text: from pathlib import Path
  import json
  from collections import OrderedDict
  import pandas as pd
  import numpy as np
  import re
  import datetime
  import h5py
  # import dask.dataframe as dd
  import struct
  
  from .BaseSource import BaseSource
  from tweezers.meta import MetaDict, UnitDict
  import hdf5storage
  import tweezers.ixo.hdf5 as h5
  
  
  class Hdf5BiotecSource(BaseSource):
      """
      Data source for \*.h5 files from the Biotec tweezers.
      """
  
      ts = None
      psd = None
      data = None
      image = None
      images = None
  
      def __init__(self, data=None, psd=None, ts=None, image=None, images=None, **kwargs):
          """
          Args:
              data (:class:`pathlib.Path`): path to data file to read, if the input is of a different type, it is given to
                                             :class:`pathlib.Path` to try to create an instance
              psd (:class:`pathlib.Path`): path to psd file
              ts (:class:`pathlib.Path`): path to ts file
              image (:class:`pathlib.Path`): path to screenshot file
              images (:class:`pathlib.Path`): path to the file containing the recorded video
              kwargs: forwared to super class
          """
  
          super().__init__(**kwargs)
  
          if ts:
              self.ts = Path(ts)
          if psd:
              self.psd = Path(psd)
          if data:
              self.data = Path(data)
          if image:
              self.image = Path(image)
          if images:
              self.images = Path(image)
  
      @staticmethod
      def isDataFile(path):
          """
          Checks if a given file is a valid data file and returns its ID and type.
  
          Args:
              path (:class:`pathlib.Path`): file to check
  
          Returns:
              `dict` with ``id`` and ``type``
          """
  
          _path = Path(path)
          m = re.match('^(?P<beadId>[0-9\-_]{19}.*#\d{3})(?P<trial>-\d{3})?\s(?P<type>[a-zA-Z]+)\.h5$',
                       _path.name)
          if m:
              ide = None
              if m.group('trial'):
                  ide = '{}{}'.format(m.group('beadId'), m.group('trial'))
              res = {'beadId': m.group('beadId'),
                     'id': ide,
                     'type': m.group('type').lower(),
                     'path': _path}
              return res
          else:
              return False
  
      @classmethod
      def getAllSources(cls, path):
          """
          Get a list of all IDs and their data soruces that are at the given path and its subfolders.
  
          Args:
              path (:class:`pathlib.Path`): root path for searching
  
          Returns:
              `dict`
          """
  
          _path = Path(path)
  
          # get a list of all files and their properties
          files = cls.getAllFiles(_path)
          sources = OrderedDict()
          sharedFiles = {}
  
          # sort files in sharedFiles and actual data files that belong to an individual trial
          for el in files:
              if el['id']:
                  if el['beadId'] not in sources.keys():
                      sources[el['beadId']] = OrderedDict()
                  if el['id'] not in sources[el['beadId']].keys():
                      sources[el['beadId']][el['id']] = cls()
                  setattr(sources[el['beadId']][el['id']], el['type'], el['path'])
              else:
                  if el['beadId'] not in sharedFiles.keys():
                      sharedFiles[el['beadId']] = {}
                  sharedFiles[el['beadId']][el['type']] = el['path']
  
          # append a reference to the shared file to each trial
          for beadId, beadData in sources.items():
              if beadId in sharedFiles.keys():
                  # for each trial
                  for trial in beadData.values():
                      # append each shared file
                      for fileType, file in sharedFiles[beadId].items():
                          setattr(trial, fileType, file)
  
          return sources
  
      @property
      def header(self):
          """
          Return which file is used for reading the metadata.
  
          Returns:
              `pathlib.Path`
          """
  
          header = None
          # order is important here for the header file
          if self.ts:
              header = self.ts
          if self.psd:
              header = self.psd
          if self.data:
              header = self.data
  
          if not header:
              raise ValueError('No header file given (probably no file given at all).')
  
          return header
  
      def getMetadata(self):
          """
          Return the metadata of the experiment.
  
          Returns:
              :class:`tweezers.MetaDict` and :class:`tweezers.UnitDict`
          """
  
          collect = h5.CustomMarshallerCollection()
          headerStr = hdf5storage.read(filename=self.header, path='/meta', marshaller_collection=collect)
  
          header = json.loads(headerStr, object_pairs_hook=OrderedDict)
          units = UnitDict(header.pop('units'))
          meta = MetaDict(header)
  
          # add column header units
          cols = self.readColumnTitles(self.header)
          units.update(cols['units'])
  
          # add id from header file name
          idDict = self.isDataFile(self.header)
          if idDict['id']:
              meta['id'] = idDict['id']
          else:
              meta['id'] = idDict['beadId']
  
          # add trap names
          meta['traps'] = meta.subDictKeys()
  
          # add ID strings safe for printing (e.g. in matplotlib legend)
          meta['beadIdSafe'] = meta['beadId'].replace('_', ' ').replace('#', '')
          meta['idSafe'] = meta['id'].replace('_', ' ').replace('#', '')
  
          # add time in timestamp format
          meta['time'] = pd.to_datetime(meta['date'], format='%d.%m.%Y %H:%M:%S').tz_localize('Europe/Berlin')
  
          return meta, units
  
      def getData(self):
          """
          Return the experiment data.
  
          Returns:
              :class:`pandas.DataFrame`
          """
  
          data = self.readToDataframe(self.data)
          return data
  
      def getPsd(self):
          """
          Returns the power spectral density (PSD) used for the calibration of the experiment by the data source.
  
          Returns:
              :class:`pandas.DataFrame`
          """
  
          # get file content
          psd = self.readToDataframe(self.psd)
          # ignore fit columns
          cols = [s for s in psd.columns if not s.lower().endswith('fit')]
          psd = psd[cols]
          return psd
  
      def getPsdFit(self):
          """
           Returns the fit to the PSD as performed by the data source.
  
          Returns:
              :class:`pandas.DataFrame`
          """
  
          # get file content
          psd = self.readToDataframe(self.psd)
          # ignore non-fit columns
          cols = [s for s in psd.columns if s.lower().endswith('fit')]
          psd = psd[['f'] + cols]
  
          # strip 'Fit' from column names
          cols = [col[:-3] for col in cols]
          psd.columns = ['f'] + cols
  
          # remove values where fit is 0, artefact of storing PSD and it's fit in the same file
          psd = psd[psd.iloc[:, 1] > 0]
  
          return psd
  
      def getTs(self):
          """
          Returns the time series recorded for the thermal calibration of the experiment. This is used to compute the
          PSD.
  
          Returns:
              :class:`pandas.DataFrame`
          """
  
          ts = self.readToDataframe(self.ts)
          ts.rename(columns={'pmXDiff': 'pmX', 'pmYDiff': 'pmY', 'aodXDiff': 'aodX', 'aodYDiff': 'aodY'},
                    inplace=True)
          # get relative time
          ts['t'] = ts.absTime - ts.absTime.iloc[0]
  
          return ts
  
      @staticmethod
      def calculateDisplacement(meta, units, data):
          """
          Calculate bead displacements from Diff signal and calibration values.
  
          Args:
              meta (:class:`.MetaDict`): metadata
              units (:class:`.UnitDict`): unit metadata
              data (:class:`pandas.DataFrame`): data
  
          Returns:
  
              Updated versions of the input parameters
  
              * meta (:class:`.MetaDict`)
              * units (:class:`.UnitDict`)
              * data (:class:`pandas.DataFrame`)
          """
  
          # calculate displacement per trap and axis
          for trap in meta['traps']:
              m = meta[trap]
              # displacement from trap signal
              data[trap + 'Disp'] = (data[trap + 'Diff'] - m['zeroOffset']) * m['displacementSensitivity']
              units[trap + 'Disp'] = 'nm'
              # displacement from video signal
              data[trap + 'DispVid'] = data[trap + 'Bead'] - data[trap + 'Trap']
  
          # invert PM bead displacement for convenience, disp is positive when pulled towards AOD ("up")
          data.pmYDisp = -data.pmYDisp
          data.pmYDispVid = -data.pmYDispVid
  
          return meta, units, data
  
      @staticmethod
      def calculateForce(meta, units, data):
          """
          Calculate forces from Diff signal and calibration values.
  
          Args:
              meta (:class:`.MetaDict`): metadata
              units (:class:`.UnitDict`): unit metadata
              data (:class:`pandas.DataFrame`): data
  
          Returns:
  
              Updated versions of the input parameters
  
              * meta (:class:`.MetaDict`)
              * units (:class:`.UnitDict`)
              * data (:class:`pandas.DataFrame`)
          """
  
          # calculate force per trap and axis
          for trap in meta['traps']:
              m = meta[trap]
              data[trap + 'Force'] = (data[trap + 'Diff'] - m['zeroOffset']) * m['forceSensitivity']
              units[trap + 'Force'] = 'pN'
  
          # invert PM force for convenience, force is positive when pulled towards AOD ("up")
          data.pmYForce = -data.pmYForce
  
          # calculate mean force per axis, only meaningful for two traps
          data['xForce'] = (data.pmXForce + data.aodXForce) / 2
          data['yForce'] = (data.pmYForce + data.aodYForce) / 2
  
          units['xForce'] = 'pN'
          units['yForce'] = 'pN'
  
          return meta, units, data
  
      @staticmethod
      def postprocessData(meta, units, data):
          """
          Modify the time array to use relative times (but keep the absolute time) and calculate forces.
  
          Args:
              meta: :class:`tweezers.MetaDict`
              units: :class:`tweezers.UnitDict`
              data: :class:`pandas.DataFrame`
  
          Returns:
              Updated versions of the input parameters
  
              * meta (:class:`.MetaDict`)
              * units (:class:`.UnitDict`)
              * data (:class:`pandas.DataFrame`)
          """
  
          # create relative time column
          data['time'] = data.absTime - data.absTime.iloc[0]
  
          data['xTrapDist'] = data.pmXTrap - data.aodXTrap
          data['yTrapDist'] = data.pmYTrap - data.aodYTrap
          units['xTrapDist'] = 'nm'
          units['yTrapDist'] = 'nm'
  
          # calculate bead distance center to center from video signal, only meaningful for two trapped beads
          data['xDistVid'] = data.pmXBead - data.aodXBead
          data['yDistVid'] = data.pmYBead - data.aodYBead
          data['distVid'] = np.sqrt((data.pmYBead - data.aodYBead)**2 + (data.pmXBead - data.aodXBead)**2)
          units['xDistVid'] = 'nm'
          units['yDistVid'] = 'nm'
          units['distVid'] = 'nm'
  
          # ensure values, set them to 0 if they come as None from the file
          for trap in meta['traps']:
              if not meta[trap].displacementSensitivity:
                  meta[trap].displacementSensitivity = 0
              if not meta[trap].forceSensitivity:
                  meta[trap].forceSensitivity = 0
  
          # calculate displacement and forces
          meta, units, data = Hdf5BiotecSource.calculateDisplacement(meta, units, data)
          meta, units, data = Hdf5BiotecSource.calculateForce(meta, units, data)
  
          # calculate bead distance center to center from trap signal
          data['xDistVolt'] = data.xTrapDist - data.pmXDisp - data.aodXDisp
          data['yDistVolt'] = data.yTrapDist - data.pmYDisp - data.aodYDisp
          data['distVolt'] = np.sqrt(data.xDistVolt**2 + data.yDistVolt**2)
          units['xDistVolt'] = 'nm'
          units['yDistVolt'] = 'nm'
          units['distVolt'] = 'nm'
  
          return meta, units, data
  
      @staticmethod
      def readColumnTitles(file):
          """
          Read the column titles and if available their units. They are expected to be given as 'f [Hz]', separated by
          tabstops.
  
          Args:
              file: (:class:`pathlib.Path`): path to file
  
          Returns:
              `dict` with keys `names` (`list`), `units` (:class:`.UnitDict`) and `n`
          """
  
          # read columns names
          with h5py.File(file, 'r') as f:
              cols = f['data'].attrs['cols']
  
          # get column title names with units
          regex = re.compile('(\w+)(?:\s\[(\w*)\])?')
          colHeaders = []
          colUnits = UnitDict()
          for col in cols:
              colStr = col.decode('utf-8')
              res = regex.match(colStr)
              colHeaders.append(res[1])
              if res.group(2):
                  colUnits[res[1]] = res[2]
  
          return {'names': colHeaders, 'units': colUnits}
  
      def readToDataframe(self, file):
          """
          Read the given file into a :class:`pandas.DataFrame`.
  
          Args:
              file (:class:`pathlib.Path`): path to file
  
          Returns:
              :class:`pandas.DataFrame`
          """
  
          cols = self.readColumnTitles(file)
          with h5py.File(file, 'r') as f:
              data = f['data'][()]
          data = pd.DataFrame(data, columns=cols['names'])
          return data
  
      def getTime(self):
          """
          Return the time of the source.
  
          Returns:
              `datetime.datetime`
          """
  
          timeStr = self.header.name[:19]
          time = datetime.datetime.strptime(timeStr, '%Y-%m-%d_%H-%M-%S')
          return time
  
      def getImages(self):
          """
          Returns the an iterator of the image data if it was recorded with the experiment.
  
          Usage example::
  
              t = TweezersData.load('path/to/data')
              for time, image in t.source.getImages():
                  print(time)
  
          Returns:
              * timestamps for each frame (`numpy.array`)
              * image data (`numpy.array`)
          """
  
          iterator = Hdf5ImageIterator(filePath=self.images, hdf5Path='/data')
          return iterator
  
      # def getDask(self, **kwargs):
      #     """
      #     Get a Dask DataFrame from the data file. Potentially useful for reading chunks of large files.
      #
      #     The Dask DataFrame must be closed using :meth:`Hdf5BiotecSource.releaseDask`.
      #
      #     Args:
      #         **kwargs: passed on to :meth:`dask.dataframe.from_array`
      #
      #     Returns:
      #         :class:`dask.dataframe`
      #     """
      #
      #     cols = self.readColumnTitles(self.data)
      #     cols = cols['names']
      #
      #     self.daskFile = h5py.File(self.data, mode='r')
      #     da = dd.from_array(self.daskFile['/data'], columns=cols, **kwargs)
      #     return da
      #
      # def releaseDask(self):
      #     """
      #     Releases the Dask DataFrame after the computations. Basically closes the file.
      #     """
      #
      #     self.daskFile.close()
  
      @staticmethod
      def getTimeFromImage(image):
          """
          Get the timestamp from an image recorded with the video stream during the experiment. The timestamps are encoded
          in the last row of each image.
  
          Timestamps are in miliseconds and can be related to the `absTime` column in the experiment data.
  
          Args:
              image (`numpy.array`): image data
  
          Returns:
              `double`
          """
  
          byteArr = image[-1, 0:8]
          time = struct.unpack('>d', byteArr)[0]
          return time
  
      def getDataIterator(self, chunksize=10000):
          """
          Get an iterator that loops through the data and returns chunks of size `chunksize`.
  
          Args:
              chunksize (int): Number of rows in the DataFrame returned in each iteration.
  
          Returns:
              :class:`Hdf5Iterator`
          """
  
          cols = self.readColumnTitles(self.data)['names']
          dataIter = Hdf5Iterator(self.data, '/data',  chunksize=chunksize, columns=cols)
          return dataIter
  
      def getDataSegment(self, tmin, tmax, chunksize=10000):
          """
          Returns the data between ``tmin`` and ``tmax`` by reading the datafile chunkwise until ``tmax`` is reached.
  
          Args:
              tmin (`float`): minimum data timestamp
              tmax (`float`): maximum data timestamp
              chunksize (`int`): number of rows to read per chunk
  
          Returns:
              :class:`pandas.DataFrame`
          """
  
          dataIter = self.getDataIterator(chunksize=chunksize)
          t0 = None
  
          # read the chunks into memory if they are within the requested limits
          df = []
          for chunk in dataIter:
              if not t0:
                  t0 = chunk.absTime.iloc[0]
  
              if chunk.absTime.iloc[0] - t0 > tmax:
                  # stop reading if the upper time limit was reached
                  break
              selection = chunk[(chunk.absTime - t0 >= tmin) & (chunk.absTime - t0 <= tmax)]
              df.append(selection)
  
          # return concatenated dataframe with all the requested data
          return pd.concat(df, ignore_index=True)
  
  
  class Hdf5Iterator:
      """
      An iterator to loop through a data table in a HDF5 data file.
      """
  
      def __init__(self, filePath, hdf5Path, chunksize=10000, columns=None):
          """
          Constructor for the iterator.
  
          Args:
              filePath (str or pathlike): the path to the HDF5 file
              hdf5Path (str): the path in the HDF5 file to the dataset to read
              chunksize (int): number of rows in the DataFrame that are returned in each iteration
              columns: names of columns for the DataFrame
          """
  
          self.filePath = filePath
          self.hdf5Path = hdf5Path
          self.chunksize = chunksize
          self.n = 0
          self.columns = columns
  
      def __iter__(self):
          """
          Actual iterator.
          Returns:
              :class:`pandas.DataFrame`
          """
  
          with h5py.File(self.filePath, 'r') as f:
              # get dataset
              dset = f[self.hdf5Path]
              # until where should we read?
              maxLen = dset.len()
              # loop through dataset
              while self.n < maxLen:
                  # get the data in the current limits and convert do pandas.DataFrame
                  data = pd.DataFrame(dset[self.n:(self.n + self.chunksize)], columns=self.columns)
                  # update current position
                  self.n += data.shape[0]
                  # return data
                  yield data
  
  
  class Hdf5ImageIterator:
      """
      An iterator to loop through the images in a HDF5 data file.
      """
  
      def __init__(self, filePath, hdf5Path):
          self.filePath = filePath
          self.hdf5Path = hdf5Path
          self.n = 0
  
      def __iter__(self):
          """
          Actual iterator.
          Returns:
              timestamp and image data as numpy array
          """
  
          with h5py.File(self.filePath, 'r') as f:
              # get dataset
              dset = f[self.hdf5Path]
              # until where should we read?
              maxLen = dset.len()
              # loop through dataset
              while self.n < maxLen:
                  # get the data in the current limits and convert do pandas.DataFrame
                  image = dset[self.n]
                  time = Hdf5BiotecSource.getTimeFromImage(image)
                  # remove row which holds the timestamp data
                  image = np.delete(image, (-1), axis=1)
                  # update current position
                  self.n += 1
                  # return data
                  yield time, image
  