624 lines
20 KiB
Python
624 lines
20 KiB
Python
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.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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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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NumberEditor,
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NumberFormatter,
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Image,
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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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ResetTool,
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Scatter,
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Select,
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Spinner,
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TableColumn,
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Tabs,
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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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zebra_data = []
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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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file_select = MultiSelect(title="Available .hdf files:", width=210, height=320)
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def _init_datatable():
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file_list = []
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for scan in zebra_data:
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file_list.append(os.path.basename(scan["original_filename"]))
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scan_table_source.data.update(
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file=file_list,
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param=[None] * len(zebra_data),
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frame=[None] * len(zebra_data),
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x_pos=[None] * len(zebra_data),
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y_pos=[None] * len(zebra_data),
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)
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scan_table_source.selected.indices = []
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scan_table_source.selected.indices = [0]
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param_select.value = "user defined"
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def _update_table():
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frame = []
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x_pos = []
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y_pos = []
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for scan in zebra_data:
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if "fit" in scan:
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framei = scan["fit"]["frame"]
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x_posi = scan["fit"]["x_pos"]
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y_posi = scan["fit"]["y_pos"]
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else:
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framei = x_posi = y_posi = None
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frame.append(framei)
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x_pos.append(x_posi)
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y_pos.append(y_posi)
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scan_table_source.data.update(frame=frame, x_pos=x_pos, y_pos=y_pos)
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def file_open_button_callback():
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nonlocal zebra_data
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zebra_data = []
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for f_name in file_select.value:
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zebra_data.append(pyzebra.read_detector_data(f_name))
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_init_datatable()
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file_open_button = Button(label="Open New", width=100)
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file_open_button.on_click(file_open_button_callback)
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def file_append_button_callback():
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for f_name in file_select.value:
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zebra_data.append(pyzebra.read_detector_data(f_name))
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_init_datatable()
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file_append_button = Button(label="Append", width=100)
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file_append_button.on_click(file_append_button_callback)
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# Scan select
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def scan_table_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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scan_table_source.selected.indices = 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 = zebra_data[new[0]]
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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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def scan_table_source_callback(_attr, _old, _new):
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pass
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scan_table_source = ColumnDataSource(dict(file=[], param=[], frame=[], x_pos=[], y_pos=[]))
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scan_table_source.selected.on_change("indices", scan_table_select_callback)
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scan_table_source.on_change("data", scan_table_source_callback)
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scan_table = DataTable(
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source=scan_table_source,
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columns=[
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TableColumn(field="file", title="file", width=150),
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TableColumn(
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field="param",
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title="param",
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formatter=num_formatter,
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editor=NumberEditor(),
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width=50,
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),
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TableColumn(field="frame", title="Frame", formatter=num_formatter, width=70),
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TableColumn(field="x_pos", title="X", formatter=num_formatter, width=70),
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TableColumn(field="y_pos", title="Y", formatter=num_formatter, width=70),
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],
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width=470, # +60 because of the index column
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height=420,
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editable=True,
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autosize_mode="none",
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)
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def param_select_callback(_attr, _old, new):
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if new == "user defined":
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param = [None] * len(zebra_data)
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else:
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# TODO: which value to take?
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param = [scan[new][0] for scan in zebra_data]
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scan_table_source.data["param"] = param
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_update_param_plot()
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param_select = Select(
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title="Parameter:",
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options=["user defined", "temp", "mf", "h", "k", "l"],
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value="user defined",
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width=145,
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)
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param_select.on_change("value", param_select_callback)
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def update_image(index=None):
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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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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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# 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(
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dict(image=[np.zeros((1, 1), dtype="float32")], x=[0], y=[0], dw=[IMAGE_W], dh=[1])
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)
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overview_plot_x_image_glyph = Image(image="image", x="x", y="y", dw="dw", dh="dh")
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overview_plot_x.add_glyph(
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overview_plot_x_image_source, overview_plot_x_image_glyph, name="image_glyph"
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)
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det_y_range = Range1d(0, IMAGE_H, bounds=(0, IMAGE_H))
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overview_plot_y = Plot(
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title=Title(text="Projections on Y-axis"),
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x_range=det_y_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_H + 22,
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)
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# ---- tools
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wheelzoomtool = WheelZoomTool(maintain_focus=False)
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overview_plot_y.toolbar.logo = None
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overview_plot_y.add_tools(
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PanTool(), BoxZoomTool(), wheelzoomtool, ResetTool(),
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)
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overview_plot_y.toolbar.active_scroll = wheelzoomtool
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# ---- axes
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overview_plot_y.add_layout(LinearAxis(axis_label="Coordinate Y, pix"), place="below")
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overview_plot_y.add_layout(
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LinearAxis(
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y_range_name="scanning_motor",
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axis_label="Scanning motor",
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major_label_orientation="vertical",
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),
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place="right",
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)
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# ---- grid lines
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overview_plot_y.add_layout(Grid(dimension=0, ticker=BasicTicker()))
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overview_plot_y.add_layout(Grid(dimension=1, ticker=BasicTicker()))
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# ---- rgba image glyph
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overview_plot_y_image_source = ColumnDataSource(
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dict(image=[np.zeros((1, 1), dtype="float32")], x=[0], y=[0], dw=[IMAGE_H], dh=[1])
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)
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overview_plot_y_image_glyph = Image(image="image", x="x", y="y", dw="dw", dh="dh")
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overview_plot_y.add_glyph(
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overview_plot_y_image_source, overview_plot_y_image_glyph, name="image_glyph"
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)
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cmap_dict = {
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"gray": Greys256,
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"gray_reversed": Greys256[::-1],
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"plasma": Plasma256,
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"cividis": Cividis256,
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}
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def colormap_callback(_attr, _old, new):
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overview_plot_x_image_glyph.color_mapper = LinearColorMapper(palette=cmap_dict[new])
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overview_plot_y_image_glyph.color_mapper = LinearColorMapper(palette=cmap_dict[new])
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colormap = Select(title="Colormap:", options=list(cmap_dict.keys()), width=210)
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colormap.on_change("value", colormap_callback)
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colormap.value = "plasma"
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PROJ_STEP = 0.1
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def proj_auto_checkbox_callback(state):
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if state:
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proj_display_min_spinner.disabled = True
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proj_display_max_spinner.disabled = True
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else:
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proj_display_min_spinner.disabled = False
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proj_display_max_spinner.disabled = False
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update_overview_plot()
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proj_auto_checkbox = CheckboxGroup(
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labels=["Projections Intensity Range"], active=[0], width=145, margin=[10, 5, 0, 5]
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)
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proj_auto_checkbox.on_click(proj_auto_checkbox_callback)
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def proj_display_max_spinner_callback(_attr, _old_value, new_value):
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proj_display_min_spinner.high = new_value - PROJ_STEP
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overview_plot_x_image_glyph.color_mapper.high = new_value
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overview_plot_y_image_glyph.color_mapper.high = new_value
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proj_display_max_spinner = Spinner(
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low=0 + PROJ_STEP,
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value=1,
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step=PROJ_STEP,
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disabled=bool(proj_auto_checkbox.active),
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width=100,
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height=31,
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)
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proj_display_max_spinner.on_change("value", proj_display_max_spinner_callback)
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def proj_display_min_spinner_callback(_attr, _old_value, new_value):
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proj_display_max_spinner.low = new_value + PROJ_STEP
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overview_plot_x_image_glyph.color_mapper.low = new_value
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overview_plot_y_image_glyph.color_mapper.low = new_value
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proj_display_min_spinner = Spinner(
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low=0,
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high=1 - PROJ_STEP,
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value=0,
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step=PROJ_STEP,
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disabled=bool(proj_auto_checkbox.active),
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width=100,
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height=31,
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)
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proj_display_min_spinner.on_change("value", proj_display_min_spinner_callback)
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def fit_event(scan):
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p0 = [1.0, 0.0, 1.0]
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maxfev = 100000
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# wave = scan["wave"]
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# ddist = scan["ddist"]
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# cell = scan["cell"]
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# gamma = scan["gamma"][0]
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# omega = scan["omega"][0]
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# nu = scan["nu"][0]
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# chi = scan["chi"][0]
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# phi = scan["phi"][0]
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scan_motor = scan["scan_motor"]
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var_angle = scan[scan_motor]
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x0 = int(np.floor(det_x_range.start))
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xN = int(np.ceil(det_x_range.end))
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y0 = int(np.floor(det_y_range.start))
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yN = int(np.ceil(det_y_range.end))
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fr0 = int(np.floor(frame_range.start))
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frN = int(np.ceil(frame_range.end))
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data_roi = scan["data"][fr0:frN, y0:yN, x0:xN]
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cnts = np.sum(data_roi, axis=(1, 2))
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coeff, _ = curve_fit(gauss, range(len(cnts)), cnts, p0=p0, maxfev=maxfev)
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# m = cnts.mean()
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# sd = cnts.std()
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# snr_cnts = np.where(sd == 0, 0, m / sd)
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frC = fr0 + coeff[1]
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var_F = var_angle[math.floor(frC)]
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var_C = var_angle[math.ceil(frC)]
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# frStep = frC - math.floor(frC)
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var_step = var_C - var_F
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# var_p = var_F + var_step * frStep
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# if scan_motor == "gamma":
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# gamma = var_p
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# elif scan_motor == "omega":
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# omega = var_p
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# elif scan_motor == "nu":
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# nu = var_p
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# elif scan_motor == "chi":
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# chi = var_p
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# elif scan_motor == "phi":
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# phi = var_p
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intensity = coeff[1] * abs(coeff[2] * var_step) * math.sqrt(2) * math.sqrt(np.pi)
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projX = np.sum(data_roi, axis=(0, 1))
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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]
|
|
|
|
scan["fit"] = {"frame": frC, "x_pos": x_pos, "y_pos": y_pos, "intensity": intensity}
|
|
|
|
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,
|
|
)
|
|
|
|
def _update_param_plot():
|
|
x = []
|
|
y = []
|
|
fit_param = fit_param_select.value
|
|
for s, p in zip(zebra_data, scan_table_source.data["param"]):
|
|
if "fit" in s and fit_param:
|
|
x.append(p)
|
|
y.append(s["fit"][fit_param])
|
|
param_plot_scatter_source.data.update(x=x, y=y)
|
|
|
|
# Parameter plot
|
|
param_plot = Plot(x_range=DataRange1d(), y_range=DataRange1d(), plot_height=400, plot_width=700)
|
|
|
|
param_plot.add_layout(LinearAxis(axis_label="Fit parameter"), place="left")
|
|
param_plot.add_layout(LinearAxis(axis_label="Parameter"), place="below")
|
|
|
|
param_plot.add_layout(Grid(dimension=0, ticker=BasicTicker()))
|
|
param_plot.add_layout(Grid(dimension=1, ticker=BasicTicker()))
|
|
|
|
param_plot_scatter_source = ColumnDataSource(dict(x=[], y=[]))
|
|
param_plot.add_glyph(param_plot_scatter_source, Scatter(x="x", y="y"))
|
|
|
|
param_plot.add_tools(PanTool(), WheelZoomTool(), ResetTool())
|
|
param_plot.toolbar.logo = None
|
|
|
|
def fit_param_select_callback(_attr, _old, _new):
|
|
_update_param_plot()
|
|
|
|
fit_param_select = Select(title="Fit parameter", options=[], width=145)
|
|
fit_param_select.on_change("value", fit_param_select_callback)
|
|
|
|
def proc_all_button_callback():
|
|
for scan in zebra_data:
|
|
fit_event(scan)
|
|
|
|
_update_table()
|
|
|
|
for scan in zebra_data:
|
|
if "fit" in scan:
|
|
options = list(scan["fit"].keys())
|
|
fit_param_select.options = options
|
|
fit_param_select.value = options[0]
|
|
break
|
|
|
|
_update_param_plot()
|
|
|
|
proc_all_button = Button(label="Process All", button_type="primary", width=145)
|
|
proc_all_button.on_click(proc_all_button_callback)
|
|
|
|
def proc_button_callback():
|
|
fit_event(det_data)
|
|
|
|
_update_table()
|
|
|
|
for scan in zebra_data:
|
|
if "fit" in scan:
|
|
options = list(scan["fit"].keys())
|
|
fit_param_select.options = options
|
|
fit_param_select.value = options[0]
|
|
break
|
|
|
|
_update_param_plot()
|
|
|
|
proc_button = Button(label="Process Current", width=145)
|
|
proc_button.on_click(proc_button_callback)
|
|
|
|
layout_controls = row(
|
|
colormap,
|
|
column(proj_auto_checkbox, row(proj_display_min_spinner, proj_display_max_spinner)),
|
|
proc_button,
|
|
proc_all_button,
|
|
)
|
|
|
|
layout_overview = column(
|
|
gridplot(
|
|
[[overview_plot_x, overview_plot_y]],
|
|
toolbar_options=dict(logo=None),
|
|
merge_tools=True,
|
|
toolbar_location="left",
|
|
),
|
|
layout_controls,
|
|
)
|
|
|
|
# Plot tabs
|
|
plots = Tabs(
|
|
tabs=[
|
|
Panel(child=layout_overview, title="single scan"),
|
|
Panel(child=column(param_plot, row(fit_param_select)), title="parameter plot"),
|
|
]
|
|
)
|
|
|
|
# Final layout
|
|
import_layout = column(
|
|
proposal_textinput,
|
|
upload_div,
|
|
upload_button,
|
|
file_select,
|
|
row(file_open_button, file_append_button),
|
|
)
|
|
|
|
scan_layout = column(scan_table, row(param_select, metadata_table))
|
|
|
|
tab_layout = column(row(import_layout, scan_layout, plots))
|
|
|
|
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))
|