Add hdf param study panel (based on hdf viewer)
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commit
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@ -8,6 +8,7 @@ from bokeh.models import Tabs, TextAreaInput
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import panel_ccl_integrate
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import panel_hdf_anatric
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import panel_hdf_param_study
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import panel_hdf_viewer
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import panel_param_study
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import panel_spind
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@ -26,15 +27,18 @@ bokeh_logger.addHandler(bokeh_handler)
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bokeh_log_textareainput = TextAreaInput(title="server output:", height=150)
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# Final layout
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tab_hdf_viewer = panel_hdf_viewer.create()
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tab_hdf_anatric = panel_hdf_anatric.create()
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tab_ccl_integrate = panel_ccl_integrate.create()
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tab_param_study = panel_param_study.create()
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tab_spind = panel_spind.create()
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doc.add_root(
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column(
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Tabs(tabs=[tab_hdf_viewer, tab_hdf_anatric, tab_ccl_integrate, tab_param_study, tab_spind]),
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Tabs(
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tabs=[
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panel_hdf_viewer.create(),
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panel_hdf_anatric.create(),
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panel_ccl_integrate.create(),
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panel_param_study.create(),
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panel_hdf_param_study.create(),
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panel_spind.create(),
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]
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),
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row(stdout_textareainput, bokeh_log_textareainput, sizing_mode="scale_both"),
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)
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)
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816
pyzebra/app/panel_hdf_param_study.py
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816
pyzebra/app/panel_hdf_param_study.py
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@ -0,0 +1,816 @@
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import base64
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import io
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import math
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import os
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import numpy as np
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from bokeh.events import MouseEnter
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from bokeh.io import curdoc
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from bokeh.layouts import column, gridplot, row
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from bokeh.models import (
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BasicTicker,
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BoxEditTool,
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BoxZoomTool,
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Button,
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CheckboxGroup,
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ColumnDataSource,
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DataRange1d,
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DataTable,
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Div,
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FileInput,
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Grid,
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MultiSelect,
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NumberFormatter,
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HoverTool,
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Image,
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Line,
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LinearAxis,
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LinearColorMapper,
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Panel,
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PanTool,
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Plot,
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Range1d,
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Rect,
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ResetTool,
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Select,
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Slider,
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Spacer,
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Spinner,
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TableColumn,
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TextInput,
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Title,
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WheelZoomTool,
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)
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from bokeh.palettes import Cividis256, Greys256, Plasma256 # pylint: disable=E0611
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from scipy.optimize import curve_fit
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import pyzebra
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IMAGE_W = 256
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IMAGE_H = 128
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IMAGE_PLOT_W = int(IMAGE_W * 2) + 52
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IMAGE_PLOT_H = int(IMAGE_H * 2) + 27
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def create():
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doc = curdoc()
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det_data = {}
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cami_meta = {}
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num_formatter = NumberFormatter(format="0.00", nan_format="")
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def file_select_update_for_proposal():
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proposal = proposal_textinput.value.strip()
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if not proposal:
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return
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for zebra_proposals_path in pyzebra.ZEBRA_PROPOSALS_PATHS:
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proposal_path = os.path.join(zebra_proposals_path, proposal)
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if os.path.isdir(proposal_path):
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# found it
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break
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else:
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raise ValueError(f"Can not find data for proposal '{proposal}'.")
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file_list = []
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for file in os.listdir(proposal_path):
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if file.endswith(".hdf"):
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file_list.append((os.path.join(proposal_path, file), file))
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file_select.options = file_list
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doc.add_periodic_callback(file_select_update_for_proposal, 5000)
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def proposal_textinput_callback(_attr, _old, _new):
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nonlocal cami_meta
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cami_meta = {}
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file_select_update_for_proposal()
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proposal_textinput = TextInput(title="Proposal number:", width=210)
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proposal_textinput.on_change("value", proposal_textinput_callback)
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def upload_button_callback(_attr, _old, new):
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nonlocal cami_meta
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proposal_textinput.value = ""
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with io.StringIO(base64.b64decode(new).decode()) as file:
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cami_meta = pyzebra.parse_h5meta(file)
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file_list = cami_meta["filelist"]
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file_select.options = [(entry, os.path.basename(entry)) for entry in file_list]
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upload_div = Div(text="or upload .cami file:", margin=(5, 5, 0, 5))
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upload_button = FileInput(accept=".cami", width=200)
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upload_button.on_change("value", upload_button_callback)
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def update_image(index=None):
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if index is None:
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index = index_spinner.value
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current_image = det_data["data"][index]
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proj_v_line_source.data.update(
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x=np.arange(0, IMAGE_W) + 0.5, y=np.mean(current_image, axis=0)
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)
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proj_h_line_source.data.update(
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x=np.mean(current_image, axis=1), y=np.arange(0, IMAGE_H) + 0.5
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)
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image_source.data.update(
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h=[np.zeros((1, 1))], k=[np.zeros((1, 1))], l=[np.zeros((1, 1))],
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)
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image_source.data.update(image=[current_image])
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if main_auto_checkbox.active:
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im_min = np.min(current_image)
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im_max = np.max(current_image)
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display_min_spinner.value = im_min
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display_max_spinner.value = im_max
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image_glyph.color_mapper.low = im_min
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image_glyph.color_mapper.high = im_max
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if "mf" in det_data:
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metadata_table_source.data.update(mf=[det_data["mf"][index]])
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else:
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metadata_table_source.data.update(mf=[None])
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if "temp" in det_data:
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metadata_table_source.data.update(temp=[det_data["temp"][index]])
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else:
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metadata_table_source.data.update(temp=[None])
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gamma, nu = calculate_pol(det_data, index)
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omega = np.ones((IMAGE_H, IMAGE_W)) * det_data["omega"][index]
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image_source.data.update(gamma=[gamma], nu=[nu], omega=[omega])
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def update_overview_plot():
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h5_data = det_data["data"]
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n_im, n_y, n_x = h5_data.shape
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overview_x = np.mean(h5_data, axis=1)
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overview_y = np.mean(h5_data, axis=2)
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overview_plot_x_image_source.data.update(image=[overview_x], dw=[n_x], dh=[n_im])
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overview_plot_y_image_source.data.update(image=[overview_y], dw=[n_y], dh=[n_im])
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if proj_auto_checkbox.active:
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im_min = min(np.min(overview_x), np.min(overview_y))
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im_max = max(np.max(overview_x), np.max(overview_y))
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proj_display_min_spinner.value = im_min
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proj_display_max_spinner.value = im_max
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overview_plot_x_image_glyph.color_mapper.low = im_min
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overview_plot_y_image_glyph.color_mapper.low = im_min
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overview_plot_x_image_glyph.color_mapper.high = im_max
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overview_plot_y_image_glyph.color_mapper.high = im_max
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frame_range.start = 0
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frame_range.end = n_im
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frame_range.reset_start = 0
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frame_range.reset_end = n_im
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frame_range.bounds = (0, n_im)
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scan_motor = det_data["scan_motor"]
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overview_plot_y.axis[1].axis_label = f"Scanning motor, {scan_motor}"
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var = det_data[scan_motor]
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var_start = var[0]
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var_end = var[-1] + (var[-1] - var[0]) / (n_im - 1)
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scanning_motor_range.start = var_start
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scanning_motor_range.end = var_end
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scanning_motor_range.reset_start = var_start
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scanning_motor_range.reset_end = var_end
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# handle both, ascending and descending sequences
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scanning_motor_range.bounds = (min(var_start, var_end), max(var_start, var_end))
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def file_select_callback(_attr, old, new):
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nonlocal det_data
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if not new:
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# skip empty selections
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return
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# Avoid selection of multiple indicies (via Shift+Click or Ctrl+Click)
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if len(new) > 1:
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# drop selection to the previous one
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file_select.value = old
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return
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if len(old) > 1:
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# skip unnecessary update caused by selection drop
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return
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det_data = pyzebra.read_detector_data(new[0], cami_meta)
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index_spinner.value = 0
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index_spinner.high = det_data["data"].shape[0] - 1
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index_slider.end = det_data["data"].shape[0] - 1
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zebra_mode = det_data["zebra_mode"]
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if zebra_mode == "nb":
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metadata_table_source.data.update(geom=["normal beam"])
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else: # zebra_mode == "bi"
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metadata_table_source.data.update(geom=["bisecting"])
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update_image(0)
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update_overview_plot()
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file_select = MultiSelect(title="Available .hdf files:", width=210, height=250)
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file_select.on_change("value", file_select_callback)
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def index_callback(_attr, _old, new):
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update_image(new)
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index_slider = Slider(value=0, start=0, end=1, show_value=False, width=400)
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index_spinner = Spinner(title="Image index:", value=0, low=0, width=100)
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index_spinner.on_change("value", index_callback)
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index_slider.js_link("value_throttled", index_spinner, "value")
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index_spinner.js_link("value", index_slider, "value")
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plot = Plot(
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x_range=Range1d(0, IMAGE_W, bounds=(0, IMAGE_W)),
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y_range=Range1d(0, IMAGE_H, bounds=(0, IMAGE_H)),
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plot_height=IMAGE_PLOT_H,
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plot_width=IMAGE_PLOT_W,
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toolbar_location="left",
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)
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# ---- tools
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plot.toolbar.logo = None
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# ---- axes
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plot.add_layout(LinearAxis(), place="above")
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plot.add_layout(LinearAxis(major_label_orientation="vertical"), place="right")
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# ---- grid lines
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plot.add_layout(Grid(dimension=0, ticker=BasicTicker()))
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plot.add_layout(Grid(dimension=1, ticker=BasicTicker()))
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# ---- rgba image glyph
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image_source = ColumnDataSource(
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dict(
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image=[np.zeros((IMAGE_H, IMAGE_W), dtype="float32")],
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h=[np.zeros((1, 1))],
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k=[np.zeros((1, 1))],
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l=[np.zeros((1, 1))],
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gamma=[np.zeros((1, 1))],
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nu=[np.zeros((1, 1))],
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omega=[np.zeros((1, 1))],
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x=[0],
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y=[0],
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dw=[IMAGE_W],
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dh=[IMAGE_H],
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)
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)
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h_glyph = Image(image="h", x="x", y="y", dw="dw", dh="dh", global_alpha=0)
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k_glyph = Image(image="k", x="x", y="y", dw="dw", dh="dh", global_alpha=0)
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l_glyph = Image(image="l", x="x", y="y", dw="dw", dh="dh", global_alpha=0)
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gamma_glyph = Image(image="gamma", x="x", y="y", dw="dw", dh="dh", global_alpha=0)
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nu_glyph = Image(image="nu", x="x", y="y", dw="dw", dh="dh", global_alpha=0)
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omega_glyph = Image(image="omega", x="x", y="y", dw="dw", dh="dh", global_alpha=0)
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plot.add_glyph(image_source, h_glyph)
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plot.add_glyph(image_source, k_glyph)
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plot.add_glyph(image_source, l_glyph)
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plot.add_glyph(image_source, gamma_glyph)
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plot.add_glyph(image_source, nu_glyph)
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plot.add_glyph(image_source, omega_glyph)
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image_glyph = Image(image="image", x="x", y="y", dw="dw", dh="dh")
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plot.add_glyph(image_source, image_glyph, name="image_glyph")
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# calculate hkl-indices of first mouse entry
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def mouse_enter_callback(_event):
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if det_data and np.array_equal(image_source.data["h"][0], np.zeros((1, 1))):
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index = index_spinner.value
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h, k, l = calculate_hkl(det_data, index)
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image_source.data.update(h=[h], k=[k], l=[l])
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plot.on_event(MouseEnter, mouse_enter_callback)
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# ---- projections
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proj_v = Plot(
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x_range=plot.x_range,
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y_range=DataRange1d(),
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plot_height=150,
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plot_width=IMAGE_PLOT_W,
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toolbar_location=None,
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)
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proj_v.add_layout(LinearAxis(major_label_orientation="vertical"), place="right")
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proj_v.add_layout(LinearAxis(major_label_text_font_size="0pt"), place="below")
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proj_v.add_layout(Grid(dimension=0, ticker=BasicTicker()))
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proj_v.add_layout(Grid(dimension=1, ticker=BasicTicker()))
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proj_v_line_source = ColumnDataSource(dict(x=[], y=[]))
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proj_v.add_glyph(proj_v_line_source, Line(x="x", y="y", line_color="steelblue"))
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proj_h = Plot(
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x_range=DataRange1d(),
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y_range=plot.y_range,
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plot_height=IMAGE_PLOT_H,
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plot_width=150,
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toolbar_location=None,
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)
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proj_h.add_layout(LinearAxis(), place="above")
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proj_h.add_layout(LinearAxis(major_label_text_font_size="0pt"), place="left")
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proj_h.add_layout(Grid(dimension=0, ticker=BasicTicker()))
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proj_h.add_layout(Grid(dimension=1, ticker=BasicTicker()))
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proj_h_line_source = ColumnDataSource(dict(x=[], y=[]))
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proj_h.add_glyph(proj_h_line_source, Line(x="x", y="y", line_color="steelblue"))
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# add tools
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hovertool = HoverTool(
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tooltips=[
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("intensity", "@image"),
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("gamma", "@gamma"),
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("nu", "@nu"),
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("omega", "@omega"),
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("h", "@h"),
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("k", "@k"),
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("l", "@l"),
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]
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)
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box_edit_source = ColumnDataSource(dict(x=[], y=[], width=[], height=[]))
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box_edit_glyph = Rect(
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x="x", y="y", width="width", height="height", fill_alpha=0, line_color="red"
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)
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box_edit_renderer = plot.add_glyph(box_edit_source, box_edit_glyph)
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boxedittool = BoxEditTool(renderers=[box_edit_renderer], num_objects=1)
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def box_edit_callback(_attr, _old, new):
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if new["x"]:
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h5_data = det_data["data"]
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x_val = np.arange(h5_data.shape[0])
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left = int(np.floor(new["x"][0]))
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right = int(np.ceil(new["x"][0] + new["width"][0]))
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bottom = int(np.floor(new["y"][0]))
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top = int(np.ceil(new["y"][0] + new["height"][0]))
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y_val = np.sum(h5_data[:, bottom:top, left:right], axis=(1, 2))
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else:
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x_val = []
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y_val = []
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roi_avg_plot_line_source.data.update(x=x_val, y=y_val)
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box_edit_source.on_change("data", box_edit_callback)
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wheelzoomtool = WheelZoomTool(maintain_focus=False)
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plot.add_tools(
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PanTool(), BoxZoomTool(), wheelzoomtool, ResetTool(), hovertool, boxedittool,
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)
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plot.toolbar.active_scroll = wheelzoomtool
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# shared frame ranges
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frame_range = Range1d(0, 1, bounds=(0, 1))
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scanning_motor_range = Range1d(0, 1, bounds=(0, 1))
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det_x_range = Range1d(0, IMAGE_W, bounds=(0, IMAGE_W))
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overview_plot_x = Plot(
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title=Title(text="Projections on X-axis"),
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x_range=det_x_range,
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y_range=frame_range,
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extra_y_ranges={"scanning_motor": scanning_motor_range},
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plot_height=400,
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plot_width=IMAGE_PLOT_W - 3,
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)
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# ---- tools
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wheelzoomtool = WheelZoomTool(maintain_focus=False)
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overview_plot_x.toolbar.logo = None
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overview_plot_x.add_tools(
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PanTool(), BoxZoomTool(), wheelzoomtool, ResetTool(),
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)
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overview_plot_x.toolbar.active_scroll = wheelzoomtool
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# ---- axes
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overview_plot_x.add_layout(LinearAxis(axis_label="Coordinate X, pix"), place="below")
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overview_plot_x.add_layout(
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LinearAxis(axis_label="Frame", major_label_orientation="vertical"), place="left"
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)
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# ---- grid lines
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overview_plot_x.add_layout(Grid(dimension=0, ticker=BasicTicker()))
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overview_plot_x.add_layout(Grid(dimension=1, ticker=BasicTicker()))
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# ---- rgba image glyph
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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=["Frame Intensity 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 Intensity 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)
|
||||
|
||||
events_data = dict(
|
||||
wave=[],
|
||||
ddist=[],
|
||||
cell=[],
|
||||
frame=[],
|
||||
x_pos=[],
|
||||
y_pos=[],
|
||||
intensity=[],
|
||||
snr_cnts=[],
|
||||
gamma=[],
|
||||
omega=[],
|
||||
chi=[],
|
||||
phi=[],
|
||||
nu=[],
|
||||
)
|
||||
doc.events_data = events_data
|
||||
|
||||
events_table_source = ColumnDataSource(events_data)
|
||||
events_table = DataTable(
|
||||
source=events_table_source,
|
||||
columns=[
|
||||
TableColumn(field="frame", title="Frame", formatter=num_formatter, width=70),
|
||||
TableColumn(field="x_pos", title="X", formatter=num_formatter, width=70),
|
||||
TableColumn(field="y_pos", title="Y", formatter=num_formatter, width=70),
|
||||
TableColumn(field="intensity", title="Intensity", formatter=num_formatter, width=70),
|
||||
TableColumn(field="gamma", title="Gamma", formatter=num_formatter, width=70),
|
||||
TableColumn(field="omega", title="Omega", formatter=num_formatter, width=70),
|
||||
TableColumn(field="chi", title="Chi", formatter=num_formatter, width=70),
|
||||
TableColumn(field="phi", title="Phi", formatter=num_formatter, width=70),
|
||||
TableColumn(field="nu", title="Nu", formatter=num_formatter, width=70),
|
||||
],
|
||||
height=150,
|
||||
width=630,
|
||||
autosize_mode="none",
|
||||
index_position=None,
|
||||
)
|
||||
|
||||
def add_event_button_callback():
|
||||
p0 = [1.0, 0.0, 1.0]
|
||||
maxfev = 100000
|
||||
|
||||
wave = det_data["wave"]
|
||||
ddist = det_data["ddist"]
|
||||
cell = det_data["cell"]
|
||||
|
||||
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]
|
||||
|
||||
events_data["wave"].append(wave)
|
||||
events_data["ddist"].append(ddist)
|
||||
events_data["cell"].append(cell)
|
||||
events_data["frame"].append(frC)
|
||||
events_data["x_pos"].append(x_pos)
|
||||
events_data["y_pos"].append(y_pos)
|
||||
events_data["intensity"].append(intensity)
|
||||
events_data["snr_cnts"].append(snr_cnts)
|
||||
events_data["gamma"].append(gamma)
|
||||
events_data["omega"].append(omega)
|
||||
events_data["chi"].append(chi)
|
||||
events_data["phi"].append(phi)
|
||||
events_data["nu"].append(nu)
|
||||
|
||||
events_table_source.data = events_data
|
||||
|
||||
add_event_button = Button(label="Add spind event", width=145)
|
||||
add_event_button.on_click(add_event_button_callback)
|
||||
|
||||
def remove_event_button_callback():
|
||||
ind2remove = events_table_source.selected.indices
|
||||
for value in events_data.values():
|
||||
for ind in reversed(ind2remove):
|
||||
del value[ind]
|
||||
|
||||
events_table_source.data = events_data
|
||||
|
||||
remove_event_button = Button(label="Remove spind event", width=145)
|
||||
remove_event_button.on_click(remove_event_button_callback)
|
||||
|
||||
metadata_table_source = ColumnDataSource(dict(geom=[""], temp=[None], mf=[None]))
|
||||
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(column(add_event_button, remove_event_button), events_table),
|
||||
)
|
||||
|
||||
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 param study")
|
||||
|
||||
|
||||
def gauss(x, *p):
|
||||
"""Defines Gaussian function
|
||||
Args:
|
||||
A - amplitude, mu - position of the center, sigma - width
|
||||
Returns:
|
||||
Gaussian function
|
||||
"""
|
||||
A, mu, sigma = p
|
||||
return A * np.exp(-((x - mu) ** 2) / (2.0 * sigma ** 2))
|
||||
|
||||
|
||||
def calculate_hkl(det_data, index):
|
||||
h = np.empty(shape=(IMAGE_H, IMAGE_W))
|
||||
k = np.empty(shape=(IMAGE_H, IMAGE_W))
|
||||
l = np.empty(shape=(IMAGE_H, IMAGE_W))
|
||||
|
||||
wave = det_data["wave"]
|
||||
ddist = det_data["ddist"]
|
||||
gammad = det_data["gamma"][index]
|
||||
om = det_data["omega"][index]
|
||||
nud = det_data["nu"]
|
||||
ub = det_data["ub"]
|
||||
geometry = det_data["zebra_mode"]
|
||||
|
||||
if geometry == "bi":
|
||||
chi = det_data["chi"][index]
|
||||
phi = det_data["phi"][index]
|
||||
elif geometry == "nb":
|
||||
chi = 0
|
||||
phi = 0
|
||||
else:
|
||||
raise ValueError(f"Unknown geometry type '{geometry}'")
|
||||
|
||||
for xi in np.arange(IMAGE_W):
|
||||
for yi in np.arange(IMAGE_H):
|
||||
h[yi, xi], k[yi, xi], l[yi, xi] = pyzebra.ang2hkl(
|
||||
wave, ddist, gammad, om, chi, phi, nud, ub, xi, yi
|
||||
)
|
||||
|
||||
return h, k, l
|
||||
|
||||
|
||||
def calculate_pol(det_data, index):
|
||||
gamma = np.empty(shape=(IMAGE_H, IMAGE_W))
|
||||
nu = np.empty(shape=(IMAGE_H, IMAGE_W))
|
||||
|
||||
ddist = det_data["ddist"]
|
||||
gammad = det_data["gamma"][index]
|
||||
nud = det_data["nu"]
|
||||
|
||||
for xi in np.arange(IMAGE_W):
|
||||
for yi in np.arange(IMAGE_H):
|
||||
gamma[yi, xi], nu[yi, xi] = pyzebra.det2pol(ddist, gammad, nud, xi, yi)
|
||||
|
||||
return gamma, nu
|
||||
Loading…
x
Reference in New Issue
Block a user