pyview/VIEWtemplates/create_measurement_list_template.py

from view.python_core.measurement_list import MeasurementList
from view.python_core.measurement_list.importers import get_importer_class
from view.python_core.flags import FlagsManager
from collections import OrderedDict
import pandas as pd
import logging

logging.basicConfig(level=logging.INFO)

# ------------------- Some parameters about experimental setup, data structure and output file type --------------------
# 3 for single wavelength Till Photonics Measurements
# 4 for two wavelength Till Photonics Measurements
# 20 for Zeiss Confocal Measurements
LE_loadExp = 3

# Mother of all Folders of your dataset
# On Windows, if you copy paths from the file explorer, make sure the string below is always of the form r"......"
STG_MotherOfAllFolders = r""

# path of the "Data" folder in VIEW organization containing the data
# On Windows, if you copy paths from the file explorer, make sure the string below is always of the form r"......"
STG_Datapath = r""

# path of the "Lists" folder in VIEW organization containing the list files
# On Windows, if you copy paths from the file explorer, make sure the string below is always of the form r"......"
STG_OdorInfoPath = r""

# Choose measurement list output extension among ".lst", ".lst.xlsx", ".settings.xlsx"
# VIEW does not support writing .xls list files anymore (nonetheless, it can read them and revise/update them to .xlsx)
measurement_output_extension = ".lst.xlsx"

# ------------------- A dictionary containing default values for metadata.----------------------------------------------
# ------------------- Only metadata included in this dictionary will be written ----------------------------------------
# ----Note that columns of the output measeurement list files will have the same order as below.------------------------

default_values = OrderedDict()

default_values['Measu'] = 0  # unique identifier for each line, corresponds to item in TILL photonics log file

default_values['Label'] = "none"
default_values['Odour'] = 'odor?'  # stimulus name, maybe extracted from label in the function "custom_func" below
default_values['OConc'] = 0  # odor concentration, maybe extracted from label in the function "custom_func" below
default_values['Analyze'] = -1  # whether to analyze in VIEWoff. Default 1

default_values['Cycle'] = 0  # how many ms per frame
default_values['DBB1'] = 'none'  # file name of raw data
default_values['UTC'] = 0  # recording time, extracted from file

default_values['PxSzX'] = '4.6'  # um per pixel, 1.5625 for 50x air objective, measured by Hanna Schnell July 2017 on Till vision system, with a binning of 8
default_values['PxSzY'] = '4.6'  # um per pixel, 1.5625 for 50x air objective, measured by Hanna Schnell July 2017 on Till vision system, with a binning of 8

default_values['Lambda'] = 0  # wavelength of stimulus. In TILL, from .log file, In Zeiss LSM, from .lsm file

# These will be automatically filed for LE_loadExp=4
default_values['dbb2'] = 'none'  # file name of raw data in dual wavelength recordings (FURA)
# To include more columns, uncomment entries below and specify a default value.
# #
# block for first stimulus
# default_values['StimON'] = -1  # stimulus onset, unit: frames, count starts at frame 1.
# default_values['StimOFF'] = -1  # stimulus offset, unit: frames, count starts at frame 1.
# default_values['StimLen'] = 0  # stimulus onset in ms from beginning - alternative to StimON
# default_values['StimONms'] = -1  # stimulus length in ms - alternative to StimOFF
# #
# block for second stimulus
# default_values['Stim2ON'] = 0  # stimulus onset, unit: frames, count starts at frame 1.
# default_values['Stim2OFF'] = 0  # stimulus offset, unit: frames, count starts at frame 1.
# default_values['Stim2Len'] = 0  # stimulus onset in ms from beginning - alternative to StimON
# default_values['Stim2ONms'] = -1  # stimulus length in ms - alternative to StimOFF
# #
# default_values['Age'] = -1
# default_values['Sex'] = 'o'
# default_values['Side'] = 'none'
# default_values['Comment'] = 'none'
# #
# default_values['MTime'] = 0
# default_values['Control'] = 0
# default_values['Pharma'] = 'none'
# default_values['PhTime'] = 0
# default_values['PhConc'] = 0
# default_values['ShiftX'] = 0
# default_values['ShiftY'] = 0
# default_values['StimISI'] = 0
# default_values['setting'] = 'none'
# default_values['dbb3'] = 'none'
# default_values['PosZ'] = 0
# default_values['Countl'] = 0
# default_values['slvFlip'] = 0
# ----------------------------------------------------------------------------------------------------------------------

# ----------------- A function used to modify list entries after automatic parsing of metadata -------------------------
# ----------------- This function indicates what needs to be done for a row --------------------------------------------
# ----------------- The same is internally applied to all rows of the measurement list----------------------------------


def custom_func(list_row: pd.Series, animal_tag: str) -> pd.Series:

    # Examples:
    # list_row["StimON"] = 25
    # list_row["Odour"] = get_odor_from_label(list_row["Label"])
    # if list_row["Measu"]
    # get Odor from another file based on the value of <animal_tag> and list_row["Label"]
    return list_row

# ----------------------------------------------------------------------------------------------------------------------

# ------------------ A function defining the criteria for excluding measurements ---------------------------------------
# ------------------ Currently applicable only for tillvision setups ---------------------------------------------------


def measurement_filter(s):
    # exclude blocks that have in the name "Snapshot" or "Delta"
    # or that do not have any "_"
    name = s["Label"]
    label_not_okay = name.count('Snapshot') > 0 or name.count('Delta') > 0 or name.count('_') < 1
    label_okay = not label_not_okay

    # exclude blocks with less than two frames or no calibration
    atleast_two_frames = False
    if type(s["Timing_ms"]) is str:
        if len(s["Timing_ms"].split(' ')) >= 2 and s["Timing_ms"] != "(No calibration available)":
            atleast_two_frames = True

    return label_okay and atleast_two_frames


# ______________________________________________________________________________________________________________________


# ------------------ names of columns that will be overwritten by old values -------------------------------------------
# ------ these will only be used if a measurement list file with the same name as current output file exists -----------

overwrite_old_values = ["Line", "PxSzX", "PxSzY", "Age", "Sex", "Prefer",
                        "Comment", "Analyze", "Odour", "OConc"]

# ______________________________________________________________________________________________________________________

if __name__ == "__main__":

    # initialize a FlagsManager object with values specified above
    flags = FlagsManager()
    flags.update_flags({"STG_MotherOfAllFolders": STG_MotherOfAllFolders,
                        "STG_OdorInfoPath": STG_OdorInfoPath,
                        "STG_Datapath": STG_Datapath})

    # initialize importer
    importer_class = get_importer_class(LE_loadExp)
    importer = importer_class(default_values)

    # open a dialog for choosing raw data files
    # this returns a dictionary where keys are animal tags (STG_ReportTag) and
    # values are lists of associated raw data files
    animal_tag_raw_data_mapping = importer.ask_for_files(default_dir=flags["STG_Datapath"])
    # make sure some files were chosen
    assert len(animal_tag_raw_data_mapping) > 0, IOError("No files were chosen!")

    for animal_tag, raw_data_files in animal_tag_raw_data_mapping.items():

        # automatically parse metadata
        metadata_df = importer.import_metadata(raw_data_files=raw_data_files,
                                               measurement_filter=measurement_filter)
        # inform user if no usable measurements were found
        if metadata_df.shape[0] == 0:
            logging.info(f"No usable measurements we found among the files "
                         f"chosen for the animal {animal_tag}. Not creating a list file")
        else:
            # create a new Measurement list object from parsed metadata
            measurement_list = MeasurementList.create_from_df(LE_loadExp=LE_loadExp,
                                                              df=metadata_df)

            # apply custom modifications
            measurement_list.update_from_custom_func(custom_func=custom_func, animal_tag=animal_tag)

            # set anaylze to 0 if raw data files don't exist
            flags.update_flags({"STG_ReportTag": animal_tag})
            measurement_list.sanitize(flags=flags,
                                      data_file_extensions=importer.movie_data_extensions)

            # construct the name of the output file
            out_file = f"{flags.get_lst_file_stem()}{measurement_output_extension}"

            # write measurement file to list
            measurement_list.write_to_list_file(lst_fle=out_file, columns2write=default_values.keys(),
                                                overwrite_old_values=overwrite_old_values)