66 lines
1.7 KiB
Python
66 lines
1.7 KiB
Python
import numpy as np
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from utils import *
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from nn import B
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from draw import draw
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import time
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def chunker(seq, size):
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return (seq[pos:pos + size] for pos in range(0, len(seq), size))
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year = MAX_YEAR - 1
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df = pd.read_pickle('data_final.p')
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latitude = np.array(df.index.get_level_values(1))
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df.loc[:, 'latitude'] = pd.Series(latitude, index=df.index)
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B.prepare_dataset(df, dataframe_to_dataset_biomes)
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B.create_model([64, 128])
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B.compile()
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for change in range(-5, 6):
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print('TEMPERATURE MODIFICATION OF {}'.format(change))
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inputs = ['elevation', 'distance_to_water']
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for season in SEASONS:
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inputs += [
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'temp_{}_{}'.format(season, year),
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'precip_{}_{}'.format(season, year)
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]
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inputs += ['latitude']
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frame = df[inputs]
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print(frame.head())
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# for season in SEASONS:
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# frame.loc[:, 'temp_{}_{}'.format(season, year)] += change
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# print(np.average(frame.loc[:, 'temp_winter_2016']))
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# index = []
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# for longitude in range(-179, 179):
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# for latitude in range(-89, 89):
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# index.append((longitude, latitude))
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columns = ['biome_num']
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new_data = pd.DataFrame(columns=columns)
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for i, chunk in enumerate(chunker(frame, B.batch_size)):
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input_data = normalize_ndarray(chunk.values)
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out = B.predict(input_data)
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new_index = np.concatenate((chunk.index.values, new_data.index.values))
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new_data = new_data.reindex(new_index)
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new_data.loc[chunk.index.values, 'biome_num'] = out
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# print(new_data['biome_num'].unique())
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draw(new_data)
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# columns = ['biome_num']
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# indices = ['longitude', 'latitude']
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# new_df = pd.DataFrame(index=indices, columns=columns)
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# new_df =
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