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import pandas as pd
import altair as alt
import numpy as np
alt.data_transformers.disable_max_rows()

from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))

import matplotlib_inline.backend_inline
matplotlib_inline.backend_inline.set_matplotlib_formats('retina')

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res_layers = list(range(1, 66))
upper_shale = [1, 2, 3, 4]
middle_shale =[29, 30, 31, 32, 33, 34]
lower_shale = [62, 63, 64, 65]
shale_layers = upper_shale + middle_shale + lower_shale
sand_layers = [x for x in res_layers if x not in shale_layers]



# create figure

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import plotly.graph_objects as go
import numpy as np
import pandas as pd
df = pd.read_csv('/Users/andreylukyanenko/Downloads/df_numpy_3d_B4_pred.csv')
# Create figure
fig = go.Figure()

# Add traces, one for each slider step
for step in range(1, 126):

Erlemar

import plotly.graph_objects as go
import numpy as np
import pandas as pd
df = pd.read_csv('/Users/andreylukyanenko/Downloads/df_numpy_3d_B4_pred.csv')
# Create figure
fig = go.Figure()

# Add traces, one for each slider step
for step in range(1, 66):
    df_small = df.loc[df['k'] == step]

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import itertools
import random
from typing import List, Tuple

import numpy as np

from interfaces.TaggingOperation import TaggingOperation
from tasks.TaskTypes import TaskType

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from fastai import *

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import os

import hydra
import pytorch_lightning as pl
from omegaconf import DictConfig
from pytorch_lightning.loggers import CometLogger, TensorBoardLogger

from src.lightning_classes.lightning_nbeats import LitM5NBeats
from src.utils.utils import set_seed

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import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline

from nltk.tokenize import TweetTokenizer
import datetime
import lightgbm as lgb
from scipy import stats

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