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import numpy as cv |
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import os |
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import plotly.express as px |
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import plotly.figure_factory as ff |
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import datetime |
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import plotly.io as pio |
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pio.kaleido.scope.mathjax = None |
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def distribute_glacier(list_of_samples): |
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list_of_glaciers = {} |
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for glacier in ['COL', 'Mapple', 'Crane', 'Jorum', 'DBE', 'SI', 'JAC']: |
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list_of_glaciers[glacier] = [sample for sample in list_of_samples if glacier in sample] |
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return list_of_glaciers |
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def create_dict(list_of_samples): |
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list_dict = [] |
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for sample in list_of_samples: |
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sample_split = sample.split('_') |
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finish_date = datetime.datetime.fromisoformat(sample_split[1]) + datetime.timedelta(days=50) |
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sample_dict = { |
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'Glacier': sample_split[0], |
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'Start': sample_split[1], |
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'Finish': str(finish_date), |
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'Satellite:': sample_split[2] |
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} |
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list_dict.append(sample_dict) |
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return list_dict |
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if __name__ == '__main__': |
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list_of_train_samples = os.listdir('/home/ho11laqe/PycharmProjects/data_raw/fronts/train') |
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list_of_test_samples = os.listdir('/home/ho11laqe/PycharmProjects/data_raw/fronts/test') |
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list_of_samples = list_of_train_samples + list_of_test_samples |
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list_of_glaciers = distribute_glacier(list_of_samples) |
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list_dict = create_dict(list_of_samples) |
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fig = px.timeline(list_dict, x_start='Start', x_end='Finish', color="Satellite:", y='Glacier', |
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color_discrete_sequence=px.colors.qualitative.G10, template="plotly_white", |
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height=300, category_orders={'Glacier': ['COL', 'Mapple', 'Crane', 'Jorum', 'DBE', 'SI', 'JAC'], |
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'Satellite:': ['ERS', 'RSAT', 'ENVISAT', 'PALSAR', 'TSX', 'TDX', |
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'S1']}) |
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fig.update_layout(legend=dict( |
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orientation="h", |
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yanchor="bottom", |
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y=1.02, |
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xanchor="right", |
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x=1, |
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), |
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margin=dict(l=0, r=0, t=0, b=0), ) |
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fig.update_layout( |
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font=dict(family="Computer Modern", size=14)) |
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fig.write_image("output/dataset_timeline.pdf", format='pdf') |
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