysis.py","file_name":"batting-first-and-chansing-analysis.py","file_ext":"py","file_size_in_byte":706,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"42670893740","text":"#1 \n# Asignaturas=[\"Matematicas\",\"Quimica\",\"Historia\",\"lengua\"]\n# Print(Asignaturas)\n\n#1\n#asignaturas=[\"Matematica\",\"Fisica\",\"Quimica\",\"Lengua\"]\n#print(asignaturas)\n\n#2\n#Lista1=[\"blog de notas\",\"lapiceras\",\"mouse\"]\n#Lista2=[\"silla\",\"computadora\",\"escritorio\"]\n#Lista3=Lista1+Lista2\n#print(\"las listas son:\", Lista3)\n\n#3 \nlista1=[\"tupla\", 1,2,3]\ntuple(lista1)\n\n#4\nEnsalada=[\"lechuga\",\"tomate\", \"cebolla\"]\nEnsalada.append(\"huevos\")\nprint(Ensalada)\n\nlen(Ensalada)\nEnsalada.pop()\nEnsalada.count(\"tomate\")","repo_name":"AleGodoy12/PythonQASemillero","sub_path":"Tamara Illanes/Ejercicio 1.py","file_name":"Ejercicio 1.py","file_ext":"py","file_size_in_byte":500,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"9400860767","text":"\"\"\"\n# Definition for a Node.\nclass Node:\n    def __init__(self, val, left=None, right=None):\n        self.val = val\n        self.left = left\n        self.right = right\n\"\"\"\n\nclass Solution:\n    def treeToDoublyList(self, root: 'Node') -> 'Node':\n        if not root: return root\n        first, last = None, None\n        def inorder(node):\n            nonlocal first, last\n            if node:\n                inorder(node.left)\n                if not first: first = node\n                if last:\n                    last.right, node.left = node, last\n                last = node\n                inorder(node.right)\n        inorder(root)\n        first.left, last.right = last, first\n        return first","repo_name":"Jason003/interview","sub_path":"databricks/Convert Binary Search Tree to Sorted Doubly Linked List.py","file_name":"Convert Binary Search Tree to Sorted Doubly Linked List.py","file_ext":"py","file_size_in_byte":701,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"81"}
+{"seq_id":"14454076958","text":"# -*- coding: utf-8 -*-  \n\n\n#上课的状态\nCLASS_STATUS = False\n\n#学生端被踢出的状态\nTICHU_STATUS = False\n\n#当前上课状态下的虚拟机的名称\nCLASS_STATUS_VM_NAME = None\n\n#终端的序列号名字\nTERMINAL_NAME = None\n\n#学生机主动点击课程，是否正在创建虚拟机\nVM_IS_CREATE_STATUS = False\n\n#系统主机名称配置文件路径\nSYS_HOSTNAME_CONFIG = None\n\n#程序运行的环境类型\nPROGRAM_RUNNING_TYPE = None\n\n#云桌面信息\nVM_INFO = None\n\n#正在运行的课程\nRUNNING_COURSE = None\n\nLANGUAGE = None\n\n\n","repo_name":"siwenhu/test_client_broadcast","sub_path":"globalvariable.py","file_name":"globalvariable.py","file_ext":"py","file_size_in_byte":548,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"36672406252","text":"elements = list()\n\nitems = int(input(\"Number of items to be added: \"))\nwhile items > 0:\n    elements.append(int(input(\"item: \")))\n    items-=1\n\nmax = elements[0]\ni=0\nwhile(i < len(elements)) :\n    if(max<elements[i]) :\n        max=elements[i]\n    i+=1\n\nprint(\"\\nMAX este: \" + str(max) )","repo_name":"dragos-vacariu/Python-Exercises","sub_path":"Basic Practice/program3 finding max of a list.py","file_name":"program3 finding max of a list.py","file_ext":"py","file_size_in_byte":286,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"35380221717","text":"#!/usr/bin/env python3\n\ndef read_file(file_name):\n  with open(file_name, \"r\") as file:\n    langs = file.readlines()\n    return [lang.strip(\"\\n\") for lang in langs if lang != \"Other\\n\"]\n\ndef counts(langs):\n  freq = {}\n  for lang in langs:\n    key = \"C/C++\" if lang == \"C\" or lang == \"C++\" else lang\n    if key not in freq:\n      freq[key] = 1\n    else:\n      freq[key] += 1\n  return freq\n\ndef compute_frequency_pcnt(lang_freqs):\n  total_obs = sum(lang_freqs.values())\n  return [[lang, (freq / total_obs) * 100] for lang, freq in lang_freqs.items()]\n\ndef show_results(items):\n  sorted_items = sorted(items, key=lambda item: item[1], reverse=True)\n  for lang, freq in sorted_items:\n    print(f\"Language: {lang}, Frequency: {freq:.2f}%\")\n\nlangs = read_file(\"langs.txt\")\nprint(\"File read successfully. Working...\")\nfreqs = counts(langs)\nfreq_pcnts = compute_frequency_pcnt(freqs)\nshow_results(freq_pcnts)\n","repo_name":"jyoo980/research-util","sub_path":"langs.py","file_name":"langs.py","file_ext":"py","file_size_in_byte":900,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"25995226151","text":"file = open(\"AdventOfCodeDay3.txt\", \"rt\")\r\n\r\n#declaring of needed global variables\r\na = 0\r\ngString = 0\r\neString = 0\r\npowerConsumption = 0\r\ngDecimal = 0\r\neDecimal = 0\r\nindex = 0\r\ng1 = 0\r\ng2 = 0\r\ng3 = 0\r\ng4 = 0\r\ng5 = 0\r\ng6 = 0\r\ng7 = 0\r\ng8 = 0\r\ng9 = 0\r\ng10 = 0\r\ng11 = 0\r\ng12 = 0\r\ne1 = 0\r\ne2 = 0\r\ne3 = 0\r\ne4 = 0\r\ne5 = 0\r\ne6 = 0\r\ne7 = 0\r\ne8 = 0\r\ne9 = 0\r\ne10 = 0\r\ne11 = 0\r\ne12 = 0\r\nbit1 = 0\r\nbit2 = 0\r\nbit3 = 0\r\nbit4 = 0\r\nbit5 = 0\r\nbit6 = 0\r\nbit7 = 0\r\nbit8 = 0\r\nbit9 = 0\r\nbit10 = 0\r\nbit11 = 0\r\nbit12 = 0\r\ncountOne1 = 0\r\ncountOne2 = 0\r\ncountOne3 = 0\r\ncountOne4 = 0\r\ncountOne5 = 0\r\ncountOne6 = 0\r\ncountOne7 = 0\r\ncountOne8 = 0\r\ncountOne9 = 0\r\ncountOne10 = 0\r\ncountOne11 = 0\r\ncountOne12 = 0\r\ncountZero1 = 0\r\ncountZero2 = 0\r\ncountZero3 = 0\r\ncountZero4 = 0\r\ncountZero5 = 0\r\ncountZero6 = 0\r\ncountZero7 = 0\r\ncountZero8 = 0\r\ncountZero9 = 0\r\ncountZero10 = 0\r\ncountZero11 = 0\r\ncountZero12 = 0\r\n\r\n#declaring list to store values\r\ndiagnosticReportList = []\r\n\r\n#loops through file and stores values in diagnosticReportList list\r\nfor line in file:\r\n    diagnosticReportList.append(line)\r\n\r\n#loops through diagnosticReportList list\r\nfor value in diagnosticReportList:\r\n\r\n    #stores the index item in a as a string\r\n    a = (diagnosticReportList[index])\r\n\r\n    #stores each character of the string from variable a in the corresponding bit variable\r\n    bit1 = (a[0])\r\n    bit2 = (a[1])\r\n    bit3 = (a[2])\r\n    bit4 = (a[3])\r\n    bit5 = (a[4])\r\n    bit6 = (a[5])\r\n    bit7 = (a[6])\r\n    bit8 = (a[7])\r\n    bit9 = (a[8])\r\n    bit10 = (a[9])\r\n    bit11 = (a[10])\r\n    bit12 = (a[11])\r\n\r\n    #reviews each bit variable to determine if a 1 or 0 should be counted in the corresponding countOne and countZero variables associated with the specific bit location in the string\r\n    if bit1 == \"1\":\r\n        countOne1 += 1\r\n\r\n    elif bit1 == \"0\":\r\n        countZero1 += 1\r\n\r\n    if bit2 == \"1\":\r\n        countOne2 += 1\r\n\r\n    elif bit2 == \"0\":\r\n        countZero2 += 1\r\n        \r\n    if bit3 == \"1\":\r\n        countOne3 += 1\r\n\r\n    elif bit3 == \"0\":\r\n        countZero3 += 1\r\n\r\n    if bit4 == \"1\":\r\n        countOne4 += 1\r\n\r\n    elif bit4 == \"0\":\r\n        countZero4 += 1\r\n\r\n    if bit5 == \"1\":\r\n        countOne5 += 1\r\n\r\n    elif bit5 == \"0\":\r\n        countZero5 += 1\r\n\r\n    if bit6 == \"1\":\r\n        countOne6 += 1\r\n\r\n    elif bit6 == \"0\":\r\n        countZero6 += 1\r\n\r\n    if bit7 == \"1\":\r\n        countOne7 += 1\r\n\r\n    elif bit7 == \"0\":\r\n        countZero7 += 1\r\n\r\n    if bit8 == \"1\":\r\n        countOne8 += 1\r\n\r\n    elif bit8 == \"0\":\r\n        countZero8 += 1\r\n\r\n    if bit9 == \"1\":\r\n        countOne9 += 1\r\n\r\n    elif bit9 == \"0\":\r\n        countZero9 += 1\r\n\r\n    if bit10 == \"1\":\r\n        countOne10 += 1\r\n\r\n    elif bit10 == \"0\":\r\n        countZero10 += 1\r\n\r\n    if bit11 == \"1\":\r\n        countOne11 += 1\r\n\r\n    elif bit11 == \"0\":\r\n        countZero11 += 1\r\n\r\n    if bit12 == \"1\":\r\n        countOne12 += 1\r\n\r\n    elif bit12 == \"0\":\r\n        countZero12 += 1\r\n\r\n    index += 1\r\n\r\n#compares the count of 1s and 0s in their corresponding bit location in the string and assigns the appropriate 1 or 0 to the bit location in the gamma and epsilon variables\r\nif countOne1 > countZero1:\r\n    g1 = \"1\"\r\n    e1 = \"0\"\r\n\r\nelif countOne1 < countZero1:\r\n    g1 = \"0\"\r\n    e1 = \"1\"\r\n\r\n\r\nif countOne2 > countZero2:\r\n    g2 = \"1\"\r\n    e2 = \"0\"\r\n\r\nelif countOne2 < countZero2:\r\n    g2 = \"0\"\r\n    e2 = \"1\"\r\n\r\n\r\nif countOne3 > countZero3:\r\n    g3 = \"1\"\r\n    e3 = \"0\"\r\n\r\nelif countOne3 < countZero3:\r\n    g3 = \"0\"\r\n    e3 = \"1\"\r\n\r\n\r\nif countOne4 > countZero4:\r\n    g4 = \"1\"\r\n    e4 = \"0\"\r\n\r\nelif countOne4 < countZero4:\r\n    g4 = \"0\"\r\n    e4 = \"1\"\r\n\r\n\r\nif countOne5 > countZero5:\r\n    g5 = \"1\"\r\n    e5 = \"0\"\r\n\r\nelif countOne5 < countZero5:\r\n    g5 = \"0\"\r\n    e5 = \"1\"\r\n\r\n\r\nif countOne6 > countZero6:\r\n    g6 = \"1\"\r\n    e6 = \"0\"\r\n\r\nelif countOne6 < countZero6:\r\n    g6 = \"0\"\r\n    e6 = \"1\"\r\n\r\n\r\nif countOne7 > countZero7:\r\n    g7 = \"1\"\r\n    e7 = \"0\"\r\n\r\nelif countOne7 < countZero7:\r\n    g7 = \"0\"\r\n    e7 = \"1\"\r\n\r\n\r\nif countOne8 > countZero8:\r\n    g8 = \"1\"\r\n    e8 = \"0\"\r\n\r\nelif countOne8 < countZero8:\r\n    g8 = \"0\"\r\n    e8 = \"1\"\r\n\r\n\r\nif countOne9 > countZero9:\r\n    g9 = \"1\"\r\n    e9 = \"0\"\r\n\r\nelif countOne9 < countZero9:\r\n    g9 = \"0\"\r\n    e9 = \"1\"\r\n\r\n\r\nif countOne10 > countZero10:\r\n    g10 = \"1\"\r\n    e10 = \"0\"\r\n\r\nelif countOne10 < countZero10:\r\n    g10 = \"0\"\r\n    e10 = \"1\"\r\n\r\n\r\nif countOne11 > countZero11:\r\n    g11 = \"1\"\r\n    e11 = \"0\"\r\n\r\nelif countOne11 < countZero11:\r\n    g11 = \"0\"\r\n    e11 = \"1\"\r\n\r\n\r\nif countOne12 > countZero12:\r\n    g12 = \"1\"\r\n    e12 = \"0\"\r\n\r\nelif countOne12 < countZero12:\r\n    g12 = \"0\"\r\n    e12 = \"1\"\r\n\r\n#concatenates the gamma and epsilon strings\r\ngString = g1 + g2 + g3 + g4 + g5 + g6 + g7 + g8 + g9 + g10 + g11 + g12\r\neString = e1 + e2 + e3 + e4 + e5 + e6 + e7 + e8 + e9 + e10 + e11 + e12\r\n\r\n#prints gamma and epsilon string variables to verify\r\nprint(gString)\r\nprint(eString)\r\n\r\n#converts the gamma and epsilon string variables into the gamma and epsilon integer variables as decimals\r\ngDecimal = int(gString, 2)\r\neDecimal = int(eString, 2)\r\n\r\n#prints gamma and epsilon integer variables to verify\r\nprint(gDecimal)\r\nprint(eDecimal)\r\n\r\n#calculates powerConsumption\r\npowerConsumption = gDecimal * eDecimal\r\n\r\n#prints desired answer\r\nprint(powerConsumption)","repo_name":"jwill2287/AdventOfCode2021","sub_path":"AdventOfCode3.1.py","file_name":"AdventOfCode3.1.py","file_ext":"py","file_size_in_byte":5239,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"73383310986","text":"#----------------------------------------------------------------------\n# Firebase Connect Part\n#----------------------------------------------------------------------\nfrom firebase import firebase\nfirebase = firebase.FirebaseApplication(\"https://capstone-776cd.firebaseio.com/\", None)\n\n\n#----------------------------------------------------------------------\n# import My lib\n#----------------------------------------------------------------------\nimport cache\n\n\n#----------------------------------------------------------------------\n# Role of Constants\n#----------------------------------------------------------------------\nCNT = 0\nMAC = 1\nSTDNUM = 2\nSTATUS_LATE_STUDENT = \"LATE\"\n\nidx = 0     # Convenience of implementation\n\n\n\n\n#----------------------------------------------------------------------\n# First-time attendance check of the day\n#----------------------------------------------------------------------\ndef Init_EscapeList(STDNUM_LIST):\n    global idx\n\n    for student in STDNUM_LIST:\n        path = 'The_number_of_escapes/' + str(idx)\n        firebase.put(path, 'STDNUM', student[STDNUM])\n        firebase.put(path, 'CNT', student[CNT])\n        firebase.put(path, 'MAC', student[MAC])\n        firebase.put(path, 'ESCAPES', 0)\n        firebase.put(path, 'STATUS', False)\n        firebase.put(path, 'IS_LATE', False)\n\n        idx += 1\n\n\n\n#----------------------------------------------------------------------\n# Add late of students\n#----------------------------------------------------------------------\ndef Add_EscapeList(ADD_LIST):\n    global idx\n\n    path = 'The_number_of_escapes/' + str(idx)\n\n    firebase.put(path, 'STDNUM', ADD_LIST[STDNUM])\n    firebase.put(path, 'CNT', ADD_LIST[CNT])\n    firebase.put(path, 'MAC', ADD_LIST[MAC])\n    firebase.put(path, 'ESCAPES', 0)\n    firebase.put(path, 'STATUS', True)\n    firebase.put(path, 'IS_LATE', True)\n\n    idx += 1\n\n    print(\"late of student : \" + ADD_LIST[STDNUM])\n\n\n#----------------------------------------------------------------------\n# Detecting people who ran away during class\n#----------------------------------------------------------------------\ndef EscapeList(STDNUM_LIST):    # STDNUM_LIST : Order by (cnt, mac, stdNum)\n    global idx\n\n    for student in STDNUM_LIST:\n        if(idx == 0):\n            Add_EscapeList(student)\n        else:\n            for cnt in range(0, idx):\n                path = 'The_number_of_escapes/' + str(cnt)\n                temp = firebase.get(path, 'MAC')\n\n                if(student[MAC] == temp):\n                    firebase.put(path, 'STATUS', True)\n                    break\n\n                Add_EscapeList(student)\n\n\n\n    for cnt in range(0, idx):\n        path = 'The_number_of_escapes/' + str(cnt)\n        temp = firebase.get(path, 'STATUS')\n\n        if not(temp):  # Logic for Student who ran away\n            tmp = firebase.get(path, 'ESCAPES')\n            tmp += 1\n            firebase.put(path, 'ESCAPES', tmp)\n\n            cache.Log(firebase.get(path, 'STDNUM'))     # Write to the log file\n\n    # status init\n    for cnt in range(0, idx):\n        path = 'The_number_of_escapes/' + str(cnt)\n        firebase.put(path, 'STATUS', False)\n\n    return idx ","repo_name":"devLupin/Capstone1","sub_path":"escape.py","file_name":"escape.py","file_ext":"py","file_size_in_byte":3175,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"4654000681","text":"class Solution:\n    def merge(self, nums1: List[int], m: int, nums2: List[int], n: int) -> None:\n        \"\"\"\n        Do not return anything, modify nums1 in-place instead.\n        \"\"\"\n        last1, last2, last = m - 1, n - 1, m + n - 1\n        \n        while last2 >= 0:\n            if last1 >= 0 and nums1[last1] >= nums2[last2]:\n                nums1[last] = nums1[last1]\n                last1 -= 1\n            else:\n                nums1[last] = nums2[last2]\n                last2 -= 1\n            last -= 1\n               \n","repo_name":"Xrenya/Algorithms","sub_path":"Leetcode/Python/_88.py","file_name":"_88.py","file_ext":"py","file_size_in_byte":528,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"72111957384","text":"import time\nimport traceback\nfrom abc import ABC\nfrom typing import Iterable, TYPE_CHECKING, Optional\n\nfrom .base_task import BaseTask\nfrom assembly_gym.environment.simulation import SimulationEnvironment\n\nif TYPE_CHECKING:\n    from task.controllers import Controller\n    from task.sensors import Sensor\n    from task.rewards import Reward\n\n\nclass SimulatedTask(BaseTask[SimulationEnvironment], ABC):\n    \"\"\"\n    An abstract base class for gym environments that use the simulated robot.\n    The task-specific aspects of the environment are defined by overriding the *_task methods.\n    \"\"\"\n\n    def __init__(self, controllers: Iterable[\"Controller[BaseTask]\"],\n                 sensors: Iterable[\"Sensor[BaseTask]\"], rewards: Iterable[\"Reward[BaseTask]\"],\n                 time_step: float = 0.005, time_limit_steps: Optional[int] = None):\n        super().__init__(controllers, sensors, rewards, time_step, time_limit_steps)\n\n    def _initialize(self) -> None:\n        self._initialize_scene()\n        self.environment.set_reset_checkpoint()\n\n    def _initialize_scene(self):\n        pass\n\n    def _restart_env(self):\n        \"\"\"\n        Deals with CoppeliaSim's bullshit.\n        :return:\n        \"\"\"\n        print(\"Terminating Simulator...\")\n        self.environment.shutdown()\n        print(\"Simulator terminated.\")\n        num_restart_attempts = 10\n        for i in range(10):\n            print(\"Trying to restart Simulator for the {}/{}. time...\".format(i + 1, num_restart_attempts))\n            try:\n                self.environment.initialize(self.time_step)\n                self._initialize()\n                print(\"Success!\")\n                return\n            except KeyboardInterrupt:\n                raise\n            except:\n                print(\"Failed with the following error message:\")\n                traceback.print_exc()\n                print(\"Retrying in 10s...\")\n                time.sleep(10)\n        print(\"Giving up :(\")\n        raise RuntimeError(\"Failed to restart Simulator\")\n","repo_name":"b4be1/rl-arch-assembly","sub_path":"python/task/simulated_task.py","file_name":"simulated_task.py","file_ext":"py","file_size_in_byte":2005,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"81"}
+{"seq_id":"40064587797","text":"import os\nimport sys\nimport urllib\nimport tarfile\nimport zipfile\nimport gzip\nimport hashlib\nimport numpy as np\nimport struct\nfrom subprocess import Popen\nfrom contextlib import contextmanager\n\n\ndataset_home = os.getenv('DATASET_HOME', 'datasets')\n\n\ndef touch(filepath, times=None):\n    with open(filepath, 'a'):\n        os.utime(filepath, times)\n\n\ndef require_dir(path):\n    if not os.path.exists(path):\n        os.makedirs(path)\n\n\ndef url_filename(url):\n    return url.split('/')[-1].split('#')[0].split('?')[0]\n\n\ndef download(url, target_dir, filename=None):\n    require_dir(target_dir)\n    if filename is None:\n        filename = url_filename(url)\n    filepath = os.path.join(target_dir, filename)\n    if sys.version_info[0] > 2:\n        urllib.request.urlretrieve(url, filepath)\n    else:\n        urllib.urlretrieve(url, filepath)\n    return filepath\n\n\n@contextmanager\ndef checkpoint(filepath):\n    try:\n        yield os.path.exists(filepath)\n    finally:\n        pass\n    touch(filepath)\n\n\ndef is_archive(filepath):\n    return (tarfile.is_tarfile(filepath) or\n            zipfile.is_zipfile(filepath) or\n            filepath[-3:].lower() == '.gz' or\n            filepath[-2:].lower() == '.z')\n\n\ndef archive_extract(filepath, target_dir):\n    target_dir = os.path.abspath(target_dir)\n    if tarfile.is_tarfile(filepath):\n        with tarfile.open(filepath, 'r') as tarf:\n            # Check that no files get extracted outside target_dir\n            for name in tarf.getnames():\n                abs_path = os.path.abspath(os.path.join(target_dir, name))\n                if not abs_path.startswith(target_dir):\n                    raise RuntimeError('Archive tries to extract files '\n                                       'outside target_dir.')\n            tarf.extractall(target_dir)\n    elif zipfile.is_zipfile(filepath):\n        with zipfile.ZipFile(filepath, 'r') as zipf:\n            zipf.extractall(target_dir)\n    elif filepath[-3:].lower() == '.gz':\n        with gzip.open(filepath, 'rb') as gzipf:\n            with open(filepath[:-3], 'wb') as outf:\n                outf.write(gzipf.read())\n    elif filepath[-2:].lower() == '.z':\n        if os.name != 'posix':\n            raise NotImplementedError('Only Linux and Mac OS X support .Z '\n                                      'compression.')\n        cmd = 'gzip -d %s' % filepath\n        retval = Popen(cmd, shell=True).wait()\n        if retval != 0:\n            raise RuntimeError('Archive file extraction failed for %s.'\n                               % filepath)\n    else:\n        raise ValueError('% is not a supported archive file.' % filepath)\n\n\ndef checksum(filename, method='sha1'):\n    data = open(filename, 'rb').read()\n    if method == 'sha1':\n        return hashlib.sha1(data).hexdigest()\n    elif method == 'md5':\n        return hashlib.md5(data).hexdigest()\n    else:\n        raise ValueError('Invalid method: %s' % method)\n\n\ndef _read_int(buf):\n    return struct.unpack('>i', buf.read(4))[0]\n\n\ndef load_idx(filepath):\n    with open(filepath, 'rb') as f:\n        magic = _read_int(f)\n        if magic == 2051:\n            shape = (_read_int(f), _read_int(f), _read_int(f))\n        elif magic == 2049:\n            shape = _read_int(f)\n        else:\n            raise RuntimeError('could not parse header correctly')\n        array = np.fromfile(f, dtype='B', count=np.prod(shape))\n        array = np.reshape(array, shape)\n    return array\n","repo_name":"andersbll/deeppy","sub_path":"deeppy/dataset/util.py","file_name":"util.py","file_ext":"py","file_size_in_byte":3414,"program_lang":"python","lang":"en","doc_type":"code","stars":1370,"dataset":"github-code","pt":"81"}
+{"seq_id":"4272935287","text":"#! /usr/local/bin python3\n# -*- coding: utf-8 -*-\n\nfrom tkinter import *\nfrom tkinter.ttk import Combobox \nfrom PIL import Image,ImageTk\nfrom os import listdir\nfrom random import randint\nfrom time import sleep\n\ndef rndX():#X座標\n    return randint(0,cv.winfo_width())\n\ndef rndY():#Y座標\n    return randint(0,cv.winfo_height())\n\ndef resize(wLow,wHigh,hLow,hHigh):\n    global img,reimg_tk,num#上の方で宣言した変数を参照すると宣言\n    reimg = img[combo.get()].resize((randint(wLow,wHigh),randint(hLow,hHigh)))#リサイズ\n    reimg_tk.append(ImageTk.PhotoImage(reimg))#挿入ように変換\n    return reimg_tk[num]\n\ndef drawer(event):\n    global imgnum\n    name = combo.get()\n    #画像を表示\n    cv.create_image(rndX(),rndY(),image=img_tk[name],anchor=CENTER,tag=(\"p\"*imgnum))\n    #画像管理用の変数を更新\n    imgnum += 1\n\ndef resizedDrawer(event):#リサイズした画像を挿入する関数\n    global imgnum,num\n    #画像を表示\n    #大きすぎる画像の出現頻度を下げる 1/6ぐらいの確率で一つ目の\n    if(randint(1,6) == 1):\n        cv.create_image(rndX(),rndY(),image=resize(1,1000,1,1000),anchor=CENTER,tag=(\"p\"*imgnum))\n    else:\n        cv.create_image(rndX(),rndY(),image=resize(1,400,1,400),anchor=CENTER,tag=(\"p\"*imgnum))\n    #配列管理用変数更新\n    num += 1\n    #画像管理用変数の更新\n    imgnum += 1\n\ndef undo(event):#画像を一つ消す\n    global imgnum\n    #表示した画像の順番が狂わないように調整\n    if(imgnum > 1):\n        imgnum -= 1\n    cv.delete(\"p\"*imgnum)\n\ndef allClear(event):\n    global imgnum,num\n    #画像を全部消す\n    for i in range(imgnum + 1):#普通の画像を全部消す\n        cv.delete(\"p\"*i)\n    del reimg_tk[:]#リサイズ画像を全部消す\n    #管理用変数リセット\n    imgnum = 1\n    num = 0\n\ndef updatePrev(event=True):\n    precv.delete(\"prev\")\n    precv.create_image(170,160,image=img_tk[combo.get()],tag=\"prev\")\n\ndef OWARI(event):\n    quit()\n\n\nif __name__ == \"__main__\":\n\n    #操作パネル管理？？\n    root = Tk()\n    root.geometry(\"400x200+810+40\")\n    root.title(\"操作\")\n\n    #キャンバス管理？？\n    sub = Toplevel()\n    sub.geometry(\"800x800+5+40\")\n    sub.title(\"てるそ\")\n\n    #これがキャンバス\n    cv = Canvas(sub,bg=\"#00a2e7\",width=800,height=800)\n    cv.pack(fill=BOTH,expand=True)\n    cv.update()\n\n    #プレビュー\n    previewer = Toplevel()\n    previewer.geometry(\"350x350+810+280\")\n    previewer.title(\"プレビュー\")\n    previewer.resizable(0,0)\n\n    precv = Canvas(previewer,bg=\"#00a2e7\",width=360,height=360)\n    precv.pack(fill=BOTH,expand=True)\n    precv.update()\n\n    #ここでいろいろな変数を宣言しておきます（いろいろ管理する為の変数が多い）\n    #画像を管理する辞書型\n    img = { i : Image.open(\"with_unkochan/\" + i) for i in sorted(listdir(\"with_unkochan\"))}\n    img_tk = { i : ImageTk.PhotoImage(Image.open(\"with_unkochan/\" + i)) for i in sorted(listdir(\"with_unkochan\"))}\n    font = \"Impact\"#                   ボタンなどのフォントを一元管理する時に使う\n    fontsize = 20#                     同様にサイズも\n    fontcolor = \"#7fffd4\" #            同様にカラーも\n    imgnum = 1#                        画像表示順番を管理する為の変数\n    reimg_tk = []#                     リサイズした画像を入れる配列\n    num = 0#                           配列を管理する為の変数\n\n    #画像選択管理\n    combo = Combobox(root,state=\"readonly\")\n    combo[\"values\"] = list(img_tk.keys())\n    combo[\"postcommand\"] = updatePrev#lambda :precv.create_image(170,160,image=img_tk[combo.get()],tag=\"prev\")\n    combo.current(0)\n    combo.pack()\n\n    #ウィジェット関連\n    #ラベルフレーム生成#操作パネル\n    F0 = LabelFrame(root,relief=\"raised\",borderwidth=15,background=\"gray\",text=\"操作パネル\",font=(font,fontsize),fg=\"#7fffd4\")\n\n    #キーバインド\n    root.focus_set()\n    root.bind_all(\"<q>\",OWARI)\n    root.bind_all(\"<Return>\",drawer)\n    root.bind_all(\"<Shift-Return>\",resizedDrawer)\n    #root.bind_all(\"<Control-Return>\",FUISION)\n    root.bind_all(\"<Delete>\",allClear)\n    root.bind_all(\"<BackSpace>\",undo)\n    root.bind_all(\"<p>\",updatePrev)\n\n    #ボタン生成\n    #生成ボタン\n    drwrbtn = Button(F0,text=u'標準画像生成',font=(font,fontsize))\n    drwrbtn.bind(\"<Button-1>\",drawer)\n    drwrbtn.grid(row=1,column=1)\n\n    #リサイズ生成ボタン\n    sdrwbtn = Button(F0,text=u'リサイズ挿入',font=(font,fontsize))\n    sdrwbtn.bind(\"<Button-1>\",resizedDrawer)\n    sdrwbtn.grid(row=2,column=1)\n\n    #合体挿入ボタン\n    #fusionner = Button(F0,text=u'三位一体挿入',font=(font,fontsize))\n    #fusionner.bind(\"<Button-1>\",FUISION)\n    #fusionner.grid(row=3,column=1)\n\n    #全消しボタン\n    clnbtn = Button(F0,text=u'画像全消し',font=(font,fontsize))\n    clnbtn.bind(\"<Button-1>\",allClear)\n    clnbtn.grid(row=2,column=2)\n\n    #undoボタン\n    undobtn = Button(F0,text=u'直前の画像を消す',font=(font,fontsize))\n    undobtn.bind(\"<Button-1>\",undo)\n    undobtn.grid(row=1,column=2)\n\n    #やめるボタン\n    exitter = Button(F0,text=u'終了',font=(font,fontsize),command=root.destroy)\n    exitter.grid(row=1,column=3)\n\n    #画面いっぱいに表示できるよ\n    F0.pack(fill=BOTH,expand=True)\n\n    root.mainloop()\n\n\n\n\n\n\n#print(sorted(listdir(\"with_unkochan\")))\n#print(list(img_tk.keys()))\n\n#j = 0\n#for i in list(img_tk.keys()):\n#    cv.create_image(j,j,image=img_tk[i])\n#    j += 300\n\n#def drawer(event):\n#    name = combo.get()\n#    cv.create_image(100,100,image=img_tk[name],anchor=CENTER)","repo_name":"YAMATA-KONZENNITTAI/FROG","sub_path":"ImageListTest.py","file_name":"ImageListTest.py","file_ext":"py","file_size_in_byte":5705,"program_lang":"python","lang":"ja","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"8634598299","text":"'''\nCreated on Sep 18, 2016\n@author: Subhash Bylaiah, Brian Trippi\n'''\n# 1)\tSet of states S: Any arrangement of 15 tiles on a 4x4 board\n#\t(Note some arrangements are not solvable)\n# 2)\tInitial State: a 15 puzzle board arrangement as given in a text file\n# 3)\tSuccessor Function SUCC: The successor function returns\n#\tthe four possible boards that result from the four possible\n#\tmoves that are available, Left, Right, Down, and Up\n# 4)\tGoal state: 15 puzzle goal state\n# 5)\tCost function: Cost = 1 for each edge traveled between nodes. The number\n#\tof edges traveled is the total cost. \n# 6)\tHeuristic Function: The heuristic funtion chooses the succesor state\n#\twith the lowest number of misplaced tiles \n\n\n# SOLUTION DISCUSSION:\n#   Implemented the Heuristic cost estimation using\n#       1) Misplaced Tiles Heuristic\n#       2) Manhattan Distance Heuristic\n#   As expected, the Manhattan Distance Heuristic works better as the heuristic is more accurate and informative\n#   than the Misplaced Tile Heuristic\n#   For the given example input board:\n#       Using Manhattan Distance Heuristic, finds solution with below characteristics\n#           ('Fringe Length:', 5812, 'Num Nodes Expanded:', 2823, 'Cost till state:', 24)\n#             TIME TAKEN:: 0.4 seconds\n#\n#       Using MISPLACED TILE Heuristic, finds solution with below characteristics\n#           ('Fringe Length:', 1367490, 'Num Nodes Expanded:', 646486, 'Cost till state:', 24)\n#             TIME TAKEN:: 89 seconds\n#\n\n# References:\n#\n\n######################################################################################################################\n#       USES PYTHON VERSION 3\n######################################################################################################################\n\n\n\n#!/usr/local/bin/python3\n\n\nimport sys\nimport pprint\nimport heapq\nimport math\nimport time\n\nif sys.version_info.major==2:\n\tprint(\"warning! use python3\")\n\n\n# This is the GOAL STATE for the 15 Puzzle\ngoal_state = [[1, 2, 3, 4],\n              [5, 6, 7, 8],\n              [9, 10, 11, 12],\n              [13, 14, 15, 0]]\n\n# goal_state = [[1, 2, 3],\n#               [4, 5, 6],\n#               [7, 8, 0]]\n\n\n# MANHATTAN OR MISPLACED\nH_FUNCTION = 'MANHATTAN'\n\n\n# Define a dictionary to represent the Directional moves based on the add/subtraction of row/col\n# Ex: if ROW is subtracted (and nothing done to Col), it represents the move DOWN\nmoves_dict = {\n        (-1,0): 'D',\n        (0,-1): 'R',\n        (0,1): 'L',\n        (1,0): 'U'\n    }\n\ndef read_input_file():\n    \"\"\"\n    Read from the input file specified on the command prompt and create the initial board configuration\n    :return: Returns the initial board config\n    \"\"\"\n    input_puzzle = []\n    # open text file from the command line and store as a matrix\n    with open(sys.argv[1], 'r') as f:\n        text_puzzle = f.read().splitlines()\n\n    for line in text_puzzle:\n        sublist = []\n        for tile in line.split(' '):\n            sublist.append(int(tile))\n        input_puzzle.append(sublist)\n    return input_puzzle\n\ndef heuristic_misplaced(puzzle_board):\n    \"\"\"\n    Function to compute the cost estimate using misplaced tiles heuristic\n    :param puzzle_board:\n    :return: Heuristic cost estimate\n    \"\"\"\n    misplaced = 0\n    for i in range(len(puzzle_board)):\n        for j in range(len(puzzle_board[0])):\n            if puzzle_board[i][j] != goal_state[i][j]:\n                misplaced += 1\n    return misplaced\n\n\ndef get_goal_row_col(tile_number):\n    \"\"\"\n    Function to compute the Goal state Row Col for any given tile number\n    :param tile_number:\n    :return: Row/Col of the goal state for the Tile\n    \"\"\"\n    return (math.floor((tile_number-1)/puzzle_length), (tile_number-1)%4)\n\n\ndef get_manhattan_dist_to_goal(board_state):\n    \"\"\"\n    Compute Manhattan distance for the given board\n    For any Tile it is computed as the absolute Vertical distance (along the row) and Horizontal Distance(along the column)\n    to reach the goal state for the tile.\n    The total distance is the sum of all the distances for all tiles in the given board config\n    :param board_state:\n    :return: Cost for the Heuristic estimate\n    \"\"\"\n    dist = 0\n    for row in range(puzzle_length):\n        for col in range(puzzle_length):\n            if board_state[row][col] != 0:\n                goal_row, goal_col = get_goal_row_col(board_state[row][col])\n                dist = dist + abs(row - goal_row) + abs( col - goal_col)\n    return dist\n\n\ndef generate_successors(state):\n    \"\"\"\n    Successor function (returns 4 nodes representing the 4 possible moves)\n    :param state:\n    :return: List of Tuples of the form (successorstate, movedirection)\n    \"\"\"\n    # Find the zero'th position\n    zero_position = [(i, state[i].index(0)) for i in range(len(state)) if 0 in state[i]][0]\n    # Create new possible posistions for the blank tile, based on the defined moves\n    new_positions = [(((zero_position[0]+i)%puzzle_length, (zero_position[1]+j)%puzzle_length), (i,j)) for i in (-1, 0, +1) for j in (-1, 0, +1) if abs(i) != abs(j)]\n    successors = []\n    # generate successors\n    for (pos, move) in new_positions:\n        succ = [row[:] for row in state]\n        succ[zero_position[0]][zero_position[1]] = succ[pos[0]][pos[1]]\n        succ[pos[0]][pos[1]] = 0\n        successors.append((succ, moves_dict[move]))\n    return successors\n\ndef is_goal_state(board_state):\n    if heuristic_misplaced(board_state) == 0:\n        return True\n\ndef get_H_cost(succ):\n    \"\"\"\n    Function to compute the Heuristic cost, for a given board state\n         Heuristic Function to be used is defined by the H_FUNCTION constant\n    :param succ: Board State\n    :return: Will return heuristic cost\n    \"\"\"\n    if H_FUNCTION == 'MANHATTAN':\n        return get_manhattan_dist_to_goal(succ)\n    elif H_FUNCTION == 'MISPLACED':\n        return heuristic_misplaced(succ)\n\n\ndef solve_puzzle(initial_state):\n    \"\"\"\n    Solution function to solve the initial Board using A* Algorithm\n        Heuristic function to be used is based on the constant H_FUNCTION\n    The fringe is managed as a Heap datastructure using the heapq module\n    The fringe is a list of tuples of the form: (Evaluated_cost, (Cost_from_Initial, Board_state, Path_From_Initial_State))\n    :param initial_state:\n    :return: Board_State, Path for the Goal state\n    \"\"\"\n\n    fringe = []\n    closed = {}\n    # heap_index = {} # Lets have a dictionary to have the index of a specific board in the heap, to make the search faster\n    if is_goal_state(initial_state):\n        return initial_state, []\n\n    heapq.heappush(fringe, (0, (0, initial_state, [])))\n\n    while fringe:\n        # print(\"Printing fringe:\")\n        # print('Fringe Length:',len(fringe),'\\n')\n        # print('\\n'.join(' '.join(map(str, row)) for row in fringe))\n        (cost, (cost_till_state, board_state, path)) = heapq.heappop(fringe)\n        # print((cost, (cost_till_state, board_state)))\n        closed[hash(str(board_state))] = True\n        if is_goal_state(board_state):\n            print('Fringe Length:',len(fringe), 'Num Nodes Expanded:', len(closed), '\\n', 'Cost till state:', cost_till_state)\n            return board_state, path\n        successors = generate_successors(board_state)\n        for succ, move in successors:\n            if closed.get(hash(str(succ))):\n                continue\n\n            h_cost = get_H_cost(succ)\n            # print('\\n', 'Cost: ', h_misplaced + cost_till_state+1, 'Heuristic:', h_misplaced)\n            # print('\\n'.join(' '.join(map(str, row)) for row in succ))\n            new_cost = h_cost + cost_till_state+1\n\n            # BELOW CODE TO CHECK AND ADD BETTER COST TO FRINGE MIGHT NOT BE NEEDED AS ITS VERY EXPENSIVE\n            # SO, EVEN IF WE ADD A LOWER COST TO THE FRINGE IT STILL IS GOING TO PROCESS THAT FIRST,\n            # INSTEAD OF A HIGHER COST ONE..\n            # THE OTHER OPTION TO TRY OUT IS TO INCLUDE A DICTIONARY BASED APPROACH AND REMOVING A\n            # NODE AS SUGGESTED ON https://docs.python.org/3/library/heapq.html\n            # BUT IN OUR CASE EVEN THAT IS NOT NEEDED AS WE ARE ONLY GOING TO PICK AN ITEM\n            # THAT IS LOWER COST\n\n            ############################################################################################################\n            # item_present = False\n            # for index, item in enumerate(fringe):\n            #     if item[1][1] == succ:\n            #         item_present = True\n            #         if item[0] > new_cost:\n            #             fringe[index], fringe[-1] = fringe[-1], fringe[index]\n            #             fringe.pop()\n            #             heapq.heapify(fringe)\n            #             heapq.heappush(fringe, (new_cost, (cost_till_state+1, succ)))\n            #\n            # if not item_present:\n            #     heapq.heappush(fringe, (new_cost, (cost_till_state + 1, succ)))\n            #     heap_index[succ] = len(fringe)\n            ############################################################################################################\n            heapq.heappush(fringe, (new_cost, (cost_till_state + 1, succ, path[:] + [move])))\n\n\ninitial_board = read_input_file()\npuzzle_length = len(initial_board)\n\nstart_time = time.time()\ngoal, path = solve_puzzle(initial_board)\nend_time = time.time()\n\nprint('\\nGoal State:')\npprint.pprint(goal, indent=4)\n\nprint(\"Time taken to Solve\", end_time - start_time, \" seconds\")\n\nprint(\"Path to goal:\")\n# print('['+'] ['.join(path) + ']')\nprint(' '.join(path))\n\n# FOR TESTING\n# successors = generate_successors(puzzle_unordered)\n# for succ in successors:\n#     print('\\n'.join(' '.join(map(str, row)) for row in succ))\n","repo_name":"ishitaganjoo/ArtificialIntelligence-Assignment1","sub_path":"problem2/solver15.py","file_name":"solver15.py","file_ext":"py","file_size_in_byte":9665,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"939207701","text":"with open('input.txt') as f:\n    raw_data = [d.rstrip().split(' ') for d in f.readlines()]\n\n\ndef do_commands(data):\n    m = {}\n    for command in data:\n        m[command[0]] = m.get(command[0], 0) + int(command[1])\n    return m['forward'] * (m['down'] - m['up'])\n\n\ndef do_commands_part2(data):\n    m = {}\n    for d in data:\n        c, i = d[0], int(d[1])\n        if c == 'down':\n            m['aim'] = m.get(\"aim\", 0) + i\n        elif c == 'up':\n            m['aim'] = m.get(\"aim\", 0) - i\n        else:\n            m['forward'] = m.get('forward', 0) + i\n            m['depth'] = m.get('depth', 0) + (m.get('aim', 0) * i)\n    return m['forward'] * m['depth']\n\n\nprint(f'Day 2, Part 1: {do_commands(raw_data)}')\nprint(f'Day 2, Part 2: {do_commands_part2(raw_data)}')\n","repo_name":"mrrobinhood5/advent-of-code","sub_path":"2021/Day 2/solution.py","file_name":"solution.py","file_ext":"py","file_size_in_byte":764,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"36628833807","text":"def czy_nieparzyste(n : int):\n    while n != 0:\n        if n % 2 == 0:\n            return False\n\n        n //= 10\n    \n    return True\n\ndef sprawdz_tablice(t, N : int):\n    for i in range(N): #wiersze\n        wiersz_ok = False\n        \n        for j in range(N): # kolumny\n            if  czy_nieparzyste(t[i][j]) == True:\n                wiersz_ok = True\n                break\n        \n        if wiersz_ok != True:\n            return False\n\n    return True\n\nn = int(input(\"Rozmiar: \"))\nt = [[int(input()) for _ in range(n)] for _ in range(n)]\n\nprint(sprawdz_tablice(t, n))","repo_name":"lkwinta/wdi","sub_path":"Workspace/Zestaw_4/t_02.py","file_name":"t_02.py","file_ext":"py","file_size_in_byte":574,"program_lang":"python","lang":"pl","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"71665709386","text":"def check(x: str, file: str):\n    list1 = [s.lower() for s in x.split()]\n    dictinary = {}\n    for s in list1:\n        if not s in dictinary:\n            dictinary[s] = 0\n        dictinary[s] += 1\n    answer  = dict(sorted(dictinary.items()))\n    answerFile = open(file, \"w\")\n    for key in answer:\n        print(key, answer[key], file = answerFile )\n    answerFile.close()\n\n","repo_name":"z3mois/ml_VMK","sub_path":"hw1/task6/task6.py","file_name":"task6.py","file_ext":"py","file_size_in_byte":376,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"81"}
+{"seq_id":"16495351095","text":"import os\n\nfrom tkinter import * \nfrom src.style.funct import *\n\npu_tableau_rectangle = []\npu_tableau_id = []\npu_tableau_dates = []\npu_tableau_badges = []\npu_tableau_entrantsortant = []\npu_tableau_place = []\npu_tableau_etat = []\npu_tableau_dateheure = []\npu_legende_rectangle = []\npu_legende_text = []\npu_pagination_rectangle = []\npu_pagination_text = []\npu_pagination_rect = []\n\npu_tableau_posx = 5\npu_tableau_posy = 63\n\ndef pu_init(box):\n\n\tset_checking_var(\"pu_page_select\", 0)\n\t#set_checking_var(\"pu_page_select\", 11)\n\tset_checking_var(\"pu_page_select_old\", 0)\n\t#set_checking_var(\"pu_page_select_old\", 11)\n\n\tload_logutilisation(get_checking_var('pu_page_select'))\n\n\tglobal pu_tableau_rectangle\n\tglobal pu_tableau_id\n\tglobal pu_tableau_dates\n\tglobal pu_tableau_badges\n\tglobal pu_tableau_entrantsortant\n\tglobal pu_tableau_place\n\tglobal pu_tableau_etat\n\tglobal pu_tableau_dateheure\n\tglobal pu_legende_rectangle\n\tglobal pu_legende_text\n\tglobal pu_pagination_rectangle\n\tglobal pu_pagination_text\n\tglobal pu_pagination_rect\n\tpu_tableau_rectangle = []\n\tpu_tableau_id = []\n\tpu_tableau_dates = []\n\tpu_tableau_badges = []\n\tpu_tableau_entrantsortant = []\n\tpu_tableau_place = []\n\tpu_tableau_etat = []\n\tpu_tableau_dateheure = []\n\tpu_legende_rectangle = []\n\tpu_legende_text = []\n\tpu_pagination_rectangle = []\n\tpu_pagination_text = []\n\tpu_pagination_rect = []\n\t\n\tpu_createlegende(box, \"#C8E6C9\", \"Codes acceptées\", 5, 5)\n\tpu_createlegende(box, \"#FFCDD2\", \"Codes refusées\", 5, 34)\n\tpu_createlegende(box, \"#9b59b6\", \"Voitures entrantes\", 190, 5)\n\tpu_createlegende(box, \"#f1c40f\", \"Voitures sortantes\", 190, 34)\n\n\tpu_createline(box, pu_tableau_posx, pu_tableau_posy, 2, \"ID\", \"ID\",\"Code d'accès\", 3, \"Place | Voiture entrante ou sortante\", \"Date et heure\")\n\tpu_createtableau(box, pu_tableau_posx, pu_tableau_posy)\n\n\tpu_createpagination(box,0 ,pu_tableau_posx, pu_tableau_posy+365, \"<\")\n\tpu_createpagination(box,1 ,pu_tableau_posx+35, pu_tableau_posy+365, \">\")\n\n\ndef pu_createlegende(box, color, text, x, y):\n\tpu_legende_text.append(box.create_text(x+24+6, y+12, text=text, fill=\"#333333\", font=\"Arial 12 bold\", anchor=W))\n\tpu_legende_rectangle.append(box.create_rectangle(x, y, x+24, y+24, width=0, fill=color))\n\ndef pu_createline(box, x, y, style, id, idshow, badges, etrantsortant, place, dateheure):\n\tif box.winfo_width()<640:\n\t\tlargeur = 640\n\telse :\n\t\tlargeur = box.winfo_width()\n\n\tif style==0:\n\t\tpu_tableau_rectangle.append(box.create_rectangle(x, y, largeur-x*2, y+20, fill='#FFCDD2', width=0))\n\telif style==1:\n\t\tpu_tableau_rectangle.append(box.create_rectangle(x, y, largeur-x*2, y+20, fill='#C8E6C9', width=0))\n\telif style==2:\n\t\tpu_tableau_rectangle.append(box.create_rectangle(x, y, largeur-x*2, y+20, fill='#9E9E9E', width=0))\n\n\tif etrantsortant==0:\n\t\tpu_tableau_etat.append(box.create_rectangle(largeur-160, y,largeur-140, y+20, fill='#9b59b6', width=0))\n\t\tpu_tableau_place.append(box.create_text(largeur-150, y+10, text=place, fill=\"#333333\", font=\"Arial 10 bold\"))\n\telif etrantsortant==1:\n\t\tpu_tableau_etat.append(box.create_rectangle(largeur-160, y,largeur-140, y+20, fill='#f1c40f', width=0))\n\t\tpu_tableau_place.append(box.create_text(largeur-150, y+10, text=place, fill=\"#333333\", font=\"Arial 10 bold\"))\n\telif etrantsortant==3:\n\t\tpu_tableau_etat.append(box.create_rectangle(largeur-160, y,largeur-140, y+20, fill='#9E9E9E', width=0))\n\t\tpu_tableau_place.append(box.create_text(largeur-150, y+10, text=place, fill=\"#333333\", font=\"Arial 10 bold\"))\n\telse:\n\t\tpu_tableau_etat.append(box.create_rectangle(largeur-160, y,largeur-140, y+20, fill='#FFCDD2', width=0))\n\t\tpu_tableau_place.append(box.create_text(largeur-150, y+10, text=place, fill=\"#FFCDD2\", font=\"Arial 10 bold\"))\n\n\tpu_tableau_id.append(box.create_text(x+5, y+10, text=idshow, fill=\"#333333\", font=\"Arial 10 bold\", anchor='w'))\n\tpu_tableau_badges.append(box.create_text(largeur*0.25, y+10, text=badges, fill=\"#333333\", font=\"Arial 10 bold\"))\n\tpu_tableau_dateheure.append(box.create_text(largeur*0.5, y+10, text=dateheure, fill=\"#333333\", font=\"Arial 10 bold\"))\n\ndef pu_updateline(box, style, id, idshow, badges, etrantsortant, place, dateheure):\n\t#ida = id+1-15*get_checking_var('pu_page_select')\n\tida = id+1\n\tif style==0:\n\t\tbox.itemconfigure(pu_tableau_rectangle[ida], fill='#FFCDD2')\n\telif style==1:\n\t\tbox.itemconfigure(pu_tableau_rectangle[ida], fill='#C8E6C9')\n\telif style==2:\n\t\tbox.itemconfigure(pu_tableau_rectangle[ida], fill='#9E9E9E')\n\tif etrantsortant==0:\n\t\tbox.itemconfigure(pu_tableau_etat[ida], fill='#9b59b6')\n\t\tbox.itemconfigure(pu_tableau_place[ida], text=place, fill=\"#333333\")\n\telif etrantsortant==1:\n\t\tbox.itemconfigure(pu_tableau_etat[ida], fill='#f1c40f')\n\t\tbox.itemconfigure(pu_tableau_place[ida], text=place, fill=\"#333333\")\n\telif etrantsortant==3:\n\t\tbox.itemconfigure(pu_tableau_etat[ida], fill='#9E9E9E')\n\t\tbox.itemconfigure(pu_tableau_place[ida], text=place, fill=\"#333333\")\n\telse:\n\t\tbox.itemconfigure(pu_tableau_etat[ida], fill='#FFCDD2')\n\t\tbox.itemconfigure(pu_tableau_place[ida], text=place, fill=\"#FFCDD2\")\n\n\tbox.itemconfigure(pu_tableau_id[ida], text=idshow, fill=\"#333333\")\n\tbox.itemconfigure(pu_tableau_badges[ida], text=badges, fill=\"#333333\")\n\tbox.itemconfigure(pu_tableau_dateheure[ida], text=dateheure, fill=\"#333333\")\n\ndef pu_createtableau(box, x, y):\n\tpu_page_select = get_checking_var('pu_page_select')\n\ta = 0\n\tb = 17*pu_page_select \n\tc = 17*(pu_page_select+1)\n\twhile b<c:\n\t\tif b<logutilisation('size', 1):\n\t\t\tpu_createline(box, x, y+20*(a+1), logutilisation('etat', a), a, logutilisation('id', a), logutilisation('code', a), logutilisation('entrantsortant', a), logutilisation('place', a), logutilisation('dateheure', a))\n\t\telse:\n\t\t\tpu_createline(box, x, y+20*(a+1), 2, a, \"\", \"\", 3, \"\", \"\")\n\t\ta += 1\n\t\tb += 1\n\ndef pu_updatetableau(box):\n\tif box.winfo_width()>640:\n\t\ta = 0\n\t\twhile a<len(pu_tableau_rectangle):\n\t\t\tbox.coords(pu_tableau_rectangle[a], pu_tableau_posx, pu_tableau_posy+20*a, box.winfo_width()-pu_tableau_posx*2, pu_tableau_posy+20*a+20)\n\t\t\tbox.coords(pu_tableau_etat[a], box.winfo_width()-160, pu_tableau_posy+20*a, box.winfo_width()-140, pu_tableau_posy+20*a+20)\n\t\t\tbox.coords(pu_tableau_place[a], box.winfo_width()-150, pu_tableau_posy+20*a+10)\n\t\t\tbox.coords(pu_tableau_badges[a], box.winfo_width()*0.25, pu_tableau_posy+20*a+10)\n\t\t\tbox.coords(pu_tableau_dateheure[a], box.winfo_width()*0.5, pu_tableau_posy+20*a+10)\n\t\t\ta += 1\n\ndef pu_createpagination(box, id, x, y, text):\n\tpu_pagination_rectangle.append(box.create_rectangle(x, y, x+30, y+26, fill='#2ecc71', width=0))\n\tpu_pagination_text.append(box.create_text(x+15, y+13, text=text, fill=\"#ecf0f1\", font=\"Arial 20\"))\n\tpu_pagination_rect.append(box.create_rectangle(x, y, x+30, y+25, width=0))\n\tbox.tag_bind(pu_pagination_rect[id], '<Enter>', lambda event, box=box, id=id: pu_paginationOver(box, id)) \n\tbox.tag_bind(pu_pagination_rect[id], '<Leave>', lambda event, box=box, id=id: pu_paginationOutOver(box, id)) \n\tbox.tag_bind(pu_pagination_rect[id], '<ButtonRelease-1>', lambda event, box=box, id=id: pu_paginationClick(box, id))\n\ndef pu_paginationOver(box, id):\n\tbox.itemconfigure(pu_pagination_rectangle[id], fill='#27ae60')\n\ndef pu_paginationOutOver(box, id):\n\tbox.itemconfigure(pu_pagination_rectangle[id], fill='#2ecc71')\n\ndef pu_paginationClick(box, id):\n\tif id==0:\n\t\tif get_checking_var('pu_page_select')<=0 or logutilisation('nbpage', 0)==0:\n\t\t\tpass\n\t\telse:\n\t\t\tset_checking_var(\"pu_page_select\", get_checking_var('pu_page_select')-1)\n\t\t\tload_logutilisation(get_checking_var('pu_page_select'))\n\telse:\n\t\tif get_checking_var('pu_page_select')>=logutilisation('nbpage', 0)-1 or logutilisation('nbpage', 0)==0:\n\t\t\tpass\n\t\telse:\n\t\t\tset_checking_var(\"pu_page_select\", get_checking_var('pu_page_select')+1)\n\t\t\tload_logutilisation(get_checking_var('pu_page_select'))\n\ndef pu_updatedatatableau(box):\n\tpu_page_select = get_checking_var('pu_page_select')\n\ta = 0\n\tb = 17*pu_page_select \n\tc = 17*(pu_page_select+1)\n\twhile b<c:\n\t\tif b<logutilisation('size', 1):\n\t\t\tpu_updateline(box, logutilisation('etat', a), a, logutilisation('id', a), logutilisation('code', a), logutilisation('entrantsortant', a), logutilisation('place', a), logutilisation('dateheure', a))\n\t\telse:\n\t\t\tpu_updateline(box, 2, a, \"\", \"\", 3, \"\", \"\")\n\t\tb += 1\n\t\ta += 1\n\n'''\n--------------------------------------------------------------------------------------\nupdate\n'''\ndef pu_update(box, command=1):\n\tif command==1:\n\t\tif get_checking_var(\"seconde\") != None:\n\t\t\tif get_checking_var(\"seconde\")!=logutilisation('seconde', 0) or get_checking_var('pu_page_select')!=get_checking_var('pu_page_select_old'):\n\t\t\t\tload_logutilisation(get_checking_var('pu_page_select'), 1)\n\t\t\t\tpu_updatedatatableau(box)\n\t\t\t\tset_checking_var(\"pu_page_select_old\", get_checking_var('pu_page_select'))\n\t\t\t\tset_checking_var(\"seconde\", logutilisation('seconde', 0))\n\t\telse:\n\t\t\tset_checking_var(\"seconde\", logutilisation('seconde', 0))\n\t\t\tset_checking_var(\"pu_page_select\", 0)\n\t\t\tset_checking_var(\"pu_page_select_old\", 0)\n\telif command==2: # la taille de la fenetre change\n\t\tpu_updatetableau(box)\n\telse:\n\t\tpass\n'''\n\n--------------------------------------------------------------------------------------\ndelete\n'''\ndef pu_delete(box):\n\tpass","repo_name":"LionelJouin/Projet-STI2D-Parking-Vertical","sub_path":"Programme python/src/pages/parkingusage.py","file_name":"parkingusage.py","file_ext":"py","file_size_in_byte":9140,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"81"}
+{"seq_id":"30348724750","text":"# -*- coding: utf-8 -*-\nimport pipe\nimport random\nimport bird\ndef choose(choice):   #随机选择难度\n    if choice==1:\n        set_easy()\n    if choice==2:\n        set_middle()\n    if choice==3:\n        set_difficult()\ndef set_easy():\n    pipe.pipeDistance=150  #设置管道的间距\n    pipe.pipeCount = 4   #管道的种类\n    bird.upSpeed = 200  #小鸟上升的速度，值越大越难\ndef set_middle():\n    pipe.pipeDistance = 100 #设置管道的间距\n    pipe.pipeCount = 4    #管道的种类\n    pipeInterval = 90     #管道的间隔\n    bird.upSpeed = 200    #小鸟上升的速度，值越大越难\ndef set_difficult():\n    pipe.pipeDistance = 100\n    pipe.pipeCount = 4\n    pipeInterval = 90\n    bird.upSpeed = 250   #小鸟速度加大了，难度增加了","repo_name":"772148702/homework","sub_path":"FlappyBirdClient/lib/difficultyset.py","file_name":"difficultyset.py","file_ext":"py","file_size_in_byte":776,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"38921762822","text":"# closure in Python\n\ndef divider(y):\n    def divide(x):\n        return x/y\n    return divide \n\n# driver code\n\nd1 = divider(2)\nd2= divider(5)\nd3= divider(4)\n\nprint(d1(2))\nprint(d2(9))\nprint(d3(10))","repo_name":"Dhiraj123-star/Python-Practice","sub_path":"closure_ex1.py","file_name":"closure_ex1.py","file_ext":"py","file_size_in_byte":196,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"34577211064","text":"x= int(input(\"enter a number\\n\"))\nans=0\nwhile ans**3 < abs(x):\n    ans+=1\nif(ans**3!= abs(x)):\n    print(\"it is not a perfect cube\")\nelse:\n    if(x<0):\n        ans = -ans\n    print(\"cube root of \"+ str(x)+\" is \"+ str(ans))\n\n\ncube=int(input(\"enter a number\"))\nfor guess in range(cube+1):\n    if(guess**2==cube):\n        print(\"cube root of\",cube ,\"is\",guess)\n","repo_name":"srinisha2628/new_practice","sub_path":"cuberoot.py","file_name":"cuberoot.py","file_ext":"py","file_size_in_byte":358,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"8146282423","text":"\"\"\"\npypi-up a simple command line tool to increase version number of package\nand release on Pypi. Also Git Tag/Push the release.\n\nIt can increase patch, minor, major, release and dev version\n\nFeatures:\n    - Increase versionning\n    - Git Tag/Push release\n    - Release to PYPI\n\nSetup:\n\nrun: pypi-up --setup\n\nManually:\n\nEdit 'setup.cfg' at the root of your package and must have the following line\n\n[pypi-up]\nversion-file = \"__about__.py\"\n\n\nThe about file must contain a variable: `__version__`\n\n__version__ = \"1.2.4\"\n\nWhat it will do, it will access the file and change the version number\n\n\n\"\"\"\nimport ConfigParser\nimport argparse\nimport os\nimport re\n\nimport sh\nfrom reversionup import Reversionup\n\nfrom pypi_up.__about__ import *\n\nCWD = os.getcwd()\nsetup_cfg = \"%s/setup.cfg\" % CWD\nabout_file = \"%s/__about__.py\" % CWD\nconf_section_name = \"pypi-up\"\n\nabout_file_content = '''\n\"\"\" __about__ \"\"\"\n\n__all__ = [\n    \"__title__\", \"__summary__\", \"__uri__\", \"__version__\", \"__author__\",\n    \"__email__\", \"__license__\", \"__copyright__\",\n]\n\n__title__ = \"\"\n__version__ = \"0.0.0\"\n__summary__ = \"\"\n__uri__ = \"\"\n__author__ = \"\"\n__email__ = \"\"\n__license__ = \"\"\n__copyright__ = \"(c) 2016 %s\" % __author__\n'''\n\n\ndef replace_in_file(file, pattern, replacement):\n    with open(file, \"r+\") as f:\n        c = re.sub(pattern, replacement, f.read())\n        f.seek(0)\n        f.truncate()\n        f.write(c)\n\n\ndef replace_file_version(file, version):\n    pattern = r'__version__\\s*=\\s*\"([\\d.\\w\\-]+)\"'\n    repl = \"__version__ = \\\"%s\\\"\" % version\n    replace_in_file(file, pattern, repl)\n\n\ndef main():\n\n    try:\n        prog = \"pypi-up\"\n        desc = \"%s %s\" % (__title__, __version__)\n        desc += \" - pypi-up a simple command line tool to increase version number of package\" \\\n                \"and release on Pypi. Also Git Tag/Push the release\"\n\n        parser = argparse.ArgumentParser(prog=prog,\n                                         description=desc)\n        parser.add_argument(\"--setup\",\n                           help=\"Setup PYPI-REL\",\n                           action=\"store_true\")\n        parser.add_argument(\"-v\", \"--version\",\n                           help=\"Show the current version\",\n                           action=\"store_true\")\n        parser.add_argument(\"-p\", \"--patch\",\n                           help=\"Increment PATCH version\",\n                           action=\"store_true\")\n        parser.add_argument(\"-m\", \"--minor\",\n                           help=\"Increment MINOR version and reset patch\",\n                           action=\"store_true\")\n        parser.add_argument(\"-j\", \"--major\",\n                           help=\"Increment MAJOR version and reset minor and patch\",\n                           action=\"store_true\")\n        parser.add_argument(\"-e\", \"--edit\",\n                           help=\"Manually enter the version number to bump\",\n                           action=\"store\")\n        parser.add_argument(\"--dry\",\n                           help=\"DRY RUN. To test the release, but it will not make any changes\",\n                           action=\"store_true\")\n        parser.add_argument(\"-x\", \"--skip-prompt\",\n                           help=\"Skip prompt\",\n                           action=\"store_true\")\n        arg = parser.parse_args()\n        config = ConfigParser.ConfigParser()\n\n        print(\"-\" * 80)\n        print(\"=== PYPI Up ===\")\n        print(\"\")\n\n        if arg.setup:\n            print(\"Setting up...\")\n            if not os.path.isfile(about_file):\n                with open(about_file, \"w+\") as f:\n                    f.write(about_file_content)\n            if not os.path.isfile(setup_cfg):\n                config.add_section(conf_section_name)\n                config.set(conf_section_name, \"version-file\", \"__about__.py\")\n                config.set(conf_section_name, \"auto-increment\", \"patch\")\n                with open(setup_cfg, \"w+\") as f:\n                    config.write(f)\n            print(\"Done!\")\n            print(\"-\" * 80)\n            exit()\n\n\n        with sh.pushd(CWD):\n            if sh.git(\"status\", \"--porcelain\").strip():\n                raise Exception(\"Repository is UNCLEAN. Commit your changes\")\n\n            config.read(setup_cfg)\n            version_file = config.get(conf_section_name, \"version-file\")\n            version_file = os.path.join(CWD, version_file)\n            if not os.path.isfile(version_file):\n                raise Exception(\"version-file '%s' is required\" % version_file)\n            auto_inc = \"\"\n            if config.has_option(conf_section_name, \"auto-increment\"):\n                auto_inc = config.get(conf_section_name, \"auto-increment\").strip()\n\n            rvnup = Reversionup(file=setup_cfg)\n            old_version = rvnup.version\n\n            def test_auto_inc(auto_inc_):\n                return auto_inc == auto_inc_ and (not arg.patch\n                         and not arg.minor\n                         and not arg.major\n                         and not arg.version\n                         and not arg.edit)\n            if arg.edit:\n                rvnup.version = arg.edit\n            elif arg.patch or test_auto_inc(\"patch\"):\n                rvnup.inc_patch()\n            elif arg.minor or test_auto_inc(\"minor\"):\n                rvnup.inc_minor()\n            elif arg.major or test_auto_inc(\"major\"):\n                rvnup.inc_major()\n            elif arg.version:\n                print(\"Current version: %s\" % rvnup.version)\n                print(\"-\" * 80)\n                exit()\n\n            if arg.dry:\n                print(\"** DRY RUNNING **\")\n                print(\"\")\n\n            print(\"* New version: %s \" % rvnup.version)\n            print(\"Old version: %s\" % old_version)\n            print(\"\")\n\n            if not arg.skip_prompt \\\n                    and raw_input(\"Continue with the release? (y | n) \").strip() == \"n\":\n                print(\"** Release Aborted\")\n                print(\"-\" * 80)\n                exit()\n\n            skip_tag = not arg.skip_prompt \\\n                       and raw_input(\"Git Tag/Push release version? (y | n) \").strip().lower() == \"n\"\n            skip_pypi = not arg.skip_prompt \\\n                        and raw_input(\"Release to PYPI? (y | n) \").strip().lower() == \"n\"\n\n            print(\"\")\n\n            if not arg.dry:\n                rvnup.write()\n                replace_file_version(version_file, rvnup.version)\n\n            if arg.dry or skip_tag:\n                print(\"- Git Tag/Push release: skipped\")\n            else:\n                tag_name = \"v%s\" % rvnup.version\n                print(\"+ Git Tag release version: %s \" % tag_name)\n                sh.git(\"add\", \".\")\n                sh.git(\"commit\", \"-m\", \"Tagged release: %s\" % tag_name)\n                sh.git(\"tag\", \"-a\", tag_name, \"-m\", tag_name)\n                print(\"+ Git Push release to repo ...\")\n                sh.git(\"push\", \"origin\", \"master\")\n                sh.git(\"push\", \"--tags\")\n\n            if arg.dry or skip_pypi:\n                print(\"- Release to Pypi: skipped\")\n            else:\n                print(\"+ Releasing to PYPI ...\")\n                sh.python(\"setup.py\", \"register\", \"-r\", \"pypi\")\n                sh.python(\"setup.py\", \"sdist\", \"upload\", \"-r\", \"pypi\")\n\n        print(\"-\" * 80)\n        print(\"\")\n\n    except Exception as ex:\n        print(\"Error: %s\" % ex.message)\n        exit(1)\n","repo_name":"mardix/pypi-up","sub_path":"pypi_up/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":7325,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"81"}
+{"seq_id":"16002223064","text":"from flask import *\napp = Flask(__name__)\n\n@app.route('/hello')\ndef hello_world():\n\tx={'numbers':[1,2,3,4]}\n\treturn jsonify(x)\n\n\n@app.route('/hai')\ndef hello_world():\n\treturn render_template('index.html')\n\nif __name__ == '__main__':\n   app.run()","repo_name":"muhammed-fanish/flask-tut","sub_path":"first.py","file_name":"first.py","file_ext":"py","file_size_in_byte":245,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"24594946644","text":"import threading\nfrom os import getenv\nfrom time import sleep\n\nfrom boltons.iterutils import first\nfrom redis import StrictRedis\nfrom redis.sentinel import Sentinel, SentinelConnectionPool\n\nfrom apierrors.errors.server_error import ConfigError, GeneralError\nfrom config import config\n\nlog = config.logger(__file__)\n\nOVERRIDE_HOST_ENV_KEY = (\"TRAINS_REDIS_SERVICE_HOST\", \"REDIS_SERVICE_HOST\")\nOVERRIDE_PORT_ENV_KEY = (\"TRAINS_REDIS_SERVICE_PORT\", \"REDIS_SERVICE_PORT\")\n\nOVERRIDE_HOST = first(filter(None, map(getenv, OVERRIDE_HOST_ENV_KEY)))\nif OVERRIDE_HOST:\n    log.info(f\"Using override redis host {OVERRIDE_HOST}\")\n\nOVERRIDE_PORT = first(filter(None, map(getenv, OVERRIDE_PORT_ENV_KEY)))\nif OVERRIDE_PORT:\n    log.info(f\"Using override redis port {OVERRIDE_PORT}\")\n\n\nclass MyPubSubWorkerThread(threading.Thread):\n    def __init__(self, sentinel, on_new_master, msg_sleep_time, daemon=True):\n        super(MyPubSubWorkerThread, self).__init__()\n        self.daemon = daemon\n        self.sentinel = sentinel\n        self.on_new_master = on_new_master\n        self.sentinel_host = sentinel.connection_pool.connection_kwargs[\"host\"]\n        self.msg_sleep_time = msg_sleep_time\n        self._running = False\n        self.pubsub = None\n\n    def subscribe(self):\n        if self.pubsub:\n            try:\n                self.pubsub.unsubscribe()\n                self.pubsub.punsubscribe()\n            except Exception:\n                pass\n            finally:\n                self.pubsub = None\n\n        subscriptions = {\"+switch-master\": self.on_new_master}\n\n        while not self.pubsub or not self.pubsub.subscribed:\n            try:\n                self.pubsub = self.sentinel.pubsub()\n                self.pubsub.subscribe(**subscriptions)\n            except Exception as ex:\n                log.warn(\n                    f\"Error while subscribing to sentinel at {self.sentinel_host} ({ex.args[0]}) Sleeping and retrying\"\n                )\n                sleep(3)\n        log.info(f\"Subscribed to sentinel {self.sentinel_host}\")\n\n    def run(self):\n        if self._running:\n            return\n        self._running = True\n\n        self.subscribe()\n\n        while self.pubsub.subscribed:\n            try:\n                self.pubsub.get_message(\n                    ignore_subscribe_messages=True, timeout=self.msg_sleep_time\n                )\n            except Exception as ex:\n                log.warn(\n                    f\"Error while getting message from sentinel {self.sentinel_host} ({ex.args[0]}) Resubscribing\"\n                )\n                self.subscribe()\n\n        self.pubsub.close()\n        self._running = False\n\n    def stop(self):\n        # stopping simply unsubscribes from all channels and patterns.\n        # the unsubscribe responses that are generated will short circuit\n        # the loop in run(), calling pubsub.close() to clean up the connection\n        self.pubsub.unsubscribe()\n        self.pubsub.punsubscribe()\n\n\n# todo,future - multi master clusters?\nclass RedisCluster(object):\n    def __init__(self, sentinel_hosts, service_name, **connection_kwargs):\n        self.service_name = service_name\n        self.sentinel = Sentinel(sentinel_hosts, **connection_kwargs)\n        self.master = None\n        self.master_host_port = None\n        self.reconfigure()\n        self.sentinel_threads = {}\n        self.listen()\n\n    def reconfigure(self):\n        try:\n            self.master_host_port = self.sentinel.discover_master(self.service_name)\n            self.master = self.sentinel.master_for(self.service_name)\n            log.info(f\"Reconfigured master to {self.master_host_port}\")\n        except Exception as ex:\n            log.error(f\"Error while reconfiguring. {ex.args[0]}\")\n\n    def listen(self):\n        def on_new_master(workerThread):\n            self.reconfigure()\n\n        for sentinel in self.sentinel.sentinels:\n            sentinel_host = sentinel.connection_pool.connection_kwargs[\"host\"]\n            self.sentinel_threads[sentinel_host] = MyPubSubWorkerThread(\n                sentinel, on_new_master, msg_sleep_time=0.001, daemon=True\n            )\n            self.sentinel_threads[sentinel_host].start()\n\n\nclass RedisManager(object):\n    def __init__(self, redis_config_dict):\n        self.aliases = {}\n        for alias, alias_config in redis_config_dict.items():\n\n            alias_config = alias_config.as_plain_ordered_dict()\n\n            is_cluster = alias_config.get(\"cluster\", False)\n\n            host = OVERRIDE_HOST or alias_config.get(\"host\", None)\n            if host:\n                alias_config[\"host\"] = host\n\n            port = OVERRIDE_PORT or alias_config.get(\"port\", None)\n            if port:\n                alias_config[\"port\"] = port\n\n            db = alias_config.get(\"db\", 0)\n\n            sentinels = alias_config.get(\"sentinels\", None)\n            service_name = alias_config.get(\"service_name\", None)\n\n            if not is_cluster and sentinels:\n                raise ConfigError(\n                    \"Redis configuration is invalid. mixed regular and cluster mode\",\n                    alias=alias,\n                )\n            if is_cluster and (not sentinels or not service_name):\n                raise ConfigError(\n                    \"Redis configuration is invalid. missing sentinels or service_name\",\n                    alias=alias,\n                )\n            if not is_cluster and (not port or not host):\n                raise ConfigError(\n                    \"Redis configuration is invalid. missing port or host\", alias=alias\n                )\n\n            if is_cluster:\n                # todo support all redis connection args via sentinel's connection_kwargs\n                del alias_config[\"sentinels\"]\n                del alias_config[\"cluster\"]\n                del alias_config[\"service_name\"]\n                self.aliases[alias] = RedisCluster(\n                    sentinels, service_name, **alias_config\n                )\n            else:\n                self.aliases[alias] = StrictRedis(**alias_config)\n\n    def connection(self, alias):\n        obj = self.aliases.get(alias)\n        if not obj:\n            raise GeneralError(f\"Invalid Redis alias {alias}\")\n        if isinstance(obj, RedisCluster):\n            obj.master.get(\"health\")\n            return obj.master\n        else:\n            obj.get(\"health\")\n            return obj\n\n    def host(self, alias):\n        r = self.connection(alias)\n        pool = r.connection_pool\n        if isinstance(pool, SentinelConnectionPool):\n            connections = pool.connection_kwargs[\n                \"connection_pool\"\n            ]._available_connections\n        else:\n            connections = pool._available_connections\n\n        if len(connections) > 0:\n            return connections[0].host\n        else:\n            return None\n\n\nredman = RedisManager(config.get(\"hosts.redis\"))\n","repo_name":"merry1314/trains-server","sub_path":"server/redis_manager.py","file_name":"redis_manager.py","file_ext":"py","file_size_in_byte":6847,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"81"}
+{"seq_id":"25792905868","text":"import torch\nfrom torch import nn\n\nfrom millet.model.backbone.common import ConvBlock, manual_pad\n\n\nclass FCNFeatureExtractor(nn.Module):\n    \"\"\"FCN feature extractor implementation for use in MILLET. Same as original architecture.\"\"\"\n\n    def __init__(self, n_in_channels: int, padding_mode: str = \"replicate\"):\n        super().__init__()\n        self.n_in_channels = n_in_channels\n        self.instance_encoder = nn.Sequential(\n            ConvBlock(n_in_channels, 128, 8, padding_mode=padding_mode),\n            ConvBlock(128, 256, 5, padding_mode=padding_mode),\n            ConvBlock(256, 128, 3, padding_mode=padding_mode),\n        )\n\n    def forward(self, x: torch.Tensor) -> torch.Tensor:\n        # PyTorch doesn't like replicate padding if the input tensor is too small, so pad manually to min length\n        min_len = 5\n        if x.shape[-1] >= min_len:\n            return self.instance_encoder(x)\n        else:\n            padded_x = manual_pad(x, min_len)\n            return self.instance_encoder(padded_x)\n","repo_name":"JAEarly/MILTimeSeriesClassification","sub_path":"millet/model/backbone/fcn.py","file_name":"fcn.py","file_ext":"py","file_size_in_byte":1019,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"81"}
+{"seq_id":"30542778658","text":"people = {\"Kyle\":{\"height_in\":75,\"address\":\"117 Boyd\"},\"Andrew\":{}}\nprint(list(people.keys()))\nprint(list(people.values()))\n\n\npeople = {}\npeople_file = open(\"people.txt\")\npeople_list = [x.strip() for x in people_file.readlines()]\n\nfor line in people_list:\n    sections = line.split(\":\") #turns the colon separated string into a list\n    name = sections[0]\n    height = int(sections[1])\n    address = sections[2]\n    if name not in people.keys():\n        people[name] = {\"height\":height,\"address\":address}\nprint(people)\n\nfor person,info in people.items():\n    print(person,info)\n\n\n\n","repo_name":"JohnsenClasses/elee2045sp22","sub_path":"dictionaries_and_strings/dictionaries.py","file_name":"dictionaries.py","file_ext":"py","file_size_in_byte":581,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"81"}
+{"seq_id":"72929366986","text":"class Solution:\n    def isValid(self, s: str) -> bool:\n        table = {\n            '(': ')',\n            '{': '}',\n            '[': ']'\n        }\n\n        stack = []\n        for char in s:\n            if char in table:\n                stack.append(char)\n            else:\n                if not stack or char != table[stack.pop()]:\n                    return False\n\n        return len(stack) == 0\n\n\nprint(Solution().isValid(\"()[]{}\"))\n","repo_name":"boorooksus/Algorithm-Study","sub_path":"LeetCode/5회차/B20-20-Valid Parentheses.py","file_name":"B20-20-Valid Parentheses.py","file_ext":"py","file_size_in_byte":437,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"38632334874","text":"# 1\n\navailableCourses = [\"Python Crash Course\", \"Java Application Development\", \"English 101\"]\n\nuserCourse = input(\"What course are you looking for? \")\n\nif userCourse in availableCourses:\n    print(\"It is available!\")\nelse:\n    print(\"Sorry, it is not available!\")","repo_name":"abbluiz/python-class","sub_path":"lab-11a.py","file_name":"lab-11a.py","file_ext":"py","file_size_in_byte":264,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"15292143687","text":"# shamelessly stolen from https://github.com/higgsfield/RL-Adventure/blob/master/common/wrappers.py\n\nimport cv2\nimport gym\nfrom gym import spaces\nfrom collections import deque\nimport numpy as np\n\n__all__ = ['atari_list', 'make_atari']\n\natari_list = ['AirRaid-v0', 'Alien-v0', 'Amidar-v0', 'Assault-v0', 'Asterix-v0', 'Asteroids-v0', 'Atlantis-v0',\n              'BankHeist-v0', 'BattleZone-v0', 'BeamRider-v0', 'Berzerk-v0', 'Bowling-v0', 'Boxing-v0',\n              'Breakout-v0',\n              'Carnival-v0', 'Centipede-v0', 'ChopperCommand-v0', 'CrazyClimber-v0',\n              'DemonAttack-v0', 'DoubleDunk-v0',\n              'ElevatorAction-v0', 'Enduro-v0', 'FishingDerby-v0', 'Freeway-v0', 'Frostbite-v0',\n              'KungFuMaster-v0',\n              'Pong-v0']\n\n\nclass EpisodicLifeEnv(gym.Wrapper):\n    def __init__(self, env):\n        \"\"\"Make end-of-life == end-of-episode, but only reset on true game over.\n        Done by DeepMind for the DQN and co. since it helps value estimation.\n        \"\"\"\n        gym.Wrapper.__init__(self, env)\n        self.lives = 0\n        self.was_real_done = True\n\n    def step(self, action):\n        obs, reward, done, info = self.env.step(action)\n        self.was_real_done = done\n        lives = self.env.unwrapped.ale.lives()\n        if 0 < lives < self.lives:\n            done = True\n        self.lives = lives\n        return obs, reward, done, info\n\n    def reset(self, **kwargs):\n        \"\"\"Reset only when lives are exhausted.\n        This way all states are still reachable even though lives are episodic,\n        and the learner need not know about any of this behind-the-scenes.\n        \"\"\"\n        if self.was_real_done:\n            obs = self.env.reset(**kwargs)\n        else:\n            # no-op step to advance from terminal/lost life state\n            obs, _, _, _ = self.env.step(0)\n        self.lives = self.env.unwrapped.ale.lives()\n        return obs\n\n\nclass ClipReward(gym.RewardWrapper):\n    def __init__(self, env):\n        super(ClipReward, self).__init__(env)\n\n    def reward(self, reward):\n        return np.sign(reward)\n\n\nclass WrapFrame(gym.ObservationWrapper):\n    def __init__(self, env):\n        \"\"\"Warp frames to 84x84 as done in the Nature paper and later work.\"\"\"\n        gym.ObservationWrapper.__init__(self, env)\n        self.width = 84\n        self.height = 84\n        self.observation_space = spaces.Box(low=0, high=255, shape=(self.height, self.width, 1), dtype=np.float32)\n\n    def observation(self, frame):\n        frame = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)\n        frame = cv2.resize(frame, (self.width, self.height), interpolation=cv2.INTER_AREA)\n        return frame[:, :, None]\n\n\nclass FrameStack(gym.Wrapper):\n    def __init__(self, env, k):\n        \"\"\"Stack k last frames\"\"\"\n        gym.Wrapper.__init__(self, env)\n        self.k = k\n        self.frames = deque([], maxlen=k)\n        shp = env.observation_space.shape\n        self.observation_space = spaces.Box(low=-1.0, high=1.0, shape=(shp[0], shp[1], shp[2] * k), dtype=np.float32)\n\n    def reset(self):\n        ob = self.env.reset()\n        for _ in range(self.k):\n            self.frames.append(ob)\n        return self._get_ob()\n\n    def step(self, action):\n        ob, reward, done, info = self.env.step(action)\n        self.frames.append(ob)\n        return self._get_ob(), reward, done, info\n\n    def _get_ob(self):\n        assert len(self.frames) == self.k\n        observation = np.array(list(self.frames), dtype=np.float32).squeeze(-1)\n        return (observation / 127.5) - 1.0\n\n\ndef make_atari(env):\n    env = gym.make(env)\n    env = EpisodicLifeEnv(env)\n    env = ClipReward(env)\n    env = WrapFrame(env)\n    env = FrameStack(env, 4)\n    return env\n","repo_name":"CherryPieSexy/policy_gradient","sub_path":"src/atari_utils.py","file_name":"atari_utils.py","file_ext":"py","file_size_in_byte":3705,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"4940224755","text":"import boto3\r\nimport json\r\nimport logging\r\nimport os\r\nfrom dbconnection import get_crendentails,cred_env\r\ndef ebs_snapshot(region_src,accountid):\r\n   ACCESS_KEY, SECRET_KEY, SESSION_TOKEN = cred_env(accountid)\r\n   logger = logging.getLogger(__name__)\r\n   # sqs = boto3.client('sqs')\r\n   # region_src = region_src\r\n   # print(region_src)\r\n   # print(ACCESS_KEY, SECRET_KEY, SESSION_TOKEN )\r\n   ec2 = boto3.resource('ec2')\r\n   if SESSION_TOKEN:\r\n      print(\"haiii\")\r\n      client_src = boto3.client('ec2',region_name=region_src,aws_access_key_id= ACCESS_KEY ,aws_secret_access_key=SECRET_KEY,aws_session_token=SESSION_TOKEN)\r\n   else:   \r\n      client_src = boto3.client('ec2',region_name=region_src,aws_access_key_id= ACCESS_KEY ,aws_secret_access_key=SECRET_KEY)\r\n   response1 = client_src.describe_snapshots(OwnerIds=[\r\n         'self'])        \r\n   b = response1['Snapshots']\r\n   if not b:\r\n      print(\"No ebs snapshots available\")\r\n   else:   \r\n   # print(b)\r\n      c= len(b)\r\n      d = []\r\n      f = []\r\n      g = []\r\n      count=0\r\n      client2 = boto3.client('organizations')   \r\n      response2 = client2.list_accounts()\r\n      if not response2[\"Accounts\"]:\r\n         print(\"No Accounts under organisation,cannot proceed further\")\r\n      else:   \r\n         for i in range(len(response2[\"Accounts\"])):   \r\n            f.append(response2[\"Accounts\"][i][\"Id\"])\r\n         print(f)   \r\n\r\n         for i in range(c):\r\n            d.append(b[i]['SnapshotId'])\r\n         print(d)\r\n         for i in d:    \r\n            snapshot = ec2.Snapshot(i)\r\n            response = snapshot.describe_attribute(\r\n               Attribute='createVolumePermission',\r\n               DryRun=False)\r\n            if (response['CreateVolumePermissions'])!= []:\r\n               for j in f:\r\n                  for i in range(len(response['CreateVolumePermissions'])):\r\n                     if(response['CreateVolumePermissions'][i][\"UserId\"]) != j:\r\n                        #  print(response['SnapshotId'])\r\n                        count = count + 1 \r\n                        if (count == len(f)):\r\n                           g.append(response['SnapshotId'])\r\n               count = 0\r\n      print(g)         \r\n         # queue_url = 'https://sqs.us-east-1.amazonaws.com/126806377151/rks-test'\r\n         # queue_url = input(\"please enter the queue url: \")\r\n         # str = ' , '.join(map(str,g))\r\n         # # for i in g:\r\n         # response = sqs.send_message(\r\n         #       QueueUrl=queue_url,\r\n         #       # DelaySeconds=10,\r\n         #       MessageAttributes={\r\n         #          'Title': {\r\n         #                'DataType': 'String',\r\n         #                'StringValue': 'The following snapshots that are not authenticated with aws accounts'\r\n         #          }\r\n         #       },\r\n         #       MessageBody = str\r\n         #    )\r\n         # print(\"The following snapshots that are not authenticated with aws accounts\\n \", str)                       \r\n#    print(response['MessageId'])\r\n   # print(response)\r\n\r\n# messages = sqs.receive_message(QueueUrl=queue_url,MaxNumberOfMessages=1, WaitTimeSeconds=1)\r\n# response = sqs.receive_message(\r\n#     QueueUrl=queue_url,\r\n#     AttributeNames=[\r\n#         'SentTimestamp'\r\n#     ],\r\n#     MaxNumberOfMessages=1,\r\n#     MessageAttributeNames=[\r\n#         'All'\r\n#     ],\r\n#     VisibilityTimeout=0,\r\n#     WaitTimeSeconds=0\r\n# )\r\n# msg=response['Messages'][0]\r\n# print(msg)\r\n# print(messages)\r\n# for mgs in messages:\r\n#        print(mgs.id)\r\n# print (\"Queue info : {}\".format(messages))\r\n# print(messages['Messages'][0])\r\n\r\n# while len(messages) > 0:\r\n   #  for message in messages:\r\n   #      mail_body = json.loads(message.body)\r\n   #      print(\"E-mail sent to: %s\" % mail_body['to'])\r\n   #      email = EmailMessage(mail_body['subject'], mail_body['message'], to=[mail_body['to']])\r\n   #      email.send()\r\n   #      message.delete()   \r\n# for msg in messages:\r\n#    logger.info(\"Received message: %s: %s\", msg.message_id, msg.body)\r\n","repo_name":"radha45/juno-image","sub_path":"ebs_snapshot_unmonitored_account.py","file_name":"ebs_snapshot_unmonitored_account.py","file_ext":"py","file_size_in_byte":4001,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"9532832161","text":"import argparse\nimport os\nimport time\nimport warnings\nwarnings.filterwarnings('ignore',category=UserWarning)\nfrom datetime import datetime\n\nCHECKPOINT_PATH = 'checkpoint'\n\n\nTIME_NOW = datetime.now().isoformat()\n\n#tensorboard log dir\nLOG_DIR = 'runs'\nCHECKPOINT_PATH = 'checkpoint'\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nimport torch.utils.data\n# from tensorboardX import SummaryWriter\nimport math\n\n# from models import *\nfrom data_loader import data_loader\nfrom helper import AverageMeter, save_checkpoint, accuracy\n\n\n\ndef adjust_learning_rate(optimizer, epoch, init_lr):\n    \"\"\"Sets the learning rate to the initial LR decayed by 10 every 30 epochs\"\"\"\n    lr = init_lr * (0.1 ** (epoch // 30))\n    for param_group in optimizer.param_groups:\n        param_group['lr'] = lr\ndef linear_learning_rate(optimizer, epoch, init_lr,T_max=100):\n    lr = 2e-5 - init_lr/T_max*(epoch+1-T_max)\n    for param_group in optimizer.param_groups:\n        param_group['lr'] = lr\n#def consine_learning_rate(optimizer, epoch, init_lr):\n #   \"\"\"Sets the learning rate to the initial LR decayed by 10 every 30 epochs\"\"\"\n  #  lr = 2e-5 + init_lr*(1+math.cos(math.pi*epoch/T_max))/2\n   # for param_group in optimizer.param_groups:\n    #    param_group['lr'] = lr\n# import imagenet_seq\n# ImagenetLoader=imagenet_seq.data.Loader\n\nmodel_names = [\n    'vgg11_cbn','vgg11_bn', 'vgg16_cbn', 'vgg16_bn',\n    'resnet18', 'resnet50', 'resnet101', 'resnet18_cbn', 'resnet50_cbn', 'resnet101_cbn', 'shufflenetv2', 'shufflenetv2_cbn'\n]\n\nparser = argparse.ArgumentParser(description='PyTorch ImageNet Training')\nparser.add_argument('-a','--arch', metavar='ARCH', default='alexnet', choices=model_names,\n                    help='model architecture: ' + ' | '.join(model_names) + ' (default: alexnet)')\nparser.add_argument('--epochs', default=90, type=int, metavar='N',\n                    help='numer of total epochs to run')\nparser.add_argument('--start_epoch', default=0, type=int, metavar='N',\n                    help='manual epoch number (useful to restarts)')\nparser.add_argument('-b', '--batch_size', default=256, type=int, metavar='N',\n                    help='mini-batch size (default: 256)')\nparser.add_argument('--lr', default=0.1, type=float, metavar='LR',\n                    help='initial learning rate')\nparser.add_argument('--momentum', default=0.9, type=float, metavar='M',\n                    help='momentum')\nparser.add_argument('--weight-decay', '--wd', default=1e-4, type=float,\n                    metavar='W', help='Weight decay (default: 1e-4)')\nparser.add_argument('-j', '--workers', default=32, type=int, metavar='N',\n                    help='number of data loading workers (default: 4)')\n#parser.add_argument('-m', '--pin-memory', dest='pin_memory', action='store_true',\n                 #   help='use pin memory')\nparser.add_argument('-p', '--pretrained', dest='pretrained', action='store_true',\n                    help='use pre-trained model')\nparser.add_argument('--print_freq', '-f', default=500, type=int, metavar='N',\n                    help='print frequency (default: 10)')\nparser.add_argument('--resume', default='', type=str, metavar='PATH',\n                    help='path to latest checkpoitn, (default: None)')\nparser.add_argument('-e', '--evaluate', dest='evaluate', action='store_true',\n                    help='evaluate model on validation set')\nparser.add_argument('-mark', type=str, default='', help='remark to this experiment')\nparser.add_argument('--warm', type=int, default=2, help='warm up training phase')\nparser.add_argument('-record', type=bool, default=True, help='whether to save checkpoint and events')\nparser.add_argument('--gpu', default = '0',help='GPU id')\nparser.add_argument('--weight', default = 'none',help='none or mean')\nargs = parser.parse_args()\nprint(TIME_NOW)\nfrom torch.optim.lr_scheduler import _LRScheduler\nclass WarmUpLR(_LRScheduler):\n    def __init__(self, optimizer, total_iters, last_epoch=-1):\n        \n        self.total_iters = total_iters\n        super().__init__(optimizer, last_epoch)\n\n    def get_lr(self):\n        return [base_lr * (0.01+self.last_epoch / (self.total_iters + 1e-8)) for base_lr in self.base_lrs]\n\n#from models.squeezenet import squeezenet1_0, squeezenet1_1\n\n#from models.densenet import densenet121, densenet161, densenet169, densenet201\nfrom models.vgg import vgg11_cbn, vgg11_bn,vgg16_cbn, vgg16_bn\n\nfrom models.resnet_nobn import resnet18_cbn, resnet50_cbn, resnet101_cbn\nfrom models.resnet_true import resnet18,  resnet50, resnet101\n     \n\ndef meanweigh(module):\n #   for name, module in container.named_modules():     \n        if isinstance(module, nn.Conv2d) and module.groups==1:  \n            datavalue=module.weight.data\n            meanvalue=datavalue.mean([1,2,3],True)\n            module.weight.data=(datavalue-meanvalue)\n     #   elif isinstance(module, nn.Linear):  \n      #      datavalue=module.weight.data\n       #     meanvalue=datavalue.mean([1],True)\n        #    module.weight.data=(datavalue-meanvalue)\n    \nbest_prec1 = 0.0\ndef train(train_loader, model, criterion, optimizer, epoch, print_freq):\n    batch_time = AverageMeter()\n    data_time = AverageMeter()\n    losses = AverageMeter()\n    top1 = AverageMeter()\n    top5 = AverageMeter()\n     \n\n    # switch to train mode\n    model.train()\n    #model.apply(freeze_bn)\n    dslen=len(train_loader)\n    end = time.time()\n    for k,(input, target) in enumerate(train_loader):\n        if epoch < args.warm:\n            warmup_scheduler.step()\n\n\n        # measure data loading time\n        if True:\n            data_time.update(time.time() - end)\n\n            target = target.cuda(non_blocking=True)\n            input = input.cuda(non_blocking=True)\n\n            # compute output\n            output = model(input)\n            loss = criterion(output, target)\n\n            # measure accuracy and record loss\n            prec= accuracy(output.data, target, topk=(1, 5))\n            losses.update(loss.item(), input.size(0))\n            top1.update(prec[0].item(), input.size(0))\n            top5.update(prec[1].item(), input.size(0))\n\n            # compute gradient and do SGD step\n            optimizer.zero_grad()\n            loss.backward()\n            optimizer.step()\n\n            # measure elapsed time\n            batch_time.update(time.time() - end)\n            end = time.time()\n        if 'mean' in args.weight:\n            model.apply(meanweigh)#(model)\n            \n        n_iter = (epoch) * len(train_loader) + k \n        if n_iter==1:\n            os.system('nvidia-smi')\n        if n_iter%print_freq==1 and epoch<args.start_epoch+5:\n            print('Epoch: [{0}][{1}/{2}]\\t'\n                   'LR: {3:.5f}\\t'\n                      'Time {batch_time.avg:.3f}\\t'\n                      'Data {data_time.avg:.3f}\\t'\n                      'LossEpoch {loss.avg:.4f}\\t'\n                      'Prec@1 {top1.avg:.3f}\\t'\n                      'Prec@5 {top5.avg:.3f}\\t'.format(\n                    epoch, n_iter,dslen, optimizer.param_groups[0]['lr'],batch_time=batch_time,\n                    data_time=data_time, loss=losses, top1=top1, top5=top5), flush=True)\n            batch_time.reset()\n            data_time.reset()\n            losses.reset()\n            top1.reset()\n            top5.reset()\n            #validatetrain(val_loader, model, criterion)\n        elif k==dslen-1:\n            model.apply(inspect_bn)\n            print('Epoch: [{0}][{1}/{2}]\\t'\n                   'LR: {3:.5f}\\t'\n                      'Time {batch_time.avg:.3f}\\t'\n                      'Data {data_time.avg:.3f}\\t'\n                      'LossEpoch {loss.avg:.4f}\\t'\n                      'Prec@1 {top1.avg:.3f}\\t'\n                      'Prec@5 {top5.avg:.3f}\\t'.format(\n                    epoch, n_iter,dslen, optimizer.param_groups[0]['lr'],batch_time=batch_time,\n                    data_time=data_time, loss=losses, top1=top1, top5=top5), flush=True)\n\n                      \n            \n           \ndef freeze_bn(m):\n    if isinstance(m, nn.BatchNorm1d):\n       # m.train()\n        m.track_running_stats=False\n\ndef inspect_bn(m):\n    if isinstance(m, nn.BatchNorm1d):\n       # m.train()\n        print(m.running_var.data[:10])\n\n#model.apply(freeze_bn)\ndef validatetrain(val_loader, model, criterion):\n    losses = AverageMeter()\n    top1 = AverageMeter()\n    top5 = AverageMeter()\n\n    model.eval()\n\n    for i, (input, target) in enumerate(val_loader):\n        target = target.cuda(non_blocking=True)\n        input = input.cuda(non_blocking=True)\n        with torch.no_grad():\n            # compute output\n            output = model(input)\n            loss = criterion(output, target)\n\n            # measure accuracy and record loss\n            prec= accuracy(output.data, target, topk=(1, 5))\n            losses.update(loss.item(), input.size(0))\n            top1.update(prec[0].item(), input.size(0))\n            top5.update(prec[1].item(), input.size(0))\n    model.train()\n    print(' *TRAINMODE Prec@1 {top1.avg:.3f} Prec@5 {top5.avg:.3f}'.format(top1=top1, top5=top5))\n\ndef validate(val_loader, model, criterion, print_freq,epoch):\n    losses = AverageMeter()\n    top1 = AverageMeter()\n    top5 = AverageMeter()\n\n    # switch to evaluate mode\n    model.eval()\n   # model.apply(unfreeze_bn)\n\n    for i, (input, target) in enumerate(val_loader):\n        target = target.cuda(non_blocking=True)\n        input = input.cuda(non_blocking=True)\n        with torch.no_grad():\n            # compute output\n            output = model(input)\n            loss = criterion(output, target)\n\n            # measure accuracy and record loss\n            prec= accuracy(output.data, target, topk=(1, 5))\n            losses.update(loss.item(), input.size(0))\n            top1.update(prec[0].item(), input.size(0))\n            top5.update(prec[1].item(), input.size(0))\n\n            # measure elapsed time\n\n            if i % print_freq == 0:\n                print('Test: [{0}][{1}/{2}]\\t'\n                      'Prec@1 {top1.val:.3f} ({top1.avg:.3f})\\t'\n                      'Prec@5 {top5.val:.3f} ({top5.avg:.3f})'.format(epoch,\n                    i, len(val_loader),  loss=losses,\n                    top1=top1, top5=top5))\n        n_iter = (epoch) * len(train_loader) + i + 1 \n                \n        # if args.record:\n        #     writer.add_scalar('Test/Average loss', losses.val,  n_iter)\n        #     writer.add_scalar('Test/Accuracy', top1.val,  n_iter)\n\n    model.train()\n    f_loss.write('\\n epoch {} test with loss {:.4f} \\n'.format(epoch,losses.avg)) \n    f_acc.write('\\n epoch {} test with top1 {:.3f} and top5 {:.3f} \\n'.format(epoch,top1.avg,top5.avg))  \n    print(' * Prec@1 {top1.avg:.3f} Prec@5 {top5.avg:.3f}'.format(top1=top1, top5=top5))\n    \n\n    return top1.avg, top5.avg\n\nif args.gpu!='-1':\n    os.environ[\"CUDA_VISIBLE_DEVICES\"] = str(args.gpu)\n    \nif not os.path.exists(LOG_DIR):\n    os.mkdir(LOG_DIR)\nif args.mark=='':\n    args.mark=TIME_NOW\n        \ncheckpoint_path = os.path.join(CHECKPOINT_PATH,args.mark)\nrunslogdir=os.path.join(LOG_DIR, args.arch, args.mark)\nif not os.path.exists(runslogdir):\n    os.makedirs(runslogdir)\nif not os.path.exists(checkpoint_path):\n    os.makedirs(checkpoint_path)\ncheckpoint_path = os.path.join(checkpoint_path, '{net}-{epoch}-{type}.pth')\n\nf_loss = open(os.path.join(runslogdir,'loss.txt'),'a') \nf_acc = open(os.path.join(runslogdir,'acc.txt'),'a') \n\n\nif __name__ == '__main__':\n\n\n        \n        \n    # create model\n    if args.pretrained:\n        print(\"=> using pre-trained model '{}'\".format(args.arch))\n    else:\n        print(\"=> creating model '{}'\".format(args.arch))\n\n\n    if args.arch == 'vgg11_cbn':\n        model = vgg11_cbn(pretrained=args.pretrained)\n    elif args.arch == 'vgg16_cbn':\n        model = vgg16_cbn()\n    elif args.arch == 'vgg19_cbn':\n        model = vgg19_cbn()\n    elif args.arch == 'vgg11_bn':\n        model = vgg11_bn(pretrained=args.pretrained)\n    elif args.arch == 'vgg16_bn':\n        model = vgg16_bn()\n    elif args.arch == 'vgg19_bn':\n        model = vgg19_bn()\n    elif args.arch == 'resnet18':\n        model = resnet18(pretrained=args.pretrained)\n    elif args.arch == 'resnet50':\n        model = resnet50(pretrained=args.pretrained)\n    elif args.arch == 'resnet101':\n        model = resnet101(pretrained=args.pretrained)\n    elif args.arch == 'resnet18_cbn':\n        model = resnet18_cbn()\n    elif args.arch == 'resnet50_cbn':\n        model = resnet50_cbn()\n    elif args.arch == 'resnet101_cbn':\n        model = resnet101_cbn()\n    elif args.arch == 'shufflenetv2':\n        from models.shufflenetv2 import shufflenet_v2_x0_5\n        model = shufflenet_v2_x0_5(pretrained=args.pretrained)\n    elif args.arch == 'shufflenetv2_cbn':\n        from models.shufflenetv2_cbn import shufflenet_v2_x0_5\n        model = shufflenet_v2_x0_5()\n    else:\n        raise NotImplementedError\n\n    # use cuda\n\n    if ',' in args.gpu:\n        #torch.distributed.init_process_group(backend='nccl', \n                   #             world_size=2, rank=0)\n        #model = torch.nn.parallel.DistributedDataParallel(model)\n        model = torch.nn.parallel.DataParallel(model)\n    model.cuda()\n\n    # Data loading\n    train_loader, val_loader = data_loader('', args.batch_size, args.workers)\n    # train_loader = ImagenetLoader(args.data+'/train', batch_size=args.batch_size, num_workers=args.workers)\n    # train_loader=val_loader\n\n    # define loss and optimizer\n    criterion = nn.CrossEntropyLoss().cuda()\n    optimizer = optim.SGD(model.parameters(), lr=args.lr * args.batch_size / 256.,\n                          momentum=args.momentum,nesterov=False, \n                          weight_decay=args.weight_decay)\n    train_scheduler = optim.lr_scheduler.MultiStepLR(optimizer, milestones=[30,60,85], gamma=0.1) #learning rate decay\n    #train_scheduler = optim.lr_scheduler.CosineAnnealingLR(optimizer,eta_min=5e-4, T_max=args.epochs) #learning rate decay\n\n\n    # optionlly resume from a checkpoint\n    if args.resume:\n        if os.path.isfile(args.resume):\n            print(\"=> loading checkpoint '{}'\".format(args.resume))\n            checkpoint = torch.load(args.resume)\n            if 'para' in checkpoint.keys():\n                optstate=checkpoint['opt']\n                checkpoint=checkpoint['para']\n                optimizer.load_state_dict(optstate)\n                print(\"=> loaded optimizer from '%s' \", args.resume)\n            try:\n                model.load_state_dict(checkpoint)\n                #print(optimizer.param_groups[0]['lr'])                \n            except:\n                state_dict=model.state_dict()\n                name_par=list(checkpoint.keys())\n                k0=0\n                for key,para in state_dict.items():\n                    if k0<len(name_par) and para.numel()==checkpoint[name_par[k0]].numel():\n                        state_dict[key]=checkpoint[name_par[k0]]\n                        k0+=1  \n                model.load_state_dict(state_dict)\n            parstr=args.resume.split('-')\n            if args.start_epoch==0:\n                args.start_epoch =int(parstr[-2])+1\n            #import math\n            #optimizer.param_groups[0]['lr']*=1/2*(1+math.cos(math.pi*args.start_epoch/130.0))\n            print(\"=> loaded checkpoint '{}' (epoch {})\".format(args.resume, int(parstr[-2])))\n            #warmup_scheduler = WarmUpLR(optimizer, 0)\n    else:\n        iter_per_epoch = len(train_loader)\n        warmup_scheduler = WarmUpLR(optimizer, iter_per_epoch*args.warm )\n        print(\"=> no checkpoint found at '{}'\".format(args.resume))\n\n\n    torch.backends.cudnn.benchmark = True\n    #torch.backends.cudnn.deterministic = False\n    \n    #torch.backends.cudnn.enabled = True\n\n    if 'mean' in args.weight:\n        model.apply(meanweigh)#(model)\n    \n\n    if args.evaluate:\n        validate(val_loader, model, criterion, args.print_freq,0)\n        assert False\n        \n    lastepoch=0\n\n    for epoch in range(args.start_epoch,args.epochs):\n        #print(optimizer.param_groups[0]['lr'])\n        if epoch >= args.warm:\n            #linear_learning_rate(optimizer, epoch, args.lr,T_max=args.epochs)\n            train_scheduler.step(epoch)\n            #print((epoch,optimizer.param_groups[0]['lr']))\n            #continue\n        # train for one epoch\n        try:\n            train(train_loader, model, criterion, optimizer, epoch, args.print_freq)\n            prec1, prec5 = validate(val_loader, model, criterion, args.print_freq,epoch)\n        except Exception as e:\n            print(e)\n            train_loader, val_loader = data_loader(args.data, args.batch_size, args.workers)\n            print('DataLoader Reset')\n            #assert False\n            continue\n        else:\n            # remember the best prec@1 and save checkpoint\n            is_best = prec1 > best_prec1\n            best_prec1 = max(prec1, best_prec1)\n            if epoch > 55 and is_best:\n                # if lastepoch>0:\n                #     os.remove(checkpoint_path.format(net=args.arch, epoch=lastepoch, type='best'))\n                torch.save(model.state_dict(), checkpoint_path.format(net=args.arch, epoch=epoch, type='best'))\n                lastepoch=epoch\n            if epoch%6==5:\n                torch.save({'para':model.state_dict(),'opt':optimizer.state_dict()}, checkpoint_path.format(net=args.arch, epoch=epoch, type='regular'))\n    f_loss.close()\n    f_acc.close()\n    print('Best Accuracy is {acc} in {epoch}'.format(acc=best_prec1,epoch=lastepoch))\n\n\n","repo_name":"Kid-key/MimicNorm","sub_path":"ImageNet/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":17441,"program_lang":"python","lang":"en","doc_type":"code","stars":19,"dataset":"github-code","pt":"81"}
+{"seq_id":"43800941225","text":"# This problem was asked by Walmart Labs.\n\n# There are M people sitting in a row of N seats, where M < N. Your task is \n# to redistribute people such that there are no gaps between any of them, \n# while keeping overall movement to a minimum.\n\n# For example, suppose you are faced with an input of [0, 1, 1, 0, 1, 0, 0, 0, 1], \n# where 0 represents an empty seat and 1 represents a person. In this case, one \n# solution would be to place the person on the right in the fourth seat. We can \n# consider the cost of a solution to be the sum of the absolute distance each \n# person must move, so that the cost here would be five.\n\n# Given an input such as the one above, return the lowest possible cost of moving \n# people to remove all gaps.\n####\ndef min_group_cost(arr):\n    n = len(arr)\n    cost_from_left = [0]*n\n    cost_from_right = [0]*n\n\n    num_ones = 0\n    cur_cost = 0\n    for i in range(n):\n        if arr[i] == 0:\n            cur_cost += num_ones\n            cost_from_left[i] = cur_cost\n        else:\n            num_ones += 1\n            cost_from_left[i] = cur_cost\n    \n    num_ones = 0\n    cur_cost = 0\n    for i in range(n-1, -1, -1):\n        if arr[i] == 0:\n            cur_cost += num_ones\n            cost_from_right[i] = cur_cost\n        else:\n            num_ones += 1\n            cost_from_right[i] = cur_cost\n    \n    return min([x+y for x, y in zip(cost_from_left, cost_from_right)])\n####\nprint(min_group_cost([0, 1, 1, 0, 1, 0, 0, 0, 1]))\n\n\n\n        \n\n\n\n\n\n","repo_name":"whoophee/DCP","sub_path":"309.py","file_name":"309.py","file_ext":"py","file_size_in_byte":1479,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"81"}
+{"seq_id":"43028798245","text":"from astro_functions import *\n\n\n# MECHANICS\n\n\nclass Hitbox:\n\n    def __init__(self, obj, obj_type, boundaries):\n        self.obj = obj\n        self.obj_type = obj_type\n\n        self.resistance = 1\n        # 2: absolute elasticity (perfectly inverts incoming velocities)\n        # >1: elasticity (bounciness)\n        # 1: absolute resistance (zeroes all incoming velocities)\n        # <1: partial resistance (slows incoming velocities, simulates viscosity for liquids)\n        # 0: no resistance (does not affect incoming velocities, simulates vacuum or simple atmosphere)\n\n        self.boundaries_old = boundaries\n        self.xi_old, self.xf_old = boundaries[0]\n        self.yi_old, self.yf_old = boundaries[1]\n        self.zi_old, self.zf_old = boundaries[2]\n        self.boundaries = boundaries\n        self.xi, self.xf = boundaries[0]\n        self.yi, self.yf = boundaries[1]\n        self.zi, self.zf = boundaries[2]\n\n    def update_vars(self):\n        self.boundaries[0] = [self.xi, self.xf]\n        self.boundaries[1] = [self.yi, self.yf]\n        self.boundaries[2] = [self.zi, self.zf]\n        self.boundaries_old[0] = [self.xi_old, self.xf_old]\n        self.boundaries_old[1] = [self.yi_old, self.yf_old]\n        self.boundaries_old[2] = [self.zi_old, self.zf_old]\n\n        self.xi_old, self.xf_old = self.xi, self.xf\n        self.yi_old, self.yf_old = self.yi, self.yf\n        self.zi_old, self.zf_old = self.zi, self.zf\n\n    def collide(self, hitbox):\n        if self.xi < hitbox.xi < self.xf or self.xi < hitbox.xf < self.xf or \\\n                self.yi < hitbox.yi < self.yf or self.yi < hitbox.yf < self.yf or \\\n                self.zi < hitbox.zi < self.zf or self.zi < hitbox.zf < self.zf or \\\n                self.xi_old < hitbox.xi < self.xi or self.xf_old < hitbox.xf < self.xf or \\\n                self.yi_old < hitbox.yi < self.yi or self.yf_old < hitbox.yf < self.yf or \\\n                self.zi_old < hitbox.zi < self.zi or self.zf_old < hitbox.zf < self.zf:\n            return True\n        else:\n            return False\n\n    def handle_collision(self, hitbox):\n        # The purpose of the following lineq() nested functions can be described using example 'zf_eq_xi':\n        # 'zf' is the output value of the function, and describes the z-position of the top side of the hitbox of 'self'\n        # at x-position 'xi' of hitbox\n        # 'xi' is the input of the function (output of nested lineq), and describes the x-position of the eastern side\n        # of the hitbox of 'hitbox' at z-position 'self.zf'\n        zf_eq_xi = lineq((self.xi, self.zf), (self.xf, self.zf),\n                         lineq((hitbox.zi, hitbox.xi), (hitbox.zf, hitbox.xi), self.zf))\n        zf_eq_xf = lineq((self.xi, self.zf), (self.xf, self.zf),\n                         lineq((hitbox.zi, hitbox.xf), (hitbox.zf, hitbox.xf), self.zf))\n        zi_eq_xi = lineq((self.xi, self.zi), (self.xf, self.zi),\n                         lineq((hitbox.zi, hitbox.xi), (hitbox.zf, hitbox.xi), self.zi))\n        zi_eq_xf = lineq((self.xi, self.zi), (self.xf, self.zi),\n                         lineq((hitbox.zi, hitbox.xf), (hitbox.zf, hitbox.xf), self.zi))\n        xf_eq_zi = lineq((self.zi, self.xf), (self.zf, self.xf),\n                         lineq((hitbox.xi, hitbox.zi), (hitbox.xf, hitbox.zi), self.xf))\n        xf_eq_zf = lineq((self.zi, self.xf), (self.zf, self.xf),\n                         lineq((hitbox.xi, hitbox.zf), (hitbox.xf, hitbox.zf), self.xf))\n        xi_eq_zi = lineq((self.zi, self.xi), (self.zf, self.xi),\n                         lineq((hitbox.xi, hitbox.zi), (hitbox.xf, hitbox.zi), self.xi))\n        xi_eq_zf = lineq((self.zi, self.xi), (self.zf, self.xi),\n                         lineq((hitbox.xi, hitbox.zf), (hitbox.xf, hitbox.zf), self.xi))\n        yf_eq_zi = lineq((self.zi, self.yf), (self.zf, self.yf),\n                         lineq((hitbox.yi, hitbox.zi), (hitbox.yf, hitbox.zi), self.yf))\n        yf_eq_zf = lineq((self.zi, self.yf), (self.zf, self.yf),\n                         lineq((hitbox.yi, hitbox.zf), (hitbox.yf, hitbox.zf), self.yf))\n        yi_eq_zi = lineq((self.zi, self.yi), (self.zf, self.yi),\n                         lineq((hitbox.yi, hitbox.zi), (hitbox.yf, hitbox.zi), self.yi))\n        yi_eq_zf = lineq((self.zi, self.yi), (self.zf, self.yi),\n                         lineq((hitbox.yi, hitbox.zf), (hitbox.yf, hitbox.zf), self.yi))\n\n        if self.collide(hitbox):\n            if self.obj_type == 'block' and hitbox.obj_type == 'block':\n                pass\n            elif self.obj_type == 'block' and hitbox.obj_type == 'entity':\n                pass\n            elif self.obj_type == 'entity' and hitbox.obj_type == 'block':\n                pass\n            elif self.obj_type == 'entity' and hitbox.obj_type == 'entity':\n                pass\n\n    def draw(self, color, player_location):\n        lines = (\n            ((self.xf, self.yf, self.zf), (self.xi, self.yf, self.zf)),\n            ((self.xi, self.yf, self.zf), (self.xi, self.yi, self.zf)),\n            ((self.xi, self.yi, self.zf), (self.xf, self.yi, self.zf)),\n            ((self.xf, self.yi, self.zf), (self.xf, self.yf, self.zf)),\n            ((self.xf, self.yf, self.zf), (self.xf, self.yf, self.zi)),\n            ((self.xi, self.yf, self.zf), (self.xi, self.yf, self.zi)),\n            ((self.xi, self.yi, self.zf), (self.xi, self.yi, self.zi)),\n            ((self.xf, self.yi, self.zf), (self.xf, self.yi, self.zi)),\n            ((self.xf, self.yf, self.zi), (self.xi, self.yf, self.zi)),\n            ((self.xi, self.yf, self.zi), (self.xi, self.yi, self.zi)),\n            ((self.xi, self.yi, self.zi), (self.xf, self.yi, self.zi)),\n            ((self.xf, self.yi, self.zi), (self.xf, self.yf, self.zi))\n        )\n        for i in range(len(lines)):\n            pygame.draw.aaline(WIN, color, grid_to_screen_coords(lines[i][0], player_location),\n                               grid_to_screen_coords(lines[i][1], player_location))\n\n\n# mobile hitbox (for player, npc, creature, vehicle, dropped item, etc)\nclass EntityHitbox:\n    def __init__(self, entity, bounds, permeable):\n        self.entity = entity\n        self.bounds = bounds\n        self.permeable = permeable\n\n\n# stationary hitbox (for blocks, geographical features, etc.)\nclass TileHitbox:\n    def __init__(self, tile, shape, scale):\n        self.tile = tile\n        self.shape = shape\n        self.scale = scale\n\n        # WHAT TO DO NEXT:\n        # - shape starts off as cell (no angled sides), allow player to change this if desired\n        # - require dimension input (length, width, height) to define cell bounds\n        # - have a plane equation to represent each side (bound) of the cell\n        # - allow player to alter \"slope\" of each face if desired\n        # - special_shapes are various presets with pre-inputted slopes to define common shapes\n        # - probably limit total sides to 6 (2 per axis, or in other words an 'initial' and 'final' bound)\n\n        self.special_shapes = [\n            'slant_un', 'slant_ue', 'slant_us', 'slant_uw', 'slant_dn', 'slant_de', 'slant_ds', 'slant_dw',\n            'slant_une', 'slant_unw', 'slant_use', 'slant_usw', 'slant_bne', 'slant_bnw', 'slant_bse', 'slant_bsw'\n            'pyramid'\n        ]  # defined as having non-flat planes as one or more of its bounds -- EXPERIMENTAL\n        self.basic_shapes = ['cell', 'slab_b', 'slab_t', 'slab_n', 'slab_e', 'slab_s']  # has all flat planes as bounds\n\n        self.resistance = 1\n        # 2: absolute elasticity (perfectly inverts incoming velocities)\n        # >1: elasticity (bounciness)\n        # 1: absolute resistance (zeroes all incoming velocities)\n        # <1: partial resistance (slows incoming velocities, simulates viscosity for liquids)\n        # 0: no resistance (does not affect incoming velocities, simulates vacuum or simple atmosphere)\n\n        self.suction = 0\n        self.suction_path = ''\n\n    def set_resistance(self, resistance):\n        if isinstance(resistance, str):\n            if resistance == 'solid':\n                self.resistance = 1\n            if resistance == 'viscous':\n                self.resistance = 0.8\n            if resistance == 'liquid':\n                self.resistance = 0.5\n            if resistance == 'gas':\n                self.resistance = 0\n        else:\n            if resistance < 0:\n                print('tile resistance cannot be less than zero')\n            else:\n                self.resistance = resistance\n\n    def set_suction(self, suction):\n        self.suction = suction\n\n    def change_shape(self, new_shape):\n        self.shape = new_shape\n\n\n# ENTITY\n\n\nclass Entity(pygame.sprite.Sprite):\n\n    def __init__(self, location, direction):\n        super().__init__()\n\n\nclass Player(pygame.sprite.Sprite):\n\n    def __init__(self, location, direction):\n        super().__init__()\n        self.x = location[0]\n        self.y = location[1]\n        self.z = location[2]\n        self.location = self.x, self.y, self.z\n\n        self.hit_box_length = 0.7\n        self.hit_box_width = 0.7\n        self.hit_box_height = 1.9\n        self.hit_box_boundaries = (round(self.x - (self.hit_box_length / 2), 1),\n                                   round(self.y - (self.hit_box_width / 2), 1), round(self.z, 1)), \\\n                                  (round(self.x + (self.hit_box_length / 2), 1),\n                                   round(self.y + (self.hit_box_width / 2), 1), round(self.z + self.hit_box_height, 1))\n\n        self.x_speed = 0.15  # in units per tick\n        self.y_speed = 0.15  # in units per tick\n\n        self.x_vel = 0\n        self.y_vel = 0\n        self.vel = round(sqrt(self.x_vel**2 + self.y_vel**2), 1)\n\n        self.initial_jump_speed = 0.2  # in units per tick\n        self.z_velocity = 0\n        self.z_speed_max = 0.5\n        self.on_ground = False\n        self.collide_ceiling = False\n        self.collide_north = False\n        self.collide_south = False\n        self.collide_east = False\n        self.collide_west = False\n\n        self.direction = [direction[0], direction[1]]\n\n        self.frame_rate = 0.1  # fraction of total time sprite frame is shown for one game tick\n\n        self.idle_width, self.idle_height = 23, 55\n\n        self.idle_image_origin = grid_origin[0] - (self.idle_width // 2), grid_origin[1] - self.idle_height + 5\n        self.idle_forward = pygame.transform.scale(pygame.image.load(r'assets\\textures\\player_idle_forward.png'),\n                                                   (self.idle_width, self.idle_height))\n        self.idle_left = pygame.transform.scale(pygame.image.load(r'assets\\textures\\player_idle_left.png'),\n                                                   (self.idle_width, self.idle_height))\n        self.idle_right = pygame.transform.scale(pygame.image.load(r'assets\\textures\\player_idle_right.png'),\n                                                (self.idle_width, self.idle_height))\n        self.idle_backward = pygame.transform.scale(pygame.image.load(r'assets\\textures\\player_idle_backward.png'),\n                                                (self.idle_width, self.idle_height))\n\n        self.run_width, self.run_height = 25, 47\n        self.run_image_origin = grid_origin[0] - (self.run_width // 2), grid_origin[1] - self.run_height + 5\n\n        self.run_total_frames = 4\n\n        self.run_forward_frame_progress = 0\n        self.run_forward = []\n        for frame_num in range(4):\n            frame = pygame.transform.scale(\n                pygame.image.load(rf'assets\\textures\\player_run_forward-{frame_num + 1}.png'),\n                (self.run_width, self.run_height))\n            self.run_forward.append(frame)\n\n        self.run_back_frame_progress = 0\n        self.run_back = []\n        for frame_num in range(4):\n            frame = pygame.transform.scale(\n                pygame.image.load(rf'assets\\textures\\player_run_back-{frame_num + 1}.png'),\n                (self.run_width, self.run_height))\n            self.run_back.append(frame)\n\n        self.side_run_width, self.side_run_height = 34, 57\n        self.side_run_image_origin = grid_origin[0] - (self.side_run_width // 2), \\\n                                     grid_origin[1] - self.side_run_height + 5\n\n        self.run_left_frame_progress = 0\n        self.run_left = []\n        for frame_num in range(4):\n            frame = pygame.transform.scale(\n                pygame.image.load(rf'assets\\textures\\player_run_left-{frame_num + 1}.png'),\n                (self.side_run_width, self.side_run_height))\n            self.run_left.append(frame)\n\n        self.run_right_frame_progress = 0\n        self.run_right = []\n        for frame_num in range(4):\n            frame = pygame.transform.scale(\n                pygame.image.load(rf'assets\\textures\\player_run_right-{frame_num + 1}.png'),\n                (self.side_run_width, self.side_run_height))\n            self.run_right.append(frame)\n\n        self.run_upper_left_frame_progress = 0\n        self.run_upper_left = []\n        for frame_num in range(4):\n            frame = pygame.transform.scale(\n                pygame.image.load(rf'assets\\textures\\player_run_upper_left-{frame_num + 1}.png'),\n                (self.run_width, self.run_height))\n            self.run_upper_left.append(frame)\n\n        self.rect = pygame.Rect(grid_origin[0] + (self.idle_width // 2), grid_origin[1] - self.idle_height,\n                                self.idle_width, self.idle_height)\n\n        self.interact_radius = 6    # blocks\n\n        self.inv = Inventory()\n\n    def update_location(self, new_location):\n        self.x = round(new_location[0], 2)\n        self.y = round(new_location[1], 2)\n        self.z = round(new_location[2], 2)\n        self.location = new_location\n        self.hit_box_boundaries = (round(self.x - (self.hit_box_length / 2), 1),\n                                   round(self.y - (self.hit_box_width / 2), 1), round(self.z, 1)), \\\n                                  (round(self.x + (self.hit_box_length / 2), 1),\n                                   round(self.y + (self.hit_box_width / 2), 1), round(self.z + self.hit_box_height, 1))\n\n    def draw(self, action):\n        if action == 'idle':\n            if self.direction == [1, 0]:\n                WIN.blit(self.idle_left, self.idle_image_origin)\n            if self.direction == [-1, 0]:\n                WIN.blit(self.idle_right, self.idle_image_origin)\n            if self.direction == [0, 1]:\n                WIN.blit(self.idle_forward, self.idle_image_origin)\n            if self.direction == [0, -1]:\n                WIN.blit(self.idle_backward, self.idle_image_origin)\n            if self.direction == [1, 1] or [-1, -1] or [1, -1] or [-1, 1]:\n                pass\n        if action == 'run':\n            if self.direction == [1, 1]:\n                frame = int(floor(self.run_upper_left_frame_progress))\n                WIN.blit(self.run_upper_left[frame], self.run_image_origin)\n                self.run_upper_left_frame_progress += self.frame_rate\n                if self.run_upper_left_frame_progress >= len(self.run_upper_left):\n                    self.run_upper_left_frame_progress *= 0\n                if round(self.run_upper_left_frame_progress, 1) % 2 == 0:\n                    step_sound = random.choice(step_sounds)\n                    pygame.mixer.Sound.play(step_sound)\n            elif self.direction == [0, -1] or self.direction == [1, -1] or self.direction == [-1, -1]:\n                frame = int(floor(self.run_back_frame_progress))\n                WIN.blit(self.run_back[frame], self.run_image_origin)\n                self.run_back_frame_progress += self.frame_rate\n                if self.run_back_frame_progress >= len(self.run_back):\n                    self.run_back_frame_progress *= 0\n                if round(self.run_back_frame_progress, 1) % 2 == 0:\n                    step_sound = random.choice(step_sounds)\n                    pygame.mixer.Sound.play(step_sound)\n            elif self.direction == [1, 0]:\n                frame = int(floor(self.run_left_frame_progress))\n                WIN.blit(self.run_left[frame], self.side_run_image_origin)\n                self.run_left_frame_progress += self.frame_rate\n                if self.run_left_frame_progress >= len(self.run_left):\n                    self.run_left_frame_progress *= 0\n                if round(self.run_left_frame_progress, 1) % 2 == 0:\n                    step_sound = random.choice(step_sounds)\n                    pygame.mixer.Sound.play(step_sound)\n            elif self.direction == [-1, 0]:\n                frame = int(floor(self.run_right_frame_progress))\n                WIN.blit(self.run_right[frame], self.side_run_image_origin)\n                self.run_right_frame_progress += self.frame_rate\n                if self.run_right_frame_progress >= len(self.run_right):\n                    self.run_right_frame_progress *= 0\n                if round(self.run_right_frame_progress, 1) % 2 == 0:\n                    step_sound = random.choice(step_sounds)\n                    pygame.mixer.Sound.play(step_sound)\n            else:\n                frame = int(floor(self.run_forward_frame_progress))\n                WIN.blit(self.run_forward[frame], self.run_image_origin)\n                self.run_forward_frame_progress += self.frame_rate\n                if self.run_forward_frame_progress >= len(self.run_forward):\n                    self.run_forward_frame_progress *= 0\n                if round(self.run_forward_frame_progress, 1) % 2 == 0:\n                    step_sound = random.choice(step_sounds)\n                    pygame.mixer.Sound.play(step_sound)\n\n    def draw_hit_box(self):\n        bix, biy, biz = self.hit_box_boundaries[0]\n        bfx, bfy, bfz = self.hit_box_boundaries[1]\n        hit_box_grid_lines = (\n            ((bfx, bfy, bfz), (bix, bfy, bfz)),\n            ((bix, bfy, bfz), (bix, biy, bfz)),\n            ((bix, biy, bfz), (bfx, biy, bfz)),\n            ((bfx, biy, bfz), (bfx, bfy, bfz)),\n            ((bfx, bfy, bfz), (bfx, bfy, biz)),\n            ((bix, bfy, bfz), (bix, bfy, biz)),\n            ((bix, biy, bfz), (bix, biy, biz)),\n            ((bfx, biy, bfz), (bfx, biy, biz)),\n            ((bfx, bfy, biz), (bix, bfy, biz)),\n            ((bix, bfy, biz), (bix, biy, biz)),\n            ((bix, biy, biz), (bfx, biy, biz)),\n            ((bfx, biy, biz), (bfx, bfy, biz))\n        )\n        for i in range(len(hit_box_grid_lines)):\n            pygame.draw.aaline(WIN, YELLOW, grid_to_screen_coords(hit_box_grid_lines[i][0], self.location),\n                               grid_to_screen_coords(hit_box_grid_lines[i][1], self.location))\n\n    def draw_interact_field(self):\n        xz_circle_width = (self.interact_radius * x_unit_length) * 2\n        xz_circle_height = (self.interact_radius * z_unit_length) * 2\n        xz_circle_origin = grid_origin[0] - (self.interact_radius * x_unit_length), \\\n                           grid_origin[1] - (self.idle_height // 2) - (self.interact_radius * z_unit_length)\n\n        xz_circle_surface = pygame.Surface((xz_circle_width, xz_circle_height), pygame.SRCALPHA)\n        xz_circle_rect = pygame.Rect((0, 0), (xz_circle_width, xz_circle_height))\n        pygame.draw.ellipse(xz_circle_surface, BLUE, xz_circle_rect, 4)\n\n        WIN.blit(xz_circle_surface, xz_circle_origin)\n\n        xy_circle_width = round(((((self.interact_radius * y_unit_length * sin(radians(60)))**2) +\n                                  ((self.interact_radius * y_unit_length * cos(radians(60))) +\n                                   (self.interact_radius * x_unit_length))**2)**(1/2)) / (2**(1/2))) * 2\n        xy_width_angle = round(atan((y_unit_length * sin(radians(60))) /\n                                    ((y_unit_length * cos(radians(60))) + x_unit_length)))\n        xy_circle_height = round(((((self.interact_radius * y_unit_length * sin(radians(60)))**2) +\n                                  (-(self.interact_radius * y_unit_length * cos(radians(60))) +\n                                   (self.interact_radius * x_unit_length))**2)**(1/2)) / (2**(1/2))) * 2\n        xy_circle_center = grid_origin[0], grid_origin[1] - (self.idle_height // 2)\n\n        xy_circle_surface = pygame.Surface((xy_circle_width, xy_circle_height), pygame.SRCALPHA)\n        xy_circle_rect = pygame.Rect(0, 0, xy_circle_width, xy_circle_height)\n        pygame.draw.ellipse(xy_circle_surface, BLUE, xy_circle_rect, 4)\n        xy_circle_surface_rotated = pygame.transform.rotate(xy_circle_surface, 25)\n        xy_circle_rect = xy_circle_surface_rotated.get_rect(center=xy_circle_center)\n\n        WIN.blit(xy_circle_surface_rotated, xy_circle_rect)\n\n        make_line(self.interact_radius * y_unit_length, x_angle,\n                  ((WIDTH // 2), (HEIGHT // 2) - (self.idle_height // 2)), BLUE)\n        make_line(-(self.interact_radius * y_unit_length), x_angle,\n                  ((WIDTH // 2), (HEIGHT // 2) - (self.idle_height // 2)), BLUE)\n\n        make_line(self.interact_radius * x_unit_length, y_angle,\n                  ((WIDTH // 2), (HEIGHT // 2) - (self.idle_height // 2)), BLUE)\n        make_line(-(self.interact_radius * x_unit_length), y_angle,\n                  ((WIDTH // 2), (HEIGHT // 2) - (self.idle_height // 2)), BLUE)\n\n        make_line(self.interact_radius * z_unit_length, z_angle,\n                  ((WIDTH // 2), (HEIGHT // 2) - (self.idle_height // 2)), BLUE)\n        make_line(-(self.interact_radius * z_unit_length), z_angle,\n                  ((WIDTH // 2), (HEIGHT // 2) - (self.idle_height // 2)), BLUE)\n\n    def check_block(self, planet, orientation):\n        blocks_info = list(find_block(self, orientation))\n        block_ids = []\n        solid_blocks = 0\n        if len(blocks_info) > 0:\n            for block_info in blocks_info:\n                chunk_ind = block_info[0]\n                block_ind = block_info[1]\n                block_id = ''\n                for block in planet.chunk(chunk_ind):\n                    if block.ind == block_ind:\n                        block_id = block_id + block.id\n                if block_id == '':\n                    block_id = block_id + '0'\n                if block_id == '0':\n                    pass\n                else:\n                    solid_blocks += 1\n        if orientation == 'below':\n            if solid_blocks > 0:\n                self.on_ground = True\n            else:\n                self.on_ground = False\n            return block_ids\n        if orientation == 'above':\n            if solid_blocks > 0:\n                self.collide_ceiling = True\n            else:\n                self.collide_ceiling = False\n            return block_ids\n        if orientation == 'north':\n            if solid_blocks > 0:\n                self.collide_north = True\n            else:\n                self.collide_north = False\n            return block_ids\n        if orientation == 'south':\n            if solid_blocks > 0:\n                self.collide_south = True\n            else:\n                self.collide_south = False\n            return block_ids\n        if orientation == 'west':\n            if solid_blocks > 0:\n                self.collide_west = True\n            else:\n                self.collide_west = False\n            return block_ids\n        if orientation == 'east':\n            if solid_blocks > 0:\n                self.collide_east = True\n            else:\n                self.collide_east = False\n            return block_ids\n\n    def initiate_jump(self):\n        if self.on_ground:\n            self.z_velocity += self.initial_jump_speed\n        else:\n            pass\n\n    def render(self, keys_pressed):\n\n        if keys_pressed[pygame.K_w] and keys_pressed[pygame.K_d] and not keys_pressed[pygame.K_s] and\\\n                not keys_pressed[pygame.K_a] or keys_pressed[pygame.K_w] and keys_pressed[pygame.K_a] and\\\n                not keys_pressed[pygame.K_s] and not keys_pressed[pygame.K_d] or keys_pressed[pygame.K_s] and\\\n                keys_pressed[pygame.K_d] and not keys_pressed[pygame.K_w] and not keys_pressed[pygame.K_a] or\\\n                keys_pressed[pygame.K_s] and keys_pressed[pygame.K_a] and not keys_pressed[pygame.K_w] and\\\n                not keys_pressed[pygame.K_d]:\n            self.x_speed = 0.1\n            self.y_speed = 0.1\n\n        if keys_pressed[pygame.K_w]:\n            if self.collide_north:\n                self.y = ceil(self.y) - (self.hit_box_length / 2)\n            else:\n                self.y += self.y_speed\n            if self.direction[1] < 1:\n                self.direction[1] += 1\n        if keys_pressed[pygame.K_s]:\n            if self.collide_south:\n                self.y = floor(self.y) + (self.hit_box_length / 2) + 0.1\n            else:\n                self.y -= self.y_speed\n            if self.direction[1] > -1:\n                self.direction[1] -= 1\n        if keys_pressed[pygame.K_a]:\n            if self.collide_west:\n                self.x = ceil(self.x) - (self.hit_box_length / 2)\n            else:\n                self.x += self.x_speed\n            if self.direction[0] < 1:\n                self.direction[0] += 1\n        if keys_pressed[pygame.K_d]:\n            if self.collide_east:\n                self.x = floor(self.x) + (self.hit_box_length / 2) + 0.1\n            else:\n                self.x -= self.x_speed\n            if self.direction[0] > -1:\n                self.direction[0] -= 1\n\n        if not keys_pressed[pygame.K_w] and not keys_pressed[pygame.K_s] and not keys_pressed[pygame.K_a] and not \\\n                keys_pressed[pygame.K_d]:\n            self.draw('idle')\n            self.x_vel = 0\n            self.y_vel = 0\n        else:\n            if self.direction[0] == 1:\n                self.x_vel = self.x_speed\n            elif self.direction[0] == -1:\n                self.x_vel = -self.x_speed\n            if self.direction[1] == 1:\n                self.y_vel = self.y_speed\n            elif self.direction[1] == -1:\n                self.y_vel = -self.y_speed\n\n            if self.collide_east or self.collide_west:\n                self.x_vel = 0\n            if self.collide_north or self.collide_south:\n                self.y_vel = 0\n\n            if self.on_ground:\n                self.draw('run')\n            else:\n                self.draw('idle')\n\n        if self.direction[0] and self.direction[1] != 0:  # this determines which direction the player faces\n            if keys_pressed[pygame.K_a] and keys_pressed[pygame.K_w] or \\\n                    not keys_pressed[pygame.K_a] and not keys_pressed[pygame.K_w]:\n                pass\n            else:\n                if keys_pressed[pygame.K_a]:\n                    self.direction[0] = 1\n                    self.direction[1] = 0\n                if keys_pressed[pygame.K_w]:\n                    self.direction[0] = 0\n                    self.direction[1] = 1\n            if keys_pressed[pygame.K_d] and keys_pressed[pygame.K_w] or \\\n                    not keys_pressed[pygame.K_d] and not keys_pressed[pygame.K_w]:\n                pass\n            else:\n                if keys_pressed[pygame.K_d]:\n                    self.direction[0] = -1\n                    self.direction[1] = 0\n                if keys_pressed[pygame.K_w]:\n                    self.direction[0] = 0\n                    self.direction[1] = 1\n            if keys_pressed[pygame.K_a] and keys_pressed[pygame.K_s] or \\\n                    not keys_pressed[pygame.K_a] and not keys_pressed[pygame.K_s]:\n                pass\n            else:\n                if keys_pressed[pygame.K_a]:\n                    self.direction[0] = 1\n                    self.direction[1] = 0\n                if keys_pressed[pygame.K_s]:\n                    self.direction[0] = 0\n                    self.direction[1] = -1\n            if keys_pressed[pygame.K_d] and keys_pressed[pygame.K_s] or \\\n                    not keys_pressed[pygame.K_d] and not keys_pressed[pygame.K_s]:\n                pass\n            else:\n                if keys_pressed[pygame.K_d]:\n                    self.direction[0] = -1\n                    self.direction[1] = 0\n                if keys_pressed[pygame.K_w]:\n                    self.direction[0] = 0\n                    self.direction[1] = 1\n\n        self.z += self.z_velocity\n        if not self.on_ground:\n            if self.z_velocity > -self.z_speed_max:\n                self.z_velocity -= GRAVITY\n        if self.on_ground and self.z_velocity < 0:\n            self.z_velocity = 0\n            self.z = ceil(self.z)\n        if not self.on_ground and self.z_velocity > 0 and self.collide_ceiling:\n            self.z = floor(self.z + self.hit_box_height) - self.hit_box_height\n            self.z_velocity = 0\n\n        self.x_speed = 0.15\n        self.y_speed = 0.15\n        self.update_location((self.x, self.y, self.z))\n\n\n# GUI\n\n\nclass Cursor:\n\n    def __init__(self):\n        self.image_width = 0\n        self.image_height = 0\n        self.image = pygame.image.load(rf'assets\\textures\\cursor.png').convert_alpha()\n        self.location = pygame.mouse.get_pos()\n        self.rect = self.image.get_rect(center=self.location)\n\n        self.selected_blocks = []\n\n    def update_pos(self):\n        pos = pygame.mouse.get_pos()\n        self.location = pos\n        self.rect = self.image.get_rect(center=self.location)\n\n    def draw(self):\n        pygame.mouse.set_cursor((8,8),(0,0),(0,0,0,0,0,0,0,0),(0,0,0,0,0,0,0,0))\n        WIN.blit(self.image, self.rect)\n\n\nclass Icon(pygame.sprite.Sprite):\n    def __init__(self, image, size, center):\n        super().__init__()\n        self.image = image\n        self.image_orig = self.image\n        self.size = size\n        self.size_base = size\n        self.cx, self.cy = center\n        self.rect = self.image.get_rect(center=(self.cx, self.cy))\n        self.scale = 1\n        self.scale_min, self.scale_max = 1, 1.05\n        self.scale_change = 0.01\n        self.scale_accel = 0.002\n        self.selected = False\n\n    def update_vars(self):\n        size_old_x, size_old_y = self.size_base\n        self.size = round(size_old_x * self.scale), round(size_old_y * self.scale)\n        self.image = pygame.transform.scale(self.image_orig, self.size)\n        self.rect = self.image.get_rect(center=(self.cx, self.cy))\n\n        if Cursor().rect.colliderect(self.rect):\n            self.selected = True\n        else:\n            self.selected = False\n\n    def handle_select1(self):\n        if self.selected:\n            if self.scale < self.scale_max:\n                self.scale += self.scale_change\n            else:\n                self.scale = self.scale_max\n        elif not self.selected:\n            if self.scale > self.scale_min:\n                self.scale -= self.scale_change\n            else:\n                self.scale = self.scale_min\n\n    def draw(self, surf):\n        surf.blit(self.image, self.rect)\n\n\nclass PlanetIcon(pygame.sprite.Sprite):\n    def __init__(self, name, directory, size, center, rotation_speed):\n        super().__init__()\n        self.name = name\n        self.size = size\n        self.center = center\n\n        self.current_frame = 3\n        self.frames = len(os.listdir(directory))\n\n        self.images = []\n        for i in range(self.frames):\n            frame_string = ''\n            if i < 10:\n                frame_string = f'00{i}'\n            if 10 <= i < 100:\n                frame_string = f'0{i}'\n            if 100 <= i < 1000:\n                frame_string = f'{i}'\n            img = pygame.image.load(directory + '\\\\' + name + '-' + frame_string + '.png')\n            img = pygame.transform.scale(img, size)\n            self.images.append(img)\n\n        self.icon = Icon(self.images[self.current_frame], size, center)\n\n        self.rotation_speed = rotation_speed\n\n        self.tick = 0\n\n    def update_vars(self):\n        self.icon.update_vars()\n\n    def handle_planet(self):\n        img = self.images[self.current_frame]\n        self.icon.image = img\n        self.icon.image_orig = img\n        if self.rotation_speed >= 1:\n            if self.tick % 1 == 0:\n                self.current_frame += self.rotation_speed\n            if self.current_frame >= self.frames:\n                self.current_frame = 0\n        if 0 < self.rotation_speed < 1:\n            if self.tick % (1 // self.rotation_speed) == 0:\n                self.current_frame += 1\n            if self.current_frame >= self.frames:\n                self.current_frame = 0\n        if -1 < self.rotation_speed < 0:\n            if self.tick % (1 // -self.rotation_speed) == 0:\n                self.current_frame -= 1\n            if self.current_frame < 0:\n                self.current_frame = self.frames\n        if self.rotation_speed <= -1:\n            if self.tick % 1 == 0:\n                self.current_frame += self.rotation_speed\n            if self.current_frame < 0:\n                self.current_frame = self.frames - 1\n        if self.rotation_speed == 0:\n            pass\n\n        self.icon.handle_select1()\n\n        self.tick += 1\n\n    def draw(self, surf):\n        self.icon.draw(surf)\n\n\nclass Slider(pygame.sprite.Sprite):\n    def __init__(self, text_dir, knob_size, slider_size, center):\n        super().__init__()\n        self.slider_image = pygame.image.load(rf'assets\\textures\\slider.png').convert_alpha()\n        self.slider_image_orig = self.slider_image\n        self.slider_size = slider_size\n        self.slider_size_base = slider_size\n\n        self.cx, self.cy = center\n        self.knob_x_min = self.cx - (self.slider_size[0] // 2) + 5\n        self.knob_x_max = self.cx + (self.slider_size[0] // 2) - 5\n        self.knob_percent = 75\n        self.knob_pos = round((self.knob_x_max - self.knob_x_min) * (self.knob_percent / 100))\n        self.slider_rect = self.slider_image.get_rect(center=(self.cx, self.cy))\n\n        self.knob_img = pygame.image.load(rf'assets\\textures\\knob.png')\n        self.knob = Icon(self.knob_img, knob_size, (self.knob_x_min + self.knob_pos, self.cy))\n\n        self.text = pygame.image.load(text_dir).convert_alpha()\n        self.text_rect = self.text.get_rect(center=(self.cx, self.cy - 15))\n\n        self.scale_min, self.scale_max = 1, 1.05\n        self.scale_change = 0.01\n        self.scale_accel = 0.002\n        self.selected = False\n        self.slidable = False\n\n    def update_vars(self):\n        self.knob.cx, self.knob.cy = self.knob_x_min + self.knob_pos, self.cy\n        self.knob.update_vars()\n        self.slider_rect = self.slider_image.get_rect(center=(self.cx, self.cy))\n        self.text_rect = self.text.get_rect(center=(self.cx, self.cy - 25))\n        self.knob_percent = (self.knob_pos / (self.knob_x_max - self.knob_x_min)) * 100\n\n    def handle_slider(self):\n        self.knob.handle_select1()\n        if self.slidable:\n            self.knob.selected = True\n            if self.knob_x_min < Cursor().location[0] < self.knob_x_max:\n                self.knob_pos = Cursor().location[0] - self.knob_x_min\n            elif Cursor().location[0] < self.knob_x_min:\n                self.knob_pos = 0\n            elif Cursor().location[0] > self.knob_x_max:\n                self.knob_pos = self.knob_x_max - self.knob_x_min\n\n    def draw(self, surf):\n        surf.blit(self.text, self.text_rect)\n        surf.blit(self.slider_image, self.slider_rect)\n        self.knob.draw(surf)\n\n\nclass Menu(pygame.sprite.Sprite):\n    def __init__(self):\n        super().__init__()\n        self.m0 = pygame.image.load(rf'assets\\textures\\menu0.png').convert_alpha()\n        self.m0_size = 305, 465\n        self.m0_location0 = WIDTH - self.m0_size[0] - 10, -self.m0_size[1]\n\n        self.mx, self.my = WIDTH - self.m0_size[0] - 10, -self.m0_size[1]\n        self.menu0_rect = self.m0.get_rect(topleft=self.m0_location0)\n        self.p0 = pygame.image.load(rf'assets\\textures\\pause0.png')\n        self.p1 = pygame.image.load(rf'assets\\textures\\pause1.png')\n        self.p2 = pygame.image.load(rf'assets\\textures\\play0.png')\n        self.p3 = pygame.image.load(rf'assets\\textures\\play1.png')\n        self.p_size = 70, 70\n        self.p_location = self.mx + (self.m0_size[0] - self.p_size[0]), self.my + (self.m0_size[1] + 5)\n\n        self.p_rect = self.p0.get_rect(topleft=self.p_location)\n        self.p_selected = False\n\n        self.resume_img = pygame.image.load(rf'assets\\textures\\resume.png')\n        self.resume = Icon(self.resume_img, (200, 40), (self.mx + 100 + 50, self.my + 20 + 30))\n\n        self.view_map_img = pygame.image.load(rf'assets\\textures\\view_map.png')\n        self.view_map = Icon(self.view_map_img, (195, 30),\n                             (self.mx + 100 + 50, self.my + 20 + 30 + 50))\n\n        self.settings_img = pygame.image.load(rf'assets\\textures\\settings.png')\n        self.settings = Icon(self.settings_img, (190, 30),\n                             (self.mx + 100 + 50, self.my + 20 + 30 + 100))\n\n        self.quick_save_img = pygame.image.load(rf'assets\\textures\\quick_save.png')\n        self.quick_save = Icon(self.quick_save_img, (230, 35),\n                             (self.mx + 100 + 50, self.my + 20 + 30 + 150))\n\n        self.save_quit_img = pygame.image.load(rf'assets\\textures\\save_quit.png')\n        self.save_quit = Icon(self.save_quit_img, (230, 35),\n                             (self.mx + 100 + 50, self.my + 20 + 30 + 200))\n\n        self.sfx = Slider(rf'assets\\textures\\sfx.png', (30, 30), (240, 15),\n                          (self.mx + 100 + 50, self.my + 20 + 30 + 300))\n        self.music = Slider(rf'assets\\textures\\music.png', (30, 30), (240, 15),\n                            (self.mx + 100 + 50, self.my + 20 + 30 + 350))\n\n        self.icons = [self.resume, self.view_map, self.settings, self.quick_save, self.save_quit]\n\n        self.sliders = [self.sfx, self.music]\n\n        self.drop_rate = 30\n        self.rise_rate = 0\n\n        self.cursor_over = False\n        self.is_dropped = False\n\n    def update_vars(self):\n        for index, icon in enumerate(self.icons):\n            icon.cx, icon.cy = self.mx + 100 + 50, self.my + 20 + 30 + (index * 60)\n        for index, slider in enumerate(self.sliders):\n            slider.cx, slider.cy = self.mx + 100 + 50, self.my + 20 + 30 + 320 + (50 * index)\n\n        self.menu0_rect = self.m0.get_rect(topleft=(self.mx, self.my))\n        self.p_rect = self.p0.get_rect(topleft=self.p_location)\n        self.p_location = self.mx + (self.m0_size[0] - self.p_size[0]), self.my + (self.m0_size[1] + 5)\n        self.update_icons()\n\n        if Cursor().rect.colliderect(self.menu0_rect):\n            self.cursor_over = True\n        else:\n            self.cursor_over = False\n\n        if Cursor().rect.colliderect(self.p_rect):\n            self.p_selected = True\n        else:\n            self.p_selected = False\n\n    def handle_movement(self):\n        if self.is_dropped:\n            self.drop()\n            self.handle_icons()\n        else:\n            self.rise()\n\n    def update_icons(self):\n        for icon in self.icons:\n            icon.update_vars()\n        for slider in self.sliders:\n            slider.update_vars()\n\n    def handle_icons(self):\n        for icon in self.icons:\n            icon.handle_select1()\n        for slider in self.sliders:\n            slider.handle_slider()\n\n    def draw_icons(self):\n        for icon in self.icons:\n            icon.draw(WIN)\n        for slider in self.sliders:\n            slider.draw(WIN)\n\n    def drop(self):\n        if self.my < 0:\n            self.my += self.drop_rate\n            if self.drop_rate > 1:\n                self.drop_rate -= 1\n        else:\n            self.my = 0\n\n    def rise(self):\n        if self.my > self.m0_location0[1]:\n            self.my -= self.drop_rate\n            self.drop_rate += 1\n        else:\n            self.my = self.m0_location0[1]\n\n    def draw(self):\n        self.update_vars()\n        WIN.blit(self.m0, (self.mx, self.my))\n        self.draw_icons()\n        if self.is_dropped:\n            if Cursor().rect.colliderect(self.p_rect):\n                WIN.blit(self.p3, self.p_location)\n            else:\n                WIN.blit(self.p2, self.p_location)\n        else:\n            if Cursor().rect.colliderect(self.p_rect):\n                WIN.blit(self.p1, self.p_location)\n            else:\n                WIN.blit(self.p0, self.p_location)\n\n\nclass Inventory(pygame.sprite.Sprite):\n    def __init__(self):\n        super().__init__()\n        self.button0_dir = rf'assets\\textures\\inv_button-0.png'\n        self.button1_dir = rf'assets\\textures\\inv_button-1.png'\n        self.button0 = pygame.image.load(self.button0_dir).convert_alpha()\n        self.button1 = pygame.image.load(self.button1_dir).convert_alpha()\n        self.image = self.button0\n        self.rect = self.button0.get_rect()\n        self.size = [round(0.33*self.rect.width), round(0.33*self.rect.height)]\n        self.x, self.y = 10, 10\n\n        self.selected = False\n        self.clicked = False\n\n        self.click_duration = 0\n\n    def update_vars(self):\n        if Cursor().rect.colliderect(self.rect):\n            self.selected = True\n        else:\n            self.selected = False\n        self.button0 = pygame.transform.scale(self.button0, self.size)\n        self.button1 = pygame.transform.scale(self.button1, self.size)\n        if self.clicked:\n            self.image = self.button1\n            self.click_duration += 1\n            if self.click_duration == (FPS // 8):\n                self.click_duration = 0\n                self.clicked = False\n        else:\n            self.image = self.button0\n        self.rect = self.button0.get_rect()\n\n    def draw(self, surf):\n        surf.blit(self.image, (self.x, self.y))\n\n\nclass GodInventory(pygame.sprite.Sprite):\n    def __init(self):\n        super().__init__()\n\n\n# WORLD\n\n\nclass Background(pygame.sprite.Sprite):\n    def __init__(self, directory):\n        super().__init__()\n        self.file_type = directory[:-4]\n        self.bg_type = ''\n        self.bg_size = WIDTH, HEIGHT\n        self.animated = False\n        self.img = os.listdir(directory)\n\n        if len(self.img) == 1:\n            pass\n        else:\n            self.animated = True\n\n        if self.animated:\n            for i in self.img:\n                img = pygame.image.load(i).convert_alpha()\n                img = pygame.transform.scale(img, self.bg_size)\n                self.img.append(img)\n        else:\n            self.img = pygame.image.load(directory + '\\\\' + self.img[0])\n            self.img = pygame.transform.scale(self.img, self.bg_size)\n\n    def draw(self, surf):\n        if self.animated:\n            pass\n        else:\n            surf.blit(self.img, (0, 0))\n\n\nclass Block(pygame.sprite.Sprite):\n\n    def __init__(self, block_id, block_ind, chunk_ind):\n        super().__init__()   # ROOT CAUSE OF LAG - FIND WAY TO REDUCE BLOCK CREATION PER FRAME\n        self.id = block_id\n        self.ind = block_ind\n        self.chunk_ind = chunk_ind\n        self.location = find_block_location(self.ind, self.chunk_ind)\n        self.x, self.y, self.z = self.location\n        self.screen_x = grid_origin[0] - (self.x * y_gap_x_comp) - \\\n                        (self.y * x_gap_x_comp) + x_gap_x_comp\n        self.screen_y = grid_origin[1] - (self.y * x_gap_y_comp) - \\\n                        (self.x * y_gap_y_comp) - (self.z * z_unit_length)\n\n        self.hit_box_length = 1\n        self.hit_box_width = 1\n        self.hit_box_height = 1\n        self.hit_box_boundaries = (self.x - self.hit_box_length, self.y - self.hit_box_width, self.z),\\\n                                  (self.x, self.y, self.z + self.hit_box_height)\n        self.texture_path = get_texture(self.id)\n        self.image = pygame.image.load(self.texture_path).convert_alpha()\n        self.image_copy = self.image.copy()\n\n        # ALPHA SETTINGS\n        self.in_front = False\n        self.trans = False\n\n        self.alpha = 255\n        self.alpha_min = 25\n        self.alpha_max = 255\n\n        self.highlight = pygame.image.load(r'assets\\textures\\block_highlight_bright.png').convert_alpha()\n\n        self.rect = self.image.get_rect()\n\n        self.is_selected = False\n        self.face_select = ''\n\n        self.resistance = 1\n\n    def selected(self, face):\n        cx, cy = Cursor().location\n        if face == 'f':  # FRONT OR SOUTH\n            if self.select_border(face, 'l', cy) < cx < self.select_border(face, 'r', cy) and \\\n                    self.select_border(face, 't', cx) < cy < self.select_border(face, 'b', cx):\n                return True\n            else:\n                return False\n        if face == 'r':  # RIGHT OR EAST\n            if self.select_border(face, 'l', cy) < cx < self.select_border(face, 'r', cy) and \\\n                    self.select_border(face, 't', cx) < cy < self.select_border(face, 'b', cx):\n                return True\n            else:\n                return False\n        if face == 't':  # TOP OR ABOVE\n            if self.select_border(face, 'l', cx) < cy < self.select_border(face, 'r', cx) and \\\n                    self.select_border(face, 't', cx) < cy < self.select_border(face, 'b', cx):\n                return True\n            else:\n                return False\n        if face == 'np':  # NO PREFERENCE\n            if self.select_border('f', 'l', cy) < cx < self.select_border('f', 'r', cy) and \\\n                    self.select_border('f', 't', cx) < cy < self.select_border('f', 'b', cx):\n                return True\n            elif self.select_border('r', 'l', cy) < cx < self.select_border('r', 'r', cy) and \\\n                    self.select_border('r', 't', cx) < cy < self.select_border('r', 'b', cx):\n                return True\n            elif self.select_border('t', 'r', cx) < cy < self.select_border('t', 'l', cx) and \\\n                    self.select_border('t', 't', cx) < cy < self.select_border('t', 'b', cx):\n                return True\n            else:\n                return False\n\n    def blit_alpha(self, surface, image, location, alpha):\n        x = location[0]\n        y = location[1]\n        temp = pygame.Surface((image.get_width(), image.get_height()))\n        temp.blit(surface, (-x, -y))\n        temp.blit(image, (0, 0))\n        temp.set_alpha(alpha)\n        surface.blit(temp, location)\n\n    def draw(self, screen):\n        if self.trans:\n            if self.alpha > self.alpha_min:\n                self.alpha -= 10\n            self.blit_alpha(screen, self.image, self.rect, self.alpha)\n        else:\n            if self.alpha < self.alpha_max:\n                self.alpha += 10\n                self.blit_alpha(screen, self.image, self.rect, self.alpha)\n            else:\n                screen.blit(self.image, self.rect)\n        if self.is_selected:\n            screen.blit(self.highlight, self.rect)\n        self.is_selected = False\n        self.face_select = ''\n\n    def select(self):\n        self.is_selected = True\n\n    def deselect(self):\n        self.is_selected = False\n\n    def update_grid_displacement(self, player_location):\n        px, py, pz = player_location\n        rel_grid_x, rel_grid_y, rel_grid_z = self.x - px, self.y - py, self.z - pz\n        self.screen_x = grid_origin[0] - (rel_grid_x * y_gap_x_comp) - (rel_grid_y * x_gap_x_comp) + x_gap_x_comp\n        self.screen_y = grid_origin[1] - (rel_grid_y * x_gap_y_comp) - (rel_grid_x * y_gap_y_comp) \\\n                        - (rel_grid_z * z_unit_length)\n        self.rect.x = round(self.screen_x)\n        self.rect.y = round(self.screen_y)\n\n    def destroy(self, tool):\n        pass\n\n    def select_border(self, face, border, var):\n        if face == 'r':\n            if border == 'l':\n                x = x_unit_length + self.screen_x\n                return x\n            if border == 'r':\n                x = x_unit_length + abs(x_gap_x_comp) + self.screen_x\n                return x\n            if border == 't':\n                y = (-(sqrt(3)) * ((var - self.screen_x) - abs(x_gap_x_comp) - x_unit_length)) + self.screen_y\n                return y\n            if border == 'b':\n                y = ((-(sqrt(3)) * ((var - self.screen_x) - abs(x_gap_x_comp) - x_unit_length)) + z_unit_length)\\\n                    + self.screen_y\n                return y\n        if face == 't':\n            if border == 'l':\n                y = (-(sqrt(3)) * ((var - self.screen_x) - abs(x_gap_x_comp))) + self.screen_y\n                return y\n            if border == 'r':\n                y = (-(sqrt(3)) * ((var - self.screen_x) - abs(x_gap_x_comp) - x_unit_length)) + self.screen_y\n                return y\n            if border == 't':\n                y = self.screen_y\n                return y\n            if border == 'b':\n                y = x_gap_y_comp + self.screen_y\n                return y\n        if face == 'f':\n            if border == 'l':\n                x = self.screen_x\n                return x\n            if border == 'r':\n                x = x_unit_length + self.screen_x\n                return x\n            if border == 't':\n                y = x_gap_y_comp + self.screen_y\n                return y\n            if border == 'b':\n                y = x_gap_y_comp + z_unit_length + self.screen_y\n                return y\n\n\nclass Chunk(pygame.sprite.Group):\n    def __init__(self, chunk_ind, planet):\n        super().__init__()\n        self.ind = chunk_ind\n        self.planet = planet\n        self.planet_file = self.planet.file\n        self.planet_data = self.planet.data\n\n        self.is_full = False\n\n        self.term_point = MAX_COORDS[0] - ((self.ind / WORLD_SIZE_X - floor(\n            self.ind / WORLD_SIZE_X)) * WORLD_SIZE_X * CHUNK_X), MAX_COORDS[1] - (floor(\n            (self.ind - (WORLD_SIZE_Y * WORLD_SIZE_X * range(WORLD_SIZE_Z)[int(floor(\n                self.ind / (WORLD_SIZE_X * WORLD_SIZE_Y)))])) / WORLD_SIZE_X) * CHUNK_Y), \\\n                          ((floor(self.ind / (WORLD_SIZE_X * WORLD_SIZE_Y)) + 1) * CHUNK_Z) - MAX_COORDS[2]\n\n    def block_sort_key(self, block):\n        return block.ind\n\n    def fill(self):\n        for i in range(CHUNK_X * CHUNK_Y * CHUNK_Z):\n            block_id = self.planet_data[self.ind][i]\n            if block_id == '0':\n                pass\n            else:\n                if self.planet.is_obscured(self.ind, i, 'all'):\n                    pass\n                else:\n                    block = Block(block_id, i, self.ind)\n                    self.add(block)\n        self.is_full = True\n\n    def reassign(self, new_ind):\n        for block in self:\n            block.kill()\n        self.empty()\n        self.ind = new_ind\n        self.fill()\n\n    def refresh(self):\n        for block in self:\n            block.kill()\n        self.fill()\n\n    def block_exists(self, block_ind):\n        found = False\n        for block in self:\n            if block.ind == block_ind:\n                found = True\n                return True\n        if not found:\n            return False\n\n    def block(self, block_ind):\n        found = False\n        for block in self:\n            if block.ind == block_ind:\n                found = True\n                return block\n        if not found:\n            return ValueError\n\n    def update_grid_displacement(self, player_location):\n        for block in self:\n            block.update_grid_displacement(player_location)\n\n    def update_file(self):\n        self.planet_file.seek(0)\n        json.dump(self.planet_data, self.planet_file)\n\n    def add_block(self, block_id, block_ind):\n        if self.block_exists(block_ind):\n            pass\n        else:\n            self.planet_data[self.ind][block_ind] = block_id\n            self.refresh()\n\n    def remove_block(self, block_ind):\n        if self.block_exists(block_ind):\n            self.planet_data[self.ind][block_ind] = '0'\n            self.refresh()\n        else:\n            pass\n\n\nclass Planet:\n\n    def __init__(self, save, name, directory):\n        # ARGS\n        self.save = save\n        self.name = name\n        self.directory = directory\n        # FILE\n        self.file = open(self.directory, 'r+')\n        self.data = json.load(self.file)\n        # BLOCKS\n        self.blocks_front = []\n        self.blocks_trans = []\n        self.selected_blocks = []\n        # CHUNKS\n        self.player_chunk_spot = ''\n        self.chunk_ind_in_range_old = set()\n        self.chunk_ind_in_range = set()\n        self.chunk_ind_arrived = []\n        self.chunk_ind_departed = []\n        self.chunks_in_range = []\n        self.chunk_ind_visited = set()\n        self.spot_x, self.spot_y, self.spot_z = 0, 0, 0\n        # ENTITIES\n        self.entities = []\n        # PLAYER\n        self.spawn_point = (0, 0, 0)\n        # TRACKING\n        self.tick = 0\n\n    def chunk_exists(self, chunk_ind):\n        found = False\n        for chunk in self.chunks_in_range:\n            if chunk.ind == chunk_ind:\n                found = True\n            else:\n                pass\n        return found\n\n    def chunk(self, chunk_ind):\n        found = False\n        for chunk in self.chunks_in_range:\n            if chunk.ind == chunk_ind:\n                found = True\n                return chunk\n            else:\n                pass\n        if not found:\n            print('chunk out of range')\n            return None\n\n    def handle_block_selection(self):\n        self.selected_blocks.clear()\n        grid_coords = screen_to_grid_pos(Cursor().location, self.save.player)  # most expensive\n        for coord in grid_coords:   # second most expensive\n            gx, gy, gz = coord\n            g_coord = ceil(gx), ceil(gy), ceil(gz)\n            chunk_ind = find_chunk_index(g_coord)\n            block_ind = find_relative_block_index(g_coord)\n            if self.chunk_exists(chunk_ind):\n                if self.chunk(chunk_ind).block_exists(block_ind):\n                    if self.data[chunk_ind][block_ind] != '0' and not self.is_obscured(chunk_ind, block_ind, 'all') and\\\n                            not self.chunk(chunk_ind).block(block_ind) in self.blocks_trans:\n                        block = self.chunk(chunk_ind).block(block_ind)\n                        if block.selected('f') and not self.is_obscured(block.chunk_ind, block.ind, 'f'):\n                            block.face_select = 'f'\n                        if block.selected('r') and not self.is_obscured(block.chunk_ind, block.ind, 'r'):\n                            block.face_select = 'r'\n                        if block.selected('t') and not self.is_obscured(block.chunk_ind, block.ind, 't'):\n                            block.face_select = 't'\n                        self.selected_blocks.append(block)\n                        block.is_selected = True\n                        break\n\n    def is_obscured(self, chunk_ind, block_ind, face):\n        if self.data[chunk_ind][block_ind] != '0':\n            max_ind = (CHUNK_X * CHUNK_Y * CHUNK_Z) - 1\n            tot_blocks = CHUNK_X * CHUNK_Y * CHUNK_Z\n\n            block_east = block_ind + 1\n            chunk_east = chunk_ind\n            if block_east % CHUNK_X == 0:\n                block_east -= CHUNK_X\n                chunk_east += 1\n\n            block_west = block_ind - 1\n            chunk_west = chunk_ind\n            if (block_west + 1) % CHUNK_X == 0:\n                block_west += CHUNK_X\n                chunk_west -= 1\n\n            block_south = block_ind + CHUNK_X\n            chunk_south = chunk_ind\n            if floor(block_south / (CHUNK_X * CHUNK_Y)) != floor(block_ind / (CHUNK_Y * CHUNK_Y)):\n                block_south -= CHUNK_X * CHUNK_Y\n                chunk_south += WORLD_SIZE_X\n\n            block_north = block_ind - CHUNK_X\n            chunk_north = chunk_ind\n            if fceil(block_north / (CHUNK_X * CHUNK_Y)) != fceil(block_ind / (CHUNK_Y * CHUNK_Y)):\n                block_north += CHUNK_X * CHUNK_Y\n                chunk_north -= WORLD_SIZE_X\n\n            block_top = block_ind + (CHUNK_X * CHUNK_Y)\n            chunk_top = chunk_ind\n            if block_top > max_ind:\n                block_top -= tot_blocks\n                chunk_top += WORLD_SIZE_X * WORLD_SIZE_Y\n\n            block_below = block_ind - (CHUNK_X * CHUNK_Y)\n            chunk_below = chunk_ind\n            if block_below < 0:\n                block_below += tot_blocks\n                chunk_below -= WORLD_SIZE_X * WORLD_SIZE_Y\n\n            if face == 'all':\n                if self.data[chunk_east][block_east] != '0' and self.data[chunk_south][block_south] != '0' and \\\n                        self.data[chunk_top][block_top] != '0' and self.data[chunk_west][block_west] != '0' and \\\n                        self.data[chunk_north][block_north] != '0' and self.data[chunk_below][block_below] != '0':\n                    return True\n                else:\n                    return False\n            if face == 'e':\n                if self.data[chunk_east][block_east] != '0':\n                    return True\n                else:\n                    return False\n            if face == 'w':\n                if self.data[chunk_west][block_west] != '0':\n                    return True\n                else:\n                    return False\n            if face == 's':\n                if self.data[chunk_south][block_south] != '0':\n                    return True\n                else:\n                    return False\n            if face == 'n':\n                if self.data[chunk_north][block_north] != '0':\n                    return True\n                else:\n                    return False\n            if face == 't':\n                if self.data[chunk_top][block_top] != '0':\n                    return True\n                else:\n                    return False\n            if face == 'b':\n                if self.data[chunk_below][block_below] != '0':\n                    return True\n                else:\n                    return False\n        else:\n            return False\n\n    def generate(self):\n        file = open(self.directory, 'w')\n        data = []\n        for i in range(WORLD_SIZE_X * WORLD_SIZE_Y * WORLD_SIZE_Z // 2):\n            chunk_data = write_chunk_list('bottom')\n            data.append(chunk_data)\n        for i in range(WORLD_SIZE_X * WORLD_SIZE_Y * WORLD_SIZE_Z // 2):\n            chunk_data = write_chunk_list('top')\n            data.append(chunk_data)\n        json.dump(data, file)\n        file.close()\n\n    def handle_render_rate(self, rate):\n        if len(self.chunk_ind_departed) and len(self.chunk_ind_arrived) > rate - 1:\n            for i in range(rate):\n                chunk = self.chunk(self.chunk_ind_departed[0])\n                if chunk in self.chunks_in_range:\n                    chunk.reassign(self.chunk_ind_arrived[0])\n                    self.chunk_ind_arrived.remove(self.chunk_ind_arrived[0])\n                    self.chunk_ind_departed.remove(self.chunk_ind_departed[0])\n\n    def render_chunks(self, player):\n        px, py, pz = player.location\n        self.chunk_ind_in_range.clear()\n\n        if self.tick % 15 == 0:\n            self.player_chunk_spot = find_chunk_spot(find_chunk_index((ceil(px), ceil(py), ceil(pz))))\n\n        max_spot_difference_x = RENDER_DISTANCE\n        max_spot_difference_y = RENDER_DISTANCE\n        max_spot_difference_z = MAX_COORDS[2] // CHUNK_Z\n\n        max_spot_x = self.player_chunk_spot[0] + max_spot_difference_x\n        if max_spot_x > WORLD_SIZE_X - 1:\n            max_spot_x = WORLD_SIZE_X - 1\n        max_spot_y = self.player_chunk_spot[1] + max_spot_difference_y\n        if max_spot_y > WORLD_SIZE_Y - 1:\n            max_spot_y = WORLD_SIZE_Y - 1\n        max_spot_z = self.player_chunk_spot[2] + max_spot_difference_z\n        if max_spot_z > WORLD_SIZE_Z - 1:\n            max_spot_z = WORLD_SIZE_Z - 1\n        min_spot_x = self.player_chunk_spot[0] - max_spot_difference_x\n        if min_spot_x < 0:\n            min_spot_x = 0\n        min_spot_y = self.player_chunk_spot[1] - max_spot_difference_y\n        if min_spot_y < 0:\n            min_spot_y = 0\n        min_spot_z = self.player_chunk_spot[2] - max_spot_difference_z\n        if min_spot_z < 0:\n            min_spot_z = 0\n\n        if self.tick == 0:\n            self.spot_x, self.spot_y, self.spot_z = min_spot_x, min_spot_y, min_spot_z\n            spot_z = min_spot_z\n            while spot_z < max_spot_z:\n                spot_y = min_spot_y\n                while spot_y < max_spot_y:\n                    spot_x = min_spot_x\n                    while spot_x < max_spot_x:\n                        if distance(self.player_chunk_spot, (spot_x, spot_y, spot_z)) < RENDER_DISTANCE:\n                            chunk_ind = spot_to_ind((spot_x, spot_y, spot_z))\n                            self.chunk_ind_in_range.add(chunk_ind)\n                            chunk = Chunk(chunk_ind, self)\n                            chunk.fill()\n                            self.chunks_in_range.append(chunk)\n                        else:\n                            pass\n                        spot_x += 1\n                    spot_y += 1\n                spot_z += 1\n            self.chunk_ind_in_range_old = self.chunk_ind_in_range.copy()\n        if self.tick > 0:\n            spot_z = min_spot_z\n            while spot_z < max_spot_z:\n                spot_y = min_spot_y\n                while spot_y < max_spot_y:\n                    spot_x = min_spot_x\n                    while spot_x < max_spot_x:\n                        if distance(self.player_chunk_spot, (spot_x, spot_y, spot_z)) < RENDER_DISTANCE:\n                            chunk_ind = spot_to_ind((spot_x, spot_y, spot_z))\n                            self.chunk_ind_in_range.add(chunk_ind)\n                        else:\n                            pass\n                        spot_x += 1\n                    spot_y += 1\n                spot_z += 1\n\n            for i in self.chunk_ind_in_range.difference(self.chunk_ind_in_range_old):\n                self.chunk_ind_arrived.append(i)\n                '''print(f'arrived {i}')'''\n\n            for i in self.chunk_ind_in_range_old.difference(self.chunk_ind_in_range):\n                self.chunk_ind_departed.append(i)\n                '''print(f'departed {i}')'''\n\n            # FOUND THE ISSUE! THESE MUST STAY EQUAL BUT THEY DO NOT WHEN THE PLAYER REACHES THE WORLD BOUNDARY.\n            # RESOLVE THIS BY FIRST PREVENTING THE DEPARTED LIST FROM FILLING UP WITH CHUNKS THAT LEAVE RENDER DISTANCE.\n            # THESE CHUNKS HAVE NO NEW INDEXES TO BE REASSIGNED TO, HENCE WHY THERE IS A CRASH REGARDING THE ABSENCE OF\n            # A TARGETED LIST ITEM.\n\n            self.handle_render_rate(10)\n            self.handle_block_selection()\n            self.blocks_front.clear()\n            self.blocks_trans.clear()\n\n            self.spot_z += 1\n            if self.spot_z == max_spot_z:\n                self.spot_z = min_spot_z\n                self.spot_y += 1\n            if self.spot_y == max_spot_y:\n                self.spot_y = min_spot_y\n                self.spot_x += 1\n            if self.spot_x == max_spot_x:\n                self.spot_x = min_spot_x\n\n        self.tick += 1\n\n        self.chunk_ind_in_range_old = self.chunk_ind_in_range.copy()\n\n    def chunk_sort_key(self, chunk):\n        return chunk.ind\n\n    def draw_blocks_behind(self, player):\n        if self.tick > 0:\n            for chunk in sorted(self.chunks_in_range, key=self.chunk_sort_key):\n                if chunk is None:\n                    pass\n                else:\n                    if chunk.is_full:\n                        chunk.update_grid_displacement(player.location)\n                        for block in chunk:\n                            bsx, bsy = block.screen_x, block.screen_y\n                            bx, by, bz = block.location\n                            px, py, pz = player.location\n\n                            p_center_s_coords = grid_to_screen_coords((px, py, pz +\n                                                                       (player.hit_box_height / 2)),\n                                                                      player.location)\n\n                            if by <= ceil(py) and bz > pz: # add bx <= ceil(px) if needed\n                                self.blocks_front.append(block)\n                                block.in_front = True\n                                if distance((bsx, bsy), p_center_s_coords) < (5 * z_unit_length) and\\\n                                        by <= ceil(py) - 1 and self.save.game.trans_blocks:\n                                    self.blocks_trans.append(block)\n                                    block.trans = True\n                            else:\n                                block.draw(WIN)\n                        chunk.update()\n                    else:\n                        pass\n        else:\n            pass\n\n    def draw_blocks_front(self):\n        for block in self.blocks_front:\n            block.draw(WIN)\n            block.in_front = False\n        for block in self.blocks_trans:\n            block.trans = False\n\n    def save_data(self):\n        self.file.seek(0)\n        json.dump(self.data, self.file)\n\n\nclass Save:\n\n    def __init__(self, name, game):\n        super().__init__()\n        self.name = name\n        self.game = game\n\n        self.player = Player((0, 0, 2), SPAWN_DIRECTION)\n        self.player_file = rf'assets\\saves\\{name}\\player_data.json'\n\n        self.planet1 = Planet(self, 'planet1', rf'assets\\saves\\{name}\\planet1_data.json')\n        self.current_planet = self.planet1\n\n    def load(self):\n        pass\n\n    def save(self):\n        pass\n\n\nclass Title:\n\n    def __init__(self):\n        self.bg = Background(rf'assets\\textures\\starry_bg')\n        self.p1 = PlanetIcon('planet1', rf'assets\\textures\\planet1', (250, 250), ((3*WIDTH) // 4, HEIGHT // 2),\n                             (1/2))\n        self.p2 = PlanetIcon('planet2', rf'assets\\textures\\planet2', (250, 250), (WIDTH // 4, HEIGHT // 2),\n                             0)\n\n        self.icons = [self.p1, self.p2]\n\n    def handle_icons(self):\n        for icon in self.icons:\n            icon.handle_planet()\n            icon.update_vars()\n\n\n# PRIMARY\n\n\nclass Game:\n\n    def __init__(self):\n        self.clock = pygame.time.Clock()\n        self.running = True\n        if not os.path.exists(r'assets\\textures\\mmm.jfif'):\n            print('lmao')\n            self.running = False\n        self.saves = [Save('save1', self)]\n        self.current_save = self.saves[0]\n\n        self.cursor = Cursor()\n        self.menu = Menu()\n        self.title = Title()\n\n        self.left_click = 1\n        self.right_click = 3\n        self.middle_click = 2\n\n        self.trans_blocks = True\n\n    def run(self):\n        if self.current_save == '':\n            pass\n        else:\n            save = self.current_save\n            planet = save.current_planet\n            player = save.player\n            pygame.mixer.music.load(to_the_stars)\n            pygame.mixer.music.play(-1)\n        while self.running:\n            self.clock.tick(FPS)\n            if self.current_save == '':\n                mouse_pressed = pygame.mouse.get_pressed()\n                keys_pressed = pygame.key.get_pressed()\n                self.cursor.update_pos()\n                self.title.handle_icons()\n                self.handle_title_events(keys_pressed, mouse_pressed)\n                self.draw_window(keys_pressed)\n            else:\n                mouse_pressed = pygame.mouse.get_pressed()\n                keys_pressed = pygame.key.get_pressed()\n                self.cursor.update_pos()\n                planet.render_chunks(player)\n                self.handle_events(player, mouse_pressed)\n                self.menu.handle_movement()\n                player.inv.update_vars()\n                self.draw_window(keys_pressed)\n                player.check_block(planet, 'below')\n                player.check_block(planet, 'north')\n                player.check_block(planet, 'south')\n                player.check_block(planet, 'east')\n                player.check_block(planet, 'west')\n                player.check_block(planet, 'above')\n\n    def change_save(self, new_save):\n        self.current_save = new_save\n\n    def handle_title_events(self, keys_pressed, mouse_pressed):\n        for event in pygame.event.get():\n            if event.type == pygame.QUIT:\n                self.running = False\n            if event.type == pygame.MOUSEBUTTONDOWN:\n                if event.button == self.left_click:\n                    if self.title.p1.icon.selected:\n                        self.current_save = self.saves[0]\n\n    def handle_events(self, player, mouse_pressed):\n        for event in pygame.event.get():\n            if event.type == pygame.QUIT:\n                self.current_save.current_planet.save_data()\n                self.running = False\n            if event.type == pygame.KEYDOWN:\n                if event.key == pygame.K_ESCAPE:\n                    pygame.mixer.Sound.play(menu_slide_sound)\n                    self.menu.is_dropped = not self.menu.is_dropped\n                if event.key == pygame.K_SPACE:\n                    player.initiate_jump()\n            if mouse_pressed[self.left_click - 1]:\n                if self.menu.sfx.knob.selected:\n                    self.menu.sfx.slidable = True\n                if self.menu.music.knob.selected:\n                    self.menu.music.slidable = True\n                    pygame.mixer.music.set_volume(self.menu.music.knob_percent / 100)\n            if not mouse_pressed[self.left_click - 1]:\n                self.menu.sfx.slidable = False\n                self.menu.music.slidable = False\n            if event.type == pygame.MOUSEBUTTONDOWN:\n                if event.button == self.left_click:\n                    if player.inv.selected:\n                        player.inv.clicked = True\n                        player.inv.image = player.inv.button1\n                    if self.menu.cursor_over or self.menu.p_selected:\n                        if self.menu.resume.selected:\n                            pygame.mixer.Sound.play(menu_slide_sound)\n                            self.menu.is_dropped = False\n                        if self.menu.quick_save.selected:\n                            self.current_save.current_planet.save_data()\n                        if self.menu.save_quit.selected:\n                            self.current_save.current_planet.save_data()\n                            self.current_save = ''\n                        if self.menu.p_selected:\n                            pygame.mixer.Sound.play(menu_slide_sound)\n                            self.menu.is_dropped = not self.menu.is_dropped\n                    else:\n                        for block in self.current_save.current_planet.selected_blocks:\n                            pygame.mixer.Sound.play(break_sound)\n                            self.current_save.current_planet.chunk(block.chunk_ind).remove_block(block.ind)\n                            for o in 'nswetb':  # for loop loads in blocks of surrounding chunks upon being exposed\n                                next_chunk_ind = chunk_ind_next(block.chunk_ind, o)\n                                if next_chunk_ind is not None:\n                                    self.current_save.current_planet.chunk(next_chunk_ind).refresh()\n                if event.button == self.right_click:\n                    for block in self.current_save.current_planet.selected_blocks:\n                        bx, by, bz = block.location\n                        if block.face_select == 'r':\n                            bx -= 1\n                            c_ind = find_chunk_index((bx, by, bz))\n                            b_ind = find_relative_block_index((bx, by, bz))\n                            pygame.mixer.Sound.play(place_sound)\n                            self.current_save.current_planet.chunk(c_ind).add_block('2', b_ind)\n                        if block.face_select == 'f':\n                            by -= 1\n                            c_ind = find_chunk_index((bx, by, bz))\n                            b_ind = find_relative_block_index((bx, by, bz))\n                            pygame.mixer.Sound.play(place_sound)\n                            self.current_save.current_planet.chunk(c_ind).add_block('2', b_ind)\n                        if block.face_select == 't':\n                            bz += 1\n                            c_ind = find_chunk_index((bx, by, bz))\n                            b_ind = find_relative_block_index((bx, by, bz))\n                            pygame.mixer.Sound.play(place_sound)\n                            self.current_save.current_planet.chunk(c_ind).add_block('2', b_ind)\n                        else:\n                            pass\n                if event.button == self.middle_click:\n                    self.trans_blocks = not self.trans_blocks\n            if event.type == pygame.VIDEORESIZE:\n                pass\n\n    def draw_window(self, keys_pressed):\n        if self.current_save == '':\n            title = self.title\n            pygame.draw.rect(WIN, WHITE, BG)\n            title.bg.draw(WIN)\n            for icon in title.icons:\n                icon.draw(WIN)\n\n            self.cursor.draw()\n            pygame.display.flip()\n        else:\n            save = self.current_save\n            planet = save.current_planet\n            player = save.player\n\n            pygame.draw.rect(WIN, WHITE, BG)\n\n            planet.draw_blocks_behind(player)\n\n            player.render(keys_pressed)\n\n            planet.draw_blocks_front()\n\n            display_coordinates(player.location)\n\n            display_advanced_info(player, self.clock)\n\n            player.draw_hit_box()\n\n            \"\"\"WIN.blit(FOG, (0, 0))\"\"\"\n\n            self.menu.draw()\n            player.inv.draw(WIN)\n\n            self.cursor.draw()\n\n            pygame.display.flip()\n\n    def new_save(self, name):\n        save = Save(name)\n        self.saves.append(save)\n\n    def delete_save(self, name):\n        for save in self.saves:\n            if save.name == name:\n                self.saves.remove(save)","repo_name":"ArtificialIntelligenceDan/Astronautica","sub_path":"astro_classes.py","file_name":"astro_classes.py","file_ext":"py","file_size_in_byte":73540,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"23425713852","text":"\"\"\"This module is a best effort first-iteration attempt to add retry logic to sqlalchemy.\n\nIt's expected when working with a remote database to encounter exceptions related to deadlocks,\ntransaction isolation measures, and overall connectivity issues.  These exceptions are almost\nalways from the DB-API level and mostly inherit from:\n    * sqlalchemy.exc.OperationalError\n    * sqlalchemy.exc.InternalError\n\nThe tenacity library can be used here to add some retry logic to sqlalchemy transactions.\n\nThere are a few ways to use tenacity:\n    * You can use the t`enacity.retry` decorator to decorate the callable with retry logic.\n    * You can use the `tenacity.Retrying` or `tenacity.AsyncRetrying` context managers to add\n      retry logic to a block of code.\n\nWARNING:  The following code in this module is experimental and needs to be completed before anything here\ncan be relied on.  It probably doesn't work in it's current form.  Use the examples under Usage instead.\n    * RetryingSession\n    * retrying_session\n    * retrying_async_session\n\n    \nUsage:\n\nExample decorator usage:\n\n    ```python\n    @tenacity.retry(**config)\n    def add_user_post(db, user_id, post_values):\n        with db.bind.Session() as session:\n            with session.begin():\n                user = session.scalars(sa.select(User).where(User.id == user_id)).one()\n                post = Post(user=user, **post_values)\n                session.add(post)\n                session.flush()\n                session.refresh(post)\n        return post\n    ```\n\nExample async decorator usage:\n    \n    ```python\n    @tenacity.retry(**config)\n    async def add_user_post(db, user_id, post_values):\n        async_bind = db.get_bind(\"async\")\n        async with async_bind.Session() as session:\n            async with session.begin():\n                user = (await session.scalars(sa.select(User).where(User.id == user_id))).one()\n                post = Post(user=user, **post_values)\n                session.add(post)\n                await session.commit()\n                await session.refresh(post)\n        return post\n    ```\n\nExample context manager usage:\n\n    ```python\n    try:\n        for attempt in tenacity.Retrying(**config):\n            with attempt:\n                with db.bind.Session() as session:\n                    with session.begin():\n                        obj = session.scalars(sa.select(User).where(User.id == 1)).one()\n                        post = Post(title=\"new post\", user=obj)\n                        session.add(Post)\n    except tenacity.RetryError:\n        pass\n    ```\n\nExample async context manager usage:\n\n    ```python\n    async_bind = db.get_bind(\"async\")\n    try:\n        async for attempt in tenacity.AsyncRetrying(**config):\n            with attempt:\n                async with async_bind.Session() as session:\n                    async with session.begin():\n                        obj = (await session.scalars(sa.select(User).where(User.id == 1))).one()\n                        post = Post(title=\"new post\", user=obj)\n                        session.add(Post)\n                        await session.commit()\n    except tenacity.RetryError:\n        pass\n    ```\n\nCheck out the docs: https://tenacity.readthedocs.io/en/latest/\nCheck out the repo: https://github.com/jd/tenacity\n\"\"\"\n\nimport logging\nfrom contextlib import asynccontextmanager\nfrom contextlib import AsyncExitStack\nfrom contextlib import contextmanager\nfrom contextlib import ExitStack\n\nimport sqlalchemy\nimport sqlalchemy.exc\nimport sqlalchemy.orm\nimport tenacity\n\n\nsa = sqlalchemy\n\nlogger = logging.getLogger(__name__)\n\n\n_RETRY_ERRORS = (\n    sa.exc.InternalError,\n    sa.exc.InvalidRequestError,\n    sa.exc.OperationalError,\n)\n\n\nretry_config = dict(\n    reraise=True,\n    retry=tenacity.retry_if_exception_type(_RETRY_ERRORS)\n    | tenacity.retry_if_exception_message(match=\"Too many connections\"),\n    stop=tenacity.stop_after_attempt(3) | tenacity.stop_after_delay(5),\n    wait=tenacity.wait_exponential(max=6, exp_base=1.5),\n    before_sleep=tenacity.before_sleep_log(logger, logging.INFO),\n)\n\nretryable = tenacity.retry(**retry_config)\nretry_context = tenacity.Retrying(**retry_config)\n\n\nclass RetryingSession(sa.orm.Session):\n    @tenacity.retry(**retry_config)\n    def _execute_internal(self, *args, **kwargs):\n        return super()._execute_internal(*args, **kwargs)\n\n\n@contextmanager\ndef retrying_session(bind, begin=True, **kwargs):\n    try:\n        for attempt in tenacity.Retrying(**retry_config, **kwargs):\n            with attempt:\n                print(\"attempt\", attempt.retry_state.attempt_number)\n                with bind.Session() as session:\n                    with ExitStack() as stack:\n                        if begin:\n                            stack.enter_context(session.begin())\n                        yield session\n    except tenacity.RetryError:\n        pass\n\n\n@asynccontextmanager\nasync def retrying_async_session(bind, begin=True, **kwargs):\n    try:\n        async for attempt in tenacity.AsyncRetrying(**retry_config, **kwargs):\n            with attempt:\n                async with bind.Session() as session:\n                    async with AsyncExitStack() as stack:\n                        if begin:\n                            await stack.enter_async_context(session.begin())\n                        yield session\n    except tenacity.RetryError:\n        pass\n","repo_name":"joeblackwaslike/quart-sqlalchemy","sub_path":"src/quart_sqlalchemy/retry.py","file_name":"retry.py","file_ext":"py","file_size_in_byte":5374,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"81"}
+{"seq_id":"880775349","text":"import random\nfrom replit import clear\n\nstages = ['''\n  +---+\n  |   |\n  O   |\n /|\\  |\n / \\  |\n      |\n=========\n''', '''\n  +---+\n  |   |\n  O   |\n /|\\  |\n /    |\n      |\n=========\n''', '''\n  +---+\n  |   |\n  O   |\n /|\\  |\n      |\n      |\n=========\n''', '''\n  +---+\n  |   |\n  O   |\n /|   |\n      |\n      |\n=========''', '''\n  +---+\n  |   |\n  O   |\n  |   |\n      |\n      |\n=========\n''', '''\n  +---+\n  |   |\n  O   |\n      |\n      |\n      |\n=========\n''', '''\n  +---+\n  |   |\n      |\n      |\n      |\n      |\n=========\n''']\nword_list = [\"apple\", \"banana\", \"cherry\", \"date\", \"grape\"]\nanswer = list(random.choice(word_list))\nletters_guessed = ['_' for i in range(0, len(answer))]\nguesses_left = 7\n\nwhile answer != letters_guessed and guesses_left != 0:\n    clear()\n    guess = input(\"Please guess an alphabet\\n\")\n    print(\" \".join(letters_guessed))\n    if guess in answer:\n        print(f\"{guess} was present in the word\")\n        for j in range(0, len(answer)):\n            if answer[j] == guess:\n                letters_guessed[j] = guess\n        print(\" \".join(letters_guessed))\n    else:\n        guesses_left -= 1\n        print(f\"Sorry, {guess} was not present in the word\")\n        print(\" \".join(letters_guessed))\n        print(f\"You have {guesses_left} guesses left\")\n        print(stages[guesses_left])\n\n\nif answer == letters_guessed:\n    print(\"You won!\")\nelse:\n    print(\"You lost!\")\n","repo_name":"datawithsumanth/100DaysofCode","sub_path":"hangman.py","file_name":"hangman.py","file_ext":"py","file_size_in_byte":1387,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"41321778043","text":"from django.contrib import admin\n\nfrom forum.models import ForumProfile, Section, Forum, Topic, Post, Search\n\nDENORMALISED_DATA_NOTICE = 'You shouldn\\'t need to edit this data manually.'\n\nclass ForumProfileAdmin(admin.ModelAdmin):\n    list_display = ('user', 'group', 'title', 'location',\n                    'post_count')\n    list_filter = ('group',)\n    fieldsets = (\n        (None, {\n            'fields': ('user', 'group', 'title', 'location', 'avatar',\n                       'website'),\n        }),\n        ('Board settings', {\n            'fields': ('timezone', 'topics_per_page', 'posts_per_page',\n                       'auto_fast_reply'),\n        }),\n        ('Denormalised data', {\n            'classes': ('collapse',),\n            'description': DENORMALISED_DATA_NOTICE,\n            'fields': ('post_count',),\n        }),\n    )\n\nclass SectionAdmin(admin.ModelAdmin):\n    list_display = ('name', 'order')\n\nclass ForumAdmin(admin.ModelAdmin):\n    list_display = ('name', 'section', 'description', 'order',\n                    'topic_count', 'locked', 'hidden')\n    list_filter = ('section',)\n    fieldsets = (\n        (None, {\n            'fields': ('name', 'section', 'description', 'order'),\n        }),\n        ('Administration', {\n            'fields': ('locked', 'hidden'),\n        }),\n        ('Denormalised data', {\n            'classes': ('collapse',),\n            'description': DENORMALISED_DATA_NOTICE,\n            'fields': ('topic_count', 'last_post_at', 'last_topic_id',\n                       'last_topic_title','last_user_id', 'last_username'),\n        }),\n    )\n    search_fields = ('name',)\n\nclass TopicAdmin(admin.ModelAdmin):\n    list_display = ('title', 'forum', 'user', 'started_at', 'post_count',\n                    'metapost_count', 'last_post_at', 'locked', 'pinned',\n                    'hidden')\n    list_filter = ('forum', 'locked', 'pinned', 'hidden')\n    fieldsets = (\n        (None, {\n            'fields': ('title', 'forum', 'user', 'description'),\n        }),\n        ('Administration', {\n            'fields': ('pinned', 'locked', 'hidden'),\n        }),\n        ('Denormalised data', {\n            'classes': ('collapse',),\n            'description': DENORMALISED_DATA_NOTICE,\n            'fields': ('post_count', 'metapost_count', 'last_post_at',\n                       'last_user_id', 'last_username'),\n        }),\n    )\n    search_fields = ('title',)\n\nclass PostAdmin(admin.ModelAdmin):\n    list_display = ('__unicode__', 'user', 'topic', 'meta', 'posted_at',\n                    'edited_at', 'user_ip')\n    list_filter = ('meta',)\n    fieldsets = (\n        (None, {\n            'fields': ('user', 'topic', 'body', 'meta', 'emoticons'),\n        }),\n        ('Denormalised data', {\n            'classes': ('collapse',),\n            'description': DENORMALISED_DATA_NOTICE,\n            'fields': ('num_in_topic',),\n        }),\n    )\n    search_fields = ('body',)\n\nclass SearchAdmin(admin.ModelAdmin):\n    list_display = ('type', 'user', 'searched_at')\n    list_filter = ('type',)\n\nadmin.site.register(ForumProfile, ForumProfileAdmin)\nadmin.site.register(Section, SectionAdmin)\nadmin.site.register(Forum, ForumAdmin)\nadmin.site.register(Topic, TopicAdmin)\nadmin.site.register(Post, PostAdmin)\nadmin.site.register(Search, SearchAdmin)\n","repo_name":"insin/forum","sub_path":"forum/admin.py","file_name":"admin.py","file_ext":"py","file_size_in_byte":3280,"program_lang":"python","lang":"en","doc_type":"code","stars":26,"dataset":"github-code","pt":"81"}
+{"seq_id":"23437996307","text":"from pyrogram import Client, filters\nimport asyncio\nimport time\nfrom pyrogram.types import ChatPermissions, Message\nfrom config import (bot, HNDLR, SUDO_USERS, LOGS_CHANNEL )\nfrom pyrogram import Client, filters\n\n\n@Client.on_message(filters.user(SUDO_USERS) & filters.command([\"pin\"], prefixes=HNDLR))\n@Client.on_message(filters.me & filters.command([\"pin\"], prefixes=HNDLR))\nasync def pin_message(client: Client, message):\n    msg_id=message.message_id\n    chat_id=message.chat.id\n    if message.reply_to_message == None:\n        await client.edit_message_text(chat_id , msg_id , \"Shall I pin your head to wall ?\")\n    else:\n        if message.chat.type == \"private\":\n            reply_msg_id=message.reply_to_message.message_id\n            await client.pin_chat_message(chat_id , reply_msg_id , both_sides=True)\n            await message.edit_text(\"Done the Job master !\")\n        else:\n            zuzu= await client.get_chat_member(chat_id , \"me\")\n            can_pin=zuzu.can_pin_messages\n            if not can_pin:\n                await client.edit_message_text(chat_id , msg_id , \"Not a admin bruh 🥱\") \n            else:         \n                reply_msg_id=message.reply_to_message.message_id\n                await client.pin_chat_message(chat_id , reply_msg_id)\n                await client.edit_message_text(chat_id , msg_id , \"Done the Job master !\")\n","repo_name":"kaal0408/Meow","sub_path":"Modules/pin.py","file_name":"pin.py","file_ext":"py","file_size_in_byte":1367,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"81"}
+{"seq_id":"34863946241","text":"\nfrom pyomo.environ import *\nimport numpy as np\nimport util\nimport config as cfg\nimport pdb\nimport pandas as pd\nimport copy\nimport dispatch_budget\nimport logging\n\ndef surplus_capacity(model):\n    return model.surplus_capacity + model.peak_penalty * model.weight_on_peak_penalty\n\ndef define_penalty_to_preference_high_cost_gen_maint_during_peak(model):\n    # if forced to choose between having high cost or low cost gen be on maintenance when load is high, we'd rather high cost gen be doing maintenance\n    # this should lower production cost overall and make maintenance schedules less random\n    return model.peak_penalty == sum([sum([model.marginal_costs[g]*model.max_load_by_group[i]*model.scheduled_maintenance[i, g] for g in model.g])\n                                      for i in model.i])\n\ndef feasible_maintenance_constraint_0(model, i, g):\n    return model.scheduled_maintenance[i, g] >= 0\n\ndef feasible_maintenance_constraint_1(model, i, g):\n    return model.scheduled_maintenance[i, g] <= 1\n\ndef define_available_gen(model, i):\n    return model.available_gen[i] == sum([(1 - model.scheduled_maintenance[i, g]) * model.pmax[g] for g in model.g])\n\ndef meet_maintenance_constraint(model, g):\n    # average maintenance across the hours == annual maintenance rate\n    return sum([model.scheduled_maintenance[i, g] * model.group_lengths[i] for i in model.i]) == model.annual_maintenace_hours[g]\n\ndef define_surplus_capacity(model, i):\n    return model.surplus_capacity >= model.available_gen[i] - model.max_load_by_group[i]\n\ndef scale_load_to_system(load, pmaxs, typical_reserve=1.15):\n    max_load = load.max()\n    sum_cap = sum(pmaxs)\n    if (max_load * typical_reserve) > sum_cap:\n        assert max_load != 0\n        load2 = load * (sum_cap / (max_load * typical_reserve))\n        return load2\n    else:\n        return load\n\ndef schedule_generator_maintenance(load, pmaxs, annual_maintenance_rates, dispatch_periods, marginal_costs, print_opt=False):\n    # annual maintenance rates must be between zero and one\n    annual_maintenance_rates = np.clip(annual_maintenance_rates, 0, 1)\n\n    # gives the index for the change between dispatch_periods\n    group_cuts = list(np.where(np.diff(dispatch_periods) != 0)[0] + 1) if dispatch_periods is not None else None\n    group_lengths = np.array([group_cuts[0]] + list(np.diff(group_cuts)) + [len(load) - group_cuts[-1]])\n    num_groups = len(group_cuts) + 1\n\n    # necessary to scale load in some cases for the optimization to work. Basically, load shouldn't be > gen\n    load_scaled = scale_load_to_system(load, pmaxs)\n    max_load_by_group = np.array([np.max(ls) for ls in np.array_split(load_scaled, np.array(group_cuts))])\n\n    annual_maintenace_hours = annual_maintenance_rates*len(load)\n\n    pmaxs_zero = np.nonzero(pmaxs==0)[0]\n    pmaxs_not_zero = np.nonzero(pmaxs)[0]\n\n    estimated_peak_penalty = sum(sum(np.outer(marginal_costs[pmaxs_not_zero],max_load_by_group).T*annual_maintenance_rates[pmaxs_not_zero]))\n    estimated_surplus_capacity = (pmaxs.sum() - max_load_by_group.min())*(1-annual_maintenance_rates.mean())\n    weight_on_peak_penalty = estimated_surplus_capacity/estimated_peak_penalty/10.\n\n    model = ConcreteModel()\n\n    # INPUT PARAMS\n    model.i = RangeSet(0, num_groups - 1)\n    model.g = RangeSet(0, len(pmaxs_not_zero) - 1)\n    model.annual_maintenace_hours = Param(model.g, initialize=dict(zip(model.g.keys(), annual_maintenace_hours[pmaxs_not_zero])))\n    model.pmax = Param(model.g, initialize=dict(zip(model.g.keys(), pmaxs[pmaxs_not_zero])))\n    model.marginal_costs = Param(model.g, initialize=dict(zip(model.g.keys(), marginal_costs[pmaxs_not_zero])))\n    model.max_load_by_group = Param(model.i, initialize=dict(zip(model.i.keys(), max_load_by_group)))\n    model.group_lengths = Param(model.i, initialize=dict(zip(model.i.keys(), group_lengths)))\n    model.weight_on_peak_penalty = Param(default=weight_on_peak_penalty)\n\n    # DECISIONS VARIABLES\n    model.available_gen = Var(model.i, within=NonNegativeReals)\n    model.scheduled_maintenance = Var(model.i, model.g, within=NonNegativeReals)\n    model.surplus_capacity = Var(within=NonNegativeReals)\n    model.peak_penalty = Var(within=NonNegativeReals)\n\n    # CONSTRAINTS\n    model.define_available_gen = Constraint(model.i, rule=define_available_gen)\n    model.feasible_maintenance_constraint_0 = Constraint(model.i, model.g, rule=feasible_maintenance_constraint_0)\n    model.feasible_maintenance_constraint_1 = Constraint(model.i, model.g, rule=feasible_maintenance_constraint_1)\n    model.meet_maintenance_constraint = Constraint(model.g, rule=meet_maintenance_constraint)\n    model.define_surplus_capacity = Constraint(model.i, rule=define_surplus_capacity)\n    model.define_penalty_to_preference_high_cost_gen_maint_during_peak = Constraint(rule=define_penalty_to_preference_high_cost_gen_maint_during_peak)\n\n    # OBJECTIVE\n    model.objective = Objective(rule=surplus_capacity, sense=minimize)\n\n    # SOLVE AND EXPORT RESULTS\n    solver = SolverFactory(cfg.solver_name or \"cbc\") # use cbc by default for testing, when you import config in a test, solver_name is None\n    results = solver.solve(model, tee=print_opt)\n    model.solutions.load_from(results)\n\n    scheduled_maintenance = np.empty((num_groups, len(pmaxs)))\n    scheduled_maintenance[:, pmaxs_zero] = annual_maintenance_rates[pmaxs_zero]\n    scheduled_maintenance[:, pmaxs_not_zero] = np.array([[model.scheduled_maintenance[i, g].value for i in model.i.keys()] for g in model.g.keys()]).T\n    return scheduled_maintenance\n\n\ndef schedule_generator_maintenance_loop(load, pmaxs, annual_maintenance_rates, dispatch_periods, scheduling_order):\n    # if nothing else, better to schedule the large generators first\n    scheduling_order = np.argsort(-pmaxs) if scheduling_order is None else scheduling_order\n\n    # annual maintenance rates must be between zero and one\n    annual_maintenance_rates = np.clip(annual_maintenance_rates, 0, 1)\n\n    # gives the index for the change between dispatch_periods\n    group_cuts = list(np.where(np.diff(dispatch_periods) != 0)[0] + 1) if dispatch_periods is not None else None\n    group_lengths = np.array([group_cuts[0]] + list(np.diff(group_cuts)) + [len(load) - group_cuts[-1]])\n    num_groups = len(group_cuts) + 1\n\n    # necessary to scale load in some cases for the optimization to work. Basically, load shouldn't be > gen\n    load_scaled = scale_load_to_system(load, pmaxs)\n    load_scaled = np.concatenate([[np.max(ls)]*gl for gl, ls in zip(group_lengths, np.array_split(load_scaled, np.array(group_cuts)))])\n\n    pmaxs_clipped = copy.deepcopy(pmaxs)\n    pmaxs_clipped = np.clip(pmaxs_clipped, 1e-1, None)\n    maintenance_energy = annual_maintenance_rates*pmaxs_clipped*len(load)\n    scheduled_maintenance = np.zeros((num_groups, len(pmaxs)))\n\n    # loop through and schedule maintenance for each generator one at a time. Update the net load after each one.\n    for i in scheduling_order:\n        energy_allocation = dispatch_budget.dispatch_to_energy_budget(load_scaled, -maintenance_energy[i], pmins=0, pmaxs=pmaxs_clipped[i])\n        scheduled_maintenance[:, i] = np.clip(np.array([np.mean(ls) for ls in np.array_split(energy_allocation, np.array(group_cuts))])/pmaxs_clipped[i], 0, 1)\n        load_scaled += np.concatenate([[sm * pmaxs[i]]*gl for gl, sm in zip(group_lengths, scheduled_maintenance[:, i])])\n\n    if not all(np.isclose(annual_maintenance_rates, (scheduled_maintenance.T * group_lengths).sum(axis=1)/len(load))):\n        logging.warning(\"scheduled maintance rates don't all match the annual maintenance rates\")\n    return scheduled_maintenance","repo_name":"energyPATHWAYS/EnergyPATHWAYS","sub_path":"energyPATHWAYS/dispatch_maintenance.py","file_name":"dispatch_maintenance.py","file_ext":"py","file_size_in_byte":7610,"program_lang":"python","lang":"en","doc_type":"code","stars":42,"dataset":"github-code","pt":"81"}
+{"seq_id":"13817534279","text":"import warnings\nimport numpy as np\nimport pandas as pd\nfrom typing import Union\n\n\nclass Sequencer:\n    # todo implement fractional differencing\n\n    def __init__(self,\n                 back: Union[list, int] = 1,\n                 forward: Union[list, int] = 1,\n                 shift: int = 0,\n                 diff: str = 'none'):\n        \"\"\"\n        Sequencer. Sequences and differentiate data.\n        The indices of back and forward start from 0. Therefore, if the output is included in the input,\n        Having forward = 4 will result in predicting the output for [t, t+1, t+2, t+3].\n        Having forward = [4] will result in making a t+4 prediction.\n        Having back = 30 will result in the all samples [t, t-1, ..., t-29] to be included.\n        Having back = [30] will result in the sample [t-30] to be included.\n\n        :param back: Int or List[int]: Input indices.\n        If list -> includes all integers within the list\n        If int -> includes that many samples back\n        :param forward: Int or List[int]: Output indices.\n        If list -> includes all integers within the list.\n        If int -> includes that many samples forward.\n        :param shift: Int: If there is a shift between input and output\n        :param diff: differencing algo, pick between 'none', 'diff', 'log_diff'\n        \"\"\"\n        # Tests\n        assert diff in ['none', 'diff', 'log_diff'], 'Diff needs to be none, diff or log_diff.'\n        if isinstance(back, int):\n            assert back > 0, \"'back' arg needs to be strictly positive.\"\n        else:\n            assert all([x >= 0 for x in back]), \"All integers in 'back' need to be positive.\"\n            assert all([x > 0 for x in np.diff(back)]), \"All integers in 'back' need to be monotonically increasing.\"\n        if isinstance(forward, int):\n            assert forward >= 0, \"'forward' arg needs to be positive\"\n        else:\n            assert all([x >= 0 for x in forward]), \"All integers in 'forward' need to be positive.\"\n            assert all([x > 0 for x in np.diff(forward)]), \\\n                \"All integers in 'forward' need to be monotonically increasing.\"\n        if diff != 'none' and isinstance(back, int):\n            assert back > 1, \"With differencing, back needs to be at least 2.\"\n        if diff != 'none' and isinstance(forward, int):\n            assert forward > 1, \"With differencing, forward needs to be at least 2.\"\n\n        # Note static args\n        self.shift = shift\n        self.diff = diff\n        self.samples = 0            # Add 1 as in/out-put start both at 0\n        self.inputConstant = 0\n        self.outputConstant = 0\n\n        # Parse args\n        if type(back) == int:\n            back = np.linspace(0, back - 1, back).astype('int')\n        elif type(back) == list:\n            back = np.array(back)\n        if type(forward) == int:\n            forward = np.linspace(0, forward - 1, forward).astype('int')\n        elif type(forward) == list:\n            forward = np.array(forward)\n\n        # Index Vectors\n        self.inputIndices = back\n        self.inputDiffIndices = None\n        self.outputIndices = forward\n        self.outputDiffIndices = None\n\n        # Differencing vectors\n        if diff != 'none':\n            # In case first is 0, we won't difference with -1, therefore, we add & skip the first\n            if self.inputIndices[0] == 0:\n                self.inputDiffIndices = self.inputIndices[:-1]\n                self.inputIndices = self.inputIndices[1:]\n            else:\n                # However, if first is nonzero, we can keep all and roll them, changing first one to 0\n                self.inputDiffIndices = np.roll(self.inputIndices, 1)\n                self.inputDiffIndices[0] = 0\n\n            # Same for output\n            if self.outputIndices[0] == 0:\n                self.outputDiffIndices = self.outputIndices[:-1]\n                self.outputIndices = self.outputIndices[1:]\n            else:\n                self.outputDiffIndices = np.roll(self.outputIndices, 1)\n                self.outputDiffIndices[0] = 0\n\n        # Number of sequence steps\n        self.nInputSteps = len(self.inputIndices)\n        self.nOutputSteps = len(self.outputIndices)\n\n        # Maximum steps\n        self.maxInputStep = max(self.inputIndices)\n        self.maxOutputStep = max(self.outputIndices)\n\n    def convert(self, x, y, flat=True):\n        \"\"\"\n        Sequences input / outputs dataframe / numpy array.\n\n        :param x: Input\n        :param y: Output\n        :param flat: Boolean. If true, a flat matrix is returned. If false, a 3D tensor is returned (numpy).\n        :return: seq_x, seq_y\n        \"\"\"\n        if isinstance(x, pd.DataFrame) or isinstance(x, pd.core.series.Series):\n            assert isinstance(y, pd.DataFrame) or isinstance(y, pd.core.series.Series), \\\n                'Input and Output need to be the same data type.'\n            return self._convert_pandas(x, y, flat=flat)\n        elif isinstance(x, np.ndarray):\n            assert isinstance(y, np.ndarray), 'Input and Output need to be the same data type.'\n            return self._convert_numpy(x, y, flat=flat)\n        else:\n            TypeError('Input & Output need to be same datatype, either Numpy or Pandas.')\n\n    def _convert_numpy(self, x, y, flat=True):\n        # Initializations\n        if x.ndim == 1:\n            x = x.reshape((-1, 1))\n        if y.ndim == 1:\n            y = y.reshape((-1, 1))\n        # Samples, interval minus sequence length (maxIn+maxOutPlus+shift)\n        self.samples += len(x) - self.maxInputStep - self.maxOutputStep - self.shift\n        features = len(x[0])\n        input_sequence = np.zeros((self.samples, self.nInputSteps, features))\n        output_sequence = np.zeros((self.samples, self.nOutputSteps))\n\n        # Sequence\n        if self.diff == 'none':\n            for i in range(self.samples):\n                input_sequence[i] = x[i + self.inputIndices]\n                output_sequence[i] = y[i + self.maxInputStep + self.shift + self.outputIndices].reshape((-1))\n\n        elif self.diff[-4:] == 'diff':\n\n            # Take log for log_diff\n            if self.diff == 'log_diff':\n                if np.min(x) < 1e-3:\n                    self.inputConstant = abs(np.min(x) * 1.001) + 1e-3\n                    warnings.warn('Small or negative input values found, adding a constant {:.2e} to input'.format(\n                        self.inputConstant))\n                if np.min(y) < 1e-3:\n                    self.outputConstant = abs(np.min(y) * 1.001) + 1e-3\n                    warnings.warn('Small or negative output values found, adding a constant {:.2e} to output'.format(\n                        self.outputConstant))\n                x = np.log(x + self.inputConstant)\n                y = np.log(y + self.outputConstant)\n\n            # Finally create the difference vector\n            for i in range(self.samples):\n                input_sequence[i] = x[i + self.inputIndices] - x[i + self.inputDiffIndices]\n                output_sequence[i] = (y[i + self.maxInputStep + self.shift + self.outputIndices] -\n                                      y[i + self.maxInputStep + self.shift + self.outputDiffIndices]).reshape((-1))\n\n        # Return\n        if flat:\n            return input_sequence.reshape((self.samples, self.nInputSteps * features)), output_sequence\n        else:\n            return input_sequence, output_sequence\n\n    def _convert_pandas(self, x, y, flat=True):\n        # Check inputs\n        if isinstance(x, pd.core.series.Series):\n            x = x.to_frame()\n        if isinstance(y, pd.core.series.Series):\n            y = y.to_frame()\n        assert len(x) == len(y)\n        assert isinstance(x, pd.DataFrame)\n        assert isinstance(y, pd.DataFrame)\n\n        # Initials\n        input_keys = x.keys()\n        output_keys = y.keys()\n        lag = None              # Input indices\n        shift = None            # Output indices\n\n        # If flat return\n        if flat:\n            # No Differencing\n            if self.diff == 'none':\n                # Input\n                for lag in self.inputIndices:\n                    keys = [key + '_' + str(lag) for key in input_keys]\n                    x[keys] = x[input_keys].shift(lag)\n\n                # Output\n                for shift in self.outputIndices:\n                    keys = [key + '_' + str(shift) for key in output_keys]\n                    y[keys] = y[output_keys].shift(-shift)\n\n            # With differencing\n            elif self.diff[-4:] == 'diff':\n                # If log_diff\n                if self.diff == 'log_diff':\n                    x = np.log(x)\n                    y = np.log(y)\n                # Input\n                for lag in self.inputIndices:\n                    # Shifted\n                    keys = [key + '_' + str(lag) for key in input_keys]\n                    x[keys] = x[input_keys].shift(lag)\n\n                    # differentiated\n                    d_keys = [key + '_d_' + str(lag) for key in input_keys]\n                    x[d_keys] = x[input_keys].shift(lag) - x[input_keys]\n\n                # Output\n                for shift in self.outputIndices:\n                    # Only differentiated\n                    keys = [key + '_' + str(shift) for key in output_keys]\n                    y[keys] = y[output_keys].shift(shift) - y[output_keys]\n\n            # Drop _0 (same as original)\n            x = x.drop([key for key in x.keys() if '_0' in key], axis=1)\n            y = y.drop([key for key in y.keys() if '_0' in key], axis=1)\n\n            # Return (first lags are NaN, last shifts are NaN\n            return x.iloc[lag:-shift if shift > 0 else None], y.iloc[lag:-shift if shift > 0 else None]\n        else:\n            x, y = x.to_numpy(), y.to_numpy()\n            return self._convert_numpy(x, y, flat=False)\n\n    def revert(self, seq_y, y_start):\n        \"\"\"\n        Reverts the sequenced vector back. Useful for sequenced predictions in production.\n        :param seq_y: The sequenced / predicted signal,\n        :param y_start: Starting vector, always necessary when differentiated.\n        :return: y: normal predicted signal, de-sequenced.\n        \"\"\"\n        print('Start Y: {}'.format(y_start))\n        assert len(seq_y.shape) == 2 and seq_y.shape[1] == self.nOutputSteps, \"revert() only suitable for output.\"\n        # assert len(y_start) == self.maxOutputStep + self.shift + 1\n\n        # Initiate\n        y = np.zeros((self.nOutputSteps, len(seq_y) + len(y_start)))\n\n        # de-sequence if diff\n        if self.diff == 'diff':\n            for i in range(self.nOutputSteps):\n                y[i, :len(y_start)] = y_start\n                for j in range(len(seq_y)):\n                    y[i, j + self.outputIndices[i]] = y[0, j + self.outputDiffIndices[i]] + seq_y[j, i]\n            return y\n\n        # de-sequence if log_diff (take log of start, add sequence log, then take exp (as x=exp(log(x)))\n        elif self.diff == 'log_diff':\n            for i in range(self.nOutputSteps):\n                y[i, :len(y_start)] = np.log(y_start + self.outputConstant)\n                for j in range(len(seq_y)):\n                    y[i, j + self.outputIndices] = y[0, j + self.outputDiffIndices] + seq_y[j, i]\n                return np.exp(y) - self.outputConstant\n\n        else:\n            if self.diff == 'none':\n                raise ValueError('With differencing set to none, reverting is not necessary.')\n            else:\n                raise ValueError('Differencing method not implemented.')\n","repo_name":"VladPerervenko/AutoML-1","sub_path":"Amplo/AutoML/Sequencer.py","file_name":"Sequencer.py","file_ext":"py","file_size_in_byte":11479,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"81"}
+{"seq_id":"11330976284","text":"from flask_api import FlaskAPI\nfrom flask_sqlalchemy import SQLAlchemy\nfrom flask import request, jsonify, abort\n\n# local import\nfrom instance.config import app_config\n\n# initialize sql-alchemy\ndb = SQLAlchemy()\n\n\ndef create_app(config_name):\n    app = FlaskAPI(__name__, instance_relative_config=True)\n    app.config.from_object(app_config[config_name])\n    app.config.from_pyfile('config.py')\n    app.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False\n    db.init_app(app)\n\n    from .models import Book\n\n    def jsonify_book(book):\n        return jsonify({\n                    'id': book.id,\n                    'name': book.name,\n                    'date_finished': book.date_finished,\n                    'author': book.author,\n                    'rank': book.rank\n                })\n\n    @app.route('/books/', methods=['POST', 'GET'])\n    def books():\n        if request.method == \"POST\":\n            name = str(request.data.get('name', ''))\n            if name:\n                book = Book(**request.data)\n                book.save()\n                response = jsonify_book(book)\n                response.status_code = 201\n                return response\n        else:\n            # GET\n            books = Book.get_all()\n            results = []\n\n            for book in books:\n                obj = {\n                    'id': book.id,\n                    'name': book.name,\n                    'date_finished': book.date_finished,\n                    'author': book.author,\n                    'rank': book.rank\n                }\n                results.append(obj)\n            response = jsonify(results)\n            response.status_code = 200\n            return response\n\n    @app.route('/books/<int:id>', methods=['GET', 'PUT', 'DELETE'])\n    def books_manipulation(id, **kwargs):\n        # retrieve a book using it's ID\n        book = Book.query.filter_by(id=id, deleted=False).first()\n        if not book:\n            # Raise an HTTPException with a 404 not found status code\n            abort(404)\n\n        if request.method == 'DELETE':\n            book.delete()\n            return {\n                       \"message\": \"book {} deleted successfully\".format(book.id)\n                   }, 200\n\n        elif request.method == 'PUT':\n            if 'rank' not in request.data:\n                response = jsonify({\n                    'no_field': 'rank field is required'\n                })\n                response.status_code = 400\n                return response\n\n            rank = str(request.data.get('rank', ''))\n            book.rank = rank\n            book.save()\n            response = jsonify_book(book)\n            response.status_code = 200\n            return response\n        else:\n            # GET\n            response = jsonify_book(book)\n            response.status_code = 200\n            return response\n\n    return app\n","repo_name":"Rikka-chan/flask_crud_test","sub_path":"src/app/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":2850,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"21246676578","text":"import argparse\nimport tempfile\nimport sys\nsys.path.append(\"..\")\nimport mitab_handler\n\nclass ArgumentParser:\n\n    def __init__(self, args=None):\n        parser = argparse.ArgumentParser(description=\"This tool will take a set of sherlock json interaction files and produce \"\n                                                     \"a single MITAB file. It will not change the order of interactions, nor will \"\n                                                     \"remove duplicate links.\")\n\n        parser.add_argument(\"-i\", \"--input-files\",\n                            help=\"path to sherlock interaction json files, separated by comma\",\n                            dest=\"input_files\",\n                            action=\"store\",\n                            required=True)\n\n        parser.add_argument(\"-o\", \"--output-file\",\n                            help=\"path to single MITAB file\",\n                            dest=\"output_file\",\n                            action=\"store\",\n                            required=True)\n\n        results = parser.parse_args(args)\n\n        self.in_files = list(map(lambda x: x.strip(), results.input_files.split(',')))\n        self.out_file = results.output_file.strip()\n\n\nif __name__ == \"__main__\":\n    params = ArgumentParser(sys.argv[1:])\n\n    temp_output_files = []\n\n    for input_file in params.in_files:\n        out_file = tempfile.NamedTemporaryFile()\n        temp_output_files.append(out_file.name)\n        print(f\"processing {input_file} --> {out_file.name}\")\n        mitab = mitab_handler.MiTabHandler()\n        mitab.parse_sherlock(input_file)\n        mitab.serialise_mitab(out_file.name)\n\n    with open(params.out_file, \"w\") as merged_output_file:\n        for temp_file_name in temp_output_files:\n            print(f\"merging {temp_file_name} --> {params.out_file}\")\n            with open(temp_file_name) as temp_file:\n                for line in temp_file:\n                    merged_output_file.write(line)\n","repo_name":"earlham-sherlock/earlham-sherlock.github.io","sub_path":"unloaders/sherlock_interactions_to_mitab.py","file_name":"sherlock_interactions_to_mitab.py","file_ext":"py","file_size_in_byte":1950,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"81"}
+{"seq_id":"30837218075","text":"import numpy as np\nimport skfmm\nimport OptPath as op\nimport matplotlib.pyplot as plt\n\n# set the problem\nX, Y = np.meshgrid(np.linspace(0,100,1001), np.linspace(0,100,1001))\n\nphi=np.ones_like(X)\nphi[:,3:10]=-1 # this needs to be robust in order to stop path finding alg. at the moment\n\nspeed=np.ones_like(X)\nspeed[(X-50)**2+(Y-50)**2<36] = 2. \n\n# solve the FMM part of the problem\ntmatrix=skfmm.travel_time(phi,speed,dx=np.asscalar(X[0,1]-X[0,0]))\n\n# now find the optimal path with two methods\nnptraj1=op.optpath_eulerforward(np.squeeze(X[0,:]),np.squeeze(Y[:,0]),tmatrix,phi,(60,60))\nnptraj2=op.optpath_scipyode(np.squeeze(X[0,:]),np.squeeze(Y[:,0]),tmatrix,phi,(60,60))\n\n# output and compare\nplt.close(\"all\")\nplt.imshow(speed,extent=[X[0,0],X[0,-1],Y[0,0],Y[-1,0]],origin='lower')\nplt.plot(nptraj1[:,1],nptraj1[:,2],'g.')\nplt.plot(nptraj2[:,1],nptraj2[:,2],'b-')\nplt.show()\n","repo_name":"wmoebius/OptPath","sub_path":"circfeature_test.py","file_name":"circfeature_test.py","file_ext":"py","file_size_in_byte":875,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"81"}
+{"seq_id":"3062986736","text":"from os import system\nfrom time import sleep\nfrom copy import deepcopy\n\ndef print_array(array,tallest_len):\n    first=array[0]\n    second=array[1]\n    third=array[2]\n    while tallest_len>=1:\n        if len(first)>=tallest_len:\n            print(\"{0:<3}\".format(first[tallest_len-1]), end='')\n        else:\n            print(\"   \", end='')\n        if len(second)>=tallest_len:\n            print(\"{0:<3}\".format(second[tallest_len-1]), end='')\n        else:\n            print(\"   \", end='')\n        if len(third)>=tallest_len:\n            print(\"{0:<3}\".format(third[tallest_len-1]), end='')\n        else:\n            print(\"   \", end='')\n        print('')\n        tallest_len-=1\n    print('__ __ __')\ndef possible_moves(array):\n    def check_which(tow1,tow2):\n        try:\n            a=tow1[-1]\n        except:\n            a=100\n        try:\n            b=tow2[-1]\n        except:\n            b=100\n        if a==b:\n            return(0)\n        elif a>b:\n            return(1)\n        else:\n            return(2)\n    first=array[0]\n    second=array[1]\n    third=array[2]\n    possibilities=[]\n    possibilities.append(check_which(first,second))\n    possibilities.append(check_which(second,third))\n    return(possibilities)\ndef move(array, possibilities, moveselect):\n    first=deepcopy(array[0])\n    second=deepcopy(array[1])\n    third=deepcopy(array[2])\n    a=possibilities[moveselect]\n    if moveselect==0:\n        if a==0:\n            post_move=[first,second,third]\n        if a==1:\n            first.append(second[-1])\n            del second[-1]\n        if a==2:\n            second.append(first[-1])\n            del first[-1]\n    elif moveselect==1:\n        if a==0:\n            post_move=[first,second,third]\n        if a==1:\n            second.append(third[-1])\n            del third[-1]\n        if a==2:\n            third.append(second[-1])\n            del second[-1]\n    post_move=[first,second,third]\n    return post_move\n\ntower1=sorted(list(set(input(\"Enter the numbers in the first tower: \").split())))\ntower1.reverse()\nfor i in range(len(tower1)):\n    tower1[i]=int(tower1[i])\ntower2=sorted(list(set(input(\"Enter the numbers in the second tower: \").split())))\ntower2.reverse()\nfor i in range(len(tower2)):\n    tower2[i]=int(tower2[i])\ntower3=sorted(list(set(input(\"Enter the numbers in the third tower: \").split())))\ntower3.reverse()\nfor i in range(len(tower3)):\n    tower3[i]=int(tower3[i])\n\n\n\nn=len(tower1)+len(tower2)+len(tower3)\n\nprevious = [tower1,tower2,tower3]\ncurrent  = [tower1,tower2,tower3]\n\nsteps=0\n\nsystem('cls')\nprint_array(current,n)\nsleep(0.5)\n\nwhile current!=[[],[],list(range(n,0,-1))]:\n    sleep(0.01)\n    system('cls')\n    moves=possible_moves(current)\n    previous.append(deepcopy(current))\n    if move(current,moves,1) not in previous:\n        current=deepcopy(move(current, moves,1)) \n    else:\n        current=deepcopy(move(current , moves, 0))\n    print_array(current,n)\n    steps+=1\n    if len(previous)>0:\n        del previous[0]\n    print(\"\\nNumber of steps taken:\", steps)  \n","repo_name":"ShubhamSKA/Games","sub_path":"Tower of Hanoi/Hanoi2.py","file_name":"Hanoi2.py","file_ext":"py","file_size_in_byte":3017,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"1611629512","text":"\"\"\"\"\"\n------------------------------------------------------------------------------\n This program takes input  N and prints a table of the\npowers of 2 that are less than or equal to 2^N.\n------------------------------------------------------------------------------\n\"\"\"\nclass Powerof2:\n\tdef power(self):\n\t\timport math\n\t\tprint(\"Enter the N value\")\n\t\tN = int(input())\n\t\tnum=0\n\t\t# as per the given condition the resultant shoulb be less than 2^31\n\t\tif(0<= N and N<31):\n\t\t\tif(N==0):\n\t\t\t\tprint(\"2 * \",N,\" = \",num)\n\t\t\tfor i in range(1,N):\n\t\t\t\tnum = pow(2,i)\n\t\t\t\tif(num <=(pow(2, N))):\n\t\t\t\t\t# Printing the table of two powers\n\t\t\t\t\tprint(\"2 pow \",i,\" = \",num)\n\t\telse:\n    \t\t\tprint(\"Given number should be less than 31\")\npowr = Powerof2()\npowr.power()\n","repo_name":"SubbuDevasani/Programs","sub_path":"1.Functional/Power of two.py","file_name":"Power of two.py","file_ext":"py","file_size_in_byte":744,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"73424040906","text":"# 创建一个字典，在其中存储三条大河流及其流经国家\nrivers = {\n    'danube': 'austria',\n    'nile': 'egypt',\n    'lancang river': 'china',\n    }\n\nfor name, city in rivers.items():\n    print(\"The \" + name.title() +\n          \" runs through \" + city.title() +\n          \".\")\n\nfor name in rivers.keys():\n    print(name.title())\n\nfor city in rivers.values():\n    print(city.title())","repo_name":"leeoohs/python","sub_path":"course_6.3/rivers_case.py","file_name":"rivers_case.py","file_ext":"py","file_size_in_byte":398,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"81"}
+{"seq_id":"31224755616","text":"from datetime import datetime\n\nimport numpy as np\nimport torch\n\n\nimport rayleaf\nfrom rayleaf.entities import Server, Client\n\n\ndef dpsgd_cnn(\n    stdev: float,\n    C: float,\n    num_rounds: int = 100,\n    eval_every: int = 10,\n    num_clients: int = 200,\n    clients_per_round: int = 40,\n    client_lr: float = 0.05,\n    batch_size: int = 64,\n    seed: int = 0,\n    num_epochs: int = 10,\n    gpus_per_client_cluster: float = 1,\n    num_client_clusters: int = 8,\n    save_model: bool = False,\n    notes: str = None\n):\n    curr_time = datetime.now().strftime(\"%Y-%m-%d_%H:%M:%S\")\n\n    class DPSGDClient(Client):\n        def train(self):\n        # def train(self, server_update):\n            self.train_model(compute_grads=True)\n\n            self.collect_metric([torch.linalg.norm(g).item() for g in self.grads], \"norms\")\n\n            for i, layer in enumerate(self.grads):\n                self.grads[i] /= max(1, torch.linalg.norm(layer) / C)\n                self.grads[i] += stdev * C * torch.randn(layer.shape)\n\n            return self.grads\n            # self.model_params = server_update\n            # grads = self.train_model(compute_grads=True)\n\n            # grads /= max(1, np.linalg.norm(grads) / C)\n\n            \n    \n\n    class DPSGDServer(Server):\n        def update_layer(self, current_params, updates: list, client_num_samples: list, num_clients: int):\n            average_grads = 0\n            for i in range(num_clients):\n                average_grads += updates[i] * client_num_samples[i]\n            \n            average_grads /= self.num_train_samples\n\n            return current_params + average_grads\n\n\n    rayleaf.run_experiment(\n        dataset = \"femnist\",\n        dataset_dir = \"data/femnist/\",\n        output_dir= f\"output/dpsgd_cnn-{curr_time}/\",\n        model = \"cnn\",\n        num_rounds = num_rounds,\n        eval_every = eval_every,\n        ServerType=DPSGDServer,\n        client_types=[(DPSGDClient, num_clients)],\n        clients_per_round = clients_per_round,\n        client_lr = client_lr,\n        batch_size = batch_size,\n        seed = seed,\n        use_val_set = False,\n        num_epochs = num_epochs,\n        gpus_per_client_cluster = gpus_per_client_cluster,\n        num_client_clusters = num_client_clusters,\n        save_model = save_model,\n        notes = notes\n    )\n","repo_name":"rizhu/rayleaf-all-experiments","sub_path":"dpsgd_cnn_exp.py","file_name":"dpsgd_cnn_exp.py","file_ext":"py","file_size_in_byte":2308,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"26271876504","text":"#!/usr/bin/python3\n\n# This program is free software; you can redistribute it and/or modify it\n# under the terms of the GNU General Public License as published by the\n# Free Software Foundation; either version 2 of the License, or (at your\n# option) any later version.\n# This program is distributed in the hope that it will be useful, but\n# WITHOUT ANY WARRANTY; without even the implied warranty of\n# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General\n# Public License for more details.\n# You should have received a copy of the GNU General Public License along\n# with this program; if not, write to the Free Software Foundation, Inc.,\n# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.\n\n# This is the original brainfuck interpreter with the pbrain extension and\n# a temporary register\n# Avaliable Operators:\n# + - < > [ ] , . ( ) : @ !\n\nimport time\nimport sys\nfrom putchar import putchar\n\nCL_S = 256         # the maximum number allowed + 1\nTP_S = 30000       # count of cells\ninput_stream = \"\"\nRT = [0 for i in range(TP_S)]            # Register tape (paper tape)\nRP = 0                                   # Register Pointer\nfunc_tape = [None for i in range(CL_S)]  # Function tape\nreg = 0                                  # Temporary register\n\ndef run_console():\n    print(\"Brainfuck Interpreter 1.0.2 (W: pbrain not usable)\")\n    print(\"Use '#' to inspect tape\")\n    while True:\n        try: execute(input(\">>> \"))\n        except (EOFError, KeyboardInterrupt): sys.exit(print())\n\ndef run_file(filename):\n    try: execute(open(filename).read())\n    except IOError: sys.exit(print(\"%s: cannot find %s: no such file\" % \\\n                (sys.argv[0], filename)))\n\ndef cur_val():\n    return RT[RP]\n\ndef add(value):\n    RT[RP] += value\n    RT[RP] %= CL_S\n\ndef mov(diff):\n    global RP\n    RP += diff\n    if RP < 0: sys.exit(\"error: tape memory out of bounds (underrun)\\n\"\\\n                 \"undershot the tape size of %d cells.\" % TP_S)\n    if RP >= TP_S: sys.exit(\"error: tape memory out of bounds (overrun)\\n\"\\\n                 \"exceeded the tape size of %d cells.\" % TP_S)\n\ndef search_loop(IT):\n    loop_lv = fdef_lv = 0\n    loop_tp = [-1 for i in range(len(IT) + 1)]\n    for i in range(len(IT)):\n        char = IT[i]\n        if char == '[':\n            index = i\n            cur_lv = loop_lv\n            while index < len(IT):\n                if IT[index] == '[': loop_lv += 1\n                elif IT[index] == ']': loop_lv -= 1\n                if IT[index] == ']' and loop_lv == cur_lv: break\n                index += 1\n            loop_tp[index] = i\n            loop_tp[i] = index\n        elif char == ']' and loop_tp[i] == -1:\n            raise Exception(\"Loop not paired.\")\n        elif char == '(':\n            index = i\n            cur_lv = fdef_lv\n            while index < len(IT):\n                if IT[index] == '(': fdef_lv += 1\n                elif IT[index] == ')': fdef_lv -= 1\n                if IT[index] == ')' and fdef_lv == cur_lv: break\n                index += 1\n            loop_tp[index] = i\n            loop_tp[i] = index\n\n        elif char == ')' and not loop_tp[i]:\n            raise Exception(\"Loop not paired.\")\n    return loop_tp\n\ndef execute(IT):                    # The instruction tape\n    loop_tape = search_loop(IT)       # Store the loop objects\n    old_IP = IP = loop_level = fdef_level = 0\n    cur_refs = -1\n    in_func = False\n    while IP < len(IT):\n        char = IT[IP]\n        diff = 0\n        if char in '-+':   add(int(char + '1'))\n        elif char in '<>': mov(ord(char) - 61)\n        elif char == '[' and cur_val() == 0: IP = loop_tape[IP]\n        elif char == ']' and cur_val() != 0: IP = loop_tape[IP]\n        elif char == '(':\n            func_tape[cur_val()] = IP\n            IP = loop_tape[IP]\n        elif char == ')':\n            if in_func:\n                IP = old_IP\n                in_func = False\n        elif char == ':':\n            print(func_tape[cur_val()])\n            if not func_tape[cur_val()]:\n                raise Exception(\"There is no such procedure.\\n\" + \\\n                        \"Procedure reference (name) is: \" + str(name))\n            else:\n                old_IP = IP\n                in_func = True\n                IP = func_tape[cur_val()]\n\n        elif char == '#':\n            print(loop_tape)\n            low = 0 if RP < 5 else RP - 5\n            high = TP_S if RP + 7 > TP_S else low + 13\n            for index in range(low, high): print(\"%5d \" % index, end='')\n            print()\n            for index in range(low, high): print(\"%5d \" % RT[index], end='')\n            print()\n            for index in range(low, high):\n                print(\"    ^ \" if index == RP else \"      \", end='')\n            print()\n\n        elif char == ',': RT[RP] = ord(sys.stdin.read(1))\n        elif char == '.': putchar(cur_val())\n        elif char == '@': reg = cur_val()\n        elif char == '!': add(reg)\n        IP += 1\n\ndef usage():\n    print(\"\"\"Usage: {prog} [OPTIONS] [file or code]\nOptions:\n        -h      Show this help message\n        -f file Use code from file\n\nUse {prog} without arguments to open console\nUse {prog} [code] to run code directly\n\"\"\".format(prog=sys.argv[0]))\n\nif __name__ == '__main__':\n    if len(sys.argv) == 3 and sys.argv[1] == '-f': run_file(sys.argv[2])\n    if len(sys.argv) == 2:\n        if sys.argv[1] == '-h':\n            usage()\n            sys.exit(0)\n        if sys.argv[1] == '-f':\n            print(\"No file specified.\")\n            sys.exit(2)\n        else:\n            execute(sys.argv[1])\n            sys.exit(0)\n    run_console()\n","repo_name":"crides/compiler","sub_path":"bf_interpreter/bfi.py","file_name":"bfi.py","file_ext":"py","file_size_in_byte":5582,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"12612677898","text":"import os\nimport json\nimport shutil\nimport subprocess\nimport math\nfrom common import count_parameters, load_all, PadSequence, training_loop, print_parameters, TensorBoardWrapper\nimport common\nimport numpy as np\nimport torch\nimport torch.nn as nn\nfrom trainer_movement_target import MovementStepper\nfrom trainer_rnn import TrainerRNN\nfrom datetime import datetime\nfrom build_order_loader import BuildOrderLoader\nfrom mappings import UnitLookup, terranUnits, zergUnits, protossUnits\nimport game_state_loader\nfrom game_state_loader import MovementTargetTrace\nfrom dataset_folder import create_datasets\nimport gzip\nimport pickle\nimport random\n\n# from pytorch_memory_utils.gpu_mem_track import MemTracker\n# Fix crash bug on some macOS versions\n\n# torch.multiprocessing.set_sharing_strategy('file_system')\n\nos.environ['KMP_DUPLICATE_LIB_OK'] = 'True'\n\n\n# @torch.jit.trace\nclass Net(nn.Module):\n    def __init__(self, num_unit_types):\n        super().__init__()\n\n        self.global_state_size = 97\n        self.minimap_size = 14\n        self.minimap_layers = 13 + 2 + 4 + 1\n\n        scale = 2\n        self.gru_hidden_size = 64 * scale\n\n        self.constant_minimap = torch.nn.Parameter(torch.randn((2, self.minimap_size, self.minimap_size), dtype=torch.float, requires_grad=True))\n\n        self.lin0 = nn.Linear(self.global_state_size, 64 * scale)\n        self.act0 = nn.LeakyReLU()\n\n        self.lin1 = nn.Linear(64 * scale, 64 * scale)\n        self.act1 = nn.LeakyReLU()\n\n        self.lin2 = nn.Linear(64 * scale, 64)\n        self.act2 = nn.LeakyReLU()\n\n        self.dropout = nn.Dropout(p=0.3)\n\n        self.lin3 = nn.Linear(64, 64)\n        self.act3 = nn.LeakyReLU()\n\n        self.gru = nn.GRUCell(input_size=self.lin3.out_features, hidden_size=self.gru_hidden_size)\n        # self.lin_gru = nn.Linear(self.lin3.out_features, self.gru_hidden_size)\n\n        self.conv1 = nn.Conv2d(in_channels=self.minimap_layers, out_channels=12 * scale, kernel_size=1)\n        self.act_c1 = nn.LeakyReLU()\n\n        self.conv2 = nn.Conv2d(in_channels=12 * scale, out_channels=12, kernel_size=3, padding=1)\n        self.act_c2 = nn.LeakyReLU()\n\n        self.conv2_2 = nn.Conv2d(in_channels=12, out_channels=6, kernel_size=3, padding=1)\n        self.act_c2_2 = nn.LeakyReLU()\n\n        # self.conv2_3 = nn.Conv2d(in_channels=6, out_channels=6, kernel_size=3, padding=1)\n        # self.act_c2_3 = nn.LeakyReLU()\n\n        # self.conv3 = nn.Conv2d(in_channels=6, out_channels=6, kernel_size=3, padding=1)\n        # self.act_c3 = nn.LeakyReLU()\n        self.conv3 = nn.Conv2d(in_channels=6, out_channels=6, kernel_size=1, padding=0)\n        self.act_c3 = nn.LeakyReLU()\n\n        self.conv_c2g_1 = nn.Conv2d(in_channels=6, out_channels=6, kernel_size=3, padding=1)\n        self.conv_c2g_2 = nn.MaxPool2d(kernel_size=2)\n        self.conv_c2g_3 = nn.Conv2d(in_channels=6, out_channels=12, kernel_size=3, padding=1)\n        self.conv_c2g_4 = nn.MaxPool2d(kernel_size=3, padding=1)\n        self.conv_c2g_5 = nn.Conv2d(in_channels=12, out_channels=self.lin2.out_features, kernel_size=3, padding=1)\n        self.conv_c2g_6 = nn.MaxPool2d(kernel_size=3, padding=0)\n        self.act_c2g_1 = nn.ReLU()\n        self.act_c2g_3 = nn.ReLU()\n        self.act_c2g_5 = nn.ReLU()\n\n        # self.lin_c2g = nn.Linear(self.minimap_size * self.minimap_size * self.conv3.out_channels, self.lin2.out_features)\n\n        self.embedding = torch.nn.Parameter(torch.rand((num_unit_types, 8), dtype=torch.float, requires_grad=True))\n\n        self.lin_t0 = nn.Linear(self.embedding.size()[1], 16)\n        self.act_t0 = nn.LeakyReLU()\n\n        self.lin_g2t = nn.Linear(self.gru_hidden_size, 16)\n\n        # self.lin_t1 = nn.Linear(16, 16)\n        # self.act_t1 = nn.LeakyReLU()\n\n        # self.lin_t2 = nn.Linear(16, 16)\n        self.act_t2 = nn.LeakyReLU()\n\n        self.lin4 = nn.Linear(self.gru_hidden_size, 64)\n        self.act4 = nn.LeakyReLU()\n\n        self.lin_t2g = nn.Linear(16, 64)\n\n        self.lin_g2c = nn.Linear(64, 12)\n        self.act_g2c = nn.LeakyReLU()\n\n        self.conv_c4_0 = nn.Conv2d(in_channels=6, out_channels=12, kernel_size=3, padding=1)\n        self.act_c4_0 = nn.LeakyReLU()\n\n        self.conv_c4_1 = nn.Conv2d(in_channels=12, out_channels=4 * scale, kernel_size=3, padding=1)\n        self.act_c4_1 = nn.LeakyReLU()\n\n        self.conv_c4_2 = nn.Conv2d(in_channels=4 * scale, out_channels=1, kernel_size=3, padding=1)\n        self.logsoftmax = nn.LogSoftmax(dim=1)\n\n        self.xs = (torch.tensor(list(range(self.minimap_size)), dtype=torch.float) / self.minimap_size).unsqueeze(1).expand(self.minimap_size, self.minimap_size)\n        self.ys = self.xs.transpose(0, 1)\n\n        # self.conv_m1 = nn.Conv2d(in_channels=1, out_channels=4, kernel_size=3, padding=0, stride=3)\n        # self.act_m1 = nn.LeakyReLU()\n\n\n        self.conv_m1_1 = nn.Conv2d(in_channels=1, out_channels=16, kernel_size=3, padding=1)\n        self.act_m1_1 = nn.ReLU()\n        self.conv_m1_2 = nn.Conv2d(in_channels=16, out_channels=16, kernel_size=3, padding=1)\n        self.act_m1_2 = nn.ReLU()\n        self.conv_m1_3 = nn.MaxPool2d(kernel_size=3)\n\n        self.conv_m2_1 = nn.Conv2d(in_channels=16, out_channels=16, kernel_size=3, padding=1)\n        self.act_m2_1 = nn.ReLU()\n        self.conv_m2_2 = nn.Conv2d(in_channels=16, out_channels=16, kernel_size=3, padding=1)\n        self.act_m2_2 = nn.ReLU()\n        self.conv_m2_3 = nn.MaxPool2d(kernel_size=2)\n\n        self.conv_m3_1 = nn.Conv2d(in_channels=16, out_channels=16, kernel_size=3, padding=1)\n        self.act_m3_1 = nn.ReLU()\n        self.conv_m3_2 = nn.Conv2d(in_channels=16, out_channels=16, kernel_size=3, padding=1)\n        self.act_m3_2 = nn.ReLU()\n        self.conv_m3_3 = nn.MaxPool2d(kernel_size=2)\n\n        self.conv_m4_1 = nn.Conv2d(in_channels=16, out_channels=4, kernel_size=1, padding=0)\n        self.act_m4_1 = nn.ReLU()\n\n        self.lin_s1 = nn.Linear(self.lin4.out_features, 1)\n        self.lin_a1 = nn.Linear(self.lin4.out_features, 1)\n\n        # self.bn_1 = nn.BatchNorm2d(16)\n        # self.bn_2 = nn.BatchNorm2d(16)\n        # self.bn_3 = nn.BatchNorm2d(16)\n        # self.bn_4 = nn.BatchNorm2d(16)\n        # self.bn_5 = nn.BatchNorm2d(16)\n\n        # self.bn_c1 = nn.BatchNorm2d(12)\n        # self.bn_c2 = nn.BatchNorm2d(12)\n\n        # self.bn_g = nn.BatchNorm(64)\n\n    def to(self, device):\n        n = super().to(device=device)\n        n.xs = n.xs.to(device=device)\n        n.ys = n.ys.to(device=device)\n        n.embedding = n.embedding.to(device=device)\n        n.constant_minimap = n.constant_minimap.to(device=device)\n        return n\n\n    def init_hidden_states(self, batch_size, device):\n        return torch.zeros((batch_size, self.gru_hidden_size), device=device)\n\n    def forward_minimap(self, minimap):\n        batch_size = minimap.size()[0]\n\n        assert minimap.size() == (batch_size, 168, 168)\n        minimap = minimap.view(batch_size, 1, 168, 168)\n        x = minimap\n\n        y = self.act_m1_1(self.conv_m1_1(x))\n        # y = self.bn_1(y)\n        x = self.act_m1_2(x + self.conv_m1_2(y))\n        # x = self.bn_2(x)\n        x = self.conv_m1_3(x)\n\n        y = self.act_m2_1(self.conv_m2_1(x))\n        # y = self.bn_3(y)\n        x = self.act_m2_2(x + self.conv_m2_2(y))\n        # x = self.bn_4(x)\n        x = self.conv_m2_3(x)\n\n        y = self.act_m3_1(self.conv_m3_1(x))\n        # y = self.bn_4(y)\n        x = self.act_m3_2(x + self.conv_m3_2(y))\n        # x = self.bn_5(x)\n        x = self.conv_m3_3(x)\n\n        x = self.act_m4_1(self.conv_m4_1(x))\n        return x\n\n    def forward(self, globalState, minimap, unit_type_counts, hiddenState, pathfinding_minimap):\n        batch_size = globalState.size()[0]\n\n        minimap = torch.cat([self.xs.expand(batch_size, 1, -1, -1), self.ys.expand(batch_size, 1, -1, -1), pathfinding_minimap, minimap], dim=1)\n\n        assert globalState.size() == (batch_size, self.global_state_size), (globalState.size(), (batch_size, self.global_state_size))\n        assert minimap.size() == (batch_size, self.minimap_layers, self.minimap_size, self.minimap_size), (minimap.size(), (batch_size, self.minimap_layers, self.minimap_size, self.minimap_size))\n\n        x = globalState\n        x = self.act0(self.lin0(x))\n        x = self.act1(self.lin1(x))\n\n        m = minimap\n        m = self.act_c1(self.conv1(m))\n        m = self.act_c2(self.conv2(m))\n        # m = self.bn_c1(m)\n        m = nn.functional.dropout(m, p=0.1)\n        m = self.act_c2_2(self.conv2_2(m))\n        # m = self.act_c2_3(self.conv2_3(m))\n        m = self.act_c3(self.conv3(m))\n        self.debug_last_minimap_output = m\n        # m3 = m.view(batch_size, -1)\n\n        c2g = self.act_c2g_1(self.conv_c2g_1(m))\n        c2g = self.conv_c2g_2(c2g)\n        c2g = self.act_c2g_3(self.conv_c2g_3(c2g))\n        c2g = self.conv_c2g_4(c2g)\n        c2g = self.act_c2g_5(self.conv_c2g_5(c2g))\n        c2g = self.conv_c2g_6(c2g).view(batch_size, 64)\n\n        # x = self.act2(self.lin2(x) + self.lin_c2g(m3))\n        x = self.act2(self.lin2(x) + c2g)\n        x = nn.functional.dropout(x, p=0.1)\n        x = self.act3(self.lin3(x))\n        # x = self.act3(self.lin_gru(x))\n        x = self.gru(x, hiddenState)\n        hiddenState = x\n\n        # TODO: Add position info as minimap layers\n\n        x = self.embedding.expand(batch_size, -1, -1)\n        scale = unit_type_counts.view(batch_size, unit_type_counts.size()[1], 1)\n        x = x * scale\n        x = self.act_t0(self.lin_t0(x))\n        # x = self.act_t1(self.lin_t1(x))\n        x = self.act_t2(x + self.lin_g2t(hiddenState).view(batch_size, 1, -1))\n        # Sum over all unit types\n        x = x.sum(dim=1)\n\n        state = self.act4(self.lin4(hiddenState) + self.lin_t2g(x))\n        # x = self.bn_g(x)\n        x = self.act_c4_0(self.conv_c4_0(m) + self.lin_g2c(state).view(batch_size, -1, 1, 1))\n        self.last_c4_0 = x\n        x = nn.functional.dropout2d(x, p=0.1)\n        x = self.act_c4_1(self.conv_c4_1(x))\n        self.last_c4_1 = x\n        x = self.conv_c4_2(x).view(batch_size, self.minimap_size * self.minimap_size)\n\n        x = self.logsoftmax(x)\n\n        y = self.lin_s1(state)\n        y = y.view(batch_size).sigmoid()\n\n        z = self.lin_a1(state)\n        z = z.view(batch_size).sigmoid()\n\n        # r = torch.zeros((batch_size, num_units, 16)).cuda()\n        # r = self.act_u3(self.lin_u3(r))\n\n        return x, hiddenState, y, z\n\n\nunitSet = {\n    \"TERRAN_BANSHEE\",\n    \"TERRAN_BATTLECRUISER\",\n    \"TERRAN_CYCLONE\",\n    \"TERRAN_GHOST\",\n    \"TERRAN_HELLION\",\n    \"TERRAN_HELLIONTANK\",\n    \"TERRAN_LIBERATOR\",\n    \"TERRAN_MARAUDER\",\n    \"TERRAN_MARINE\",\n    \"TERRAN_MEDIVAC\",\n    \"TERRAN_MISSILETURRET\",\n    \"TERRAN_RAVEN\",\n    \"TERRAN_REAPER\",\n    \"TERRAN_SCV\",\n    \"TERRAN_SIEGETANK\",\n    \"TERRAN_THOR\",\n    \"TERRAN_VIKINGFIGHTER\",\n    \"TERRAN_WIDOWMINE\",\n}\n\nmodule_name = \"movement_target\"\n# device = torch.device(\"cpu\")\nsmall_input = False\n\nprint(\"Loading\")\ndata_paths = [\"training_data/replays/s3\"]\ncache_dir = f\"training_cache/{module_name}\"\n\n# unit_lookup = UnitLookup(terranUnits + zergUnits + protossUnits)\nunit_lookup = UnitLookup(protossUnits)\nbuildOrderLoader = BuildOrderLoader(unit_lookup, 1.0)\nprint(\"A\")\n# gpu_tracker.track()\n\ndevice = None\nmodel = None\noptimizer = None\ntrainer = None\n\nlearning_rate = 0.004\nlearning_rate_decay = 500\nlearning_rate_final = 0.001\ntest_split = 0.1\nbatch_size = 48\ntensor_version = 8\n\ncurrent_step = 0\n# gpu_tracker.track()\n\ntraining_losses = []\ntest_losses = []\n\nprint(\"D\")\n\n\ndef init_network(device):\n    global model, optimizer, trainer\n    model = Net(unit_lookup.num_units)\n    model = model.to(device)\n    optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)\n    trainer = TrainerRNN(model, optimizer, action_loss_weights=[1, 1], device=device, sampleClass=MovementTargetTrace, stepperClass=MovementStepper, step_size=1)    \n\n\npadding = PadSequence(MovementTargetTrace(\n    states='pack-stack-timewise',\n    replay_path=None,\n    minimap_states='pack-stack-timewise',\n    data_path=None,\n    playerID=None,\n    target_positions='pack-stack-timewise',\n    unit_type_counts='pack-stack-timewise',\n    pathfinding_minimap='stack',\n    fraction_similar_orders='pack-stack-timewise',\n    attack_order='pack-stack-timewise',\n))\n\n\ndef visualize(epoch):\n    init_network(device=torch.device(\"cpu\"))\n\n    import matplotlib\n    matplotlib.use('TkAgg')\n    import matplotlib.pyplot as plt\n    # Turn on non-blocking plotting mode\n    plt.ion()\n\n    print(\"E\")\n    load_weights(f\"models/{module_name}_{epoch}.weights\")\n    print(\"F\")\n    memory, test_memory = create_datasets(cache_dir, test_split, suffix=f\".{tensor_version}.pt\")\n    print(\"G\")\n    for sampleIndex in range(1000):\n        sample = memory[sampleIndex]\n        print(\"H\")\n        with gzip.open(sample.data_path, \"rb\") as f:\n            session = pickle.load(f)\n\n        print(\"I\")\n        stepper = trainer.stepperClass(model, padding([sample])[0], device, 0, lambda a, b: 0, step_size=1)\n        print(\"J\")\n        observationSession = {\n            \"observations\": session[\"observations\"][sample.playerID - 1],\n            \"gameInfo\": session[\"gameInfo\"],\n            \"replayInfo\": session[\"replayInfo\"]\n        }\n        map_size = game_state_loader.find_map_size(observationSession, sample.playerID)\n        print(\"K\")\n        while True:\n            timestep = stepper.timestep\n            print(\"Eval\")\n            model.eval()\n            with torch.no_grad():\n                res = stepper.step()\n            if res is None:\n                break\n\n            outputs = stepper.outputs.detach().cpu().exp()\n\n            if sample.minimap_states[timestep][-1].sum() == 0:\n                print(\"Skipping frame\")\n                continue\n\n            print(\"Plotting\")\n            playerIndex = sample.playerID - 1\n            units = observationSession[\"observations\"][\"rawUnits\"][timestep][\"units\"]\n            our_units = [u for u in units if u[\"owner\"] == sample.playerID and game_state_loader.isMovableUnit(u, buildOrderLoader)]\n            our_buildings = [u for u in units if u[\"owner\"] == sample.playerID and not game_state_loader.isMovableUnit(u, buildOrderLoader)]\n\n            mirror = False #sample.playerID == 2\n            coords = np.array([game_state_loader.transform_coord(u[\"pos\"], map_size, mirror) for u in our_units])\n            coords2 = np.array([game_state_loader.transform_coord(u[\"pos\"], map_size, mirror) for u in our_buildings])\n\n            fig, axs = plt.subplots(nrows=6, ncols=8, figsize=(16, 8), num=1, clear=True)\n\n            plt.sca(axs[0, 0])\n            edgecolors = [(0, 0, 0, 1) if unit_lookup.military_units_mask[unit_lookup.unit_index_map[u[\"unit_type\"]]] else (0, 0, 0, 0) for u in our_units]\n\n            enemy_units = [u for u in units if u[\"owner\"] != sample.playerID and u[\"unit_type\"] in unit_lookup.unit_index_map]\n            enemy_coords = np.array([game_state_loader.transform_coord(u[\"pos\"], map_size, mirror) for u in enemy_units]).reshape(-1, 2)\n            print(\"A\")\n\n            plt.scatter(enemy_coords[:, 0], enemy_coords[:, 1], c=\"#e41a1c\")\n            plt.scatter(coords2[:, 0], coords2[:, 1], c=\"#999999\")\n            plt.scatter(coords[:, 0], coords[:, 1], cmap=plt.cm.viridis, edgecolors=edgecolors)\n\n            plt.xlim((-0.1, 1.1))\n            plt.ylim((-0.1, 1.1))\n            # plt.colorbar()\n            plt.clim((0, 1))\n            axs[0, 0].set(aspect='equal')\n\n            plt.sca(axs[1, 0])\n            plt.scatter(enemy_coords[:, 0], enemy_coords[:, 1], c=\"#e41a1c\")\n            # plt.scatter(coords[:, 0], coords[:, 1], c=sample.movement[timestep], cmap=plt.cm.viridis)\n\n            plt.xlim((-0.1, 1.1))\n            plt.ylim((-0.1, 1.1))\n            # plt.colorbar()\n            plt.clim((0, 1))\n            axs[1, 0].set(aspect='equal')\n\n            layers = sample.minimap_states[timestep].size()[0]\n            for i in range(layers // 2):\n                for j in range(2):\n                    plt.sca(axs[j, i + 1])\n                    plt.imshow(sample.minimap_states[timestep][i + j * layers // 2].transpose(0, 1), origin=\"lower\")\n\n            minimap = model.debug_last_minimap_output.detach().cpu()\n            for i in range(6):\n                plt.sca(axs[2, i])\n                plt.imshow(minimap[0, i].transpose(0, 1), origin=\"lower\")\n\n            minimap = model.last_c4_0.detach().cpu()\n            for i in range(8):\n                plt.sca(axs[4, i])\n                plt.imshow(minimap[0, i].transpose(0, 1), origin=\"lower\")\n\n            minimap = model.last_c4_1.detach().cpu()\n            for i in range(8):\n                plt.sca(axs[5, i])\n                plt.imshow(minimap[0, i].transpose(0, 1), origin=\"lower\")\n\n            plt.sca(axs[3, 0])\n            plt.imshow(outputs[0].view(model.minimap_size, model.minimap_size).transpose(0, 1), origin=\"lower\")\n            targetCoord = sample.target_positions[timestep].argmax().item()\n            xc = targetCoord // model.minimap_size\n            yc = targetCoord % model.minimap_size\n\n            w = 0.5\n            plt.plot([xc + w, xc - w, xc - w, xc + w, xc + w], [yc + w, yc + w, yc - w, yc - w, yc + w], c=\"#FF0000\")\n\n            targetCoord = sample.minimap_states[timestep][-3].argmax().item()\n            xc = targetCoord // model.minimap_size\n            yc = targetCoord % model.minimap_size\n            w = 0.4\n            plt.plot([xc + w, xc - w, xc - w, xc + w, xc + w], [yc + w, yc + w, yc - w, yc - w, yc + w], c=\"#000000\")\n\n            targetCoord = sample.minimap_states[timestep][-2].argmax().item()\n            xc = targetCoord // model.minimap_size\n            yc = targetCoord % model.minimap_size\n            w = 0.8\n            plt.plot([xc + w, xc - w, xc - w, xc + w, xc + w], [yc + w, yc + w, yc - w, yc - w, yc + w], c=\"#0000FF\")\n\n            print(\"B\")\n\n            plt.sca(axs[3, 1])\n            plt.imshow(sample.target_positions[timestep].transpose(0, 1), origin=\"lower\")\n\n            plt.sca(axs[3, 2])\n            plt.imshow(sample.pathfinding_minimap.transpose(0, 1), origin=\"lower\")\n\n            # for i in range(2):\n            #     plt.sca(axs[3, 2 + i])\n            #     plt.imshow(model.constant_minimap[i].detach().cpu().transpose(0, 1), origin=\"lower\")\n\n            for i in range(4):\n                plt.sca(axs[3, 3 + i])\n                plt.imshow(stepper.batch.pathfinding_minimap.detach()[0, i, :, :].cpu().transpose(0, 1), origin=\"lower\")\n\n            plt.sca(axs[2, 7])\n            plt.bar([0], [stepper.outputs_keep_orders.detach().item()])\n            plt.ylim((0, 1))\n            print(\"C\")\n            # plt.tight_layout()\n            plt.pause(0.01)\n            # stepper.outputs\n\n\ndef cache_tensors():\n    init_network(device=torch.device(\"cpu\"))\n\n    def load_state(s, target_filepath):\n        def save_sample(sample):\n            with gzip.open(target_filepath, 'wb') as f:\n                torch.save(sample, f)\n        game_state_loader.loadSessionMovementTarget(s, buildOrderLoader, save_sample, None)\n\n    common.cache_tenors(data_paths, cache_dir, small_input, load_state, tensor_version)\n\n\ndef learning_rate_by_time(epoch):\n    return learning_rate - (learning_rate - learning_rate_final) * min(epoch / learning_rate_decay, 1)\n\n\nassert learning_rate_by_time(0) == learning_rate\nassert abs(learning_rate_by_time(learning_rate_decay) - learning_rate_final) < 0.00001\nassert abs(learning_rate_by_time(learning_rate_decay + 100) - learning_rate_final) < 0.00001\n\n\ndef plot(tensorboard_writer):\n    tensorboard_writer.add_embedding(model.embedding.data, metadata=[u[0] for u in unit_lookup.units], global_step=current_step, tag=\"unit embedding\")\n\n\ndef save_tensorboard_graph(memory, tensorboard_writer):\n    # sample = memory[0]\n    # tensorboard_writer.add_graph(model, torch.tensor([sample.state], dtype=torch.float, device=device), True)\n    pass\n\n\ndef train(comment):\n    init_network(device=torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\"))\n\n    print_parameters(model)\n    print(f\"Parameters: {count_parameters(model)}\")\n\n    global training_losses_by_time, current_step\n    tensorboard_writer = TensorBoardWrapper(log_dir=f\"tensorboard/{module_name}/{datetime.now():%Y-%m-%d_%H:%M} {comment}\")\n    memory, test_memory = create_datasets(cache_dir, test_split, suffix=f\".{tensor_version}.pt\")\n    save_tensorboard_graph(test_memory, tensorboard_writer)\n\n    training_generator = torch.utils.data.DataLoader(memory, batch_size=batch_size, shuffle=True, pin_memory=True, num_workers=2, collate_fn=padding)\n    testing_generator = torch.utils.data.DataLoader(test_memory, batch_size=batch_size, shuffle=True, pin_memory=True, num_workers=1, collate_fn=padding)\n\n    for epoch, current_step in training_loop(training_generator, testing_generator, trainer, tensorboard_writer):\n        for g in optimizer.param_groups:\n            g['lr'] = learning_rate_by_time(epoch)\n            print(\"Set learning rate to \", g['lr'])\n\n        if epoch > 1:\n            save(epoch)\n\n            if (epoch % 5) == 0:\n                plot(tensorboard_writer)\n\n\ndef save(epoch):\n    torch.save(model.state_dict(), f\"models/{module_name}_\" + str(epoch) + \".weights\")\n\n\ndef load_weights(file):\n    model.load_state_dict(torch.load(file, map_location='cpu'))\n\n# import matplotlib\n# matplotlib.use('TkAgg')\n# import matplotlib.pyplot as plt\n# Turn on non-blocking plotting mode\n# plt.ion()\n\nclass Stepper:\n    def __init__(self):\n        init_network(device=torch.device(\"cpu\"))\n\n        self.stepper = trainer.stepperClass(model, None, device, 0, lambda a, b: 0, step_size=1)\n        self.stepper.init_hidden_states(batch_size=1)\n\n    def step(self, json_data, unit_tag_mask, playerID):\n        # fig, axs = plt.subplots(1, 1, num=1, clear=True)\n\n        observer_session = json.loads(json_data)\n        trace = game_state_loader.loadSessionMovementTarget2(observer_session, playerID, buildOrderLoader, set(unit_tag_mask), \"invalid\")\n        self.stepper.set_batch(padding([trace])[0])\n        self.stepper.step()\n        result = self.stepper.outputs.detach().cpu().exp()\n        should_keep_order = self.stepper.outputs_keep_orders.item()\n        should_keep_order = random.random() < should_keep_order**(1/2)\n\n        is_attack_order = self.stepper.outputs_attack_order.item()\n        is_attack_order = random.random() < is_attack_order\n\n        probs = result.numpy().flatten()\n        probs = probs**3\n        probs /= probs.sum()\n        index = np.random.choice(14*14, p=probs)\n        index = probs.argmax()\n        # coord = (index % 14, index // 14)\n        coord = (index // 14, index % 14)\n        target_coord = game_state_loader.inverse_transform_coord_minimap(coord, game_state_loader.find_map_size(observer_session, playerID), 14, False)\n\n        # axs[0].imshow(result.tranpose(0, 1), origin='lower')\n        # plt.pause(0.001)\n\n        print(target_coord)\n\n\n        return target_coord, should_keep_order, is_attack_order\n\nif __name__ == \"__main__\":\n    common.train_interface(cache_tensors, train, visualize)\n","repo_name":"HalfVoxel/sc2-voxelbot","sub_path":"bot/python/predictor_movement_target.py","file_name":"predictor_movement_target.py","file_ext":"py","file_size_in_byte":22970,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"42121646942","text":"import turtle\n\n\nn = 7\nangle = (180-360/n)/3                                           \nturtle.speed(6)\n\nwhile True:\n    turtle.forward(100)\n    turtle.left(180-angle)\n    if abs(turtle.pos()) < 1:\n        break\n    \nturtle.mainloop() \nprint(angle)\n","repo_name":"Drozdovsimple/project_first","sub_path":"star_3.py","file_name":"star_3.py","file_ext":"py","file_size_in_byte":248,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"28538661500","text":"from turtle import *\nfrom sympy import *\nimport turtle\nfrom random import randint\n\nclass Drawer():\n    def __init__(self):\n        self.t = None\n        self.colors = [\"Red\", \"Green\", \"Blue\"]\n        self.is_drawn_grid = False\n    \n    def draw_grid(self):\n        self.t = turtle.Turtle()\n        turtle.setup(width = 700, height = 700)\n        self.t.penup()\n        self.t.setx(-1920)\n        self.t.pendown()\n        self.t.speed(100)\n        self.t.pensize(3)\n        self.t.setposition(1920, 0)\n        #t.goto(-1920, 0)\n        self.t.setposition(0, 1200)\n        self.t.goto(0, -1200)\n        self.t.penup()\n        self.t.setx(-1920)\n        self.t.sety(-1200)\n        self.t.pendown()\n\n        self.t.pensize(0.3)\n        \n        y = -1200\n        \"\"\"\n        for i in range(100):\n            self.t.goto(1920, y)\n            self.t.penup()\n            self.t.goto(-1920, y)\n            self.t.pendown()\n            y+=100\n            \n        x = -1920\n        \n        for i in range (100):\n            self.t.goto(x, 1200)\n            self.t.penup()\n            self.t.goto(x, -1200)\n            self.t.pendown()\n            x+=100\n        \"\"\"\n    def calc_func(self, str_expr):\n        list_x = []\n        list_y = []\n\n        x = symbols(\"x\")\n\n        expr = sympify(str_expr)\n\n        i = -10\n        \n        while (i < 10):\n            list_x.append(i)\n            i += 0.1\n            \n        for i in range(len(list_x)):\n            list_y.append(expr.subs(x, list_x[i]))\n\n        return (list_x, list_y)\n\n    def draw_func(self, str_expr):\n        if not self.is_drawn_grid:\n            self.draw_grid()\n            self.is_drawn_grid = True\n        self.t.pensize(3)\n        coords = self.calc_func(str_expr)\n        self.t.penup()\n        self.t.color(self.colors[randint(0, 2)])\n        self.t.goto(coords[0][0]*100, coords[1][0]*100)\n        self.t.pendown()\n        for i in range(len(coords[0])):\n            self.t.goto(coords[0][i]*100, coords[1][i]*100)\n\n\n\n\n\n\n\n\n\n\n","repo_name":"NickSot/Math","sub_path":"FuncsAndLibs/funcs.py","file_name":"funcs.py","file_ext":"py","file_size_in_byte":1996,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"71082940426","text":"# Dylan Andrews, dmandrew@usc.edu\n# ITP 115, Fall 2020\n# Assignment 10\n# Description:\n# This program will simulate a user's music library using an already-created music library, and save the music library\n# with any changes back in the same file\n\nimport MusicLibraryHelper\nimport random\n\n# displays menu with all the options for the music platform\ndef displayMenu():\n    print(\"Welcome to Your Music Library\\nOptions:\\n\\t1) Display Library\\n\\t2) Display all artists\\n\\t3) Add an album\"\n          \"\\n\\t4) Delete an album\\n\\t5) Delete an artist\\n\\t6) Search library\\n\\t7) Generate a random playlist\\n\\t8) Exit\")\n\n# allows user to display library, lists all artists and their albums\ndef displayLibrary(musicLibDictionary):\n    artists = list(musicLibDictionary.keys())\n    albumsList = list(musicLibDictionary.values())\n    i = 0\n    while i < len(artists):\n        print(\"Artist:\", artists[i])\n        c = 0\n        print(\"Albums:\")\n        artistAlbumList = list(albumsList[i])\n        while c < len(artistAlbumList):\n            print(\"\\t -\", artistAlbumList[c])\n            c += 1\n        i += 1\n    print(\"\\n\")\n\n# displays all artists in library\ndef displayArtists(musicLibDictionary):\n    print(\"Displaying all artists:\")\n    artists = list(musicLibDictionary.keys())\n    i = 0\n    while i < len(artists):\n        print(\"-\", artists[i])\n        i += 1\n\n# allows user to add an album to an existing artist\ndef addAlbum(musicLibDictionary):\n    artists = list(musicLibDictionary)\n    artist = input(\"Enter artist: \")\n    artist = artist.strip().lower().title()\n    while artist not in artists:\n        artist = input(\"Enter artist in library: \")\n        artist = artist.strip().lower().title()\n    album = input(\"Enter album: \")\n    album = album.lower().title().strip()\n    musicLibDictionary[artist] = album\n\n# allows user to delete an album in library, returns true or false based on whether it was able to delete something\ndef deleteAlbum(musicLibDictionary):\n    artists = list(musicLibDictionary.keys())\n    artistDelete = input(\"Enter artist: \")\n    artistDelete = artistDelete.lower().title().strip()\n    while artistDelete not in artists:\n        artistDelete = input(\"Enter artist in library: \")\n        artistDelete = artistDelete.lower().title().strip()\n    albumDelete = input(\"Enter album: \")\n    albumDelete = albumDelete.lower().title().strip()\n    albumList = musicLibDictionary.get(artistDelete)\n    if albumDelete not in albumList:\n        return False\n    else:\n        albumList.remove(albumDelete)\n        musicLibDictionary[artistDelete] = albumList\n        return True\n\n\n\n# allows user to delete an artist in library and all of their albums, returns true or false based on if it was able to\n# delete something\ndef deleteArtist(musicLibDictionary):\n    artists = list(musicLibDictionary.keys())\n    artistDelete = input(\"Enter artist: \")\n    artistDelete = artistDelete.strip().lower().title()\n    if artistDelete not in artists:\n        return False\n    else:\n        del musicLibDictionary[artistDelete]\n        return True\n\n\n# allows user to search library for key words and display albums and artists containing the word / phrase\ndef searchLibrary(musicLibDictionary):\n    searchOriginal = input(\"Please enter a search term: \")\n    artists = list(musicLibDictionary.keys())\n    albums = list(musicLibDictionary.values())\n    search = searchOriginal.strip().lower().title()\n    print(\"Artists containing '\" + searchOriginal + \"'\")\n    i = 0\n    resultsArtists = False\n    while i < len(artists):\n        if search in artists[i]:\n            print(\"- \" + artists[i])\n            resultsArtists = True\n        i += 1\n    if resultsArtists == False:\n        print(\"\\tNo results\")\n    print(\"Albums containing '\" + searchOriginal + \"'\")\n    c = 0\n    resultsAlbums = False\n    while c < len(albums):\n        artistAlbumList = list(albums[c])\n        h = 0\n        while h < len(artistAlbumList):\n            if search in artistAlbumList[h]:\n                print(\"-\", artistAlbumList[h])\n                resultsAlbums = True\n            h += 1\n        c += 1\n    if resultsAlbums == False:\n        print(\"\\tNo results\")\n\n# generates a random playlist of the artists and albums\ndef generateRandomPlaylist(musicLibDictionary):\n    artists = list(musicLibDictionary.keys())\n    editArtists = list(musicLibDictionary.keys())\n    albums = list(musicLibDictionary.values())\n    i = 0\n    print(\"Here is your random playlist:\")\n    while i < len(artists):\n        number = random.randrange(0, len(editArtists))\n        num2 = random.randrange(0, len(albums[number]))\n        artistAlbumList = list(albums[number])\n        print(\"-\", artistAlbumList[num2], \"by\", editArtists[number])\n        editArtists.pop(number)\n        albums.pop(number)\n        i += 1\n\n# runs the library program until user enters 8 and saves it after they press 8\ndef main():\n    library = MusicLibraryHelper.loadLibrary(\"musicLibrary.dat\")\n    print(library)\n    choice = 0\n    while int(choice) != 8:\n        displayMenu()\n        choice = input(\"> \")\n        while (choice.isdigit() == False) or (int(choice) not in range(1, 9)):\n            choice = input(\"Make a proper selection (1-8): \")\n        if int(choice) == 1:\n            displayLibrary(library)\n        elif int(choice) == 2:\n            displayArtists(library)\n        elif int(choice) == 3:\n            addAlbum(library)\n        elif int(choice) == 4:\n            delete = deleteAlbum(library)\n            if delete == True:\n                print(\"Delete album success!\")\n            elif delete == False:\n                print(\"Delete album failed.\")\n        elif int(choice) == 5:\n            artistDelete = deleteArtist(library)\n            if artistDelete == True:\n                print(\"Delete artist success!\")\n            elif artistDelete == False:\n                print(\"Delete artist failed.\")\n        elif int(choice) == 6:\n            searchLibrary(library)\n        elif int(choice) == 7:\n            generateRandomPlaylist(library)\n    print(\"Saving music library...\")\n    MusicLibraryHelper.saveLibrary(\"musicLibrary.dat\", library)\n    print(\"Goodbye!\")\n\n\n\n\n\n\n\nmain()\n\n","repo_name":"dandrews19/MusicLibrarySimulator","sub_path":"ITP115_A10_Andrews_Dylan.py","file_name":"ITP115_A10_Andrews_Dylan.py","file_ext":"py","file_size_in_byte":6139,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"70015078344","text":"#!/usr/bin/python3\nimport sys, time, datetime\nsys.path.extend(['..', '.'])\nfrom collections import *\nfrom main import run\ndef get_day(): return 2\ndef get_year(): return 2019\n\ndef p1(v):\n    digs = [int(x) for x in v.split(',')]\n    return run_prog(digs, 12, 2)\n\n    return digs[0]\n\ndef run_prog(digs, i, j):\n    digs[1] = i\n    digs[2] = j\n    ip = 0\n    while ip < len(digs)-3:\n        op, a, b, c = digs[ip:ip + 4]\n        if op == 99: break\n        elif op == 1:\n            digs[c] = digs[a] + digs[b]\n        elif op == 2:\n            digs[c] = digs[a] * digs[b]\n        else:\n            print('wtf, op: {}'.format(op))\n            return\n        ip += 4\n    return digs[0]\n\n\ndef p2(v):\n    digs = [int(x) for x in v.split(',')]\n    for i in range(10000):\n        x = run_prog(list(digs), i//100, i%100)\n        if x == 19690720:\n            return i\n    return 0\n\n\nif __name__ == '__main__':\n    run(get_year(), get_day(), p1, p2)\n","repo_name":"exoji2e/aoc2019","sub_path":"02/day02.py","file_name":"day02.py","file_ext":"py","file_size_in_byte":938,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"81"}
+{"seq_id":"11359132349","text":"import pandas as pd\nimport plotly.express as px\nimport argparse\n\nparser = argparse.ArgumentParser()\nparser.add_argument(\"--df\", nargs='+') # 'etomidate_apo_lipids'\nargs = parser.parse_args()\n\ndata_file = args.df\ndata = pd.DataFrame()\n\nfor df in data_file:\n\n    single_data = pd.read_csv(df)\n    data = pd.concat([data, single_data])\n\nprint(data)\n\nfig = px.box(data, x='interface', y='distance', hover_name='system', facet_col='ligand_state', facet_row='ligand_type', color='system')\nfig.write_html('gate_resid_dist' + '.html')\n\n#fig2 = px.line(data, x='frame', y='distance', hover_name='system', facet_col='ligand_state', facet_row='ligand_type', color='system')\n#fig2.write_html('gate_resid_traj' + '.html')\n","repo_name":"michal2am/bioscripts","sub_path":"stockholm/mdanalysis/gate_resid_dist_ploter.py","file_name":"gate_resid_dist_ploter.py","file_ext":"py","file_size_in_byte":709,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"81"}
+{"seq_id":"6065494971","text":"import notes.note as modelo\n\n\n\nclass Acciones:\n\n    def NewNotes(self, Usuario):\n        name = Usuario['name']\n        lastname = Usuario['lastname']\n        id = Usuario['_id']\n        print(f' {name} {lastname} Add New Note')\n        title = input('Title Note: ').capitalize()\n        description = input('description Note: ').capitalize()\n\n        note = modelo.Note(title, description, id )\n        save = note.SaveNote()\n\n\n    def ShowNotes(self, Usuario):\n        name = Usuario['name']\n        lastname = Usuario['lastname']\n        id = Usuario['_id']\n        print(f'Get All notes For User: {name} {lastname} ')\n        note = modelo.Note(None, None, id)\n        getAll = note.GetAllNotes()\n    \n    def DeleteNote(self,Usuario):\n        print('We procced to Delete the Note please Write the Title')\n        title = input('Escribe el Nombre de la nota que deseas Borrar: ').capitalize()\n        note = modelo.Note(title, None, None)\n        #confirm = input('Are you Sure?: ').lower()\n        delete = note.DeleteOneNotes()","repo_name":"katchvidal/legendary-robot","sub_path":"src/notes/acciones.py","file_name":"acciones.py","file_ext":"py","file_size_in_byte":1033,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"31148160467","text":"from django.db.models import get_models, get_app\nfrom rest_framework.routers import DefaultRouter, flatten, Route, replace_methodname\nfrom django.core.exceptions import ImproperlyConfigured\nfrom rest_framework import views\nfrom rest_framework.response import Response\nfrom rest_framework.reverse import reverse\n\nfrom easyapi.permissions import IsStaff\nfrom easyapi.viewsets import InstanceViewSet\n\n\n__author__ = 'mikhailturilin'\n\nINSTANCE_METHOD_ROUTE = Route(\n    url=r'^{prefix}/{lookup}/{methodname}{trailing_slash}$',\n    mapping={\n        '{httpmethod}': '{methodname}',\n    },\n    name='{basename}-{methodnamehyphen}',\n    initkwargs={}\n)\n\nMANAGER_METHOD_ROUTE = Route(\n    url=r'^{prefix}-manager/{methodname}{trailing_slash}$',\n    mapping={\n        '{httpmethod}': '{methodname}',\n    },\n    name='{basename}-manager-{methodnamehyphen}',\n    initkwargs={}\n)\n\n\nclass EasyApiRouter(DefaultRouter):\n    permission_classes = (IsStaff,)\n\n\n    def __init__(self, namespace=None, **kwargs):\n        super(EasyApiRouter, self).__init__(**kwargs)\n        self.namespace = namespace\n\n\n    def known_actions(self):\n        the_known_actions = flatten([route.mapping.values() for route in self.routes])\n        return the_known_actions\n\n    def compute_routes(self, wrapper, method_name, route):\n        httpmethods = wrapper.bind_to_methods\n\n        dynamic_routes = []\n        if httpmethods:\n            if method_name in self.known_actions():\n                raise ImproperlyConfigured('Cannot use @rest_method decorator on '\n                                           'method \"%s\" as it is an existing route' % method_name)\n            httpmethods = [method.lower() for method in httpmethods]\n            dynamic_routes.append((httpmethods, method_name))\n\n\n        # Dynamic routes (@link or @action decorator)\n        instance_routes = []\n        for httpmethods, method_name in dynamic_routes:\n            initkwargs = route.initkwargs.copy()\n            # initkwargs.update(getattr(viewset, method_name).kwargs)\n            instance_routes.append(Route(\n                url=replace_methodname(route.url, method_name),\n                mapping=dict((httpmethod, method_name) for httpmethod in httpmethods),\n                name=replace_methodname(route.name, method_name),\n                initkwargs=initkwargs,\n            ))\n\n        return instance_routes\n\n    def get_routes(self, viewset):\n        routes = super(EasyApiRouter, self).get_routes(viewset)\n\n        if issubclass(viewset, InstanceViewSet):\n            for method_name in viewset.instance_rest_methods():\n                inst_wrapper = viewset.get_instance_method(method_name)\n                instance_routes = self.compute_routes(inst_wrapper, method_name, INSTANCE_METHOD_ROUTE)\n\n                routes.extend(instance_routes)\n\n            for method_name in viewset.manager_rest_methods():\n                inst_wrapper = viewset.get_manager_method(method_name)\n                manager_routes = self.compute_routes(inst_wrapper, method_name, MANAGER_METHOD_ROUTE)\n\n                routes.extend(manager_routes)\n\n        return routes\n\n    def get_method_map(self, viewset, method_map):\n        is_instance_viewset = issubclass(viewset, InstanceViewSet)\n\n        bound_methods = {}\n        for method, action in method_map.items():\n            if hasattr(viewset, action) \\\n                    or (is_instance_viewset and action in viewset.instance_rest_methods()) \\\n                    or (is_instance_viewset and action in viewset.manager_rest_methods()):\n                bound_methods[method] = action\n        return bound_methods\n\n\n    def get_api_root_view(self):\n        \"\"\"\n        I ovveriden this function to include namespaces\n        \"\"\"\n        api_root_dict = {}\n        list_name = self.routes[0].name\n        for prefix, viewset, basename in self.registry:\n            api_root_dict[prefix] = list_name.format(basename=basename)\n\n        outer_self = self\n\n        class APIRoot(views.APIView):\n            _ignore_model_permissions = True\n            permission_classes = outer_self.permission_classes\n\n            def get(self, request, format=None):\n                ret = {}\n                for key, url_name in api_root_dict.items():\n                    if outer_self.namespace:\n                        url_name = \"%s:%s\" % (outer_self.namespace, url_name)\n                    ret[key] = reverse(url_name, request=request, format=format)\n                return Response(ret)\n\n        return APIRoot.as_view()\n\n    @property\n    def urls(self):\n        urls = super(EasyApiRouter, self).urls\n        if self.namespace:\n            return urls, self.namespace, self.namespace\n\n        return urls\n\n\ndef ViewSetFactory(the_model):\n    class _ModelViewSet(InstanceViewSet):\n        model = the_model\n\n    return _ModelViewSet\n\n\nclass AutoAppRouter(EasyApiRouter):\n    def __init__(self, app_name, **kwargs):\n        super(AutoAppRouter, self).__init__(**kwargs)\n\n        app = get_app(app_name)\n        for model in get_models(app):\n            viewset_class = ViewSetFactory(model)\n            self.register(model.__name__.lower(), viewset_class)\n\n\nclass AutoAppListRouter(EasyApiRouter):\n    def __init__(self, *app_names, **kwargs):\n        super(AutoAppListRouter, self).__init__(**kwargs)\n\n        for app_name in app_names:\n            app = get_app(app_name)\n            for model in get_models(app):\n                viewset_class = ViewSetFactory(model)\n                prefix = \"%s/%s\" % (app_name, model.__name__.lower())\n                self.register(prefix, viewset_class)\n\n\n","repo_name":"mturilin/easyapi","sub_path":"easyapi/router.py","file_name":"router.py","file_ext":"py","file_size_in_byte":5574,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"6079297184","text":"import os\nimport torch\nimport torch.nn as nn\nimport torch.backends.cudnn as cudnn\nimport wandb\n\nfrom torchvision import datasets\nfrom model import Classifier\nfrom utils import parse\nfrom transforms import dataTransforms\n\ndef validate(model, criterion):\n    # Single iteration\n    for inputs, labels in dataloader:\n        inputs = inputs.to(device)\n        labels = labels.to(device)\n        samples = dataloader.dataset.samples\n\n        # forward\n        with torch.no_grad():\n            outputs = model(inputs)\n            _, preds = torch.max(outputs, 1)\n            loss = criterion(outputs, labels)\n\n        # statistics\n        running_loss = loss.item() * inputs.size(0)\n        running_corrects = torch.sum(preds == labels.data)\n\n    # wandb.log({\n    #     \"val_loss\": running_loss / valSetSize,\n    #     \"val_acc\": running_corrects.double() / valSetSize\n    # })\n\n    return samples, torch.stack((labels,preds), dim=1)\n\nif __name__ == '__main__':\n    cudnn.benchmark = True  # fixed size inputs\n\n    device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n    criterion = nn.CrossEntropyLoss()\n\n    valSet = datasets.ImageFolder(os.path.join('dset', 'val'), dataTransforms['val'])\n    valSetSize = len(valSet)\n    dataloader = torch.utils.data.DataLoader(valSet, batch_size = valSetSize,\n                                                shuffle=False, num_workers=4)\n\n    classifier = Classifier().to(device)\n    classifier.load('old_checkpoint.pt')\n    classifier.eval()\n\n    # wandb.init(\n    #     project = \"ilmos\",\n    #     name = f\"val_UTHZ5B82_epoch_36\"\n    # )\n\n    samples, stacked = validate(classifier, criterion)\n    zipped = zip(samples, stacked)\n    matrix = torch.zeros(2, 2, dtype=torch.int64)\n    \n    for sample, p in zipped:\n        gt, pred = p.tolist()\n        matrix[gt, pred] += 1\n\n        if gt != pred:\n            print(sample)\n\n    print(matrix)\n    # wandb.finish()","repo_name":"kayberd/ilmos","sub_path":"cv/validate.py","file_name":"validate.py","file_ext":"py","file_size_in_byte":1922,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"81"}
+{"seq_id":"69893945225","text":"import copy\ndef main():\n    while (True):\n        print(\"==========================动态分区==========================\")\n        choose = int(input(\"选择算法\\n1-首次适应\\n2-循环首次适应\\n3-最佳适应\\n4-最坏适应\\n\"))\n        # memory = [16, 16, 8, 32, 64, 32, 8, 16, 64]\n        # processNeed = [7, 18, 9, 20, 35, 8, ]\n        memory = [int(i) for i in input(\"请输入内存空间\\n\").split()]\n        processNeed = [int(i) for i in input(\"请输入进程需求\\n\").split()]\n        print(\"分区情况如下\\n\")\n        if choose == 1:\n            firstPartion(copy.copy(memory), processNeed)\n        elif choose == 2:\n            cycleFirstPartion(copy.copy(memory), processNeed)\n        elif choose == 3:\n            bestPartition(copy.copy(memory), processNeed)\n        elif choose == 4:\n            worstPartition(copy.copy(memory), processNeed)\n\n\ndef firstPartion(memory, processNeed):\n    for pid in range(len(processNeed)):\n        for addr in range(len(memory)):\n            if isinstance(memory[addr], int) and memory[addr] > processNeed[pid]:\n                if addr != len(memory) - 1:\n                    memory.insert(addr + 1, memory[addr] - processNeed[pid])\n                else:\n                    memory.append(memory[addr] - processNeed[pid])\n                memory[addr] = chr(65 + pid)\n                break\n            elif isinstance(memory[addr], int) and memory[addr] == processNeed[pid]:\n                memory[addr] = chr(65 + pid)\n                break\n\n    print(memory)\n\n\ndef cycleFirstPartion(memory, processNeed):\n    pid = 0\n    while (True):\n        addr = 0\n        while (addr != len(memory)):\n            if isinstance(memory[addr], int) and memory[addr] > processNeed[pid]:\n                if addr != len(memory) - 1:\n                    memory.insert(addr + 1, memory[addr] - processNeed[pid])\n                else:\n                    memory.append(memory[addr] - processNeed[pid])\n                memory[addr] = chr(65 + pid)\n                addr += 1\n                pid += 1\n                if pid == len(processNeed): break\n            elif isinstance(memory[addr], int) and memory[addr] == processNeed[pid]:\n                memory[addr] = chr(65 + pid)\n                pid += 1\n                if pid == len(processNeed): break\n            addr += 1\n        if (pid == len(processNeed)): break\n    print(memory)\n\n\ndef bestPartition(memory, processNeed):\n    for pid in range(len(processNeed)):\n        bestIndex = -1\n        bestSize = float(\"inf\")\n        for addr in range(len(memory)):\n            if isinstance(memory[addr], int) and memory[addr] >= processNeed[pid] and memory[addr] < bestSize:\n                bestIndex = addr\n                bestSize = memory[addr]\n        if bestIndex != -1:\n            if memory[bestIndex] > processNeed[pid]:\n                if bestIndex != len(memory) - 1:\n                    memory.insert(bestIndex + 1, memory[bestIndex] - processNeed[pid])\n                else:\n                    memory.append(memory[bestIndex] - processNeed[pid])\n                memory[bestIndex] = chr(65 + pid)\n                if pid != len(processNeed) - 1:\n                    pid += 1\n                else:\n                    break\n\n            elif memory[bestIndex] == processNeed[pid]:\n                memory[bestIndex] = chr(65 + pid)\n                if pid != len(processNeed) - 1:\n                    pid += 1\n                else:\n                    break\n        else:\n            print(\"内存不足\")\n    print(memory)\n\n\ndef worstPartition(memory, processNeed):\n    for pid in range(len(processNeed)):\n        worstIndex = -1\n        worstSize = 0\n        for addr in range(len(memory)):\n            if isinstance(memory[addr], int) and memory[addr] >= processNeed[pid] and memory[addr] > worstSize:\n                worstIndex = addr\n                worstSize = memory[addr]\n        if worstIndex != -1:\n            if memory[worstIndex] > processNeed[pid]:\n                if worstIndex != len(memory) - 1:\n                    memory.insert(worstIndex + 1, memory[worstIndex] - processNeed[pid])\n                else:\n                    memory.append(memory[worstIndex] - processNeed[pid])\n                memory[worstIndex] = chr(65 + pid)\n                if pid != len(processNeed) - 1:\n                    pid += 1\n                else:\n                    break\n\n            elif memory[worstIndex] == processNeed[pid]:\n                memory[worstIndex] = chr(65 + pid)\n                if pid != len(processNeed) - 1:\n                    pid += 1\n                else:\n                    break\n        else:\n            print(\"内存不足\")\n    print(memory)\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"AmmoMercy/OSexps","sub_path":"Memory.py","file_name":"Memory.py","file_ext":"py","file_size_in_byte":4717,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"10497213301","text":"import warnings\n\nfrom typing import Generator, Union\n\nimport faster_whisper\nimport whisper\n\nfrom tqdm import tqdm\n\nfrom tafrigh.config import Config\nfrom tafrigh.types.whisper.type_hints import WhisperModel\n\n\nclass WhisperRecognizer:\n    def __init__(self, verbose: bool):\n        self.verbose = verbose\n\n    def recognize(\n        self,\n        file_path: str,\n        model: WhisperModel,\n        whisper_config: Config.Whisper,\n    ) -> Generator[dict[str, float], None, list[dict[str, Union[str, float]]]]:\n        with warnings.catch_warnings():\n            warnings.simplefilter('ignore')\n\n            if isinstance(model, whisper.Whisper):\n                whisper_generator = self._recognize_stable_whisper(file_path, model, whisper_config)\n            elif isinstance(model, faster_whisper.WhisperModel):\n                whisper_generator = self._recognize_faster_whisper(file_path, model, whisper_config)\n\n            while True:\n                try:\n                    yield next(whisper_generator)\n                except StopIteration as e:\n                    return e.value\n\n    def _recognize_stable_whisper(\n        self,\n        audio_file_path: str,\n        model: whisper.Whisper,\n        whisper_config: Config.Whisper,\n    ) -> Generator[dict[str, float], None, list[dict[str, Union[str, float]]]]:\n        yield {'progress': 0.0, 'remaining_time': None}\n\n        segments = model.transcribe(\n            audio=audio_file_path,\n            verbose=self.verbose,\n            task=whisper_config.task,\n            language=whisper_config.language,\n            beam_size=whisper_config.beam_size,\n        ).segments\n\n        return [\n            {\n                'start': segment.start,\n                'end': segment.end,\n                'text': segment.text.strip(),\n            }\n            for segment in segments\n        ]\n\n    def _recognize_faster_whisper(\n        self,\n        audio_file_path: str,\n        model: faster_whisper.WhisperModel,\n        whisper_config: Config.Whisper,\n    ) -> Generator[dict[str, float], None, list[dict[str, Union[str, float]]]]:\n        segments, info = model.transcribe(\n            audio=audio_file_path,\n            task=whisper_config.task,\n            language=whisper_config.language,\n            beam_size=whisper_config.beam_size,\n        )\n\n        converted_segments = []\n        last_end = 0\n        with tqdm(\n            total=round(info.duration, 2),\n            unit='sec',\n            bar_format='{desc}: {percentage:.2f}%|{bar}| {n:.2f}/{total_fmt} [{elapsed}<{remaining}, {rate_fmt}{postfix}]',\n            disable=self.verbose is not False,\n        ) as pbar:\n            for segment in segments:\n                converted_segments.append(\n                    {\n                        'start': segment.start,\n                        'end': segment.end,\n                        'text': segment.text.strip(),\n                    }\n                )\n\n                pbar_update = min(segment.end - last_end, info.duration - pbar.n)\n                pbar.update(pbar_update)\n                last_end = segment.end\n\n                yield {\n                    'progress': round(pbar.n / pbar.total * 100, 2),\n                    'remaining_time': (pbar.total - pbar.n) / pbar.format_dict['rate']\n                    if pbar.format_dict['rate'] and pbar.total\n                    else None,\n                }\n\n        return converted_segments\n","repo_name":"ieasybooks/tafrigh","sub_path":"tafrigh/recognizers/whisper_recognizer.py","file_name":"whisper_recognizer.py","file_ext":"py","file_size_in_byte":3421,"program_lang":"python","lang":"en","doc_type":"code","stars":52,"dataset":"github-code","pt":"81"}
+{"seq_id":"73317760906","text":"from aiohttp import web\nfrom app.settings import BASE_DIR\nimport json\nfrom app.code_wheel import get_code\n\n\nasync def index(request):\n    return web.FileResponse(BASE_DIR / \"public\" / \"index.html\")\n\n\nasync def games(request):\n    data = {\"some\": \"data\"}\n    return web.json_response(data)\n\n\nasync def code_wheel(request):\n    static_wheel = request.query.get(\"static_wheel\")\n    moving_wheel = request.query.get(\"moving_wheel\")\n    cell = request.query.get(\"cell\")\n\n    codes_file = open(BASE_DIR / \"app/data/indy4a_code_wheel.json\")\n    codes_string = codes_file.read()\n    codes = json.loads(codes_string)\n\n    code = get_code(static_wheel.upper(), moving_wheel.upper(), int(cell), codes)\n\n    return web.json_response({\"code\": code})\n\n\ndef add_routes(app):\n    app.router.add_route(\"GET\", \"/\", index)\n    app.router.add_route(\"GET\", \"/api/games\", games)\n    app.router.add_route(\"GET\", \"/api/wheel\", code_wheel)\n    app.router.add_static(\n        \"/static/\", path=BASE_DIR / \"public\" / \"static\", name=\"static\"\n    )\n","repo_name":"arnaugm/game-catalog","sub_path":"app/routes.py","file_name":"routes.py","file_ext":"py","file_size_in_byte":1019,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"12707018884","text":"# def initialize(context):\n#     set_benchmark('000300.XSHG')\n#     set_option('use_real_price',True)\n#     set_option('order_volume_ratio', 1)\n#     set_order_cost(OrderCost(open_tax=0, close_tax=0.001, open_commission=0.0003, close_today_commission=0,close_commission=0.0003,min_commission=5), type='stock')\n#     g.security = '000300.XSHG'   # 可多加股票\n#     g.M = 15   # 20日均线\n#     g.N = 2.2    # N\n    \n# def handle_data(context,data):\n#     sr = attribute_history(g.security,g.M)['close']\n#     ma20 = sr.mean()                                 # 20日均线\n#     up = ma20 + g.N * sr.std()\n#     down = ma20 - g.N * sr.std()\n#     q=attribute_history(g.security,1)['high'].mean()\n#     v=attribute_history(g.security,1)['close'].mean()\n#     p = get_current_data()[g.security].day_open      # 开盘价\n#     amount = context.portfolio.positions[g.security].total_amount # 总持仓 。或者开仓均线去表示\n#     cash = context.portfolio.available_cash                       # 可用资金\n    \n#     # 如果p小于down，且没有持仓，则 买入\n#     # 如果p大于up，且有持仓，则 卖出\n#     if  q> up and amount == 0:\n#         order_value(g.security,cash) \n#     elif v< down and amount > 0:\n#         order_target(g.security,0)\nimport pandas as pd\nimport numpy as np\nfrom matplotlib import pyplot as plt\nfrom utils import sim_trade2, plot_idx, calc_sharpe_ratio, show_rate_stat, calc_drawdown, add_line\n\ndef load_data():\n    return pd.read_csv(\"SH000300.csv\")\n\ndef baseline(plt, data: pd.DataFrame):\n    N = 2.2\n    sr = data['Close']\n    ma20 = sr.rolling(20).mean()\n    std = sr.rolling(20).std()\n    up = ma20 + N * std\n    down = ma20 - N * std\n    q=data['High']\n    k=data['Close']\n    v=data['Low']\n    holds = np.zeros_like(sr.values)\n    buy = q>up\n    sell = (k<down)\n    short = (v<down)\n    for i in range(1, len(holds)):\n        if buy[i]:\n            holds[i] = 1\n        elif sell[i] or short[i]:\n            holds[i] = 0\n        else:\n            holds[i] = holds[i-1]\n    return holds\n\ndef signal(data: pd.DataFrame, period=20):\n    def linear(series: pd.Series):\n        n = len(series)\n        x = np.array(range(n), dtype=np.float32)\n        x -= np.mean(x)\n        # 计算自变量的均值和因变量的均值\n        y_mean = np.mean(series)\n        # 计算自变量和因变量的差值乘积之和以及自变量的差值平方和\n        xy_diff_sum = np.sum(x * (series - y_mean))\n        x_diff_squared_sum = np.sum(x ** 2)\n        # 计算回归系数\n        slope = xy_diff_sum / x_diff_squared_sum\n        return slope\n\n    def no_lin_std(series: pd.Series):\n        slope = linear(series)\n        x = np.array(range(len(series)), dtype=np.float32) * slope\n        x -= np.mean(x)\n        series = series - series.mean() - x\n        return series.std()\n    \n    sr = data['Close']\n    ma = sr.rolling(period).mean()\n    std = sr.rolling(period).std()\n    lin = sr.rolling(period).apply(linear)\n    no_lin = sr.rolling(period).apply(no_lin_std)\n    return ma, std, lin, no_lin\n\n\ndef get_signal2(plt, data: pd.DataFrame, do_short=False):\n    # sharp = 0.75\n    N = 2.1\n    sr = data['Close']\n\n    ma40, std40, lin40, no_lin40 = signal(data, 40)\n    ma20, std20, lin20, no_lin20 = signal(data, 20)\n    ma10, std10, lin10, no_lin10 = signal(data, 10)\n\n    # up20 = ma20 + N * no_lin20 + lin20 * 10\n    up20 = ma20 + N * no_lin20 + lin20.apply(lambda x:x if x>0 else 0) * 10\n    # up20 = ma20 + N * std20# + lin20.apply(lambda x:x if x>0 else 0) * 10\n    down20 = ma20 - N * no_lin20 + lin40 * 10\n    # down20 = ma20 - N * sr.rolling(20).std()\n    add_line(plt, up20.fillna(sr[0]), normalize=True, label=\"up20\")\n    add_line(plt, down20.fillna(sr[0]), normalize=True, label=\"down20\")\n    buy = data['High'] > up20\n    sell = data['Low'] < down20\n\n    holds = np.zeros_like(sr.values)\n\n    for i in range(1, len(holds)):\n        h = holds[i-1]\n        if buy[i]:\n            holds[i] = 1\n        elif sell[i]:\n            holds[i] = 0\n        elif h < 0:\n            # 做空头寸衰减\n            holds[i] = 0.95 * h\n        else:\n            # 持仓\n            holds[i] = h\n    add_line(plt, holds.astype(np.int8), label=\"holds\")\n    return holds\n\ndef get_signal3(plt, data: pd.DataFrame, do_short=False):\n    # SOTA!\n    N = 2.1\n    sr = data['Close']\n\n    ma80, std80, lin80, no_lin80 = signal(data, 80)\n    ma40, std40, lin40, no_lin40 = signal(data, 40)\n    ma20, std20, lin20, no_lin20 = signal(data, 20)\n    ma10, std10, lin10, no_lin10 = signal(data, 10)\n\n    # up20 = ma20 + N * no_lin20 + lin20 * 10\n    up20 = ma20 + N * no_lin20 + lin20.apply(lambda x:x if x>0 else 0) * 10\n    # up20 = ma20 + N * std20# + lin20.apply(lambda x:x if x>0 else 0) * 10\n    down20 = ma20 - N * no_lin20 + lin40 * 10\n    # down20 = ma20 - N * sr.rolling(20).std()\n    # add_line(plt, up20.fillna(sr[0]), normalize=True, label=\"up20\")\n    # add_line(plt, down20.fillna(sr[0]), normalize=True, label=\"down20\")\n    buy = data['High'] > up20\n    sell = data['Low'] < down20\n\n    holds = pd.Series(np.zeros_like(sr.values))\n    for i in range(1, len(holds)):\n        h = holds[i-1]\n        if buy[i]:\n            holds[i] = 1\n        elif sell[i]:\n            holds[i] = 0\n        elif h < 0:\n            # 做空头寸衰减\n            holds[i] = 0.95 * h\n        else:\n            # 持仓\n            holds[i] = h\n    add_line(plt, holds, label=\"holds\")\n\n    mean_holds = holds.rolling(80).mean()\n\n    M = 1.4\n    P = 1.2\n    _up20 = ma20 + 3.0 * no_lin20 + lin40.apply(lambda x:x if x<0 else -x) * 10\n    # _up20 = ma20 + M * std20# + lin40 * 10\n    # up20 = ma20 + N * std20# + lin20.apply(lambda x:x if x>0 else 0) * 10\n    _down20 = ma20 - M * std20 - P * std40 - lin40.apply(lambda x:x if x<0 else -x) * 10 - (lin80 - lin40).apply(lambda x:x if x>0 else 0)\n    # down20 = ma20 - N * sr.rolling(20).std()\n    __down20 = ma20 - M * std20 - P * std40\n    add_line(plt, _up20.fillna(sr[0]), normalize=True, label=\"up20\")\n    add_line(plt, __down20.fillna(sr[0]), normalize=True, label=\"ma40+M*no_lin40\")\n    add_line(plt, _down20.fillna(sr[0]), normalize=True, label=\"down20\")\n\n    short = data['Close'] < _down20\n    run = data['Close'] > _up20\n\n    short_holds = np.zeros_like(sr.values)\n\n    for i in range(1, len(holds)):\n        h = short_holds[i-1]\n        # 检验是否存在趋势\n        if short[i]:\n            short_holds[i] = -1\n        elif run[i]:\n            short_holds[i] = 0\n        elif h < 0:\n            # 做空头寸衰减\n            short_holds[i] = 0.999 * h\n        else:\n            # 持仓\n            short_holds[i] = h\n    add_line(plt, short_holds, label=\"short_holds\")\n    return short_holds + holds\n\n\ndata = load_data()\nplt.figure(figsize=(16,4))\nadd_line(plt, data['Close'].values, normalize=True, label='close')\n# add_line(plt, data['High'].values, normalize=True, label='high')\n# add_line(plt, data['Low'].values, normalize=True, label='low')\n\n# baseline\nbaseline_holds = baseline(plt, data)\nbaseline_capital = sim_trade2(data[\"Close\"].values, baseline_holds, friction = True, friction_rate = 0.001, print_func = lambda x: None, date = None)\nadd_line(plt, baseline_capital, normalize=True, label='baseline')\nprint(f\"baseline sharp: {calc_sharpe_ratio(baseline_capital)}\")\n\n# ours\nholds = get_signal3(plt, data)\ncapital = sim_trade2(data[\"Close\"].values, holds, friction = True, friction_rate = 0.001, print_func = lambda x: None, date = None)\nadd_line(plt, capital, normalize=True, label='ours')\nprint(f\"our sharp: {calc_sharpe_ratio(capital)}\")\nshow_rate_stat(data['Close'].values, capital, 250)\nplt.grid(); plt.legend()\n# plt.savefig(\"with_short.png\")\nplt.show()","repo_name":"IcyChlorine/Quant9","sub_path":"quant.py","file_name":"quant.py","file_ext":"py","file_size_in_byte":7678,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"4127056668","text":"def search_subsequence(file):\n    first = \"\"\n    with open(file, \"r\") as f:\n        sub = \"\"\n        for line in f:\n            if line[0] == \">\":\n                if sub:\n                    first = sub\n                sub = \"\"\n                continue\n            sub += line.strip()\n    \n    index = 0\n    indeces = list()\n    for nuc_i in range(len(first)):\n        if first[nuc_i] == sub[index]:\n            indeces.append(str(nuc_i+1))\n            index+=1\n        if index == len(sub):\n            break\n    \n    with open(\"resultsseq.txt\", \"w\") as f:\n        f.write(\" \".join(indeces))\n\nsearch_subsequence('rosalind_sseq.txt')","repo_name":"elisa2602/PCS2_HOMEWORK","sub_path":"pcs2_hm5/sseq.py","file_name":"sseq.py","file_ext":"py","file_size_in_byte":633,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"43325580645","text":"\"\"\"\n    Tests about menu management\n\"\"\"\nimport unittest\n\nfrom flask import json\n\nfrom . import ApiTestCase\n\n\nclass ItemModelCase(ApiTestCase):\n\n    def test_items_post(self):\n        self.assertEquals(\n            self.client.post(self.init_data['RESTAURANT_CNAME'] + '/items',\n                             headers={'Authorization': 'JWT ' + self.john_token},\n                             data=json.dumps(dict(\n                                title='Delicioso prato de batata',\n                                price=10,\n                                description='Batata rustica com amendoin e \\\n                                    alecrim e canela')),\n                             content_type='application/json').status_code, 201)\n        self.assertEquals(\n            self.client.post(self.init_data['RESTAURANT_CNAME'] + '/items',\n                             headers={'Authorization': 'JWT ' + self.karl_token},\n                             data=json.dumps(dict(\n                                title='Lentilhas com bardana',\n                                price=12,\n                                description='Lentilha cozida com raiz de bardana')),\n                             content_type='application/json').status_code, 201)\n        self.assertEquals(\n            self.client.post(self.init_data['RESTAURANT_CNAME'] + '/items',\n                             headers={'Authorization': 'JWT ' + self.susan_token},\n                             data=json.dumps(dict(\n                                title='Shitake',\n                                price=13,\n                                description='Sopa de cogumelos')),\n                             content_type='application/json').status_code, 403)\n\n\nif __name__ == '__main__':\n    unittest.main(verbosity=2)\n","repo_name":"du2x/pystro","sub_path":"tests/test_menu_api.py","file_name":"test_menu_api.py","file_ext":"py","file_size_in_byte":1775,"program_lang":"python","lang":"en","doc_type":"code","stars":20,"dataset":"github-code","pt":"81"}
+{"seq_id":"39834563204","text":"from math import floor, sqrt\ndef solution(brown, yellow):\n    answer = []\n    area = brown + yellow\n    mid = int(sqrt(area))\n\n    for i in range(mid, area+1):\n        if not area % i:\n            temp = brown\n            border = 2*i + 2*area//i - 4\n            idx = 0\n            right = True\n            while temp != 0:\n                if temp < 0:\n                    right = False\n                    break\n                temp -= (border + (8*idx))\n                idx += 1\n            if right:\n                answer.append(i)\n                answer.append(area//i)\n                break\n    answer.sort(reverse=True)\n    return answer","repo_name":"devhyojin/Algorithm","sub_path":"Programmers/[Programmers]코딩테스트고득점Kit_정렬_카펫.py","file_name":"[Programmers]코딩테스트고득점Kit_정렬_카펫.py","file_ext":"py","file_size_in_byte":645,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"81"}
+{"seq_id":"74238939465","text":"n = int(input(\"Enter number of students: \"))\r\nlst_1 = list()\r\n\r\nfor i in range(0,n):\r\n    r_no =int(input(f\"Enter roll no. of student {i+1}: \"))\r\n    name = input(\"Enter name: \")\r\n    m1 =int(input(\"Enter marks in subject1: \"))\r\n    m2 =int(input(\"Enter marks in subject2: \"))\r\n    m3 =int(input(\"Enter marks in subject3: \"))\r\n    l1=[r_no,name,m1,m2,m3]\r\n    tpl = tuple(l1)\r\n    lst_1.append(tpl)\r\n\r\ntpl=tuple(lst_1)\r\nwhile True:\r\n    print(\"1.All elements 2.Element with python 3.Sorted tuple 4.Exit\")\r\n    choice=input(\"Enter a choice: \")\r\n    if choice ==\"1\":\r\n        print(\"All elements:\")\r\n        for t in tpl:\r\n            print(t)\r\n    elif choice ==\"2\":\r\n        print(\"Element with python:\")\r\n        for t in tpl:\r\n            if \"python\" in t:\r\n                print(t)\r\n    elif choice==\"3\":\r\n        print(\"Sorted tules according to name:\")\r\n        print(sorted(tpl,key=lambda x: x[1]))\r\n    elif choice==\"4\":\r\n        exit()\r\n    \r\n","repo_name":"Haris90400/python-lab","sub_path":"Python-lab exp/exp4.py","file_name":"exp4.py","file_ext":"py","file_size_in_byte":951,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"36374868128","text":"# %% package imports\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport clipboard_and_style_sheet as cr\nfrom scipy.integrate import simpson\nfrom tqdm import tqdm\n\n\n# %% load the data, for some reason the first spectrum always looks wonky ...\n# s_1 = np.load(\"temp/img1.npy\", mmap_mode=\"r+\")\n# s_2 = np.load(\"temp/img2.npy\", mmap_mode=\"r+\")\n\n# s_1 = s_1[1:]\n# s_2 = s_2[1:]\n\n# x = np.arange(0, s_1.shape[1] * 5, 5)\n# y = np.arange(0, s_1.shape[0] * 5, 5)\n\n# %% ------------------- f0 removal -------------------------------------------\n# filt_s1 = np.array(\n#     [\n#         [19400, 19480],\n#         [21500, 21750],\n#         [25850, 26025],\n#         [34480, 34560],\n#         [0, 20],\n#         [38860, 38900],\n#     ]\n# )\n\n# filt_s2 = np.array(\n#     [\n#         [2410, 2450],\n#         [19425, 19455],\n#         [21537, 21753],\n#         [25836, 26000],\n#         [34440, 34540],\n#         [0, 40],\n#         [38860, 38890],\n#     ]\n# )\n\n\n# def apply_filter(s, filt):\n#     for f in filt:\n#         s[f[0] : f[1]] = 0\n\n\n# s_1.resize(s_1.shape[0] * s_1.shape[1], s_1.shape[2])\n# for n, s in enumerate(tqdm(s_1)):\n#     apply_filter(s, filt_s1)\n\n# s_2.resize(s_2.shape[0] * s_2.shape[1], s_2.shape[2])\n# for n, s in enumerate(tqdm(s_2)):\n#     apply_filter(s, filt_s2)\n\n# %% ---------------- combine images ------------------------------------------\n# s_total = np.hstack(\n#     (\n#         s_1[:, :120],\n#         s_2,\n#     )\n# )\n\n# np.save(\"temp/s_total.npy\", s_total)\n\n# %% --------------- analyze combined image -----------------------------------\ns_t = np.load(\"temp/s_total.npy\", mmap_mode=\"r\")\n\n# ---------- single spectrum\n# t = np.fft.fftshift(np.fft.irfft(s_t[s_t.shape[0] // 2, s_t.shape[1] // 2 + 0]))\n\n# t_bckgnd = np.fft.fftshift(np.fft.irfft(s_t[0, 0]))\n# t_bckgnd = np.fft.fftshift(np.fft.irfft(s_t[-1, -1]))\n# t_bckgnd = np.load(\"avg_off_bio_sample.npy\")\n\n# ppifg = 77760\n# center = ppifg // 2\n\n# resolution = 50  # GHz\n# apod = int(np.round(ppifg / resolution))\n# if apod % 2 == 1:\n#     apod += 1\n\n# t = t[center - apod // 2 : center + apod // 2]\n# ft = abs(np.fft.rfft(np.fft.ifftshift(t)))\n\n# t_bckgnd = t_bckgnd[center - apod // 2 : center + apod // 2]\n# ft_bckgnd = abs(np.fft.rfft(np.fft.ifftshift(t_bckgnd)))\n\n# absorbance = -np.log(ft / ft_bckgnd)\n\n# freq = np.fft.rfftfreq(len(t))\n# (ind,) = np.logical_and(0.05 < freq, freq < 0.1912).nonzero()\n\n# fig, ax = plt.subplots(1, 2, figsize=np.array([11.51, 4.8]))\n# ax[0].plot(freq[ind], ft_bckgnd[ind], \".-\")\n# ax[0].plot(freq[ind], ft[ind], \".-\")\n# ax[1].plot(freq[ind], absorbance[ind])\n\n# ---------- total image\nppifg = 77760\ncenter = ppifg // 2\n\nresolution = 228  # GHz\napod = int(np.round(ppifg / resolution))\nif apod % 2 == 1:\n    apod += 1\n\nimg = np.zeros((s_t.shape[0], s_t.shape[1], len(np.fft.rfftfreq(apod))))\nshape_img = img.shape\nshape_s = s_t.shape\n\nt_bckgnd = np.fft.fftshift(np.fft.irfft(s_t[-1, -1]))\nt_b = t_bckgnd[center - apod // 2 : center + apod // 2]\nft_b = abs(np.fft.rfft(np.fft.ifftshift(t_b)))\n\ns_t.resize(s_t.shape[0] * s_t.shape[1], s_t.shape[2])\nimg.resize(img.shape[0] * img.shape[1], img.shape[2])\nfor n, s in enumerate(tqdm(s_t)):\n    t = np.fft.fftshift(np.fft.irfft(s))\n    t = t[center - apod // 2 : center + apod // 2]\n    ft = abs(np.fft.rfft(np.fft.ifftshift(t)))\n    absorbance = np.log(ft_b / ft)\n    img[n] = absorbance\n\nimg.resize(*shape_img)\ns_t.resize(*shape_s)\n\n# np.save(\"temp/img_absorbance_100GHz.npy\", img)\n\n# %% ---------------------- plotting ------------------------------------------\n# ppifg = 77760\n# center = ppifg // 2\n\n# resolution = 228  # GHz\n# apod = int(np.round(ppifg / resolution))\n# if apod % 2 == 1:\n#     apod += 1\n# img = np.load(f\"temp/img_absorbance_{resolution}GHz.npy\")\n\n# nu_grid = np.fft.rfftfreq(apod, d=1e-9) * ppifg\n# nu_grid += nu_grid[-1] * 2\n# wl_grid = 299792458 / nu_grid * 1e6\n# wnum_grid = 1e4 / wl_grid\n\n# # %%\n# (ind,) = np.logical_and(3.1927 < wl_grid, wl_grid < 3.6914).nonzero()\n# fig, ax = plt.subplots(1, 1)\n# ax.plot(wnum_grid[ind], img[img.shape[0] // 2, img.shape[1] // 2][ind])\n# conversion = lambda x: 1e4 / x\n# ax2 = ax.secondary_xaxis(\"top\", functions=(conversion, conversion))\n# ax2.set_xlabel(\"wavelength ($\\\\mathrm{\\\\mu m}$)\")\n# ax.set_xlabel(\"wavenumber ($\\\\mathrm{cm^{-1}}$)\")\n# ax.set_ylabel(\"absorbance\")\n# fig.tight_layout()\n\n# # %%\n# freq = np.fft.rfftfreq(apod)\n# i_1 = img[:, :, np.argmin(abs(freq - 0.12471))]\n# i_2 = img[:, :, np.argmin(abs(freq - 0.09845))]\n# i_3 = simpson(\n#     img[:, :, np.argmin(abs(freq - 0.11647)) : np.argmin(abs(freq - 0.13521))], axis=-1\n# )\n\n# fig, ax = plt.subplots(1, 3, figsize=np.array([12.59, 4.8]))\n# ax[0].imshow(i_1)\n# ax[0].set_title(\"peak 1 height\")\n# ax[1].imshow(i_2)\n# ax[1].set_title(\"peak 2 height\")\n# ax[2].imshow(i_3)\n# ax[2].set_title(\"integrating over peak 1\")\n# fig.tight_layout()\n\n# %% diagnose\n# (ind,) = np.logical_and(3.1927 < wl_grid, wl_grid < 3.6914).nonzero()\n# fig, ax = plt.subplots(1, 1)\n# save = False\n# for _, n in enumerate(tqdm(ind)):\n#     ax.clear()\n#     ax.set_title(\n#         \"$\\\\mathrm{\\\\lambda}=$\" + f\"{np.round(wl_grid[n], 2)}\" + \" $\\\\mathrm{\\\\mu m}$\"\n#     )\n#     ax.imshow(img[:, :, n])\n#     fig.tight_layout()\n#     if save:\n#         plt.savefig(f\"fig/{n}.png\")\n#     else:\n#         plt.pause(0.05)\n","repo_name":"peter-chang62/Microscope_Data_Analysis","sub_path":"04-05-2023_data.py","file_name":"04-05-2023_data.py","file_ext":"py","file_size_in_byte":5274,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"17960981541","text":"from config import converter\nfrom converter.base_converter import StorageItem\nfrom operation.base_operation import BaseOperation\nfrom operation.atomic_operation import AtomicOperation\nfrom operation.rename_operation import RenameOperation\n\nimport os\nimport utils.math as math_utils\nimport utils.string_utils as string_utils\n\ndef getRenameOperation(dir: str) -> BaseOperation:\n    items: list[StorageItem] = []\n    pending_dirs = [dir]\n    while len(pending_dirs) > 0:\n        local_pending_dirs = []\n        pending_dirs = [string_utils.to_folder_path(i) for i in pending_dirs]\n        for pending_dir in pending_dirs:\n            if not os.path.isdir(pending_dir):\n                raise FileNotFoundError(pending_dir + ' is not a valid directory')\n            for item_name in os.listdir(pending_dir):\n                path = pending_dir + item_name\n                item = StorageItem(path)\n                if converter.match(item):\n                    items.append(item)\n                if item.isDirectory() and converter.recursive(item):\n                    local_pending_dirs.append(item.getPath())\n        pending_dirs = local_pending_dirs\n\n    rename_ops = []\n    items.sort(key=lambda i : i.getOriginalPath())\n    if len(items) > 0:\n        converter.convert(items)\n        digits = math_utils.get_digits(len(items))\n        for i, item in enumerate(items):\n            print('%%0%dd: \"%%s\" -> \"%%s\"' % digits % (i + 1, item.getOriginalName(), item.getName()))\n        input('Press ENTER to proceed...')\n    for item in items:\n        new_path = string_utils.to_parent_path(item.getOriginalPath()) + item.getName()\n        rename_ops.append(RenameOperation(item.getOriginalPath(), new_path))\n\n    return AtomicOperation(rename_ops)\n\ndef main():\n    target_dir = input('Enter target directory:\\n')\n    target_dir = string_utils.to_folder_path(target_dir)\n    getRenameOperation(target_dir).do()\n\nif __name__ == '__main__':\n    main()","repo_name":"aicd0/RenameMachine","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1939,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"17571399603","text":"from datetime import datetime\n\nfrom mongoengine.errors import FieldDoesNotExist\n\n\ndef update_fields(update_request_body, stored_item):\n    unknown_fields = \"\"\n    for field in update_request_body:\n        try:\n            stored_item[field] = update_request_body[field]\n        except KeyError as e:\n            unknown_fields += str(e)\n\n    if unknown_fields:\n        raise FieldDoesNotExist(unknown_fields)\n    stored_item[\"updated_at\"] = datetime.now()\n","repo_name":"Hack-it-spiration/his_api","sub_path":"src/features/common/helpers/update.py","file_name":"update.py","file_ext":"py","file_size_in_byte":456,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"28774988754","text":"from itertools import accumulate\n\nN = int(input())\na = list(accumulate([int(i) for i in input().split()]))\nl, r = 0, 1\nans = float(\"inf\")\nfor i in range(1, N-2):\n    while abs(a[i] - 2 * a[l+1]) < abs(a[i] - 2 * a[l]): l += 1\n    while abs(a[-1] - 2 * a[r+1] + a[i]) < abs(a[-1] - 2 * a[r] + a[i]): r += 1\n    t = [a[l], a[i] - a[l], a[r] - a[i], a[-1] - a[r]]\n    ans = min(ans, max(t) - min(t))\nprint(ans)\n\n\n'''\n#Original\n\nimport queue\n\nN = int(input())\nll, lr = a[1] - a[0], a[2] - a[1]\nA = [int(i) for i in input().split()]\n\na = [0]\nsum_A = 0\nfor i in range(N):\n    sum_A += A[i]\n    a.append(sum_A)\n\nll, lr = queue.Queue(), queue.Queue()\nrl, rr = queue.Queue(), queue.Queue()\nll.put(a[1] - a[0]), lr.put(a[2] - a[1])\n\n#右の初期最適解を求める\n\n\nans = float(\"inf\")\nfor i in range(2, N - 1):\n\n\nprint(a)\n'''\n","repo_name":"yasunariston/atCoder","sub_path":"ABC/ABC102/D.py","file_name":"D.py","file_ext":"py","file_size_in_byte":820,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"37289271844","text":"class defVal:\n    def __init__(self):\n        self.title = ''\n        self.title_short = ''\n        self.artist = ''\n        self.artist_short = ''\n        self.desc = ''\n        self.bpm = 120\n        self.preview_start = 10000\n        self.preview_end = 25000\n        self.length = 80\n        self.countin = 4\n        self.gates = [20, 40, 60]\n        self.game_speed = 1.0\n        self.true_values = False\n        self.midi_file = \"\"\n        self.track1 = \"\"\n        self.track2 = \"\"\n        self.track3 = \"\"\n        self.track4 = \"\"\n        self.track5 = \"\"\n        self.track6 = \"\"\n        self.bg_track = \"\"\n        self.flow1 = \"\"\n        self.flow2 = \"\"\n        self.flow3 = \"\"\n        self.ps4mode = False\n        self.track1_att = 0.0\n        self.track2_att = 0.0\n        self.track3_att = 0.0\n        self.track4_att = 0.0\n        self.track5_att = 0.0\n        self.track6_att = 0.0\n        self.bg_track_att = 0.0\n        self.flow1_att = 0.0\n        self.flow2_att = \"\"\n        self.flow3_att = \"\"\n        self.save_path = \"\"\n        self.save_file = \"\"\n","repo_name":"AddyMills/Amplitude-Compiler","sub_path":"scripts/CompClasses.py","file_name":"CompClasses.py","file_ext":"py","file_size_in_byte":1068,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"81"}
+{"seq_id":"39961846027","text":"from model.users import Users\nimport random\nimport allure\n\n\ndef test_edit_user(app, db, check_ui):\n    app.open_home_page()\n    with allure.step('Given a user list. Empty - add user'):\n        user1 = Users(users_name=\"специальный юзер\", users_lastname=\"Никалаев\", home_phone=\"2345678\", mobile_phone=\"89655783498\", work_phone=\"812-567-90-89\", phone2=\"812-789-56-37\")\n        if len(db.get_user_list()) == 0:\n            app.user.add_new_user(user1)\n    with allure.step('Given a user list'):\n        old_users = db.get_user_list()\n    with allure.step('Given random user from the list'):\n        user2 = random.choice(old_users)\n        index = old_users.index(user2)\n    user3 = Users(users_name=\"Николай\", users_lastname=\"Николаев\", name_company=u\"ооо\\\"далеко и близко\\\"\")\n    user3.id = user2.id\n    with allure.step('When i edit a user %s to the list' % user3):\n        app.user.edit_user_by_id(user2.id, user3)\n    with allure.step('Then the new user list is equal to the old list with the edit user'):\n        new_users = db.get_user_list()\n        assert len(old_users) == len(new_users)\n        old_users[index] = user3\n        if check_ui:\n            assert sorted(old_users, key=Users.id_or_max) == sorted(new_users, key=Users.id_or_max)\n\n\n","repo_name":"annadenils/python_training","sub_path":"test/test_edit_user.py","file_name":"test_edit_user.py","file_ext":"py","file_size_in_byte":1305,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"14824608404","text":"from common.server_process import server, RedisClient\nfrom common.common import *\nfrom common.constants import *\nimport threading\nimport socket\nfrom uuid import uuid4\nfrom common.socket_id import *\nimport sys\nimport logging\nimport traceback\nfrom scene.scene_handler import *\nfrom scene.dialog_handler import *\nfrom player.player_handler import *\n\n\nuuidChars = (\"a\", \"b\", \"c\", \"d\", \"e\", \"f\",\n             \"g\", \"h\", \"i\", \"j\", \"k\", \"l\", \"m\", \"n\", \"o\", \"p\", \"q\", \"r\", \"s\",\n             \"t\", \"u\", \"v\", \"w\", \"x\", \"y\", \"z\", \"0\", \"1\", \"2\", \"3\", \"4\", \"5\",\n             \"6\", \"7\", \"8\", \"9\", \"A\", \"B\", \"C\", \"D\", \"E\", \"F\", \"G\", \"H\", \"I\",\n             \"J\", \"K\", \"L\", \"M\", \"N\", \"O\", \"P\", \"Q\", \"R\", \"S\", \"T\", \"U\", \"V\",\n             \"W\", \"X\", \"Y\", \"Z\")\n\n\ndef short_uuid():\n    uuid = str(uuid4()).replace('-', '')\n    result = ''\n    for i in range(0,8):\n        sub = uuid[i * 4: i * 4 + 4]\n        x = int(sub, 16)\n        result += uuidChars[x % 0x3E]\n    return result\n\n\nclass GameServer(threading.Thread):\n    def __init__(self):\n        super(GameServer, self).__init__()\n        self.players = []  # 存放所有在此game server维护的玩家id\n        self.uuid = short_uuid()\n        self.socket = socket.socket()\n\n    def connect(self):\n        \"\"\"\n        只有game server进程才进行connect\n        :return:\n        \"\"\"\n        self.socket.connect(('127.0.0.1', 9093))\n\n    def run(self):\n        from common.common import sprint\n        sprint('game server进程启动...')\n        while True:\n            try:\n                _bytes = self.socket.recv(4)  # 阻塞获取2字节, 如果有则是消息长度\n                msg_len = int.from_bytes(_bytes, byteorder='big')  # 消息长度, 2字节\n                msg = b''\n                while len(msg) < msg_len:\n                    msg += self.socket.recv(msg_len - len(msg))  # 获取消息内容\n                self.recv_handler(msg)\n            except ConnectionResetError as e:\n                sprint('接收服务器数据异常, 请尝试重新登陆:' + str(e))\n                exit()\n            except BaseException as e:\n                print(e)\n                print(traceback.print_exc())\n                # raise e\n\n    def recv_handler(self, recv_bytes):\n        msg = json.loads(recv_bytes)\n        pid = msg['pid']  # 玩家id\n        cmd = msg['cmd']\n        # print('game server recv:', msg)\n\n        if cmd == C_更新坐标:\n            x, y, map_id = msg['x'], msg['y'], msg['mapid']\n            rset(pid, CHAR, x, 'mx')\n            rset(pid, CHAR, y, 'my')\n            rset(pid, CHAR, int(map_id), '地图')\n        elif cmd == C_进入场景:\n            map_id = msg['map_id']\n            player_enter_scene(pid, map_id)\n        elif cmd == C_发送路径:\n            path = msg['路径']\n            # print('发送路径,', pid, path)\n            player_set_path_request(pid, path)\n        elif cmd == C_角色发言:\n            ch = msg['频道']\n            text = msg['内容']\n            player_speak(pid, ch, text)\n        elif cmd == C_点击NPC:\n            npc_id = msg['id']\n            trigger_npc_dialog(pid, npc_id)\n        elif cmd == C_传送点传送:\n            portal_id = msg['portal_id']\n            if str(portal_id) in PORTALS:\n                target_map = int(PORTALS[str(portal_id)]['目的地'])\n                target_x = int(PORTALS[str(portal_id)]['目的地x'])\n                target_y = int(PORTALS[str(portal_id)]['目的地y'])\n                scene_transfer(pid, target_map, target_x, target_y)\n        elif cmd == C_对话选项:\n            print('对话选项:', msg)\n            map_id = msg['map_id']  # 对话发生时的地图id\n            name = msg['name']  # 对话对象的名称\n            option = msg['option']\n            id = msg['id']\n            dialog_type = msg['type']\n            if dialog_type == 'npc':\n                trigger_npc_response(pid, id, option)\n        elif cmd == C_角色升级:\n            player_level_up(pid)\n        elif cmd == C_攻击玩家:\n            pass\n\n\ndef start_game_server():\n    redis_client = RedisClient()\n    server.redis_conn = redis_client.conn\n\n    game_server = GameServer()\n    server.game_server = game_server\n    game_server.connect()\n    game_server.start()\n\n    send2gw(GAME_SERVER_REGISTER_CMD, dict(uuid=game_server.uuid))\n    load_npc_objects()\n    # import_npc_objects()\n\n\nstart_game_server()\n","repo_name":"1992leiting/pygame_nt","sub_path":"server_redis/game_server.py","file_name":"game_server.py","file_ext":"py","file_size_in_byte":4386,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"44009698843","text":"import re\n\ndef match_twitter_handle():\n    # Twitter Username: \"@username\"\n    twitter_pattern = r\"@(\\w+)\"\n\n    # Get user input\n    input_string = input(\"Enter a Twitter handle: \")\n\n    # Match against the regular expression pattern\n    twitter_match = re.match(twitter_pattern, input_string)\n\n    if twitter_match:\n        username = twitter_match.group(1)\n        print(\"Valid Twitter Username:\", username)\n    else:\n        print(\"Invalid Twitter Username\")\n\n# Call the function\nmatch_twitter_handle()\n","repo_name":"WENDPANGA1/alu_regex_hackathon_group2","sub_path":"twitterhandle.py","file_name":"twitterhandle.py","file_ext":"py","file_size_in_byte":506,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"42410184495","text":"\"\"\" Defines classes which represent gates, as well as supporting functions \"\"\"\nfrom __future__ import division, print_function, absolute_import, unicode_literals\n#*****************************************************************\n#    pyGSTi 0.9:  Copyright 2015 Sandia Corporation\n#    This Software is released under the GPL license detailed\n#    in the file \"license.txt\" in the top-level pyGSTi directory\n#*****************************************************************\n\nimport numpy as _np\nimport scipy.linalg as _spl\nimport functools as _functools\nimport copy as _copy\n\nfrom ..      import optimize as _opt\nfrom ..tools import matrixtools as _mt\nfrom ..tools import gatetools as _gt\nfrom ..tools import jamiolkowski as _jt\nfrom ..tools import basistools as _bt\nfrom ..tools import slicetools as _slct\nfrom . import gaugegroup as _gaugegroup\nfrom . import gatesetmember as _gatesetmember\nfrom ..baseobjs import ProtectedArray as _ProtectedArray\n\nIMAG_TOL = 1e-7 #tolerance for imaginary part being considered zero\n\ndef optimize_gate(gateToOptimize, targetGate):\n    \"\"\"\n    Optimize the parameters of gateToOptimize so that the\n      the resulting gate matrix is as close as possible to\n      targetGate's matrix.\n\n    This is trivial for the case of FullyParameterizedGate\n      instances, but for other types of parameterization\n      this involves an iterative optimization over all the\n      parameters of gateToOptimize.\n\n    Parameters\n    ----------\n    gateToOptimize : Gate\n      The gate to optimize.  This object gets altered.\n\n    targetGate : Gate\n      The gate whose matrix is used as the target.\n\n    Returns\n    -------\n    None\n    \"\"\"\n\n    #TODO: cleanup this code:\n    if isinstance(gateToOptimize, StaticGate):\n        return #nothing to optimize\n\n    if isinstance(gateToOptimize, FullyParameterizedGate):\n        if(targetGate.dim != gateToOptimize.dim): #special case: gates can have different overall dimension\n            gateToOptimize.dim = targetGate.dim   #  this is a HACK to allow model selection code to work correctly\n        gateToOptimize.set_value(targetGate)     #just copy entire overall matrix since fully parameterized\n        return\n\n    assert(targetGate.dim == gateToOptimize.dim) #gates must have the same overall dimension\n    targetMatrix = _np.asarray(targetGate)\n    def _objective_func(param_vec):\n        gateToOptimize.from_vector(param_vec)\n        return _mt.frobeniusnorm(gateToOptimize - targetMatrix)\n\n    x0 = gateToOptimize.to_vector()\n    minSol = _opt.minimize(_objective_func, x0, method='BFGS', maxiter=10000, maxfev=10000,\n                           tol=1e-6, callback=None)\n\n    gateToOptimize.from_vector(minSol.x)\n    #print(\"DEBUG: optimized gate to min frobenius distance %g\" %\n    #      _mt.frobeniusnorm(gateToOptimize-targetMatrix))\n\n\ndef compose(gate1, gate2, basis, parameterization=\"auto\"):\n    \"\"\"\n    Returns a new Gate that is the composition of gate1 and gate2.\n\n    The resulting gate's matrix == dot(gate1, gate2),\n     (so gate1 acts *second* on an input) and the type of Gate instance\n     returned will depend on how much of the parameterization in gate1\n     and gate2 can be preserved in the resulting gate.\n\n    Parameters\n    ----------\n    gate1 : Gate\n        Gate to compose as left term of matrix product (applied second).\n\n    gate2 : Gate\n        Gate to compose as right term of matrix product (applied first).\n\n    basis : {'std', 'gm', 'pp', 'qt'} or Basis object\n        The source and destination basis, respectively.  Allowed\n        values are Matrix-unit (std), Gell-Mann (gm), Pauli-product (pp),\n        and Qutrit (qt) (or a custom basis object).\n\n    parameterization : {\"auto\",\"full\",\"TP\",\"linear\",\"static\"}, optional\n        The parameterization of the resulting gates.  The default, \"auto\",\n        attempts to convert to the most restrictive common parameterization.\n\n    Returns\n    -------\n    Gate\n       The composed gate.\n    \"\"\"\n\n    #Find the most restrictive common parameterization that both gate1\n    # and gate2 can be cast/converted into. Utilized converstions are:\n    #\n    # Static => TP (sometimes)\n    # Static => Linear\n    # Static => Full\n    # Linear => TP (sometimes)\n    # Linear => Full\n    # TP => Full\n\n    if parameterization == \"auto\":\n        if any([isinstance(g, FullyParameterizedGate) for g in (gate1,gate2)]):\n            paramType = \"full\"\n        elif any([isinstance(g, TPParameterizedGate) for g in (gate1,gate2)]):\n            paramType = \"TP\" #update to \"full\" below if TP-conversion\n                             #not possible?\n        elif any([isinstance(g, LinearlyParameterizedGate)\n                  for g in (gate1,gate2)]):\n            paramType = \"linear\"\n        else:\n            assert( isinstance(gate1, StaticGate)\n                    and isinstance(gate2, StaticGate) )\n            paramType = \"static\"\n    else:\n        paramType = parameterization #user-specified final parameterization\n\n    #Convert to paramType as necessary\n    cgate1 = convert(gate1, paramType, basis)\n    cgate2 = convert(gate2, paramType, basis)\n\n    # cgate1 and cgate2 are the same type, so can invoke the gate's compose method\n    return cgate1.compose(cgate2)\n\n\n\ndef convert(gate, toType, basis):\n    \"\"\"\n    Convert gate to a new type of parameterization, potentially creating\n    a new Gate object.  Raises ValueError for invalid conversions.\n\n    Parameters\n    ----------\n    gate : Gate\n        Gate to convert\n\n    toType : {\"full\", \"TP\", \"CPTP\", \"H+S\", \"S\", \"static\", \"GLND\"}\n        The type of parameterizaton to convert to.\n\n    basis : {'std', 'gm', 'pp', 'qt'} or Basis object\n        The basis for `gate`.  Allowed values are Matrix-unit (std),\n        Gell-Mann (gm), Pauli-product (pp), and Qutrit (qt)\n        (or a custom basis object).\n\n    Returns\n    -------\n    Gate\n       The converted gate, usually a distinct\n       object from the gate object passed as input.\n    \"\"\"\n    if toType == \"full\":\n        if isinstance(gate, FullyParameterizedGate):\n            return gate #no conversion necessary\n        else:\n            ret = FullyParameterizedGate( gate )\n            return ret\n\n    elif toType == \"TP\":\n        if isinstance(gate, TPParameterizedGate):\n            return gate #no conversion necessary\n        else:\n            return TPParameterizedGate( gate )\n              # above will raise ValueError if conversion cannot be done\n\n    elif toType == \"linear\":\n        if isinstance(gate, LinearlyParameterizedGate):\n            return gate #no conversion necessary\n        elif isinstance(gate, StaticGate):\n            real = _np.isclose(_np.linalg.norm( gate.imag ),0)\n            return LinearlyParameterizedGate(gate, _np.array([]), {}, real)\n        else:\n            raise ValueError(\"Cannot convert type %s to LinearlyParameterizedGate\"\n                             % type(gate))\n\n    elif toType in (\"CPTP\",\"H+S\",\"S\",\"GLND\"):\n        RANK_TOL = 1e-6        \n        J = _jt.fast_jamiolkowski_iso_std(gate, basis) #Choi mx basis doesn't matter\n        if _np.linalg.matrix_rank(J, RANK_TOL) == 1: \n            unitary_post = gate # when 'gate' is unitary\n        else: unitary_post = None\n\n        nQubits = _np.log2(gate.dim)/2.0\n        bQubits = bool(abs(nQubits-round(nQubits)) < 1e-10) #integer # of qubits?\n        \n        proj_basis = \"pp\" if (basis == \"pp\" or bQubits) else basis\n        ham_basis = proj_basis if toType in (\"CPTP\",\"H+S\") else None\n        nonham_basis = proj_basis\n        nonham_diagonal_only = bool(toType in (\"H+S\",\"S\") )\n        cptp = False if toType == \"GLND\" else True #only \"General LiNDbladian\" is non-cptp\n        truncate=True\n\n        if isinstance(gate, LindbladParameterizedGate) and \\\n           _np.linalg.norm(gate.unitary_postfactor-unitary_post) < 1e-6 \\\n           and ham_basis==gate.ham_basis and nonham_basis==gate.other_basis \\\n           and cptp==gate.cptp and nonham_diagonal_only==gate.nonham_diagonal_only \\\n           and basis==gate.matrix_basis:\n            return gate #no conversion necessary\n        else:\n            return LindbladParameterizedGate(\n                gate, unitary_post, ham_basis, nonham_basis, cptp,\n                nonham_diagonal_only, truncate, basis)\n        \n\n    elif toType == \"static\":\n        if isinstance(gate, StaticGate):\n            return gate #no conversion necessary\n        else:\n            return StaticGate( gate )\n\n    else:\n        raise ValueError(\"Invalid toType argument: %s\" % toType)\n\n\ndef finite_difference_deriv_wrt_params(gate, eps=1e-7):\n    \"\"\"\n    Computes a finite-difference Jacobian for a Gate object.\n\n    The returned value is a matrix whose columns are the vectorized\n    derivatives of the flattened gate matrix with respect to a single\n    gate parameter, matching the format expected from the gate's\n    `deriv_wrt_params` method.\n\n    Parameters\n    ----------\n    gate : Gate\n        The gate object to compute a Jacobian for.\n        \n    eps : float, optional\n        The finite difference step to use.\n\n    Returns\n    -------\n    numpy.ndarray\n        An M by N matrix where M is the number of gate elements and\n        N is the number of gate parameters. \n    \"\"\"\n    dim = gate.get_dimension()\n    gate2 = gate.copy()\n    p = gate.to_vector()\n    fd_deriv = _np.empty((dim,dim,gate.num_params()), 'd') #assume real (?)\n\n    for i in range(gate.num_params()):\n        p_plus_dp = p.copy()\n        p_plus_dp[i] += eps\n        gate2.from_vector(p_plus_dp)\n        fd_deriv[:,:,i] = (gate2-gate)/eps\n\n    fd_deriv.shape = [dim**2,gate.num_params()]\n    return fd_deriv\n\n\ndef check_deriv_wrt_params(gate, deriv_to_check=None, eps=1e-7):\n    \"\"\"\n    Checks the `deriv_wrt_params` method of a Gate object.\n\n    This routine is meant to be used as an aid in testing and debugging\n    gate classes by comparing the finite-difference Jacobian that\n    *should* be returned by `gate.deriv_wrt_params` with the one that\n    actually is.  A ValueError is raised if the two do not match.\n\n    Parameters\n    ----------\n    gate : Gate\n        The gate object to test.\n\n    deriv_to_check : numpy.ndarray or None, optional\n        If not None, the Jacobian to compare against the finite difference\n        result.  If None, `gate.deriv_wrt_parms()` is used.  Setting this\n        argument can be useful when the function is called *within* a Gate\n        class's `deriv_wrt_params()` method itself as a part of testing.\n        \n    eps : float, optional\n        The finite difference step to use.\n\n    Returns\n    -------\n    None\n    \"\"\"\n    fd_deriv = finite_difference_deriv_wrt_params(gate, eps)\n    if deriv_to_check is None:\n        deriv_to_check = gate.deriv_wrt_params()\n\n    #print(\"Deriv shapes = %s and %s\" % (str(fd_deriv.shape),\n    #                                    str(deriv_to_check.shape)))\n    #print(\"finite difference deriv = \\n\",fd_deriv)\n    #print(\"deriv_wrt_params deriv = \\n\",deriv_to_check)\n    #print(\"deriv_wrt_params - finite diff deriv = \\n\",\n    #      deriv_to_check - fd_deriv)\n    for i in range(deriv_to_check.shape[0]):\n        for j in range(deriv_to_check.shape[1]):\n            diff = abs(deriv_to_check[i,j] - fd_deriv[i,j])\n            if diff > 10*eps:\n                print(\"deriv_chk_mismatch: (%d,%d): %g (comp) - %g (fd) = %g\" %\n                      (i,j,deriv_to_check[i,j],fd_deriv[i,j],diff))\n\n    if _np.linalg.norm(fd_deriv - deriv_to_check)/fd_deriv.size > 10*eps:\n        raise ValueError(\"Failed check of deriv_wrt_params:\\n\" +\n                         \" norm diff = %g\" % \n                         _np.linalg.norm(fd_deriv - deriv_to_check))\n\n\n#Note on initialization sequence of Gates within a GateSet:\n# 1) a GateSet is constructed (empty)\n# 2) a Gate is constructed - apart from a GateSet if it's locally parameterized,\n#    otherwise with explicit reference to an existing GateSet's labels/indices.\n#    All gates (GateSetMember objs in general) have a \"gpindices\" member which\n#    can either be initialized upon construction or set to None, which signals\n#    that the GateSet must initialize it.\n# 3) the Gate is assigned/added to a dict within the GateSet.  As a part of this\n#    process, the Gate's 'gpindices' member is set, if it isn't already, and the\n#    GateSet's \"global\" parameter vector (and number of params) is updated as\n#    needed to accomodate new parameters.\n#\n# Note: gpindices may be None (before initialization) or any valid index\n#  into a 1D numpy array (e.g. a slice or integer array).  It may NOT have\n#  any repeated elements.\n#\n# When a Gate is removed from the GateSet, parameters only used by it can be\n# removed from the GateSet, and the gpindices members of existing gates\n# adjusted as needed.\n#\n# When derivatives are taken wrt. a gateset parameter (1 col of a jacobian)\n# derivatives wrt each gate that includes that parameter in its gpindices\n# must be processed.\n\n\nclass Gate(_gatesetmember.GateSetMember):\n    \"\"\" Base class for all gate representations \"\"\"\n    \n    def __init__(self, dim):\n        \"\"\" Initialize a new Gate \"\"\"\n        super(Gate, self).__init__(dim)\n        \n    def acton(self, state):\n        \"\"\" Act this gate map on an input state \"\"\"\n        raise NotImplementedError(\"acton(...) not implemented!\")\n\n    def transform(self, S):\n        \"\"\" Update gate G with inv(S) * G * S.\"\"\"\n        raise NotImplementedError(\"This gate cannot be transform()'d\")\n\n    def depolarize(self, amount):\n        \"\"\" Depolarize gate by the given amount. \"\"\"\n        raise NotImplementedError(\"This gate cannot be depolarize()'d\")\n\n    def rotate(self, amount, mxBasis=\"gm\"):\n        \"\"\" Rotate gate by the given amount. \"\"\"\n        raise NotImplementedError(\"This gate cannot be rotate()'d\")\n\n    def frobeniusdist2(self, otherGate, transform=None, inv_transform=None):\n        \"\"\" \n        Return the squared frobenius distance between this gate\n        and `otherGate`, optionally transforming this gate first\n        using `transform` and `inv_transform`.\n        \"\"\"\n        raise NotImplementedError(\"frobeniusdist2(...) not implemented!\")\n\n    def frobeniusdist(self, otherGate, transform=None, inv_transform=None):\n        \"\"\" \n        Return the frobenius distance between this gate\n        and `otherGate`, optionally transforming this gate first\n        using `transform` and `inv_transform`.\n        \"\"\"\n        return _np.sqrt(self.frobeniusdist2(otherGate, transform, inv_transform))\n\n    def jtracedist(self, otherGate, transform=None, inv_transform=None):\n        \"\"\" \n        Return the Jamiolkowski trace distance between this gate\n        and `otherGate`, optionally transforming this gate first\n        using `transform` and `inv_transform`.\n        \"\"\"\n        raise NotImplementedError(\"jtracedist(...) not implemented!\")\n\n    def diamonddist(self, otherGate, transform=None, inv_transform=None):\n        \"\"\" \n        Return the diamon distance between this gate\n        and `otherGate`, optionally transforming this gate first\n        using `transform` and `inv_transform`.\n        \"\"\"\n        raise NotImplementedError(\"diamonddist(...) not implemented!\")\n\n\n    #Pickle plumbing\n    def __setstate__(self, state):\n        self.__dict__.update(state)\n\n\n#class GateMap(Gate):\n#    def __init__(self, dim):\n#        \"\"\" Initialize a new Gate \"\"\"\n#        super(GateMap, self).__init__(dim)\n\n    \nclass GateMatrix(Gate):\n    \"\"\"\n    Excapulates a parameterization of a gate matrix.  This class is the\n    common base class for all specific parameterizations of a gate.\n    \"\"\"\n\n    def __init__(self, mx=None):\n        \"\"\" Initialize a new Gate \"\"\"\n        self.base = mx\n        super(GateMatrix, self).__init__(self.base.shape[0])\n        \n    def acton(self, state):\n        \"\"\" Act this gate matrix on an input state (left-multiply w/matrix) \"\"\"\n        return _np.dot(self.base, state) #TODO: return a SPAMVec?\n\n    def frobeniusdist2(self, otherGate, transform=None, inv_transform=None):\n        \"\"\" \n        Return the squared frobenius difference between this gate and\n        `otherGate`, optionally transforming this gate first using matrices\n        `transform` and `inv_transform`.\n        \"\"\"\n        if transform is None and inv_transform is None:\n            return _gt.frobeniusdist2(self.base,otherGate)\n        else:\n            return _gt.frobeniusdist2(_np.dot(\n                    inv_transform,_np.dot(self.base,transform)),\n                    otherGate)\n\n    def residuals(self, otherGate, transform=None, inv_transform=None):\n        \"\"\"\n        The per-element difference between this `GateMatrix` and `otherGate`,\n        possibly after transforming this gate as \n        `G => inv_transform * G * transform`.\n\n        Parameters\n        ----------\n        otherGate : GateMatrix\n            The gate to compare against.\n\n        transform, inv_transform : numpy.ndarray, optional\n            The transform and its inverse, respectively, to apply before\n            taking the element-wise difference.\n\n        Returns\n        -------\n        numpy.ndarray\n            A 1D-array of size equal to that of the flattened gate matrix.\n        \"\"\"\n        if transform is None and inv_transform is None:\n            return _gt.residuals(self.base,otherGate)\n        else:\n            return _gt.residuals(_np.dot(\n                    inv_transform,_np.dot(self.base,transform)),\n                    otherGate)\n\n    def jtracedist(self, otherGate, transform=None, inv_transform=None):\n        \"\"\" \n        Return the Jamiolkowski trace distance between this gate\n        and `otherGate`, optionally transforming this gate first\n        using `transform` and `inv_transform`.\n        \"\"\"\n        if transform is None and inv_transform is None:\n            return _gt.jtracedist(self.base,otherGate)\n        else:\n            return _gt.jtracedist(_np.dot(\n                    inv_transform,_np.dot(self.base,transform)),\n                    otherGate)\n\n    def diamonddist(self, otherGate, transform=None, inv_transform=None):\n        \"\"\" \n        Return the diamon distance between this gate\n        and `otherGate`, optionally transforming this gate first\n        using `transform` and `inv_transform`.\n        \"\"\"\n        if transform is None and inv_transform is None:\n            return _gt.diamonddist(self.base,otherGate)\n        else:\n            return _gt.diamonddist(_np.dot(\n                    inv_transform,_np.dot(self.base,transform)),\n                    otherGate)\n\n    def deriv_wrt_params(self, wrtFilter=None):\n        \"\"\"\n        Construct a matrix whose columns are the vectorized\n        derivatives of the flattened gate matrix with respect to a\n        single gate parameter.  Thus, each column is of length\n        gate_dim^2 and there is one column per gate parameter. An\n        empty 2D array in the StaticGate case (num_params == 0).\n\n        Returns\n        -------\n        numpy array\n            Array of derivatives with shape (dimension^2, num_params)\n        \"\"\"\n        raise NotImplementedError(\"deriv_wrt_params(...) is not implemented\")\n\n    def has_nonzero_hessian(self):\n        \"\"\" \n        Returns whether this gate has a non-zero Hessian with\n        respect to its parameters, i.e. whether it only depends\n        linearly on its parameters or not.\n\n        Returns\n        -------\n        bool\n        \"\"\"\n        return True #conservative\n\n    def hessian_wrt_params(self, wrtFilter1=None, wrtFilter2=None):\n        \"\"\"\n        Construct the Hessian of this gate with respect to its parameters.\n\n        This function returns a tensor whose first axis corresponds to the\n        flattened gate matrix and whose 2nd and 3rd axes correspond to the\n        parameters that are differentiated with respect to.\n\n        Parameters\n        ----------\n        wrtFilter1, wrtFilter2 : list\n            Lists of indices of the paramters to take first and second\n            derivatives with respect to.  If None, then derivatives are\n            taken with respect to all of the gate's parameters.\n\n        Returns\n        -------\n        numpy array\n            Hessian with shape (dimension^2, num_params1, num_params2)\n        \"\"\"\n        raise NotImplementedError(\"hessian_wrt_params(...) is not implemented\")\n\n\n    #Handled by derived classes\n    #def __str__(self):\n    #    s = \"Gate with shape %s\\n\" % str(self.base.shape)\n    #    s += _mt.mx_to_string(self.base, width=4, prec=2)\n    #    return s\n\n    #Access to underlying ndarray\n    def __getitem__( self, key ):\n        self.dirty = True\n        return self.base.__getitem__(key)\n\n    def __getslice__(self, i,j):\n        self.dirty = True\n        return self.__getitem__(slice(i,j)) #Called for A[:]\n\n    def __setitem__(self, key, val):\n        self.dirty = True\n        return self.base.__setitem__(key,val)\n\n    def __getattr__(self, attr):\n        #use __dict__ so no chance for recursive __getattr__\n        ret = getattr(self.__dict__['base'],attr)\n        if(self.base.shape != (self.dim,self.dim)):\n            raise ValueError(\"Cannot change shape of Gate\")\n        self.dirty = True\n        return ret\n\n\n    #Mimic array behavior\n    def __pos__(self):         return self.base\n    def __neg__(self):         return -self.base\n    def __abs__(self):         return abs(self.base)\n    def __add__(self,x):       return self.base + x\n    def __radd__(self,x):      return x + self.base\n    def __sub__(self,x):       return self.base - x\n    def __rsub__(self,x):      return x - self.base\n    def __mul__(self,x):       return self.base * x\n    def __rmul__(self,x):      return x * self.base\n    def __truediv__(self, x):  return self.base / x\n    def __rtruediv__(self, x): return x / self.base\n    def __floordiv__(self,x):  return self.base // x\n    def __rfloordiv__(self,x): return x // self.base\n    def __pow__(self,x):       return self.base ** x\n    def __eq__(self,x):        return self.base == x\n    def __len__(self):         return len(self.base)\n    def __int__(self):         return int(self.base)\n    def __long__(self):        return int(self.base)\n    def __float__(self):       return float(self.base)\n    def __complex__(self):     return complex(self.base)\n\n\n\n    @staticmethod\n    def convert_to_matrix(M):\n        \"\"\"\n        Static method that converts a matrix-like object to a 2D numpy array.\n\n        Parameters\n        ----------\n        M : array_like\n\n        Returns\n        -------\n        numpy array\n        \"\"\"\n        if isinstance(M, Gate):\n            dim = M.dim\n            matrix = _np.asarray(M).copy()\n              # Gate objs should also derive from ndarray\n        else:\n            try:\n                dim = len(M)\n                d2  = len(M[0]) #pylint : disable=unused-variable\n            except:\n                raise ValueError(\"%s doesn't look like a 2D array/list\" % M)\n            if any([len(row) != dim for row in M]):\n                raise ValueError(\"%s is not a *square* 2D array\" % M)\n\n            ar = _np.array(M)\n            if _np.all(_np.isreal(ar)):\n                matrix = _np.array(ar.real, 'd')\n            else:\n                matrix = _np.array(ar, 'complex')\n\n\n        if len(matrix.shape) != 2:\n            raise ValueError(\"%s has %d dimensions when 2 are expected\"\n                             % (M, len(matrix.shape)))\n\n        if matrix.shape[0] != matrix.shape[1]:\n            raise ValueError(\"%s is not a *square* 2D array\" % M)\n\n        return matrix\n\n\n\nclass StaticGate(GateMatrix):\n    \"\"\"\n    Encapsulates a gate matrix that is completely fixed, or \"static\", meaning\n      that is contains no parameters.\n    \"\"\"\n\n    def __init__(self, M):\n        \"\"\"\n        Initialize a StaticGate object.\n\n        Parameters\n        ----------\n        M : array_like or Gate\n            a square 2D array-like or Gate object representing the gate action.\n            The shape of M sets the dimension of the gate.\n        \"\"\"\n        GateMatrix.__init__(self, GateMatrix.convert_to_matrix(M))\n\n\n    def set_value(self, M):\n        \"\"\"\n        Attempts to modify gate parameters so that the specified raw\n        gate matrix becomes mx.  Will raise ValueError if this operation\n        is not possible.\n\n        Parameters\n        ----------\n        M : array_like or Gate\n            An array of shape (dim, dim) or Gate representing the gate action.\n\n        Returns\n        -------\n        None\n        \"\"\"\n        mx = GateMatrix.convert_to_matrix(M)\n        if(mx.shape != (self.dim, self.dim)):\n            raise ValueError(\"Argument must be a (%d,%d) matrix!\"\n                             % (self.dim,self.dim))\n        self.base[:,:] = _np.array(mx)\n        self.dirty = True\n\n\n    def num_params(self):\n        \"\"\"\n        Get the number of independent parameters which specify this gate.  Zero\n        in the case of a StaticGate.\n\n        Returns\n        -------\n        int\n           the number of independent parameters.\n        \"\"\"\n        return 0 # no parameters\n\n\n    def to_vector(self):\n        \"\"\"\n        Get the gate parameters as an array of values.  An empty array in the\n        case of StaticGate.\n\n        Returns\n        -------\n        numpy array\n            The gate parameters as a 1D array with length num_params().\n        \"\"\"\n        return _np.array([], 'd') # no parameters\n\n\n    def from_vector(self, v):\n        \"\"\"\n        Initialize the gate using a vector of parameters.\n\n        Parameters\n        ----------\n        v : numpy array\n            The 1D vector of gate parameters.  Length\n            must == num_params()\n\n        Returns\n        -------\n        None\n        \"\"\"\n        assert(len(v) == 0) #should be no parameters, and nothing to do\n\n\n    def deriv_wrt_params(self, wrtFilter=None):\n        \"\"\"\n        Construct a matrix whose columns are the vectorized\n        derivatives of the flattened gate matrix with respect to a\n        single gate parameter.  Thus, each column is of length\n        gate_dim^2 and there is one column per gate parameter. An\n        empty 2D array in the StaticGate case (num_params == 0).\n\n        Returns\n        -------\n        numpy array\n            Array of derivatives with shape (dimension^2, num_params)\n        \"\"\"\n        derivMx = _np.zeros((self.dim**2,0),'d')\n        if wrtFilter is None:\n            return derivMx\n        else:\n            return _np.take( derivMx, wrtFilter, axis=1 )\n\n        \n    def has_nonzero_hessian(self):\n        \"\"\" \n        Returns whether this gate has a non-zero Hessian with\n        respect to its parameters, i.e. whether it only depends\n        linearly on its parameters or not.\n\n        Returns\n        -------\n        bool\n        \"\"\"\n        return False\n\n\n    def copy(self, parent=None):\n        \"\"\"\n        Copy this gate.\n\n        Returns\n        -------\n        Gate\n            A copy of this gate.\n        \"\"\"\n        return self._copy_gpindices( StaticGate(self.base), parent)\n\n\n    def transform(self, S):\n        \"\"\"\n        Update gate matrix G with inv(S) * G * S.\n\n        Generally, the transform function updates the *parameters* of \n        the gate such that the resulting gate matrix is altered as \n        described above.  If such an update cannot be done (because\n        the gate parameters do not allow for it), ValueError is raised.\n\n        In this particular case *no* transforms are possible since a\n        StaticGate has no parameters to modify.  Thus, this function\n        *always* raises a ValueError.\n\n        Parameters\n        ----------\n        S : GaugeGroupElement\n            A gauge group element which specifies the \"S\" matrix \n            (and it's inverse) used in the above similarity transform.\n        \"\"\"\n        raise ValueError(\"Cannot transform a StaticGate - it has no parameters!\")\n        #self.set_value(_np.dot(Si,_np.dot(self.base, S)))\n\n    def depolarize(self, amount):\n        \"\"\"\n        Depolarize this gate by the given `amount`.\n\n        Generally, the depolarize function updates the *parameters* of \n        the gate such that the resulting gate matrix is depolarized.  If\n        such an update cannot be done (because the gate parameters do not\n        allow for it), ValueError is raised.\n\n        In this particular case *no* alterations are possible since a\n        StaticGate has no parameters to modify.  Thus, this function\n        *always* raises a ValueError.\n\n        Parameters\n        ----------\n        amount : float or tuple\n            The amount to depolarize by.  If a tuple, it must have length\n            equal to one less than the dimension of the gate. In standard\n            bases, depolarization corresponds to multiplying the gate matrix\n            by a diagonal matrix whose first diagonal element (corresponding\n            to the identity) equals 1.0 and whose subsequent elements \n            (corresponding to non-identity basis elements) equal\n            `1.0 - amount[i]` (or just `1.0 - amount` if `amount` is a\n            float).\n\n        Returns\n        -------\n        None\n        \"\"\"\n        raise ValueError(\"Cannot depolarize a StaticGate - it has no parameters!\")\n\n\n    def rotate(self, amount, mxBasis=\"gm\"):\n        \"\"\"\n        Rotate this gate by the given `amount`.\n\n        Generally, the rotate function updates the *parameters* of \n        the gate such that the resulting gate matrix is rotated.  If\n        such an update cannot be done (because the gate parameters do not\n        allow for it), ValueError is raised.\n\n        In this particular case *no* alterations are possible since a\n        StaticGate has no parameters to modify.  Thus, this function\n        *always* raises a ValueError.\n\n        Parameters\n        ----------\n        amount : tuple of floats, optional\n            Specifies the rotation \"coefficients\" along each of the non-identity\n            Pauli-product axes.  The gate's matrix `G` is composed with a\n            rotation operation `R`  (so `G` -> `dot(R, G)` ) where `R` is the\n            unitary superoperator corresponding to the unitary operator \n            `U = exp( sum_k( i * rotate[k] / 2.0 * Pauli_k ) )`.  Here `Pauli_k`\n            ranges over all of the non-identity un-normalized Pauli operators.\n\n        mxBasis : {'std', 'gm', 'pp', 'qt'} or Basis object\n            The source and destination basis, respectively.  Allowed\n            values are Matrix-unit (std), Gell-Mann (gm), Pauli-product (pp),\n            and Qutrit (qt) (or a custom basis object).\n\n        Returns\n        -------\n        None\n        \"\"\"\n        raise ValueError(\"Cannot rotate a StaticGate - it has no parameters!\")\n\n\n    def compose(self, otherGate):\n        \"\"\"\n        Create and return a new gate that is the composition of this gate\n        followed by otherGate, which *must be another StaticGate*.  (For more\n        general compositions between different types of gates, use the module-\n        level compose function.)  The returned gate's matrix is equal to\n        dot(this, otherGate).\n\n        Parameters\n        ----------\n        otherGate : StaticGate\n            The gate to compose to the right of this one.\n\n        Returns\n        -------\n        StaticGate\n        \"\"\"\n        assert( isinstance(otherGate, StaticGate) )\n        return StaticGate( _np.dot( self.base, otherGate.base ) )\n\n\n    def __str__(self):\n        s = \"Static gate with shape %s\\n\" % str(self.base.shape)\n        s += _mt.mx_to_string(self.base, width=4, prec=2)\n        return s\n\n\n\nclass FullyParameterizedGate(GateMatrix):\n    \"\"\"\n    Encapsulates a gate matrix that is fully parameterized, that is,\n      each element of the gate matrix is an independent parameter.\n    \"\"\"\n\n    def __init__(self, M):\n        \"\"\"\n        Initialize a FullyParameterizedGate object.\n\n        Parameters\n        ----------\n        M : array_like or Gate\n            a square 2D array-like or Gate object representing the gate action.\n            The shape of M sets the dimension of the gate.\n        \"\"\"\n        M2 = GateMatrix.convert_to_matrix(M)\n        GateMatrix.__init__(self,M2)\n\n    def set_value(self, M):\n        \"\"\"\n        Attempts to modify gate parameters so that the specified raw\n        gate matrix becomes mx.  Will raise ValueError if this operation\n        is not possible.\n\n        Parameters\n        ----------\n        M : array_like or Gate\n            An array of shape (dim, dim) or Gate representing the gate action.\n\n        Returns\n        -------\n        None\n        \"\"\"\n        mx = GateMatrix.convert_to_matrix(M)\n        if(mx.shape != (self.dim, self.dim)):\n            raise ValueError(\"Argument must be a (%d,%d) matrix!\"\n                             % (self.dim,self.dim))\n        self.base[:,:] = _np.array(mx)\n        self.dirty = True\n\n\n    def num_params(self):\n        \"\"\"\n        Get the number of independent parameters which specify this gate.\n\n        Returns\n        -------\n        int\n           the number of independent parameters.\n        \"\"\"\n        return self.dim**2\n\n\n    def to_vector(self):\n        \"\"\"\n        Get the gate parameters as an array of values.\n\n        Returns\n        -------\n        numpy array\n            The gate parameters as a 1D array with length num_params().\n        \"\"\"\n        return self.base.flatten() #.real in case of complex matrices?\n\n\n    def from_vector(self, v):\n        \"\"\"\n        Initialize the gate using a vector of parameters.\n\n        Parameters\n        ----------\n        v : numpy array\n            The 1D vector of gate parameters.  Length\n            must == num_params()\n\n        Returns\n        -------\n        None\n        \"\"\"\n        assert(self.base.shape == (self.dim, self.dim))\n        self.base[:,:] = v.reshape( (self.dim,self.dim) )\n        self.dirty = True\n\n\n    def deriv_wrt_params(self, wrtFilter=None):\n        \"\"\"\n        Construct a matrix whose columns are the vectorized\n        derivatives of the flattened gate matrix with respect to a\n        single gate parameter.  Thus, each column is of length\n        gate_dim^2 and there is one column per gate parameter.\n\n        Returns\n        -------\n        numpy array\n            Array of derivatives with shape (dimension^2, num_params)\n        \"\"\"\n        derivMx = _np.identity( self.dim**2, 'd' )\n        if wrtFilter is None:\n            return derivMx\n        else:\n            return _np.take( derivMx, wrtFilter, axis=1 )\n\n        \n    def has_nonzero_hessian(self):\n        \"\"\" \n        Returns whether this gate has a non-zero Hessian with\n        respect to its parameters, i.e. whether it only depends\n        linearly on its parameters or not.\n\n        Returns\n        -------\n        bool\n        \"\"\"\n        return False\n\n\n    def copy(self, parent=None):\n        \"\"\"\n        Copy this gate.\n\n        Returns\n        -------\n        Gate\n            A copy of this gate.\n        \"\"\"\n        return self._copy_gpindices( FullyParameterizedGate(self.base), parent )\n\n\n    def transform(self, S):\n        \"\"\"\n        Update gate matrix G with inv(S) * G * S,\n\n        Generally, the transform function updates the *parameters* of \n        the gate such that the resulting gate matrix is altered as \n        described above.  If such an update cannot be done (because\n        the gate parameters do not allow for it), ValueError is raised.\n\n        In this particular case *any* transform of the appropriate\n        dimension is possible, since all gate matrix elements are parameters.\n\n        Parameters\n        ----------\n        S : GaugeGroupElement\n            A gauge group element which specifies the \"S\" matrix \n            (and it's inverse) used in the above similarity transform.\n        \"\"\"\n        Smx = S.get_transform_matrix()\n        Si  = S.get_transform_matrix_inverse()\n        self.set_value(_np.dot(Si,_np.dot(self.base, Smx)))\n\n\n    def depolarize(self, amount):\n        \"\"\"\n        Depolarize this gate by the given `amount`.\n\n        Generally, the depolarize function updates the *parameters* of \n        the gate such that the resulting gate matrix is depolarized.  If\n        such an update cannot be done (because the gate parameters do not\n        allow for it), ValueError is raised.\n\n        Parameters\n        ----------\n        amount : float or tuple\n            The amount to depolarize by.  If a tuple, it must have length\n            equal to one less than the dimension of the gate. In standard\n            bases, depolarization corresponds to multiplying the gate matrix\n            by a diagonal matrix whose first diagonal element (corresponding\n            to the identity) equals 1.0 and whose subsequent elements \n            (corresponding to non-identity basis elements) equal\n            `1.0 - amount[i]` (or just `1.0 - amount` if `amount` is a\n            float).\n\n        Returns\n        -------\n        None\n        \"\"\"\n        if isinstance(amount,float):\n            D = _np.diag( [1]+[1-amount]*(self.dim-1) )\n        else:\n            assert(len(amount) == self.dim-1)\n            D = _np.diag( [1]+list(1.0 - _np.array(amount,'d')) )\n        self.set_value(_np.dot(D,self))\n\n\n    def rotate(self, amount, mxBasis=\"gm\"):\n        \"\"\"\n        Rotate this gate by the given `amount`.\n\n        Generally, the rotate function updates the *parameters* of \n        the gate such that the resulting gate matrix is rotated.  If\n        such an update cannot be done (because the gate parameters do not\n        allow for it), ValueError is raised.\n\n        Parameters\n        ----------\n        amount : tuple of floats, optional\n            Specifies the rotation \"coefficients\" along each of the non-identity\n            Pauli-product axes.  The gate's matrix `G` is composed with a\n            rotation operation `R`  (so `G` -> `dot(R, G)` ) where `R` is the\n            unitary superoperator corresponding to the unitary operator \n            `U = exp( sum_k( i * rotate[k] / 2.0 * Pauli_k ) )`.  Here `Pauli_k`\n            ranges over all of the non-identity un-normalized Pauli operators.\n\n        mxBasis : {'std', 'gm', 'pp', 'qt'} or Basis object\n            The source and destination basis, respectively.  Allowed\n            values are Matrix-unit (std), Gell-Mann (gm), Pauli-product (pp),\n            and Qutrit (qt) (or a custom basis object).\n\n        Returns\n        -------\n        None\n        \"\"\"\n        rotnMx = _gt.rotation_gate_mx(amount,mxBasis)\n        self.set_value(_np.dot(rotnMx,self))\n\n\n    def __str__(self):\n        s = \"Fully Parameterized gate with shape %s\\n\" % str(self.base.shape)\n        s += _mt.mx_to_string(self, width=4, prec=2)\n        return s\n\n\n    def compose(self, otherGate):\n        \"\"\"\n        Create and return a new gate that is the composition of this gate\n        followed by otherGate, which *must be another FullyParameterizedGate*.\n        (For more general compositions between different types of gates, use\n        the module-level compose function.)  The returned gate's matrix is\n        equal to dot(this, otherGate).\n\n        Parameters\n        ----------\n        otherGate : FullyParameterizedGate\n            The gate to compose to the right of this one.\n\n        Returns\n        -------\n        FullyParameterizedGate\n        \"\"\"\n        assert( isinstance(otherGate, FullyParameterizedGate) )\n        return FullyParameterizedGate( _np.dot( self.base, otherGate.base) )\n    \n\nclass TPParameterizedGate(GateMatrix):\n    \"\"\"\n    Encapsulates a gate matrix that is fully parameterized except for\n    the first row, which is frozen to be [1 0 ... 0] so that the action\n    of the gate, when interpreted in the Pauli or Gell-Mann basis, is\n    trace preserving (TP).\n    \"\"\"\n\n    def __init__(self, M):\n        \"\"\"\n        Initialize a TPParameterizedGate object.\n\n        Parameters\n        ----------\n        M : array_like or Gate\n            a square 2D numpy array representing the gate action.  The\n            shape of this array sets the dimension of the gate.\n        \"\"\"\n        #Gate.__init__(self, Gate.convert_to_matrix(M))\n        mx = GateMatrix.convert_to_matrix(M)\n        if not (_np.isclose(mx[0,0], 1.0) and \\\n                _np.allclose(mx[0,1:], 0.0)):\n            raise ValueError(\"Cannot create TPParameterizedGate: \" +\n                             \"invalid form for 1st row!\")\n        GateMatrix.__init__(self, _ProtectedArray(mx,\n                       indicesToProtect=(0, slice(None,None,None))))\n\n\n    def set_value(self, M):\n        \"\"\"\n        Attempts to modify gate parameters so that the specified raw\n        gate matrix becomes mx.  Will raise ValueError if this operation\n        is not possible.\n\n        Parameters\n        ----------\n        M : array_like or Gate\n            An array of shape (dim, dim) or Gate representing the gate action.\n\n        Returns\n        -------\n        None\n        \"\"\"\n        mx = GateMatrix.convert_to_matrix(M)\n        if(mx.shape != (self.dim, self.dim)):\n            raise ValueError(\"Argument must be a (%d,%d) matrix!\"\n                             % (self.dim,self.dim))\n        if not (_np.isclose(mx[0,0], 1.0) and _np.allclose(mx[0,1:], 0.0)):\n            raise ValueError(\"Cannot set TPParameterizedGate: \" +\n                             \"invalid form for 1st row!\" )\n            #For further debugging:  + \"\\n\".join([str(e) for e in mx[0,:]])\n        self.base[1:,:] = mx[1:,:]\n        self.dirty = True\n\n\n    def num_params(self):\n        \"\"\"\n        Get the number of independent parameters which specify this gate.\n\n        Returns\n        -------\n        int\n           the number of independent parameters.\n        \"\"\"\n        return self.dim**2 - self.dim\n\n\n    def to_vector(self):\n        \"\"\"\n        Get the gate parameters as an array of values.\n\n        Returns\n        -------\n        numpy array\n            The gate parameters as a 1D array with length num_params().\n        \"\"\"\n        return self.base.flatten()[self.dim:] #.real in case of complex matrices?\n\n\n    def from_vector(self, v):\n        \"\"\"\n        Initialize the gate using a vector of parameters.\n\n        Parameters\n        ----------\n        v : numpy array\n            The 1D vector of gate parameters.  Length\n            must == num_params()\n\n        Returns\n        -------\n        None\n        \"\"\"\n        assert(self.base.shape == (self.dim, self.dim))\n        self.base[1:,:] = v.reshape((self.dim-1,self.dim))\n        self.dirty = True\n\n\n    def deriv_wrt_params(self, wrtFilter=None):\n        \"\"\"\n        Construct a matrix whose columns are the vectorized\n        derivatives of the flattened gate matrix with respect to a\n        single gate parameter.  Thus, each column is of length\n        gate_dim^2 and there is one column per gate parameter.\n\n        Returns\n        -------\n        numpy array\n            Array of derivatives with shape (dimension^2, num_params)\n        \"\"\"\n        derivMx = _np.identity( self.dim**2, 'd' )\n        derivMx = derivMx[:,self.dim:] #remove first gate_dim cols ( <=> first-row parameters )\n\n        if wrtFilter is None:\n            return derivMx\n        else:\n            return _np.take( derivMx, wrtFilter, axis=1 )\n\n\n    def has_nonzero_hessian(self):\n        \"\"\" \n        Returns whether this gate has a non-zero Hessian with\n        respect to its parameters, i.e. whether it only depends\n        linearly on its parameters or not.\n\n        Returns\n        -------\n        bool\n        \"\"\"\n        return False\n\n\n    def copy(self, parent=None):\n        \"\"\"\n        Copy this gate.\n\n        Returns\n        -------\n        Gate\n            A copy of this gate.\n        \"\"\"\n        return self._copy_gpindices( TPParameterizedGate(self.base), parent )\n\n\n    def transform(self, S):\n        \"\"\"\n        Update gate matrix G with inv(S) * G * S,\n\n        Generally, the transform function updates the *parameters* of \n        the gate such that the resulting gate matrix is altered as \n        described above.  If such an update cannot be done (because\n        the gate parameters do not allow for it), ValueError is raised.\n\n        In this particular case any TP gauge transformation is possible,\n        i.e. when `S` is an instance of `TPGaugeGroupElement` or \n        corresponds to a TP-like transform matrix.\n\n        Parameters\n        ----------\n        S : GaugeGroupElement\n            A gauge group element which specifies the \"S\" matrix \n            (and it's inverse) used in the above similarity transform.\n        \"\"\"\n        Smx = S.get_transform_matrix()\n        Si  = S.get_transform_matrix_inverse()\n        self.set_value(_np.dot(Si,_np.dot(self.base, Smx)))\n\n\n    def depolarize(self, amount):\n        \"\"\"\n        Depolarize this gate by the given `amount`.\n\n        Generally, the depolarize function updates the *parameters* of \n        the gate such that the resulting gate matrix is depolarized.  If\n        such an update cannot be done (because the gate parameters do not\n        allow for it), ValueError is raised.\n\n        Parameters\n        ----------\n        amount : float or tuple\n            The amount to depolarize by.  If a tuple, it must have length\n            equal to one less than the dimension of the gate. In standard\n            bases, depolarization corresponds to multiplying the gate matrix\n            by a diagonal matrix whose first diagonal element (corresponding\n            to the identity) equals 1.0 and whose subsequent elements \n            (corresponding to non-identity basis elements) equal\n            `1.0 - amount[i]` (or just `1.0 - amount` if `amount` is a\n            float).\n\n        Returns\n        -------\n        None\n        \"\"\"\n        if isinstance(amount,float):\n            D = _np.diag( [1]+[1-amount]*(self.dim-1) )\n        else:\n            assert(len(amount) == self.dim-1)\n            D = _np.diag( [1]+list(1.0 - _np.array(amount,'d')) )\n        self.set_value(_np.dot(D,self))\n\n\n    def rotate(self, amount, mxBasis=\"gm\"):\n        \"\"\"\n        Rotate this gate by the given `amount`.\n\n        Generally, the rotate function updates the *parameters* of \n        the gate such that the resulting gate matrix is rotated.  If\n        such an update cannot be done (because the gate parameters do not\n        allow for it), ValueError is raised.\n\n        Parameters\n        ----------\n        amount : tuple of floats, optional\n            Specifies the rotation \"coefficients\" along each of the non-identity\n            Pauli-product axes.  The gate's matrix `G` is composed with a\n            rotation operation `R`  (so `G` -> `dot(R, G)` ) where `R` is the\n            unitary superoperator corresponding to the unitary operator \n            `U = exp( sum_k( i * rotate[k] / 2.0 * Pauli_k ) )`.  Here `Pauli_k`\n            ranges over all of the non-identity un-normalized Pauli operators.\n\n        mxBasis : {'std', 'gm', 'pp', 'qt'} or Basis object\n            The source and destination basis, respectively.  Allowed\n            values are Matrix-unit (std), Gell-Mann (gm), Pauli-product (pp),\n            and Qutrit (qt) (or a custom basis object).\n\n        Returns\n        -------\n        None\n        \"\"\"\n        rotnMx = _gt.rotation_gate_mx(amount,mxBasis)\n        self.set_value(_np.dot(rotnMx,self))\n\n\n    def compose(self, otherGate):\n        \"\"\"\n        Create and return a new gate that is the composition of this gate\n        followed by otherGate, which *must be another TPParameterizedGate*.\n        (For more general compositions between different types of gates, use\n        the module-level compose function.)  The returned gate's matrix is\n        equal to dot(this, otherGate).\n\n        Parameters\n        ----------\n        otherGate : TPParameterizedGate\n            The gate to compose to the right of this one.\n\n        Returns\n        -------\n        TPParameterizedGate\n        \"\"\"\n        assert( isinstance(otherGate, TPParameterizedGate) )\n        return TPParameterizedGate( _np.dot( self.base, otherGate ) )\n\n\n    def __str__(self):\n        \"\"\" Return string representation \"\"\"\n        s = \"TP Parameterized gate with shape %s\\n\" % str(self.base.shape)\n        s += _mt.mx_to_string(self.base, width=4, prec=2)\n        return s\n\n\nclass LinearlyParameterizedElementTerm(object):\n    \"\"\"\n    Encapsulates a single term within a LinearlyParameterizedGate.\n    \"\"\"\n    def __init__(self, coeff=1.0, paramIndices=[]):\n        \"\"\"\n        Create a new LinearlyParameterizedElementTerm\n\n        Parameters\n        ----------\n        coeff : float\n            The term's coefficient\n\n        paramIndices : list\n            A list of integers, specifying which parameters are muliplied\n            together (and finally, with `coeff`) to form this term.\n        \"\"\"\n        self.coeff = coeff\n        self.paramIndices = paramIndices\n\n    def copy(self, parent=None):\n        \"\"\" Copy this term. \"\"\"\n        return LinearlyParameterizedElementTerm(self.coeff, self.paramIndices[:])\n\n\nclass LinearlyParameterizedGate(GateMatrix):\n    \"\"\"\n    Encapsulates a gate matrix that is parameterized such that each\n    element of the gate matrix depends only linearly on any parameter.\n    \"\"\"\n\n    def __init__(self, baseMatrix, parameterArray, parameterToBaseIndicesMap,\n                 leftTransform=None, rightTransform=None, real=False):\n        \"\"\"\n        Initialize a LinearlyParameterizedGate object.\n\n        Parameters\n        ----------\n        basematrix : numpy array\n            a square 2D numpy array that acts as the starting point when\n            constructin the gate's matrix.  The shape of this array sets\n            the dimension of the gate.\n\n        parameterArray : numpy array\n            a 1D numpy array that holds the all the parameters for this\n            gate.  The shape of this array sets is what is returned by\n            value_dimension(...).\n\n        parameterToBaseIndicesMap : dict\n            A dictionary with keys == index of a parameter\n            (i.e. in parameterArray) and values == list of 2-tuples\n            indexing potentially multiple gate matrix coordinates\n            which should be set equal to this parameter.\n\n        leftTransform : numpy array or None, optional\n            A 2D array of the same shape as basematrix which left-multiplies\n            the base matrix after parameters have been evaluated.  Defaults to\n            no transform.\n\n        rightTransform : numpy array or None, optional\n            A 2D array of the same shape as basematrix which right-multiplies\n            the base matrix after parameters have been evaluated.  Defaults to\n            no transform.\n\n        real : bool, optional\n            Whether or not the resulting gate matrix, after all\n            parameter evaluation and left & right transforms have\n            been performed, should be real.  If True, ValueError will\n            be raised if the matrix contains any complex or imaginary\n            elements.\n        \"\"\"\n\n        baseMatrix = _np.array( GateMatrix.convert_to_matrix(baseMatrix), 'complex')\n          #complex, even if passed all real base matrix\n\n        elementExpressions = {}\n        for p,ij_tuples in list(parameterToBaseIndicesMap.items()):\n            for i,j in ij_tuples:\n                assert((i,j) not in elementExpressions) #only one parameter allowed per base index pair\n                elementExpressions[(i,j)] = [ LinearlyParameterizedElementTerm(1.0, [p]) ]\n\n        typ = \"d\" if real else \"complex\"\n        mx = _np.empty( baseMatrix.shape, typ )\n        self.baseMatrix = baseMatrix\n        self.parameterArray = parameterArray\n        self.numParams = len(parameterArray)\n        self.elementExpressions = elementExpressions\n\n        I = _np.identity(self.baseMatrix.shape[0],'d')\n        self.leftTrans = leftTransform if (leftTransform is not None) else I\n        self.rightTrans = rightTransform if (rightTransform is not None) else I\n        self.enforceReal = real\n        mx.flags.writeable = False # only _construct_matrix can change array\n\n        GateMatrix.__init__(self, mx)\n        self._construct_matrix() # construct base from the parameters\n\n\n    def _construct_matrix(self):\n        \"\"\"\n        Build the internal gate matrix using the current parameters.\n        \"\"\"\n        matrix = self.baseMatrix.copy()\n        for (i,j),terms in self.elementExpressions.items():\n            for term in terms:\n                param_prod = _np.prod( [ self.parameterArray[p] for p in term.paramIndices ] )\n                matrix[i,j] += term.coeff * param_prod\n        matrix = _np.dot(self.leftTrans, _np.dot(matrix, self.rightTrans))\n\n        if self.enforceReal:\n            if _np.linalg.norm(_np.imag(matrix)) > IMAG_TOL:\n                raise ValueError(\"Linearly parameterized matrix has non-zero\" +\n                        \"imaginary part (%g)!\" % _np.linalg.norm(_np.imag(matrix)))\n            matrix = _np.real(matrix)\n\n        assert(matrix.shape == (self.dim,self.dim))\n        self.base = matrix\n        self.base.flags.writeable = False\n\n\n    def set_value(self, M):\n        \"\"\"\n        Attempts to modify gate parameters so that the specified raw\n        gate matrix becomes mx.  Will raise ValueError if this operation\n        is not possible.\n\n        Parameters\n        ----------\n        M : numpy array or Gate\n            A numpy array of shape (dim, dim) or Gate representing the gate action.\n\n        Returns\n        -------\n        None\n        \"\"\"\n        mx = GateMatrix.convert_to_matrix(M)\n        if(mx.shape != (self.dim, self.dim)):\n            raise ValueError(\"Argument must be a (%d,%d) matrix!\" % (self.dim,self.dim))\n        raise ValueError(\"Currently, directly setting the matrix of a\" +\n                         \" LinearlyParameterizedGate object is not\" +\n                         \" supported.  Please use the from_vector method.\")\n\n\n    def num_params(self):\n        \"\"\"\n        Get the number of independent parameters which specify this gate.\n\n        Returns\n        -------\n        int\n           the number of independent parameters.\n        \"\"\"\n        return self.numParams\n\n\n    def to_vector(self):\n        \"\"\"\n        Extract a vector of the underlying gate parameters from this gate.\n\n        Returns\n        -------\n        numpy array\n            a 1D numpy array with length == num_params().\n        \"\"\"\n        return self.parameterArray #.real in case of complex matrices\n\n\n    def from_vector(self, v):\n        \"\"\"\n        Initialize the gate using a vector of its parameters.\n\n        Parameters\n        ----------\n        v : numpy array\n            The 1D vector of gate parameters.  Length\n            must == num_params().\n\n        Returns\n        -------\n        None\n        \"\"\"\n        self.parameterArray = v\n        self._construct_matrix()\n        self.dirty = True\n\n\n    def deriv_wrt_params(self, wrtFilter=None):\n        \"\"\"\n        Construct a matrix whose columns are the vectorized\n        derivatives of the flattened gate matrix with respect to a\n        single gate parameter.  Thus, each column is of length\n        gate_dim^2 and there is one column per gate parameter.\n\n        Returns\n        -------\n        numpy array\n            Array of derivatives, shape == (dimension^2, num_params)\n        \"\"\"\n        derivMx = _np.zeros( (self.numParams, self.dim, self.dim), 'complex' )\n        for (i,j),terms in self.elementExpressions.items():\n            for term in terms:\n                params_to_mult = [ self.parameterArray[p] for p in term.paramIndices ]\n                for k,p in enumerate(term.paramIndices):\n                    param_partial_prod = _np.prod( params_to_mult[0:k] + params_to_mult[k+1:] ) # exclude k-th factor\n                    derivMx[p,i,j] += term.coeff * param_partial_prod\n        \n        derivMx = _np.dot(self.leftTrans, _np.dot(derivMx, self.rightTrans)) # (d,d) * (P,d,d) * (d,d) => (d,P,d)\n        derivMx = _np.rollaxis(derivMx,1,3) # now (d,d,P)\n        derivMx = derivMx.reshape([self.dim**2, self.numParams]) # (d^2,P) == final shape\n\n        if self.enforceReal:\n            assert(_np.linalg.norm(_np.imag(derivMx)) < IMAG_TOL)\n            derivMx = _np.real(derivMx)\n\n        if wrtFilter is None:\n            return derivMx\n        else:\n            return _np.take( derivMx, wrtFilter, axis=1 )\n\n        \n    def has_nonzero_hessian(self):\n        \"\"\" \n        Returns whether this gate has a non-zero Hessian with\n        respect to its parameters, i.e. whether it only depends\n        linearly on its parameters or not.\n\n        Returns\n        -------\n        bool\n        \"\"\"\n        return False\n\n\n    def copy(self, parent=None):\n        \"\"\"\n        Copy this gate.\n\n        Returns\n        -------\n        Gate\n            A copy of this gate.\n        \"\"\"\n        #Construct new gate with no intial elementExpressions\n        newGate = LinearlyParameterizedGate(self.baseMatrix, self.parameterArray.copy(),\n                                            {}, self.leftTrans.copy(), self.rightTrans.copy(),\n                                            self.enforceReal)\n\n        #Deep copy elementExpressions into new gate\n        for tup, termList in list(self.elementExpressions.items()):\n            newGate.elementExpressions[tup] = [ term.copy() for term in termList ]\n        newGate._construct_matrix()\n\n        return self._copy_gpindices( newGate, parent )\n\n\n    def transform(self, S):\n        \"\"\"\n        Update gate matrix G with inv(S) * G * S,\n\n        Generally, the transform function updates the *parameters* of \n        the gate such that the resulting gate matrix is altered as \n        described above.  If such an update cannot be done (because\n        the gate parameters do not allow for it), ValueError is raised.\n\n        Parameters\n        ----------\n        S : GaugeGroupElement\n            A gauge group element which specifies the \"S\" matrix \n            (and it's inverse) used in the above similarity transform.\n        \"\"\"\n        #Currently just raise error - in general there is no *unique* \n        # parameter alteration that would affect a similarity transform,\n        # and even if one exists it may be difficult to find efficiently.\n        raise ValueError(\"Invalid transform for this LinearlyParameterizedGate\")\n    \n        #OLD (TODO: remove)\n        #self.leftTrans = _np.dot(Si,self.leftTrans)\n        #self.rightTrans = _np.dot(self.rightTrans,S)\n        #self._construct_matrix()\n\n\n    def depolarize(self, amount):\n        \"\"\"\n        Depolarize this gate by the given `amount`.\n\n        Generally, the depolarize function updates the *parameters* of \n        the gate such that the resulting gate matrix is depolarized.  If\n        such an update cannot be done (because the gate parameters do not\n        allow for it), ValueError is raised.\n\n        Parameters\n        ----------\n        amount : float or tuple\n            The amount to depolarize by.  If a tuple, it must have length\n            equal to one less than the dimension of the gate. In standard\n            bases, depolarization corresponds to multiplying the gate matrix\n            by a diagonal matrix whose first diagonal element (corresponding\n            to the identity) equals 1.0 and whose subsequent elements \n            (corresponding to non-identity basis elements) equal\n            `1.0 - amount[i]` (or just `1.0 - amount` if `amount` is a\n            float).\n\n        Returns\n        -------\n        None\n        \"\"\"\n        raise ValueError(\"Cannot depolarize a LinearlyParameterizedGate (no general procedure)!\")\n\n\n    def rotate(self, amount, mxBasis=\"gm\"):\n        \"\"\"\n        Rotate this gate by the given `amount`.\n\n        Generally, the rotate function updates the *parameters* of \n        the gate such that the resulting gate matrix is rotated.  If\n        such an update cannot be done (because the gate parameters do not\n        allow for it), ValueError is raised.\n\n        Parameters\n        ----------\n        amount : tuple of floats, optional\n            Specifies the rotation \"coefficients\" along each of the non-identity\n            Pauli-product axes.  The gate's matrix `G` is composed with a\n            rotation operation `R`  (so `G` -> `dot(R, G)` ) where `R` is the\n            unitary superoperator corresponding to the unitary operator \n            `U = exp( sum_k( i * rotate[k] / 2.0 * Pauli_k ) )`.  Here `Pauli_k`\n            ranges over all of the non-identity un-normalized Pauli operators.\n\n        mxBasis : {'std', 'gm', 'pp', 'qt'} or Basis object\n            The source and destination basis, respectively.  Allowed\n            values are Matrix-unit (std), Gell-Mann (gm), Pauli-product (pp),\n            and Qutrit (qt) (or a custom basis object).\n\n        Returns\n        -------\n        None\n        \"\"\"\n        raise ValueError(\"Cannot rotate a LinearlyParameterizedGate (no general procedure)!\")\n\n\n\n    def compose(self, otherGate):\n        \"\"\"\n        Create and return a new gate that is the composition of this gate\n        followed by otherGate, which *must be another LinearlyParameterizedGate*.\n        (For more general compositions between different types of gates, use\n        the module-level compose function.)  The returned gate's matrix is\n        equal to dot(this, otherGate).\n\n        Parameters\n        ----------\n        otherGate : LinearlyParameterizedGate\n            The gate to compose to the right of this one.\n\n        Returns\n        -------\n        LinearlyParameterizedGate\n        \"\"\"\n        assert( isinstance(otherGate, LinearlyParameterizedGate) )\n\n        ### Implementation Notes ###\n        #\n        # Let self == L1 * A * R1, other == L2 * B * R2\n        #   where  [A]_ij = a_ij + sum_l c^(ij)_l T^(ij)_l  so that\n        #      a_ij == base matrix of self, c's are term coefficients, and T's are parameter products\n        #   and similarly [B]_ij = b_ij + sum_l d^(ij)_l R^(ij)_l.\n        #\n        # We want in the end a gate with matrix:\n        #   L1 * A * R1 * L2 * B * R2 == L1 * (A * W * B) * R2,  (where W := R1 * L2 )\n        #   which is a linearly parameterized gate with leftTrans == L1, rightTrans == R2\n        #   and a parameterized part == (A * W * B) which can be written with implied sum on k,n:\n        #\n        #  [A * W * B]_ij\n        #   = (a_ik + sum_l c^(ik)_l T^(ik)_l) * W_kn * (b_nj + sum_m d^(nj)_m R^(nj)_m)\n        #\n        #   = a_ik * W_kn * b_nj +\n        #     a_ik * W_kn * sum_m d^(nj)_m R^(nj)_m +\n        #     sum_l c^(ik)_l T^(ik)_l * W_kn * b_nj +\n        #     (sum_l c^(ik)_l T^(ik)_l) * W_kn * (sum_m d^(nj)_m R^(nj)_m)\n        #\n        #   = aWb_ij   # (new base matrix == a*W*b)\n        #     aW_in * sum_m d^(nj)_m R^(nj)_m +   # coeffs w/params of otherGate\n        #     sum_l c^(ik)_l T^(ik)_l * Wb_kj +   # coeffs w/params of this gate\n        #     sum_m,l c^(ik)_l W_kn d^(nj)_m T^(ik)_l R^(nj)_m) # coeffs w/params of both gates\n        #\n\n        W = _np.dot(self.rightTrans, otherGate.leftTrans)\n        baseMx = _np.dot(self.baseMatrix, _np.dot(W, otherGate.baseMatrix)) # aWb above\n        paramArray = _np.concatenate( (self.parameterArray, otherGate.parameterArray), axis=0)\n        composedGate = LinearlyParameterizedGate(baseMx, paramArray, {},\n                                                 self.leftTrans, otherGate.rightTrans,\n                                                 self.enforceReal and otherGate.enforceReal)\n\n        # Precompute what we can before the compute loop\n        aW = _np.dot(self.baseMatrix, W)\n        Wb = _np.dot(W, otherGate.baseMatrix)\n\n        kMax,nMax = (self.dim,self.dim) #W.shape\n        offset = len(self.parameterArray) # amt to offset parameter indices of otherGate\n\n        # Compute  [A * W * B]_ij element expression as described above\n        for i in range(self.baseMatrix.shape[0]):\n            for j in range(otherGate.baseMatrix.shape[1]):\n                terms = []\n                for n in range(nMax):\n                    if (n,j) in otherGate.elementExpressions:\n                        for term in otherGate.elementExpressions[(n,j)]:\n                            coeff = aW[i,n] * term.coeff\n                            paramIndices = [ p+offset for p in term.paramIndices ]\n                            terms.append( LinearlyParameterizedElementTerm( coeff, paramIndices ) )\n\n                for k in range(kMax):\n                    if (i,k) in self.elementExpressions:\n                        for term in self.elementExpressions[(i,k)]:\n                            coeff = term.coeff * Wb[k,j]\n                            terms.append( LinearlyParameterizedElementTerm( coeff, term.paramIndices ) )\n\n                            for n in range(nMax):\n                                if (n,j) in otherGate.elementExpressions:\n                                    for term2 in otherGate.elementExpressions[(n,j)]:\n                                        coeff = term.coeff * W[k,n] * term2.coeff\n                                        paramIndices = term.paramIndices + [ p+offset for p in term2.paramIndices ]\n                                        terms.append( LinearlyParameterizedElementTerm( coeff, paramIndices ) )\n\n                composedGate.elementExpressions[(i,j)] = terms\n\n        composedGate._construct_matrix()\n        return composedGate\n\n\n    def __str__(self):\n        s = \"Linearly Parameterized gate with shape %s, num params = %d\\n\" % \\\n            (str(self.base.shape), self.numParams)\n        s += _mt.mx_to_string(self.base, width=5, prec=1)\n        s += \"\\nParameterization:\"\n        for (i,j),terms in self.elementExpressions.items():\n            tStr = ' + '.join([ '*'.join([\"p%d\"%p for p in term.paramIndices])\n                                for term in terms] )\n            s += \"Gate[%d,%d] = %s\\n\" % (i,j,tStr)\n        return s\n\n\n#Or CommutantParameterizedGate(Gate): ?\nclass EigenvalueParameterizedGate(GateMatrix):\n    \"\"\"\n    Encapsulates a real gate matrix that is parameterized only by its\n    eigenvalues, which are assumed to be either real or to occur in\n    conjugate pairs.  Thus, the number of parameters is equal to the\n    number of eigenvalues.\n    \"\"\"\n\n    def __init__(self, matrix, includeOffDiagsInDegen2Blocks=False,\n                 TPconstrainedAndUnital=False):\n        \"\"\"\n        Initialize a LinearlyParameterizedGate object.\n\n        Parameters\n        ----------\n        matrix : numpy array\n            a square 2D numpy array that gives the raw gate matrix to\n            paramterize.  The shape of this array sets the dimension\n            of the gate.\n\n        includeOffDiagsInDegen2Blocks : bool\n            If True, include as parameters the (initially zero) \n            off-diagonal elements in degenerate 2x2 blocks of the \n            the diagonalized gate matrix (no off-diagonals are \n            included in blocks larger than 2x2).  This is an option\n            specifically used in the intelligent fiducial pair\n            reduction (IFPR) algorithm.\n\n        TPconstrainedAndUnital : bool\n            If True, assume the top row of the gate matrix is fixed\n            to [1, 0, ... 0] and should not be parameterized, and verify\n            that the matrix is unital.  In this case, \"1\" is always a\n            fixed (not-paramterized0 eigenvalue with eigenvector\n            [1,0,...0] and if includeOffDiagsInDegen2Blocks is True\n            any off diagonal elements lying on the top row are *not*\n            parameterized as implied by the TP constraint.\n        \"\"\"\n\n        def cmplx_compare(ia,ib):\n            \"\"\"Helper for comparing complex numbers\"\"\"\n            a,b = evals[ia], evals[ib]\n            if a.real < b.real:   return -1\n            elif a.real > b.real: return  1\n            elif a.imag < b.imag: return -1\n            elif a.imag > b.imag: return  1\n            else: return 0\n        cmplx_compare_key = _functools.cmp_to_key(cmplx_compare)\n\n        def isreal(a):\n            \"\"\" b/c numpy's isreal tests for strict equality w/0 \"\"\"\n            return _np.isclose(_np.imag(a),0.0)\n\n        # Since matrix is real, eigenvalues must either be real or occur in\n        #  conjugate pairs.  Find and sort by conjugate pairs.\n\n        assert(_np.linalg.norm(_np.imag(matrix)) < IMAG_TOL ) #matrix should be real\n        evals, B = _np.linalg.eig(matrix) # matrix == B * diag(evals) * Bi\n        dim = len(evals)\n\n        #Sort eigenvalues & eigenvectors by:\n        # 1) unit eigenvalues first (with TP eigenvalue first of all)\n        # 2) non-unit real eigenvalues in order of magnitude\n        # 3) complex eigenvalues in order of real then imaginary part\n\n        unitInds = []; realInds = []; complexInds = []\n        for i,ev in enumerate(evals):\n            if _np.isclose(ev,1.0): unitInds.append(i)\n            elif isreal(ev): realInds.append(i)\n            else: complexInds.append(i)\n\n        if TPconstrainedAndUnital:\n            #check matrix is TP and unital\n            unitRow = _np.zeros( (len(evals)), 'd'); unitRow[0] = 1.0\n            assert( _np.allclose(matrix[0,:],unitRow) )\n            assert( _np.allclose(matrix[:,0],unitRow) )\n\n            #find the eigenvector with largest first element and make sure\n            # this is the first index in unitInds\n            k = _np.argmax([B[0,i] for i in unitInds])\n            if k != 0:  #swap indices 0 <-> k in unitInds\n                t = unitInds[0]; unitInds[0] = unitInds[k]; unitInds[k] = t\n\n            #Assume we can recombine unit-eval eigenvectors so that the first\n            # one (actually the closest-to-unit-row one) == unitRow and the\n            # rest do not have any 0th component.\n            iClose = _np.argmax( [abs(B[0,ui]) for ui in unitInds] )\n            B[:, unitInds[iClose] ] = unitRow\n            for i,ui in enumerate(unitInds):\n                if i == iClose: continue\n                B[0, ui] = 0.0; B[:,ui] /= _np.linalg.norm(B[:,ui])\n\n        realInds = sorted(realInds, key=lambda i: -abs(evals[i]))\n        complexInds = sorted(complexInds, key=cmplx_compare_key)\n        new_ordering = unitInds + realInds + complexInds\n\n        #Re-order the eigenvalues & vectors\n        sorted_evals = _np.zeros(evals.shape,'complex')\n        sorted_B = _np.zeros(B.shape, 'complex')\n        for i,indx in enumerate(new_ordering):\n            sorted_evals[i] = evals[indx]\n            sorted_B[:,i] = B[:,indx]\n\n        #Save the final list of (sorted) eigenvalues & eigenvectors\n        self.evals = sorted_evals\n        self.B = sorted_B\n        self.Bi = _np.linalg.inv(sorted_B)\n\n        self.options = { 'includeOffDiags': includeOffDiagsInDegen2Blocks,\n                         'TPandUnital': TPconstrainedAndUnital }\n\n        #Check that nothing has gone horribly wrong\n        assert(_np.allclose(_np.dot(\n                    self.B,_np.dot(_np.diag(self.evals),self.Bi)), matrix))\n                \n        #Build a list of parameter descriptors.  Each element of self.params\n        # is a list of (prefactor, (i,j)) tuples.\n        self.params = []        \n        i = 0; N = len(self.evals); processed = [False]*N\n        while i < N:\n            if processed[i]:\n                i += 1; continue\n\n            # Find block (i -> j) of degenerate eigenvalues\n            j = i+1\n            while j < N and _np.isclose(self.evals[i],self.evals[j]): j += 1\n            blkSize = j-i\n\n            #Add eigenvalues as parameters\n            ev = self.evals[i] #current eigenvalue being processed\n            if isreal(ev):\n                \n                # Side task: for a *real* block of degenerate evals, we want\n                # to ensure the eigenvectors are real, which numpy doesn't\n                # always guarantee (could be conj. pairs for instance).\n\n                # Solve or Cmx: [v1,v2,v3,v4]Cmx = [v1',v2',v3',v4'] ,\n                # where ' qtys == real, so Im([v1,v2,v3,v4]Cmx) = 0\n                # Let Cmx = Cr + i*Ci, v1 = v1.r + i*v1.i, etc.,\n                #  then solve [v1.r, ...]Ci + [v1.i, ...]Cr = 0\n                #  which can be cast as [Vr,Vi]*[Ci] = 0\n                #                               [Cr]      (nullspace of V)\n                # Note: only involve complex evecs (don't disturb TP evec!)            \n                evecIndsToMakeReal = []\n                for k in range(i,j): \n                    if _np.linalg.norm(self.B[:,k].imag) >= IMAG_TOL:\n                        evecIndsToMakeReal.append(k)\n\n                nToReal = len(evecIndsToMakeReal)\n                if nToReal > 0:\n                    vecs = _np.empty( (dim,nToReal),'complex')\n                    for ik,k in enumerate(evecIndsToMakeReal): \n                        vecs[:,ik] = self.B[:,k]\n                    V = _np.concatenate((vecs.real, vecs.imag), axis=1)\n                    nullsp = _mt.nullspace(V); \n                    if nullsp.shape[1] < nToReal: #DEBUG\n                        raise ValueError(\"Nullspace only has dimension %d when %d was expected! (i=%d, j=%d, blkSize=%d)\\nevals = %s\" \\\n                                         % (nullsp.shape[1],nToReal, i,j,blkSize,str(self.evals)) )\n                    assert(nullsp.shape[1] >= nToReal),\"Cannot find enough real linear combos!\"\n                    nullsp = nullsp[:,0:nToReal] #truncate #cols if there are more than we need\n    \n                    Cmx = nullsp[nToReal:,:] + 1j*nullsp[0:nToReal,:] # Cr + i*Ci\n                    new_vecs = _np.dot(vecs,Cmx)\n                    assert(_np.linalg.norm(new_vecs.imag) < IMAG_TOL), \"Imaginary mag = %g!\" % _np.linalg.norm(new_vecs.imag)\n                    for ik,k in enumerate(evecIndsToMakeReal): \n                        self.B[:,k] = new_vecs[:,ik]\n                    self.Bi = _np.linalg.inv(self.B)                \n\n                \n                #Now, back to constructing parameter descriptors...\n                for k in range(i,j):\n                    if TPconstrainedAndUnital and k == 0: continue\n                    prefactor = 1.0; mx_indx = (k,k)\n                    self.params.append( [(prefactor, mx_indx)] )\n                    processed[k] = True\n            else:\n                iConjugate = {}\n                for k in range(i,j):\n                    #Find conjugate eigenvalue to eval[k]\n                    conj = _np.conj(self.evals[k]) # == conj(ev), indep of k\n                    conjB = _np.conj(self.B[:,k])\n                    for l in range(j,N):\n                        if _np.isclose(conj,self.evals[l]) and \\\n                                _np.allclose(conjB, self.B[:,l]):\n                            self.params.append( [  # real-part param\n                                    (1.0, (k,k)),  # (prefactor, index)\n                                    (1.0, (l,l)) ] ) \n                            self.params.append( [  # imag-part param\n                                    (1j, (k,k)),  # (prefactor, index)\n                                    (-1j, (l,l)) ] ) \n                            processed[k] = processed[l] = True\n                            iConjugate[k] = l #save conj. pair index for below\n                            break\n                    else:\n                        raise ValueError(\"Could not find conjugate pair \" \n                                         + \" for %s\" % self.evals[k])\n            \n            \n            if includeOffDiagsInDegen2Blocks and blkSize == 2:\n                #Note: could remove blkSize == 2 condition or make this a \n                # separate option.  This is useful currently so that we don't\n                # add lots of off-diag elements in accidentally-degenerate \n                # cases, but there's probabaly a better heuristic for this, such\n                # as only including off-diag els for unit-eigenvalue blocks\n                # of size 2 (?)\n                for k1 in range(i,j-1):\n                    for k2 in range(k1+1,j):\n                        if isreal(ev):\n                            # k1,k2 element\n                            if not TPconstrainedAndUnital or k1 != 0:\n                                self.params.append( [(1.0, (k1,k2))] )\n\n                            # k2,k1 element\n                            if not TPconstrainedAndUnital or k2 != 0:\n                                self.params.append( [(1.0, (k2,k1))] )\n                        else:\n                            k1c, k2c = iConjugate[k1],iConjugate[k2]\n\n                            # k1,k2 element\n                            self.params.append( [  # real-part param\n                                    (1.0, (k1,k2)),\n                                    (1.0, (k1c,k2c)) ] ) \n                            self.params.append( [  # imag-part param\n                                    (1j, (k1,k2)),\n                                    (-1j, (k1c,k2c)) ] ) \n\n                            # k2,k1 element\n                            self.params.append( [  # real-part param\n                                    (1.0, (k2,k1)),\n                                    (1.0, (k2c,k1c)) ] ) \n                            self.params.append( [  # imag-part param\n                                    (1j, (k2,k1)),\n                                    (-1j, (k2c,k1c)) ] )\n\n            i = j #advance to next block\n\n        #Allocate array of parameter values (all zero initially)\n        self.paramvals = _np.zeros(len(self.params), 'd')\n\n        #Finish Gate construction\n        mx = _np.empty( matrix.shape, \"d\" )\n        mx.flags.writeable = False # only _construct_matrix can change array\n        GateMatrix.__init__(self, mx)\n        self._construct_matrix() # construct base from the parameters\n\n\n    def _construct_matrix(self):\n        \"\"\"\n        Build the internal gate matrix using the current parameters.\n        \"\"\"\n        base_diag = _np.diag(self.evals)\n        for pdesc,pval in zip(self.params, self.paramvals):\n            for prefactor,(i,j) in pdesc:\n                base_diag[i,j] += prefactor * pval\n        matrix = _np.dot(self.B,_np.dot(base_diag,self.Bi))\n        assert(_np.linalg.norm(matrix.imag) < IMAG_TOL)\n        assert(matrix.shape == (self.dim,self.dim))\n        self.base = matrix.real\n        self.base.flags.writeable = False\n\n    def set_value(self, M):\n        \"\"\"\n        Attempts to modify gate parameters so that the specified raw\n        gate matrix becomes mx.  Will raise ValueError if this operation\n        is not possible.\n\n        Parameters\n        ----------\n        M : numpy array or Gate\n            A numpy array of shape (dim, dim) or Gate representing the gate action.\n\n        Returns\n        -------\n        None\n        \"\"\"\n        mx = GateMatrix.convert_to_matrix(M)\n        if(mx.shape != (self.dim, self.dim)):\n            raise ValueError(\"Argument must be a (%d,%d) matrix!\" % (self.dim,self.dim))\n        raise ValueError(\"Currently, directly setting the matrix of an\" +\n                         \" EigenvalueParameterizedGate object is not\" +\n                         \" supported.  Please use the from_vector method.\")\n\n    def num_params(self):\n        \"\"\"\n        Get the number of independent parameters which specify this gate.\n\n        Returns\n        -------\n        int\n           the number of independent parameters.\n        \"\"\"\n        return len(self.paramvals)\n\n\n    def to_vector(self):\n        \"\"\"\n        Extract a vector of the underlying gate parameters from this gate.\n\n        Returns\n        -------\n        numpy array\n            a 1D numpy array with length == num_params().\n        \"\"\"\n        return self.paramvals\n\n\n    def from_vector(self, v):\n        \"\"\"\n        Initialize the gate using a vector of its parameters.\n\n        Parameters\n        ----------\n        v : numpy array\n            The 1D vector of gate parameters.  Length\n            must == num_params().\n\n        Returns\n        -------\n        None\n        \"\"\"\n        assert(len(v) == self.num_params())\n        self.paramvals = v\n        self._construct_matrix()\n        self.dirty = True\n\n\n    def deriv_wrt_params(self, wrtFilter=None):\n        \"\"\"\n        Construct a matrix whose columns are the vectorized\n        derivatives of the flattened gate matrix with respect to a\n        single gate parameter.  Thus, each column is of length\n        gate_dim^2 and there is one column per gate parameter.\n\n        Returns\n        -------\n        numpy array\n            Array of derivatives, shape == (dimension^2, num_params)\n        \"\"\"\n        \n        # matrix = B * diag * Bi, and only diag depends on parameters\n        #  (and only linearly), so:\n        # d(matrix)/d(param) = B * d(diag)/d(param) * Bi\n\n        derivMx = _np.zeros( (self.dim**2, self.num_params()), 'd' )\n\n        # Compute d(diag)/d(param) for each params, then apply B & Bi\n        for k,pdesc in enumerate(self.params):\n            dMx = _np.zeros( (self.dim,self.dim), 'complex')\n            for prefactor,(i,j) in pdesc:\n                dMx[i,j] = prefactor\n            tmp = _np.dot(self.B, _np.dot(dMx, self.Bi))\n            if _np.linalg.norm(tmp.imag) >= IMAG_TOL: #just a warning until we figure this out.\n                print(\"EigenvalueParameterizedGate deriv_wrt_params WARNING:\" + \n                      \" Imag part = \",_np.linalg.norm(tmp.imag),\" pdesc = \",pdesc)\n            #assert(_np.linalg.norm(tmp.imag) < IMAG_TOL), \\\n            #       \"Imaginary mag = %g!\" % _np.linalg.norm(tmp.imag)\n            derivMx[:,k] = tmp.real.flatten()\n\n        if wrtFilter is None:\n            return derivMx\n        else:\n            return _np.take( derivMx, wrtFilter, axis=1 )\n\n        \n    def has_nonzero_hessian(self):\n        \"\"\" \n        Returns whether this gate has a non-zero Hessian with\n        respect to its parameters, i.e. whether it only depends\n        linearly on its parameters or not.\n\n        Returns\n        -------\n        bool\n        \"\"\"\n        return False\n\n\n    def copy(self, parent=None):\n        \"\"\"\n        Copy this gate.\n\n        Returns\n        -------\n        Gate\n            A copy of this gate.\n        \"\"\"\n        #Construct new gate with dummy identity mx\n        newGate = EigenvalueParameterizedGate(_np.identity(self.dim,'d'))\n        \n        #Deep copy data\n        newGate.evals = self.evals.copy()\n        newGate.B = self.B.copy()\n        newGate.Bi = self.Bi.copy()\n        newGate.params = self.params[:] #copies tuple elements\n        newGate.paramvals = self.paramvals.copy()\n        newGate.options = self.options.copy()\n        newGate._construct_matrix()\n\n        return self._copy_gpindices( newGate, parent )\n\n\n    def transform(self, S):\n        \"\"\"\n        Update gate matrix G with inv(S) * G * S,\n\n        Generally, the transform function updates the *parameters* of \n        the gate such that the resulting gate matrix is altered as \n        described above.  If such an update cannot be done (because\n        the gate parameters do not allow for it), ValueError is raised.\n\n        Parameters\n        ----------\n        S : GaugeGroupElement\n            A gauge group element which specifies the \"S\" matrix \n            (and it's inverse) used in the above similarity transform.\n        \"\"\"\n        #Currently just raise error, as a general similarity transformation\n        # *will* affect the eigenvalues.  In the future, perhaps we could allow\n        # *unitary* type similarity transformations.\n        raise ValueError(\"Invalid transform for this EigenvalueParameterizedGate\")\n\n        #OLD:\n        #if Si is None: Si = _np.linalg.inv(S)\n        #self.B = _np.dot(Si,self.B)\n        #self.Bi = _np.dot(self.Bi,S)\n        #self._construct_matrix()\n\n\n    def depolarize(self, amount):\n        \"\"\"\n        Depolarize this gate by the given `amount`.\n\n        Generally, the depolarize function updates the *parameters* of \n        the gate such that the resulting gate matrix is depolarized.  If\n        such an update cannot be done (because the gate parameters do not\n        allow for it), ValueError is raised.\n\n        Parameters\n        ----------\n        amount : float or tuple\n            The amount to depolarize by.  If a tuple, it must have length\n            equal to one less than the dimension of the gate. In standard\n            bases, depolarization corresponds to multiplying the gate matrix\n            by a diagonal matrix whose first diagonal element (corresponding\n            to the identity) equals 1.0 and whose subsequent elements \n            (corresponding to non-identity basis elements) equal\n            `1.0 - amount[i]` (or just `1.0 - amount` if `amount` is a\n            float).\n\n        Returns\n        -------\n        None\n        \"\"\"\n        #TODO: maybe could do this if we wanted to get clever: if one eigenvector is the\n        # identity we could reduce all the eigenvalues corresponding to *other* evecs.\n        raise ValueError(\"Cannot depolarize a EigenvalueParameterizedGate\")\n\n\n    def rotate(self, amount, mxBasis=\"gm\"):\n        \"\"\"\n        Rotate this gate by the given `amount`.\n\n        Generally, the rotate function updates the *parameters* of \n        the gate such that the resulting gate matrix is rotated.  If\n        such an update cannot be done (because the gate parameters do not\n        allow for it), ValueError is raised.\n\n        Parameters\n        ----------\n        amount : tuple of floats, optional\n            Specifies the rotation \"coefficients\" along each of the non-identity\n            Pauli-product axes.  The gate's matrix `G` is composed with a\n            rotation operation `R`  (so `G` -> `dot(R, G)` ) where `R` is the\n            unitary superoperator corresponding to the unitary operator \n            `U = exp( sum_k( i * rotate[k] / 2.0 * Pauli_k ) )`.  Here `Pauli_k`\n            ranges over all of the non-identity un-normalized Pauli operators.\n\n        mxBasis : {'std', 'gm', 'pp', 'qt'} or Basis object\n            The source and destination basis, respectively.  Allowed\n            values are Matrix-unit (std), Gell-Mann (gm), Pauli-product (pp),\n            and Qutrit (qt) (or a custom basis object).\n\n        Returns\n        -------\n        None\n        \"\"\"\n        raise ValueError(\"Cannot rotate an EigenvalueParameterizedGate!\")\n\n\n    def compose(self, otherGate):\n        \"\"\"\n        Create and return a new gate that is the composition of this gate\n        followed by otherGate, which *must be another \n        EigenvalueParameterizedGate*.  (For more general compositions between\n        different types of gates, use the module-level compose function.)  The\n        returned gate's matrix is equal to dot(this, otherGate).\n\n        Parameters\n        ----------\n        otherGate : EigenvalueParameterizedGate\n            The gate to compose to the right of this one.\n\n        Returns\n        -------\n        EigenvalueParameterizedGate\n        \"\"\"\n        assert( isinstance(otherGate, EigenvalueParameterizedGate) )\n\n        composed_mx = _np.dot(self, otherGate)\n        return EigenvalueParameterizedGate(composed_mx,\n                                           self.options['includeOffDiags'],\n                                           self.options['TPandUnital'])\n\n    def __str__(self):\n        s = \"Eigenvalue Parameterized gate with shape %s, num params = %d\\n\" % \\\n            (str(self.base.shape), self.num_params())\n        s += _mt.mx_to_string(self.base, width=5, prec=1)\n        return s\n\n\n\nclass LindbladParameterizedGate(GateMatrix):\n    \"\"\"\n    Encapsulates a gate matrix that is parameterized by a Lindblad-form\n    expression, such that each parameter multiplies a particular term in\n    the Lindblad form that is exponentiated to give the gate matrix up\n    to an optional unitary prefactor).  The basis used by the Lindblad\n    form is referred to as the \"projection basis\".\n    \"\"\"\n    \n    def __init__(self, gateMatrix, unitaryPostfactor=None,\n                 ham_basis=\"pp\", nonham_basis=\"pp\", cptp=True,\n                 nonham_diagonal_only=False, truncate=True, mxBasis=\"pp\"):\n        \"\"\"\n        Initialize a LindbladParameterizedGate object.\n\n        Parameters\n        ----------\n        gateMatrix : numpy array\n            a square 2D numpy array that gives the raw gate matrix, assumed to\n            be in the `mxBasis` basis, to parameterize.  The shape of this\n            array sets the dimension of the gate. If None, then it is assumed\n            equal to `unitaryPostfactor` (which cannot also be None). The\n            quantity `gateMatrix inv(unitaryPostfactor)` is parameterized via\n            projection onto the Lindblad terms.\n            \n        unitaryPostfactor : numpy array, optional\n            a square 2D numpy array of the same size of `gateMatrix` (if\n            not None).  This matrix specifies a part of the gate action \n            to remove before parameterization via Lindblad projections.\n            Typically, this is a target (desired) gate operation such \n            that only the erroneous part of the gate (i.e. the gate \n            relative to the target), which should be close to the identity,\n            is parameterized.  If none, the identity is used by default.\n\n        ham_basis: {'std', 'gm', 'pp', 'qt'}, list of matrices, or Basis object\n            The basis is used to construct the Stochastic-type lindblad error\n            Allowed values are Matrix-unit (std), Gell-Mann (gm), Pauli-product (pp),\n            and Qutrit (qt), list of numpy arrays, or a custom basis object.\n\n        other_basis: {'std', 'gm', 'pp', 'qt'}, list of matrices, or Basis object\n            The basis is used to construct the Stochastic-type lindblad error\n            Allowed values are Matrix-unit (std), Gell-Mann (gm), Pauli-product (pp),\n            and Qutrit (qt), list of numpy arrays, or a custom basis object.\n\n        cptp : bool, optional\n            Whether or not the new gate should be constrained to CPTP.\n            (if True, see behavior or `truncate`).\n\n        nonham_diagonal_only : boolean, optional\n            If True, only *diagonal* Stochastic (non-Hamiltonain) terms are\n            included in the parameterization.\n\n        truncate : bool, optional\n            Whether to truncate the projections onto the Lindblad terms in\n            order to preserve CPTP (when necessary).  If False, then an \n            error is thrown when `cptp == True` and when Lindblad projections\n            result in a non-positive-definite matrix of non-Hamiltonian term\n            coefficients.\n\n        mxBasis : {'std', 'gm', 'pp', 'qt'} or Basis object\n            The source and destination basis, respectively.  Allowed\n            values are Matrix-unit (std), Gell-Mann (gm), Pauli-product (pp),\n            and Qutrit (qt) (or a custom basis object).\n        \"\"\"\n        if gateMatrix is None:\n            assert(unitaryPostfactor is not None), \"arguments cannot both be None\"\n            gateMatrix = unitaryPostfactor\n        if unitaryPostfactor is None:\n            unitaryPostfactor = _np.identity(gateMatrix.shape[0],'d')\n\n        self.cptp = cptp\n        self.nonham_diagonal_only = nonham_diagonal_only\n        \n        d2 = gateMatrix.shape[0]\n        d = int(round(_np.sqrt(d2)))\n        assert(d*d == d2), \"Gate dim must be a perfect square\"\n        self.unitary_postfactor = unitaryPostfactor\n\n        self.ham_basis = ham_basis\n        self.other_basis = nonham_basis\n        self.matrix_basis = mxBasis\n\n        #For now, always use pauli-product basis (should be able to use\n        # gm also, but one thing at a time).\n        errgen = _gt.error_generator(gateMatrix, unitaryPostfactor, mxBasis, \"logGTi\")\n        self._set_params_from_errgen(errgen, truncate)\n          #also sets self.hamGens and self.otherGens as side effect (TODO FIX)\n          # and self.ham_basis_size and self.other_basis_size\n\n        assert(not _np.array_equal(gateMatrix,unitaryPostfactor) or\n               _np.allclose(self.paramvals,0.0,atol=1e-6))\n\n        #Assume gate in in the pauli-product basis for now, just for\n        # simplicity.  I think everything should work fine in any other\n        # basis with the same trace(BiBj) = delta_ij property (e.g. Gell-Mann)\n        mxBasisToStd = _bt.transform_matrix(mxBasis, \"std\", d)\n        self.leftTrans  = _np.linalg.inv(mxBasisToStd)\n        self.rightTrans = mxBasisToStd\n\n        #initialize intermediate storage for matrix and for deriv computation\n        bsO = self.other_basis_size\n        if bsO > 0:\n            self.Lmx = _np.zeros((bsO-1,bsO-1),'complex')\n        else: self.Lmx = None\n\n        GateMatrix.__init__(self, self.unitary_postfactor)\n        self._construct_matrix() # construct base from the parameters\n\n        if not truncate:\n            assert(not _np.array_equal(gateMatrix,self.base)), \\\n                   \"Gate matrix must be truncated and truncate == False!\"\n\n\n    def _set_params_from_errgen(self, errgen, truncate):\n        d2 = self.unitary_postfactor.shape[0]\n\n        hamC, otherC, self.hamGens, self.otherGens = \\\n            _gt.lindblad_errgen_projections(\n                errgen, self.ham_basis, self.other_basis, self.matrix_basis, normalize=False,\n                return_generators=True, other_diagonal_only=self.nonham_diagonal_only) # in std basis\n\n        if self.hamGens is not None:\n            bsH = self.hamGens.shape[0]+1 #projection-basis size (not nec. == d2)\n            assert(self.hamGens.shape == (bsH-1,d2,d2))\n            \n            assert(_np.isclose(_np.linalg.norm(hamC.imag),0)), \\\n                \"Hamiltoian coeffs are not all real!\"\n            hamParams = hamC.real\n        else:\n            bsH = 0\n            hamParams = _np.empty(0,'d')\n            \n\n        if self.otherGens is not None:\n            bsO = self.otherGens.shape[0]+1 #projection-basis size (not nec. == d2)\n\n            if self.nonham_diagonal_only:\n                assert(self.otherGens.shape == (bsO-1,d2,d2))\n                assert(_np.isclose(_np.linalg.norm(_np.imag(otherC)),0))\n                #assert(_np.all(_np.isreal(otherC))) #sometimes fails even when imag to machine prec\n    \n                if self.cptp: #otherParams is a 1D vector of the sqrts of diagonal els\n                    otherC = otherC.clip(1e-16,1e100) #must be positive\n                    otherParams = _np.sqrt(otherC.real) # shape (bs-1,)\n                else: #otherParams is a 1D vector of the real diagonal els of otherC\n                    otherParams = otherC.real # shape (bs-1,)\n    \n            else:\n                assert(self.otherGens.shape == (bsO-1,bsO-1,d2,d2))\n                assert(_np.isclose(_np.linalg.norm(otherC-otherC.T.conjugate())\n                                   ,0)), \"other coeff mx is not Hermitian!\"\n    \n                otherParams = _np.empty((bsO-1,bsO-1),'d')\n    \n                if self.cptp: #otherParams mx stores Cholesky decomp\n    \n                    #push any slightly negative evals of otherC positive so that\n                    # the Cholesky decomp will work.\n                    evals,U = _np.linalg.eig(otherC)\n                    Ui = _np.linalg.inv(U)\n    \n                    assert(truncate or all([ev >= -1e-12 for ev in evals])), \\\n                        \"gateMatrix must be CPTP (truncate == False)!\"\n    \n                    pos_evals = evals.clip(1e-16,1e100)\n                    otherC = _np.dot(U,_np.dot(_np.diag(pos_evals),Ui))\n                    try:\n                        Lmx = _np.linalg.cholesky(otherC)\n                    except _np.linalg.LinAlgError: #Lmx not postitive definite?\n                        pos_evals = evals.clip(1e-12,1e100) #try again with 1e-12\n                        otherC = _np.dot(U,_np.dot(_np.diag(pos_evals),Ui))\n                        Lmx = _np.linalg.cholesky(otherC)\n    \n                    for i in range(bsO-1):\n                        assert(_np.linalg.norm(_np.imag(Lmx[i,i])) < IMAG_TOL)\n                        otherParams[i,i] = Lmx[i,i].real\n                        for j in range(i):\n                            otherParams[i,j] = Lmx[i,j].real\n                            otherParams[j,i] = Lmx[i,j].imag\n    \n                else: #otherParams mx stores otherC (hermitian) directly\n                    for i in range(bsO-1):\n                        assert(_np.linalg.norm(_np.imag(otherC[i,i])) < IMAG_TOL)\n                        otherParams[i,i] = otherC[i,i].real\n                        for j in range(i):\n                            otherParams[i,j] = otherC[i,j].real\n                            otherParams[j,i] = otherC[i,j].imag\n        else:\n            bsO = 0\n            otherParams = _np.empty(0,'d')\n\n        self.paramvals = _np.concatenate( (hamParams, otherParams.flat) )\n        self.ham_basis_size = bsH\n        self.other_basis_size = bsO\n\n        #True unless bsH,bsO are zero\n        #if nonham_diagonal_only:\n        #    assert(self.paramvals.shape == ((bsH-1)+(bsO-1),))\n        #else:\n        #    assert(self.paramvals.shape == ((bsH-1)+(bsO-1)**2,))\n\n\n    def _construct_matrix(self):\n        \"\"\"\n        Build the internal gate matrix using the current parameters.\n        \"\"\"\n        d2 = self.dim\n        bsH = self.ham_basis_size\n        bsO = self.other_basis_size\n\n        # self.paramvals = [hamCoeffs] + [otherParams]\n        #  where hamCoeffs are *real* and of length d2-1 (self.dim == d2)\n        if bsH > 0:\n            hamCoeffs = self.paramvals[0:bsH-1]\n            nHam = bsH-1\n        else:\n            nHam = 0\n\n        #built up otherCoeffs based on self.cptp and self.nonham_diagonal_only\n        if bsO > 0:\n            if self.nonham_diagonal_only:\n                otherParams = self.paramvals[nHam:]\n                assert(otherParams.shape == (bsO-1,))\n    \n                if self.cptp:\n                    otherCoeffs = otherParams**2 #Analagous to L*L_dagger\n                else:\n                    otherCoeffs = otherParams\n            else:\n                otherParams = self.paramvals[nHam:].reshape((bsO-1,bsO-1))\n    \n                if self.cptp:\n                    #  otherParams is an array of length (bs-1)*(bs-1) that\n                    #  encodes a lower-triangular matrix \"Lmx\" via:\n                    #  Lmx[i,i] = otherParams[i,i]\n                    #  Lmx[i,j] = otherParams[i,j] + 1j*otherParams[j,i] (i > j)\n                    for i in range(bsO-1):\n                        self.Lmx[i,i] = otherParams[i,i]\n                        for j in range(i):\n                            self.Lmx[i,j] = otherParams[i,j] + 1j*otherParams[j,i]\n            \n                    #The matrix of (complex) \"other\"-coefficients is build by\n                    # assuming Lmx is its Cholesky decomp; means otherCoeffs\n                    # is pos-def.\n\n                    # NOTE that the Cholesky decomp with all positive real diagonal\n                    # elements is *unique* for a given positive-definite otherCoeffs\n                    # matrix, but we don't care about this uniqueness criteria and so\n                    # the diagonal els of Lmx can be negative and that's fine -\n                    # otherCoeffs will still be posdef.\n                    otherCoeffs = _np.dot(self.Lmx,self.Lmx.T.conjugate())\n    \n                    #DEBUG - test for pos-def\n                    #evals = _np.linalg.eigvalsh(otherCoeffs)\n                    #DEBUG_TOL = 1e-16; #print(\"EVALS DEBUG = \",evals)\n                    #assert(all([ev >= -DEBUG_TOL for ev in evals]))\n    \n                else:\n                    #otherParams holds otherCoeff real and imaginary parts directly\n                    otherCoeffs = _np.empty((bsO-1,bsO-1),'complex')\n                    for i in range(bsO-1):\n                        otherCoeffs[i,i] = otherParams[i,i]\n                        for j in range(i):\n                            otherCoeffs[i,j] = otherParams[i,j] +1j*otherParams[j,i]\n                            otherCoeffs[j,i] = otherParams[i,j] -1j*otherParams[j,i]\n\n        #Finally, build gate matrix from generators and coefficients:\n        if bsH > 0:\n            lnd_error_gen = _np.einsum('i,ijk', hamCoeffs, self.hamGens)\n        else:\n            lnd_error_gen = _np.zeros( (d2,d2), 'complex')\n\n        if bsO > 0:\n            if self.nonham_diagonal_only:\n                lnd_error_gen += _np.einsum('i,ikl', otherCoeffs, self.otherGens)\n            else:\n                lnd_error_gen += _np.einsum('ij,ijkl', otherCoeffs, self.otherGens)\n\n        lnd_error_gen = _np.dot(self.leftTrans, _np.dot(\n                lnd_error_gen, self.rightTrans)) #basis chg\n        self.err_gen = lnd_error_gen\n        self.exp_err_gen = _spl.expm(lnd_error_gen)\n        matrix = _np.dot(self.exp_err_gen, self.unitary_postfactor)\n\n        #gen0_pp = hamGens_pp[0] #_np.dot(self.leftTrans, _np.dot(self.hamGens[0], self.rightTrans))\n        #print(\"DEBUG CONSTRUCTED BASE:\\n\", self.unitary_postfactor)\n        #print(\"DEBUG CONSTRUCTED GEN:\\n\", lnd_error_gen)\n        #print(\"DEBUG CONSTRUCTED EXP(GEN):\\n\", _spl.expm(lnd_error_gen))\n        #print(\"DEBUG CONSTRUCTED EXP(GEN)*GEN0:\\n\", _np.dot(_spl.expm(lnd_error_gen), gen0_pp))\n        #print(\"DEBUG CONSTRUCTED MX:\\n\", matrix)\n        #print(\"DEBUG GEN0:\\n\", gen0_pp)\n\n        assert(_np.linalg.norm(matrix.imag) < IMAG_TOL)\n        assert(matrix.shape == (self.dim,self.dim))\n\n        self.base = matrix.real\n        self.base.flags.writeable = False\n        self.base_deriv = None\n        self.base_hessian = None        \n\n        ##TEST FOR CP: DEBUG!!!\n        #from ..tools import jamiolkowski as _jt\n        #evals = _np.linalg.eigvals(_jt.jamiolkowski_iso(matrix,\"pp\"))\n        #if min(evals) < -1e-6:\n        #    print(\"Choi eigenvalues of final mx = \",sorted(evals))\n        #    print(\"Choi eigenvalues of final mx (pp Choi) = \",sorted(_np.linalg.eigvals(_jt.jamiolkowski_iso(matrix,\"pp\",\"pp\"))))\n        #    print(\"Choi evals of exp(gen) = \", sorted(_np.linalg.eigvals(_jt.jamiolkowski_iso(self.exp_err_gen,\"pp\"))))\n        #\n        #    ham_error_gen = _np.einsum('i,ijk', hamCoeffs, self.hamGens)\n        #    other_error_gen = _np.einsum('ij,ijkl', otherCoeffs, self.otherGens)\n        #    ham_error_gen = _np.dot(self.leftTrans, _np.dot(ham_error_gen, self.rightTrans))\n        #    other_error_gen = _np.dot(self.leftTrans, _np.dot(other_error_gen, self.rightTrans))\n        #    ham_exp_err_gen = _spl.expm(ham_error_gen)\n        #    other_exp_err_gen = _spl.expm(other_error_gen)\n        #    print(\"Choi evals of exp(hamgen) = \", sorted(_np.linalg.eigvals(_jt.jamiolkowski_iso(ham_exp_err_gen,\"pp\"))))\n        #    print(\"Choi evals of exp(othergen) = \", sorted(_np.linalg.eigvals(_jt.jamiolkowski_iso(other_exp_err_gen,\"pp\"))))\n        #    print(\"Evals of otherCoeffs = \",sorted(_np.linalg.eigvals(otherCoeffs)))\n        #    assert(False)\n        \n\n    def set_value(self, M):\n        \"\"\"\n        Attempts to modify gate parameters so that the specified raw\n        gate matrix becomes mx.  Will raise ValueError if this operation\n        is not possible.\n\n        Parameters\n        ----------\n        M : numpy array or Gate\n            A numpy array of shape (dim, dim) or Gate representing the gate action.\n\n        Returns\n        -------\n        None\n        \"\"\"\n        mx = GateMatrix.convert_to_matrix(M)\n        if(mx.shape != (self.dim, self.dim)):\n            raise ValueError(\"Argument must be a (%d,%d) matrix!\" % (self.dim,self.dim))\n        raise ValueError(\"Currently, directly setting the matrix of an\" +\n                         \" LindbladParameterizedGate object is not\" +\n                         \" supported.  Please use the from_vector method.\")\n\n    def num_params(self):\n        \"\"\"\n        Get the number of independent parameters which specify this gate.\n\n        Returns\n        -------\n        int\n           the number of independent parameters.\n        \"\"\"\n        return len(self.paramvals)\n\n\n    def to_vector(self):\n        \"\"\"\n        Extract a vector of the underlying gate parameters from this gate.\n\n        Returns\n        -------\n        numpy array\n            a 1D numpy array with length == num_params().\n        \"\"\"\n        return self.paramvals\n\n\n    def from_vector(self, v):\n        \"\"\"\n        Initialize the gate using a vector of its parameters.\n\n        Parameters\n        ----------\n        v : numpy array\n            The 1D vector of gate parameters.  Length\n            must == num_params().\n\n        Returns\n        -------\n        None\n        \"\"\"\n        assert(len(v) == self.num_params())\n        self.paramvals = v\n        self._construct_matrix()\n        self.dirty = True\n\n\n    def old_deriv_wrt_params(self, wrtFilter=None):\n        \"\"\"\n        Construct a matrix whose columns are the vectorized\n        derivatives of the flattened gate matrix with respect to a\n        single gate parameter.  Thus, each column is of length\n        gate_dim^2 and there is one column per gate parameter.\n\n        Returns\n        -------\n        numpy array\n            Array of derivatives, shape == (dimension^2, num_params)\n        \"\"\"\n        if self.base_deriv is None:\n            d2 = self.dim\n            bsH = self.ham_basis_size\n            bsO = self.other_basis_size\n            TERM_TOL = 1e-12\n    \n            #Deriv wrt hamiltonian params\n            if bsH > 0:\n                dHdp = _np.einsum(\"ik,akl,lj->ija\", self.leftTrans, self.hamGens, self.rightTrans)\n                series = last_commutant = term = dHdp; i=2\n                while _np.amax(_np.abs(term)) > TERM_TOL: #_np.linalg.norm(term)\n                    commutant = _np.einsum(\"ik,kja->ija\",self.err_gen,last_commutant) - \\\n                        _np.einsum(\"ika,kj->ija\",last_commutant,self.err_gen)\n                    term = 1/_np.math.factorial(i) * commutant\n                    series += term #1/_np.math.factorial(i) * commutant\n                    last_commutant = commutant; i += 1\n                dH = _np.einsum('ika,kl,lj->ija', series, self.exp_err_gen, self.unitary_postfactor)\n                dH = dH.reshape((d2**2,bsH-1)) # [iFlattenedGate,iHamParam]\n                nHam = bsH-1\n            else:\n                dH = _np.empty( (d2**2,0), 'd') #so concat works below\n                nHam = 0\n    \n            #Deriv wrt other params\n            if bsO > 0:\n                if self.nonham_diagonal_only:\n                    otherParams = self.paramvals[nHam:]\n                \n                    # Derivative of exponent wrt other param; shape == [d2,d2,bs-1]\n                    if self.cptp:\n                        dOdp  = _np.einsum('alj,a->lja', self.otherGens, 2*otherParams)\n                    else:\n                        dOdp  = _np.einsum('alj->lja', self.otherGens)\n                \n                    #apply basis transform\n                    dOdp  = _np.einsum('lk,kna,nj->lja', self.leftTrans, dOdp, self.rightTrans)\n                    assert(_np.linalg.norm(_np.imag(dOdp)) < IMAG_TOL)\n                \n                    #take d(matrix-exp) using series approximation\n                    series = last_commutant = term = dOdp; i=2\n                    while _np.amax(_np.abs(term)) > TERM_TOL: #_np.linalg.norm(term)\n                        commutant = _np.einsum(\"ik,kja->ija\",self.err_gen,last_commutant) - \\\n                            _np.einsum(\"ika,kj->ija\",last_commutant,self.err_gen)\n                        term = 1/_np.math.factorial(i) * commutant\n                        series += term #1/_np.math.factorial(i) * commutant\n                        last_commutant = commutant; i += 1\n                    dO = _np.einsum('ika,kl,lj->ija', series, self.exp_err_gen, self.unitary_postfactor) # dim [d2,d2,bs-1]\n                    dO = dO.reshape(d2**2,bsO-1)\n                \n                \n                else: #all lindblad terms included                \n                    if self.cptp:\n                        L,Lbar = self.Lmx,self.Lmx.conjugate()\n                        F1 = _np.tril(_np.ones((bsO-1,bsO-1),'d'))\n                        F2 = _np.triu(_np.ones((bsO-1,bsO-1),'d'),1) * 1j\n                \n                          # Derivative of exponent wrt other param; shape == [d2,d2,bs-1,bs-1]\n                        dOdp  = _np.einsum('amlj,mb,ab->ljab', self.otherGens, Lbar, F1) #only a >= b nonzero (F1)\n                        dOdp += _np.einsum('malj,mb,ab->ljab', self.otherGens, L, F1)    # ditto\n                        dOdp += _np.einsum('bmlj,ma,ab->ljab', self.otherGens, Lbar, F2) #only b > a nonzero (F2)\n                        dOdp += _np.einsum('mblj,ma,ab->ljab', self.otherGens, L, F2.conjugate()) # ditto\n                    else:\n                        F0 = _np.identity(bsO-1,'d')\n                        F1 = _np.tril(_np.ones((bsO-1,bsO-1),'d'),-1)\n                        F2 = _np.triu(_np.ones((bsO-1,bsO-1),'d'),1) * 1j\n                \n                          # Derivative of exponent wrt other param; shape == [d2,d2,bs-1,bs-1]\n                        dOdp  = _np.einsum('ablj,ab->ljab', self.otherGens, F0)  # a == b case\n                        dOdp += _np.einsum('ablj,ab->ljab', self.otherGens, F1) + \\\n                                _np.einsum('balj,ab->ljab', self.otherGens, F1) # a > b (F1)\n                        dOdp += _np.einsum('balj,ab->ljab', self.otherGens, F2) - \\\n                                _np.einsum('ablj,ab->ljab', self.otherGens, F2) # a < b (F2)\n                \n                    #apply basis transform\n                    dOdp  = _np.einsum('lk,knab,nj->ljab', self.leftTrans, dOdp, self.rightTrans)\n                    assert(_np.linalg.norm(_np.imag(dOdp)) < IMAG_TOL)\n                \n                    #take d(matrix-exp) using series approximation\n                    series = last_commutant = term = dOdp; i=2\n                    while _np.amax(_np.abs(term)) > TERM_TOL: #_np.linalg.norm(term)\n                        commutant = _np.einsum(\"ik,kjab->ijab\",self.err_gen,last_commutant) - \\\n                            _np.einsum(\"ikab,kj->ijab\",last_commutant,self.err_gen)\n                        term = 1/_np.math.factorial(i) * commutant\n                        series += term #1/_np.math.factorial(i) * commutant\n                        last_commutant = commutant; i += 1\n                    dO = _np.einsum('ikab,kl,lj->ijab', series, self.exp_err_gen, self.unitary_postfactor) # dim [d2,d2,bs-1,bs-1]\n                    dO = dO.reshape(d2**2,(bsO-1)**2)\n            else:\n                dO = _np.empty( (d2**2,0), 'd') #so concat works below\n            \n            derivMx = _np.concatenate((dH,dO), axis=1)\n            assert(_np.linalg.norm(_np.imag(derivMx)) < IMAG_TOL)\n            derivMx = _np.real(derivMx)\n            self.base_deriv = derivMx\n\n            #check_deriv_wrt_params(self, derivMx, eps=1e-7)\n            #fd_deriv = finite_difference_deriv_wrt_params(self, eps=1e-7)\n            #derivMx = fd_deriv\n\n        if wrtFilter is None:\n            return self.base_deriv\n        else:\n            return _np.take( self.base_deriv, wrtFilter, axis=1 )\n\n    def _dHdp(self):\n        return _np.einsum(\"ik,akl,lj->ija\", self.leftTrans, self.hamGens, self.rightTrans)\n\n    def _dOdp(self):\n        bsH = self.ham_basis_size\n        bsO = self.other_basis_size\n        nHam = bsH-1 if (bsH > 0) else 0\n        \n        assert(bsO > 0),\"Cannot construct dOdp when other_basis_size == 0!\"\n        if self.nonham_diagonal_only:\n            otherParams = self.paramvals[nHam:]\n            \n            # Derivative of exponent wrt other param; shape == [d2,d2,bs-1]\n            if self.cptp:\n                dOdp  = _np.einsum('alj,a->lja', self.otherGens, 2*otherParams)\n            else:\n                dOdp  = _np.einsum('alj->lja', self.otherGens)\n                            \n        else: #all lindblad terms included                \n            if self.cptp:\n                L,Lbar = self.Lmx,self.Lmx.conjugate()\n                F1 = _np.tril(_np.ones((bsO-1,bsO-1),'d'))\n                F2 = _np.triu(_np.ones((bsO-1,bsO-1),'d'),1) * 1j\n                \n                  # Derivative of exponent wrt other param; shape == [d2,d2,bs-1,bs-1]\n                dOdp  = _np.einsum('amlj,mb,ab->ljab', self.otherGens, Lbar, F1) #only a >= b nonzero (F1)\n                dOdp += _np.einsum('malj,mb,ab->ljab', self.otherGens, L, F1)    # ditto\n                dOdp += _np.einsum('bmlj,ma,ab->ljab', self.otherGens, Lbar, F2) #only b > a nonzero (F2)\n                dOdp += _np.einsum('mblj,ma,ab->ljab', self.otherGens, L, F2.conjugate()) # ditto\n            else:\n                F0 = _np.identity(bsO-1,'d')\n                F1 = _np.tril(_np.ones((bsO-1,bsO-1),'d'),-1)\n                F2 = _np.triu(_np.ones((bsO-1,bsO-1),'d'),1) * 1j\n            \n                # Derivative of exponent wrt other param; shape == [d2,d2,bs-1,bs-1]\n                dOdp  = _np.einsum('ablj,ab->ljab', self.otherGens, F0)  # a == b case\n                dOdp += _np.einsum('ablj,ab->ljab', self.otherGens, F1) + \\\n                        _np.einsum('balj,ab->ljab', self.otherGens, F1) # a > b (F1)\n                dOdp += _np.einsum('balj,ab->ljab', self.otherGens, F2) - \\\n                        _np.einsum('ablj,ab->ljab', self.otherGens, F2) # a < b (F2)\n\n        # apply basis transform\n        tr = len(dOdp.shape) #tensor rank\n        assert( (tr-2) in (1,2)), \"Currently, dodp can only have 1 or 2 derivative dimensions\"\n\n        if tr == 3:\n            dOdp  = _np.einsum('lk,kna,nj->lja', self.leftTrans, dOdp, self.rightTrans)\n        elif tr == 4:\n            dOdp  = _np.einsum('lk,knab,nj->ljab', self.leftTrans, dOdp, self.rightTrans)\n        assert(_np.linalg.norm(_np.imag(dOdp)) < IMAG_TOL)\n        return dOdp\n\n\n    def _d2Odp2(self):\n        bsH = self.ham_basis_size\n        bsO = self.other_basis_size\n        nHam = bsH-1 if (bsH > 0) else 0\n        d2 = self.dim\n        \n        assert(bsO > 0),\"Cannot construct dOdp when other_basis_size == 0!\"\n        if self.nonham_diagonal_only:\n            otherParams = self.paramvals[nHam:]\n            nP = len(otherParams); assert(nP == bsO-1)\n            \n            # Derivative of exponent wrt other param; shape == [d2,d2,bs-1]\n            if self.cptp:\n                d2Odp2  = _np.einsum('alj,aq->ljaq', self.otherGens, 2*_np.identity(nP,'d'))\n            else:\n                d2Odp2  = _np.zeros([d2,d2,nP,nP],'d')\n                            \n        else: #all lindblad terms included                \n            if self.cptp:\n                nP = bsO-1\n                d2Odp2  = _np.zeros([d2,d2,nP,nP,nP,nP],'complex') #yikes! maybe make this SPARSE in future?\n                \n                #Note: correspondence w/Erik's notes: a=alpha, b=beta, q=gamma, r=delta\n                # indices of d2Odp2 are [i,j,a,b,q,r]\n                \n                def iter_base_ab_qr(ab_inc_eq, qr_inc_eq):\n                    \"\"\" Generates (base,ab,qr) tuples such that `base` runs over\n                        all possible 'other' params and 'ab' and 'qr' run over\n                        parameter indices s.t. ab > base and qr > base.  If\n                        ab_inc_eq == True then the > becomes a >=, and likewise\n                        for qr_inc_eq.  Used for looping over nonzero hessian els. \"\"\"\n                    for _base in range(nP):\n                        start_ab = _base if ab_inc_eq else _base+1\n                        start_qr = _base if qr_inc_eq else _base+1\n                        for _ab in range(start_ab,nP):\n                            for _qr in range(start_qr,nP):\n                                yield (_base,_ab,_qr)\n                                \n                for base,a,q in iter_base_ab_qr(True,True): # Case1: base=b=r, ab=a, qr=q\n                    d2Odp2[:,:,a,base,q,base] = self.otherGens[a,q] + self.otherGens[q,a]\n                for base,a,r in iter_base_ab_qr(True,False): # Case2: base=b=q, ab=a, qr=r\n                    d2Odp2[:,:,a,base,base,r] = -1j*self.otherGens[a,r] + 1j*self.otherGens[r,a]\n                for base,b,q in iter_base_ab_qr(False,True): # Case3: base=a=r, ab=b, qr=q\n                    d2Odp2[:,:,base,b,q,base] = 1j*self.otherGens[b,q] - 1j*self.otherGens[q,b]\n                for base,b,r in iter_base_ab_qr(False,False): # Case4: base=a=q, ab=b, qr=r\n                    d2Odp2[:,:,base,b,base,r] = self.otherGens[b,r] + self.otherGens[r,b]\n                \n            else:\n                d2Odp2  = _np.zeros([d2,d2,nP,nP,nP,nP],'d') #all params linear\n\n        # apply basis transform\n        tr = len(d2Odp2.shape) #tensor rank\n        assert( (tr-2) in (2,4)), \"Currently, d2Odp2 can only have 2 or 4 derivative dimensions\"\n\n        if tr == 4:\n            d2Odp2  = _np.einsum('lk,knaq,nj->ljaq', self.leftTrans, d2Odp2, self.rightTrans)\n        elif tr == 6:\n            d2Odp2  = _np.einsum('lk,knabqr,nj->ljabqr', self.leftTrans, d2Odp2, self.rightTrans)\n        assert(_np.linalg.norm(_np.imag(d2Odp2)) < IMAG_TOL)\n        return d2Odp2\n\n\n    def deriv_wrt_params(self, wrtFilter=None):\n        \"\"\"\n        Construct a matrix whose columns are the vectorized\n        derivatives of the flattened gate matrix with respect to a\n        single gate parameter.  Thus, each column is of length\n        gate_dim^2 and there is one column per gate parameter.\n\n        Returns\n        -------\n        numpy array\n            Array of derivatives, shape == (dimension^2, num_params)\n        \"\"\"\n        if self.base_deriv is None:\n            d2 = self.dim\n            bsH = self.ham_basis_size\n            bsO = self.other_basis_size\n    \n            #Deriv wrt hamiltonian params\n            if bsH > 0:\n                dexpL = _dexpX(self.err_gen, self._dHdp(), self.exp_err_gen,\n                               self.unitary_postfactor)\n                dH = dexpL.reshape((d2**2,bsH-1)) # [iFlattenedGate,iHamParam]\n            else:\n                dH = _np.empty( (d2**2,0), 'd') #so concat works below\n    \n            #Deriv wrt other params\n            if bsO > 0:\n                dexpL = _dexpX(self.err_gen, self._dOdp(), self.exp_err_gen,\n                               self.unitary_postfactor)\n\n                #Reshape so index as [iFlattenedGate,iOtherParam]\n                # 2nd dim will be bsO-1 or (bsO-1)**2 depending on tensor rank\n                dO = dexpL.reshape((d2**2,-1)) \n            else:\n                dO = _np.empty( (d2**2,0), 'd') #so concat works below\n            \n            derivMx = _np.concatenate((dH,dO), axis=1)\n            assert(_np.linalg.norm(_np.imag(derivMx)) < IMAG_TOL)\n            derivMx = _np.real(derivMx)\n            self.base_deriv = derivMx\n\n            #check_deriv_wrt_params(self, derivMx, eps=1e-7)\n            #fd_deriv = finite_difference_deriv_wrt_params(self, eps=1e-7)\n            #derivMx = fd_deriv\n\n        if wrtFilter is None:\n            return self.base_deriv\n        else:\n            return _np.take( self.base_deriv, wrtFilter, axis=1 )\n\n    def has_nonzero_hessian(self):\n        \"\"\" \n        Returns whether this gate has a non-zero Hessian with\n        respect to its parameters, i.e. whether it only depends\n        linearly on its parameters or not.\n\n        Returns\n        -------\n        bool\n        \"\"\"\n        return True\n\n    def hessian_wrt_params(self, wrtFilter1=None, wrtFilter2=None):\n        \"\"\"\n        Construct the Hessian of this gate with respect to it's parameters.\n\n        This function returns a tensor whose first axis corresponds to the\n        flattened gate matrix and whose 2nd and 3rd axes correspond to the\n        parameters that are differentiated with respect to.\n\n        Parameters\n        ----------\n        wrtFilter1, wrtFilter2 : list\n            Lists of indices of the paramters to take first and second\n            derivatives with respect to.  If None, then derivatives are\n            taken with respect to all of the gate's parameters.\n\n        Returns\n        -------\n        numpy array\n            Hessian with shape (dimension^2, num_params1, num_params2)\n        \"\"\"\n        if self.base_hessian is None:\n            d2 = self.dim\n            bsH = self.ham_basis_size\n            bsO = self.other_basis_size\n            nHam = bsH-1 if (bsH > 0) else 0\n\n            #Split hessian in 4 pieces:   d2H  |  dHdO\n            #                             dHdO |  d2O\n            # But only d2O is non-zero - and only when cptp == True\n\n            nTotParams = self.num_params()\n            hessianMx = _np.zeros( (d2**2, nTotParams, nTotParams), 'd' )\n    \n            #Deriv wrt other params\n            if bsO > 0: #if there are any \"other\" params\n                dOdp = self._dOdp()\n                d2Odp2 = self._d2Odp2()\n                tr = len(dOdp.shape)\n                    \n                series, series2 = _d2expSeries(self.err_gen, dOdp, d2Odp2)\n                term1 = series2\n                if tr == 3: #one deriv dimension \"a\"\n                    term2 = _np.einsum(\"ija,jkq->ikaq\",series,series)\n                    d2expL = _np.einsum(\"ikaq,kl,lj->ijaq\", term1+term2,\n                                      self.exp_err_gen, self.unitary_postfactor)\n                    dO = d2expL.reshape((d2**2,bsO-1,bsO-1))\n                    #d2expL.shape = (d2**2,bsO-1,bsO-1); dO = d2expL\n\n                elif tr == 4: #two deriv dimension \"ab\"\n                    term2 = _np.einsum(\"ijab,jkqr->ikabqr\",series,series)\n                    d2expL = _np.einsum(\"ikabqr,kl,lj->ijabqr\", term1+term2,\n                                      self.exp_err_gen, self.unitary_postfactor)\n                    dO = d2expL.reshape((d2**2, (bsO-1)**2, (bsO-1)**2 ))\n                    #d2expL.shape = (d2**2, (bsO-1)**2, (bsO-1)**2); dO = d2expL\n\n                #dO has been reshape so index as [iFlattenedGate,iDeriv1,iDeriv2]\n                assert(_np.linalg.norm(_np.imag(dO)) < IMAG_TOL)\n                hessianMx[:,nHam:,nHam:] = _np.real(dO) # d2O block of hessian\n\n            self.base_hessian = hessianMx\n\n            #TODO: check hessian with finite difference here?\n            \n        if wrtFilter1 is None:\n            if wrtFilter2 is None:\n                return self.base_hessian\n            else:\n                return _np.take(self.base_hessian, wrtFilter2, axis=2 )\n        else:\n            if wrtFilter2 is None:\n                return _np.take(self.base_hessian, wrtFilter1, axis=1 )\n            else:\n                return _np.take( _np.take(self.base_hessian, wrtFilter1, axis=1),\n                                 wrtFilter2, axis=2 )\n\n\n    def copy(self, parent=None):\n        \"\"\"\n        Copy this gate.\n\n        Returns\n        -------\n        Gate\n            A copy of this gate.\n        \"\"\"\n        #Construct new gate with dummy identity mx\n        newGate = LindbladParameterizedGate(None,self.unitary_postfactor.copy(),\n                                            _copy.deepcopy(self.ham_basis),\n                                            _copy.deepcopy(self.other_basis),\n                                            self.cptp,self.nonham_diagonal_only,\n                                            True, _copy.deepcopy(self.matrix_basis))\n        \n        #Deep copy data\n        newGate.paramvals = self.paramvals.copy()\n        newGate._construct_matrix()\n\n        return self._copy_gpindices( newGate, parent )\n\n\n    def transform(self, S):\n        \"\"\"\n        Update gate matrix G with inv(S) * G * S,\n\n        Generally, the transform function updates the *parameters* of \n        the gate such that the resulting gate matrix is altered as \n        described above.  If such an update cannot be done (because\n        the gate parameters do not allow for it), ValueError is raised.\n\n        Parameters\n        ----------\n        S : GaugeGroupElement\n            A gauge group element which specifies the \"S\" matrix \n            (and it's inverse) used in the above similarity transform.\n        \"\"\"\n        if isinstance(S, _gaugegroup.UnitaryGaugeGroupElement) or \\\n           isinstance(S, _gaugegroup.TPSpamGaugeGroupElement):\n            U = S.get_transform_matrix()\n            Uinv = S.get_transform_matrix_inverse()\n\n            #just conjugate postfactor and Lindbladian exponent by U:\n            self.unitary_postfactor = _np.dot(Uinv,_np.dot(self.unitary_postfactor, U))\n            self.err_gen = _np.dot(Uinv,_np.dot(self.err_gen, U))\n            self._set_params_from_errgen(self.err_gen, truncate=True)\n            self._construct_matrix()\n            self.dirty = True\n            #Note: truncate=True above because some unitary transforms seem to\n            ## modify eigenvalues to be negative beyond the tolerances\n            ## checked when truncate == False.  I'm not sure why this occurs,\n            ## since a true unitary should map CPTP -> CPTP...\n\n            #CHECK WITH OLD (passes) TODO move to unit tests?\n            #tMx = _np.dot(Uinv,_np.dot(self.base, U)) #Move above for checking\n            #tGate = LindbladParameterizedGate(tMx,self.unitary_postfactor,\n            #                                self.ham_basis, self.other_basis,\n            #                                self.cptp,self.nonham_diagonal_only,\n            #                                True, self.matrix_basis)\n            #assert(_np.linalg.norm(tGate.paramvals - self.paramvals) < 1e-6)\n        else:\n            raise ValueError(\"Invalid transform for this LindbladParameterizedGate: type %s\"\n                             % str(type(S)))\n\n\n    def depolarize(self, amount):\n        \"\"\"\n        Depolarize this gate by the given `amount`.\n\n        Generally, the depolarize function updates the *parameters* of \n        the gate such that the resulting gate matrix is depolarized.  If\n        such an update cannot be done (because the gate parameters do not\n        allow for it), ValueError is raised.\n\n        Parameters\n        ----------\n        amount : float or tuple\n            The amount to depolarize by.  If a tuple, it must have length\n            equal to one less than the dimension of the gate. In standard\n            bases, depolarization corresponds to multiplying the gate matrix\n            by a diagonal matrix whose first diagonal element (corresponding\n            to the identity) equals 1.0 and whose subsequent elements \n            (corresponding to non-identity basis elements) equal\n            `1.0 - amount[i]` (or just `1.0 - amount` if `amount` is a\n            float).\n\n        Returns\n        -------\n        None\n        \"\"\"\n        if isinstance(amount,float):\n            D = _np.diag( [1]+[1-amount]*(self.dim-1) )\n        else:\n            assert(len(amount) == self.dim-1)\n            D = _np.diag( [1]+list(1.0 - _np.array(amount,'d')) )\n        mx = _np.dot(D,self.base)\n        tGate = LindbladParameterizedGate(mx,self.unitary_postfactor,\n                                          self.ham_basis, self.other_basis,\n                                          self.cptp,self.nonham_diagonal_only,\n                                          True, self.matrix_basis)\n\n        #Note: truncate=True to be safe\n        self.paramvals[:] = tGate.paramvals[:]\n        self._construct_matrix()\n        self.dirty = True\n\n\n    def rotate(self, amount, mxBasis=\"gm\"):\n        \"\"\"\n        Rotate this gate by the given `amount`.\n\n        Generally, the rotate function updates the *parameters* of \n        the gate such that the resulting gate matrix is rotated.  If\n        such an update cannot be done (because the gate parameters do not\n        allow for it), ValueError is raised.\n\n        Parameters\n        ----------\n        amount : tuple of floats, optional\n            Specifies the rotation \"coefficients\" along each of the non-identity\n            Pauli-product axes.  The gate's matrix `G` is composed with a\n            rotation operation `R`  (so `G` -> `dot(R, G)` ) where `R` is the\n            unitary superoperator corresponding to the unitary operator \n            `U = exp( sum_k( i * rotate[k] / 2.0 * Pauli_k ) )`.  Here `Pauli_k`\n            ranges over all of the non-identity un-normalized Pauli operators.\n\n        mxBasis : {'std', 'gm', 'pp', 'qt'} or Basis object\n            The source and destination basis, respectively.  Allowed\n            values are Matrix-unit (std), Gell-Mann (gm), Pauli-product (pp),\n            and Qutrit (qt) (or a custom basis object).\n\n        Returns\n        -------\n        None\n        \"\"\"\n        rotnMx = _gt.rotation_gate_mx(amount,mxBasis)\n        mx = _np.dot(rotnMx,self.base)\n        tGate = LindbladParameterizedGate(mx,self.unitary_postfactor,\n                                          self.ham_basis, self.other_basis,\n                                          self.cptp,self.nonham_diagonal_only,\n                                          True, self.matrix_basis)\n        #Note: truncate=True to be safe\n        self.paramvals[:] = tGate.paramvals[:]\n        self._construct_matrix()\n        self.dirty = True\n\n\n    def compose(self, otherGate):\n        \"\"\"\n        Create and return a new gate that is the composition of this gate\n        followed by otherGate, which *must be another \n        LindbladParameterizedGate*.  (For more general compositions between\n        different types of gates, use the module-level compose function.)  The\n        returned gate's matrix is equal to dot(this, otherGate).\n\n        Parameters\n        ----------\n        otherGate : LindbladParameterizedGate\n            The gate to compose to the right of this one.\n\n        Returns\n        -------\n        LindbladParameterizedGate\n        \"\"\"\n        assert( isinstance(otherGate, LindbladParameterizedGate) )\n        assert(self.cptp == otherGate.cptp)\n        assert(self.nonham_diagonal_only == otherGate.nonham_diagonal_only)\n\n        composed_mx = _np.dot(self, otherGate)\n        return LindbladParameterizedGate(composed_mx,self.unitary_postfactor,\n                                          self.ham_basis, self.other_basis,\n                                          self.cptp,self.nonham_diagonal_only,\n                                          True, self.matrix_basis)\n\n\n    def __str__(self):\n        s = \"Lindblad Parameterized gate with shape %s, num params = %d\\n\" % \\\n            (str(self.base.shape), self.num_params())\n        s += _mt.mx_to_string(self.base, width=5, prec=1)\n        return s\n\n\n\ndef _dexpSeries(X, dX):\n    TERM_TOL = 1e-12\n    tr = len(dX.shape) #tensor rank of dX; tr-2 == # of derivative dimensions\n    assert( (tr-2) in (1,2)), \"Currently, dX can only have 1 or 2 derivative dimensions\"\n    series = last_commutant = term = dX; i=2\n\n    #take d(matrix-exp) using series approximation\n    while _np.amax(_np.abs(term)) > TERM_TOL: #_np.linalg.norm(term)\n        if tr == 3:\n            commutant = _np.einsum(\"ik,kja->ija\",X,last_commutant) - \\\n                        _np.einsum(\"ika,kj->ija\",last_commutant,X)\n        elif tr == 4:\n            commutant = _np.einsum(\"ik,kjab->ijab\",X,last_commutant) - \\\n                    _np.einsum(\"ikab,kj->ijab\",last_commutant,X)\n        term = 1/_np.math.factorial(i) * commutant\n        series += term #1/_np.math.factorial(i) * commutant\n        last_commutant = commutant; i += 1\n    return series\n\ndef _d2expSeries(X, dX, d2X):\n    TERM_TOL = 1e-12\n    tr = len(dX.shape) #tensor rank of dX; tr-2 == # of derivative dimensions\n    tr2 = len(d2X.shape) #tensor rank of dX; tr-2 == # of derivative dimensions\n    assert( (tr-2,tr2-2) in [(1,2),(2,4)]), \"Current support for only 1 or 2 derivative dimensions\"\n    \n    series = term = last_commutant = dX\n    series2 = last_commutant2 = term2 = d2X\n    i=2\n\n    #take d(matrix-exp) using series approximation\n    while _np.amax(_np.abs(term)) > TERM_TOL or _np.amax(_np.abs(term2)) > TERM_TOL:\n        if tr == 3:\n            commutant = _np.einsum(\"ik,kja->ija\",X,last_commutant) - \\\n                        _np.einsum(\"ika,kj->ija\",last_commutant,X)\n            commutant2A = _np.einsum(\"ikq,kja->ijaq\",dX,last_commutant) - \\\n                    _np.einsum(\"ika,kjq->ijaq\",last_commutant,dX)\n            commutant2B = _np.einsum(\"ik,kjaq->ijaq\",X,last_commutant2) - \\\n                    _np.einsum(\"ikaq,kj->ijaq\",last_commutant,X)\n\n        elif tr == 4:\n            commutant = _np.einsum(\"ik,kjab->ijab\",X,last_commutant) - \\\n                    _np.einsum(\"ikab,kj->ijab\",last_commutant,X)\n            commutant2A = _np.einsum(\"ikqr,kjab->ijabqr\",dX,last_commutant) - \\\n                    _np.einsum(\"ikab,kjqr->ijabqr\",last_commutant,dX)\n            commutant2B = _np.einsum(\"ik,kjabqr->ijabqr\",X,last_commutant2) - \\\n                    _np.einsum(\"ikabqr,kj->ijabqr\",last_commutant2,X)\n\n        term = 1/_np.math.factorial(i) * commutant\n        term2 = 1/_np.math.factorial(i) * (commutant2A + commutant2B)\n        series += term \n        series2 += term2\n        last_commutant = commutant\n        last_commutant2 = (commutant2A + commutant2B)\n        i += 1        \n    return series, series2\n\ndef _dexpX(X,dX,expX=None,postfactor=None):\n    \"\"\" \n    Computes the derivative of the exponential of X(t) using\n    the Haddamard lemma series expansion.\n\n    Parameters\n    ----------\n    X : ndarray\n        The 2-tensor being exponentiated\n\n    dX : ndarray\n        The derivative of X; can be either a 3- or 4-tensor where the\n        3rd+ dimensions are for (multi-)indexing the parameters which\n        are differentiated w.r.t.  For example, in the simplest case\n        dX is a 3-tensor s.t. dX[i,j,p] == d(X[i,j])/dp.\n\n    expX : ndarray, optional\n        The value of `exp(X)`, which can be specified in order to save\n        a call to `scipy.linalg.expm`.  If None, then the value is\n        computed internally.\n\n    postfactor : ndarray, optional\n        A 2-tensor of the same shape as X that post-multiplies the\n        result.\n\n    Returns\n    -------\n    ndarray\n        The derivative of `exp(X)*postfactor` given as a tensor with the\n        same shape and axes as `dX`.\n    \"\"\"\n    tr = len(dX.shape) #tensor rank of dX; tr-2 == # of derivative dimensions\n    assert( (tr-2) in (1,2)), \"Currently, dX can only have 1 or 2 derivative dimensions\"\n\n    series = _dexpSeries(X,dX)\n    if expX is None: expX = _spl.expm(X)\n    \n    if tr == 3:\n        dExpX = _np.einsum('ika,kj->ija', series, expX)\n        if postfactor is not None:\n            dExpX = _np.einsum('ila,lj->ija', dExpX, postfactor)\n    elif tr == 4:\n        dExpX = _np.einsum('ikab,kj->ijab', series, expX)\n        if postfactor is not None:\n            dExpX = _np.einsum('ilab,lj->ijab', dExpX, postfactor)\n            \n    return dExpX\n\n\n\nclass TPInstrumentGate(GateMatrix):\n    \"\"\"\n    A partial implementation of :class:`Gate` which encapsulates an element of a\n    :class:`TPInstrument`.  Instances rely on their parent being a \n    `TPInstrument`.\n    \"\"\"\n\n    def __init__(self, param_gates, index):\n        \"\"\"\n        Initialize a TPInstrumentGate object.\n\n        Parameters\n        ----------\n        param_gates : list of Gate objects\n            A list of the underlying gate objects which constitute a simple\n            parameterization of a :class:`TPInstrument`.  Namely, this is\n            the list of [MT,D1,D2,...Dn] gates which parameterize *all* of the\n            `TPInstrument`'s elements.\n\n        index : int\n            The index indicating which element of the `TPInstrument` the\n            constructed object is.  Must be in the range \n            `[0,len(param_gates)-1]`.\n        \"\"\"\n        self.param_gates = param_gates\n        self.index = index\n        GateMatrix.__init__(self, _np.identity(param_gates[0].dim,'d'))\n        self._construct_matrix()\n\n        #Set our own parent and gpindices based on param_gates\n        # (this breaks the usual paradigm of having the parent object set these,\n        #  but the exception is justified b/c the parent has set these members\n        #  of the underlying 'param_gates' gates)\n        self.dependents = [0,index+1] if index < len(param_gates)-1 \\\n                          else list(range(len(param_gates)))\n          #indices into self.param_gates of the gates this gate depends on\n        self.set_gpindices(_slct.list_to_slice(\n            _np.concatenate( [ param_gates[i].gpindices_as_array()\n                               for i in self.dependents ], axis=0),True,False),\n                           param_gates[0].parent) #use parent of first param gate\n                                                  # (they should all be the same)\n\n\n    def _construct_matrix(self):\n        \"\"\"\n        Mi = Di + MT for i = 1...(n-1)\n           = -(n-2)*MT-sum(Di) = -(n-2)*MT-[(MT-Mi)-n*MT] for i == (n-1)\n        \"\"\"\n        nEls = len(self.param_gates)\n        if self.index < nEls-1:\n            self.base = _np.asarray( self.param_gates[self.index+1]\n                                     + self.param_gates[0] )\n        else:\n            assert(self.index == nEls-1), \\\n                \"Invalid index %d > %d\" % (self.index,nEls-1)\n            self.base = _np.asarray( -sum(self.param_gates)\n                                     -(nEls-3)*self.param_gates[0] )\n        \n        assert(self.base.shape == (self.dim,self.dim))\n        self.base.flags.writeable = False\n\n        \n\n    def set_value(self, M):\n        \"\"\"\n        Attempts to modify gate parameters so that the specified raw\n        gate matrix becomes mx.  Will raise ValueError if this operation\n        is not possible.\n\n        Parameters\n        ----------\n        M : array_like or Gate\n            An array of shape (dim, dim) or Gate representing the gate action.\n\n        Returns\n        -------\n        None\n        \"\"\"\n        raise ValueError(\"Cannot set the value of a TPInstrumentGate directly!\")\n\n\n    def deriv_wrt_params(self, wrtFilter=None):\n        \"\"\"\n        Construct a matrix whose columns are the vectorized\n        derivatives of the flattened gate matrix with respect to a\n        single gate parameter.  Thus, each column is of length\n        gate_dim^2 and there is one column per gate parameter. An\n        empty 2D array in the StaticGate case (num_params == 0).\n\n        Returns\n        -------\n        numpy array\n            Array of derivatives with shape (dimension^2, num_params)\n        \"\"\"\n        Np = len(self.gpindices_as_array())\n        derivMx = _np.zeros((self.dim**2,Np),'d')\n        Nels = len(self.param_gates)\n\n        off = 0\n        if self.index < Nels-1: # matrix = Di + MT = param_gates[index+1] + param_gates[0]\n            for i in [self.index+1, 0]:\n                Np = self.param_gates[i].num_params()\n                derivMx[:,off:off+Np] = self.param_gates[i].deriv_wrt_params()\n                off += Np\n\n        else: # matrix = -(nEls-1)*MT-sum(Di)\n            Np = self.param_gates[0].num_params()\n            derivMx[:,off:off+Np] = -(Nels-1)*self.param_gates[0].deriv_wrt_params()\n            off += Np\n\n            for i in range(1,Nels):\n                Np = self.param_gates[i].num_params()\n                derivMx[:,off:off+Np] = -self.param_gates[i].deriv_wrt_params()\n                off += Np\n        \n        if wrtFilter is None:\n            return derivMx\n        else:\n            return _np.take( derivMx, wrtFilter, axis=1 )\n\n        \n    def has_nonzero_hessian(self):\n        \"\"\" \n        Returns whether this gate has a non-zero Hessian with\n        respect to its parameters, i.e. whether it only depends\n        linearly on its parameters or not.\n\n        Returns\n        -------\n        bool\n        \"\"\"\n        return False\n\n\n\n    def from_vector(self, v):\n        \"\"\"\n        Initialize this partially-implemented gate using a vector of its parameters.\n\n        Parameters\n        ----------\n        v : numpy array\n            The 1D vector of parameters.\n\n        Returns\n        -------\n        None\n        \"\"\"\n        #Rely on the Instrument ordering of it's elements: if we're being called\n        # to init from v then this is within the context of a TPInstrument's gates\n        # having been compiled and now being initialized from a vector (within a\n        # calculator).  We rely on the Instrument elements having their\n        # from_vector() methods called in self.index order.\n        \n        if self.index < len(self.param_gates)-1: #final element doesn't need to init any param gates\n            for i in self.dependents: #re-init all my dependents (may be redundant)\n                if i == 0 and self.index > 0: continue # 0th param-gate already init by index==0 element\n                pgate_local_inds = _gatesetmember._decompose_gpindices(\n                    self.gpindices, param_gates[i].gpindices)\n                param_gates[i].from_vector( v[pgate_local_inds] )\n                \n        self._construct_matrix()\n\n\n    def copy(self, parent=None):\n        \"\"\"\n        Copy this gate.\n\n        Returns\n        -------\n        Gate\n            A copy of this gate.\n        \"\"\"\n        assert(parent is None),\"TPInstrumentGate set's it's own parent, so `parent` arg of copy must be None\"\n        return self._copy_gpindices( TPInstrumentGate(self.param_gates,self.index), parent)\n          #Note: this doesn't deep-copy self.param_gates, since the TPInstrumentGate doesn't really own these.\n\n    def __str__(self):\n        s = \"TPInstrumentGate with shape %s\\n\" % str(self.base.shape)\n        s += _mt.mx_to_string(self.base, width=4, prec=2)\n        return s\n\n","repo_name":"visalso/pyGSTi-Rice","sub_path":"build/lib/pygsti/objects/gate.py","file_name":"gate.py","file_ext":"py","file_size_in_byte":141089,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"37034251600","text":"import tkinter\nimport requests\nfrom tkinter import font\nfrom tkinter import messagebox\nimport urllib.parse as urlparse\nfrom urllib.parse import urlencode\nimport json\nimport sys\n\ndef sendRequestButtonClicked():\n\turl=URLTextfeild.get()\n\tif(url==\"\"):\n\t\tmessagebox.showerror(\"Error\", \"URL Field should not be empty!\")\n\t\treturn\n\tprint(url)\n\ttry:\n\t\tmethodValue=currvalue.get()\n\t\t#sending Request\n\t\tif methodValue==\"GET\":\n\t\t\tresponse=requests.get(url)\t\t\t\n\t\telif methodValue==\"POST\":\n\t\t\tbody=bodyText.get(\"1.0\",\"end-1c\")\n\t\t\tresponse=requests.post(url=url,data=body)\n\t\telse:\n\t\t\tpass\n\t\t#response evaluation\n\t\tif response.status_code==200:\n\t\t\tresponseTextLabel.config(text=response.status_code,bg=\"green\",fg=\"black\")\n\t\t\tresponseHeaderText.config(state=\"normal\",fg='green')\n\t\t\tresponseHeaderText.insert(1.0,json.dumps(dict(response.headers),indent=1))\n\t\t\tresponseHeaderText.config(state=\"disabled\")\n\t\t\tresponseBodyText.config(state=\"normal\",fg='green')\n\t\t\tresponseBodyText.insert(1.0,response.text)\n\t\t\tresponseBodyText.config(state=\"disabled\")\n\t\telse:\n\t\t\tresponseTextLabel.config(text=response.status_code,bg=\"red\",fg=\"black\")\t\t\t\n\t\t\tresponseHeaderText.config(state=\"normal\",fg='red')\n\t\t\tresponseHeaderText.insert(1.0,json.dumps(dict(response.headers),indent=1))\n\t\t\tresponseHeaderText.config(state=\"disabled\")\n\t\t\tresponseBodyText.config(state=\"normal\",fg='red')\n\t\t\tresponseBodyText.insert(1.0,response.text)\n\t\t\tresponseBodyText.config(state=\"disabled\")\t\t\n\texcept requests.exceptions.RequestException as e:\n\t\tmessagebox.showerror(\"Error\",str(e))\n\texcept requests.exceptions.HTTPError as e:\n\t\tmessagebox.showerror(\"Error\",str(e))\n\texcept Exception as e: \n\t\tmessagebox.showerror(\"Error\",str(e))\n\t\t\t\n\ndef AddQueryString():\n\turl=URLTextfeild.get()\n\tif(url==\"\"):\n\t\tmessagebox.showerror(\"Error\", \"URL Field should not be empty!\")\n\t\treturn\n\tname=queryParameterNameLabelfeild.get()\n\tvalue=queryParameterValueLabelfeild.get()\n\tif name==\"\" or value==\"\":\n\t\tmessagebox.showerror(\"Error\", \"Query Paramter Name and Query Paramter Value both are required!\")\n\t\treturn\n\tparams = {name:value}\n\turl_parts = list(urlparse.urlparse(url))\n\tquery = dict(urlparse.parse_qsl(url_parts[4]))\n\tquery.update(params)\n\turl_parts[4] = urlencode(query)\n\turl=urlparse.urlunparse(url_parts)\n\tprint(url)\n\tURLTextfeild.delete(0,\"end\")\n\tURLTextfeild.insert(0,url)\n\t\n\n\n\nif __name__==\"__main__\":\n\ttop=tkinter.Tk()\n\ttop.geometry(\"%dx%d+%d+%d\" %(1000,600,160,50))\n\ttop.title(\"RestletServices Checker\")\n\ttop.resizable(0,0)\n\tcanvas=tkinter.Canvas(top,bg=\"white\",height=600,width=1000)\n\tcanvas.pack()\n\n\n\n#METHOD AND OPTIONS\t\n\tmethodLabel=tkinter.Label(canvas,text=\"METHOD\",bg=\"white\" ,fg=\"Black\" ,font=font.Font( family=\"{Times}\", size=12, weight='bold'))\n\tmethodLabel.place(x=10,y=10)\n\toptionsfont =font.Font(family=\"{Times}\",size=11,weight=\"bold\")\n\tOPTIONS=[\"GET\",\"POST\"]\n\tcurrvalue=tkinter.StringVar(canvas)\n\tcurrvalue.set(OPTIONS[0])\n\toptionmenu=tkinter.OptionMenu(canvas,currvalue,*OPTIONS)\n\toptionmenu.config(font=optionsfont,bg=\"lemon chiffon\",activebackground=\"navajo white\",width=8)\n\toptionmenu['menu'].config(font=font.Font(family=\"{Times}\",size=10),bg='navajo white',activebackground=\"lemon chiffon\")\n\toptionmenu.place(x=12,y=32)\n\n\n#URL LABEL AND SEND BUTTON\n\tURLLabel=tkinter.Label(canvas,text=\"URL\",bg=\"white\",fg=\"black\",font=font.Font(family=\"{Times}\",size=12,weight='bold'))\n\tURLLabel.place(x=150,y=10)\n\tURLTextfeild=tkinter.Entry(canvas,bg=\"white\",bd=1,font=font.Font(family=\"{Times}\",size=15),justify=\"left\",width=66)\n\tURLTextfeild.place(x=150,y=32,height=34,width=700)\n\tsendbutton=tkinter.Button(bg=\"lemon chiffon\",activebackground=\"navajo white\",bd=4,fg=\"black\",font=font.Font(family=\"{Times}\",size=12,weight=\"bold\",slant=\"italic\"),text=\"SEND\",command=sendRequestButtonClicked)\n\tsendbutton.place(x=868,y=31,height=36,width=120)\n\n#SENDING QUERY PART\n\tqueryParameterLabel=tkinter.Label(canvas,text=\"QUERY PARAMETERS\",bg=\"white\",fg=\"black\",font=font.Font(family=\"{Times}\",size=12,weight='bold'))\n\tqueryParameterLabel.place(x=10,y=90)\n\tqueryParameterNameLabel=tkinter.Label(canvas,text=\"Name:\",bg=\"white\",fg=\"black\",font=font.Font(family=\"{Times}\",size=10,weight='bold'))\n\tqueryParameterNameLabel.place(x=15,y=118)\n\tqueryParameterNameLabelfeild=tkinter.Entry(canvas,bg=\"white\",bd=1,font=font.Font(family=\"{Times}\",size=11),justify=\"left\",width=30)\n\tqueryParameterNameLabelfeild.place(x=68,y=118,height=20,width=128)\n\tqueryParameterValueLabel=tkinter.Label(canvas,text=\"Value:\",bg=\"white\",fg=\"black\",font=font.Font(family=\"{Times}\",size=10,weight='bold'))\n\tqueryParameterValueLabel.place(x=205,y=118)\n\tqueryParameterValueLabelfeild=tkinter.Entry(canvas,bg=\"white\",bd=1,font=font.Font(family=\"{Times}\",size=11),justify=\"left\",width=30)\n\tqueryParameterValueLabelfeild.place(x=258,y=118,height=20,width=128)\n\taddQuerybutton=tkinter.Button(bg=\"lemon chiffon\",activebackground=\"navajo white\",bd=4,fg=\"black\",font=font.Font(family=\"{Times}\",size=11,weight=\"bold\",slant=\"italic\"),text=\"+\",command=AddQueryString)\n\taddQuerybutton.place(x=398,y=115,height=26,width=30)\n\n\n#SENDING BODY PART\n\tbodyLabel=tkinter.Label(canvas,text=\"BODY REQUEST\",bg=\"white\",fg=\"black\",font=font.Font(family=\"{Times}\",size=12,weight='bold'))\n\tbodyLabel.place(x=10,y=158)\n\tbodyTextYScrollbar=tkinter.Scrollbar(canvas,orient=\"vertical\")\n\tbodyTextYScrollbar.place(x=413,y=183,height=387,width=18)\n\tbodyTextXScrollbar=tkinter.Scrollbar(canvas,orient=\"horizontal\")\n\tbodyTextXScrollbar.place(x=21,y=553,height=18,width=392)\n\tbodyText=tkinter.Text(canvas,bg=\"white\",wrap=\"none\",font=font.Font(family=\"{Times}\",size=11),width=43,yscrollcommand=bodyTextYScrollbar.set,xscrollcommand=bodyTextXScrollbar.set)\n\tbodyTextYScrollbar.config(command=bodyText.yview,activebackground=\"navajo white\",bg=\"lemon chiffon\",bd=4)\n\tbodyTextXScrollbar.config(command=bodyText.xview,activebackground=\"navajo white\",bg=\"lemon chiffon\",bd=4)\n\tbodyText.place(x=20,y=183,height=370)\n\n\n#DRAWING LINE BETWEEN RESPONSE AND REQUEST\n\tcanvas.create_line(452,90,452,570,width=2,fill=\"gray25\")\n\n\n#RESPONSE PART\n\tresponseLabel=tkinter.Label(canvas,text=\"RESPONSE\",bg=\"white\",fg=\"black\",font=font.Font(family=\"{Times}\",size=12,weight='bold'))\t\n\tresponseLabel.place(x=470,y=90)\n\tresponseTextLabel=tkinter.Label(canvas,anchor=\"nw\",bg=\"white\",fg=\"black\",font=font.Font(family=\"{Times}\",size=11,weight='bold'),bd=1,justify=\"left\",borderwidth=2,relief='ridge')\n\tresponseTextLabel.place(x=477,y=115,width=500,height=25)\n\n\n#RESPONSE HEADER PART\n\tresponseHeaderLabel=tkinter.Label(canvas,text=\"RESPONSE HEADERS\",bg=\"white\",fg=\"black\",font=font.Font(family=\"{Times}\",size=12,weight='bold'))\n\tresponseHeaderLabel.place(x=470,y=158)\n\tresponseHeaderYScrollbar=tkinter.Scrollbar(canvas,orient=\"vertical\")\n\tresponseHeaderYScrollbar.place(x=960,y=185,height=96,width=18)\n\tresponseHeaderXScrollbar=tkinter.Scrollbar(canvas,orient=\"horizontal\")\n\tresponseHeaderXScrollbar.place(x=478,y=263,height=18,width=482)\n\tresponseHeaderText=tkinter.Text(canvas,bg=\"white\",wrap='none',font=font.Font(family=\"{Times}\",size=11),width=43,yscrollcommand=responseHeaderYScrollbar.set,xscrollcommand=responseHeaderXScrollbar.set)\n\tresponseHeaderText.config(state=\"disabled\")\n\tresponseHeaderYScrollbar.config(command=responseHeaderText.yview,activebackground=\"navajo white\",bg=\"lemon chiffon\",bd=4)\n\tresponseHeaderXScrollbar.config(command=responseHeaderText.xview,activebackground=\"navajo white\",bg=\"lemon chiffon\",bd=4)\n\tresponseHeaderText.place(x=478,y=185,width=482,height=78)\n\n\n#RESPONSE BODY PART\n\tresponseBodyHeaderLabel=tkinter.Label(canvas,text=\"RESPONSE BODY\",bg=\"white\",fg=\"black\",font=font.Font(family=\"{Times}\",size=12,weight='bold'))\n\tresponseBodyHeaderLabel.place(x=470,y=296)\n\tresponseBodyYScrollbar=tkinter.Scrollbar(canvas,orient=\"vertical\")\n\tresponseBodyYScrollbar.place(x=957,y=316,height=255,width=18)\n\tresponseBodyXScrollbar=tkinter.Scrollbar(canvas,orient=\"horizontal\")\n\tresponseBodyXScrollbar.place(x=475,y=553,height=18,width=482)\n\tresponseBodyText=tkinter.Text(canvas,bg=\"white\",wrap='none',font=font.Font(family=\"{Times}\",size=11),width=43,yscrollcommand=responseBodyYScrollbar.set,xscrollcommand=responseBodyXScrollbar.set)\n\tresponseBodyText.config(state=\"disabled\")\n\tresponseBodyYScrollbar.config(command=responseBodyText.yview,activebackground=\"navajo white\",bg=\"lemon chiffon\",bd=4)\n\tresponseBodyXScrollbar.config(command=responseBodyText.xview,activebackground=\"navajo white\",bg=\"lemon chiffon\",bd=4)\n\tresponseBodyText.place(x=475,y=316,height=237,width=482)\n\n\n\ttop.mainloop()\n","repo_name":"pk111297/RestServicesChecker","sub_path":"finalRC.py","file_name":"finalRC.py","file_ext":"py","file_size_in_byte":8455,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"81"}
+{"seq_id":"12629826478","text":"import sys\n\n\ndef get_gcd(num1: int, num2: int):\n    if num2 == 0:\n        return num1\n    else:\n        return get_gcd(num2, num1 % num2)\n\n\ndef get_lcm(num1: int, num2: int):\n    return num1 * num2 // get_gcd(num1, num2)\n\n\nT = int(sys.stdin.readline())\nans = list()\n\nfor i in range(T):\n    a, b = map(int, sys.stdin.readline().split())\n    ans.append(str(get_lcm(a, b)))\n\nprint('\\n'.join(ans))\n","repo_name":"jh9875/PS","sub_path":"Python/BOJ/[1934] 최소공배수/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":394,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"5284703546","text":"#!/usr/bin/python\nimport sys\nimport csv\nimport numpy as np\nimport pandas as pd\nimport cv2\nimport os\nimport argparse\n\nap = argparse.ArgumentParser()\nap.add_argument(\"-c\", \"--csv\", nargs='?', const='test.csv', type=str, help = \"path to the csv file\")\nap.add_argument(\"-p\", \"--processed\", help = \"path to for processed image\")\nap.add_argument(\"-i\", \"--intensity\", nargs='?', const=128, type=int, help = \"crosstalk points intensity\")\nap.add_argument(\"-d\", \"--default\", nargs='?', const=0, type=int, help = \"defalut black/white\")\nargs = vars(ap.parse_args())\n\nif len( sys.argv ) < 3:\n\tap.print_help( sys.stderr )\n\tsys.exit( 1 )\ncsv_file = args[ \"csv\" ]\nprocessed_image = args[ \"processed\" ]\nintensity = int( args[ \"intensity\" ] )\ndefault_intensity = int( args[ \"default\" ] )\ndata = pd.read_csv( csv_file )  # path of the .csv file#print(data.shape)  # to check the shape\nprint (data.shape)\nimage_shape = data.iloc[ 0 ]\nprint ( ( image_shape[ 'y' ], image_shape[ 'x' ] ) )\nimage = np.full(( image_shape[ 'y' ], image_shape[ 'x' ], 3), default_intensity, dtype=np.uint8)\n\nwhite = [ intensity, intensity , intensity ]\nfor i in range( 1, data.shape[ 0 ] ):  #data.shape[0] gives no. of rows\n\tface = data.iloc[ i ]  # remove one row from the data\n\timage[ face[ 'y' ], face[ 'x' ] ] = white  # send this row of to the function \n\ncv2.imshow( 'img', image )\ncv2.imwrite( processed_image, image )\ncv2.waitKey( 5000 )\n\n","repo_name":"jemishbpatel/crosstalk_prediction_model","sub_path":"csv_to_image.py","file_name":"csv_to_image.py","file_ext":"py","file_size_in_byte":1404,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"3622245893","text":"from flask import Flask,render_template,request,jsonify\nimport pickle\nimport numpy as np\nfrom sklearn import preprocessing\napp = Flask('__name__')\nmodel=pickle.load(open('MERISkill.sav','rb'))\n\n@app.route('/')\ndef home():\n    return render_template('index.html')\ndef predict_api(feature):\n    '''\n    \n    '''\n    feature=[float(x) for x in request.form.values()]\n    \n    asarray=np.asarray(feature).reshape (1,-1)\n    prediction=model.predict(asarray)\n    # data = request.get_json(force=True)\n    # prediction = model.predict([np.array(list(feature.values()))])\n    \n    return prediction[0]\n\n\n\n@app.route('/predict',methods=[\"POST\"])\n\n\ndef predict():\n    if request.method == 'POST':\n        feature= request.form.to_dict()\n        feature=list(feature.values())\n        feature=list(map(float, feature))\n        predict=predict_api(feature)\n\n        if  float (predict)==1:\n            prediction ='positive'\n            # print( 'RESULT: POSITIVE')\n        else:\n            # print(' RESULT: NEGATIVE')\n            prediction = 'negative'\n        return render_template('predict.html',prediction = prediction)  \n  \n    \n\n    \n\nif(__name__=='__main__'):\n    app.run(debug=True)\n","repo_name":"Maokadtech/MERISKILL-INTERNSHIP","sub_path":"PROJECT 3/diabetes app/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1184,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"81"}
+{"seq_id":"38854394456","text":"## This file contains functions common to charting datasets where a slider picks a time period and normalized data is \n## shown in the figure\n\nimport json\nimport pprint\nimport numpy as np\nimport pandas as pd\nimport altair as alt\nimport geopandas as gpd\nfrom shapely import wkt\nimport matplotlib.pyplot as plt\n\n\n\nclass NormalizedDataSliderVisualization:\n        def __init__(self, area_geofile : str = '../assets/plss_subbasin.geojson',\n                         base_geofile:str = 'https://raw.githubusercontent.com/codeforgermany/click_that_hood/main/public/data/california-counties.geojson'):\n           \n            # this stores the California county map with couty boundaries as a base\n            geo_json_file_loc = base_geofile\n            base_gdf = gpd.read_file(geo_json_file_loc)\n            base_gdf.set_crs('epsg:4326')\n\n            #Set the class's base chart \n            self.county_base = alt.Chart(base_gdf).mark_geoshape(\n                                stroke='black',\n                                strokeWidth=1\n                            ).encode(\n                                color= alt.value('lightgray'),\n                                opacity=alt.value(.3),\n                            ).properties(\n                                width=500,\n                                height=500\n                            )\n\n            #This stores the San Joaquin River Basin a base geodataframe\n            self.plss_gdf = gpd.read_file(area_geofile)\n            self.plss_range = self.plss_gdf.dissolve(by='TownshipRange').reset_index()\n                        \n\n\n\n        def view_attribute_per_year(self, df, color_col='GSE_GWE_NORMALIZED', time_col = 'YEAR'):\n            \"\"\"\n                    This function charts out a geodataframe with a slider that controls the data in the dataframe by the position of the slider indicating a Year period\n                    The color of the values is scaled by the color_col in the dataframe\n\n                    The df will have a geometry of points or polygons. But it is left joined with a dataframe with polygons and hence polygins will be charted \n            \n            \"\"\"\n            #Limit the time range so that the chart can be shown\n            df = df[df[time_col] >= 2014]\n            #By left joining the plss dataframe we replace the poin\n            df_poly = self.plss_range.sjoin(df)\n\n            min_year_num = df[time_col].min()\n            max_year_num = df[time_col].max()\n            slider = alt.binding_range(\n                min=min_year_num,\n                max=max_year_num,\n                step=1,\n                name=\"Year: \",\n            )\n\n            slider_selection = alt.selection_single(\n                fields=[f\"{time_col}\"], bind=slider, name=\"Year:\",\n                init={f\"{time_col}\": 2014}\n            )\n\n            self.area_slider_chart =  alt.Chart(  df_poly).mark_geoshape(\n                                                        ).encode( \n                                                            color= alt.Color(f'{color_col}', scale=alt.Scale(scheme='purpleorange')),\n                                                            tooltip= list(df_poly.columns)\n                                                        ).transform_filter(\n                                                            slider_selection\n                                                        ).add_selection(\n                                                            slider_selection\n                                                        ).properties( \n\n                                                            width=500,\n                                                            height=500\n                                                        )\n            return  self.county_base + self.area_slider_chart \n\n\n        def explore_data(self, df, year_column:str, color_column:str, year:int = 2021,):\n            \"\"\"\n                    This function converts a poitn geometry dataframe into a polygon geometry dataframe by left joining a plss of san joaquin county\n                    it then calls geopandas explore function\n            \"\"\"\n            df = df[df[year_column] == year]\n\n            df_poly = self.plss_range.sjoin(df)\n            \n            return df_poly.explore(color_column)\n\n\n","repo_name":"sjtalkar/milestone2_waterwells_deepnote","sub_path":"eda/geodataframe_visualization_class.py","file_name":"geodataframe_visualization_class.py","file_ext":"py","file_size_in_byte":4322,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"73641188745","text":"from sqlalchemy import Column, func, String, SmallInteger, Integer, DateTime, Boolean, Float, Date\nfrom sqlalchemy.schema import Table, ForeignKey\nfrom sqlalchemy.orm import relationship\nfrom sqlalchemy.sql.expression import text\nfrom webapp import app\nfrom webapp.models import CustomBase\nfrom flask import g\n\n'''\n\tFogbugzUserCase.cost()\n\t\t-> Case.cost()\n\t\t\t-> Milestone.cost() : Case.cost() for all cases\n\t\t\t-> Milestone.billable_cost() : Case.cost() for all non-comped, non-deliverable cases\n\t\t\t\t-> Invoice.billable_cost()\n\t\t\t-> Category.cost()\n\t\t\t-> Deliverable.balance() : estimate minus refunded amount and non-comped cases\n\t\t\t-> Deliverable.cost_in_milestone() : Case.cost() for all cases if case in given milestone\n\n\tMilestone.billable_cost() -> Milestone.finvoicedamount upon Invoice Finalization\n'''\n\nclass Company(CustomBase):\n\n\t__tablename__ = 'company'\n\n\tname = Column(String(255))\n\taddressline1 = Column(String(255))\n\taddressline2 = Column(String(255))\n\taddressline3 = Column(String(255))\n\tinvoices = relationship('Invoice', backref='company', lazy='dynamic')\n\nclass Customer(CustomBase):\n\n\t__tablename__ = 'customer'\n\n\tname = Column(String(255))\n\taddressline1 = Column(String(255))\n\taddressline2 = Column(String(255))\n\taddressline3 = Column(String(255))\n\tinvoices = relationship('Invoice', backref='customer', lazy='dynamic')\n\nclass Project(CustomBase):\n\n\t__tablename__ = 'project'\n\n\tixproject = Column(Integer, unique=True)\n\tsproject = Column(String(255))\n\tmilestones = relationship('Milestone', backref='project', lazy='dynamic')\n\n\tdef __init__(self, ixproject, sproject):\n\t\tself.ixproject = ixproject\n\t\tself.sproject = sproject\n\nclass Milestone(CustomBase):\n\n\t__tablename__ = 'milestone'\n\n\tixfixfor = Column(Integer, unique=True)\n\tixproject = Column(Integer, ForeignKey('project.ixproject'), nullable=False)\n\tsfixfor = Column(String(255), nullable=False)\n\tdtstart = Column(DateTime, nullable=True)\n\tdt = Column(DateTime, nullable=False)\n\tbfrozen = Column(Boolean, default=False)\n\tinvoice_id = Column(Integer, ForeignKey('invoice.id'), nullable=True)\n\tbinvoiced = Column(Boolean, default=False)\n\tfinvoicedamount = Column(Float, nullable=True)\n\tbpaid = Column(Boolean, default=False)\n\tdtpaid = Column(DateTime, nullable=True)\n\tfpaidamount = Column(Float, nullable=True)\n\tcases = relationship('Case', backref='milestone', lazy='dynamic')\n\tcategories = relationship('Category', backref='milestone', lazy='dynamic')\n\tdeliverable_id = Column(Integer, ForeignKey('deliverable.id'), nullable=True)\n\n\tdef __init__(self, ixfixfor, sfixfor, ixproject, dt, dtstart):\n\t\tself.ixfixfor = ixfixfor\n\t\tself.sfixfor = sfixfor\n\t\tself.ixproject = ixproject\n\t\tself.dt = dt\n\t\tself.dtstart = dtstart\n\n\tdef bfrozen_label_class(self):\n\t\treturn \"info\" if self.bfrozen else \"default\"\n\n\tdef invoiced_label_class(self):\n\t\treturn \"info\" if self.invoice else \"default\"\n\n\tdef bpaid_label_class(self):\n\t\treturn \"info\" if self.bpaid else \"default\"\n\n\tdef billable_cost(self):\n\t\t''' Sum of cost of all cases, accounting for comping and deliverable assignment .'''\n\t\tif self.deliverable:\n\t\t\tnon_comped_non_deliverable_case_cost = 0\n\t\telse:\n\t\t\tnon_comped_non_deliverable_case_cost = sum([ \n\t\t\t\tc.cost() \n\t\t\t\tfor c \n\t\t\t\tin self.cases \n\t\t\t\tif (\n\t\t\t\t\tnot c.fogbugzusercases[0].bcomped \n\t\t\t\t\t\tand not c.deliverable) \n\t\t\t])\n\t\t\t\n\t\treturn non_comped_non_deliverable_case_cost\n\n\tdef deliverables(self):\n\t\treturn set([ c.deliverable for c in self.cases if c.deliverable ])\n\n\tdef get_categories(self, include_empty_categories=True):\n\t\tcategories = self.categories\n\t\tif not include_empty_categories:\n\t\t\tcategories = [ c for c in categories if c.cases.count() > 0 ]\n\n\t\treturn categories\n\n\tdef uncategorized_cases(self, include_zero_cost=True):\n\t\tcases = self.cases.filter(Case.category_id==None)\n\t\tif not include_zero_cost:\n\t\t\tcases = [ c for c in cases if c.cost() > 0 ]\n\n\t\treturn cases\n\n\tdef no_charge_cases(self):\n\t\tcases = [ c for c in self.cases if c.cost() == 0 ]\n\t\treturn cases\n\n\tdef comped_cases(self):\n\n\t\tcases = [ c for c in self.cases if c.fogbugzusercases[0].bcomped ]\n\t\treturn cases\n\n\tdef cost(self):\n\t\t''' Case cost rollup '''\n\n\t\treturn sum([ c.cost() for c in self.cases ])\n\t\t\nclass Case(CustomBase):\n\n\t__tablename__ = 'fbcase'\n\n\tixbug = Column(Integer, unique=True)\n\tixfixfor = Column(Integer, ForeignKey('milestone.ixfixfor'))\n\tstitle = Column(String(255), nullable=False)\n\tscategory = Column(String(255), nullable=False)\n\tsticket = Column(String(255), nullable=False)\n\tixpriority = Column(Integer)\n\tsstatus = Column(String(255))\n\tixpersonresolvedby = Column(Integer, ForeignKey('fogbugzuser.ixperson'), nullable=True)\n\tfogbugzusercases = relationship('FogbugzUserCase', backref='case', lazy='dynamic')\n\tcategory_id = Column(Integer, ForeignKey('category.id'), nullable=True)\n\tdeliverable_id = Column(Integer, ForeignKey('deliverable.id'), nullable=True)\n\n\tdef __init__(self, ixbug, ixfixfor, stitle, ixpriority, sstatus, scategory, sticket, ixpersonresolvedby):\n\t\tself.ixbug = ixbug\n\t\tself.ixfixfor = ixfixfor\n\t\tself.stitle = stitle\n\t\tself.ixpriority = ixpriority\n\t\tself.sstatus = sstatus\n\t\tself.scategory = scategory\n\t\tself.sticket = sticket\n\t\tself.ixpersonresolvedby = ixpersonresolvedby\n\n\t\t#self.ixpersonresolvedby = int(c.ixpersonresolvedby.string)\n\t\t#self.hrsElapsed = float(c.hrselapsed.string)\n\t\t#self.hrsElapsedExtra = float(c.hrselapsedextra.string)\n\n\tdef billable(self):\n\t\treturn self.fogbugzusercases[0].fhours > 0\n\n\tdef cost(self):\n\t\t''' Billable amount for a particular employee's time on this case '''\n\t\t# -- TODO: we assume there is only one FBUC\n\t\treturn self.fogbugzusercases[0].cost()\n\n\tdef ticket_url(self):\n\t\tif not g.print_view:\n\t\t\treturn \"<a href='%s%s?%s' target='_blank' title=\\\"%s\\\">%s</a>\" %(app.config['FOGBUGZ_URL'], app.config['CASE_ENDPOINT'], self.sticket, self.stitle, self.ixbug)\n\t\telse:\n\t\t\treturn \"%s\" % self.ixbug\n\nclass FogbugzUser(CustomBase):\n\n\t__tablename__ = 'fogbugzuser'\n\n\tixperson = Column(Integer, unique=True)\n\tsfullname = Column(String(255), nullable=False)\n\tfrate = Column(Float, nullable=False, default=0.0)\n\tcases = relationship('Case', backref='fogbugzuser', lazy='dynamic')\n\tfogbugzusercases = relationship('FogbugzUserCase', backref='fogbugzuser', lazy='dynamic')\n\n\tdef __init__(self, ixperson, sfullname):\n\t\tself.ixperson = ixperson\n\t\tself.sfullname = sfullname\n\nclass FogbugzUserCase(CustomBase):\n\n\t__tablename__ = 'fogbugzusercase'\n\n\tixperson = Column(Integer, ForeignKey('fogbugzuser.ixperson'))\n\tixbug = Column(Integer, ForeignKey('fbcase.ixbug'))\n\tbcomped = Column(Boolean, nullable=False, server_default='f')\n\tfhours = Column(Float, nullable=False, default=0.0)\n\tfhours_override = Column(Float, nullable=False, default=0.0)\n\tfrate_override = Column(Float, nullable=False, default=0.0)\n\n\tdef __init__(self, ixperson, ixbug, fhours):\n\t\tself.ixperson = ixperson\n\t\tself.ixbug = ixbug\n\t\tself.fhours = fhours\n\n\tdef cost(self):\n\t\trate = self.frate_override if self.frate_override > 0 else self.fogbugzuser.frate\n\t\thours = self.fhours_override if self.fhours_override > 0 else self.fhours\n\t\tcost = hours*rate\n\t\trounded_cost = float(\"%.2f\" % (cost))\n\n\t\t#app.logger.info(cost - rounded_cost)\n\t\treturn rounded_cost\n\nclass Category(CustomBase):\n\n\t__tablename__ = 'category'\n\n\tmilestone_id = Column(Integer, ForeignKey('milestone.id'), nullable=False)\n\tcases = relationship('Case', backref='category', lazy='dynamic')\n\tname = Column(String(255), nullable=False)\n\n\tdef __init__(self, name, milestone_id):\n\t\tself.name = name\n\t\tself.milestone_id = milestone_id\n\n\tdef cost(self):\n\t\treturn sum([ c.cost() for c in self.cases ])\n\nclass Deliverable(CustomBase):\n\n\t__tablename__ = 'deliverable'\n\n\tcases = relationship('Case', backref='deliverable', lazy='dynamic')\n\tmilestones = relationship('Milestone', backref='deliverable', lazy='dynamic')\n\tname = Column(String(255), nullable=False)\n\tfestimate = Column(Float, nullable=False, server_default='0.0')\n\tfrefunded = Column(Float, nullable=False, server_default='0.0')\n\tbinvoiced = Column(Boolean, nullable=False, server_default='f')\n\tbpaid = Column(Boolean, nullable=False, server_default='f')\n\tinvoice_id = Column(Integer, ForeignKey('invoice.id'), nullable=True)\n\trefund_invoice_id = Column(Integer, ForeignKey('invoice.id'), nullable=True)\n\n\tdef __init__(self, name, festimate):\n\t\tself.name = name\n\t\tself.festimate = festimate\n\n\tdef editable(self):\n\t\treturn (not self.invoice or not self.invoice.state == 'final') \\\n\t\t\tand (not self.refund_invoice or not self.refund_invoice.state == 'final')\n\n\tdef refundable(self):\n\t\treturn self.invoice and self.invoice.state == 'final' and self.frefunded == 0 and self.balance() != 0\n\n\tdef balance(self):\n\t\tb = self.festimate - self.frefunded - sum([ c.cost() for c in self.cases if not c.fogbugzusercases[0].bcomped ])\n\t\t# -- sometimes a fraction of a cent will keep the balance open\n\t\t# -- actually with the rounded_cost fix in FogbugzUserCase.cost() this may no longer be an issue\n\t\tif abs(b) < 0.01:\n\t\t\tapp.logger.warn('Deliverable.balance() reports a value less than a penny - trunced to zero')\n\t\t\tb = 0\n\n\t\treturn b\n\n\tdef cases_in_milestone(self, milestone):\n\t\t''' The intersection of the deliverable's cases and the given milestone's cases '''\n\t\treturn [ c for c in self.cases if c in milestone.cases ]\n\n\tdef cost_in_milestone(self, milestone):\n\t\t''' Billable amount of the intersection of the deliverable's cases and the given milestone's cases '''\n\t\treturn sum([ c.cost() for c in self.cases if c in milestone.cases ])\n\nclass Invoice(CustomBase):\n\n\t__tablename__ = 'invoice'\n\n\tcompany_id = Column(Integer, ForeignKey('company.id'), nullable=False)\n\tcustomer_id = Column(Integer, ForeignKey('customer.id'), nullable=False)\n\tstate = Column(String(20), nullable=False, server_default='final')\n\tmilestones = relationship('Milestone', backref='invoice', lazy='dynamic')\n\tunpaid_deliverables = relationship('Deliverable', foreign_keys=[Deliverable.invoice_id], backref='invoice', lazy='dynamic')\n\trefund_deliverables = relationship('Deliverable', foreign_keys=[Deliverable.refund_invoice_id], backref='refund_invoice', lazy='dynamic')\n\n\tdef billable_cost(self):\n\n\t\treturn sum([ m.billable_cost() for m in self.milestones.all() ]) \\\n\t\t\t+ sum([ d.festimate for d in self.unpaid_deliverables ]) \\\n\t\t\t- sum([ d.frefunded for d in self.refund_deliverables ]) \n\n\tdef deliverables(self):\n\t\treturn set([ m.deliverable for m in self.milestones if m.deliverable ])\n\nclass Payment(CustomBase):\n\n\t__tablename__ = 'payment'\n\n\tfamount = Column(Float, nullable=False, server_default='0.0')\n\tdate_deposited = Column(Date, nullable=False, server_default=text('current_date'))\n\tcustomer_id = Column(Integer, ForeignKey('customer.id'), nullable=False)\n\tcompany_id = Column(Integer, ForeignKey('company.id'), nullable=False)\n\nclass InvoicePayment(CustomBase):\n\n\t__tablename__ = 'invoicepayment'\n\n\tinvoice_id = Column(Integer, ForeignKey('invoice.id'), nullable=False)\n\tpayment_id = Column(Integer, ForeignKey('payment.id'), nullable=False)\n\tfamount_apportioned = Column(Float, nullable=False, server_default='0.0')","repo_name":"tpalko/fogbugz-headlamp","sub_path":"webapp/invoicing/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":11009,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"74013642504","text":"\"\"\"Scipy cookbook implementations of the Savitzky Golay filter for xr.DataArrays.\"\"\"\nimport warnings\nfrom math import factorial\n\nimport numpy as np\nimport scipy.signal\n\nimport typing\nimport xarray as xr\nfrom arpes.provenance import update_provenance\nfrom arpes.typing import DataType\n\n__all__ = (\"savitzky_golay\",)\n\n\n@update_provenance(\"Savitzky Golay Filter\")\ndef savitzky_golay(\n    data: typing.Union[DataType, list, np.ndarray], window_size, order, deriv=0, rate=1, dim=None\n):\n    \"\"\"Implements a Savitzky Golay filter with given window size.\n\n    You can specify \"pass through\" dimensions\n    which will not be touched with the `dim` argument. This allows for filtering each frame of a map or each equal-energy\n    contour in a 3D dataset, for instance.\n\n    Args:\n        data: Input data.\n        window_size: Number of points in the window that the filter uses locally.\n        order: The polynomial order used in the convolution.\n        deriv\n        rate\n        dim\n\n    Returns:\n        Smoothed data.\n    \"\"\"\n    if isinstance(\n        data,\n        (\n            list,\n            np.ndarray,\n        ),\n    ):\n        return savitzky_golay_array(data, window_size, order, deriv, rate)\n\n    if len(data.dims) == 1:\n        transformed_data = savitzky_golay_array(data.values, window_size, order, deriv, rate)\n    else:\n        # only 1D, 2D, 3D supported for the moment\n        assert len(data.dims) <= 3\n\n        if deriv == 0:\n            deriv = None\n\n        if len(data.dims) == 3:\n            if dim is None:\n                dim = data.dims[-1]\n            return data.G.map_axes(\n                dim, lambda d, _: savitzky_golay(d, window_size, order, deriv, rate)\n            )\n\n        if len(data.dims) == 2:\n            if dim is None:\n                transformed_data = savitzky_golay_2d(\n                    data.values, window_size, order, derivative=deriv\n                )\n            else:\n                return data.G.map_axes(\n                    dim,\n                    lambda d, _: savitzky_golay(\n                        d, window_size, order, deriv=deriv or 0, rate=rate, dim=None\n                    ),\n                )\n\n    return xr.DataArray(transformed_data, data.coords, data.dims, attrs=data.attrs.copy())\n\n\ndef savitzky_golay_2d(z, window_size, order, derivative=None):\n    \"\"\"Implementation from the scipy cookbook before the Savitzky Golay filter was supported.\n\n    This is changed now, so we should ideally migrate to use the new scipy implementation.\n\n    Args:\n        z\n        window_size\n        order\n        derivative\n\n    Returns:\n        Smoothed data\n    \"\"\"\n    # number of terms in the polynomial expression\n    n_terms = (order + 1) * (order + 2) / 2.0\n\n    if window_size % 2 == 0 or window_size < 1:\n        warnings.warn(\"window_size size must be a positive odd number\")\n        window_size = np.max([0, window_size])\n        window_size += 1\n\n    if window_size ** 2 < n_terms:\n        raise ValueError(\"order is too high for the window size\")\n\n    half_size = window_size // 2\n\n    # exponents of the polynomial.\n    # p(x,y) = a0 + a1*x + a2*y + a3*x^2 + a4*y^2 + a5*x*y + ...\n    # this line gives a list of two item tuple. Each tuple contains\n    # the exponents of the k-th term. First element of tuple is for x\n    # second element for y.\n    # Ex. exps = [(0,0), (1,0), (0,1), (2,0), (1,1), (0,2), ...]\n    exps = [(k - n, n) for k in range(order + 1) for n in range(k + 1)]\n\n    # coordinates of points\n    ind = np.arange(-half_size, half_size + 1, dtype=np.float64)\n    dx = np.repeat(ind, window_size)\n    dy = np.tile(ind, [window_size, 1]).reshape(\n        window_size ** 2,\n    )\n\n    # build matrix of system of equation\n    A = np.empty((window_size ** 2, len(exps)))\n    for i, exp in enumerate(exps):\n        A[:, i] = (dx ** exp[0]) * (dy ** exp[1])\n\n    # pad input array with appropriate values at the four borders\n    new_shape = z.shape[0] + 2 * half_size, z.shape[1] + 2 * half_size\n    Z = np.zeros((new_shape))\n    # top band\n    band = z[0, :]\n    Z[:half_size, half_size:-half_size] = band - np.abs(np.flipud(z[1 : half_size + 1, :]) - band)\n    # bottom band\n    band = z[-1, :]\n    Z[-half_size:, half_size:-half_size] = band + np.abs(\n        np.flipud(z[-half_size - 1 : -1, :]) - band\n    )\n    # left band\n    band = np.tile(z[:, 0].reshape(-1, 1), [1, half_size])\n    Z[half_size:-half_size, :half_size] = band - np.abs(np.fliplr(z[:, 1 : half_size + 1]) - band)\n    # right band\n    band = np.tile(z[:, -1].reshape(-1, 1), [1, half_size])\n    Z[half_size:-half_size, -half_size:] = band + np.abs(\n        np.fliplr(z[:, -half_size - 1 : -1]) - band\n    )\n    # central band\n    Z[half_size:-half_size, half_size:-half_size] = z\n\n    # top left corner\n    band = z[0, 0]\n    Z[:half_size, :half_size] = band - np.abs(\n        np.flipud(np.fliplr(z[1 : half_size + 1, 1 : half_size + 1])) - band\n    )\n    # bottom right corner\n    band = z[-1, -1]\n    Z[-half_size:, -half_size:] = band + np.abs(\n        np.flipud(np.fliplr(z[-half_size - 1 : -1, -half_size - 1 : -1])) - band\n    )\n\n    # top right corner\n    band = Z[half_size, -half_size:]\n    Z[:half_size, -half_size:] = band - np.abs(\n        np.flipud(Z[half_size + 1 : 2 * half_size + 1, -half_size:]) - band\n    )\n    # bottom left corner\n    band = Z[-half_size:, half_size].reshape(-1, 1)\n    Z[-half_size:, :half_size] = band - np.abs(\n        np.fliplr(Z[-half_size:, half_size + 1 : 2 * half_size + 1]) - band\n    )\n\n    # solve system and convolve\n    if derivative is None:\n        m = np.linalg.pinv(A)[0].reshape((window_size, -1))\n        return scipy.signal.fftconvolve(Z, m, mode=\"valid\")\n    elif derivative == \"col\":\n        c = np.linalg.pinv(A)[1].reshape((window_size, -1))\n        return scipy.signal.fftconvolve(Z, -c, mode=\"valid\")\n    elif derivative == \"row\":\n        r = np.linalg.pinv(A)[2].reshape((window_size, -1))\n        return scipy.signal.fftconvolve(Z, -r, mode=\"valid\")\n    elif derivative == \"both\":\n        c = np.linalg.pinv(A)[1].reshape((window_size, -1))\n        r = np.linalg.pinv(A)[2].reshape((window_size, -1))\n        return scipy.signal.fftconvolve(Z, -r, mode=\"valid\"), scipy.signal.fftconvolve(\n            Z, -c, mode=\"valid\"\n        )\n\n\ndef savitzky_golay_array(y, window_size, order, deriv=0, rate=1):\n    r\"\"\"Smooth (and optionally differentiate) data with a Savitzky-Golay filter.\n\n    The Savitzky-Golay filter removes high frequency noise from data.\n    It has the advantage of preserving the original shape and\n    features of the signal better than other types of filtering\n    approaches, such as moving averages techniques.\n\n    Notes:\n        The Savitzky-Golay is a type of low-pass filter, particularly\n        suited for smoothing noisy data. The main idea behind this\n        approach is to make for each point a least-square fit with a\n        polynomial of high order over a odd-sized window centered at\n        the point.\n\n    Examples:\n        t = np.linspace(-4, 4, 500)\n        y = np.exp( -t**2 ) + np.random.normal(0, 0.05, t.shape)\n        ysg = savitzky_golay(y, window_size=31, order=4)\n        import matplotlib.pyplot as plt\n        plt.plot(t, y, label='Noisy signal')\n        plt.plot(t, np.exp(-t**2), 'k', lw=1.5, label='Original signal')\n        plt.plot(t, ysg, 'r', label='Filtered signal')\n        plt.legend()\n        plt.show()\n\n    References:\n        * [1] A. Savitzky, M. J. E. Golay, Smoothing and Differentiation of\n          Data by Simplified Least Squares Procedures. Analytical\n          Chemistry, 1964, 36 (8), pp 1627-1639.\n        * [2] Numerical Recipes 3rd Edition: The Art of Scientific Computing\n          W.H. Press, S.A. Teukolsky, W.G. Vetterling, B.P. Flannery\n        Cambridge University Press ISBN-13: 9780521880688\n\n    Args:\n        y : array_like, shape (N,)\n          the values of the time history of the signal.\n        window_size : int\n          the length of the window. Must be an odd integer number.\n        order : int\n          the order of the polynomial used in the filtering.\n          Must be less then `window_size` - 1.\n        deriv: int\n          the order of the derivative to compute (default = 0 means only smoothing)\n\n    Returns:\n        ndarray, shape (N)\n        the smoothed signal (or it's n-th derivative).\n    \"\"\"\n    try:\n        window_size = np.abs(np.int(window_size))\n        order = np.abs(np.int(order))\n    except ValueError:\n        raise ValueError(\"window_size and order have to be of type int\")\n\n    if window_size % 2 != 1 or window_size < 1:\n        warnings.warn(\"window_size size must be a positive odd number\")\n        window_size = np.max([0, window_size])\n        window_size += 1\n\n    if window_size < order + 2:\n        raise TypeError(\"window_size is too small for the polynomials order\")\n\n    order_range = range(order + 1)\n    half_window = (window_size - 1) // 2\n    # precompute coefficients\n    b = np.mat([[k ** i for i in order_range] for k in range(-half_window, half_window + 1)])\n    m = np.linalg.pinv(b).A[deriv] * rate ** deriv * factorial(deriv)\n    # pad the signal at the extremes with\n    # values taken from the signal itself\n    firstvals = y[0] - np.abs(y[1 : half_window + 1][::-1] - y[0])\n    lastvals = y[-1] + np.abs(y[-half_window - 1 : -1][::-1] - y[-1])\n    y = np.concatenate((firstvals, y, lastvals))\n    return np.convolve(m[::-1], y, mode=\"valid\")\n","repo_name":"chstan/arpes","sub_path":"arpes/analysis/savitzky_golay.py","file_name":"savitzky_golay.py","file_ext":"py","file_size_in_byte":9411,"program_lang":"python","lang":"en","doc_type":"code","stars":28,"dataset":"github-code","pt":"81"}
+{"seq_id":"23337011184","text":"def divide():\r\n\r\n\ttry:\r\n\r\n\t   op1=(float(input(\"Introduce el primer numero: \")))\r\n\r\n\t   op2=(float(input(\"Introduce el primer numero: \")))\r\n\r\n\t   print(\"La division es: \" + str(op1/op2))\r\n\r\n\texcept ValueError:\r\n\t\tprint(\"El valor introducido no es correcto\")\r\n\r\n\texcept ZeroDivisionError:\r\n\t\tprint(\"No se puede dividir entre cero\")\r\n\r\n\tfinally:\t#Siempre se va a ejecutar lo que siga de este comando\r\n\r\n\t   print(\"Calculo finalizado\")\r\n\r\ndivide()","repo_name":"pmendizabal/auxiliary_files_about_python_course","sub_path":"Clase EXCEPCIONES 2.py","file_name":"Clase EXCEPCIONES 2.py","file_ext":"py","file_size_in_byte":444,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"12568308866","text":"from myCodes import utilities\nimport os\n\n\n\ndef distributer():\n    all_urls = utilities.read_json(\"/home/c6/Documents/OpenWPM2/datadir/callable_urls.json\")\n    out_dir = \"/home/c6/Documents/OpenWPM2/datadir/distributed_dataset\"\n    counter = 0\n    counter_name = 1\n    splitter = 15000\n\n    while counter != 675000:\n        new_list_name = \"list\" + str(counter_name)\n        new_list = all_urls[counter:splitter]\n        counter = splitter\n        splitter = splitter + 15000\n        counter_name += 1\n        utilities.write_json(os.path.join(out_dir, new_list_name+\".json\"), new_list)\n\n    new_list_name = \"list\" + str(counter_name)\n    new_list = all_urls[counter:]\n    utilities.write_json(os.path.join(out_dir, new_list_name+\".json\"), new_list)\n\n    all_sublists = utilities.get_files_in_a_directory(out_dir)\n    for file in all_sublists:\n        file = utilities.read_json(file)\n        print(len(file))\n\n\ndef sanitiser():\n    all_urls = utilities.read_json(\"/home/c6/Documents/OpenWPM2/datadir/callable_urls.json\")\n    setify = set()\n    sub_lists = utilities.get_files_in_a_directory(\"/home/c6/Documents/OpenWPM2/datadir/distributed_dataset\")\n    for sublist in sub_lists:\n        sublist_content = utilities.read_json(sublist)\n        for url in sublist_content:\n            setify.add(url)\n    print(len(setify))\n    print(len(all_urls))\n\n\nsanitiser()","repo_name":"pooneh-nb/openwpm_","sub_path":"myCodes/distributer.py","file_name":"distributer.py","file_ext":"py","file_size_in_byte":1360,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"6080819894","text":"import ast\nimport pytest\nfrom robocop.rules import Rule, RuleSeverity\nimport robocop.exceptions\n\n\n@pytest.fixture\ndef valid_msg():\n    msg = (\n        \"some-message\",\n        \"Some description\",\n        RuleSeverity.WARNING\n    )\n    return Rule('0101', msg)\n\n\n@pytest.fixture\ndef valid_msg_with_conf():\n    msg = (\n        \"some-message\",\n        \"Some description\",\n        RuleSeverity.WARNING,\n        ('param_name', 'param_priv_name', int)\n    )\n    return Rule('0101', msg)\n\n\nINVALID_MSG_MISSING_SEVERITY = (\n    \"some-message\",\n    \"Some description\",\n)\n\n\nINVALID_MSG_MISSING_DESC_SEV = (\n    \"some-message\"\n)\n\n\nclass TestMessage:\n    def test_get_fullname(self, valid_msg):  # noqa\n        msg = valid_msg.prepare_message(source=None, node=None, lineno=None, col=None, end_lineno=None, end_col=None)\n        assert msg.get_fullname() == 'W0101 (some-message)'\n\n    @staticmethod\n    def change_severity(msg, severity):\n        msg.change_severity(severity)\n        return msg\n\n    @pytest.mark.parametrize('severity, exp_sev', [\n        ('e', 'E'),\n        ('error', 'E'),\n        ('i', 'I'),\n        ('info', 'I'),\n        ('w', 'W'),\n        ('warning', 'W'),\n        ('eRROr', 'E'),\n        ('Warning', 'W')\n    ])\n    def test_change_message_severity(self, valid_msg, severity, exp_sev):  # noqa\n        assert TestMessage.change_severity(valid_msg, severity).severity.value == exp_sev\n\n    @pytest.mark.parametrize('severity', [\n        'invalid',\n        1,\n        'errorE',\n        None,\n        dict()\n    ])\n    def test_change_message_severity_invalid(self, valid_msg, severity):  # noqa\n        with pytest.raises(robocop.exceptions.InvalidRuleSeverityError) as err:\n            valid_msg.change_severity(severity)\n        assert rf\"Fatal error: Tried to configure message some-message with invalid severity: {severity}\" in str(err)\n\n    def test_get_configurable_existing(self, valid_msg_with_conf):  # noqa\n        assert valid_msg_with_conf.get_configurable('param_name') == ('param_name', 'param_priv_name', int)\n\n    def test_get_configurable_empty_configurables(self, valid_msg):  # noqa\n        assert valid_msg.get_configurable('param_name') is None\n\n    def test_get_configurable_non_existing(self, valid_msg_with_conf):  # noqa\n        assert valid_msg_with_conf.get_configurable('invalid_param') is None\n\n    @pytest.mark.parametrize('msg', [\n        INVALID_MSG_MISSING_SEVERITY,\n        INVALID_MSG_MISSING_DESC_SEV,\n        (),\n        None,\n        1\n    ])\n    def test_parse_invalid_body(self, msg):\n        with pytest.raises(robocop.exceptions.InvalidRuleBodyError) as err:\n            Rule('0101', msg)\n        assert rf\"Fatal error: Rule '0101' has invalid body:\\n{msg}\" in str(err)\n\n    @pytest.mark.parametrize('configurable', [\n        [None],\n        [1],\n        [()],\n        [('some', 'some', int, 5, 5)],\n        [('some', 'some', str), None]\n    ])\n    def test_parse_invalid_configurable(self, configurable):\n        msg = (\n            \"some-message\",\n            \"Some description\",\n            RuleSeverity.WARNING\n        )\n        body = msg + tuple(configurable)\n        with pytest.raises(robocop.exceptions.InvalidRuleConfigurableError) as err:\n            Rule('0101', body)\n        assert rf\"Fatal error: Rule '0101' has invalid configurable:\\n{body}\" in str(err)\n\n    @pytest.mark.parametrize('configurable', [\n        [('some', 'some', int)],\n        [(1, 2, 3)],\n        [('some', 'some', int), ('some2', 'some2', str)]\n    ])\n    def test_parse_valid_configurable(self, configurable):\n        msg = (\n            \"some-message\",\n            \"Some description\",\n            RuleSeverity.WARNING\n        )\n        body = msg + tuple(configurable)\n        rule = Rule('0101', body)\n        assert rule.configurable == configurable\n\n    @pytest.mark.parametrize('source, range, range_exp', [\n        ('path/to/file1.robot', (None, None, None, None), (10, 0, 10, 0)),\n        ('path/to/file1.robot', (15, None, None, 7), (15, 0, 15, 7)),\n        ('path/to/file1.robot', (None, 20, 20, None), (10, 20, 20, 20)),\n        ('path/to/file2.robot', (15, 200, None, None), (15, 200, 15, 200))\n    ])\n    def test_prepare_message(self, valid_msg, source, range, range_exp):  # noqa\n        node = ast.AST()\n        node.lineno = 10\n        lineno, col, end_lineno, end_col = range\n        lineno_exp, col_exp, end_lineno_exp, end_col_exp = range_exp\n        msg = valid_msg.prepare_message(\n            source=source,\n            node=node,\n            lineno=lineno,\n            col=col,\n            end_lineno=end_lineno,\n            end_col=end_col\n        )\n        assert msg.line == lineno_exp\n        assert msg.col == col_exp\n        assert msg.end_line == end_lineno_exp\n        assert msg.end_col == end_col_exp\n        assert msg.source == source\n\n    @pytest.mark.parametrize('args, desc, exp_error', [\n        ([], \"Some description %s and %d\", \"not enough arguments for format string\"),\n        ([1, 'smth'], \"Some description %s and %d\", \"%d format: a number is required, not str\"),\n        (['smth'], \"Some description %s and %d\", \"not enough arguments for format string\"),\n        (['smth', 1, 10], \"Some description %s and %d\", \"not all arguments converted during string formatting\"),\n        ([10], \"Some description\", \"not all arguments converted during string formatting\"),\n    ])\n    def test_prepare_invalid_message_invalid_arg(self, valid_msg, args, desc, exp_error):  # noqa\n        node = ast.AST()\n        node.lineno = 10\n        valid_msg.desc = desc\n        with pytest.raises(robocop.exceptions.InvalidRuleUsageError) as err:\n            valid_msg.prepare_message(\n                *args,\n                source='file1.robot',\n                node=node,\n                lineno=None,\n                col=None,\n                end_lineno=None,\n                end_col=None\n            )\n        assert rf\"Fatal error: Rule '0101' failed to prepare message description with error: {exp_error}\" in str(err)\n","repo_name":"TrendingTechnology/robotframework-robocop","sub_path":"tests/utest/test_messages.py","file_name":"test_messages.py","file_ext":"py","file_size_in_byte":5985,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"81"}
+{"seq_id":"19114749530","text":"import struct\nimport os\n\ndef read(path:str):\n    f = open(path, \"rb\")\n    arr = []\n    for i in range(1000):\n        byte = struct.unpack('f', f.read(4))\n        arr.append(byte[0])\n\n    return arr\n\ndef convert(path:str):\n    new_path = path.split('.')[0] + '.txt'\n    f = open(\"../files/\" + new_path, 'w')\n    arr = read(path)\n    for line in arr:\n        f.write(str(line) + \"\\n\")\n\ndef listdir(path:str):\n    return [str(f) for f in os.listdir(path)]\n\ndef convertAll(path:str):\n    arr = listdir(path)\n    #print(arr)\n    for f in arr:\n        convert(path+f)\n\ndef indexfile(path:str):\n    arr = []\n    for f in listdir(path):\n        try:\n            if f.split('.')[1] == 'txt':\n                arr.append(f)\n        except:\n            continue\n    return arr\ndef createIndexFile(arr):\n    f = open('./indexfile.txt', 'w')\n    for l in arr:\n        f.write(l+'\\n')\ntry:        \n    convertAll('orig/')\nexcept:\n    pass\ncreateIndexFile(indexfile('./'))\n","repo_name":"fernandomflopes/UsandoOpenMpi","sub_path":"files/orig/convert.py","file_name":"convert.py","file_ext":"py","file_size_in_byte":957,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"73987367944","text":"from django.urls import path\nfrom .views import *\nfrom . import views\n\nurlpatterns = [\n    # Home related urls-----------------------------------------------------------------------------\n    path('upload_uace/', UACEUploadView.as_view(), name='upload_uace' ),\n    path('upload_uce/', UCEUploadView.as_view(), name='upload_uce' ),\n    path('upload_upe/', UPEUploadView.as_view(), name='upload_upe' ),\n    path('upload_service_provider/', ServiceProviderUploadView.as_view(), name='upload_service_provider' ),\n    path('upload_district/', DistrictUploadView.as_view(), name='upload_district' ),\n    path('upload_county/', CountyUploadView.as_view(), name='upload_county' ),\n    path('upload_subcounty/', SubCountyUploadView.as_view(), name='upload_subcounty' ),\n    path('upload_parish/', ParishUploadView.as_view(), name='upload_parish' ),\n    path('upload_school/', SchoolUploadView.as_view(), name='upload_school' ),\n    path('upload_secondary_school/', SecondarySchoolUploadView.as_view(), name='upload_secondary_school' ),\n    path('upload_tertiary_school/', TertiarySchoolUploadView.as_view(), name='upload_tertiary_school' ),\n    path('', views.home, name='ministry-home'),\n    path('schools_in_region_chart/<int:pk>/', views.schools_region_chart, name='schools-in-region-chart'),\n    path('schools_in_year_chart/<int:pk>/', views.schools_year_chart, name='schools-in-year-chart'),\n    path('operation_status_chart/<int:pk>/', views.operation_status_chart, name='operation-status-chart'),\n    path('founder_chart/<int:pk>/', views.founder_chart, name='founder-chart'),\n    path('funder_chart/<int:pk>/', views.funder_chart, name='funder-chart'),\n    path('category_chart/<int:pk>/', views.category_chart, name='category-chart'),\n    path('section_chart/<int:pk>/', views.section_chart, name='section-chart'),\n    path('registry_chart/<int:pk>/', views.registry_status_chart, name='registry-status-chart'),\n    path('rural_urban_chart/<int:pk>/', views.rural_urban_chart, name='rural-urban-chart'),\n    path('access_chart/<int:pk>/', views.access_chart, name='access-chart'),\n    path('nearest_school_chart/<int:pk>/', views.nearest_school_chart, name='nearest-school-chart'),\n    path('deo_office_chart/<int:pk>/', views.deo_office_chart, name='deo-office-chart'),\n    path('pie-chart/', views.pie_chart, name='pie-chart'),\n    # Student's Tab ----------------------------------------------------------------------------\n    path('students/<int:pk>/repeaters/', views.repeaters, name='repeaters'),\n    path('students/<int:pk>/nationality/', views.nationality, name='nationality'),\n    path('students/<int:pk>/physical_streams/', views.physical_streams, name='physical-streams'),\n    path('students/<int:pk>/orphans/', views.orphans, name='orphans'),\n    path('students/<int:pk>/special_needs/', views.special_needs, name='special-needs'),\n    path('students/<int:pk>/seating_and_writing_space/', views.seating_and_writing_space, name='seating-and-writing-space'),\n    path('students/<int:pk>/transfered_students/', views.transfered_students, name='transfered-students'),\n    path('students/<int:pk>/examinations/', views.examinations, name='examinations'),\n    path('students/<int:pk>/proposed_intake/', views.proposed_intake, name='proposed-intake'),\n    path('students/<int:pk>/new_entrants/', views.new_entrants, name='new-entrants'),\n    path('students/<int:pk>/enrolments/', views.enrolments, name='enrolments'),\n    path('ministry_add_students/<int:pk>/enrolments/', views.ministry_add_students, name='ministry-add-students'),\n    # School related urls-----------------------------------------------------------------------------\n    path('schools/<int:pk>/district', views.view_schools, name='schools'),\n    path('schools/<int:pk>/op_status', views.operation_status, name='operation-status'),\n    path('schools/<int:pk>/founder', views.founder, name='founder'),\n    path('schools/<int:pk>/funder', views.funders, name='funder'),\n    path('schools/<int:pk>/category', views.category, name='categories'),\n    path('schools/<int:pk>/section', views.section, name='sections'),\n    path('schools/<int:pk>/reg_status', views.registration_status, name='registration-status'),\n    path('schools/<int:pk>/access', views.access, name='accesses'),\n    path('schools/<int:pk>/urban', views.rural_urban, name='rural-urban'),\n    path('schools/<int:pk>/nearest_school', views.nearest_school, name='nearest-school'),\n    path('schools/<int:pk>/nearest_deo', views.nearest_deo, name='nearest-deo'),\n    path('schools/new/', SchoolCreateView.as_view(), name='new-school'),\n    path('school/<int:pk>/details', SchoolDetailView.as_view(), name='school-details'),\n    path('schools/<int:pk>/update/', SchoolUpdateView.as_view(), name='update-school'),\n    path('schools/<int:pk>/facility/',FacilityCreateView.as_view(), name='new-facility'),\n    path('schools/ownership/', OwnershipCreateView.as_view(), name='ownership'),\n    path('schools/ownership/<int:pk>/update/', OwnershipUpdateView.as_view(), name='ownership-update'),\n    path('schools/access/', AccessCreateView.as_view(), name='access'),\n    path('schools/access/<int:pk>/update/', AccessUpdateView.as_view(), name='access-update'),\n    path('schools/section/', SectionCreateView.as_view(), name='section'),\n    path('schools/section/<int:pk>/update/', SectionUpdateView.as_view(), name='section-update'),\n    path('schools/level/', LevelCreateView.as_view(), name='level'),\n    path('schools/level/<int:pk>/update/', LevelUpdateView.as_view(), name='level-update'),\n    path('schools/category/', CategoryCreateView.as_view(), name='category'),\n    path('schools/category/<int:pk>/update/', CategoryUpdateView.as_view(), name='category-update'),\n    path('schools/schtype/', SchtypeCreateView.as_view(), name='schtype'),\n    path('schools/schtype/<int:pk>/update/', SchtypeUpdateView.as_view(), name='schtype-update'),\n    path('schools/regstatus/', RegstatusCreateView.as_view(), name='regstatus'),\n    path('schools/regstatus/<int:pk>/update/', RegstatusUpdateView.as_view(), name='regstatus-update'),\n    path('schools/cennostatus/', CennostatusCreateView.as_view(), name='cennostatus'),\n    path('schools/cennostatus/<int:pk>/update/', CennostatusUpdateView.as_view(), name='cennostatus-update'),\n    path('schools/district/<int:pk>/update/', DistrictUpdateView.as_view(), name='district-update'),\n    path('schools/district/', DistrictCreateView.as_view(), name='district'),\n    path('schools/region/', RegionCreateView.as_view(), name='region'),\n    path('schools/region/<int:pk>/update/', RegionUpdateView.as_view(), name='region-update'),\n    # Facility related urls-----------------------------------------------------------------------------\n    path('schools/factype/', FacilityTypeView.as_view(), name='factype'),\n    path('schools/factype/<int:pk>/update/', FacilityTypeUpdateView.as_view(), name='factype-update'),\n    path('schools/facstatus/', FacilityStatusView.as_view(), name='facstatus'),\n    path('schools/facstatus/<int:pk>/update/', FacilityStatusUpdateView.as_view(), name='facstatus-update'),\n    \n    # Teacher related urls-----------------------------------------------------------------------------\n\n    path('teachers/<int:pk>/region', views.region_teachers, name='region-teachers'),\n    path('teachers/<int:pk>/non_teaching_staff', views.non_teaching_staff, name='non-teaching-staff'),\n    path('teachers/<int:pk>/qualification', views.qualification_teachers, name='qualification-teachers'),\n    path('teachers/<int:pk>/left_teachers', views.left_teachers, name='left-teachers'),\n    path('teachers/<int:pk>/education', views.education_teachers, name='education-teachers'),\n    path('teachers/', views.teachers, name='strack-teachers'),\n    path('teachers/new/', TeacherCreateView.as_view(), name='new-teacher'),\n    path('teachers/view/', TeacherListView.as_view(), name='view-teachers'),\n    path('teachers/transfer/view/', TeacherTransferListView.as_view(), name='view-teacher-transfers'),\n    path('teachers/<int:pk>/', TeacherDetailView.as_view(), name='teacher-details'),\n    path('teachers/<int:pk>/update/', TeacherUpdateView.as_view(), name='update-teacher'),\n    path('teachers/gender/', GenderCreateView.as_view(), name='gender'),\n    path('teachers/gender/<int:pk>/update/', GenderUpdateView.as_view(), name='update-gender'),\n    path('teachers/status/', TeacherStatusCreateView.as_view(), name='teacher-status'),\n    path('teachers/status/<int:pk>/update/', TeacherStatusUpdateView.as_view(), name='update-teacher-status'),\n    path('teachers/transfer/', TransferTeacherCreateView.as_view(), name='transfer-teacher'),\n    path('teachers/transfer/<int:pk>/update/', TransferTeacherUpdateView.as_view(), name='update-transfer'),\n    path('teachers/designation/', DesignationCreateView.as_view(), name='designation'),\n    path('teachers/designation/<int:pk>/update/', DesignationUpdateView.as_view(), name='update-designation'),\n    # DEO related urls-----------------------------------------------------------------------------\n    path('DEOs/', views.deos, name='strack-deos'),\n    path('DEOs/new/', DeoCreateView.as_view(), name='new-deo'),\n    path('DEOs/view/', DeoListView.as_view(), name='view-deos'),\n    path('DEOs/<int:pk>/', DeoDetailView.as_view(), name='deo-details'),\n    path('DEOs/<int:pk>/update/', DeoUpdateView.as_view(), name='update-deo'),\n    path('DEOs/transfer/', TransferDeoCreateView.as_view(), name='transfer-deo'),\n    path('DEOs/transfer/<int:pk>/update/', TransferDeoUpdateView.as_view(), name='update-deo-transfer'),\n    # Resource related urls-----------------------------------------------------------------------------\n    path('infrastructure/<int:pk>/rooms', views.building_rooms, name='building-rooms'),\n    path('infrastructure/<int:pk>/rooms_needed', views.rooms_needed, name='rooms-needed'),\n    path('infrastructure/<int:pk>/rooms_under_construction', views.rooms_under_construction, name='rooms-under-construction'),\n    path('resources/', views.resources, name='strack-resources'),\n    path('resources/type/', ResourceTypeCreateView.as_view(), name='resource-type'),\n    path('resources/type/<int:pk>/update/', ResourceTypeUpdateView.as_view(), name='update-resource-type'),\n    path('resources/source/', ResourceSourceCreateView.as_view(), name='resource-source'),\n    path('resources/source/<int:pk>/update/', ResourceSourceUpdateView.as_view(), name='update-resource-source'),\n    path('resources/resource/', ResourceCreateView.as_view(), name='resource'),\n    path('resources/resource/<int:pk>/', ResourceDetailView.as_view(), name='resource-details'),\n    path('resources/view/', ResourceListView.as_view(), name='view-resources'),\n    path('resources/resource/<int:pk>/update/', ResourceUpdateView.as_view(), name='update-resource'),\n    path('resources/allocate/', AllocateResourceCreateView.as_view(), name='allocate-resource'),\n    path('resources/allocate/view/', AllocateResourceListView.as_view(), name='allocated-resources'),\n    path('resources/allocate/<int:pk>/update/', AllocateResourceUpdateView.as_view(), name='update-allocate-resource'),\n    # Marketing related urls-----------------------------------------------------------------------------\n    path('marketing/', views.marketing, name='strack-marketing'),\n    path('marketing/new-product/', ProductCreateView.as_view(), name='product'),\n    path('marketing/<int:pk>/update/', ProductUpdateView.as_view(), name='update-product'),\n    path('marketing/<int:pk>/', ProductDetailView.as_view(), name='product-detail'),\n    path('marketing/user-products/', UserProducts.as_view(), name='user-products'),\n    path('marketing/product-status/', ProductStatusCreateView.as_view(), name='product-status'),\n    path('marketing/product-status/<int:pk>/update/', ProductStatusUpdateView.as_view(), name='update-product-status'),\n\n    # Communication related urls-----------------------------------------------------------------------------\n    path('communication/', views.communication, name='communication'),\n    path('resource/', views.download_resource, name='download-resource'),\n    # Settings related urls-----------------------------------------------------------------------------\n    path('settings/', views.settings, name='strack-settings'),\n    path('subjects/', SubjectCreateView.as_view(), name='subject'),\n    path('subjects/<int:pk>/update/', SubjectUpdateView.as_view(), name='update-subject'),\n\n]\n","repo_name":"ssempuuma-moses-mozart/Oncisms","sub_path":"ministry/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":12413,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"72465031944","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sat Mar 28 21:45:36 2020\n\n@author: whc\n\"\"\"\nimport random\ndef read_txt(txt_path):\n    f = open(txt_path,\"r\")   \n    data_list = f.readlines()\n    f.close()         \n    return data_list\n\ndef randm_list(num_list,rscale):\n    new_num_list = []\n    for num in num_list:\n        rd =(random.randint(0,rscale*100))/100*(-1)**(random.randint(0,4))\n        new_num_list.append(float(num[:-1]) + rd)\n    return new_num_list\n    \ndef print_list(l):\n    for ll in l:\n        print(ll)\n    \ndef main():\n    txt_path = \"vv.txt\"\n    rscale = 6\n    print_list(randm_list(read_txt(txt_path),rscale))\n    \nmain()","repo_name":"hanchenn/python_demos","sub_path":"getfile_randomly.py","file_name":"getfile_randomly.py","file_ext":"py","file_size_in_byte":634,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"4864961240","text":"import numpy as np\nfrom PIL import Image, ImageDraw\n\n\ndef get_bb(x, y, width, height, angle):\n    bb = np.array([(0, 0), (width, 0), (width, height), (0, height), (0, 0)])\n    theta = (np.pi / 180.0) * angle\n    R = np.array([[np.cos(theta), -np.sin(theta)], [np.sin(theta), np.cos(theta)]])\n    offset = np.array([x, y])\n    transformed_bb = np.dot(bb, R) + offset\n    return transformed_bb\n\n\ndef GT(x, y, w, h, sx, sy, label, bg):\n    gt = np.ones([sx, sy]) * bg\n    gt = Image.fromarray(gt)\n    draw = ImageDraw.Draw(gt)\n    bb = get_bb(x, y, w, h, 0)\n    draw.polygon([tuple(p) for p in bb], fill=label)\n\n    return (gt)\n","repo_name":"MohsenFayyaz89/C3DSegmentation","sub_path":"generateGT.py","file_name":"generateGT.py","file_ext":"py","file_size_in_byte":625,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"81"}
+{"seq_id":"39296504032","text":"\nclass ROI_Cal():\n    \"\"\"\n    Project to use in the future.\n    total income, total expenses, total invested\n    *** total income and total expenses --- on a month to month basis***\n    ***cash flow --- income minus expenses***\n    *** total invested --- how much you paid to acquire the property.***\n    ***annual cash flow --- cash flow on a yearly basis***\n    \"\"\"   \n    def __init__(self):\n        self.total_income = 0\n        self.total_expenses = 0\n        self.total_invested = 0\n        self.annual_cash = 0\n\n    def income_cal(self):\n        inc = int(input(\"input your total income on a monthly basis for one or more properties. \"))\n        self.total_income = inc\n        print(self.total_income)\n\n    def exp_cal(self):\n        exp = int(input(\"input your total expenses on a monthly basis for one or more properties. \"))\n        self.total_expenses = exp \n        print(self.total_expenses)\n\n    def cash_f(self):\n        flow = self.total_income - self.total_expenses\n        self.annual_cash = flow * 12\n        print(flow)\n        print(self.annual_cash)\n\n    def Roi(self):\n        totIn = int(input(\"input your total invested to acquire the property \"))\n        self.total_invested = totIn\n        ROI = self.annual_cash / self.total_invested * 100\n        print(f\"Your Rate of return for this property portfolio is{ROI}%\")\n        print(f\"The average return of the S&P500 is 10%\")\n        print(f\"The average return of the U.S overall stock market is 8%\")\n        print(f\"the average return of 401Ks and pension funds are 7%\")\n\n    def run(self):\n        self.income_cal()\n        self.exp_cal()\n        self.cash_f()\n        self.Roi()\n\n\nmyrental = ROI_Cal()\nmyrental.run()","repo_name":"qwertyNippon/week3BiggerPockectsHw","sub_path":"biggerPocketsHw.py","file_name":"biggerPocketsHw.py","file_ext":"py","file_size_in_byte":1695,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"22862330920","text":"from django.shortcuts import render, redirect\nfrom django.contrib import auth, messages\nfrom accounts import forms\nfrom todolist import models, forms\n\nfrom django.contrib.auth.decorators import login_required\n\n@login_required(login_url='/user/login/')\ndef menu(request):\n    all_todo = models.Todo.objects.all()\n    return render(request, 'menu.html', {'todos': all_todo})\n\n@login_required(login_url='/user/login/')\ndef detail(request, todo_id):\n    todo = models.Todo.objects.get(id=todo_id)\n    return render(request, 'detail.html', {'todo': todo})\n\n@login_required(login_url='/user/login/')\ndef create(request):\n    if request.method == \"POST\":\n        form = forms.CreateTodoForm(request.POST)\n        if form.is_valid():\n            form.save()\n            messages.success(request, \"successfully created todo\", \"success\")\n            return redirect('menu')\n        else:\n            messages.error(request, \"unsuccessfully create Form\", \"danger\")\n            return redirect('menu')\n    else:\n        form = forms.CreateTodoForm()\n        return render(request, 'create.html', {'create_form': form})\n\n@login_required(login_url='/user/login/')\ndef update(request, todo_id):\n    todo = models.Todo.objects.get(id=todo_id)\n    if request.method == \"POST\":\n        form = forms.UpdateTodoForm(request.POST, instance=todo)\n        if form.is_valid():\n            form.save()\n            messages.success(request, \"successfully\", \"success\")\n            return redirect('menu')\n        else:\n            messages.error(request, \"Unsuccessfully update todo\", \"danger\")\n            return redirect('menu')\n    else:\n        form = forms.UpdateTodoForm(instance=todo)\n        return render(request, 'update.html', {'update_form': form})        \n\n@login_required(login_url='/user/login/')    \ndef delete(request, todo_id):\n    todo = models.Todo.objects.get(id=todo_id).delete()\n    messages.success(request, \"todo Successfully deleted\", 'success')\n    return redirect('menu')\n","repo_name":"ArmiinJP/Learn_Django","sub_path":"third-Project/todolist/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1973,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"19642465571","text":"\r\n# getting data\r\nwith open(\"day1.in\") as file:\r\n    data = [i for i in file.read().strip().split(\"\\n\")]\r\n\r\n# print(data)\r\n\r\n# stating my variables\r\nmax_value = 0\r\nmax2 = 0\r\nmax3 = 0\r\ncount = 0\r\n# if statement counting the totals\r\nfor item in data:\r\n    if item == '':\r\n        count = 0\r\n    else:\r\n        num = int(item)\r\n        count += num\r\n\r\n    if count > max_value:\r\n        max_value = count\r\n    elif count > max2:\r\n        max2 = count\r\n    elif count > max3:\r\n        max3 = count\r\n\r\nprint(\"Answer to part 1:\", max_value)\r\nprint(\"Answer to part 2:\", max_value+max2+max3)\r\n","repo_name":"JoannaKonerska/Advent_of_code_2022","sub_path":"day1.py","file_name":"day1.py","file_ext":"py","file_size_in_byte":585,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"72744187786","text":"import sys\na = int(sys.stdin.readline())\nsys.setrecursionlimit(10**6)\nresult = []\nfor _ in range(a):\n  m,n,k = map(int,sys.stdin.readline().split())\n  kimchi = [[0]*m for _ in range(n)]\n  visit = [[False]*m for _ in range(n)]\n  count =0\n  def dfs(x,y):\n    dx = [0,1,0,-1]\n    dy = [1,0,-1,0]\n    if x<0 or x>=n or y>=m or y<0:\n      return False\n    if visit[x][y]==False and kimchi[x][y]==1:\n      visit[x][y] = True\n      for i in range(4):\n        cdx = x+dx[i]\n        cdy = y+dy[i]\n        dfs(cdx,cdy)\n      return True\n    return False\n  for i in range(k):\n    c,d = map(int , sys.stdin.readline().split())\n    kimchi[d][c] = 1\n  for i in range(n):\n    for j in range(m):\n      if dfs(i,j)==True:\n        count+=1\n  result.append(count)\nfor i in result:\n  print(i)\n  ","repo_name":"kangjunyoung37/algorizm-python","sub_path":"탐색/1012번 유기농배추.py","file_name":"1012번 유기농배추.py","file_ext":"py","file_size_in_byte":775,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"81"}
+{"seq_id":"41682894486","text":"# -*- coding: utf-8 -*-\n\n# Import Python libs\nfrom __future__ import absolute_import\nimport logging\n\n# Import Salt libs\nimport salt.minion\nimport salt.utils.verify\nimport salt.utils.jid\nfrom salt.utils.event import tagify\n\n\nlog = logging.getLogger(__name__)\n\n\ndef store_job(opts, load, event=None, mminion=None):\n    # If the return data is invalid, just ignore it\n    if any(key not in load for key in ('return', 'jid', 'id')):\n        return False\n    if not salt.utils.verify.valid_id(opts, load['id']):\n        return False\n    if mminion is None:\n        mminion = salt.minion.MasterMinion(opts, states=False, rend=False)\n    if load['jid'] == 'req':\n        # The minion is returning a standalone job, request a jobid\n        load['arg'] = load.get('arg', load.get('fun_args', []))\n        load['tgt_type'] = 'glob'\n        load['tgt'] = load['id']\n        prep_fstr = '{0}.prep_jid'.format(opts['master_job_cache'])\n        load['jid'] = mminion.returners[prep_fstr](\n            nocache=load.get('nocache', False))\n\n        # save the load, since we don't have it\n        saveload_fstr = '{0}.save_load'.format(opts['master_job_cache'])\n        mminion.returners[saveload_fstr](load['jid'], load)\n    elif salt.utils.jid.is_jid(load['jid']):\n        # Store the jid\n        jidstore_fstr = '{0}.prep_jid'.format(opts['master_job_cache'])\n        mminion.returners[jidstore_fstr](False, passed_jid=load['jid'])\n    if event:\n        # If the return data is invalid, just ignore it\n        log.info('Got return from {id} for job {jid}'.format(**load))\n        event.fire_event(\n            load, tagify([load['jid'], 'ret', load['id']], 'job'))\n        event.fire_ret_load(load)\n\n    # if you have a job_cache, or an ext_job_cache, don't write to\n    # the regular master cache\n    if not opts['job_cache'] or opts.get('ext_job_cache'):\n        return\n\n    # otherwise, write to the master cache\n    fstr = '{0}.returner'.format(opts['master_job_cache'])\n    mminion.returners[fstr](load)\n# vim:set et sts=4 ts=4 tw=80:\n","repo_name":"c-nixon/salt","sub_path":"salt/utils/job.py","file_name":"job.py","file_ext":"py","file_size_in_byte":2026,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"81"}
+{"seq_id":"13980607289","text":"class Node:\r\n    def __init__(self, value):\r\n        self.value = value\r\n        self.next = None\r\n        self.prev = None\r\n\r\n\r\nclass Queue:\r\n    def __init__(self):\r\n        self.head = None\r\n        self.tail = None\r\n\r\n    def put(self, value):\r\n        node = Node(value)\r\n        if self.tail:\r\n            self.tail.next = node\r\n            node.prev = self.tail\r\n        else:\r\n            self.head = node\r\n        self.tail = node\r\n\r\n    def get(self):\r\n        if self.head:\r\n            value = self.head.value\r\n            self.head = self.head.next\r\n            if self.head:\r\n                self.head.prev = None\r\n            return value\r\n\r\n    def min(self):\r\n        node = self.head\r\n        min_value = node.value\r\n        while node:\r\n            min_value = min(min_value, node.value)\r\n            node = node.next\r\n        return min_value\r\n\r\n    def max(self):\r\n        node = self.head\r\n        max_value = node.value\r\n        while node:\r\n            max_value = max(max_value, node.value)\r\n            node = node.next\r\n        return max_value\r\n\r\nqueue = Queue()\r\n\r\n# Добавляем элементы\r\nqueue.put(10)\r\nqueue.put(5)\r\nqueue.put(20)\r\n\r\n# Проверяем минимум\r\nprint(\"Минимум:\", queue.min())\r\n\r\n# Добавляем еще\r\nqueue.put(3)\r\n\r\n# Проверяем минимум снова\r\nprint(\"Новый минимум:\", queue.min())\r\n\r\n# Проверяем максимум\r\nprint(\"Максимум:\", queue.max())\r\n\r\n# Добавляем еще\r\nqueue.put(30)\r\n\r\n# Проверяем новый максимум\r\nprint(\"Новый максимум:\", queue.max())\r\n","repo_name":"FyGeR2017/PythonSkillBox","sub_path":"skillbox/mod5/task2.py","file_name":"task2.py","file_ext":"py","file_size_in_byte":1632,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"28235727055","text":"from PyQt6.QtWidgets import  QMainWindow, QFileDialog,QApplication\r\nfrom PyQt6.QtCore import Qt\r\nfrom PyQt6 import QtGui\r\nfrom PyQt6.uic import loadUi\r\nfrom IntermidiateWindow import *\r\nfrom ErrorWindow import *\r\nimport sys\r\n\r\n\r\nclass MainWindow(QMainWindow):\r\n    def __init__(self):\r\n        super(MainWindow,self).__init__()\r\n        loadUi(\"MainWindow.ui\",self)\r\n\r\n        self.currentPath = None\r\n        self.compiler = None\r\n        self.numberLine()\r\n        self.init()\r\n        self.textEdit.setPlaceholderText(\"Type your text here...\")\r\n        self.setWindowIcon(QtGui.QIcon('compiler.png'))\r\n\r\n        self.actionNew.triggered.connect(self.newFile)\r\n        self.actionSave.triggered.connect(self.saveFile)\r\n        self.actionSave_as.triggered.connect(self.saveFileAs)\r\n        self.actionOpen.triggered.connect(self.openFile)\r\n        self.actionUndo.triggered.connect(self.undo)\r\n        self.actionRedo.triggered.connect(self.redo)\r\n        self.actionCut.triggered.connect(self.cut)\r\n        self.actionCopy.triggered.connect(self.copy)\r\n        self.actionPaste.triggered.connect(self.paste)\r\n        self.pushButton.clicked.connect(self.compile)\r\n        self.pushButton1.clicked.connect(self.selectCompiler)\r\n\r\n    def newFile(self):\r\n        self.textEdit.clear()\r\n        self.setWindowTitle(\"Untitled\")\r\n        self.currentPath = None\r\n\r\n    def saveFile(self):\r\n        if self.currentPath is not None:\r\n            with open(self.currentPath, 'w') as file:\r\n                file.write(self.textEdit.toPlainText())\r\n        else:\r\n            self.saveFileAs()\r\n\r\n    def saveFileAs(self):\r\n        pathName = QFileDialog.getSaveFileName(self, 'Save file', '', 'Cimple files(*.ci)')\r\n        if(pathName[0]!=\"\"):\r\n            with open(pathName[0], 'w') as file:\r\n                file.write(self.textEdit.toPlainText())\r\n            self.currentPath = pathName[0]\r\n            self.setWindowTitle(pathName[0])\r\n        else:\r\n            return\r\n\r\n    def openFile(self):\r\n        fileName = QFileDialog.getOpenFileName(self, 'Open file','', 'Cimple files(*.ci)')\r\n        if(fileName[0]!=\"\"):\r\n            self.setWindowTitle(fileName[0])\r\n            with open(fileName[0], 'r') as file:\r\n                fileText = file.read()\r\n                self.textEdit.setText(fileText)\r\n            self.currentPath = fileName[0]\r\n        else:\r\n            return\r\n        \r\n    def selectCompiler(self):\r\n        fileName = QFileDialog.getOpenFileName(self, 'Choose Compiler','', 'Python files (*.py)')\r\n        self.compiler = fileName[0]\r\n\r\n    def undo(self):\r\n        self.textEdit.undo()\r\n\r\n    def redo(self):\r\n        self.textEdit.redo()\r\n\r\n    def copy(self):\r\n        self.textEdit.copy()\r\n\r\n    def cut(self):\r\n        self.textEdit.cut()\r\n\r\n    def paste(self):\r\n        self.textEdit.paste()\r\n\r\n    def compile(self):\r\n        if self.compiler == '' or self.compiler == None :\r\n            self.ErrWindow = ErrorWindow()\r\n            self.ErrWindow.show()\r\n            self.ErrWindow.label.setText(\"Choose Compiler first\")\r\n            self.ErrWindow.setWindowTitle(\"Error Window\") \r\n        else:\r\n            self.InterWindow = IntermidiateWindow()\r\n            self.InterWindow.setPath(self.currentPath)\r\n            self.InterWindow.setCompiler(self.compiler)\r\n            self.InterWindow.run()\r\n        \r\n      \r\n    def numberLine(self):\r\n        self.textEdit.textChanged.connect(self.updateLineNumbers)\r\n        self.lineNumberEdit = self.textBrowser\r\n        self.lineNumberEdit.setVerticalScrollBarPolicy(Qt.ScrollBarPolicy.ScrollBarAlwaysOff)\r\n            \r\n    def updateLineNumbers(self):\r\n        text = self.textEdit.toPlainText()\r\n        lineCount = text.count('\\n') + 1\r\n        lineNumbersText = '\\n'.join(map(str, range(1, lineCount + 1)))\r\n        self.lineNumberEdit.setPlainText(lineNumbersText)\r\n\r\n    def init(self):\r\n        self.textEdit.verticalScrollBar().valueChanged.connect(self.syncScrollBars)\r\n        self.textBrowser.verticalScrollBar().valueChanged.connect(self.syncScrollBars)\r\n\r\n    def syncScrollBars(self):\r\n        scrollValue = self.sender().value()\r\n        self.textEdit.verticalScrollBar().setValue(scrollValue)\r\n        self.textBrowser.verticalScrollBar().setValue(scrollValue)\r\n\r\n    def resizeEvent(self, event):\r\n        newSize = event.size()\r\n        self.textEdit.setGeometry(50, 30, newSize.width() - 70, newSize.height() - 120)\r\n        self.textBrowser.setGeometry(10, 30, 51, newSize.height() - 120)\r\n        self.pushButton.move(10, newSize.height() - 80)\r\n        self.pushButton1.move(newSize.width()-100, 0)\r\n        self.label.move(newSize.width()-150, 0)\r\n    \r\n\r\n\r\nif __name__ == '__main__':\r\n    app = QApplication(sys.argv)\r\n    ui = MainWindow()\r\n    ui.show()\r\n    app.exec()    \r\n","repo_name":"KostasTritsonis/compiler","sub_path":"New_folder/MainWindow.py","file_name":"MainWindow.py","file_ext":"py","file_size_in_byte":4785,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"10331703069","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nimport nltk as nltk\nfrom collections import Counter\nimport itertools\n\n''' \nAuthor: Chris Hokamp\nVectorBuilder uses NLTK to take a document (a string), and return a vector of word counts\n    - look at a list of tuples (feature, class)\n\n'''\n\n# arg 'documents' is assumed to be a list of tuples, where each tuple is of the form (string, classLabel)\nclass WordVectorBuilder:\n    def __init__(self, lang, documents):\n        self.sentence_tokenizer = nltk.data.load('tokenizers/punkt/english.pickle')\n        self.word_tokenize = nltk.tokenize.word_tokenize\n        self.stemmer = nltk.stem.snowball.SnowballStemmer(lang)\n        self.vectorCollection = self.buildCollection(documents)\n        self.features, self.classes = self.uniqueFeaturesAndClasses()\n\n    #returns a list of tuples with the format ({docVector}, class)\n    def buildCollection(self, documents):\n        return [(self.getCountsVector(doc), c) for doc, c in documents]     \n    #return the set of all unique tokens\n    def uniqueFeaturesAndClasses(self):\n        l = []\n        c = []\n        for vec, cName in self.vectorCollection: #very messy! \n            l = l + vec.keys()\n            c.append(cName) \n        return list(set(l)), list(set(c))\n\n    def getCountsVector(self, text):\n        stemmed = self.preprocess(text)\n        countsVector = Counter(stemmed)  \n        return countsVector\n   \n    def preprocess(self, text):\n        #convert text to unicode if it's not?\n        sents = self.sentence_tokenizer.tokenize(text)\n        toks = [self.word_tokenize(sen) for sen in self.sentence_tokenizer.tokenize(text)]\n        stemmed = []\n        for sen in toks:\n            for tok in sen:\n                try:\n                    #print tok\n                    stemmed.append(self.stemmer.stem(tok))\n                except UnicodeDecodeError:\n                    pass\n        #This was throwing UnicodeDecodeError\n        #stemmed = [self.stemmer.stem(tok) for sen in toks for tok in sen]\n        return stemmed\n","repo_name":"chrishokamp/naive-bayes-python","sub_path":"VectorBuilder.py","file_name":"VectorBuilder.py","file_ext":"py","file_size_in_byte":2034,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"37475408998","text":"#! /usr/bin/python\n# $Header$\n\nimport numpy as np\nimport copy\nfrom colorama import init, Fore, Back, Style\n\nclass Moves:\n  pass\n\n  \n\n'''\n  here are the move 54x54 matrices for a 3x3x3 rubiks cube\n  and some tests\n'''\n\nCUBE_SIZE = 3 # 3x3x3 cube\nNUMBER_OF_SQUARES = 6 # a cube has 6 squares\nMOVES_PER_SLICE = 4 # a move can be 90, 180, 270 or 360(identity) degree\nTOTAL_SQUARE_ELEMENTS = NUMBER_OF_SQUARES * CUBE_SIZE * CUBE_SIZE # a cube has 6 squares with CUBE_SIZE*CUBE_SIZE elements\n\n# for internal tests ( should not be used anywhere else )\n# there are a unique number in each element to see errors in move matrices\n# a real cube later run has only [0..NUMBER_OF_SQUARES-1] values\n__IDENTITY_ARRAY = np.array( [ k for k in range( TOTAL_SQUARE_ELEMENTS ) ] )\n\n'''\nfor displaying nxnxn cube over multiple lines\n\n3x3x3 cube output layout (indices)\n( 0- 3)  00 01 02\n( 3- 6)  03 04 05\n( 6- 9)  06 07 08\n( 9-21)  09 10 11 12 13 14 15 16 17 18 19 20\n(21-33)  21 22 23 24 25 26 27 28 29 30 31 32\n(33-45)  33 34 35 36 37 38 39 40 41 42 43 44\n(45-48)  45 46 47\n(48-51)  48 49 50\n(51-54)  51 52 53\n\n2x2x2 cube output layout (indices)\n( 0- 2) 00 01\n( 2- 4) 02 03\n( 4-12) 04 05 06 07 08 09 10 11\n(12-20) 12 13 14 15 16 17 18 19\n(20-22) 20 21\n(22-24) 22 23\n'''\ndef line_display_offset( s:int = CUBE_SIZE ):\n  num_elems_per_line = []\n  num_elems_per_line.extend( [                             s for k in range( s ) ] )\n  num_elems_per_line.extend( [ ( NUMBER_OF_SQUARES - 2 ) * s for k in range( s ) ] )\n  num_elems_per_line.extend( [                             s for k in range( s ) ] )\n\n  offsets_1 = [ sum( num_elems_per_line[:k] ) for k in range( len( num_elems_per_line ) ) ]\n  offsets_2 = offsets_1[1:]\n  offsets_2.append( NUMBER_OF_SQUARES * s * s )\n\n  offset_pairs = list( tuple( zip( tuple( offsets_1 ), tuple( offsets_2 ) ) ) )\n\n  # if you want to understand the construction please set debug_print = True\n  debug_print = False\n  #debug_print = True\n  if debug_print:\n    print( f'num_elems_per_line: {num_elems_per_line}')\n    print( f'offsets_1: {offsets_1}')\n    print( f'offsets_2: {offsets_2}')\n    print( f'offset_pairs: {offset_pairs}')\n  return offset_pairs\n  \nLINE_DISPLAY_OFFSETS = line_display_offset()\n  \n# types\npermut_type = list[ list [ int ] ]\n\ndef display_test_array( cube:str = __IDENTITY_ARRAY ) -> str:\n  out = ''\n  for ( x, y ) in LINE_DISPLAY_OFFSETS:\n    out += ' '.join( [ f'{cube[ i ]:02d}' for i in range( x, y ) ] ) + '\\n'\n  return out\n\n\n'''\nThere are 3 possible moves per slice (90, 180 and 270 degrees, 360 is the identity or zero move)\nWith a fix upper left back corner we have 3 move directions to use (front,right and down)\nThe number of slices is 1 less than the dimension of the cube\nfor eg. a 2x2x2 cube, we have   1 slice,  3 moves and 3 directions -> 1*3*3     =       9 possible moves\nfor eg. a 3x3x3 cube, we have   2 slices, 3 moves and 3 directions -> 2*3*3     =      18 possible moves\nfor eg. a nxnxn cube, we have n-1 slices, 3 moves and 3 directions -> (n-1)*3*3 = (n-1)*9 possible moves\n\nfor all subsequent moves we have 3 moves less to try, because we do not have to turn the same slice twice\neg. when we start with a front move F(90) and have a second F(180) we could have started with a single F(270) move\neg for 3*3*3 cube Sn = 1 + 18 * sum( 15^k ) k [0..(n-1)]\neg for 2*2*2 cube Sn = 1 +  9 * sum(  6^k ) k [0..(n-1)]\n'''\ndef calc_move_sum(  n: int ) -> int:\n  num_moves = len( MOVES_INDEX )\n  return 1 + num_moves * sum( [ ( num_moves - MOVES_PER_SLICE + 1 ) ** k for k in range( n ) ] )\n\n'''\n  size: size of cube array\n  permuts: a list of all 4 element permutation to execute\n\n  Example permutation with 4 elements\n\n  Cube C eg one square:\n  1 2 -> 4 1\n  4 3    3 2\n\n  Permutation (rotate clock wise) P:\n  ( 1 2 3 4 )\n  ( 4 1 2 3 )\n\n  Orthogonal Matrix M:\n        1 2 3 4\n  \n  1  ( 0 0 0 1 )   ( 1 )   ( 4 )\n  2  | 1 0 0 0 | * | 2 | = | 1 |\n  3  | 0 1 0 0 |   | 3 |   | 2 |\n  4  ( 0 0 1 0 )   ( 4 )   ( 3 )\n'''\ndef construct_rotator_matrix( size: int, permuts: permut_type = [] ):\n  # start with identity and modify the permut stuff\n  # dtype i8 seems to be the quickest one\n  matrix = np.identity( size, dtype = 'i8' )\n  for perm in permuts:\n    # do the permutation\n    matrix[ perm[ 0 ], perm[ 3 ] ] = 1\n    matrix[ perm[ 3 ], perm[ 2 ] ] = 1\n    matrix[ perm[ 2 ], perm[ 1 ] ] = 1\n    matrix[ perm[ 1 ], perm[ 0 ] ] = 1\n    for elem in perm:\n      # unset diagonal elements\n      matrix[ elem, elem ] = 0\n  return( matrix )\n\ndef display_rotator_matrix( mat )->str:\n  # find actual input parameter variable name\n  init( autoreset = True )\n  globals_dict = globals()\n  locals_dict  = locals()\n  dict = globals_dict | locals_dict\n  my_name = [ var_name for var_name in dict if dict[ var_name ] is mat ]\n  disp_name = 'unknown'\n  try:\n    disp_name = my_name[ 0 ]\n  except:\n    pass\n  my_size = len( mat )\n  out = ''\n  out += f\"Colored rotator matrix: {disp_name}, size {my_size}\" + '\\n'\n  out += '    ' + f''.join( [f'{n:3}' for n in range( my_size )] ) + '\\n' # rows titles\n  out += f'{Style.RESET_ALL}\\n'.join( [ f'{idx:2}: ' + ''.join( [ Fore.RED + f'{item:3}' if item > 0 else Fore.GREEN + f'{item:3}' for item in row ] ) for idx, row in enumerate( mat ) ] ) + '\\n'\n  return out\n\n# move matrices\nIDENT = construct_rotator_matrix( TOTAL_SQUARE_ELEMENTS )\n  \nF11 = construct_rotator_matrix( TOTAL_SQUARE_ELEMENTS,\n  [\n    [  6, 12, 47, 44 ],\n    [  7, 24, 46, 32 ],\n    [  8, 36, 45, 20 ],\n    [  9, 11, 35, 33 ],\n    [ 10, 23, 34, 21 ],\n  ] )\nF12 = F11.dot( F11 )\nF13 = F12.dot( F11 )\nF14 = F13.dot( F11 ) # identity\n\nF21 = construct_rotator_matrix( TOTAL_SQUARE_ELEMENTS,\n  [\n    [  3, 13, 50, 43 ],\n    [  4, 25, 49, 31 ],\n    [  5, 37, 48, 19 ],\n] )\nF22 = F21.dot( F21 )\nF23 = F22.dot( F21 )\nF24 = F23.dot( F21 ) # identity\n\nR11 = construct_rotator_matrix( TOTAL_SQUARE_ELEMENTS,\n  [\n    [  2, 39, 47, 11 ],\n    [  5, 27, 50, 23 ],\n    [  8, 15, 53, 35 ],\n    [ 12, 14, 38, 36 ],\n    [ 13, 26, 37, 24 ],\n  ] )\nR12 = R11.dot( R11 )\nR13 = R12.dot( R11 )\nR14 = R13.dot( R11 ) # identity\n\nR21 = construct_rotator_matrix( TOTAL_SQUARE_ELEMENTS,\n  [\n    [  1, 40, 46, 10 ],\n    [  4, 28, 49, 22 ],\n    [  7, 16, 52, 34 ],\n  ] )\nR22 = R21.dot( R21 )\nR23 = R22.dot( R21 )\nR24 = R23.dot( R21 ) # identity\n\nD11 = construct_rotator_matrix( TOTAL_SQUARE_ELEMENTS,\n  [\n    [ 33, 36, 39, 42 ],\n    [ 34, 37, 40, 43 ],\n    [ 35, 38, 41, 44 ],\n    [ 45, 47, 53, 51 ],\n    [ 46, 50, 52, 48 ],\n  ] )\nD12 = D11.dot( D11 )\nD13 = D12.dot( D11 )\nD14 = D13.dot( D11 ) # identity\n\nD21 = construct_rotator_matrix( TOTAL_SQUARE_ELEMENTS,\n  [\n    [ 21, 24, 27, 30 ],\n    [ 22, 25, 28, 31 ],\n    [ 23, 26, 29, 32 ],\n] )\nD22 = D21.dot( D21 )\nD23 = D22.dot( D21 )\nD24 = D23.dot( D21 ) # identity\n\n# enumerate moves\nF11I = 0\nF21I = 1\nR11I = 2\nR21I = 3\nD11I = 4\nD21I = 5\n\nF12I = 6\nF22I = 7\nR12I = 8\nR22I = 9\nD12I = 10\nD22I = 11\n\nF13I = 12\nF23I = 13\nR13I = 14\nR23I = 15\nD13I = 16\nD23I = 17\n\nMOVES_INDEX = [ # 0,    1,    2,    3,    4,    5\n                  F11I, F21I, R11I, R21I, D11I, D21I,\n                  F12I, F22I, R12I, R22I, D12I, D22I,\n                  F13I, F23I, R13I, R23I, D13I, D23I,\n            ] \n\n# permutation matrices\nMOVES = [ # 0,   1,   2,   3,   4,   5\n            F11, F21, R11, R21, D11, D21,\n            F12, F22, R12, R22, D12, D22,\n            F13, F23, R13, R23, D13, D23,\n        ] \n\n# my moves\nDISPLAY_MOVES = {\n  'standard' :\n  [ # 0,     1,     2,     3,     4,     5\n    \"F\",   \"S\",   \"R\",   \"M\",   \"D\",   \"E\",\n    \"FF\",  \"SS\",  \"RR\",  \"MM\",  \"DD\",  \"EE\",\n    \"F'\",  \"S'\",  \"R'\",  \"M'\",  \"D'\",  \"E'\",\n  ],\n  'mine' :\n  [ # 0,     1,     2,     3,     4,     5\n    'F1',  'F2',  'R1',  'R2',  'D1',  'D2',\n    'F12', 'F22', 'R12', 'R22', 'D12', 'D22',\n    'F1-', 'F2-', 'R1-', 'R2-', 'D1-', 'D2-',\n  ],\n  'color' :\n  [ # 0,     1,     2,     3,     4,     5\n    'Y ',  'y ',  'O ',  'o ',  'G ',  'g',\n    'Y2',  'y2',  'O2',  'o2',  'G2',  'g2',\n    'Y-',  'y-',  'O-',  'o-',  'G-',  'g-',\n  ],\n}\n\ndef execute_move_sequence( mat, move_sequence ):\n  for move in move_sequence:\n    mat = move.dot( mat )\n  return mat  \n\nif __name__ == \"__main__\": # pragma: no cover\n  m = Moves()\n  # execute only if run as a script\n  print( DISPLAY_MOVES )\n  print( MOVES_INDEX )\n\n  for idx, mat in enumerate( MOVES ): \n    print( f'Index: {idx}: {mat}' )\n    # lasts quite long\n    #display_rotator_matrix( mat )\n\n  print( display_rotator_matrix( IDENT ) )\n  print( display_rotator_matrix( MOVES[ 0 ] ) )\n\n  # print out the number of moves for a specific recursion level\n  gods_number = 20\n  for n in range( 0, gods_number + 1 ):\n    # eg. 1234567 -> 1'234'567\n    print( f' {n:2d}: {calc_move_sum( n ):,}'.replace( ',', \"'\" ) )\n\n  for num in range( 5 ):\n    print( f'Cube size: {num}' )\n    op = line_display_offset( num )\n    print( f'Offset pairs[{len( op ):02d}]: {op}' )\n    print( 80 * '=' )\n\n  print( display_rotator_matrix( IDENT ) )\n  print( display_rotator_matrix( MOVES[ 0 ] ) )\n\n  print( display_test_array() )\n  print( display_test_array( __IDENTITY_ARRAY ) )\n\n  move_array = MOVES\n  move_array = [ D11 ]\n  result_array = copy.copy( __IDENTITY_ARRAY )\n  for mat in move_array:\n    result_array = mat.dot( result_array )\n  \n  print( display_test_array( result_array ) )\n\n  # find identity move sequences (repeated)\n  result = IDENT\n  for cnt in range( 1, 1001 ):\n    move_list = MOVES\n    move_list = [ F11, R11, D11, F12, R12, D12, F13, R13, D13, F21, R21, D21, F22, R22, D22, F23, F23, F23 ]\n    result = execute_move_sequence( result, move_list )\n    if ( result == IDENT ).all():\n      print( display_rotator_matrix( result ) )\n      print( f'{cnt}' )\n      break\n  \n","repo_name":"Hanspeter-Schinz/Rubik3Matrix","sub_path":"src/Moves/Moves.py","file_name":"Moves.py","file_ext":"py","file_size_in_byte":9635,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"21632064980","text":"def make_inequiv_pairs(n):\n    \"\"\" Convert from gap permutation pairs output to txt format\n        gap format is assumed to be stored in Pairs-n-1-1.txt\n    \"\"\"\n    path = \"data10/\"\n    fname = f\"Pairs-{n}-1-1.txt\"\n    with open(f\"{path}{fname}\", \"r\") as f:\n        lst = f.readlines()\n    inequiv_parser_pairs(lst, f\"{path}New_{fname}\", n)\n\ndef inequiv_parser_pairs(lst, out_file, n, save_chunk=100000):\n    \"\"\" parse gap txt output in lst\n        make new format\n    \"\"\"\n    pairs_lst = []\n    count = 0\n    with open(out_file, \"w\") as f:\n        i = 0\n        while i < len(lst):\n            r = lst[i]\n            row_count = 0\n            # cycles of first perm in pair\n            middle = r[1:-1]\n            left_paren = middle.find(\"[\")\n            right_paren = middle.find(\"],\")\n            first_perm = middle[left_paren:right_paren+1]\n            if first_perm.find(\".\") < 0:\n                first_perm = first_perm.split()\n                first_lst = [int(i.strip(\",\")) for i in first_perm if i.strip(\",\").isdigit()]\n            else:\n                first_lst = [i+1 for i in range(n)] # is identity\n\n            # cycles of second perm in pair\n            sec_half = middle[right_paren+1:]\n            sec_left_paren = sec_half.find(\"[\")\n            if sec_left_paren < 0: # on a half line\n                i += 1\n                r = lst[i]\n                middle = r[1:-1]\n                sec_left_paren = middle.find(\"[\")\n                sec_half = middle\n            sec_right_paren = sec_half.rfind(\"]\")\n            sec_perm = sec_half[sec_left_paren:sec_right_paren+1]\n            if sec_perm.find(\".\") < 0:\n                sec_perm = sec_perm.split()\n                sec_lst = [int(i.strip(\",\")) for i in sec_perm if i.strip(\",\").isdigit()]\n            else:\n                sec_lst = [i+1 for i in range(n)] # is identity\n\n            pairs_lst.append((first_lst, sec_lst))\n            count += 1\n            if count >= save_chunk:\n                # save memory by writing in chunks\n                f.writelines((str(pair)[1:-1]+\"\\n\" for pair in pairs_lst))\n                del pairs_lst\n                pairs_lst = []\n                count = 0\n            i+=1\n\n        f.writelines((str(pair)[1:-1]+\"\\n\" for pair in pairs_lst))\n    del pairs_lst\n    print(\"done saving\")\n\ndef read_inequiv_pairs(n):\n    \"\"\" read New_Pairs-n-1-1.txt and return pairs of permutations\n    \"\"\"\n    with open(f\"data10/New_Pairs-{n}-1-1.txt\", \"r\") as f:\n        lst = f.readlines()\n    perms = []\n    for r in lst:\n        middle = r[0:-1]\n        left_paren = middle.find(\"[\")\n        right_paren = middle.find(\"],\")\n        first_perm = middle[left_paren+1:right_paren]\n        first_perm = first_perm.replace(\",\", \" \").split()\n        first_lst = [int(i) for i in first_perm]\n\n        sec_half = middle[right_paren+1:]\n        sec_left_paren = sec_half.find(\"[\")\n        sec_right_paren = sec_half.rfind(\"]\")\n        sec_perm = sec_half[sec_left_paren+1:sec_right_paren]\n        sec_perm = sec_perm.replace(\",\", \" \").split()\n        sec_lst = [int(i) for i in sec_perm]\n        perms.append((first_lst, sec_lst))\n    return perms\n\n","repo_name":"cptq/ds-spectra","sub_path":"src/inequivpairs.py","file_name":"inequivpairs.py","file_ext":"py","file_size_in_byte":3136,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"81"}
+{"seq_id":"23957405715","text":"# -*- coding: utf-8 -*-\n\nimport itertools\nimport numpy as np\nimport tensorflow as tf\n\nfrom tensorpack.models import Conv2D, FixedUnPooling, MaxPooling, layer_register\nfrom tensorpack.tfutils.argscope import argscope\nfrom tensorpack.tfutils.scope_utils import under_name_scope\nfrom tensorpack.tfutils.summary import add_moving_summary\nfrom tensorpack.tfutils.tower import get_current_tower_context\n\n#from net.config import config as cfg\nfrom train_config import config as cfg\nfrom net.model_box import roi_align\nfrom net.model_rpn import generate_rpn_proposals, rpn_losses\nfrom net.utils.box_ops import area as tf_area\n\n\n\n\n@under_name_scope()\ndef fpn_map_rois_to_levels(boxes):\n    \"\"\"\n    Assign boxes to level 2~5.\n\n    Args:\n        boxes (nx4):\n\n    Returns:\n        [tf.Tensor]: 4 tensors for level 2-5. Each tensor is a vector of indices of boxes in its level.\n        [tf.Tensor]: 4 tensors, the gathered boxes in each level.\n\n    Be careful that the returned tensor could be empty.\n    \"\"\"\n    sqrtarea = tf.sqrt(tf_area(boxes))\n    level = tf.cast(tf.floor(\n        4 + tf.log(sqrtarea * (1. / 224) + 1e-6) * (1.0 / np.log(2))), tf.int32)\n\n    # RoI levels range from 2~5 (not 6)\n    level_ids = [\n        tf.where(level <= 2),\n        tf.where(tf.equal(level, 3)),   # == is not supported\n        tf.where(tf.equal(level, 4)),\n        tf.where(level >= 5)]\n    level_ids = [tf.reshape(x, [-1], name='roi_level{}_id'.format(i + 2))\n                 for i, x in enumerate(level_ids)]\n    num_in_levels = [tf.size(x, name='num_roi_level{}'.format(i + 2))\n                     for i, x in enumerate(level_ids)]\n    add_moving_summary(*num_in_levels)\n\n    level_boxes = [tf.gather(boxes, ids) for ids in level_ids]\n    return level_ids, level_boxes\n\n\n\ndef multilevel_roi_align(features, rcnn_boxes, resolution):\n    \"\"\"\n    Args:\n        features ([tf.Tensor]): 4 FPN feature level 2-5\n        rcnn_boxes (tf.Tensor): nx4 boxes\n        resolution (int): output spatial resolution\n    Returns:\n        NxC x res x res\n    \"\"\"\n    assert len(features) == 4, features\n    # Reassign rcnn_boxes to levels\n    level_ids, level_boxes = fpn_map_rois_to_levels(rcnn_boxes)\n    all_rois = []\n\n    # Crop patches from corresponding levels\n    for i, boxes, featuremap in zip(itertools.count(), level_boxes, features):\n        with tf.name_scope('roi_level{}'.format(i + 2)):\n            boxes_on_featuremap = boxes * (1.0 / cfg.FPN.ANCHOR_STRIDES[i])\n            all_rois.append(roi_align(featuremap, boxes_on_featuremap, resolution))\n\n    # this can fail if using TF<=1.8 with MKL build\n    all_rois = tf.concat(all_rois, axis=0)  # NCHW\n\n\n    # Unshuffle to the original order, to match the original samples\n    level_id_perm = tf.concat(level_ids, axis=0)  # A permutation of 1~N\n    level_id_invert_perm = tf.invert_permutation(level_id_perm)\n    all_rois = tf.gather(all_rois, level_id_invert_perm)\n    return all_rois\n\n\ndef multilevel_rpn_losses(\n        multilevel_anchors, multilevel_label_logits, multilevel_box_logits):\n    \"\"\"\n    Args:\n        multilevel_anchors: #lvl RPNAnchors\n        multilevel_label_logits: #lvl tensors of shape HxWxA\n        multilevel_box_logits: #lvl tensors of shape HxWxAx4\n\n    Returns:\n        label_loss, box_loss\n    \"\"\"\n    num_lvl = len(cfg.FPN.ANCHOR_STRIDES)\n    assert len(multilevel_anchors) == num_lvl\n    assert len(multilevel_label_logits) == num_lvl\n    assert len(multilevel_box_logits) == num_lvl\n\n    losses = []\n    with tf.name_scope('rpn_losses'):\n        for lvl in range(num_lvl):\n            anchors = multilevel_anchors[lvl]\n\n\n\n\n            label_loss, box_loss = rpn_losses(\n                anchors.gt_labels, anchors.encoded_gt_boxes(),\n                multilevel_label_logits[lvl], multilevel_box_logits[lvl],\n                name_scope='level{}'.format(lvl + 2))\n            losses.extend([label_loss, box_loss])\n\n        total_label_loss = tf.add_n(losses[::2], name='label_loss')\n        total_box_loss = tf.add_n(losses[1::2], name='box_loss')\n        add_moving_summary(total_label_loss, total_box_loss)\n    return [total_label_loss, total_box_loss]\n\ndef generate_fpn_proposals(\n        multilevel_pred_boxes, multilevel_label_logits, image_shape2d,training):\n    \"\"\"\n    Args:\n        multilevel_pred_boxes: #lvl HxWxAx4 boxes\n        multilevel_label_logits: #lvl tensors of shape HxWxA\n\n    Returns:\n        boxes: kx4 float\n        scores: k logits\n    \"\"\"\n    num_lvl = len(cfg.FPN.ANCHOR_STRIDES)\n    assert len(multilevel_pred_boxes) == num_lvl\n    assert len(multilevel_label_logits) == num_lvl\n\n\n    all_boxes = []\n    all_scores = []\n    if cfg.FPN.PROPOSAL_MODE == 'Level':\n        fpn_nms_topk = cfg.RPN.TRAIN_PER_LEVEL_NMS_TOPK if training else cfg.RPN.TEST_PER_LEVEL_NMS_TOPK\n        for lvl in range(num_lvl):\n            with tf.name_scope('Lvl{}'.format(lvl + 2)):\n                pred_boxes_decoded = multilevel_pred_boxes[lvl]\n\n                proposal_boxes, proposal_scores = generate_rpn_proposals(\n                    tf.reshape(pred_boxes_decoded, [-1, 4]),\n                    tf.reshape(multilevel_label_logits[lvl], [-1]),\n                    image_shape2d, fpn_nms_topk)\n                all_boxes.append(proposal_boxes)\n                all_scores.append(proposal_scores)\n\n        proposal_boxes = tf.concat(all_boxes, axis=0)  # nx4\n        proposal_scores = tf.concat(all_scores, axis=0)  # n\n        # Here we are different from Detectron.\n        # Detectron picks top-k within the batch, rather than within an image. However we do not have a batch.\n        proposal_topk = tf.minimum(tf.size(proposal_scores), fpn_nms_topk)\n        proposal_scores, topk_indices = tf.nn.top_k(proposal_scores, k=proposal_topk, sorted=False)\n        proposal_boxes = tf.gather(proposal_boxes, topk_indices)\n    else:\n        for lvl in range(num_lvl):\n            with tf.name_scope('Lvl{}'.format(lvl + 2)):\n                pred_boxes_decoded = multilevel_pred_boxes[lvl]\n                all_boxes.append(tf.reshape(pred_boxes_decoded, [-1, 4]))\n                all_scores.append(tf.reshape(multilevel_label_logits[lvl], [-1]))\n        all_boxes = tf.concat(all_boxes, axis=0)\n        all_scores = tf.concat(all_scores, axis=0)\n        proposal_boxes, proposal_scores = generate_rpn_proposals(\n            all_boxes, all_scores, image_shape2d,\n            cfg.RPN.TRAIN_PRE_NMS_TOPK if training else cfg.RPN.TEST_PRE_NMS_TOPK,\n            cfg.RPN.TRAIN_POST_NMS_TOPK if training else cfg.RPN.TEST_POST_NMS_TOPK)\n\n    tf.sigmoid(proposal_scores, name='probs')  # for visualization\n    return tf.stop_gradient(proposal_boxes, name='boxes'), \\\n        tf.stop_gradient(proposal_scores, name='scores')\n\n\n","repo_name":"610265158/frcnn","sub_path":"net/model_fpn.py","file_name":"model_fpn.py","file_ext":"py","file_size_in_byte":6681,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"6500484109","text":"from datetime import datetime\n\nfrom django.http import HttpRequest\nfrom django.shortcuts import render\n\n\ndef setup_useragent_on_request_middleware(get_response):\n    # print('initial call')\n\n    def middleware(request: HttpRequest):\n        # print('before get response')\n        request.user_agent = request.META['HTTP_USER_AGENT']\n        # request.user_agent = request.META.get('HTTP_USER_AGENT', 'test/client')\n        response = get_response(request)\n        # print('after get response')\n\n        return response\n\n    return middleware\n\n\nclass CountRequestMiddleware:\n    def __init__(self, get_response):\n        self.get_response = get_response\n        self.requests_count = 0\n        self.responses_count = 0\n        self.exceptions_count = 0\n\n    def __call__(self, request: HttpRequest):\n        self.requests_count += 1\n        # print('requests count', self.requests_count)\n        response = self.get_response(request)\n        self.responses_count += 1\n        # print('responses count', self.responses_count)\n        return response\n\n    def process_exception(self, request: HttpRequest, exception: Exception):\n        self.exceptions_count += 1\n        # print('got', self.exceptions_count, 'exceptions so far')\n\n\nclass ThrottlingMiddleware:\n    def __init__(self, get_response):\n        self.get_response = get_response\n        self.request_time = dict()\n\n    def __call__(self, request: HttpRequest):\n        user_ip = request.META.get('REMOTE_ADDR')\n        if not self.request_time:\n\n            self.request_time[user_ip] = datetime.now()\n\n        else:\n            if user_ip in self.request_time.keys():\n                time_delta = (datetime.now() - self.request_time[user_ip]).seconds\n\n                if time_delta < 1:\n                    return render(request, 'requestdataapp/request-error.html')\n#\n#             self.request_time[user_ip] = datetime.now()\n#\n#         response = self.get_response(request)\n#         return response\n","repo_name":"NataliyaYunusova/python_django","sub_path":"mysite/requestdataapp/middlewares.py","file_name":"middlewares.py","file_ext":"py","file_size_in_byte":1962,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"17798935858","text":"def merge(left, right):\n    res=[]\n    l=0\n    r=0\n    while l < len(left) and r < len(right):\n        if left[l] < right[r]:\n            res.append(left[l])\n            l += 1\n        else:\n            res.append(right[r])\n            r += 1\n    if l < (len(left)):\n        for i in range(l, len(left)):\n            res.append(left[i])\n    if r < (len(right)):\n        for i in range(r,len(right)):\n            res.append(right[i])\n    return res\n\ndef mergeSort(nums):\n    '''\n    merge sort\n    '''\n    if len(nums)==1:\n        return nums\n    midIndex=len(nums)//2\n    left_side=mergeSort(nums[midIndex:])\n    right_side=mergeSort(nums[:midIndex])\n    return merge(left_side, right_side)\n\n\nnums=[-4,0,7,4,5,-5,2,8,6,1,4,19,-1,0,-7,-1]\nprint(str(nums)+\" - Before\")\nnums=mergeSort(nums)\nprint(str(nums)+\" - After\")\n","repo_name":"egcode/algorithms-datastructures-patterns","sub_path":"Algorithms/Python/MergeSort.py","file_name":"MergeSort.py","file_ext":"py","file_size_in_byte":816,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"20482366959","text":"\"\"\"voir ma sol de course schedule 1 super important : https://leetcode.com/problems/course-schedule/submissions/\nce pb est le meme que 1 mais on nous demande de nous rendre en plus si il ya pas de loop l'ordre des cours qui doit etre pris . en faite ca revient tout simplement a retourner le topological order car le topological\norder est un ordre ou u apparait avant v si u->v donc ici aussi on veut que un cours u soit avant un cours v si u depend de v . il existe plusieur possibilite dans le topological order donc c'est pour cela qu'il ya \nplusieur reponse possible (voir explication dans le lien precedent). \"\"\"\n\"\"\"#first sol #not my sol #dfs recursive #Topological sort # TC/ SC O (V+E)  #same code as course schedule 1 with few changement (3 lignes de codes) #voit TC et SC explication ds course schedule 1  \"\"\"\n\nclass Solution:\n    def findOrder(self, numCourses: int, prerequisites: List[List[int]]) -> List[int]:\n \n            # build Adjacency list from Edges list\n            adjList  = defaultdict(set)\n            for crs, pre in prerequisites: #chaque element de prerequesites est une list de deux element le cours et son cours prerequis\n                adjList[crs].add(pre)   # on aura donc un dictio qui represente un graph car la valeurs de la vertex key represente les voisin ds le graph de ce vertex\n            \n    \n            # Each vertex can have 3 different states:\n            # state 0   : vertex is not visited. It's a default state.\n            # state -1  : vertex qui a son dfs en cours, cad on a pas fini de faire dfs sur ses descendants. \n            # state 1   : vertex qui a fini son dfs\n            state = [0] * numCourses   # au debut tout les vertex ne sont pas visiter \n            stack = []  # le stack va contenir l'ordre topologique \n    \n    \n    \n            def dfs(v,stack):\n                #if state[v] == 1:\n                    # dans ce cas ca veut dire qu'on a deja fini de faire precedement le dfs de ce vertex donc pas besoin de refaire , on return false ( cad que il ya pas de cycle jusqu'a present ) \n                    # et donc ca veut dire  qu'on ne rappele pas le dfs recursivement pour ses descendant)           \n                #    return False\n                if state[v] == -1:\n                    # ca veut dire que on est en cours du dfs de ce vertex donc comme on retombe sur lui pdt son propre dfs ca veut dire qu'il ya un cycle. \n                    return True \n    \n                # on arrive ici si le state du node est ni -1 ni 1 il sera donc 0 ,donc maintenant on marque se node comme etant en cours de son dfs cad -1\n                state[v] = -1\n\n            \n               # cette boucle est tres importante a comprendre : \n               # on appele recursivement dfs sur tout les voisin de v qui eux meme vont ensuite appeler dfs sur leur voisin et ainsi de suite jusqu'a ce qu'il y'ai plus de voisin. \n               # si il ya plus de voisin alors on ne va pas rentrer dans le for car adjList == [] dans ce cas, et on va donc continuer le code : state[v] = 1  cad v est marquer comme node qui a fini sont dfs\n               # puis on return false , donc quand il ya pas de voisin il ya pas d'appel recursive du dfs le dfs retournera false donc si il ya pas de voisin c'est une condition d'arret. \n               # une autre condition d'arret est si un cycle est detecter alors dfs ne s'appel plus recursivement il rend true .\n               # apres avoir eu un condition d'arret dfs rend True si c'est un cycle et False si il ya pas de voisin , donc comme dfs est appeler dans la boucle for alors if dfs(i) avec i un node qui enclanche \n               # une condition d'arret alors dfs va rendre true ou false si il rend true du a un cycle alors le dfs qui a appeler dfs i va rendre true ce qui va faire que aussi la dfs d'en haut sera true est ainsi de\n               # de suite . mais si on a aucun cercle alors dfs(i) va tjrs etre false donc aucun dfs va etre True donc if dfs(i) dans le for va tjrs etre false donc tout les dfs vont finir le for sans rendre qqch\n               # il vont continuer les lignes apres for qui sont state[v]=1 et return False cad il vont tous rendre False et donc en remontant la condition if dfs(i) va tjrs etre false. \n               # app : 0->1->2  pas de cycle\n               #\n               #                    False : donc if dfs(1)== if False donc rentre pas ds if , on continue car for se termine , donc suite de dfs(0): state[0]=1 et return False cad dfs(0) return False\n               #                   -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------   \n               # dfs(0) :          |                                                                                                                                                                                   |   \n               # state [0] =-1     |                              False: donc if dfs(2)==if False donc rentre pas ds if ,on continue car for terminer donc suite dfs(1): state[1]=1 et return False, donc dfs(1) return False \n               # for i in [1]:     v                                  -------------------------------------------------------------------------------------------------------                  \n               #   if dfs(i) cad dfs(1) -->  dfs(1) : state[1]=-1     |                                                                                                     |\n               #                                     for i in [2]:    v                                                                                                     |   \n               #                                        if dfs(i) cad dfs(2) --> dfs (2) : state [2]=-1                                                                     |                                                 \n               #                                                                           for i in [] : #pas de voisin donc pas d'appel de dfs car rentre pas ds for       |\n               #                                                                           state[2]=1                                                                       |     \n               #                                                                           return False ---------------------------------------------------------------------\n               #               \n               # app : 0->1->0  avc cycle\n               #                                 True : donc if dfs(1)== if True donc rentre ds if donc dfs(0) return True  cad il ya un cycle \n               #                   -----------------------------------------------------------------------------------------------------------\n               #                   |                                                                                                         |   \n               # state [0] =-1     |                             True : donc if dfs(0)==if True donc rentre ds if et donc dfs(1) va return True \n               # for i in [1]:     v                                  -------------------------------------------------------------------------------------------------------                  \n               #   if dfs(i) cad dfs(1) -->  dfs(1) : state[1]=-1     |                                                                                                     |\n               #                                     for i in [0]:    v                                                                                                     |   \n               #                                        if dfs(i) cad dfs(0) --> dfs (0) : state [0]==-1   donc dfs(0) return True  -----------------------------------------\n               #                                                                           \n               #  remarque : ca change pas cbm de voisin il ya car apartir du moment ou un voisin return true alors on rentre dans la condition du for et donc on return True on continue pasle for ni les lignes d'apres\n               #             donc mem si tous les voisin sont false sauf un qui est true alors le dfs va rendre true et va remonter en haut ce qui va faire que meme celui d'en haut va rentrer ds la condition if du\n               # for ce qui va faire qu'il va return True et ainsi de suite jusqu'a que la fct va rendre true jusqu'en haut du retoure de la recursion .\n                for i in adjList[v]:  \n                    if state[i] != 1 : # on appele recursivement dfs sur tout les voisins de v qu'on leur a pas deja fini leur dfs car si on leur a deja fini leur dfs pas besoin de leur refaire encore une fois dfs \n                        if dfs(i,stack) :    #dfs est True si il ya un cycle\n                            return True  \n    \n                state[v] = 1      # cette ligne se fait apres qu'on remonte de la recursion seulement si tout les dfs des voisin etait False car sinon on aurait return True avant et donc cette ligne ne s'execute pas \n                                  # car la fct a deja return cad elle a fini avant de lire cette ligne\n                stack.append(v)  #on append au stack en remontant de la recursion donc les premier dans le stack vont etre ce de la fin de la recursion cad les cours que les autres depende d'eux \n                return False      \n            \n            \n            \n            \n            # ces lignes sont de la fct exterieur et non dfs voir indentation \n            # we traverse each vertex using DFS, if we find a cycle, stop and return\n            for v in range(numCourses):\n                if state[v] != 1 : #cad que les node qu'on leur a pas deja fait dfs car ca sert a rien de refaire \n                    if dfs(v,stack):  # si on a un cycle quand on fait dfs(v) alors dfs(v) sera True donc l'algo va return une empty list car il ya pas de reponse possible \n                        return []\n\n            return stack  #si on est arriver ici ca veut dire qu'on a pas detecter de cycle donc notre algo return l'ordre topologique\n        \n        \n\"\"\"#solution 2 #not my sol #  topological sort a l'aide de khan algoritme  #BFS iterative #easy\n\"\"\"\n\n\n\nclass Solution:\n    def findOrder(self, numCourses: int, prerequisites: List[List[int]]) -> List[int]:\n \t\t\t\t                 \n            return self.topoBFS(numCourses, prerequisites) \n            \n    def topoBFS(self, numNodes, prerequisites):\n        \n            # build Adjacency list from Edges list\n            adjList  = defaultdict(set)\n            for crs, pre in prerequisites: # chaque element de prerequesites est une list de deux element le cours et son cours prerequis\n                adjList[pre].add(crs)   # on aura donc un dictio qui represente un graph car la valeurs de la vertex key represente les voisin ds le graph de ce vertex : pre->crs #inverse d'en haut\n    \n            # 1. A list stores No. of incoming edges of each vertex\n            inDegrees = [0] * numNodes\n            for crs, pre in prerequisites: #chaque element de prerequesites est une list de deux element le cours et son cours prerequis\n                # crs and pre form a directed edge pre->crs cad il ya un incoming egdes dans pre\n                inDegrees[crs] += 1\n     \n            # 2. a queue of all vertices with no incoming edge at least one such node must exist in a non-empty acyclic graph.\n            # vertices in this queue have the same order as the eventual topological sort.\n            queue = deque()\n            for v in range(numNodes):\n                if inDegrees[v] == 0:  #cad il ya pas de incoming edge ds v donc il ya pas de prerequis pour v\n                    queue.append(v)\n    \n            # initialize count of visited vertices\n            count = 0\n            # an empty list that will contain the final topological order\n            topoOrder = []\n    \n            while queue:\n                # pop a vertex from front of queue.\n                # depending on the order that vertices are removed from queue,  a different solution is created\n                v = queue.popleft()\n                # append it to topoOrder\n                topoOrder.append(v)\n    \n                # increase count by 1\n                count += 1\n    \n                # for each descendant of current vertex, reduce its in-degree by 1 car le descendant aura maintenant un incomming edges en moins\n                for neighboor in adjList[v]:\n                    inDegrees[neighboor] -= 1\n                    # if in-degree becomes 0, add it to queue\n                    if inDegrees[neighboor] == 0:\n                        queue.append(neighboor)\n    \n            if count != numNodes: # si on a pas tout les vertex dans topoOrder ca veut dire qu'il ya un cycle ( y'a un vertex qui aura le inDegree > 0 donc rentrera pas dans topoOrder ) est donc on return []\n                return []  # graph has at least one cycle \n            else:\n                return topoOrder\n    \n        \n       \n\n\n\n","repo_name":"rtn75000/leetcode-pb","sub_path":"210. Course Schedule II/solution.py","file_name":"solution.py","file_ext":"py","file_size_in_byte":13079,"program_lang":"python","lang":"fr","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"1496570226","text":"import numpy as np\n\ndata = np.array(open('6a.txt').read().split('\\n'))\n\ndays = 256\noriginal = np.array(data[0].split(',')).astype(np.int8)\n\nfor i in range(days // 2):\n    original = original - 1\n    # for j in range(len(copied)):\n    #     if copied[j] == -1:\n    #         copied[j] = original[j]\n    #         original = np.append(original, 6)\n    #         copied = np.append(copied, 8)\n    w = original == -1\n    count = len(original[w])\n    original[w] = 6\n    original = np.append(original, [8 for j in range(count)]).astype(np.int8)\n    print(f'Day {i if i >= 10 else \"0\" + str(i)}')\n\no1 = original[0::2*int(len(original)//3)]\no2 = original[2*int(len(original)//3)::len(original)]\n\nfor i in range(days // 2, days):\n    o1 = o1 - 1\n    # for j in range(len(copied)):\n    #     if copied[j] == -1:\n    #         copied[j] = original[j]\n    #         original = np.append(original, 6)\n    #         copied = np.append(copied, 8)\n    w = o1 == -1\n    count = len(o1[w])\n    o1[w] = 6\n    o1 = np.append(o1, [8 for j in range(count)]).astype(np.int8)\n    print(f'Day {i if i >= 10 else \"0\" + str(i)}')\n\nfor i in range(days // 2, days):\n    o2 = o2 - 1\n    # for j in range(len(copied)):\n    #     if copied[j] == -1:\n    #         copied[j] = original[j]\n    #         original = np.append(original, 6)\n    #         copied = np.append(copied, 8)\n    w = o2 == -1\n    count = len(o2[w])\n    o2[w] = 6\n    o2 = np.append(o2, [8 for j in range(count)]).astype(np.int8)\n    print(f'Day {i if i >= 10 else \"0\" + str(i)}')\n\nprint(len(o1) + len(o2))\n","repo_name":"MatejHav/aoc2021","sub_path":"solutions/day06/day6a.py","file_name":"day6a.py","file_ext":"py","file_size_in_byte":1546,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"15859760756","text":"import torchxrayvision as xrv\nimport torchvision\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom torch.utils.tensorboard import SummaryWriter\nimport skimage\n\nclass Net(nn.Module):\n    def __init__(self):\n        super().__init__()\n\n        self.conv1 = nn.Conv2d(3, 32, 3, padding = \"same\")\n        self.relu1 = F.relu\n        self.pool1 = nn.MaxPool2d(2, 2)\n        self.dropout1 = nn.Dropout(p = 0.3)\n\n        self.conv2 = nn.Conv2d(32, 64, 3, padding = \"same\")\n        self.relu2 = F.relu\n        self.pool2 = nn.MaxPool2d(2, 2)\n        self.dropout2 = nn.Dropout(p = 0.3)\n\n        self.conv3 = nn.Conv2d(64, 128, 3, padding = \"same\")\n        self.relu3 = F.relu\n        self.pool3 = nn.MaxPool2d(2, 2)\n        self.dropout3 = nn.Dropout(p = 0.4)\n\n        self.conv4 = nn.Conv2d(128, 256, 3, padding = \"same\")\n        self.relu4 = F.relu\n        self.pool4 = nn.MaxPool2d(2, 2)\n        self.dropout4 = nn.Dropout(p = 0.4)\n\n\n        self.conv5 = nn.Conv2d(256, 512, 3, padding = \"same\")\n        self.relu5 = F.relu\n        self.pool5 = nn.MaxPool2d(2, 2)\n        self.dropout5 = nn.Dropout(p = 0.4)\n\n        self.flatten = nn.Flatten()\n\n        self.linear1 = nn.Linear(512, 80)\n        self.relu6 = F.relu\n        self.dropout6 = nn.Dropout(p = 0.3)\n\n        self.linear2 = nn.Linear(80, 20)\n\n    def forward(self, x):\n\n        x = self.conv1(x)\n        x = self.relu1(x)\n        x = self.pool1(x)\n        x = self.dropout1(x)\n\n        x = self.conv2(x)\n        x = self.relu2(x)\n        x = self.pool2(x)\n        x = self.dropout2(x)\n\n        x = self.conv3(x)\n        x = self.relu3(x)\n        x = self.pool3(x)\n        x = self.dropout3(x)\n\n        x = self.conv4(x)\n        x = self.relu4(x)\n        x = self.pool4(x)\n        x = self.dropout4(x)\n\n        x = self.conv5(x)\n        x = self.relu5(x)\n        x = self.pool5(x)\n        x = self.dropout5(x)\n\n        x = self.flatten(x)\n\n        x = self.linear1(x)\n        x = self.relu6(x)\n        x = self.dropout6(x)\n\n        x = self.linear2(x)\n        \n        return x\n\n\n# Prepare the image:\nimg = skimage.io.imread(\"D:\\\\School\\\\2023 Winter\\\\Term Project\\\\Model Visualization\\\\european-rabbit-in-australia.jpg\")\nimg = xrv.datasets.normalize(img, 255) # convert 8-bit image to [-1024, 1024] range\nimg = img.mean(2)[None, ...] # Make single color channel\n\ntransformImageNet = torchvision.transforms.Compose([xrv.datasets.XRayCenterCrop(),xrv.datasets.XRayResizer(224)])\nimgImageNet = transformImageNet(img)\nimgImageNet = torch.from_numpy(imgImageNet)\nimgImageNet = torch.unsqueeze(imgImageNet, 0)\nimgImageNet = torch.cat([imgImageNet,imgImageNet,imgImageNet], dim = 1)\n\ntransformCifar10 = torchvision.transforms.Compose([xrv.datasets.XRayCenterCrop(),xrv.datasets.XRayResizer(32)])\nimgCifar10 = transformCifar10(img)\nimgCifar10 = torch.from_numpy(imgCifar10)\nimgCifar10 = torch.unsqueeze(imgCifar10, 0)\nimgCifar10 = torch.cat([imgCifar10,imgCifar10,imgCifar10], dim = 1)\n\n\n\nimageNetModel = torchvision.models.resnet18(pretrained = True)\ncifar10Network = torch.load(\"D:\\\\School\\\\2023 Winter\\\\Term Project\\\\Program Output\\\\Simple Model.pt\")\n\nwriter = SummaryWriter(\"D:\\\\School\\\\2023 Winter\\\\Term Project\\\\Model Visualization\\\\Cifar 10 Network\\\\\")\nwriter.add_graph(cifar10Network, imgCifar10)\nwriter.close()\n\nwriter = SummaryWriter(\"D:\\\\School\\\\2023 Winter\\\\Term Project\\\\Model Visualization\\\\ImageNet Model\\\\\")\nwriter.add_graph(imageNetModel, imgImageNet)\nwriter.close()\n\n","repo_name":"GuillaumeLaporte/Friends-Networks","sub_path":"modelPrinting.py","file_name":"modelPrinting.py","file_ext":"py","file_size_in_byte":3454,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"24396004835","text":"\nimport pyttsx3\n\nengine = pyttsx3.init('sapi5')\nvoices = engine.getProperty('voices')\nprint(voices[0].id)\nengine.setProperty('voice',voices[0].id)\nengine.setProperty('rate',100)\n\ndef speak(audio):\n    engine.say(audio)\n    engine.runAndWait()\n\ndef arriving_call():\n    train_no = input(\"enter the train number: \")\n    from_station = input(\"station comming from: \")\n    via_stations = input(\"Via stations: \")\n    to_station = input(\"station going to: \")\n    train_name = input(\"Train name: \")\n    platform_number = input(\"Enter platform number: \")\n    train_no_sort = \"\"\n    for i in train_no:\n        train_no_sort = train_no_sort+\" \"+i\n\n    announcement = f\"may i have your attention please! Train number {train_no_sort}  from {from_station} via {via_stations} to {to_station} {train_name} is on time and arrving on platform number {platform_number}\"\n\n    speak(announcement)\n\narriving_call()\n","repo_name":"anand-ryuzagi/Python-Railway-Announcement","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":894,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"41922151073","text":"import pandas as pd\nimport numpy as np\nfrom pandas import DataFrame\nfrom pandas import Series\n\nposi=pd.read_csv(\"E:\\\\data\\\\python_file\\\\bad_driving_exchange_csv_file\\\\AA00002_bd.csv\")\n\nnum=posi.shape[0]       #读取的记录行数\n\nbad_driving_time = np.array(posi[\"bad_driving_time\"][0:num]) # 获取不良驾驶发生的时间\nbad_driving_lat = np.array(posi[\"bad_driving_lat\"][0:num]) # 获取纬度值\nbad_driving_lng = np.array(posi[\"bad_driving_lng\"][0:num]) # 获取经度值 \nbad_driving_name = np.array(posi[\"bad_driving_name\"][0:num]) #获取不良驾驶的类型\nbad_driving_info = np.array(posi[\"bad_driving_info\"][0:num])  #获取不良驾驶的详细信息\n\nfrom urllib.request import urlopen, quote\nimport json\n\n#定义一个将wgs84坐标系转化为百度坐标系的函数\ndef wgs84tobaidu(x,y):\n\tdata=str(x)+','+str(y);\n\toutput = 'json'\n\turl='http://api.map.baidu.com/geoconv/v1/?coords='+data+'&from=1&to=5&output=' +output+'&ak=S2HzBb9CHpFRM55s8H8bjkkrUGBmGhIY'  #ak为百度api的密钥\n\treq = urlopen(url)\n\tres = req.read().decode() \n\ttemp = json.loads(res)\n\tbaidu_x=0\n\tbaidu_y=0\n\tif temp['status']==0:\n\t\tbaidu_x=temp['result'][0]['x']\n\t\tbaidu_y=temp['result'][0]['y']\n\n\treturn baidu_x,baidu_y\n\t\nex_lng=[]\nex_lat=[]\nex_location=[]\n\nfor i in range(num):\n\tx,y=wgs84tobaidu(bad_driving_lng[i],bad_driving_lat[i])\n\tex_lng.append(x)\n\tex_lat.append(y)\nfor i in range(num):\n\tlocation_str=str(ex_lat[i])+','+str(ex_lng[i])     #将转化成功的坐标合并在一起\n\tex_location.append(location_str)\n\t\nimport requests\nimport json\n\nex_district=[]\nex_city=[]\nex_province=[]\n\n#利用百度api，根据经纬度得出具体的地理位置（省、市、县）\nfor i in range(num):\n\tr = requests.get(url='http://api.map.baidu.com/geocoder/v2/', params={'location':ex_location[i],'ak':'S2HzBb9CHpFRM55s8H8bjkkrUGBmGhIY','output':'json'})\n\tresult = r.json()\n\tdistrict = result['result']['addressComponent']['district']\n\tcity = result['result']['addressComponent']['city']\n\tprovince = result['result']['addressComponent']['province']\n\n\tex_district.append(district)     #获取县\n\tex_city.append(city)     #获取市\n\tex_province.append(province)     #获取省份\n\t\n#df=DataFrame({'ex_province':Series(ex_province),'ex_city':Series(ex_city),'ex_district':Series(ex_district)})\n#df.to_csv('E:\\\\data\\\\AA00002_bd_ex1.csv',index=False,encoding=\"utf_8_sig\")\n\nposi1=pd.read_csv(\"E:\\\\data\\\\python_file\\\\bad_driving_exchange_csv_file\\\\qx_data.csv\")     #读取气象数据的csv文件\n\nnum1=posi1.shape[0]       #读取气象数据的记录行数\n\nqx_province = np.array(posi1[\"province\"][0:num1]) # 获取省份\nqx_city = np.array(posi1[\"prefecture_city\"][0:num1]) # 获取市\nqx_district = np.array(posi1[\"county\"][0:num1]) # 获取县\nqx_wind_power = np.array(posi1[\"wind_power\"][0:num1])   #获取风力级别\nqx_conditions = np.array(posi1[\"conditions\"][0:num1])     #获取天气信息\nqx_rain_amount = np.array(posi1[\"precipitation\"][0:num1])   #获取降雨量\nqx_date = np.array(posi1[\"record_date\"][0:num1])      #获取时间\n\nfrom pandas import to_datetime\n\nposi_dt=to_datetime(posi.bad_driving_time,format='%Y/%m/%d %H:%M:%S')\n\nposi_day=posi_dt.dt.day\nposi_month=posi_dt.dt.month\nposi_year=posi_dt.dt.year\n\nex_date=[]\nfor i in range(num):\n\tex_date.append(str(posi_day[i])+'/'+str(posi_month[i])+'/'+str(posi_year[i]))    #将日期数据转化为日/月/年的字符串格式，方便后续与气象数据中的日期相比较\n\nfor i in range(num):\n\tex_province[i]=ex_province[i][:2]    #去除“省”字样\n\tex_city[i]=ex_city[i][:2]     #去除“市”\n\tl=len(ex_district[i])\n\tex_district[i]=ex_district[i][:l-1]     #去除“县/区”字样\n\t\nex_wind_power=[]\nex_conditions=[]\nex_rain_amount=[]\n\nfor i in range(num):\n\tex_wind_power.append('')\n\tex_conditions.append('')\n\tex_rain_amount.append('')\n\n#通过省、市、县、日期与气象数据中的相应属性列进行匹配，获取某天、某地区的具体天气情况\nfor i in range(num):\n\tfor k in range(num1):\n\t\tif ex_province[i]==qx_province[k] and ex_city[i]==qx_city[k] and ex_district[i]==qx_district[k] and ex_date[i]==qx_date[k]: \n\t\t\tex_wind_power[i]=qx_wind_power[k]\n\t\t\tex_conditions[i]=qx_conditions[k]\n\t\t\tex_rain_amount[i]=qx_rain_amount[k]\n\nfor i in range(num):\n\tif ex_conditions[i]=='':\n\t\tfor k in range(num1):\n\t\t\tif ex_province[i]==qx_province[k] and ex_city[i]==qx_city[k] and ex_date[i]==qx_date[k]:\n\t\t\t\tex_wind_power[i]=qx_wind_power[k]\n\t\t\t\tex_conditions[i]=qx_conditions[k]\n\t\t\t\tex_rain_amount[i]=qx_rain_amount[k]\n\n#存储多行与多列的数据集合\ndf=DataFrame({'bad_driving_time':Series(bad_driving_time),'bad_driving_lng':Series(bad_driving_lng),'bad_driving_lat':Series(bad_driving_lat),'bad_driving_name':Series(bad_driving_name),'bad_driving_info':Series(bad_driving_info),'ex_province':Series(ex_province),'ex_city':Series(ex_city),'ex_district':Series(ex_district),'ex_wind_power':Series(ex_wind_power),'ex_conditions':Series(ex_conditions),'ex_rain_amount':Series(ex_rain_amount)})\n#存储为csv文件，并保存至相应文件夹\ndf.to_csv('E:\\\\data\\\\python_file\\\\bad_driving_exchange_csv_file\\\\AA00002_bd_ex.csv',index=False,encoding=\"utf_8_sig\")","repo_name":"wonderccc/TeddyContest","sub_path":"python_code/bd_qx_ex.py","file_name":"bd_qx_ex.py","file_ext":"py","file_size_in_byte":5161,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"41472617184","text":"if __name__ == '__main__':\n    import pytex; __package__ = 'pytex.elements'  # @UnusedImport @ReservedAssignment\n\nimport warnings\nfrom contextlib import contextmanager\nfrom .types.nested_dict import NestedDict\n\nclass Context:\n    '''Defines a TeX processor state. It maps names to macros and environments, \n    it store global variables for the TeX processor, current environment, etc.'''\n    PACKAGE = None\n\n    def __init__(self, packages=[], macros={}):\n        self._init_class()\n        self._macro_table = NestedDict()\n        self._packages = [[]]\n        self._namespace = {}\n\n        # Load TeX and LaTeX macros\n        packages = self.PACKAGE.load_packages(packages, load_latex=True)\n        for package in packages:\n            self.load_package(package)\n\n    def get_macro(self, macro_name, warn=2):\n        '''Return the macro if it exists in the macro table'''\n\n        try:\n            return self._macro_table[macro_name]\n        except KeyError:\n            if warn >= 2:\n                raise ValueError('macro not found: %s' % macro_name)\n            elif warn == 1:\n                warnings.warn('creating macro \\\\%s' % macro_name)\n\n            new = self._new_macro(macro_name)\n            self.save_macro(new)\n            return new\n\n    def save_macro(self, macro):\n        '''Saves a macro to the macros table'''\n\n        self._macro_table[macro.macro_name] = macro\n\n    def get_environment(self, envname, warn=2):\n        '''Return the macro if it exists in the macro table'''\n\n        try:\n            return self.get_macro(envname, warn=2).environment\n        except ValueError:\n            if warn >= 2:\n                raise ValueError('environment not found: %s' % envname)\n            elif warn == 1:\n                warnings.warn('creating a new %s environment' % envname)\n\n            new = self._new_environment(envname)\n            self.save_macro(new.begin_macro)\n            self.save_macro(new.end_macro)\n            return new\n\n    def save_environment(self, env):\n        '''Register an environment to the macro table'''\n\n        self.save_macro(env.begin_macro)\n        self.save_macro(env.end_macro)\n\n    def load_package(self, package):\n        '''Load all macros from the given Package object.'''\n\n        if isinstance(package, str):\n            package = self.PACKAGE.load_package(package)\n            return self.load_package(package)\n\n        for _, obj in package.items():\n            if isinstance(obj, type):\n                if issubclass(obj, self._MACRO):\n                    self.save_macro(obj)\n                elif issubclass(obj, self._ENVIRONMENT):\n                    self.save_environment(obj)\n\n        self._packages[-1].append(package.name)\n\n    def set_variable(self, varname, value):\n        '''Set the value of a context variable'''\n\n        self._namespace[varname] = value\n\n    def get_variable(self, varname, *args):\n        '''Get the value for a variable or the second argument if this variable\n        does not exist'''\n\n        if len(args) > 1:\n            raise TypeError('get_variable(varname[,default]) can be called with one or two positional arguments')\n        try:\n            return self._namespace[varname]\n        except KeyError:\n            if args:\n                return args[0]\n            else:\n                raise ValueError(\"variable doesn't exist: %s\" % varname)\n\n    def begin_group(self):\n        '''Begin a new group. Define a nested namespace for the group.'''\n        self._macro_table.up()\n\n    def end_group(self):\n        '''Ends the namespace defined by begin_group()'''\n        self._macro_table.down()\n\n    @contextmanager\n    def grouping(self):\n        '''Context manager for safely creating new groups'''\n        # __enter__()\n        self.begin_group()\n\n        # Execute block\n        yield\n\n        # __exit__()\n        self.end_group()\n\n    @classmethod\n    def _init_class(cls):\n        if cls.PACKAGE is None:\n            from pytex import package\n            cls.PACKAGE = package\n\n    def copy(self):\n        warnings(NotImplemented)\n        return self\n\nif __name__ == '__main__':\n    import doctest\n    doctest.testmod(report=True, optionflags=doctest.REPORT_ONLY_FIRST_FAILURE)\n","repo_name":"fabiommendes/pytex","sub_path":"pytex/src/pytex/context.py","file_name":"context.py","file_ext":"py","file_size_in_byte":4197,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"3613594765","text":"import matplotlib.pyplot as plt\nimport pandas as pd\nimport scipy\n\nimport scikits.bootstrap as bootstrap\n\ndata = pd.read_csv('bat_data.csv')\ndata_input = pd.read_csv('bat_data.csv')\nprint('All Bats Species Statistical Summary')\nprint(data.describe())\n\nprint('All Bat Species correlation coefficient between FA(mm) and Wt(g)')\nprint(data['FA(mm)'].corr(data['Wt(g)']))\nprint('\\n-------------------------------------')\n\nspecies_length = data[[\"Species\", \"FA(mm)\"]]\nspecies_len = data[\"FA(mm)\"]\n\nspecies_length.hist()\nspecies_length.boxplot()\nplt.title(\"All Bat Species Bat Length N = 267\")\nplt.show()\nspecies_length.hist()\nplt.title(\"All Bat Species  Length-FA(mm) N = 267\")\nplt.show()\n\nCIs = bootstrap.ci(data=species_len, statfunction=scipy.mean)\nprint('Confidence Interval FA(mm) All Species Bats Low {} High {}'.format(CIs[0], CIs[1]))\nprint('\\n-------------------------------------')\n\nbat_weight = data[[\"Species\", \"Wt(g)\"]]\nspecies_wt = data['Wt(g)']\n\nCIwt = bootstrap.ci(data=species_wt, statfunction=scipy.mean)\nprint('Confidence Interval Wt(g) All Species Bats Low {} High {}'.format(CIwt[0], CIwt[1]))\nprint('\\n-------------------------------------')\n\nbat_weight.boxplot()\nplt.title('All Bat Species Weight - Wt(g) N= 267')\nplt.show()\nbat_weight.hist()\nplt.title('All Bat Species Histogram (Bat Weight in grams) N = 267')\nplt.show()\n\nsubgrp_Mcriteria = data['Sex'] == 'M'\nsubgrp_Fcriteria = data['Sex'] == 'F'\n\noverall_female = data[subgrp_Fcriteria]\noverall_male = data[subgrp_Mcriteria]\n\nprint('All Species Female only correlation coefficient between FA(mm) and Wt(g)')\nprint(overall_female['FA(mm)'].corr(overall_female['Wt(g)']))\nprint('\\n')\nprint('All Species Male only correlation coefficient between FA(mm) and Wt(g)')\nprint(overall_male['FA(mm)'].corr(overall_male['Wt(g)']))\n\nprint('\\nAll Species Female Bat Summary Statistics')\nprint(overall_female.describe())\nprint('\\nAll Species Male Bat Summary Statistics')\nprint(overall_male.describe())\n\nCIwt = bootstrap.ci(data=overall_male['Wt(g)'], statfunction=scipy.mean)\nprint('Confidence Interval Male Wt(g) All Species Bats Low {} High {}'.format(CIwt[0], CIwt[1]))\nprint('\\n-------------------------------------')\n\nCIwt = bootstrap.ci(data=overall_female['Wt(g)'], statfunction=scipy.mean)\nprint('Confidence Interval Female Wt(g) All Bats Low {} High {}'.format(CIwt[0], CIwt[1]))\nprint('\\n-------------------------------------')\n\nCIwt = bootstrap.ci(data=overall_male['FA(mm)'], statfunction=scipy.mean)\nprint('Confidence Interval Male FA(mm) All Bats Low {} High {}'.format(CIwt[0], CIwt[1]))\nprint('\\n-------------------------------------')\n\nCIwt = bootstrap.ci(data=overall_female['FA(mm)'], statfunction=scipy.mean)\nprint('Confidence Interval Female FA(mm) All Bats Low {} High {}'.format(CIwt[0], CIwt[1]))\nprint('\\n-------------------------------------')\n\noverall_female.boxplot()\nplt.title('Female only - All Species N=142')\nplt.show()\noverall_male.boxplot()\nplt.title('Male only - All Species N=125')\nplt.show()\n#\nsubgrp_late_capture_time = data['Time'] == '21:00'\nsubgrp_early_capture_time = data['Time'] == '7:00'\n\nearly_bats = data[subgrp_early_capture_time]\nlate_bats = data[subgrp_late_capture_time]\n\nearly_bats.boxplot()\nplt.title('Early Capture - All Species N = 78')\nplt.show()\nearly_bats.hist()\nplt.title('Early Capture - All Wt(g) Species N = 78')\nplt.show()\n\nlate_bats.boxplot()\nplt.title('Late capture - All Species N = 189')\nplt.show()\nlate_bats.hist()\nplt.title('Late capture - All Species Wt(g) N = 189')\nplt.show()\n\nprint('Early Correlation Coefficient')\nprint(early_bats['FA(mm)'].corr(early_bats['Wt(g)']))\n\nCIwt = bootstrap.ci(data=early_bats['FA(mm)'], statfunction=scipy.mean)\nprint('Confidence Interval Early FA(mm) All Species Bats Low {} High {}'.format(CIwt[0], CIwt[1]))\nprint('\\n-------------------------------------')\n\nCIwt = bootstrap.ci(data=early_bats['Wt(g)'], statfunction=scipy.mean)\nprint('Confidence Interval Early Wt(g) All Species Bats Low {} High {}'.format(CIwt[0], CIwt[1]))\nprint('\\n-------------------------------------')\n\nprint('Late Correlation Coefficient')\nprint(late_bats['FA(mm)'].corr(late_bats['Wt(g)']))\n\nCIwt = bootstrap.ci(data=late_bats['FA(mm)'], statfunction=scipy.mean)\nprint('Confidence Interval Late FA(mm) All Species Bats Low {} High {}'.format(CIwt[0], CIwt[1]))\nprint('\\n-------------------------------------')\n\nCIwt = bootstrap.ci(data=late_bats['Wt(g)'], statfunction=scipy.mean)\nprint('Confidence Interval Late Wt(g) All Species Bats Low {} High {}'.format(CIwt[0], CIwt[1]))\nprint('\\n-------------------------------------')\n\nprint('All Species Late Capture Time')\nprint(data[subgrp_late_capture_time].describe())\n\nprint('All Species Early Capture Time')\nprint(data[subgrp_early_capture_time].describe())\n\noverall_male_early = data[subgrp_Mcriteria & subgrp_early_capture_time]\noverall_male_late = data[subgrp_Mcriteria & subgrp_late_capture_time]\n\noverall_male_early.boxplot()\nplt.title('Male only Early - All Species')\nplt.show()\noverall_male_late.boxplot()\nplt.title('Male only Late - All Species')\nplt.show()\n\noverall_female_early = data[subgrp_Fcriteria & subgrp_early_capture_time]\noverall_female_late = data[subgrp_Fcriteria & subgrp_late_capture_time]\n\noverall_female_early.boxplot()\nplt.title('Female only Early - All Species N=51')\nplt.show()\noverall_female_late.boxplot()\nplt.title('Female only Late - All Species')\nplt.show()\n\nprint('All Species Female Early Statistical Summary N=91')\nprint(overall_female_early.describe())\nprint('All Species Female Late Statistical Summary')\nprint(overall_female_late.describe())\n\nprint('All Species Male Early Statistical Summary N=27')\nprint(overall_male_early.describe())\nprint('All Species Male Late Statistical Summary N=98')\nprint(overall_male_late.describe())\n\nprint('Male only Early Correlation Coefficient')\nprint(overall_male_early['FA(mm)'].corr(overall_male_early['Wt(g)']))\n\nCIwt = bootstrap.ci(data=overall_male_early['FA(mm)'], statfunction=scipy.mean)\nprint('Confidence Interval Early Male FA(mm) All Species Bats Low {} High {}'.format(CIwt[0], CIwt[1]))\nprint('\\n-------------------------------------')\n\nprint('Male Only Late All Species correlation coefficient')\nprint(overall_male_late['FA(mm)'].corr(overall_male_late['Wt(g)']))\n\nCIwt = bootstrap.ci(data=overall_male_late['FA(mm)'], statfunction=scipy.mean)\nprint('Confidence Interval Late Male FA(mm) All Species Low {} High {}'.format(CIwt[0], CIwt[1]))\nprint('\\n-------------------------------------')\n\nprint('All Species female Early correlation coefficient')\nprint(overall_female_early['FA(mm)'].corr(overall_female_early['Wt(g)']))\n\nCIwt = bootstrap.ci(data=overall_female_early['FA(mm)'], statfunction=scipy.mean)\nprint('Confidence Interval All Species Early Female FA(mm) All Bats Low {} High {}'.format(CIwt[0], CIwt[1]))\nprint('\\n-------------------------------------')\n\nprint('All Species female Late correlation coefficient')\nprint(overall_female_late['FA(mm)'].corr(overall_female_late['Wt(g)']))\nprint('\\n-------------------------------------')\n\nCIwt = bootstrap.ci(data=overall_female_late['FA(mm)'], statfunction=scipy.mean)\nprint('Confidence Interval All Species Late Female FA(mm) All Bats Low {} High {}'.format(CIwt[0], CIwt[1]))\nprint('\\nEnd of All Species Analysis')\n\nsubgrp_trap_location = data['Trap']\n\nplt.scatter(data['Band'], data['Wt(g)'], c=data['Sex'] == 'F')\nplt.title('All Species Scatterplot N = 267, female in yellow')\nplt.show()\n\nsubgrp_len_gt = data[\"FA(mm)\"] > [data[\"FA(mm)\"].mean()]\n\nsubgrp_wt_gt = data[\"Wt(g)\"] > [data[\"Wt(g)\"].mean()]\n\nsubgrp1_type_intermedia = data['Species'] == 'Kerivoula intermedia'\nsubgrp1_type_papillosa = data['Species'] == 'Kerivoula papillosa'\nsubgrp1_type_pellucida = data['Species'] == 'Kerivoula pellucida'\nsubgrp1_general_kerivoula = data['Species'] == 'Kerivoula'\n\nsubgrp2_general_hipposideros = data['Species'] == 'Hipposideros'\nsubgrp2_type_cervinus = data['Species'] == 'Hipposideros cervinus'\n\nsubgrp3_general_rhinolophus = data['Species'] == 'Rhinolophus'\nsubgrp3_type_trifoliatus = data['Species'] == 'Rhinolophus trifoliatus'\n\n# Female late capture\nfm_combine_grp_time = subgrp1_type_intermedia & subgrp_Fcriteria & subgrp_late_capture_time\n# Female intermedia group\nfm_combine_grp = subgrp_Fcriteria & subgrp1_type_intermedia\n# Female early capture\nfm_combine_grp_time_early = subgrp1_type_intermedia & subgrp_Fcriteria & subgrp_early_capture_time\n\n# Male late capture\nm_combine_grp_time = subgrp1_type_intermedia & subgrp_Mcriteria & subgrp_late_capture_time\n# Male group\nm_combine_grp = subgrp_Mcriteria & subgrp1_type_intermedia\n# Male early capture group\nm_combine_grp_time_early = subgrp1_type_intermedia & subgrp_Mcriteria & subgrp_early_capture_time\n\n\nintermedia_female_stats = data[fm_combine_grp]\nintermedia_female_late_stats_time = data[fm_combine_grp_time]\nintermedia_female_early_stats_time = data[fm_combine_grp_time_early]\n\nintermedia_female_late_stats_time_weight = intermedia_female_late_stats_time['Wt(g)']\nintermedia_female_early_stats_time_weight = intermedia_female_early_stats_time['Wt(g)']\nintermedia_female_stats_time_weight = intermedia_female_stats['Wt(g)']\n\nintermedia_female_late_stats_time_size = intermedia_female_late_stats_time['FA(mm)']\nintermedia_female_early_stats_time_size = intermedia_female_early_stats_time['FA(mm)']\nintermedia_female_stats_time_size = intermedia_female_stats['FA(mm)']\n\nprint('Kerivoula intermedia Species Female Only Statistics')\nprint(intermedia_female_stats.describe())\nprint('Kerivoula intermedia Species Late Female Only Statistics')\nprint(intermedia_female_late_stats_time.describe())\nprint('Kerivoula intermedia Species Early Female Only Statistics')\nprint(intermedia_female_early_stats_time.describe())\nprint('\\n')\n\nCIs = bootstrap.ci(data=intermedia_female_stats_time_weight, statfunction=scipy.mean)\nprint ('Kerivoula intermedia Female Weight confidence intervals Low:{} High{}'.format(CIs[0], CIs[1]))\nCIs = bootstrap.ci(data=intermedia_female_late_stats_time_weight, statfunction=scipy.mean)\nprint ('Kerivoula intermedia Late Weight Female confidence interval Low:{} High{}'.format(CIs[0], CIs[1]))\n# CIs = bootstrap.ci(data =intermedia_female_early_stats_time_weight, statfunction=scipy.mean)\n# print ('Kerivoula intermedia Early Weight Female confidence intervals Low:{} High{}'.format(CIs[0], CIs[1]))\nCIs = bootstrap.ci(data=intermedia_female_stats_time_size, statfunction=scipy.mean)\nprint ('Kerivoula intermedia Female FA(mm) confidence interval Low:{} High{}'.format(CIs[0], CIs[1]))\nCIs = bootstrap.ci(data=intermedia_female_late_stats_time_size, statfunction=scipy.mean)\nprint ('Kerivoula intermedia Late Female FA(mm) confidence interval Low:{} High{}'.format(CIs[0], CIs[1]))\nCIs = bootstrap.ci(data=intermedia_female_early_stats_time_size, statfunction=scipy.mean)\nprint ('Kerivoula intermedia Early Female FA(mm) confidence interval Low:{} High{}'.format(CIs[0], CIs[1]))\n\nintermedia_female_stats.boxplot()\nplt.title('Kerivoula Female intermedia N = 11')\nplt.show()\nintermedia_female_late_stats_time.boxplot()\nplt.title('Kerivoula intermedia Female Late Capture Time N = 6')\nplt.show()\nintermedia_female_early_stats_time.boxplot()\nplt.title('Kerivoula intermedia Female Early Capture Time N = 5')\nplt.show()\n\nintermedia_male_stats = data[m_combine_grp]\nintermedia_male_late_stats_time = data[m_combine_grp_time]\nintermedia_male_early_stats_time = data[m_combine_grp_time_early]\n\nintermedia_male_late_stats_time_weight = intermedia_male_late_stats_time['Wt(g)']\nintermedia_male_early_stats_time_weight = intermedia_male_early_stats_time['Wt(g)']\nintermedia_male_stats_time_weight = intermedia_male_stats['Wt(g)']\n\nintermedia_male_late_stats_time_size = intermedia_male_late_stats_time['FA(mm)']\nintermedia_male_early_stats_time_size = intermedia_male_early_stats_time['FA(mm)']\nintermedia_male_stats_time_size = intermedia_male_stats['FA(mm)']\n\nprint('Kerivoula intermedia Male Summary Statistics')\nprint(intermedia_male_stats.describe())\nprint('Kerivoula intermedia Male Late Summary Statistics')\nprint(intermedia_male_late_stats_time.describe())\nprint('Kerivoula intermedia Male Early Summary Statistics')\nprint(intermedia_male_early_stats_time.describe())\n\nCIs = bootstrap.ci(data=intermedia_male_stats_time_weight, statfunction=scipy.mean)\nprint ('Kerivoula intermedia Male Weight confidence interval Low:{} High{}'.format(CIs[0], CIs[1]))\n\nCIs = bootstrap.ci(data=intermedia_male_late_stats_time_weight, statfunction=scipy.mean)\nprint ('Kerivoula intermedia Male Late Weight confidence interval Low:{} High{}'.format(CIs[0], CIs[1]))\n\nCIs = bootstrap.ci(data=intermedia_male_early_stats_time_weight, statfunction=scipy.mean)\nprint ('Kerivoula intermedia Male Early Weight confidence interval Low:{} High{}'.format(CIs[0], CIs[1]))\n\nCIs = bootstrap.ci(data=intermedia_male_stats_time_size, statfunction=scipy.mean)\nprint ('Kerivoula intermedia Male Size FA(mm) confidence interval Low:{} High{}'.format(CIs[0], CIs[1]))\n\nCIs = bootstrap.ci(data=intermedia_male_late_stats_time_size, statfunction=scipy.mean)\nprint ('Kerivoula intermedia Male Late Size FA(mm) confidence interval Low:{} High{}'.format(CIs[0], CIs[1]))\n\nCIs = bootstrap.ci(data=intermedia_male_early_stats_time_size, statfunction=scipy.mean)\nprint ('Kerivoula intermedia Male Early Size FA(mm) confidence interval Low:{} High{}'.format(CIs[0], CIs[1]))\n\nintermedia_male_stats.boxplot()\nplt.title('Kerivoula Male intermedia N = 54')\nplt.show()\n\nintermedia_male_late_stats_time.boxplot()\nplt.title('Kerivoula intermedia Male Late Capture Time N = 45')\nplt.show()\n\nintermedia_male_early_stats_time.boxplot()\nplt.title('Kerivoula intermedia Male Early Capture Time N = 09')\nplt.show()\n","repo_name":"derrelldunn/MATH5378-FINAL","sub_path":"bat_data_file_open.py","file_name":"bat_data_file_open.py","file_ext":"py","file_size_in_byte":13674,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"33336168808","text":"import pytest\nfrom src.sorting import sort_by\n\n\n@pytest.fixture\ndef mock_jobs():\n    return [\n        {\"title\": \"Job1\", \"min_salary\": 15, \"max_salary\": 85,\n            \"date_posted\": '2022-05-28'},\n        {\"title\": \"Job2\", \"min_salary\": 20, \"max_salary\": 60,\n            \"date_posted\": '2022-06-28'},\n        {\"title\": \"Job3\", \"min_salary\": 3, \"max_salary\": 50,\n            \"date_posted\": '2022-07-28'},\n    ]\n\n\ndef test_sort_by_criteria(mock_jobs):\n    sort_by(mock_jobs, \"min_salary\")\n    assert mock_jobs[0][\"min_salary\"] == 3\n    sort_by(mock_jobs, \"max_salary\")\n    assert mock_jobs[0][\"max_salary\"] == 85\n    sort_by(mock_jobs, \"min_salary\")\n    assert mock_jobs[0][\"date_posted\"] == '2022-07-28'\n","repo_name":"EletroCP/job-insights","sub_path":"tests/sorting/test_sorting.py","file_name":"test_sorting.py","file_ext":"py","file_size_in_byte":704,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"40276608215","text":"#!/usr/bin/env python\n# coding:utf-8\n\nimport myDetectronModel as DecModel\n\nimport subFunctions as sub\n\ndef main():\n    #print(torch.__version__, torch.cuda.is_available())\n    print(\"start\")\n    \n    model1=DecModel.myModel()\n    \n    # 転移学習前\n    # model1.ExecInference_OutputImage(imageFilePathName=\"./images/input.jpg\",\n    #                                 OutputFilePathNam=\"./images/output1.jpg\")\n    # \n    # sub.PrintLogFormat(\"Start TransferLearning. ------------\")\n    # 転移学習\n    \n    categoriesInfo = sub.readJson(\"./CoCoSample/trainval.json\")[\"categories\"]\n    print(categoriesInfo)\n    \n    # 転移学習実行\n    # model1.TransferLearning(traningDataName=\"CoCoSample\", \n    #                         classesName=categoriesInfo,\n    #                         CoCoFilePathName=\"./CoCoSample/trainval.json\",\n    #                         imageRootPath=\"./CoCoSample/images\")\n    \n    model1.TransferLearningDataAugmentation(traningDataName=\"CoCoSample\", \n                            classesName=categoriesInfo,\n                            CoCoFilePathName=\"./CoCoSample/trainval.json\",\n                            imageRootPath=\"./CoCoSample/images\")\n    \n    sub.PrintLogFormat(\"End TransferLearning. ------------\")    \n\n    # 転移学習後    \n    model1.ExecInference_OutputImage(imageFilePathName=\"./images/input.jpg\",\n                                    OutputFilePathNam=\"./images/output2.jpg\")\n    model1.ExecInference_OutputImage(imageFilePathName=\"./images/input_b.jpg\",\n                                    OutputFilePathNam=\"./images/output3.jpg\")\n\n    print(\"end\")\n    \nif __name__ == '__main__':\n    main()\n\n","repo_name":"Y-Yoshimoto/PythonDevKit","sub_path":"RCNN-detection/TrainerRole/dectrainer/src/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1652,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"29930712283","text":"import unittest\nfrom typing import Tuple, List\n\nfrom multiprocess.connection import Pipe\n\ntry:\n    from multiprocess.connection import PipeConnection\nexcept ImportError:\n    PipeConnection = object\n\nfrom ....characters.moves import MoveDescriptor\nfrom ....characters.units import Entity\nfrom ....board import Builder\nfrom ....characters.moves import Path\nfrom ....characters.units import Unit\nfrom ....controls.controllers import Bot\nfrom ....controls.events import BotEvent, SpecialEvent\nfrom ....controls.wrappers.bot import BotControllerWrapper\nfrom ....game import Core, UnfeasibleMoveException, API\n\nMOVE1 = \"MOVE1\"\nMOVE2 = \"MOVE2\"\nMSG1 = \"DO_MOVE1\"\nMSG2 = \"DO_MOVE2\"\n\n\nclass ExampleBotControllerWrapper(BotControllerWrapper):\n    def isMoveDescriptorAllowed(self, move_descriptor) -> bool:\n        return type(move_descriptor) == str and move_descriptor[0:4] == 'MOVE'\n\n\nclass ExampleAPI(API):\n    def _decodeMoveFromPositiveNumber(self, player_number: int, encoded_move: int) -> MoveDescriptor:\n        pass\n\n    def _encodeMoveIntoPositiveNumber(self, player_number: int, move_descriptor: MoveDescriptor) -> int:\n        pass\n\n    def createMoveForDescriptor(self, unit: Unit, move_descriptor: MoveDescriptor, max_moves: int = -1,\n                                force: bool = False, is_step: bool=False) -> Path:\n        raise UnfeasibleMoveException()\n\n    def __init__(self, game: Core):\n        super().__init__(game)\n        self.move1 = 0\n        self.move2 = 0\n\n    def isItOneTestMethod(self):\n        if isinstance(self.game, ExampleGame):\n            return True\n        return False\n\n    def performMove1(self):\n        self.move1 += 1\n\n    def performMove2(self):\n        self.move2 += 1\n\n\nclass ExampleGame(Core):\n    @property\n    def _teamKillAllowed(self) -> bool:\n        return False\n\n    @property\n    def _suicideAllowed(self) -> bool:\n        return False\n\n    def _collidePlayers(self, player1, player2, tile_id, frontal: bool = False, entity: Entity=None):\n        pass\n\n\nclass ExampleBot(Bot):\n    @property\n    def possibleMoves(self) -> List[MoveDescriptor]:\n        return []\n\n    def _getGameStateAPI(self, game: Core):\n        return ExampleAPI(game)\n\n    def reactToEvents(self, events: List[BotEvent]):\n        for event in events:\n\n            new_move_event = event.moveDescriptor\n            if new_move_event == MOVE1:\n                self.gameState.performMove1()\n            elif new_move_event == MOVE2:\n                self.gameState.performMove2()\n        return super().reactToEvents(events)\n\n    def _isMoveInteresting(self, player_number: int, new_move_event) -> bool:\n        return True\n\n    def _isMoveAllowed(self, move: str) -> bool:\n        if type(move) == str and move[0:4] == 'MOVE':\n            return True\n        return False\n\n    def selectMoveFollowingTeammateMessage(self, teammate_number: int, message):\n        if message == MSG1:\n            return MOVE1\n        elif message == MSG2:\n            return MOVE2\n\n    def _selectNewMove(self, game_state: ExampleAPI):\n        return \"MOVE1-\" + str(game_state.move1) + '/' + \"MOVE2-\" + str(game_state.move2)\n\n\nclass TestBotControllerWrapper(unittest.TestCase):\n    def setUp(self):\n        self.game = ExampleGame(Builder(10, 10, 7, 6).create())\n        self.game.addUnit(Unit(1), 1, (0, 0))\n        self.bot1 = ExampleBot(1)\n        self.bot1.gameState = self.game.copy()\n        self.linker1 = ExampleBotControllerWrapper(self.bot1)\n        self.game.addUnit(Unit(2), 1, (0, 0))\n        self.bot2 = ExampleBot(2)\n        self.bot2.gameState = self.game.copy()\n        self.linker2 = ExampleBotControllerWrapper(self.bot2)\n        self.game_info_pipe_parent1, self.game_info_pipe_child1 = Pipe()  # type: Tuple[PipeConnection, PipeConnection]\n        self.game_info_pipe_parent2, self.game_info_pipe_child2 = Pipe()  # type: Tuple[PipeConnection, PipeConnection]\n        self.move_pipe_parent1, self.move_pipe_child1 = Pipe()  # type: Tuple[PipeConnection, PipeConnection]\n        self.move_pipe_parent2, self.move_pipe_child2 = Pipe()  # type: Tuple[PipeConnection, PipeConnection]\n        self.linker1.setMainPipe(self.move_pipe_child1)\n        self.linker1.setGameInfoPipe(self.game_info_pipe_child1)\n        self.linker2.setMainPipe(self.move_pipe_child2)\n        self.linker2.setGameInfoPipe(self.game_info_pipe_child2)\n        self.collaboration_pipe_1, self.collaboration_pipe_2 = Pipe()\n        self.linker1.addCollaborationPipe(2, self.collaboration_pipe_1)\n        self.linker2.addCollaborationPipe(1, self.collaboration_pipe_2)\n\n    def test_invalid_type_sent(self):\n        \"\"\"\n        Tests that the linker raises an error when a message that is not a \"BotEvent\" is sent\n        \"\"\"\n        self.move_pipe_parent1.send(\"\")\n        self.assertRaises(TypeError, self.linker1._routine, self.game_info_pipe_child1, self.move_pipe_child1)\n\n    def test_send_move(self):\n        \"\"\"\n        Tests that moves are sent correctly, that they affect the GameState and that the AI responds well\n        \"\"\"\n        move1_event = BotEvent(1, MOVE1)\n        move2_event = BotEvent(1, MOVE2)\n        self.move_pipe_parent1.send(move1_event)\n        self.move_pipe_parent1.send(move1_event)\n        self.move_pipe_parent1.send(move1_event)\n        self.move_pipe_parent1.send(move2_event)\n        self.linker1._routine()\n        self.assertFalse(self.move_pipe_parent1.poll())\n        self.linker1._routine()\n        self.assertTrue(self.move_pipe_parent1.poll())\n        self.assertEqual(self.move_pipe_parent1.recv(), \"MOVE1-3/MOVE2-1\")\n\n    def test_send_message_to_teammate(self):\n        \"\"\"\n        Tests that messages are sent well between two teammates\n        \"\"\"\n        self.bot1.sendMessageToTeammate(2, MSG1)\n        self.linker1._routine()  # Will send the message\n        self.linker2._routine()  # Will receive the message\n        self.assertTrue(self.move_pipe_parent2.poll())\n        self.assertEqual(self.move_pipe_parent2.recv(), \"MOVE1\")\n\n    def test_send_end_event(self):\n        \"\"\"\n        Checks if the linker's logical loop ends correctly when it receives the end event\n        \"\"\"\n        self.game_info_pipe_parent1.send(SpecialEvent(SpecialEvent.END))\n        self.linker1.run()\n        # Should run indefinitely if no flag was sent\n        self.assertTrue(True)\n\n    def test_unit_dead(self):\n        \"\"\"\n        Checks if the linker blocks the incoming message of a dead unit, and starts to send again when resurrected\n        \"\"\"\n        self.game_info_pipe_parent1.send(SpecialEvent(SpecialEvent.UNIT_KILLED))\n        self.move_pipe_parent1.send(BotEvent(1, MOVE1))\n        self.linker1._routine()\n        self.assertFalse(self.move_pipe_parent1.poll())\n        self.linker1._routine()  # Message blocked\n        self.assertFalse(self.move_pipe_parent1.poll())\n        self.game_info_pipe_parent1.send(SpecialEvent(SpecialEvent.RESURRECT_UNIT))\n        self.move_pipe_parent1.send(BotEvent(1, MOVE2))\n        self.linker1._routine()  # Message received\n        self.linker1._routine()  # Message sent\n        self.assertTrue(self.move_pipe_parent1.poll())\n        # The message \"MOVE1\" was correctly received while the unit was dead => the game state is updated\n        # while the unit is dead\n        self.assertEqual(self.move_pipe_parent1.recv(), \"MOVE1-1/MOVE2-1\")\n","repo_name":"Angeall/pyTGF","sub_path":"pytgf/test/test_controls/wrappers/test_bot_wrapper.py","file_name":"test_bot_wrapper.py","file_ext":"py","file_size_in_byte":7317,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"3774349335","text":"from random import randint\n\nimport pygame\nfrom phoenyx import Engine\n\nfrom boid import Boid\nfrom quadtree import Circle, Point, Quadtree, Rect\n\n\ndef _map(x: float, x0: float, x1: float, y0: float, y1: float) -> float:\n    \"\"\"\n    linear interpolation\n    \"\"\"\n    return (y0 * (x1 - x) + y1 * (x - x0)) / (x1 - x0)\n\n\nclass Population:\n    \"\"\"\n    Population\n    ==========\n    Group of Boids\n\n    Population has :\n     * a ``Group`` of Boids\n     * a ``quadtree`` recursive structure\n    \"\"\"\n    def __init__(self, renderer: Engine, win: tuple, n: int = 10) -> None:\n        self._renderer = renderer\n        self._win = win\n        self.pop = pygame.sprite.Group()\n\n        self.qt = Quadtree(\n            Rect(renderer, self._win[0] // 2, self._win[1] // 2, self._win[0] // 2, self._win[1] // 2))\n\n        for _ in range(n):\n            self.pop.add(Boid(renderer, randint(0, win[0]), randint(0, win[1]), win))\n\n    def show(self) -> None:\n        \"\"\"\n        calls draw methods from Engine to draw Sprite Group\n        \"\"\"\n        self._renderer.sprites(self.pop)\n\n    def build(self) -> None:\n        \"\"\"\n        Rebuilds the Quadtree\n        \"\"\"\n        self.qt = Quadtree(\n            Rect(self._renderer, self._win[0] // 2, self._win[1] // 2, self._win[0] // 2, self._win[1] // 2))\n\n        for boid in self.pop:\n            point = Point(boid.rect.x, boid.rect.y, boid)\n            self.qt.insert(point)\n\n    def update(self, a: float = 1, b: float = 1, c: float = 1) -> tuple:\n        \"\"\"\n        Updates all population\n\n        Parameters\n        ----------\n            a : (float, optional)\n                coefficient for alignment\n                Defaults to 1\n            b : (float, optional)\n                coefficient for cohesion\n                Defaults to 1\n            c : (float, optional)\n                coefficient for separation\n                Defaults to 1\n        Returns\n        -------\n            tuple : total neighbors, velocity, brute and modified force\n        \"\"\"\n        total, vel = 0, 0\n        brute, modified = 0, 0\n\n        for boid in self.pop.sprites()[:]:\n            points =\\\n                self.qt.query(Circle(self._renderer, boid.rect.x, boid.rect.y, boid.RADIUS)) +\\\n                self.qt.query(Circle(self._renderer, boid.rect.x + self._win[0], boid.rect.y, boid.RADIUS)) +\\\n                self.qt.query(Circle(self._renderer, boid.rect.x - self._win[0], boid.rect.y, boid.RADIUS)) +\\\n                self.qt.query(Circle(self._renderer, boid.rect.x, boid.rect.y + self._win[1], boid.RADIUS)) +\\\n                self.qt.query(Circle(self._renderer, boid.rect.x, boid.rect.y - self._win[1], boid.RADIUS))\n            others = [p.data for p in points]\n\n            tot, br, mo = boid.flock(others, a, b, c)\n            total += tot\n            brute += br\n            modified += mo\n\n        for boid in self.pop:\n            vel += boid.move()\n\n        self.build()\n        return total, vel, brute, modified\n","repo_name":"ThomasByr/flocking_simulation","sub_path":"population.py","file_name":"population.py","file_ext":"py","file_size_in_byte":2965,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"32562352645","text":"#This programme reads in two numbers, #divides the first one by the second \n#and gives the integer result and the remainder\n#Author: Ruth McQuillan\n\nx = int (input (\"Please enter first number:  \"))                                      #this is asking for the numerator\ny = int (input (\"Please enter the number you wish to divide by: \"))                   #this is asking for the denominator\nanswer = x//y                                                                         #this gives the answer as an integer ( without the remainder)\nremainder = x % y                                                                     #this gives the remainder\n\nprint (\"{} divided by {} is {} with remainder {}\" .format (x, y,answer,remainder))    #output command and format","repo_name":"G00398347/programming2021","sub_path":"week02/lab2.3.3div.py","file_name":"lab2.3.3div.py","file_ext":"py","file_size_in_byte":764,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"21098019109","text":"from alembic import op\nimport sqlalchemy as sa\nfrom sqlalchemy.dialects import mysql\n\n# revision identifiers, used by Alembic.\nrevision = '05df3bf17d09'\ndown_revision = '6056d0d0eff9'\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.add_column('pipelines', sa.Column('emails_for_notifications', sa.String(length=255), nullable=True))\n    # ### end Alembic commands ###\n\n\ndef downgrade():\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.drop_column('pipelines', 'emails_for_notifications')\n    # ### end Alembic commands ###\n","repo_name":"google/crmint","sub_path":"backend/migrations/versions/05df3bf17d09_add_emails_for_notifications.py","file_name":"05df3bf17d09_add_emails_for_notifications.py","file_ext":"py","file_size_in_byte":629,"program_lang":"python","lang":"en","doc_type":"code","stars":153,"dataset":"github-code","pt":"81"}
+{"seq_id":"21577659641","text":"import sys\nsys.path.append(\"pypace\")\nimport missingData as md\nimport numpy as np\nimport matplotlib as mpl\nimport config\nif ( not config.enableShow ):\n    mpl.use(\"Agg\")\nmpl.rcParams[\"svg.fonttype\"] = \"none\"\nmpl.rcParams[\"axes.unicode_minus\"] = False\nfrom matplotlib import pyplot as plt\nimport pickle as pck\nimport copy\nimport missingDataInitGenerator as mdig\nimport h5py as h5\nimport time\n\nNUMBER_OF_ITERATIONS=1000\nRELATIVE_ERROR = 5E-2\nreconstruct = \"data/average_NiAu_sample1_3D_50_1.npy\"\nkspace = \"data/NiAu_sample1_3D.npy\"\n\ndef main():\n    timestamp = str( time.strftime(\"%Y_%m_%d_%H_%M_%S\") )\n    print (\"Started on %s\"%(timestamp))\n    useCubicMask = False\n    ds = 1\n    scattered = np.load( kspace )\n    realsp = np.load( reconstruct )\n    realspPadded = np.zeros(scattered.shape)\n    start = int( scattered.shape[0]/4 )\n    end = int( 3*scattered.shape[0]/4 )\n\n    realspPadded[start:end,start:end,start:end] = realsp\n    mask = np.zeros( scattered.shape, dtype=np.uint8 )\n    mask[scattered>1E-16*scattered.max()] = 1\n\n    support = np.zeros( realspPadded.shape, dtype=np.uint8 )\n    support[realspPadded>1E-6*realspPadded.max()] = 1\n\n    del realsp, realspPadded\n\n    if ( useCubicMask ):\n        mask[:,:,:] = 1\n        w = mask.shape[0]/4\n        center = mask.shape[0]/2\n        start = int(center-w/2)\n        end = int(center+w/2)\n        mask[start:end,start:end,start:end] = 0\n    support = support[::ds,::ds,::ds]\n    mask = mask[::ds,::ds,::ds]\n    #mdata = md.MissingDataAnalyzer( mask, support )\n    #constraints = [md.FourierConstraint(mdata),md.RealSpaceConstraint(mdata)]#,md.NormalizationConstraint(mdata)]\n\n    nfunctions = 1\n    initGenerator = mdig.InitialGenerator( mask.shape )\n    initconditions = initGenerator.generate()\n    images = []\n    orthogonalConstraints = []\n    for i in range( len(initconditions) ):\n        mdata = md.MissingDataAnalyzer( mask, support )\n        constraints = [md.FourierConstraint(mdata),md.RealSpaceConstraint(mdata)]#,md.NormalizationConstraint(mdata)]\n        #constraints += orthogonalConstraints\n\n        result = mdata.solve( constraints, niter=NUMBER_OF_ITERATIONS, relerror=RELATIVE_ERROR, show=False, initial=initconditions[i].astype(np.float64),\n        zeroLimit=1E-4 )\n\n        fig = mdata.plot( mdata.getImg() )\n        fig.savefig( \"data/unconstrained%d_%s.svg\"%(i,timestamp) )\n        #orthogonalConstraints.append( md.OrthogonalConstraint( mdata, copy.deepcopy(mdata.getImg()) ) )\n        images.append( copy.deepcopy(result) )\n        mdata.allErrors = []\n        mdata.bestError = 1E30\n\n    # Save the unconstrained modes\n    with h5.File( \"data/unconstrainedModes%s.h5\"%(timestamp), 'w' ) as hf:\n        hf.create_dataset(\"support\", data=support)\n        hf.create_dataset(\"mask\", data=mask)\n        for i in range( len(images) ):\n            group = hf.create_group(\"mode%d\"%(i) )\n            group.create_dataset( \"img\", data=images[i][\"image\"] )\n            group.create_dataset( \"error\", data=images[i][\"error\"] )\n            group.attrs[\"status\"] = images[i][\"status\"]\n            group.attrs[\"message\"] = images[i][\"message\"]\n            group.attrs[\"cnstpow\"] = images[i][\"constrainedPower\"]\n            group.attrs[\"besterr\"] = images[i][\"bestError\"]\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"davidkleiven/PyPACE","sub_path":"example/unconstrainedDiffMap.py","file_name":"unconstrainedDiffMap.py","file_ext":"py","file_size_in_byte":3283,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"28430577294","text":"import os\nimport torch\nfrom torch import nn\nimport torch.nn.parallel\n\nfrom opts import parse_opts\nfrom mean import get_mean, get_std\nfrom spatial_transforms import (\n    Compose, Normalize, Scale, CenterCrop, CornerCrop, MultiScaleCornerCrop,\n    MultiScaleRandomCrop, RandomHorizontalFlip, ToTensor)\nfrom temporal_transforms import LoopPadding, TemporalRandomCrop\nfrom datasets.kinetics_episode import make_video_names, KineticsVideoList\nfrom datasets.something_episode import SomethingVideoList, make_something_video_names\nfrom datasets.ucf101_episode import UCFVideoList, make_ucf_video_names\n\nfrom utils import setup_logger, AverageMeter, count_acc, euclidean_metric\nimport time\nfrom batch_sampler import CategoriesSampler\nimport torch.optim as optim\nfrom clip_model import generate_model\n\nimport numpy as np\nimport pickle\n\ndef l2_norm(input):\n    input_size = input.size()\n    buffer = torch.pow(input, 2)\n\n    normp = torch.sum(buffer, 1).add_(1e-10)\n    norm = torch.sqrt(normp)\n\n    _output = torch.div(input, norm.view(-1, 1).expand_as(input))\n\n    output = _output.view(input_size)\n\n    return output\n\n\ndef weights_init(m):\n    classname = m.__class__.__name__\n    if classname.find('Linear') != -1:\n        m.weight.data.normal_(0.0, 0.02)\n        m.bias.data.fill_(0)\n\n\nclass CLASSIFIER(nn.Module):\n    def __init__(self, input_dim, nclass):\n        super(CLASSIFIER, self).__init__()\n        self.fc = nn.Linear(input_dim, nclass)\n        self.logic = nn.LogSoftmax(dim=1)\n\n    def forward(self, x):\n        o = self.logic(self.fc(x))\n        return o\n\ndef get_classifier_weights(embedding_shot, target_shot, lr=0.01, nepoch=5):\n    classifier = CLASSIFIER(embedding_shot.size(1), opt.test_way)\n    classifier.apply(weights_init)\n    criterion = nn.NLLLoss()\n\n    classifier.cuda()\n    criterion.cuda()\n\n    optimizer = optim.Adam(classifier.parameters(), lr=lr, betas=(0.5, 0.999))\n    for i in range(nepoch):\n        optimizer.zero_grad()\n        output = classifier(embedding_shot)\n        loss = criterion(output, target_shot)\n        #print(loss.data)\n        loss.backward()\n        optimizer.step()\n\n    return classifier.fc.weight.data\n\n\ndef train_epoch(support_data_loader, model, classifier, criterion, optimizer):\n    classifier.train()\n    support_clip_embedding = torch.FloatTensor(opt.test_novel_way*opt.shot*opt.n_samples_for_each_video, opt.emb_dim).cuda()\n    support_clip_label = torch.LongTensor(opt.test_novel_way*opt.shot*opt.n_samples_for_each_video).cuda()\n    batch_size = opt.n_samples_for_each_video\n    with torch.no_grad():\n        cur_loc = 0\n        for i, (data, label) in enumerate(support_data_loader):\n            batch_embedding = model(data.cuda())\n            cur_batch = batch_embedding.size(0)\n            support_clip_embedding[cur_loc:cur_loc+cur_batch] = batch_embedding.squeeze()\n            support_clip_label[cur_loc:cur_loc+cur_batch] = label.cuda()\n            cur_loc += cur_batch\n\n    optimizer.zero_grad()\n    output = classifier(support_clip_embedding)\n    loss = criterion(output, support_clip_label)\n    loss.backward()\n    optimizer.step()\n\n\ndef val_epoch(query_novel_data_loader, query_base_data_loader, model, classifier):\n    classifier.eval()\n    query_novel_clip_embedding = torch.FloatTensor(opt.test_novel_way * opt.query_novel * opt.n_val_samples, opt.emb_dim).cuda()\n    query_base_clip_embedding = torch.FloatTensor(opt.test_base_way * opt.query_base * opt.n_val_samples, opt.emb_dim).cuda()\n\n    with torch.no_grad():\n        cur_loc = 0\n        for i, (data, label) in enumerate(query_novel_data_loader):\n            batch_embedding = model(data.cuda())\n            cur_batch = batch_embedding.size(0)\n            query_novel_clip_embedding[cur_loc:cur_loc+cur_batch] = batch_embedding.squeeze()\n            cur_loc += cur_batch\n\n        cur_loc = 0\n        for i, (data, label) in enumerate(query_base_data_loader):\n            batch_embedding = model(data.cuda())\n            cur_batch = batch_embedding.size(0)\n            query_base_clip_embedding[cur_loc:cur_loc+cur_batch] = batch_embedding.squeeze()\n            cur_loc += cur_batch\n\n\n        base_clip_logits = torch.exp(classifier(query_base_clip_embedding))\n        base_logits = base_clip_logits.reshape(opt.query_base * opt.test_base_way, opt.n_val_samples, -1).mean(dim=1)\n\n        novel_clip_logits = torch.exp(classifier(query_novel_clip_embedding))\n        novel_logits = novel_clip_logits.reshape(opt.query_novel * opt.test_novel_way, opt.n_val_samples, -1).mean(dim=1)\n\n        all_logits = torch.cat((base_logits, novel_logits))\n\n        query_base_labels = torch.arange(opt.test_base_way).repeat(opt.query_base)\n        query_novel_labels = torch.arange(opt.test_novel_way).repeat(opt.query_novel) + opt.test_base_way\n        query_all_labels = torch.cat((query_base_labels, query_novel_labels)).cuda()\n\n        is_base = torch.arange(opt.test_base_way*opt.query_base)\n        is_novel = torch.arange(opt.test_base_way*opt.query_base, opt.test_base_way*opt.query_base+opt.test_novel_way*opt.query_novel)\n\n        predicted_labels = torch.argmax(all_logits, dim=1)\n        # print(predicted_labels)\n        # print(query_all_labels)\n        acc_all = (predicted_labels == query_all_labels).type(torch.cuda.FloatTensor).mean().item()\n        acc_base = (predicted_labels[is_base] == query_all_labels[is_base]).type(torch.cuda.FloatTensor).mean().item()\n        acc_novel = (predicted_labels[is_novel] == query_all_labels[is_novel]).type(torch.cuda.FloatTensor).mean().item()\n\n\n    return acc_novel, acc_base, acc_all, predicted_labels[is_novel]\n\n\ndef append_base_weight(classifier, base_weight):\n\n    weight = torch.cat((base_weight, classifier.fc.weight.data))\n    classifier.fc = nn.Linear(opt.emb_dim,opt.test_base_way+opt.test_novel_way)\n    classifier.fc = classifier.fc.cuda()\n\n    classifier.fc.weight.data = weight\n\n    return classifier\n\n\ndef meta_test_episode(support_data_loader, query_novel_data_loader, query_base_data_loader, model, base_weight, opt):\n    model.eval()\n    # train classifier\n    classifier = CLASSIFIER(opt.emb_dim, opt.test_novel_way)\n    classifier.apply(weights_init)\n    criterion = nn.NLLLoss()\n    classifier.cuda()\n    criterion.cuda()\n    optimizer = optim.Adam(classifier.parameters(), lr=opt.lr, betas=(0.5, 0.999))\n\n    for i in range(opt.nepoch):\n        train_epoch(support_data_loader, model, classifier, criterion, optimizer)\n        #acc = val_epoch(query_data_loader, model, classifier)\n        #print(acc)\n\n    classifier = append_base_weight(classifier, base_weight)\n    acc_novel, acc_base, acc_all, predicted_novel_labels = val_epoch(query_novel_data_loader, query_base_data_loader, model, classifier)\n    return acc_novel, acc_base, acc_all, predicted_novel_labels\n\n\nif __name__ == '__main__':\n    opt = parse_opts()\n    print(opt)\n\n    opt.scales = [opt.initial_scale]\n    for i in range(1, opt.n_scales):\n        opt.scales.append(opt.scales[-1] * opt.scale_step)\n\n    opt.arch = '{}-{}'.format(opt.clip_model, opt.clip_model_depth)\n    opt.mean = get_mean(opt.norm_value)\n    opt.std = get_std(opt.norm_value)\n\n    if not os.path.exists(opt.result_path):\n        os.makedirs(opt.result_path)\n\n    # Setup logging system\n    logger = setup_logger(\n        \"validation\",\n        opt.result_path,\n        0,\n        'results.txt'\n    )\n    logger.debug(opt)\n    print(opt.lr)\n    if opt.gpu is not None:\n        print(\"Use GPU: {} for training\".format(opt.gpu))\n\n    torch.backends.cudnn.benchmark = True\n\n    torch.manual_seed(opt.manual_seed)\n\n    model = generate_model(opt)\n    if opt.resume_path:\n        print('loading pretrained model {}'.format(opt.resume_path))\n        pretrain = torch.load(opt.resume_path)\n        model.load_state_dict(pretrain['state_dict'])\n\n    base_weight = model.module.fc.weight.data\n    model = nn.Sequential(*list(model.module.children())[:-1])\n    #print(model)\n\n    if opt.no_mean_norm and not opt.std_norm:\n        norm_method = Normalize([0, 0, 0], [1, 1, 1])\n    elif not opt.std_norm:\n        norm_method = Normalize(opt.mean, [1, 1, 1])\n    else:\n        norm_method = Normalize(opt.mean, opt.std)\n\n\n    if opt.train_crop == 'random':\n        crop_method = MultiScaleRandomCrop(opt.scales, opt.sample_size)\n    elif opt.train_crop == 'corner':\n        crop_method = MultiScaleCornerCrop(opt.scales, opt.sample_size)\n    elif opt.train_crop == 'center':\n        crop_method = MultiScaleCornerCrop(\n            opt.scales, opt.sample_size, crop_positions=['c'])\n\n    train_spatial_transform = Compose([\n        crop_method,\n        RandomHorizontalFlip(),\n        ToTensor(opt.norm_value), norm_method\n    ])\n    train_temporal_transform = TemporalRandomCrop(opt.sample_duration)\n\n    test_spatial_transform = Compose([\n        Scale(opt.sample_size),\n        CenterCrop(opt.sample_size),\n        ToTensor(opt.norm_value), norm_method\n    ])\n    test_temporal_transform = TemporalRandomCrop(opt.sample_duration)\n\n    if opt.dataset == 'kinetics100':\n        test_novel_videos, test_novel_labels = make_video_names(opt.test_novel_video_path, opt.test_novel_list_path)\n        test_base_videos, test_base_labels = make_video_names(opt.test_base_video_path, opt.test_base_list_path)\n\n    elif opt.dataset == 'something':\n        test_novel_videos, test_novel_labels = make_something_video_names(opt.test_novel_video_path, opt.test_novel_list_path)\n        test_base_videos, test_base_labels = make_something_video_names(opt.test_base_video_path, opt.test_base_list_path)\n\n    elif opt.dataset == 'ucf101':\n        test_novel_videos, test_novel_labels = make_ucf_video_names(opt.test_novel_video_path, opt.test_novel_list_path)\n        test_base_videos, test_base_labels = make_ucf_video_names(opt.test_base_video_path, opt.test_base_list_path)\n\n\n    episode_novel_sampler = CategoriesSampler(test_novel_labels,\n                                opt.nepisode, opt.test_novel_way, opt.shot + opt.query_novel)\n\n    episode_base_sampler = CategoriesSampler(test_base_labels,\n                                opt.nepisode, opt.test_base_way, opt.query_base)\n\n\n    episode_time = AverageMeter()\n    accuracies_base = AverageMeter()\n    accuracies_novel = AverageMeter()\n    accuracies_all = AverageMeter()\n\n\n    n_classes = len(np.unique(test_base_labels)) + len(np.unique(test_novel_labels))\n\n    confusion_matrix = torch.zeros(n_classes, n_classes)\n\n\n    for i, (batch_novel_idx, batch_base_idx) in enumerate(zip(episode_novel_sampler, episode_base_sampler)):\n        k = opt.test_novel_way * opt.shot\n        support_videos = [test_novel_videos[j] for j in batch_novel_idx[:k]]\n        support_labels = torch.arange(opt.test_novel_way).repeat(opt.shot)\n\n        query_novel_videos = [test_novel_videos[j] for j in batch_novel_idx[k:]]\n        query_novel_labels = torch.arange(opt.test_novel_way).repeat(opt.query_novel)\n\n        query_base_videos = [test_base_videos[j] for j in batch_base_idx]\n        query_base_labels = torch.arange(opt.test_base_way).repeat(opt.query_base)\n\n        cur_classes = list(np.arange(opt.test_base_way))\n        for j in batch_novel_idx[:opt.test_novel_way]:\n            cur_classes.append(test_novel_labels[j]+len(np.unique(test_base_labels)))\n\n        if opt.dataset == 'kinetics100':\n            support_data_loader = torch.utils.data.DataLoader(\n                KineticsVideoList(\n                    support_videos,\n                    support_labels,\n                    spatial_transform=test_spatial_transform,\n                    temporal_transform=test_temporal_transform,\n                    n_samples_for_each_video=opt.n_samples_for_each_video),\n                batch_size=opt.batch_size,\n                shuffle=False,\n                num_workers=opt.n_threads,\n                pin_memory=True)\n\n            query_novel_data_loader = torch.utils.data.DataLoader(\n                KineticsVideoList(\n                    query_novel_videos,\n                    query_novel_labels,\n                    spatial_transform=test_spatial_transform,\n                    temporal_transform=test_temporal_transform,\n                    n_samples_for_each_video=opt.n_val_samples),\n                batch_size=opt.batch_size,\n                shuffle=False,\n                num_workers=opt.n_threads,\n                pin_memory=True)\n\n            query_base_data_loader = torch.utils.data.DataLoader(\n                KineticsVideoList(\n                    query_base_videos,\n                    query_base_labels,\n                    spatial_transform=test_spatial_transform,\n                    temporal_transform=test_temporal_transform,\n                    n_samples_for_each_video=opt.n_val_samples),\n                batch_size=opt.batch_size,\n                shuffle=False,\n                num_workers=opt.n_threads,\n                pin_memory=True)\n\n\n        elif opt.dataset == 'something':\n            support_data_loader = torch.utils.data.DataLoader(\n                SomethingVideoList(\n                    support_videos,\n                    support_labels,\n                    spatial_transform=test_spatial_transform,\n                    temporal_transform=test_temporal_transform,\n                    n_samples_for_each_video=opt.n_samples_for_each_video),\n                batch_size=opt.batch_size,\n                shuffle=False,\n                num_workers=opt.n_threads,\n                pin_memory=True)\n\n            query_novel_data_loader = torch.utils.data.DataLoader(\n                SomethingVideoList(\n                    query_novel_videos,\n                    query_novel_labels,\n                    spatial_transform=test_spatial_transform,\n                    temporal_transform=test_temporal_transform,\n                    n_samples_for_each_video=opt.n_val_samples),\n                batch_size=opt.batch_size,\n                shuffle=False,\n                num_workers=opt.n_threads,\n                pin_memory=True)\n\n            query_base_data_loader = torch.utils.data.DataLoader(\n                SomethingVideoList(\n                    query_base_videos,\n                    query_base_labels,\n                    spatial_transform=test_spatial_transform,\n                    temporal_transform=test_temporal_transform,\n                    n_samples_for_each_video=opt.n_val_samples),\n                batch_size=opt.batch_size,\n                shuffle=False,\n                num_workers=opt.n_threads,\n                pin_memory=True)\n\n        elif opt.dataset == 'ucf101':\n            support_data_loader = torch.utils.data.DataLoader(\n                UCFVideoList(\n                    support_videos,\n                    support_labels,\n                    spatial_transform=test_spatial_transform,\n                    temporal_transform=test_temporal_transform,\n                    n_samples_for_each_video=opt.n_samples_for_each_video),\n                batch_size=opt.batch_size,\n                shuffle=False,\n                num_workers=opt.n_threads,\n                pin_memory=True)\n\n            query_novel_data_loader = torch.utils.data.DataLoader(\n                UCFVideoList(\n                    query_novel_videos,\n                    query_novel_labels,\n                    spatial_transform=test_spatial_transform,\n                    temporal_transform=test_temporal_transform,\n                    n_samples_for_each_video=opt.n_val_samples),\n                batch_size=opt.batch_size,\n                shuffle=False,\n                num_workers=opt.n_threads,\n                pin_memory=True)\n\n            query_base_data_loader = torch.utils.data.DataLoader(\n                UCFVideoList(\n                    query_base_videos,\n                    query_base_labels,\n                    spatial_transform=test_spatial_transform,\n                    temporal_transform=test_temporal_transform,\n                    n_samples_for_each_video=opt.n_val_samples),\n                batch_size=opt.batch_size,\n                shuffle=False,\n                num_workers=opt.n_threads,\n                pin_memory=True)\n\n        end_time = time.time()\n        acc_novel, acc_base, acc_all, predicted_novel_labels = meta_test_episode(support_data_loader, query_novel_data_loader, query_base_data_loader, model, base_weight,  opt)\n        accuracies_novel.update(acc_novel)\n        accuracies_base.update(acc_base)\n        accuracies_all.update(acc_all)\n\n        episode_time.update(time.time() - end_time)\n\n        logger.info('Episode: [{0}/{1}]\\t'\n              'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\\t'\n              'Acc_novel {acc_novel.val:.3f} ({acc_novel.avg:.3f})\\t'\n              'Acc_base {acc_base.val:.3f} ({acc_base.avg:.3f})\\t'\n              'Acc_all {acc_all.val:.3f} ({acc_all.avg:.3f})\\t'.format(\n                  i + 1,\n                  opt.nepisode,\n                  batch_time=episode_time,\n                  acc_novel=accuracies_novel,\n                  acc_base=accuracies_base,\n                  acc_all=accuracies_all))\n\n\n        #\n        # for t, p in zip(query_novel_labels.view(-1), predicted_novel_labels.view(-1)):\n        #     confusion_matrix[cur_classes[t.long()+opt.test_base_way], cur_classes[p.long()]] += 1\n        #\n        #\n        # if i % 10 == 0:\n        #     confusion_matrix_save = confusion_matrix.numpy()\n        #     with open(os.path.join(opt.result_path, 'conf_mat_' + str(i) + '.pkl'), 'wb') as fp:\n        #         pickle.dump(confusion_matrix_save, fp)","repo_name":"xianyongqin/few-shot-video-classification","sub_path":"tsl_gfsv.py","file_name":"tsl_gfsv.py","file_ext":"py","file_size_in_byte":17427,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"81"}
+{"seq_id":"30506393806","text":"from django.urls import path\nfrom libraryapp import views\n \nurlpatterns = [\n\n    path(\"signaction/\",views.signaction,name=\"signaction\"),\n    path(\"\",views.loginaction,name=\"loginaction\"),\n    path(\"addbook/\",views.addbook,name=\"addbook\"),\n    path(\"dashborad/\",views.dashborad,name=\"dashborad\"),\n    path(\"update/\",views.update,name=\"update\"),\n    path(\"updatebook/\",views.updatebook,name=\"updatebook\"),\n    path(\"deletebook/\",views.deletebook,name=\"deletebook\"),\n    path(\"registration/\",views.registration,name=\"registration\"),\n    path(\"login/\",views.login,name=\"login\"),\n    path(\"deletebook/\",views.deletebook,name=\"deletebook\"),\n\n]","repo_name":"AkashTarmale97/keywordio-project","sub_path":"libraryapp/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":637,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"11620283005","text":"from ctypes import Structure, c_double, POINTER, c_float, c_int, c_uint, c_uint32, c_int64, c_long, c_ulong, c_ulonglong, c_void_p, c_char_p, CFUNCTYPE, byref, create_string_buffer, addressof, pointer, cast\nfrom . import clibrebound, Escape, NoParticles, Encounter, SimulationError, ParticleNotFound\nfrom .particle import Particle\nfrom .units import units_convert_particle, check_units, convert_G\nfrom .tools import hash as rebhash\nimport math\nimport os\nimport sys\nimport ctypes.util\nimport warnings\nfrom collections import MutableMapping\n\ntry:\n    import pkg_resources\nexcept: \n    # Fails on python3, but not important\n    pass\nimport types\n      \n### The following enum and class definitions need to\n### consitent with those in rebound.h\n        \nINTEGRATORS = {\"ias15\": 0, \"whfast\": 1, \"sei\": 2, \"leapfrog\": 4, \"hermes\": 5, \"none\": 7, \"janus\": 8, \"mercurius\": 9}\nBOUNDARIES = {\"none\": 0, \"open\": 1, \"periodic\": 2, \"shear\": 3}\nGRAVITIES = {\"none\": 0, \"basic\": 1, \"compensated\": 2, \"tree\": 3, \"mercurius\": 4}\nCOLLISIONS = {\"none\": 0, \"direct\": 1, \"tree\": 2, \"mercurius\": 3}\nVISUALIZATIONS = {\"none\": 0, \"opengl\": 1, \"webgl\": 2}\nCOORDINATES = {\"jacobi\": 0, \"democraticheliocentric\": 1, \"whds\": 2}\nBINARY_WARNINGS = [\n    (\"Cannot read binary file. Check filename and file contents.\", 1),\n    (\"Binary file was saved with a different version of REBOUND. Binary format might have changed.\", 2),\n    (\"You have to reset function pointers after creating a reb_simulation struct with a binary file.\", 4),\n    (\"Binary file might be corrupted. Number of particles found does not match particle number expected.\", 8),\n    (\"Unknown field found in binary file.\", 128)\n]\n\nclass reb_hash_pointer_pair(Structure):\n    _fields_ = [(\"hash\", c_uint32),\n                (\"index\", c_int)]\n\nclass reb_vec3d(Structure):\n    _fields_ = [(\"x\", c_double),\n                (\"y\", c_double),\n                (\"z\", c_double)]\n\nclass reb_dp7(Structure):\n    _fields_ = [(\"p0\", POINTER(c_double)),\n                (\"p1\", POINTER(c_double)),\n                (\"p2\", POINTER(c_double)),\n                (\"p3\", POINTER(c_double)),\n                (\"p4\", POINTER(c_double)),\n                (\"p5\", POINTER(c_double)),\n                (\"p6\", POINTER(c_double))]\n\nclass reb_ghostbox(Structure):\n    _fields_ = [(\"shiftx\", c_double),\n                (\"shifty\", c_double),\n                (\"shiftz\", c_double),\n                (\"shiftvx\", c_double),\n                (\"shiftvy\", c_double),\n                (\"shiftvz\", c_double)]\n\nclass reb_collision(Structure):\n    _fields_ = [(\"p1\", c_int),\n                (\"p2\", c_int),\n                (\"gb\", reb_ghostbox),\n                (\"time\", c_double),\n                (\"ri\", c_int)]\n\nclass reb_simulation_integrator_sei(Structure):\n    \"\"\"\n    This class is an abstraction of the C-struct reb_simulation_integrator_sei.\n    It controls the behaviour of the symplectic SEI integrator for shearing\n    sheet calculations. It is described in Rein and Tremaine (2011).\n    \n    This struct should be accessed via the simulation class only. Here is an \n    example:\n\n    >>> sim = rebound.Simulation()\n    >>> sim.ri_sei.OMEGA =  1.58\n    \n    :ivar float OMEGA:          \n        The epicyclic frequency OMEGA. For simulations making use of shearing \n        sheet boundary conditions, REBOUND needs to know the epicyclic frequency. \n        By default OMEGA is 1. For more details read Rein and Tremaine 2011.\n    :ivar float OMEGAZ:          \n        The z component of the epicyclic frequency OMEGA. By default it is assuming\n        OMEGAZ is the same as OMEGA.\n    \"\"\"\n    _fields_ = [(\"OMEGA\", c_double),\n                (\"OMEGAZ\", c_double),\n                (\"lastdt\", c_double),\n                (\"sindt\", c_double),\n                (\"tandt\", c_double),\n                (\"sindtz\", c_double),\n                (\"tandtz\", c_double)]\n\nclass reb_simulation_integrator_ias15(Structure):\n    _fields_ = [(\"epsilon\", c_double),\n                (\"min_dt\", c_double),\n                (\"epsilon_global\", c_uint),\n                (\"iterations_max_exceeded\", c_ulong),\n                (\"allocatedN\", c_int),\n                (\"at\", POINTER(c_double)),\n                (\"x0\", POINTER(c_double)),\n                (\"v0\", POINTER(c_double)),\n                (\"a0\", POINTER(c_double)),\n                (\"csx\", POINTER(c_double)),\n                (\"csv\", POINTER(c_double)),\n                (\"csa0\", POINTER(c_double)),\n                (\"g\", reb_dp7),\n                (\"b\", reb_dp7),\n                (\"csb\", reb_dp7),\n                (\"e\", reb_dp7),\n                (\"br\", reb_dp7),\n                (\"er\", reb_dp7)]\n\nclass reb_simulation_integrator_whfast(Structure):\n    \"\"\"\n    This class is an abstraction of the C-struct reb_simulation_integrator_whfast.\n    It controls the behaviour of the symplectic WHFast integrator described \n    in Rein and Tamayo (2015).\n    \n    This struct should be accessed via the simulation class only. Here is an \n    example:\n\n    >>> sim = rebound.Simulation()\n    >>> sim.ri_whfast.corrector =  11\n\n    \n    :ivar int corrector:      \n        The order of the symplectic corrector in the WHFast integrator.\n        By default the symplectic correctors are turned off (=0). For high\n        accuracy simulation set this value to 11. For more details read \n        Rein and Tamayo (2015).\n    :ivar int recalculate_coordinates_this_timestep:\n        Sets a flag that tells WHFast that the particles have changed.\n        Setting this flag to 1 (default 0) triggers the WHFast integrator\n        to recalculate Jacobi/heliocenctric coordinates. This is needed \n        if the user changes the particle position, velocity or mass \n        inbetween timesteps.  After every timestep the flag is set back \n        to 0, so if you continuously update the particles manually, \n        you need to set this flag to 1 after every timestep.\n    :ivar string coordinates:\n        Sets the internal coordinate system that WHFast is using. By default\n        it uses ``'jacobi'`` (=0) coordinates. Other options are \n        ``'democraticheliocentric'`` (=1) and ``'whds'`` (=2). See Hernandez \n        and Dehnen (2017) for more information.\n    :ivar int safe_mode:\n        If safe_mode is 1 (default) particles can be modified between\n        timesteps and particle velocities and positions are always synchronised.\n        If you set safe_mode to 0, the speed and accuracy of WHFast improve.\n        However, make sure you are aware of the consequences. Read the iPython tutorial\n        on advanced WHFast usage to learn more.\n    \"\"\"\n    _fields_ = [(\"corrector\", c_uint),\n                (\"_coordinates\", c_uint),\n                (\"recalculate_coordinates_this_timestep\", c_uint),\n                (\"safe_mode\", c_uint),\n                (\"p_jh\", POINTER(Particle)),\n                (\"keep_unsynchronized\", c_uint),\n                (\"is_synchronized\", c_uint),\n                (\"allocatedN\", c_uint),\n                (\"timestep_warning\", c_uint),\n                (\"recalculate_coordinates_but_not_synchronized_warning\", c_uint)]\n    @property\n    def coordinates(self):\n        \"\"\"\n        Get or set the internal coordinate system.\n\n        Available coordinate systems are:\n\n        - ``'jacobi'`` (default)\n        - ``'democraticheliocentric'``\n        - ``'whds'``\n        \"\"\"\n        i = self._coordinates\n        for name, _i in COORDINATES.items():\n            if i==_i:\n                return name\n        return i\n    @coordinates.setter\n    def coordinates(self, value):\n        if isinstance(value, int):\n            self._coordinates = c_uint(value)\n        elif isinstance(value, basestring):\n            value = value.lower()\n            if value in COORDINATES: \n                self._coordinates = COORDINATES[value]\n            else:\n                raise ValueError(\"Warning. Coordinate system not found.\")\n\nclass Orbit(Structure):\n    \"\"\"\n    A class containing orbital parameters for a particle.\n    This is an abstraction of the reb_orbit data structure in C.\n\n    When using the various REBOUND functions using Orbits, all angles are in radians. \n    The following image illustrated the most important angles used.\n    In REBOUND the reference direction is the positive x direction, the reference plane\n    is the xy plane.\n    \n    .. image:: images/orbit.png\n       :width: 500px\n       :height: 450px\n\n    Image from wikipedia. CC-BY-SA-3.\n\n    Attributes\n    ----------\n    d       : float           \n        radial distance from reference \n    v       : float         \n        velocity relative to central object's velocity\n    h       : float           \n        specific angular momentum\n    P       : float           \n        orbital period (negative if hyperbolic)\n    n       : float           \n        mean motion    (negative if hyperbolic)\n    a       : float           \n        semimajor axis\n    e       : float           \n        eccentricity\n    inc     : float           \n        inclination\n    Omega   : float           \n        longitude of ascending node\n    omega   : float           \n        argument of pericenter\n    pomega  : float           \n        longitude of pericenter\n    f       : float           \n        true anomaly\n    M       : float           \n        mean anomaly\n    l       : float           \n        mean longitude = Omega + omega + M\n    theta   : float           \n        true longitude = Omega + omega + f\n    T       : float\n        time of pericenter passage\n    \"\"\"\n    _fields_ = [(\"d\", c_double),\n                (\"v\", c_double),\n                (\"h\", c_double),\n                (\"P\", c_double),\n                (\"n\", c_double),\n                (\"a\", c_double),\n                (\"e\", c_double),\n                (\"inc\", c_double),\n                (\"Omega\", c_double),\n                (\"omega\", c_double),\n                (\"pomega\", c_double),\n                (\"f\", c_double),\n                (\"M\", c_double),\n                (\"l\", c_double),\n                (\"theta\", c_double),\n                (\"T\", c_double)]\n\n    def __str__(self):\n        \"\"\"\n        Returns a string with the semi-major axis and eccentricity of the orbit.\n        \"\"\"\n        return \"<rebound.Orbit instance, a={0} e={1} inc={2} Omega={3} omega={4} f={5}>\".format(str(self.a),str(self.e), str(self.inc), str(self.Omega), str(self.omega), str(self.f))\n\n\n\nclass Simulation(Structure):\n    \"\"\"\n    REBOUND Simulation Object.\n\n    This object encapsulated an entire REBOUND simulation. \n    It is an abstraction of the C struct reb_simulation.\n    You can create mutiple REBOUND simulations (the c library is thread safe). \n\n    Examples\n    --------\n    Most simulation parameters can be directly changed with the property syntax:\n\n    >>> sim = rebound.Simulation()\n    >>> sim.G = 1.                  # Sets the graviational constant (default 1)\n    >>> sim.softening = 1.          # Sets the graviational softening parameter (default 0)\n    >>> sim.testparticle_type = 1   # Allows massive particles to feel influence from testparticles (default 0)\n    >>> sim.dt = 0.1                # Sets the timestep (will change for adaptive integrators such as IAS15).\n    >>> sim.t = 0.                  # Sets the current simulation time (default 0)\n    >>> print(sim.N)                # Gets the current number of particles\n    >>> print(sim.N_active)         # Gets the current number of active particles\n\n    \"\"\"\n    def __init__(self):\n        clibrebound.reb_init_simulation(byref(self))\n        self.save_messages = 1 # Warnings will be checked within python\n    \n    @classmethod\n    def from_archive(cls, filename,snapshot=-1):\n        \"\"\"\n        Loads a REBOUND simulation from a SimulationArchive binary file.\n        It uses the last snapshot in that file unless otherwise specified. \n        This function is only really useful for restarting a simulation. \n        For full access to the Simulation Archive, use the \n        SimulationArchive class.\n        \n        After loading the REBOUND simulation from file, you need to reset \n        any function pointers manually. Please read all documentation \n        before using this function as it has several requirements to work\n        correctly.\n        \n        Arguments\n        ---------\n        filename : str\n            Filename of the binary file.\n        snapshot : int (optional)\n            If given, load the snapshot with this index. Otherwise \n            load last snapshot.\n        \n        Returns\n        ------- \n        A rebound.Simulation object.\n        \n        \"\"\"\n        sim = Simulation.from_file(filename)\n        clibrebound.reb_simulationarchive_load_snapshot.restype = c_int\n        err = clibrebound.reb_simulationarchive_load_snapshot(byref(sim),c_char_p(filename.encode(\"ascii\")),snapshot)\n        if err != 0:\n            raise AttributeError(\"Error loading simulation from archive.\")\n        return sim\n\n    @classmethod\n    def from_file(cls, filename):\n        \"\"\"\n        Loads a REBOUND simulation from a file.\n        \n        After loading the REBOUND simulation from file, you need to reset any function pointers manually.\n        \n        Arguments\n        ---------\n        filename : str\n            Filename of the binary file.\n        \n        Returns\n        ------- \n        A rebound.Simulation object.\n        \n        Examples\n        --------\n        The following example creates a simulation, saves it to a file and then creates\n        a copy of the simulation store in the binary file.\n\n        >>> sim = rebound.Simulation()\n        >>> sim.add(m=1.)\n        >>> sim.add(m=1.e-3,x=1.,vy=1.)\n        >>> sim.save(\"simulation.bin\")\n        >>> sim_copy = rebound.Simulation.from_file(\"simulation.bin\")\n        \"\"\"\n        w = c_int(0)\n        sim = Simulation()\n        clibrebound.reb_create_simulation_from_binary_with_messages(byref(sim), c_char_p(filename.encode(\"ascii\")),byref(w))\n        if w.value & 1:     # Major error\n            raise ValueError(BINARY_WARNINGS[0])\n        for message, value in BINARY_WARNINGS:  # Just warnings\n            if w.value & value and value!=1:\n                warnings.warn(message, RuntimeWarning)\n        return sim\n\n    def getWidget(self,**kwargs):\n        \"\"\"\n        Wrapper function that returns a new widget attached to this simulation.\n\n        Widgets provide real-time 3D visualizations from within an Jupyter notebook.\n        See the Widget class for more details on the possible arguments.\n        \n        \n        Arguments\n        ---------\n        All arguments passed to this wrapper function will be passed to /Widget class.\n        \n        Returns\n        ------- \n        A rebound.Widget object.\n        \n        Examples\n        --------\n\n        >>> sim = rebound.Simulation()\n        >>> sim.add(m=1.)\n        >>> sim.add(m=1.e-3,x=1.,vy=1.)\n        >>> sim.getWidget()\n\n        \"\"\"\n        from .widget import Widget # ondemand\n        from ipywidgets import DOMWidget\n        from IPython.display import display, HTML\n        if not hasattr(self, '_widgets'):\n            self._widgets = []\n            def display_heartbeat(simp):\n                for w in self._widgets:\n                    w.refresh(simp,isauto=1)\n            self.visualization = VISUALIZATIONS[\"webgl\"] \n            clibrebound.reb_display_init_data(byref(self));\n            self._dhbf = AFF(display_heartbeat)\n            self._display_heartbeat = self._dhbf\n            display(HTML(Widget.getClientCode())) # HACK! Javascript should go into custom.js\n        newWidget = Widget(self,**kwargs)\n        self._widgets.append(newWidget)\n        newWidget.refresh(isauto=0)\n        return newWidget\n    \n    def refreshWidgets(self):\n        \"\"\"\n        This function manually refreshed all widgets attached to this simulation.\n        \n        You want to call this function if any particle data has been manually changed.\n        \"\"\"\n        if hasattr(self, '_widgets'):\n            for w in self._widgets:\n                w.refresh(isauto=0)\n        else:\n            raise RuntimeError(\"No widgets found\")\n\n\n# Simulation Archive tools\n    @property \n    def simulationarchive_filename(self):\n        \"\"\"\n        Filename where to store SimulationArchive\n        \"\"\"\n        return self._sa_filename.value.decode(\"ascii\")\n    @simulationarchive_filename.setter\n    def simulationarchive_filename(self, filename):\n        self._sa_filename = c_char_p(filename.encode(\"ascii\")) # keep a reference to string\n        self._simulationarchive_filename = self._sa_filename\n    \n    def estimateSimulationArchiveSize(self, tmax):\n        \"\"\"\n        This function estimates the SimulationArchive file size (in bytes)\n        before a simulation is run. This is useful to check if the interval\n        results in a resonable filesize.\n\n        Note that the simulation setup needs to be complete, that is\n        with all the particles present, before this function can return \n        meaningful results.\n        \n        Arguments\n        ---------\n        tmax : float\n            Maximum integration time in current code units.\n        \"\"\"\n        if self.simulationarchive_interval ==0.:\n            raise RuntimeError(\"Need to set simulationarchive_interval before estimating filesize.\")\n        if self.N==0:\n            raise RuntimeError(\"Need to add particles to simulation before estimating filesize.\")\n\n        clibrebound.reb_simulationarchive_estimate_size.restype = c_long\n        estsize = clibrebound.reb_simulationarchive_estimate_size(byref(self), c_double(tmax))\n        self.process_messages()\n        return estsize\n        \n    def initSimulationArchive(self, filename, interval=None, interval_walltime=None):\n        \"\"\"\n        This function initializes the Simulation Archive so that\n        binary data can be outputted to the SimulationArchive file \n        during the simulation.\n\n        \n        Arguments\n        ---------\n        filename : str\n            Filename of the binary file.\n        interval : float\n            Interval between outputs in code units.\n        interval_walltime : float\n            Interval between outputs in wall time (seconds). \n            Useful when using IAS15 with adaptive timesteps. \n        \n        Examples\n        --------\n        The following example creates a simulation, then \n        initializes the Simulation Archive and integrates\n        it forward in time. \n\n        >>> sim = rebound.Simulation()\n        >>> sim.add(m=1.)\n        >>> sim.add(m=1.e-3,x=1.,vy=1.)\n        >>> sim.initSimulationArchive(\"sa.bin\",interval=1000.)\n        >>> sim.integrate(1e8)\n        \"\"\"\n        self.simulationarchive_filename = filename\n        if interval is None and interval_walltime is None:\n            raise AttributeError(\"Need to specify either interval or interval_walltime.\")\n        self.simulationarchive_walltime = 0.\n        self.simulationarchive_next = 0.\n        self.simulationarchive_interval = 0. \n        self.simulationarchive_interval_walltime = 0.\n        if interval:\n            self.simulationarchive_interval = interval\n        if interval_walltime:\n            self.simulationarchive_interval_walltime = interval_walltime\n\n    \n    def process_messages(self):\n        clibrebound.reb_get_next_message.restype = c_int\n        buf = create_string_buffer(c_int.in_dll(clibrebound, \"reb_max_messages_length\").value)\n        while clibrebound.reb_get_next_message(byref(self), buf):\n            msg = buf.value.decode(\"ascii\")\n            if msg[0]=='w':\n                warnings.warn(msg[1:], RuntimeWarning)\n            elif msg[0]=='e':\n                raise RuntimeError(msg[1:])\n\n\n    def __del__(self):\n        if self._b_needsfree_ == 1: # to avoid, e.g., sim.particles[1]._sim.contents.G creating a Simulation instance to get G, and then freeing the C simulation when it immediately goes out of scope\n            clibrebound.reb_free_pointers(byref(self))\n\n# Status functions\n    def status(self):\n        \"\"\" \n        Prints a summary of the current status \n        of the simulation.\n        \"\"\"\n        from rebound import __version__, __build__\n        s= \"\"\n        s += \"---------------------------------\\n\"\n        s += \"REBOUND version:     \\t%s\\n\" %__version__\n        s += \"REBOUND built on:    \\t%s\\n\" %__build__\n        s += \"Number of particles: \\t%d\\n\" %self.N       \n        s += \"Selected integrator: \\t\" + self.integrator + \"\\n\"       \n        s += \"Simulation time:     \\t%.16e\\n\" %self.t\n        s += \"Current timestep:    \\t%f\\n\" %self.dt\n        if self.N>0:\n            s += \"---------------------------------\\n\"\n            for p in self.particles:\n                s += str(p) + \"\\n\"\n        s += \"---------------------------------\"\n        print(s)\n\n# Set function pointer for additional forces\n    @property\n    def additional_forces(self):\n        \"\"\"\n        Get or set a function pointer for calculating additional forces in the simulation.\n\n        The argument can be a python function or something that can \n        be cast to a C function of type CFUNCTYPE(None,POINTER(Simulaton)). \n        If the forces are velocity dependent, the flag \n        force_is_velocity_dependent needs to be set to 1. Otherwise\n        the particle structures might contain incorrect velocity \n        values.\n        \"\"\"\n        raise AttributeError(\"You can only set C function pointers from python.\")\n    @additional_forces.setter\n    def additional_forces(self, func):\n        if hasattr(self, '_extras_ref'): # using REBOUNDx\n            raise Warning(\"Overwriting REBOUNDx operators. If you want to use REBOUNDx and your own custom effects, add them through REBOUNDx.  See https://github.com/dtamayo/reboundx/blob/master/ipython_examples/Custom_Effects.ipynb for a tutorial.\")\n        self._afp = AFF(func)\n        self._additional_forces = self._afp\n\n    @property\n    def pre_timestep_modifications(self):\n        \"\"\"\n        Get or set a function pointer for pre-timestep modifications.\n\n        The argument can be a python function or something that can be cast to a C function or a\n        python function.\n        \"\"\"\n        raise AttributeError(\"You can only set C function pointers from python.\")\n    @pre_timestep_modifications.setter\n    def pre_timestep_modifications(self, func):\n        if hasattr(self, '_extras_ref'): # using REBOUNDx\n            raise Warning(\"Overwriting REBOUNDx operators. If you want to use REBOUNDx and your own custom effects, add them through REBOUNDx.  See https://github.com/dtamayo/reboundx/blob/master/ipython_examples/Custom_Effects.ipynb for a tutorial.\")\n        self._pretmp = AFF(func)\n        self._pre_timestep_modifications = self._pretmp\n    \n    @property\n    def post_timestep_modifications(self):\n        \"\"\"\n        Get or set a function pointer for post-timestep modifications.\n\n        The argument can be a python function or something that can be cast to a C function or a\n        python function.\n        \"\"\"\n        raise AttributeError(\"You can only set C function pointers from python.\")\n    @post_timestep_modifications.setter\n    def post_timestep_modifications(self, func):\n        if hasattr(self, '_extras_ref'): # using REBOUNDx\n            raise AttributeError(\"You cannot access post_timestep_modifications after adding REBOUNDx to a simulation.  Instead, add your own custom effects through REBOUNDx.  See https://github.com/dtamayo/reboundx/blob/master/ipython_examples/Custom_Effects.ipynb for a tutorial.\")\n        self._posttmp = AFF(func)\n        self._post_timestep_modifications = self._posttmp\n \n    @property\n    def heartbeat(self):\n        \"\"\"\n        Set a function pointer for a heartbeat function.\n        The heartbeat function is called every timestep and can be used\n        to monitor long simulations, check for stalled simulations and \n        output debugging information.\n     \n        The argument can be a python function or something that can be \n        cast to a C function or a python function.\n\n        The function called will receive a pointer to the simulation\n        object as its argument.\n        \n        Examples\n        --------\n\n        >>> import rebound\n        >>> sim = rebound.Simulation()\n        >>> sim.add(m=1.)\n        >>> sim.add(m=1e-3, a=1)\n        >>> def heartbeat(sim):\n        >>>     # sim is a pointer to the simulation object,\n        >>>     # thus use contents to access object data.\n        >>>     # See ctypes documentation for details.\n        >>>     print(sim.contents.t)\n        >>> sim.heartbeat = heartbeat\n        >>> sim.integrate(1.)\n\n        \"\"\"\n        raise AttributeError(\"You can only set C function pointers from python (not get).\")\n    @heartbeat.setter\n    def heartbeat(self, func):\n        self._hb = AFF(func)\n        self._heartbeat = self._hb\n\n    @property \n    def coefficient_of_restitution(self):\n        \"\"\"\n        Get or set a function pointer that defined the coefficient of restitution.\n        \"\"\"\n        raise AttributeError(\"You can only set C function pointers from python.\")\n    @coefficient_of_restitution.setter\n    def coefficient_of_restitution(self, func):\n        self._corfp = CORFF(func)\n        self._coefficient_of_restitution = self._corfp\n    \n    @property \n    def collision_resolve(self):\n        \"\"\"\n        Get or set a function pointer for collision resolving routine.\n        \n        Possible options for setting:\n          1) Function pointer\n          2) \"merge\": two colliding particles will merge) \n          3) \"hardsphere\": two colliding particles will bounce of using a set coefficient of restitution\n        \"\"\"\n        raise AttributeError(\"You can only set C function pointers from python.\")\n    @collision_resolve.setter\n    def collision_resolve(self, func):\n        if func is \"merge\":\n            clibrebound.reb_set_collision_resolve.restype = None\n            clibrebound.reb_set_collision_resolve(byref(self), clibrebound.reb_collision_resolve_merge)\n        elif func is \"hardsphere\":\n            clibrebound.reb_set_collision_resolve.restype = None\n            clibrebound.reb_set_collision_resolve(byref(self), clibrebound.reb_collision_resolve_hardsphere)\n        else:\n            self._colrfp = COLRFF(func)\n            self._collision_resolve = self._colrfp\n    \n    @property \n    def free_particle_ap(self):\n        \"\"\"\n        Get or set a function pointer for freeing the ap pointer whenever sim.remove is called.\n        \"\"\"\n        raise AttributeError(\"You can only set C function pointers from python.\")\n    @free_particle_ap.setter\n    def free_particle_ap(self, func):\n        self._fpa = FPA(func)\n        self._free_particle_ap = self._fpa\n\n# Setter/getter of parameters and constants\n    @property \n    def N_real(self):\n        \"\"\"\n        Get the current number of real (i.e. no variational/shadow) particles in the simulation.\n        \"\"\"\n        return self.N-self.N_var\n\n    @property\n    def integrator(self):\n        \"\"\"\n        Get or set the intergrator module.\n\n        Available integrators are:\n\n        - ``'ias15'`` (default)\n        - ``'whfast'``\n        - ``'sei'``\n        - ``'leapfrog'``\n        - ``'hermes'``\n        - ``'janus'``\n        - ``'mercurius'``\n        - ``'bs'``\n        - ``'none'``\n        \n        Check the online documentation for a full description of each of the integrators. \n        \"\"\"\n        i = self._integrator\n        for name, _i in INTEGRATORS.items():\n            if i==_i:\n                return name\n        return i\n    @integrator.setter\n    def integrator(self, value):\n        if isinstance(value, int):\n            self._integrator = c_int(value)\n        elif isinstance(value, basestring):\n            debug.integrator_fullname = value\n            debug.integrator_package = \"REBOUND\"\n            value = value.lower()\n            if value in INTEGRATORS: \n                self._integrator = INTEGRATORS[value]\n            elif value.lower() == \"mercury\":\n                debug.integrator_package = \"MERCURY\"\n            elif value.lower() == \"swifter-whm\":\n                debug.integrator_package = \"SWIFTER\"\n            elif value.lower() == \"swifter-symba\":\n                debug.integrator_package = \"SWIFTER\"\n            elif value.lower() == \"swifter-helio\":\n                debug.integrator_package = \"SWIFTER\"\n            elif value.lower() == \"swifter-tu4\":\n                debug.integrator_package = \"SWIFTER\"\n            else:\n                raise ValueError(\"Warning. Integrator not found.\")\n    \n    @property\n    def boundary(self):\n        \"\"\"\n        Get or set the boundary module.\n\n        Available boundary modules are:\n\n        - ``'none'`` (default)\n        - ``'open'``\n        - ``'periodic'``\n        - ``'shear'``\n        \n        Check the online documentation for a full description of each of the modules. \n        \"\"\"\n        i = self._boundary\n        for name, _i in BOUNDARIES.items():\n            if i==_i:\n                return name\n        return i\n    @boundary.setter\n    def boundary(self, value):\n        if isinstance(value, int):\n            self._boundary = c_int(value)\n        elif isinstance(value, basestring):\n            value = value.lower()\n            if value in BOUNDARIES: \n                self._boundary = BOUNDARIES[value]\n            else:\n                raise ValueError(\"Warning. Boundary condition module not found.\")\n\n    @property\n    def gravity(self):\n        \"\"\"\n        Get or set the gravity module.\n\n        Available gravity modules are:\n\n        - ``'none'`` \n        - ``'basic'`` (default)\n        - ``'compensated'``\n        - ``'tree'``\n        \n        Check the online documentation for a full description of each of the modules. \n        \"\"\"\n        i = self._gravity\n        for name, _i in GRAVITIES.items():\n            if i==_i:\n                return name\n        return i\n    @gravity.setter\n    def gravity(self, value):\n        if isinstance(value, int):\n            self._gravity = c_int(value)\n        elif isinstance(value, basestring):\n            value = value.lower()\n            if value in GRAVITIES: \n                self._gravity = GRAVITIES[value]\n            else:\n                raise ValueError(\"Warning. Gravity module not found.\")\n\n    @property\n    def collision(self):\n        \"\"\"\n        Get or set the collision module.\n\n        Available collision modules are:\n\n        - ``'none'`` (default)\n        - ``'direct'``\n        - ``'tree'``\n        - ``'mercurius'`` \n        \n        Check the online documentation for a full description of each of the modules. \n        \"\"\"\n        i = self._collision\n        for name, _i in COLLISIONS.items():\n            if i==_i:\n                return name\n        return i\n    @collision.setter\n    def collision(self, value):\n        if isinstance(value, int):\n            self._collision = c_int(value)\n        elif isinstance(value, basestring):\n            value = value.lower()\n            if value in COLLISIONS: \n                self.collision = COLLISIONS[value]\n            else:\n                raise ValueError(\"Warning. Collision module not found.\")\n\n# Units\n\n    @property\n    def units(self):\n        \"\"\"\n        Tuple of the units for length, time and mass.  Can be set in any order, and strings are not case-sensitive.  See ipython_examples/Units.ipynb for more information.  You can check the units' exact values and add Additional units in rebound/rebound/units.py.  Units should be set before adding particles to the simulation (will give error otherwise).\n\n        Currently supported Units \n        -------------------------\n\n        Times:\n        Hr          : Hours\n        Yr          : Julian years\n        Jyr         : Julian years\n        Sidereal_yr : Sidereal year\n        Yr2pi       : Year divided by 2pi, with year defined as orbital period of planet at 1AU around 1Msun star\n        Kyr         : Kiloyears (Julian)\n        Myr         : Megayears (Julian)\n        Gyr         : Gigayears (Julian)\n\n        Lengths:\n        M           : Meters\n        Cm          : Centimeters\n        Km          : Kilometers\n        AU          : Astronomical Units\n\n        Masses:\n        Kg          : Kilograms\n        Msun        : Solar masses\n        Mmercury    : Mercury masses\n        Mvenus      : Venus masses\n        Mearth      : Earth masses\n        Mmars       : Mars masses\n        Mjupiter    : Jupiter masses\n        Msaturn     : Saturn masses\n        Muranus     : Neptune masses\n        Mpluto      : Pluto masses\n        \n        Examples\n        --------\n        \n        >>> sim = rebound.Simulation()\n        >>> sim.units = ('yr', 'AU', 'Msun')\n\n        \"\"\"\n        if not hasattr(self, '_units'):\n            self._units = {'length':None, 'mass':None, 'time':None}\n\n        return self._units\n\n    @units.setter\n    def units(self, newunits):\n        if not hasattr(self, '_units'):\n            self._units = {'length':None, 'mass':None, 'time':None}\n        newunits = check_units(newunits)        \n        if self.N>0: # some particles are loaded\n            raise AttributeError(\"Error:  You cannot set the units after populating the particles array.  See ipython_examples/Units.ipynb.\")\n        self.update_units(newunits) \n\n    def update_units(self, newunits): \n        self._units['length'] = newunits[0] \n        self._units['time'] = newunits[1] \n        self._units['mass'] = newunits[2] \n        self.G = convert_G(self._units['length'], self._units['time'], self._units['mass'])\n\n    def convert_particle_units(self, *args): \n        \"\"\"\n        Will convert the units for the simulation (i.e. convert G) as well as the particles' cartesian elements.\n        Must have set sim.units ahead of calling this function so REBOUND knows what units to convert from.\n\n        Parameters\n        ----------\n        3 strings corresponding to units of time, length and mass.  Can be in any order and aren't case sensitive.        You can add new units to rebound/rebound/units.py\n        \"\"\"\n        if None in self.units.values():\n            raise AttributeError(\"Must set sim.units before calling convert_particle_units in order to know what units to convert from.\")\n        new_l, new_t, new_m = check_units(args)\n        for p in self.particles:\n            units_convert_particle(p, self._units['length'], self._units['time'], self._units['mass'], new_l, new_t, new_m)\n        self.update_units((new_l, new_t, new_m))\n\n# Variational Equations\n    def add_variation(self,order=1,first_order=None, first_order_2=None, testparticle=-1):\n        \"\"\" \n        This function adds a set of variational particles to the simulation. \n\n        If there are N real particles in the simulation, this functions adds N additional variational \n        particles. To see how many particles (real and variational) are in a simulation, use ``'sim.N'``. \n        To see how many variational particles are in a simulation use ``'sim.N_var'``.\n\n        Currently Leapfrog, WHFast and IAS15 support first order variational equations. IAS15 also\n        supports second order variational equations.\n\n        Parameters\n        ----------\n        order : integer, optional\n            By default the function adds a set of first order variational particles to the simulation. Set this flag to 2 for second order.\n        first_order : Variation, optional\n            Second order variational equations depend on their corresponding first order variational equations. \n            This parameter expects the Variation object corresponding to the first order variational equations. \n        first_order_2 : Variation, optional\n            Same as first_order. But allows to set two different indicies to calculate off-diagonal elements. \n            If omitted, then first_order will be used for both first order equations.\n        testparticle : int, optional\n            If set to a value >= 0, then only one variational particle will be added and be treated as a test particle.\n            \n\n        Returns\n        -------\n        Returns Variation object (a copy--you can only modify it through its particles property or vary method). \n        \"\"\"\n        cur_var_config_N = self.var_config_N\n        if order==1:\n            index = clibrebound.reb_add_var_1st_order(byref(self),c_int(testparticle))\n        elif order==2:\n            if first_order is None:\n                raise AttributeError(\"Please specify corresponding first order variational equations when initializing second order variational equations.\")\n            if first_order_2 is None:\n                first_order_2 = first_order\n            index = clibrebound.reb_add_var_2nd_order(byref(self),c_int(testparticle),c_int(first_order.index),c_int(first_order_2.index))\n        else:\n            raise AttributeError(\"Only variational equations of first and second order are supported.\")\n\n        # Need a copy because location of original might shift if more variations added\n        s = Variation.from_buffer_copy(self.var_config[cur_var_config_N])\n\n        return s\n        \n# MEGNO\n    def init_megno(self):\n        \"\"\"\n        This function initialises the chaos indicator MEGNO particles and enables their integration.\n\n        MEGNO is short for Mean Exponential Growth of Nearby orbits. It can be used to test\n        if a system is chaotic or not. In the backend, the integrator is integrating an additional set\n        of particles using the variational equation. Note that variational equations are better \n        suited for this than shadow particles. MEGNO is currently only supported in the IAS15 \n        and WHFast integrators.\n\n        This function also needs to be called if you are interested in the Lyapunov exponent as it is\n        calculate with the help of MEGNO. See Rein and Tamayo 2015 for details on the implementation.\n\n        For more information on MENGO see e.g. http://dx.doi.org/10.1051/0004-6361:20011189\n        \"\"\"\n        clibrebound.reb_tools_megno_init(byref(self))\n    \n    def calculate_megno(self):\n        \"\"\"\n        Return the current MEGNO value.\n        Note that you need to call init_megno() before the start of the simulation.\n        \"\"\"\n        if self._calculate_megno==0:\n            raise RuntimeError(\"MEGNO cannot be calculated. Make sure to call init_megno() after adding all particles but before integrating the simulation.\")\n\n        clibrebound.reb_tools_calculate_megno.restype = c_double\n        return clibrebound.reb_tools_calculate_megno(byref(self))\n    \n    def calculate_lyapunov(self):\n        \"\"\"\n        Return the current Lyapunov Characteristic Number (LCN).\n        Note that you need to call init_megno() before the start of the simulation.\n        To get a timescale (the Lyapunov timescale), take the inverse of this quantity.\n        \"\"\"\n        if self._calculate_megno==0:\n            raise RuntimeError(\"Lyapunov Characteristic Number cannot be calculated. Make sure to call init_megno() after adding all particles but before integrating the simulation.\")\n\n        clibrebound.reb_tools_calculate_lyapunov.restype = c_double\n        return clibrebound.reb_tools_calculate_lyapunov(byref(self))\n    \n# Particle add function, used to be called particle_add() and add_particle() \n    def add(self, particle=None, **kwargs):   \n        \"\"\"\n        Adds a particle to REBOUND. Accepts one of the following:\n\n        1) A single Particle structure.\n        2) The particle's mass and a set of cartesian coordinates: m,x,y,z,vx,vy,vz.\n        3) The primary as a Particle structure, the particle's mass and a set of orbital elements: primary,m,a,anom,e,omega,inv,Omega,MEAN (see :class:`.Orbit` for the definition of orbital elements).\n        4) A name of an object (uses NASA Horizons to look up coordinates)\n        5) A list of particles or names.\n        \"\"\"\n        if particle is not None:\n            if isinstance(particle, Particle):\n                if (self.gravity == \"tree\" or self.collision == \"tree\") and self.root_size <=0.:\n                    raise ValueError(\"The tree code for gravity and/or collision detection has been selected. However, the simulation box has not been configured yet. You cannot add particles until the the simulation box has a finite size.\")\n\n                clibrebound.reb_add(byref(self), particle)\n            elif isinstance(particle, list):\n                for p in particle:\n                    self.add(p, **kwargs)\n            elif isinstance(particle,str):\n                if None in self.units.values():\n                    self.units = ('AU', 'yr2pi', 'Msun')\n                self.add(horizons.getParticle(particle, **kwargs), hash=particle)\n                units_convert_particle(self.particles[-1], 'km', 's', 'kg', self._units['length'], self._units['time'], self._units['mass'])\n            else: \n                raise ValueError(\"Argument passed to add() not supported.\")\n        else: \n            self.add(Particle(simulation=self, **kwargs))\n        if hasattr(self, '_widgets'):\n            self._display_heartbeat(pointer(self))\n\n# Particle getter functions\n    @property\n    def particles(self):\n        \"\"\"\n        Returns a Particles object that allows users to access particles like a dictionary using indices, hashes, or strings. \n\n        The Particles object uses pointers and thus the contents update \n        as the simulation progresses. Note that the pointers could change,\n        for example when a particle is added or removed from the simulation. \n        \"\"\"\n        particles = Particles(self)\n        return particles\n\n    @particles.deleter\n    def particles(self):\n        \"\"\"\n        Remove all particles from the simulation\n        \"\"\"\n        clibrebound.reb_remove_all(byref(self))\n        self.process_messages()\n\n    def remove(self, index=None, hash=None, keepSorted=True):\n        \"\"\" \n        Removes a particle from the simulation.\n\n        Parameters\n        ----------\n        index : int, optional\n            Specify particle to remove by index.\n        hash : c_uint32 or string, optional\n            Specifiy particle to remove by hash (if a string is passed, the corresponding hash is calculated).\n        keepSorted : bool, optional\n            By default, remove preserves the order of particles in the particles array. \n            Might set it to zero in cases with many particles and many removals to speed things up.\n        \"\"\"\n        if index is not None:\n            clibrebound.reb_remove(byref(self), index, keepSorted)\n        if hash is not None:\n            hash_types = c_uint32, c_uint, c_ulong\n            PY3 = sys.version_info[0] == 3\n            if PY3:\n                string_types = str,\n                int_types = int,\n            else:\n                string_types = basestring,\n                int_types = int, long\n            if isinstance(hash, string_types):\n                clibrebound.reb_remove_by_hash(byref(self), rebhash(hash), keepSorted)\n            elif isinstance(hash, int_types):\n                clibrebound.reb_remove_by_hash(byref(self), c_uint32(hash), keepSorted)\n            elif isinstance(hash, hash_types):\n                clibrebound.reb_remove_by_hash(byref(self), hash, keepSorted)\n        if hasattr(self, '_widgets'):\n            self._display_heartbeat(pointer(self))\n\n        self.process_messages()\n\n    def particles_ascii(self, prec=8):\n        \"\"\"\n        Returns an ASCII string with all particles' masses, radii, positions and velocities.\n\n        Parameters\n        ----------\n        prec : int, optional\n            Number of digits after decimal point. Default 8.\n        \"\"\"\n        s = \"\"\n        for p in self.particles:\n            s += ((\"%%.%de \"%prec) * 8)%(p.m, p.r, p.x, p.y, p.z, p.vx, p.vy, p.vz) + \"\\n\"\n        if len(s):\n            s = s[:-1]\n        return s\n    \n    def add_particles_ascii(self, s):\n        \"\"\"\n        Adds particles from an ASCII string. \n\n        Parameters\n        ----------\n        s : string\n            One particle per line. Each line should include particle's mass, radius, position and velocity.\n        \"\"\"\n        for l in s.split(\"\\n\"):\n            r = l.split()\n            if len(r):\n                try:\n                    r = [float(x) for x in r]\n                    p = Particle(simulation=self, m=r[0], r=r[1], x=r[2], y=r[3], z=r[4], vx=r[5], vy=r[6], vz=r[7])\n                    self.add(p)\n                except:\n                    raise AttributeError(\"Each line requires 8 floats corresponding to mass, radius, position (x,y,z) and velocity (x,y,z).\")\n\n# Orbit calculation\n    def calculate_orbits(self, heliocentric=False, barycentric=False):\n        \"\"\" \n        Calculate orbital parameters for all partices in the simulation.\n        By default this functions returns the orbits in Jacobi coordinates. \n\n        If MEGNO is enabled, variational particles will be ignored.\n\n        Parameters\n        ----------\n        heliocentric : bool, optional\n            Set the parameter heliocentric to True to return orbits referenced to sim.particles[0].\n        barycentric : bool, optional\n            Set the parameter barycentric to True to return orbits referenced to the system's barycenter.\n            \n\n        Returns\n        -------\n        Returns an array of Orbits of length N-1.\n        \"\"\"\n        _particles_tmp = self.particles\n        orbits = []\n        \n        jacobi = True\n        com = _particles_tmp[0]\n        if heliocentric is True:\n            jacobi = False\n        if barycentric is True:\n            com = self.calculate_com()\n            jacobi = False\n\n        clibrebound.reb_get_com_of_pair.restype = Particle\n        for i in range(1,self.N_real):\n            orbits.append(_particles_tmp[i].calculate_orbit(primary=com))\n            if jacobi is True:\n                com = clibrebound.reb_get_com_of_pair(com, _particles_tmp[i])\n\n        return orbits\n\n# COM calculation \n    def calculate_com(self, first=0, last=None):\n        \"\"\"\n        Returns the center of momentum for all particles in the simulation.\n\n        Parameters\n        ----------\n        first: int, optional\n            If ``first`` is specified, only calculate the center of momentum starting\n            from index=``first``.\n        last : int or None, optional\n            If ``last`` is specified only calculate the center of momentum up to \n            (but excluding) index=``last``.  Same behavior as Python's range function.\n\n        Examples\n        --------\n        >>> sim = rebound.Simulation()\n        >>> sim.add(m=1, x=-20)\n        >>> sim.add(m=1, x=-10)\n        >>> sim.add(m=1, x=0)\n        >>> sim.add(m=1, x=10)\n        >>> sim.add(m=1, x=20)\n        >>> com = sim.calculate_com()\n        >>> com.x\n        0.0 \n        >>> com = sim.calculate_com(first=2,last=4) # Considers indices 2,3\n        >>> com.x\n        5.0\n\n        \"\"\"\n        if last is None:\n            last = self.N_real\n        clibrebound.reb_get_com_range.restype = Particle\n        return clibrebound.reb_get_com_range(byref(self), c_int(first), c_int(last))\n\n# Tools\n    def serialize_particle_data(self,**kwargs):\n        \"\"\"\n        Fast way to access serialized particle data via numpy arrays.\n\n        This function can directly set the values of numpy arrays to\n        current particle data. This is significantly faster than accessing\n        particle data via `sim.particles` as all the copying is done \n        on the C side. \n        No memory is allocated by this function.\n        It expects correctly sized numpy arrays as arguments. The argument\n        name indicates what kind of particle data is written to the array. \n        \n        Possible argument names are \"hash\", \"m\", \"r\", \"xyz\", \"vxvyvz\".\n        The datatype for the \"hash\" array needs to be uint32. The other arrays\n        expect a datatype of float64. The lengths of \"hash\", \"m\", \"r\" arrays\n        need to be at least sim.N. The lengths of xyz and vxvyvz need\n        to be at least 3*sim.N. Exceptions are raised otherwise.\n\n        Note that this routine is only intended for special use cases\n        where speed is an issue. For normal use, it is recommended to\n        access particle data via the `sim.particles` array. Be aware of\n        potential issues that arrise by directly accesing the memory of\n        numpy arrays (see numpy documentation for more details).\n\n        Examples\n        --------\n        This sets an array to the xyz positions of all particles:\n\n        >>> import numpy as np\n        >>> a = np.zeros((sim.N,3),dtype=\"float64\")\n        >>> sim.serialize_particle_data(xyz=a)\n        >>> print(a)\n\n        To get all current radii of particles:\n\n        >>> a = np.zeros(sim.N,dtype=\"float64\")\n        >>> sim.serialize_particle_data(r=a)\n        >>> print(a)\n        \n        To get all current radii and hashes of particles:\n\n        >>> a = np.zeros(sim.N,dtype=\"float64\")\n        >>> b = np.zeros(sim.N,dtype=\"uint32\")\n        >>> sim.serialize_particle_data(r=a,hash=b)\n        >>> print(a,b)\n\n        \"\"\"\n        N = self.N\n        possible_keys = [\"hash\",\"m\",\"r\",\"xyz\",\"vxvyvz\"]\n        d = {x:None for x in possible_keys}\n        for k,v in kwargs.items():\n            if k in d:\n                if k == \"hash\":\n                    if v.dtype!= \"uint32\":\n                        raise AttributeError(\"Expected 'uint32' data type for '%s' array.\"%k)\n                    if v.size<N:\n                        raise AttributeError(\"Array '%s' is not large enough.\"%k)\n                    d[k] = v.ctypes.data_as(ctypes.POINTER(ctypes.c_uint32))\n                else:\n                    if v.dtype!= \"float64\":\n                        raise AttributeError(\"Expected 'float64' data type for %s array.\"%k)\n                    if k in [\"xyz\", \"vxvyvz\"]:\n                        minsize = 3*N\n                    else:\n                        minsize = N\n                    if v.size<minsize:\n                        raise AttributeError(\"Array '%s' is not large enough.\"%k)\n                    d[k] = v.ctypes.data_as(ctypes.POINTER(ctypes.c_double))\n            else:\n                raise AttributeError(\"Only '%s' are currently supported attributes for serialization.\" % \"', '\".join(d.keys()))\n\n        clibrebound.reb_serialize_particle_data(byref(self), d[\"hash\"], d[\"m\"], d[\"r\"], d[\"xyz\"], d[\"vxvyvz\"])\n\n    def move_to_com(self):\n        \"\"\"\n        This function moves all particles in the simulation to a center of momentum frame.\n        In that frame, the center of mass is at the origin and does not move.\n        It makes sense to call this function at the beginning of the integration, especially \n        for the high accuray integrators IAS15 and WHFast.\n        \"\"\"\n        clibrebound.reb_move_to_com(byref(self))\n    \n    def calculate_energy(self):\n        \"\"\"\n        Returns the sum of potential and kinetic energy of all particles in the simulation.\n        \"\"\"\n        clibrebound.reb_tools_energy.restype = c_double\n        return clibrebound.reb_tools_energy(byref(self))\n   \n    def calculate_angular_momentum(self):\n        \"\"\"\n        Returns a list of the three (x,y,z) components of the total angular momentum of all particles in the simulation.\n        \"\"\"\n        clibrebound.reb_tools_angular_momentum.restype = reb_vec3d\n        L = clibrebound.reb_tools_angular_momentum(byref(self))\n        return [L.x, L.y, L.z]\n\n    def configure_box(self, boxsize, root_nx=1, root_ny=1, root_nz=1):\n        \"\"\"\n        Initialize the simulation box.\n\n        This function only needs to be called it boundary conditions other than \"none\"\n        are used. In such a case the boxsize must be known and is set with this function.\n\n        Parameters\n        ----------\n        boxsize : float, optional\n            The size of one root box.\n        root_nx, root_ny, root_nz : int, optional\n            The number of root boxes in each direction. The total size of the simulation box\n            will be ``root_nx * boxsize``, ``root_ny * boxsize`` and ``root_nz * boxsize``.\n            By default there will be exactly one root box in each direction.\n        \"\"\"\n        clibrebound.reb_configure_box(byref(self), c_double(boxsize), c_int(root_nx), c_int(root_ny), c_int(root_nz))\n        return\n   \n    def configure_ghostboxes(self, nghostx=0, nghosty=0, nghostz=0):\n        \"\"\"\n        Initialize the ghost boxes.\n\n        This function only needs to be called it boundary conditions other than \"none\" or\n        \"open\" are used. In such a case the number of ghostboxes must be known and is set \n        with this function. \n        \n        Parameters\n        ----------\n        nghostx, nghosty, nghostz : int\n            The number of ghost boxes in each direction. All values default to 0 (no ghost boxes).\n        \"\"\"\n        clibrebound.nghostx = c_int(nghostx)\n        clibrebound.nghosty = c_int(nghosty)\n        clibrebound.nghostz = c_int(nghostz)\n        return\n\n\n# Input/Output routines\n    def save(self, filename):\n        \"\"\"\n        Save the entire REBOUND simulation to a binary file.\n        \"\"\"\n        clibrebound.reb_output_binary(byref(self), c_char_p(filename.encode(\"ascii\")))\n\n# Integration\n    def step(self):\n        \"\"\"\n        Perform exactly one integration step with REBOUND. This function is rarely needed.\n        Instead, use integrate().\n        \"\"\"\n        clibrebound.reb_step(byref(self))\n        self.process_messages()\n\n    def integrate(self, tmax, exact_finish_time=1):\n        \"\"\"\n        Main integration function. Call this function when you have setup your simulation and want to integrate it forward (or backward) in time. The function might be called many times to integrate the simulation in steps and create outputs in-between steps.\n        \n        Parameters\n        ----------\n        tmax : float\n            The final time of your simulation. If the current time is 100, and tmax=200, then after the calling the integrate routine, the time has advanced to t=200. If tmax is larger than or equal to the current time, no integration will be performed.\n        exact_finish_time: int, optional\n            This argument determines whether REBOUND should try to finish at the exact time (tmax) you give it or if it is allowed to overshoot. Overshooting could happen if one starts at t=0, has a timestep of dt=10 and wants to integrate to tmax=25. With ``exact_finish_time=1``, the integrator will choose the last timestep such that t is exactly 25 after the integration, otherwise t=30. Note that changing the timestep does affect the accuracy of symplectic integrators negatively.\n        \n        Exceptions\n        ----------\n        Exceptions are thrown when no more particles are left in the simulation or when a generic integration error occured. \n        If you specified exit_min_distance or exit_max_distance, then additional exceptions might thrown for escaping particles or particles that undergo a clos encounter.\n        \n        Examples\n        -------- \n        The typical usage is as follows. Note the use of ``np.linspace`` to create equally spaced outputs. \n        Using ``np.logspace`` can be used to easily produce logarithmically spaced outputs.\n\n        >>> import numpy as np\n        >>> for time in np.linspace(0,100.,10): \n        >>>     sim.integrate(time)\n        >>>     perform_output(sim)\n        \n        \"\"\"\n        if debug.integrator_package ==\"REBOUND\":\n            self.exact_finish_time = c_int(exact_finish_time)\n            ret_value = clibrebound.reb_integrate(byref(self), c_double(tmax))\n            if ret_value == 1:\n                raise SimulationError(\"An error occured during the integration.\")\n            if ret_value == 2:\n                raise NoParticles(\"No more particles left in simulation.\")\n            if ret_value == 3:\n                raise Encounter(\"Two particles had a close encounter (d<exit_min_distance).\")\n            if ret_value == 4:\n                raise Escape(\"A particle escaped (r>exit_max_distance).\")\n            if ret_value == 5:\n                raise Escape(\"User caused exit. Simulation did not finish.\") # should not occur in python\n            if ret_value == 6:\n                raise KeyboardInterrupt\n        else:\n            debug.integrate_other_package(tmax,exact_finish_time)\n        self.process_messages()\n\n    def integrator_synchronize(self):\n        \"\"\"\n        Call this function if safe-mode is disabled and you need synchronize particle positions and velocities between timesteps.\n        \"\"\"\n        clibrebound.reb_integrator_synchronize(byref(self))\n    \n    def tree_update(self):\n        \"\"\"\n        Call this function to update the tree structure manually after removing particles.\n        \"\"\"\n        clibrebound.reb_tree_update(byref(self))\n    \nclass Variation(Structure):\n    \"\"\"\n    REBOUND Variational Configuration Object.\n\n    This object encapsulated the configuration of one set of variational \n    equations in a REBOUND simulation.  It is an abstraction of the \n    C struct reb_variational_configuration.\n\n    None of the fields in this struct should be changed after it has\n    been initialized.\n\n    One rebound simulation can include any number of first and second order \n    variational equations.\n\n    Note that variations are only encoded as particles for convenience.  \n    A variational particle's position and velocity should be interpreted as a derivative, i.e. how much that position orvelocity varies with respect to the first or second-order variation.  \n    See ipython_examples/VariationalEquations.ipynb and Rein and Tamayo (2016) for details.\n\n    Examples\n    --------\n\n    >>> sim = rebound.Simulation()          # Create a simulation\n    >>> sim.add(m=1.)                       # Add a star\n    >>> sim.add(m=1.e-3, a=1.)              #     a planet\n    >>> var_config = sim.add_variation()    # Add a set of variational equations. \n    >>> var_config.particles[1].x = 1.      # Initialize the variational particle corresponding to the planet\n    >>> sim.integrate(100.)                 # Integrate the simulation\n    >>> print(var_config.particles[0].vx)   # Print the velocity of the variational particle corresponding to the star\n    \"\"\"\n    _fields_ = [\n                (\"_sim\", POINTER(Simulation)),\n                (\"order\", c_int),\n                (\"index\", c_int),\n                (\"testparticle\", c_int),\n                (\"index_1st_order_a\", c_int),\n                (\"index_1st_order_b\", c_int)]\n\n    def vary(self, particle_index, variation, variation2=None, primary=None):\n        \"\"\"\n        This function can be used to initialize the variational particles that are \n        part of a Variation.\n    \n        Note that rather than using this convenience function, one can \n        also directly manipulate the particles' coordinate using the following\n        syntax:\n\n        >>> var = sim.add_variation()\n        >>> var.particles[0].x = 1.\n\n        The ``vary()`` function is useful for initializing variations corresponding to \n        changes in one of the orbital parameters for a particle on a bound \n        Keplerian orbit.\n\n        The function supports both first and second order variations in the following\n        classical orbital parameters:\n          a, e, inc, omega, Omega, f\n        as well as the Pal (2009) coordinates: \n          a, h, k, ix, iy, lambda\n        and in both cases the mass m of the particle. The advantage of the Pal coordinate\n        system is that all derivatives are well behaved (infinitely differentiable).\n        Classical orbital parameters on the other hand exhibit coordinate singularities, \n        for example when e=0.\n        \n        The following example initializes the variational particles corresponding to a \n        change in the semi-major axis of the particle with index 1:\n        \n        >>> var = sim.add_variation()\n        >>> var.vary(1,\"a\")\n\n        Parameters\n        ----------\n        particle_index : int\n            The index of the particle that should be varied. The index starts at 0 and runs through N-1. The first particle added to a simulation receives the index 0, the second 1, and the on.\n        variation : string\n            This parameter determines which orbital parameter is varied. \n        variation2: string, optional\n            This is only used for second order variations which can depend on two varying parameters. If omitted, then it is assumed that the parameter variation is variation2.\n        primary: Particle, optional\n            By default variational particles are created in the Heliocentric frame. \n            Set this parameter to use any other particles as a primary (e.g. the center of mass).\n        \"\"\"\n        if self.order==2 and variation2 is None:\n            variation2 = variation\n        if self._sim is not None:\n            sim = self._sim.contents\n            particles = sim.particles\n        else:\n            raise RuntimeError(\"Something went wrong. Cannot seem to find simulation corresponding to variation.\")\n        if self.testparticle >= 0:\n            particles[self.index] = Particle(simulation=sim,particle=particles[particle_index], variation=variation, variation2=variation2, primary=primary)\n        else:\n            particles[self.index + particle_index] = Particle(simulation=sim,particle=particles[particle_index], variation=variation, variation2=variation2, primary=primary)\n\n    @property\n    def particles(self):\n        \"\"\"\n        Access the variational particles corresponding to this set of variational equations.\n\n        The function returns a list of particles which are sorted in the same way as those in \n        sim.particles\n\n        The particles are pointers and thus can be modified. \n\n        If there are N real particles, this function will also return a list of N particles (all of which are \n        variational particles). \n        \"\"\"\n        sim = self._sim.contents\n        ps = []\n        if self.testparticle>=0:\n            N = 1\n        else:\n            N = sim.N-sim.N_var \n        \n        ParticleList = Particle*N\n        ps = ParticleList.from_address(ctypes.addressof(sim._particles.contents)+self.index*ctypes.sizeof(Particle))\n        return ps\n\nclass reb_particle_int(Structure):\n    _fields_ = [\n                (\"x\", c_int64),\n                (\"y\", c_int64),\n                (\"z\", c_int64),\n                (\"vx\", c_int64),\n                (\"vy\", c_int64),\n                (\"vz\", c_int64),\n                ]\n\nclass reb_simulation_integrator_janus(Structure):\n    _fields_ = [\n                (\"scale_pos\",c_double),\n                (\"scale_vel\",c_double),\n                (\"order\", c_uint),\n                (\"recalculate_integer_coordinates_this_timestep\", c_uint),\n                (\"p_int\", POINTER(reb_particle_int)),\n                (\"allocated_N\",c_uint),\n                ]\n\nclass reb_simulation_integrator_mercurius(Structure):\n    _fields_ = [(\"rcrit\", c_double),\n                (\"recalculate_coordinates_this_timestep\", c_uint),\n                (\"recalculate_rhill_this_timestep\", c_uint),\n                (\"safe_mode\", c_uint),\n                (\"keep_unsynchronized\", c_uint),\n                (\"_is_synchronized\", c_uint),\n                (\"_mode\", c_uint),\n                (\"_encounterN\", c_uint),\n                (\"_globalN\", c_uint),\n                (\"_globalNactive\", c_uint),\n                (\"_allocatedN\", c_uint),\n                (\"_rhillallocatedN\", c_uint),\n                (\"_encounterAllocatedN\", c_uint),\n                (\"_m0\", c_double),\n                (\"_rhill\", c_void_p),\n                (\"_rhillias15\", c_void_p),\n                (\"_encounterIndicies\", c_void_p),\n                (\"_encounterParticles\", POINTER(Particle)),\n                (\"_p_hold\", POINTER(Particle)),\n                ]\n\nclass reb_simulation_integrator_hermes(Structure):\n    _fields_ = [(\"mini\", POINTER(Simulation)),\n                (\"global\", POINTER(Simulation)),\n                (\"hill_switch_factor\", c_double),\n                (\"solar_switch_factor\", c_double),\n                (\"adaptive_hill_switch_factor\", c_int),\n                (\"mini_active\", c_int),\n                (\"_current_hill_switch_factor\", c_double),\n                (\"_energy_before_timestep\", c_double),\n                (\"_collision_this_global_dt\", c_int),\n                (\"_global_index_from_mini_index\", POINTER(c_int)),\n                (\"_global_index_from_mini_index_N\",c_int),\n                (\"_global_index_from_mini_index_Nmax\",c_int),\n                (\"_is_in_mini\", POINTER(c_int)),\n                (\"_is_in_mini_Nmax\", c_int),\n                (\"_a_i\", POINTER(c_double)),\n                (\"_a_f\", POINTER(c_double)),\n                (\"_a_Nmax\", c_int),\n                (\"_timestep_too_large_warning\", c_int),\n                (\"_steps\", c_ulonglong),\n                (\"_steps_miniactive\", c_ulonglong),\n                (\"_steps_miniN\", c_ulonglong),\n                ]\n\nclass timeval(Structure):\n    _fields_ = [(\"tv_sec\",c_long),(\"tv_usec\",c_long)]\n\nclass reb_display_data(Structure):\n    _fields_ = [(\"r\", POINTER(Simulation)),\n                (\"r_copy\", POINTER(Simulation)),\n                (\"particle_data\", c_void_p),\n                (\"orbit_data\", c_void_p),\n                (\"particles_copy\", POINTER(Particle)),\n                (\"p_jh_copy\", POINTER(Particle)),\n                (\"allocated_N\", c_ulong),\n                (\"allocated_N_whfast\", c_ulong),\n                (\"opengl_enabled\", c_int),\n                (\"scale\", c_double),\n                (\"mouse_x\", c_double),\n                (\"mouse_y\", c_double),\n                (\"retina\", c_double),\n                # ignoring other data (never used)\n                ]\n\n\n# Setting up fields after class definition (because of self-reference)\nSimulation._fields_ = [\n                (\"t\", c_double),\n                (\"G\", c_double),\n                (\"softening\", c_double),\n                (\"dt\", c_double),\n                (\"dt_last_done\", c_double),\n                (\"N\", c_int),\n                (\"N_var\", c_int),\n                (\"var_config_N\", c_int),\n                (\"var_config\", POINTER(Variation)),\n                (\"N_active\", c_int),\n                (\"testparticle_type\", c_int),\n                (\"_particle_lookup_table\", POINTER(reb_hash_pointer_pair)),\n                (\"hash_ctr\", c_int),\n                (\"N_lookup\", c_int),\n                (\"allocatedN_lookup\", c_int),\n                (\"allocated_N\", c_int),\n                (\"_particles\", POINTER(Particle)),\n                (\"gravity_cs\", POINTER(reb_vec3d)),\n                (\"gravity_cs_allocatedN\", c_int),\n                (\"tree_root\", c_void_p),\n                (\"tree_needs_update\", c_int),\n                (\"opening_angle2\", c_double),\n                (\"_status\", c_int),\n                (\"exact_finish_time\", c_int),\n                (\"force_is_velocity_dependent\", c_uint),\n                (\"gravity_ignore_10\", c_uint),\n                (\"output_timing_last\", c_double),\n                (\"_display_clock\", c_ulong),\n                (\"save_messages\", c_int),\n                (\"messages\", c_void_p),\n                (\"exit_max_distance\", c_double),\n                (\"exit_min_distance\", c_double),\n                (\"usleep\", c_double),\n                (\"display_data\", POINTER(reb_display_data)),\n                (\"track_energy_offset\", c_int),\n                (\"energy_offset\", c_double),\n                (\"boxsize\", reb_vec3d),\n                (\"boxsize_max\", c_double),\n                (\"root_size\", c_double),\n                (\"root_n\", c_int),\n                (\"root_nx\", c_int),\n                (\"root_ny\", c_int),\n                (\"root_nz\", c_int),\n                (\"nghostx\", c_int),\n                (\"nghosty\", c_int),\n                (\"nghostz\", c_int),\n                (\"collision_resolve_keep_sorted\", c_int),\n                (\"collisions\", c_void_p),\n                (\"collisions_allocatedN\", c_int),\n                (\"minimum_collision_celocity\", c_double),\n                (\"collisions_plog\", c_double),\n                (\"max_radius\", c_double*2),\n                (\"collisions_Nlog\", c_long),\n                (\"_calculate_megno\", c_int),\n                (\"megno_Ys\", c_double),\n                (\"megno_Yss\", c_double),\n                (\"megno_cov_Yt\", c_double),\n                (\"megno_var_t\", c_double),\n                (\"megno_mean_t\", c_double),\n                (\"megno_mean_Y\", c_double),\n                (\"megno_n\", c_long),\n                (\"simulationarchive_size_first\", c_long),\n                (\"simulationarchive_size_snapshot\", c_long),\n                (\"simulationarchive_interval\", c_double),\n                (\"simulationarchive_interval_walltime\", c_double),\n                (\"simulationarchive_next\", c_double),\n                (\"_simulationarchive_filename\", c_char_p),\n                (\"simulationarchive_walltime\", c_double),\n                (\"simulationarchive_time\", timeval),\n                (\"_visualization\", c_int),\n                (\"_collision\", c_int),\n                (\"_integrator\", c_int),\n                (\"_boundary\", c_int),\n                (\"_gravity\", c_int),\n                (\"ri_sei\", reb_simulation_integrator_sei), \n                (\"ri_whfast\", reb_simulation_integrator_whfast),\n                (\"ri_ias15\", reb_simulation_integrator_ias15),\n                (\"ri_hermes\", reb_simulation_integrator_hermes),\n                (\"ri_mercurius\", reb_simulation_integrator_mercurius),\n                (\"ri_janus\", reb_simulation_integrator_janus),\n                (\"_additional_forces\", CFUNCTYPE(None,POINTER(Simulation))),\n                (\"_pre_timestep_modifications\", CFUNCTYPE(None,POINTER(Simulation))),\n                (\"_post_timestep_modifications\", CFUNCTYPE(None,POINTER(Simulation))),\n                (\"_heartbeat\", CFUNCTYPE(None,POINTER(Simulation))),\n                (\"_display_heartbeat\", CFUNCTYPE(None,POINTER(Simulation))),\n                (\"_coefficient_of_restitution\", CFUNCTYPE(c_double,POINTER(Simulation), c_double)),\n                (\"_collision_resolve\", CFUNCTYPE(c_int,POINTER(Simulation), reb_collision)),\n                (\"_free_particle_ap\", CFUNCTYPE(None, POINTER(Particle))),\n                (\"extras\", c_void_p),\n                 ]\n\nParticle._fields_ = [(\"x\", c_double),\n                (\"y\", c_double),\n                (\"z\", c_double),\n                (\"vx\", c_double),\n                (\"vy\", c_double),\n                (\"vz\", c_double),\n                (\"ax\", c_double),\n                (\"ay\", c_double),\n                (\"az\", c_double),\n                (\"m\", c_double),\n                (\"r\", c_double),\n                (\"lastcollision\", c_double),\n                (\"c\", c_void_p),\n                (\"_hash\", c_uint32),\n                (\"ap\", c_void_p),\n                (\"_sim\", POINTER(Simulation))]\n\nPOINTER_REB_SIM = POINTER(Simulation) \nAFF = CFUNCTYPE(None,POINTER_REB_SIM)\nCORFF = CFUNCTYPE(c_double,POINTER_REB_SIM, c_double)\nCOLRFF = CFUNCTYPE(c_int, POINTER_REB_SIM, reb_collision)\nFPA = CFUNCTYPE(None, POINTER(Particle))\n\nclass Particles(MutableMapping):\n    \"\"\"\n    This class allows the user to access particles like a dictionary using the particle's 1) index 2) hash 3) string (which will be converted to hash).\n    Allows for negative indices and slicing.\n    \"\"\"\n    def __init__(self, sim):\n        self.sim = sim\n\n    @property\n    def _ps(self):\n        ParticleList = Particle*self.sim.N\n        return ParticleList.from_address(ctypes.addressof(self.sim._particles.contents))\n\n    def __getitem__(self, key):\n        hash_types = c_uint32, c_uint, c_ulong\n        PY3 = sys.version_info[0] == 3\n        if PY3:\n            string_types = str,\n            int_types = int,\n        else:\n            string_types = basestring,\n            int_types = int, long,\n       \n        if isinstance(key, slice):\n            return [self[i] for i in range(*key.indices(len(self)))]\n\n        if isinstance(key, int_types):\n            if key < 0: # accept negative indices\n                key += self.sim.N\n            if key < 0 or key >= self.sim.N:\n                raise AttributeError(\"Index {0} used to access particles out of range.\".format(key))\n            return self._ps[key]\n\n        else:\n            clibrebound.reb_get_particle_by_hash.restype = POINTER(Particle)\n            if isinstance(key, string_types):\n                key = rebhash(key)\n            elif not isinstance(key, hash_types):\n                raise AttributeError(\"Expecting string, integer or ctypes.c_uint32 as argument to sim.particles.  See UniquelyIdentifyingParticles.ipynb ipython_example.\")\n\n            ptr = clibrebound.reb_get_particle_by_hash(byref(self.sim), key) \n            \n            if ptr:\n                p = Particle\n                return p.from_address(ctypes.addressof(ptr.contents))\n            else:\n                raise ParticleNotFound(\"Particle was not found in the simulation.\") \n\n    def __setitem__(self, key, value):\n        if isinstance(value, Particle):\n            value._sim = pointer(self.sim)\n            p = self[key]\n            if p.index == -1:\n                raise AttributeError(\"Can't set particle (particle not found in simulation).\")\n            else:\n                self._ps[p.index] = value\n\n    def __delitem__(self, key):\n        pass\n\n    def __iter__(self):\n        for p in self._ps:\n            yield p\n\n    def __len__(self):\n        return self.sim.N\n\n# Import at the end to avoid circular dependence\nfrom . import horizons\nfrom . import debug\n","repo_name":"shangfei/rebound-sph","sub_path":"rebound/simulation.py","file_name":"simulation.py","file_ext":"py","file_size_in_byte":73350,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"81"}
+{"seq_id":"20311987650","text":"\ndef sum_of_unique(nums):\n    list1 = []\n    for i in set(nums):\n        if nums.count(i) == 1:\n            list1.append(i)\n    return sum(list1)\n\nnums = [1,2,3,2]\nprint(sum_of_unique(nums))\n\n\n\n'''\n\n定义函数实现：输出100以内的孪生质数\n\n学生质数就是指相差2的质数对，例如3和5,5和7,11和13\n\n\n'''\n\ndef func(num):\n    if num < 2:\n        return False\n    for i in range(2,num):\n        if (num % i) == 0 :\n            return False\n        \n    return True\n\ndef func2(n = 100): #  默认参数\n    for i in range(n):\n        if func(i) and func(i+2):\n            print(i,i+2)\n\n\nfunc2(200)\n\n\n\n\n\nfor right in range(1,10):\n    for left in range(1,right + 1):\n        print(f'{left}X{right}={left*right}',end='\\t')\n    print()\n\n    \nprint('hello world')\n","repo_name":"CosmicVoyager-StarryDreamer/gitworkspace","sub_path":"gittest.py","file_name":"gittest.py","file_ext":"py","file_size_in_byte":778,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"13057259484","text":"\"\"\"\nLogger configuration\n\"\"\"\nimport os\nimport platform\nimport multiprocessing\nfrom collections import OrderedDict\nimport logbook\n\nBANNER_WIDTH = 70\n\nclass RockfishLogger(logbook.Logger):\n\n    def __init__(self, *args, **kwargs):\n\n        logbook.Logger.__init__(self, *args, **kwargs)\n    \n        self.banner0 = BANNER_WIDTH * '='\n\n    def header(self):\n        \n        from rockfish2 import __version__, __file__\n        \n        self.info('\\n{:}\\nRockfish -- Version {:}\\n{:}',\n                self.banner0, __version__, self.banner0)\n\n        self.debug('Logger: {:} ({:})', logbook.__name__, logbook.__version__)\n        self.debug('Log level: {:}', self.level_name)\n        self.log_system_info(level=1)\n\n    def _get_system_info(self):\n        \"\"\"\n        Log information about the system \n        \"\"\"\n        stats = OrderedDict()\n        stats['Python version'] = platform.python_version()\n        stats['Python compiler'] = platform.python_compiler()\n        stats['System'] = platform.system()\n        stats['Machine'] = platform.machine()\n        stats['Processor'] = platform.processor()\n        stats['CPU count'] = multiprocessing.cpu_count()\n        stats['Architecture'] = platform.architecture()[0]\n        stats['Node'] = platform.node()\n\n        return stats\n\n    system_info = property(fget=_get_system_info)\n\n    def log_system_info(self, level=1):\n        \"\"\"\n        Log system information\n        \"\"\"\n        stats = self.system_info\n        for stat in stats:\n            self.log(level, '{:}: {:}', stat, stats[stat])\n\n","repo_name":"rvbelefonte/Rockfish2","sub_path":"rockfish2/utils/logging.py","file_name":"logging.py","file_ext":"py","file_size_in_byte":1547,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"6446920655","text":"# Exemplo de como a solução via equações normais\n# produz um resultado com erro grande quando a matriz\n# A é mal condicionada.\n# Exemplo adaptado de:\n# https://andreask.cs.illinois.edu/cs357-s15/public/demos/09-svd-applications/Least%20Squares%20using%20the%20SVD.html\n\nimport numpy as np\nimport leastsquares as ls\n\nnp.random.seed(12)\nA = np.random.randn(6, 4)\nb = np.random.randn(6)\nA[3] = A[4] + A[5]\nA[1] = A[5] + A[1]\nA[2] = A[3] + A[1]\nA[0] = A[3] + A[1]\n\nx_neq = ls.neq(A, b)\nx_svd = ls.svd(A, b)\n\nprint(\"Norma do vetor residual (equações normais): \" + str(np.linalg.norm(A @ x_neq - b)))\nprint(\"Norma do vetor residual (SVD): \" + str(np.linalg.norm(A @ x_svd - b)))","repo_name":"Salies/lstsq","sub_path":"src/neqerr.py","file_name":"neqerr.py","file_ext":"py","file_size_in_byte":679,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"22261460243","text":"from collections import defaultdict\n\n\nclass Node:\n  def __init__(self):\n    self.children = defaultdict(Node)\n    self.isEndwith = False\n\n\nclass WordDictionary:\n\n  def __init__(self):\n    \"\"\"\n    Initialize your data structure here.\n    \"\"\"\n    self.root = Node()\n\n  def addWord(self, word):\n    \"\"\"\n    Adds a word into the data structure.\n    :type word: str\n    :rtype: void\n    \"\"\"\n    cur = self.root\n    for c in word:\n      cur = cur.children[c]\n    cur.isEndwith = True\n\n  def search(self, word):\n    \"\"\"\n    Returns if the word is in the data structure. A word could contain the dot character '.' to represent any one letter.\n    :type word: str\n    :rtype: bool\n    \"\"\"\n    cur = self.root\n    for i, l in enumerate(word):\n      if l == '.':\n        for k in cur.children:\n          chdTrie = WordDictionary()\n          chdTrie.root = cur.children[k]\n          if chdTrie.search(word[i + 1:]):\n            return True\n        return False\n      elif l not in cur.children:\n        return False\n      cur = cur.children[l]\n    return cur.isEndwith\n\n# Your WordDictionary object will be instantiated and called as such:\n# obj = WordDictionary()\n# obj.addWord(word)\n# param_2 = obj.search(word)\n","repo_name":"GuanzhouSong/Leetcode_Python","sub_path":"Leetcode/211. Add and Search Word - Data structure design.py","file_name":"211. Add and Search Word - Data structure design.py","file_ext":"py","file_size_in_byte":1202,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"26243052988","text":"# -*- coding: utf-8 -*-\n\n#    This file is part of Gertrude.\n#\n#    Gertrude is free software; you can redistribute it and/or modify\n#    it under the terms of the GNU General Public License as published by\n#    the Free Software Foundation; either version 3 of the License, or\n#    (at your option) any later version.\n#\n#    Gertrude 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 Gertrude; if not, see <http://www.gnu.org/licenses/>.\n\nfrom __future__ import unicode_literals\nfrom __future__ import print_function\nfrom builtins import str\nfrom facture import *\nfrom cotisation import CotisationException\nfrom ooffice import *\n\n\nclass ExportComptaCotisationsModifications:\n    title = \"Export compta\"\n    template = \"Export compta cotisations.txt\"\n\n    def __init__(self, inscrits, periode, site=None):\n        self.inscrits = inscrits\n        self.periode = periode\n        self.site = site\n        if site:\n            self.default_output = \"Export compta cotisations %s %s %d.txt\" % (site.nom, months[periode.month - 1], periode.year)\n        else:\n            self.default_output = \"Export compta cotisations %s %d.txt\" % (months[periode.month - 1], periode.year)\n        self.email_to = None\n        self.multi = False\n        self.email = False\n        self.errors = {}\n\n    @staticmethod\n    def generate_inextenso_line_mvt(inscrit, date, total):\n        template = \"%(date)s;VEN;%(compte)s;%(debit)s;%(credit)s;%(libelle)s\"\n        result = [\n            template % {\n                \"date\": date2str(date),\n                \"compte\": \"706100\",\n                \"debit\": \"\",\n                \"credit\": (\"%.02f\" % total).replace(\".\", \",\"),\n                \"libelle\": \"Facture %s %s %d\" % (inscrit.nom.upper(), months[date.month - 1], date.year)\n               },\n            template % {\n                \"date\": date2str(date),\n                \"compte\": inscrit.famille.code_client,\n                \"debit\": (\"%.02f\" % total).replace(\".\", \",\"),\n                \"credit\": \"\",\n                \"libelle\": \"Facture %s %s %d\" % (inscrit.nom.upper(), months[date.month - 1], date.year)\n            }\n        ]\n        return \"\\n\".join(result)\n\n    @staticmethod\n    def generate_ciel_line_mvt(inscrit, date, total):\n        template = '\"77\"\\t\"VT\"\\t\"%(date)s\"\\t\"%(client)s\"\\t\"%(nom)s\"\\t\"%(total)s\"\\tD\\tB\\t\"COTISATION %(nom)s\"\\t\"10\"\\t\"%(date)s\"\\n' + \\\n                   '\"77\"\\t\"VT\"\\t\"%(date)s\"\\t\"706410\"\\t\"PARTICIPATION FAMILIALE\"\\t\"%(total)s\"\\tC\\tB\\t\"COTISATION %(nom)s\"\"10\"'\n        result = template % {\"date\": date2str(date),\n                             \"client\": inscrit.famille.code_client,\n                             \"nom\": inscrit.nom.upper(),\n                             \"total\": \"%.02f\" % total\n                             }\n        return str(result)\n\n    @staticmethod\n    def generate_ciel_line_tiers(inscrit):\n        template = '\"%(client)s\"\\t\"%(nom)s\"\\t\"SR\"\\t\"FRA\"'\n        result = template % {\"client\": inscrit.famille.code_client,\n                             \"nom\": inscrit.nom.upper(),\n                             }\n        return str(result)\n\n    def execute(self, text):\n        text = text.decode(\"latin-1\")\n        if \"<lines-ciel-mvt>\" in text or \"<lines-ciel-tiers>\" in text:\n            return self.execute_ciel(text)\n        elif \"<lines-inextenso-mvt>\" in text:\n            return self.execute_inextenso(text)\n        else:\n            return self.execute_ebp(text)\n\n    def generate_ciel_sections(self):\n        mvt, tiers = [], []\n        for inscrit in self.inscrits:\n            try:\n                facture = Facture(inscrit, self.periode.year, self.periode.month, NO_NUMERO)\n            except CotisationException as e:\n                self.errors[\"%s %s\" % (inscrit.prenom, inscrit.nom)] = e.errors\n                continue\n            date = GetMonthStart(self.periode)\n            mvt.append(self.generate_ciel_line_mvt(inscrit, date, facture.total))\n            tiers.append(self.generate_ciel_line_tiers(inscrit))\n        return \"\\n\".join(mvt), \"\\n\".join(tiers)\n\n    def execute_ciel(self, text):\n        mvt_section, tiers_section = self.generate_ciel_sections()\n        text = text.replace(\"<lines-ciel-mvt>\", mvt_section)\n        text = text.replace(\"<lines-ciel-tiers>\", tiers_section)\n        return text.encode(\"latin-1\"), self.errors\n\n    def execute_inextenso(self, text):\n        result = []\n        for inscrit in self.inscrits:\n            try:\n                facture = Facture(inscrit, self.periode.year, self.periode.month, NO_NUMERO)\n            except CotisationException as e:\n                self.errors[\"%s %s\" % (inscrit.prenom, inscrit.nom)] = e.errors\n                continue\n            date = GetMonthStart(self.periode)\n            result.append(self.generate_inextenso_line_mvt(inscrit, date, facture.total))\n        return (\"\\n\".join(result)).encode(\"latin-1\"), self.errors\n\n    def execute_ebp(self, text):\n        errors = {}\n        result = []\n        replacements = {}\n        lines = text.splitlines(3)\n        for i, line in enumerate(lines):\n            if i == 0:\n                result.append(line)\n            elif i == 1:\n                template = line\n            else:\n                if \"=\" in line:\n                    try:\n                        key, value = line.split(\"=\", 1)\n                        replacements[key.strip()] = value.strip()\n                    except:\n                        pass\n\n        # print replacements\n\n        indexOperation = 0\n        for inscrit in self.inscrits:\n            try:\n                facture = Facture(inscrit, self.periode.year, self.periode.month, NO_NUMERO)\n            except CotisationException as e:\n                errors[\"%s %s\" % (inscrit.prenom, inscrit.nom)] = e.errors\n                continue\n\n            indexOperation += 1\n            date = GetMonthEnd(self.periode)\n            if len(database.creche.sites) > 1 and facture.site:\n                site = facture.site.nom\n            else:\n                site = database.creche.nom\n\n            fields = {'date-fin-mois': '%.2d/%.2d/%d' % (date.day, date.month, date.year),\n                      'index-operation': \"%04d\" % indexOperation,\n                      'nom': inscrit.nom.upper(),\n                      'mois': months[date.month - 1].lower(),\n                      }\n\n            for i in range(5):\n                if i == 0 and facture.total:\n                    fields['numero-compte'] = \"411%s\" % inscrit.nom.upper()[:5]\n                    fields['debit'] = facture.total\n                    fields['credit'] = 0\n                    fields['activite'] = \"\"\n                    fields['plan-analytique'] = \"\"\n                    fields['poste-analytique'] = \"\"\n                elif i == 1 and facture.total - facture.supplement_activites:\n                    fields['numero-compte'] = 70600000\n                    fields['debit'] = 0\n                    fields['credit'] = facture.total - facture.supplement_activites\n                    fields['activite'] = 'garde'\n                    fields['plan-analytique'] = \"SITES\"\n                    fields['poste-analytique'] = site\n                elif i == 2 and facture.supplement_activites:\n                    fields['numero-compte'] = 70710000\n                    fields['debit'] = 0\n                    fields['credit'] = facture.supplement_activites\n                    fields['activite'] = 'activites'\n                    fields['plan-analytique'] = \"SITES\"\n                    fields['poste-analytique'] = site\n                elif i == 3 and facture.frais_inscription_reservataire:\n                    fields['numero-compte'] = \"411RESERVATAIRE\"\n                    fields['debit'] = facture.frais_inscription_reservataire\n                    fields['credit'] = 0\n                    fields['activite'] = \"\"\n                    fields['plan-analytique'] = \"\"\n                    fields['poste-analytique'] = \"\"\n                elif i == 4 and facture.frais_inscription_reservataire:\n                    fields['numero-compte'] = 70820000\n                    fields['debit'] = 0\n                    fields['credit'] = facture.frais_inscription_reservataire\n                    fields['activite'] = \"frais_inscription\"\n                    fields['plan-analytique'] = \"SITES\"\n                    fields['poste-analytique'] = site\n                else:\n                    break\n\n                line = template\n                for field in fields:\n                    value = str(fields[field])\n                    if value in replacements.keys():\n                        value = replacements[value]\n                    line = line.replace(\"<%s>\" % field, value)\n                result.append(line)\n\n        return (\"\".join(result)).encode(\"latin-1\"), errors\n\n\nclass ExportComptaReglementsModifications(object):\n    def __init__(self, inscrits, periode):\n        self.template = 'Export compta reglements.txt'\n        self.inscrits = inscrits\n        self.periode = periode\n        self.default_output = \"Export compta reglements %s %d.txt\" % (months[periode.month - 1], periode.year)\n        self.email_to = None\n        self.multi = False\n        self.email = False\n        self.errors = {}\n\n    @staticmethod\n    def generate_ciel_line_mvt_inscrit(inscrit, date, total):\n        template = '\"78\"\\t\"BQ\"\\t\"%(date)s\"\\t\"%(client)s\"\\t\"%(nom)s\"\\t\"%(total)s\"\\tC\\tB\\t\"COTISATION %(mois)s\"\\t\"10\"\\t\"%(date)s\"'\n        result = template % {\"date\": date,\n                             \"mois\": months[date.month - 1].upper(),\n                             \"total\": \"%.02f\" % total,\n                             \"client\": inscrit.famille.code_client,\n                             \"nom\": inscrit.nom.upper(),\n                             }\n        return str(result)\n\n    @staticmethod\n    def generate_ciel_line_mvt_banque(date, total):\n        template = '\"78\"\\t\"BQ\"\\t\"%(date)s\"\\t\"512000\"\\t\"Banque\"\\t\"%(total)s\"\\tD\\tB\\t\"RECETTES COTISATION %(mois)s\"\\t\"10\"'\n        result = template % {\"date\": date,\n                             \"total\": \"%.02f\" % total,\n                             \"mois\": months[date.month-1].upper(),\n                             }\n        return str(result)\n\n    @staticmethod\n    def generate_ciel_line_tiers(inscrit):\n        template = '\"%(client)s\"\\t\"%(nom)s\"\\t\"SR\"\\t\"FRA\"'\n        result = template % {\"client\": inscrit.famille.code_client,\n                             \"nom\": inscrit.nom.upper(),\n                             }\n        return str(result)\n\n    def generate_ciel_sections(self):\n        mvt, tiers = [], []\n        total = 0.0\n        for inscrit in self.inscrits:\n            encaissements = [encaissement for encaissement in inscrit.famille.encaissements if encaissement.date.month == self.periode.month]\n            for encaissement in encaissements:\n                mvt.append(self.generate_ciel_line_mvt_inscrit(inscrit, encaissement.date, encaissement.valeur))\n                total += encaissement.valeur\n            if encaissements:\n                tiers.append(self.generate_ciel_line_tiers(inscrit))\n        mvt.append(self.generate_ciel_line_mvt_banque(GetMonthEnd(self.periode), total))\n        return \"\\n\".join(mvt), \"\\n\".join(tiers)\n\n    def execute(self, text):\n        return self.execute_ciel(text)\n\n    def execute_ciel(self, text):\n        mvt_section, tiers_section = self.generate_ciel_sections()\n        text = text.replace(\"<lines-ciel-mvt>\", mvt_section)\n        text = text.replace(\"<lines-ciel-tiers>\", tiers_section)\n        return text, self.errors\n","repo_name":"studio1247/gertrude","sub_path":"generation/export_compta.py","file_name":"export_compta.py","file_ext":"py","file_size_in_byte":11755,"program_lang":"python","lang":"en","doc_type":"code","stars":14,"dataset":"github-code","pt":"81"}
+{"seq_id":"41116323917","text":"# Licensed under an MIT open source license - see LICENSE\nfrom __future__ import print_function, absolute_import, division\n\nimport pytest\n\nimport numpy as np\nimport numpy.testing as npt\nimport astropy.units as u\nimport os\n\ntry:\n    import pyfftw\n    PYFFTW_INSTALLED = True\nexcept ImportError:\n    PYFFTW_INSTALLED = False\n\nfrom ..statistics import VCS, VCS_Distance\nfrom ._testing_data import \\\n    dataset1, dataset2, computed_data, computed_distances\n\n\ndef test_VCS_method():\n    tester = VCS(dataset1[\"cube\"]).run(high_cut=0.3 / u.pix,\n                                       low_cut=3e-2 / u.pix,\n                                       verbose=True,\n                                       save_name='test.png')\n    os.system(\"rm test.png\")\n\n    # Test fitting with bootstrapping\n    tester.fit_pspec(bootstrap=True,\n                     high_cut=0.3 / u.pix,\n                     low_cut=3e-2 / u.pix)\n\n    npt.assert_allclose(tester.ps1D, computed_data['vcs_val'])\n\n    npt.assert_allclose(tester.slope, computed_data['vcs_slopes'])\n\n    # Test loading and saving\n    tester.save_results(\"vcs_output.pkl\", keep_data=False)\n\n    saved_tester = VCS.load_results(\"vcs_output.pkl\")\n\n    # Remove the file\n    os.remove(\"vcs_output.pkl\")\n\n    npt.assert_allclose(saved_tester.ps1D, computed_data['vcs_val'])\n\n    npt.assert_allclose(saved_tester.slope, computed_data['vcs_slopes'])\n\n\ndef test_VCS_distance():\n    tester_dist = \\\n        VCS_Distance(dataset1[\"cube\"], dataset2[\"cube\"],\n                     fit_kwargs=dict(high_cut=0.3 / u.pix,\n                                     low_cut=3e-2 / u.pix))\n    tester_dist.distance_metric(verbose=True, save_name='test.png')\n    os.system(\"rm test.png\")\n\n    npt.assert_almost_equal(tester_dist.distance,\n                            computed_distances['vcs_distance'])\n\n    # With pre-computed VCS classes as inputs\n\n    tester_dist2 = \\\n        VCS_Distance(tester_dist.vcs1, tester_dist.vcs2,\n                     fit_kwargs=dict(high_cut=0.3 / u.pix,\n                                     low_cut=3e-2 / u.pix))\n    tester_dist2.distance_metric()\n\n    npt.assert_almost_equal(tester_dist2.distance,\n                            computed_distances['vcs_distance'])\n\n    # With fresh VCS classes as inputs\n    tester = VCS(dataset1[\"cube\"])\n    tester2 = VCS(dataset2[\"cube\"])\n\n    tester_dist3 = \\\n        VCS_Distance(tester, tester2,\n                     fit_kwargs=dict(high_cut=0.3 / u.pix,\n                                     low_cut=3e-2 / u.pix))\n    tester_dist3.distance_metric()\n\n    npt.assert_almost_equal(tester_dist3.distance,\n                            computed_distances['vcs_distance'])\n\n\ndef test_VCS_method_fitlimits():\n\n    high_cut = 0.17 / u.pix\n    low_cut = 0.02 / u.pix\n\n    tester = VCS(dataset1[\"cube\"])\n    tester.run(high_cut=high_cut, low_cut=low_cut)\n\n    # Convert to spectral units\n    high_cut = \\\n        high_cut.value / np.abs(dataset1['cube'][1]['CDELT3'] * u.m / u.s)\n    low_cut = \\\n        low_cut.value / np.abs(dataset1['cube'][1]['CDELT3'] * u.m / u.s)\n    tester2 = VCS(dataset1[\"cube\"])\n    tester2.run(high_cut=high_cut, low_cut=low_cut)\n\n    npt.assert_allclose(tester.slope, tester2.slope, atol=0.02)\n\n\n@pytest.mark.skipif(\"not PYFFTW_INSTALLED\")\ndef test_VCS_method_fftw():\n    tester = VCS(dataset1[\"cube\"]).run(high_cut=0.3 / u.pix,\n                                       low_cut=3e-2 / u.pix,\n                                       use_pyfftw=True,\n                                       threads=1)\n\n    npt.assert_allclose(tester.ps1D, computed_data['vcs_val'])\n\n    npt.assert_allclose(tester.slope, computed_data['vcs_slopes'])\n","repo_name":"Astroua/TurbuStat","sub_path":"turbustat/tests/test_vcs.py","file_name":"test_vcs.py","file_ext":"py","file_size_in_byte":3634,"program_lang":"python","lang":"en","doc_type":"code","stars":60,"dataset":"github-code","pt":"81"}
+{"seq_id":"6218980814","text":"import unittest\nimport re\nimport janome\nfrom collections import defaultdict\nfrom janome.tokenizer import Tokenizer\n\nclass PrepareChain(object):\n\n    BEGIN = \"__BEGIN_SENTENCE__\"\n    END = \"__END_SENTENCE__\"\n\n    def __init__(self, name):\n        # 名前をプッシュ\n        self.name = name\n\n        fname = \"resources/\"+name+\".txt\"\n        with open(fname, mode=\"r\") as file:\n            text = file.read()\n\n        self.text = text\n\n        # 形態素解析用タガー\n        self.tagger = Tokenizer()\n\n    def make_triplet_freqs(self):\n        messages = self._divide_text(self.text)\n\n        # 3つ組の出現回数\n        triplet_freqs = defaultdict(int)\n\n        # センテンス毎に3つ組にする\n        for message in messages:\n            # 形態素解析\n            morphemes = self._morphological_analysis(message)\n            # 3つ組をつくる\n            triplets = self._make_triplet(morphemes)\n            # 出現回数を加算\n            for (triplet, n) in triplets.items():\n                triplet_freqs[triplet] += n\n\n        return triplet_freqs\n\n    def _divide_text(self, text):\n        messages = text.split(\"\\n\")\n\n        return messages\n\n    def _morphological_analysis(self, message):\n        # 一文を形態素解析する\n        sentence = message.encode(\"utf-8\")\n        morphemes = self.tagger.tokenize(message, wakati=True)\n\n        return morphemes\n\n    def _make_triplet(self, morphemes):\n        # 形態素解析で分割された配列を、形態素毎に3つ組にしてその出現回数を数える\n\n        # 3つ組をつくれない場合は終える\n        if len(morphemes) < 3:\n            return {}\n\n        # 出現回数の辞書\n        triplet_freqs = defaultdict(int)\n\n        # 繰り返し\n        for i in range(len(morphemes)-2):\n            triplet = tuple(morphemes[i:i+3])\n            triplet_freqs[triplet] += 1\n\n        # beginを追加\n        triplet = (PrepareChain.BEGIN, morphemes[0], morphemes[1])\n        triplet_freqs[triplet] = 1\n\n        # endを追加\n        triplet = (morphemes[-2], morphemes[-1], PrepareChain.END)\n        triplet_freqs[triplet] = 1\n\n        return triplet_freqs\n\n    def save(self, triplet_freqs, init=False):\n        name = self.name\n        # 3つ組毎に出現回数をcsvに保存\n        fname = \"corpus/\"+name+\"_corpus.csv\"\n        datas = [(triplet[0], triplet[1], triplet[2], freq) for (triplet, freq) in triplet_freqs.items()]\n\n        # 初期化から始める場合\n        if init:\n            # データ整形\n            with open(fname, mode=\"w\") as file:\n                file.write(\"prefix1,prefix2,suffix,freq\\n\")\n\n        # 保存して終了\n        with open(fname, mode=\"a\") as file:\n            for triplet, freq in triplet_freqs.items():\n                file.write(triplet[0]+\",\"+triplet[1]+\",\"+triplet[2]+\",\"+str(freq)+\"\\n\")\n\nif __name__ == '__main__':\n    # unittest.main()\n\n    name = \"アスカ\"\n    PC = PrepareChain(name)\n    triplet_freqs = PC.make_triplet_freqs()\n    PC.save(triplet_freqs, True)\n","repo_name":"278Mt/AsukaGen","sub_path":"ag/PrepareChain.py","file_name":"PrepareChain.py","file_ext":"py","file_size_in_byte":3053,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"12165997931","text":"#-*- coding: utf-8 -*-\n\nfrom __future__ import unicode_literals\n\nfrom caliop.helpers import renderer\n\n\ndef includeme(config):\n    \"\"\"\n    Serve a static JSON based REST API.\n    \"\"\"\n    config.commit()\n    config.add_renderer('text_plain', renderer.TextPlainRenderer)\n    config.add_renderer('json', renderer.JsonRenderer)\n    config.add_renderer('simplejson', renderer.JsonRenderer)\n    config.add_renderer('part', renderer.PartRenderer)\n\n    # Activate cornice in any case and scan\n    config.scan('caliop.views.api.api')\n\n    config.add_route('contact.info', '/api/mock/contact/info')\n    config.add_view('caliop.views.api.ContactInfo',\n                    route_name='contact.info',\n                    renderer='json')\n\n    config.add_route('contact.login', '/api/mock/contact/login')\n    config.add_view('caliop.views.api.ContactLogin',\n                    route_name='contact.login',\n                    renderer='json')\n","repo_name":"LaurentChemla/CaliOpen","sub_path":"caliop/caliop/views/api/config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":928,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"81"}
+{"seq_id":"35290034088","text":"from flask import Flask, jsonify\nfrom flask import render_template\nfrom flask_cors import CORS, cross_origin\nfrom mongo_db_script import MongoDB\n#from . import app\n\napp = Flask(__name__)\nIP = '0.0.0.0'\nPORT = 3333\ncors = CORS(app)\napp.config['CORS_HEADERS'] = 'Content-Type'\n\nmdb = MongoDB()\n\ndef prepare_data(coursor):\n    for i, item in enumerate (coursor):\n        del item [\"_id\"]\n        coursor [i] = item\n    return jsonify (coursor)\n\n@app.route(\"/classic/\")\n@cross_origin()\ndef classic():\n    # return render_template(\"classic.html\")\n    coursor = list (mdb.classic_collection.find({}))\n    return prepare_data(coursor)\n\n@app.route(\"/mystic/\")\n@cross_origin()\ndef mystic():\n    # return render_template(\"mystic.html\")\n    coursor = list (mdb.mystic_collection.find({}))\n    return prepare_data(coursor)\n\n@app.route(\"/fantasy/\")\n@cross_origin()\ndef fantasy():\n    # return render_template(\"fantasy.html\")\n    coursor = list (mdb.fantasy_collection.find({}))\n    return prepare_data(coursor)\n\n@app.route(\"/romance/\")\n@cross_origin()\ndef romance():\n    # return render_template(\"romance.html\")\n    coursor = list (mdb.romance_collection.find({}))\n    return prepare_data(coursor)\n\n@app.route(\"/detectiv/\")\n@cross_origin()\ndef detectiv():\n    # return render_template(\"detectiv.html\")\n    coursor = list (mdb.detectiv_collection.find({}))\n    return prepare_data(coursor)\n\n@app.route(\"/kids/\")\n@cross_origin()\ndef kids():\n    # return render_template(\"kids.html\")\n    coursor = list (mdb.kids_collection.find({}))\n    return prepare_data(coursor)\n\nif __name__== '__main__':\n    app.run(IP, port=PORT, debug=True)\n","repo_name":"helgadev/Specter","sub_path":"hello_app/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1617,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"22110580919","text":"# coding=utf-8\nimport os\nimport pytest\n\nROOT_DIRECTORY = \"DRLimplementation\"\nTARGET_WORKING_DIRECTORY = ROOT_DIRECTORY\n\n# pytestmark = pytest.mark.skip(\"all tests still WIP\")  # (Priority) todo:implement --> coverage: then remove line\n\n# TARGET_WORKING_DIRECTORY = \"SoftActorCritic\"\n\ndef set_up_cwd(initial_CWD):\n    print(\"\\n:: START set_up_cwd, Initial was: \", initial_CWD)\n    \n    path_basename = os.path.basename(initial_CWD)\n    \n    if path_basename == TARGET_WORKING_DIRECTORY:\n        pass\n    elif path_basename == ROOT_DIRECTORY:\n        # then we must get one level down in directory tree\n        os.chdir(TARGET_WORKING_DIRECTORY)\n        print(\":: change cwd to: \", os.getcwd())\n    else:\n        # we are to deep in directory tree\n        while os.path.basename(os.getcwd()) != TARGET_WORKING_DIRECTORY:\n            os.chdir(\"..\")\n            print(\":: CD to parent\")\n    return None\n\n\ndef return_to_initial_working_directory(initial_CWD):\n    print(\":: return_to_initial_working_directory\")\n    if os.path.basename(os.getcwd()) != os.path.basename(initial_CWD):\n        os.chdir(initial_CWD)\n        print(\":: change cwd to: \", os.getcwd())\n    print(\":: Teardown END\\n\")\n    return None\n\n\n@pytest.fixture(scope=\"function\")\ndef set_up_PWD_to_project_root():\n    initial_CWD = os.getcwd()\n    \n    set_up_cwd(initial_CWD)\n    \n    yield\n    \n    return_to_initial_working_directory(initial_CWD)\n\n\ndef test_SoftActorCritic_agent_PLAY_Pendulum_command_line_invocation(set_up_PWD_to_project_root):\n    from os import system\n    \n    out = system(\"python -m SoftActorCritic --playPendulum --play_for=6 --testRun\")\n    \n    # Note: exit(0) <==> clean exit without any errors/problems\n    assert 0 == out, \"Agent invocated from command line exited with error {}\".format(out)\n\n\ndef test_SoftActorCritic_agent_PLAY_LUNAR_command_line_invocation(set_up_PWD_to_project_root):\n    from os import system\n    \n    out = system(\"python -m SoftActorCritic --playLunar --play_for=6 --testRun\")\n    \n    # Note: exit(0) <==> clean exit without any errors/problems\n    assert 0 == out, \"Agent invocated from command line exited with error {}\".format(out)\n\n\ndef test_SoftActorCritic_agent_PLAY_HARD_LUNAR_command_line_invocation(set_up_PWD_to_project_root):\n    from os import system\n    \n    out = system(\"python -m SoftActorCritic --playHardLunar --play_for=6 --testRun\")\n    \n    # Note: exit(0) <==> clean exit without any errors/problems\n    assert 0 == out, \"Agent invocated from command line exited with error {}\".format(out)\n\n\n# @pytest.mark.skip(reason=\"Mute for now\")\ndef test_SoftActorCritic_agent_train_BipedalWalker_command_line_invocation(set_up_PWD_to_project_root):\n    from os import system\n    \n    out = system(\"python -m SoftActorCritic --trainBipedalWalker --testRun\")\n    \n    # Note: exit(0) <==> clean exit without any errors/problems\n    assert 0 == out, \"Agent invocated from command line exited with error {}\".format(out)\n\n\n# @pytest.mark.skip(reason=\"Mute for now\")\ndef test_SoftActorCritic_agent_train_Pendulum_command_line_invocation(set_up_PWD_to_project_root):\n    from os import system\n    \n    out = system(\"python -m SoftActorCritic --trainPendulum --testRun\")\n    \n    # Note: exit(0) <==> clean exit without any errors/problems\n    assert 0 == out, \"Agent invocated from command line exited with error {}\".format(out)\n\n\n# @pytest.mark.skip(reason=\"Mute for now\")\ndef test_SoftActorCritic_agent_train_LunarLander_command_line_invocation(set_up_PWD_to_project_root):\n    from os import system\n    \n    out = system(\"python -m SoftActorCritic --trainLunarLander --testRun\")\n    \n    # Note: exit(0) <==> clean exit without any errors/problems\n    assert 0 == out, \"Agent invocated from command line exited with error {}\".format(out)\n\n\n# @pytest.mark.skip(reason=\"Problem solved\")\ndef test_SoftActorCritic_agent_train_command_line_invocation_RERUN_PASS(set_up_PWD_to_project_root):\n    from os import system\n    \n    out = system(\"python -m SoftActorCritic --trainPendulumTESTrerun --renderTraining --rerun 2\")\n    # out = system(\"python -m SoftActorCritic --trainPendulumTESTrerun --testRun --rerun 2\")\n    \n    # Note: exit(0) <==> clean exit without any errors/problems\n    assert 0 == out, \"Agent invocated from command line exited with error {}\".format(out)\n","repo_name":"RedLeader962/LectureDirigeDRLimplementation","sub_path":"DRLimplementation/tests/test_C_aSoftActorCritic/test_SoftActorCritic_from_command_line.py","file_name":"test_SoftActorCritic_from_command_line.py","file_ext":"py","file_size_in_byte":4292,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"81"}
+{"seq_id":"12231910915","text":"import vtk\n# from vtk import qt\n# qt.QVTKRWIBase = \"QGLWidget\"\nfrom vtk.qt.QVTKRenderWindowInteractor import QVTKRenderWindowInteractor\n\n\nclass VTKWidget(QVTKRenderWindowInteractor):\n    def __init__(self, parent=None, **kw):\n        QVTKRenderWindowInteractor.__init__(self, parent, **kw)\n        \"\"\"\n         # create the widget\n            widget = QVTKRenderWindowInteractor()\n            widget.Initialize()\n            widget.Start()\n            # if you don't want the 'q' key to exit comment this.\n            widget.AddObserver(\"ExitEvent\", lambda o, e, a=app: a.quit())\n        \n            ren = vtk.vtkRenderer()\n            widget.GetRenderWindow().AddRenderer(ren)\n        \n            cone = vtk.vtkConeSource()\n            cone.SetResolution(8)\n        \n            coneMapper = vtk.vtkPolyDataMapper()\n            coneMapper.SetInputConnection(cone.GetOutputPort())\n        \n            coneActor = vtk.vtkActor()\n            coneActor.SetMapper(coneMapper)\n        \n            ren.AddActor(coneActor)\n\n        \"\"\"\n        self.tracks = []\n        self.Initialize()\n        self.Start()\n        self.SetInteractorStyle(vtk.vtkInteractorStyleTrackballCamera())\n        self.ren = vtk.vtkRenderer()\n        self.actor = vtk.vtkActor()\n        self.mapper = vtk.vtkPolyDataMapper()\n        self.line_poly_data = vtk.vtkPolyData()\n\n        # all you need to reset the tracks\n        self.lines_cells = vtk.vtkCellArray()\n        self.points = vtk.vtkPoints()\n        self.colors = vtk.vtkUnsignedCharArray()\n        self.point_actors = []\n        self.text_actors = []\n\n        self.GetRenderWindow().AddRenderer(self.ren)\n        self.mapper.SetInputData(self.line_poly_data)\n\n        self.actor.SetMapper(self.mapper)\n\n        self.actor.GetProperty().SetLineWidth(3)\n        self.actor.GetProperty().SetRenderLinesAsTubes(True)\n\n        self.ren.AddActor(self.actor)\n        self.ren.SetBackground(1, 1, 1)\n\n    def add_track(self, track, updated_color = []):\n        color = updated_color if len(updated_color)> 1 else track.color\n        # color[-1] = 255\n        self.create_line(list(track.time_position_map.values()), color)\n        self.tracks.append(track)\n\n    def create_line(self, pos, color):\n        _points = pos\n        _color = color\n        _line = vtk.vtkPolyLine()\n\n        # we have _points\n        _total_points_in_track = len(_points)\n        _line.GetPointIds().SetNumberOfIds(_total_points_in_track)\n        # set color to line\n\n        self.colors.SetNumberOfComponents(4)\n\n        for index, p in enumerate(_points):\n            self.points.InsertNextPoint(p)\n            _line.GetPointIds().SetId(index, self.points.GetNumberOfPoints() - 1)\n            self.colors.InsertNextTuple(_color)\n\n        self.lines_cells.InsertNextCell(_line)\n\n    def display_points(self, time_points, track_id=None):\n        self.__clear_points()\n        colors = vtk.vtkNamedColors()\n        track = None\n        if track_id is None:\n            track = self.tracks[0]\n        else:\n            track = self.__get_track_by_id(track_id)\n            if track is None:\n                return\n\n        for t_point in time_points:\n            point = track.time_position_map.get(t_point, None)\n            if point is None:\n                continue\n            point_source = vtk.vtkSphereSource()\n            point_source.SetCenter(point)\n            point_source.SetRadius(0.2)\n            point_mapper = vtk.vtkPolyDataMapper()\n            point_mapper.SetInputConnection(point_source.GetOutputPort())\n            point_actor = vtk.vtkActor()\n            point_actor.SetMapper(point_mapper)\n            point_actor.GetProperty().SetColor(colors.GetColor3d(\"Red\"))\n            self.ren.AddActor(point_actor)\n            self.point_actors.append(point_actor)\n\n        self.render_lines()\n\n    def highlight_track(self, track):\n        # self.display_points(list(track.time_position_map.keys()), track.track_id)\n        self.__reset()\n        # self.__change_opacity(track)\n        for _track in self.tracks:\n            if _track.track_id == track.track_id:\n                color = _track.color\n                color[-1] = 255\n                self.create_line(list(_track.time_position_map.values()), color)\n            else:\n                color = _track.color\n                color[-1] = 20\n                self.create_line(list(_track.time_position_map.values()), color)\n\n        self.render_lines()\n\n    def render_lines(self):\n        self.line_poly_data.GetPointData().SetScalars(self.colors)\n        self.line_poly_data.SetPoints(self.points)\n        self.line_poly_data.SetLines(self.lines_cells)\n        self.Render()\n\n    def __get_track_by_id(self, id):\n        for t in self.tracks:\n            if t.track_id == id:\n                return t\n        return None\n\n    def __clear_points(self):\n        for a in self.point_actors:\n            self.ren.RemoveActor(a)\n            del a\n\n        for b in self.text_actors:\n            self.ren.RemoveActor(b)\n            del b\n\n    def __reset(self):\n        self.lines_cells = vtk.vtkCellArray()\n        self.points = vtk.vtkPoints()\n        self.colors = vtk.vtkUnsignedCharArray()\n","repo_name":"zeroth/cellphy","sub_path":"cellphy/tracking_analyzer_gui/VTKWidget.py","file_name":"VTKWidget.py","file_ext":"py","file_size_in_byte":5161,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"81"}
+{"seq_id":"22238401644","text":"import os\nimport glob\nimport psycopg2\nimport datetime\nimport pandas as pd\nfrom pandas import Series, DataFrame\nfrom sql_queries import *\n\n\ndef process_song_file(cur, filepath):\n    \"\"\"\n    \n    - Processes a single song file    \n    - Inserts the record into DB\n       \n    \"\"\"\n    # open song file\n    df = pd.read_json(filepath, typ = Series)\n\n    # insert song record\n    song_data = [\n        df.values[6],\n        df.values[7],\n        df.values[1],\n        df.values[9],\n        df.values[8]   \n    ]\n    cur.execute(song_table_insert, song_data)\n    \n    # insert artist record\n    artist_data = [\n        df.values[1],\n        df.values[5],\n        df.values[4],\n        df.values[2],\n        df.values[3],\n    ]\n    cur.execute(artist_table_insert, artist_data)\n\n\ndef process_log_file(cur, filepath):\n    \"\"\"\n    \n    - Processes a single log file    \n    - Inserts the time data records into DB by filtering with NextSong and converting timestamp column to datetime\n    - Inserts user data and songplay data\n    \n    \"\"\"\n    # open log file\n    df = pd.read_json(filepath, lines=True)\n\n    # filter by NextSong action\n    df = df.loc[df['page'] == \"NextSong\"]\n\n    # convert timestamp column to datetime\n    t = pd.to_datetime(df['ts'], unit='ms')\n    \n    # insert time data records\n    time_data = (t.astype('datetime64').values, t.dt.hour.values, t.dt.day.values, t.dt.week.values, t.dt.month.values, t.dt.year.values, t.dt.weekday.values)\n    column_labels = ('timestamp', 'hour', 'day', 'week', 'month', 'year', 'weekday')\n    \n    items = {}\n    for index, i in enumerate(time_data):\n        items[column_labels[index]]=i\n        \n    time_df = pd.DataFrame(items)\n\n    for i, row in time_df.iterrows():\n        cur.execute(time_table_insert, list(row))\n\n    # load user table\n    user_df = df[[\"userId\", \"firstName\", \"lastName\", \"gender\", \"level\"]]\n\n    # insert user records\n    for i, row in user_df.iterrows():\n        cur.execute(user_table_insert, row)\n\n    # insert songplay records\n    for index, row in df.iterrows():\n\n        # get songid and artistid from song and artist tables\n        cur.execute(song_select, (row.song, row.artist, row.length))\n        results = cur.fetchone()\n\n        if results:\n            songId, artistId = results\n            # insert songplay record\n            songplay_data = [datetime.datetime.fromtimestamp(row.ts / 1e3), row.userId, row.level, songId, artistId, row.sessionId, row.location, row.userAgent]\n            cur.execute(songplay_table_insert, songplay_data)\n\n\ndef process_data(cur, conn, filepath, func):\n    \"\"\"\n    \n    - Processes all files with .json extension    \n    \n    \"\"\"\n    # get all files matching extension from directory\n    all_files = []\n    for root, dirs, files in os.walk(filepath):\n        files = glob.glob(os.path.join(root,'*.json'))\n        for f in files :\n            all_files.append(os.path.abspath(f))\n\n    # get total number of files found\n    num_files = len(all_files)\n    print('{} files found in {}'.format(num_files, filepath))\n\n    # iterate over files and process\n    for i, datafile in enumerate(all_files, 1):\n        func(cur, datafile)\n        conn.commit()\n        print('{}/{} files processed.'.format(i, num_files))\n\n\ndef main():\n    \"\"\"\n    \n    - Creates and connects to the sparkifydb\n    - Process the song data and log data\n    \n    \"\"\"\n    conn = psycopg2.connect(\"\")\n    cur = conn.cursor()\n\n    process_data(cur, conn, filepath='data/song_data', func=process_song_file)\n    process_data(cur, conn, filepath='data/log_data', func=process_log_file)\n\n    conn.close()\n\n\nif __name__ == \"__main__\":\n    main()","repo_name":"PuneethaJangirL/Data-modeling-with-Postgres","sub_path":"etl.py","file_name":"etl.py","file_ext":"py","file_size_in_byte":3627,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"39685179530","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\n\"\"\" VGGSound Audio-Visual  dataset processing for  training audio-visual correlation models\"\"\"\n\nimport os\nimport sys\nimport logging\nimport re\nimport json\nimport glob\n\nfrom PIL import Image\nimport numpy as np\nimport random\n\nimport torch\nfrom torch.utils.data import Dataset\n\n#sys.path.append(\"../../..\")\n\n#root folder for vgg visual and audio data\naud_root = \"data/vggsound/audio_feats/fb_48k\"\nframe_root = \"data/vggsound/frames_1fps\"\n\ndef getFrames(f):\n    #return the frames in the video \n    frames = []\n    #Extract number of video frames from file names which have format: name_nframes. \n    #frames.append(f+\"/image_001.jpg\") #select first frame\n    video_frames = sorted(glob.glob(os.path.join(frame_root, f) + \"/*.jpg\"))\n    nframes = len(video_frames)\n    #if nframes > 1: #if video has more frames, select a few more \n    #   nf = int(nframes/2) + 1\n    for nf in range(nframes):\n          frames.append(f+\"/image_{:03}.jpg\".format(nf+1))\n    assert(len(set(frames)) == len(frames)) #ensure the frame list has no duplicates\n    #print(\"number of samples=%d\"%(len(frames)))\n    return frames\n\ndef getCorAud(img_path):\n     #get the audio file  and starting frame number\n     afile = img_path.split(\"/\")\n     fn = afile[-1]  #get visual frame number\n     fn, _ = os.path.splitext(fn)  #remove image extension\n     #audio frame starts from 0, vframe starts from 1. So aframe = vframe -1\n     fn = int(fn.rsplit(\"_\") [1])-1  \n     #fn = 0\n     afile = \"/\".join(afile[:-1]) + \"_\" + str(fn)    \n     return afile\n \ndef getDataList(v2a_map):\n    \"\"\" Get the data list from the video to pos/neg aud mapping\"\"\"\n    \n    #video to audio mapping\n    with open(v2a_map, \"r\")  as fp:\n       v2a = json.load(fp)\n    data_list = []   \n    for k, v in v2a.items():\n        sample = {\"vis\": k, \"label\": v[\"label\"]}\n        data_list.append(sample)\n\n    return data_list\n\n\nclass VGGSound(Dataset):\n    \n    \"\"\" Iterates over the game data frame file list and returns the data necessary for \n        training audio-visual correlation models.\n    \"\"\"\n    def __init__(self, conf, data_file, vis_transform):\n       \n       self.conf = conf\n       self.vis_transform = vis_transform\n       self.aud_transform = self.getAudTransform(self.conf.mean_norm)\n\n       \n       #list of frames and corresponding audio label (single label for pairwise sim)\n       self.data_list = getDataList(data_file)\n       \n       # Map visual files to labels consisting of sfx file indices \n       #self.filterFrames(frames, labels)       \n       print(\"loaded labels2vid:{}\".format(len(self.data_list))) \n       logging.info(\"loaded vis2aud_idx:{}\".format(len(self.data_list)))  \n\n       np.random.seed(144)\n       random.seed(144)\n       \n    def filterFrames(self):\n        \"\"\" Get a subset of the data\"\"\"\n\n       \n        #limit the training data\n        if (not self.conf.demo) and len(self.frames) > 150000:      \n          #select a smaller subset  \n          random.seed(144)\n          #print(len(self.frames))\n          #rind = random.sample(range(len(self.frames)), 1500)  \n          rind = random.sample(range(len(self.frames)), 150000)  \n          self.frames = [self.frames[i] for i in rind]\n          self.labels = [self.labels[i] for i in rind]\n\n        print(\"Reduced num frames = {}. num labels={}\".format(len(self.frames), len(self.labels)))\n\n        return   \n\n       \n    def __len__(self):\n       \"\"\" return length of the dataset \"\"\"\n      \n       return len(self.data_list)\n   \n\n    def globalContrastNorm(self, img):\n        \"\"\" global contrast pixel normalization \"\"\"\n        \n        eps = 1e-09\n        lmda = 5\n        s = 1\n        img_res = img.resize((224, 224))\n        x = np.array(img_res)\n        x_avg = np.mean(x)\n    \n        x_new = x - x_avg\n        contrast = np.sqrt(lmda + np.mean(x_new**2))\n        new_img = s*x_new/ max(contrast, eps)\n        new_img = np.transpose(new_img, (0,2,1))\n        new_img = np.transpose(new_img, (1,0,2))\n        new_img = torch.from_numpy(new_img).float()\n        return new_img\n    \n    def getVisFrame(self, img_path):\n       #return video frame given the path \n        \n       \n       ip = os.path.join(frame_root, img_path)\n       x = Image.open(ip)\n       #print(img_path, \"original shape=\", x.shape)\n       #x = self.globalContrastNorm(x)\n       \n       #apply data transform if required\n       if self.vis_transform is not None:\n       #   #print(x.size)\n          x = self.vis_transform(x)\n       return x   \n     \n    def getAudTransform(self, mean_norm):\n        \"\"\" Get audio transform parameters, if required \"\"\"\n        \n        params = {} \n        params[\"aud_frames\"] = self.conf.aud_frames\n        \n        #mean normalization  for noisy audio\n        if mean_norm:\n            #normalize, if filterbank audio features are used as labels\n            afeat = self.conf.getTagFeat()\n            if afeat.startswith(\"fb_vad\"):\n               #load the precomputed mean and variance for the training features \n               mv = (np.load(\"lists/vggsound_train_mean_var.npy\"))\n        else:\n            mv = None\n        if mv is not None:\n                  params[\"mean\"] = mv[0]\n                  eps = 1e-8\n                  params[\"std\"] = np.sqrt(mv[1]) + eps\n   \n        return params\n\n    \n    def transformAud(self, afeat, params):\n        \"\"\" \n            If the number of feature frames is < 100, we pad by replicating the \n            features so that the number of frames = 100. This is to ensure \n            that we can use  1 sec of audio feature for the clean label \n            (1 sec = ~94 frames).\n            If the audio is longer than 1 sec, a random 1 sec sample is extracted.\n            Audio feature is then normalized using training data's mean and var.\n            Features are transposed and expanded to get a channel first format.\n        \"\"\"\n        \n        \n        mod_feat = afeat\n        #print(\"afeat shape\", afeat.shape)\n        if params is not None:\n          nframes = params[\"aud_frames\"] #default= 100, corresponds to 1 sec audio\n          #restrict window length to 1 sec for training. No restriction during evaluation or embedding generation.\n          if nframes is not None: \n            #pad by wrapping the features.   #smallest number of audio frames = 47\n            if afeat.shape[0] < nframes:\n              mod_feat = np.pad(mod_feat,((0, nframes-afeat.shape[0]), (0, 0)), mode='wrap')\n              #print(\"padding\")\n              #   mod_feat = np.concatenate((mod_feat, afeat), axis=0)\n            elif afeat.shape[0] > nframes: #pick a random 1sec audio from the clean sfx if it is longer than 1 sec.\n              #start_idx = np.random.choice(range(0, (afeat.shape[0]-nframes+1)))\n              #mod_feat = mod_feat[start_idx:start_idx+nframes,:]  \n              mod_feat = mod_feat[:nframes,:]  #first nframes to make the model predictable\n          if \"mean\" in params.keys():    \n             #normalize the audio sample. #standard normalization\n             mod_feat = (mod_feat - params[\"mean\"])/params[\"std\"] \n             #print(mod_feat)\n        mod_feat = mod_feat.astype(float)     \n        #transpose to (feature dim X num frames) format\n        mod_feat = np.transpose(mod_feat)\n        #expand dim to get (channel X feature dim X num frames) format. channel=1.\n        #This will allow the audio data to be input to a 2D conv net.\n        mod_feat =  np.expand_dims(mod_feat, axis=0)  \n        return mod_feat                 \n         \n    \n    def getAudioLabel(self, afile, frame_num=0):\n       \"\"\" Get the audio label features \"\"\"\n       \n       \n       afeat = self.conf.getTagFeat()\n       if afeat.startswith(\"fb_vad\"):\n          y = np.load(afile)\n          #print(afile, y.shape)\n          #entire video in vggsound data has the same audio label. So instead of \n          #just extracting 1 sec audio corresponding to the video frame, we average the sound features \n          #over the entire video duration (frame_num = video duration)\n          start = frame_num*100\n          if start > y.shape[0]:\n              start = 0 * 100 #get the first 1 sec if audio is shorter then the video length\n          y = y[start:] #start+frame_num*100]\n          #print(afile, frame_num, y.shape)\n          #avg_1sec = np.zeros((100, y.shape[1]))\n          #for i in range(frame_num):\n          #    avg_1sec = np.sum(avg_1sec, y[i*100: (i+1)*100])\n          #audio features with fixed frame length, normalized, transposed, and expanded to 3D.  \n          y = self.transformAud(y, self.aud_transform)\n         \n       return y\n\n    def getNegativeLabel(self, vid, lab):\n      \"\"\" Get a negative label (audio unrelated to video)\"\"\"\n\n      #get all the labels that dont match the video label\n      not_labs = list(set(list(self.labels2vid.keys())) - set([lab]))\n      #pick a random label from the complement      \n      neg = np.random.choice(not_labs)\n      #get the videos having this negative label\n      not_labs = self.labels2vid[neg]\n      #pick a random video from the complement list      \n      neg = np.random.choice(not_labs)\n      #extract name and frame number\n      #print(\"neg\", neg)\n      #neg, fn = neg.rsplit(\"_\", 1)\n      fn = 0 #get 1 sec audio from start (can also be a random 1sec audio)\n      #get the audio path for the selected video\n      neg_file = os.path.join(aud_root, neg)+\".npy\" \n   \n      return neg_file, fn\n   \n    \n      \n    def __getitem__(self, n):\n       \"\"\" Return a (single visual frame, matching audio for positive label, visual frame filename, label) tuple \n       \"\"\"  \n       element = self.data_list[n]\n       video = element[\"vis\"]\n       vframes = glob.glob(os.path.join(frame_root, video) + \"/*.jpg\")\n       #pick a random video frame for the video       \n       img_path = np.random.choice(vframes) \n       x = self.getVisFrame(img_path)\n       #print(element, img_path)\n       \n       #get corresponding audio frame\n       frame = img_path.split(\"/\")[-1]\n       afn = getCorAud(video+ \"/\" + frame)\n       afn = afn.rsplit(\"_\")\n       #print(\"afn split\", afn) \n       afile =  os.path.join(aud_root,\"_\".join(afn[:-1])) +\".npy\"      \n       fn=int(afn[-1])\n       #print(\"afile\", afile, \"fn\", fn) \n       assert (os.path.exists(afile))\n       y = self.getAudioLabel(afile, fn) \n       afile = os.path.splitext(afile)[0] + \"_%s.npy\"%(str(fn)) \n       #print(afile)\n        \n       sample = {\"vis\": x, \"aud\":y, \"v_fn\": img_path, \"a_fn\": afile, \"label\": element[\"label\"]}  \n          \n       #print(sample)\n              \n       return sample\n   \n    \n    \n","repo_name":"athena913/aud_vis_correlation_vggsound","sub_path":"data/vggsound_dataset_contrast.py","file_name":"vggsound_dataset_contrast.py","file_ext":"py","file_size_in_byte":10574,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"25472314269","text":"from lukefi.metsi.data.model import ReferenceTree, TreeStratum\nfrom lukefi.metsi.forestry.forestry_utils import find_matching_stratum_by_diameter\n\nSTRATUM_SUPPLEMENT = 1\nINITIAL_TREE_SUPPLEMENT = 2\nSAME_TREE_DIAMETER_SUPPLEMENT = 3\nSAME_TREE_D13_AGE_SUPPLEMENT = 4\n\n\nclass SupplementStrategy:\n    def __init__(self, rt: ReferenceTree):\n        self.reference_tree_id: str = rt.identifier\n        self.solved: bool = False\n        self.strategy: int = None\n        self.tree_identifier: str = None\n\n\ndef perform_supplementing(tree_and_strategy: list[tuple[ReferenceTree, SupplementStrategy]],\n                          age_trees: list[ReferenceTree],\n                          age_stratums: list[TreeStratum]) -> list[ReferenceTree]:\n    for (rt, s) in tree_and_strategy:\n        if s.strategy is STRATUM_SUPPLEMENT:\n            same_species_strata = [\n                stratum for stratum in age_stratums if stratum.species == rt.species and stratum.has_diameter()\n            ]\n            stratum = find_matching_stratum_by_diameter(rt, same_species_strata)\n            rt.breast_height_age = stratum.breast_height_age\n            rt.biological_age = stratum.biological_age\n        elif s.strategy is INITIAL_TREE_SUPPLEMENT:\n            supplement_tree = next((tree for tree in age_trees if tree.identifier == s.tree_identifier))\n            rt.breast_height_age = supplement_tree.breast_height_age\n            rt.biological_age = supplement_tree.biological_age\n        elif s.strategy is SAME_TREE_DIAMETER_SUPPLEMENT:\n            rt.breast_height_age = 2 * rt.breast_height_diameter\n            rt.biological_age = 9 + 2 * rt.breast_height_diameter\n        elif s.strategy is SAME_TREE_D13_AGE_SUPPLEMENT:\n            rt.breast_height_age = 2 * rt.breast_height_diameter\n            # Q: bio age calculation is done with 2*diameter as breast_height_age is this correct?\n            rt.biological_age = rt.breast_height_age + 9\n        else:\n            raise UserWarning('error: no supplementing strategy for tree id ' + str(rt))\n    reference_trees = [i[0] for i in tree_and_strategy]\n    return reference_trees\n\n\ndef final_tree_strategy(reference_tree):\n    \"\"\" Final strategy for trees that have no age.\n\n    param: reference_tree: Tree with no age\n    \"\"\"\n    strategy = SupplementStrategy(reference_tree)\n    if not reference_tree.has_biological_age():\n        strategy.solved = True\n        strategy.strategy = SAME_TREE_DIAMETER_SUPPLEMENT\n        return strategy\n    if not reference_tree.has_height_over_130_cm():\n        strategy.solved = True\n        strategy.strategy = SAME_TREE_D13_AGE_SUPPLEMENT\n        return strategy\n    return strategy\n\n\ndef tree_strategy(reference_tree: ReferenceTree, age_trees: list[ReferenceTree]):\n    \"\"\" Secondary startegy for trees that have no age\n\n    param: reference_tree: Tree with no age\n    param: age_trees: Subset of trees that have age\n    \"\"\"\n    strategy = SupplementStrategy(reference_tree)\n    for age_tree in age_trees:\n        if reference_tree.compare_species(age_tree):\n            strategy.solved = True\n            strategy.strategy = INITIAL_TREE_SUPPLEMENT\n            strategy.tree_identifier = age_tree.identifier\n            return strategy\n    return strategy\n\n\ndef stratum_strategy(reference_tree: ReferenceTree, stratums: list[TreeStratum]):\n    \"\"\" Default strategy for trees that do not have age\n\n    param: reference_tree: Tree which does not yet have a age\n    param: startums: Subset of stratums that have age\n    \"\"\"\n    strategy = SupplementStrategy(reference_tree)\n    for stratum in stratums:\n        if reference_tree.compare_species(stratum) and stratum.has_diameter():\n            strategy.solved = True\n            strategy.strategy = STRATUM_SUPPLEMENT\n            return strategy\n    return strategy\n\n\ndef solve_supplement_strategy(no_age_trees: list[ReferenceTree],\n                              age_trees: list[ReferenceTree],\n                              age_stratums: list[TreeStratum]) -> list[SupplementStrategy]:\n    \"\"\" Solveing a supplement strategy happens in a priority order in which stratum strategy\n    is with highest priority and using same same tree to supplement the lowest.\"\"\"\n    supplement_strategies = []\n    for rt in no_age_trees:\n        strategy = stratum_strategy(rt, age_stratums)\n        if not strategy.solved:\n            strategy = tree_strategy(rt, age_trees)\n        if not strategy.solved:\n            strategy = final_tree_strategy(rt)\n        if strategy.solved:\n            supplement_strategies.append((rt, strategy))\n        else:\n            raise UserWarning('error: supplement strategy for tree number' + str(rt.identifier) + ' can not be solved')\n    return supplement_strategies\n\n\ndef supplement_age_for_reference_trees(reference_trees: list[ReferenceTree],\n                                       stratums: list[TreeStratum]) -> list[ReferenceTree]:\n    \"\"\" Supplementing of reference trees that have no d13 age.\n    Supplementing happens from subsets of stratums and trees that have d13 age.\n    Based on a priority a strategy to supplement is selected and supplementing is performed.\n    \"\"\"\n    no_age_trees = list(filter(lambda t: t.breast_height_age is None and t.has_height_over_130_cm(), reference_trees))\n    age_trees = list(filter(lambda t: t.breast_height_age or 0 > 0.0, reference_trees))\n    age_stratums = list(filter(lambda s: s.breast_height_age > 0.0, stratums))\n    trees_and_strategies = solve_supplement_strategy(no_age_trees, age_trees, age_stratums)\n    return perform_supplementing(trees_and_strategies, age_trees, age_stratums)\n    # TODO: Remove zero stem stratums. See vmi-data-converter issue #55.\n","repo_name":"lukefi/metsi","sub_path":"lukefi/metsi/forestry/preprocessing/age_supplementing.py","file_name":"age_supplementing.py","file_ext":"py","file_size_in_byte":5662,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"81"}
+{"seq_id":"12794559388","text":"import _thread\nimport time\nfrom threading import Thread, Lock\n\nimport datetime\nfrom pymodbus.client.sync import ModbusTcpClient\nfrom pymodbus.datastore import ModbusSlaveContext, ModbusServerContext\nfrom pymodbus.datastore import ModbusSparseDataBlock\nfrom pymodbus.server.sync import ModbusTcpServer\n\nfrom building import Building\n\ntime_address = 0\nready_flag_address = 0\nt_o_address = 100\nt_zco_address = 200\nt_cob_address = 422\nf_cob_address = 420\nub_address = 424\ntr_address = 426\nCONTROLLER_IP = '192.168.1.111'\nLOGGER_IP = '192.168.1.222'\nOWN_IP = '192.168.1.202'\nPORT = 5555\nDT = 60\nmutex = Lock()\n\n\n# example function registered after as a time callback\ndef step(time_start_arg):\n\tmutex.acquire()\n\ttime_start = datetime.datetime.now()\n\tprint(\"\\n Start:\" + str(time_start))\n\tbuilding.building_simulation_step(server.get_time(), DT, float(server.get_t_zco_from_registers()[0]) / 100,\n\t\t\t\t\t\t\t\t\t  float(server.get_t_o_from_registers()[0]) / 100)\n\t# building.building_simulation_step(server.get_time(), DT, 40000.0 / 100, 27300.0 / 100)\n\tprint(\"Sending updates\")\n\ttry:\n\t\tlogger.send_update(building)\n\texcept:\n\t\tprint(\"Error with sending data to Logger\")\n\ttry:\n\t\twater_flow_controller.send_update(building)\n\texcept:\n\t\tprint(\"Error with sending data to Water Flow Controller\")\n\tprint(building)\n\tserver.set_ready_flag()\n\ttime_end = datetime.datetime.now()\n\tprint(\"End: \" + str(time_end))\n\tprint(\"Duration of step: \" + str(time_end - time_start_arg))\n\tmutex.release()\n\n\nclass Receiver(ModbusTcpClient):\n\tdef __init__(self, host, port, name):\n\t\tsuper(Receiver, self).__init__(host, port)\n\t\tself.name = name\n\t\tself.initialization = True\n\n\tdef connected(self):\n\t\treturn self.socket\n\n\tdef get_name(self):\n\t\treturn self.name\n\n\nclass WaterFlowController(Receiver):\n\tdef __init__(self):\n\t\tsuper(WaterFlowController, self).__init__(CONTROLLER_IP, PORT, 'WaterFlowController')\n\n\tdef send_update(self, building_arg):\n\t\t# super(WaterFlowController, self).write_registers(t_cob_address, [int(building_arg.t_cob * 100), 0,\n\t\t# \t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t int(building_arg.f_cob * 1000000)])\n\n\t\tsuper(WaterFlowController, self).write_register(t_cob_address, int(building_arg.t_cob * 100))\n\t\tsuper(WaterFlowController, self).write_register(f_cob_address, int(building_arg.f_cob * 1000000))\n\n\nclass Logger(Receiver):\n\tdef __init__(self):\n\t\tsuper(Logger, self).__init__(LOGGER_IP, PORT, 'Logger')\n\n\tdef send_update(self, building_arg):\n\t\t# super(Logger, self).write_registers(t_cob_address,\n\t\t# \t\t\t\t\t\t\t\t\t[int(building_arg.t_cob * 100), 69, int(building_arg.f_cob * 1000000), 69,\n\t\t# \t\t\t\t\t\t\t\t\t int(building_arg.ub * 100), 69, int(building_arg.t_ro * 100)])\n\t\t# print([int(building_arg.t_cob * 100), 69, int(building_arg.f_cob * 1000000), 6969,\n\t\t# \t   int(building_arg.ub * 100), 0, int(building_arg.t_ro * 100)])\n\n\t\tsuper(Logger, self).write_register(t_cob_address, int(building_arg.t_cob * 100))\n\t\tsuper(Logger, self).write_register(f_cob_address, int(building_arg.f_cob * 1000000))\n\t\tsuper(Logger, self).write_register(ub_address, int(building_arg.ub * 100))\n\t\tsuper(Logger, self).write_register(tr_address, int(building_arg.t_ro * 100))\n\n\nclass CoilsDataBlock(ModbusSparseDataBlock):\n\tdef __init__(self, values):\n\t\tsuper(CoilsDataBlock, self).__init__(values)\n\t\tself.time_flag_callback = None\n\n\tdef setValues(self, address, values):\n\t\tsuper(CoilsDataBlock, self).setValues(address, values)\n\t\tif address == ready_flag_address:\n\t\t\tif values == [False]:\n\t\t\t\t_thread.start_new_thread(self.time_flag_callback, (datetime.datetime.now(),))\n\n\tdef set_time_flag_callback(self, function):\n\t\tself.time_flag_callback = function\n\n\nclass HoldingRegisterDataBlock(ModbusSparseDataBlock):\n\tdef __init__(self, values):\n\t\tsuper(HoldingRegisterDataBlock, self).__init__(values)\n\t\tself.timestamp = 0\n\t\tself.time_callback = None\n\n\tdef setValues(self, address, values):\n\t\tsuper(HoldingRegisterDataBlock, self).setValues(address, values)\n\t\tif address == time_address:\n\t\t\tself.timestamp = self.__calculate_timestamp__(values)\n\n\tdef get_time(self):\n\t\treturn self.timestamp\n\n\t@staticmethod\n\tdef __calculate_timestamp__(register_values):\n\t\ttime1 = register_values[0]\n\t\ttime2 = register_values[1]\n\t\treturn (time1 << 16) | time2\n\n\nclass Server:\n\tdef __init__(self, address, port):\n\t\tself.holding_register_block = HoldingRegisterDataBlock.create()\n\t\tself.coil_block = CoilsDataBlock.create()\n\t\tself.store = ModbusSlaveContext(hr=self.holding_register_block, co=self.coil_block, zero_mode=True)\n\t\tself.context = ModbusServerContext(slaves=self.store, single=True)\n\t\tself.server = ModbusTcpServer(self.context, address=(address, port))\n\t\tself.thread = Thread(target=self.__run_thread__, args=())\n\t\tself.holding_register_block.setValues(t_o_address, 277)\n\t\tself.holding_register_block.setValues(t_zco_address, 400)\n\n\tdef __run_thread__(self):\n\t\tself.server.serve_forever()\n\n\tdef run(self):\n\t\tself.thread.start()\n\n\tdef stop(self):\n\t\tself.server.server_close()\n\t\tself.server.shutdown()\n\t\tself.thread.join()\n\n\tdef get_holding_register(self, index):\n\t\treturn self.holding_register_block.getValues(index, 1)\n\n\tdef set_time_flag_callback(self, function):\n\t\tself.coil_block.set_time_flag_callback(function)\n\n\tdef set_ready_flag(self):\n\t\tself.coil_block.setValues(ready_flag_address, True)\n\n\tdef get_ready_flag(self):\n\t\treturn self.coil_block.getValues(ready_flag_address, 1)\n\n\tdef get_time(self):\n\t\treturn self.holding_register_block.get_time()\n\n\tdef get_t_o_from_registers(self):\n\t\treturn self.holding_register_block.getValues(t_o_address, 1)\n\n\tdef get_t_zco_from_registers(self):\n\t\treturn self.holding_register_block.getValues(t_zco_address, 1)\n\n\nif __name__ == '__main__':\n\t# start new server\n\tserver = Server('', PORT)\n\t# add a callback function which will be called with new timestamp each time new time is received\n\tserver.set_time_flag_callback(step)\n\t# run the server\n\tserver.run()\n\tprint('Server is running')\n\n\twater_flow_controller = WaterFlowController()\n\tlogger = Logger()\n\tbuilding = Building()\n\tclose = False\n\twhile not close:\n\t\ttry:\n\t\t\ttime.sleep(10)\n\t\t\tinput_str = input()\n\t\t\tif input_str == 'close':\n\t\t\t\tclose = True\n\t\texcept KeyboardInterrupt:\n\t\t\tclose = True\n\n\tprint('Server is closing')\n\t# remember to stop server while closing your application\n\tserver.stop()\n\n\tprint('finished!')\n","repo_name":"Dilong-D/modbus_multimaster_tcp_ip_building","sub_path":"modbus_multimaster.py","file_name":"modbus_multimaster.py","file_ext":"py","file_size_in_byte":6235,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"5636445258","text":"#!/usr/bin/env python3\n#\n# problem statement:\n#\n#   If we list all the natural numbers below 10 that are multiples of 3 or 5, we get 3, 5, 6 and 9.\n#   The sum of these multiples is 23.\n#\n#   Find the sum of all the multiples of 3 or 5 below 1000.\n#\n\n#import pprint           # pprint.pprint([\"hello\", \"world\"])\n\nsum = 0\nfor x in range(3, 1000):\n    if x % 3 == 0 or x % 5 == 0:\n        sum += x\n\nprint(\"the total is: \", sum)\n","repo_name":"DeeNewcum/learn_python","sub_path":"project_euler/pe1.py","file_name":"pe1.py","file_ext":"py","file_size_in_byte":426,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"42119927942","text":"def check_eligibility(age, years_cit):\n\tif age >= 30 and years_cit >= 9:\n\t\tprint(\"This person is eligible to be a US Senator or Representative\")\n\telif age >= 25 and years_cit >= 7:\n\t\tprint(\"This person is eligible to be a US Representative\")\n\telse:\n\t\tprint(\"This person is not eligible to be a Senator or a Representative\")\n\ndef main():\n\tage = int(input(\"How old is this person?\"))\n\tyearsofcit = int(input(\"How long has this person been a citizen?\"))\n\tcheck_eligibility(age, yearsofcit)\n\nmain()","repo_name":"rabisnath/CAAP-CS","sub_path":"Exercises/Ch 7/number8.py","file_name":"number8.py","file_ext":"py","file_size_in_byte":494,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"39623309528","text":"# from re import S\n# from unittest import result\n\n# 나무의수 n, 나무의길이 m\nn, m = list(map(int, input().split(' ')))\n\n# 각 나무의 개별 높이 정보를 입력\narray = list(map(int, input().split()))\n\n# 이진 탐색을 위한 시작점과 끝점 설정\nstart = 0\nend = max(array)\n\n# 이진 탐색 수행 (반복적) #H가 높아지면 가져가는 나무의 길이가 낮아지고 H가 낮아지면 가져가는 나무의 길이가 길어진다.\nresult = 0\nwhile(start <= end):\n    total = 0    # 가져갈 나무의길이\n    mid = (start + end) // 2\n    for x in array:\n        #잘랐을 때의 나무길이의 총합 계산\n        if x > mid:\n            total += x - mid\n        #나무길이가 부족할 경우 더 많이 자르기 (왼쪽부분 탐색)\n    if total < m :\n        end = mid -1\n    #나무길이가 충분 충분할 경우 덜 자르기 (오른쪽 부분 탐색)\n    else:\n        result = mid # 최대한 덜 잘랐을 때가 정답이므로, 여기에서 result에 기록\n        start = mid + 1\n    \n# end start mid 마지막 서클 전 확인 해보기 \n \n# 정답출력\nprint(result)\n\n\n# # 반복문\n# def binary_search(arr,val):\n#     first, last = 0, arr.len()\n#     while first <=last:\n#         mid = (first + last) // 2\n#         if arr[mid] == val: return mid\n#         if arr[mid] > val: last -mid -1\n#         else: first = mid +1\n#     return -1\n\n# # 재귀\n# def binary_search(arr, target, low = None, high =None):\n#     low, high = low or 0, high or len(arr) -1\n#     if low > high:\n#         return -1\n#     mid = (low + high) // 2\n#     if arr[mid] > target:\n#         return binary_search(arr, target, low, mid)\n#     if arr[mid] == target:\n#         return mid\n#     if arr[mid] < target:\n#         return binary_search(arr, target, mid + 1,)\n\n","repo_name":"Gi-Youn-Oh/jungle-week02","sub_path":"Gi-youn/week02/2_2805나무자르기.py","file_name":"2_2805나무자르기.py","file_ext":"py","file_size_in_byte":1805,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"24378708216","text":"from parse_query import Parse\nimport pandas as pd\nimport utils\nimport numpy as np\nfrom vis.Rank import find_rank_render\nfrom pandas.api.types import is_string_dtype\nfrom pandas.api.types import is_numeric_dtype\nfrom builtins import list\n\ndef rank_answer(query,table_path):\n  #Answer questions about rank\n  option={}\n  table=pd.read_csv(table_path)\n  Parse_instance = Parse(data_url=table_path)\n  respon = Parse_instance.analyze_query(query)\n  taskMap = respon.get('taskMap')\n  y_label = taskMap.get('rank')[0].get('attributes')[0]\n  if len(taskMap) >= 2:\n      x_label = \"\"\n      Filter = taskMap.get('filter')\n      F = \"\"\n      for filter in Filter:\n          filter_Phrase = filter.get(\"queryPhrase\")\n          filter_opertaor = filter.get(\"operator\")\n          values = filter.get(\"values\")\n          filter_attributes = filter.get(\"attributes\")[0]\n          if filter_opertaor == \"EQ\" and is_string_dtype(table[filter_attributes]):\n              table[filter_attributes] = table[filter_attributes].str.lower()\n              table = table[table[filter_attributes].str.contains(values.lower())]\n              F = F + filter_Phrase + \" \" + values + \" \" + filter_attributes\n          elif filter_opertaor == \"EQ\" and is_numeric_dtype(table[filter_attributes]):\n              table[filter_attributes] = table[filter_attributes].astype(int)\n              table = table[table[filter_attributes] == int(values)]\n              F = F + filter_Phrase + \" \" + values + \" \" + filter_attributes\n              x_label = filter_attributes\n          elif filter_opertaor == \"LT\" and is_numeric_dtype(table[filter_attributes]):\n              table[filter_attributes] = table[filter_attributes].astype(int)\n              table = table[table[filter_attributes] <= int(values)]\n              F = F + filter_Phrase + \" \" + values + \" \" + filter_attributes\n              x_label = filter_attributes\n          elif filter_opertaor == \"GT\" and is_numeric_dtype(table[filter_attributes]) and filter_Phrase != \"over\":\n              table[filter_attributes] = table[filter_attributes].astype(int)\n              table = table[table[filter_attributes] >= int(values)]\n              F = F + filter_Phrase + \" \" + values + \" \" + filter_attributes\n              x_label = filter_attributes\n          elif filter_Phrase != \"over\":\n            x_label = filter_attributes\n          elif filter_attributes == \"RANGE\" and is_numeric_dtype(table[filter_attributes]):\n              table[filter_attributes] = table[filter_attributes].astype(int)\n              table = table[\n                  (table[filter_attributes] <= int(values[0])) & (table[filter_attributes] >= int(values[1]))]\n              F = F + filter_Phrase + \" \" + values[0] + \" and \" + values[1] + \" \" + filter_attributes\n              x_label = filter_attributes\n      x = []\n      y = list(table[y_label])\n      if len(x_label) == 0:\n          Label = []\n          for attribute in table.columns:\n              if attribute.lower() in utils.time:\n                  Label.append(attribute)\n          for attribute in table.columns:\n              if attribute.lower() not in utils.time and is_string_dtype(table[attribute]):\n                  Label.append(attribute)\n          if len(Label)!=0:\n              x_label = Label[0]\n              x = list(table[x_label])\n          else:\n              x_label = \"Serial\"\n              for i in range(len(y)):\n                  x.append(i + 1)\n      else:\n          x = list(table[x_label])\n      y_rank = np.array(y)\n      index = np.argsort(y_rank)\n      X = []\n      Y = []\n      for i in range(len(index)):\n         if x_label!=\"Serial\":\n           X.append(x[index[i]])\n         else:\n           X.append(i+1)\n         Y.append(y[index[i]])\n      answer=\"\"\n      answer+=\"In \"+y_label+\": \"+\"the max value is \"+str(Y[0])+\", the min value is \"+str(Y[len(Y)-1])+\" \"+F\n      option = find_rank_render(X, Y, query, table_path,answer)\n  elif len(taskMap)==1:\n    rank_attribute = taskMap.get('rank')[0].get('attributes')[0]\n    rank_queryPhrase = taskMap.get('rank')[0].get('queryPhrase')\n    y = list(table[rank_attribute])\n    y_rank = np.array(y)\n    index = np.argsort(y_rank)\n    Y = []\n    for i in range(len(index)):\n      Y.append(y[index[i]])\n    x_attributes=[]\n    table_attributes = table.columns\n    for attribute in table_attributes:\n      if attribute.lower() in utils.time:\n        x_attributes.append(attribute)\n      elif is_string_dtype(table[attribute]) or attribute.lower() in utils.time:\n        x_attributes.append(attribute)\n    X=[]\n    for attribute in x_attributes:\n      X.append(list(table[attribute]))\n    if len(X)==0:\n      for i in range(len(Y)):\n        X.append(i+1)\n      answer = \"\"\n      answer += \"In \" + y_label + \": \" + \"the max value is \" + str(Y[0]) + \", the min value is \" + str(\n        Y[len(Y) - 1])\n      option=find_rank_render(X, Y, query, table_path,answer)\n    else:\n      answer = \"\"\n      answer += \"In \" + y_label + \": \" + \"the max value is \" + str(Y[0]) + \", the min value is \" + str(\n        Y[len(Y) - 1])\n      option=find_rank_render(X[0], Y, query, table_path,answer)\n  return {\"option\":option,\"attributes\":[y_label],\"task\":\"rank\"}","repo_name":"jiangqicd/DT2VIS","sub_path":"dt2vis/answerRank.py","file_name":"answerRank.py","file_ext":"py","file_size_in_byte":5167,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"81"}
+{"seq_id":"3810443581","text":"import numpy as np\r\nimport pygad\r\nimport pyswarms as ps\r\nfrom pyswarm import pso\r\nfrom sys import argv\r\nimport indicators as ind\r\nfrom symbols import *\r\nimport multiprocessing as mp\r\n\r\nyears = 10\r\n\r\n#end = 20211204\r\nend = 20221013\r\nstart = end - years*10000\r\n\r\ndef normalize():\r\n   def rd(n):\r\n      return str(round(n/1000000,1))\r\n   w = open(\"data/USA500IDXUSD1.degap.csv\",'w')\r\n   r = open('data/USA500IDXUSD1.csv','r')\r\n   r.readline()\r\n   while True: \r\n      d,t,o,h,l,c,a,v = ind.parseLine(r,1)\r\n      if d==-1: break\r\n      w.write(str(d)+\",\"+t+\",\"+rd(o)+\",\"+rd(h)+\",\"+rd(l)+\",\"+rd(c)+\",\"+rd(a)+\",\"+str(v)+\"\\n\")\r\n   w.close()\r\n   r.close()\r\n\r\ndef read_data():\r\n   r = open('data/USA500IDXUSD1.degap.csv','r')\r\n   r.readline()\r\n   d=0\r\n   result = []\r\n   while d<start: \r\n      d,t,o,h,l,c,a,v = ind.parseLine(r,1)\r\n   while True:\r\n      d,t,o,h,l,c,a,v = ind.parseLine(r,1)\r\n      if d==-1: break\r\n      dd = [d,t,o,h,l,c]\r\n      result.append(dd)\r\n   return result\r\n\r\ndata= read_data()\r\n\r\ndef martingale(x):\r\n   def buy_empty():\r\n      nonlocal t\r\n      for tt in t:\r\n         if tt['typ']==\"B\": return False\r\n      return True\r\n\r\n   def sell_empty():\r\n      nonlocal t\r\n      for tt in t:\r\n         if tt['typ']==\"S\": return False\r\n      return True\r\n\r\n   def buy_first():\r\n      nonlocal t\r\n      t.append({'typ':\"B\", 'price':o+spread/2, 'lot':max(0.1,round(b*risk,1))})\r\n\r\n   def sell_first():\r\n      nonlocal t\r\n      t.append({'typ':\"S\", 'price':o-spread/2, 'lot':max(0.1,round(b*risk,1))})\r\n\r\n   def close_all_buy():\r\n      nonlocal t,b\r\n      ttt=[]\r\n      for tt in t:\r\n         if tt['typ']==\"S\": ttt.append(tt)\r\n      t=ttt\r\n      b=b+tbp\r\n\r\n   def close_all_sell():\r\n      nonlocal t,b\r\n      ttt=[]\r\n      for tt in t:\r\n         if tt['typ']==\"B\": ttt.append(tt)\r\n      t=ttt\r\n      b=b+tsp\r\n\r\n   def lowest_buy():\r\n      nonlocal t\r\n      min=999999\r\n      v=0\r\n      for tt in t:\r\n         if tt['typ']==\"B\" and tt['price']<min: \r\n            min = tt['price']\r\n            v = tt['lot']\r\n      return [min,v]\r\n\r\n   def highest_sell():\r\n      nonlocal t\r\n      max=-999999\r\n      v=0\r\n      for tt in t:\r\n         if tt['typ']==\"S\" and tt['price']>max: \r\n            max = tt['price']\r\n            v = tt['lot']\r\n      return [max,v]\r\n\r\n   def buy_next(v):\r\n      nonlocal t\r\n      t.append({'typ':\"B\", 'price':o+spread/2, 'lot':max(0.1,round(v*exp,1))})\r\n\r\n   def sell_next(v):\r\n      nonlocal t\r\n      t.append({'typ':\"S\", 'price':o-spread/2, 'lot':max(0.1,round(v*exp,1))})\r\n\r\n   def total_buy_profits():\r\n      nonlocal t,o\r\n      p = 0\r\n      for tt in t:\r\n         if tt['typ']==\"B\": p = p + tt['lot'] * ((o-spread/2)-tt['price'])\r\n      return p\r\n\r\n   def total_sell_profits():\r\n      nonlocal t,o\r\n      p = 0\r\n      for tt in t:\r\n         if tt['typ']==\"S\": p = p + tt['lot'] * (tt['price']-(o+spread/2))\r\n      return p\r\n\r\n   global data\r\n   risk = x[0]\r\n   distance = x[1]\r\n   exp = x[2]\r\n   tp = x[3]\r\n   sl = x[4]\r\n   wait = int(x[5])\r\n   surge = x[6]\r\n\r\n   last = len(data)\r\n   i=0\r\n   b=1000    # balance\r\n   e=1000    # equity\r\n   eMax=e    # equity max\r\n   minEP=1   # minimum equity proportion\r\n   ol=0      # old lots\r\n   t=[]      # trades\r\n\r\n   j=0\r\n   ow=[]\r\n\r\n   while i<wait: \r\n      i=int(i)\r\n      o = data[i][2]      \r\n      ow.append(o)\r\n      i=i+1\r\n\r\n   while i<last-1:\r\n      i=int(i)\r\n      o = data[i][2]\r\n      w = ow[0]\r\n      ow = ow[1:]\r\n      ow.append(o)\r\n      spread = max(round(o/6000,1),0.2)\r\n\r\n      if e<100: \r\n         print(\"bust at \"+str(data[i][0]))\r\n         return 999999\r\n      if e/b<sl:\r\n         t = []\r\n         b = e\r\n      if e      > eMax : eMax = e\r\n      if e/eMax < minEP: minEP = e / eMax\r\n\r\n      isRising = (o>w)\r\n      tbp = total_buy_profits()\r\n      tsp = total_sell_profits()\r\n      if buy_empty()  and      isRising : buy_first()\r\n      if sell_empty() and (not isRising): sell_first()\r\n      if      isRising and o>ow[-2]+surge*o: close_all_sell()\r\n      if (not isRising) and o<ow[-2]-surge*o: close_all_buy()\r\n      [lb, v] = lowest_buy()\r\n      if isRising and o<lb - distance*o: buy_next(v)\r\n      elif tbp>tp*b: close_all_buy()\r\n      [hs, v] = highest_sell()\r\n      if (not isRising) and o>hs + distance*o: sell_next(v)\r\n      elif tsp>tp*b: close_all_sell()\r\n      e = b + tbp + tsp\r\n      i=i+1\r\n      #print(data[i][0],data[i][1],o,round(b,2),round(e,2),round(tp,2),tp*b,round(tbp,2),round(tsp,2),t)\r\n      #if i%10000==0: print(data[i][0],o,round(e,2),t)\r\n   print(round(e))\r\n   return -e\r\n\r\n\r\nmin_cost = 1\r\nbest_pos = []\r\noptimizer = 0\r\nmaxim = -1\r\n\r\ndef obj_function_pool(x):\r\n   global min_cost\r\n   global best_pos\r\n   r = pool.map(martingale,x)   \r\n   for j in range(len(r)): \r\n      if r[j]<min_cost:\r\n         min_cost = r[j]\r\n         best_pos = x[j]\r\n   print(best_pos)\r\n   return r\r\n\r\nif __name__ == '__main__':\r\n   pool = mp.Pool(33)\r\n   qq = mp.Queue()\r\n   if argv[1]==\"normalize\":\r\n      normalize()\r\n   elif argv[1]==\"test\":\r\n      print(str(len(data))+\" records read\")\r\n      martingale([2.55694721e-04, 5.91813579e-02, 2.89255651e+00, 1.59839499e-02, 7.42409324e-02, 1.06277872e+02, 2.50461312e-02])\r\n      martingale([1.61691665e-04, 3.11414662e-02, 4.26896235e+00, 6.65749179e-02, 5.91987482e-01, 2.74519767e+01, 2.53710627e-02])\r\n   elif argv[1]==\"pso\":\r\n                     # risk  distance  exp    tp    sl    wait  surge\r\n      lb = np.array([0.0001,  0.001,  1.05, 0.001, 0.05,    2, 0.005])\r\n      ub = np.array([0.001 ,    0.1,     5,   0.1,  0.9 , 1440, 0.03 ])\r\n      bounds = (lb, ub)\r\n      options = {'c1': 1.5, 'c2': 3, 'w':0.1}\r\n      optimizer = ps.single.GlobalBestPSO(n_particles=256, dimensions=7, options=options, bounds=bounds)\r\n      cost, pos = optimizer.optimize(obj_function_pool, 10000)\r\n      print(optimizer.swarm.best_pos[optimizer.swarm.pbest_cost.argmin(axis=0)])\r\n      #xopt, fopt = pso(gridder2, lb, ub, maxiter=10, swarmsize=10, debug=True)\r\n      print(pos)\r\n      #print(xopt)\r\n\r\n   elif argv[1]==\"ga\":\r\n      lb = np.array([0.0001,   1, 1000, 0.1,  1, 1.01])\r\n      ub = np.array([0.01  , 120,10000, 0.8, 60, 1.2])      \r\n      def fitness_func(f,i):\r\n         return gridder(i,f[0],f[1],f[2],f[3],f[4],f[5])\r\n      gene_space = [{'low':0.0001, 'high':0.01, 'step':0.0001},\r\n                     {'low':1, 'high':120, 'step':1},\r\n                     {'low':1000, 'high':10000, 'step':100},\r\n                     {'low':0.1, 'high':0.8, 'step':0.01},\r\n                     {'low':1, 'high':60, 'step':1},\r\n                     {'low':1.01, 'high':1.2, 'step':0.01}]         \r\n      fitness_function = fitness_func\r\n      num_generations = 10\r\n      num_parents_mating = 4\r\n      sol_per_pop = 8\r\n      num_genes = 6\r\n      parent_selection_type = \"sss\"\r\n      keep_parents = 1\r\n      crossover_type = \"single_point\"\r\n      mutation_type = \"random\"\r\n      mutation_percent_genes = 30\r\n      ga_instance = pygad.GA(\r\n                       gene_space=gene_space,\r\n                       num_generations=num_generations,\r\n                       num_parents_mating=num_parents_mating,\r\n                       fitness_func=fitness_function,\r\n                       sol_per_pop=sol_per_pop,\r\n                       num_genes=num_genes,\r\n                       parent_selection_type=parent_selection_type,\r\n                       keep_parents=keep_parents,\r\n                       crossover_type=crossover_type,\r\n                       mutation_type=mutation_type,\r\n                       mutation_percent_genes=mutation_percent_genes)\r\n\r\n      ga_instance.run()\r\n\r\n      solution, solution_fitness, solution_idx = ga_instance.best_solution()\r\n      print(\"Parameters of the best solution : {solution}\".format(solution=solution))\r\n      print(\"Fitness value of the best solution = {solution_fitness}\".format(solution_fitness=solution_fitness))\r\n\r\n      prediction = gridder(0,solution[0],solution[1],solution[2],solution[3],solution[4],solution[5])\r\n      print(\"Predicted output based on the best solution : {prediction}\".format(prediction=prediction))","repo_name":"binarybehemoth/forex-ml","sub_path":"martingale.py","file_name":"martingale.py","file_ext":"py","file_size_in_byte":8004,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"34471749538","text":"import numpy as np\nfrom scipy import stats\nimport matplotlib.pyplot as plt\nimport time\nfrom statsmodels.stats.proportion import proportion_confint\nimport multiprocessing as mp\nimport pickle\n    \ndef get_monte_carlo_sample(p, n_permutation=5000, random_state=0):\n    \"\"\" Generate monte carlo samples given the p-value.\n        The samples are binary indicating if an extreme event occurs\n    \"\"\"\n    # Full Monte Carlo permutation\n    np.random.seed(random_state)\n    n_feature = p.shape[0]\n    U = np.random.binomial(1, p, size=(n_permutation, n_feature))\n    p_fmc = (np.sum(U, axis=0)+1)/(n_permutation+1)\n    t_fmc = np.ones([n_feature], dtype=int) * n_permutation\n    return U, p_fmc, t_fmc\n\n\"\"\"\n    Adaptive Monte Carlo test\n\"\"\"\ndef amt(f_sample, data, n_hypothesis, n_fMC=None, alpha=0.1, increment=1.1,\n        batch_size_start=100, delta=None, output_folder=None, verbose=False, title='',\n        random_state=0, output_data_folder=None, n_core=2):\n    \"\"\" Adaptive Monte Carlo test via multi-armed bandits.\n    Args:\n        f_sample (f(data,n_sample,sample_start)): the function to acquire MC samples.\n        data: the data to generate MC samples, input for f_sample.\n        n_hypothesis (int): the number of hypotheses.\n        n_fMC (int): the maximum number of MC samples for each hypothesis.\n        alpha (float): the nominal FDR level. \n        increment (float, >1): the multiplicative increment of the batch sizes.\n        delta (float): the error probability.\n        output_folder (str): the folder to output intermediate results. \n        verbose (boolean): if to output some intermediate results. \n      \n    Returns:\n        x_order ((d,) ndarray): the order (of x_val) from smallest alt/null ratio to \n            the largest.\n    \"\"\"\n    if output_folder is not None:\n        fout = open(output_folder+'/result_amt%s'%title, 'w')\n    start_time = time.time()\n    # Initialization\n    if delta is None:\n        delta = 1 / n_hypothesis\n    delta_CI = delta/n_hypothesis/np.log(n_fMC)\n    # delta_CI = delta\n    ind_sample = np.ones([n_hypothesis], dtype=bool)\n    p_hat_ub = np.ones([n_hypothesis], dtype=float)\n    p_hat_lb = np.zeros([n_hypothesis], dtype=float)    \n    r_hat = n_hypothesis\n    tau_hat = r_hat/n_hypothesis*alpha    \n    # n_amt: number of MC samples\n    n_amt = np.zeros([n_hypothesis], dtype=int)\n    # n_amt: number of MC sample batches\n    n_amt_batch = np.zeros([n_hypothesis], dtype=int)\n    # s_amt: number of successes\n    s_amt = np.zeros([n_hypothesis], dtype=int)\n    # Set the batch size\n    n_batch = np.ceil(np.log(1 - n_fMC/batch_size_start*(1-increment))\n                   / np.log(increment))\n    batch_size = np.ceil(batch_size_start * increment**(np.arange(n_batch))).astype(int)\n    batch_size[-1] = n_fMC - np.sum(batch_size[:-1])\n    if verbose:\n        print('# Initialization parameters')\n        print('# n_hypothesis=%d, n_fMC=%d, alpha=%0.2f, increment=%0.2f'%\n              (n_hypothesis, n_fMC, alpha, increment))\n        print('# delta', delta)\n        print('# delta_CI', delta_CI)\n        print('# r_hat=%d, tau_hat=%0.4f'%(r_hat, tau_hat))\n        print('# batch_size', batch_size)\n        print('# sum of batch size = %d'%np.sum(batch_size))\n        print('# Initialization completed: time=%0.3fs'%(start_time - time.time())) \n    if output_folder is not None:\n        fout.write('# Initialization parameters\\n')\n        fout.write('# n_hypothesis=%d, n_fMC=%d, alpha=%0.2f, increment=%0.2f\\n'%\n                   (n_hypothesis, n_fMC, alpha, increment))\n        fout.write('# delta=%0.4f\\n'%delta)\n        fout.write('# delta_CI=%0.4f\\n'%delta_CI)\n        fout.write('# r_hat=%d, tau_hat=%0.4f\\n'%(r_hat, tau_hat))\n        fout.write('# batch_size: %s\\n'%batch_size)\n        fout.write('# sum of batch size = %d\\n'%np.sum(batch_size))\n        fout.write('# Initialization completed: time=%0.3fs\\n\\n'%(start_time - time.time()))\n    if output_data_folder is not None:\n        res_dic = {'p_hat_ub':p_hat_ub, 'p_hat_lb':p_hat_lb, \n                   'n_amt':n_amt, 's_amt': s_amt, 'ind_sample':ind_sample,\n                   'r_hat':r_hat, 'tau_hat':tau_hat}\n        with open(output_data_folder+'/init.pickle', \"wb\") as f:\n            pickle.dump(res_dic, f)         \n    i_itr=0\n    while True:\n        # Record the data\n        if output_data_folder is not None:\n            res_dic = {'p_hat_ub':p_hat_ub, 'p_hat_lb':p_hat_lb, \n                       'n_amt':n_amt, 's_amt': s_amt, 'ind_sample':ind_sample,\n                       'r_hat':r_hat, 'tau_hat':tau_hat}\n            with open(output_data_folder+'/itr%d.pickle'%i_itr, \"wb\") as f:\n                pickle.dump(res_dic, f)\n        # Sample\n        temp_n_amt_new = batch_size[n_amt_batch[ind_sample]]\n        s_amt[ind_sample] = s_amt[ind_sample] + f_sample(data, ind_sample, \n                                                         temp_n_amt_new, \n                                                         n_amt[ind_sample],\n                                                         random_state=random_state*1000+i_itr,\n                                                         n_core=n_core)\n        n_amt_batch[ind_sample] += 1 \n        n_amt[ind_sample] = n_amt[ind_sample] + temp_n_amt_new                     \n        # Update CIs                \n        p_hat_lb[ind_sample],p_hat_ub[ind_sample] = update_p(s_amt[ind_sample], \n                                                             n_amt[ind_sample],\n                                                             n_fMC,\n                                                             extreme_prob=delta_CI)\n        # Update estimates\n        n_g = np.sum(p_hat_lb > tau_hat)\n        while (r_hat > n_hypothesis - n_g):\n            r_hat = n_hypothesis - n_g\n            tau_hat = r_hat/n_hypothesis*alpha\n            n_g = np.sum(p_hat_lb > tau_hat)\n        n_l = np.sum(p_hat_ub <= tau_hat)\n        ind_sample = (p_hat_lb <= tau_hat) & (p_hat_ub > tau_hat)\\\n                     & (p_hat_lb != p_hat_ub)\n        # Record\n        n_u = n_hypothesis - n_g - n_l\n        if output_folder is not None:\n            fout.write('# avg_sample=%0.1f, tau_hat=%0.5f, r_hat=%d, n_u=%d, n_g=%d, n_l=%d, '\n                        %(np.mean(n_amt), tau_hat, r_hat, n_u, n_g, n_l))\n            fout.write('time=%0.1fs\\n'%(time.time()-start_time))\n        if verbose:            \n            print('# %d, avg_sample=%0.1f, tau=%0.5f, r_hat=%d, n_u=%d, n_g=%d, n_l=%d'\\\n                  %(i_itr, np.mean(n_amt), tau_hat, r_hat, n_u, n_g, n_l))\n        i_itr = i_itr+1\n        # Exit condition\n        if np.sum(ind_sample)==0:\n            break\n    if output_folder is not None:\n        fout.write('\\n# AMT finished\\n')\n        fout.write('# avg_sample=%0.1f, tau_hat=%0.5f, r_hat=%d, n_u=%d, n_g=%d, n_l=%d, '\n                   %(np.mean(n_amt), tau_hat, r_hat, n_u, n_g, n_l))\n        fout.write('time=%0.1fs\\n'%(time.time()-start_time))\n        fout.write('# discoveries=%d'%(np.sum(p_hat_ub<=tau_hat)))\n        fout.close()\n    if output_data_folder is not None:\n        res_dic = {'p_hat_ub':p_hat_ub, 'p_hat_lb':p_hat_lb, \n                   'n_amt':n_amt, 's_amt': s_amt, 'ind_sample':ind_sample,\n                   'r_hat':r_hat, 'tau_hat':tau_hat}\n        with open(output_data_folder+'/final.pickle', \"wb\") as f:\n            pickle.dump(res_dic, f)\n    p_hat = s_amt/n_amt\n    return p_hat_ub, p_hat_lb, p_hat, tau_hat, n_amt\n\n\"\"\"\n    Sample dummy (this part should be parallelized for large-scale problems)\n\"\"\" \ndef f_sample_dummy(data, ind_sample, n_new_sample, sample_start=None,\n                   random_state=None, n_core=2):\n    \"\"\" Adaptive Monte Carlo test via multi-armed bandits.\n    Args:\n        data ((n,m) NumPy boolean array): the fMC samples.\n        ind_sample ((m,) NumPy boolean array): the hypothesis that need new samples\n        n_new_sample ((n_active,) NumPy int array): the number of fMC samples for each hypothesis.\n        sample_start ((n_active,) NumPy int array): the number of fMC samples each hypothesis\n            has so far.\n      \n    Returns:\n        n_success ((m,) NumPy int array): the number of successes for each hypothesis.\n    \"\"\" \n    n,m = data.shape\n    n_active = np.sum(ind_sample)\n    n_success = np.zeros([n_active], dtype=int)\n    if sample_start is None:\n        sample_start = np.zeros([n_active], dtype=int)\n    sample_end = sample_start + n_new_sample\n    for i,i_origin in enumerate(np.arange(m)[ind_sample]):\n        n_success[i] = np.sum(data[sample_start[i]:sample_end[i], i_origin])\n    return n_success\n\n\"\"\"\n    f_sample for GWAS\n\"\"\"\ndef f_sample_chi2(data, ind_sample, n_new_sample, sample_start=None, n_core=2,\n                  random_state=None):\n    X = data['X'][:,ind_sample]\n    y = data['y']\n    Exp = data['Exp'][:, ind_sample]\n    r_Exp = data['r_Exp'][:, ind_sample]\n    chi2_obs = data['chi2_obs'][ind_sample]  \n    n_active = np.sum(ind_sample)\n    n_success = np.zeros([n_active], dtype=int)\n    n_permute = np.max(n_new_sample) # Parallelization\n    # Compute the MC samples \n    if n_core==1:\n        B = permute_chi2_batch(y, X, Exp, r_Exp, chi2_obs, n_permute)\n    else:\n        Y_input = []\n        n_permute_core = int(n_permute/n_core)\n        for i_core in range(n_core):\n            if i_core != n_core-1:\n                Y_input.append([y, X, Exp, r_Exp, chi2_obs, n_permute_core,\n                                random_state*n_core+i_core])\n            else:\n                Y_input.append([y, X, Exp, r_Exp, chi2_obs, \n                                int(n_permute-(n_core-1)*n_permute_core),\n                                random_state*n_core+i_core])\n        po = mp.Pool(n_core)\n        res = po.map(permute_chi2_batch_wrapper, Y_input)\n        po.close()\n        B = res[0]\n        # print(np.mean(B, axis=0)[0:5])\n        for i_core in range(1,n_core):\n            # print(np.mean(res[i_core], axis=0)[0:5])\n            B = np.concatenate([B, res[i_core]], axis=0)        \n    for i in range(n_active):\n        n_success[i] = np.sum(B[0:n_new_sample[i], i])\n    return n_success\n\ndef permute_chi2_batch_ncore(y, X, Exp, r_Exp, chi2_obs, n_permute, random_state=0,\n                             verbose=False, n_core=32):\n    Y_input = []\n    n_permute_core = int(n_permute/n_core)\n    for i_core in range(n_core):\n        if i_core != n_core-1:\n            Y_input.append([y, X, Exp, r_Exp, chi2_obs, n_permute_core,\n                            random_state*n_core+i_core])\n        else:\n            Y_input.append([y, X, Exp, r_Exp, chi2_obs, \n                            int(n_permute-(n_core-1)*n_permute_core),\n                            random_state*n_core+i_core])\n    po = mp.Pool(n_core)\n    res = po.map(permute_chi2_batch_wrapper, Y_input)\n    po.close()\n    B = res[0]\n    for i_core in range(1,n_core):\n        B = np.concatenate([B, res[i_core]], axis=0)\n    return B\n\ndef permute_chi2_batch_wrapper(data):\n    return permute_chi2_batch(data[0], data[1], data[2], \n                              data[3], data[4], data[5], \n                              random_state = data[6])\n\ndef permute_chi2_batch(y, X, Exp, r_Exp, chi2_obs, n_permute, random_state=None,\n                       verbose=False):\n    if random_state is not None:\n        np.random.seed(random_state)\n    start_time = time.time()\n    n_feature = X.shape[1]\n    B = np.zeros([n_permute, n_feature], dtype=bool)\n    for i_permute in range(n_permute):\n        y_new = np.random.permutation(y)\n        temp_chi2 = compute_chi2(y_new, X, Exp, r_Exp)\n        B[i_permute,:] = (temp_chi2>=chi2_obs)\n        if verbose:\n            if i_permute%1000==0:\n                print('%d/%d, time=%0.1fs'%(i_permute, n_permute, time.time()-start_time))\n    return B\n\ndef compute_chi2(y, X, Exp, r_Exp):\n    # start_time = time.time()\n    Obs = np.zeros([8, X.shape[1]], dtype=int)\n    for iy in range(2):\n        temp = X[y==iy,:]\n        for ix in range(4):\n            Obs[iy*4+ix,:] = np.sum(temp==ix, axis=0)\n    chi2 = np.sum((Obs-Exp)**2*r_Exp, axis=0)\n    # print('Time=%0.3fs'%(time.time()-start_time))\n    return chi2\n\n\"\"\"\n    Sample correlation (this part should be parallelized for large-scale problems)\n    think of this part later ...\n\"\"\" \ndef f_sample_correlation(X, y, ni, ti=None):\n    \"\"\" Adaptive Monte Carlo test via multi-armed bandits.\n    Args:\n        X ((n,m) NumPy boolean array): the fMC samples.\n        y ((n,m) NumPy boolean array): the fMC samples.\n        ni ((m,) NumPy int array): the number of fMC samples for each hypothesis.\n        ti ((m,) NumPy int array): the number of fMC samples each hypothesis has so far.\n      \n    Returns:\n        si ((m,) NumPy int array): the number of successes for each hypothesis.\n    \"\"\"\n    return si \n\n\n\"\"\"\n    Old implementation\n\"\"\"\n# ada_permute\ndef amc_test(U, alpha=0.1, step_size=10, extreme_prob=0.01, max_iter = 20000,\\\n             p_true=None, output_folder=None, verbose=False):\n    # Initialization\n    n_max, m = U.shape\n    ind_u = np.ones([m], dtype=bool)\n    p_hat_ub = np.ones([m], dtype=float)\n    p_hat_lb = np.zeros([m], dtype=float)    \n    r_hat = m\n    tau_hat = r_hat/m*alpha    \n    # t: number of MC samples\n    t = np.zeros([m], dtype=int)\n    # s: number of successes\n    s = np.zeros([m], dtype=int)\n    n_itr = 0\n    while True:\n        # New Monte Carlo samples\n        temp = t[ind_u]\n        t[ind_u] = (t[ind_u] + step_size).clip(max=n_max)\n        s[ind_u] = s[ind_u] + sample_MC(U[:,ind_u], temp, t[ind_u])                        \n        # Update CIs                \n        p_hat_lb[ind_u],p_hat_ub[ind_u] = update_p(s[ind_u], t[ind_u], n_max,\\\n                                                   extreme_prob=extreme_prob)\n        # Update estimates\n        n_g = np.sum(p_hat_lb > tau_hat)\n        while (r_hat > m - n_g):\n            r_hat = m - n_g\n            tau_hat = r_hat/m*alpha\n            n_g = np.sum(p_hat_lb > tau_hat)\n        n_l = np.sum(p_hat_ub <= tau_hat)\n        ind_u = (p_hat_lb <= tau_hat) & (p_hat_ub > tau_hat)\\\n                & (p_hat_lb != p_hat_ub)\n        if np.sum(ind_u)==0:\n            break\n        n_itr += 1\n        # Record\n        if verbose and (n_itr%20==0):\n            n_u = m - n_g - n_l\n            tau_true = bh(p_true, alpha=alpha)\n            print('## n_itr=%d, avg_sample=%0.1f'%(n_itr, np.mean(t)))\n            print('r_hat=%d, n_u=%d, n_g=%d, n_l=%d'\\\n                  %(r_hat, n_u, n_g, n_l))\n            print('tau=%0.5f, tau_true=%0.5f'%(tau_hat, tau_true))\n            h = (p_true<=tau_true)\n            h_hat = (p_hat_ub<=tau_hat)\n            print('D_ap=%d, D_overlap=%d, D_true=%d, '\\\n                  %(result_compare(h_hat, h)))\n        if n_itr > max_iter:\n            print('## Early exit!')\n        #     n_pts = 100\n        #     plot_ind = np.argsort(p_true)[:n_pts]\n        #     temp_U = ((p_hat_lb <= tau) & (p_hat_ub >=tau))[plot_ind]     \n        #     y_val = 0.5*(p_hat_lb[plot_ind] + p_hat_ub[plot_ind])\n        #     y_err = p_hat_ub[plot_ind] - y_val  \n        #     xaxis = np.arange(n_pts)+1\n        #     plt.figure(figsize = [6, 5])\n        #     plot_p_sort(p_true, n_pts=n_pts, label='full_mc')\n        #     plt.scatter(xaxis[temp_U], p_true[plot_ind][temp_U], color='r', label='uncertain pts')\n        #     plt.errorbar(xaxis, y_val, yerr = y_err, alpha=0.6)\n        #     plt.ylim([0, 0.03])\n        #     plt.plot([1, n_pts], [tau, tau], color='r', label='tau_hat')\n        #     plt.legend(loc='upper left')\n        #     plt.title('avg_sample=%0.1f, r_hat=%d, |U|=%d, |L|=%d, |S|=%d'\\\n        #               %(np.mean(t), r_hat, n_uncertain, n_larger, n_smaller))\n        #     if output_folder is not None:\n        #         figname = 'progress_%d'%n_itr\n        #         plt.savefig(output_folder + '/' + figname + '.png')\n        #         plt.savefig(output_folder + '/' + figname + '.pdf')\n        #     plt.show()\n            \n        # if n_smaller >= r_hat:\n        #     tau_true = bh(p_true, alpha=alpha)\n        #     if verbose:\n        #         print('## n_itr=%d, avg_sample=%0.1f'%(n_itr, np.mean(t)))\n        #         print('r_hat=%d, |U|=%d, |L|=%d, |S|=%d'%(r_hat, n_uncertain, n_larger, n_smaller))\n        #         print('tau=%0.5f, tau_true=%0.5f'%(tau, tau_true))\n        #         h = (p_true<=tau_true)\n        #         h_hat = (p_hat_ub<=tau)\n        #         print('D_ap=%d, D_true=%d, D_overlap=%d'\\\n        #               %(np.sum(h_hat), np.sum(h), np.sum(h_hat*h))) \n        #         n_pts = 100\n        #         plot_ind = np.argsort(p_true)[:n_pts]\n        #         temp_U = ((p_hat_lb <= tau) & (p_hat_ub >=tau))[plot_ind]     \n        #         y_val = 0.5*(p_hat_lb[plot_ind] + p_hat_ub[plot_ind])\n        #         y_err = p_hat_ub[plot_ind] - y_val  \n        #         xaxis = np.arange(n_pts)+1\n        #         plt.figure(figsize = [6, 5])\n        #         plot_p_sort(p_true, n_pts=n_pts, label='full_mc')\n        #         plt.scatter(xaxis[temp_U], p_true[plot_ind][temp_U], color='r',\\\n        #                     label='uncertain pts')\n        #         plt.errorbar(xaxis, y_val, yerr = y_err, alpha=0.6)\n        #         plt.ylim([0, 0.03])\n        #         plt.plot([1, n_pts], [tau, tau], color='r', label='tau_hat')\n        #         plt.legend(loc='upper left')\n        #         plt.title('avg_sample=%0.1f, r_hat=%d, |U|=%d, |L|=%d, |S|=%d'\\\n        #               %(np.mean(t), r_hat, n_uncertain, n_larger, n_smaller))\n        #         if output_folder is not None:\n        #             figname = 'progress_%d'%n_itr\n        #             plt.savefig(output_folder + '/' + figname + '.png')\n        #             plt.savefig(output_folder + '/' + figname + '.pdf')\n        #         plt.show()\n        #     break \n        \n    return p_hat_ub, tau_hat, t\n\ndef sample_MC(U, t_start, t_end):\n    n_feature = U.shape[1]\n    s = np.zeros([n_feature], dtype=int)\n    for i_feature in range(n_feature):\n        s[i_feature] = np.sum(U[t_start[i_feature]:t_end[i_feature], i_feature])\n    return s\n\n# def compute_s(t, U_cumsum):\n#     n_feature = U_cumsum.shape[1]\n#     s = np.zeros([n_feature], dtype=int)\n#     for i_feature in range(n_feature):\n#         s[i_feature] = np.sum(U_cumsum[t[i_feature]-1, i_feature])\n#     return s\n    \ndef update_p(s, t, n_max, extreme_prob=0.01):\n    \"\"\" Compute the binomial confidence intervals to update the p-values.\n    \n    Args:\n        s (ndarray): number of successes for each feature.\n        t (ndarray): total number of trials for each feature.\n        alpha (float): alpha/2 is used for both half_tails.\n        \n    To-do: \n        Consolidate this part.\n    \"\"\"\n    # p_hat_lb, p_hat_ub = proportion_confint(s, t, alpha=extreme_prob, method='beta')\n    p_hat_lb, p_hat_ub = proportion_confint(s, t, alpha=extreme_prob, method='agresti_coull')\n    # p_hat_lb[np.isnan(p_hat_lb)] = 0\n    # p_hat_ub[np.isnan(p_hat_ub)] = 1\n    # For points that all sampled are exhausted.\n    ind_sample_end = (t==n_max)\n    p_hat_lb[ind_sample_end] = (s[ind_sample_end]+1)/(t[ind_sample_end]+1)\n    p_hat_ub[ind_sample_end] = (s[ind_sample_end]+1)/(t[ind_sample_end]+1)\n    return p_hat_lb, p_hat_ub\n\n\"\"\"\n    Baseline methods\n\"\"\"\ndef ada_permute_oracle(U, p_true, alpha=0.1, step_size=10, option='strong', verbose=False):\n    # Set up parameters\n    n_permutation, n_feature = U.shape\n    t = np.zeros([n_feature], dtype=int)\n    p_hat_ub = np.ones([n_feature], dtype=float)\n    p_hat_lb = np.zeros([n_feature], dtype=float)\n    U_cumsum = np.cumsum(U, axis=0)\n    n_itr = 0\n    if option == 'strong':\n        tau_true = bh(p_true, alpha)\n    elif option == 'weak':\n        sort_ind = np.argsort(p_true)\n        tau_true = np.zeros([n_feature], dtype=float)\n        tau_true[sort_ind] = (np.arange(n_feature)+1)/n_feature*alpha\n        tau_true = tau_true.clip(min=bh(p_true, alpha))\n    while True:\n        # Permutation samples\n        ind_uncertain = (p_hat_lb <= tau_true) & (p_hat_ub >=tau_true)\\\n                         & (p_hat_lb != p_hat_ub)\n        n_uncertain = np.sum(ind_uncertain)\n        t[ind_uncertain] += step_size\n        t = t.clip(max=n_permutation)\n        # Update parameters\n        s = compute_s(t, U_cumsum)\n        p_hat_lb, p_hat_ub = update_p(s, t, n_permutation, extreme_prob=0.01)\n        if n_itr % 200 == 0 and verbose:            \n            print('## n_itr=%d, avg_sample=%0.1f, |U|=%d'%(n_itr, np.mean(t), n_uncertain))\n        n_itr += 1\n        if n_uncertain == 0:\n            break\n    return p_hat_ub, t\n\ndef sMC(U, r=10):\n    \"\"\" Sequenctial monte carlo method as described in the 1991 paper.\n    \"\"\"\n    n_permutation, n_feature = U.shape\n    cumsum_U = np.cumsum(U, axis=0)\n    p_sMC = np.zeros([n_feature], dtype=float)\n    t_sMC = np.zeros([n_feature], dtype=int)\n    # sequential Monte Carlo\n    for i_feature in range(n_feature):\n        if cumsum_U[-1, i_feature] < r:\n            p_sMC[i_feature] = (cumsum_U[-1, i_feature]+1) / (n_permutation+1)\n            t_sMC[i_feature] = n_permutation\n        else:\n            B = np.where(cumsum_U[:, i_feature] == r)[0][0] + 1\n            p_sMC[i_feature] = r/B\n            t_sMC[i_feature] = B\n    return p_sMC, t_sMC\n    \ndef bh(p, alpha=0.1):\n    sort_ind = np.argsort(p)\n    p = p[sort_ind]\n    m = p.shape[0]\n    threshold = (np.arange(m)+1)*alpha/m\n    if np.sum(p <= threshold) == 0:\n        return 0\n    else:\n        critical_ind = np.where(p <= threshold)[0][-1]\n        return threshold[critical_ind]\n\n\"\"\"\n    Anciliary functions\n\"\"\"\ndef plot_p_sort(p, alpha=0.1, sort_ind=None, n_pts=100, label=None):\n    m = p.shape[0]\n    if sort_ind is None:\n        sort_ind = np.argsort(p)\n        t_bh = bh(p, alpha)\n        plt.plot([1, n_pts], [t_bh, t_bh], linestyle='--', linewidth = 1,\\\n                 color='limegreen', label='bh threshold')\n    p_sort = p[sort_ind]    \n    x_axis = np.arange(m)+1\n    bh_threshold = alpha*(np.arange(m)+1)/m    \n    plt.scatter(x_axis[:n_pts], p_sort[:n_pts], s=16, alpha=0.4, label=label)\n    plt.plot(x_axis[:n_pts], bh_threshold[:n_pts], linestyle='--', color='r')\n    plt.xlabel('p-value rank')\n    plt.ylabel('p-value')\n    \ndef plot_p_sort_with_ci(t, U, sort_ind, n_pts = 100, label=None):\n    n_permutation,n_featuer = U.shape\n    U_cumsum = np.cumsum(U, axis=0)\n    s = compute_s(t, U_cumsum)\n    p_hat_lb, p_hat_ub = update_p(s, t, n_permutation, extreme_prob=0.01)\n    plot_ind = sort_ind[:n_pts] \n    xaxis = np.arange(n_pts)+1\n    y_val = 0.5*(p_hat_lb[plot_ind] + p_hat_ub[plot_ind])\n    y_err = p_hat_ub[plot_ind] - y_val    \n    plt.errorbar(xaxis, y_val, yerr = y_err, alpha=0.6, label=label)\n    plt.title('Avg. sample = %0.1f'%(np.mean(t)))\n    \ndef plot_pmc(p, n_mc, label=None):\n    sort_ind = np.argsort(p)\n    m = p.shape[0]\n    p = p[sort_ind]\n    n_mc =n_mc[sort_ind]\n    ci = np.sqrt(p*(1-p)/n_mc)*2\n    plt.errorbar(np.arange(m)+1, p, yerr = ci, alpha=0.8, label=label)\n    \ndef result_compare(h_hat, h_true):\n    D_hat = np.sum(h_hat)\n    D_true = np.sum(h_true)\n    D_overlap = np.sum(h_hat*h_true)\n    return D_hat, D_overlap, D_true\n \n\"\"\"\n    Deprecated\n\"\"\"\ndef ada_permute_1(U, alpha=0.1, step_size=5, verbose=False, p_true=None):\n    # Set up parameters\n    n_permutation, n_feature = U.shape\n    U_pt = np.ones([n_feature], dtype=bool)\n    R_pt = np.zeros([n_feature], dtype=bool)\n    A_pt = np.zeros([n_feature], dtype=bool)\n    t = np.zeros([n_feature], dtype=int)\n    # p_hat = np.zeros([n_feature], dtype=float)\n    p_hat_ub = np.ones([n_feature], dtype=float)\n    p_hat_lb = np.zeros([n_feature], dtype=float)\n    # r_hat = np.zeros([n_feature], dtype=int)\n    r_hat_ub = np.ones([n_feature], dtype=int)*n_feature\n    r_hat_lb = np.ones([n_feature], dtype=int)\n    n_itr = 0\n    while (np.sum(U_pt)>0):\n        # Permutation samples\n        t[U_pt] += step_size\n        # Update parameters\n        s = np.zeros([n_feature], dtype=int)\n        for i_feature in range(n_feature):\n            s[i_feature] = np.sum(U[:t[i_feature], i_feature])\n        p_hat_lb, p_hat_ub = update_p(s, t, n_permutation, extreme_prob=0.05)\n        r_hat_lb, r_hat_ub = update_r(p_hat_lb, p_hat_ub)\n        # Update points\n        ind_A = p_hat_lb > (r_hat_ub*alpha/n_feature)\n        U_pt[ind_A] = False\n        A_pt[ind_A] = True\n        ind_R = p_hat_ub > (r_hat_lb*alpha/n_feature)\n        n_itr += 1\n        if verbose:\n            print('## n_itr=%d, avg_sample=%0.1f'%(n_itr, np.mean(t)))\n            print('|U|=%d, |A|=%d, |R|=%d'%(U_pt.sum(), A_pt.sum(), R_pt.sum()))\n            print(r_hat_ub[0:5])\n            print(r_hat_lb[0:5])\n            sort_ind = np.argsort(p_true)\n            temp_lb = p_hat_lb[sort_ind]\n            temp_ub = p_hat_ub[sort_ind]\n            temp_rlb = r_hat_lb[sort_ind]\n            temp_rub = r_hat_ub[sort_ind]\n            temp_U_pt = U_pt[sort_ind]\n            y_val = (temp_lb+temp_ub)/2\n            y_err = temp_ub-y_val\n            n_pts = 200\n            xaxis = np.arange(n_feature)+1\n            plt.figure()\n            plot_p_sort(p_true, n_pts=n_pts, sort_ind=None, label='full_mc')\n            plt.scatter(xaxis[:n_pts][temp_U_pt[:n_pts]],\\\n                        p_true[sort_ind][:n_pts][temp_U_pt[:n_pts]],\\\n                        alpha=0.4, color='r')\n            plt.errorbar(xaxis[:n_pts], y_val[:n_pts], yerr = y_err[:n_pts], alpha=0.6)\n            plt.plot(xaxis[:n_pts], (temp_rlb[:n_pts]*alpha/n_feature),\\\n                     linestyle='--', color='r')\n            plt.plot(xaxis[:n_pts], (temp_rub[:n_pts]*alpha/n_feature),\\\n                     linestyle='--', color='r')\n            plt.show()\n            print('')\n        if n_itr == 5:\n            break\n    return t\n\ndef update_r(p_hat_lb, p_hat_ub):\n    \"\"\" Compute the binomial confidence intervals\n    \n    Args:\n        s (ndarray): number of successes for each feature.\n        t (ndarray): total number of trials for each feature.\n        alpha (float): alpha/2 is used for both half_tails.\n    \"\"\"\n    n_feature = p_hat_lb.shape[0]\n    r_hat_ub = np.zeros([n_feature], dtype=int)\n    r_hat_lb = np.zeros([n_feature], dtype=int)\n    for i_feature in range(n_feature):\n        r_hat_ub[i_feature] = np.sum(p_hat_ub[i_feature] < p_hat_lb)\n        r_hat_lb[i_feature] = np.sum(p_hat_lb[i_feature] > p_hat_ub)\n    return r_hat_lb, r_hat_ub","repo_name":"martinjzhang/AMT","sub_path":"amt/method.py","file_name":"method.py","file_ext":"py","file_size_in_byte":26275,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"81"}
+{"seq_id":"10485280466","text":"\"\"\"empty message\n\nRevision ID: 653ab361dc49\nRevises: 9dbeb508c2e2\nCreate Date: 2019-08-31 05:22:27.597875\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\nimport app\nfrom sqlalchemy.dialects import postgresql\n\n# revision identifiers, used by Alembic.\nrevision = '653ab361dc49'\ndown_revision = '1e91172e3671'\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.add_column('account', sa.Column('bse_symbol', sa.String(length=32), nullable=True))\n    op.add_column('account', sa.Column('fsym_id', sa.String(length=128), nullable=True))\n    op.add_column('account', sa.Column('nse_symbol', sa.String(length=32), nullable=True))\n    op.add_column('account', sa.Column('perm_security_id', sa.String(length=128), nullable=True))\n    # op.add_column('account', sa.Column('ec_identifier', sa.String(length=128), nullable=True))\n    op.add_column('account', sa.Column('website', sa.String(length=512), nullable=True))\n    op.add_column('management_profile', sa.Column('email', app.base.model_fields.LCString(length=128), nullable=True))\n    # ### end Alembic commands ###\n\n\ndef downgrade():\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.drop_column('management_profile', 'email')\n    op.drop_column('account', 'website')\n    # op.drop_column('account', 'ec_identifier')\n    op.drop_column('account', 'perm_security_id')\n    op.drop_column('account', 'nse_symbol')\n    op.drop_column('account', 'fsym_id')\n    op.drop_column('account', 'bse_symbol')\n    # ### end Alembic commands ###\n","repo_name":"Witzcode0/Exchange-connect","sub_path":"migrations/versions/653ab361dc49_.py","file_name":"653ab361dc49_.py","file_ext":"py","file_size_in_byte":1581,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"11947889955","text":"from Node import *\r\nimport sympy as sm\r\n\r\nclass Inductor():\r\n    def __init__(self, name, n1, n2, L):\r\n        self.i = (-1/float(L))*(sm.integrate((n1.volt - n2.volt), t))\r\n        try:\r\n            self.i.doit()\r\n        except:\r\n            self.i = self.i\r\n        self.name = name\r\n        self.res = (n1.volt - n2.volt)/self.i\r\n        n1.branches.append(self.i)\r\n        n2.branches.append(-self.i)\r\n    def print(self, time):\r\n        if solved == True:\r\n            self.__init__(self, self.name, self.n1, self.n2, self.L)\r\n        print(f'Inductor {self.name} has {simplify(self.i.subs(t, time))} current at time {time}')\r\n\r\n\r\nA = Node('A', '4*sin(t)')\r\nC = Node('C')\r\nB = gnd\r\n\r\nL1 = Inductor('L1', A, B, 2)\r\nL2 = Inductor('L2', A, C, 1)\r\nL3 = Inductor('L3', C, B, 1)\r\nSolveNodes()\r\nL1 = Inductor('L1', A, B, 2)\r\nL2 = Inductor('L2', A, C, 1)\r\nL3 = Inductor('L3', C, B, 1)\r\nL1.print(t)\r\nL2.print(t)\r\nC.print()","repo_name":"krishnask18/Linear_Network_Solver","sub_path":"Ind.py","file_name":"Ind.py","file_ext":"py","file_size_in_byte":919,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"22696169621","text":"import tkinter as tk\n\ndef remove_newlines(input_text):\n    # Replace all newline characters with an empty string\n    result = input_text.replace(\"\\n\", \"\")\n    return result\n\ndef process_text():\n    input_text = input_textbox.get(\"1.0\", \"end-1c\")\n    output_text = remove_newlines(input_text)\n    \n    # Append the output to the existing text in the output textbox\n    output_textbox.insert(tk.END, output_text + \"\\n\")\n    \n    # Clear the input textbox\n    input_textbox.delete(\"1.0\", tk.END)\n\n# Create the main application window\napp = tk.Tk()\napp.title(\"Remove Newlines\")\n\n# Input Textbox\ninput_label = tk.Label(app, text=\"Paste your text with newlines:\")\ninput_label.pack()\ninput_textbox = tk.Text(app, height=10, width=50)\ninput_textbox.pack()\n\n# Preview Button\npreview_button = tk.Button(app, text=\"Preview\", command=process_text)\npreview_button.pack()\n\n# Output Textbox\noutput_label = tk.Label(app, text=\"Output (newlines removed):\")\noutput_label.pack()\noutput_textbox = tk.Text(app, height=10, width=50)\noutput_textbox.pack()\n\n# Run the main event loop\napp.mainloop()\n","repo_name":"noblea30/numerade_git","sub_path":"facilitate_update.py","file_name":"facilitate_update.py","file_ext":"py","file_size_in_byte":1075,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"24381879645","text":"#! /usr/bin/env python\n\nimport argparse\nimport csv\nimport math\nimport os\nimport pneumodel_.trial as pn\nimport sys\nimport time\n\ndef _parse_args():\n  parser = argparse.ArgumentParser(description='Compute the time-varying prevalence in each vaccine trial arm.')\n  parser.add_argument('-l', '--logs-folder', required=True, help='Path to a folder containing event logs from one or more simulation runs.')\n  parser.add_argument('-d', '--days-between', type=int, required=True, help='Time interval, in days, between prevalence computations.')\n  parser.add_argument('-f', '--follow-up-days', type=int, required=True, help='Number of days to follow the trial participants, starting from birth.')\n\n  return parser.parse_args()\n\ndef _get_num_runs(logs_folder):\n  n = 0\n  while(True):\n    path = os.path.join(logs_folder, 'trial-{}'.format(n))\n    if not os.path.exists(path):\n      break\n    else:\n      n += 1\n  return n\n\ndef _elapsed(start_time):\n  T = time.time() - start_time\n  return T // 60, T % 60\n\ndef _transpose_csv(path, out):\n  with open(path, 'r') as f:\n    zipped = zip(*csv.reader(f))\n  with open(out, 'w') as f:\n    csv.writer(f).writerows(zipped)\n\nif __name__ == '__main__':\n  t0 = time.time()\n\n  # get arguments from command line\n  args = _parse_args()\n  num_samples = int(math.ceil(args.follow_up_days / float(args.days_between))) + 1\n  days_since_birth = [k * args.days_between for k in range(num_samples)]\n\n  print('---Generate Prevalence Trajectories---')\n  print('Event logs from {}'.format(os.path.abspath(args.logs_folder)))\n  print('Compute prevalences every {} days until at least {} days after birth.'.format(args.days_between, args.follow_up_days))\n\n  # create directory to store csv files\n  dest_dir = os.path.join(args.logs_folder, 'trial-prevalences-{}-{}'.format(args.days_between, args.follow_up_days))\n  try:\n    os.mkdir(dest_dir)\n  except OSError:\n    raise RuntimeError('Trying to create {}, but it already exists'.format(dest_dir))\n  print('Prevalences will be written to {}'.format(os.path.abspath(dest_dir)))\n  \n  # create a csv file for each trial arm\n  trial_0 = pn.Trial(args.logs_folder, 0)\n  arm_names_0 = trial_0.arms.keys()\n  for a in arm_names_0:\n    with open(os.path.join(dest_dir, '{}.csv.temp'.format(a)), 'w') as f:\n      D = max(trial_0.arms[a].config.schedule)\n      days_since_vac = [d - D for d in days_since_birth] # days since vaccination\n      w = csv.writer(f, quoting=csv.QUOTE_NONE)\n      w.writerow(['dsb'] + days_since_birth)\n      w.writerow(['dsv'] + days_since_vac)\n\n  # process each run\n  num_runs = _get_num_runs(args.logs_folder)\n  for i in range(num_runs):\n    t00 = time.time()\n    sys.stdout.write('Processing run {} of {}'.format(i + 1, num_runs))\n    sys.stdout.flush()\n  \n    trial = pn.Trial(args.logs_folder, i)\n    arm_names = trial.arms.keys()\n    assert(sorted(arm_names) == sorted(arm_names_0))\n    for a in arm_names:\n      sys.stdout.write('.')\n      sys.stdout.flush()\n\n      xs = pn.get_prevalence_trajectory(trial.arms[a], days_since_birth, first_day=0)\n      with open(os.path.join(dest_dir, '{}.csv.temp'.format(a)), 'a') as f:\n        writer = csv.writer(f, quoting=csv.QUOTE_NONE)\n        writer.writerow(['run-{}'.format(i)] + ['{:.5f}'.format(x) for x in xs])\n\n    sys.stdout.write('{:.0f}m{:.1f}s\\n'.format(*_elapsed(t00)))\n    sys.stdout.flush()\n\n  # transpose the csv.temp files\n  for filename in os.listdir(dest_dir):\n    if filename.endswith('.csv.temp'):\n      filepath = os.path.join(dest_dir, filename)\n      out = os.path.join(dest_dir, '{}.csv'.format(filename.split('.')[0]))\n      _transpose_csv(filepath, out)\n      os.remove(filepath)\n\n  sys.stdout.write('{:.0f}m{:.1f}s\\n'.format(*_elapsed(t0)))\n  sys.stdout.flush()\n","repo_name":"lucymli/Pneumo-ABM","sub_path":"scripts/generate_prevalence_trajectories.py","file_name":"generate_prevalence_trajectories.py","file_ext":"py","file_size_in_byte":3715,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"10857538887","text":"from itertools import permutations\ndef solution(k, dungeons):\n    answer = 0\n    \n    for per in permutations(dungeons, len(dungeons)):\n        hp = k\n        count = 0 \n        for p in per:\n            if hp >= p[0] :\n                hp -= p[1]\n                count += 1\n        if answer < count :\n            answer = count\n    \n    \n    return answer\n","repo_name":"dongzooo/Coding-test-with-Py","sub_path":"완전탐색/피로도.py","file_name":"피로도.py","file_ext":"py","file_size_in_byte":357,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"33488196844","text":"# Definition for a binary tree node.\n# class TreeNode(object):\n#     def __init__(self, x):\n#         self.val = x\n#         self.left = None\n#         self.right = None\n\nclass Solution(object):\n    def findBottomLeftValue(self, root):\n        \"\"\"\n        :type root: TreeNode\n        :rtype: int\n        \"\"\"\n        la = [root]\n        while la:\n            lb = []\n            for x in la:\n                if x.left: lb.append(x.left)\n                if x.right: lb.append(x.right)\n            if not lb: return la[0].val\n            la = lb\n        return None\n\n","repo_name":"XiongQiuQiu/leetcode-slove","sub_path":"Algorithms/513-Find-Bottom-Left-Tree-Value.py","file_name":"513-Find-Bottom-Left-Tree-Value.py","file_ext":"py","file_size_in_byte":565,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"81"}
+{"seq_id":"21789704189","text":"hrs = input(\"Enter Hours:\")\r\nh = float(hrs)\r\nrate = input(\"Enter Rate:\")\r\nr = float(rate)\r\nif h > 40:\r\n    overtime = (h-40)*(r*1.5)\r\n    standard = 40\r\n    pay = overtime + (standard*r)\r\n    print(float(pay))\r\nelse:\r\n    print(h*r)\r\n","repo_name":"Nullblano/Python_Learning","sub_path":"Course One/Assignment_03_01.py","file_name":"Assignment_03_01.py","file_ext":"py","file_size_in_byte":234,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"35541387855","text":"''' Simple lirc setup tool - view part. '''\n\nimport os.path\nimport os\n\nfrom gi.repository import Gtk         # pylint: disable=no-name-in-module\n\nimport baseview\n\nfrom baseview import _hasitem\nfrom baseview import _on_window_delete_event_cb\n\nREMOTES_LIST_URL = \"http://lirc-remotes.sourceforge.net/remotes.list\"\n_DEBUG = 'LIRC_DEBUG' in os.environ\n\nNO_REMOTE_INFO = \"\"\"\nIf you select this option, you need to provide a lircd.conf file\nlater, either by finding it elsewhere or by recording your own using\nthe irrecord tool. Normally you want to install this file as\n/etc/lirc/lircd.conf.\"\"\"\n\nDEVINPUT_INFO = \"\"\"\nThe devinput driver uses the linux kernel decoding rather than lirc's.\nIt does not support as many devices as lirc, but for supported devices\nthis is an easy setup. It has some limitations, notably it does not\nsupport sending IR signals (ir blasting).\"\"\"\n\nDEFAULT_INFO = \"\"\"\nThe default driver uses the kernel IR drivers, but does it's own\ndecoding. It can be used with most devices supported by the kernel\nand also some other devices, notably many serial and parallell ones.\nIt needs to be configured which is normally more work than the devinput\ndriver, but supports more devices and full functionality.\"\"\"\n\nPRECONFIG_INFO = \"\"\"\nFor some capture devices which cannot be used with the default or the devinput\ndriver lirc has specific support, often a lirc driver and/or kernel\nconfiguration of the standard driver(s). If you can find your device here\nit will configure this support. \"\"\"\n\nMAIN_HELP = \"\"\"\n\n<big>LIRC Configuration Tool Help.</big>\n\nNOTE: This tool is in alpha stage! Here are numerous bugs and shortcomings.\nPlease report issues on the mailing list or issue tracker at\nhttps://sourceforge.net/projects/lirc/!\n\nThe tool allows you to configure lirc. This is done in three steps.\n\nIn the first you should select a configuration file which corresponds to\nyour remote. You can search for remotes from existing ones, browse brands\nor select to not use any pre-configured file. In the last case you\nprobably wants to record your own configuration file using irrecord(1)\nlater.  This is the top pane of the window\n\nIn the second step you should select a driver which can handle your\ncapture device e. g., a usb dongle or a DIY serial device. This is the\nbottom pane of the window.\n\nActually, it doesn't really matter if you select remote or capture device\nfirst. You can do it in any order.\n\nIf you select a remote which only can be used with a specific capture\ndevice the capture device will be updated automagically. Likewise, if you\nselect a capture device which requires a specific remote the remote will be\nupdated.\n\nIn the last step you should install the configuration. This will write a set\nof configuration files to the results directory, normally lirc-setup.conf.d.\nYou should then install these files into their proper locations, see the\nREADME file in the results directory.\"\"\"\n\n\ndef _get_lines_by_letter(lines):\n    '''' Return a dictionary keyed with first letter of lines. '''\n\n    all_keys = [chr(c) for c in range(ord('a'), ord('z') + 1)]\n    all_keys.extend([chr(c) for c in range(ord('0'), ord('9') + 1)])\n    lines_by_letter = {}\n    for c in all_keys:\n        lines_by_letter[c] = []\n    for l in lines:\n        lines_by_letter[l[0].lower()].extend([l])\n    return lines_by_letter\n\n\nclass View(baseview.Baseview):\n    ''' The View part of the MVC pattern. '''\n\n    def __init__(self, builder, model):\n        self.builder = builder\n        baseview.Baseview.__init__(self, self)\n        self.model = model\n        self.controller = None\n        self.selected = None\n        self._main_window_connect()\n        model.add_listener(self.on_model_change)\n\n    def _main_window_connect(self):\n        ''' Connect signals for main window. '''\n\n        def on_view_config_btn_cb(btn):\n            ''' User pressed the view config file button. '''\n            label = self.builder.get_object('selected_remote_lbl')\n            self.controller.show_remote(label.get_text())\n            return True\n\n        def on_info_btn_clicked(txt):\n            ''' Handle ? help button  buttons. '''\n            lbl = self.builder.get_object('selected_remote_lbl').get_text()\n            if _DEBUG:\n                print(\"Selected remote label: \" + lbl)\n            self.show_text(txt, \"lirc: help\")\n\n        def on_search_config_btn_clicked_cb(btn, data=None):\n            ''' User clicked \"Search remote\" button. '''\n            lbl = self.builder.get_object('search_config_entry')\n            self.controller.select_remote(lbl.get_text())\n\n        clicked_connects = [\n            ('preconfig_device_btn', lambda b: self.show_preconfig_dialog()),\n            ('view_config_btn', on_view_config_btn_cb),\n            ('view_driver_btn', lambda b: self.show_driver()),\n            ('main_browse_btn', lambda b: self.show_config_browse_cb()),\n            ('no_remote_btn', lambda b: self.model.set_manual_remote()),\n            ('devinput_btn', lambda b: self.controller.show_devinput()),\n            ('default_btn', lambda b: self.controller.show_default()),\n            ('exit_btn', lambda b: Gtk.main_quit()),\n            ('search_btn', on_search_config_btn_clicked_cb),\n            ('install_btn', lambda b: self.controller.write_results()),\n            ('restart_btn', lambda b: self.controller.restart()),\n            ('no_remote_help_btn',\n             lambda b: on_info_btn_clicked(NO_REMOTE_INFO)),\n            ('devinput_help_btn',\n             lambda b: on_info_btn_clicked(DEVINPUT_INFO)),\n            ('default_help_btn',\n             lambda b: on_info_btn_clicked(DEFAULT_INFO)),\n            ('main_help_btn', lambda b: on_info_btn_clicked(MAIN_HELP)),\n            ('preconfig_help_btn',\n             lambda b: on_info_btn_clicked(PRECONFIG_INFO))\n        ]\n        w = self.builder.get_object('main_window')\n        w.connect('delete-event', lambda w, e: Gtk.main_quit())\n        for btn_name, handler in clicked_connects:\n            self.builder.get_object(btn_name).connect('clicked', handler)\n\n    def show_text(self, text, title=\"lirc: show file\", on_ok_cb=None):\n        ''' Read-only text display in a textview. '''\n\n        def cb_on_view_ok_btn_clicked(button, data=None):\n            ''' OK button on view_some_text window. '''\n            button.get_toplevel().hide()\n            if on_ok_cb:\n                on_ok_cb()\n            else:\n                return True\n\n        text = text.replace(\"&\", \"&amp;\")\n        self.builder.get_object(\"show_text_label\").set_markup(text)\n        w = self.builder.get_object('view_text_window')\n        w.set_title(title)\n        if not self.test_and_set_connected('view_text_window'):\n            w.connect('delete-event', _on_window_delete_event_cb)\n            b = self.builder.get_object('view_text_ok_btn')\n            b.connect('clicked', cb_on_view_ok_btn_clicked)\n        w.show_all()\n\n    def set_controller(self, controller):\n        ''' Set the controller, a circular dependency. '''\n        self.controller = controller\n\n    def on_model_change(self):\n        ''' Update view to match model.'''\n        text = \"(None)\"\n        sense_conf = False\n        if self.model.has_lircd_conf():\n            text = self.model.config['lircd_conf']\n            if not self.model.is_remote_manual():\n                sense_conf = True\n        self.builder.get_object('selected_remote_lbl').set_text(text)\n        self.builder.get_object('drv_select_remote_lbl').set_text(text)\n        self.builder.get_object('view_config_btn').set_sensitive(sense_conf)\n\n        text = 'Selected capture device (None)'\n        sensitive = True if self.model.has_label() else False\n        if sensitive:\n            text = self.model.config['label']\n        self.builder.get_object('view_driver_btn').set_sensitive(sensitive)\n        self.builder.get_object('selected_driver_lbl').set_text(text)\n        self.builder.get_object('selected_driver_lbl').queue_draw()\n        btn = self.builder.get_object('install_btn')\n        btn.set_sensitive(self.model.is_installable())\n\n    def load_configs(self):\n        ''' Load config files into model. -> control... '''\n        self.model.load_configs()\n        if not self.model.configs:\n            self.view.show_warning(\"Cannot find the configuration files\")\n\n    def show_driver(self):\n        ''' Display data for current driver. '''\n\n        def format_path(config, key):\n            ''' Format a lircd.conf/lircmd.conf entry. '''\n            if not _hasitem(config, key):\n                return 'Not set'\n            return os.path.basename(config[key])\n\n        items = [\n            ('Driver', 'driver'),\n            ('lircd.conf file', 'lircd_conf'),\n            ('lircmd.conf file', 'lircmd_conf'),\n            ('lircd --device option', 'device'),\n            ('Kernel modules required and loaded', 'modprobe'),\n            ('Kernel module setup', 'modsetup'),\n            ('Blacklisted kernel modules', 'conflicts')\n        ]\n        s = ''\n        for label, key in items:\n            s += \"%-36s: \" % label\n            if key in ['lircd_conf', 'lircmd_conf']:\n                s += format_path(self.model.config, key)\n            else:\n                s += str(self.model.config[key]) \\\n                    if key in self.model.config else 'Not set'\n            s += \"\\n\"\n        s = \"<tt>\" + s + \"</tt>\"\n        self.show_info('lirc: Driver configuration', s)\n\n    def show_select_lpt_window(self, module):\n        ''' Show window for selecting lpt1, lpt2... '''\n\n        ports = {'lpt1': ['7', '0x378'],\n                 'lpt2': ['7', '0x278'],\n                 'lpt3': ['5', '0x3bc'],\n                 'custom': ['0', '0']}\n\n        def on_select_back_cb(button, data=None):\n            ''' User clicked the Back button. '''\n            button.get_toplevel().hide()\n\n        def on_select_next_cb(button, data=None):\n            ''' User clicked the Next button. '''\n            group = self.builder.get_object('lpt1_lpt_btn').get_group()\n            for b in group:\n                if not b.get_active():\n                    continue\n                btn_id = b.lirc_id\n                if btn_id == 'custom':\n                    entry = self.builder.get_object('lpt_iobase_entry')\n                    iobase = entry.get_text()\n                    entry = self.builder.get_object('lpt_irq_entry')\n                    irq = entry.get_text()\n                else:\n                    iobase = ports[btn_id][0]\n                    irq = ports[btn_id][1]\n                modsetup = '%s: iobase=%s irq=%s' % (module, iobase, irq)\n            button.get_toplevel().hide()\n            self.controller.modinit_done(modsetup)\n\n        for btn in ports.keys():\n            b = self.builder.get_object(btn + '_lpt_btn')\n            b.lirc_id = btn\n        if not self.test_and_set_connected('select_lpt_window'):\n            b = self.builder.get_object('lpt_next_btn')\n            b.connect('clicked', on_select_next_cb)\n            b = self.builder.get_object('lpt_back_btn')\n            b.connect('clicked', on_select_back_cb)\n        w = self.builder.get_object('select_lpt_window')\n        w.show_all()\n\n    def show_select_udp_port_window(self):\n        ''' Let user select port for the UDP driver. '''\n\n        def on_udp_port_next_cb(button, data=None):\n            ''' User clicked the Next button. '''\n            entry = self.builder.get_object('udp_port_entry')\n            numtext = entry.get_text()\n            try:\n                num = int(numtext)\n            except ValueError:\n                num = -1\n            num = -1 if num > 65535 else num\n            num = -1 if num <= 1024 else num\n            if num == -1:\n                self.show_warning(\"Illegal port number\")\n                return True\n            else:\n                self.model.set_device(numtext)\n                self.controller.modinit_done(None)\n                device_dict = dict(self.model.find_config('id', 'udp'))\n                device_dict['label'] += ' ' + numtext\n                device_dict['device'] = numtext\n                self.model.set_capture_device(device_dict)\n            button.get_toplevel().hide()\n\n        def on_udp_port_back_cb(button, data=None):\n            ''' User clicked the Back button. '''\n            button.get_toplevel().hide()\n\n        if not self.test_and_set_connected('select_udp_port_window'):\n            b = self.builder.get_object('udp_port_next_btn')\n            b.connect('clicked', on_udp_port_next_cb)\n            b = self.builder.get_object('udp_port_back_btn')\n            b.connect('clicked', on_udp_port_back_cb)\n        w = self.builder.get_object('select_udp_port_window')\n        entry = self.builder.get_object('udp_port_entry')\n        entry.set_text(\"8765\")\n        w.show_all()\n\n    def show_select_com_window(self, module):\n        ''' Show window for selecting com1, com2... '''\n\n        ports = {'com1': ['4', '0x3f8'],\n                 'com2': ['3', '0x2f8'],\n                 'com3': ['4', '0x3e8'],\n                 'com4': ['3', '0x2e8'],\n                 'custom': ['0', '0']}\n\n        def on_com_select_next_cb(button, data=None):\n            ''' User clicked the Next button. '''\n            group = self.builder.get_object('com1_btn').get_group()\n            for b in group:\n                if not b.get_active():\n                    continue\n                btn_id = b.lirc_id\n                if btn_id == 'custom':\n                    entry = self.builder.get_object('custom_iobase_entry')\n                    iobase = entry.get_text()\n                    entry = self.builder.get_object('custom_irq_entry')\n                    irq = entry.get_text()\n                else:\n                    iobase = ports[btn_id][1]\n                    irq = ports[btn_id][0]\n                modsetup = '%s: iobase=%s irq=%s' % (module, iobase, irq)\n            self.controller.modinit_done(modsetup)\n            button.get_toplevel().hide()\n\n        def on_com_select_back_cb(button, data=None):\n            ''' User clicked the Back button. '''\n            button.get_toplevel().hide()\n\n        for btn in ports.keys():\n            b = self.builder.get_object(btn + '_btn')\n            b.lirc_id = btn\n        if not self.test_and_set_connected('select_com_window'):\n            b = self.builder.get_object('select_com_next_btn')\n            b.connect('clicked', on_com_select_next_cb)\n            b = self.builder.get_object('select_com_back_btn')\n            b.connect('clicked', on_com_select_back_cb)\n            self.connected.add('select_com_window')\n        w = self.builder.get_object('select_com_window')\n        w.show_all()\n\n    def show_preconfig_dialog(self):\n        ''' Show the preconfigured devices main dialog. '''\n\n        menu_label_by_id = {\n            'usb': ('preconfig_menu_1', 'USB Devices'),\n            'other_serial': ('preconfig_menu_2', 'Other serial devices'),\n            'tv_card': ('preconfig_menu_3', 'TV card devices'),\n            'pda': ('preconfig_menu_4', 'PDA:s'),\n            'other': ('preconfig_menu_5',\n                      'Other (MIDI, Bluetooth, udp, etc.)'),\n            'soundcard': ('preconfig_menu_6',\n                          'Home-brew (soundcard input)'),\n            'home_brew': ('preconfig_menu_7',\n                          'Home-brew serial and parallel devices'),\n            'irda': ('preconfig_menu_8', 'IRDA/CIR hardware')\n        }\n\n        def get_selected_cb(button, data=None):\n            ''' Return currently selected menu option. '''\n            for menu_label in menu_label_by_id.values():\n                b = self.builder.get_object(menu_label[0])\n                if b.get_active():\n                    self.selected = b.lirc_id\n                    return True\n            return False\n\n        w = self.builder.get_object('preconfig_window')\n        if not self.test_and_set_connected('preconfig_window'):\n            w.connect('delete-event', _on_window_delete_event_cb)\n            b = self.builder.get_object('preconfig_back_btn')\n            b.connect('clicked', lambda b: w.hide())\n            b = self.builder.get_object('preconfig_next_btn')\n            b.connect('clicked',\n                      lambda b: self.show_preconfig_select(self.selected))\n            for id_, menu_label in menu_label_by_id.items():\n                b = self.builder.get_object(menu_label[0])\n                b.connect('toggled', get_selected_cb)\n                b.lirc_id = id_\n            get_selected_cb(None)\n        w.show_all()\n\n    def show_preconfig_select(self, menu=None):\n        ''' User has selected configuration submenu, present options. '''\n\n        def build_treeview(menu):\n            ''' Construct the configurations points liststore treeview. '''\n\n            treeview = self._create_treeview('preconfig_items_view',\n                                             ['Configuration'])\n            treeview.get_selection().connect('changed',\n                                             on_treeview_change_cb)\n            liststore = treeview.get_model()\n            liststore.clear()\n            configs = self.model.get_configs()\n            labels = [configs[l]['label']\n                      for l in configs.keys() if configs[l]['menu'] == menu]\n            for l in sorted(labels):\n                liststore.append([l])\n\n        def on_treeview_change_cb(selection, data=None):\n            ''' User selected a row i. e., a config. '''\n            (model, iter_) = selection.get_selected()\n            if not iter_:\n                return\n            label = self.builder.get_object('preconfig_select_lbl')\n            label.set_text(model[iter_][0])\n\n        def on_preconfig_next_clicked_cb(button, data=None):\n            ''' User pressed 'Next' button. '''\n            label = self.builder.get_object('preconfig_select_lbl')\n            device = self.model.find_config('label', label.get_text())\n            self.model.set_capture_device(device)\n            button.get_toplevel().hide()\n            self.builder.get_object('preconfig_window').hide()\n            self.controller.start_check()\n            return True\n\n        w = self.builder.get_object('preconfig_select_window')\n        if not self.test_and_set_connected('preconfig_select_window'):\n            build_treeview(menu)\n            w.connect('delete-event', _on_window_delete_event_cb)\n            b = self.builder.get_object('preconfig_select_back_btn')\n            b.connect('clicked', lambda b: w.hide())\n            b = self.builder.get_object('preconfig_select_next_btn')\n            b.connect('clicked', on_preconfig_next_clicked_cb)\n        w.show_all()\n\n    def show_single_remote(self, line):\n        ''' Display search results of a single match. '''\n\n        def on_next_btn_clicked_cb(btn, id_):\n            ''' User clicked \"Next\" button. '''\n            self.controller.set_remote(id_)\n            btn.get_toplevel().hide()\n\n        words = line.split(';')\n        id_ = words[0] + '/' + words[1]\n        s = \"Path: \" + id_\n        self.builder.get_object('single_remote_config_lbl').set_text(s)\n        s = \"Supported remotes: \" + words[4]\n        self.builder.get_object('single_remote_supports_lbl').set_text(s)\n        w = self.builder.get_object('single_remote_select_window')\n        if not self.test_and_set_connected('single_remote_select_window'):\n            btn = self.builder.get_object('single_remote_next_btn')\n            btn.connect('clicked', on_next_btn_clicked_cb, id_)\n            btn = self.builder.get_object('single_remote_back_btn')\n            btn.connect('clicked', lambda b: w.hide())\n        w.show_all()\n\n    def show_search_results_select(self, lines):\n        ''' User  has entered a search pattern, let her choose match.'''\n\n        def on_select_change_cb(selection, data=None):\n            ''' User changed the selected lircd.conf option. '''\n            (model, iter_) = selection.get_selected()\n            if not iter_:\n                return\n            remote = model[iter_][0] + '/' + model[iter_][1]\n            self.builder.get_object('selected_config_lbl').set_text(remote)\n            self.builder.get_object('search_next_btn').set_sensitive(True)\n\n        def on_search_next_btn_cb(button, data=None):\n            ''' User pressed 'Next' button.'''\n            label = self.builder.get_object('selected_config_lbl')\n            self.controller.set_remote(label.get_text())\n            button.get_toplevel().hide()\n            return True\n\n        def load_liststore(treeview, found):\n            ''' Reload the liststore data model. '''\n            liststore = treeview.get_model()\n            liststore.clear()\n            for l in found:\n                words = l.split(';')\n                liststore.append([words[0], words[1], words[4]])\n            treeview.set_model(liststore)\n\n        treeview = self._create_treeview('search_results_view',\n                                         ['vendor', 'lircd.conf', 'device'])\n        load_liststore(treeview, lines)\n        if not self.test_and_set_connected('search_select_window'):\n            treeview.get_selection().connect('changed', on_select_change_cb)\n            b = self.builder.get_object('search_back_btn')\n            b.connect('clicked', lambda b, d=None: b.get_toplevel().hide())\n            b = self.builder.get_object('search_next_btn')\n            b.connect('clicked', on_search_next_btn_cb)\n        w = self.builder.get_object('search_select_window')\n        w.show_all()\n\n    def show_config_browse_cb(self, button=None, data=None):\n        ''' User clicked browse configs button. '''\n\n        def build_treeview():\n            ''' Construct the remotes browse liststore treeview. '''\n            treeview = self._create_treeview('config_browse_view', ['path'])\n            treestore = Gtk.TreeStore(str)\n            treeview.set_model(treestore)\n            return treeview\n\n        def fill_treeview(treeview):\n            ''' Fill the treestore with browse options. '''\n            treestore = treeview.get_model()\n            if hasattr(treestore, 'lirc_is_inited'):\n                return\n            treestore.clear()\n            lines = self.model.get_remotes_list(self)\n            lines_by_letter = _get_lines_by_letter(lines)\n            for c in sorted(lines_by_letter.keys()):\n                iter_ = treestore.insert_with_values(None, -1, [0], [c])\n                done = []\n                for l in lines_by_letter[c]:\n                    w = [l.split(';')[0]]\n                    if w[0] in done:\n                        continue\n                    done.extend(w)\n                    subiter = treestore.insert_with_values(iter_, -1, [0], w)\n                    my_lines = [l for l in lines_by_letter[c]\n                                if l.startswith(w[0] + ';')]\n                    for ml in my_lines:\n                        words = ml.split(';')\n                        label = [words[1] + \"/\" + words[4]]\n                        treestore.insert_with_values(subiter, -1, [0], label)\n\n            treestore.lirc_is_inited = True\n\n        def on_select_change_cb(selection, data=None):\n            ''' User changed the selected lircd.conf browse  option. '''\n            (model, treeiter) = selection.get_selected()\n            if not treeiter or not model[treeiter].get_parent():\n                return True\n            item = model[treeiter][0].split('/')[0]\n            item = model[treeiter].get_parent()[0] + \"/\" + item\n            self.controller.set_remote(item)\n            lbl = self.builder.get_object('config_browse_select_lbl')\n            lbl.set_text(item)\n            btn = self.builder.get_object('config_browse_next_btn')\n            btn.set_sensitive(True)\n            return True\n\n        def on_config_browse_next_btn_cb(button, data=None):\n            ''' User presses 'Next' button. '''\n            label = self.builder.get_object('config_browse_select_lbl')\n            self.controller.set_remote(label.get_text())\n            button.get_toplevel().hide()\n            return True\n\n        def on_config_browse_view_btn_cb(button, data=None):\n            ''' User presses 'View' button. '''\n            lbl = self.builder.get_object('config_browse_select_lbl')\n            self.controller.show_remote(lbl.get_text())\n\n        w = self.builder.get_object('config_browse_window')\n        if not self.test_and_set_connected('config_browse_window'):\n            w.connect('delete-event', _on_window_delete_event_cb)\n            b = self.builder.get_object('config_browse_back_btn')\n            b.connect('clicked', lambda b, d=None: b.get_toplevel().hide())\n            b = self.builder.get_object('config_browse_next_btn')\n            b.connect('clicked', on_config_browse_next_btn_cb)\n            b = self.builder.get_object('config_browse_view_btn')\n            b.connect('clicked', on_config_browse_view_btn_cb)\n            treeview = build_treeview()\n            fill_treeview(treeview)\n            treeview.get_selection().connect('changed', on_select_change_cb)\n        w.show_all()\n\n\n# vim: set expandtab ts=4 sw=4:\n","repo_name":"andyvand/LIRC","sub_path":"tools/lirc-setup/mvc_view.py","file_name":"mvc_view.py","file_ext":"py","file_size_in_byte":25002,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"81"}
+{"seq_id":"5375614879","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Sat Apr  9 16:34:46 2022\r\n\r\n@author: Naman Jain\r\n\"\"\"\r\n\r\nimport pandas as pd\r\nimport matplotlib.pyplot as plt\r\nimport numpy as np\r\nimport cv2\r\n\r\nimport tensorflow as tf\r\nfrom tensorflow import keras \r\nfrom keras.models import Sequential,Model\r\nfrom keras.layers import Dense, Dropout, Flatten\r\n#from keras.layers import Conv2D, MaxPooling2D, LeakyReLU, UpSampling2D, BatchNormalization, Concatenate, Input\r\nfrom keras.layers import *\r\nfrom tensorflow.keras.optimizers import *\r\n\r\n\r\ndf = pd.read_csv('train_set/label_data_0531.csv')\r\n\r\nx=[]\r\nl0 = []\r\nl1 = []\r\nnew_paths = []\r\n\r\npaths = df.iloc[:,-1].values\r\nlane0 = df.iloc[:,df.columns.str.startswith('lanes/0')].values\r\nlane1 = df.iloc[:,df.columns.str.startswith('lanes/1')].values\r\nlane2 = df.iloc[:,df.columns.str.startswith('lanes/2')].values\r\nlane3 = df.iloc[:,df.columns.str.startswith('lanes/3')].values\r\nlane4 = df.iloc[:,df.columns.str.startswith('lanes/4')].values\r\nh_sample = df.iloc[:,df.columns.str.startswith('h_sample')].values\r\n\r\n\r\nprint(len(lane0[0]))\r\n\r\nfull0 = np.asarray(np.where((lane0 < 0).sum(axis=1) <= 0))\r\nfull1 = np.asarray(np.where((lane1 < 0).sum(axis=1) <= 0))\r\n\r\nmatch = (np.intersect1d(full0, full1))\r\n\r\n#print(len(full0[0]))\r\n    \r\nfor i in full0:\r\n    #print(i)\r\n    l0 = lane0[i,:]\r\n    new_paths = paths[i]\r\n    \r\nfor i in full1:\r\n    #print(i)\r\n    l1 = lane1[i,:]\r\n    #new_paths = paths[i]    \r\n\r\n    \r\nlane = np.concatenate((lane0, lane1, lane2, lane3), axis=1)\r\n#lane = np.reshape(lane, (4, len(lane0), len(lane0[1])))\r\n\r\n\r\nfor j in range(0, len(paths)):\r\n\r\n    \r\n    p = paths[j]\r\n    p = 'train_set/' + p\r\n    #p = 'train_set/clips/0531/1492626287507231547/20.jpg'\r\n    \r\n    image = cv2.imread(p)\r\n    img = image[160:710:10,0:1000,:]\r\n    \r\n    #cv2.imshow('image',img)\r\n    #cv2.waitKey(1000)\r\n    \r\n    img = cv2.cvtColor(img, cv2.COLOR_RGB2YUV)\r\n    img = cv2.GaussianBlur(img, (3,3), 0)\r\n    img = cv2.resize(img, (200,66))\r\n    img = img/255\r\n    \r\n    #cv2.imshow('image', img)\r\n    #cv2.waitKey(1000)\r\n    \r\n    #img = np.resize(img, (90,160,3))\r\n    #gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\r\n    x.append(img)\r\n    \r\n#cv2.destroyAllWindows()\r\n\r\nx = np.array(x)\r\ny = lane0\r\n\r\nx_vals=[]\r\ny_vals=[]\r\n\r\ncoef_matrix=[]\r\n#print(len(y))\r\n\r\nfor i in range(0, len(y)):\r\n    for j in range(0, len(y[1])):\r\n        #print(i,j)\r\n        if(y[i,j]!=-2 and np.isnan(y[i,j]) == False):\r\n            x_vals.append(y[i,j])\r\n            y_vals.append(h_sample[i,j])\r\n    X = np.array(x_vals)\r\n    Y = np.array(y_vals)\r\n    x_vals.clear()\r\n    y_vals.clear()\r\n    coef = np.polyfit(Y,X,1)\r\n    coef_matrix.append(coef)      \r\n\r\ncoef_matrix = np.array(coef_matrix)\r\ny = coef_matrix\r\nnum_output = len(y[0])\r\n\r\n# for i in range(0,len(coef_matrix)):\r\n#     for j in range(0,len(coef_matrix[1])):\r\n#         if(coef_matrix[i][j] > -.1 and coef_matrix[i][j] < 0):\r\n#             coef_matrix[i][j] = 0\r\n#         if(coef_matrix[i][j] > )\r\n\r\ndef lane_net():\r\n    \r\n    model = Sequential()\r\n    model.add(Conv2D(30, (5, 5), input_shape=(66, 200, 3)))\r\n    model.add(LeakyReLU(alpha=0.05))\r\n    model.add(MaxPooling2D(pool_size=(2, 2)))\r\n    \r\n    model.add(Conv2D(15, (3, 3)))\r\n    model.add(LeakyReLU(alpha=0.05))\r\n    model.add(MaxPooling2D(pool_size=(2, 2)))\r\n    \r\n    model.add(Conv2D(10, (3, 3)))\r\n    model.add(LeakyReLU(alpha=0.05))\r\n    model.add(MaxPooling2D(pool_size=(2, 2)))\r\n    \r\n    model.add(Conv2D(5, (3, 3)))\r\n    model.add(LeakyReLU(alpha=0.05))\r\n    model.add(MaxPooling2D(pool_size=(2, 2)))\r\n    \r\n    model.add(Flatten())\r\n    model.add(Dense(150))\r\n    model.add(LeakyReLU(alpha=0.05))\r\n    \r\n    model.add(Dense(125))\r\n    model.add(LeakyReLU(alpha=0.05))\r\n    \r\n    model.add(Dense(100))\r\n    model.add(LeakyReLU(alpha=0.05))\r\n    \r\n    #model.add(Dropout(0.5))\r\n    model.add(Dense(num_output))\r\n    model.add(LeakyReLU(alpha=0.05))\r\n    # Compile model\r\n    model.compile(optimizer='adam',loss='mse', metrics=['accuracy'])\r\n    #model.compile(Adam(lr = 0.01), loss='categorical_crossentropy', metrics=['accuracy'])\r\n    return model\r\n    \r\n#model = lane_net()\r\n#print(model.summary())\r\n\r\n#history = model.fit(x,y,validation_split=0.3,epochs=5,batch_size=50, verbose=1)\r\n\r\n\r\ndef nvidia_model():\r\n      model = Sequential()\r\n      model.add(Conv2D(24, kernel_size=(5,5), strides=(2,2), input_shape=(66,200,3),activation='tanh'))\r\n \r\n      model.add(Conv2D(36, kernel_size=(5,5), strides=(2,2), activation='tanh'))\r\n      model.add(Conv2D(48, kernel_size=(5,5), strides=(2,2), activation='tanh'))\r\n      model.add(Conv2D(64, kernel_size=(3,3), activation='tanh'))\r\n      model.add(Conv2D(64, kernel_size=(3,3), activation='tanh'))\r\n      #model.add(Dropout(0.5))\r\n \r\n \r\n      model.add(Flatten())\r\n      model.add(Dense(100, activation='tanh'))\r\n      model.add(Dropout(0.5))\r\n \r\n \r\n      model.add(Dense(50, activation='tanh'))\r\n      model.add(Dropout(0.5))\r\n      \r\n      model.add(Dense(25, activation ='tanh'))\r\n      model.add(Dropout(0.5))\r\n      \r\n      model.add(Dense(num_output))\r\n \r\n      optimizer= Adam(learning_rate=1e-5)\r\n      model.compile(loss='mse', optimizer=optimizer, metrics=['accuracy'])\r\n \r\n      return model\r\n  \r\nmodel = nvidia_model()\r\nprint(model.summary())\r\n\r\nhistory = model.fit(x,coef_matrix,validation_split=0.3, epochs=50,batch_size=5, verbose=1, shuffle=True)\r\n\r\n# plt.plot(history.history['loss'])\r\n# plt.plot(history.history['val_loss'])\r\n# plt.title('model loss')\r\n# plt.ylabel('loss')\r\n# plt.xlabel('epoch')\r\n# plt.legend(['train', 'test'], loc='upper left')\r\n# plt.show()\r\n\r\n# model.save('allpaths1.h5')\r\n    \r\n# plt.plot(history.history['accuracy'])\r\n# plt.plot(history.history['val_accuracy'])\r\n# plt.title('model accuracy')\r\n# plt.ylabel('accuracy')\r\n# plt.xlabel('epoch')\r\n# plt.legend(['train', 'test'], loc='upper left')\r\n# plt.show()\r\n\r\n#import random\r\n\r\n# x_pred = []\r\n# index = random.randint(0,len(paths))\r\n# rand_path = paths[index]\r\n# rand_path = 'train_set/' + rand_path\r\n# #p = 'train_set/clips/0531/1492626287507231547/20.jpg'\r\n\r\n# image = cv2.imread(rand_path)\r\n\r\n# img = image[160:710:10,0:1000,:]\r\n\r\n# #cv2.imshow('image',img)\r\n# #cv2.waitKey(1000)\r\n\r\n# img = cv2.cvtColor(img, cv2.COLOR_RGB2YUV)\r\n# img = cv2.GaussianBlur(img, (3,3), 0)\r\n# img = cv2.resize(img, (200,66))\r\n# img = img/255\r\n\r\n# x_pred.append(img)\r\n# x_pred = np.array(x_pred)\r\n# y = h_sample[0]\r\n\r\n# model = keras.models.load_model('newpaths_93.h5')\r\n\r\n\r\n# pred = model.predict(x_pred)\r\n# pred = np.resize(pred, (56))\r\n\r\n# #solver = np.poly1d(coef)\r\n# #x_val = solver(y)\r\n\r\n# for i in range(0,len(y)):\r\n#     #print(pred[i])\r\n#     cv2.drawMarker(image, (int(pred[i]),int(y[i])), (0,0,255), markerSize = 10, thickness =2)\r\n#     #print(i)\r\n\r\n# cv2.imshow('image',image)\r\n# cv2.waitKey(0)\r\n       \r\n# cv2.destroyAllWindows()\r\n\r\n# print(coef_matrix[0:4,:])\r\n# \r\n\r\nx=[]\r\n\r\nfor j in range(0, len(paths)):\r\n    p = paths[j]\r\n    p = 'train_set/' + p\r\n    #p = 'train_set/clips/0531/1492626287507231547/20.jpg'\r\n    \r\n    image = cv2.imread(p)\r\n    image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\r\n    x.append(image)\r\n    \r\nx = np.array(x)\r\n\r\ndef unet():\r\n    inputs = Input((1280,720,1))\r\n    conv1 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(inputs)\r\n    conv1 = BatchNormalization()(conv1)\r\n    conv1 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv1)\r\n    conv1 = BatchNormalization()(conv1)\r\n    pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)\r\n    conv2 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool1)\r\n    conv2 = BatchNormalization()(conv2)\r\n    conv2 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv2)\r\n    conv2 = BatchNormalization()(conv2)\r\n    pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)\r\n    conv3 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool2)\r\n    conv3 = BatchNormalization()(conv3)\r\n    conv3 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv3)\r\n    conv3 = BatchNormalization()(conv3)\r\n    pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)\r\n    conv4 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool3)\r\n    conv4 = BatchNormalization()(conv4)\r\n    conv4 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv4)\r\n    conv4 = BatchNormalization()(conv4)\r\n    drop4 = Dropout(0.5)(conv4)\r\n    pool4 = MaxPooling2D(pool_size=(2, 2))(drop4)\r\n\r\n    conv5 = Conv2D(1024, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool4)\r\n    conv5 = BatchNormalization()(conv5)\r\n    conv5 = Conv2D(1024, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv5)\r\n    conv5 = BatchNormalization()(conv5)\r\n    drop5 = Dropout(0.5)(conv5)\r\n\r\n    up6 = Conv2D(512, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(drop5))\r\n    merge6 = concatenate([drop4,up6], axis = 3)\r\n    conv6 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge6)\r\n    conv6 = BatchNormalization()(conv6)\r\n    conv6 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv6)\r\n    conv6 = BatchNormalization()(conv6)\r\n\r\n    up7 = Conv2D(256, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(conv6))\r\n    merge7 = concatenate([conv3,up7], axis = 3)\r\n    conv7 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge7)\r\n    conv7 = BatchNormalization()(conv7)\r\n    conv7 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv7)\r\n    conv7 = BatchNormalization()(conv7)\r\n\r\n    up8 = Conv2D(128, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(conv7))\r\n    merge8 = concatenate([conv2,up8], axis = 3)\r\n    conv8 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge8)\r\n    conv8 = BatchNormalization()(conv8)\r\n    conv8 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv8)\r\n    conv8 = BatchNormalization()(conv8)\r\n\r\n    up9 = Conv2D(64, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(conv8))\r\n    merge9 = concatenate([conv1,up9], axis = 3)\r\n    conv9 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge9)\r\n    conv9 = BatchNormalization()(conv9)\r\n    conv9 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv9)\r\n    conv9 = BatchNormalization()(conv9)\r\n    conv9 = Conv2D(2, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv9)\r\n    conv9 = BatchNormalization()(conv9)\r\n    conv10 = Conv2D(1, 1, activation = 'sigmoid')(conv9)\r\n    \r\n    model = Model(inputs = inputs, outputs = conv10)\r\n    model.compile(optimizer=Adam(lr=1e-3), loss='mse', metrics =['accuracy'])\r\n    return model\r\n\r\nmodel = unet()\r\nprint(model.summary())\r\n","repo_name":"Namanj1298/Lane-Detection-Project","sub_path":"neural.py","file_name":"neural.py","file_ext":"py","file_size_in_byte":11258,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"72763368266","text":"computer = 0\r\nplayer = 0\r\nwin = False\r\n\r\n\r\nwhile not win:\r\n    var = input(\"Enter : Rock,Paper,Scissor :\")\r\n\r\n    if var == \"Rock\" or var == \"rock\":\r\n        print(\"Computer Chooses Paper!\")\r\n        computer += 1\r\n        print(f\"You have {player} points!\")\r\n        print(f\"Computer have {computer} Points!\")\r\n    \r\n    elif var == \"Paper\" or var == \"paper\":\r\n        print(\"Computer Chooses Scissor!\")\r\n        computer += 1\r\n        print(f\"You have {player} points!\")\r\n        print(f\"Computer have {computer} Points!\")\r\n\r\n    elif var == \"Scissor\" or var == \"scissor\":\r\n        print(\"Computer Chooses Rock!\")\r\n        computer += 1\r\n        print(f\"You have {player} points!\")\r\n        print(f\"Computer have {computer} Points!\")\r\n\r\n    if computer == 3:\r\n        print(\"Computer Has Won!!\")\r\n        print(\"Want To Play Again??\")\r\n        reply = input()\r\n        if reply == \"yes\" or reply == \"Yes\":\r\n            win = False\r\n\r\n        if reply == \"no\" or reply == \"No\":\r\n            win = True\r\n\r\n            ","repo_name":"Coding-With-Serwin/Rock-Paper-Scissor-Game","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1018,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"81"}
+{"seq_id":"71783499144","text":"from pathlib import Path\n\nfrom datascience_model_commons.model import BaseModel, ModelException\nimport numpy as np\nimport pandas as pd\nimport tensorflow as tf\nimport tensorflow_text  # noqa: F401\nimport yaml\n\nfrom .arms_of_kamaji import series_map\n\nDEFAULT_VALUES_DICT = {tf.string: (tf.string, \"\"), tf.float32: (tf.float32, 0.0)}\n\n\n# Enable loading of metadata without model source code\nclass SafeLoaderIgnoreUnknown(yaml.SafeLoader):\n    def ignore_unknown(self, node):\n        return None\n\n\nclass Model(BaseModel):\n    def __init__(self, model_path):\n        from datascience_model_commons.transactions.business_rules.dutch_tax_rules import (\n            apply_tax_rules as apply_nl_tax_rules,\n        )\n        from datascience_model_commons.transactions.business_rules.uk_tax_rules import (\n            apply_tax_rules as apply_uk_tax_rules,\n        )\n\n        model_directory = Path(__file__).parent\n        with open(model_directory / \"tax_config.yaml\", \"r\") as f:\n            self.tax_config_dict = yaml.load(f, Loader=SafeLoaderIgnoreUnknown)\n            self.tax_mapping_dict = self.tax_config_dict.get(\"mapping\")\n            self.tax_label_column = self.tax_config_dict.get(\"tax_label_column\")\n\n        # Dummy function (no operation) just returns the input_df\n        def apply_nop(input_df: pd.DataFrame):\n            return input_df\n\n        self.tax_function_dict = {\n            \"NL\": apply_nl_tax_rules,\n            \"GB\": apply_uk_tax_rules,\n            \"\": apply_nop,\n        }\n\n        # Default mapping is combination of UK and NL mapping\n        tax_all_mapping = {}\n        for local_mapping in self.tax_mapping_dict.values():\n            for transaction_type, mapping in local_mapping.items():\n                if transaction_type not in tax_all_mapping:\n                    tax_all_mapping[transaction_type] = {}\n                tax_all_mapping[transaction_type].update(mapping)\n        self.tax_mapping_dict[\"\"] = tax_all_mapping\n\n        self.load_model(model_path=model_path)\n\n    def load_model(self, model_path):\n        def find_artifact(root):\n            import os\n\n            # Traverse root to find model signature files (.pb file extension)\n            for root, dirs, files in os.walk(root):\n                for file in files:\n                    if file.endswith(\".pb\"):\n                        return root\n\n            raise ModelException(\"No .pb model artifact found!\")\n\n        try:\n            artifact_location = find_artifact(root=model_path)\n            model = tf.saved_model.load(artifact_location)\n        except Exception as e:\n            raise ModelException(f\"Unable to load model {model_path}\") from e\n\n        metadata_path = f\"{model_path}/training_metadata.yaml\"\n        try:\n            with open(metadata_path, \"r\") as stream:\n                SafeLoaderIgnoreUnknown.add_constructor(\n                    None, SafeLoaderIgnoreUnknown.ignore_unknown\n                )\n                root = yaml.load(stream, Loader=SafeLoaderIgnoreUnknown)\n                categories = root[\"categories\"]\n        except IOError as ioerr:\n            raise ModelException(\n                f'Error reading yaml config file: \"{metadata_path}\"'\n            ) from ioerr\n        except yaml.YAMLError as yerr:\n            raise ModelException(f'Unable to parse \"{metadata_path}\"') from yerr\n\n        self.model = model\n        self.categories = categories\n        self.predict_function = self.model.signatures[\"serving_default\"]\n        self.input_tensors = [\n            tensor\n            for tensor in self.predict_function.inputs\n            if \"unknown\" not in tensor.name\n        ]\n        self.input_description = {\n            tensor.name.split(\":\")[0]: DEFAULT_VALUES_DICT.get(tensor.dtype)\n            for tensor in self.input_tensors\n        }\n\n    def df_to_tensor_dict(self, df):\n        N = df.shape[0]\n        return {\n            key: np.atleast_2d(\n                df[key].values.astype(dtype=dtype.as_numpy_dtype)\n                if key in df\n                else np.repeat(default_value, N)\n            ).T\n            for key, (dtype, default_value) in self.input_description.items()\n        }\n\n    def scores_to_category(self, scores):\n        return np.choose(scores.argmax(axis=1), self.categories)\n\n    @staticmethod\n    def extract_country_code(df: pd.DataFrame):\n        # Make sure country code field is available\n        if \"country_code\" not in df:\n            df = df.assign(country_code=\"\")\n        else:\n            df[\"country_code\"].fillna(\"\", inplace=True)\n\n        # Select transactions that are missing country code\n        has_no_country_code = df[\"country_code\"] == \"\"\n        # and where counterparty_iban is available\n        if \"bank_counterparty_iban\" not in df:\n            df = df.assign(counterparty_iban=\"\")\n        has_iban = df[\"bank_counterparty_iban\"].fillna(\"\") != \"\"\n        selected_records = has_no_country_code & has_iban\n\n        # Take first country code letters from counterparty_iban\n        derived_country_code = df.loc[\n            selected_records, \"bank_counterparty_iban\"\n        ].str.slice(start=0, stop=2)\n        # and assign to country code\n        df.loc[selected_records, \"country_code\"] = derived_country_code\n\n        # ToDo: Make sure country code becomes available from serving\n        # assign country_code=GB when account type is CURRENT_ACCOUNT\n        # and country_code could not be derived from bank_counterparty_iban\n        has_no_country_code = df[\"country_code\"] == \"\"\n        is_current_account = df[\"counter_account_type\"] == \"CURRENT_ACCOUNT\"\n        selected_records = has_no_country_code & is_current_account\n        df.loc[selected_records, \"country_code\"] = \"GB\"\n\n        return df\n\n    def apply_tax_rules(self, input_df: pd.DataFrame) -> pd.DataFrame:\n        \"\"\"\n        input = Rules column\n        output = SME categories\n        \"\"\"\n        for country_code, local_tax_mapping in self.tax_mapping_dict.items():\n            subset_df = input_df.loc[input_df[\"country_code\"] == country_code]\n\n            # Nothing to do continue to next country code\n            if len(subset_df) == 0:\n                continue\n\n            tax_mapping_function = self.tax_function_dict.get(country_code)\n            subset_df = tax_mapping_function(subset_df)\n            # if subset_df is empty no tax mapping should take place\n            if self.tax_label_column in subset_df:\n                for transaction_type, mapping in local_tax_mapping.items():\n                    to_be_mapped_df = subset_df.loc[\n                        (subset_df[\"transaction_type\"] == transaction_type)\n                        & (\n                            subset_df[self.tax_label_column].notna()\n                        )  # only when the rules apply\n                    ]\n                    # loc only changes the relevant transactions and columns in input_df\n                    input_df.loc[to_be_mapped_df.index, \"category\"] = to_be_mapped_df[\n                        self.tax_label_column\n                    ].map(\n                        mapping\n                    )  # this applies the tax re-mapping\n                    input_df.loc[\n                        to_be_mapped_df.index, \"category_source\"\n                    ] = \"ModelPrediction\"\n        return input_df\n\n    def apply_kamaji_the_counterparty_mapper(\n        self, input_df: pd.DataFrame\n    ) -> pd.DataFrame:\n\n        if \"counterparty_name\" in input_df:\n            kamaji_answer = series_map(input_df.counterparty_name)\n            return kamaji_answer\n\n        else:\n            nones = pd.Series(None, input_df.index, dtype=str)\n            return nones\n\n    def apply_business_rules(self, input_df: pd.DataFrame) -> pd.DataFrame:\n        \"\"\"Apply business rules here, category column should be overwritten where business rules apply\"\"\"\n        input_df = self.extract_country_code(df=input_df)\n\n        result_df = self.apply_tax_rules(input_df=input_df)\n\n        # Make sure interest paid after tax .* deducted is categorised as Other Income as it is not Loans\n        result_df.loc[\n            result_df.description.str.lower().str.match(\n                \"interest paid after tax .* deducted\"\n            ),\n            [\"category\", \"category_source\"],\n        ] = (\"Other Income\", \"ModelPrediction\")\n\n        return result_df[[\"category\", \"category_source\"]]\n\n    def predict(self, *, df: pd.DataFrame):\n        if df.empty:  # Nothing to predict so return empty dataframe\n            categories = pd.Series(dtype=object)\n            source = pd.Series(dtype=object)\n        else:\n            predictions = self.predict_function(**self.df_to_tensor_dict(df))\n            scores = predictions[\"postprocessed_model\"].numpy()\n            categories = self.scores_to_category(scores=scores)\n            source = np.atleast_1d(predictions[\"postprocessed_model_1\"].numpy()).astype(\n                str\n            )\n\n        stage_two = self.apply_business_rules(\n            input_df=df.assign(\n                category=categories,\n                category_source=source,\n            )\n        )\n\n        kamajis_answer = self.apply_kamaji_the_counterparty_mapper(df)\n        stage_two[\"category\"][~kamajis_answer.isna()] = kamajis_answer[\n            ~kamajis_answer.isna()\n        ]\n\n        return stage_two\n","repo_name":"Yolt-group/categories-model","sub_path":"categories_model/predict/sme.py","file_name":"sme.py","file_ext":"py","file_size_in_byte":9293,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"81"}
+{"seq_id":"12540857411","text":"import numpy as np\nimport pandas as pd\nimport tensorflow as tf\nfrom tensorflow.keras import layers\nfrom sklearn.preprocessing import MinMaxScaler\nfrom tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping, LearningRateScheduler\nimport matplotlib.pyplot as plt\n\n# Load and preprocess the data\ndata = pd.read_csv('stock_prices.csv')  # Replace 'stock_prices.csv' with your own dataset\ndata = data['Close'].values  # Assuming the 'Close' column contains the target variable\ndata = data.reshape(-1, 1)  # Reshape to a 2D array\n\n# Split the data into train and test sets\ntrain_size = int(len(data) * 0.8)\ntrain_data = data[:train_size]\ntest_data = data[train_size:]\n\n# Normalize the data\nscaler = MinMaxScaler(feature_range=(0, 1))\ntrain_data = scaler.fit_transform(train_data)\ntest_data = scaler.transform(test_data)\n\n# Prepare the data for training\ndef prepare_data(data, time_steps):\n    X, y = [], []\n    for i in range(len(data) - time_steps - 1):\n        X.append(data[i : (i + time_steps)])\n        y.append(data[i + time_steps])\n    return np.array(X), np.array(y)\n\ntime_steps = 10  # Number of previous time steps to consider for prediction\nX_train, y_train = prepare_data(train_data, time_steps)\nX_test, y_test = prepare_data(test_data, time_steps)\n\n# Build the RNN-Attention model\ninputs = tf.keras.Input(shape=(time_steps, 1))\ngru = layers.GRU(64, return_sequences=True)(inputs)\nattention = layers.Attention()(gru)\noutput = layers.Dense(1)(attention)\nmodel = tf.keras.Model(inputs=inputs, outputs=output)\n\n# Compile the model\nmodel.compile(optimizer='adam', loss='mean_squared_error')\n\n# Define callbacks\ncheckpoint = ModelCheckpoint(\"model_checkpoint.h5\", save_best_only=True, save_weights_only=True, verbose=1)\nearly_stopping = EarlyStopping(patience=10, restore_best_weights=True)\nlr_scheduler = LearningRateScheduler(lambda epoch: 0.001 if epoch < 10 else 0.0001)\n\n# Train the model\nhistory = model.fit(\n    X_train,\n    y_train,\n    epochs=100,\n    batch_size=32,\n    validation_data=(X_test, y_test),\n    callbacks=[checkpoint, early_stopping, lr_scheduler]\n)\n\n# Evaluate the model\ntrain_predictions = model.predict(X_train)\ntest_predictions = model.predict(X_test)\n\n# Denormalize the predictions\ntrain_predictions = scaler.inverse_transform(train_predictions)\ny_train = scaler.inverse_transform(y_train)\ntest_predictions = scaler.inverse_transform(test_predictions)\ny_test = scaler.inverse_transform(y_test)\n\n# Plot the predictions\nplt.plot(y_train, label='Actual Train')\nplt.plot(train_predictions, label='Predicted Train')\nplt.plot(len(y_train) + np.arange(len(y_test)), y_test, label='Actual Test')\nplt.plot(len(y_train) + np.arange(len(test_predictions)), test_predictions, label='Predicted Test')\nplt.legend()\nplt.show()\n","repo_name":"devfinwiz/Fin-Maestro-Web","sub_path":"MLModels/rnn_attention_model.py","file_name":"rnn_attention_model.py","file_ext":"py","file_size_in_byte":2751,"program_lang":"python","lang":"en","doc_type":"code","stars":140,"dataset":"github-code","pt":"81"}
+{"seq_id":"74938099146","text":"from factory_functions import *\nimport time\nif __name__ == '__main__':\n    while True:\n        client_input = input('\\nWelcome to the BMW Factory in China!! what would you like to do? '\n                             '\\nEnter \"1\" to run tests on our factory or \"2\" to create your own personal '\n                             'Ultimate Driving Experience..!\\n:')\n        if client_input == '1':\n            print('lets run some successful tests!')\n            time.sleep(0.8)\n            print('hopefully...')\n            time.sleep(1)\n            from factory_test import *\n\n        elif client_input == '2':\n            print('lets make your your personal own'\n                             'Ultimate Driving Experience..!')\n            arg1 = input('what material do you want!')\n            arg2 = input('Do you want handmade experience to tailor to your needs? - TYPE \"labour\"?\\n..OR our state of the art... '\n                         'sewing machine!. - ENTER \"sewing machine\"')\n            result = run_factory(arg1, arg2)\n            print(result)\n        elif client_input == 'stop':\n                break\n        else:\n            print('sorry that\"s not a valid option! Please try again')\n","repo_name":"jdst103/BMW-factory","sub_path":"run_BMW_factory.py","file_name":"run_BMW_factory.py","file_ext":"py","file_size_in_byte":1194,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"32217900841","text":"from accounts.models import Account\nfrom accounts import constants\nimport logging\nlogger = logging.getLogger(__name__)\n\n\ndef account_context(request):\n    context = {\n        'account' : None\n    }\n    if request.user.is_authenticated:\n        try:\n            account = Account.objects.get(user=request.user)\n            context['account'] = account\n            context['ACCOUNT_TYPE'] : constants.ACCOUNT_TYPE\n        except Account.DoesNotExist :\n            logging.warning(f\"No account found for user {request.user.username}\")\n        \n    return context","repo_name":"cyrildzumts/django-accounts","sub_path":"accounts/context_processors.py","file_name":"context_processors.py","file_ext":"py","file_size_in_byte":559,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"1323110879","text":"# This file is for functions that will alter certain behaviors for your system\n\nfrom django.utils.crypto import get_random_string\n\ndef get_random_slug(obj):\n    # This is to get a random and unique slug for a posts\n    if not obj.slug: # if no slug present\n        obj.slug = get_random_string(16)\n        is_not_unique = True\n\n        while is_not_unique:\n            is_not_unique = False\n\n            obj_with_slug = type(obj).objects.filter(slug=obj.slug)\n            if len(obj_with_slug) > 0:\n                is_not_unique = True # slug not unique\n\n            bad_words = ['fuck', 'shit']\n            if obj.slug in bad_words:\n                is_not_unique = True # no bad words\n\n            if is_not_unique:\n                obj.slug = get_random_string(16) # continue looking","repo_name":"mhrdp/vue_django_test","sub_path":"backdoor/functions.py","file_name":"functions.py","file_ext":"py","file_size_in_byte":784,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"35829613115","text":"from spack import *\n\nclass Garply(Package):\n    \"\"\"Garply is a toy package\"\"\"\n    homepage = \"https://github.com/amundson/garply\"\n    url      = \"http://compacc.fnal.gov/~amundson/garply-1.0.tar.gz\"\n\n    version('1.0.1', git='git@github.com:amundson/garply.git',\n            tag='v1.0.1')\n    version('1.0', '13a24801512e5b577afde076d2e908d3')\n\n    depends_on(\"cmake@3.0:\")\n\n    def install(self, spec, prefix):\n        cmake(\".\", *std_cmake_args)\n        make()\n        make(\"install\")\n","repo_name":"amundson/metasynrepo","sub_path":"packages/garply/package.py","file_name":"package.py","file_ext":"py","file_size_in_byte":487,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"16038024283","text":"import pandas as pd\n\nclass ScrapedDataProcessor():\n\n    def __init__(self):\n        self\n\n    def load(self, resource):\n\n        df = pd.read_json(resource)\n\n        print(df.columns)\n        df.set_index('name', inplace=True)\n\n        print(df)\n\n        return df","repo_name":"Stefangralo/universidades-phonebook","sub_path":"universidades_phonebook/loader/ScrapedDataProcessor.py","file_name":"ScrapedDataProcessor.py","file_ext":"py","file_size_in_byte":264,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"32377934230","text":"\"\"\"\nIn this little assignment you are given a string of space separated numbers, \nand have to return the highest and lowest number.\n\nExamples\nhigh_and_low(\"1 2 3 4 5\")  # return \"5 1\"\nhigh_and_low(\"1 2 -3 4 5\") # return \"5 -3\"\nhigh_and_low(\"1 9 3 4 -5\") # return \"9 -5\"\nNotes\nAll numbers are valid Int32, no need to validate them.\nThere will always be at least one number in the input string.\nOutput string must be two numbers separated by a single space, and highest number is first.\n\n\"\"\"\n\ndef high_and_low(numbers):\n    # Split the input string into a list of numbers\n    numbers_list = [int(num) for num in numbers.split()]\n\n    # Find the highest and lowest numbers\n    highest = max(numbers_list)\n    lowest = min(numbers_list)\n\n    # Return the result as a formatted string\n    result_str = f\"{highest} {lowest}\"\n\n    return result_str\n\nresult = high_and_low(\"1 2 3 4 5\")\nprint(result)  # Output: \"5 1\"","repo_name":"Jared951/codewars","sub_path":"high_low.py","file_name":"high_low.py","file_ext":"py","file_size_in_byte":908,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"41676282386","text":"import billboard\n\n\n\nf=open('C:\\\\Users\\\\BSE\\\\Desktop\\\\csgraduation\\\\realchart.txt','w')\n\nfor i in range(0,20):\n    chart=billboard.ChartData('hot-100', date=str(1998+i)+'-08-26', fetch=True)\n\n    for j in range(0, 100):\n        song=chart[j]\n        f.write(song.artist+\"\\n\")\n        f.write(song.title+\"\\n\")\n    \nf.close()\n","repo_name":"Wonhyeok/SKKU-Grad-Project","sub_path":"Get Artist Song List.py","file_name":"Get Artist Song List.py","file_ext":"py","file_size_in_byte":323,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"74794832265","text":"# Definition for a binary tree node.\n# class TreeNode:\n#     def __init__(self, val=0, left=None, right=None):\n#         self.val = val\n#         self.left = left\n#         self.right = right\nclass Solution:\n    def deleteNode(self, root: Optional[TreeNode], key: int) -> Optional[TreeNode]:\n        def findSuccessor(node):\n            while node and node.left:\n                node = node.left\n            return node.val\n        \n        def findPredecessor(node):\n            while node and node.right:\n                node = node.right\n            return node.val\n        \n        def delete(node, target):\n            if not node:\n                return node\n            \n            if node.val < target:\n                node.right = delete(node.right, target)\n            elif node.val > target:\n                node.left = delete(node.left, target)\n            else:\n                if not node.left and not node.right:\n                    node = None\n                elif node.right:\n                    successor = findSuccessor(node.right)\n                    node.val = successor\n                    node.right = delete(node.right, successor)\n                else:\n                    predecessor = findPredecessor(node.left)\n                    node.val = predecessor\n                    node.left = delete(node.left, predecessor)\n            return node\n        \n        return delete(root, key)\n            \n","repo_name":"novayo/LeetCode","sub_path":"0450_Delete_Node_in_a_BST/try_2.py","file_name":"try_2.py","file_ext":"py","file_size_in_byte":1424,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"81"}
+{"seq_id":"29148497943","text":"'''\nTask №4\n\nThere is an array of numbers - arr [1, 3, 6, 2, 2, 0, 4, 5] \nthere is a number target = 5.\nCount the number of pairs in the array, the sum of which will give target\n'''\nimport numpy as np\n\n\ndef task(arr : list, target : int):\n    res = 0\n\n    for i in arr:\n        for j in arr:\n            if i + j == target and i != j:\n                res += 1\n\n    return res // 2\n\n\narr = [1, 3, 6, 2, 2, 0, 4, 5]\ntarget = 5\n\nprint(task(arr, target))","repo_name":"SerhiiKorniichuk1/Home_task_basic","sub_path":"task4_1.py","file_name":"task4_1.py","file_ext":"py","file_size_in_byte":452,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"74045903626","text":"from app.telegram.core.handlers import bot, log, main_markup\nfrom app.telegram.schedule import data, controller\nimport telebot\n\n\ndef attach_schedule_module():\n\n    @bot.message_handler(commands=['config_schedule'])\n    def schedule_configuration(message):\n        log(data.MODULE_NAME, message)\n\n        if message.text == \"/config_schedule\":\n            print(f\"CONFIG SCHEDULE pressed by {message.from_user.id}\")\n            user = controller.get_user(message.from_user.id)\n            if not user:\n                controller.register_user(\n                    message.from_user.id, message.from_user.username)\n\n            options = telebot.types.ReplyKeyboardMarkup(True, False)\n            group_levels = controller.get_group_levels()\n            options.add(*group_levels)\n            msg = bot.send_message(\n                message.chat.id, data.REQUEST_COURSE, reply_markup=options)\n            bot.register_next_step_handler(\n                msg, process_course_step, group_levels)\n\n    def process_course_step(message, group_levels):\n        log(data.MODULE_NAME, message)\n\n        if not message.text:\n            bot.send_message(message.chat.id, data.MESSAGE_ERROR,\n                             reply_markup=main_markup)\n            return\n\n        course = message.text\n        if course not in group_levels:\n            bot.send_message(message.chat.id, data.MESSAGE_ERROR,\n                             reply_markup=main_markup)\n            return\n        print(f\"{course} pressed by {message.from_user.id}\")\n        options = telebot.types.ReplyKeyboardMarkup(True, False)\n        specific_groups = controller.get_specific_group(course)\n        options.add(*specific_groups)\n        msg = bot.send_message(\n            message.chat.id, data.REQUEST_GROUP, reply_markup=options)\n        bot.register_next_step_handler(\n            msg, process_group_step, specific_groups)\n\n    def process_group_step(message, specific_groups):\n        log(data.MODULE_NAME, message)\n        if not message.text:\n            bot.send_message(message.chat.id, data.MESSAGE_ERROR,\n                             reply_markup=main_markup)\n            return\n\n        if message.text not in specific_groups:\n            bot.send_message(message.chat.id, data.MESSAGE_ERROR,\n                             reply_markup=main_markup)\n            return\n        specific_group = message.text\n        print(f\"{specific_group} pressed by {message.from_user.id}\")\n        controller.add_user_group(specific_group, message.from_user.id)\n        bot.send_message(message.chat.id, data.MESSAGE_SETTINGS_SAVED,\n                         reply_markup=main_markup)\n\n    @bot.message_handler(regexp=f\"^({data.TEXT_BUTTON_NOW}|\"\n                         f\"{data.TEXT_BUTTON_DAY}|\"\n                         f\"{data.TEXT_BUTTON_WEEK})$\"\n                         )\n    def main_buttons_handler(message):\n        log(data.MODULE_NAME, message)\n        user = controller.get_user(message.chat.id)\n        if not user or not user.group_id:\n            bot.send_message(\n                message.chat.id, data.MESSAGE_USER_NOT_configD, reply_markup=main_markup)\n            return\n        if user.handle != message.from_user.username:\n            controller.update_user_alias(\n                message.from_user.id, message.from_user.username)\n\n        if message.text == data.TEXT_BUTTON_NOW:\n            print(f\"NOW pressed by {message.from_user.id}\")\n            send_current_schedule(message.chat.id, message.from_user.id)\n        elif message.text == data.TEXT_BUTTON_DAY:\n            print(f\"DAY pressed by {message.from_user.id}\")\n            markup = telebot.types.ReplyKeyboardMarkup(True)\n            buttons = list()\n            day_of_week = controller.get_day_of_the_week()\n            for i, day in enumerate(data.TEXT_DAYS_OF_WEEK):\n                buttons.append(telebot.types.KeyboardButton(\n                    day if day_of_week != i else day + \"⭐\"))\n            markup.add(*buttons)\n            bot.send_message(\n                message.chat.id, data.REQUEST_WEEKDAY, reply_markup=markup)\n        elif message.text == data.TEXT_BUTTON_WEEK:\n            print(f\"WEEK pressed by {message.from_user.id}\")\n            send_week_schedule(message.from_user.id)\n\n    def send_current_schedule(chat_id, about_user_id):\n        current_lesson = controller.get_current_lesson(about_user_id)\n        next_lesson = controller.get_next_lesson(about_user_id)\n\n        reply = data.HEADER_NOW + \\\n            controller.get_str_current(\n                current_lesson) if current_lesson else \"\"\n\n        if next_lesson:\n            reply += data.HEADER_NEXT + controller.get_str_future(next_lesson)\n        else:\n            reply += data.HEADER_NO_NEXT_LESSONS\n        bot.send_message(chat_id, reply, reply_markup=main_markup)\n\n    def send_week_schedule(user_id):\n        slots = controller.get_week_lessons(user_id)\n\n        reply = data.HEADER_WEEK\n        week_schedule = {0: [], 1: [], 2: [], 3: [], 4: [], 5: []}\n        for slot in slots:\n            weekday = controller.get_time_in_timezone_arrow(\n                slot.start_time).weekday()\n            week_schedule[weekday].append(slot)\n\n        for key, value in week_schedule.items():\n            reply += f\"\\n\\n{data.DAYS_OF_WEEK[key]}:\\n\" + \\\n                (data.MESSAGE_NO_LESSON if not value else\n                 data.HEADER_SEPARATOR.join(\n                     controller.print_slot(lesson) for lesson in value))\n        bot.send_message(user_id, reply, reply_markup=main_markup)\n\n    @bot.message_handler(regexp=f\"^({'|'.join(data.TEXT_DAYS_OF_WEEK)})⭐?$\")\n    def weekday_select_handler(message):\n        log(data.MODULE_NAME, message)\n\n        user = controller.get_user(message.chat.id)\n        if not user or not user.group_id:\n            bot.send_message(\n                message.chat.id, data.MESSAGE_USER_NOT_configD, reply_markup=main_markup)\n            return\n\n        weekday = message.text[:2]\n        print(f\"{weekday} pressed by {message.from_user.id}\")\n        if weekday not in data.TEXT_DAYS_OF_WEEK:\n            return\n\n        schedule = controller.get_day_lessons(\n            message.from_user.id, day=data.TEXT_DAYS_OF_WEEK.index(weekday))\n\n        reply = data.MESSAGE_FREE_DAY if not schedule else \\\n            data.HEADER_SEPARATOR.join(\n                controller.print_slot(lesson) for lesson in schedule)\n        bot.send_message(message.chat.id, reply, reply_markup=main_markup)\n\n    @bot.message_handler(commands=['link'])\n    def get_link(message):\n        log(data.MODULE_NAME, message)\n        print(f\"link pressed by {message.from_user.id}\")\n        bot.send_message(message.chat.id, data.MESSAGE_FULL_WEEK,\n                         reply_markup=main_markup, parse_mode=\"MarkdownV2\")\n\n    @bot.message_handler(commands=['week_number'])\n    def get_week_number(message):\n        log(data.MODULE_NAME, message)\n        print(f\"week number pressed by {message.from_user.id}\")\n        week_num = controller.get_week_number()\n        reply = f\"Week {week_num}\"\n        bot.send_message(message.from_user.id, reply, reply_markup=main_markup)\n","repo_name":"Danielatonge/scheduler-backend","sub_path":"app/telegram/schedule/handlers.py","file_name":"handlers.py","file_ext":"py","file_size_in_byte":7120,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"71424513865","text":"import importlib\nimport json\nimport random\nimport string\n\nfrom behave import step, given, then, when\nfrom typing import List, Any, Optional\nfrom hamcrest import assert_that, equal_to, has_entry\n\nfrom genyrator import (\n    Entity, create_entity, Column, create_column, create_identifier_column,\n    string_to_type_option,\n)\nfrom genyrator.entities.Entity import (\n    string_to_operation_option, all_operations\n)\nfrom genyrator.entities.Column import IdentifierColumn\nfrom genyrator.entities.Schema import create_schema, Schema\nfrom test.e2e.steps.common import make_request\n\n\ndef _random_string(n: int) -> str:\n    return ''.join(random.choices(string.ascii_letters, k=n))\n\n\ndef _create_test_entity(columns: List[Column], identifier_column: IdentifierColumn) -> Entity:\n    return create_entity(\n        class_name=_random_string(24),\n        identifier_column=identifier_column,\n        columns=columns,\n    )\n\n\ndef _create_schema(context: Any, module_name: Optional[str] = None):\n    entity_name = context.entity_name if hasattr(context, 'entity_name') else None\n    operations = context.operations if hasattr(context, 'operations') else all_operations\n    entity = create_entity(\n        class_name=_random_string(36) if entity_name is None else entity_name,\n        identifier_column=context.identifier_column,\n        columns=context.columns,\n        operations=operations,\n    )\n    module_name = _random_string(14) if module_name is None else module_name\n    schema = create_schema(\n        module_name='output.{}'.format(module_name),\n        entities=[entity],\n        file_path=['output', module_name],\n    )\n    context.schema = schema\n    context.module_name = 'output.{}'.format(module_name)\n    context.module_path = 'output/{}'.format(module_name)\n\n\n@given('I have an entity \"{name}\" with properties')\ndef step_impl(context: Any, name: str):\n    context.entity_name = name\n    entity_with_properties(context)\n\n\n@given('I have operation options \"{operation}\"')\ndef step_impl(context, operation: str):\n    context.operations = set([string_to_operation_option(o) for o in operation.split(', ')])\n\n\n@given(\"I have an entity with properties\")\ndef entity_with_properties(context: Any):\n    columns = []\n    for row in context.table:\n        if 'nullable' in row.headings:\n            nullable = row['nullable'] == 'True'\n        else:\n            nullable = False\n        columns.append(create_column(\n            row['name'], string_to_type_option(row['type']), nullable=nullable\n        ))\n    context.columns = columns\n\n\n@step('identifier column \"{name}\" with type \"{column_type}\"')\ndef step_impl(context: Any, name: str, column_type: str):\n    context.identifier_column = create_identifier_column(\n        name, string_to_type_option(column_type),\n    )\n\n\n@step(\"I create a schema from those entities\")\ndef step_impl(context: Any):\n    _create_schema(context)\n\n\n@step('I create a schema \"{schema_name}\" from those entities')\ndef step_impl(context: Any, schema_name: str):\n    _create_schema(context, schema_name)\n\n\n@step(\"I write that schema\")\ndef write_schema(context: Any):\n    schema: Schema = context.schema\n    schema.write_files()\n\n\n@step(\"I import the generated app\")\n@then(\"I can import the generated app\")\ndef import_app(context):\n    context.generated_module = generated_module = importlib.import_module(context.module_name)\n    context.app = generated_module.app\n\n\n@step(\"I initialize the database\")\ndef init_db(context):\n    db = context.generated_module.db\n    with context.app.app_context():\n        db.drop_all()\n        db.create_all()\n\n\n@step(\"I can run the generated app\")\ndef run_app(context):\n    app = context.app\n    app.testing = True\n    context.client = client = app.test_client()\n    response = client.get('/')\n    assert_that(response.status_code, equal_to(200))\n\n\n@step(\"the app is running\")\ndef app_is_running(context: Any):\n    write_schema(context)\n    import_app(context)\n    init_db(context)\n    run_app(context)\n\n\n@step('I make a \"{method}\" request to \"{path}\"')\ndef step_impl(context, method: str, path: str):\n    context.response = make_request(context.client, path, method)\n\n\n@when('I make a \"{method}\" request to \"{path}\" with parameters \"{parameters}\"')\ndef step_impl(context: Any, method: str, path: str, parameters: str):\n    context.response = make_request(context.client, path, method, parameters)\n\n\n@then('I get http status \"{status}\"')\ndef step_impl(context: Any, status: int):\n    assert_that(context.response.status_code, equal_to(int(status)))\n\n\n@given(\"I have text data\")\ndef step_impl(context):\n    context.data = context.text\n\n\n@step(\"I have json data\")\ndef have_json_data(context):\n    context.data = json.loads(context.text)\n\n\n@step('I make a \"{method}\" request to \"{path}\" with that json data')\ndef step_impl(context: Any, method: str, path: str):\n    data = context.data\n    context.response = make_request(context.client, path, method, data=data)\n\n\n@step('I can get entity \"{path}\"')\ndef step_impl(context, path: str):\n    _i_can_get_entity(context, path)\n\n\n@step('I can get entity \"{path_template}\" using that response data')\ndef stemp_impl(context, path_template: str):\n    path = path_template.format(**context.response.json)\n    _i_can_get_entity(context, path)\n\n\ndef _i_can_get_entity(context, path: str):\n    context.response = response = make_request(context.client, path, 'get')\n    assert_that(response.status_code, equal_to(200))\n\n\n@step('I cannot get entity \"{path}\"')\ndef step_impl(context, path: str):\n    assert_that(make_request(context.client, path, 'get').status_code, equal_to(404))\n\n\n@step(\"that response matches the original data\")\ndef step_impl(context):\n    response_data = context.response.json\n    original_data = context.data\n    assert_that(response_data, equal_to(original_data))\n\n\n@step('that response matches the original data plus \"{id_field}\"')\ndef step_impl(context, id_field: str):\n    response_data = context.response.json\n    original_data = context.data\n    original_data[id_field] = response_data[id_field]\n\n    assert_that(response_data, equal_to(original_data))\n\n\n@step('the response has \"{field}\" with value \"{value}\"')\ndef response_has_field_value(context, field: str, value: Any):\n    assert_that(json.loads(context.response.data), has_entry(field, value))\n\n\n@step('I load data for \"{entity_name}\"')\ndef step_impl(context, entity_name):\n    db = context.generated_module.db\n    model_module = importlib.import_module(\n        '.'.join([context.module_name, 'sqlalchemy', 'model', entity_name])\n    )\n    constructor = getattr(model_module, entity_name)\n    args = {}\n    for row in context.table:\n        args[row['name']] = row['value']\n    args['id'] = None\n    model = constructor(**args)\n    with context.app.app_context():\n        db.session.add(model)\n        db.session.commit()\n\n\n@step('making \"{operations_list}\" requests to \"{endpoint}\" gives http status \"{status}\"')\ndef step_impl(context, operations_list: str, endpoint: str, status: int):\n    for operation in operations_list.split(','):\n        response = make_request(\n            client=context.client,\n            endpoint=endpoint,\n            method=operation,\n        )\n        assert_that(response.status_code, equal_to(int(status)), f'{operation}')\n\n\n@step(\"I create an example app\")\ndef step_impl(context):\n        context.execute_steps(\"\"\"\n        Given I have an entity \"ExampleEntity\" with properties\n          | name      | type |\n          | test_name | str  |\n          And identifier column \"test_id\" with type \"int\"\n          And I create a schema from those entities\n          And the app is running\n          And I load data for \"ExampleEntity\"\n          | name      | value |\n          | test_name | test  |\n          | test_id   | 3     |\n    \"\"\")\n\n\n@when('I create a fixture for the \"{entity_name}\" entity')\ndef step_impl(context, entity_name: str):\n    fixture_module_name = f'{context.module_name}.sqlalchemy.fixture.{entity_name}'\n    fixture_module = importlib.import_module(fixture_module_name)\n    fixture = getattr(fixture_module, f'{entity_name}Factory')\n    with context.app.app_context():\n        instance = fixture.create()\n        context.generated_module.db.session.commit()\n\n\n@then('the db contains \"{count}\" \"{model_name}\" entity')\ndef step_impl(context, count: str, model_name: str):\n    model_module_name = f'{context.module_name}.sqlalchemy.model.{model_name}'\n    model_module = importlib.import_module(model_module_name)\n    model = getattr(model_module, model_name)\n    with context.app.app_context():\n        result = model.query.all()\n        assert_that(len(result), equal_to(int(count)))\n","repo_name":"jumblesale/genyrator","sub_path":"test/e2e/steps/generated_app_steps.py","file_name":"generated_app_steps.py","file_ext":"py","file_size_in_byte":8611,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"81"}
+{"seq_id":"34328286627","text":"import random\r\nimport copy\r\nimport math\r\n\r\ninner_nodes = 15\r\noutput_nodes = 7\r\n\r\nparticles_const = 20\r\nepochs = 100\r\nc1 = 0.6\r\nc2 = 1.95\r\nw = 0.00051231\r\nmin_velocity = -10\r\nmax_velocity = 10\r\n\r\n##sigmoids\r\ndef bipolarSigmoid(x):\r\n    return (2 / (1 + math.exp(-1*x ))) - 1\r\n\r\n##swarm for pso\r\nclass Swarm():\r\n    def __init__(self, particles):\r\n        self.particles = particles\r\n        self.gbestScore = -1000\r\n        self.gbest = []\r\n\r\n    def update(self):\r\n        for part in self.particles:\r\n            if part.score > self.gbestScore:\r\n                self.gbestScore = part.score\r\n                self.gbest = part.pos\r\n            if part.score > part.pbestScore:\r\n                part.pbestScore = part.score\r\n                part.pbest = part.pos\r\n\r\n            part.nxtVelocity = []\r\n            for i in range(len(part.velocity)):\r\n                temp = (w*part.velocity[i]) + (c1*random.uniform(-1,1)*(part.pbest[i] - part.pos[i]))\r\n                temp = temp + (c2*random.uniform(-1,1)*(self.gbest[i] - part.pos[i]))\r\n                if temp > max_velocity:\r\n                    temp = max_velocity\r\n                    part.nxtVelocity.append(temp)\r\n                elif temp < min_velocity:\r\n                    temp = min_velocity\r\n                    part.nxtVelocity.append(temp)\r\n                else:\r\n                    part.nxtVelocity.append(temp)\r\n\r\n\r\n            #update velocity\r\n            for i in range(len(part.velocity)):\r\n                    if part.pos[i]+ part.nxtVelocity[i] > 3:\r\n                        part.pos[i] = 3\r\n                    elif part.pos[i]+ part.nxtVelocity[i] < -3:\r\n                        part.pos[i] = -3\r\n                    else:\r\n                        part.pos[i] = part.pos[i]+ part.nxtVelocity[i]\r\n\r\n\r\n\r\n\r\n\r\n##particle for pso\r\nclass Particle():\r\n    def __init__ (self):\r\n        self.score = 0\r\n        self.pos = self.initPos()\r\n        self.nxtVelocity = []\r\n        self.velocity = self.initVel()\r\n        self.pbest = self.velocity\r\n        self.pbestScore = -1000\r\n\r\n    def initPos(self):\r\n        temp = (63*inner_nodes) + (inner_nodes*output_nodes)\r\n        arr = []\r\n        for i in range(temp):\r\n            arr.append(random.uniform(-2,2))\r\n\r\n        ##biases\r\n        arr.append(10)\r\n        arr.append(5)\r\n        return arr\r\n\r\n    def initVel(self):\r\n        temp = (63*inner_nodes) + (inner_nodes*output_nodes)\r\n        arr = []\r\n        for i in range(temp):\r\n            arr.append(random.uniform(min_velocity,max_velocity))\r\n\r\n        ##biases\r\n        arr.append(random.uniform(-1,1))\r\n        arr.append(random.uniform(-1,1))\r\n        return arr\r\n\r\n\r\n##node object for net\r\nclass Node():\r\n    def __init__ (self):\r\n        self.signal = 0\r\n\r\nclass Net():\r\n    def __init__(self):\r\n        self.net = []\r\n    def buildNet(self):\r\n        ##output layer\r\n        outputNodes = []\r\n        for i in range(output_nodes):\r\n            node = Node()\r\n            outputNodes.append(node)\r\n\r\n        ##median layer\r\n        medianNodes = []\r\n        for i in range(inner_nodes):\r\n            node = Node()\r\n            medianNodes.append(node)\r\n\r\n        ##input layer\r\n        inputNodes = []\r\n        for i in range(63):\r\n            node = Node()\r\n            inputNodes.append(node)\r\n\r\n        self.net.append(inputNodes)\r\n        self.net.append(medianNodes)\r\n        self.net.append(outputNodes)\r\n\r\n    def feed(self, chars, particle):\r\n        count = 0\r\n        for i in chars:\r\n            if i != '.':\r\n                self.net[0][count].signal = 1\r\n            else:\r\n                self.net[0][count].signal = -1\r\n            count = count+1\r\n\r\n        self.feedForward(particle)\r\n\r\n    def feedForward(self, particle):\r\n        temp = 0\r\n        weights = []\r\n        ##getting right weights from particle\r\n        for j in self.net[0]:\r\n            k = particle.pos[(inner_nodes*temp):(inner_nodes*(temp+1))]\r\n            weights.append(k)\r\n            temp = temp+1\r\n\r\n        tempArr = particle.pos[63*inner_nodes:]\r\n        arr = []\r\n        temp = 0\r\n        for j in self.net[1]:\r\n            k = tempArr[(output_nodes*temp):(output_nodes*(temp+1))]\r\n            arr.append(k)\r\n            temp = temp+1\r\n\r\n        count = 0\r\n        ##first layer\r\n        for i in self.net[1]:\r\n            sum = 0\r\n            temp = 0\r\n            for j in self.net[0]:\r\n                sum = sum + (j.signal*weights[temp][count])\r\n                temp = temp+1\r\n            sum = particle.pos[-2] + sum\r\n            i.signal = bipolarSigmoid(sum)\r\n            count = count + 1\r\n\r\n        ##second layer\r\n        count = 0\r\n        for i in self.net[2]:\r\n            sum = 0\r\n            temp = 0\r\n            for j in self.net[1]:\r\n                sum = sum + (j.signal*arr[temp][count])\r\n                temp = temp+1\r\n            sum = particle.pos[-1] + sum\r\n            i.signal = bipolarSigmoid(sum)\r\n            count = count + 1\r\n\r\n\r\ntarget = [[1,-1,-1,-1,-1, -1, -1],[-1,1,-1,-1,-1, -1, -1],[-1,-1, 1,-1,-1, -1, -1],[-1,-1,-1, 1,-1, -1, -1],[-1,-1,-1,-1, 1, -1, -1],[-1,-1,-1,-1,-1, 1, -1],[-1,-1,-1,-1,-1, -1, 1]]\r\ndef train(netw,swarm):\r\n    f = open('TrainData.txt', 'r')\r\n    for i in range(epochs):\r\n        for part in swarm.particles:\r\n            part.score = 0\r\n        ##process\r\n        letter = 0\r\n        count= 0\r\n        chars = []\r\n        for line in f:\r\n            line = line.rstrip()\r\n            if count < 8:\r\n                for i in line:\r\n                    chars.append(i)\r\n                count = count +1\r\n            else:\r\n                ##feed\r\n                if letter < 7:\r\n                    for part in swarm.particles:\r\n                        netw.feed(chars,part)\r\n                        #part.checkScore(netw.net[2],target[letter])\r\n                        temp = copy.deepcopy(netw.net[2])\r\n                        final = []\r\n                        count  = 0\r\n                        for i in temp:\r\n                            final.append([i.signal,count])\r\n                            count = count+1\r\n                        temp = sorted(final).pop()\r\n                        asc = temp[1]\r\n                        if asc == letter:\r\n                            part.score = part.score+1\r\n\r\n                    letter = letter + 1\r\n                else:\r\n                    letter = 0\r\n                    for part in swarm.particles:\r\n                        netw.feed(chars,part)\r\n                        #part.checkScore(netw.net[2],target[letter])\r\n                        temp = copy.deepcopy(netw.net[2])\r\n                        final = []\r\n                        count  = 0\r\n                        for i in temp:\r\n                            final.append([i.signal,count])\r\n                            count = count+1\r\n                        temp = sorted(final).pop()\r\n                        asc = temp[1]\r\n                        if asc == letter:\r\n                            part.score = part.score+1\r\n\r\n                    letter = letter + 1\r\n                ##continue\r\n                chars = []\r\n                count = 0\r\n\r\n        swarm.update()\r\n\r\n        f.seek(0)\r\n    f.close()\r\n\r\n\r\n##test\r\nf = open('TestData.txt', 'r')\r\navgTotal = 0\r\nfor runs in range(10):\r\n    particles = []\r\n    for i in range(particles_const):\r\n        part = Particle()\r\n        particles.append(part)\r\n\r\n    swarm = Swarm(particles)\r\n    netw = Net()\r\n    netw.buildNet()\r\n    print('swarming particles, may take a bit depending on # of epochs')\r\n    train(netw,swarm)\r\n\r\n\r\n    print('particle scores from final run:')\r\n    for i in swarm.particles:\r\n        print(str(i.score/21) + \" percent accurate\")\r\n\r\n    print('global best accuracy: '+str(swarm.gbestScore/21))\r\n\r\n    print('Testing with global best performing particle')\r\n    bestParticle = Particle()\r\n    bestParticle.pos = swarm.gbest\r\n    ##process\r\n    letter = 0\r\n    count= 0\r\n    chars = []\r\n    numRight = 0\r\n    total = 0\r\n    for line in f:\r\n        line = line.rstrip()\r\n        print(line)\r\n        if count < 8:\r\n            for i in line:\r\n                chars.append(i)\r\n            count = count +1\r\n        else:\r\n            ##feed\r\n            netw.feed(chars, bestParticle)\r\n            temp = copy.deepcopy(netw.net[2])\r\n            print('---------------------------')\r\n            print(\"for letter \" + str(letter)+ \":\")\r\n            final = []\r\n            count  = 0\r\n            for i in temp:\r\n                final.append([i.signal,count])\r\n                count = count+1\r\n            temp = sorted(final).pop()\r\n            print(temp)\r\n            asc = temp[1]\r\n            if asc == 0:\r\n                asc = 'A'\r\n            elif asc == 1:\r\n                asc = 'B'\r\n            elif asc == 2:\r\n                asc = 'C'\r\n            elif asc == 3:\r\n                asc = 'D'\r\n            elif asc == 4:\r\n                asc = 'E'\r\n            elif asc == 5:\r\n                asc = 'J'\r\n            elif asc == 6:\r\n                asc = 'K'\r\n            print(\"Computer guessed: \" + asc)\r\n            ##continue\r\n            if letter == temp[1]:\r\n                numRight = numRight + 1\r\n            else:\r\n                print('Result was wrong, computers second choice:')\r\n                temp = sorted(final)\r\n                temp.pop()\r\n                temp = temp.pop()\r\n                print(temp)\r\n                asc = temp[1]\r\n                if asc == 0:\r\n                    asc = 'A'\r\n                elif asc == 1:\r\n                    asc = 'B'\r\n                elif asc == 2:\r\n                    asc = 'C'\r\n                elif asc == 3:\r\n                    asc = 'D'\r\n                elif asc == 4:\r\n                    asc = 'E'\r\n                elif asc == 5:\r\n                    asc = 'J'\r\n                elif asc == 6:\r\n                    asc = 'K'\r\n                print(\"Computer guessed: \" + asc)\r\n            print('---------------------------')\r\n            chars = []\r\n            count = 0\r\n            if letter < 6:\r\n                letter = letter +1\r\n            else:\r\n                letter = 0\r\n            total = total + 1\r\n    print(\"accuracy:\" + str((numRight/total)*100))\r\n    avgTotal = avgTotal+ (numRight/total)*100\r\n    f.seek(0)\r\n    runs = runs + 1\r\nprint(\"Average accuracy:\")\r\nprint(avgTotal/10)\r\nf.close()\r\n","repo_name":"notaturkey/Training-a-Nueral-Net-with-a-Particle-Swarm","sub_path":"mainPSO.py","file_name":"mainPSO.py","file_ext":"py","file_size_in_byte":10322,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"18212891967","text":"from datetime import datetime, date, timedelta\n\ndef Transform( oldfield, settings, debug ):\n    loglines = []\n    default_term = settings.get( 'default_term', 'Unknown' )\n    if not oldfield:\n        return default_term, loglines\n    scoretype = settings.get( 'scoretype', '' )\n    if scoretype.lower() == 'sat':\n        fieldparts = oldfield.split( '-' )\n        if len( fieldparts ) != 2:\n            if debug:\n                loglines.append( 'graduation date in unknown format, returning original field' )\n            return oldfield, loglines\n        gradyear = int( fieldparts[0] )\n        gradmonth = int( fieldparts[1] )\n    elif scoretype.lower() == 'act':\n        try:\n            gradyear = int( oldfield )\n        except ValueError:\n            if debug:\n                loglines.append( 'data in field cannot be converted to integer, returning original field' )\n            return oldfield, loglines\n        gradmonth = 5\n    else:\n        if debug:\n            loglines.append( 'no valid score type provided, returning original field' )\n        return oldfield, loglines\n    entrytermformat = settings.get( 'entrytermformat', 'Fall %Y' )\n    breakmonth = settings.get( 'breakmonth', 8)\n    thisyear = datetime.now().year\n    thismonth = datetime.now().month\n    entry_year = False\n    if (gradyear >= (thisyear + 1)) and (gradmonth <= breakmonth):\n        entry_year = datetime( year=gradyear, month=gradmonth, day=1 )\n    elif (gradyear >= (thisyear + 1)) and (gradmonth > breakmonth):\n        entry_year = datetime( year=gradyear+1, month=gradmonth, day=1 )\n    elif (gradyear == thisyear) and (thismonth <= breakmonth) and (gradmonth <= breakmonth):\n        entry_year = datetime( year=gradyear, month=gradmonth, day=1 )\n    elif (gradyear == thisyear) and (thismonth >= breakmonth) and (gradmonth > breakmonth):\n        entry_year = datetime( year=gradyear+1, month=gradmonth, day=1 )\n    elif (gradyear == (thisyear - 1)) and (thismonth <= breakmonth) and (gradmonth > breakmonth):\n        entry_year = datetime( year=thisyear, month=gradmonth, day=1 )\n    if entry_year:\n        newfield = entry_year.strftime( entrytermformat )\n    else:\n        newfield = default_term\n    if debug:\n        loglines.append( 'using grad date of %s returning entry term of %s' % (oldfield, newfield) )\n    return newfield, loglines\n","repo_name":"pkscout/exautomation","sub_path":"resources/transforms/fieldtransforms/score-entryterm.py","file_name":"score-entryterm.py","file_ext":"py","file_size_in_byte":2336,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"43706635289","text":"flag=0\nstr2=\"\"\nstr1=input(\"enter the no \")\nfor x in str1:\n    if(x==\"-\"):\n        flag=1\n        break\nlist1=str1.split()\nvar=len(list1)\ninc=0\nflage2=1\nwhile var>0:\n    str2=list1[inc]\n    if(str2==str2[::-1]):\n        flage2=0\n        \n    inc=inc+1\n    var=var-1\n\nif((flag==0)and(flage2==0)):\n    print (True)\nelse:\n    print (False)","repo_name":"abhinavsoni2601/fosk_work","sub_path":"day 2/ch3.py","file_name":"ch3.py","file_ext":"py","file_size_in_byte":335,"program_lang":"python","lang":"de","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"41413953585","text":"import os\nimport sys\n\n\nimport traci\nimport numpy as np\nimport csv\n\nclass Environment:\n    place_lenght = 7.5\n    place_offset = 8.50\n    lane_len = 10\n    lane_ids = ['-gneE0_0', '-gneE0_1', '-gneE1_0', '-gneE1_1', '-gneE2_0', '-gneE2_1', '-gneE3_0', '-gneE3_1']\n    \n    def __init__(self, label='default', gui_f=False, writer_accumWaitingTime=None, writer_numOfVehicles = None, writer_trafficDensity = None, fnames = None):\n        self.label = label\n        self.wt_last = 0.\n        self.number_of_cars = 0\n        self.writer_accumWaitingTime = writer_accumWaitingTime\n        self.writer_numOfVehicles = writer_numOfVehicles\n        self.writer_numOfVehicles = writer_numOfVehicles\n        self.writer_trafficDensity = writer_trafficDensity\n        self.fnames = fnames\n        self.step_number = 0\n\n        if 'SUMO_HOME' in os.environ:\n            tools = os.path.join(os.environ['SUMO_HOME'], 'tools')\n            sys.path.append(tools)\n        else:\n            sys.exit(\"please declare environment variable 'SUMO_HOME'\")\n\n        exe = 'sumo-gui' if gui_f else 'sumo'\n        sumoBinary = os.path.join(os.environ['SUMO_HOME'], 'bin', exe)\n        self.sumoCmd = [sumoBinary, '-c', 'Intersection/intersection.sumocfg']\n\n        return\n\n    def take_positions(self):\n        state = np.zeros(self.lane_len * 8 + 4, dtype=np.float32)\n\n        for ilane in range(0, 8):\n            lane_id = self.lane_ids[ilane]\n            number_of_cars = traci.lane.getLastStepVehicleNumber(lane_id)\n            cars = traci.lane.getLastStepVehicleIDs(lane_id)\n            for icar in cars:\n                xcar, ycar = traci.vehicle.getPosition(icar)\n                if ilane < 2:\n                    pos = (ycar - self.place_offset) / self.place_lenght\n                elif ilane < 4:\n                    pos = (xcar - self.place_offset) / self.place_lenght\n                elif ilane < 6:\n                    pos = (-ycar - self.place_offset) / self.place_lenght\n                else:\n                    pos = (-xcar - self.place_offset) / self.place_lenght\n                if pos > self.lane_len - 1.:\n                    continue\n                pos = np.clip(pos, 0., self.lane_len - 1. - 1e-6)\n                ipos = int(pos)\n                state[int(ilane * self.lane_len + ipos)] += 1. - pos + ipos\n                state[int(ilane * self.lane_len + ipos + 1)] += pos - ipos\n            state[self.lane_len * 8:self.lane_len * 8+4] = np.eye(4)[traci.trafficlight.getPhase('gneJ00')]\n\n        return state\n\n# -------------------------------------------------------------- WRITING DATA TO CSV FILE -------------------------------------------------------------- #\n    def writeAccumulatedTimetoCSV(self):\n        time_result = [None] * 4\n        if self.writer_accumWaitingTime != None:\n            for id in range(1,len(self.fnames)):\n                time_result[id-1] = traci.edge.getWaitingTime(self.fnames[id])\n            self.writer_accumWaitingTime.writerow({self.fnames[0]: self.step_number, self.fnames[1]: time_result[0], self.fnames[2]: time_result[1], self.fnames[3]: time_result[2], self.fnames[4]: time_result[3]})\n    \n    def writeNumOfVehicletoCSV(self):\n        vehicle_result = [None] * 4 \n        if self.writer_numOfVehicles != None:\n            for id in range(1,len(self.fnames)):\n                vehicle_result[id-1] = traci.edge.getLastStepVehicleNumber(self.fnames[id])\n            self.writer_numOfVehicles.writerow({self.fnames[0]: self.step_number, self.fnames[1]: vehicle_result[0], self.fnames[2]: vehicle_result[1], self.fnames[3]: vehicle_result[2], self.fnames[4]: vehicle_result[3]})\n\n    def writeTrafficDensitytoCSV(self):\n        density_result = [None] * 4\n        if self.writer_trafficDensity != None:\n            for id in range(1,len(self.fnames)):\n                numberOfVehicle = traci.edge.getLastStepVehicleNumber(self.fnames[id])\n                density_result[id-1] = numberOfVehicle / traci.lane.getLength(self.fnames[id] + \"_0\")\n            self.writer_trafficDensity.writerow({self.fnames[0]: self.step_number, self.fnames[1]: density_result[0], self.fnames[2]: density_result[1], self.fnames[3]: density_result[2], self.fnames[4]: density_result[3]})\n# -------------------------------------------------------------- WRITING DATA TO CSV FILE -------------------------------------------------------------- #\n\n    def step_d(self, action):\n        done = False\n        # traci.switch(self.label)\n        \n        action = np.squeeze(action)\n        #print(\"action: \" , action)\n        traci.trafficlight.setPhase('gneJ00', action)\n\n        traci.simulationStep()\n        self.writeAccumulatedTimetoCSV()\n        self.writeNumOfVehicletoCSV()\n        self.writeTrafficDensitytoCSV()\n        traci.simulationStep()\n        self.step_number += 1\n        self.writeAccumulatedTimetoCSV()\n        self.writeNumOfVehicletoCSV()\n        self.writeTrafficDensitytoCSV()\n        self.number_of_cars += traci.simulation.getDepartedNumber()\n\n        state = self.take_positions()\n\n        wt = 0\n        for ilane in range(0, 8):\n            lane_id = self.lane_ids[ilane]\n            wt += traci.lane.getWaitingTime(lane_id)\n        reward = - (wt - self.wt_last)*0.004\n\n        if self.number_of_cars > 250:\n            done = True\n\n        self.step_number += 1\n        return state, reward, done, np.array([[reward]])\n\n    def reset(self):\n        self.wt_last = 0.\n        self.number_of_cars = 0\n        traci.start(self.sumoCmd, label=self.label)\n        traci.trafficlight.setProgram('gneJ00', '0')\n        traci.simulationStep()\n        self.writeAccumulatedTimetoCSV()\n        self.writeNumOfVehicletoCSV()\n        self.writeTrafficDensitytoCSV()\n        traci.simulationStep()\n        self.step_number += 1\n        return self.take_positions()\n\n    def close(self):\n        traci.close()\n","repo_name":"iulasbayram/Intelligent-Traffic-Lights","sub_path":"environment.py","file_name":"environment.py","file_ext":"py","file_size_in_byte":5840,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"81"}
+{"seq_id":"39384948451","text":"import os \nimport praw\nimport wget\n\ntry:\n    reddit = praw.Reddit(client_id = \"your_client_id_here\",client_secret = \"your_secret_key_here\" , user_agent = \"Ninebit\")\n    subreddit = reddit.subreddit(\"wallpaper\")\n    for post in subreddit.hot(limit = 1):\n        filename = wget.download(post.url)\n        uri = \"file:///\"+str(os.path.abspath(filename))\n        query = \"/usr/bin/gsettings set org.gnome.desktop.background picture-uri \"+uri\n        os.system(query)\nexcept:\n    print(\"Exception\")    \n","repo_name":"ShreyanshShandilya/wallpaper_linux","sub_path":"wallpaper.py","file_name":"wallpaper.py","file_ext":"py","file_size_in_byte":499,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"81"}
+{"seq_id":"41198476683","text":"\"\"\"Iranian-specific form helpers.\"\"\"\nimport re\n\nfrom django.core.exceptions import ImproperlyConfigured, ValidationError\nfrom django.forms.fields import CharField, RegexField, Select\nfrom django.utils.translation import gettext_lazy as _\n\nfrom .ir_provinces import PROVINCE_CHOICES\n\n\nclass IRProvinceSelect(Select):\n    \"\"\"\n    A Select widget that uses a list of Iran provinces cities as its choices.\n\n    .. versionadded:: 2.2\n    \"\"\"\n\n    def __init__(self, attrs=None):\n        super().__init__(attrs, choices=PROVINCE_CHOICES)\n\n\nclass IRPostalCodeField(RegexField):\n    \"\"\"\n    A form field that validates its input as an Iran postal code.\n\n    Valid form is XXXXXXXXXX where X represents integer.\n\n    Validate code:\n        - don't use 0 in first 5 digit\n        - don't use 2 in postal code\n        - First 4 digit is not the same\n        - The 5th digit cannot be 5\n        - all digits aren't the same\n\n    .. versionadded:: 2.2\n    \"\"\"\n\n    default_error_messages = {\n        'invalid': _('Enter a postal code in the format XXXXXXXXXX - digits only'),\n    }\n\n    def __init__(self, **kwargs):\n        super().__init__(r'\\b(?!(\\d)\\1{3})[13-9]{4}[1346-9][013-9]{5}\\b$', **kwargs)\n\n    def clean(self, value):\n        if value not in self.empty_values:\n            value = value.replace(' ', '')\n        return super().clean(value)\n\n\nclass IRIDNumberField(CharField):\n    \"\"\"\n    A form field that validates its input as an Iranian identification number.\n\n    Valid form is per the Iranian ID specification.\n\n    Persian documentation :\n        http://www.aliarash.com/article/codemeli/codemeli.htm\n\n        There isn't good English documentation available for the Iranian identification number. Non-Persian speakers\n        will need to use an online translation service to read this documentation.\n\n    .. versionadded:: 2.2\n    \"\"\"\n    id_number_re = re.compile(r'^\\d{10}$')\n\n    default_error_messages = {\n        'invalid': _('Enter a valid ID number.'),\n    }\n\n    def clean(self, value):\n        value = super().clean(value)\n\n        if value in self.empty_values:\n            return value\n\n        match = self.id_number_re.match(value)\n        if not match:\n            raise ValidationError(self.error_messages['invalid'], code='invalid')\n\n        check = int(value[9])\n        s = sum([int(value[x]) * (10 - x) for x in range(9)]) % 11\n\n        if (2 > s == check) or (s >= 2 and check + s == 11):\n            return value\n        else:\n            raise ValidationError(self.error_messages['invalid'], code='invalid')\n","repo_name":"django/django-localflavor","sub_path":"localflavor/ir/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":2538,"program_lang":"python","lang":"en","doc_type":"code","stars":774,"dataset":"github-code","pt":"81"}
+{"seq_id":"42909964976","text":"import sys\nimport getopt\nsys.path.append(\"D:\\\\Documentos\\\\INNOVATE\\\\GH\\\\proyectox\\\\Simetrias\\\\Utils\")\nfrom Visualization_utilities import *\nfrom Utilities import *\nfrom MF import *\nfrom transformation import Transformation\nfrom Signatures import Signature\n\n#GEOMETRY LOAD\npath=\"D:\\Documentos\\INNOVATE\\lib\\symmetry_detection_python\\Linea 12.pts\"\ndp=Geometry_load(path=path,\n                visualization=True,\n                voxel_down_sample=0.01,\n                geometry_type=\"pointCloud\")\n#SIGNATURE GENERATION\nindexes=np.arange(np.asarray(dp.points).shape[0])\npt = o3d.geometry.KDTreeFlann(dp)\nsignatures=Signature(dp,pt,indexes)\nSignature().v_build(signatures,NN_Criteria=\"KNN\",\n                    rad=np.std(np.asarray(dp.points)-np.mean(np.asarray(dp.points),axis=0))/4,\n                    NN=30)\n#REMOVE DISCONTINUITIES\nsignaturesp=remove_discontinuities_by_curvature(dp,signatures,pt,curvature=0.4,radius=0.02)\nprint(len(signaturesp))\n#POINT PRUNNING\npp=prune_points(signaturesp,\"sphericity\",0.0001)\nprint(\"numero de puntos umbilicales\")\nprint(len(pp))\n#RANDOM SAMPLE\nrat=0.1/32\nppp=random_sample(pp,rat)\nprint(\"numero de puntos aleatorios\")\nprint(len(ppp))\nnp.asarray(dp.points).shape\n#TRANSFORMATION SPACE GENERATION\n[trans_s,rs,KDT,query]=build_pairing_kd_tree(dp,pp,rad=10000.6,rand_samp_percentage=rat,\n                                             rigid=True,only_reflections=True,NN=50)\nprint(\"numero de puntos en el espacio de transformadas\")\nprint(len(trans_s))\nz=0\npoints=Transformation().v_toPoint(trans_s[:,z])\nprint(\"z=0\")\nprint(points)\n\nembedding= MDS(n_components=2,n_jobs=4)\npoints = embedding.fit_transform(points)\nprint(\"z=0\")\nprint(points)\nplot=np.concatenate((np.zeros((points.shape[0],1)),points0),axis=1)\n\nbd=4.5*(((np.max(points0[:,0])*1.2-np.min(points0[:,0])*1.8)/200+(np.max(points0[:,0])*1.2-np.min(points0[:,0])*1.8)/200)/2)\nprint(bd)\n\nmodelo_kde=KernelDensity(kernel='epanechnikov',bandwidth=bd).fit(points)\ndensityPlot(modelo_kde,points)\n","repo_name":"grmc1999/Structural_recognition","sub_path":"Simetrias/Visualization_by_density.py","file_name":"Visualization_by_density.py","file_ext":"py","file_size_in_byte":1979,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"11138603450","text":"from pyPS4Controller.controller import Controller\nimport rclpy\nfrom geometry_msgs.msg import Twist\nfrom rclpy.node import Node\nimport threading\nimport cv2\nfrom pop import Util, Pilot\nimport os\nimport time\n\nspeed = 0.0\nsteering =0.0\ncapture = 0\nfork = 0\ncrossroads = 0\npath = '/home/soda/Documents/car/self_driving_car/tools/lane'\n\nclass ps4controller(Controller):\n    def __init__(self, **kwargs):\n        super().__init__('ps4controller')\n        Controller.__init__(self, **kwargs)\n\n    def on_R1_press(self):\n        global fork\n        if fork == 0:\n            fork = 1\n        else:\n            fork = 0\n    def read_events(self):\n        print(\"asdfsadf\")\n\n    def on_L1_press(self):\n        global crossroads\n        if crossroads == 0:\n            crossroads = 1\n        else:\n            crossroads = 0\n    \n                   \n    def on_square_press(self):\n        print(\"capture\")\n        global capture\n        capture = 1\n        \n    def on_L3_y_at_rest(self):\n        global speed\n        speed = 0.0\n\n    def on_R3_x_at_rest(self):\n        global steering\n        steering = 0.0\n\n    def on_L3_up(self, value):\n        global speed\n        speed = -value*50/32767\n        print(value)\n\n    def on_L3_down(self, value):\n        global speed\n        speed = -value*50/32767\n        print(value)\n\n    def on_R3_left(self, value):\n        global steering\n        steering = value*1/32767\n        print(value)\n\n    def on_R3_right(self, value):\n        global steering\n        steering = value*1/32767\n        print(value)\n\nclass DriveController(Node):\n    def __init__(self, **kwargs):\n        super().__init__('drive_controller')\n        self.get_logger().info(\"Node Started\")\n        self.cmd_vel_pub = self.create_publisher(Twist, \"/cmd_vel\", 10) \n        timer_period = 0.03\n        self.timer = self.create_timer(timer_period, self.cmd_vel_callback)\n        \n    def cmd_vel_callback(self):\n        my_msg = Twist()\n        global speed\n        my_msg.linear.x = speed\n        global steering\n        my_msg.angular.z = steering\n        self.cmd_vel_pub.publish(my_msg)\n\ndef ps4_thread(name):\n    while(True):\n        try:   \n            controller = ps4controller(interface=\"/dev/input/js0\").listen(timeout=5)\n        except:\n            print(\"disconnect\")\n            \ndef driver_thread(name):\n    Util.enable_imshow()\n    Car = Pilot.AutoCar()\n    gstr = Util.gstrmer(width =640, height= 480, fps =30, flip =0)\n    camera = cv2.VideoCapture(gstr, cv2.CAP_GSTREAMER)    \n    global capture\n    max = check_file_number(path)\n    status = \"\"\n    while(True):\n        if crossroads == 1:\n            Car.setPixelDisplay(2**0, [255,0,0])\n        else:\n            Car.setPixelDisplay(2**0, [0,0,0])\n\n        if fork == 1:\n            Car.setPixelDisplay(2**7, [255,0,0])\n        else:\n            Car.setPixelDisplay(2**7, [0,0,0])\n\n        \n        if capture == 1:\n            Car.alarm(scale=4, pitch = 8, duration = 0.3)\n            Car.setPixelDisplay(2**3, [0,255,0])\n            time.sleep(0.1)\n            Car.setPixelDisplay(2**3, [0,0,0])\n            ret, frame = camera.read()\n            if crossroads == 1:\n                status = \"crossroads\"\n            elif fork == 1:\n                status = \"fork\"\n            else:\n                status = \"line\"\n            max = max + 1\n            cv2.imwrite(os.path.join(path , f'{max}_{status}.jpg'), frame)\n            capture = 0    \n    \ndef check_file_number(path):\n    file_list = os.listdir(path)\n    max_number = 0\n    for file in file_list:\n        number = int(file.split('_')[0])\n        if number > max_number:\n            max_number = number\n    return max_number\n\ndef main(args=None):\n    t1 = threading.Thread(target=ps4_thread, args=(1,))\n    t2 = threading.Thread(target=driver_thread, args=(1,))\n    t1.start()\n    t2.start()\n    rclpy.init(args=args)\n    drive_control = DriveController()\n    try:    \n        rclpy.spin(drive_control)\n    except KeyboardInterrupt:\n        pass\n    drive_control.destroy_node()\n    rclpy.shutdown()\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"nkcong206/autonomous_car","sub_path":"tools/lane_dataset.py","file_name":"lane_dataset.py","file_ext":"py","file_size_in_byte":4069,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"15813648775","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Nov. 8 23:43:59 2018\ndata: Pittsburgh Bridges Data Set with Version 1\n@author: Angela (Qiqian) Yang\n\"\"\"\n# import statement \nimport pandas as pd \nimport numpy as np\nimport matplotlib.pyplot as plt\n\nurl = \"https://archive.ics.uci.edu/ml/machine-learning-databases/bridges/bridges.data.version1\"\nmy_data = pd.read_csv(url, header=None)\nmy_data.columns = ['IDENTIF', 'RIVER', 'LOCATION', 'ERECTED', 'PURPOSE',\n                   'LENGTH', 'LANES', 'CLEAR-G', 'T-OR-D', 'MATERIAL',\n                   'SPAN', 'REL-L', 'TYPE']\n\nmy_data.dtypes\n\n## 1. Replace missing numeric data\n# look up missing values, and get to know data types of them\nmy_data = my_data.replace(to_replace=\"?\", value=float(\"NaN\"))\nmy_data.isnull().sum()\n# impute missing value in LENGTH to its median value \nmy_data.loc[:, \"LENGTH\"] = pd.to_numeric(my_data.loc[:, \"LENGTH\"], errors='coerce')\nHasNan = np.isnan(my_data.loc[:,\"LENGTH\"])\nmy_data.loc[HasNan, \"LENGTH\"] = np.nanmedian(my_data.loc[:,\"LENGTH\"])\n# drop other columns, which can't be imputed by numeric data \nmy_data = my_data.dropna(axis=0)\n\n\n## 2. Account for outlier values in numeric columns \nx = np.array(my_data['LENGTH'])\nLimitHi = np.mean(x) + 2*np.std(x)\nLimitLo = np.mean(x) - 2*np.std(x)\nFlagOutlier = (x < LimitLo) | (x > LimitHi)\nprint ('the histogram plot for the hours-per-week column is below:')\nplt.hist(my_data.loc[~FlagOutlier, 'LENGTH'], bins = 10, color=[1, 1, 0, 0.5])\nplt.hist(my_data.loc[FlagOutlier, 'LENGTH'], bins = 10, color=[0, 0, 1, 0.5])\nplt.xlabel('length of the bridge')\nplt.title(\"Compare Distributions between inliers and outliers\")\nplt.show()\nprint ('below are the value counts for outliers in LENGTH column')\nprint (my_data.loc[FlagOutlier, 'LENGTH'].value_counts())\n# because the outliers are only three rows, approcimitely 4% of the whole data\n# so I decided to simply remove the outliers\nmy_data = my_data.loc[~FlagOutlier]\n\n\n## 3. Normalize numeric values \nlength =np.array(my_data['LENGTH'])\n# use min-max normalized as an example\nlength_mm = (length - np.min(length))/(np.max(length) - np.min(length)) \n# look at original data distributrions \nplt.hist(length, bins = 20, color=[0, 0, 0, 1])\nplt.title(\"Original Bridge Length Distribution\")\nplt.xlabel('length of the bridge')\nplt.show()\nplt.hist(length_mm, bins = 20, color=[1, 1, 0, 1])\nplt.title(\"MinMax-normalization of Original Length\")\nplt.xlabel('length of the bridge')\nplt.show()\n\n\n## 4. Bin numeric variables\n# Decide to have 3 bins\nNumberOfBins = 3\nx = np.array(my_data['LENGTH'])\n# equal frequency binning\nApproxBinCount = len(x)/NumberOfBins\nprint(\"\\n########\\n\\n Each bin should contain approximately\", ApproxBinCount, \"elements.\")\nprint(np.sort(x))\n# Bins with 26, 26, and 25 elements\n# Calculate the bin edge value\nMinBin = float('-inf')  \nMaxBin1 = 1092.5\nMaxBin2 = 1500.5\nMaxBin = float('inf')\nprint(\"\\n########\\n\\n Bin 1 is from \", MinBin, \" to \", MaxBin1)\nprint(\" Bin 2 is greater than \", MaxBin1, \" up to \", MaxBin2)\nprint(\" Bin 3 is greater than \", MaxBin2, \" up to \", MaxBin)\n\n# Empty starting point for equal-frequency-binned array\nBinned_EF = np.array([\" \"]*len(x)) \nBinned_EF[(MinBin < x) & (x <= MaxBin1)] = \"L\" # Low\nBinned_EF[(MaxBin1 < x) & (x <= MaxBin2)] = \"M\" # Med\nBinned_EF[(MaxBin2 < x) & (x  < MaxBin)] = \"H\" # High\nprint(\" x binned into 3 equal-freq1uency bins: \")\nprint(Binned_EF)\n\n\n## 5. Consolidate categorical data\n# for bridge type, I want to consolidate Cont-T with SIMPLE-T \nmy_data.loc[my_data.loc[:, \"TYPE\"] == \"CONT-T\", \"TYPE\"] = \"SIMPLE-T\"\n\n\n## 6. One-hot encode categorical data with at least 3 categories\n# encode the river \nprint (my_data['RIVER'].value_counts())\nmy_data.loc[:, \"A_RIVER\"] = (my_data.loc[:, \"RIVER\"] == \"A\").astype(int)\nmy_data.loc[:, \"M_RIVER\"] = (my_data.loc[:, \"RIVER\"] == \"M\").astype(int)\nmy_data.loc[:, \"O_RIVER\"] = (my_data.loc[:, \"RIVER\"] == \"O\").astype(int)\n\n\n## 7. Remove obsolete columns\n# Remove 'RIVER', because it already presented by above three columns\nmy_data = my_data.drop(\"RIVER\", axis=1)\n\n## 8. Write out data \nmy_data.to_csv('AngelaQiqianYang-M02-Dataset.csv', index=False)\n\n## 8. Final Comment\n# With the Pittsburgh bridges dataset, I mostly focus on bridge length. \n# Because this is the only column that could transfer to numeric value and won't mislead the model. \n# there are three extreme long bridge. It is the reason that three outliers showed up in the database. \n# I use min-max normalization is because the length values has no outliers already, and min-max is easy to understand and explain \n# I binned length in equal frequency binned array is because I want to see where the length distribution divided. \n# I chose to consolidate the bridge types, because there are two types really similar to each other. \n# I chose to encode the RIVER, because river represent really different geographic location, so it worth to seperate them and treat them as three different valuables. \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","repo_name":"AngelaQYang/Self-Study","sub_path":"UW_data_science/AneglaQYang-Milestone02.py","file_name":"AneglaQYang-Milestone02.py","file_ext":"py","file_size_in_byte":4959,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"10480882316","text":"\"\"\"\nModels for \"activity institution\" package.\n\"\"\"\n\nfrom sqlalchemy import CheckConstraint, UniqueConstraint\n\nfrom app import db\nfrom app.base.models import BaseModel\nfrom app.base.model_fields import LCString, ChoiceString\n\n\nclass ActivityInstitution(BaseModel):\n\n    __tablename__ = 'activity_institution'\n\n    activity_id = db.Column(db.BigInteger, db.ForeignKey(\n        'activity.id', name='institute_activity_id_fkey', ondelete=\"CASCADE\"), \n    nullable=False)\n\n    account_name = db.Column(db.String(256))\n\n    account_id = db.Column(db.BigInteger, db.ForeignKey(\n        'account.id', name='institute_account_id_fkey', ondelete='CASCADE'))\n\n    factset_entity_id = db.Column(db.String(256))\n\n    account = db.relationship('Account', backref=db.backref(\n        'institution_account', lazy='dynamic'), foreign_keys='ActivityInstitution.account_id')\n\n    # activity = db.relationship('Activity', backref=db.backref(\n    #     'activity_institution_activity', uselist=True),\n    #     primaryjoin='ActivityInstitution.activity_id == Activity.row_id')\n\n    def __init__(self, *args, **kwargs):\n        super(ActivityInstitution, self).__init__(*args, **kwargs)\n\n    def __repr__(self):\n        return '<ActivityInstitution %r>' % (self.account_name)\n\n\nclass ActivityInstitutionParticipant(BaseModel):\n\n    __tablename__ = 'activity_institution_participant'\n\n    activity_id = db.Column(db.BigInteger, db.ForeignKey(\n        'activity.id', name='institute_participant_activity_id_fkey', ondelete=\"CASCADE\"), \n    nullable=False)\n\n    participant_name = db.Column(db.String(256))\n\n    user_id = db.Column(db.BigInteger, db.ForeignKey(\n        'user_profile.id', name='institution_participant_user_id_fkey', ondelete='CASCADE'))\n\n    contact_id = db.Column(db.Integer, db.ForeignKey(\n        'crm_contact.id', name='institution_contact_id_fkey', ondelete='CASCADE'))\n\n    people_id = db.Column(db.String(256))\n\n    users = db.relationship('UserProfile', backref=db.backref(\n        'institution_participant_users', lazy='dynamic'), \n    foreign_keys='ActivityInstitutionParticipant.user_id')\n\n    contacts = db.relationship('CRMContact', backref=db.backref(\n        'institution_contacts', lazy='dynamic'), \n    foreign_keys='ActivityInstitutionParticipant.contact_id')\n\n    # activity = db.relationship('Activity', backref=db.backref(\n    #     'activity_institution_activity', uselist=True),\n    #     primaryjoin='ActivityInstitution.activity_id == Activity.row_id')\n\n    def __init__(self, account_name=None, participant_name=None, user_id=None, contact_id=None, people_id=None, *args, **kwargs):\n        self.activity_id = account_name\n        self.participant_name = participant_name\n        self.user_id = user_id\n        self.people_id = people_id\n        self.contact_id = contact_id\n        super(ActivityInstitutionParticipant, self).__init__(*args, **kwargs)\n\n    def __repr__(self):\n        return '<ActivityInstitutionParticipant %r>' % (self.row_id)","repo_name":"Witzcode0/Exchange-connect","sub_path":"app/activity/activities_institution/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":2965,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"6595061820","text":"from flaskblog import create_app #this import is because we are running app here (this is going to import from __init__.py, app need to exist in __init__.py )\n\napp = create_app()\n\n# __name__ == '__main__' if we are running this script directly from python script, otherwise is not true\nif( __name__ == '__main__'):  #The only purpose of this is to grab application and run it\n    app.run(debug=True)\n\n\n\n\n","repo_name":"Pioter44/Flaskblog_pkyziol_2","sub_path":"run.py","file_name":"run.py","file_ext":"py","file_size_in_byte":404,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"15671917967","text":"# Requires the PyMongo package.\r\n# https://api.mongodb.com/python/current\r\n\r\nclient = MongoClient('mongodb+srv://bedu-admin:hola123@bedu-cluster.0fhlu.mongodb.net/?authSource=admin&replicaSet=atlas-x7e8us-shard-0&readPreference=primary&appname=MongoDB%20Compass&ssl=true')\r\nresult = client['sample_airbnb']['listingsAndReviews'].aggregate([\r\n    {\r\n        '$match': {\r\n            'property_type': re.compile(r\"house|home(?i)\"), \r\n            'bedrooms': {\r\n                '$gte': 1\r\n            }\r\n        }\r\n    }, {\r\n        '$addFields': {\r\n            'costo_recamara': {\r\n                '$divide': [\r\n                    '$price', '$bedrooms'\r\n                ]\r\n            }\r\n        }\r\n    }, {\r\n        '$group': {\r\n            '_id': '$address.country', \r\n            'costo_promedio': {\r\n                '$avg': '$costo_recamara'\r\n            }\r\n        }\r\n    }, {\r\n        '$project': {\r\n            'costo_promedio': {\r\n                '$trunc': [\r\n                    '$costo_promedio', 2\r\n                ]\r\n            }\r\n        }\r\n    }\r\n])","repo_name":"ParamontHdzGlz/BEDU-Data-Analysis","sub_path":"Modulo1-Introduccion-a-Bases-de-Datos/sesion-06/reto-01/reto-01.py","file_name":"reto-01.py","file_ext":"py","file_size_in_byte":1063,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"29963847615","text":"import hoomd\nimport datetime\n\n\nclass Status():\n\n    def __init__(self, system):\n        self.system = system\n\n    @property\n    def seconds_remaining(self):\n        try:\n            return (self.system.final_timestep - self.system.timestep) / self.system.tps\n        except ZeroDivisionError:\n            return 0\n\n    @property\n    def etr(self):\n        return str(datetime.timedelta(seconds=self.seconds_remaining))\n        \n\nclass Thermo():\n\n    def __init__(self, system):\n        thermo = hoomd.md.compute.ThermodynamicQuantities(filter=hoomd.filter.All())\n        system.operations.computes.append(thermo)\n        \n        self.thermo = thermo\n        \n    @property\n    def kinetic_temperature(self):\n        try:\n            return self.thermo.kinetic_temperature\n        except hoomd.error.DataAccessError:\n            return 0.\n\n\ndef get_logger(system, quantities=[]):\n    \"\"\"Compute logged quantities\"\"\"\n\n    status = Status(system)\n    thermo = Thermo(system)\n    \n    logger = hoomd.logging.Logger(categories=['scalar', 'string'])\n        \n    logger.add(system, quantities=['timestep', 'tps'] + quantities)\n    \n    logger[('Status', 'etr')] = (status, 'etr', 'string')\n    logger[('Thermo', 'kinetic_temperature')] = (thermo, 'kinetic_temperature', 'scalar')\n\n    return logger\n\n\ndef table_formatter(logger, period=5000, **kwargs):\n    \"\"\"Set tabulated logs\"\"\"\n    \n    table = hoomd.write.Table(trigger=hoomd.trigger.Periodic(period=int(period)),\n                              logger=logger, **kwargs)\n                             \n    return table\n","repo_name":"open2c/polychrom-hoomd","sub_path":"polychrom_hoomd/log.py","file_name":"log.py","file_ext":"py","file_size_in_byte":1566,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"81"}
+{"seq_id":"5586408485","text":"from main.inat_adapter import InatAdapter\nfrom main.models import Observation\n\n\ndef get_adapter_for_origin(origin, load_event=None):\n    if origin == 'inaturalist':\n        return InatAdapter(origin=origin, load_event=load_event)\n    if origin == 'biodiversity4all':\n        return InatAdapter(origin=origin, load_event=load_event)\n    # elif origin == 'gbif_obervation_org':\n    #     return GBIFAdapter(origin=origin)\n    else:\n        raise KeyError\n\n\ndef replay_event(event, clear=False):\n    adapter = get_adapter_for_origin(event.origin, event)\n    if clear:\n        hydrated = adapter.hydrate_all()\n        Observation.objects.filter(origin=event.origin).delete()\n        Observation.objects.bulk_create(hydrated)\n    else:\n        raw_data = adapter.read_raw_data()\n        for raw_obs in raw_data:\n            try:\n                existing_obs = Observation.objects.filter(origin=event.origin).get(native_id=raw_obs['id'])\n                updated_obs = adapter.copy(existing_obs, raw_obs)\n                updated_obs.save()\n            except Observation.DoesNotExist:\n                observation = adapter.hydrate(raw_obs)\n                observation.save()\n\n\ndef load_from_file(file_name, origin):\n    adapter = get_adapter_for_origin(origin)\n    hydrated = adapter.hydrate_from_file(file_name)\n    for obs in hydrated:\n        try:\n            existing_obs = Observation.objects.filter(origin=origin).get(native_id=obs.native_id)\n            updated_obs = adapter.clone_overwrite(existing_obs, obs)\n            updated_obs.save()\n        except Observation.DoesNotExist:\n            obs.save()\n","repo_name":"aescobarr/data_aggregator_api","sub_path":"main/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":1606,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"32805065967","text":"from .FetchBase.FetchBase import FetchBase\nimport requests\nimport logging\nimport pyquery as pq\nfrom asyncio import sleep\nimport aiohttp\nfrom bot.fetch_modules.FetchBase.utils import send_request_multiple, send_data, headers\n\n\nclass Fetch_shikimori(FetchBase):\n    def __init__(self, bot, running, check_id):\n        self.running = running\n        self.check_id = check_id\n        self.bot = bot\n\n        self.fetch_name = 'shikimori'\n        self.accuracy = 0.65\n        self.max_page = 0\n\n        self.endpoint = 'https://shikimori.one/mangas/page/'\n        self.output = []\n\n    async def _get_max_pages(self):\n        first_page_json = requests.get(url=f'{self.endpoint}1.json', headers=headers).json()\n        return first_page_json['pages_count']\n\n    async def _fetch_page(self, session, url):\n        result = await send_request_multiple(session=session, url=url)\n        if result:\n            query = pq.PyQuery(result)\n            container = query('.cc-entries > article.c-column')\n            for item in container.items():\n                link = item('a').attr('href')\n                other_names_data = await send_request_multiple(session=session, url=f'{link}/other_names')\n                original_name = list(pq.PyQuery(other_names_data).find('.line-container .value').items())[0]\n\n                self.output.append({\n                    'ru_title': item('span.name-ru').text(),\n                    'en_title': item('span.name-en').text(),\n                    'orig_title': original_name.text(),\n                    'dir': link\n                })\n\n                await sleep(0.3)\n\n    async def prepare(self):\n        logging.info(f'FETCH {self.fetch_name.upper()}: prepare stage start')\n        self.max_page = await self._get_max_pages()\n\n    async def run(self):\n        logging.info(f'FETCH {self.fetch_name.upper()}: run stage start')\n        async with aiohttp.ClientSession(headers=headers) as session:\n            #await self._fetch_page(session=session, url=f'{self.endpoint}1')\n            for page in range(1, self.max_page + 1):\n                await self._fetch_page(session=session, url=f'{self.endpoint}{page}')\n                await sleep(0.5)\n\n            logging.info(f'FETCH {self.fetch_name.upper()}: run stage complete')\n\n    async def complete(self):\n        logging.info(f'FETCH {self.fetch_name.upper()}: complete stage start')\n        await send_data(self.output, self.running, self.check_id, self.bot, self.fetch_name,\n                        ru_key='ru_title', en_key='en_title', orig_key='orig_title', dir=\"dir\") \n        logging.info(f'FETCH {self.fetch_name.upper()}: complete stage complete')\n\n                \n\n\n\n    ","repo_name":"EmperorBeliver/fetch_sites_bot","sub_path":"bot/fetch_modules/Fetch_shikimori.py","file_name":"Fetch_shikimori.py","file_ext":"py","file_size_in_byte":2668,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"81"}
+{"seq_id":"35410610818","text":"import math\n\n#Corelation formula \ndef Internal_correlation(x,y):\n    xy=[]\n    for index,value in enumerate(x):\n        xy.append(value * y[index])\n    X=[]\n    for value in x:\n        X.append(value*value)\n    Y=[]\n    for value in y:\n        Y.append(value*value)\n        \n    return(((sum(xy)*len(x))-(sum(x)*sum(y)))/math.sqrt(((sum(X)*len(x))-(sum(x)*sum(x)))*((sum(Y)*len(x))-(sum(y)*sum(y)))))\n    \n    \n    \n#Multiple argument handeling for y\ndef Correlation(x,y):\n    for value in y:\n        print(Internal_correlation(x,value))\n    \n    \n\n#Sample arguments\ndependentVariable = [43,21,25,42,57,59]\nindependentVariable = [[99,65,79,75,87,81],[99,65,79,75,87,81],[99,65,79,75,87,81]]\n\n\n\n# Calling the fucntion\nCorrelation(dependentVariable,independentVariable)\n","repo_name":"NarasimmanSaravana1994/DataScience-MachineLearning-","sub_path":"correlation.py","file_name":"correlation.py","file_ext":"py","file_size_in_byte":768,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"81"}
+{"seq_id":"71980063945","text":"import yaml\nimport logging\nimport pydash as _\nimport os\nimport mimetypes\nfrom pathlib import *\nfrom multiprocessing.dummy import Pool as ThreadPool\nimport isodate\nimport boto3\nimport botocore\nfrom braceexpand import braceexpand\nfrom sys import exit\n\n# Configuration\n\ndef _config_file(filename):\n    try:\n        with open(filename, \"r\") as stream:\n            return yaml.load(stream)\n    except yaml.YAMLError as exc:\n        log.error(\"%s was not a valid YAML file\", filename)\n        exit(2)\n    except:\n        log.info(\"%s could not be opened, ignoring\", filename)\n\n    return {}\n\n\n\n# YAML Parsers\n\ndef _parse_cache_control(directive):\n    response = []\n\n    # If the directive is a string, then return itself\n    # CacheControl: max-age=3600\n    if isinstance(directive, str):\n        return directive\n\n    # The following conditions assume that the directive is an object\n    if \"must-revalidate\" in directive:\n        if directive[\"must-revalidate\"] is True:\n            response.append(\"must-revalidate\")\n\n    if \"no-cache\" in directive:\n        if directive[\"no-cache\"] is True:\n            response.append(\"no-cache\")\n\n    if \"no-store\" in directive:\n        if directive[\"no-store\"] is True:\n            response.append(\"no-store\")\n\n    if \"no-transform\" in directive:\n        if directive[\"no-transform\"] is True:\n            response.append(\"no-transform\")\n\n    if \"public\" in directive:\n        if directive[\"public\"] is True:\n            response.append(\"public\")\n\n    if \"private\" in directive:\n        if directive[\"private\"] is True:\n            response.append(\"private\")\n\n    if \"proxy-revalidate\" in directive:\n        if directive[\"proxy-revalidate\"] is True:\n            response.append(\"proxy-revalidate\")\n\n    if \"max-age\" in directive:\n        response.append('max-age=\"%s\"' % (\n            _parse_date_directive_to_seconds(directive[\"max-age\"])\n        ))\n\n    if \"s-maxage\" in directive:\n        response.append('s-maxage=\"%s\"' % (\n            _parse_date_directive_to_seconds(directive[\"s-maxage\"])\n        ))\n\n    return \", \".join(response)\n\n\ndef _parse_date_directive_to_seconds(directive):\n    # Digit directives are in seconds\n    if directive.isdigit():\n        return directive\n\n    # If the directive is a string\n    if isinstance(directive, str):\n        try:\n            date = isodate.parse_duration(directive)\n            return (date.days * (60 * 60 * 24)) + date.seconds\n        except Exception as e:\n            log.exception(e)\n\n    print(\"Could not parse directive as date: \" + directive)\n\n\ndef _parse_mime_type_to_content_type(mime_type):\n    if not isinstance(mime_type, str):\n        return None\n    elif mime_type.startswith(\"text/\"):\n        return \"%s; charset=UTF-8\" % (mime_type)\n    else:\n        return mime_type\n\n\n\n# Local files\n\ndef job_files(job):\n    directory_src = Path(job[\"src\"]).resolve()\n    match_globs = []\n    match_files = set([])\n\n    # Read include/exclude from the configuration\n    if \"match\" in job:\n        list(map(\n            lambda x: match_globs.extend(list(braceexpand(x))),\n            job[\"match\"].splitlines()\n        ))\n\n    # If there is no inclusion, include everything\n    if not match_globs:\n        match_globs.append(\"**/*\")\n        match_globs.append(\"!**/.DS_Store\")\n\n    # Find files\n    for i in match_globs:\n        if i.startswith(\"!\"):\n            match_files -= set(directory_src.glob(i[1:])) # remove\n        else:\n            match_files |= set(directory_src.glob(i)) # append\n\n    return match_files\n\n\n\n# Upload\n\ndef upload_file(local_filepath, destination_key):\n    # Identify MIME type and encoding\n    content_mimetype, content_encoding = mimetypes.guess_type(\n        str(local_filepath)\n    )\n\n    # Extra Args for S3 file\n    file_settings = {}\n\n    if content_mimetype in config[\"mime-types\"]:\n        file_settings = config[\"mime-types\"][content_mimetype]\n\n    # Default headers\n    extra_args = { \"ACL\": \"public-read\" }\n\n    content_type = _parse_mime_type_to_content_type(content_mimetype)\n    if not content_type is None:\n        extra_args[\"ContentType\"] = content_type\n\n    if \"CacheControl\" in file_settings:\n        extra_args[\"CacheControl\"] = _parse_cache_control(file_settings[\"CacheControl\"])\n\n    # If the item has been encoded\n    if not content_encoding is None:\n        extra_args[\"ContentEncoding\"] = content_encoding\n\n    S3Bucket.upload_file(\n        str(local_filepath),\n        destination_key,\n        extra_args\n    )\n\n\n\ndef upload_job(job):\n    if \"name\" in job:\n        log.info(\"Starting job: %s\", job[\"name\"])\n    else:\n        log.info(\"Starting job\")\n\n    if not \"src\" in job:\n        job[\"src\"] = os.environ[\"WERCKER_ROOT\"]\n\n    if not \"dest\" in job:\n        job[\"dest\"] = \"\"\n\n    files = job_files(job)\n\n    def _threadsafe_upload_file(local_filepath):\n        # Only upload files\n        if not local_filepath.is_file():\n            return True\n\n        local_filename = local_filepath.relative_to(Path(job[\"src\"]).resolve())\n        destination_key = os.path.join(job[\"dest\"], str(local_filename))\n\n        attempts = 5\n        for attempt in range(1, attempts):\n            try:\n                upload_file(local_filepath, destination_key)\n                log.debug(\n                    \"%s => %s (uploaded; attempts: %s)\",\n                    local_filename,\n                    destination_key,\n                    attempt\n                )\n\n                return True\n\n            except Exception as e:\n                log.debug(\n                    \"%s => %s (failed; attempt: %s)\",\n                    local_filename,\n                    destination_key,\n                    attempt\n                )\n                log.debug(e)\n\n        log.exception(\n            \"%s => %s (aborted; attempts: %s)\",\n            local_filename,\n            destination_key,\n            attempt\n        )\n        return False\n\n    pool = ThreadPool(10)\n    results = pool.map(_threadsafe_upload_file, files)\n    pool.close()\n    pool.join()\n\n    return all(results)\n\n\n\ndef upload_jobs():\n    if not \"jobs\" in config:\n        log.error(\"Could not find jobs in yaml configuration\")\n        raise\n\n    pool = ThreadPool(1)\n    results = pool.map(upload_job, config[\"jobs\"])\n    pool.close()\n    pool.join()\n\n    return all(results)\n\n\n\n# Init\n\nif __name__ == \"__main__\":\n\n    # Setup logging\n    logging.basicConfig(level=logging.DEBUG, format=\"%(message)s\")\n    logging.getLogger(\"boto3\").propagate = False\n    logging.getLogger(\"botocore\").propagate = False\n    logging.getLogger(\"s3transfer\").propagate = False\n    log = logging.getLogger(os.environ[\"WERCKER_STEP_NAME\"])\n\n    # Establish connection to Amazon Web Services\n    AWSSession = boto3.Session(\n        aws_access_key_id=os.environ[\"WERCKER_AWS_S3_DEPLOY_AWS_ACCESS_KEY_ID\"],\n        aws_secret_access_key=os.environ[\"WERCKER_AWS_S3_DEPLOY_AWS_SECRET_ACCESS_KEY\"],\n        region_name=os.environ[\"WERCKER_AWS_S3_DEPLOY_AWS_REGION\"],\n    )\n\n    # Establish connection to S3\n    S3Resource = AWSSession.resource(\"s3\")\n    S3Bucket = S3Resource.Bucket(os.environ[\"WERCKER_AWS_S3_DEPLOY_TARGET_BUCKET\"])\n\n    # Setup MIME types\n    mimetypes.init([\n        os.environ[\"WERCKER_STEP_ROOT\"] + \"/mime.types\",\n        os.environ[\"WERCKER_ROOT\"] + \"/mime.types\"\n    ])\n\n    mimetypes.encodings_map[\".gzip\"] = \"gzip\"\n\n    # Compile the configuration for the application\n    # pydash's _defaults_deep orders from highest to lowest precedence\n    config = _.defaults_deep(\n        # Project configuration\n        _config_file(os.environ[\"WERCKER_AWS_S3_DEPLOY_CONFIGURATION_FILE\"]),\n\n        # Global configuration\n        _config_file(os.path.join(\n            os.environ[\"WERCKER_STEP_ROOT\"],\n            \"aws-s3-deploy.yml\"\n        )),\n\n        # Placeholders\n        { \"version\": \"1\", \"mime-types\": {} }\n    )\n\n    # Exit with 0 status on success\n    # Exit with 1 status on failure\n    exit(0 if upload_jobs() else 1)\n","repo_name":"bashaus/wercker-aws-s3-deploy","sub_path":"run.py","file_name":"run.py","file_ext":"py","file_size_in_byte":7906,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"38553181624","text":"from constants import *\nimport xlrd\nimport workbook_extractor\nimport handler\n\n\ndevice_path = \"{}device.xlsx\".format(DATA_PATH)\nworkbook = xlrd.open_workbook(device_path)\ndevices = workbook_extractor.collect_devices(workbook)\n\nrequest_path = \"{}request.xlsx\".format(DATA_PATH)\nworkbook = xlrd.open_workbook(request_path)\nrequests = workbook_extractor.collect_requests(workbook)\nfor request in requests:\n    print(\"request:\", request)\n    called_devices = handler.handle(request, devices)\n\n    for device_score, id, action_0_score, action_1_score in called_devices[:5]:\n        action = 'action_0' if action_0_score > action_1_score else 'action_1'\n        print (\"score = {:.2f}, device = {}, type = {}, action = {}\".\n               format(device_score, devices[id]['name'], devices[id]['type'], devices[id][action]))\n    print ('---------------------\\n')","repo_name":"alannguyencs/control_device_smart_home","sub_path":"src/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":854,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"28745398588","text":"import numpy as np; import pandas as pd\nfrom sympy import ordered\n\n\ndef practice():\n    values = pd.Series(['apple', 'orange', 'apple', 'apple'] *2)\n    \n    print(\"\\noriginal series:\\n\", values)\n    print(\"\\nunique values: \\n\", pd.unique(values))\n    print(\"\\ncounting each unique values:\\n\", pd.value_counts(values))\n    \n    a = pd.Series([0, 1, 0, 0] * 2)\n    b = pd.Series(['apple', 'orange'])\n    \n    print(b.take(a))\n    \ndef practice_02():\n    fruits = ['apple', 'orange', 'apple', 'apple'] * 2\n    n = len(fruits) #Out[]: 8\n    df = pd.DataFrame({'fruit' : fruits,\n                       'basket_id' : np.arange(n),\n                       'count' : np.random.randint(3, 15, size = n),\n                       'weight' : np.random.uniform(0, 4, size = n)},\n                      columns = ['basket_id', 'fruit', 'count', 'weight'])\n    print(\"\\noriginal dataframe:\\n\", df)\n    \n    fruit_cat = df['fruit'].astype('category')\n    print(\"\\nfruit category:\\n\", fruit_cat)\n    \n    c = fruit_cat.values\n    print(\"\\ntype:\\n\", type(c))\n    \n    print(c.codes)\n    \n    df['fruit'] = df['fruit'].astype('category')\n    print(\"\\n\", df.fruit)\n    \n    #직접 생성\n    my_categories = pd.Categorical(['foo', 'bar', 'baz', 'foo', 'bar'])\n    print(\"\\nby pd.categorical: \\n\", my_categories)\n    \n    #from_codes 함수 이용\n    categories = ['foo', 'bar', 'baz']\n    codes = [0, 1, 2, 0, 0, 1]\n    my_cats_2 = pd.Categorical.from_codes(codes, categories)\n    print(\"\\nby from_codes:\\n\", my_cats_2)\n    \n    #ordered = True 사용하여 순서 나타내주기\n    ordered_cat = pd.Categorical.from_codes(codes, categories, ordered = True)\n    print(\"\\nordered = True, ordered cat: \\n\", ordered_cat)\n    \n    #as_ordered() 사용하여 순서 나타내주기\n    print(\"\\nas_ordered:\\n\", my_cats_2.as_ordered())\n    \n    \ndef calculation():\n    np.random.seed(12345)\n    draws = np.random.randn(1000)\n    print(\"\\ndraws:\\n\", draws[:5])\n    \n    # .qcut -> 범주형 데이터 나누기\n    bins = pd.qcut(draws, 4) #4 sections 으로 나누기\n    print(\"\\nbins:\\n\", bins)\n    \n    bins = pd.qcut(draws, 4, labels = ['Q1', 'Q2', 'Q3', 'Q4'])\n    print(\"\\nbins with labels:\\n\", bins)\n    \n    print(\"\\nbins.codes:\\n\", bins.codes[:10])\n\n    bins = pd.Series(bins, name = 'quartile')\n    results = (pd.Series(draws).groupby(bins).agg(['count', 'min', 'max']).reset_index())\n    \n    print(\"\\nresults:\\n\", results)\n\ndef practice_03():\n    n = 10000000\n    draws = pd.Series(np.random.randn(n))\n    labels = pd.Series(['foo', 'bar', 'baz', 'qux'] * (n // 4))\n    categories = labels.astype('category') #lables를 categorical 하게 바꿔 적은 메모리 사용\n\ndef categorical():\n    s = pd.Series(['a', 'b', 'c', 'd'] * 2)\n    cat_s = s.astype('category')\n    print(\"\\ncat_s as categorical:\\n\", cat_s)\n    \n    print(\"\\ncodes:\\n\", cat_s.cat.codes)\n    \n    print(\"\\ncategories:\\n\", cat_s.cat.categories)\n    \n    actual_categories = ['a', 'b', 'c', 'd', 'e']\n    cat_s2 = cat_s.cat.set_categories(actual_categories)\n    print(\"\\ncat_s2:\\n\", cat_s2)\n    \n    print(cat_s.value_counts())\n    \n    cat_s3 = cat_s[cat_s.isin(['a', 'b'])]\n    \n    print(\"\\ncat_s3:\\n\", cat_s3)\n    \n    print(\"\\nremoved unused categories:\\n\", cat_s3.cat.remove_unused_categories())\n    \ndef dummy():\n    cat_s = pd.Series(['a', 'b', 'c', 'd'] * 2, dtype = 'category')\n    print(\"\\ndummies:\\n\", pd.get_dummies(cat_s)) #pd.get_dummies -> changes 1D categorical data to DataFrame\n    \ndef groupby():\n    df = pd.DataFrame({'key' : ['a', 'b', 'c'] * 4,\n                       'value' : np.arange(12.)})\n    print(\"\\n original dataframe:\\n\", df)\n    \n    g = df.groupby('key').value\n    print(\"\\n mean of each keys:\\n\", g.mean())\n    \n    print(\"\\ntransform: \\n\", g.transform(lambda x: x.mean()))\n    \n    print(\"\\n'mean' agg:\\n\", g.transform('mean'))\n\n    print(\"\\n x = x * 2:\\n\", g.transform(lambda x: x* 2))\n    \n    print(\"\\nrank of g:\\n\", g.transform(lambda x: x.rank(ascending = False)))\n    \n    def normalize(x):\n        return (x - x.mean()) / x.std()\n\n    print(\"\\nnormalized:\\n\", g.transform(normalize))\n    \n    print(\"\\n.apply:\\n\", g.apply(normalize))\n    \n    #page 497 복습\n    \ndef time():\n    n = 15\n    times = pd.date_range('2017-05-20 00:00', freq = '1min', periods = n)\n    df = pd.DataFrame({'time' : times, 'values' : np.arange(n)})\n    \n    print(\"\\ntime series data frame:\\n\", df)\n    \n    df.set_index('time').resample('5min').count() #'time' 으로 색인한 후 리샘플\n    df2 = pd.DataFrame({'time' : times.repeat(3),\n                        'key' : np.tile(['a', 'b', 'c'], n),\n                        'value' : np.arange(n *3)})\n    print(\"\\ndf2:\\n\", df2[:7])\n    \n    '''\n    time_key = pd.TimeGrouper('5min') #pands.TimeGrouper -> 'key' 의 각 값에 대해 같은 리샘플을 수행\n    \n    resampled = (df2.set_index('time').groupby(['key', time_key]).sum())\n    print(\"\\nresampled:\\n\", resampled)\n    \n    '''\n    #page 500 다시\n    \n#method 연결 기법: DataFrame.assign method\n'''\n#실용적이지 않은 방법\ndf2 = df.copy()\ndf2['k'] =v\n\n#실용적인 방법\ndf2 = df.assign(k = v)\n'''\n#값을 직접 대입하는 것이 assign 보단 빠름\n#하지만 assign 이용시 method 를 연결해서 사용 가능\n\n#page 502 - 504 복습\n\n#pipe method\n  \n    \n    \n    \n    \n    \nif __name__ == \"__main__\":\n    #practice()\n    #practice_02()\n    #calculation()\n    #practice_03()\n    #categorical()\n    #dummy()\n    #groupby()\n    time()\n    ","repo_name":"sbandrew117/pythonfordataanalysis","sub_path":"basic_09.py","file_name":"basic_09.py","file_ext":"py","file_size_in_byte":5461,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"74080383625","text":"import requests\n\nfrom tss_cmd.api.api_globals import SESSION, TSS_ENDPOINT, CONN_TIMEOUT\n\n\ndef post(url: str, data: dict) -> requests.Response:\n    \"\"\"Makes an authorized POST request\"\"\"\n    return SESSION.post(\n        TSS_ENDPOINT + url,\n        data=data,\n        timeout=CONN_TIMEOUT,\n    )\n\n\ndef get(url: str) -> requests.Response:\n    return SESSION.get(\n        TSS_ENDPOINT + url,\n        timeout=CONN_TIMEOUT,\n    )\n","repo_name":"Cod3dDOT/exploiting_tss","sub_path":"tss_cmd/api/reqs.py","file_name":"reqs.py","file_ext":"py","file_size_in_byte":425,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"45460738556","text":"import logging\nimport json\nfrom urllib.request import urlopen, Request\nimport urllib.error\n\n\nclass DomainRecordChanger:\n    def __init__(self, domain_config, ip_addrs):\n        self.enabled = domain_config.get(\"enabled\", False)\n        self.zoneID = domain_config.get(\"zoneID\", None)\n        self.apiToken = domain_config.get(\"apiToken\", None)\n        self.sub_domains = domain_config.get(\"subdomains\", [])\n        self.domain_name = domain_config.get(\"domainName\", None)\n        self.record_type = domain_config.get(\"recordType\", \"A\")\n        # self.ip_value = str(urlopen(ip_url[self.record_type]).read(), encoding='utf-8').strip()\n        self.ip_value = ip_addrs[self.record_type]\n        self.allow_new = domain_config.get(\"createNewRecord\", False)\n        self.request_header = {\n            \"Authorization\": \"Bearer %s\" % self.apiToken,\n            \"Content-Type\": \"application/json\"\n        }\n\n    @staticmethod\n    def do_request(request):\n        try:\n            response = urlopen(request)\n        except urllib.error.HTTPError as error_response:\n            response = error_response\n        return json.loads(response.read())\n\n    def describe_record(self, full_domain_name):\n        request_url = \"https://api.cloudflare.com/client/v4/zones/%s/dns_records?type=%s&name=%s\" % \\\n                      (self.zoneID, self.record_type, full_domain_name)\n        describe_record_request = Request(request_url, headers=self.request_header)\n        response_dict = self.do_request(describe_record_request)\n        if response_dict[\"success\"]:\n            return response_dict[\"result_info\"][\"total_count\"], response_dict[\"result\"]\n        else:\n            for error_part in response_dict[\"errors\"]:\n                logging.error(\"Describe domain record for %s error. Error code: %d, %s\" %\n                              (full_domain_name, error_part[\"code\"], error_part[\"message\"]))\n            return -1, None\n\n    def create_record(self, full_domain_name, ttl, is_proxied):\n        request_url = \"https://api.cloudflare.com/client/v4/zones/%s/dns_records\" % self.zoneID\n        request_data = {\n            \"type\": self.record_type,\n            \"name\": full_domain_name,\n            \"content\": self.ip_value,\n            \"ttl\": ttl,\n            \"proxied\": is_proxied\n        }\n        create_record_request = Request(request_url, data=bytes(json.dumps(request_data), encoding=\"utf-8\"),\n                                        headers=self.request_header, method='POST')\n        response_dict = self.do_request(create_record_request)\n        if response_dict[\"success\"]:\n            logging.info(\n                \"Successfully created an %s record for %s.\" % (self.record_type, full_domain_name))\n        else:\n            for error_part in response_dict[\"errors\"]:\n                logging.error(\"Failed to create and %s record for %s. Error code: %d, %s\" %\n                              (self.record_type, full_domain_name, error_part[\"code\"], error_part[\"message\"]))\n\n    def update_record(self, full_domain_name, record_id, ttl, is_proxied):\n        request_url = \"https://api.cloudflare.com/client/v4/zones/%s/dns_records/%s\" % (self.zoneID, record_id)\n        request_data = {\n            \"type\": self.record_type,\n            \"name\": full_domain_name,\n            \"content\": self.ip_value,\n            \"ttl\": ttl,\n            \"proxied\": is_proxied\n        }\n        update_record_request = Request(request_url, data=bytes(json.dumps(request_data), encoding=\"utf-8\"),\n                                        headers=self.request_header, method='PUT')\n        response_dict = self.do_request(update_record_request)\n        if response_dict[\"success\"]:\n            logging.info(\"Successfully updated the %s record for %s.\" % (self.record_type, full_domain_name))\n        else:\n            for error_part in response_dict[\"errors\"]:\n                logging.error(\"Update domain record for %s error. Error code: %d, %s\" %\n                              (full_domain_name, error_part[\"code\"], error_part[\"message\"]))\n\n    def change_single_domain(self, subdomain_settings):\n        subdomain_name = subdomain_settings.get(\"name\", None)\n        subdomain_ttl = subdomain_settings.get(\"ttl\", 1)\n        subdomain_proxied = subdomain_settings.get(\"proxied\", True)\n        if subdomain_name is None:\n            logging.error('Subdomain missing \"name\" field, skipped.')\n            return\n        elif subdomain_name == \"\" or subdomain_name == \"@\":\n            full_domain_name = self.domain_name\n        else:\n            full_domain_name = subdomain_name + \".\" + self.domain_name\n        record_count, record_content = self.describe_record(full_domain_name)\n        if record_count == -1:\n            logging.error(\"Describe record error.\")\n        elif record_count == 0:\n            if self.allow_new:\n                logging.debug(\"The Domain %s doesn't have a %s record. Will create a new record for it.\" %\n                             (full_domain_name, self.record_type))\n                self.create_record(full_domain_name, subdomain_ttl, subdomain_proxied)\n            else:\n                logging.debug(\"The Domain %s doesn't have a %s record. Creating new record is disabled.\" %\n                             (full_domain_name, self.record_type))\n        elif record_count == 1:\n            if (record_content[0][\"content\"] != self.ip_value) or (record_content[0][\"proxied\"] != subdomain_proxied) \\\n                    or (record_content[0][\"ttl\"] != subdomain_ttl):\n                self.update_record(full_domain_name, record_content[0]['id'], subdomain_ttl, subdomain_proxied)\n            else:\n                logging.debug(\"%s record for %s did not change since it is same as the machine.\" %\n                             (self.record_type, full_domain_name))\n        else:\n            logging.error(\"There are more than 1 %s record for %s. Record for it was left unchanged.\" %\n                          (self.record_type, full_domain_name))\n\n    def start_ddns(self):\n        if self.enabled:\n            logging.debug(\"Start DDNS for %s record under %s.\" % (self.record_type, self.domain_name))\n            logging.debug(\"Got ip address %s\" % self.ip_value)\n            if self.domain_name is None:\n                logging.error(\"Corrupted configuration, missing domainName. DDNS ended.\")\n                return\n            elif self.zoneID is None:\n                logging.error(\"Corrupted configuration, missing ZoneID. DDNS ended.\")\n                return\n            elif self.apiToken is None:\n                logging.error(\"Corrupted configuration, missing ApiToken. DDNS ended.\")\n                return\n            else:\n                for single_subdomain in self.sub_domains:\n                    self.change_single_domain(single_subdomain)\n                logging.debug(\"DDNS for %s ended.\" % self.domain_name)\n        else:\n            logging.debug(\"DDNS for %s is disabled.\" % self.domain_name)\n","repo_name":"un-lock-able/cloudflare-ddns","sub_path":"DomainRecordChanger.py","file_name":"DomainRecordChanger.py","file_ext":"py","file_size_in_byte":6928,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"21332326345","text":"\r\nimport os\r\nimport json\r\nfrom PIL import Image\r\n\r\nfrom datetime import date, datetime\r\nimport pytz #time zone info\r\n\r\nfrom constants import LOCALTZ, BG_IMG_GREYSCALE\r\n\r\n#encoding of json files gathered from MTF\r\nMTF_JSON_ENCODING = 'ANSI'\r\n\r\n#----------\r\n#private\r\n\r\ndef interaction_data(scout_subfolder_path):    \r\n    interactions = None\r\n    try:\r\n        interactions_path = scout_subfolder_path + os.sep + \"scout_interactions.json\"\r\n        assert(os.path.isfile(interactions_path))\r\n        with open(interactions_path, 'r', encoding=MTF_JSON_ENCODING) as interactions_file:\r\n            interactions = json.load(interactions_file)\r\n    except Exception as e:\r\n        print(\"Problem loading interaction data in \" + scout_subfolder_path + \"\\n\" + str(e))\r\n        interactions = None\r\n    finally:\r\n        return interactions\r\n    \r\ndef calibration_image(scout_subfolder_path):\r\n    im = None\r\n    try:\r\n        image_path = None\r\n        for filename in os.listdir(scout_subfolder_path):\r\n            if filename.endswith(\"jpg\") and filename.startswith(\"scout\"):\r\n                image_path = scout_subfolder_path + os.sep + filename\r\n        im = Image.open(image_path)\r\n        if BG_IMG_GREYSCALE: im = im.convert('L')\r\n    except Exception as e:\r\n        print(\"Problem loading calibration image in \" + scout_subfolder_path + \"\\n\" + str(e))\r\n    finally:\r\n        return im\r\n \r\ndef parsed_set_date(set_subfolder_name):\r\n    #set_subfolder_name should be of the form \"[scoutname]_[MMDDYYYY]\"\r\n    try:\r\n        datestr = folder_name.split(\"_\")[1]\r\n        date = datetime.strptime(datestr, \"%m%d%Y\").date()\r\n    except:\r\n        print(\"problem parsing date string in \" + folder_name)\r\n        date = date(10, 1, 1)\r\n    finally:\r\n        return date\r\n        \r\ndef parsed_interaction_time(interaction_time_string):\r\n\r\n    #parse the time string into a python datetime object\r\n    dt = datetime.strptime(interaction_time_string, \"%Y-%m-%dT%H:%M:%SZ\")\r\n    #mtf records timestamps in UTC, so we need to tell the datetime object to interpret the parsed dates at UTC\r\n    dt = pytz.utc.localize(dt)\r\n    #tell the datetime object to change time display to eastern\r\n    dt = dt.astimezone(pytz.timezone(LOCALTZ))\r\n    \r\n    return dt\r\n    \r\ndef parse_interaction_times(interactions):\r\n    #replace string in \"EnteredAt\" field with a parsed datetime object in the local timezone.\r\n    if interactions == None: return None\r\n    for i in interactions: i['EnteredAt'] = parsed_interaction_time(i['EnteredAt'])\r\n    return interactions\r\n    \r\n# from https://stackoverflow.com/questions/9427163/remove-duplicate-dict-in-list-in-python\r\ndef deduplicate(list_of_jsons):\r\n    #return [elem for n, elem in enumerate(list) if elem not in list[n + 1:]] \r\n    #return [dict(t) for t in set([tuple(d.items()) for d in list])]\r\n    set_of_jsons = {json.dumps(d, sort_keys=True) for d in list_of_jsons}\r\n    return [json.loads(t) for t in set_of_jsons]\r\n \r\n#returns tuple of (interactions, calibration image) \r\n#entertimes returned in strings, not parsed into datetime objects\r\ndef from_set_private(scout_folder_path, set_subfolder_name):\r\n    path = scout_folder_path + os.sep + set_subfolder_name\r\n    if os.path.isdir(path): return (interaction_data(path), calibration_image(path))\r\n    else: return (None, None)\r\n \r\n#-------------------\r\n#public\r\n\r\n#returns tuple of (interactions, calibration image) \r\n#enter times parsed into datetime objects \r\ndef from_set(scout_folder_path, set_subfolder_name):\r\n    (interactions, im) = from_set_private(scout_folder_path, set_subfolder_name)\r\n    return (parse_interaction_times(interactions), im)\r\n \r\n#returns tuple of (interactions, calibration image, set subfolder name)\r\ndef from_most_recent_set(scout_folder_path):\r\n    \r\n    #iterate through subfolders, sorted by parsed date from most recent to oldest\r\n    for set_subfolder_name in sorted(os.listdir(scout_folder_path), key=parsed_set_date, reverse=True):\r\n        (interactions, im) = from_set(scout_folder_path, set_subfolder_name)\r\n        if interactions != None and im != None:\r\n            return (interactions, im, set_subfolder_name)\r\n    print(\"No usable set subfolders in \" + scout_folder_path + \"\\n\")\r\n    return (None, None, None) \r\n \r\n#the calibration image returned from the following methods is a bit tricky.=\r\n#combining data with different calibration images can be dubious if the images\r\n#are angled differently to a signifigant degree (which is very possible).\r\n#so using the following options assumes that either:\r\n#   a) the calibration image won't be used (such as in the case of time charts)\r\n#   b) the user has already looked through the images manually and confirmed\r\n#       that they are all sufficiently similar in angle for the data to\r\n#       match up reasonably. \r\n#\r\n#additionally, in the case of b) the user should select the \"best\" calibration\r\n#image (the sharpest and most free of people and temoprary objects and \r\n#put a copy of it in the scout's folder (the main folder, not a set subfolder).\r\n#if an image exists there, these functions will return it as the overall calibration\r\n#image to display as background. if not, an arbitrary one will be selected.\r\n \r\n#returns tuple of (interactions, calibration image)    \r\ndef combined_from_sets(scout_folder_path, set_subfolder_names):\r\n    im_overall = None\r\n    im_overall = calibration_image(scout_folder_path)\r\n    \r\n    interactions_all = []\r\n    for set_subfolder_name in set_subfolder_names:\r\n        (interactions, im) = from_set_private(scout_folder_path, set_subfolder_name)\r\n        if interactions != None: interactions_all = interactions_all + interactions\r\n        if im_overall == None: im_overall = im\r\n        \r\n    return (parse_interaction_times(deduplicate(interactions_all)), im_overall) \r\n    #dedupe interactions_all in case data was not cleared between collecting datasets\r\n \r\n#returns tuple of (interactions, calibration image)    \r\ndef combined_from_all_sets(scout_folder_path):\r\n    return combined_from_sets(scout_folder_path, os.listdir(scout_folder_path))\r\n    \r\n#returns tuple of (interactions, calibration image)    \r\n#min_date and max_date should be datetime objects\r\n#weeksdays to include should be a list of numbers between 1 and 7 \r\ndef from_date_range(scout_folder_path, date_range, weekdays_to_include=range(7)):\r\n    (interactions, im) = combined_from_all_sets(scout_folder_path)\r\n    \r\n    clipped_interactions = []\r\n    for i in interactions:\r\n        dt = i['EnteredAt']\r\n        if dt.weekday() in weekdays_to_include and date_range[0] <= dt.date() <= date_range[1]:\r\n            clipped_interactions.append(i)\r\n            \r\n    return (clipped_interactions, im)\r\n    \r\n    '''\r\n    relevant_sets = []\r\n    #iterate through subfolders, sorted by parsed date from oldest to most recent\r\n    for set_subfolder_name in sorted(os.listdir(scout_folder_path), key=parsed_set_date):\r\n        if parsed_set_date(set_subfolder_name)\r\n        \r\n        (interactions, im) = from_set_private(scout_folder_path, set_subfolder_name)\r\n        if interactions != None and im != None:\r\n            return (interactions, im, set_subfolder_name)\r\n    print(\"No usable set subfolders in \" + scout_folder_path + \"\\n\")\r\n    return (None, None, None) \r\n    '''\r\n            ","repo_name":"carnegielibrary/measure-the-future","sub_path":"load_mtf_data.py","file_name":"load_mtf_data.py","file_ext":"py","file_size_in_byte":7274,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"26223827724","text":"def changerxtoy(n):\n    if n  ==  \"\":\n        return \"\"\n    elif n[len(n) -  1]  ==  \"x\":\n        n = list(n)\n        n[len(n) - 1] = \"y\"\n        n = \"\".join(n)\n    name = n[:-1]\n    return changerxtoy(name) + n[len(n)-1]\n\nif __name__ == \"__main__\":\n    stringval = \"yyhxyi\"\n    print(changerxtoy(stringval))","repo_name":"mosot624/pythonRecursiveChallenges","sub_path":"changexy-recursion.py","file_name":"changexy-recursion.py","file_ext":"py","file_size_in_byte":308,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"71684150026","text":"# IOT RF controller\nimport sys\nimport time\nfrom rpi_rf import RFDevice # pylint: disable=import-error\nimport json\nimport threading\nsys.path.insert(0, \"..\")\nimport iot # pylint: disable=import-error\n\n# Initialize RF devices\nrfRecv = RFDevice(27)\nrfTrans = RFDevice(17)\nrfRecv.enable_rx()\nrfTrans.enable_tx()\n\n# Receiving variables\nrecvTime = None\nlastCodeReceived = None\nlastPulseLength = None\nlastProtocol = None\n\n# Data functions\ndef getCodes():\n    # Returns all the codes in the json\n    dataFile = open(\"codes.json\", \"r\")\n    data = dataFile.read()\n    data = json.loads(data)\n\n    dataFile.close()\n\n    return data\n\ndef writeCodes(data:dict):\n    # Writes the data to the json file\n    dataFile = open(\"codes.json\", \"w\")\n    data = json.dumps(data)\n    \n    dataFile.write(data)\n    dataFile.close()\n\n# Create receive thread\ndef receiveData():\n    # Open globals\n    global recvTime\n    global lastCodeReceived\n    global lastPulseLength\n    global lastProtocol\n\n    # Receive the code with given parameters\n    print(\"Listening\")\n    while rfRecv.rx_enabled:\n        # Check if code changed\n        if rfRecv.rx_code_timestamp != recvTime:\n            recvTime = rfRecv.rx_code_timestamp\n            lastPulseLength = rfRecv.rx_pulselength\n            lastProtocol = rfRecv.rx_proto\n\n            lastCodeReceived = rfRecv.rx_code\n            print(\"Received code:\", lastCodeReceived)\n\n        time.sleep(0.01) # Stall program until received a transmission\n\ndataThread = threading.Thread(target=receiveData, args=())\ndataThread.start()\n\n# Define command functions\ndef transmitDecimal(args, sender):\n    # Transmits the code with given parameters\n    print(\"Transmitting code!\")\n    rfTrans.tx_code(int(args[0]), int(args[1]), int(args[2]))\n\ndef addCode(args, sender):\n    # Define data\n    name = args[0]\n\n    # Wait until button pressed\n    print(\"Listening to keypress\")\n    startTime = (time.perf_counter() * 1000000)\n    \n    while recvTime == None or recvTime < startTime:\n        time.sleep(0.1)\n\n    # Acknowledge the code and test it, let the sender know\n    rfIOT.give(\"route \" + sender + \" testing_code \" + str(lastCodeReceived) + \" \" + str(lastPulseLength) + \" \" + str(lastProtocol))\n    rfIOT.take()\n\n    # Adds a code to a database\n    print(\"Code test\")\n    codes = getCodes()\n    codes[name] = {\n        \"code\": lastCodeReceived,\n        \"pulse\": lastPulseLength,\n        \"proto\": lastProtocol\n    }\n\n    # Wait 5 seconds, then send the code\n    time.sleep(5)\n    transmitDecimal([lastCodeReceived, lastProtocol, lastPulseLength], sender)\n\n    # Wait for response\n    res = rfIOT.take()\n    if res != sender + \" accepted\":\n        print(\"Code not correct, end execution before saving\")\n        return\n\n    # Write the codes to file if successful\n    print(\"Code written\")\n    writeCodes(codes)\n\ndef sendCode(args, sender):\n    # Get code list\n    codes = getCodes()\n\n    codeData = codes[args[0]] # Get data from name\n\n    # Only continue if data exists\n    if codeData == None: return\n\n    # Send pulse\n    print(\"Transmitting code:\", codeData)\n    transmitDecimal([codeData[\"code\"], codeData[\"proto\"], codeData[\"pulse\"]], sender)\n\ndef sendLastCode(args, sender):\n    # Skip if no last code\n    if lastCodeReceived == None:\n        print(\"No valid code.\")\n        return\n\n    # Send the TX of the last code\n    print(\"Transmitting code:\", lastCodeReceived)\n    transmitDecimal([lastCodeReceived, lastProtocol, lastPulseLength], sender)\n\n# Initialize IOT\nrfIOT = iot.IOT(\"rfcontrol\", \"iotserver\", 5623) # Define the ID, first argument is IP, second argument is the port\nrfIOT.start() # Start the connection to the IOT server\n\nprint(\"\\n====================== UPDATE ======================\")\nprint(\"Current version: \" + rfIOT.currentVersion)\nrfIOT.checkUpdate() # Update the currentVersion to the one the server jas\nprint(\"Server version: \" + rfIOT.currentVersion)\nprint(\"====================================================\\n\")\n\n# Define commands\nrfIOT.defineCommand(\"transmit\", transmitDecimal)\nrfIOT.defineCommand(\"add_code\", addCode)\nrfIOT.defineCommand(\"send_code\", sendCode)\nrfIOT.defineCommand(\"send_last\", sendLastCode)\n\n# Accept commands\ntry:\n    while True:\n        rfIOT.takeCommand()\nexcept KeyboardInterrupt:\n    print(\"Clean exit!\")\n\n# Cleanup IOT\nrfIOT.stop() # Stop the command to the IOT server\n\n# Cleanup RF\nrfRecv.cleanup()\nrfTrans.cleanup()\ndataThread.join()\n","repo_name":"garrettblankenship456/smartdevicenetwork","sub_path":"iot_device/device_custom/rfcontrol.py","file_name":"rfcontrol.py","file_ext":"py","file_size_in_byte":4397,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"33156108192","text":"import logging\nimport pprint\nimport werkzeug\n\nfrom odoo import http, _\nfrom odoo.http import request\nfrom odoo.exceptions import ValidationError\nfrom odoo.addons.payment import utils as payment_utils\nfrom odoo.addons.payment_authorize.controllers.main import AuthorizeController\nfrom odoo.addons.payment.controllers.post_processing import PaymentPostProcessing\n\n_logger = logging.getLogger(__name__)\n\n\nclass AuthorizeControllerInherit(AuthorizeController):\n\n    @http.route('/payment/authorize/payment', type='json', auth='public')\n    def authorize_payment(self, reference, partner_id, access_token, opaque_data):\n        \"\"\" Make a payment request and handle the response.\n\n        :param str reference: The reference of the transaction\n        :param int partner_id: The partner making the transaction, as a `res.partner` id\n        :param str access_token: The access token used to verify the provided values\n        :param dict opaque_data: The payment details obfuscated by Authorize.Net\n        :return: None\n        \"\"\"\n        # Check that the transaction details have not been altered\n        if not payment_utils.check_access_token(access_token, reference, partner_id):\n            raise ValidationError(\"Authorize.Net: \" + _(\"Received tampered payment request data.\"))\n\n        # Make the payment request to Authorize.Net\n        tx_sudo = request.env['payment.transaction'].sudo().search([('reference', '=', reference)])\n        response_content = tx_sudo._authorize_create_transaction_request(opaque_data)\n\n        # Check the Order and respective website related with the transaction\n        # Check the payment_return url for the success and error pages\n        # Pass the transaction_id on the session\n        sale_order_ids = tx_sudo.sale_order_ids.ids\n        sale_order = request.env['sale.order'].sudo().search([\n            ('id', 'in', sale_order_ids), ('website_id', '!=', False)\n        ], limit=1)\n\n        # Get Website\n        website = sale_order.website_id\n        # Redirect to VSF\n        # vsf_payment_success_return_url = website.vsf_payment_success_return_url\n        # vsf_payment_error_return_url = website.vsf_payment_error_return_url\n\n        request.session[\"__payment_monitored_tx_ids__\"] = [tx_sudo.id]\n\n        # Handle the payment request response\n        _logger.info(\n            \"payment request response for transaction with reference %s:\\n%s\",\n            reference, pprint.pformat(response_content)\n        )\n        tx_sudo._handle_notification_data('authorize', {'response': response_content})\n\n        if tx_sudo.created_on_vsf:\n            status_code = response_content.get('x_response_code', '3')\n            if status_code == '1':  # Approved\n                status_type = response_content.get('x_type').lower()\n                if status_type in ('auth_capture', 'prior_auth_capture'):\n                    # Confirm sale order\n                    PaymentPostProcessing().poll_status()\n\n                    # Redirect to Success Page\n                    # return werkzeug.utils.redirect(vsf_payment_success_return_url)\n                    return response_content\n\n            # Declined or Error or Held for Review\n            elif status_code in ['2', '3', '4']:\n                # Redirect to Error Page\n                # return werkzeug.utils.redirect(vsf_payment_error_return_url)\n                return response_content\n\n            return '[accepted]'  # Acknowledge the notification\n","repo_name":"odoogap/vuestorefront-extra","sub_path":"payment_authorize_vsf/controllers/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3445,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"41273293654","text":"import pandas as pd\nimport numpy as np\nfrom random import random\n\ndef pd_readcsv(filename, date_index_name=\"DATETIME\"):\n    \"\"\"\n    Reads a pandas data frame, with time index labelled\n    package_name(/path1/path2.., filename\n\n    :param filename: Filename with extension\n    :type filename: str\n\n    :param date_index_name: Column name of date index\n    :type date_index_name: list of str\n\n\n    :returns: pd.DataFrame\n\n    \"\"\"\n\n    ans = pd.read_csv(filename)\n    #ans.index = pd.to_datetime(ans[date_index_name]).values\n    ans.index = ans[date_index_name].values\n\n    del ans[date_index_name]\n\n    ans.index.name = None\n\n    return ans\n\ndef geomean(x):\n    if type(x) is pd.core.frame.DataFrame:\n        return x.apply(geomean, 0)\n    prod_returns=x+1.0\n    cum_prod_returns=prod_returns.cumprod()\n    final_pr=cum_prod_returns.values[-1]\n    avg_pr=final_pr**(1/float(len(x.index)))\n    return avg_pr-1.0\n\ndef geostd(x):\n    if type(x) is pd.core.frame.DataFrame:\n        return x.apply(geostd, 0)\n    gm=geomean(x)\n    \n    def _gsone(xitem, gm):\n        return (np.log((1+xitem)/(1+gm)))**2.0\n        \n    items=[_gsone(xitem, gm) for xitem in x.values]\n    \n    return np.exp((np.mean(items))**.5)-1.0\n\n\ndata=pd_readcsv(\"/home/rob/workspace/systematictradingexamples/plots_for_perhaps/USrealreturns.csv\", \"year\")\n\n## resampled version\nperiod_length=50\nmonte_runs=1000\n\neqbeatbonds=0\neqbeatcash=0\nbobeatcash=0\neqbeatport=0\n\nfor monte in range(monte_runs):\n    choose=[int(random()*len(data.index)) for notused in range(period_length)]\n    subdata=data.iloc[choose,]\n    subdata.index=pd.date_range(pd.datetime(1990,1,1), periods=period_length, freq=\"A\")\n    portfolio=subdata.SP500*.6+subdata.US10*.4\n    \n    gmeans=geomean(subdata)\n    if gmeans.SP500>gmeans.US10:\n        eqbeatbonds=eqbeatbonds+1\n    if gmeans.SP500>gmeans.TBILL:\n        eqbeatcash=eqbeatcash+1\n    if gmeans.US10>gmeans.TBILL:\n        bobeatcash=bobeatcash+1\n        \n    if gmeans.SP500>geomean(portfolio):\n        eqbeatport=eqbeatport+1\n        \n## portfolio optimisation\nwts=[0.4, 0.6]\nwts=wts+[1-sum(wts)]\n\nportfolio=data.SP500*wts[0]+data.US10*wts[1]+data.TBILL*wts[2]\n\nprint(geomean(portfolio))\nprint(geostd(portfolio))\nprint((geomean(portfolio) - 0.005)/geostd(portfolio))\n\n","repo_name":"robcarver17/systematictradingexamples","sub_path":"plots_for_perhaps/realreturns.py","file_name":"realreturns.py","file_ext":"py","file_size_in_byte":2261,"program_lang":"python","lang":"en","doc_type":"code","stars":238,"dataset":"github-code","pt":"81"}
+{"seq_id":"4896095392","text":"from random import randint\nimport requests\nimport random\n\nsammaF = []     #en array som ser till att inte samma fråga ställs flera gånger\n\nantalF = 1      #hur många frågor koden har stält\npoints = 0      #hur många poäng spelaren har\n\nurl = \"https://opentdb.com/api.php?amount=50&difficulty=medium\"     #genom att ta bort allt efter medium så behövde jag inte använda lika många replace\nr = requests.get(url)\nR = r.json()\n\nNamn = (input(\"\\n     . : FRÅGESPORT : . \\n----------------------------\\nVälkommen till Vem Kan Allt!\\nDu kommer att få 10 stycken kunskapsfågor.\\nFår du alla rätt så vinner du\\nAbsolut Ingenting!\\n\\nVi kan börja med ditt namn\\n> \"))\nNamn = Namn.title()\n\nRedo = (input(\"Dåså \"+ Namn +\", är du redo?\\n> \"))\nRedo = Redo.title()\n\nif Redo == \"Ja\":\n    print(\"Då kör vi!\\n\")\n\nelif Redo == \"Nej\":\n    print(\"Nähä, men vi kör igång endå!\\n\")\n\nelse:\n    print(\"Jag tolkar det som ett ja så då drar vi igång!\\n\")\n\nwhile antalF <11:       #det går att ändra hur många frågor man vill ha genom att ändra detta värde\n    \n    l = randint(0,49)\n\n    if l != sammaF:     #här är funktionen som gör så att inte samma fråga ställs flera gånger\n        sammaF.append(l)\n\n        Fråga = R[\"results\"][l][\"question\"]\n        Rätt = R[\"results\"][l][\"correct_answer\"]\n        Alt = R[\"results\"][l][\"incorrect_answers\"]\n\n        Fråga = Fråga.replace('&quot;','')\n        Fråga = Fråga.replace('&ldquo;','')\n        Fråga = Fråga.replace('&rdquo;','')\n        Fråga = Fråga.replace('&#039','')\n                                                    #det krävs fortfarande några replase för att få koden att se bra ut\n        for items in range(len(Alt)):\n            if \"&quot;\" in Alt[items]:\n                Alt[items]= Alt[items].replace(\"&quot;\",\"\")\n        for items in range(len(Alt)):\n            if \"&ldquo;\" in Alt[items]:\n                Alt[items]= Alt[items].replace(\"&ldquo;\",\"\")\n        for items in range(len(Alt)):\n            if \"&rdquo;\" in Alt[items]:\n                Alt[items]= Alt[items].replace(\"&rdquo;\",\"\")\n        for items in range(len(Alt)):\n            if \"&#039\" in Alt[items]:\n                Alt[items]= Alt[items].replace(\"&#039\",\"\")\n        \n        Alt.append(Rätt)\n        random.shuffle(Alt)     #blandar alternativen i arrayen så att rätt svar inte är på samma plats varje gång\n\n        if len(Alt) == 2:\n            print(Fråga + \"\\n\",Alt[0],\" - \",Alt[1])\n\n        elif len(Alt) == 3:\n            print(Fråga + \"\\n\",Alt[0],\" - \",Alt[1],\" - \",Alt[2])        #ibland är det färre än fyra altenaativ. dessa if/elif satser gör att den bara skriver ut rätt antal alternativ\n        \n        elif len(Alt) == 4:\n            print(Fråga + \"\\n\",Alt[0],\" - \",Alt[1],\" - \",Alt[2],\" - \",Alt[3])\n\n        svartf = (input(\"> \"))\n        svartf=svartf.title()\n\n        if svartf == Rätt and antalF<10:\n            antalF +=1\n            points +=1\n            print(\"Helt rätt, vidare till nästa fråga nummer\",antalF,\"\\n\")       \n            \n        elif svartf != Rätt and antalF<10:\n            antalF +=1\n            print(\"Tyvär är det fel, rätt svar är\", Rätt+\", vi går vidare till fråga nummer\",antalF,\"\\n\")\n        \n        elif svartf == Rätt and antalF == 10:\n            points +=1\n            print(\"Helt rätt, det var sista frågan. Här är ditt resultat:\\nAv 10 möjliga fick du\",points)\n            if points < 6:\n                print(\"Du kan bättre.\\n\")\n            else:\n                print(\"Bra jobbat!\\n\")\n            break\n        \n        elif svartf != Rätt and antalF == 10:\n            print(\"Tyvär är det fel, rätt svar är\", Rätt+\". Det var sista frågan, här är ditt resultat\\nAv 10 möjliga fick du\",points)\n            if points < 6:\n                print(\"Du kan bättre.\\n\")\n            else:\n                print(\"Bra jobbat!\\n\")\n            break","repo_name":"abbindustrigymnasium/python-kompendium-ABBjaceng","sub_path":"Pytonprodjekt/Frågesport.py","file_name":"Frågesport.py","file_ext":"py","file_size_in_byte":3904,"program_lang":"python","lang":"sv","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"35396090020","text":"from Seq1 import Seq\n\nPRACTICE = 1\nEXERCISE = 4\nprint(f\"-----| Practice {PRACTICE}, Exercise {EXERCISE} |------\")\n\n\ns1 = Seq()\ns2 = Seq(\"ACTGA\")\ns3 = Seq(\"Invalid sequence\")\n\nprint(f\"Sequence 1: (Length: {s1.len()}\"\")\", s1,\n      f\"\\nSequence 2: (Length: {s2.len()}\"\")\",s2,\n      f\"\\nSequence 3: (Length: {s3.len()}\"\")\", s3)","repo_name":"estherpj/2021-2022-PNE-Practices","sub_path":"P1/ex4.py","file_name":"ex4.py","file_ext":"py","file_size_in_byte":324,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"21878810516","text":"# Project imports\nfrom src.data import colors, settings, emojis\nfrom src.utils import time_util\n\n# External imports\nimport discord\n\n\nasync def move_message(bot, message):\n    \"\"\"\n    Move the current message to MOVE_TO channel (in config)\n\n    Args:\n        bot (BotClient): bot to perform action on\n        message (discord.Message): message to move\n    \"\"\"\n    # Filter spam message\n    raw_message = message.content\n    if len(raw_message) <= 5:\n        await message.add_reaction(emojis.QUESTION)\n        return\n\n    # Generate message embedded\n    embedded = generate_embedded(message.author, message.content, message.attachments)\n    # Delete message\n    await message.delete()\n    # Send confirm message\n    await message.channel.send(content=f\"`{time_util.formatted_now(include_date=True)}` >> Your report has been registered {emojis.CHECK}\")\n    # Fetch MOVE_TO channel\n    channel = await bot.fetch_channel(settings.MOVE_TO_CHANNEL)\n    # Send message to MOVE_TO channel\n    sent_message = await channel.send(embed=embedded)\n\n    # Scan for attachments\n    if not message.attachments:\n        return\n\n    # If there is attachments, send attachment messages\n    for attachment in message.attachments:\n        file = await attachment.to_file()\n        await sent_message.reply(content=f\"Attached file `{attachment.filename}`:\", file=file)\n\n\ndef generate_embedded(author, raw_message, attachments, is_dm=False):\n    title = f\"Message from {author.display_name}#{author.discriminator}\"\n    if is_dm:\n        title = \"[DM] \" + title\n\n    embedded = discord.Embed(\n        title=title,\n        description=f\"Timestamp (UTC): {time_util.formatted_now(include_date=True)[:-4]}\",\n        color=colors.COLOR_HELP\n    )\n    embedded.add_field(name=\"**Message from:**\", value=f\"> {author.mention}\", inline=False)\n    if not raw_message:\n        raw_message = \"*no text message*\"\n    embedded.add_field(name=\"**Message details:**\", value=f\"> {raw_message}\", inline=False)\n    if attachments:\n        # Add attachments field\n        embedded.add_field(name=\"**Message attachments:**\", value=f\"> {settings.SEP.join(att.filename for att in attachments)}\", inline=False)\n    return embedded\n","repo_name":"OneMelon8/BasePython","sub_path":"src/utils/MoveMessageUtil.py","file_name":"MoveMessageUtil.py","file_ext":"py","file_size_in_byte":2184,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"1235850046","text":"#!/usr/bin/env python\n\n# -*- coding: utf-8 -*-\n\n## QC: Quality control based on tissues volumes:\n##  Compute tissue volumes based on BrainVISA segmentation\n##\n## Parameters:\n## in_file : a text file that contains:\n##  - the site\n##  - the subject ID\n##  - path to the segmented images\n##\n## out_file : a file where the results will be written\n##\n## Then execute the R script: \"01qc_tisues-volumes_brainvisa-segmentation.R\" to make a\n## nice plot:\n## $ Rscript 01qc_tisues-volumes_brainvisa-segmentation.R in out\n\nimport sys\nimport nibabel\n\nDEFAULT_GM_VALUE = 100\nDEFAULT_WM_VALUE = 200\n\ndef tissues_volumes(gm_wm_image, gm_value = DEFAULT_GM_VALUE, wm_value = DEFAULT_WM_VALUE):\n    gm_wm_arr = gm_wm_image.get_data()\n\n    # classify\n    #    print \"classify/\",;sys.stdout.flush()\n    gm_bool_arr = (gm_wm_arr == gm_value)\n    wm_bool_arr = (gm_wm_arr == wm_value)\n\n    gm = gm_bool_arr.sum()\n    wm = wm_bool_arr.sum()\n\n    return [gm, wm]\n\nif __name__ == '__main__':\n    # Parse CLI\n    import argparse\n    parser = argparse.ArgumentParser(description=\"\"\"Compute the volumes of grey and white matter of a BrainVisa segmentation.\n                          The goal is to compare it with SPM in order to detect outliers.\n                          The input is a text file where each line contains the site, the subject ID and the path of the segmented image.\n                          The output is a CSV file that contains the volumes.\"\"\")\n    parser.add_argument('--gm_value', type=int, default=DEFAULT_GM_VALUE,\n                        help='Value for grey matter in segmented images (default %i)' % DEFAULT_GM_VALUE)\n    parser.add_argument('--wm_value', type=int, default=DEFAULT_WM_VALUE,\n                        help='Value for white matter in segmented images (default %i)' % DEFAULT_WM_VALUE)\n\n    # Positionnal arguments\n    parser.add_argument('segmented_files', nargs='?', type=argparse.FileType('r'),\n                        default=sys.stdin,\n                        help='File containing the filenames (default stdin)')\n    parser.add_argument('outfile', nargs='?', type=argparse.FileType('w'),\n                        default=sys.stdout,\n                        help ='Output file (default stdout)')\n    args = parser.parse_args()\n\n    # Write output header\n    OUT_HEADER = \"center\"+\" \"+\"subject\"+\" \"+\"dimX\"+\" \"+\"dimY\"+\" \"+\"dimZ\"+\" \"+ \\\n                 \"voxsizeX\"+\" \"+\"voxsizeY\"+\" \"+\"voxsizeZ\"+\" \"+ \\\n                 \"nvoxGM\"+\" \"+\"nvoxWM\"\n    OUT_FORMAT=\"{center} {subject} {dim[0]} {dim[1]} {dim[2]} {vox_sizes[0]} {vox_sizes[1]} {vox_sizes[2]} {nvoxGM} {nvoxWM}\"\n    args.outfile.write(OUT_HEADER + \"\\n\")\n\n    # Call comparison method for each segmented file\n    for line in args.segmented_files:\n        center, subject, gm_wm_filename = line.split()\n        gm_wm_image = nibabel.load(gm_wm_filename)\n        gm_wm_image_header = gm_wm_image.get_header()\n        nvoxGM, nvoxWM = tissues_volumes(gm_wm_image)\n\n        # Write volumes to file\n        dat = OUT_FORMAT.format(center=center, subject=subject,\n                                dim=gm_wm_image.shape,\n                                vox_sizes=gm_wm_image_header['pixdim'][1:4],\n                                nvoxGM=nvoxGM, nvoxWM=nvoxWM)\n        args.outfile.write(dat+\"\\n\")\n        args.outfile.flush()\n\n    args.outfile.close()","repo_name":"neurospin/scripts","sub_path":"2013_adni/qc_scripts/01qc_tissues-volumes_brainvisa-segmentation.py","file_name":"01qc_tissues-volumes_brainvisa-segmentation.py","file_ext":"py","file_size_in_byte":3314,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"81"}
+{"seq_id":"21795764754","text":"# Complete the function below.\n\ndef improbabilityCalculator(coordinates, remove):\n    if not coordinates:\n        return coordinates\n\n    if remove >= len(coordinates):\n        return '0'\n    elif remove < 0:\n        return coordinates\n    elif remove == 0:\n        return coordinates\n\n    lHalf = coordinates[:remove+1]\n    rHalf = coordinates[remove:]\n\n    # find min number in the left half\n    min = 10\n    i = 0\n    anchor = -1\n\n    while i < len(lHalf):\n        if int(lHalf[i]) < min:\n            min = int(lHalf[i])\n            anchor = i\n        i += 1\n\n#     if the anchor is the last element in lhalf, we'll just remove the head\n    if anchor == len(lHalf) - 1:\n        return coordinates[remove:]\n    else:\n        lHalf = lHalf[anchor]\n\n    return lHalf + getRightHalf(rHalf, 1)\n\n#     right half\ndef getRightHalf(coordinates, remove):\n    while remove > 0:\n        for i, v in enumerate(coordinates):\n            if coordinates[i] > coordinates[i+1]:\n                del coordinates[i]\n            else:\n                del coordinates[i+1]\n            break\n        remove -= 1\n    return ''.join(coordinates)\n\nprint(improbabilityCalculator(\"746209249\",5))\nprint(improbabilityCalculator(\"64738929\",0))\nprint(improbabilityCalculator(\"12345\",6))\nprint(improbabilityCalculator(\"43597658\", 2))\n","repo_name":"akshayjai1/InterviewPractice","sub_path":"nineChapters/improbabilityCalculator.py","file_name":"improbabilityCalculator.py","file_ext":"py","file_size_in_byte":1305,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"72083263304","text":"from setuptools import setup, find_packages\n\nversion = \"0.3.1\"\n\nwith open(\"README.md\", \"r\", encoding=\"utf-8\") as f:\n    long_description = f.read()\n\nwith open(\"requirements.txt\") as f:\n    required = f.read().splitlines()\n\nsetup(\n    name=\"mkdocs-custom-tags-attributes\",\n    version=version,\n    description=\"Adding custom attributes using hashtags.\",\n    long_description=long_description,\n    long_description_content_type=\"text/markdown\",\n    url=\"https://github.com/Mara-Li/mkdocs-custom-tags-attributes\",\n    author=\"Mara-Li\",\n    author_email=\"mara-li@outlook.fr\",\n    license=\"GPL-3\",\n    python_requires=\">=3.7\",\n    install_requires=required,\n    tests_require=[\"pytest\"],\n    packages=find_packages(),\n    keywords=\"mkdocs, custom attributes, markdown extension, markdown, md, attribute list, attr_list\",\n    entry_points={\n        \"mkdocs.plugins\": [\n            \"custom-attributes = custom_attributes.plugin:TagsAttributePlugins\"\n        ]\n    },\n)\n","repo_name":"ObsidianPublisher/mkdocs-custom-tags-attributes","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":962,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"81"}
+{"seq_id":"33235697308","text":"from starlette.middleware.cors import CORSMiddleware\nfrom fastapi.staticfiles import StaticFiles\nfrom fastapi import FastAPI\n\nfrom backend.routes import routers, create_docs_route\nfrom backend.utils import extend_openapi\nfrom backend.config import settings\n\n\ndef create_app() -> FastAPI:\n    app = FastAPI(docs_url=None)\n    app.mount(\"/static\", StaticFiles(directory=\"backend/static\"), name=\"static\")\n    create_docs_route(app)\n    app.include_router(routers)\n    app.add_middleware(\n        CORSMiddleware,\n        allow_origins=settings.APP_CORS_ORIGINS,\n        allow_credentials=True,\n        allow_methods=[\"*\"],\n        allow_headers=[\"*\"],\n    )\n    app.openapi_schema = extend_openapi(app)\n    return app\n\n\napp = create_app()\n","repo_name":"Demetrous-fd/GATW","sub_path":"backend/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":735,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"27106828127","text":"from collections import defaultdict\nimport sys\nsys.setrecursionlimit(10 ** 7)\n\nn = int(input())\ng = defaultdict(list)\n\nfor _ in range(n - 1):\n    u, v = map(int, input().split())\n    g[u].append(v)\n    g[v].append(u)\n\nans = [None] * n\nm = 0\n\ndef dfs(x, p):\n    global m, n, g\n    mi = float('inf')\n    ma = float('-inf')\n\n    for y in g[x]:\n        if y == p:\n            continue\n        a, b = dfs(y, x)\n        mi = min(mi, a)\n        ma = max(ma, b)\n\n    if ma < 0:\n        m += 1\n        mi = m\n        ma = m\n\n    ans[x - 1] = (mi, ma)\n    return (mi, ma)\n\ndfs(1, 0)\nfor a in ans:\n    print(*a)","repo_name":"silphire/atcoder","sub_path":"abc240/e.py","file_name":"e.py","file_ext":"py","file_size_in_byte":600,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"7341149281","text":"from fastapi import FastAPI, Depends, Request\nfrom loguru import logger\n\nfrom config import settings\nfrom utils.utils import check_token\nfrom modules.todos import todos as todos_routes\nfrom utils.meili_search import Client\n\napp = FastAPI(debug=settings.debug, dependencies=[Depends(check_token)],\n              responses={\n                  401: {'description': 'Not authorized'}\n              })\napp.include_router(todos_routes, prefix='/todos')\n\n\n@app.middleware(\"http\")\nasync def print_request(request: Request, call_next):\n    print(request.headers)\n    response = await call_next(request)\n    return response\n\n\n@app.on_event('startup')\nasync def init_meili_client_and_check_connection():\n    app.state.meili_client = Client('http://localhost', 7700)\n    is_meili_health = await app.state.meili_client.health()\n    if not is_meili_health:\n        logger.error('Couldn\\'t connect to MeiliSearch by address http://localhost:7700')\n        raise ConnectionError('Couldn\\'t connect to MeiliSearch by address http://localhost:7700')\n\n\n@app.on_event('shutdown')\nasync def close_meili_search_client():\n    await app.state.meili_client.close()\n","repo_name":"Svirex/assistant","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1140,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"41578597724","text":"#Ningning winter Giselle Karina\nimport os\nimport cv2\nimport time\nimport numpy as np\nimport tensorflow as tf\nimport matplotlib.pyplot as plt\nfrom tensorflow.keras.models import load_model\nmodel = load_model(\"model/face_aespa.h5\")\nclass_names = ['Giselle', 'Karina', 'Ningning', \"Winter\"]\nnet = cv2.dnn.readNetFromCaffe(\"model/deploy.prototxt\", \"model/model.caffemodel\")\ncap = cv2.VideoCapture(\"aespa.mp4\")\nwhile True:\n    ret, frame = cap.read()\n    if frame is None:\n        break\n    frame_ = frame.copy()\n    h, w = frame.shape[:2]\n    blob = cv2.dnn.blobFromImage(frame, 1.0, (300, 300), [104, 117, 123], False, False)\n    net.setInput(blob)\n    detections = net.forward()\n    bboxes = []\n    for i in range(detections.shape[2]):\n        confidence = detections[0, 0, i, 2]\n        if confidence > 0.5:\n            x1 = int(detections[0, 0, i, 3] * w)\n            y1 = int(detections[0, 0, i, 4] * h)\n            x2 = int(detections[0, 0, i, 5] * w)\n            y2 = int(detections[0, 0, i, 6] * h)\n            img = frame_[y1:y2, x1:x2]\n            try:\n                img_ = cv2.resize(img, (180, 180))\n                img_ = cv2.cvtColor(img_, cv2.COLOR_BGR2RGB)\n                img_array = tf.keras.utils.img_to_array(img_)\n                img_array = tf.expand_dims(img_array, 0)\n                predictions = model.predict(img_array)\n                score = round((100 * np.max(predictions)), 2)\n                name = class_names[np.argmax(predictions)]\n                cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 2)\n                cv2.putText(frame, name, (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)\n                cv2.putText(frame, str(score), (x1, y2 + 25), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)\n            except:\n                print(\"none Face\")\n    f_ = cv2.resize(frame, dsize=None, fx=0.5, fy=0.5)\n    cv2.imshow(\"frame\", f_)\n    key = cv2.waitKey(1)\n    if key == ord('q'):\n        break\n\ncap.release()\ncv2.destroyAllWindows()\n","repo_name":"KhanhNguyen1308/Bai_tap_Neural_Network","sub_path":"Face_recognition/face_recognition.py","file_name":"face_recognition.py","file_ext":"py","file_size_in_byte":1986,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"33238524418","text":"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom skimage import filters\n\n\nclass FocalLoss(nn.Module):\n\tdef __init__(self, gamma = 2):\n\t\tsuper().__init__()\n\t\tself.gamma = gamma\n\n\tdef forward(self, input, target):\n\t\tif not (target.size() == input.size()):\n\t\t\traise ValueError(\"Target size ({}) must be the same as input size({})\".format(target.size(), input.size()))\n\n\t\tmax_val = (-input).clamp(min=0)\n\t\tloss = input - input * target + max_val + ((-max_val).exp() + (-input - max_val).exp()).log()\n\n\t\tinvprobs = F.logsigmoid(-input * (target * 2 - 1))\n\t\tloss = (invprobs * self.gamma).exp() * loss\n\n\t\treturn loss.mean()\n\nclass TverskyLoss:\n\tdef __init__(self, alpha, torch_device):\n\t\tself.a = alpha\n\t\tself.b = 1 - alpha\n\t\tself.smooth = torch.tensor(1.0, device = torch_device)\n\n\tdef __call__(self, predict, target_):\n\t\tpredict = F.sigmoid(predict)\n\t\ttarget_f = target_.view(-1) \t# g\n\t\tpredict_f = predict.view(-1) \t# p\n\n\n\t\t# PG + a * P_G + b * G_P\n\t\tPG = (predict_f * target_f).sum()\n\t\tP_G = (predict_f * (1 - target_f)).sum() * self.a\n\t\tG_P = ((1-predict_f) * target_f).sum() * self.b\n\n\t\tloss = PG / (PG + P_G + G_P + self.smooth)\n\t\treturn loss * -1\n\ndef dice_coeff_loss(prob, label, nlabels=1):\n\tmax_val, pred = prob.max(1)\n\tfg_mask = max_val.gt(0.5).type_as(label)\n\n\t# masking\n\tdices_per_label = []\n\tsmooth = 1e-10\n\teps = 1e-8\n\tfor l in range(0, nlabels):\n\t\tdices = []\n\t\tfor n in range(prob.size(0)):\n\t\t\tlabel_p = label[n].eq(l).float()\n\n\t\t\tif l == 0:\n\t\t\t\tprob_l = 1-max_val[n]\n\t\t\telse:\n\t\t\t\tprob_l = prob[n, l-1, :, :, :]\n\t\t\tprob_l = torch.clamp(prob_l, eps, 1.0-eps)\n\n\t\t\t# calc accuracy\n\t\t\tjacc = 1.0 - torch.clamp(( ((prob_l * label_p).sum() + smooth) / ((label_p**2).sum() + (prob_l**2).sum() - (prob_l * label_p).sum() + smooth ) ), 0.0, 1.0)\n\t\t\tdices.append(jacc.view(-1))\n\t\tdices_per_label.append(torch.mean(torch.cat(dices)).view(-1))\n\treturn torch.mean(torch.cat(dices_per_label))\n\nif __name__ == \"__main__\":\n\n\tdef get_grad(*args):\n\t\tprint(\"Grad : \\n\", args)\n\n\n\ttarget = torch.tensor([[[0,1,0],[1,1,1],[0,1,0]]], dtype=torch.float, requires_grad=True)\n\tpredicted = torch.tensor([[[1,1,0],[0,0,0],[1,0,0]]], dtype=torch.float, requires_grad=True)\n\tprint(\"Prediction : \\n\", predicted); print(\"GroudTruth : \\n\", target)\n\tpredicted.register_hook(get_grad)\n\n\tloss = TverskyLoss(0.3, torch.device(\"cpu\"))\n\tl = loss(predicted, target)\n\tprint(\"Loss : \", l)\n\tl.backward()\n\n","repo_name":"aktivhoon/segmentation","sub_path":"loss.py","file_name":"loss.py","file_ext":"py","file_size_in_byte":2396,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"81"}
+{"seq_id":"40698152738","text":"from actions import intro, choose_your_stats, objects, confrontation, item_pickup, cont, m_creation\n\n\ngame_score = 0\nintro()\nplayer1 = choose_your_stats()\ngame_rounds = int(input('How many monsters would you like to fight?: '))\nprint()\ncurrent_round = 1\ngame_on = True\nwhile game_on:\n    for i in range(1, game_rounds + 1):\n        monster1 = m_creation()\n        damage_count = monster1.health\n        (item1, item2, item3, item4, item5, item6) = objects()\n        print(f\"\\nConfrontation number {current_round}\\n----------------------\\n\")\n        cont()\n        game_on = confrontation(player1, monster1)\n        if game_on == 'dead':\n            break\n        item_found = item_pickup(item1, item2, item3, item4, item5, item6)\n        if item_found == 'gs':\n            player1.health += item2.effect\n            print(f\"Health changed by {item2.effect}. Player health is now: {player1.health}\")\n            cont()\n        if item_found == 'gr':\n            player1.strength += item4.effect\n            print(f\"Strength changed by {item4.effect}. Player strength is now: {player1.strength}\")\n            cont()\n        if item_found == 'gb':\n            player1.speed += item6.effect\n            print(f\"Speed changed by {item6.effect}. Player speed is now: {player1.speed}\")\n            cont()\n        if item_found == 'b':\n            player1.speed += item5.effect\n            print(f\"Speed changed by {item5.effect}. Player speed is now: {player1.speed}\")\n            cont()\n        if item_found == 'r':\n            player1.strength += item3.effect\n            print(f\"Strength changed by {item3.effect}. Player strength is now: {player1.strength}\")\n            cont()\n        if item_found == 's':\n            player1.health += item1.effect\n            print(f\"Health changed by {item1.effect}. Player health is now: {player1.health}\")\n            cont()\n        if player1.health <= 0:\n            print(f'{player1.name} has died. GAME OVER')\n            break\n        current_round += 1\n        game_score += damage_count\n        if current_round == 5 and game_rounds > 5:\n            print(f'Congrats, {player1.name} made it five rounds. They found a nice fire to relax by. Health +50.')\n            player1.health += 50\n    if game_on == 'dead':\n        break\n    else:\n        print(f'YOU WIN! You had {player1.health} health points left.\\n')\n        if player1.health >= 50:\n            print('You cleared the cave easily.')\n            game_score += 20\n        if 11 <= player1.health < 20:\n            print('You\\'re a little scratched, but alive.')\n            game_score += 10\n        if player1.health < 11:\n            print('You barely made it out alive.')\n        game_on = False\n\ngame_score *= current_round\nprint('\\nThe game has ended.')\nprint(f'\\nYour score was {game_score}.')\n","repo_name":"jekijo/DungeonGame","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2803,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"81"}
+{"seq_id":"7192879263","text":"def extract_parent_and_child_nodes(cmd: str):\n    splt = cmd.split(' ')\n    return [splt[1], splt[7]]\n\nf = open('task.data', 'r')\ncmds = [] \nfor line in f:\n    cmds.append(extract_parent_and_child_nodes(line.replace('\\n','')))\nf.close()\n\ndef find_parents(cmds):\n    parents = list(set(map(lambda a: a[0], cmds)))\n    children = list(set(map(lambda a: a[1], cmds)))\n    super_parents = []\n    for parent in parents:\n        if parent not in children:\n            super_parents.append(parent)\n    return super_parents\n\n\nparents = find_parents(cmds)\n\nsuper_parent = '*'\nfor parent in parents:\n    cmds.append(['*', parent])\n\navailable_tasks = []\n\norder = super_parent\ndef get_available_tasks(cmds, parent):\n    connected_to_master = {}\n    for cmd in cmds:\n        if cmd[0] != parent:\n            connected_to_master[cmd[1]] = False\n        elif cmd[1] not in connected_to_master:\n            connected_to_master[cmd[1]] = True\n    return list(set(map(lambda a : a[1], filter(lambda a: connected_to_master[a[1]] , cmds))))\n\nwhile len(cmds) > 0:\n    new_tasks = get_available_tasks(cmds, super_parent)\n    available_tasks = list(set(available_tasks + new_tasks))\n    available_tasks.sort()\n    current_task = available_tasks[0]\n    order += current_task\n    available_tasks.remove(current_task)\n    def mapper(a):\n        if a[0] == current_task:\n            a[0] = super_parent\n        return a\n    cmds = list(map(mapper,filter(lambda a: a[0]!= super_parent, cmds)))\n\nprint(order.replace('*',''))\n","repo_name":"CaptainCuddleCube/advent_of_code_2018","sub_path":"day7/task1.py","file_name":"task1.py","file_ext":"py","file_size_in_byte":1496,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"5416949240","text":"import io\nimport json\nimport os\nimport pathlib\nimport pprint\nimport re\nimport subprocess\nimport sys\nimport tempfile\nfrom os import path\nfrom typing import List, Optional, Tuple, Union\n\nfrom docutils import nodes\nfrom docutils.nodes import Element, Node\nfrom docutils.parsers.rst import Directive\nfrom docutils.parsers.rst.states import Inliner\nfrom docutils.statemachine import StringList, string2lines\nfrom sphinx import addnodes\nfrom sphinx.addnodes import desc_signature, pending_xref\nfrom sphinx.application import Sphinx\nfrom sphinx.builders import Builder\nfrom sphinx.directives import ObjectDescription\nfrom sphinx.domains import Domain, ObjType\nfrom sphinx.environment import BuildEnvironment\nfrom sphinx.locale import _\nfrom sphinx.roles import XRefRole\nfrom sphinx.util import logging\nfrom sphinx.util.console import red\nfrom sphinx.util.docfields import Field, GroupedField\nfrom sphinx.util.nodes import make_refnode\nfrom sphinx.util.typing import OptionSpec\n\n__version__ = (\n    open(path.join(path.dirname(__file__), \"version.lisp-expr\"))\n    .read()\n    .strip('\"')\n)\n\n\nALL_TYPES = [\n    \"macro\",\n    \"function\",\n    \"genericFunction\",\n    \"setf\",\n    \"variable\",\n    \"class\",\n]\nupper_symbols = re.compile(r\"([^a-z\\s\\\"`]*[A-Z]{2,}[^a-z\\s\\\"`:]*)($|\\s)\")\n\nLISP_DATA = {}\n\nlambda_list_keywords = [\n    \"&allow-other-keys\",\n    \"&key\",\n    \"&rest\",\n    \"&aux\",\n    \"&optional\",\n]\n\nlogger = logging.getLogger(__name__)\n\n\ndef node_to_dict(node):\n    name = getattr(node, \"tagname\", node)\n    if getattr(node, \"rawsource\", None):\n        return {name: node.rawsource}\n    nodes = {name: []}\n    for child in node.children:\n        nodes[name].append(node_to_dict(child))\n    return nodes\n\n\ndef debug_print(node):\n    \"\"\"Useful in pdb sessions.\"\"\"\n    node = node_to_dict(node)\n    pprint.pprint(node)\n\n\ndef bool_option(arg: None) -> bool:\n    \"\"\"Used to convert flag options to directives.\n\n    (Instead of directives.flag(), which returns None).\n    \"\"\"\n    return True\n\n\n# An almost exact copy of Peter Norvig's scheme parser\n# http://norvig.com/lispy.html\ndef _read(s):\n    \"\"\"Read a Scheme expression from a string.\"\"\"\n    return _read_from(_tokenize(s))\n\n\ndef _tokenize(s: str) -> List[str]:\n    \"\"\"Convert a string into a list of tokens.\"\"\"\n    return s.replace(\"(\", \" ( \").replace(\")\", \" ) \").split()\n\n\ndef _read_from(tokens):\n    \"\"\"Read an expression from a sequence of tokens.\"\"\"\n    if len(tokens) == 0:\n        raise SyntaxError(\"unexpected EOF while reading\")\n    token = tokens.pop(0)\n    if \"(\" == token:\n        L = []\n        while tokens[0] != \")\":\n            L.append(_read_from(tokens))\n        tokens.pop(0)  # pop off ')'\n        return L\n    elif \")\" == token:\n        raise SyntaxError(\"unexpected )\")\n    else:\n        return token\n\n\n# end of http://norvig.com/lispy.html\n\n\nclass desc_clparameterlist(addnodes.desc_parameterlist):\n    \"\"\"Node for a common lisp parameter list.\"\"\"\n\n    child_text_separator = \" \"\n\n\n# v is short for visit\n# d is short for depart\n\n\ndef v_clparameterlist(self, node):\n    self.first_param = True\n    self.body.append(\" \")\n    self.body.append(\"(\")\n    self.param_separator = node.child_text_separator\n\n\ndef d_clparameterlist(self, node):\n    self.body.append(\")\")\n\n\ndef v_latex_clparameterlist(self, node):\n    if not hasattr(self, \"parameter_stack\"):\n        self.parameter_stack = []\n\n    if len(self.parameter_stack) == 0:\n        # close name, open parameterlist\n        self.body.append(\"}{\")\n    else:\n        self.body.append(\" (\")\n\n    self.parameter_stack.append(node)\n    self.param_separator = node.child_text_separator\n\n\ndef d_latex_clparameterlist(self, node):\n    self.parameter_stack.pop()\n    if len(self.parameter_stack) == 0:\n        # close parameterlist, open return annotation\n        self.body.append(\"}{\")\n    else:\n        self.body.append(\" )\")\n\n\nclass desc_clparameter(addnodes.desc_parameter):\n    \"\"\"Node for a common lisp parameter item.\"\"\"\n\n\ndef d_clparameter(self, node):\n    pass\n\n\ndef v_html_clparameter(self, node):\n    if self.body[-1] != (\"(\"):\n        self.body.append(self.param_separator)\n    if node.hasattr(\"lambda_keyword\"):\n        self.body.append('<em class=\"lambda_keyword text-muted\">')\n    elif node.hasattr(\"keyword\"):\n        self.body.append('<em class=\"keyword text-muted\">')\n    elif not node.hasattr(\"noemph\"):\n        self.body.append(\"<em>\")\n\n\ndef d_html_clparameter(self, node):\n    if node.hasattr(\"lambda_keyword\"):\n        self.body.append(\"</em>\")\n    elif node.hasattr(\"keyword\"):\n        self.body.append(\"</em>\")\n    elif not node.hasattr(\"noemph\"):\n        self.body.append(\"</em>\")\n\n\ndef v_latex_clparameter(self, node):\n    if self.body[-1] != (\"(\"):\n        self.body.append(self.param_separator)\n    if node.hasattr(\"lambda_keyword\") or node.hasattr(\"keyword\"):\n        pass\n    elif not node.hasattr(\"noemph\"):\n        self.body.append(r\"\\emph{\")\n\n\ndef d_latex_clparameter(self, node):\n    if node.hasattr(\"lambda_keyword\") or node.hasattr(\"keyword\"):\n        pass\n    elif not node.hasattr(\"noemph\"):\n        self.body.append(\"}\")\n\n\ndef v_texinfo_clparameter(self, node):\n    if not self.first_param:\n        self.body.append(self.param_separator)\n    else:\n        self.first_param = False\n    text = self.escape(node.astext())\n    # replace no-break spaces with normal ones\n    text = text.replace(\" \", \"@w{ }\")\n    self.body.append(text)\n    raise nodes.SkipNode\n\n\ndef v_text_clparameter(self, node):\n    if not self.first_param:\n        self.add_text(self.param_separator)\n    else:\n        self.first_param = False\n    self.add_text(node.astext())\n    raise nodes.SkipNode\n\n\ndef v_bs_html_desc_type(self, node):\n    self.body.append(self.param_separator)\n    self.body.append(self.starttag(node, \"tt\", \"\", CLASS=\"desc-type\"))\n\n\ndef d_bs_html_desc_type(self, node):\n    self.body.append(\"</tt>\")\n\n\ndef v_html_desc_type(self, node):\n    self.body.append(self.param_separator)\n\n\ndef specializer_qualify_symbols(\n    symbols: List[str], package: Optional[str]\n) -> List[str]:\n    \"\"\"Qualify symbols, for specializers.\"\"\"\n\n    def qualify(symbol):\n        if isinstance(symbol, list):\n            return [symbol[0].upper(), qualify(symbol[1])]\n        # Depending on the splitting, it might be that EQ is a single\n        # token, or split in 2.\n        if symbol.startswith(\":\"):\n            return [\"EQ\", \"KEYWORD%s\" % symbol.upper()]\n        if symbol.lower() in lambda_list_keywords:\n            return symbol.upper()\n\n        # TODO (RS) this needs to be smarter what happens if there is an\n        # internal symbol instead of an external one?\n        if \":\" not in symbol and package:\n            return (package + \":\" + symbol).upper()\n        else:\n            return symbol.upper()\n\n    return [qualify(symbol) for symbol in symbols]\n\n\ndef parse_specializer_argument(argument: str, package: Optional[str]):\n    \"\"\"Convert the users arguments into a sexp list.\"\"\"\n    argument = argument.strip()\n    # Always wrap in paren\n    if not (argument.startswith(\"(\") and argument.endswith(\")\")):\n        argument = \"(%s)\" % argument\n    return specializer_qualify_symbols(_read(argument), package)\n\n\ndef parse_specializer_lisp_data(argument: str, package: Optional[str]):\n    \"\"\"Process the specializer from LISP data to make sure it has the same\n    formatting as the processed argument string.\"\"\"\n    argument = \" \".join(argument).strip()\n    return parse_specializer_argument(argument, package)\n\n\ndef normalize_specializer(specializer, package):\n    \"\"\"Convert the specializer into it's normal form as a string.\"\"\"\n    if isinstance(specializer, list):\n        specializer = parse_specializer_lisp_data(specializer, package)\n    else:\n        specializer = parse_specializer_argument(specializer, package)\n    return specializer_to_str(specializer)\n\n\ndef specializer_to_str(specializer):\n    output = io.StringIO()\n\n    def join_sexp(sexp):\n        output.write(\"(\")\n        for i, atom in enumerate(sexp):\n            if isinstance(atom, list):\n                join_sexp(atom)\n            else:\n                output.write(atom)\n            # Don't print a space after the last item.\n            if i < len(sexp) - 1:\n                output.write(\" \")\n        output.write(\")\")\n\n    join_sexp(specializer)\n    output.seek(0)\n    return output.read()\n\n\ndef generic_xref(symbol: str, state, package: str, node_type=nodes.inline):\n    xref = \"See also: :cl:generic:`{} <{}:{}>`\".format(\n        symbol,\n        package,\n        symbol,\n    )\n    lines = string2lines(xref)\n    node = node_type()\n    state.nested_parse(StringList(lines), 0, node)\n    return node\n\n\ndef specializer_name_xref(\n    symbol: str, sexp, inliner: Inliner, package: str, lineno: int\n):\n    \"\"\"Generate a link to a method.\n\n    The output of this function is the partner to the output of\n    CLMethod.get_index_name\n    \"\"\"\n    target = normalize_specializer(sexp, package)\n    target = \"{} <{} {}>\".format(\n        local_atom(package, symbol.lower()),\n        symbol.lower(),\n        target.lower(),\n    )\n    xref = \":cl:method:`{}`\".format(target)\n    node = CLXRefRole()(\"cl:method\", xref, target, lineno, inliner)\n    return nodes.inline(\"\", \"\", node[0][0])\n\n\ndef local_atom(package: str, atom: str, private: bool = True) -> str:\n    \"\"\"If the atom has a package qualifier then remove it.\"\"\"\n    split = [atom]\n    if \"::\" in atom:\n        if private:\n            split = atom.split(\"::\", 1)\n        else:\n            split = [atom]\n    elif \":\" in atom:\n        split = atom.split(\":\", 1)\n\n    if len(split) == 1:\n        return split[0]\n\n    if split[0].upper() == package.upper():\n        return split[-1]\n\n    # Remove the KEYWORD from those symbols\n    if split[0].upper() == \"KEYWORD\":\n        return \":\" + split[-1]\n\n    return atom\n\n\ndef qualify_atom(package: str, atom: str) -> str:\n    \"\"\"If the atom is not qualified then quailify it.\"\"\"\n    if \"::\" in atom:\n        return atom\n    elif \":\" in atom:\n        return atom\n    return package + \":\" + atom\n\n\ndef fieldlist_index(node):\n    \"\"\"Find the index of a field list in a content node.\"\"\"\n    for i, n in enumerate(node):\n        if isinstance(n, nodes.field_list):\n            return i\n\n\ndef get_content_node(node):\n    \"\"\"Search through and find the content node from a signature.\"\"\"\n    for subnode in node:\n        if isinstance(subnode, addnodes.desc):\n            for subsubnode in subnode:\n                if isinstance(subsubnode, addnodes.desc_content):\n                    return subsubnode\n\n\nclass LispDataError(Exception):\n    pass\n\n\ndef get_lisp_object(package, name, objtype):\n    symbol = (\"%s:%s\" % (package, name)).upper()\n    objtype = objtype.strip()\n\n    data = None\n    try:\n        if objtype in [\"function\", \"macro\", \"generic\", \"method\"]:\n            data = LISP_DATA[symbol][\"function\"]\n        else:\n            data = LISP_DATA[symbol][objtype]\n    except KeyError:\n        raise LispDataError(\n            \"Missing lisp data for package %s, name %s, objtype %s\"\n            % (package, name, objtype)\n        )\n    if not data:\n        raise LispDataError(\n            \"Missing lisp data for package %s, name %s, objtype %s\"\n            % (package, name, objtype)\n        )\n    return data\n\n\nclass SpecializerField(Field):\n    \"\"\"\"\"\"\n\n    is_grouped = True\n    list_type = nodes.bullet_list\n\n    def __init__(\n        self, name, names=(), label=None, rolename=None, can_collapse=False\n    ):\n        Field.__init__(self, name, names, label, True, rolename)\n        self.can_collapse = can_collapse\n\n    def make_field(self, domain, items):\n        fieldname = nodes.field_name(\"\", self.label)\n        listnode = self.list_type()\n        for content in items:\n            par = nodes.paragraph()\n            par += content\n            listnode += nodes.list_item(\"\", par)\n        fieldbody = nodes.field_body(\"\", listnode)\n        return nodes.field(\"\", fieldname, fieldbody)\n\n\nclass SEXP(object):\n    \"\"\"An SEXP argument list builder.\n\n    This class takes an sexp and some types and will create a pretty\n    argument list.\n    \"\"\"\n\n    def __init__(self, sexp, types=None, show_defaults=False, package=None):\n        if not isinstance(sexp, list):\n            self.sexp = _read(sexp)\n        else:\n            self.sexp = sexp\n        self.types = types\n        if self.types:\n            if len(self.sexp) < len(types):\n                raise ValueError(\n                    \"Type expression %r, has more atoms than the sexp it's annotating %r\"\n                    % (types, sexp)\n                )\n            for i, type in enumerate(self.types):\n                self.sexp[i] = [\n                    self.sexp[i].lower(),\n                    self._type_node(type, package),\n                ]\n        self.show_defaults = show_defaults\n        self.show_defaults = True\n        self.package = package\n\n    def _type_node(self, type_name, package):\n        if type_name.lower().startswith(\"(eq \"):\n            type_name = type_name.strip(\"()\").split()[1]\n            type_node = addnodes.pending_xref(\n                \"\", refdomain=\"cl\", reftype=\"class\", reftarget=type_name\n            )\n            name = local_atom(package, type_name, private=False)\n            type_node += addnodes.desc_type(name, name)\n            return [addnodes.desc_sig_keyword(\"eq\", \"eq\"), type_node]\n\n        type_node = addnodes.pending_xref(\n            \"\", refdomain=\"cl\", reftype=\"class\", reftarget=type_name\n        )\n        name = local_atom(package, type_name, private=False)\n        type_node += addnodes.desc_type(name, name)\n        return type_node\n\n    def as_parameterlist(self, function_name):\n        if isinstance(function_name, str) and function_name.startswith(\n            \"(SETF\"\n        ):\n            # split up the \"(setf foo)\" string\n\n            setf, name = function_name[1:-1].lower().split(\" \")\n            name = local_atom(self.package, name, private=False)\n            name = addnodes.desc_name(name, name)\n            desc_sexplist = desc_clparameterlist()\n            if self.types:\n                # this is a generic, so the first parameter becomes the value\n                value = desc_clparameterlist()\n                [self.render_atom(e, value) for e in self.sexp[0]]\n                if value[-1].tagname == \"desc_sig_space\":\n                    value.pop()  # if the last element is whitespace pop it\n                self.sexp = self.sexp[1:]\n            else:\n                value = desc_clparameter(\"value\", \"value\")\n            desc_sexplist.append(addnodes.desc_sig_keyword(setf, setf))\n            desc_sexplist.append(self.render_parameterlist(prepend_node=name))\n            desc_sexplist.append(value)\n            return desc_sexplist\n        else:\n            return self.render_parameterlist(prepend_node=function_name)\n\n    def render_parameterlist(self, signode=None, prepend_node=None, sexp=None):\n        desc_sexplist = desc_clparameterlist()\n        if prepend_node:\n            desc_sexplist.append(prepend_node)\n        if signode:\n            signode.append(desc_sexplist)\n        for atom in sexp or self.sexp:\n            if isinstance(atom, list):\n                if self.show_defaults:\n                    self.render_parameterlist(signode=desc_sexplist, sexp=atom)\n                    desc_sexplist.append(addnodes.desc_sig_space())\n                else:\n                    self.render_atom(atom[0], desc_sexplist)\n            else:\n                self.render_atom(atom, desc_sexplist)\n        if desc_sexplist[-1].tagname == \"desc_sig_space\":\n            desc_sexplist.pop()  # if the last element is whitespace pop it\n        return desc_sexplist\n\n    def render_atom(self, token, signode, noemph=True):\n        \"\"\"add syntax hi-lighting to interesting atoms.\"\"\"\n        if not isinstance(token, nodes.Element):\n            param = desc_clparameter(token, local_atom(self.package, token))\n            if token.lower() in lambda_list_keywords:\n                param = addnodes.desc_sig_keyword(\n                    token, local_atom(self.package, token)\n                )\n                param[\"lambda_keyword\"] = True\n            if token.startswith(\":\"):\n                param[\"keyword\"] = True\n            signode.append(param)\n            signode.append(addnodes.desc_sig_space())\n        else:\n            signode.append(token)\n            signode.append(addnodes.desc_sig_space())\n\n\nclass CLsExp(ObjectDescription):\n\n    doc_field_types = [\n        GroupedField(\n            \"parameter\",\n            label=_(\"Parameters\"),\n            names=(\n                \"param\",\n                \"parameter\",\n                \"arg\",\n                \"argument\",\n                \"keyword\",\n                \"kwparam\",\n            ),\n        ),\n        Field(\n            \"returnvalue\",\n            label=_(\"Returns\"),\n            has_arg=False,\n            names=(\"returns\", \"return\"),\n        ),\n    ]\n\n    option_spec: OptionSpec = {\n        \"nodoc\": bool_option,\n        \"noindex\": bool_option,\n        \"noinitargs\": bool_option,\n    }\n\n    lispobj = False\n\n    def cl_lisp_data(self):\n        \"\"\"Initialize the self.lispobj object.\n\n        This is done using data that is passed to the application from a\n        CL lisp process.\n        \"\"\"\n        if self.lispobj is not False:\n            return self.lispobj\n\n        self.lispobj = None\n        try:\n            self.lispobj = get_lisp_object(\n                self.cl_package, self.cl_symbol_name, self.objtype\n            )\n        except LispDataError as e:\n            self.state_machine.reporter.warning(e)\n\n        return self.lispobj\n\n    def cl_handle_signature(self, sig: str, signode: desc_signature):\n        pass\n\n    def handle_signature(\n        self, sig: str, signode: desc_signature\n    ) -> Tuple[str, str]:\n        \"\"\"This method sets up the entire object, it's the first.\"\"\"\n        self.cl_package = self.env.temp_data.get(\"cl:package\").lower()\n        self.cl_symbol_name = sig.split(\" \")[0].lower()\n\n        signode.append(\n            addnodes.desc_annotation(\n                str(self.get_signature_prefix()), self.get_signature_prefix()\n            )\n        )\n\n        self.cl_handle_signature(sig, signode)\n\n        if not self.cl_symbol_name:\n            self.state_machine.reporter.warning(\n                \"Unknown symbol type for signature %s\" % self.cl_symbol_name\n            )\n\n        return self.objtype, self.cl_symbol_name\n\n    def get_field_list(self, node):\n        \"\"\"Return the node's field list, if there isn't one then create it\n        first.\"\"\"\n        # Add a field list if there isn't one\n        if not node[1][-1].children:\n            node[1][-1].append(nodes.field_list())\n        if not isinstance(node[1][-1][0], nodes.field_list):\n            node[1][-1].append(nodes.field_list())\n        return node[1][-1][-1]\n\n    def get_index_text(self, name, type):\n        return _(\"%s (Lisp %s)\") % (name.lower().split(\":\")[-1], type)\n\n    def get_index_name(self, name, type):\n        return type + \":\" + name\n\n    def get_signature_prefix(self):\n        return self.objtype + \" \"\n\n    def add_target_and_index(self, name, sig, signode):\n        # node target\n        type, name = name\n\n        if \"cl:package\" in self.env.temp_data:\n            package = self.options.get(\n                \"module\", self.env.temp_data.get(\"cl:package\")\n            )\n            name = package.lower() + \":\" + name\n        else:\n            return\n\n        indexname = self.get_index_name(name, type)\n        if name not in self.state.document.ids:\n            signode[\"names\"].append(name)\n            signode[\"ids\"].append(indexname)\n            signode[\"first\"] = not self.names\n            self.state.document.note_explicit_target(signode)\n            inv = self.env.domaindata[\"cl\"][\"symbols\"]\n            # TODO (RS) reenable this checking based on doc and type.\n            # if name in inv:\n            #     self.state_machine.reporter.warning(\n            #         'duplicate symbol description of %s, ' % name +\n            #         'other instance in ' + self.env.doc2path(inv[name][0]),\n            #         line=self.lineno)\n            if name in inv:\n                inv[name].append((self.env.docname, self.objtype))\n            else:\n                inv[name] = [(self.env.docname, self.objtype)]\n\n        indextext = self.get_index_text(name, type)\n        if indextext:\n            self.indexnode[\"entries\"].append(\n                (\"single\", indextext, indexname, \"\", None)\n            )\n\n    def before_content(self):\n        if \"nodoc\" in self.options:\n            return\n        package = self.env.temp_data.get(\"cl:package\")\n        name = self.names[0][1]\n        if not package:\n            self.state_machine.reporter.warning(\n                \"No package specified for symbol %s.\" % name\n            )\n            return\n        string = self.cl_doc_string()\n        if not string:\n            return\n        lines = string2lines(string) + [\"\"]\n        self.content = StringList(lines) + self.content\n\n    def cl_doc_string(self, objtype=None):\n        \"\"\"Resolve a symbols doc string.\n\n        Will raise KeyError if the symbol can't be found.\n        \"\"\"\n        objtype = objtype or self.objtype\n        lispobj = get_lisp_object(\n            self.cl_package, self.cl_symbol_name, objtype\n        )\n        if \"documentation\" in lispobj:\n            return lispobj[\"documentation\"]\n\n        self.state_machine.reporter.warning(\n            \"Can't find symbol {}:{}\".format(\n                self.cl_package, self.cl_symbol_name\n            )\n        )\n        return \"\"\n\n\nclass CLVariable(CLsExp):\n    def cl_handle_signature(self, sig: str, signode: desc_signature):\n        \"\"\"Perform CLOS specific signature additions.\"\"\"\n        name = addnodes.desc_name(self.cl_symbol_name, self.cl_symbol_name)\n        signode.append(name)\n\n\nclass CLClass(CLsExp):\n    def cl_handle_signature(self, sig: str, signode: desc_signature):\n        \"\"\"Perform CLOS specific signature additions.\"\"\"\n        name = addnodes.desc_name(self.cl_symbol_name, self.cl_symbol_name)\n        signode.append(name)\n\n        # Add CLOS Slots\n        lispobj = self.cl_lisp_data()\n        slots = lispobj[\"slots\"]\n        if slots and \"noinitargs\" not in self.options:\n            # TODO add slot details if describing a class\n            for slot in slots:\n                initarg = slot.get(\"initarg\")\n                if initarg and initarg.lower() != \"nil\":\n                    slotarg = addnodes.literal_emphasis(\n                        slot.get(\"name\"), slot.get(\"name\")\n                    )\n                    slotsig = initarg.lower() + \" \"\n                    signode.append(\n                        addnodes.desc_optional(slotsig, slotsig, slotarg)\n                    )\n\n\nclass CLFunction(CLsExp):\n    def get_signature_prefix(self):\n        return self.objtype\n\n    def cl_handle_signature(self, sig: str, signode: desc_signature):\n        \"\"\"Perform CLOS specific signature additions.\"\"\"\n        name = addnodes.desc_name(self.cl_symbol_name, self.cl_symbol_name)\n\n        lispobj = self.cl_lisp_data()\n        lisp_args = lispobj[\"arguments\"].strip() or \"()\"\n        try:\n            sexp = SEXP(\n                lisp_args.lower(),\n                show_defaults=self.env.app.config.cl_show_defaults,\n                package=self.env.temp_data.get(\"cl:package\"),\n            )\n            arg_list = sexp.as_parameterlist(name)\n            signode.append(arg_list)\n        except ValueError as e:\n            self.state_machine.reporter.warning(e)\n\n    def transform_content(self, contentnode: addnodes.desc_content) -> None:\n        dl = addnodes.desc()\n        dl[\"objtype\"] = \"setfFunction\"\n        dd = addnodes.desc_content()\n        dd.append(dl)\n\n        if self.cl_handle_setf(dl):\n            contentnode.children.insert(0, dl)\n\n    def cl_handle_setf(self, contentnode):\n        try:\n            setfobj = get_lisp_object(\n                self.cl_package, self.cl_symbol_name, \"setfFunction\"\n            )\n        except LispDataError:\n            return False\n\n        if setfobj[\"type\"] == \"genericFunction\":\n            self.cl_handle_setf_methods(contentnode, setfobj[\"methods\"])\n        else:\n            self.cl_handle_setf_function(contentnode, setfobj)\n        return True\n\n    def cl_handle_setf_function(self, methodnode, setfobj):\n        signode = addnodes.desc_signature(\"\", \"\")\n        sexp = SEXP(\n            setfobj[\"arguments\"].lower(),\n            show_defaults=self.env.app.config.cl_show_defaults,\n            package=self.cl_package,\n        )\n        arg_list = sexp.as_parameterlist(setfobj[\"name\"])\n        signode.append(arg_list)\n        methodnode.append(signode)\n\n    def cl_handle_setf_methods(self, methodnode, methodsobj):\n        for method in methodsobj:\n            signode = addnodes.desc_signature(\"\", \"\")\n            sexp = SEXP(\n                method[\"arguments\"].lower(),\n                types=method[\"specializer\"],\n                show_defaults=self.env.app.config.cl_show_defaults,\n                package=self.cl_package,\n            )\n            arg_list = sexp.as_parameterlist(method[\"name\"])\n            signode.append(arg_list)\n            methodnode.append(signode)\n\n        if methodsobj:\n            return True\n\n\nclass CLMacro(CLFunction):\n    pass\n\n\nclass CLGeneric(CLFunction):\n\n    option_spec: OptionSpec = {\n        \"nodoc\": bool_option,\n        \"noindex\": bool_option,\n        \"nospecializers\": bool_option,\n    }\n\n    def transform_content(self, contentnode: addnodes.desc_content) -> None:\n        if self.objtype != \"generic\":\n            return\n        if \"nospecializers\" in self.options:\n            return\n        package = self.env.temp_data.get(\"cl:package\")\n        name = self.cl_symbol_name\n        lispobj = get_lisp_object(package, name, self.objtype)\n\n        dl = addnodes.desc()\n        # This objtype setting is needed to support the latex output.\n        dl[\"objtype\"] = \"method\"\n        dd = addnodes.desc_content()\n        dd.append(dl)\n        contentnode.children.insert(0, dl)\n        for method in lispobj[\"methods\"]:\n            signode = addnodes.desc_signature(\"\", \"\")\n            # signode.append(addnodes.desc_annotation(\"method\", \"method\"))\n            name = local_atom(package, method[\"name\"]).lower()\n            ref = specializer_name_xref(\n                method[\"name\"],\n                method[\"specializer\"],\n                self.state.inliner,\n                package,\n                self.lineno,\n            )\n            function_name = addnodes.desc_name()\n            function_name.append(ref)\n            sexp = SEXP(\n                method[\"arguments\"],\n                types=method[\"specializer\"],\n                show_defaults=self.env.app.config.cl_show_defaults,\n                package=self.env.temp_data.get(\"cl:package\"),\n            )\n            arg_list = sexp.as_parameterlist(function_name)\n            signode.append(arg_list)\n            dl.append(signode)\n        super(CLGeneric, self).cl_handle_setf(dl)\n\n\nclass CLMethod(CLFunction):\n\n    option_spec: OptionSpec = {\n        \"nodoc\": bool_option,\n        \"noindex\": bool_option,\n        \"noinherit\": bool_option,\n        \"nospecializers\": bool_option,\n        \"nolinkgeneric\": bool_option,\n    }\n\n    doc_field_types = [\n        Field(\n            \"specializer\",\n            label=_(\"Specializer\"),\n            has_arg=False,\n            names=(\"specializer\"),\n        ),\n        GroupedField(\n            \"parameter\",\n            label=_(\"Parameters\"),\n            names=(\n                \"param\",\n                \"parameter\",\n                \"arg\",\n                \"argument\",\n                \"keyword\",\n                \"kwparam\",\n            ),\n        ),\n        Field(\n            \"returnvalue\",\n            label=_(\"Returns\"),\n            has_arg=False,\n            names=(\"returns\", \"return\"),\n        ),\n    ]\n\n    def cl_method_specializer(self):\n        \"\"\"\"\"\"\n        return normalize_specializer(\n            self.arguments[0].split(\" \")[1:], self.cl_package\n        )\n\n    def cl_method(self, sig: str, package: str):\n        \"\"\"Convert find a method using the sig and package.\n\n        sig would be e.g. `my-method (eq foobar) my-class`\n        \"\"\"\n        symbol_name = sig.split(\" \")[0]  # get the name of the method\n        lispobj = get_lisp_object(package, symbol_name, \"method\")\n\n        # remove the function name from the specializer arguments\n        key = self.cl_method_specializer()\n\n        try:\n            return next(\n                m\n                for m in lispobj[\"methods\"]\n                if normalize_specializer(m[\"specializer\"], self.cl_package)\n                == key\n            )\n        except StopIteration:\n            specializers = [\n                normalize_specializer(m[\"specializer\"], self.cl_package)\n                for m in lispobj[\"methods\"]\n            ]\n            self.state_machine.reporter.warning(\n                \"Can't find method %s:%s specializer %s, \"\n                \"available specializers are %s\"\n                % (package, symbol_name, key, specializers)\n            )\n\n    def cl_setf_methods(self, symbol_name: str, package: str):\n        \"\"\"Convert find a method using the sig and package.\n\n        sig would be e.g. `my-method (eq foobar) my-class`\n        \"\"\"\n        lispobj = get_lisp_object(package, symbol_name, \"setfFunction\")\n\n        # remove the function name and the first type from the\n        # specializer arguments\n        key = self.cl_method_specializer()\n\n        try:\n            return [\n                m\n                for m in lispobj[\"methods\"]\n                # Remove the first type from the specializer\n                if normalize_specializer(m[\"specializer\"][1:], self.cl_package)\n                == key\n            ]\n        except StopIteration:\n            specializers = [\n                normalize_specializer(m[\"specializer\"], self.cl_package)\n                for m in lispobj[\"methods\"]\n            ]\n            self.state_machine.reporter.warning(\n                \"Can't find method %s:%s specializer %s, \"\n                \"available specializers are %s\"\n                % (package, symbol_name, key, specializers)\n            )\n\n    def cl_handle_setf(self, contentnode):\n        try:\n            setfobj = get_lisp_object(\n                self.cl_package, self.cl_symbol_name, \"setfFunction\"\n            )\n        except LispDataError:\n            return False\n\n        if setfobj[\"type\"] == \"genericFunction\":\n            return self.cl_handle_setf_methods(\n                contentnode,\n                self.cl_setf_methods(self.cl_symbol_name, self.cl_package),\n            )\n        else:\n            self.cl_handle_setf_function(contentnode, setfobj)\n        return True\n\n    def cl_handle_signature(self, sig: str, signode: desc_signature):\n        \"\"\"Perform Method specific signature additions.\"\"\"\n        name = addnodes.desc_name(self.cl_symbol_name, self.cl_symbol_name)\n\n        lispobj = self.cl_lisp_data()\n        lisp_args = lispobj[\"arguments\"].strip() or \"()\"\n        # returns none if not found\n        types = self.cl_method(sig, self.cl_package)\n        if types:\n            types = types[\"specializer\"]\n        try:\n            sexp = SEXP(\n                lisp_args.lower(),\n                types=types,\n                show_defaults=self.env.app.config.cl_show_defaults,\n                package=self.env.temp_data.get(\"cl:package\"),\n            )\n            arg_list = sexp.as_parameterlist(name)\n            signode.append(arg_list)\n        except ValueError as e:\n            self.state_machine.reporter.warning(e)\n\n    def get_index_name(self, name, type):\n        \"\"\"Generate a name that will be used to create anchors on the page for\n        each method.\"\"\"\n        return \"%s:%s %s\" % (\n            type,\n            qualify_atom(self.cl_package, self.cl_symbol_name).lower(),\n            self.cl_method_specializer().lower(),\n        )\n\n    def get_index_text(self, name, type):\n        specializer = self.cl_method_specializer()\n        return _(\"%s (%s) (Lisp %s)\") % (\n            name.lower().split(\":\")[-1],\n            specializer.lower(),\n            type,\n        )\n\n    def add_target_and_index(\n        self, name_obj: Tuple[str, str], sig: str, signode: desc_signature\n    ) -> None:\n        # node target\n        type, name = name_obj\n\n        if \"cl:package\" in self.env.temp_data:\n            package = self.options.get(\n                \"module\", self.env.temp_data.get(\"cl:package\")\n            )\n            name = package.lower() + \":\" + name\n        else:\n            return\n\n        indexname = self.get_index_name(name, type)\n        if name not in self.state.document.ids:\n            signode[\"names\"].append(name)\n            signode[\"ids\"].append(indexname)\n            signode[\"first\"] = not self.names\n            self.state.document.note_explicit_target(signode)\n            inv = self.env.domaindata[\"cl\"][\"methods\"]\n            # TODO (RS) reenable this checking based on doc and type.\n            # if name in inv:\n            #     self.state_machine.reporter.warning(\n            #         'duplicate symbol description of %s, ' % name +\n            #         'other instance in ' + self.env.doc2path(inv[name][0]),\n            #         line=self.lineno)\n\n            sig = self.cl_method_specializer()\n            if name in inv:\n                inv[name][sig] = (self.env.docname, self.objtype)\n            else:\n                inv[name] = {sig: (self.env.docname, self.objtype)}\n\n        indextext = self.get_index_text(name, type)\n        if indextext:\n            self.indexnode[\"entries\"].append(\n                (\"single\", indextext, indexname, \"\", None)\n            )\n\n    def cl_doc_string(self):\n        \"\"\"Resolve a symbols doc string.\n\n        Will raise KeyError if the symbol can't be found.\n        \"\"\"\n        doc = \"\"\n        method = self.cl_method(self.arguments[0], self.cl_package)\n        if method:\n            doc = method[\"documentation\"]\n\n        if doc:\n            return doc\n\n        if \"noinherit\" not in self.options:\n            return super(CLMethod, self).cl_doc_string(\"generic\")\n        return \"\"\n\n    def run(self) -> List[Node]:\n        result = super(CLMethod, self).run()\n        field_list = self.get_field_list(result)\n        package = self.env.temp_data.get(\"cl:package\")\n\n        if \"nolinkgeneric\" not in self.options:\n            spec = generic_xref(\n                self.cl_symbol_name,\n                self.state,\n                package=package,\n            )\n            field_list.append(spec)\n\n        return result\n\n\nclass CLCurrentPackage(Directive):\n    \"\"\"This directive is just to tell Sphinx that we're documenting stuff in\n    namespace foo.\"\"\"\n\n    has_content = False\n    required_arguments = 1\n    optional_arguments = 0\n    final_argument_whitespace = True\n    option_spec: OptionSpec = {}\n\n    def run(self):\n        env = self.state.document.settings.env\n        env.temp_data[\"cl:package\"] = self.arguments[0].upper()\n        # index_package(self.arguments[0].upper())\n        return []\n\n\nclass CLXRefRole(XRefRole):\n    def process_link(\n        self,\n        env: BuildEnvironment,\n        refnode: Element,\n        has_explicit_title: bool,\n        title: str,\n        target: str,\n    ) -> Tuple[str, str]:\n        if not has_explicit_title:\n            target = target.lstrip(\"~\")  # only has a meaning for the title\n            # if the first character is a tilde, don't display the package\n            if title[0:1] == \"~\":\n                symbol = title[1:].split(\":\")\n                # package = symbol[0]\n                title = symbol[-1]\n                if target[0] == \":\":\n                    title = \":\" + title\n        return title, target\n\n\nclass CLDomain(Domain):\n    \"\"\"CL language domain.\"\"\"\n\n    name = \"cl\"\n    label = \"Common Lisp\"\n\n    object_types = {\n        \"package\": ObjType(_(\"package\"), \"package\"),\n        \"function\": ObjType(_(\"function\"), \"function\"),\n        \"macro\": ObjType(_(\"macro\"), \"macro\"),\n        \"variable\": ObjType(_(\"variable\"), \"variable\"),\n        \"class\": ObjType(_(\"class\"), \"class\"),\n        \"generic\": ObjType(_(\"generic\"), \"generic\"),\n        \"method\": ObjType(_(\"method\"), \"method\"),\n    }\n\n    directives = {\n        \"package\": CLCurrentPackage,\n        \"function\": CLFunction,\n        \"generic\": CLGeneric,\n        \"macro\": CLMacro,\n        \"variable\": CLVariable,\n        \"class\": CLClass,\n        \"method\": CLMethod,\n    }\n\n    roles = {\n        \"symbol\": CLXRefRole(),\n        \"function\": CLXRefRole(),\n        \"generic\": CLXRefRole(),\n        \"macro\": CLXRefRole(),\n        \"variable\": CLXRefRole(),\n        \"class\": CLXRefRole(),\n        \"method\": CLXRefRole(),\n    }\n    initial_data = {\n        \"symbols\": {},\n        \"methods\": {},\n    }\n\n    def find_obj(\n        self, env: BuildEnvironment, name: str\n    ) -> Optional[Union[List[Tuple[str, List[Tuple[str, str]]]], filter]]:\n        \"\"\"Find a Lisp symbol for \"name\", perhaps using the given package\n        Return a list of (name, object entry) tuples.\"\"\"\n        symbols = self.data[\"symbols\"]\n        name = name.lower()\n        if \":\" in name:\n            if name in symbols:\n                return [(name, symbols[name])]\n        else:\n\n            def filter_symbols(symbol):\n                symbol = symbol[0]\n                if name == symbol:\n                    return True\n                if \":\" in symbol:\n                    symbol = symbol.split(\":\")[1]\n                    if name == symbol:\n                        return True\n                return False\n\n            return filter(filter_symbols, symbols.items())\n\n    def find_method(\n        self, env: BuildEnvironment, name: str, node: pending_xref\n    ) -> Optional[List[Tuple[str, str]]]:\n        \"\"\"Find a Lisp symbol for \"name\", perhaps using the given package\n        Return a list of (name, object entry) tuples.\"\"\"\n        methods = self.data[\"methods\"]\n        name = name.lower()\n        sexp = name.split(\" \")\n        generic = sexp[0]\n        specializer = \" \".join(sexp[1:]).upper()\n\n        if generic in methods:\n            if specializer in methods[generic]:\n                return [methods[generic][specializer]]\n            else:\n                logger.warning(\"can't find method %s\" % (name), location=node)\n        else:\n            logger.warning(\"can't find generic %s\" % (name), location=node)\n\n    def resolve_xref(\n        self,\n        env: BuildEnvironment,\n        fromdocname: str,\n        builder: Builder,\n        typ: str,\n        target: str,\n        node: pending_xref,\n        contnode: Element,\n    ) -> Optional[Element]:\n        if \" \" in target:\n            matches = self.find_method(env, target.upper(), node)\n        else:\n            matches = self.find_obj(env, target.upper())\n\n        if not matches:\n            return None\n\n        matches = [*matches]\n        if len(matches) == 0:\n            logger.warning(\n                \"no target found for cross-reference %r\" % (target),\n                location=node,\n            )\n            return None\n        elif len(matches) > 1:\n            logger.warning(\n                \"more than one target found for cross-reference \"\n                \"%r: %s\" % (target, \", \".join(match[0] for match in matches)),\n                location=node,\n            )\n        # TODO (RS) this just chooses the first symbol, instead every\n        # symbol should be presented.\n\n        if \" \" in target:\n            sexp = target.split(\" \")\n            generic = sexp[0].lower()\n            specializer = \" \".join(sexp).lower()\n            name = generic\n            filename = matches[0][0]  # the first filename\n            link = \"method\" + \":\" + specializer\n        else:\n            name = matches[0][0]  # the symbol name\n            filename = matches[0][1][0][0]  # the first filename\n            type = matches[0][1][0][1]  # the first type\n            link = type + \":\" + name\n        return make_refnode(\n            builder, fromdocname, filename, link, contnode, name\n        )\n\n    def get_symbols(self):\n        for refname, docs in self.data[\"symbols\"].items():\n            for (docname, type) in docs:\n                yield (refname, refname, type, docname, refname, 1)\n\n\ndef save_cldomain_output(output: bytes):\n    \"\"\"Save a copy of the clgit output for debugging.\"\"\"\n    fd, path = tempfile.mkstemp(\".log\", \"cldomain-err-\")\n    os.write(fd, output)\n    os.close(fd)\n    return path\n\n\ndef index_packages(\n    systems: List[str],\n    system_paths: List[str],\n    packages: List[str],\n    cl_debug: bool,\n) -> None:\n    \"\"\"Call an external lisp program that will return a dictionary of doc\n    strings for all public symbols.\"\"\"\n    cldomain_exe = [\n        str(pathlib.Path(__file__).parent.resolve().joinpath(\"cldomain.ros\"))\n    ]\n    cldomain_args = [\n        \"--package\",\n        \",\".join(packages),\n        \"--system\",\n        \",\".join(systems),\n        \"--path\",\n        \",\".join(system_paths),\n    ]\n    env = os.environ.copy()\n    raw_output = subprocess.check_output(cldomain_exe + cldomain_args, env=env)\n    try:\n        lisp_data = json.loads(raw_output)\n        if cl_debug:\n            pprint.pprint(lisp_data)\n    except Exception:\n        dump_path = save_cldomain_output(raw_output)\n        print(\n            red(\n                \"A error occurred with the json output from cldomain's\"\n                \" lisp inspector,  this has been dumped to %s if you \"\n                \"intend on submitting a bug please include this file \"\n                \"with the sphinx error log.\" % dump_path\n            ),\n            file=sys.stderr,\n        )\n        raise\n    LISP_DATA.update(lisp_data)\n\n\ndef load_packages(app: Sphinx) -> None:\n    packages = []\n    systems = []\n    system_paths = []\n    if app.config.cl_packages:\n        logger.info(\n            \"DEPRECATED: The cl_packages variable has been \"\n            \"replaced by cl_systems and will be removed in the future.\"\n        )\n        for package, system_path in app.config.cl_packages.items():\n            packages.append(package.upper())\n            systems.append(package)\n            system_paths.append(system_path)\n    if app.config.cl_systems:\n        for system in app.config.cl_systems:\n            systems.append(system[\"name\"])\n\n            if \"path\" in system:\n                system_paths.append(system[\"path\"])\n\n            if \"packages\" in system:\n                for package in system[\"packages\"]:\n                    packages.append(package.upper())\n            else:\n                packages.append(system[\"name\"].upper())\n\n    if not packages:\n        logger.warn(\"No CL packages specified.\")\n        return None\n\n    logger.info(\n        \"Collecting Lisp docstrings from %s...\"\n        % \", \".join(str(x) for x in systems)\n    )\n    index_packages(\n        systems,\n        system_paths,\n        packages,\n        app.config.cl_debug,\n    )\n\n\ndef uppercase_symbols(app, docname, source):\n    \"\"\"For each line in a list replace all uppercase symbols with a sphinx\n    references.\"\"\"\n    for i, line in enumerate(source):\n        source[i] = re.sub(upper_symbols, r\":cl:symbol:`~\\g<1>`\\g<2>\", line)\n\n\ndef add_node(class_name, node, visit, depart=None):\n    \"\"\"Register a node's visitor functions with a class, if is available.\"\"\"\n\n    def import_class(cl):\n        d = cl.rfind(\".\")\n        classname = cl[d + 1 : len(cl)]\n        m = __import__(cl[0:d], globals(), locals(), [classname])\n        return getattr(m, classname)\n\n    try:\n        translator = import_class(class_name)\n    except (ImportError, AttributeError):\n        return None\n    setattr(translator, \"visit_\" + node.__name__, visit)\n    if depart:\n        setattr(translator, \"depart_\" + node.__name__, depart)\n\n\nadd_node(\n    \"sphinx_bootstrap_theme.BootstrapTranslator\",\n    desc_clparameterlist,\n    v_clparameterlist,\n    d_clparameterlist,\n)\n\nadd_node(\n    \"sphinx_bootstrap_theme.BootstrapTranslator\",\n    desc_clparameter,\n    v_html_clparameter,\n    d_html_clparameter,\n)\n\nadd_node(\n    \"sphinx_bootstrap_theme.BootstrapTranslator\",\n    addnodes.desc_type,\n    v_bs_html_desc_type,\n    d_bs_html_desc_type,\n)\n\nadd_node(\n    \"sphinx.writers.html.HTMLTranslator\", addnodes.desc_type, v_html_desc_type\n)\n","repo_name":"russell/sphinxcontrib-cldomain","sub_path":"sphinxcontrib/cldomain/cldomain.py","file_name":"cldomain.py","file_ext":"py","file_size_in_byte":44245,"program_lang":"python","lang":"en","doc_type":"code","stars":14,"dataset":"github-code","pt":"81"}
+{"seq_id":"24084987121","text":"# windows\r\nimport ctypes\r\nfrom ctypes import wintypes\r\n\r\nuser32 = ctypes.windll.user32\r\n\r\nh_wnd = user32.GetForegroundWindow()\r\npid = wintypes.DWORD()\r\nuser32.GetWindowThreadProcessId(h_wnd, ctypes.byref(pid))\r\nprint(pid.value)\r\n\r\n# linux\r\nimport sys\r\nimport os\r\nimport subprocess\r\nimport re\r\n\r\ndef get_active_window_title():\r\n    root = subprocess.Popen(['xprop', '-root', '_NET_ACTIVE_WINDOW'], stdout=subprocess.PIPE)\r\n    stdout, stderr = root.communicate()\r\n\r\n    m = re.search(b'^_NET_ACTIVE_WINDOW.* ([\\w]+)$', stdout)\r\n    if m != None:\r\n        window_id = m.group(1)\r\n        window = subprocess.Popen(['xprop', '-id', window_id, 'WM_NAME'], stdout=subprocess.PIPE)\r\n        stdout, stderr = window.communicate()\r\n    else:\r\n        return None\r\n\r\n    match = re.match(b\"WM_NAME\\(\\w+\\) = (?P<name>.+)$\", stdout)\r\n    if match != None:\r\n        return match.group(\"name\").strip(b'\"')\r\n\r\n    return None\r\n\r\nif __name__ == \"__main__\":\r\n    print(get_active_window_title())","repo_name":"nopeless/python-curses-moba-comments","sub_path":"activewindow.py","file_name":"activewindow.py","file_ext":"py","file_size_in_byte":979,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"81"}
+{"seq_id":"6149409518","text":"import os\nfrom http.client import HTTPConnection\n\nimport paramiko\nimport simplejson\nfrom django.http import HttpResponse\nfrom django.shortcuts import render\n\n\ndef Inicio(request):\n    lstHora = []\n    count = 0\n    while count < 24:\n        lstHora.append(count)\n        count += 1\n    return render(request, \"views/inicio.html\", context={\"lstHora\": lstHora})\n\n\ndef EjecutarRF1(request, json):\n    lstData = simplejson.loads(json)\n    objHoraInicio = lstData[\"HoraInicio\"]\n    objHoraFin = lstData[\"HoraFin\"]\n    objParams = \"{0} {1}\".format(objHoraInicio, objHoraFin)\n    return EjecutarReto(objParams, \"config/rf1.txt\")\n\n\ndef EjecutarRF2(request, json):\n    lstData = simplejson.loads(json)\n    objZonaDesde = lstData[\"ZonaDesde\"]\n    objZonaHasta = lstData[\"ZonaHasta\"]\n    objMes = lstData[\"Mes\"]\n    objParams = \"{0} {1} {2}\".format(objZonaDesde, objZonaHasta, objMes)\n    return EjecutarReto(objParams, \"config/rf2.txt\")\n\n\ndef EjecutarRF3(request, json):\n    lstData = simplejson.loads(json)\n    objHoraInicio = lstData[\"HoraInicio\"]\n    objHoraFin = lstData[\"HoraFin\"]\n    objZonaHasta = lstData[\"ZonaHasta\"]\n    objParams = \"{0} {1} {2}\".format(objHoraInicio, objHoraFin, objZonaHasta)\n    return EjecutarReto(objParams, \"config/rf3.txt\")\n\n\ndef EjecutarRA2(request):\n    return EjecutarReto(\"\", \"config/ra2.txt\")\n\n\ndef ConsultarRespuesta(request, json):\n    try:\n        lstData = simplejson.loads(json)\n        objFileName = lstData[\"FileName\"]\n        objFileName = objFileName.replace(\"-\", \"/\")\n\n        objDirName = os.path.dirname(os.path.realpath(\"__file__\"))\n        objFileConfig = open(os.path.join(objDirName, objFileName), \"r\")\n        objConfig = objFileConfig.read().split(\"\\n\")\n\n        objResultFileName = objConfig[0]\n        objResultFilePath = objConfig[1]\n        objHadoopTarget = objConfig[5]\n        objLocalFilePath = objConfig[6]\n        objResultFile = os.path.join(objResultFilePath, objResultFileName)\n        objLocalFile = os.path.join(objLocalFilePath, objResultFileName)\n        objHadoopResultFile = os.path.join(objHadoopTarget, objResultFileName)\n\n        RunCmd(\"hadoop fs -get {0} {1}\".format(objHadoopResultFile, objResultFilePath))\n        DownloadFile(objResultFile, objLocalFile)\n\n        objArchivo = open(objLocalFile, \"r\")\n        objResult = objArchivo.read()\n    except OSError as err:\n        objResult = \"IN_PROCESS\"\n        # objResult = \"OS error: {0}\".format(err)\n    return HttpResponse(objResult)\n\n\ndef EjecutarReto(objParams, objFileName):\n    try:\n        objDirName = os.path.dirname(os.path.realpath(\"__file__\"))\n        objFileConfig = open(os.path.join(objDirName, objFileName), \"r\")\n        objConfig = objFileConfig.read().split(\"\\n\")\n\n        objResultFileName = objConfig[0]\n        objResultFilePath = objConfig[1]\n        objJarJobPath = objConfig[2]\n        objJarJobName = objConfig[3]\n        objHadoopSource = objConfig[4]\n        objHadoopTarget = objConfig[5]\n        objLocalFilePath = objConfig[6]\n        objResultFile = os.path.join(objResultFilePath, objResultFileName)\n        objLocalFile = os.path.join(objLocalFilePath, objResultFileName)\n\n        if not os.path.isdir(objLocalFilePath):\n            os.mkdir(objLocalFilePath)\n\n        if os.path.isfile(objLocalFile):\n            os.remove(objLocalFile)\n\n        RunCmd(\"mkdir -p {0}\".format(objResultFilePath))\n        RunCmd(\"rm {0}\".format(objResultFile))\n        RunCmd(\"hadoop fs -rm -r {0}\".format(objHadoopTarget))\n        RunCmd(\"hadoop jar {0} {1} {2} {3} {4}\".format(\n            objJarJobPath,\n            objJarJobName,\n            objHadoopSource,\n            objHadoopTarget,\n            objParams))\n\n        objResult = \"OK\"\n    except OSError as err:\n        objResult = \"OS error: {0}\".format(err)\n\n    return HttpResponse(objResult)\n\n\ndef RunCmd(objCommand):\n    host_proxy = \"connect2.virtual.uniandes.edu.co\"\n    port_proxy = 443\n    host_server = \"bigdata-cluster1-ambari.virtual.uniandes.edu.co\"\n    port_server = 22\n    user_server = \"bigdata09\"\n    pass_server = \"Rojo2020\"\n\n    http_con = HTTPConnection(host_proxy, port_proxy)\n    http_con.set_tunnel(host_server, port_server)\n    http_con.connect()\n    sock = http_con.sock\n    ssh = paramiko.SSHClient()\n    ssh.set_missing_host_key_policy(paramiko.AutoAddPolicy())\n    ssh.connect(hostname=host_server, username=user_server, password=pass_server, sock=sock)\n    ssh.exec_command(objCommand)\n\n    ssh.close()\n    http_con.close()\n\n\ndef DownloadFile(objRemoteFile, objLocalFile):\n    host_proxy = \"connect2.virtual.uniandes.edu.co\"\n    port_proxy = 443\n    host_server = \"bigdata-cluster1-ambari.virtual.uniandes.edu.co\"\n    port_server = 22\n    user_server = \"bigdata09\"\n    pass_server = \"Rojo2020\"\n\n    http_con = HTTPConnection(host_proxy, port_proxy)\n    http_con.set_tunnel(host_server, port_server)\n    http_con.connect()\n    sock = http_con.sock\n    ssh = paramiko.SSHClient()\n    ssh.set_missing_host_key_policy(paramiko.AutoAddPolicy())\n    ssh.connect(hostname=host_server, username=user_server, password=pass_server, sock=sock)\n    sftp = ssh.open_sftp()\n    sftp.get(objRemoteFile, objLocalFile)\n\n    sftp.close()\n    ssh.close()\n    http_con.close()\n","repo_name":"nicxleo/202020-Grupo09","sub_path":"aplicaciones/taller_1/front_taller_1/apps/consultas/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":5179,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"41694665924","text":"\"\"\"\nGiven an array nums containing n + 1 integers where each integer is between 1 and n (inclusive), prove that at least one\nduplicate number must exist. Assume that there is only one duplicate number, find the duplicate one.\n\nNote:\nYou must not modify the array (assume the array is read only).\nYou must use only constant, O(1) extra space.\nYour runtime complexity should be less than O(n^2).\nThere is only one duplicate number in the array, but it could be repeated more than once.\n\"\"\"\n__author__ = 'Daniel'\n\n\nclass Solution(object):\n    def findDuplicate(self, nums):\n        \"\"\"\n        Condition for convert the array problem to linked list problem:\n        CANNOT contains integer with 0; otherwise cycle: A = [1, 0]\n\n        Degenerated case: if there is only one duplicates, just do arithmetic.\n\n        For possibly multiple duplications: Floyd's loop detection\n        Abstract the array to linked list, current index as value, current value as next\n        Need to derive the math proof\n\n        Abstract array to linked list\n        :type nums: List[int]\n        :rtype: int\n        \"\"\"\n        f, s = 0, 0\n        while True:\n            f = nums[nums[f]]\n            s = nums[s]\n            if f == s:\n                break\n\n        t = 0\n        while t != s:\n            t = nums[t]\n            s = nums[s]\n\n        return t\n\n\nif __name__ == \"__main__\":\n    assert Solution().findDuplicate([1, 2, 3 ,4, 5, 5]) == 5\n\n","repo_name":"algorhythms/LeetCode","sub_path":"287 Find the Duplicate Number.py","file_name":"287 Find the Duplicate Number.py","file_ext":"py","file_size_in_byte":1432,"program_lang":"python","lang":"en","doc_type":"code","stars":843,"dataset":"github-code","pt":"81"}
+{"seq_id":"36317772881","text":"# import matplotlib.pyplot as plt\n# import json\n#\n# with open(\"checkpoints/d_scores.json\") as f:\n#     j = json.load(f)\n#     plt.plot(j['D_A'], '.')\n#     plt.title('D_A Scores')\n#     plt.show()\n#\n#     plt.plot(j['D_B'], '.')\n#     plt.title('D_B Scores')\n#     plt.show()\n#\n#     plt.plot(j['A_gt'], '.')\n#     plt.title('A_gt Scores')\n#     plt.show()\n\nimport matplotlib.pyplot as plt\nimport json\n\nwith open(\"checkpoints/d_scores_train.json\") as f:\n    j_train = json.load(f)\nwith open(\"checkpoints/d_scores_test.json\") as f:\n    j_test = json.load(f)\n\nfig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=(8,3))\n\n# x = j_train['D_A']\nx = sorted(j_train['D_A'], reverse=True)\nax1.plot(x, '.', label=\"D_X\")\n# x = j_train['D_B']\nx = sorted(j_train['D_B'], reverse=True)\nax1.plot(x, '.', label=\"D_Y\")\n# x = j_train['A_gt']\nx = sorted(j_train['A_gt'], reverse=True)\nax1.plot(x, '.', label='Groundtruth')\n\nax1.set_title('D Scores Train')\nax1.axhline(y=0.5, color='brown')\nax1.legend(fontsize=9)\nax1.set_ylim([-0.7, 1.25])\n\n\n# x = j_test['D_A']\nx = sorted(j_test['D_A'], reverse=True)\nax2.plot(x, '.', label='D_X')\n# x = j_test['D_B']\nx = sorted(j_test['D_B'], reverse=True)\nax2.plot(x, '.', label=\"D_Y\")\n\n# x = j_test['A_gt']\nx = sorted(j_test['A_gt'], reverse=True)\nax2.plot(x, '.', label='Groundtruth')\n\nax2.set_title('D Scores Test')\nax2.axhline(y=0.5, color='brown')\nax2.legend(fontsize=9)\nax2.set_ylim([-0.7, 1.3])\n\n# fig, ax = plt.subplots(nrows=2, ncols=3, figsize=(13,6))\n#\n# # x = j_train['D_A']\n# x = sorted(j_train['D_A'], reverse=True)\n# ax[0][0].plot(x, '.')\n# ax[0][0].set_title('D_A Scores Train')\n# ax[0][0].axhline(y=0.5, color='g')\n# # ax[0][0].set_xlabel(\"Examples\")\n# # ax[0][0].set_ylabel(\"\")\n#\n# # x = j_train['D_B']\n# x = sorted(j_train['D_B'], reverse=True)\n# ax[0][1].plot(x, '.')\n# ax[0][1].set_title('D_B Scores Train')\n# ax[0][1].axhline(y=0.5, color='g')\n#\n# # x = j_train['A_gt']\n# x = sorted(j_train['D_B'], reverse=True)\n# ax[0][2].plot(x, '.')\n# ax[0][2].set_title('Groundtruth Scores Train')\n# ax[0][2].axhline(y=0.5, color='g')\n#\n# # x = j_test['D_A']\n# x = sorted(j_test['D_A'], reverse=True)\n# ax[1][0].plot(x, '.')\n# ax[1][0].set_title('D_A Scores Test')\n# ax[1][0].axhline(y=0.5, color='g')\n#\n# # x = j_test['D_B']\n# x = sorted(j_test['D_B'], reverse=True)\n# ax[1][1].plot(x, '.')\n# ax[1][1].set_title('D_B Scores Test')\n# ax[1][1].axhline(y=0.5, color='g')\n#\n# # x = j_test['A_gt']\n# x = sorted(j_test['D_B'], reverse=True)\n# ax[1][2].plot(x, '.')\n# ax[1][2].set_title('Groundtruth Scores Test')\n# ax[1][2].axhline(y=0.5, color='g')\n\nfig.tight_layout()\n# plt.show()\n\nplt.savefig(\"checkpoints/d_scores.png\")","repo_name":"wnls/cyclegan","sub_path":"d_score.py","file_name":"d_score.py","file_ext":"py","file_size_in_byte":2650,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"38094980649","text":"import numpy as np\nfrom numpy.testing import assert_almost_equal, assert_array_less, assert_equal\nfrom skcv.multiview.util.synthetic_point_cloud import *\n\ndef test_random_sphere():\n    n_points = 10\n    radius = 7\n    center = np.array((1, 2, 3))\n\n    points = random_sphere(n_points, radius=radius, center=center)\n\n    norm = np.linalg.norm(points - center[:, np.newaxis], axis=0)\n\n    assert_almost_equal(radius*np.ones(n_points), norm)\n\n\ndef test_random_ball():\n    n_points = 10\n    radius = 7\n    center = np.array((1, 2, 3))\n\n    points = random_ball(n_points, radius=radius, center=center)\n\n    norm = np.linalg.norm(points - center[:, np.newaxis], axis=0)\n\n    assert_array_less(norm, radius*np.ones(n_points))\n\n\ndef test_random_cube():\n\n    n_points = 10\n    size = 5\n    center = np.array((1, 2, 3))\n\n    points = random_cube(n_points, size, center)\n    points_c = points - center[:, np.newaxis]\n\n    in_cube = np.zeros(10, dtype=np.bool)\n\n    for i in range(3):\n        b = (abs(points_c[i, :]) - 0.5*size) < 1e-6\n        in_cube = np.logical_or(in_cube, b)\n\n    assert_equal(np.all(in_cube), True)","repo_name":"guillempalou/scikit-cv","sub_path":"skcv/multiview/util/tests/test_point_clouds.py","file_name":"test_point_clouds.py","file_ext":"py","file_size_in_byte":1109,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"81"}
+{"seq_id":"16721130868","text":"\"\"\"\nНапишите программу, которая определит позицию второго вхождения строки в списке либо сообщит, что её нет.\n\n*Пример:*\n\n- список: [\"qwe\", \"asd\", \"zxc\", \"qwe\", \"ertqwe\"], ищем: \"qwe\", ответ: 3\n- список: [\"йцу\", \"фыв\", \"ячс\", \"цук\", \"йцукен\", \"йцу\"], ищем: \"йцу\", ответ: 5\n- список: [\"йцу\", \"фыв\", \"ячс\", \"цук\", \"йцукен\"], ищем: \"йцу\", ответ: -1\n- список: [\"123\", \"234\", 123, \"567\"], ищем: \"123\", ответ: -1\n- список: [], ищем: \"123\", ответ: -1\n\"\"\"\n\n\n\nlst1 = [\"qwe\", \"asd\", \"zxc\", \"qwe\", \"ertqwe\"]\nfind_str = \"qwe\"\ncounter = 0\nconditPoint = 2\n\nfor idx, value in enumerate(lst1):\n    print(f'test Cou {counter}, idx {idx}, value {value}')\n    if value == find_str:\n        print(f'test 1if')\n        counter +=1\n    if counter == conditPoint:\n        print(f'Result: {idx}')\n        break\nif counter != conditPoint:\n    print('-1')\n","repo_name":"G-Relaxant/PythonSeminarsAndHomework","sub_path":"Seminar4/Task5.py","file_name":"Task5.py","file_ext":"py","file_size_in_byte":1044,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"42036035084","text":"from django.shortcuts import render\nfrom .models import Reserva\nfrom .forms import FormReserva, FormActualizarReserva\n\n# Create your views here.\n\n\ndef index(request):\n    return render(request, \"index.html\")\n\n\ndef crear_reserva(request):\n    form = FormReserva()\n    if request.method == \"POST\":\n        form = FormReserva(request.POST)\n        if form.is_valid():\n            form.save()\n        return index(request)\n    data = {\"form\": form}\n    return render(request, \"crear_reserva.html\", data)\n\n\ndef listar_reservas(request):\n    reserva = Reserva.objects.all()\n    data = {\"reserva\": reserva}\n    return render(request, \"listar_reserva.html\", data)\n\n\ndef editar_reserva(request, id):\n    queryset = Reserva.objects.get(id=id)\n    form = FormActualizarReserva(instance=queryset)\n    if request.method == \"POST\":\n        form = FormActualizarReserva(request.POST, instance=queryset)\n        if form.is_valid():\n            form.save()\n        return listar_reservas(request)\n    data = {\"form\": form}\n    return render(request, \"editar_reserva.html\", data)\n\n\ndef eliminar_reserva(request, id):\n    reserva = Reserva.objects.get(id=id)\n    reserva.delete()\n    return listar_reservas(request)\n","repo_name":"LloyolaB/django-prueba2","sub_path":"reservas/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1197,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"74519246023","text":"from google.auth.transport.requests import Request\nfrom google.oauth2.credentials import Credentials\nfrom google_auth_oauthlib.flow import InstalledAppFlow\nfrom googleapiclient.discovery import build\nfrom googleapiclient.errors import HttpError\nfrom googleapiclient.http import MediaFileUpload , MediaIoBaseUpload\nimport os\nimport os.path\nfrom io import BytesIO\nimport io\nimport hashlib\n\n\ndef get_credentials():\n    SCOPES=[\"https://www.googleapis.com/auth/drive\"]\n    creds = None\n    if os.path.exists('token.json'):\n        creds = Credentials.from_authorized_user_file('token.json', SCOPES)\n    if not creds or not creds.valid:\n        if creds and creds.expired and creds.refresh_token:\n            creds.refresh(Request())\n        else:\n            flow = InstalledAppFlow.from_client_secrets_file(\"ChApp/creds.json\",SCOPES)\n            creds = flow.run_local_server(port=0)\n        with open('token.json', 'w') as token:\n            token.write(creds.to_json())\n\n    return creds\n\n\n\ndef Upload(file_data,file_name,user_name,file_mimetype):\n    creds = get_credentials()\n\n    folder_name = user_name\n    try:\n        service = build(\"drive\",\"v3\",credentials=creds)\n        response = service.files().list(q=f\"name='{folder_name}' and mimeType='application/vnd.google-apps.folder'\",spaces='drive').execute()\n        if not response['files']:\n            file_metadata={\n                \"name\" : folder_name,\n                \"mimeType\" : \"application/vnd.google-apps.folder\"\n            }\n            file = service.files().create(body=file_metadata,fields = \"id\").execute()\n            folder_id = file.get(\"id\")\n        else:\n            folder_id = response['files'][0][\"id\"]\n        file_metadata={\n            \"name\" : file_name,\n            \"parents\" : [folder_id]\n        }\n        media = MediaIoBaseUpload(io.BytesIO(file_data), mimetype=file_mimetype)\n        upload_file=service.files().create(body = file_metadata,media_body=media,fields = \"id\").execute()\n        print(f\"FILE UPLOADED TO GOOGLE DRIVE {upload_file.get('id')}\")\n        return upload_file.get(\"id\")\n\n    except HttpError as ex:\n        print(str(ex))\n\n\n\ndef HashFile(file_data):\n    sha256 = hashlib.sha256()\n    sha256.update(file_data)\n    return sha256.hexdigest()\n\n\ndef ListFile(user_name):\n    creds = get_credentials()\n    try:\n        # Build the Google Drive API client\n        service = build('drive', 'v3', credentials=creds)\n\n        # Get the list of files in the user's folder\n        folder_name = user_name\n        folder_query = \"name='{}' and mimeType='application/vnd.google-apps.folder'\".format(folder_name)\n        folder_results = service.files().list(q=folder_query, fields=\"nextPageToken, files(id, name)\").execute()\n        folder_items = folder_results.get('files', [])\n        if not folder_items:\n            message = 'No files found.'\n        else:\n            # Get the list of files in the user's folder\n            file_query = \"'{}' in parents\".format(folder_items[0]['id'])\n            file_results = service.files().list(q=file_query, fields=\"nextPageToken, files(id, name, createdTime)\").execute()\n            file_items = file_results.get('files', [])\n            if not file_items:\n                message = 'No files found.'\n                print('error')\n            else:\n                return file_items\n                \n\n    except HttpError as error:\n        print(str(error))\n\n\n\n\n","repo_name":"itami23/CloudHive","sub_path":"ChApp/functions.py","file_name":"functions.py","file_ext":"py","file_size_in_byte":3408,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"72292826184","text":"import statistics\nfrom collections import Counter\n\n\n# calculate the Euclidean distance between two vectors\ndef euclidean_distance(row1, row2):\n    distance = 0.0\n    for i in range(len(row1)):\n        distance += (row1[i] - row2[i]) ** 2\n    from math import sqrt\n    return sqrt(distance)\n\n\ndef most_frequent(List):\n    occurence_count = Counter(List)\n    return occurence_count.most_common(1)[0][0]\n\n\ndef mean_standard_deviation(errorRate):\n    meanList = []\n    sdList = []\n    # print(errorRate)\n    for datapoints in range(0,len(errorRate[0])):\n        eachErrorRate = []\n        for run in range(0,len(errorRate)):\n            eachErrorRate.append(errorRate[run][datapoints])\n        meanList.append('%.2f'%statistics.mean(eachErrorRate))\n        sdList.append('%.2f'%statistics.stdev(eachErrorRate))\n    return meanList, sdList\n","repo_name":"Dhirajdgandhi/Artificial_Intelligence","sub_path":"utility.py","file_name":"utility.py","file_ext":"py","file_size_in_byte":835,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"34983387537","text":"from django.conf.urls import url\r\n\r\nfrom . import views\r\n\r\napp_name = 'adcalendar'\r\n\r\nurlpatterns = [\r\n    url(r'^$', views.index, name='index'),\r\n\turl('index.html',  views.index, name='index'),\r\n\turl(r'^viewCase$' , views.viewCase , name = 'viewCase'),\r\n\turl(r'^addCase$' , views.addCase , name = 'addCase'),\r\n\turl(r'^update$', views.update , name = 'update'),\r\n\turl(r'^updateCase$', views.updateCase , name = 'updateCase'),\r\n]\r\n\r\n\r\n\r\n","repo_name":"naman0805/adcalendar","sub_path":"project/mysite/adcalendar/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":436,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"74021142346","text":"import csv\nimport sys\n\nfrom ing2ynab.RowReader import RowReader\n\n\nclass Ing2ynab(object):\n\n    def __init__(self, data):\n        self.data_in = data\n        self.data_out = []\n\n    @classmethod\n    def fromfilename(cls, file_in):\n        data = []\n        try:\n            with open(file_in) as File:\n                #               just to skip first line\n                lv_first_line = File.readline()\n                csvReader = csv.reader(File)\n                for row in csvReader:\n                    data.append(row)\n        except IOError:\n            sys.exit(\"input file error\",\n                     \"input file not found\\nplease check the input file name\")\n        return cls(data=data)\n\n    @classmethod\n    def fromlist(cls, data):\n        return cls(data=data)\n\n    def convert(self):\n        for row in self.data_in:\n            rowReader = RowReader(row)\n            data_row = {'Date': rowReader.get_date(),\n                        'Payee': rowReader.get_payee(),\n                        'Category': rowReader.get_category(),\n                        'Memo': rowReader.get_memo(),\n                        'Outflow': rowReader.get_outflow(),\n                        'Inflow': rowReader.get_inflow()}\n            self.data_out.append(data_row)\n\n    def to_csv(self, outputfile):\n\n        try:\n            data_row = {}\n            with open(outputfile, 'w+', newline='') as file:\n                lr_writer = csv.writer(file, quoting=csv.QUOTE_NONNUMERIC)\n\n                #                   write header row\n                lr_writer.writerow(['Date', 'Payee', 'Category',\n                                    'Memo', 'Outflow', 'Inflow'])\n\n                #                   write the rest\n                for jdx, data_row in enumerate(self.data_out):\n                    lr_writer.writerow([data_row.get('Date'),\n                                        data_row.get('Payee'),\n                                        data_row.get('Category'),\n                                        data_row.get('Memo'),\n                                        data_row.get('Outflow'),\n                                        data_row.get('Inflow')])\n            print(\"Wrote output to: \" + outputfile)\n        except IOError:\n            sys.exit(\"Output file error\")\n","repo_name":"Tbutje/ING2YNAB","sub_path":"ing2ynab/Ing2ynab.py","file_name":"Ing2ynab.py","file_ext":"py","file_size_in_byte":2271,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"28447150213","text":"__author__ = 'endru'\nimport Dev.Config.ConfigHandler as CF\nimport os\nimport math\n\nclass ARConfig:\n    def __init__(self):\n        self.dirPath=os.path.dirname(os.path.abspath(__file__))\n        self.file_name=\"ausrichtung.ini\"\n        self.config = CF.ConfigHandler(self.dirPath +\"/\"+ self.file_name)\n        self.angle2Step = self.config.get_float(\"Ausrichtung\", \"angle2Step\", 2970.8922) #float(100*float(140)/12)/math.pi*8\n        self.dir_pin = self.config.get_number(\"Ausrichtung\", \"dir_pin\", 10)\n        self.pulse_pin =self.config.get_number(\"Ausrichtung\", \"pulse_pin\", 22)\n        self.enable_pin =self.config.get_number(\"Ausrichtung\", \"enable_pin\", 24)\n        self.microsteps1_pin =self.config.get_number(\"Ausrichtung\", \"microsteps1_pin\", 9)\n        self.microsteps2_pin =self.config.get_number(\"Ausrichtung\", \"microsteps2_pin\", 11)\n        self.min_delay =self.config.get_number(\"Ausrichtung\", \"min_delay\", 300)\n        self.max_delay =self.config.get_number(\"Ausrichtung\", \"max_delay\", 2000)\n        self.acc =self.config.get_number(\"Ausrichtung\", \"acc\", 100)\n        self.max_steps =self.config.get_number(\"Ausrichtung\", \"max_steps\", 3200)\n\n    def set_angle2Step(self, angle2Step):\n        self.config.set_float(\"Ausrichtung\", \"angle2Step\", angle2Step)\n        self.angle2Step=angle2Step\n\n    def save_config(self):\n        self.config.set_float(\"Ausrichtung\", \"angle2Step\", self.angle2Step)\n        self.config.set_number(\"Ausrichtung\", \"dir_pin\", self.dir_pin)\n        self.config.set_number(\"Ausrichtung\", \"pulse_pin\", self.pulse_pin)\n        self.config.set_number(\"Ausrichtung\", \"enable_pin\", self.enable_pin)\n        self.config.set_number(\"Ausrichtung\", \"microsteps1_pin\", self.microsteps1_pin)\n        self.config.set_number(\"Ausrichtung\", \"microsteps2_pin\", self.microsteps2_pin)\n        self.config.set_number(\"Ausrichtung\", \"min_delay\", self.min_delay)\n        self.config.set_number(\"Ausrichtung\", \"max_delay\", self.max_delay)\n        self.config.set_number(\"Ausrichtung\", \"acc\", self.acc)\n        self.config.set_number(\"Ausrichtung\", \"max_steps\", self.max_steps)\n","repo_name":"4ndri/hslu_pren_grp19_2014","sub_path":"Dev/Treiber/Ausrichtung/config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":2090,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"81"}
+{"seq_id":"9260020522","text":"import numpy as np\r\nfrom numpy import pi, cos, sin\r\nimport matplotlib.pyplot as plt\r\nimport scipy.fftpack\r\n\r\n\r\nsample_rate = 5000\r\n\r\n# definindo a frequencia de corte\r\n\r\nfc = 0.014\r\n\r\n# definindo o roll-off\r\nBW = 0.5\r\n# M = 4 / BW\r\nM = 8\r\nK = 1\r\n\r\ni = np.arange(0, M, 0.001)\r\n\r\n# 16-4\r\nh = K * (sin(2*pi*fc*(i-M/2))/(i-M/2)) * (0.42 - 0.5*cos(2*pi*i/M) + 0.08*cos(4*pi*i/M))\r\n\r\n\r\n###############\r\n#   plot\r\nplt.subplot(2, 1, 1)\r\nplt.plot(i, h, label=\"Windowed\")\r\nplt.legend()\r\nplt.xlabel(\"Time [s]\")\r\nplt.ylabel(\"Amplitude\")\r\n\r\n#retorna a transformada de fourier\r\nyf = scipy.fftpack.fft(h)\r\n\r\nplt.subplot(2, 1, 2)\r\nplt.plot(i, yf, 'b')\r\nplt.xlabel(\"Freq\")\r\nplt.ylabel(\"Magnitude\")\r\n\r\nplt.grid()\r\nplt.show()","repo_name":"diogomarchi/Processador-Digital-de-sinais","sub_path":"aula-9/windowed_design.py","file_name":"windowed_design.py","file_ext":"py","file_size_in_byte":706,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"81"}
+{"seq_id":"3263830290","text":"import json\n\nwith open(\"./tailwindPaste.js\") as file:\n    data = file.read().splitlines()\n\ntailwindArray = []\ncssArray = []\n\nfor line in data:\n    if \".\" in line:\n        if \":\" not in line:\n            tailwindArray.append(line.split(\".\")[1])\n    if \";\" in line:\n        if \"=\" not in line:\n            if \"(\" not in line:\n                if \")\" not in line:\n                    cssArray.append(line.strip())\nprint(len(tailwindArray))\nprint(len(cssArray))\n\nshadowResult = {\n    \"shadow\": \"box-shadow: 0 1px 3px 0 rgba(0, 0, 0, 0.1), 0 1px 2px 0 rgba(0, 0, 0,0.06);\",\n    \"shadow-md\": \"box-shadow: 0 4px 6px -1px rgba(0, 0, 0, 0.1), 0 2px 4px -1px rgba(0,0, 0, 0.06);\",\n    \"shadow-lg\": \"box-shadow: 0 10px 15px -3px rgba(0, 0, 0, 0.1), 0 4px 6px -2px rgba(0, 0, 0, 0.05);\",\n    \"shadow-xl\": \"box-shadow: 0 20px 25px -5px rgba(0, 0, 0, 0.1), 0 10px 10px -5px rgba(0, 0, 0, 0.04);\",\n    \"shadow-xs\": \"box-shadow: 0 0 0 1px rgba(0, 0, 0, 0.05);\",\n    \"shadow-sm\": \"box-shadow: 0 1px 2px 0 rgba(0, 0, 0, 0.05);\",\n    \"shadow-2xl\": \"box-shadow: 0 25px 50px -12px rgba(0, 0, 0, 0.25);\",\n    \"shadow-inner\": \"box-shadow: inset 0 2px 4px 0 rgba(0, 0, 0, 0.06);\",\n    \"shadow-outline\": \"box-shadow: 0 0 0 3px rgba(66, 153, 225, 0.5);\",\n    \"shadow-none\": \"box-shadow: none;\",\n}\n\nwith open(\"./tailwindPaste2.js\") as file:\n    data = file.read().splitlines()\n\ntailwindArray2 = []\ncssArray2 = []\n\nfor line in data:\n    if \".grid-cols\" in line:\n        tailwindArray2.append(line.split(\".\")[1])\n    if \"grid-template-columns\" in line:\n        cssArray2.append(line.strip())\n\n# tempResult = {}\n\nresult = {}\nfor i in range(len(tailwindArray)):\n    key = tailwindArray[i]\n    value = cssArray[i]\n    result[key] = value\nfor key in shadowResult:\n    value = shadowResult[key]\n    result[key] = value\n\nfor i in range(len(tailwindArray2)):\n    key = tailwindArray2[i]\n    value = cssArray2[i]\n    result[key] = value\n\nprint(len(result))\n# print(tempResult)\n\n# with open(\"tailwindDict.json\", \"w\") as file:\n#     file.write(json.dumps(result))  # use `json.loads` to do the reverse\n\nwith open(\"../client/src/tailwindDict.json\", \"w\") as file:\n    file.write(json.dumps(result))  # use `json.loads` to do the reverse\n\n","repo_name":"grobelDev/tailwind-to-css-converter","sub_path":"tailwindDict/parseTailwindPaste.py","file_name":"parseTailwindPaste.py","file_ext":"py","file_size_in_byte":2196,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"21006669479","text":"#Authour: Sanu\n\n\npreheat_oven = lambda : print('Preheating oven')\nput_croissants_in = lambda: print('putting croissants in')\nwait_five_min = lambda: print('Waiting five mins')\ntake_croissants_out = lambda: print('Take croissants out (and eat them!)')\n\n#preheat_oven()\n#put_croissants_in()\n#wait_five_min()\n#take_croissants_out()\n\n\n\"\"\"\ndef perform_steps(*function):\n    for function in function:\n        function()\n\n\nperform_steps(preheat_oven,\n        put_croissants_in,\n        wait_five_min,\n        take_croissants_out)\n\n\"\"\"\n\ndef create_recipe(*functions):\n    def run_all():\n        for function in functions:\n            function()\n    return run_all\n\n\nrecipe = create_recipe(preheat_oven,\n        put_croissants_in,\n        wait_five_min,\n        take_croissants_out)\n\nrecipe()\n\n","repo_name":"skysoft999/python_skill_learningpath","sub_path":"returnfn_fromotherfn.py","file_name":"returnfn_fromotherfn.py","file_ext":"py","file_size_in_byte":785,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"6143542542","text":"# coding: utf-8\n\nimport math, os, errno, random, tqdm, subprocess, re, pickle, shutil\nimport numpy as np\n\nclass Utils:\n\n\t@staticmethod\n\tdef rm_dir(thedir):\n\t\tif os.path.exists(thedir):\n\t\t\tshutil.rmtree(thedir)\n\n\t@staticmethod\n\tdef copy_dir(src, dst):\n\t\tUtils.rm_dir(dst)\n\t\tshutil.copytree(src, dst)\n\n\t@staticmethod\n\tdef move_dir(src, dst):\n\t\tUtils.rm_dir(dst)\n\t\tshutil.move(src, dst)\t\n\t\n\t#=======================================================================\n\t# Color map\n\t#=======================================================================\n\n\t@staticmethod\n\tdef color_map(val, vals, colors):\n\t\tif val < vals[0]:\n\t\t\tval = vals[0]\n\t\tif val > vals[-1]:\n\t\t\tval = vals[-1]\n\n\t\tfor v in range(len(vals) - 1):\n\t\t\tif val <= vals[v + 1]:\n\t\t\t\tcolorR = int(colors[v][0:2], 16), int(colors[v + 1][0:2], 16)\n\t\t\t\tcolorG = int(colors[v][2:4], 16), int(colors[v + 1][2:4], 16)\n\t\t\t\tcolorB = int(colors[v][4:6], 16), int(colors[v + 1][4:6], 16)\n\n\t\t\t\tinterp = (val - vals[v]) / (vals[v + 1] - vals[v])\n\t\t\t\tcolorRint = interp * (colorR[1] - colorR[0]) + colorR[0]\n\t\t\t\tcolorGint = interp * (colorG[1] - colorG[0]) + colorG[0]\n\t\t\t\tcolorBint = interp * (colorB[1] - colorB[0]) + colorB[0]\n\n\t\t\t\treturn (int(0.5 + colorRint), int(0.5 + colorGint), int(0.5 + colorBint))\n\n\t\treturn (0, 0, 0)\n\n\t#=======================================================================\n\t# Wind\n\t#=======================================================================\n\n\t@staticmethod\n\tdef wind_UV_to_n(UVcols, n):\n\t\tassert (UVcols.shape[1] == 2)\n\t\tnbVals = UVcols.shape[0]\n\n\t\tif False:\n\t\t\tres = np.empty((nbVals, n), dtype=np.float)\n\t\t\tfor ia in range(n):\n\t\t\t\ta = float(ia) / float(n) * 2.0 * math.pi\n\t\t\t\tres[:, ia] = np.clip(math.cos(a) * UVcols[:, 0] + math.sin(a) * UVcols[:, 1], a_min=0.0, a_max=None)\n\t\telse:\n\t\t\tangle = np.mod((np.around(np.arctan2(UVcols[:,1], UVcols[:,0]) / (2.0*math.pi) * n) + n/2).astype(int), n)\n\n\t\t\tres = np.zeros((nbVals, n), dtype=np.float)\n\t\t\tres[np.arange(nbVals), angle] = np.sqrt(UVcols[:, 0]*UVcols[:, 0] + UVcols[:, 1]*UVcols[:, 1])\n\t\t\t#print res\n\n\n\t\treturn res\n\n\t# X_wind_UV must be UVUVUVUV...\n\t@staticmethod\n\tdef convert_wind_matrix(X_wind_UV, nbWindDims):\n\t\tX_wind = np.empty((X_wind_UV.shape[0], nbWindDims*X_wind_UV.shape[1]//2), dtype=np.float)\n\t\tfor w in range(X_wind_UV.shape[1]//2):\n\t\t\tX_wind[:,range(8*w,8*(w+1))] = Utils.wind_UV_to_n(X_wind_UV[:,range(2*w,2*(w+1))], nbWindDims)\n\t\treturn X_wind\n\n\t#=======================================================================\n\t#\n\t#=======================================================================\n\n\t@staticmethod\n\tdef compute_normalization_coeffs(X):\n\t\txArrayMean = np.mean(X, axis=0)\n\t\txArraySquaredMean = np.square(xArrayMean)\n\t\txArrayMeanSquare = np.mean(np.square(X), axis=0)\n\t\txArrayStd = np.sqrt(xArrayMeanSquare - xArraySquaredMean)\n\n\t\treturn xArrayMean, xArrayStd\n\n\t@staticmethod\n\tdef apply_normalization(X, normalization_mean, normalization_std):\n\t\tfor d in range(X.shape[0]):\n\t\t\tX[d, :] = (X[d, :] - normalization_mean.transpose()) / normalization_std.transpose()\n\n\t#=======================================================================\n\t#\n\t#=======================================================================\n\n\t@staticmethod\n\tdef convert_longitude(lon):\n\t\tif lon <= 180.0:\n\t\t\treturn lon\n\t\telse:\n\t\t\treturn lon - 360\n\n\t#=======================================================================\n\t#\n\t#=======================================================================\n\n\t@staticmethod\n\tdef get_elapsed_time(searched):\n\t\toutput = subprocess.Popen([\"ps\", \"-eo\", \"pid,etime,args\"], encoding='utf8', stdout=subprocess.PIPE).communicate()[0]\n\t\tresults = []\n\t\tfor line in output.split(\"\\n\"):\n\t\t\tif searched in line:\n\t\t\t\tsplitted = line.split()\n\t\t\t\tif len(splitted) >= 2:\n\t\t\t\t\tpid = int(splitted[0])\n\t\t\t\t\tres = re.findall(\"(([0-9]+)\\\\-)?(([0-9]+):)?([0-9]+):([0-9]+)\", splitted[1])\n\t\t\t\t\tif len(res) > 0:\n\t\t\t\t\t\t_, days, _, hours, minutes, seconds = res[0]\n\t\t\t\t\t\tif days == '':\n\t\t\t\t\t\t\tdays = '0'\n\t\t\t\t\t\tif hours == '':\n\t\t\t\t\t\t\thours = '0'\n\n\t\t\t\t\t\tnbElapsedSeconds = int(seconds) + 60 * int(minutes) + 3600 * int(hours) + 24 * 3600 * int(days)\n\n\t\t\t\t\t\tresults += [(pid, nbElapsedSeconds)]\n\t\treturn results\n","repo_name":"AntoineMeler/Paraglidable","sub_path":"neural_network/inc/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":4166,"program_lang":"python","lang":"en","doc_type":"code","stars":75,"dataset":"github-code","pt":"81"}
+{"seq_id":"74664367946","text":"import streamlit as st\nimport pandas as pd\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nimport graph_func as fun\nimport data_func as dfun\n\nfrom sklearn.preprocessing import LabelEncoder\n\n\nst.title(\"Data Analysis App by Tanmaya Kumar Sahni\")\nst.write(\"Github: https://github.com/tanmaya48/Streamlit_herokuapp\")\nst.write(\"   \")\n\nst.write(\"This is a demo file, you can use any other csv on the web\")\npath = st.text_input('file name',value = \"titanic.csv\")\n\n\n\npages = ['Home','Graphs','Outliers']\n\nloaded = False\n\nif path:\n    try:\n        df = pd.read_csv(path) \n\n        le = LabelEncoder()\n\n        cols = df.columns\n\n        for col in cols:\n            if df[col].dtypes == object:\n\n                c = df[col]\n\n                df[col] = c\n\n\n        loaded = True\n        st.write(\"File has been loaded\")\n\n        v = st.checkbox('View data')\n        if v:\n            st.write(df)\n    except:\n        st.write(\"No such file found\") \n        \n\n\nreps = ['Column Statistics','Distribution Plot','Bar Plot','Correlation Heatmap','Scatter Plot']\n\n\n\nrad =st.sidebar.selectbox(\"Navigation\",pages)\n\n\nif rad == \"Home\":\n    st.write('')\nelif rad == \"Graphs\":    \n\n    if loaded:\n\n        representation = st.selectbox('Representation',reps)\n\n        if representation == 'Column Statistics':\n            fun.col_stats(st,df)\n\n        elif representation == 'Distribution Plot':\n            fun.dist_plot(st,df) \n\n        elif representation == 'Bar Plot':\n            fun.bar_plot(st,df)\n\n\n        elif representation == 'Correlation Heatmap':\n            fun.corr_plot(st,df)\n\n        elif representation == 'Scatter Plot':\n            fun.scat_plot(st,df)\n\nelif rad == \"Outliers\":\n    \n    col = st.selectbox('Column',list(df.columns))\n    impact = st.selectbox('Impact',list(df.columns))\n\n    if df[impact].nunique() < 20:\n        dfun.outlier_analysis(st,df,col,impact)\n\n    else:\n        st.write('Choose a Categorical variable for testing impact over')    \n\n\n\n\n    \n","repo_name":"tanmaya48/Streamlit_herokuapp","sub_path":"app_EDA.py","file_name":"app_EDA.py","file_ext":"py","file_size_in_byte":1973,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"71347026186","text":"from ..models import Playlist, db, environment, SCHEMA\nfrom sqlalchemy.sql import text\nimport random\nfrom random import randint, sample\n\n\nplaylist_names = [\n    \"Groovy Vibes\",\n    \"Chill Out Mix\",\n    \"Summer Jams\",\n    \"Indie Anthems\",\n    \"Throwback Classics\",\n    \"Electro Beats\",\n    \"Acoustic Serenity\",\n    \"Rock Legends\",\n    \"R&B Soulful Hits\",\n    \"Latin Fiesta\",\n    \"Country Roads\",\n    \"Hip-Hop Hype\",\n    \"Alternative Vibes\",\n    \"Reggae Rhythms\",\n    \"Piano Melodies\",\n    \"EDM Party Anthems\",\n    \"Bluesy Tunes\",\n    \"Jazz Fusion\",\n    \"Folk & Americana\",\n    \"Pop Extravaganza\"\n]\n\n\nplaylist_descriptions = [\n    \"Let's get this party started.\",\n    \"This playlist be romantic as heck.  Man I miss my ex\",\n    \"Bubble bath mix.  For after a hard day at the office\",\n    \"I will get in shape this year!  This is my inspiration.\",\n    \"Road trip mix.\",\n    \"I'm angry at the world and I'm never coming out.\",\n    \"Just calm down.  calm.....down\",\n    \"Throwback mix.  For back when everything made sense\",\n    \"I just wanna dance\",\n    \"Meditation music.\"\n]\n\n\n\n\n\n# blank_playlist={\"id\":0, \"name\":'random','cover_image_url':'random','description':'randomtextupto1000','user_id':'random 1 to 3'}\ncounter = 0\ndef seed_playlists():\n    master_playlist=[]\n\n\n    for i in range (1,4):\n        # new_user_id = i\n        rand_idx_list = sample(range(20), 10)\n        counter = 0\n        for idx in rand_idx_list:\n            new_name = playlist_names[idx]\n            new_description = playlist_descriptions[counter]\n            counter += 1\n\n            new_playlist = Playlist(\n                name=new_name,\n                cover_image_url = None,\n                description = new_description,\n                user_id = i\n                )\n\n            master_playlist.append(new_playlist)\n    db.session.add_all(master_playlist)\n    db.session.commit()\n\ndef undo_playlists():\n        if environment == \"production\":\n            db.session.execute(f\"TRUNCATE table {SCHEMA}.playlists RESTART IDENTITY CASCADE;\")\n        else:\n            db.session.execute(text(\"DELETE FROM playlists\"))\n\n        db.session.commit()\n","repo_name":"Mirabordem/Moodify","sub_path":"app/seeds/playlist_seeds.py","file_name":"playlist_seeds.py","file_ext":"py","file_size_in_byte":2127,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}
+{"seq_id":"23882583362","text":"from itertools import combinations\nimport math\n\nfrom utils.utils import load_input\n\n\nTARGET_NUMBER = 2020\n\ndef get_multiple_of_components(values, combination_size=2):\n    for combo in combinations(values, combination_size):\n        if sum(combo) == TARGET_NUMBER:\n            return math.prod(combo)\n    return 0\n\n\ndef part1(values):\n    # halfway_number = math.ceil(TARGET_NUMBER)\n    # for i in range(halfway_number + 1):\n    #     if i in values and (TARGET_NUMBER - i) in values:\n    #         return i * (TARGET_NUMBER - i)\n    # return 0\n    return get_multiple_of_components(values)\n\n\ndef part2(values):\n    return get_multiple_of_components(values, combination_size=3)\n\n\nif __name__ == \"__main__\":\n    parse_line_func = lambda l: int(l.replace(\"\\n\", \"\"))\n\n    # test against example\n    test_values = load_input(path=\"example.txt\", parsing_func=parse_line_func)\n    assert part1(test_values) == 514579\n\n    # test against real data\n    input_values = load_input(parsing_func=parse_line_func)\n    print(\"part 1: {}\".format(part1(input_values)))\n    print(\"part 2: {}\".format(part2(input_values)))\n","repo_name":"r-bex/adventofcode","sub_path":"2020/day1/script.py","file_name":"script.py","file_ext":"py","file_size_in_byte":1104,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"81"}