583 lines
19 KiB
Python
583 lines
19 KiB
Python
import base64
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import io
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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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CellEditor,
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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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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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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():
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if data_source.value == "proposal number":
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proposal_path = proposal_textinput.name
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if proposal_path:
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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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else:
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file_select.options = []
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else: # "cami file"
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if not cami_meta:
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file_select.options = []
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return
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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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def data_source_callback(_attr, _old, _new):
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file_select_update()
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data_source = Select(
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title="Data Source:",
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value="proposal number",
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options=["proposal number", "cami file"],
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width=210,
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)
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data_source.on_change("value", data_source_callback)
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doc.add_periodic_callback(file_select_update, 5000)
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def proposal_textinput_callback(_attr, _old, _new):
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file_select_update()
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proposal_textinput = doc.proposal_textinput
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proposal_textinput.on_change("name", 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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with io.StringIO(base64.b64decode(new).decode()) as file:
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cami_meta = pyzebra.parse_h5meta(file)
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data_source.value = "cami file"
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file_select_update()
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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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if "mf" in det_data:
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metadata_table_source.data.update(mf=[det_data["mf"][0]])
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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"][0]])
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else:
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metadata_table_source.data.update(temp=[None])
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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", editor=CellEditor(), 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(
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field="frame", title="Frame", formatter=num_formatter, editor=CellEditor(), width=70
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),
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TableColumn(
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field="x_pos", title="X", formatter=num_formatter, editor=CellEditor(), width=70
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),
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TableColumn(
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field="y_pos", title="Y", formatter=num_formatter, editor=CellEditor(), width=70
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),
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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_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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# normalize for simpler colormapping
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overview_max_val = max(np.max(overview_x), np.max(overview_y))
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overview_x = 1000 * overview_x / overview_max_val
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overview_y = 1000 * overview_y / overview_max_val
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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 = 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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metadata_table_source = ColumnDataSource(dict(geom=[""], temp=[None], mf=[None]))
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metadata_table = DataTable(
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source=metadata_table_source,
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columns=[
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TableColumn(field="geom", title="Geometry", width=100),
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TableColumn(field="temp", title="Temperature", formatter=num_formatter, width=100),
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TableColumn(field="mf", title="Magnetic Field", formatter=num_formatter, width=100),
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],
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width=300,
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height=50,
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autosize_mode="none",
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index_position=None,
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)
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def _update_param_plot():
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x = []
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y = []
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fit_param = fit_param_select.value
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for s, p in zip(zebra_data, scan_table_source.data["param"]):
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if "fit" in s and fit_param:
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x.append(p)
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y.append(s["fit"][fit_param])
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param_plot_scatter_source.data.update(x=x, y=y)
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# Parameter plot
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param_plot = Plot(x_range=DataRange1d(), y_range=DataRange1d(), plot_height=400, plot_width=700)
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param_plot.add_layout(LinearAxis(axis_label="Fit parameter"), place="left")
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param_plot.add_layout(LinearAxis(axis_label="Parameter"), place="below")
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param_plot.add_layout(Grid(dimension=0, ticker=BasicTicker()))
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param_plot.add_layout(Grid(dimension=1, ticker=BasicTicker()))
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param_plot_scatter_source = ColumnDataSource(dict(x=[], y=[]))
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param_plot.add_glyph(param_plot_scatter_source, Scatter(x="x", y="y"))
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param_plot.add_tools(PanTool(), WheelZoomTool(), ResetTool())
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param_plot.toolbar.logo = None
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def fit_param_select_callback(_attr, _old, _new):
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_update_param_plot()
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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:
|
|
pyzebra.fit_event(
|
|
scan,
|
|
int(np.floor(frame_range.start)),
|
|
int(np.ceil(frame_range.end)),
|
|
int(np.floor(det_y_range.start)),
|
|
int(np.ceil(det_y_range.end)),
|
|
int(np.floor(det_x_range.start)),
|
|
int(np.ceil(det_x_range.end)),
|
|
)
|
|
|
|
_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():
|
|
pyzebra.fit_event(
|
|
det_data,
|
|
int(np.floor(frame_range.start)),
|
|
int(np.ceil(frame_range.end)),
|
|
int(np.floor(det_y_range.start)),
|
|
int(np.ceil(det_y_range.end)),
|
|
int(np.floor(det_x_range.start)),
|
|
int(np.ceil(det_x_range.end)),
|
|
)
|
|
|
|
_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(
|
|
data_source,
|
|
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")
|