world-ecoregion/predict.py

import numpy as np 

from utils import *
from nn import B, compile_b
from draw import draw
import time

def chunker(seq, size):
    return (seq[pos:pos + size] for pos in range(0, len(seq), size))


def predicted_map(path=None):
    year = MAX_YEAR - 1

    df = pd.read_pickle('data.p')

    print('TEMPERATURE MODIFICATION OF {}'.format(change))

    inputs = ['elevation', 'distance_to_water', 'latitude']

    for season in SEASONS:
        inputs += [
            'temp_{}_{}'.format(season, year),
            'precip_{}_{}'.format(season, year)
        ]

    print(inputs)

    # print(inputs)
    frame = df[inputs + ['longitude']]
    # print(frame.head())

    for season in SEASONS:
        frame.loc[:, 'temp_{}_{}'.format(season, year)] += change

    columns = ['latitude', 'longitude', 'biome_num']
    new_data = pd.DataFrame(columns=columns)

    for i, chunk in enumerate(chunker(frame, B.batch_size)):
        if chunk.shape[0] < B.batch_size:
            continue
        input_data = normalize_ndarray(chunk.loc[:, inputs].values)
        out = B.predict(input_data)

        f = pd.DataFrame({
            'longitude': chunk.loc[:, 'longitude'], 
            'latitude': chunk.loc[:, 'latitude'],
            'biome_num': out
        }, columns=columns)
        new_data = new_data.append(f)

    draw(new_data, path=path)

if __name__ == "__main__":
    compile_b()
    predicted_map()