{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"!pip install -q git+https://github.com/mnansary/gsoc-wav2vec2.git","metadata":{"executionInfo":{"elapsed":5454,"status":"ok","timestamp":1657278018808,"user":{"displayName":"kugelblitz 1729","userId":"14824418471120817982"},"user_tz":-360},"id":"2FjtlpKILn8e","outputId":"eb050008-442b-4b8e-f463-7f844e6cc907","papermill":{"duration":14.20055,"end_time":"2022-07-13T11:18:15.565426","exception":false,"start_time":"2022-07-13T11:18:01.364876","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2022-08-23T11:29:46.989210Z","iopub.execute_input":"2022-08-23T11:29:46.989570Z","iopub.status.idle":"2022-08-23T11:30:00.452200Z","shell.execute_reply.started":"2022-08-23T11:29:46.989482Z","shell.execute_reply":"2022-08-23T11:30:00.450742Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Data Access","metadata":{"id":"sr3PVY2aNmx9","papermill":{"duration":0.024399,"end_time":"2022-07-13T11:18:15.617793","exception":false,"start_time":"2022-07-13T11:18:15.593394","status":"completed"},"tags":[]}},{"cell_type":"code","source":"from kaggle_datasets import KaggleDatasets\nGCS_PATH=KaggleDatasets().get_gcs_path(\"tfrecs-for-arijitx\")\nGCS_PATH_TRAIN=KaggleDatasets().get_gcs_path(\"buet-dl-tpu-records-part1\")\nimport os \n#------------------------------\n# change able params\n#------------------------------\nTRAIN_GCS_PATTERNS = [os.path.join(GCS_PATH_TRAIN,\"voted\",\"*/*.tfrecord\")]\n \nEVAL_GCS_PATTERNS = [os.path.join(GCS_PATH,\"eval\",\"*/*.tfrecord\")]\n\nPER_REPLICA_BATCH_SIZE = 16 # this is a safe batch size \nEPOCHS = 10 # change this as needed .. keep the kaggle allowed TPU limit of 9 hours in mind \n\n#------------------------------\n# fixed params while creating the tfrecords\n#------------------------------\nREC_SIZE=256 \nVOCAB =[' ', '', '', '', '', '', '', '', '', \n '', '', '', '', '', '', '', '', \n '', '', '', '', '', '', '', '', \n '', '', '', '', '', '।', 'ঁ', 'ং', 'ঃ', 'অ', 'আ', 'ই', \n 'ঈ', 'উ', 'ঊ', 'ঋ', 'এ', 'ঐ', 'ও', 'ঔ', 'ক', 'খ', 'গ', 'ঘ', 'ঙ', 'চ', 'ছ', 'জ', 'ঝ', 'ঞ', \n 'ট', 'ঠ', 'ড', 'ঢ', 'ণ', 'ত', 'থ', 'দ', 'ধ', 'ন', 'প', 'ফ', 'ব', 'ভ', 'ম', 'য', 'র', 'ল',\n 'শ', 'ষ', 'স', 'হ', '', 'া', 'ি', 'ী', 'ু', 'ূ', 'ৃ', 'ে', 'ৈ', 'ো', 'ৌ', '্', 'ৎ', \n '', 'ড়', 'ঢ়', 'য়', '০', '১', '২', '৩', '৪', '৫', '৬', '৭', '৮', '৯', '', '', \n '\\u200d', '', '', '', '', '']\n\nprint(\"Vocab Len:\",len(VOCAB))\nprint(\"Pad Id:\",VOCAB.index(\"\"))","metadata":{"id":"bB9uvms3Ln8h","papermill":{"duration":0.520515,"end_time":"2022-07-13T11:18:16.214944","exception":false,"start_time":"2022-07-13T11:18:15.694429","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2022-08-23T11:30:00.454943Z","iopub.execute_input":"2022-08-23T11:30:00.455243Z","iopub.status.idle":"2022-08-23T11:30:01.255517Z","shell.execute_reply.started":"2022-08-23T11:30:00.455208Z","shell.execute_reply":"2022-08-23T11:30:01.254584Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Imports and data ","metadata":{"id":"-h_SBtPBSXUT","papermill":{"duration":0.023917,"end_time":"2022-07-13T11:18:16.308703","exception":false,"start_time":"2022-07-13T11:18:16.284786","status":"completed"},"tags":[]}},{"cell_type":"code","source":"#-------------------------------\n# imports\n#-------------------------------\nos.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' \nimport random\nimport tensorflow as tf\nimport transformers\nimport matplotlib.pyplot as plt\nimport numpy as np \nimport pandas as pd\nfrom tqdm.auto import tqdm\nfrom IPython.display import display,Audio\nfrom wav2vec2 import RobustWav2Vec2Config,Wav2Vec2,CTCLoss\ntqdm.pandas()\n\n#--------------------------\n# GCS Paths and tfrecords\n#-------------------------\ntrain_recs=[]\neval_recs =[]\ndef get_tfrecs(gcs_pattern):\n file_paths = tf.io.gfile.glob(gcs_pattern)\n random.shuffle(file_paths)\n print(\"found \",len(file_paths), \"tfrecords\")\n return file_paths\n\nfor gcs in TRAIN_GCS_PATTERNS:\n print(\"Looking into gcs path:\",gcs)\n train_recs+=get_tfrecs(gcs)\nfor gcs in EVAL_GCS_PATTERNS:\n print(gcs)\n eval_recs+=get_tfrecs(gcs)\n\nprint(\"Total Eval-recs:\",len(eval_recs))\nprint(\"Total Train-recs:\",len(train_recs))\n#------------------------------------------------\n# change config\n#------------------------------------------------\nconfig = RobustWav2Vec2Config()\nconfig.pad_id=VOCAB.index(\"\")","metadata":{"executionInfo":{"elapsed":4450,"status":"ok","timestamp":1657278025337,"user":{"displayName":"kugelblitz 1729","userId":"14824418471120817982"},"user_tz":-360},"id":"Z7fTXNgsLn8i","outputId":"cd14876a-6b69-46ff-c901-859d70f457e5","papermill":{"duration":8.607519,"end_time":"2022-07-13T11:18:24.941917","exception":false,"start_time":"2022-07-13T11:18:16.334398","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2022-08-23T11:30:01.256826Z","iopub.execute_input":"2022-08-23T11:30:01.257167Z","iopub.status.idle":"2022-08-23T11:30:09.782378Z","shell.execute_reply.started":"2022-08-23T11:30:01.257131Z","shell.execute_reply":"2022-08-23T11:30:09.781398Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#----------------------------------------------------------\n# Detect hardware, return appropriate distribution strategy\n#----------------------------------------------------------\n# TPU detection. No parameters necessary if TPU_NAME environment variable is set. On Kaggle this is always the case.\ntry:\n tpu = tf.distribute.cluster_resolver.TPUClusterResolver() \n print('Running on TPU ', tpu.master())\nexcept ValueError:\n tpu = None\n\nif tpu:\n tf.config.experimental_connect_to_cluster(tpu)\n tf.tpu.experimental.initialize_tpu_system(tpu)\n strategy = tf.distribute.experimental.TPUStrategy(tpu)\n tf.config.optimizer.set_jit(True)\nelse:\n strategy = tf.distribute.get_strategy() \n # default distribution strategy in Tensorflow. Works on CPU and single GPU.\n\nprint(\"REPLICAS: \", strategy.num_replicas_in_sync)\n\n#-------------------------------------\n# batching , strategy and steps\n#-------------------------------------\nif strategy.num_replicas_in_sync==1:\n BATCH_SIZE = PER_REPLICA_BATCH_SIZE\nelse:\n BATCH_SIZE = PER_REPLICA_BATCH_SIZE*strategy.num_replicas_in_sync\n\n# set \nSTEPS_PER_EPOCH = (len(train_recs)*REC_SIZE)//(BATCH_SIZE)\nEVAL_STEPS = (len(eval_recs)*REC_SIZE)//(2*BATCH_SIZE)\nprint(\"Batch Size:\",BATCH_SIZE)\nprint(\"Steps:\",STEPS_PER_EPOCH)\nprint(\"Eval Steps:\",EVAL_STEPS)","metadata":{"executionInfo":{"elapsed":11626,"status":"ok","timestamp":1657278036958,"user":{"displayName":"kugelblitz 1729","userId":"14824418471120817982"},"user_tz":-360},"id":"aoOTdXZpLn8j","outputId":"e365a2b1-b3bf-4e99-9bb5-7697a70b3e68","papermill":{"duration":6.493472,"end_time":"2022-07-13T11:18:31.506845","exception":false,"start_time":"2022-07-13T11:18:25.013373","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2022-08-23T11:30:09.784153Z","iopub.execute_input":"2022-08-23T11:30:09.784463Z","iopub.status.idle":"2022-08-23T11:30:16.032968Z","shell.execute_reply.started":"2022-08-23T11:30:09.784436Z","shell.execute_reply":"2022-08-23T11:30:16.032015Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"class cfg:\n audio_shape = (246000,) # this is actually fixed for the pretrained weights we are using -- highets audio length=15 secs\n label_shape = (250,) # this is actually fixed for the pretrained weights we are using \n sample_rate = 16000\n shuffle_buffer = 1024\n batch_size = BATCH_SIZE\n vocab_len = len(VOCAB) \n embed_dim = 1024\n ","metadata":{"id":"ydv0NrOtSLvI","papermill":{"duration":0.036038,"end_time":"2022-07-13T11:18:31.615917","exception":false,"start_time":"2022-07-13T11:18:31.579879","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2022-08-23T11:30:16.034419Z","iopub.execute_input":"2022-08-23T11:30:16.034632Z","iopub.status.idle":"2022-08-23T11:30:16.040431Z","shell.execute_reply.started":"2022-08-23T11:30:16.034607Z","shell.execute_reply":"2022-08-23T11:30:16.039049Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#------------------------------\n# parsing tfrecords \n#------------------------------\ndef normalize(x):\n # -> (1, seqlen)\n mean = tf.reduce_mean(x, axis=-1, keepdims=True)\n var = tf.math.reduce_variance(x, axis=-1, keepdims=True)\n return tf.squeeze((x - mean) / tf.sqrt(var + 1e-5))\n\ndef read_raw_audio(audio):\n wave,rate = tf.audio.decode_wav(audio, desired_channels=1, desired_samples=-1)\n return tf.reshape(wave, shape=[-1]) \n \ndef preprocess_example(audio,label):\n with tf.device(\"/CPU:0\"):\n signal = normalize(read_raw_audio(audio))\n label = tf.strings.to_number(tf.strings.split(label), out_type=tf.int32)\n return signal,label\n\ndef data_input_fn(recs): \n '''\n This Function generates data from gcs\n * The parser function should look similiar now because of datasetEDA\n '''\n def _parser(example): \n feature ={ 'audio' : tf.io.FixedLenFeature([],tf.string) ,\n 'label' : tf.io.FixedLenFeature([],tf.string) \n } \n example=tf.io.parse_single_example(example,feature)\n audio,label=preprocess_example(**example)\n return audio,label\n # fixed code (for almost all tfrec training)\n dataset = tf.data.TFRecordDataset(recs)\n dataset = dataset.map(_parser)\n dataset = dataset.shuffle(cfg.shuffle_buffer,reshuffle_each_iteration=True)\n dataset = dataset.repeat()\n dataset = dataset.padded_batch(cfg.batch_size, padded_shapes=(cfg.audio_shape[0],cfg.label_shape[0]), padding_values=(0.0,VOCAB.index(\"\")))\n dataset = dataset.prefetch(tf.data.experimental.AUTOTUNE)\n dataset = dataset.apply(tf.data.experimental.ignore_errors())\n return dataset","metadata":{"id":"r6HBseySLn8k","papermill":{"duration":0.042851,"end_time":"2022-07-13T11:18:31.684622","exception":false,"start_time":"2022-07-13T11:18:31.641771","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2022-08-23T11:30:16.043193Z","iopub.execute_input":"2022-08-23T11:30:16.043544Z","iopub.status.idle":"2022-08-23T11:30:16.057093Z","shell.execute_reply.started":"2022-08-23T11:30:16.043504Z","shell.execute_reply":"2022-08-23T11:30:16.056040Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train_ds=data_input_fn(train_recs)\neval_ds =data_input_fn(eval_recs)","metadata":{"id":"Qt44lD2ZLn8m","papermill":{"duration":0.703473,"end_time":"2022-07-13T11:18:32.412591","exception":false,"start_time":"2022-07-13T11:18:31.709118","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2022-08-23T11:30:16.058654Z","iopub.execute_input":"2022-08-23T11:30:16.059170Z","iopub.status.idle":"2022-08-23T11:30:16.607572Z","shell.execute_reply.started":"2022-08-23T11:30:16.059138Z","shell.execute_reply":"2022-08-23T11:30:16.606602Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Visualize","metadata":{"id":"kr2LdngtSGfy","papermill":{"duration":0.023526,"end_time":"2022-07-13T11:18:32.460498","exception":false,"start_time":"2022-07-13T11:18:32.436972","status":"completed"},"tags":[]}},{"cell_type":"code","source":"#------------------------------\n# view data\n#------------------------------\nfor x,y in eval_ds.take(1):\n signal=x[0].numpy()\n display(Audio(data=signal, rate=cfg.sample_rate))\n label=y[0].numpy()\n sen=\"\".join([VOCAB[int(i)] for i in label])\n print(\"label:\",sen)\n print(\"input shape:\",x.shape)\n print(\"output shape:\",y.shape)","metadata":{"executionInfo":{"elapsed":15132,"status":"ok","timestamp":1657278064182,"user":{"displayName":"kugelblitz 1729","userId":"14824418471120817982"},"user_tz":-360},"id":"_5jRxL1OLn8m","outputId":"c2491bf9-4bbc-40dc-8a17-f6570b9e3fc3","papermill":{"duration":6.663317,"end_time":"2022-07-13T11:18:39.147769","exception":false,"start_time":"2022-07-13T11:18:32.484452","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2022-08-23T11:30:16.609098Z","iopub.execute_input":"2022-08-23T11:30:16.609428Z","iopub.status.idle":"2022-08-23T11:30:22.540403Z","shell.execute_reply.started":"2022-08-23T11:30:16.609385Z","shell.execute_reply":"2022-08-23T11:30:22.538882Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Modeling","metadata":{"id":"RapqzxDCLn8n","papermill":{"duration":0.040983,"end_time":"2022-07-13T11:18:39.23034","exception":false,"start_time":"2022-07-13T11:18:39.189357","status":"completed"},"tags":[]}},{"cell_type":"code","source":"SUFFIX = \":0\"\nMAPPING = (\n (\"layer_norm.weight\", \"layer_norm/gamma\"),\n (\"layer_norm.bias\", \"layer_norm.beta\"),\n (\"weight\", \"kernel\"),\n (\".\", \"/\"),\n)\n\nSPECIAL_MAPPING_WITH_HEAD = {\n \"wav2vec2.encoder.pos_conv_embed.conv.weight_g\": \"wav2vec2/encoder/pos_conv_embed/conv/weight_g:0\",\n \"wav2vec2.encoder.pos_conv_embed.conv.weight_v\": \"wav2vec2/encoder/pos_conv_embed/conv/weight_v:0\",\n}\n\n\n\ndef replace(k):\n \"\"\"\n Converts PyTorch state_dict keys to TensorFlow varible name.\n \"\"\"\n for hf_v, tf_v in MAPPING:\n k = k.replace(hf_v, tf_v)\n return k + SUFFIX\n\n\ndef get_tf_pretrained_model(hf_model_id,tf_model):\n \"\"\"\n Converts HuggingFace PyTorch weights to TensorFlow compatible weights.\n \"\"\"\n hf_model = transformers.Wav2Vec2ForCTC.from_pretrained(hf_model_id)\n\n hf_state_dict = hf_model.state_dict()\n\n tf_variables = tf_model.variables\n tf_variables_dict = {}\n for v in tf_variables:\n tf_variables_dict[v.name] = v\n\n tf_weights = []\n extra_keys = []\n for k in tqdm(hf_state_dict):\n if k in SPECIAL_MAPPING_WITH_HEAD:\n new_k = (SPECIAL_MAPPING_WITH_HEAD[k])\n else:\n new_k = replace(k)\n if new_k not in tf_variables_dict.keys():\n extra_keys.append(k)\n print(f\"SKIPPING {k}\")\n continue\n\n \n array = hf_state_dict[k].numpy()\n\n if k in SPECIAL_MAPPING_WITH_HEAD:\n array = np.transpose(array, axes=(2, 1, 0))\n elif \"kernel\" in new_k:\n array = np.transpose(array)\n\n tf_weights.append((tf_variables_dict[new_k], array))\n\n print(\"EXTRA KEYS:\\n\", extra_keys)\n\n tf.keras.backend.batch_set_value(tf_weights)\n return tf_model","metadata":{"papermill":{"duration":0.061113,"end_time":"2022-07-13T11:18:39.416096","exception":false,"start_time":"2022-07-13T11:18:39.354983","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2022-08-23T11:30:22.542239Z","iopub.execute_input":"2022-08-23T11:30:22.543107Z","iopub.status.idle":"2022-08-23T11:30:22.558715Z","shell.execute_reply.started":"2022-08-23T11:30:22.543066Z","shell.execute_reply":"2022-08-23T11:30:22.557597Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Build the model","metadata":{"papermill":{"duration":0.040709,"end_time":"2022-07-13T11:18:39.498572","exception":false,"start_time":"2022-07-13T11:18:39.457863","status":"completed"},"tags":[]}},{"cell_type":"code","source":"def create_model(cfg):\n inputs = tf.keras.Input(shape=cfg.audio_shape)\n # avoid using spec augmentation\n config.apply_spec_augment=False\n # freeze feature extractor\n states = Wav2Vec2(config)(inputs)\n logits= tf.keras.layers.Dense(cfg.vocab_len,name=\"lm_head\")(states)\n model = tf.keras.Model(inputs=inputs, outputs=logits)\n return model\n","metadata":{"papermill":{"duration":0.052328,"end_time":"2022-07-13T11:18:39.592254","exception":false,"start_time":"2022-07-13T11:18:39.539926","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2022-08-23T11:30:22.562415Z","iopub.execute_input":"2022-08-23T11:30:22.563381Z","iopub.status.idle":"2022-08-23T11:30:22.585107Z","shell.execute_reply.started":"2022-08-23T11:30:22.563316Z","shell.execute_reply":"2022-08-23T11:30:22.584053Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"with strategy.scope():\n model=get_tf_pretrained_model(\"arijitx/wav2vec2-xls-r-300m-bengali\",create_model(cfg))\n # freeze feature extractor\n model.layers[1].freeze_feature_extractor()\n model.load_weights(\"../input/final-model-dlsprint/model_final_semi_50.h5\")\nmodel.summary()","metadata":{"executionInfo":{"elapsed":5343,"status":"ok","timestamp":1657278099188,"user":{"displayName":"kugelblitz 1729","userId":"14824418471120817982"},"user_tz":-360},"id":"tauFmCJRLn8p","outputId":"e240cfe6-d25b-4637-dbfe-92685690822f","papermill":{"duration":101.600259,"end_time":"2022-07-13T11:20:21.322264","exception":false,"start_time":"2022-07-13T11:18:39.722005","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2022-08-23T11:30:22.586561Z","iopub.execute_input":"2022-08-23T11:30:22.586811Z","iopub.status.idle":"2022-08-23T11:32:57.460752Z","shell.execute_reply.started":"2022-08-23T11:30:22.586781Z","shell.execute_reply":"2022-08-23T11:32:57.459484Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":" \n# early stopping\nearly_stopping = tf.keras.callbacks.EarlyStopping(patience=10, \n verbose=1, \n mode = 'auto') \nlr_reducer=tf.keras.callbacks.ReduceLROnPlateau( patience=3)\n\nmodel_save=tf.keras.callbacks.ModelCheckpoint(\"final.h5\",\n save_best_only=True,\n save_weights_only=True,\n verbose=1)\ncallbacks = [model_save]\n\nwith strategy.scope():\n #lr_schedule = tf.keras.optimizers.schedules.ExponentialDecay(initial_learning_rate=0.000001,\n # decay_steps=10000,\n # decay_rate=0.95,)\n lr_schedule = tf.keras.experimental.CosineDecay(initial_learning_rate=0.0000007,\n decay_steps=10000,\n alpha= 0.02)\n\n loss_fn = CTCLoss(config, (PER_REPLICA_BATCH_SIZE,cfg.audio_shape[0]), division_factor=PER_REPLICA_BATCH_SIZE)\n model.compile(optimizer=tf.keras.optimizers.Adam(lr_schedule),\n loss=loss_fn)","metadata":{"id":"ji6rjq9xR7ZA","papermill":{"duration":0.110017,"end_time":"2022-07-13T11:20:21.562484","exception":false,"start_time":"2022-07-13T11:20:21.452467","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2022-08-23T11:32:57.463368Z","iopub.execute_input":"2022-08-23T11:32:57.463837Z","iopub.status.idle":"2022-08-23T11:32:57.514411Z","shell.execute_reply.started":"2022-08-23T11:32:57.463790Z","shell.execute_reply":"2022-08-23T11:32:57.513563Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"history=model.fit(train_ds,\n epochs=30,\n steps_per_epoch=STEPS_PER_EPOCH,\n verbose=1,\n validation_data=eval_ds,\n validation_steps=EVAL_STEPS, \n callbacks=callbacks)","metadata":{"executionInfo":{"elapsed":9777524,"status":"ok","timestamp":1657287889381,"user":{"displayName":"kugelblitz 1729","userId":"14824418471120817982"},"user_tz":-360},"id":"xsZRmDMyLn8q","outputId":"4449c29f-0b2d-4461-f0e5-9ea3f7f762fc","papermill":{"duration":23618.062932,"end_time":"2022-07-13T17:53:59.66916","exception":false,"start_time":"2022-07-13T11:20:21.606228","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2022-08-23T11:32:57.516113Z","iopub.execute_input":"2022-08-23T11:32:57.516438Z","iopub.status.idle":"2022-08-23T16:36:47.893615Z","shell.execute_reply.started":"2022-08-23T11:32:57.516397Z","shell.execute_reply":"2022-08-23T16:36:47.891603Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"curves={}\nfor key in history.history.keys():\n curves[key]=history.history[key]\ncurves=pd.DataFrame(curves)\ncurves.to_csv(f\"history.csv\",index=False)","metadata":{"id":"zzgH25SYLn8q","papermill":{"duration":4.654906,"end_time":"2022-07-13T17:54:24.899969","exception":false,"start_time":"2022-07-13T17:54:20.245063","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2022-08-23T16:36:47.899096Z","iopub.execute_input":"2022-08-23T16:36:47.899469Z","iopub.status.idle":"2022-08-23T16:36:47.953869Z","shell.execute_reply.started":"2022-08-23T16:36:47.899428Z","shell.execute_reply":"2022-08-23T16:36:47.952901Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"curves","metadata":{"executionInfo":{"elapsed":462,"status":"ok","timestamp":1657293740858,"user":{"displayName":"kugelblitz 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