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import numpy as np
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import pandas as pd
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from scipy import interpolate
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global dataWriter
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def convert_file(file_path):
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suffix = file_path.split('.')[-1]
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if suffix not in ['txt', 'csv', 'xy']:
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Warning(f'File {file_path} not supported, skiping...')
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return None
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if suffix == 'txt':
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return txt_to_csv(file_path)
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elif suffix == 'csv':
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return csv_to_csv(file_path)
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elif suffix == 'xy':
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return xy_to_csv(file_path)
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def txt_to_csv(file_path):
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f = open(file_path, 'r')
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rows = []
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for line in f.readlines():
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line = line.strip('\n')
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line = line.replace('\t', ' ')
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line = [x for x in line.split(' ') if x != '']
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if len(line) == 3:
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try:
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line = [line[0], float(line[1])-float(line[2])]
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except ValueError:
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continue
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elif len(line) < 2 or len(line) > 3:
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continue
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rows.append(line)
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f.close()
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outData = upsample(rows)
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return outData
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def csv_to_csv(file_path):
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fromData = pd.read_csv(file_path).values
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outData = upsample(list(fromData))
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return outData
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def xy_to_csv(file_path):
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return txt_to_csv(file_path)
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def upsample(rows):
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if len(rows) == 0:
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Warning('Empty data!')
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return None
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rows.insert(0, ['10', rows[0][1]]) if float(rows[0][0]) > 10 else None
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rows.append(['80', rows[-1][1]]) if float(rows[-1][0]) < 80 else None
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rowsData = np.array(rows, dtype=np.float32)
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f = interpolate.interp1d(rowsData[:, 0], rowsData[:, 1], kind='slinear')
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xnew = np.linspace(10, 80, 4500)
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ynew = f(xnew)
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return ynew |
