def modify_doc(doc): source = ColumnDataSource( dict(x=[1, 2], y=[1, 1], width=[0.5, 0.5], height=[0.5, 0.5])) plot = Plot(height=400, width=400, x_range=Range1d(0, 3), y_range=Range1d(0, 3), min_border=0) renderer = plot.add_glyph( source, Rect(x='x', y='y', width='width', height='height')) tool = BoxEditTool(dimensions='both', num_objects=num_objects, renderers=[renderer]) plot.add_tools(tool) plot.toolbar.active_multi = tool div = Div(text='False') def cb(attr, old, new): if cds_data_almost_equal(new, expected): div.text = 'True' source.on_change('data', cb) code = RECORD("matches", "div.text") plot.add_tools( CustomAction(callback=CustomJS(args=dict(div=div), code=code))) doc.add_root(column(plot, div))
def initialize(self, plot_id=None): plot = self.plot data = plot.handles['cds'].data element = self.plot.current_frame stream = self.streams[0] kwargs = {} if stream.num_objects: kwargs['num_objects'] = stream.num_objects xs, ys, widths, heights = [], [], [], [] for x, y in zip(data['xs'], data['ys']): x0, x1 = (np.nanmin(x), np.nanmax(x)) y0, y1 = (np.nanmin(y), np.nanmax(y)) xs.append((x0+x1)/2.) ys.append((y0+y1)/2.) widths.append(x1-x0) heights.append(y1-y0) data = {'x': xs, 'y': ys, 'width': widths, 'height': heights} data.update({vd.name: element.dimension_values(vd, expanded=False) for vd in element.vdims}) rect_source = ColumnDataSource(data=data) style = self.plot.style[self.plot.cyclic_index] style.pop('cmap', None) r1 = plot.state.rect('x', 'y', 'width', 'height', source=rect_source, **style) plot.handles['rect_source'] = rect_source if stream.styles: self._create_style_callback(rect_source, r1.glyph, 'x') box_tool = BoxEditTool(renderers=[r1], **kwargs) plot.state.tools.append(box_tool) if plot.handles['glyph_renderer'] in self.plot.state.renderers: self.plot.state.renderers.remove(plot.handles['glyph_renderer']) super(CDSCallback, self).initialize() data = self._process_msg({'data': data})['data'] for stream in self.streams: stream.update(data=data)
def initialize(self): try: from bokeh.models import BoxEditTool except: param.main.warning('BoxEdit requires bokeh >= 0.12.14') return plot = self.plot element = self.plot.current_frame xs, ys, widths, heights = [], [], [], [] for el in element.split(): x0, x1 = el.range(0) y0, y1 = el.range(1) xs.append((x0+x1)/2.) ys.append((y0+y1)/2.) widths.append(x1-x0) heights.append(y1-y0) data = {'x': xs, 'y': ys, 'width': widths, 'height': heights} data.update({vd.name: [] for vd in element.vdims}) rect_source = ColumnDataSource(data=data) style = self.plot.style[self.plot.cyclic_index] style.pop('cmap', None) r1 = plot.state.rect('x', 'y', 'width', 'height', source=rect_source, **style) plot.handles['rect_source'] = rect_source box_tool = BoxEditTool(renderers=[r1]) plot.state.tools.append(box_tool) self.plot.state.renderers.remove(plot.handles['glyph_renderer']) super(BoxEditCallback, self).initialize() for stream in self.streams: stream.update(data=self._process_msg({'data': data})['data'])
def _make_plot(dimensions="both", num_objects=0): source = ColumnDataSource(dict(x=[1, 2], y=[1, 1], width=[0.5, 0.5], height=[0.5, 0.5])) plot = Plot(plot_height=400, plot_width=400, x_range=Range1d(0, 3), y_range=Range1d(0, 3), min_border=0) renderer = plot.add_glyph(source, Rect(x='x', y='y', width='width', height='height')) tool = BoxEditTool(dimensions=dimensions, num_objects=num_objects, renderers=[renderer]) plot.add_tools(tool) plot.toolbar.active_multi = tool code = RECORD("x", "source.data.x") + RECORD("y", "source.data.y") + RECORD("width", "source.data.width") + RECORD("height", "source.data.height") plot.add_tools(CustomAction(callback=CustomJS(args=dict(source=source), code=code))) plot.toolbar_sticky = False return plot
def initialize(self, plot_id=None): try: from bokeh.models import BoxEditTool except: param.main.param.warning('BoxEdit requires bokeh >= 0.12.14') return plot = self.plot data = plot.handles['cds'].data element = self.plot.current_frame stream = self.streams[0] kwargs = {} if stream.num_objects: if bokeh_version >= '1.0.0': kwargs['num_objects'] = stream.num_objects else: param.main.param.warning( 'Specifying num_objects to BoxEdit stream requires ' 'a bokeh version >=1.0.0.') xs, ys, widths, heights = [], [], [], [] for x, y in zip(data['xs'], data['ys']): x0, x1 = (np.nanmin(x), np.nanmax(x)) y0, y1 = (np.nanmin(y), np.nanmax(y)) xs.append((x0 + x1) / 2.) ys.append((y0 + y1) / 2.) widths.append(x1 - x0) heights.append(y1 - y0) data = {'x': xs, 'y': ys, 'width': widths, 'height': heights} data.update({ vd.name: element.dimension_values(vd, expanded=False) for vd in element.vdims }) rect_source = ColumnDataSource(data=data) style = self.plot.style[self.plot.cyclic_index] style.pop('cmap', None) r1 = plot.state.rect('x', 'y', 'width', 'height', source=rect_source, **style) plot.handles['rect_source'] = rect_source box_tool = BoxEditTool(renderers=[r1], **kwargs) plot.state.tools.append(box_tool) if plot.handles['glyph_renderer'] in self.plot.state.renderers: self.plot.state.renderers.remove(plot.handles['glyph_renderer']) super(CDSCallback, self).initialize() data = self._process_msg({'data': data})['data'] for stream in self.streams: stream.update(data=data)
def initialize(self, plot_id=None): from .path import PathPlot stream = self.streams[0] cds = self.plot.handles['cds'] kwargs = {} if stream.num_objects: kwargs['num_objects'] = stream.num_objects if stream.tooltip: kwargs[CUSTOM_TOOLTIP] = stream.tooltip renderer = self.plot.handles['glyph_renderer'] if isinstance(self.plot, PathPlot): renderer = self._path_initialize() if stream.styles: self._create_style_callback(cds, renderer.glyph) box_tool = BoxEditTool(renderers=[renderer], **kwargs) self.plot.state.tools.append(box_tool) self._update_cds_vdims(cds.data) super(CDSCallback, self).initialize()
def fig_sample02(name): x_ax_typ = bc.AxisTyp.X_LINEAR set_title = "bokeh plot sample [" + name + "]" set_tools = "reset" p = figure(x_axis_type=x_ax_typ, tools=set_tools, title=set_title) output_file_name = "out/bokeh_plot_sample_" + name + ".html" output_file(output_file_name, title=set_title) src = ColumnDataSource({ 'x': [5, 2, 8], 'y': [5, 7, 8], 'width': [2, 1, 2], 'height': [2, 1, 1.5], 'alpha': [0.5, 0.5, 0.5] }) renderer = p.rect('x', 'y', 'width', 'height', source=src, alpha='alpha') draw_tool = BoxEditTool(renderers=[renderer], empty_value=1) p.add_tools(draw_tool) p.toolbar.active_drag = draw_tool show(p) # open a browser
def _make_plot(dimensions="both", num_objects: int = 0) -> Plot: source = ColumnDataSource( dict(x=[1, 2], y=[1, 1], width=[0.5, 0.5], height=[0.5, 0.5])) plot = Plot(height=400, width=400, x_range=Range1d(0, 3), y_range=Range1d(0, 3), min_border=0) renderer = plot.add_glyph( source, Rect(x='x', y='y', width='width', height='height')) tool = BoxEditTool(dimensions=dimensions, num_objects=num_objects, renderers=[renderer]) plot.add_tools(tool) plot.toolbar.active_multi = tool code = RECORD("x", "source.data.x", final=False) + \ RECORD("y", "source.data.y", final=False) + \ RECORD("width", "source.data.width", final=False) + \ RECORD("height", "source.data.height") plot.tags.append( CustomJS(name="custom-action", args=dict(source=source), code=code)) plot.toolbar_sticky = False return plot
def create(): doc = curdoc() det_data = {} cami_meta = {} def proposal_textinput_callback(_attr, _old, new): nonlocal cami_meta proposal = new.strip() for zebra_proposals_path in pyzebra.ZEBRA_PROPOSALS_PATHS: proposal_path = os.path.join(zebra_proposals_path, proposal) if os.path.isdir(proposal_path): # found it break else: raise ValueError(f"Can not find data for proposal '{proposal}'.") file_list = [] for file in os.listdir(proposal_path): if file.endswith(".hdf"): file_list.append((os.path.join(proposal_path, file), file)) file_select.options = file_list cami_meta = {} proposal_textinput = TextInput(title="Proposal number:", width=210) proposal_textinput.on_change("value", proposal_textinput_callback) def upload_button_callback(_attr, _old, new): nonlocal cami_meta with io.StringIO(base64.b64decode(new).decode()) as file: cami_meta = pyzebra.parse_h5meta(file) file_list = cami_meta["filelist"] file_select.options = [(entry, os.path.basename(entry)) for entry in file_list] upload_div = Div(text="or upload .cami file:", margin=(5, 5, 0, 5)) upload_button = FileInput(accept=".cami", width=200) upload_button.on_change("value", upload_button_callback) def update_image(index=None): if index is None: index = index_spinner.value current_image = det_data["data"][index] proj_v_line_source.data.update(x=np.arange(0, IMAGE_W) + 0.5, y=np.mean(current_image, axis=0)) proj_h_line_source.data.update(x=np.mean(current_image, axis=1), y=np.arange(0, IMAGE_H) + 0.5) image_source.data.update( h=[np.zeros((1, 1))], k=[np.zeros((1, 1))], l=[np.zeros((1, 1))], ) image_source.data.update(image=[current_image]) if main_auto_checkbox.active: im_min = np.min(current_image) im_max = np.max(current_image) display_min_spinner.value = im_min display_max_spinner.value = im_max image_glyph.color_mapper.low = im_min image_glyph.color_mapper.high = im_max if "mf" in det_data: metadata_table_source.data.update(mf=[det_data["mf"][index]]) else: metadata_table_source.data.update(mf=[None]) if "temp" in det_data: metadata_table_source.data.update(temp=[det_data["temp"][index]]) else: metadata_table_source.data.update(temp=[None]) gamma, nu = calculate_pol(det_data, index) omega = np.ones((IMAGE_H, IMAGE_W)) * det_data["omega"][index] image_source.data.update(gamma=[gamma], nu=[nu], omega=[omega]) def update_overview_plot(): h5_data = det_data["data"] n_im, n_y, n_x = h5_data.shape overview_x = np.mean(h5_data, axis=1) overview_y = np.mean(h5_data, axis=2) overview_plot_x_image_source.data.update(image=[overview_x], dw=[n_x], dh=[n_im]) overview_plot_y_image_source.data.update(image=[overview_y], dw=[n_y], dh=[n_im]) if proj_auto_checkbox.active: im_min = min(np.min(overview_x), np.min(overview_y)) im_max = max(np.max(overview_x), np.max(overview_y)) proj_display_min_spinner.value = im_min proj_display_max_spinner.value = im_max overview_plot_x_image_glyph.color_mapper.low = im_min overview_plot_y_image_glyph.color_mapper.low = im_min overview_plot_x_image_glyph.color_mapper.high = im_max overview_plot_y_image_glyph.color_mapper.high = im_max frame_range.start = 0 frame_range.end = n_im frame_range.reset_start = 0 frame_range.reset_end = n_im frame_range.bounds = (0, n_im) scan_motor = det_data["scan_motor"] overview_plot_y.axis[1].axis_label = f"Scanning motor, {scan_motor}" var = det_data[scan_motor] var_start = var[0] var_end = var[-1] + (var[-1] - var[0]) / (n_im - 1) scanning_motor_range.start = var_start scanning_motor_range.end = var_end scanning_motor_range.reset_start = var_start scanning_motor_range.reset_end = var_end # handle both, ascending and descending sequences scanning_motor_range.bounds = (min(var_start, var_end), max(var_start, var_end)) def file_select_callback(_attr, old, new): nonlocal det_data if not new: # skip empty selections return # Avoid selection of multiple indicies (via Shift+Click or Ctrl+Click) if len(new) > 1: # drop selection to the previous one file_select.value = old return if len(old) > 1: # skip unnecessary update caused by selection drop return det_data = pyzebra.read_detector_data(new[0]) if cami_meta and "crystal" in cami_meta: det_data["ub"] = cami_meta["crystal"]["UB"] index_spinner.value = 0 index_spinner.high = det_data["data"].shape[0] - 1 index_slider.end = det_data["data"].shape[0] - 1 zebra_mode = det_data["zebra_mode"] if zebra_mode == "nb": metadata_table_source.data.update(geom=["normal beam"]) else: # zebra_mode == "bi" metadata_table_source.data.update(geom=["bisecting"]) update_image(0) update_overview_plot() file_select = MultiSelect(title="Available .hdf files:", width=210, height=250) file_select.on_change("value", file_select_callback) def index_callback(_attr, _old, new): update_image(new) index_slider = Slider(value=0, start=0, end=1, show_value=False, width=400) index_spinner = Spinner(title="Image index:", value=0, low=0, width=100) index_spinner.on_change("value", index_callback) index_slider.js_link("value_throttled", index_spinner, "value") index_spinner.js_link("value", index_slider, "value") plot = Plot( x_range=Range1d(0, IMAGE_W, bounds=(0, IMAGE_W)), y_range=Range1d(0, IMAGE_H, bounds=(0, IMAGE_H)), plot_height=IMAGE_PLOT_H, plot_width=IMAGE_PLOT_W, toolbar_location="left", ) # ---- tools plot.toolbar.logo = None # ---- axes plot.add_layout(LinearAxis(), place="above") plot.add_layout(LinearAxis(major_label_orientation="vertical"), place="right") # ---- grid lines plot.add_layout(Grid(dimension=0, ticker=BasicTicker())) plot.add_layout(Grid(dimension=1, ticker=BasicTicker())) # ---- rgba image glyph image_source = ColumnDataSource( dict( image=[np.zeros((IMAGE_H, IMAGE_W), dtype="float32")], h=[np.zeros((1, 1))], k=[np.zeros((1, 1))], l=[np.zeros((1, 1))], gamma=[np.zeros((1, 1))], nu=[np.zeros((1, 1))], omega=[np.zeros((1, 1))], x=[0], y=[0], dw=[IMAGE_W], dh=[IMAGE_H], )) h_glyph = Image(image="h", x="x", y="y", dw="dw", dh="dh", global_alpha=0) k_glyph = Image(image="k", x="x", y="y", dw="dw", dh="dh", global_alpha=0) l_glyph = Image(image="l", x="x", y="y", dw="dw", dh="dh", global_alpha=0) gamma_glyph = Image(image="gamma", x="x", y="y", dw="dw", dh="dh", global_alpha=0) nu_glyph = Image(image="nu", x="x", y="y", dw="dw", dh="dh", global_alpha=0) omega_glyph = Image(image="omega", x="x", y="y", dw="dw", dh="dh", global_alpha=0) plot.add_glyph(image_source, h_glyph) plot.add_glyph(image_source, k_glyph) plot.add_glyph(image_source, l_glyph) plot.add_glyph(image_source, gamma_glyph) plot.add_glyph(image_source, nu_glyph) plot.add_glyph(image_source, omega_glyph) image_glyph = Image(image="image", x="x", y="y", dw="dw", dh="dh") plot.add_glyph(image_source, image_glyph, name="image_glyph") # ---- projections proj_v = Plot( x_range=plot.x_range, y_range=DataRange1d(), plot_height=150, plot_width=IMAGE_PLOT_W, toolbar_location=None, ) proj_v.add_layout(LinearAxis(major_label_orientation="vertical"), place="right") proj_v.add_layout(LinearAxis(major_label_text_font_size="0pt"), place="below") proj_v.add_layout(Grid(dimension=0, ticker=BasicTicker())) proj_v.add_layout(Grid(dimension=1, ticker=BasicTicker())) proj_v_line_source = ColumnDataSource(dict(x=[], y=[])) proj_v.add_glyph(proj_v_line_source, Line(x="x", y="y", line_color="steelblue")) proj_h = Plot( x_range=DataRange1d(), y_range=plot.y_range, plot_height=IMAGE_PLOT_H, plot_width=150, toolbar_location=None, ) proj_h.add_layout(LinearAxis(), place="above") proj_h.add_layout(LinearAxis(major_label_text_font_size="0pt"), place="left") proj_h.add_layout(Grid(dimension=0, ticker=BasicTicker())) proj_h.add_layout(Grid(dimension=1, ticker=BasicTicker())) proj_h_line_source = ColumnDataSource(dict(x=[], y=[])) proj_h.add_glyph(proj_h_line_source, Line(x="x", y="y", line_color="steelblue")) # add tools hovertool = HoverTool(tooltips=[ ("intensity", "@image"), ("gamma", "@gamma"), ("nu", "@nu"), ("omega", "@omega"), ("h", "@h"), ("k", "@k"), ("l", "@l"), ]) box_edit_source = ColumnDataSource(dict(x=[], y=[], width=[], height=[])) box_edit_glyph = Rect(x="x", y="y", width="width", height="height", fill_alpha=0, line_color="red") box_edit_renderer = plot.add_glyph(box_edit_source, box_edit_glyph) boxedittool = BoxEditTool(renderers=[box_edit_renderer], num_objects=1) def box_edit_callback(_attr, _old, new): if new["x"]: h5_data = det_data["data"] x_val = np.arange(h5_data.shape[0]) left = int(np.floor(new["x"][0])) right = int(np.ceil(new["x"][0] + new["width"][0])) bottom = int(np.floor(new["y"][0])) top = int(np.ceil(new["y"][0] + new["height"][0])) y_val = np.sum(h5_data[:, bottom:top, left:right], axis=(1, 2)) else: x_val = [] y_val = [] roi_avg_plot_line_source.data.update(x=x_val, y=y_val) box_edit_source.on_change("data", box_edit_callback) wheelzoomtool = WheelZoomTool(maintain_focus=False) plot.add_tools( PanTool(), BoxZoomTool(), wheelzoomtool, ResetTool(), hovertool, boxedittool, ) plot.toolbar.active_scroll = wheelzoomtool # shared frame ranges frame_range = Range1d(0, 1, bounds=(0, 1)) scanning_motor_range = Range1d(0, 1, bounds=(0, 1)) det_x_range = Range1d(0, IMAGE_W, bounds=(0, IMAGE_W)) overview_plot_x = Plot( title=Title(text="Projections on X-axis"), x_range=det_x_range, y_range=frame_range, extra_y_ranges={"scanning_motor": scanning_motor_range}, plot_height=400, plot_width=IMAGE_PLOT_W - 3, ) # ---- tools wheelzoomtool = WheelZoomTool(maintain_focus=False) overview_plot_x.toolbar.logo = None overview_plot_x.add_tools( PanTool(), BoxZoomTool(), wheelzoomtool, ResetTool(), ) overview_plot_x.toolbar.active_scroll = wheelzoomtool # ---- axes overview_plot_x.add_layout(LinearAxis(axis_label="Coordinate X, pix"), place="below") overview_plot_x.add_layout(LinearAxis(axis_label="Frame", major_label_orientation="vertical"), place="left") # ---- grid lines overview_plot_x.add_layout(Grid(dimension=0, ticker=BasicTicker())) overview_plot_x.add_layout(Grid(dimension=1, ticker=BasicTicker())) # ---- rgba image glyph overview_plot_x_image_source = ColumnDataSource( dict(image=[np.zeros((1, 1), dtype="float32")], x=[0], y=[0], dw=[IMAGE_W], dh=[1])) overview_plot_x_image_glyph = Image(image="image", x="x", y="y", dw="dw", dh="dh") overview_plot_x.add_glyph(overview_plot_x_image_source, overview_plot_x_image_glyph, name="image_glyph") det_y_range = Range1d(0, IMAGE_H, bounds=(0, IMAGE_H)) overview_plot_y = Plot( title=Title(text="Projections on Y-axis"), x_range=det_y_range, y_range=frame_range, extra_y_ranges={"scanning_motor": scanning_motor_range}, plot_height=400, plot_width=IMAGE_PLOT_H + 22, ) # ---- tools wheelzoomtool = WheelZoomTool(maintain_focus=False) overview_plot_y.toolbar.logo = None overview_plot_y.add_tools( PanTool(), BoxZoomTool(), wheelzoomtool, ResetTool(), ) overview_plot_y.toolbar.active_scroll = wheelzoomtool # ---- axes overview_plot_y.add_layout(LinearAxis(axis_label="Coordinate Y, pix"), place="below") overview_plot_y.add_layout( LinearAxis( y_range_name="scanning_motor", axis_label="Scanning motor", major_label_orientation="vertical", ), place="right", ) # ---- grid lines overview_plot_y.add_layout(Grid(dimension=0, ticker=BasicTicker())) overview_plot_y.add_layout(Grid(dimension=1, ticker=BasicTicker())) # ---- rgba image glyph overview_plot_y_image_source = ColumnDataSource( dict(image=[np.zeros((1, 1), dtype="float32")], x=[0], y=[0], dw=[IMAGE_H], dh=[1])) overview_plot_y_image_glyph = Image(image="image", x="x", y="y", dw="dw", dh="dh") overview_plot_y.add_glyph(overview_plot_y_image_source, overview_plot_y_image_glyph, name="image_glyph") roi_avg_plot = Plot( x_range=DataRange1d(), y_range=DataRange1d(), plot_height=150, plot_width=IMAGE_PLOT_W, toolbar_location="left", ) # ---- tools roi_avg_plot.toolbar.logo = None # ---- axes roi_avg_plot.add_layout(LinearAxis(), place="below") roi_avg_plot.add_layout(LinearAxis(major_label_orientation="vertical"), place="left") # ---- grid lines roi_avg_plot.add_layout(Grid(dimension=0, ticker=BasicTicker())) roi_avg_plot.add_layout(Grid(dimension=1, ticker=BasicTicker())) roi_avg_plot_line_source = ColumnDataSource(dict(x=[], y=[])) roi_avg_plot.add_glyph(roi_avg_plot_line_source, Line(x="x", y="y", line_color="steelblue")) cmap_dict = { "gray": Greys256, "gray_reversed": Greys256[::-1], "plasma": Plasma256, "cividis": Cividis256, } def colormap_callback(_attr, _old, new): image_glyph.color_mapper = LinearColorMapper(palette=cmap_dict[new]) overview_plot_x_image_glyph.color_mapper = LinearColorMapper( palette=cmap_dict[new]) overview_plot_y_image_glyph.color_mapper = LinearColorMapper( palette=cmap_dict[new]) colormap = Select(title="Colormap:", options=list(cmap_dict.keys()), width=210) colormap.on_change("value", colormap_callback) colormap.value = "plasma" STEP = 1 def main_auto_checkbox_callback(state): if state: display_min_spinner.disabled = True display_max_spinner.disabled = True else: display_min_spinner.disabled = False display_max_spinner.disabled = False update_image() main_auto_checkbox = CheckboxGroup(labels=["Main Auto Range"], active=[0], width=145, margin=[10, 5, 0, 5]) main_auto_checkbox.on_click(main_auto_checkbox_callback) def display_max_spinner_callback(_attr, _old_value, new_value): display_min_spinner.high = new_value - STEP image_glyph.color_mapper.high = new_value display_max_spinner = Spinner( low=0 + STEP, value=1, step=STEP, disabled=bool(main_auto_checkbox.active), width=100, height=31, ) display_max_spinner.on_change("value", display_max_spinner_callback) def display_min_spinner_callback(_attr, _old_value, new_value): display_max_spinner.low = new_value + STEP image_glyph.color_mapper.low = new_value display_min_spinner = Spinner( low=0, high=1 - STEP, value=0, step=STEP, disabled=bool(main_auto_checkbox.active), width=100, height=31, ) display_min_spinner.on_change("value", display_min_spinner_callback) PROJ_STEP = 0.1 def proj_auto_checkbox_callback(state): if state: proj_display_min_spinner.disabled = True proj_display_max_spinner.disabled = True else: proj_display_min_spinner.disabled = False proj_display_max_spinner.disabled = False update_overview_plot() proj_auto_checkbox = CheckboxGroup(labels=["Projections Auto Range"], active=[0], width=145, margin=[10, 5, 0, 5]) proj_auto_checkbox.on_click(proj_auto_checkbox_callback) def proj_display_max_spinner_callback(_attr, _old_value, new_value): proj_display_min_spinner.high = new_value - PROJ_STEP overview_plot_x_image_glyph.color_mapper.high = new_value overview_plot_y_image_glyph.color_mapper.high = new_value proj_display_max_spinner = Spinner( low=0 + PROJ_STEP, value=1, step=PROJ_STEP, disabled=bool(proj_auto_checkbox.active), width=100, height=31, ) proj_display_max_spinner.on_change("value", proj_display_max_spinner_callback) def proj_display_min_spinner_callback(_attr, _old_value, new_value): proj_display_max_spinner.low = new_value + PROJ_STEP overview_plot_x_image_glyph.color_mapper.low = new_value overview_plot_y_image_glyph.color_mapper.low = new_value proj_display_min_spinner = Spinner( low=0, high=1 - PROJ_STEP, value=0, step=PROJ_STEP, disabled=bool(proj_auto_checkbox.active), width=100, height=31, ) proj_display_min_spinner.on_change("value", proj_display_min_spinner_callback) def hkl_button_callback(): index = index_spinner.value h, k, l = calculate_hkl(det_data, index) image_source.data.update(h=[h], k=[k], l=[l]) hkl_button = Button(label="Calculate hkl (slow)", width=210) hkl_button.on_click(hkl_button_callback) def events_list_callback(_attr, _old, new): doc.events_list_spind.value = new events_list = TextAreaInput(rows=7, width=830) events_list.on_change("value", events_list_callback) doc.events_list_hdf_viewer = events_list def add_event_button_callback(): diff_vec = [] p0 = [1.0, 0.0, 1.0] maxfev = 100000 wave = det_data["wave"] ddist = det_data["ddist"] gamma = det_data["gamma"][0] omega = det_data["omega"][0] nu = det_data["nu"][0] chi = det_data["chi"][0] phi = det_data["phi"][0] scan_motor = det_data["scan_motor"] var_angle = det_data[scan_motor] x0 = int(np.floor(det_x_range.start)) xN = int(np.ceil(det_x_range.end)) y0 = int(np.floor(det_y_range.start)) yN = int(np.ceil(det_y_range.end)) fr0 = int(np.floor(frame_range.start)) frN = int(np.ceil(frame_range.end)) data_roi = det_data["data"][fr0:frN, y0:yN, x0:xN] cnts = np.sum(data_roi, axis=(1, 2)) coeff, _ = curve_fit(gauss, range(len(cnts)), cnts, p0=p0, maxfev=maxfev) m = cnts.mean() sd = cnts.std() snr_cnts = np.where(sd == 0, 0, m / sd) frC = fr0 + coeff[1] var_F = var_angle[math.floor(frC)] var_C = var_angle[math.ceil(frC)] frStep = frC - math.floor(frC) var_step = var_C - var_F var_p = var_F + var_step * frStep if scan_motor == "gamma": gamma = var_p elif scan_motor == "omega": omega = var_p elif scan_motor == "nu": nu = var_p elif scan_motor == "chi": chi = var_p elif scan_motor == "phi": phi = var_p intensity = coeff[1] * abs( coeff[2] * var_step) * math.sqrt(2) * math.sqrt(np.pi) projX = np.sum(data_roi, axis=(0, 1)) coeff, _ = curve_fit(gauss, range(len(projX)), projX, p0=p0, maxfev=maxfev) x_pos = x0 + coeff[1] projY = np.sum(data_roi, axis=(0, 2)) coeff, _ = curve_fit(gauss, range(len(projY)), projY, p0=p0, maxfev=maxfev) y_pos = y0 + coeff[1] ga, nu = pyzebra.det2pol(ddist, gamma, nu, x_pos, y_pos) diff_vector = pyzebra.z1frmd(wave, ga, omega, chi, phi, nu) d_spacing = float(pyzebra.dandth(wave, diff_vector)[0]) diff_vector = diff_vector.flatten() * 1e10 dv1, dv2, dv3 = diff_vector diff_vec.append(diff_vector) if events_list.value and not events_list.value.endswith("\n"): events_list.value = events_list.value + "\n" events_list.value = ( events_list.value + f"{x_pos} {y_pos} {intensity} {snr_cnts} {dv1} {dv2} {dv3} {d_spacing}" ) add_event_button = Button(label="Add spind event") add_event_button.on_click(add_event_button_callback) metadata_table_source = ColumnDataSource( dict(geom=[""], temp=[None], mf=[None])) num_formatter = NumberFormatter(format="0.00", nan_format="") metadata_table = DataTable( source=metadata_table_source, columns=[ TableColumn(field="geom", title="Geometry", width=100), TableColumn(field="temp", title="Temperature", formatter=num_formatter, width=100), TableColumn(field="mf", title="Magnetic Field", formatter=num_formatter, width=100), ], width=300, height=50, autosize_mode="none", index_position=None, ) # Final layout import_layout = column(proposal_textinput, upload_div, upload_button, file_select) layout_image = column( gridplot([[proj_v, None], [plot, proj_h]], merge_tools=False)) colormap_layout = column( colormap, main_auto_checkbox, row(display_min_spinner, display_max_spinner), proj_auto_checkbox, row(proj_display_min_spinner, proj_display_max_spinner), ) layout_controls = column( row(metadata_table, index_spinner, column(Spacer(height=25), index_slider)), row(add_event_button, hkl_button), row(events_list), ) layout_overview = column( gridplot( [[overview_plot_x, overview_plot_y]], toolbar_options=dict(logo=None), merge_tools=True, toolbar_location="left", ), ) tab_layout = row( column(import_layout, colormap_layout), column(layout_overview, layout_controls), column(roi_avg_plot, layout_image), ) return Panel(child=tab_layout, title="hdf viewer")
from bokeh.models import BoxEditTool, ColumnDataSource from bokeh.plotting import figure, output_file, show output_file("tools_box_edit.html") p = figure(x_range=(0, 10), y_range=(0, 10), width=400, height=400, title='Box Edit Tool') src = ColumnDataSource({ 'x': [5, 2, 8], 'y': [5, 7, 8], 'width': [2, 1, 2], 'height': [2, 1, 1.5], 'alpha': [0.5, 0.5, 0.5] }) renderer = p.rect('x', 'y', 'width', 'height', source=src, alpha='alpha') draw_tool = BoxEditTool(renderers=[renderer], empty_value=1) p.add_tools(draw_tool) p.toolbar.active_drag = draw_tool show(p)
def create(): det_data = {} roi_selection = {} upload_div = Div(text="Open .cami file:") def upload_button_callback(_attr, _old, new): with io.StringIO(base64.b64decode(new).decode()) as file: h5meta_list = pyzebra.parse_h5meta(file) file_list = h5meta_list["filelist"] filelist.options = file_list filelist.value = file_list[0] upload_button = FileInput(accept=".cami") upload_button.on_change("value", upload_button_callback) def update_image(index=None): if index is None: index = index_spinner.value current_image = det_data["data"][index] proj_v_line_source.data.update(x=np.arange(0, IMAGE_W) + 0.5, y=np.mean(current_image, axis=0)) proj_h_line_source.data.update(x=np.mean(current_image, axis=1), y=np.arange(0, IMAGE_H) + 0.5) image_source.data.update( h=[np.zeros((1, 1))], k=[np.zeros((1, 1))], l=[np.zeros((1, 1))], ) image_source.data.update(image=[current_image]) if auto_toggle.active: im_max = int(np.max(current_image)) im_min = int(np.min(current_image)) display_min_spinner.value = im_min display_max_spinner.value = im_max image_glyph.color_mapper.low = im_min image_glyph.color_mapper.high = im_max def update_overview_plot(): h5_data = det_data["data"] n_im, n_y, n_x = h5_data.shape overview_x = np.mean(h5_data, axis=1) overview_y = np.mean(h5_data, axis=2) overview_plot_x_image_source.data.update(image=[overview_x], dw=[n_x]) overview_plot_y_image_source.data.update(image=[overview_y], dw=[n_y]) if frame_button_group.active == 0: # Frame overview_plot_x.axis[1].axis_label = "Frame" overview_plot_y.axis[1].axis_label = "Frame" overview_plot_x_image_source.data.update(y=[0], dh=[n_im]) overview_plot_y_image_source.data.update(y=[0], dh=[n_im]) elif frame_button_group.active == 1: # Omega overview_plot_x.axis[1].axis_label = "Omega" overview_plot_y.axis[1].axis_label = "Omega" om = det_data["rot_angle"] om_start = om[0] om_end = (om[-1] - om[0]) * n_im / (n_im - 1) overview_plot_x_image_source.data.update(y=[om_start], dh=[om_end]) overview_plot_y_image_source.data.update(y=[om_start], dh=[om_end]) def filelist_callback(_attr, _old, new): nonlocal det_data det_data = pyzebra.read_detector_data(new) index_spinner.value = 0 index_spinner.high = det_data["data"].shape[0] - 1 update_image(0) update_overview_plot() filelist = Select() filelist.on_change("value", filelist_callback) def index_spinner_callback(_attr, _old, new): update_image(new) index_spinner = Spinner(title="Image index:", value=0, low=0) index_spinner.on_change("value", index_spinner_callback) plot = Plot( x_range=Range1d(0, IMAGE_W, bounds=(0, IMAGE_W)), y_range=Range1d(0, IMAGE_H, bounds=(0, IMAGE_H)), plot_height=IMAGE_H * 3, plot_width=IMAGE_W * 3, toolbar_location="left", ) # ---- tools plot.toolbar.logo = None # ---- axes plot.add_layout(LinearAxis(), place="above") plot.add_layout(LinearAxis(major_label_orientation="vertical"), place="right") # ---- grid lines plot.add_layout(Grid(dimension=0, ticker=BasicTicker())) plot.add_layout(Grid(dimension=1, ticker=BasicTicker())) # ---- rgba image glyph image_source = ColumnDataSource( dict( image=[np.zeros((IMAGE_H, IMAGE_W), dtype="float32")], h=[np.zeros((1, 1))], k=[np.zeros((1, 1))], l=[np.zeros((1, 1))], x=[0], y=[0], dw=[IMAGE_W], dh=[IMAGE_H], )) h_glyph = Image(image="h", x="x", y="y", dw="dw", dh="dh", global_alpha=0) k_glyph = Image(image="k", x="x", y="y", dw="dw", dh="dh", global_alpha=0) l_glyph = Image(image="l", x="x", y="y", dw="dw", dh="dh", global_alpha=0) plot.add_glyph(image_source, h_glyph) plot.add_glyph(image_source, k_glyph) plot.add_glyph(image_source, l_glyph) image_glyph = Image(image="image", x="x", y="y", dw="dw", dh="dh") plot.add_glyph(image_source, image_glyph, name="image_glyph") # ---- projections proj_v = Plot( x_range=plot.x_range, y_range=DataRange1d(), plot_height=200, plot_width=IMAGE_W * 3, toolbar_location=None, ) proj_v.add_layout(LinearAxis(major_label_orientation="vertical"), place="right") proj_v.add_layout(LinearAxis(major_label_text_font_size="0pt"), place="below") proj_v.add_layout(Grid(dimension=0, ticker=BasicTicker())) proj_v.add_layout(Grid(dimension=1, ticker=BasicTicker())) proj_v_line_source = ColumnDataSource(dict(x=[], y=[])) proj_v.add_glyph(proj_v_line_source, Line(x="x", y="y", line_color="steelblue")) proj_h = Plot( x_range=DataRange1d(), y_range=plot.y_range, plot_height=IMAGE_H * 3, plot_width=200, toolbar_location=None, ) proj_h.add_layout(LinearAxis(), place="above") proj_h.add_layout(LinearAxis(major_label_text_font_size="0pt"), place="left") proj_h.add_layout(Grid(dimension=0, ticker=BasicTicker())) proj_h.add_layout(Grid(dimension=1, ticker=BasicTicker())) proj_h_line_source = ColumnDataSource(dict(x=[], y=[])) proj_h.add_glyph(proj_h_line_source, Line(x="x", y="y", line_color="steelblue")) # add tools hovertool = HoverTool(tooltips=[("intensity", "@image"), ("h", "@h"), ("k", "@k"), ("l", "@l")]) box_edit_source = ColumnDataSource(dict(x=[], y=[], width=[], height=[])) box_edit_glyph = Rect(x="x", y="y", width="width", height="height", fill_alpha=0, line_color="red") box_edit_renderer = plot.add_glyph(box_edit_source, box_edit_glyph) boxedittool = BoxEditTool(renderers=[box_edit_renderer], num_objects=1) def box_edit_callback(_attr, _old, new): if new["x"]: h5_data = det_data["data"] x_val = np.arange(h5_data.shape[0]) left = int(np.floor(new["x"][0])) right = int(np.ceil(new["x"][0] + new["width"][0])) bottom = int(np.floor(new["y"][0])) top = int(np.ceil(new["y"][0] + new["height"][0])) y_val = np.sum(h5_data[:, bottom:top, left:right], axis=(1, 2)) else: x_val = [] y_val = [] roi_avg_plot_line_source.data.update(x=x_val, y=y_val) box_edit_source.on_change("data", box_edit_callback) wheelzoomtool = WheelZoomTool(maintain_focus=False) plot.add_tools( PanTool(), BoxZoomTool(), wheelzoomtool, ResetTool(), hovertool, boxedittool, ) plot.toolbar.active_scroll = wheelzoomtool # shared frame range frame_range = DataRange1d() det_x_range = DataRange1d() overview_plot_x = Plot( title=Title(text="Projections on X-axis"), x_range=det_x_range, y_range=frame_range, plot_height=400, plot_width=400, toolbar_location="left", ) # ---- tools wheelzoomtool = WheelZoomTool(maintain_focus=False) overview_plot_x.toolbar.logo = None overview_plot_x.add_tools( PanTool(), BoxZoomTool(), wheelzoomtool, ResetTool(), ) overview_plot_x.toolbar.active_scroll = wheelzoomtool # ---- axes overview_plot_x.add_layout(LinearAxis(axis_label="Coordinate X, pix"), place="below") overview_plot_x.add_layout(LinearAxis(axis_label="Frame", major_label_orientation="vertical"), place="left") # ---- grid lines overview_plot_x.add_layout(Grid(dimension=0, ticker=BasicTicker())) overview_plot_x.add_layout(Grid(dimension=1, ticker=BasicTicker())) # ---- rgba image glyph overview_plot_x_image_source = ColumnDataSource( dict(image=[np.zeros((1, 1), dtype="float32")], x=[0], y=[0], dw=[1], dh=[1])) overview_plot_x_image_glyph = Image(image="image", x="x", y="y", dw="dw", dh="dh") overview_plot_x.add_glyph(overview_plot_x_image_source, overview_plot_x_image_glyph, name="image_glyph") det_y_range = DataRange1d() overview_plot_y = Plot( title=Title(text="Projections on Y-axis"), x_range=det_y_range, y_range=frame_range, plot_height=400, plot_width=400, toolbar_location="left", ) # ---- tools wheelzoomtool = WheelZoomTool(maintain_focus=False) overview_plot_y.toolbar.logo = None overview_plot_y.add_tools( PanTool(), BoxZoomTool(), wheelzoomtool, ResetTool(), ) overview_plot_y.toolbar.active_scroll = wheelzoomtool # ---- axes overview_plot_y.add_layout(LinearAxis(axis_label="Coordinate Y, pix"), place="below") overview_plot_y.add_layout(LinearAxis(axis_label="Frame", major_label_orientation="vertical"), place="left") # ---- grid lines overview_plot_y.add_layout(Grid(dimension=0, ticker=BasicTicker())) overview_plot_y.add_layout(Grid(dimension=1, ticker=BasicTicker())) # ---- rgba image glyph overview_plot_y_image_source = ColumnDataSource( dict(image=[np.zeros((1, 1), dtype="float32")], x=[0], y=[0], dw=[1], dh=[1])) overview_plot_y_image_glyph = Image(image="image", x="x", y="y", dw="dw", dh="dh") overview_plot_y.add_glyph(overview_plot_y_image_source, overview_plot_y_image_glyph, name="image_glyph") def frame_button_group_callback(_active): update_overview_plot() frame_button_group = RadioButtonGroup(labels=["Frames", "Omega"], active=0) frame_button_group.on_click(frame_button_group_callback) roi_avg_plot = Plot( x_range=DataRange1d(), y_range=DataRange1d(), plot_height=IMAGE_H * 3, plot_width=IMAGE_W * 3, toolbar_location="left", ) # ---- tools roi_avg_plot.toolbar.logo = None # ---- axes roi_avg_plot.add_layout(LinearAxis(), place="below") roi_avg_plot.add_layout(LinearAxis(major_label_orientation="vertical"), place="left") # ---- grid lines roi_avg_plot.add_layout(Grid(dimension=0, ticker=BasicTicker())) roi_avg_plot.add_layout(Grid(dimension=1, ticker=BasicTicker())) roi_avg_plot_line_source = ColumnDataSource(dict(x=[], y=[])) roi_avg_plot.add_glyph(roi_avg_plot_line_source, Line(x="x", y="y", line_color="steelblue")) cmap_dict = { "gray": Greys256, "gray_reversed": Greys256[::-1], "plasma": Plasma256, "cividis": Cividis256, } def colormap_callback(_attr, _old, new): image_glyph.color_mapper = LinearColorMapper(palette=cmap_dict[new]) overview_plot_x_image_glyph.color_mapper = LinearColorMapper( palette=cmap_dict[new]) overview_plot_y_image_glyph.color_mapper = LinearColorMapper( palette=cmap_dict[new]) colormap = Select(title="Colormap:", options=list(cmap_dict.keys())) colormap.on_change("value", colormap_callback) colormap.value = "plasma" radio_button_group = RadioButtonGroup(labels=["nb", "nb_bi"], active=0) STEP = 1 # ---- colormap auto toggle button def auto_toggle_callback(state): if state: display_min_spinner.disabled = True display_max_spinner.disabled = True else: display_min_spinner.disabled = False display_max_spinner.disabled = False update_image() auto_toggle = Toggle(label="Auto Range", active=True, button_type="default") auto_toggle.on_click(auto_toggle_callback) # ---- colormap display max value def display_max_spinner_callback(_attr, _old_value, new_value): display_min_spinner.high = new_value - STEP image_glyph.color_mapper.high = new_value display_max_spinner = Spinner( title="Maximal Display Value:", low=0 + STEP, value=1, step=STEP, disabled=auto_toggle.active, ) display_max_spinner.on_change("value", display_max_spinner_callback) # ---- colormap display min value def display_min_spinner_callback(_attr, _old_value, new_value): display_max_spinner.low = new_value + STEP image_glyph.color_mapper.low = new_value display_min_spinner = Spinner( title="Minimal Display Value:", high=1 - STEP, value=0, step=STEP, disabled=auto_toggle.active, ) display_min_spinner.on_change("value", display_min_spinner_callback) def hkl_button_callback(): index = index_spinner.value setup_type = "nb_bi" if radio_button_group.active else "nb" h, k, l = calculate_hkl(det_data, index, setup_type) image_source.data.update(h=[h], k=[k], l=[l]) hkl_button = Button(label="Calculate hkl (slow)") hkl_button.on_click(hkl_button_callback) selection_list = TextAreaInput(rows=7) def selection_button_callback(): nonlocal roi_selection selection = [ int(np.floor(det_x_range.start)), int(np.ceil(det_x_range.end)), int(np.floor(det_y_range.start)), int(np.ceil(det_y_range.end)), int(np.floor(frame_range.start)), int(np.ceil(frame_range.end)), ] filename_id = filelist.value[-8:-4] if filename_id in roi_selection: roi_selection[f"{filename_id}"].append(selection) else: roi_selection[f"{filename_id}"] = [selection] selection_list.value = str(roi_selection) selection_button = Button(label="Add selection") selection_button.on_click(selection_button_callback) # Final layout layout_image = column( gridplot([[proj_v, None], [plot, proj_h]], merge_tools=False), row(index_spinner)) colormap_layout = column(colormap, auto_toggle, display_max_spinner, display_min_spinner) hkl_layout = column(radio_button_group, hkl_button) layout_overview = column( gridplot( [[overview_plot_x, overview_plot_y]], toolbar_options=dict(logo=None), merge_tools=True, ), frame_button_group, ) tab_layout = row( column( upload_div, upload_button, filelist, layout_image, row(colormap_layout, hkl_layout), ), column( roi_avg_plot, layout_overview, row(selection_button, selection_list), ), ) return Panel(child=tab_layout, title="Data Viewer")
def create_image_figure( image_source: ColumnDataSource, roi_source: ColumnDataSource, vector_source: ColumnDataSource, ) -> Figure: try: image = image_source.data['image'][0] width = image.shape[1] height = image.shape[0] except IndexError: width = 800 height = 800 plot = figure( plot_width=min(width, 800), plot_height=min(height, 800), x_range=[0, width], y_range=[0, height], title='Selected Image', name='image_figure', ) plot.image( image='image', x=0, y=0, dw='dw', dh='dh', source=image_source, palette='Spectral11', name='image_plot', ) hover = HoverTool(tooltips=[('x', '$x'), ('y', '$y'), ('value', '@image')]) r1 = plot.rect( x='x', y='y', width='width', height='height', source=roi_source, fill_alpha=0.5, fill_color='#DAF7A6', name='rois', ) lines = plot.multi_line( xs='xs', ys='ys', source=vector_source, line_color='red', line_width=2, name='vectors', ) circles = plot.circle( x=[], y=[], size=10, color='yellow', ) plot.tools = [ hover, BoxEditTool(renderers=[r1]), PolyDrawTool(renderers=[lines]), PolyEditTool(renderers=[lines], vertex_renderer=circles), ] plot.toolbar.active_inspect = [] return plot