<jupyter_start><jupyter_text>Read EOPatch files<jupyter_code>import os
import numpy as np
from marshmallow import fields, Schema
from marshmallow.validate import OneOf
from eolearn.core import EOPatch, FeatureType
eop = EOPatch.load("../../eopatches/eopatch_5453")
eop
eop.meta_info["ct_code_list"]
eop.meta_info["ct_eu_hierarchy"]<jupyter_output>/home/elena/anaconda3/envs/aitlas-py37/lib/python3.7/site-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
  and should_run_async(code)<jupyter_text>Each EOPatch is 100x100 pixels, containing timeseries for each pixel for multiple features<jupyter_code>import matplotlib.pyplot as plt
plt.figure(figsize=(10,10))
plt.imshow(eop.data['FEATURES_S2'][12][...,[0,1,2]] * 2.5)<jupyter_output>/home/elena/anaconda3/envs/aitlas-py37/lib/python3.7/site-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
  and should_run_async(code)<jupyter_text>Each EOPatch contains information about the "crop fields" it contains?<jupyter_code>eop.vector_timeless["CROP_TYPE_GDF"]
eop.vector_timeless["CROP_TYPE_GDF"].iloc[0].geometry<jupyter_output>/home/elena/anaconda3/envs/aitlas-py37/lib/python3.7/site-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
  and should_run_async(code)<jupyter_text>Crop types mask<jupyter_code>import matplotlib.pyplot as plt
plt.figure(figsize=(10,10))
plt.imshow(eop.mask_timeless['CROP_TYPE'])<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.09853842527109855, 'fraction_numerical': 0.028288543140028287, 'mean_word_length': 7.556451612903226, 'pattern_counts': {'":': 0, '<': 14, '<?xml version=': 0, '>': 14, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '4', 'dir': 'jupyter-structured-clean-dedup', 'id': '183138', 'license': 'MIT', 'n_tokens_mistral': 762, 'n_tokens_neox': 723, 'n_words': 161, 'path': 'examples/breizhcrops_eopatch_data_analysis.ipynb', 'repo_name': 'biasvariancelabs/aitlas', 'size': '2121'}