588 lines
21 KiB
Python
588 lines
21 KiB
Python
import base64
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import io
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import os
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import tempfile
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import types
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from copy import deepcopy
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import numpy as np
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from bokeh.layouts import column, row
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from bokeh.models import (
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Asterisk,
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BasicTicker,
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Button,
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CheckboxEditor,
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ColumnDataSource,
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CustomJS,
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DataRange1d,
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DataTable,
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Div,
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Dropdown,
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FileInput,
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Grid,
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Line,
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LinearAxis,
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MultiSelect,
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NumberEditor,
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Panel,
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PanTool,
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Plot,
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RadioButtonGroup,
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ResetTool,
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Scatter,
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Select,
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Spacer,
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Span,
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Spinner,
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TableColumn,
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TextAreaInput,
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TextInput,
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Toggle,
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WheelZoomTool,
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Whisker,
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)
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import pyzebra
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from pyzebra.ccl_io import AREA_METHODS
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javaScript = """
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setTimeout(function() {
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const filename = 'output' + js_data.data['ext']
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const blob = new Blob([js_data.data['cont']], {type: 'text/plain'})
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const link = document.createElement('a');
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document.body.appendChild(link);
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const url = window.URL.createObjectURL(blob);
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link.href = url;
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link.download = filename;
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link.click();
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window.URL.revokeObjectURL(url);
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document.body.removeChild(link);
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}, 500);
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"""
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PROPOSAL_PATH = "/afs/psi.ch/project/sinqdata/2020/zebra/"
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def create():
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det_data = {}
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fit_params = {}
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peak_pos_textinput_lock = False
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js_data = ColumnDataSource(data=dict(cont=[], ext=[]))
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def proposal_textinput_callback(_attr, _old, new):
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ccl_path = os.path.join(PROPOSAL_PATH, new.strip())
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ccl_file_list = []
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for file in os.listdir(ccl_path):
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if file.endswith(".ccl"):
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ccl_file_list.append((os.path.join(ccl_path, file), file))
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file_select.options = ccl_file_list
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file_select.value = ccl_file_list[0][0]
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proposal_textinput = TextInput(title="Enter proposal number:", default_size=145, disabled=True)
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proposal_textinput.on_change("value", proposal_textinput_callback)
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def _init_datatable():
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scan_list = list(det_data["scan"].keys())
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file_list = []
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extra_meta = det_data.get("extra_meta", {})
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for scan_id in scan_list:
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if scan_id in extra_meta:
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f_path = extra_meta[scan_id]["original_filename"]
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else:
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f_path = det_data["meta"]["original_filename"]
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_, f_name = os.path.split(f_path)
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file_list.append(f_name)
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scan_table_source.data.update(
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file=file_list,
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scan=scan_list,
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param=[""] * len(scan_list),
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peaks=[0] * len(scan_list),
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fit=[0] * len(scan_list),
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export=[True] * len(scan_list),
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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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def file_select_callback(_attr, _old, new):
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nonlocal det_data
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with open(new) as file:
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_, ext = os.path.splitext(new)
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det_data = pyzebra.parse_1D(file, ext)
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_init_datatable()
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file_select = Select(title="Available .dat files", disabled=True)
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file_select.on_change("value", file_select_callback)
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def upload_button_callback(_attr, _old, new):
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nonlocal det_data
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det_data = {}
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for f_str, f_name in zip(new, upload_button.filename):
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with io.StringIO(base64.b64decode(f_str).decode()) as file:
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_, ext = os.path.splitext(f_name)
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if det_data:
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append_data = pyzebra.parse_1D(file, ext)
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pyzebra.unified_merge(det_data, append_data)
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else:
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det_data = pyzebra.parse_1D(file, ext)
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_init_datatable()
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upload_button = FileInput(accept=".dat", multiple=True)
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upload_button.on_change("value", upload_button_callback)
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def append_upload_button_callback(_attr, _old, new):
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for f_str, f_name in zip(new, append_upload_button.filename):
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with io.StringIO(base64.b64decode(f_str).decode()) as file:
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_, ext = os.path.splitext(f_name)
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append_data = pyzebra.parse_1D(file, ext)
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pyzebra.unified_merge(det_data, append_data)
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_init_datatable()
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append_upload_button = FileInput(accept=".dat", multiple=True)
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append_upload_button.on_change("value", append_upload_button_callback)
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def _update_table():
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num_of_peaks = [len(scan.get("peak_indexes", [])) for scan in det_data["scan"].values()]
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fit_ok = [(1 if "fit" in scan else 0) for scan in det_data["scan"].values()]
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scan_table_source.data.update(peaks=num_of_peaks, fit=fit_ok)
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def _update_plot(scan):
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nonlocal peak_pos_textinput_lock
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peak_pos_textinput_lock = True
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y = scan["Counts"]
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x = scan["om"]
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plot_scatter_source.data.update(x=x, y=y, y_upper=y + np.sqrt(y), y_lower=y - np.sqrt(y))
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num_of_peaks = len(scan.get("peak_indexes", []))
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if num_of_peaks is not None and num_of_peaks > 0:
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peak_indexes = scan["peak_indexes"]
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if len(peak_indexes) == 1:
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peak_pos_textinput.value = str(x[peak_indexes[0]])
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else:
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peak_pos_textinput.value = str([x[ind] for ind in peak_indexes])
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plot_peak_source.data.update(x=x[peak_indexes], y=scan["peak_heights"])
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plot_line_smooth_source.data.update(x=x, y=scan["smooth_peaks"])
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else:
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peak_pos_textinput.value = None
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plot_peak_source.data.update(x=[], y=[])
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plot_line_smooth_source.data.update(x=[], y=[])
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peak_pos_textinput_lock = False
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fit = scan.get("fit")
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if fit is not None:
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x = scan["fit"]["x_fit"]
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plot_gauss_source.data.update(x=x, y=scan["fit"]["comps"]["gaussian"])
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plot_bkg_source.data.update(x=x, y=scan["fit"]["comps"]["background"])
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params = fit["result"].params
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fit_output_textinput.value = (
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"Gaussian: centre = %9.4f, sigma = %9.4f, area = %9.4f \n"
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"background: slope = %9.4f, intercept = %9.4f \n"
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"Int. area = %9.4f +/- %9.4f \n"
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"fit area = %9.4f +/- %9.4f \n"
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"ratio((fit-int)/fit) = %9.4f"
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% (
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params["g_cen"].value,
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params["g_width"].value,
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params["g_amp"].value,
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params["slope"].value,
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params["intercept"].value,
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fit["int_area"].n,
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fit["int_area"].s,
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params["g_amp"].value,
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params["g_amp"].stderr,
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(params["g_amp"].value - fit["int_area"].n) / params["g_amp"].value,
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)
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)
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numfit_min, numfit_max = fit["numfit"]
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if numfit_min is None:
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numfit_min_span.location = None
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else:
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numfit_min_span.location = x[numfit_min]
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if numfit_max is None:
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numfit_max_span.location = None
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else:
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numfit_max_span.location = x[numfit_max]
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else:
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plot_gauss_source.data.update(x=[], y=[])
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plot_bkg_source.data.update(x=[], y=[])
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fit_output_textinput.value = ""
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numfit_min_span.location = None
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numfit_max_span.location = None
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# Main plot
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plot = Plot(x_range=DataRange1d(), y_range=DataRange1d(), plot_height=400, plot_width=700)
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plot.add_layout(LinearAxis(axis_label="Counts"), place="left")
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plot.add_layout(LinearAxis(axis_label="Omega"), place="below")
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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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plot_scatter_source = ColumnDataSource(dict(x=[0], y=[0], y_upper=[0], y_lower=[0]))
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plot.add_glyph(plot_scatter_source, Scatter(x="x", y="y", line_color="steelblue"))
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plot.add_layout(Whisker(source=plot_scatter_source, base="x", upper="y_upper", lower="y_lower"))
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plot_line_smooth_source = ColumnDataSource(dict(x=[0], y=[0]))
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plot.add_glyph(
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plot_line_smooth_source, Line(x="x", y="y", line_color="steelblue", line_dash="dashed")
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)
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plot_gauss_source = ColumnDataSource(dict(x=[0], y=[0]))
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plot.add_glyph(plot_gauss_source, Line(x="x", y="y", line_color="red", line_dash="dashed"))
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plot_bkg_source = ColumnDataSource(dict(x=[0], y=[0]))
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plot.add_glyph(plot_bkg_source, Line(x="x", y="y", line_color="green", line_dash="dashed"))
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plot_peak_source = ColumnDataSource(dict(x=[], y=[]))
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plot.add_glyph(plot_peak_source, Asterisk(x="x", y="y", size=10, line_color="red"))
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numfit_min_span = Span(location=None, dimension="height", line_dash="dashed")
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plot.add_layout(numfit_min_span)
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numfit_max_span = Span(location=None, dimension="height", line_dash="dashed")
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plot.add_layout(numfit_max_span)
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plot.add_tools(PanTool(), WheelZoomTool(), ResetTool())
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plot.toolbar.logo = None
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# Scan select
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def scan_table_select_callback(_attr, old, new):
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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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_update_plot(det_data["scan"][scan_table_source.data["scan"][new[0]]])
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scan_table_source = ColumnDataSource(dict(file=[], scan=[], param=[], peaks=[], fit=[], export=[]))
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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(field="scan", title="scan", width=50),
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TableColumn(field="param", title="param", width=50),
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TableColumn(field="peaks", title="Peaks", width=50),
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TableColumn(field="fit", title="Fit", width=50),
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TableColumn(field="export", title="Export", editor=CheckboxEditor(), width=50),
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],
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width=400,
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index_position=None,
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editable=True,
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fit_columns=False,
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)
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scan_table_source.selected.on_change("indices", scan_table_select_callback)
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def _get_selected_scan():
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selected_index = scan_table_source.selected.indices[0]
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selected_scan_id = scan_table_source.data["scan"][selected_index]
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return det_data["scan"][selected_scan_id]
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def peak_pos_textinput_callback(_attr, _old, new):
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if new is not None and not peak_pos_textinput_lock:
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scan = _get_selected_scan()
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peak_ind = (np.abs(scan["om"] - float(new))).argmin()
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scan["peak_indexes"] = np.array([peak_ind], dtype=np.int64)
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scan["peak_heights"] = np.array([scan["smooth_peaks"][peak_ind]])
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_update_table()
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_update_plot(scan)
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peak_pos_textinput = TextInput(title="Peak position:", default_size=145)
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peak_pos_textinput.on_change("value", peak_pos_textinput_callback)
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peak_int_ratio_spinner = Spinner(
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title="Peak intensity ratio:", value=0.8, step=0.01, low=0, high=1, default_size=145
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)
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peak_prominence_spinner = Spinner(title="Peak prominence:", value=50, low=0, default_size=145)
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smooth_toggle = Toggle(label="Smooth curve", default_size=145)
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window_size_spinner = Spinner(title="Window size:", value=7, step=2, low=1, default_size=145)
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poly_order_spinner = Spinner(title="Poly order:", value=3, low=0, default_size=145)
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integ_from = Spinner(title="Integrate from:", default_size=145)
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integ_to = Spinner(title="to:", default_size=145)
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def fitparam_reset_button_callback():
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...
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fitparam_reset_button = Button(label="Reset to defaults", default_size=145, disabled=True)
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fitparam_reset_button.on_click(fitparam_reset_button_callback)
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def fitparams_add_dropdown_callback(click):
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new_tag = str(fitparams_select.tags[0]) # bokeh requires (str, str) for MultiSelect options
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fitparams_select.options.append((new_tag, click.item))
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fit_params[new_tag] = fitparams_factory(click.item)
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fitparams_select.tags[0] += 1
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fitparams_add_dropdown = Dropdown(
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label="Add fit function",
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menu=[
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("Background", "background"),
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("Gauss", "gauss"),
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("Voigt", "voigt"),
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("Pseudo Voigt", "pseudovoigt"),
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("Pseudo Voigt1", "pseudovoigt1"),
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],
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default_size=145,
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)
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fitparams_add_dropdown.on_click(fitparams_add_dropdown_callback)
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def fitparams_select_callback(_attr, old, new):
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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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fitparams_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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if new:
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fitparams_table_source.data.update(fit_params[new[0]])
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else:
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fitparams_table_source.data.update(dict(param=[], guess=[], vary=[], min=[], max=[]))
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fitparams_select = MultiSelect(options=[], height=120, default_size=145)
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fitparams_select.tags = [0]
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fitparams_select.on_change("value", fitparams_select_callback)
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def fitparams_remove_button_callback():
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if fitparams_select.value:
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sel_tag = fitparams_select.value[0]
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del fit_params[sel_tag]
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for elem in fitparams_select.options:
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if elem[0] == sel_tag:
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fitparams_select.options.remove(elem)
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break
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fitparams_select.value = []
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fitparams_remove_button = Button(label="Remove fit function", default_size=145)
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fitparams_remove_button.on_click(fitparams_remove_button_callback)
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def fitparams_factory(function):
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if function == "background":
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params = ["slope", "offset"]
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elif function == "gauss":
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params = ["center", "sigma", "amplitude"]
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elif function == "voigt":
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params = ["center", "sigma", "amplitude", "gamma"]
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elif function == "pseudovoigt":
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params = ["center", "sigma", "amplitude", "fraction"]
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elif function == "pseudovoigt1":
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params = ["center", "g_sigma", "l_sigma", "amplitude", "fraction"]
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else:
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raise ValueError("Unknown fit function")
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n = len(params)
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fitparams = dict(
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param=params, guess=[None] * n, vary=[True] * n, min=[None] * n, max=[None] * n,
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)
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return fitparams
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fitparams_table_source = ColumnDataSource(dict(param=[], guess=[], vary=[], min=[], max=[]))
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fitparams_table = DataTable(
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source=fitparams_table_source,
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columns=[
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TableColumn(field="param", title="Parameter"),
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TableColumn(field="guess", title="Guess", editor=NumberEditor()),
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TableColumn(field="vary", title="Vary", editor=CheckboxEditor()),
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TableColumn(field="min", title="Min", editor=NumberEditor()),
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TableColumn(field="max", title="Max", editor=NumberEditor()),
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],
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height=200,
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width=350,
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index_position=None,
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editable=True,
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auto_edit=True,
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)
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# start with `background` and `gauss` fit functions added
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fitparams_add_dropdown_callback(types.SimpleNamespace(item="background"))
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fitparams_add_dropdown_callback(types.SimpleNamespace(item="gauss"))
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fit_output_textinput = TextAreaInput(title="Fit results:", width=450, height=400)
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def _get_peakfind_params():
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return dict(
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int_threshold=peak_int_ratio_spinner.value,
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prominence=peak_prominence_spinner.value,
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smooth=smooth_toggle.active,
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window_size=window_size_spinner.value,
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poly_order=poly_order_spinner.value,
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)
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def peakfind_all_button_callback():
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peakfind_params = _get_peakfind_params()
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for scan in det_data["scan"].values():
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pyzebra.ccl_findpeaks(scan, **peakfind_params)
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_update_table()
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_update_plot(_get_selected_scan())
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peakfind_all_button = Button(label="Peak Find All", button_type="primary", default_size=145)
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peakfind_all_button.on_click(peakfind_all_button_callback)
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def peakfind_button_callback():
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scan = _get_selected_scan()
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pyzebra.ccl_findpeaks(scan, **_get_peakfind_params())
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_update_table()
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_update_plot(scan)
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peakfind_button = Button(label="Peak Find Current", default_size=145)
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peakfind_button.on_click(peakfind_button_callback)
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def _get_fit_params():
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return dict(
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guess=fit_params["1"]["guess"] + fit_params["0"]["guess"],
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vary=fit_params["1"]["vary"] + fit_params["0"]["vary"],
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constraints_min=fit_params["1"]["min"] + fit_params["0"]["min"],
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constraints_max=fit_params["1"]["max"] + fit_params["0"]["max"],
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numfit_min=integ_from.value,
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numfit_max=integ_to.value,
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binning=bin_size_spinner.value,
|
|
)
|
|
|
|
def fit_all_button_callback():
|
|
fit_params = _get_fit_params()
|
|
for scan in det_data["scan"].values():
|
|
# fit_params are updated inplace within `fitccl`
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|
pyzebra.fitccl(scan, **deepcopy(fit_params))
|
|
|
|
_update_plot(_get_selected_scan())
|
|
_update_table()
|
|
|
|
fit_all_button = Button(label="Fit All", button_type="primary", default_size=145)
|
|
fit_all_button.on_click(fit_all_button_callback)
|
|
|
|
def fit_button_callback():
|
|
scan = _get_selected_scan()
|
|
pyzebra.fitccl(scan, **_get_fit_params())
|
|
|
|
_update_plot(scan)
|
|
_update_table()
|
|
|
|
fit_button = Button(label="Fit Current", default_size=145)
|
|
fit_button.on_click(fit_button_callback)
|
|
|
|
def area_method_radiobutton_callback(_attr, _old, new):
|
|
det_data["meta"]["area_method"] = AREA_METHODS[new]
|
|
|
|
area_method_radiobutton = RadioButtonGroup(
|
|
labels=["Fit area", "Int area"], active=0, default_size=145,
|
|
)
|
|
area_method_radiobutton.on_change("active", area_method_radiobutton_callback)
|
|
|
|
bin_size_spinner = Spinner(title="Bin size:", value=1, low=1, step=1, default_size=145)
|
|
|
|
lorentz_toggle = Toggle(label="Lorentz Correction", default_size=145)
|
|
|
|
preview_output_textinput = TextAreaInput(title="Export file preview:", width=450, height=400)
|
|
|
|
def preview_output_button_callback():
|
|
if det_data["meta"]["indices"] == "hkl":
|
|
ext = ".comm"
|
|
elif det_data["meta"]["indices"] == "real":
|
|
ext = ".incomm"
|
|
|
|
with tempfile.TemporaryDirectory() as temp_dir:
|
|
temp_file = temp_dir + "/temp"
|
|
export_data = deepcopy(det_data)
|
|
for s, export in zip(scan_table_source.data["scan"], scan_table_source.data["export"]):
|
|
if not export:
|
|
del export_data["scan"][s]
|
|
pyzebra.export_comm(export_data, temp_file, lorentz=lorentz_toggle.active)
|
|
|
|
with open(f"{temp_file}{ext}") as f:
|
|
preview_output_textinput.value = f.read()
|
|
|
|
preview_output_button = Button(label="Preview file", default_size=220)
|
|
preview_output_button.on_click(preview_output_button_callback)
|
|
|
|
def export_results(det_data):
|
|
if det_data["meta"]["indices"] == "hkl":
|
|
ext = ".comm"
|
|
elif det_data["meta"]["indices"] == "real":
|
|
ext = ".incomm"
|
|
|
|
with tempfile.TemporaryDirectory() as temp_dir:
|
|
temp_file = temp_dir + "/temp"
|
|
export_data = deepcopy(det_data)
|
|
for s, export in zip(scan_table_source.data["scan"], scan_table_source.data["export"]):
|
|
if not export:
|
|
del export_data["scan"][s]
|
|
pyzebra.export_comm(export_data, temp_file, lorentz=lorentz_toggle.active)
|
|
|
|
with open(f"{temp_file}{ext}") as f:
|
|
output_content = f.read()
|
|
|
|
return output_content, ext
|
|
|
|
def save_button_callback():
|
|
cont, ext = export_results(det_data)
|
|
js_data.data.update(cont=[cont], ext=[ext])
|
|
|
|
save_button = Button(label="Download file", button_type="success", default_size=220)
|
|
save_button.on_click(save_button_callback)
|
|
save_button.js_on_click(CustomJS(args={"js_data": js_data}, code=javaScript))
|
|
|
|
findpeak_controls = column(
|
|
row(peak_pos_textinput, column(Spacer(height=19), smooth_toggle)),
|
|
row(peak_int_ratio_spinner, peak_prominence_spinner),
|
|
row(window_size_spinner, poly_order_spinner),
|
|
row(peakfind_button, peakfind_all_button),
|
|
)
|
|
|
|
fitpeak_controls = row(
|
|
column(fitparams_add_dropdown, fitparams_select, fitparams_remove_button),
|
|
fitparams_table,
|
|
Spacer(width=20),
|
|
column(
|
|
row(integ_from, integ_to),
|
|
row(bin_size_spinner, column(Spacer(height=19), lorentz_toggle)),
|
|
row(fitparam_reset_button, area_method_radiobutton),
|
|
row(fit_button, fit_all_button),
|
|
),
|
|
)
|
|
|
|
export_layout = column(preview_output_textinput, row(preview_output_button, save_button))
|
|
|
|
upload_div = Div(text="Or upload .dat files:")
|
|
append_upload_div = Div(text="append extra .dat files:")
|
|
tab_layout = column(
|
|
row(proposal_textinput, file_select),
|
|
row(
|
|
column(Spacer(height=5), upload_div),
|
|
upload_button,
|
|
column(Spacer(height=5), append_upload_div),
|
|
append_upload_button,
|
|
),
|
|
row(scan_table, plot, Spacer(width=30), fit_output_textinput, export_layout),
|
|
row(findpeak_controls, Spacer(width=30), fitpeak_controls),
|
|
)
|
|
|
|
return Panel(child=tab_layout, title="param study")
|