analysisDir

Class to manage a list of files loaded from a folder.

Source code in sanpy/analysisDir.py

class analysisDir:
    """Class to manage a list of files loaded from a folder.
    """

    sanpyColumns = _sanpyColumns
    # Dict of dict of column names and bookkeeping info.

    # 20231230, get this from sanpyapp fileloader keys
    # theseFileTypes = [".abf", ".atf", ".sanpy", ".tif"]  # .dat .czi
    # File types to load.

    def __init__(
        self,
        path: str = None,
        filePath : str = None,
        sanPyWindow : "sanpy.interface.SanPyWindow" = None,
        fileLoaderDict : dict = None,
        autoLoad: bool = False,
        folderDepth: Optional[int] = None,
    ):
        """Load and manage a list of files in a folder path.

        Use this as the main pandasModel for file list myTableView.

        TODO: extend to link to folder in cloud (start with box and/or github)

        Parameters
        ----------
        path (str):
            Path to folder
        filePath (str):
            Path to one file
        sanPyWindow (sanpy.interface.SanPyWindow)
            PyQt, used to signal progress on loading
        fileLoaderDict (dict):
            Dict with file extension keys (no dot)
        autoLoad (bool):
            If True then 
        folderDepth (int):
            Folder depth to recurse if loading folder path.

        Notes
        -----
        - Some functions are so self can mimic a pandas dataframe used by pandasModel.
            (shape, loc, loc_setter, iloc, iLoc_setter, columns, append, drop, sort_values, copy)
        - 202312 adding filepath to load just one file
        """

        self._filePath = filePath
        if filePath is not None and os.path.isfile(filePath):
            self._filePath = filePath
            path = os.path.split(filePath)[0]

        logger.info(f'{path} self._filePath:{self._filePath}')

        self.path: str = path

        self._sanPyWindow = sanPyWindow
        # used to signal on building initial db

        self._fileLoaderDict = fileLoaderDict
        # dist with file extension keys

        self.autoLoad = autoLoad
        # not used

        self.folderDepth = folderDepth

        self._isDirty = False
        # keep track if analysis was changed and prompt on quit

        # self._poolDf = None
        """See pool_ functions"""

        # keys are full path to file, if from cloud, key is 'cloud/<filename>'
        # holds bAnalysisObjects
        # needs to be a list so we can have files more than one
        # self.fileList = [] #OrderedDict()

        # TODO: refactor, we are not using the csv parth of this, just the filename
        # name of database file created/loaded from folder path
        self.dbFile = "sanpy_recording_db.csv"

        self._df = self.loadHdf()
        if self._df is None:
            # did not load h5 file
            self._df = self.loadFolder(loadData=autoLoad)
            self._updateLoadedAnalyzed()
        elif self._fileLoaderDict is not None:
            logger.info(f'sync existing df with filePath: {self._filePath}')
            self.syncDfWithPath()

        # if we have a filePath and not in df then add it
        if self._filePath is not None:

            logger.info('self._df')
            print(self._df)

        # self._df = self.loadFolder(loadData=autoLoad)

        #
        self._checkColumns()
        self._updateLoadedAnalyzed()

    @property
    def theseFileTypes(self):
        """Get list of file extensions we will load.
        """
        if self._fileLoaderDict is not None:
            return list(self._fileLoaderDict.keys())

    def findFileRow(self, filename):
        # filename = os.path.split(filePath)[1]
        fileIndexList = self._df.index[self._df['File'] == filename].tolist()
        if fileIndexList:
            rowIdx = fileIndexList[0]
            return rowIdx
        else:
            logger.warning(f"Did not find file {filename} in {self._df['File'].tolist()}")

    def __iter__(self):
        self._iterIdx = -1
        return self

        # self._iterIdx = 0
        # logger.info(f'making iter for bAnalysisDir')
        # print(self._df)
        # x = self._df.loc[self._iterIdx]["_ba"]
        # return x

    def __next__(self):
        self._iterIdx += 1
        if self._iterIdx >= self.numFiles:
            self._iterIdx = -1  # reset to initial value
            raise StopIteration
        else:
            return self._df.loc[self._iterIdx]["_ba"]

        # if self._iterIdx < self.numFiles:
        #     x = self._df.loc[self._iterIdx]["_ba"]
        #     self._iterIdx += 1
        #     return x
        # else:
        #     raise StopIteration

    def __str__(self):
        totalDurSec = self._df["Dur(s)"].sum()
        theStr = f"analysisDir Num Files: {len(self)} Total Dur(s): {totalDurSec}"
        return theStr

    @property
    def isDirty(self):
        return self._isDirty

    def __len__(self):
        return len(self._df)

    @property
    def numFiles(self):
        return len(self._df)

    @property
    def shape(self):
        """
        Can't just return shape of _df, columns (like 'ba') may have been added
        Number of columns is based on self.columns
        """
        # return self._df.shape
        numRows = self._df.shape[0]
        numCols = len(self.columns)
        return (numRows, numCols)

    @property
    def loc(self):
        """Mimic pandas df.loc[]"""
        return self._df.loc

    @loc.setter
    def loc_setter(self, rowIdx, colStr, value):
        self._df.loc[rowIdx, colStr] = value

    @property
    def iloc(self):
        # mimic pandas df.iloc[]
        return self._df.iloc

    @iloc.setter
    def iLoc_setter(self, rowIdx, colIdx, value):
        self._df.iloc[rowIdx, colIdx] = value
        self._isDirty = True

    @property
    def at(self):
        # mimic pandas df.at[]
        return self._df.at

    @at.setter
    def at_setter(self, rowIdx, colStr, value):
        self._df.at[rowIdx, colStr] = value
        self._isDirty = True

    """
    @property
    def iat(self):
        # mimic pandas df.iat[]
        return self._df.iat

    @iat.setter
    def iat_setter(self, rowIdx, colStr, value):
        self._df.iat[rowIdx, colStr] = value
        self._isDirty = True
    """

    @property
    def index(self):
        return self._df.index

    @property
    def columns(self):
        # return list of column names
        return list(self.sanpyColumns.keys())

    def copy(self):
        return self._df.copy()

    def sort_values(self, Ncol, order):
        logger.info(f"sorting by column {self.columns[Ncol]} with order:{order}")
        self._df = self._df.sort_values(self.columns[Ncol], ascending=not order)
        # print(self._df)

    @property
    def columnsDict(self):
        return self.sanpyColumns

    def columnIsEditable(self, colName):
        return self.sanpyColumns[colName]["isEditable"]

    def columnIsCheckBox(self, colName):
        """All bool columns are checkbox

        TODO: problems with using type=bool and isinstance(). Kust using str 'bool'
        """
        type = self.sanpyColumns[colName]["type"]
        # isBool = isinstance(type, bool)
        isBool = type == "bool"
        # logger.info(f'{colName} {type(type)}, type:{type} {isBool}')
        return isBool

    def getDataFrame(self):
        """Get the underlying pandas DataFrame."""
        return self._df

    @property
    def numFiles(self):
        """Get the number of files. same as len()."""
        return len(self._df)

    def copyToClipboard(self):
        """
        TODO: Is this used or is copy to clipboard in pandas model?
        """
        if self.getDataFrame() is not None:
            self.getDataFrame().to_clipboard(sep="\t", index=False)
            logger.info("Copied to clipboard")

    def getPathFromRelPath(self, relPath):
        """Get full path to file from relPath.

        Uses analysisDir folder path.
        """
        if relPath.startswith("/"):
            relPath = relPath[1:]

        fullFilePath = os.path.join(self.path, relPath)

        """
        print('xxx', self.path)
        print('xxx', relPath)
        print('xxx', fullFilePath)
        """

        return fullFilePath

    def saveHdf(self):
        """Save file table and any number of loaded and analyzed bAnalysis.

        Set file table 'uuid' column when we actually save a bAnalysis

        Important: Order matters
            (1) Save bAnalysis first, it updates uuid in file table.
            (2) Save file table with updated uuid
        """
        start = time.time()

        df = self.getDataFrame()

        # the compressed version from the last save
        hdfFile = os.path.splitext(self.dbFile)[0] + ".h5"
        hdfFilePath = pathlib.Path(self.path) / hdfFile

        logger.info(f"Saving db (will be compressed) {hdfFilePath}")

        # save each bAnalysis
        for row in range(len(df)):
            ba = df.at[row, "_ba"]
            if ba is not None:
                didSave = ba._saveHdf_pytables(hdfFilePath)
                if didSave:
                    # we are now saved into h5 file, remember uuid to load
                    # print('xxx SETTING dir uuid')
                    df.at[row, "uuid"] = ba.uuid

        # rebuild (L, A, S) columns
        self._updateLoadedAnalyzed()

        #
        # save file database
        logger.info(f"    saving file db with {len(df)} rows")
        print(df)

        dbKey = os.path.splitext(self.dbFile)[0]
        df = df.drop("_ba", axis=1)  # don't ever save _ba, use it for runtime

        # hdfStore[dbKey] = df  # save it
        df.to_hdf(hdfFilePath, dbKey)

        #
        self._isDirty = False  # if true, prompt to save on quit

        # rebuild the file to remove old changes and reduce size
        # self._rebuildHdf()
        sanpy.h5Util._repackHdf(hdfFilePath)

        # list the keys in the file
        # sanpy.h5Util.listKeys(hdfFilePath)

        stop = time.time()
        logger.info(f"Saving took {round(stop-start,2)} seconds")

    def loadHdf(self, path=None, verbose=False):
        """Load the database key from an h5 file.

        We do not load analy anlysis until user clicks on row, see loadOneAnalysis()
        """
        if path is None:
            path = self.path
        self.path = path

        df = None
        hdfFile = os.path.splitext(self.dbFile)[0] + ".h5"
        hdfPath = pathlib.Path(self.path) / hdfFile
        if not hdfPath.is_file():
            return

        # logger.info(f"Loading existing folder h5 file {hdfPath}")
        # sanpy.h5Util.listKeys(hdfPath)

        _start = time.time()
        dbKey = os.path.splitext(self.dbFile)[0]

        try:
            df = pd.read_hdf(hdfPath, dbKey)
        except KeyError as e:
            # file is corrupt !!!
            logger.error(f'    Load h5 failed, did not find dbKey:"{dbKey}" {e}')

        if df is not None:
            # _ba is for runtime, assign after loading from either (abf or h5)
            df["_ba"] = None

            # fix bug during dev of ba metadata
            # df['Sex'] = 'unknown'

            if verbose:
                logger.info("    loaded db df")

            _stop = time.time()
            # logger.info(f"Loading took {round(_stop-_start,2)} seconds")

            # if we are one file then make sure file is in df

        return df

    def loadOneAnalysis(self, path, uuid=None, allowAutoLoad=True, verbose=False):
        """Load one bAnalysis either from original file path or uuid of h5 file.

        If from h5, we still need to reload sweeps !!!
        They are binary and fast, saving to h5 (in this case) is slow.
        """
        if verbose:
            logger.info(f'path:"{path}" uuid:"{uuid}" allowAutoLoad:"{allowAutoLoad}"')

        hdfPath = self._getHdfFile()

        # grab the fileLoaderDict from our app
        # if it is None then bAnalysis will load this (from disk)
        # if self.mySanPyWindow is not None:
        #     _fileLoaderDict = self.mySanPyWindow.getSanPyApp().getFileLoaderDict()
        # else:
        #     _fileLoaderDict = None

        ba = None
        if uuid is not None and uuid:
            # load from h5
            if verbose:
                logger.info(f"    Retreiving uuid from hdf file {uuid}")

            # load from abf
            ba = sanpy.bAnalysis(path, fileLoaderDict=self._fileLoaderDict, verbose=verbose)

            # load analysis from h5 file, will fail if uuid is not in file
            ba._loadHdf_pytables(hdfPath, uuid)

        if allowAutoLoad and ba is None:
            # load from path
            ba = sanpy.bAnalysis(path, fileLoaderDict=self._fileLoaderDict, verbose=verbose)
            if verbose:
                logger.info(f"    Loaded ba from path {path} and now ba:{ba}")
        #
        return ba

    def _getHdfFile(self):
        hdfFile = os.path.splitext(self.dbFile)[0] + ".h5"
        hdfPath = os.path.join(self.path, hdfFile)
        return hdfPath

    def _deleteFromHdf(self, uuid):
        """Delete uuid from h5 file.

        Each bAnalysis detection get a unique uuid.
        """
        if uuid is None or not uuid:
            return
        logger.info(f"deleting from h5 file uuid:{uuid}")

        _hdfFile = os.path.splitext(self.dbFile)[0] + ".h5"
        hdfPath = pathlib.Path(self.path) / _hdfFile

        # tmpHdfPath = self._getTmpHdfFile()

        removed = False
        with pd.HDFStore(hdfPath) as hdfStore:
            try:
                hdfStore.remove(uuid)
                removed = True
            except KeyError:
                logger.error(f"Did not find uuid {uuid} in h5 file {hdfPath}")

        #
        if removed:
            # will rebuild on next save
            # self._rebuildHdf()
            self._updateLoadedAnalyzed()
            self._isDirty = True  # if true, prompt to save on quit

    def loadFolder(self, path=None, loadData=False) -> pd.DataFrame:
        """Parse a folder and load all (abf, csv, ...).

        Only called if no h5 file.

        TODO: get rid of loading database from .csv (it is replaced by .h5 file)
        TODO: extend the logic to load from cloud (after we were instantiated)
        """
        logger.info("Loading folder from scratch (no h5 file)")

        start = time.time()
        if path is None:
            path = self.path
        self.path = path

        df = pd.DataFrame(columns=self.sanpyColumns.keys())
        df = self._setColumnType(df)

        # get list of all abf/csv/tif files
        fileList = self.getFileList(path)
        _numFilesToLoad = len(fileList)
        start = time.time()
        # build new db dataframe
        listOfDict = []
        for rowIdx, fullFilePath in enumerate(fileList):

            self.signalWindow(
                f'Loading file {rowIdx+1} of {_numFilesToLoad} "{fullFilePath}"'
            )

            # rowDict is what we are showing in the file table
            # abb debug vue, set loadData=True
            # loads bAnalysis
            ba, rowDict = self.getFileRow(fullFilePath, loadData=loadData)

            if rowDict is None:
                logger.warning(f'error loading file {fullFilePath}')
                continue

            # as we parse the folder, don't load ALL files (will run out of memory)
            if loadData:
                rowDict["_ba"] = ba
            else:
                rowDict["_ba"] = None  # ba

            # do not assign uuid until bAnalysis is saved in h5 file
            # rowDict['uuid'] = ''

            # logger.info(f'    row:{rowIdx} relPath:{relPath} fullFilePath:{fullFilePath}')

            listOfDict.append(rowDict)

        stop = time.time()
        logger.info(f"Loading {len(listOfDict)} files took {round(stop-start,3)} seconds.")

        df = pd.DataFrame(listOfDict)
        df = self._setColumnType(df)

        return df

    def _checkColumns(self):
        """Check columns in loaded vs sanpyColumns (and vica versa).
        """
        if self._df is None:
            return

        verbose = True
        loadedColumns = self._df.columns
        for col in loadedColumns:
            if col not in self.sanpyColumns.keys():
                # loaded has unexpected column, leave it
                if verbose:
                    logger.info(
                        f'did not find loaded col: "{col}" in sanpyColumns.keys() ... ignore it'
                    )
        for col in self.sanpyColumns.keys():
            if not col in loadedColumns:
                # loaded is missing expected, add it
                logger.info(
                    f'did not find sanpyColumns.keys() col: "{col}" in loadedColumns ... adding col'
                )
                self._df[col] = ""

    def _updateLoadedAnalyzed(self, theRowIdx=None):
        """Refresh Loaded (L) and Analyzed (A) columns.

        Arguments:
            theRowIdx (int): Update just one row

        TODO: For kymograph, add rows (left, top, right, bottom) and update
        """
        if self._df is None:
            return
        for rowIdx in range(len(self._df)):
            if theRowIdx is not None and theRowIdx != rowIdx:
                continue

            ba = self._df.loc[rowIdx, "_ba"]  # Can be None

            # uuid = self._df.at[rowIdx, 'uuid']
            #
            # loaded
            if self.isLoaded(rowIdx):
                theChar = "\u2022"  # FILLED BULLET
            # elif uuid:
            #    #theChar = '\u25CB'  # open circle
            #    theChar = '\u25e6'  # white bullet
            else:
                theChar = ""
            # self._df.iloc[rowIdx, loadedCol] = theChar
            self._df.loc[rowIdx, "L"] = theChar
            #
            # analyzed
            if self.isAnalyzed(rowIdx):
                theChar = "\u2022"  # FILLED BULLET
                self._df.loc[rowIdx, "N"] = ba.numSpikes
                _numErrors = ba.numErrors
                if _numErrors is None:
                    _numErrors = ''
                # logger.warning(f'setting E to _numErrors {_numErrors}')
                self._df.loc[rowIdx, "E"] = _numErrors
            # elif uuid:
            #    #theChar = '\u25CB'
            #    theChar = '\u25e6'  # white bullet
            else:
                theChar = ""
                self._df.loc[rowIdx, "A"] = ""
            # self._df.iloc[rowIdx, analyzedCol] = theChar
            self._df.loc[rowIdx, "A"] = theChar
            #
            # saved
            if self.isSaved(rowIdx):
                theChar = "\u2022"  # FILLED BULLET
            else:
                theChar = ""
            # self._df.iloc[rowIdx, savedCol] = theChar
            self._df.loc[rowIdx, "S"] = theChar
            #
            # start(s) and stop(s) from ba detectionDict
            if self.isAnalyzed(rowIdx):
                # set table to values we just detected with
                startSec = ba.getDetectionDict()["startSeconds"]
                stopSec = ba.getDetectionDict()["stopSeconds"]
                self._df.loc[rowIdx, "Start(s)"] = startSec
                self._df.loc[rowIdx, "Stop(s)"] = stopSec

                dvdtThreshold = ba.getDetectionDict()["dvdtThreshold"]
                mvThreshold = ba.getDetectionDict()["mvThreshold"]
                self._df.loc[rowIdx, "dvdtThreshold"] = dvdtThreshold
                self._df.loc[rowIdx, "mvThreshold"] = mvThreshold

                #
                # TODO: remove start of ba._path that corresponds to our current folder path
                # will allow our save db to be modular

                # relPth should usually be filled in ???
                """
                relPath = self.getPathFromRelPath(ba._path)
                self._df.loc[rowIdx, 'relPath'] = relPath
                """

                # logger.info('maybe put back in')
                # print(f'    self._df.loc[rowIdx, "relPath"] is "{self._df.loc[rowIdx, "relPath"]}"')

            # aug 2023, update meta data columns
            if ba is not None:
                for k,v in ba.metaData.items():
                    self._df.loc[rowIdx, k] = v

            # kymograph interface
            # 20230602, don't show rect in interface
            # if ba is not None and ba.fileLoader.isKymograph():
            #     kRect = ba.fileLoader.getKymographRect()

            #     # print(kRect)
            #     # sys.exit(1)

            #     if kRect is None:
            #         logger.error(f"Got None kymograph rect")
            #     else:
            #         self._df.loc[rowIdx, "kLeft"] = kRect[0]
            #         self._df.loc[rowIdx, "kTop"] = kRect[1]
            #         self._df.loc[rowIdx, "kRight"] = kRect[2]
            #         self._df.loc[rowIdx, "kBottom"] = kRect[3]
            #
            # TODO: remove start of ba._path that corresponds to our current folder path
            # will allow our save db to be modular
            # self._df.loc[rowIdx, 'path'] = ba._path

    """
    def setCellValue(self, rowIdx, colStr, value):
        self._df.loc[rowIdx, colStr] = value
    """

    def isLoaded(self, rowIdx):
        isLoaded = self._df.loc[rowIdx, "_ba"] is not None
        return isLoaded

    def isAnalyzed(self, rowIdx):
        isAnalyzed = False
        ba = self._df.loc[rowIdx, "_ba"]
        # print('isAnalyzed()', rowIdx, ba)
        # if ba is not None:
        # print('qqq', rowIdx, ba, type(ba))
        # sanpy.bAnalysis_.bAnalysis
        # if isinstance(ba, sanpy.bAnalysis):
        if ba is not None:
            try:
                isAnalyzed = ba.isAnalyzed()
            except(AttributeError) as e:
                logger.error(f'rowIdx {rowIdx} ba is "{ba}" but expecting bAnalysis_')
                logger.error('self._df:')
                print(self._df)
                return False
        return isAnalyzed

    def analysisIsDirty(self, rowIdx):
        """Analysis is dirty when there has been detection but not saved to h5."""
        isDirty = False
        ba = self._df.loc[rowIdx, "_ba"]
        if isinstance(ba, sanpy.bAnalysis):
            isDirty = ba.isDirty()
        return isDirty

    def hasDirty(self):
        """Return true if any bAnalysis in list has been analyzed but not saved (e.g. is dirty)"""
        haveDirty = False
        numRows = len(self._df)
        for rowIdx in range(numRows):
            if self.analysisIsDirty(rowIdx):
                haveDirty = True

        return haveDirty

    def isSaved(self, rowIdx):
        uuid = self._df.at[rowIdx, "uuid"]
        return len(uuid) > 0

    def getAnalysis(self, rowIdx, allowAutoLoad=True, verbose=False) -> sanpy.bAnalysis:
        """Get bAnalysis object, will load if necc.

        Args:
            rowIdx (int): Row index from table, corresponds to row in self._df
            allowAutoLoad (bool)
        Return:
            bAnalysis
        """
        file = self._df.loc[rowIdx, "File"]
        ba = self._df.loc[rowIdx, "_ba"]
        uuid = self._df.loc[rowIdx, "uuid"]  # if we have a uuid bAnalysis is saved in h5f
        # filePath = os.path.join(self.path, file)
        # logger.info(f'Found _ba in file db with ba:"{ba}" {type(ba)}')
        # logger.info(f'rowIdx: {rowIdx} ba:{ba}')

        if ba is None or ba == "":
            # logger.info('did not find _ba ... loading from abf file ...')
            # working on kymograph
            #                 relPath = self.getPathFromRelPath(ba._path)
            relPath = self._df.loc[rowIdx, "relPath"]
            filePath = self.getPathFromRelPath(relPath)

            ba = self.loadOneAnalysis(
                filePath, uuid, allowAutoLoad=allowAutoLoad, verbose=verbose
            )
            # load
            """
            logger.info(f'Loading bAnalysis from row {rowIdx} "{filePath}"')
            ba = sanpy.bAnalysis(filePath)
            """
            if ba is None:
                logger.warning(
                    f'Did not load row {rowIdx} path: "{filePath}". Analysis was probably not saved'
                )
            else:
                self._df.at[rowIdx, "_ba"] = ba
                # does not get a uuid until save into h5
                if uuid:
                    # there was an original uuid (in table), means we are saved into h5
                    self._df.at[rowIdx, "uuid"] = uuid
                    if uuid != ba.uuid:
                        logger.error(
                            "Loaded uuid does not match existing in file table"
                        )
                        logger.error(f"  Loaded {ba.uuid}")
                        logger.error(f"  Existing {uuid}")

                # kymograph, set ba rect from table
                # if ba is not None and ba.fileLoader.isKymograph():
                #     left = self._df.loc[rowIdx, "kLeft"]
                #     top = self._df.loc[rowIdx, "kTop"]
                #     right = self._df.loc[rowIdx, "kRight"]
                #     bottom = self._df.loc[rowIdx, "kBottom"]

                #     # on first load, these will be empty
                #     # grab rect from ba (in _updateLoadedAnalyzed())
                #     if left == "" or top == "" or right == "" or bottom == "":
                #         pass
                #     else:
                #         theRect = [left, top, right, bottom]
                #         logger.info(f"  theRect:{theRect}")
                #         ba.fileLoader._updateTifRoi(theRect)

                #
                # update stats of table load/analyzed columns
                self._updateLoadedAnalyzed()

        return ba

    def _setColumnType(self, df):
        """Needs to be called every time a df is created.
        Ensures proper type of columns following sanpyColumns[key]['type']
        """
        # print('columns are:', df.columns)
        for col in df.columns:
            # when loading from csv, 'col' may not be in sanpyColumns
            if not col in self.sanpyColumns:
                logger.warning(f'Column "{col}" is not in sanpyColumns -->> ignoring')
                continue
            colType = self.sanpyColumns[col]["type"]
            # print(f'  _setColumnType() for "{col}" is type "{colType}"')
            # print(f'    df[col]:', 'len:', len(df[col]))
            # print(df[col])
            if colType == str:
                df[col] = df[col].replace(np.nan, "", regex=True)
                df[col] = df[col].astype(str)
            elif colType == int:
                pass
                # print('!!! df[col]:', df[col])
                # df[col] = df[col].astype(int)
            elif colType == float:
                # error if ''
                df[col] = df[col].astype(float)
            elif colType == bool:
                df[col] = df[col].astype(bool)
            else:
                logger.warning(f'Did not parse col "{col}" with type "{colType}"')
        #
        return df

    def getFileRow(self, path, loadData=False):
        """Get dict representing one file (row in table). Loads bAnalysis to get headers.

        On load error of proper file type (abf, csv), ba.loadError==True

        Args:
            path (Str): Full path to file.
            #rowIdx (int): Optional row index to assign in column 'Idx'

        Return:
            (tuple): tuple containing:

            - ba (bAnalysis): [sanpy.bAnalysis](/api/bAnalysis).
            - rowDict (dict): On success, otherwise None.
                    fails when path does not lead to valid bAnalysis file.
        """
        if not os.path.isfile(path):
            logger.warning(f'Did not find file "{path}"')
            return None, None
        fileType = os.path.splitext(path)[1]
        # if fileType:
        #     fileType = fileType[1:]  # [1:] to strip period
        if fileType not in self.theseFileTypes:  
            logger.warning(f'Did not load file type "{fileType}"')
            return None, None

        # grab the fileLoaderDict from our app
        # if it is None then bAnalysis will load this (from disk)
        # if self.mySanPyWindow is not None:
        #     _fileLoaderDict = self.mySanPyWindow.getSanPyApp().getFileLoaderDict()
        # else:
        #     _fileLoaderDict = None

        # load bAnalysis
        # logger.info(f'Loading bAnalysis "{path}"')
        # loadData is false, load header
        ba = sanpy.bAnalysis(path,
                             loadData=loadData,
                             fileLoaderDict=self._fileLoaderDict)

        if ba.loadError:
            logger.error(f'Error loading bAnalysis file "{path}"')
            # return None, None

        # not sufficient to default everything to empty str ''
        # sanpyColumns can only have type in ('float', 'str')
        rowDict = dict.fromkeys(self.sanpyColumns.keys(), "")
        for k in rowDict.keys():
            if self.sanpyColumns[k]["type"] == str:
                rowDict[k] = ""
            elif self.sanpyColumns[k]["type"] == float:
                rowDict[k] = np.nan

        # if rowIdx is not None:
        #    rowDict['Idx'] = rowIdx

        """
        if ba.loadError:
            rowDict['I'] = 0
        else:
            rowDict['I'] = 2 # need 2 because checkbox value is in (0,2)
        """

        if ba.loadError:
            return None, None

        rowDict["File"] = ba.fileLoader.filename  # os.path.split(ba.path)[1]
        rowDict["Dur(s)"] = ba.fileLoader.recordingDur

        rowDict["Channels"] = ba.fileLoader.numChannels  # Theanne

        rowDict["Sweeps"] = ba.fileLoader.numSweeps

        # TODO: here, we do not get an epoch table until the file is loaded !!!
        rowDict["Epochs"] = ba.fileLoader.numEpochs  # Theanne, data has to be loaded

        rowDict["kHz"] = ba.fileLoader.recordingFrequency
        rowDict["Mode"] = ba.fileLoader.recordingMode.value

        # rowDict['dvdtThreshold'] = 20
        # rowDict['mvThreshold'] = -20
        if ba.isAnalyzed():
            dDict = ba.getDetectionDict()
            # rowDict['I'] = dDict.getValue('include')
            rowDict["dvdtThreshold"] = dDict.getValue("dvdtThreshold")
            rowDict["mvThreshold"] = dDict.getValue("mvThreshold")
            rowDict["Start(s)"] = dDict.getValue("startSeconds")
            rowDict["Stop(s)"] = dDict.getValue("stopSeconds")

        # add parent1, parent2, parent3
        _path, _file = os.path.split(path)
        _path, _parent1 = os.path.split(_path)
        _path, _parent2 = os.path.split(_path)
        _path, _parent3 = os.path.split(_path)
        rowDict['parent1'] = _parent1
        rowDict['parent2'] = _parent2
        rowDict['parent3'] = _parent3

        # aug 2023,  adding bAnalysis metadata columns
        for k,v in ba.metaData.items():
            rowDict[k] = v

        # remove the path to the folder we have loaded
        relPath = path.replace(self.path, "")

        # logger.info(f'xxx self.path: "{self.path}"')
        # logger.info(f'xxx path: "{path}"')
        # logger.info(f'xxx relPath: "{relPath}"')

        if relPath.startswith("/"):
            # so we can use os.path.join()
            relPath = relPath[1:]
        # added 20230505 working with johnson in 1313 to fix windows bug ???
        if relPath.startswith("\\"):
            # so we can use os.path.join()
            relPath = relPath[1:]

        rowDict["relPath"] = relPath

        #logger.info(f'2) xxx relPath: "{relPath}"')
        # logger.info('qqq')
        # print(rowDict)

        return ba, rowDict

    def getFileList(self,
                    path: str = None,
                    santanaTif=False
                    ) -> List[str]:
        """Get file paths from path.

        Uses self.theseFileTypes

        """

        # to open just one file
        # if forceFolder:
        #     # we are forcing reload of an entire folder
        #     self._filePath = None

        if self._filePath is not None:
            logger.info(f'returning one file {self._filePath}')
            return [self._filePath]

        if path is None:
            path = self.path

        fileList = getFileList(path, self.theseFileTypes, self.folderDepth)
        if santanaTif:
            fileList = stripSantanaTif(fileList)
        return fileList

        logger.warning("Remember: MODIFIED TO LOAD TIF FILES IN SUBFOLDERS")
        count = 1
        tmpFileList = []
        folderDepth = self.folderDepth  # if none then all depths
        excludeFolders = ["analysis", "hide"]
        for root, subdirs, files in os.walk(path):
            subdirs[:] = [d for d in subdirs if d not in excludeFolders]

            print(f'count:{count} folderDepth:{folderDepth}')
            print('  root:', root)
            print('  subdirs:', subdirs)
            print('  files:', files)

            # strip out folders that start with __
            # _parentFolder = os.path.split(root)[1]
            # print('root:', root)
            # print('  parentFolder:', _parentFolder)
            # if _parentFolder.startswith('__'):
            if "__" in root:
                logger.info(f"SKIPPING based on path root:{root}")
                continue

            if os.path.split(root)[1] == "analysis":
                # don't load from analysis/ folder, we save analysis there
                continue

            # if os.path.split(root)[1] == 'hide':
            #     # special case/convention, don't load from 'hide' folders
            #     continue

            for file in files:
                # TODO (cudmore) parse all our fileLoader(s) for a list
                _, _ext = os.path.splitext(file)
                if _ext in self.theseFileTypes:
                    oneFile = os.path.join(root, file)
                    tmpFileList.append(oneFile)

            count += 1
            if folderDepth is not None and count > folderDepth:
                break

        fileList = []
        for file in sorted(tmpFileList):
            if file.startswith("."):
                continue
            # ignore our database file
            if file == self.dbFile:
                continue

            # tmpExt is like .abf, .csv, etc
            tmpFileName, tmpExt = os.path.splitext(file)
            if tmpExt in self.theseFileTypes:
                # if getFullPath:
                #     #file = os.path.join(path, file)
                #     file = pathlib.Path(path) / file
                #     file = str(file)  # return List[str] NOT List[PosixPath]
                fileList.append(file)
        #
        logger.info(f"found {len(fileList)} files ...")
        return fileList

    def getRowDict(self, rowIdx):
        """
        Return a dict with selected row as dict (includes detection parameters).

        Important to return a copy as our '_ba' is a pointer to bAnalysis.

        Returns:
            theRet (dict): Be sure to make a deep copy of ['_ba'] if neccessary.
        """
        theRet = {}
        # use columns in main sanpyColumns, not in df
        # for colStr in self.columns:
        for colStr in self._df.columns:
            # theRet[colStr] = self._df.loc[rowIdx, colStr]
            theRet[colStr] = self._df.loc[rowIdx, colStr]
        # theRet['_ba'] = theRet['_ba'].copy()
        return theRet

    def appendRow(self, rowDict=None, ba=None):
        """Append an empty row."""

        # logger.info('')
        # print('    rowDict:', rowDict)
        # print('    ba:', ba)

        rowSeries = pd.Series()
        if rowDict is not None:
            # rowSeries = pd.Series(rowDict)
            rowSeries = pd.DataFrame([rowDict])

            # self._data.iloc[row] = rowSeries
            # self._data = self._data.reset_index(drop=True)

        newRowIdx = len(self._df)  # append this row

        df = self._df
        # logger.warning(f"need to replace append with concat")
        #df = df.append(rowSeries, ignore_index=True)

        logger.info('concat this rowSeries')
        print(rowSeries)
        print('to this df')
        print(df)

        df = pd.concat([df, rowSeries], axis=0, ignore_index=True)

        # df = pd.concat([df,rowSeries], ignore_index=True, axis=1)
        df = df.reset_index(drop=True)

        if ba is not None:
            df.loc[newRowIdx, "_ba"] = ba

        print('')
        logger.info('=== after concat')
        print(df)

        #
        self._df = df

    def unloadRow(self, rowIdx):
        self._df.loc[rowIdx, "_ba"] = None
        self._updateLoadedAnalyzed()

    def removeRowFromDatabase(self, rowIdx):
        # delete from h5 file
        uuid = self._df.at[rowIdx, "uuid"]
        self._deleteFromHdf(uuid)

        # clear uuid
        self._df.at[rowIdx, "uuid"] = ""

        self._updateLoadedAnalyzed()

    def deleteRow(self, rowIdx):
        df = self._df

        # delete from h5 file
        uuid = df.at[rowIdx, "uuid"]
        self._deleteFromHdf(uuid)

        # delete from df/model
        df = df.drop([rowIdx])
        df = df.reset_index(drop=True)
        self._df = df

        self._updateLoadedAnalyzed()

    def _old_duplicateRow(self, rowIdx):
        """Depreciated, Was used to have different conditions within a recording,
        this is now handled by condiiton column.
        """
        # duplicate rowIdx
        newIdx = rowIdx + 0.5

        rowDict = self.getRowDict(rowIdx)

        # CRITICAL: Need to make a deep copy of the _ba pointer to bAnalysis object
        logger.info(f"copying {type(rowDict['_ba'])} {rowDict['_ba']}")
        baNew = copy.deepcopy(rowDict["_ba"])

        # copy of bAnalysis needs a new uuid
        new_uuid = (
            sanpy._util.getNewUuid()
        )  # 't' + str(uuid.uuid4())   #.replace('-', '_')
        logger.info(f"assigning new uuid {new_uuid} to {baNew}")

        if baNew.uuid == new_uuid:
            logger.error("!!!!!!!!!!!!!!!!!!!!!!!!!CRITICAL, new uuid is same as old")

        baNew.uuid = new_uuid

        rowDict["_ba"] = baNew
        rowDict["uuid"] = baNew.uuid  # new row can never have same uuid as old

        dfRow = pd.DataFrame(rowDict, index=[newIdx])

        df = self._df
        df = df.append(dfRow, ignore_index=True)
        df = df.sort_values(by=["File"], axis="index", ascending=True, inplace=False)
        df = df.reset_index(drop=True)
        self._df = df

        self._updateLoadedAnalyzed()

    def syncDfWithPath(self):
        """Sync path with existing df. Used to detect new/removed files.

        If we currently have just one file (self._filePath) we will trash it and load a folder

        Notes
        -----
        20231230, trying to use this to open a one file window with an exiting h5 file.
        """

        pathFileList = self.getFileList()

        # our currently loaded files
        dfFileList = self._df["File"].tolist()

        logger.info(f'dfFileList: {dfFileList}')
        # print('    === pathFileList (on drive):')
        # print('    ', pathFileList)
        # print('    === dfFileList (in table):')
        # print('    ', dfFileList)

        addedToDf = False

        # look for files in path not in df
        for pathFile in pathFileList:
            fileName = os.path.split(pathFile)[1]
            if fileName not in dfFileList:
                logger.info(f'   Found file in path "{fileName}" not in df')

                # load bAnalysis and get df column values
                addedToDf = True

                ba, rowDict = self.getFileRow(pathFile)  # loads bAnalysis

                if rowDict is not None:
                    # listOfDict.append(rowDict)

                    # TODO: get this into getFileROw()
                    # logger.warning("bug 20220718, not sure we need this ???")
                    # print(rowDict)

                    # rowDict['relPath'] = pathFile
                    rowDict["_ba"] = None

                    self.appendRow(rowDict=rowDict, ba=None)

        # look for files in df not in path
        # for dfFile in dfFileList:
        #     if not dfFile in pathFileList:
        #         logger.info(f'Found file in df "{dfFile}" not in path')

        if addedToDf:
            df = self._df
            df = df.sort_values(
                by=["File"], axis="index", ascending=True, inplace=False
            )
            df = df.reset_index(drop=True)
            self._df = df

        self._updateLoadedAnalyzed()

    def pool_build(self, uniqueColumn=None, allowAutoLoad=False, includeNo=True, verbose=False):
        """Build one df with all analysis. Use this in plot tool plugin.

        Parameters
        ----------
        uniqueColumn : str
            Name of column to prepend to File column to make a unique name.
            Use 'parant2' for Kymograph tif files exported from Olympus.
        includeNo : boolean
            if True then include files with metadata 'Include' of no.
        """
        if verbose:
            logger.info("")

        masterDf = None

        # for row in range(self.numFiles):
        for rowIdx, rowDict in self._df.iterrows():
            if (not includeNo) and (rowDict['Include'] == 'no'):
                if verbose:
                    logger.info(f'  rowIdx:{rowIdx} Include is "no"')
                continue

            ba = self.getAnalysis(rowIdx, allowAutoLoad=allowAutoLoad)
            if ba is None:
                continue

            if not ba.isAnalyzed():
                if verbose:
                    logger.info(f"  rowIdx:{rowIdx} not analyzed")
                continue

            oneDf = ba.asDataFrame(regenerateAnalysisDataFrame=True)

            if oneDf is not None:

                self.signalWindow(f'Adding "{ba.fileLoader.filename}"', verbose=verbose)

                oneDf["File Number"] = int(rowIdx)

                # 20240114
                oneDf['File Path'] = ba.fileLoader.filepath

                uniqueName = os.path.splitext(ba.fileLoader.filename)[0]
                if uniqueColumn is not None:
                    uniqueName = rowDict[uniqueColumn] + '-' + uniqueName
                oneDf["Unique Name"] = uniqueName

                # logger.warning('TEMPORARY WHILE WORKING ON KYM POOLING !!!!!!!!!!!!!!!!!!!!!!!!!')
                # logger.warning('randomly assigning sex to male, female, unknown')
                # sexList = ['male', 'female', 'unknown']
                # oneDf['Sex'] = random.choice(sexList)
                oneDf_thresholdVal = oneDf['thresholdVal'].to_numpy()  # take off potential
                oneDf_thresholdVal_mean = np.nanmean(oneDf_thresholdVal)
                if oneDf_thresholdVal_mean > 0.5685522031727147:  # mean of all thresholdVal
                    # print(f'oneDf_thresholdVal_mean:{oneDf_thresholdVal_mean} male')
                    oneDf['Sex'] ='male'  # pandas dataframe columns are Capitalized !!!!!
                else:
                    oneDf['Sex'] = 'female'
                    # print(f'oneDf_thresholdVal_mean:{oneDf_thresholdVal_mean} female')

                # print('FINAL SEX IS !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!')
                # print(oneDf['sex'])
                # drop some redundant analysis results (not in file metadata)

                if masterDf is None:
                    masterDf = oneDf
                else:
                    masterDf = pd.concat([masterDf, oneDf], ignore_index=True)
        #
        if masterDf is None:
            if verbose:
                logger.error("Did not find any analysis.")
        else:
            # add an index column (for plotting)
            masterDf['index'] = [x for x in range(len(masterDf))]
            if verbose:
                logger.info(f"final num spikes {len(masterDf)}")

            # # randomly assign sex based on mena +/- STD of take of potential
            # _thresholdVal = masterDf['thresholdVal'].to_numpy()  # take off potential
            # _thresholdVal_mean = np.nanmean(_thresholdVal)
            # # _thresholdVal_mean: 0.5685522031727147
            # logger.error(f'  remember, setting rows based on takeoff potential _thresholdVal_mean: {_thresholdVal_mean}')
            # for _idx, _row in masterDf.iterrows():
            #     logger.error(f' _idx:{_idx} thresholdVal:{_row["thresholdVal"]}')
            #     if _row['thresholdVal'] > _thresholdVal_mean:
            #         print('  -->> male')
            #         masterDf.at[_idx, 'sex'] = 'male'
            #     else:
            #         masterDf.at[_idx, 'sex'] = 'female'
            #         print('  -->> male')

        # print(masterDf.head())
        #self._poolDf = masterDf

        return masterDf

    def signalWindow(self, str, verbose=True):
        """Update status bar of SanPy window.

        TODO make this a signal and connect app to it.
            Will not be able to do this, we need to run outside Qt
        """
        if self._sanPyWindow is not None:
            self._sanPyWindow.slot_updateStatus(str)
        elif verbose:
            logger.info(str)

    def api_getFileHeaders(self):
        headerList = []
        df = self.getDataFrame()
        for row in range(len(df)):
            # ba = self.getAnalysis(row)  # do not call this, it will load
            ba = df.at[row, "_ba"]
            if ba is not None:
                headerDict = ba.api_getHeader()
                headerList.append(headerDict)
        #
        return headerList

Attributes

loc property

Mimic pandas df.loc[]

numFiles property

Get the number of files. same as len().

shape property

Can't just return shape of _df, columns (like 'ba') may have been added Number of columns is based on self.columns

theseFileTypes property

Get list of file extensions we will load.

Functions

__init__(path=None, filePath=None, sanPyWindow=None, fileLoaderDict=None, autoLoad=False, folderDepth=None)

Load and manage a list of files in a folder path.

Use this as the main pandasModel for file list myTableView.

TODO: extend to link to folder in cloud (start with box and/or github)

Parameters:

Name	Type	Description	Default
path	str	Path to folder	None
filePath	str	Path to one file	None
sanPyWindow	SanPyWindow	PyQt, used to signal progress on loading	None
fileLoaderDict	dict	Dict with file extension keys (no dot)	None
autoLoad	bool	If True then	False
folderDepth	Optional[int]	Folder depth to recurse if loading folder path.	None

Notes

• Some functions are so self can mimic a pandas dataframe used by pandasModel. (shape, loc, loc_setter, iloc, iLoc_setter, columns, append, drop, sort_values, copy)
• 202312 adding filepath to load just one file

Source code in sanpy/analysisDir.py

def __init__(
    self,
    path: str = None,
    filePath : str = None,
    sanPyWindow : "sanpy.interface.SanPyWindow" = None,
    fileLoaderDict : dict = None,
    autoLoad: bool = False,
    folderDepth: Optional[int] = None,
):
    """Load and manage a list of files in a folder path.

    Use this as the main pandasModel for file list myTableView.

    TODO: extend to link to folder in cloud (start with box and/or github)

    Parameters
    ----------
    path (str):
        Path to folder
    filePath (str):
        Path to one file
    sanPyWindow (sanpy.interface.SanPyWindow)
        PyQt, used to signal progress on loading
    fileLoaderDict (dict):
        Dict with file extension keys (no dot)
    autoLoad (bool):
        If True then 
    folderDepth (int):
        Folder depth to recurse if loading folder path.

    Notes
    -----
    - Some functions are so self can mimic a pandas dataframe used by pandasModel.
        (shape, loc, loc_setter, iloc, iLoc_setter, columns, append, drop, sort_values, copy)
    - 202312 adding filepath to load just one file
    """

    self._filePath = filePath
    if filePath is not None and os.path.isfile(filePath):
        self._filePath = filePath
        path = os.path.split(filePath)[0]

    logger.info(f'{path} self._filePath:{self._filePath}')

    self.path: str = path

    self._sanPyWindow = sanPyWindow
    # used to signal on building initial db

    self._fileLoaderDict = fileLoaderDict
    # dist with file extension keys

    self.autoLoad = autoLoad
    # not used

    self.folderDepth = folderDepth

    self._isDirty = False
    # keep track if analysis was changed and prompt on quit

    # self._poolDf = None
    """See pool_ functions"""

    # keys are full path to file, if from cloud, key is 'cloud/<filename>'
    # holds bAnalysisObjects
    # needs to be a list so we can have files more than one
    # self.fileList = [] #OrderedDict()

    # TODO: refactor, we are not using the csv parth of this, just the filename
    # name of database file created/loaded from folder path
    self.dbFile = "sanpy_recording_db.csv"

    self._df = self.loadHdf()
    if self._df is None:
        # did not load h5 file
        self._df = self.loadFolder(loadData=autoLoad)
        self._updateLoadedAnalyzed()
    elif self._fileLoaderDict is not None:
        logger.info(f'sync existing df with filePath: {self._filePath}')
        self.syncDfWithPath()

    # if we have a filePath and not in df then add it
    if self._filePath is not None:

        logger.info('self._df')
        print(self._df)

    # self._df = self.loadFolder(loadData=autoLoad)

    #
    self._checkColumns()
    self._updateLoadedAnalyzed()

analysisIsDirty(rowIdx)

Analysis is dirty when there has been detection but not saved to h5.

Source code in sanpy/analysisDir.py

def analysisIsDirty(self, rowIdx):
    """Analysis is dirty when there has been detection but not saved to h5."""
    isDirty = False
    ba = self._df.loc[rowIdx, "_ba"]
    if isinstance(ba, sanpy.bAnalysis):
        isDirty = ba.isDirty()
    return isDirty

appendRow(rowDict=None, ba=None)

Append an empty row.

Source code in sanpy/analysisDir.py

def appendRow(self, rowDict=None, ba=None):
    """Append an empty row."""

    # logger.info('')
    # print('    rowDict:', rowDict)
    # print('    ba:', ba)

    rowSeries = pd.Series()
    if rowDict is not None:
        # rowSeries = pd.Series(rowDict)
        rowSeries = pd.DataFrame([rowDict])

        # self._data.iloc[row] = rowSeries
        # self._data = self._data.reset_index(drop=True)

    newRowIdx = len(self._df)  # append this row

    df = self._df
    # logger.warning(f"need to replace append with concat")
    #df = df.append(rowSeries, ignore_index=True)

    logger.info('concat this rowSeries')
    print(rowSeries)
    print('to this df')
    print(df)

    df = pd.concat([df, rowSeries], axis=0, ignore_index=True)

    # df = pd.concat([df,rowSeries], ignore_index=True, axis=1)
    df = df.reset_index(drop=True)

    if ba is not None:
        df.loc[newRowIdx, "_ba"] = ba

    print('')
    logger.info('=== after concat')
    print(df)

    #
    self._df = df

columnIsCheckBox(colName)

All bool columns are checkbox

TODO: problems with using type=bool and isinstance(). Kust using str 'bool'

Source code in sanpy/analysisDir.py

def columnIsCheckBox(self, colName):
    """All bool columns are checkbox

    TODO: problems with using type=bool and isinstance(). Kust using str 'bool'
    """
    type = self.sanpyColumns[colName]["type"]
    # isBool = isinstance(type, bool)
    isBool = type == "bool"
    # logger.info(f'{colName} {type(type)}, type:{type} {isBool}')
    return isBool

copyToClipboard()

TODO: Is this used or is copy to clipboard in pandas model?

Source code in sanpy/analysisDir.py

def copyToClipboard(self):
    """
    TODO: Is this used or is copy to clipboard in pandas model?
    """
    if self.getDataFrame() is not None:
        self.getDataFrame().to_clipboard(sep="\t", index=False)
        logger.info("Copied to clipboard")

getAnalysis(rowIdx, allowAutoLoad=True, verbose=False)

Get bAnalysis object, will load if necc.

Args: rowIdx (int): Row index from table, corresponds to row in self._df allowAutoLoad (bool) Return: bAnalysis

Source code in sanpy/analysisDir.py

def getAnalysis(self, rowIdx, allowAutoLoad=True, verbose=False) -> sanpy.bAnalysis:
    """Get bAnalysis object, will load if necc.

    Args:
        rowIdx (int): Row index from table, corresponds to row in self._df
        allowAutoLoad (bool)
    Return:
        bAnalysis
    """
    file = self._df.loc[rowIdx, "File"]
    ba = self._df.loc[rowIdx, "_ba"]
    uuid = self._df.loc[rowIdx, "uuid"]  # if we have a uuid bAnalysis is saved in h5f
    # filePath = os.path.join(self.path, file)
    # logger.info(f'Found _ba in file db with ba:"{ba}" {type(ba)}')
    # logger.info(f'rowIdx: {rowIdx} ba:{ba}')

    if ba is None or ba == "":
        # logger.info('did not find _ba ... loading from abf file ...')
        # working on kymograph
        #                 relPath = self.getPathFromRelPath(ba._path)
        relPath = self._df.loc[rowIdx, "relPath"]
        filePath = self.getPathFromRelPath(relPath)

        ba = self.loadOneAnalysis(
            filePath, uuid, allowAutoLoad=allowAutoLoad, verbose=verbose
        )
        # load
        """
        logger.info(f'Loading bAnalysis from row {rowIdx} "{filePath}"')
        ba = sanpy.bAnalysis(filePath)
        """
        if ba is None:
            logger.warning(
                f'Did not load row {rowIdx} path: "{filePath}". Analysis was probably not saved'
            )
        else:
            self._df.at[rowIdx, "_ba"] = ba
            # does not get a uuid until save into h5
            if uuid:
                # there was an original uuid (in table), means we are saved into h5
                self._df.at[rowIdx, "uuid"] = uuid
                if uuid != ba.uuid:
                    logger.error(
                        "Loaded uuid does not match existing in file table"
                    )
                    logger.error(f"  Loaded {ba.uuid}")
                    logger.error(f"  Existing {uuid}")

            # kymograph, set ba rect from table
            # if ba is not None and ba.fileLoader.isKymograph():
            #     left = self._df.loc[rowIdx, "kLeft"]
            #     top = self._df.loc[rowIdx, "kTop"]
            #     right = self._df.loc[rowIdx, "kRight"]
            #     bottom = self._df.loc[rowIdx, "kBottom"]

            #     # on first load, these will be empty
            #     # grab rect from ba (in _updateLoadedAnalyzed())
            #     if left == "" or top == "" or right == "" or bottom == "":
            #         pass
            #     else:
            #         theRect = [left, top, right, bottom]
            #         logger.info(f"  theRect:{theRect}")
            #         ba.fileLoader._updateTifRoi(theRect)

            #
            # update stats of table load/analyzed columns
            self._updateLoadedAnalyzed()

    return ba

getDataFrame()

Get the underlying pandas DataFrame.

Source code in sanpy/analysisDir.py

def getDataFrame(self):
    """Get the underlying pandas DataFrame."""
    return self._df

getFileList(path=None, santanaTif=False)

Get file paths from path.

Uses self.theseFileTypes

Source code in sanpy/analysisDir.py

def getFileList(self,
                path: str = None,
                santanaTif=False
                ) -> List[str]:
    """Get file paths from path.

    Uses self.theseFileTypes

    """

    # to open just one file
    # if forceFolder:
    #     # we are forcing reload of an entire folder
    #     self._filePath = None

    if self._filePath is not None:
        logger.info(f'returning one file {self._filePath}')
        return [self._filePath]

    if path is None:
        path = self.path

    fileList = getFileList(path, self.theseFileTypes, self.folderDepth)
    if santanaTif:
        fileList = stripSantanaTif(fileList)
    return fileList

    logger.warning("Remember: MODIFIED TO LOAD TIF FILES IN SUBFOLDERS")
    count = 1
    tmpFileList = []
    folderDepth = self.folderDepth  # if none then all depths
    excludeFolders = ["analysis", "hide"]
    for root, subdirs, files in os.walk(path):
        subdirs[:] = [d for d in subdirs if d not in excludeFolders]

        print(f'count:{count} folderDepth:{folderDepth}')
        print('  root:', root)
        print('  subdirs:', subdirs)
        print('  files:', files)

        # strip out folders that start with __
        # _parentFolder = os.path.split(root)[1]
        # print('root:', root)
        # print('  parentFolder:', _parentFolder)
        # if _parentFolder.startswith('__'):
        if "__" in root:
            logger.info(f"SKIPPING based on path root:{root}")
            continue

        if os.path.split(root)[1] == "analysis":
            # don't load from analysis/ folder, we save analysis there
            continue

        # if os.path.split(root)[1] == 'hide':
        #     # special case/convention, don't load from 'hide' folders
        #     continue

        for file in files:
            # TODO (cudmore) parse all our fileLoader(s) for a list
            _, _ext = os.path.splitext(file)
            if _ext in self.theseFileTypes:
                oneFile = os.path.join(root, file)
                tmpFileList.append(oneFile)

        count += 1
        if folderDepth is not None and count > folderDepth:
            break

    fileList = []
    for file in sorted(tmpFileList):
        if file.startswith("."):
            continue
        # ignore our database file
        if file == self.dbFile:
            continue

        # tmpExt is like .abf, .csv, etc
        tmpFileName, tmpExt = os.path.splitext(file)
        if tmpExt in self.theseFileTypes:
            # if getFullPath:
            #     #file = os.path.join(path, file)
            #     file = pathlib.Path(path) / file
            #     file = str(file)  # return List[str] NOT List[PosixPath]
            fileList.append(file)
    #
    logger.info(f"found {len(fileList)} files ...")
    return fileList

getFileRow(path, loadData=False)

Get dict representing one file (row in table). Loads bAnalysis to get headers.

On load error of proper file type (abf, csv), ba.loadError==True

Args: path (Str): Full path to file. #rowIdx (int): Optional row index to assign in column 'Idx'

Return: (tuple): tuple containing:

- ba (bAnalysis): [sanpy.bAnalysis](/api/bAnalysis).
- rowDict (dict): On success, otherwise None.
        fails when path does not lead to valid bAnalysis file.

Source code in sanpy/analysisDir.py

def getFileRow(self, path, loadData=False):
    """Get dict representing one file (row in table). Loads bAnalysis to get headers.

    On load error of proper file type (abf, csv), ba.loadError==True

    Args:
        path (Str): Full path to file.
        #rowIdx (int): Optional row index to assign in column 'Idx'

    Return:
        (tuple): tuple containing:

        - ba (bAnalysis): [sanpy.bAnalysis](/api/bAnalysis).
        - rowDict (dict): On success, otherwise None.
                fails when path does not lead to valid bAnalysis file.
    """
    if not os.path.isfile(path):
        logger.warning(f'Did not find file "{path}"')
        return None, None
    fileType = os.path.splitext(path)[1]
    # if fileType:
    #     fileType = fileType[1:]  # [1:] to strip period
    if fileType not in self.theseFileTypes:  
        logger.warning(f'Did not load file type "{fileType}"')
        return None, None

    # grab the fileLoaderDict from our app
    # if it is None then bAnalysis will load this (from disk)
    # if self.mySanPyWindow is not None:
    #     _fileLoaderDict = self.mySanPyWindow.getSanPyApp().getFileLoaderDict()
    # else:
    #     _fileLoaderDict = None

    # load bAnalysis
    # logger.info(f'Loading bAnalysis "{path}"')
    # loadData is false, load header
    ba = sanpy.bAnalysis(path,
                         loadData=loadData,
                         fileLoaderDict=self._fileLoaderDict)

    if ba.loadError:
        logger.error(f'Error loading bAnalysis file "{path}"')
        # return None, None

    # not sufficient to default everything to empty str ''
    # sanpyColumns can only have type in ('float', 'str')
    rowDict = dict.fromkeys(self.sanpyColumns.keys(), "")
    for k in rowDict.keys():
        if self.sanpyColumns[k]["type"] == str:
            rowDict[k] = ""
        elif self.sanpyColumns[k]["type"] == float:
            rowDict[k] = np.nan

    # if rowIdx is not None:
    #    rowDict['Idx'] = rowIdx

    """
    if ba.loadError:
        rowDict['I'] = 0
    else:
        rowDict['I'] = 2 # need 2 because checkbox value is in (0,2)
    """

    if ba.loadError:
        return None, None

    rowDict["File"] = ba.fileLoader.filename  # os.path.split(ba.path)[1]
    rowDict["Dur(s)"] = ba.fileLoader.recordingDur

    rowDict["Channels"] = ba.fileLoader.numChannels  # Theanne

    rowDict["Sweeps"] = ba.fileLoader.numSweeps

    # TODO: here, we do not get an epoch table until the file is loaded !!!
    rowDict["Epochs"] = ba.fileLoader.numEpochs  # Theanne, data has to be loaded

    rowDict["kHz"] = ba.fileLoader.recordingFrequency
    rowDict["Mode"] = ba.fileLoader.recordingMode.value

    # rowDict['dvdtThreshold'] = 20
    # rowDict['mvThreshold'] = -20
    if ba.isAnalyzed():
        dDict = ba.getDetectionDict()
        # rowDict['I'] = dDict.getValue('include')
        rowDict["dvdtThreshold"] = dDict.getValue("dvdtThreshold")
        rowDict["mvThreshold"] = dDict.getValue("mvThreshold")
        rowDict["Start(s)"] = dDict.getValue("startSeconds")
        rowDict["Stop(s)"] = dDict.getValue("stopSeconds")

    # add parent1, parent2, parent3
    _path, _file = os.path.split(path)
    _path, _parent1 = os.path.split(_path)
    _path, _parent2 = os.path.split(_path)
    _path, _parent3 = os.path.split(_path)
    rowDict['parent1'] = _parent1
    rowDict['parent2'] = _parent2
    rowDict['parent3'] = _parent3

    # aug 2023,  adding bAnalysis metadata columns
    for k,v in ba.metaData.items():
        rowDict[k] = v

    # remove the path to the folder we have loaded
    relPath = path.replace(self.path, "")

    # logger.info(f'xxx self.path: "{self.path}"')
    # logger.info(f'xxx path: "{path}"')
    # logger.info(f'xxx relPath: "{relPath}"')

    if relPath.startswith("/"):
        # so we can use os.path.join()
        relPath = relPath[1:]
    # added 20230505 working with johnson in 1313 to fix windows bug ???
    if relPath.startswith("\\"):
        # so we can use os.path.join()
        relPath = relPath[1:]

    rowDict["relPath"] = relPath

    #logger.info(f'2) xxx relPath: "{relPath}"')
    # logger.info('qqq')
    # print(rowDict)

    return ba, rowDict

getPathFromRelPath(relPath)

Get full path to file from relPath.

Uses analysisDir folder path.

Source code in sanpy/analysisDir.py

def getPathFromRelPath(self, relPath):
    """Get full path to file from relPath.

    Uses analysisDir folder path.
    """
    if relPath.startswith("/"):
        relPath = relPath[1:]

    fullFilePath = os.path.join(self.path, relPath)

    """
    print('xxx', self.path)
    print('xxx', relPath)
    print('xxx', fullFilePath)
    """

    return fullFilePath

getRowDict(rowIdx)

Return a dict with selected row as dict (includes detection parameters).

Important to return a copy as our '_ba' is a pointer to bAnalysis.

Returns: theRet (dict): Be sure to make a deep copy of ['_ba'] if neccessary.

Source code in sanpy/analysisDir.py

def getRowDict(self, rowIdx):
    """
    Return a dict with selected row as dict (includes detection parameters).

    Important to return a copy as our '_ba' is a pointer to bAnalysis.

    Returns:
        theRet (dict): Be sure to make a deep copy of ['_ba'] if neccessary.
    """
    theRet = {}
    # use columns in main sanpyColumns, not in df
    # for colStr in self.columns:
    for colStr in self._df.columns:
        # theRet[colStr] = self._df.loc[rowIdx, colStr]
        theRet[colStr] = self._df.loc[rowIdx, colStr]
    # theRet['_ba'] = theRet['_ba'].copy()
    return theRet

hasDirty()

Return true if any bAnalysis in list has been analyzed but not saved (e.g. is dirty)

Source code in sanpy/analysisDir.py

def hasDirty(self):
    """Return true if any bAnalysis in list has been analyzed but not saved (e.g. is dirty)"""
    haveDirty = False
    numRows = len(self._df)
    for rowIdx in range(numRows):
        if self.analysisIsDirty(rowIdx):
            haveDirty = True

    return haveDirty

loadFolder(path=None, loadData=False)

Parse a folder and load all (abf, csv, ...).

Only called if no h5 file.

TODO: get rid of loading database from .csv (it is replaced by .h5 file) TODO: extend the logic to load from cloud (after we were instantiated)

Source code in sanpy/analysisDir.py

def loadFolder(self, path=None, loadData=False) -> pd.DataFrame:
    """Parse a folder and load all (abf, csv, ...).

    Only called if no h5 file.

    TODO: get rid of loading database from .csv (it is replaced by .h5 file)
    TODO: extend the logic to load from cloud (after we were instantiated)
    """
    logger.info("Loading folder from scratch (no h5 file)")

    start = time.time()
    if path is None:
        path = self.path
    self.path = path

    df = pd.DataFrame(columns=self.sanpyColumns.keys())
    df = self._setColumnType(df)

    # get list of all abf/csv/tif files
    fileList = self.getFileList(path)
    _numFilesToLoad = len(fileList)
    start = time.time()
    # build new db dataframe
    listOfDict = []
    for rowIdx, fullFilePath in enumerate(fileList):

        self.signalWindow(
            f'Loading file {rowIdx+1} of {_numFilesToLoad} "{fullFilePath}"'
        )

        # rowDict is what we are showing in the file table
        # abb debug vue, set loadData=True
        # loads bAnalysis
        ba, rowDict = self.getFileRow(fullFilePath, loadData=loadData)

        if rowDict is None:
            logger.warning(f'error loading file {fullFilePath}')
            continue

        # as we parse the folder, don't load ALL files (will run out of memory)
        if loadData:
            rowDict["_ba"] = ba
        else:
            rowDict["_ba"] = None  # ba

        # do not assign uuid until bAnalysis is saved in h5 file
        # rowDict['uuid'] = ''

        # logger.info(f'    row:{rowIdx} relPath:{relPath} fullFilePath:{fullFilePath}')

        listOfDict.append(rowDict)

    stop = time.time()
    logger.info(f"Loading {len(listOfDict)} files took {round(stop-start,3)} seconds.")

    df = pd.DataFrame(listOfDict)
    df = self._setColumnType(df)

    return df

loadHdf(path=None, verbose=False)

Load the database key from an h5 file.

We do not load analy anlysis until user clicks on row, see loadOneAnalysis()

Source code in sanpy/analysisDir.py

def loadHdf(self, path=None, verbose=False):
    """Load the database key from an h5 file.

    We do not load analy anlysis until user clicks on row, see loadOneAnalysis()
    """
    if path is None:
        path = self.path
    self.path = path

    df = None
    hdfFile = os.path.splitext(self.dbFile)[0] + ".h5"
    hdfPath = pathlib.Path(self.path) / hdfFile
    if not hdfPath.is_file():
        return

    # logger.info(f"Loading existing folder h5 file {hdfPath}")
    # sanpy.h5Util.listKeys(hdfPath)

    _start = time.time()
    dbKey = os.path.splitext(self.dbFile)[0]

    try:
        df = pd.read_hdf(hdfPath, dbKey)
    except KeyError as e:
        # file is corrupt !!!
        logger.error(f'    Load h5 failed, did not find dbKey:"{dbKey}" {e}')

    if df is not None:
        # _ba is for runtime, assign after loading from either (abf or h5)
        df["_ba"] = None

        # fix bug during dev of ba metadata
        # df['Sex'] = 'unknown'

        if verbose:
            logger.info("    loaded db df")

        _stop = time.time()
        # logger.info(f"Loading took {round(_stop-_start,2)} seconds")

        # if we are one file then make sure file is in df

    return df

loadOneAnalysis(path, uuid=None, allowAutoLoad=True, verbose=False)

Load one bAnalysis either from original file path or uuid of h5 file.

If from h5, we still need to reload sweeps !!! They are binary and fast, saving to h5 (in this case) is slow.

Source code in sanpy/analysisDir.py

def loadOneAnalysis(self, path, uuid=None, allowAutoLoad=True, verbose=False):
    """Load one bAnalysis either from original file path or uuid of h5 file.

    If from h5, we still need to reload sweeps !!!
    They are binary and fast, saving to h5 (in this case) is slow.
    """
    if verbose:
        logger.info(f'path:"{path}" uuid:"{uuid}" allowAutoLoad:"{allowAutoLoad}"')

    hdfPath = self._getHdfFile()

    # grab the fileLoaderDict from our app
    # if it is None then bAnalysis will load this (from disk)
    # if self.mySanPyWindow is not None:
    #     _fileLoaderDict = self.mySanPyWindow.getSanPyApp().getFileLoaderDict()
    # else:
    #     _fileLoaderDict = None

    ba = None
    if uuid is not None and uuid:
        # load from h5
        if verbose:
            logger.info(f"    Retreiving uuid from hdf file {uuid}")

        # load from abf
        ba = sanpy.bAnalysis(path, fileLoaderDict=self._fileLoaderDict, verbose=verbose)

        # load analysis from h5 file, will fail if uuid is not in file
        ba._loadHdf_pytables(hdfPath, uuid)

    if allowAutoLoad and ba is None:
        # load from path
        ba = sanpy.bAnalysis(path, fileLoaderDict=self._fileLoaderDict, verbose=verbose)
        if verbose:
            logger.info(f"    Loaded ba from path {path} and now ba:{ba}")
    #
    return ba

pool_build(uniqueColumn=None, allowAutoLoad=False, includeNo=True, verbose=False)

Build one df with all analysis. Use this in plot tool plugin.

Parameters:

Name	Type	Description	Default
uniqueColumn	str	Name of column to prepend to File column to make a unique name. Use 'parant2' for Kymograph tif files exported from Olympus.	None
includeNo	boolean	if True then include files with metadata 'Include' of no.	True

Source code in sanpy/analysisDir.py

def pool_build(self, uniqueColumn=None, allowAutoLoad=False, includeNo=True, verbose=False):
    """Build one df with all analysis. Use this in plot tool plugin.

    Parameters
    ----------
    uniqueColumn : str
        Name of column to prepend to File column to make a unique name.
        Use 'parant2' for Kymograph tif files exported from Olympus.
    includeNo : boolean
        if True then include files with metadata 'Include' of no.
    """
    if verbose:
        logger.info("")

    masterDf = None

    # for row in range(self.numFiles):
    for rowIdx, rowDict in self._df.iterrows():
        if (not includeNo) and (rowDict['Include'] == 'no'):
            if verbose:
                logger.info(f'  rowIdx:{rowIdx} Include is "no"')
            continue

        ba = self.getAnalysis(rowIdx, allowAutoLoad=allowAutoLoad)
        if ba is None:
            continue

        if not ba.isAnalyzed():
            if verbose:
                logger.info(f"  rowIdx:{rowIdx} not analyzed")
            continue

        oneDf = ba.asDataFrame(regenerateAnalysisDataFrame=True)

        if oneDf is not None:

            self.signalWindow(f'Adding "{ba.fileLoader.filename}"', verbose=verbose)

            oneDf["File Number"] = int(rowIdx)

            # 20240114
            oneDf['File Path'] = ba.fileLoader.filepath

            uniqueName = os.path.splitext(ba.fileLoader.filename)[0]
            if uniqueColumn is not None:
                uniqueName = rowDict[uniqueColumn] + '-' + uniqueName
            oneDf["Unique Name"] = uniqueName

            # logger.warning('TEMPORARY WHILE WORKING ON KYM POOLING !!!!!!!!!!!!!!!!!!!!!!!!!')
            # logger.warning('randomly assigning sex to male, female, unknown')
            # sexList = ['male', 'female', 'unknown']
            # oneDf['Sex'] = random.choice(sexList)
            oneDf_thresholdVal = oneDf['thresholdVal'].to_numpy()  # take off potential
            oneDf_thresholdVal_mean = np.nanmean(oneDf_thresholdVal)
            if oneDf_thresholdVal_mean > 0.5685522031727147:  # mean of all thresholdVal
                # print(f'oneDf_thresholdVal_mean:{oneDf_thresholdVal_mean} male')
                oneDf['Sex'] ='male'  # pandas dataframe columns are Capitalized !!!!!
            else:
                oneDf['Sex'] = 'female'
                # print(f'oneDf_thresholdVal_mean:{oneDf_thresholdVal_mean} female')

            # print('FINAL SEX IS !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!')
            # print(oneDf['sex'])
            # drop some redundant analysis results (not in file metadata)

            if masterDf is None:
                masterDf = oneDf
            else:
                masterDf = pd.concat([masterDf, oneDf], ignore_index=True)
    #
    if masterDf is None:
        if verbose:
            logger.error("Did not find any analysis.")
    else:
        # add an index column (for plotting)
        masterDf['index'] = [x for x in range(len(masterDf))]
        if verbose:
            logger.info(f"final num spikes {len(masterDf)}")

        # # randomly assign sex based on mena +/- STD of take of potential
        # _thresholdVal = masterDf['thresholdVal'].to_numpy()  # take off potential
        # _thresholdVal_mean = np.nanmean(_thresholdVal)
        # # _thresholdVal_mean: 0.5685522031727147
        # logger.error(f'  remember, setting rows based on takeoff potential _thresholdVal_mean: {_thresholdVal_mean}')
        # for _idx, _row in masterDf.iterrows():
        #     logger.error(f' _idx:{_idx} thresholdVal:{_row["thresholdVal"]}')
        #     if _row['thresholdVal'] > _thresholdVal_mean:
        #         print('  -->> male')
        #         masterDf.at[_idx, 'sex'] = 'male'
        #     else:
        #         masterDf.at[_idx, 'sex'] = 'female'
        #         print('  -->> male')

    # print(masterDf.head())
    #self._poolDf = masterDf

    return masterDf

saveHdf()

Save file table and any number of loaded and analyzed bAnalysis.

Set file table 'uuid' column when we actually save a bAnalysis

Important: Order matters (1) Save bAnalysis first, it updates uuid in file table. (2) Save file table with updated uuid

Source code in sanpy/analysisDir.py

def saveHdf(self):
    """Save file table and any number of loaded and analyzed bAnalysis.

    Set file table 'uuid' column when we actually save a bAnalysis

    Important: Order matters
        (1) Save bAnalysis first, it updates uuid in file table.
        (2) Save file table with updated uuid
    """
    start = time.time()

    df = self.getDataFrame()

    # the compressed version from the last save
    hdfFile = os.path.splitext(self.dbFile)[0] + ".h5"
    hdfFilePath = pathlib.Path(self.path) / hdfFile

    logger.info(f"Saving db (will be compressed) {hdfFilePath}")

    # save each bAnalysis
    for row in range(len(df)):
        ba = df.at[row, "_ba"]
        if ba is not None:
            didSave = ba._saveHdf_pytables(hdfFilePath)
            if didSave:
                # we are now saved into h5 file, remember uuid to load
                # print('xxx SETTING dir uuid')
                df.at[row, "uuid"] = ba.uuid

    # rebuild (L, A, S) columns
    self._updateLoadedAnalyzed()

    #
    # save file database
    logger.info(f"    saving file db with {len(df)} rows")
    print(df)

    dbKey = os.path.splitext(self.dbFile)[0]
    df = df.drop("_ba", axis=1)  # don't ever save _ba, use it for runtime

    # hdfStore[dbKey] = df  # save it
    df.to_hdf(hdfFilePath, dbKey)

    #
    self._isDirty = False  # if true, prompt to save on quit

    # rebuild the file to remove old changes and reduce size
    # self._rebuildHdf()
    sanpy.h5Util._repackHdf(hdfFilePath)

    # list the keys in the file
    # sanpy.h5Util.listKeys(hdfFilePath)

    stop = time.time()
    logger.info(f"Saving took {round(stop-start,2)} seconds")

signalWindow(str, verbose=True)

Update status bar of SanPy window.

TODO make this a signal and connect app to it. Will not be able to do this, we need to run outside Qt

Source code in sanpy/analysisDir.py

def signalWindow(self, str, verbose=True):
    """Update status bar of SanPy window.

    TODO make this a signal and connect app to it.
        Will not be able to do this, we need to run outside Qt
    """
    if self._sanPyWindow is not None:
        self._sanPyWindow.slot_updateStatus(str)
    elif verbose:
        logger.info(str)

syncDfWithPath()

Sync path with existing df. Used to detect new/removed files.

If we currently have just one file (self._filePath) we will trash it and load a folder

Notes

20231230, trying to use this to open a one file window with an exiting h5 file.

Source code in sanpy/analysisDir.py

def syncDfWithPath(self):
    """Sync path with existing df. Used to detect new/removed files.

    If we currently have just one file (self._filePath) we will trash it and load a folder

    Notes
    -----
    20231230, trying to use this to open a one file window with an exiting h5 file.
    """

    pathFileList = self.getFileList()

    # our currently loaded files
    dfFileList = self._df["File"].tolist()

    logger.info(f'dfFileList: {dfFileList}')
    # print('    === pathFileList (on drive):')
    # print('    ', pathFileList)
    # print('    === dfFileList (in table):')
    # print('    ', dfFileList)

    addedToDf = False

    # look for files in path not in df
    for pathFile in pathFileList:
        fileName = os.path.split(pathFile)[1]
        if fileName not in dfFileList:
            logger.info(f'   Found file in path "{fileName}" not in df')

            # load bAnalysis and get df column values
            addedToDf = True

            ba, rowDict = self.getFileRow(pathFile)  # loads bAnalysis

            if rowDict is not None:
                # listOfDict.append(rowDict)

                # TODO: get this into getFileROw()
                # logger.warning("bug 20220718, not sure we need this ???")
                # print(rowDict)

                # rowDict['relPath'] = pathFile
                rowDict["_ba"] = None

                self.appendRow(rowDict=rowDict, ba=None)

    # look for files in df not in path
    # for dfFile in dfFileList:
    #     if not dfFile in pathFileList:
    #         logger.info(f'Found file in df "{dfFile}" not in path')

    if addedToDf:
        df = self._df
        df = df.sort_values(
            by=["File"], axis="index", ascending=True, inplace=False
        )
        df = df.reset_index(drop=True)
        self._df = df

    self._updateLoadedAnalyzed()