def import_data(input_file, file_type=None):
"""Convenience function for importing recognized file formats and saving the results to NDIToolbox data folder.
Primarily used for multiprocess pools.
input_file - name of the input data file. If file_type is not specified (default),
type of file is assumed based on file extension.
file_type - (optional) specify the file format. Must be one of the file formats
supported by NDIToolbox. Currently supported: 'image', 'nditoolbox',
'utwin', 'csv', 'winspect', 'dicom' (use the available_file_types
function to retrieve a list of supported types). If not specified,
format is assumed based on file extension.
"""
data = read_data(input_file, file_type)
if hasattr(data, "keys"):
# Handle multiple datasets
for dataset in data:
root, ext = os.path.splitext(os.path.basename(input_file))
output_fname = os.path.join(pathfinder.data_path(), root + "_" + dataset + ".hdf5")
dataio.save_data(output_fname, data[dataset])
else:
# Handle single dataset
root, ext = os.path.splitext(os.path.basename(input_file))
output_fname = os.path.join(pathfinder.data_path(), root + ".hdf5")
dataio.save_data(output_fname, data)
def on_slice_data(self, evt):
"""Handles request to export a slice of data"""
slice_dlg = dlg.ExportSliceDialog(parent=self.view, datafile=self.view.data_panel.data)
if slice_dlg.ShowModal() == wx.ID_OK:
try:
wx.BeginBusyCursor()
sliced_data = dataio.get_data(self.view.data_panel.data, slice_dlg.get_slice())
sliced_data_fname = "_".join(["sliced",
os.path.basename(self.view.data_panel.data)])
fname_dlg = wx.TextEntryDialog(parent=self.view, message="Please specify a filename for the sliced data.",
caption="Save Sliced Data", defaultValue=sliced_data_fname)
if fname_dlg.ShowModal() == wx.ID_OK:
dest_fname = os.path.join(pathfinder.data_path(), fname_dlg.GetValue())
dataio.save_data(dest_fname, sliced_data)
self.view.data_panel.populate()
except TypeError: # bad dimensions
err_dlg = wx.MessageDialog(self.view, message="Specified dimensions out of range for this data.",
caption="Unable To Slice Data", style=wx.ICON_ERROR)
err_dlg.ShowModal()
err_dlg.Destroy()
except ValueError: # zero-length slices, etc.
err_dlg = wx.MessageDialog(self.view, message="Zero-length slices are not permitted.",
caption="Unable To Slice Data", style=wx.ICON_ERROR)
err_dlg.ShowModal()
err_dlg.Destroy()
finally:
wx.EndBusyCursor()
slice_dlg.Destroy()
def on_slice_data(self, evt):
"""Handles request to export a slice of data"""
slice_dlg = dlg.ExportSliceDialog(parent=self.view, datafile=self.view.data_panel.data)
if slice_dlg.ShowModal() == wx.ID_OK:
try:
wx.BeginBusyCursor()
sliced_data = dataio.get_data(self.view.data_panel.data, slice_dlg.get_slice())
sliced_data_fname = "_".join(["sliced",
os.path.basename(self.view.data_panel.data)])
fname_dlg = wx.TextEntryDialog(parent=self.view, message="Please specify a filename for the sliced data.",
caption="Save Sliced Data", defaultValue=sliced_data_fname)
if fname_dlg.ShowModal() == wx.ID_OK:
dest_fname = os.path.join(pathfinder.data_path(), fname_dlg.GetValue())
dataio.save_data(dest_fname, sliced_data)
self.view.data_panel.populate()
except TypeError: # bad dimensions
err_dlg = wx.MessageDialog(self.view, message="Specified dimensions out of range for this data.",
caption="Unable To Slice Data", style=wx.ICON_ERROR)
err_dlg.ShowModal()
err_dlg.Destroy()
except ValueError: # zero-length slices, etc.
err_dlg = wx.MessageDialog(self.view, message="Zero-length slices are not permitted.",
caption="Unable To Slice Data", style=wx.ICON_ERROR)
err_dlg.ShowModal()
err_dlg.Destroy()
finally:
wx.EndBusyCursor()
slice_dlg.Destroy()
def run_plugin(toolkit, input_file, toolkit_config=None, file_type=None, save_data=True):
"""Convenience function for creating and executing BatchPluginAdapters and optionally saving
results to NDIToolbox data folder, e.g. for multiprocessing Pools.
toolkit - name of plugin class (NOT name of plugin file): e.g. MedianFilterPlugin,
not medfilter_plugin.py. Must be an installed NDIToolbox plugin.
input_file - name of the input data file. If file_type is not specified (default),
type of file is assumed based on file extension.
toolkit_config - (optional) JSON configuration file for the toolkit.
file_type - (optional) specify the file format. Must be one of the file formats
supported by NDIToolbox. Currently supported: 'image', 'nditoolbox',
'utwin', 'csv', 'winspect', 'dicom' (use the available_file_types
function to retrieve a list of supported types). If not specified,
format is assumed based on file extension.
save_data - (optional) if True, resultant data are saved to a new HDF5 data file with
the same basename as the input file. Defaults to True.
"""
batch_runner = BatchPluginAdapter(toolkit, input_file, toolkit_cfg=toolkit_config, filetype=file_type)
batch_runner.run()
if save_data:
if hasattr(batch_runner.data, "keys"):
# Handle multiple datasets
for dataset in batch_runner.data:
root, ext = os.path.splitext(os.path.basename(input_file))
output_fname = os.path.join(pathfinder.batchoutput_path(), root + "_" + dataset + ".hdf5")
dataio.save_data(output_fname, batch_runner.data[dataset])
else:
# Handle single dataset
root, ext = os.path.splitext(os.path.basename(input_file))
output_fname = os.path.join(pathfinder.batchoutput_path(), root + ".hdf5")
dataio.save_data(output_fname, batch_runner._data)
def on_save_data(self, evt):
"""Handles request to save current data set to disk"""
default_path, default_file = os.path.split(self.model.data_file)
wild_card = "NDIToolbox data files (*.hdf5)|*.hdf5|All files (*.*)|*.*"
save_dlg = wx.FileDialog(self.view, message="Save File As...",
defaultDir=default_path,
defaultFile=default_file,
wildcard=wild_card,
style=wx.SAVE | wx.OVERWRITE_PROMPT)
if save_dlg.ShowModal() == wx.ID_OK:
dataio.save_data(save_dlg.GetPath(), self.data)
self.view.parent.refresh()
save_dlg.Destroy()
def test_save_data(self):
"""Verify save_data function saves NumPy array to disk"""
sample_filename = "test_savedata.dat"
sample_path = os.path.join(os.path.dirname(__file__), sample_filename)
dataio.save_data(sample_path, self.sample_data)
self.assertTrue(os.path.exists(sample_path + ".hdf5"))
with h5py.File(sample_path + ".hdf5", "r") as fidin:
froot, ext = os.path.splitext(os.path.basename(sample_filename))
for key in fidin.keys():
if key.startswith(froot):
read_data = fidin[key][...]
self.assertTrue(np.array_equal(self.sample_data, read_data))
if os.path.exists(sample_path + ".hdf5"):
os.remove(sample_path + ".hdf5")
def on_save_data(self, evt):
"""Handles request to save current data set to disk"""
default_path, default_file = os.path.split(self.model.data_file)
wild_card = "NDIToolbox data files (*.hdf5)|*.hdf5|All files (*.*)|*.*"
save_dlg = wx.FileDialog(self.view,
message="Save File As...",
defaultDir=default_path,
defaultFile=default_file,
wildcard=wild_card,
style=wx.SAVE | wx.OVERWRITE_PROMPT)
if save_dlg.ShowModal() == wx.ID_OK:
dataio.save_data(save_dlg.GetPath(), self.data)
self.view.parent.refresh()
save_dlg.Destroy()
def test_save_data(self):
"""Verify save_data function saves NumPy array to disk"""
sample_filename = "test_savedata.dat"
sample_path = os.path.join(os.path.dirname(__file__), sample_filename)
dataio.save_data(sample_path, self.sample_data)
self.assertTrue(os.path.exists(sample_path + ".hdf5"))
with h5py.File(sample_path + ".hdf5", "r") as fidin:
froot, ext = os.path.splitext(os.path.basename(sample_filename))
for key in fidin.keys():
if key.startswith(froot):
read_data = fidin[key][...]
self.assertTrue(np.array_equal(self.sample_data,
read_data))
if os.path.exists(sample_path + ".hdf5"):
os.remove(sample_path + ".hdf5")
def create_datafile(self, ext=".hdf5"):
"""Returns a NamedTemporaryFile containing NumPy data. Caller responsible for deletion."""
temp_file = tempfile.NamedTemporaryFile(suffix=ext, delete=False)
rnd_data = [random.uniform(-5, 5) for i in range(11)]
dataio.save_data(temp_file.name, np.array(rnd_data))
return temp_file.name
def create_datafile(self, ext=".hdf5"):
"""Returns a NamedTemporaryFile containing NumPy data. Caller responsible for deletion."""
temp_file = tempfile.NamedTemporaryFile(suffix=ext, delete=False)
rnd_data = [random.uniform(-5, 5) for i in range(11)]
dataio.save_data(temp_file.name, np.array(rnd_data))
return temp_file.name
def save_data(cls, file_name, data):
"""Saves NumPy array data to the specified file name"""
dataio.save_data(file_name, data)
def save_data(cls, file_name, data):
"""Saves NumPy array data to the specified file name"""
dataio.save_data(file_name, data)
