diff options
Diffstat (limited to 'examples/hdf5widget.py')
| -rwxr-xr-x | examples/hdf5widget.py | 357 |
1 files changed, 214 insertions, 143 deletions
diff --git a/examples/hdf5widget.py b/examples/hdf5widget.py index 0d45b8f..187480e 100755 --- a/examples/hdf5widget.py +++ b/examples/hdf5widget.py @@ -1,8 +1,7 @@ #!/usr/bin/env python -# coding: utf-8 # /*########################################################################## # -# Copyright (c) 2016-2019 European Synchrotron Radiation Facility +# Copyright (c) 2016-2021 European Synchrotron Radiation Facility # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal @@ -25,12 +24,12 @@ # ###########################################################################*/ """Qt Hdf5 widget examples""" +import html import logging import sys import tempfile import numpy -import six logging.basicConfig() _logger = logging.getLogger("hdf5widget") @@ -40,13 +39,14 @@ try: # it should be loaded before h5py import hdf5plugin # noqa except ImportError: - message = "Module 'hdf5plugin' is not installed. It supports some hdf5"\ - + " compressions. You can install it using \"pip install hdf5plugin\"." + message = ( + "Module 'hdf5plugin' is not installed. It supports some hdf5" + + ' compressions. You can install it using "pip install hdf5plugin".' + ) _logger.warning(message) import h5py import silx.gui.hdf5 -import silx.utils.html from silx.gui import qt from silx.gui.data.DataViewerFrame import DataViewerFrame from silx.gui.widgets.ThreadPoolPushButton import ThreadPoolPushButton @@ -59,7 +59,7 @@ _file_cache = {} def str_attrs(str_list): """Return a numpy array of unicode strings""" - text_dtype = h5py.special_dtype(vlen=six.text_type) + text_dtype = h5py.special_dtype(vlen=str) return numpy.array(str_list, dtype=text_dtype) @@ -76,10 +76,12 @@ def get_hdf5_with_all_types(): g.create_dataset("scalar", data=10) g.create_dataset("list", data=numpy.arange(10)) base_image = numpy.arange(10**2).reshape(10, 10) - images = [ base_image, - base_image.T, - base_image.size - 1 - base_image, - base_image.size - 1 - base_image.T] + images = [ + base_image, + base_image.T, + base_image.size - 1 - base_image, + base_image.size - 1 - base_image.T, + ] dtype = images[0].dtype data = numpy.empty((10 * 10, 10, 10), dtype=dtype) for i in range(10 * 10): @@ -127,8 +129,12 @@ def get_hdf5_with_all_links(): alltypes_filename = get_hdf5_with_all_types() h5["external_link_to_group"] = h5py.ExternalLink(alltypes_filename, "/arrays") - h5["external_link_to_dataset"] = h5py.ExternalLink(alltypes_filename, "/arrays/cube") - h5["external_link_to_nothing"] = h5py.ExternalLink(alltypes_filename, "/foo/bar/2000") + h5["external_link_to_dataset"] = h5py.ExternalLink( + alltypes_filename, "/arrays/cube" + ) + h5["external_link_to_nothing"] = h5py.ExternalLink( + alltypes_filename, "/foo/bar/2000" + ) h5["external_link_to_missing_file"] = h5py.ExternalLink("missing_file.h5", "/") h5.close() @@ -233,14 +239,18 @@ def get_hdf5_with_external_recursive_links(): g.create_dataset("dataset", data=numpy.int64(10)) h5_1["soft_link_to_group"] = h5py.SoftLink("/group") h5_1["external_link_to_link"] = h5py.ExternalLink(tmp2.name, "/soft_link_to_group") - h5_1["external_link_to_recursive_link"] = h5py.ExternalLink(tmp2.name, "/external_link_to_recursive_link") + h5_1["external_link_to_recursive_link"] = h5py.ExternalLink( + tmp2.name, "/external_link_to_recursive_link" + ) h5_1.close() g = h5_2.create_group("group") g.create_dataset("dataset", data=numpy.int64(10)) h5_2["soft_link_to_group"] = h5py.SoftLink("/group") h5_2["external_link_to_link"] = h5py.ExternalLink(tmp1.name, "/soft_link_to_group") - h5_2["external_link_to_recursive_link"] = h5py.ExternalLink(tmp1.name, "/external_link_to_recursive_link") + h5_2["external_link_to_recursive_link"] = h5py.ExternalLink( + tmp1.name, "/external_link_to_recursive_link" + ) h5_2.close() _file_cache[ID] = (tmp1, tmp2) @@ -260,186 +270,197 @@ def get_hdf5_with_nxdata(): g0d = h5.create_group("scalars") g0d0 = g0d.create_group("0D_scalar") - g0d0.attrs["NX_class"] = u"NXdata" - g0d0.attrs["signal"] = u"scalar" + g0d0.attrs["NX_class"] = "NXdata" + g0d0.attrs["signal"] = "scalar" g0d0.create_dataset("scalar", data=10) g0d1 = g0d.create_group("2D_scalars") - g0d1.attrs["NX_class"] = u"NXdata" - g0d1.attrs["signal"] = u"scalars" - ds = g0d1.create_dataset("scalars", data=numpy.arange(3*10).reshape((3, 10))) - ds.attrs["interpretation"] = u"scalar" + g0d1.attrs["NX_class"] = "NXdata" + g0d1.attrs["signal"] = "scalars" + ds = g0d1.create_dataset("scalars", data=numpy.arange(3 * 10).reshape((3, 10))) + ds.attrs["interpretation"] = "scalar" g0d1 = g0d.create_group("4D_scalars") - g0d1.attrs["NX_class"] = u"NXdata" - g0d1.attrs["signal"] = u"scalars" - ds = g0d1.create_dataset("scalars", data=numpy.arange(2*2*3*10).reshape((2, 2, 3, 10))) - ds.attrs["interpretation"] = u"scalar" + g0d1.attrs["NX_class"] = "NXdata" + g0d1.attrs["signal"] = "scalars" + ds = g0d1.create_dataset( + "scalars", data=numpy.arange(2 * 2 * 3 * 10).reshape((2, 2, 3, 10)) + ) + ds.attrs["interpretation"] = "scalar" # SPECTRA g1d = h5.create_group("spectra") g1d0 = g1d.create_group("1D_spectrum") - g1d0.attrs["NX_class"] = u"NXdata" - g1d0.attrs["signal"] = u"count" + g1d0.attrs["NX_class"] = "NXdata" + g1d0.attrs["signal"] = "count" g1d0.attrs["auxiliary_signals"] = str_attrs(["count.5", "count2"]) - g1d0.attrs["axes"] = u"energy_calib" + g1d0.attrs["axes"] = "energy_calib" g1d0.attrs["uncertainties"] = str_attrs(["energy_errors"]) g1d0.create_dataset("count", data=numpy.arange(10)) - g1d0.create_dataset("count.5", data=.5*numpy.arange(10)) - d2 = g1d0.create_dataset("count2", data=2*numpy.arange(10)) - d2.attrs["long_name"] = u"count multiplied by 2" - g1d0.create_dataset("energy_calib", data=(10, 5)) # 10 * idx + 5 - g1d0.create_dataset("energy_errors", data=3.14*numpy.random.rand(10)) + g1d0.create_dataset("count.5", data=0.5 * numpy.arange(10)) + d2 = g1d0.create_dataset("count2", data=2 * numpy.arange(10)) + d2.attrs["long_name"] = "count multiplied by 2" + g1d0.create_dataset("energy_calib", data=(10, 5)) # 10 * idx + 5 + g1d0.create_dataset("energy_errors", data=3.14 * numpy.random.rand(10)) g1d0.create_dataset("title", data="Title example provided as dataset") g1d1 = g1d.create_group("2D_spectra") - g1d1.attrs["NX_class"] = u"NXdata" - g1d1.attrs["signal"] = u"counts" - ds = g1d1.create_dataset("counts", data=numpy.arange(3*10).reshape((3, 10))) - ds.attrs["interpretation"] = u"spectrum" + g1d1.attrs["NX_class"] = "NXdata" + g1d1.attrs["signal"] = "counts" + ds = g1d1.create_dataset("counts", data=numpy.arange(3 * 10).reshape((3, 10))) + ds.attrs["interpretation"] = "spectrum" g1d2 = g1d.create_group("4D_spectra") - g1d2.attrs["NX_class"] = u"NXdata" - g1d2.attrs["signal"] = u"counts" + g1d2.attrs["NX_class"] = "NXdata" + g1d2.attrs["signal"] = "counts" g1d2.attrs["axes"] = str_attrs(["energy"]) - ds = g1d2.create_dataset("counts", data=numpy.arange(2*2*3*10).reshape((2, 2, 3, 10))) - ds.attrs["interpretation"] = u"spectrum" - g1d2.create_dataset("errors", data=4.5*numpy.random.rand(2, 2, 3, 10)) - ds = g1d2.create_dataset("energy", data=5+10*numpy.arange(15), - shuffle=True, compression="gzip") - ds.attrs["long_name"] = u"Calibrated energy" + ds = g1d2.create_dataset( + "counts", data=numpy.arange(2 * 2 * 3 * 10).reshape((2, 2, 3, 10)) + ) + ds.attrs["interpretation"] = "spectrum" + g1d2.create_dataset("errors", data=4.5 * numpy.random.rand(2, 2, 3, 10)) + ds = g1d2.create_dataset( + "energy", data=5 + 10 * numpy.arange(15), shuffle=True, compression="gzip" + ) + ds.attrs["long_name"] = "Calibrated energy" ds.attrs["first_good"] = 3 ds.attrs["last_good"] = 12 - g1d2.create_dataset("energy_errors", data=10*numpy.random.rand(15)) + g1d2.create_dataset("energy_errors", data=10 * numpy.random.rand(15)) # IMAGES g2d = h5.create_group("images") g2d0 = g2d.create_group("2D_regular_image") - g2d0.attrs["NX_class"] = u"NXdata" - g2d0.attrs["signal"] = u"image" + g2d0.attrs["NX_class"] = "NXdata" + g2d0.attrs["signal"] = "image" g2d0.attrs["auxiliary_signals"] = str_attrs(["image2", "image3"]) g2d0.attrs["axes"] = str_attrs(["rows_calib", "columns_coordinates"]) - g2d0.attrs["title"] = u"Title example provided as group attr" - g2d0.create_dataset("image", data=numpy.arange(4*6).reshape((4, 6))) - g2d0.create_dataset("image2", data=1/(1.+numpy.arange(4*6).reshape((4, 6)))) - ds = g2d0.create_dataset("image3", data=-numpy.arange(4*6).reshape((4, 6))) - ds.attrs["long_name"] = u"3rd image (2nd auxiliary)" + g2d0.attrs["title"] = "Title example provided as group attr" + g2d0.create_dataset("image", data=numpy.arange(4 * 6).reshape((4, 6))) + g2d0.create_dataset("image2", data=1 / (1.0 + numpy.arange(4 * 6).reshape((4, 6)))) + ds = g2d0.create_dataset("image3", data=-numpy.arange(4 * 6).reshape((4, 6))) + ds.attrs["long_name"] = "3rd image (2nd auxiliary)" ds = g2d0.create_dataset("rows_calib", data=(10, 5)) - ds.attrs["long_name"] = u"Calibrated Y" - g2d0.create_dataset("columns_coordinates", data=0.5+0.02*numpy.arange(6)) + ds.attrs["long_name"] = "Calibrated Y" + g2d0.create_dataset("columns_coordinates", data=0.5 + 0.02 * numpy.arange(6)) g2d4 = g2d.create_group("RGBA_image") - g2d4.attrs["NX_class"] = u"NXdata" - g2d4.attrs["signal"] = u"image" - g2d4.attrs["auxiliary_signals"] = u"squared image" + g2d4.attrs["NX_class"] = "NXdata" + g2d4.attrs["signal"] = "image" + g2d4.attrs["auxiliary_signals"] = "squared image" g2d4.attrs["axes"] = str_attrs(["rows_calib", "columns_coordinates"]) - rgba_image = numpy.linspace(0, 1, num=7*8*3).reshape((7, 8, 3)) - rgba_image[:, :, 1] = 1 - rgba_image[:, :, 1] # invert G channel to add some color + rgba_image = numpy.linspace(0, 1, num=7 * 8 * 3).reshape((7, 8, 3)) + rgba_image[:, :, 1] = 1 - rgba_image[:, :, 1] # invert G channel to add some color ds = g2d4.create_dataset("image", data=rgba_image) - ds.attrs["interpretation"] = u"rgba-image" + ds.attrs["interpretation"] = "rgba-image" ds = g2d4.create_dataset("squared image", data=rgba_image**2) - ds.attrs["interpretation"] = u"rgba-image" + ds.attrs["interpretation"] = "rgba-image" ds = g2d4.create_dataset("rows_calib", data=(10, 5)) - ds.attrs["long_name"] = u"Calibrated Y" - g2d4.create_dataset("columns_coordinates", data=0.5+0.02*numpy.arange(8)) + ds.attrs["long_name"] = "Calibrated Y" + g2d4.create_dataset("columns_coordinates", data=0.5 + 0.02 * numpy.arange(8)) g2d1 = g2d.create_group("2D_irregular_data") - g2d1.attrs["NX_class"] = u"NXdata" - g2d1.attrs["signal"] = u"data" + g2d1.attrs["NX_class"] = "NXdata" + g2d1.attrs["signal"] = "data" g2d1.attrs["axes"] = str_attrs(["rows_coordinates", "columns_coordinates"]) - g2d1.create_dataset("data", data=numpy.arange(64*128).reshape((64, 128))) - g2d1.create_dataset("rows_coordinates", data=numpy.arange(64) + numpy.random.rand(64)) - g2d1.create_dataset("columns_coordinates", data=numpy.arange(128) + 2.5 * numpy.random.rand(128)) + g2d1.create_dataset("data", data=numpy.arange(64 * 128).reshape((64, 128))) + g2d1.create_dataset( + "rows_coordinates", data=numpy.arange(64) + numpy.random.rand(64) + ) + g2d1.create_dataset( + "columns_coordinates", data=numpy.arange(128) + 2.5 * numpy.random.rand(128) + ) g2d2 = g2d.create_group("3D_images") - g2d2.attrs["NX_class"] = u"NXdata" - g2d2.attrs["signal"] = u"images" - ds = g2d2.create_dataset("images", data=numpy.arange(2*4*6).reshape((2, 4, 6))) - ds.attrs["interpretation"] = u"image" + g2d2.attrs["NX_class"] = "NXdata" + g2d2.attrs["signal"] = "images" + ds = g2d2.create_dataset("images", data=numpy.arange(2 * 4 * 6).reshape((2, 4, 6))) + ds.attrs["interpretation"] = "image" g2d3 = g2d.create_group("5D_images") - g2d3.attrs["NX_class"] = u"NXdata" - g2d3.attrs["signal"] = u"images" + g2d3.attrs["NX_class"] = "NXdata" + g2d3.attrs["signal"] = "images" g2d3.attrs["axes"] = str_attrs(["rows_coordinates", "columns_coordinates"]) - ds = g2d3.create_dataset("images", data=numpy.arange(2*2*2*4*6).reshape((2, 2, 2, 4, 6))) - ds.attrs["interpretation"] = u"image" - g2d3.create_dataset("rows_coordinates", data=5+10*numpy.arange(4)) - g2d3.create_dataset("columns_coordinates", data=0.5+0.02*numpy.arange(6)) + ds = g2d3.create_dataset( + "images", data=numpy.arange(2 * 2 * 2 * 4 * 6).reshape((2, 2, 2, 4, 6)) + ) + ds.attrs["interpretation"] = "image" + g2d3.create_dataset("rows_coordinates", data=5 + 10 * numpy.arange(4)) + g2d3.create_dataset("columns_coordinates", data=0.5 + 0.02 * numpy.arange(6)) # SCATTER g = h5.create_group("scatters") gd0 = g.create_group("x_y_scatter") - gd0.attrs["NX_class"] = u"NXdata" - gd0.attrs["signal"] = u"y" + gd0.attrs["NX_class"] = "NXdata" + gd0.attrs["signal"] = "y" gd0.attrs["axes"] = str_attrs(["x"]) - gd0.attrs["title"] = u"simple y = f(x) scatters cannot be distinguished from curves" + gd0.attrs["title"] = "simple y = f(x) scatters cannot be distinguished from curves" gd0.create_dataset("y", data=numpy.random.rand(128) - 0.5) - gd0.create_dataset("x", data=2*numpy.random.rand(128)) - gd0.create_dataset("x_errors", data=0.05*numpy.random.rand(128)) - gd0.create_dataset("errors", data=0.05*numpy.random.rand(128)) + gd0.create_dataset("x", data=2 * numpy.random.rand(128)) + gd0.create_dataset("x_errors", data=0.05 * numpy.random.rand(128)) + gd0.create_dataset("errors", data=0.05 * numpy.random.rand(128)) gd1 = g.create_group("x_y_value_scatter") - gd1.attrs["NX_class"] = u"NXdata" - gd1.attrs["signal"] = u"values" + gd1.attrs["NX_class"] = "NXdata" + gd1.attrs["signal"] = "values" gd1.attrs["auxiliary_signals"] = str_attrs(["values.5", "values2"]) gd1.attrs["axes"] = str_attrs(["x", "y"]) - gd1.attrs["title"] = u"x, y, values scatter with asymmetric y_errors" - gd1.create_dataset("values", data=3.14*numpy.random.rand(128)) - gd1.create_dataset("values.5", data=0.5*3.14*numpy.random.rand(128)) - gd1.create_dataset("values2", data=2.*3.14*numpy.random.rand(128)) + gd1.attrs["title"] = "x, y, values scatter with asymmetric y_errors" + gd1.create_dataset("values", data=3.14 * numpy.random.rand(128)) + gd1.create_dataset("values.5", data=0.5 * 3.14 * numpy.random.rand(128)) + gd1.create_dataset("values2", data=2.0 * 3.14 * numpy.random.rand(128)) gd1.create_dataset("y", data=numpy.random.rand(128)) - y_errors = [0.03*numpy.random.rand(128), 0.04*numpy.random.rand(128)] + y_errors = [0.03 * numpy.random.rand(128), 0.04 * numpy.random.rand(128)] gd1.create_dataset("y_errors", data=y_errors) - ds = gd1.create_dataset("x", data=2*numpy.random.rand(128)) - ds.attrs["long_name"] = u"horizontal axis" - gd1.create_dataset("x_errors", data=0.02*numpy.random.rand(128)) + ds = gd1.create_dataset("x", data=2 * numpy.random.rand(128)) + ds.attrs["long_name"] = "horizontal axis" + gd1.create_dataset("x_errors", data=0.02 * numpy.random.rand(128)) # NDIM > 3 g = h5.create_group("cubes") gd0 = g.create_group("3D_cube") - gd0.attrs["NX_class"] = u"NXdata" - gd0.attrs["signal"] = u"cube" + gd0.attrs["NX_class"] = "NXdata" + gd0.attrs["signal"] = "cube" gd0.attrs["axes"] = str_attrs(["img_idx", "rows_coordinates", "cols_coordinates"]) - gd0.create_dataset("cube", data=numpy.arange(4*5*6).reshape((4, 5, 6))) + gd0.create_dataset("cube", data=numpy.arange(4 * 5 * 6).reshape((4, 5, 6))) gd0.create_dataset("img_idx", data=numpy.arange(4)) - gd0.create_dataset("rows_coordinates", data=0.1*numpy.arange(5)) + gd0.create_dataset("rows_coordinates", data=0.1 * numpy.arange(5)) gd0.create_dataset("cols_coordinates", data=[0.2, 0.3]) # linear calibration gd1 = g.create_group("5D") - gd1.attrs["NX_class"] = u"NXdata" - gd1.attrs["signal"] = u"hypercube" - gd1.create_dataset("hypercube", - data=numpy.arange(2*3*4*5*6).reshape((2, 3, 4, 5, 6))) + gd1.attrs["NX_class"] = "NXdata" + gd1.attrs["signal"] = "hypercube" + gd1.create_dataset( + "hypercube", data=numpy.arange(2 * 3 * 4 * 5 * 6).reshape((2, 3, 4, 5, 6)) + ) gd2 = g.create_group("3D_nonlinear_scaling") - gd2.attrs["NX_class"] = u"NXdata" - gd2.attrs["signal"] = u"cube" + gd2.attrs["NX_class"] = "NXdata" + gd2.attrs["signal"] = "cube" gd2.attrs["axes"] = str_attrs(["img_idx", "rows_coordinates", "cols_coordinates"]) - gd2.create_dataset("cube", data=numpy.arange(4*5*6).reshape((4, 5, 6))) - gd2.create_dataset("img_idx", data=numpy.array([2., -0.1, 8, 3.14])) - gd2.create_dataset("rows_coordinates", data=0.1*numpy.arange(5)) - gd2.create_dataset("cols_coordinates", data=[0.1, 0.6, 0.7, 8., 8.1, 8.2]) - + gd2.create_dataset("cube", data=numpy.arange(4 * 5 * 6).reshape((4, 5, 6))) + gd2.create_dataset("img_idx", data=numpy.array([2.0, -0.1, 8, 3.14])) + gd2.create_dataset("rows_coordinates", data=0.1 * numpy.arange(5)) + gd2.create_dataset("cols_coordinates", data=[0.1, 0.6, 0.7, 8.0, 8.1, 8.2]) # invalid NXdata g = h5.create_group("invalid") g0 = g.create_group("invalid NXdata") - g0.attrs["NX_class"] = u"NXdata" + g0.attrs["NX_class"] = "NXdata" g1 = g.create_group("invalid NXentry") - g1.attrs["NX_class"] = u"NXentry" - g1.attrs["default"] = u"missing NXdata group" + g1.attrs["NX_class"] = "NXentry" + g1.attrs["default"] = "missing NXdata group" g2 = g.create_group("invalid NXroot") - g2.attrs["NX_class"] = u"NXroot" - g2.attrs["default"] = u"invalid NXentry in NXroot" + g2.attrs["NX_class"] = "NXroot" + g2.attrs["default"] = "invalid NXentry in NXroot" g20 = g2.create_group("invalid NXentry in NXroot") - g20.attrs["NX_class"] = u"NXentry" - g20.attrs["default"] = u"missing NXdata group" + g20.attrs["NX_class"] = "NXentry" + g20.attrs["default"] = "missing NXdata group" h5.close() @@ -544,11 +565,21 @@ class Hdf5TreeViewExample(qt.QMainWindow): self.__treeview.findHdf5TreeModel().appendFile(file_name) self.__treeview.activated.connect(self.displayData) - self.__treeview.activated.connect(lambda index: self.displayEvent("activated", index)) - self.__treeview.clicked.connect(lambda index: self.displayEvent("clicked", index)) - self.__treeview.doubleClicked.connect(lambda index: self.displayEvent("doubleClicked", index)) - self.__treeview.entered.connect(lambda index: self.displayEvent("entered", index)) - self.__treeview.pressed.connect(lambda index: self.displayEvent("pressed", index)) + self.__treeview.activated.connect( + lambda index: self.displayEvent("activated", index) + ) + self.__treeview.clicked.connect( + lambda index: self.displayEvent("clicked", index) + ) + self.__treeview.doubleClicked.connect( + lambda index: self.displayEvent("doubleClicked", index) + ) + self.__treeview.entered.connect( + lambda index: self.displayEvent("entered", index) + ) + self.__treeview.pressed.connect( + lambda index: self.displayEvent("pressed", index) + ) self.__treeview.addContextMenuCallback(self.customContextMenu) # lambda function will never be called cause we store it as weakref @@ -558,8 +589,7 @@ class Hdf5TreeViewExample(qt.QMainWindow): self.__treeview.addContextMenuCallback(self.__store_lambda) def displayData(self): - """Called to update the dataviewer with the selected data. - """ + """Called to update the dataviewer with the selected data.""" selected = list(self.__treeview.selectedH5Nodes()) if len(selected) == 1: # Update the viewer for a single selection @@ -570,10 +600,10 @@ class Hdf5TreeViewExample(qt.QMainWindow): self.__dataViewer.setData(data) def displayEvent(self, eventName, index): - """Called to log event in widget - """ + """Called to log event in widget""" + def formatKey(name, value): - name, value = silx.utils.html.escape(str(name)), silx.utils.html.escape(str(value)) + name, value = html.escape(str(name)), html.escape(str(value)) return "<li><b>%s</b>: %s</li>" % (name, value) text = "<html>" @@ -657,10 +687,20 @@ class Hdf5TreeViewExample(qt.QMainWindow): for obj in selectedObjects: if obj.ntype is h5py.File: action = qt.QAction("Remove %s" % obj.local_filename, event.source()) - action.triggered.connect(lambda: self.__treeview.findHdf5TreeModel().removeH5pyObject(obj.h5py_object)) + action.triggered.connect( + lambda: self.__treeview.findHdf5TreeModel().removeH5pyObject( + obj.h5py_object + ) + ) menu.addAction(action) - action = qt.QAction("Synchronize %s" % obj.local_filename, event.source()) - action.triggered.connect(lambda: self.__treeview.findHdf5TreeModel().synchronizeH5pyObject(obj.h5py_object)) + action = qt.QAction( + "Synchronize %s" % obj.local_filename, event.source() + ) + action.triggered.connect( + lambda: self.__treeview.findHdf5TreeModel().synchronizeH5pyObject( + obj.h5py_object + ) + ) menu.addAction(action) def __hdf5ComboChanged(self, index): @@ -687,7 +727,10 @@ class Hdf5TreeViewExample(qt.QMainWindow): combo.addItem("Containing 10000 datasets", get_hdf5_with_10000_datasets) combo.addItem("Containing 100000 datasets", get_hdf5_with_100000_datasets) combo.addItem("Containing recursive links", get_hdf5_with_recursive_links) - combo.addItem("Containing external recursive links", get_hdf5_with_external_recursive_links) + combo.addItem( + "Containing external recursive links", + get_hdf5_with_external_recursive_links, + ) combo.addItem("Containing NXdata groups", get_hdf5_with_nxdata) combo.activated.connect(self.__hdf5ComboChanged) content.layout().addWidget(combo) @@ -732,26 +775,40 @@ class Hdf5TreeViewExample(qt.QMainWindow): panel.layout().addWidget(option) sorting = qt.QCheckBox("Enable sorting", option) - sorting.setChecked(treeview.selectionMode() == qt.QAbstractItemView.MultiSelection) + sorting.setChecked( + treeview.selectionMode() == qt.QAbstractItemView.MultiSelection + ) sorting.toggled.connect(lambda: treeview.setSortingEnabled(sorting.isChecked())) option.layout().addWidget(sorting) multiselection = qt.QCheckBox("Multi-selection", option) - multiselection.setChecked(treeview.selectionMode() == qt.QAbstractItemView.MultiSelection) + multiselection.setChecked( + treeview.selectionMode() == qt.QAbstractItemView.MultiSelection + ) switch_selection = lambda: treeview.setSelectionMode( - qt.QAbstractItemView.MultiSelection if multiselection.isChecked() - else qt.QAbstractItemView.SingleSelection) + qt.QAbstractItemView.MultiSelection + if multiselection.isChecked() + else qt.QAbstractItemView.SingleSelection + ) multiselection.toggled.connect(switch_selection) option.layout().addWidget(multiselection) filedrop = qt.QCheckBox("Drop external file", option) filedrop.setChecked(treeview.findHdf5TreeModel().isFileDropEnabled()) - filedrop.toggled.connect(lambda: treeview.findHdf5TreeModel().setFileDropEnabled(filedrop.isChecked())) + filedrop.toggled.connect( + lambda: treeview.findHdf5TreeModel().setFileDropEnabled( + filedrop.isChecked() + ) + ) option.layout().addWidget(filedrop) filemove = qt.QCheckBox("Reorder files", option) filemove.setChecked(treeview.findHdf5TreeModel().isFileMoveEnabled()) - filemove.toggled.connect(lambda: treeview.findHdf5TreeModel().setFileMoveEnabled(filedrop.isChecked())) + filemove.toggled.connect( + lambda: treeview.findHdf5TreeModel().setFileMoveEnabled( + filedrop.isChecked() + ) + ) option.layout().addWidget(filemove) option.layout().addStretch(1) @@ -762,18 +819,32 @@ class Hdf5TreeViewExample(qt.QMainWindow): autosize = qt.QCheckBox("Auto-size headers", option) autosize.setChecked(treeview.header().hasAutoResizeColumns()) - autosize.toggled.connect(lambda: treeview.header().setAutoResizeColumns(autosize.isChecked())) + autosize.toggled.connect( + lambda: treeview.header().setAutoResizeColumns(autosize.isChecked()) + ) option.layout().addWidget(autosize) columnpopup = qt.QCheckBox("Popup to hide/show columns", option) columnpopup.setChecked(treeview.header().hasHideColumnsPopup()) - columnpopup.toggled.connect(lambda: treeview.header().setEnableHideColumnsPopup(columnpopup.isChecked())) + columnpopup.toggled.connect( + lambda: treeview.header().setEnableHideColumnsPopup(columnpopup.isChecked()) + ) option.layout().addWidget(columnpopup) define_columns = qt.QComboBox() - define_columns.addItem("Default columns", treeview.findHdf5TreeModel().COLUMN_IDS) - define_columns.addItem("Only name and Value", [treeview.findHdf5TreeModel().NAME_COLUMN, treeview.findHdf5TreeModel().VALUE_COLUMN]) - define_columns.activated.connect(lambda index: treeview.header().setSections(define_columns.itemData(index))) + define_columns.addItem( + "Default columns", treeview.findHdf5TreeModel().COLUMN_IDS + ) + define_columns.addItem( + "Only name and Value", + [ + treeview.findHdf5TreeModel().NAME_COLUMN, + treeview.findHdf5TreeModel().VALUE_COLUMN, + ], + ) + define_columns.activated.connect( + lambda index: treeview.header().setSections(define_columns.itemData(index)) + ) option.layout().addWidget(define_columns) option.layout().addStretch(1) @@ -791,7 +862,7 @@ def main(filenames): sys.excepthook = qt.exceptionHandler window = Hdf5TreeViewExample(filenames) window.show() - result = app.exec_() + result = app.exec() # remove ending warnings relative to QTimer app.deleteLater() sys.exit(result) |