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"""
Extract forces and extension timeseries data from Excel spreadsheet files (.xls) provided by Michael T. Woodside
REQUIREMENTS:
* xlrd (pure Python Excel spreadsheet (.xls) file reader) - http://www.lexicon.net/sjmachin/xlrd.htm
"""
# =============================================================================================
# IMPORTS
# =============================================================================================
import sys
import bz2
import tempfile
from pathlib import Path
from numpy import zeros, float64
try:
import xlrd # pure Python Excel spreadsheet (.xls) file reader
except ImportError:
print("ERROR: This example requires xlrd, run:", " pip install xlrd", sep="\n")
sys.exit()
# =============================================================================================
# PARAMETERS
# =============================================================================================
# location of original data (.xls files)
original_data_directory = Path("original-data")
# location for where processed data is to be stored
processed_data_directory = Path("processed-data")
# prefixes of datasets to process (filenames are f'({prefix}_data.xls')
datasets = ["20R55_4T"]
# =============================================================================================
# SUBROUTINES
# =============================================================================================
# =============================================================================================
# MAIN
# =============================================================================================
def main():
ORIGINAL_DATA = Path(original_data_directory)
PROCESSED_DATA = Path(processed_data_directory)
# process all datasets
for dataset in datasets:
print(f"Extracting data from dataset '{dataset}'...")
# Extract compressed Excel spreadsheet file and load it with xlrd
with bz2.open(ORIGINAL_DATA / f"{dataset}_data.xls.bz2") as f:
with tempfile.NamedTemporaryFile(suffix=".xls", delete=False) as temp:
temp.write(f.read())
workbook = xlrd.open_workbook(temp.name)
# DEBUG
print(
f"Workbook contains {workbook.nsheets:d} worksheets:", workbook.sheet_names(), sep="\n"
)
# Get the first worksheet.
worksheet = workbook.sheet_by_index(0)
# DEBUG
print(
f"1st worksheet '{worksheet.name}' has {worksheet.ncols:d} columns and {worksheet.nrows:d} rows."
)
# Extract biasing forces.
K = worksheet.ncols - 1
biasing_force_k = zeros([K], float64)
for k in range(K):
biasing_force_k[k] = worksheet.cell_value(rowx=1, colx=1 + k)
print(f"{K:d} biasing forces (in pN):", biasing_force_k, sep="\n")
# Write biasing forces.
filename = PROCESSED_DATA / f"{dataset}.forces"
print(f"Writing biasing forces to '{filename}'...")
with open(filename, "w") as outfile:
for k in range(K):
if k > 0:
outfile.write(" ")
outfile.write(f"{biasing_force_k[k]:.2f}")
# Read trajectories.
T_max = worksheet.nrows - 3
x_kt = zeros([K, T_max])
for k in range(K):
print(f"Reading trajectory {k + 1:d} / {K + 1:d}...")
for t in range(T_max):
x_kt[k, t] = worksheet.cell_value(colx=1 + k, rowx=3 + t)
print(f"Read {K:d} trajectories of {T_max:d} samples each.")
# Write trajectories.
filename = PROCESSED_DATA / f"{dataset}.trajectories"
print(f"Writing trajectories to '{filename}'...")
with open(filename, "w") as outfile:
for t in range(T_max):
for k in range(K):
if k > 0:
outfile.write(" ")
outfile.write(f"{x_kt[k, t]:f}")
outfile.write("\n")
if __name__ == "__main__":
main()
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