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92 lines
2.3 KiB
92 lines
2.3 KiB
""" Sensospot Data Parser |
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Parsing the numerical output from Sensovations Sensospot image analysis. |
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""" |
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__version__ = "0.6.0" |
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import sys |
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from pathlib import Path |
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import click |
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import pandas |
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from .parser import parse_file, parse_folder # noqa: F401 |
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from .parameters import ExposureInfo # noqa: F401 |
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DEFAULT_OUTPUT_FILENAME = "collected_data.csv" |
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@click.command() |
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@click.argument( |
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"source", |
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type=click.Path( |
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exists=True, |
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file_okay=False, |
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dir_okay=True, |
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readable=True, |
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writable=True, |
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), |
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) |
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@click.option( |
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"-o", |
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"--outfile", |
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default=DEFAULT_OUTPUT_FILENAME, |
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help=( |
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"Output file name, use a dash '-' for stdout, " |
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f"default: '{DEFAULT_OUTPUT_FILENAME}'" |
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), |
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) |
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@click.option( |
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"-q", |
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"--quiet", |
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is_flag=True, |
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default=False, |
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help="Ignore Sanity Check", |
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) |
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@click.option( |
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"-r", |
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"--recurse", |
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is_flag=True, |
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default=False, |
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help="Recurse into folders one level down", |
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) |
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def main(source, outfile, quiet=False, recurse=False): |
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if recurse: |
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_parse_recursive(source, outfile, quiet) |
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else: |
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_parse_one_folder(source, outfile, quiet) |
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def _output(data, folder, outfile): |
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"""output a datafarme to stdout or csv file |
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data: the pandas dataframe |
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folder: the folder to save the file to |
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outfile: the name of the outfile, '-' will output to stdout |
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""" |
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if outfile.strip() == "-": |
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data.to_csv(sys.stdout, sep="\t") |
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else: |
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csv_file = Path(folder) / outfile |
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data.to_csv(csv_file, sep="\t") |
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def _parse_one_folder(source, outfile, quiet): |
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"""parses the data of one folder""" |
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source_path = Path(source) |
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# read the raw data of a folder |
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raw_data = parse_folder(source_path, quiet=quiet) |
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_output(raw_data, source_path, outfile) |
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return raw_data |
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def _parse_recursive(source, outfile, quiet): |
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"""parses all folder one level down and collects the data""" |
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child_outfile = DEFAULT_OUTPUT_FILENAME |
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source_path = Path(source) |
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folders = (i for i in source_path.iterdir() if i.is_dir()) |
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collection = (_parse_one_folder(f, child_outfile, quiet) for f in folders) |
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collected_data = pandas.concat(collection, ignore_index=True).reset_index() |
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_output(collected_data, source_path, outfile)
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