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@ -568,11 +568,11 @@ class FitAnalyser():
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:type input_core_dims: list or array like, optional
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:param dask: over write of the same argument in xarray.apply_ufunc,, defaults to 'parallelized'
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:type dask: str, optional
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:param vectorize: over write of the same argument in xarray.apply_ufunc,, defaults to True
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:param vectorize: over write of the same argument in xarray.apply_ufunc, defaults to True
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:type vectorize: bool, optional
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:param keep_attrs: over write of the same argument in xarray.apply_ufunc,, defaults to True
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:param keep_attrs: over write of the same argument in xarray.apply_ufunc, defaults to True
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:type keep_attrs: bool, optional
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:param daskKwargs: over write of the same argument in xarray.apply_ufunc,, defaults to None
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:param daskKwargs: over write of the same argument in xarray.apply_ufunc, defaults to None
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:type daskKwargs: dict, optional
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:return: The guessed initial parameters for the fit
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:rtype: xarray DataArray
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@ -891,7 +891,7 @@ class FitAnalyser():
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return res.reshape(shape, order='F')
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def eval(self, fitResultArray, x=None, y=None, output_core_dims=None, prefix="", dask='parallelized', vectorize=True, daskKwargs=None, **kwargs):
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"""_summary_
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"""Call the eval function of the fit model to calculate the curve.
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:param fitResultArray: The result of fit
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:type fitResultArray: xarray DataArray
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@ -11,7 +11,17 @@ from datetime import datetime
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def _read_globals_attrs(variable_attrs, context=None):
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"""Combine attributes from different variables according to combine_attrs"""
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"""Find global parameters of shots, including scan axes.
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:param variable_attrs: The attrs of current shot.
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:type variable_attrs: dict
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:param context: _description_, defaults to None
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:type context: _type_, optional
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:return: The globals attrs of the whole shot.
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:rtype: dict
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"""
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# Combine attributes from different variables according to combine_attrs
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if not variable_attrs:
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# no attributes to merge
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return None
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@ -86,12 +96,31 @@ def _read_globals_attrs(variable_attrs, context=None):
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def _read_shot_number_from_hdf5(x):
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"""Add the current shot number to the data read from HDF5 file.
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:param x: The data of current shot
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:type x: xarray DataArray
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:return: The data with current shot number
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:rtype: xarray DataArray
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"""
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filePath = x.encoding["source"]
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shotNum = filePath.split("_")[-1].split("_")[-1].split(".")[0]
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return x.assign(shotNum=shotNum)
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def _assign_scan_axis_partial(x, datesetOfGlobal, fullFilePath):
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"""Find and add the scan axes to the data read from HDF5 file.
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:param x: The data of current shot
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:type x: xarray DataArray
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:param datesetOfGlobal: The xarray DataSet stored the information of global parameters
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:type datesetOfGlobal: xarray DataSet
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:param fullFilePath: The full and absolute file path of current shot
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:type fullFilePath: str
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:return: The data of current shot with scan axes
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:rtype: xarray DataArray
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"""
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scanAxis = datesetOfGlobal.scanAxis
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filePath = x.encoding["source"].replace("\\", "/")
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shotNum = np.where(fullFilePath==filePath)
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@ -109,15 +138,42 @@ def _assign_scan_axis_partial(x, datesetOfGlobal, fullFilePath):
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def _update_globals_attrs(variable_attrs, context=None):
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# for live plot panel
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pass
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def update_hdf5_file():
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# for live plot panel
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pass
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def read_hdf5_file(filePath, group=None, datesetOfGlobal=None, preprocess=None, join="outer", parallel=True, engine="h5netcdf", phony_dims="access", excludeAxis=[], maxFileNum=None, **kwargs):
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"""Read the data from HDF5 files in given path.
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:param filePath: The path of HDF5 files, which python glob.glob() can read. It has to end with '.h5'.
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:type filePath: str
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:param group: The path of the group in HDF5 file where data is, defaults to None. Please use '/', instead of '\\'
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:type group: str, optional
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:param datesetOfGlobal: A xarry dataSet stored the global parameters of the data, defaults to None
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:type datesetOfGlobal: xarry DataSet, optional
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:param preprocess: The function you want to run for each file after read before combination, defaults to None
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:type preprocess: a handle to function, optional
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:param join: over write of the same argument in xarray.open_mfdataset, defaults to "outer"
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:type join: str, optional
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:param parallel: over write of the same argument in xarray.open_mfdataset, defaults to True
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:type parallel: bool, optional
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:param engine: The engine to read HDF5 file, defaults to "h5netcdf"
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:type engine: str, optional
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:param phony_dims: Please read the introduction of h5netcdf package, defaults to "access"
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:type phony_dims: str, optional
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:param excludeAxis: The name of axes, whose value changes together with scan axes, defaults to []
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:type excludeAxis: list, optional
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:param maxFileNum: The maximal number of files to read, defaults to None
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:type maxFileNum: int, optional
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:return: A xarray dataSet contain the data read from specified HDF5 file, including scan axes and shot number.
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:rtype: xarray DataSet
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"""
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filePath = np.sort(np.atleast_1d(filePath))
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filePathAbs = []
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@ -192,6 +248,18 @@ def read_hdf5_file(filePath, group=None, datesetOfGlobal=None, preprocess=None,
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def _assign_scan_axis_partial_and_remove_everything(x, datesetOfGlobal, fullFilePath):
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"""Find ONLY and add ONLY the scan axes to the data read from HDF5 file.
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:param x: The data of current shot
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:type x: xarray DataArray
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:param datesetOfGlobal: The xarray DataSet stored the information of global parameters
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:type datesetOfGlobal: xarray DataSet
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:param fullFilePath: The full and absolute file path of current shot
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:type fullFilePath: str
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:return: The data of current shot with scan axes
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:rtype: xarray DataArray
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"""
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scanAxis = datesetOfGlobal.scanAxis
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filePath = x.encoding["source"].replace("\\", "/")
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shotNum = np.where(fullFilePath==filePath)
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@ -209,12 +277,44 @@ def _assign_scan_axis_partial_and_remove_everything(x, datesetOfGlobal, fullFile
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def _read_run_time_from_hdf5(x):
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"""Find the run time of give data read from HDF5 file.
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:param x: The data of current shot
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:type x: xarray DataArray
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:return: The data of current shot with last modification time
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:rtype: xarray DataArray
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"""
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runTime = datetime.strptime(x.attrs['run time'], '%Y%m%dT%H%M%S')
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return runTime
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def read_hdf5_run_time(filePath, group=None, datesetOfGlobal=None, preprocess=None, join="outer", parallel=True, engine="h5netcdf", phony_dims="access", excludeAxis=[], maxFileNum=None, **kwargs):
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"""Read the run time from HDF5 files in given path.
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:param filePath: The path of HDF5 files, which python glob.glob() can read. It has to end with '.h5'.
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:type filePath: str
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:param group: The path of the group in HDF5 file where run time is, defaults to None. Please use '/', instead of '\\'
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:type group: str, optional
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:param datesetOfGlobal: A xarry dataSet stored the global parameters of the data, defaults to None
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:type datesetOfGlobal: xarry DataSet, optional
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:param preprocess: The function you want to run for each file after read before combination, defaults to None
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:type preprocess: a handle to function, optional
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:param join: over write of the same argument in xarray.open_mfdataset, defaults to "outer"
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:type join: str, optional
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:param parallel: over write of the same argument in xarray.open_mfdataset, defaults to True
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:type parallel: bool, optional
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:param engine: The engine to read HDF5 file, defaults to "h5netcdf"
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:type engine: str, optional
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:param phony_dims: Please read the introduction of h5netcdf package, defaults to "access"
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:type phony_dims: str, optional
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:param excludeAxis: The name of axes, whose value changes together with scan axes, defaults to []
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:type excludeAxis: list, optional
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:param maxFileNum: The maximal number of files to read, defaults to None
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:type maxFileNum: int, optional
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:return: A xarray dataSet contain the data read from specified HDF5 file.
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:rtype: xarray DataSet
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"""
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filePath = np.sort(np.atleast_1d(filePath))
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filePathAbs = []
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@ -289,6 +389,29 @@ def read_hdf5_run_time(filePath, group=None, datesetOfGlobal=None, preprocess=No
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def read_hdf5_global(filePath, preprocess=None, join="outer", combine="nested", parallel=True, engine="h5netcdf", phony_dims="access", excludeAxis=[], maxFileNum=None, **kwargs):
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"""Read the global parameters and find scan axes, from HDF5 files in given path.
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:param filePath: The path of HDF5 files, which python glob.glob() can read. It has to end with '.h5'.
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:type filePath: str
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:param preprocess: The function you want to run for each file after read before combination, defaults to None
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:type preprocess: a handle to function, optional
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:param join: over write of the same argument in xarray.open_mfdataset, defaults to "outer"
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:type join: str, optional
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:param combine: over write of the same argument in xarray.open_mfdataset, defaults to "nested"
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:type combine: str, optional
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:param parallel: over write of the same argument in xarray.open_mfdataset, defaults to True
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:type parallel: bool, optional
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:param engine: The engine to read HDF5 file, defaults to "h5netcdf"
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:type engine: str, optional
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:param phony_dims: Please read the introduction of h5netcdf package, defaults to "access"
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:type phony_dims: str, optional
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:param excludeAxis: The name of axes, whose value changes together with scan axes, defaults to []
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:type excludeAxis: list, optional
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:param maxFileNum: The maximal number of files to read, defaults to None
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:type maxFileNum: int, optional
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:return: A xarray dataSet contain the data read from specified HDF5 file.
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:rtype: xarray DataSet
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"""
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filePath = np.sort(np.atleast_1d(filePath))
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@ -330,6 +453,13 @@ def read_hdf5_global(filePath, preprocess=None, join="outer", combine="nested",
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def _read_csv_file_pandas(filePath, **kwargs):
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"""Read csv file using pandas package function read_csv()
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:param filePath:The path of csv files.
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:type filePath: str
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:return: A xarray DataSet stored the data
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:rtype: xarray DataSet
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"""
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res = pd.read_csv(filePath, **kwargs)
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@ -338,6 +468,13 @@ def _read_csv_file_pandas(filePath, **kwargs):
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def _read_csv_file_dask(filePath, **kwargs):
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"""Read csv file using dask package function read_csv()
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:param filePath:The path of csv files.
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:type filePath: str
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:return: A xarray DataSet stored the data
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:rtype: xarray DataSet
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"""
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res = df.read_csv(filePath, **kwargs)
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@ -346,6 +483,26 @@ def _read_csv_file_dask(filePath, **kwargs):
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def read_csv_file(filePath, maxFileNum=None, dask='parallelized', vectorize=True, csvEngine='pandas', daskKwargs={}, csvKwargs={}, **kwargs):
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"""Read the data from csv files in given path.
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:param filePath: The path of csv files, which python glob.glob() can read. It has to end with '.csv'.
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:type filePath: str
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:param maxFileNum: The maximal number of files to read, defaults to None
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:type maxFileNum: int, optional
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:param dask: over write of the same argument in xarray.apply_ufunc, defaults to 'parallelized'
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:type dask: str, optional
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:param vectorize: over write of the same argument in xarray.apply_ufunc, defaults to True
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:type vectorize: bool, optional
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:param csvEngine: The engine to read csv file, defaults to 'pandas'
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:type csvEngine: str, optional
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:param daskKwargs: over write of the same argument in xarray.apply_ufunc, defaults to {}
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:type daskKwargs: dict, optional
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:param csvKwargs: The kwargs send to csvEngine, defaults to {}
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:type csvKwargs: dict, optional
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:return: A xarray DataSet stored the data
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:rtype: xarray DataSet
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"""
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filePath = np.sort(np.atleast_1d(filePath))
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filePathAbs = []
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@ -9,7 +9,7 @@ import xarray as xr
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def get_mask(dataArray):
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"""generate a bool mask array for given dataArray
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"""Generate a bool mask array for given dataArray
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:param dataArray: The given dataArray
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:type dataArray: xarray DataArray
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@ -20,7 +20,10 @@ def get_mask(dataArray):
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def remove_bad_shots(dataArray, **kwargs):
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"""copy and remove bad shots from the dataArray
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"""Copy and remove bad shots from the dataArray by setting the value to np.nan.
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If you want fully delete those nan data, please use the function xarray.DataArray.dropna() (see this link https://docs.xarray.dev/en/stable/generated/xarray.DataArray.dropna.html).
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Here is an example for indexing the bad shots: remove_bad_shots(dataArray, axis_1 = the value (not index) of axis_1, axis_2 = the value of axis_2, ...)
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For more detials please read 'Positional indexing' section in this link https://docs.xarray.dev/en/stable/user-guide/indexing.html#positional-indexing.
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:param dataArray: The given dataArray
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:type dataArray: xarray DataArray
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@ -33,7 +36,7 @@ def remove_bad_shots(dataArray, **kwargs):
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def auto_rechunk(dataSet):
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"""rechunk the dataSet or dataArray using auto rechunk function
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"""Rechunk the dataSet or dataArray using auto rechunk function
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:param dataSet: The given dataArray or dataSet
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:type dataSet: xarray DataArray or xarray DataSet
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@ -48,7 +51,7 @@ def auto_rechunk(dataSet):
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def copy_chunk(dataSet, dataChunk):
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"""copy the chunk and apply to another dataArray or dataSet
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"""Copy the chunk and apply to another dataArray or dataSet
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:param dataSet: The dataArray or dataSet will be chunked
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:type dataSet: xarray DataArray or xarray DataSet
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@ -66,16 +69,16 @@ def copy_chunk(dataSet, dataChunk):
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def get_h5_file_path(folderpath, maxFileNum=None, filename='*.h5',):
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"""_summary_
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"""Get all the path of HDF5 files in specific folder
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:param folderpath: _description_
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:type folderpath: _type_
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:param maxFileNum: _description_, defaults to None
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:type maxFileNum: _type_, optional
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:param filename: _description_, defaults to '*.h5'
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:param folderpath: the path of the folder
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:type folderpath: str
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:param maxFileNum: the maximal number of returned files, defaults to None
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:type maxFileNum: int, optional
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:param filename: a string to specify the type of the file to read, defaults to '*.h5'
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:type filename: str, optional
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:return: _description_
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:rtype: _type_
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:return: the found file path
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:rtype: 1D numpy array
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"""
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filepath = np.sort(glob.glob(folderpath + filename))
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if maxFileNum is None:
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@ -85,15 +88,42 @@ def get_h5_file_path(folderpath, maxFileNum=None, filename='*.h5',):
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def get_date():
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"""Return the date of today in a format compatible with file path
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:return: the date of today in a format compatible with file path
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:rtype: str
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"""
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today = date.today()
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return today.strftime("%Y/%m/%d")
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def _combine_uncertainty(value, std):
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"""Give a list of value and standard deviation, combine them to a number with unceuncertainty (ufloat), and return them in another list.
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See this link https://pythonhosted.org/uncertainties/
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:param value: The value
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:type value: float, or array like
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:param std: The standard deviation
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:type std: float, or array like
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:return: The combined value and standard deviation
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:rtype: ufloat, or uncertainties uarray
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"""
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return unp.uarray(value, std)
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def combine_uncertainty(value, std, dask='parallelized', **kwargs):
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"""Give a xarray DataArray of value and standard deviation, combine them to a number with unceuncertainty (ufloat), and return them in another xarray DataArray .
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See this link https://pythonhosted.org/uncertainties/
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:param value: The value
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:type value: xarray DataArray
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:param std: The standard deviation
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:type std: xarray DataArray
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:param dask: over write of the same argument in xarray.apply_ufunc, defaults to 'parallelized'
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:type dask: str, optional
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:return: The combined value and standard deviation
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:rtype: xarray DataArray
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"""
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kwargs.update(
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{
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@ -105,15 +135,38 @@ def combine_uncertainty(value, std, dask='parallelized', **kwargs):
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def _seperate_uncertainty_single(data):
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"""From a number with unceuncertainty, read out the value and standard deviation
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:param data: The number with unceuncertainty
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:type data: ufloat
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:return: a tuple of (value, standard deviations)
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:rtype: tuple of two floats
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"""
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return data.n, data.s
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def _seperate_uncertainty(data):
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"""From a list of numbers with unceuncertainty, read out the values and standard deviations
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:param data: The list of numbers with unceuncertainty
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:type data: ufloat, or uncertainties uarray
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:return: a tuple of (a numpy array of value, a numpy array of standard deviations)
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:rtype: tuple of two numpy arrays
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"""
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func = np.vectorize(_seperate_uncertainty_single)
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return func(data)
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def seperate_uncertainty(data, dask='parallelized', **kwargs):
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"""From a xarray DataArray of numbers with unceuncertainty, read out the values and standard deviations
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:param data: The xarray DataArray of numbers with unceuncertainty
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:type data: xarray DataArray
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:param dask: over write of the same argument in xarray.apply_ufunc, defaults to 'parallelized'
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:type dask: str, optional
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:return: a tuple of (a xarray DataArray of value, a xarray DataArray of standard deviations)
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:rtype: tuple of two xarray DataArray
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"""
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kwargs.update(
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{
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@ -126,6 +179,14 @@ def seperate_uncertainty(data, dask='parallelized', **kwargs):
|
||||
|
||||
|
||||
def get_scanAxis(dataSet):
|
||||
"""Give a numpy array of names of scan axes.
|
||||
|
||||
:param dataSet: The xarray DataSet stored the data.
|
||||
:type dataSet: xarray DataSet
|
||||
:return: The names of scan axes
|
||||
:rtype: a numpy array
|
||||
"""
|
||||
|
||||
res = dataSet.scanAxis
|
||||
|
||||
if len(res) == 0:
|
||||
@ -139,6 +200,11 @@ def get_scanAxis(dataSet):
|
||||
|
||||
|
||||
def print_scanAxis(dataSet):
|
||||
"""Print the names and the values of scan axes.
|
||||
|
||||
:param dataSet: The xarray DataSet stored the data.
|
||||
:type dataSet: xarray DataSet
|
||||
"""
|
||||
scanAxis = dataSet.scanAxis
|
||||
|
||||
scan = {}
|
||||
@ -153,9 +219,21 @@ def print_scanAxis(dataSet):
|
||||
)
|
||||
print("The detected scaning axes and values are: \n")
|
||||
print(scan)
|
||||
|
||||
|
||||
def print_scanAxis_original(dataSet):
|
||||
# print the original (unsorted) scan axes.
|
||||
pass
|
||||
|
||||
|
||||
def calculate_mean(dataSet):
|
||||
"""Calculte the mean along repetition axis 'runs'
|
||||
|
||||
:param dataSet: The xarray DataSet or DataArray stored the data.
|
||||
:type dataSet: xarray DataSet or DataArray
|
||||
:return: The mean value
|
||||
:rtype: xarray DataSet or DataArray
|
||||
"""
|
||||
if 'runs' in dataSet.dims:
|
||||
return dataSet.mean(dim='runs')
|
||||
else:
|
||||
@ -163,6 +241,13 @@ def calculate_mean(dataSet):
|
||||
|
||||
|
||||
def calculate_std(dataSet):
|
||||
"""Calculte the standard deviation along repetition axis 'runs'
|
||||
|
||||
:param dataSet: The xarray DataSet or DataArray stored the data.
|
||||
:type dataSet: xarray DataSet or DataArray
|
||||
:return: The standard deviation
|
||||
:rtype: xarray DataSet or DataArray
|
||||
"""
|
||||
if 'runs' in dataSet.dims:
|
||||
return dataSet.std(dim='runs')
|
||||
else:
|
||||
@ -170,13 +255,22 @@ def calculate_std(dataSet):
|
||||
|
||||
|
||||
def extract_temperature_from_fit():
|
||||
# by giving the shot number, read data, fit the center of could and fit with ToF, give the temperature of the clound
|
||||
pass
|
||||
|
||||
|
||||
def extract_condensate_fraction_from_fit():
|
||||
# by giving the shot number, read data, fit the of could, give the condenstate fraction of the clound
|
||||
pass
|
||||
|
||||
def swap_xy(dataSet):
|
||||
"""Swap the x ans y axis.
|
||||
|
||||
:param dataSet: The xarray DataSet or DataArray stored the data.
|
||||
:type dataSet: xarray DataSet or DataArray
|
||||
:return: The xarray DataSet or DataArray with swapped x and y axis.
|
||||
:rtype: xarray DataSet or DataArray
|
||||
"""
|
||||
dataSet = dataSet.rename_dims(dict(x='__x'))
|
||||
dataSet = dataSet.rename_dims(dict(y='x'))
|
||||
dataSet = dataSet.rename_dims(dict(__x='y'))
|
||||
|
||||
Loading…
Reference in New Issue
Block a user