def get_save_layout_button(enable_button, from_main):
from astronomicAL.utils import save_config
if ("save_button" not in settings.keys()) or from_main:
settings["save_button"] = pn.widgets.Button(
name="Save Current Configuration", disabled=not (enable_button)
)
layout_dict = {}
text_area_input = TextAreaInput(value="")
text_area_input.on_change(
"value",
partial(
save_config.save_config_file_cb,
trigger_text=text_area_input,
autosave=False,
),
)
settings["save_button"].jscallback(
clicks=save_config.save_layout_js_cb,
args=dict(text_area_input=text_area_input),
)
settings["save_button"].on_click(_save_layout_button_cb)
return settings["save_button"]
if not from_main:
settings["save_button"].disabled = not (enable_button)
return settings["save_button"]
def test_single_row_copy_with_zero(self, bokeh_model_page):
data = {'x': [1,2,3,4], 'y': [0, 0, 0, 0], 'd': ['foo', 'bar', 'baz', 'quux']}
source = ColumnDataSource(data)
table = DataTable(columns=[
TableColumn(field="x", title="x"),
TableColumn(field="y", title="y"),
TableColumn(field="d", title="d"),
], source=source)
text_input = TextAreaInput(css_classes=["foo"])
text_input.js_on_change('value', CustomJS(code=RECORD("value", "cb_obj.value")))
page = bokeh_model_page(column(table, text_input))
# select the third row
row = get_table_row(page.driver, 2)
row.click()
enter_text_in_element(page.driver, row, Keys.INSERT, mod=Keys.CONTROL, click=0, enter=False)
input_el = page.driver.find_element_by_css_selector('.foo')
enter_text_in_element(page.driver, input_el, Keys.INSERT, mod=Keys.SHIFT, enter=False)
#enter_text_in_element(page.driver, input_el, "")
sleep(0.5)
results = page.results
assert results['value'] == '1\t2\t0\tbar\n'
assert page.has_no_console_errors()
def milestone_tab(self):
self.milestone_subtitle = Div(text="Milestones & Major Accomplishments", css_classes=['subt-style'])
self.milestone_inst = Div(text="Record any major milestones or accomplishments that occur throughout a night and the exposure numbers that correspond to it. If applicable, indicate the ID of exposures to ignore in a series.", css_classes=['inst-style'],width=1000)
self.milestone_input = TextAreaInput(placeholder="Description", rows=10, cols=3, max_length=5000)
self.milestone_exp_start = TextInput(title ='Exposure Start', placeholder='12345', value=None)
self.milestone_exp_end = TextInput(title='Exposure End', placeholder='12345', value=None)
self.milestone_exp_excl = TextInput(title='Excluded Exposures', placeholder='12346', value=None)
self.milestone_btn = Button(label='Add Milestone', css_classes=['add_button'])
self.milestone_alert = Div(text=' ', css_classes=['alert-style'])
self.summary = TextAreaInput(rows=6, title='End of Night Summary',max_length=5000)
self.summary_btn = Button(label='Add Summary', css_classes=['add_button'], width=300)
def modify_doc(doc):
source = ColumnDataSource(dict(x=[1, 2], y=[1, 1], val=["a", "b"]))
plot = Plot(plot_height=400, plot_width=400, x_range=Range1d(0, 1), y_range=Range1d(0, 1), min_border=0)
plot.add_glyph(source, Circle(x='x', y='y', size=20))
code = RECORD("data", "s.data")
plot.add_tools(CustomAction(callback=CustomJS(args=dict(s=source), code=code)))
text_input = TextAreaInput(cols=20, css_classes=["foo"])
def cb(attr, old, new):
foo.append((old, new))
source.data['val'] = [old, new]
text_input.on_change('value', cb)
doc.add_root(column(text_input, plot))
def modify_doc(doc):
source = ColumnDataSource(dict(x=[1, 2], y=[1, 1], val=["a", "b"]))
plot = Plot(plot_height=400, plot_width=400, x_range=Range1d(0, 1), y_range=Range1d(0, 1), min_border=0)
plot.add_glyph(source, Circle(x='x', y='y', size=20))
code = RECORD("data", "s.data")
plot.add_tools(CustomAction(callback=CustomJS(args=dict(s=source), code=code)))
text_input = TextAreaInput(cols=20, css_classes=["foo"])
def cb(attr, old, new):
foo.append((old, new))
source.data['val'] = [old, new]
text_input.on_change('value', cb)
doc.add_root(column(text_input, plot))
def create_text(cont):
"""
Create a box to enter text for each tab.
Args:
cont (str): Continent name.
Returns:
text_input (:obj:`TextAreaInput`): Text input box.
"""
text_input = TextAreaInput(
value="",
rows=6,
title="Manually enter line-separated countries, hit tab when finished")
text_input.on_change("value", text_update)
return text_input
def test_displays_placeholder(self, bokeh_model_page):
text_input = TextAreaInput(placeholder="placeholder", css_classes=["foo"])
page = bokeh_model_page(text_input)
input_div = page.driver.find_element_by_class_name('foo')
el = input_div.find_element_by_tag_name("textarea")
assert el.get_attribute('placeholder') == "placeholder"
assert page.has_no_console_errors()
def __init__(self):
Report.__init__(self, 'OS')
self.title = Div(text="DESI Nightly Intake - Operating Scientist", css_classes=['h1-title-style'], width=1000)# width=800, style={'font-size':'24pt','font-style':'bold'})
desc = """
The Operating Scientist (OS) is responsible for initializing the Night Log. Connect to an existing Night Log using the date or initialize tonight's log.
Throughout the night, enter information about the exposures, weather, and problems. Complete the OS Checklist at least once every hour.
"""
self.instructions = Div(text=desc+self.time_note.text, css_classes=['inst-style'], width=500)
self.line = Div(text='-----------------------------------------------------------------------------------------------------------------------------', width=1000)
self.line2 = Div(text='-----------------------------------------------------------------------------------------------------------------------------', width=1000)
self.init_bt = Button(label="Initialize Tonight's Log", css_classes=['init_button'])
self.LO = Select(title='Lead Observer', value='Choose One', options=self.lo_names)
self.OA = Select(title='Observing Assistant', value='Choose One', options=self.oa_names)
self.page_logo = Div(text="<img src='OS_Report/static/logo.png'>", width=350, height=300)
self.contributer_list = TextAreaInput(placeholder='Contributer names (include all)', rows=2, cols=3, title='Names of all Contributers')
self.contributer_btn = Button(label='Update Contributer List', css_classes=['add_button'], width=300)
self.connect_hdr = Div(text="Connect to Existing Night Log", css_classes=['subt-style'], width=800)
self.init_hdr = Div(text="Initialize Tonight's Night Log", css_classes=['subt-style'], width=800)
self.check_subtitle = Div(text="OS Checklist", css_classes=['subt-style'])
self.checklist_inst = Div(text="Every hour, the OS is expected to monitor several things. After completing these tasks, record at what time they were completed. Be honest please!", css_classes=['inst-style'], width=1000)
self.checklist.labels = ["Did you check the weather?", "Did you check the guiding?", "Did you check the positioner temperatures?","Did you check the FXC?", "Did you check the Cryostat?", "Did you do a connectivity aliveness check?","Did you check the Spectrograph Chiller?"]
self.nl_submit_btn = Button(label='Submit NightLog & Publish Nightsum', width=300, css_classes=['add_button'])
self.header_options = ['Startup','Calibration (Arcs/Twilight)','Focus','Observation','Other Acquisition','Comment']
def test_displays_placeholder(self,
bokeh_model_page: BokehModelPage) -> None:
text_input = TextAreaInput(placeholder="placeholder")
page = bokeh_model_page(text_input)
el = find_element_for(page.driver, text_input, "textarea")
assert el.get_attribute('placeholder') == "placeholder"
assert page.has_no_console_errors()
def test_displays_text_input(self, bokeh_model_page):
text_input = TextAreaInput(css_classes=["foo"])
page = bokeh_model_page(text_input)
input_div = page.driver.find_element_by_class_name('foo')
el = input_div.find_element_by_tag_name("textarea")
assert el.tag_name == 'textarea'
assert page.has_no_console_errors()
def test_displays_text_input(self,
bokeh_model_page: BokehModelPage) -> None:
text_input = TextAreaInput()
page = bokeh_model_page(text_input)
el = find_element_for(page.driver, text_input, "textarea")
assert el.tag_name == 'textarea'
assert page.has_no_console_errors()
def plan_tab(self):
self.plan_subtitle = Div(text="Night Plan", css_classes=['subt-style'])
self.plan_inst = Div(text="Input the major elements of the Night Plan found at the link below in the order expected for their completion.", css_classes=['inst-style'], width=1000)
self.plan_txt = Div(text='<a href="https://desi.lbl.gov/trac/wiki/DESIOperations/ObservingPlans/">Tonights Plan Here</a>', css_classes=['inst-style'], width=500)
self.plan_order = TextInput(title ='Expected Order:', placeholder='1', value=None)
self.plan_input = TextAreaInput(placeholder="description", rows=8, cols=3, title="Describe item of the night plan:",max_length=5000)
self.plan_btn = Button(label='Add', css_classes=['add_button'])
self.plan_alert = Div(text=' ', css_classes=['alert-style'])
def test_server_on_change_round_trip(
self, bokeh_server_page: BokehServerPage) -> None:
text_input = TextAreaInput(cols=20)
page = bokeh_server_page(mk_modify_doc(text_input))
el = find_element_for(page.driver, text_input, "textarea")
enter_text_in_element(page.driver, el, "val1" + Keys.TAB)
page.eval_custom_action()
results = page.results
assert results['data']['val'] == ["", "val1"]
p.yaxis.visible = None
p.xgrid.grid_line_color = None
p.ygrid.grid_line_color = None
status = Paragraph(text="", width=900, height=100)
tags_text = PreText(text="", width=400)
profile_tags = CheckboxButtonGroup(labels=tags["PROFILE"],
active=[],
width=800)
pol_tags = CheckboxButtonGroup(labels=tags["POLARIZATION"], active=[])
freq_tags = CheckboxButtonGroup(labels=tags["FREQUENCY"], active=[])
time_tags = CheckboxButtonGroup(labels=tags["TIME"], active=[])
observation_tags = CheckboxButtonGroup(labels=tags["OBSERVATION"], active=[])
apply_tags = Button(label="Apply tags", button_type="success")
save = Button(label="Save tags", button_type="success")
comments = TextAreaInput(value="", title="User comments", rows=6)
getlatest = Toggle(label="Get latest webshot", button_type="primary")
def get_url(psrname):
url = "https://www.atnf.csiro.au/people/joh414/meerkat/{0}.html".format(
psrname)
return url
def capture_screenshot(url):
status.text = "Procuring a snapshot from the TPA webpage..."
CHROME_PATH = '/usr/bin/google-chrome-stable'
CHROMEDRIVER_PATH = '/usr/bin/chromedriver'
WINDOW_SIZE = "900,600"
chrome_options = Options()
def create():
doc = curdoc()
det_data = {}
cami_meta = {}
def proposal_textinput_callback(_attr, _old, new):
nonlocal cami_meta
proposal = new.strip()
for zebra_proposals_path in pyzebra.ZEBRA_PROPOSALS_PATHS:
proposal_path = os.path.join(zebra_proposals_path, proposal)
if os.path.isdir(proposal_path):
# found it
break
else:
raise ValueError(f"Can not find data for proposal '{proposal}'.")
file_list = []
for file in os.listdir(proposal_path):
if file.endswith(".hdf"):
file_list.append((os.path.join(proposal_path, file), file))
file_select.options = file_list
cami_meta = {}
proposal_textinput = TextInput(title="Proposal number:", width=210)
proposal_textinput.on_change("value", proposal_textinput_callback)
def upload_button_callback(_attr, _old, new):
nonlocal cami_meta
with io.StringIO(base64.b64decode(new).decode()) as file:
cami_meta = pyzebra.parse_h5meta(file)
file_list = cami_meta["filelist"]
file_select.options = [(entry, os.path.basename(entry))
for entry in file_list]
upload_div = Div(text="or upload .cami file:", margin=(5, 5, 0, 5))
upload_button = FileInput(accept=".cami", width=200)
upload_button.on_change("value", upload_button_callback)
def update_image(index=None):
if index is None:
index = index_spinner.value
current_image = det_data["data"][index]
proj_v_line_source.data.update(x=np.arange(0, IMAGE_W) + 0.5,
y=np.mean(current_image, axis=0))
proj_h_line_source.data.update(x=np.mean(current_image, axis=1),
y=np.arange(0, IMAGE_H) + 0.5)
image_source.data.update(
h=[np.zeros((1, 1))],
k=[np.zeros((1, 1))],
l=[np.zeros((1, 1))],
)
image_source.data.update(image=[current_image])
if main_auto_checkbox.active:
im_min = np.min(current_image)
im_max = np.max(current_image)
display_min_spinner.value = im_min
display_max_spinner.value = im_max
image_glyph.color_mapper.low = im_min
image_glyph.color_mapper.high = im_max
if "mf" in det_data:
metadata_table_source.data.update(mf=[det_data["mf"][index]])
else:
metadata_table_source.data.update(mf=[None])
if "temp" in det_data:
metadata_table_source.data.update(temp=[det_data["temp"][index]])
else:
metadata_table_source.data.update(temp=[None])
gamma, nu = calculate_pol(det_data, index)
omega = np.ones((IMAGE_H, IMAGE_W)) * det_data["omega"][index]
image_source.data.update(gamma=[gamma], nu=[nu], omega=[omega])
def update_overview_plot():
h5_data = det_data["data"]
n_im, n_y, n_x = h5_data.shape
overview_x = np.mean(h5_data, axis=1)
overview_y = np.mean(h5_data, axis=2)
overview_plot_x_image_source.data.update(image=[overview_x],
dw=[n_x],
dh=[n_im])
overview_plot_y_image_source.data.update(image=[overview_y],
dw=[n_y],
dh=[n_im])
if proj_auto_checkbox.active:
im_min = min(np.min(overview_x), np.min(overview_y))
im_max = max(np.max(overview_x), np.max(overview_y))
proj_display_min_spinner.value = im_min
proj_display_max_spinner.value = im_max
overview_plot_x_image_glyph.color_mapper.low = im_min
overview_plot_y_image_glyph.color_mapper.low = im_min
overview_plot_x_image_glyph.color_mapper.high = im_max
overview_plot_y_image_glyph.color_mapper.high = im_max
frame_range.start = 0
frame_range.end = n_im
frame_range.reset_start = 0
frame_range.reset_end = n_im
frame_range.bounds = (0, n_im)
scan_motor = det_data["scan_motor"]
overview_plot_y.axis[1].axis_label = f"Scanning motor, {scan_motor}"
var = det_data[scan_motor]
var_start = var[0]
var_end = var[-1] + (var[-1] - var[0]) / (n_im - 1)
scanning_motor_range.start = var_start
scanning_motor_range.end = var_end
scanning_motor_range.reset_start = var_start
scanning_motor_range.reset_end = var_end
# handle both, ascending and descending sequences
scanning_motor_range.bounds = (min(var_start,
var_end), max(var_start, var_end))
def file_select_callback(_attr, old, new):
nonlocal det_data
if not new:
# skip empty selections
return
# Avoid selection of multiple indicies (via Shift+Click or Ctrl+Click)
if len(new) > 1:
# drop selection to the previous one
file_select.value = old
return
if len(old) > 1:
# skip unnecessary update caused by selection drop
return
det_data = pyzebra.read_detector_data(new[0])
if cami_meta and "crystal" in cami_meta:
det_data["ub"] = cami_meta["crystal"]["UB"]
index_spinner.value = 0
index_spinner.high = det_data["data"].shape[0] - 1
index_slider.end = det_data["data"].shape[0] - 1
zebra_mode = det_data["zebra_mode"]
if zebra_mode == "nb":
metadata_table_source.data.update(geom=["normal beam"])
else: # zebra_mode == "bi"
metadata_table_source.data.update(geom=["bisecting"])
update_image(0)
update_overview_plot()
file_select = MultiSelect(title="Available .hdf files:",
width=210,
height=250)
file_select.on_change("value", file_select_callback)
def index_callback(_attr, _old, new):
update_image(new)
index_slider = Slider(value=0, start=0, end=1, show_value=False, width=400)
index_spinner = Spinner(title="Image index:", value=0, low=0, width=100)
index_spinner.on_change("value", index_callback)
index_slider.js_link("value_throttled", index_spinner, "value")
index_spinner.js_link("value", index_slider, "value")
plot = Plot(
x_range=Range1d(0, IMAGE_W, bounds=(0, IMAGE_W)),
y_range=Range1d(0, IMAGE_H, bounds=(0, IMAGE_H)),
plot_height=IMAGE_PLOT_H,
plot_width=IMAGE_PLOT_W,
toolbar_location="left",
)
# ---- tools
plot.toolbar.logo = None
# ---- axes
plot.add_layout(LinearAxis(), place="above")
plot.add_layout(LinearAxis(major_label_orientation="vertical"),
place="right")
# ---- grid lines
plot.add_layout(Grid(dimension=0, ticker=BasicTicker()))
plot.add_layout(Grid(dimension=1, ticker=BasicTicker()))
# ---- rgba image glyph
image_source = ColumnDataSource(
dict(
image=[np.zeros((IMAGE_H, IMAGE_W), dtype="float32")],
h=[np.zeros((1, 1))],
k=[np.zeros((1, 1))],
l=[np.zeros((1, 1))],
gamma=[np.zeros((1, 1))],
nu=[np.zeros((1, 1))],
omega=[np.zeros((1, 1))],
x=[0],
y=[0],
dw=[IMAGE_W],
dh=[IMAGE_H],
))
h_glyph = Image(image="h", x="x", y="y", dw="dw", dh="dh", global_alpha=0)
k_glyph = Image(image="k", x="x", y="y", dw="dw", dh="dh", global_alpha=0)
l_glyph = Image(image="l", x="x", y="y", dw="dw", dh="dh", global_alpha=0)
gamma_glyph = Image(image="gamma",
x="x",
y="y",
dw="dw",
dh="dh",
global_alpha=0)
nu_glyph = Image(image="nu",
x="x",
y="y",
dw="dw",
dh="dh",
global_alpha=0)
omega_glyph = Image(image="omega",
x="x",
y="y",
dw="dw",
dh="dh",
global_alpha=0)
plot.add_glyph(image_source, h_glyph)
plot.add_glyph(image_source, k_glyph)
plot.add_glyph(image_source, l_glyph)
plot.add_glyph(image_source, gamma_glyph)
plot.add_glyph(image_source, nu_glyph)
plot.add_glyph(image_source, omega_glyph)
image_glyph = Image(image="image", x="x", y="y", dw="dw", dh="dh")
plot.add_glyph(image_source, image_glyph, name="image_glyph")
# ---- projections
proj_v = Plot(
x_range=plot.x_range,
y_range=DataRange1d(),
plot_height=150,
plot_width=IMAGE_PLOT_W,
toolbar_location=None,
)
proj_v.add_layout(LinearAxis(major_label_orientation="vertical"),
place="right")
proj_v.add_layout(LinearAxis(major_label_text_font_size="0pt"),
place="below")
proj_v.add_layout(Grid(dimension=0, ticker=BasicTicker()))
proj_v.add_layout(Grid(dimension=1, ticker=BasicTicker()))
proj_v_line_source = ColumnDataSource(dict(x=[], y=[]))
proj_v.add_glyph(proj_v_line_source,
Line(x="x", y="y", line_color="steelblue"))
proj_h = Plot(
x_range=DataRange1d(),
y_range=plot.y_range,
plot_height=IMAGE_PLOT_H,
plot_width=150,
toolbar_location=None,
)
proj_h.add_layout(LinearAxis(), place="above")
proj_h.add_layout(LinearAxis(major_label_text_font_size="0pt"),
place="left")
proj_h.add_layout(Grid(dimension=0, ticker=BasicTicker()))
proj_h.add_layout(Grid(dimension=1, ticker=BasicTicker()))
proj_h_line_source = ColumnDataSource(dict(x=[], y=[]))
proj_h.add_glyph(proj_h_line_source,
Line(x="x", y="y", line_color="steelblue"))
# add tools
hovertool = HoverTool(tooltips=[
("intensity", "@image"),
("gamma", "@gamma"),
("nu", "@nu"),
("omega", "@omega"),
("h", "@h"),
("k", "@k"),
("l", "@l"),
])
box_edit_source = ColumnDataSource(dict(x=[], y=[], width=[], height=[]))
box_edit_glyph = Rect(x="x",
y="y",
width="width",
height="height",
fill_alpha=0,
line_color="red")
box_edit_renderer = plot.add_glyph(box_edit_source, box_edit_glyph)
boxedittool = BoxEditTool(renderers=[box_edit_renderer], num_objects=1)
def box_edit_callback(_attr, _old, new):
if new["x"]:
h5_data = det_data["data"]
x_val = np.arange(h5_data.shape[0])
left = int(np.floor(new["x"][0]))
right = int(np.ceil(new["x"][0] + new["width"][0]))
bottom = int(np.floor(new["y"][0]))
top = int(np.ceil(new["y"][0] + new["height"][0]))
y_val = np.sum(h5_data[:, bottom:top, left:right], axis=(1, 2))
else:
x_val = []
y_val = []
roi_avg_plot_line_source.data.update(x=x_val, y=y_val)
box_edit_source.on_change("data", box_edit_callback)
wheelzoomtool = WheelZoomTool(maintain_focus=False)
plot.add_tools(
PanTool(),
BoxZoomTool(),
wheelzoomtool,
ResetTool(),
hovertool,
boxedittool,
)
plot.toolbar.active_scroll = wheelzoomtool
# shared frame ranges
frame_range = Range1d(0, 1, bounds=(0, 1))
scanning_motor_range = Range1d(0, 1, bounds=(0, 1))
det_x_range = Range1d(0, IMAGE_W, bounds=(0, IMAGE_W))
overview_plot_x = Plot(
title=Title(text="Projections on X-axis"),
x_range=det_x_range,
y_range=frame_range,
extra_y_ranges={"scanning_motor": scanning_motor_range},
plot_height=400,
plot_width=IMAGE_PLOT_W - 3,
)
# ---- tools
wheelzoomtool = WheelZoomTool(maintain_focus=False)
overview_plot_x.toolbar.logo = None
overview_plot_x.add_tools(
PanTool(),
BoxZoomTool(),
wheelzoomtool,
ResetTool(),
)
overview_plot_x.toolbar.active_scroll = wheelzoomtool
# ---- axes
overview_plot_x.add_layout(LinearAxis(axis_label="Coordinate X, pix"),
place="below")
overview_plot_x.add_layout(LinearAxis(axis_label="Frame",
major_label_orientation="vertical"),
place="left")
# ---- grid lines
overview_plot_x.add_layout(Grid(dimension=0, ticker=BasicTicker()))
overview_plot_x.add_layout(Grid(dimension=1, ticker=BasicTicker()))
# ---- rgba image glyph
overview_plot_x_image_source = ColumnDataSource(
dict(image=[np.zeros((1, 1), dtype="float32")],
x=[0],
y=[0],
dw=[IMAGE_W],
dh=[1]))
overview_plot_x_image_glyph = Image(image="image",
x="x",
y="y",
dw="dw",
dh="dh")
overview_plot_x.add_glyph(overview_plot_x_image_source,
overview_plot_x_image_glyph,
name="image_glyph")
det_y_range = Range1d(0, IMAGE_H, bounds=(0, IMAGE_H))
overview_plot_y = Plot(
title=Title(text="Projections on Y-axis"),
x_range=det_y_range,
y_range=frame_range,
extra_y_ranges={"scanning_motor": scanning_motor_range},
plot_height=400,
plot_width=IMAGE_PLOT_H + 22,
)
# ---- tools
wheelzoomtool = WheelZoomTool(maintain_focus=False)
overview_plot_y.toolbar.logo = None
overview_plot_y.add_tools(
PanTool(),
BoxZoomTool(),
wheelzoomtool,
ResetTool(),
)
overview_plot_y.toolbar.active_scroll = wheelzoomtool
# ---- axes
overview_plot_y.add_layout(LinearAxis(axis_label="Coordinate Y, pix"),
place="below")
overview_plot_y.add_layout(
LinearAxis(
y_range_name="scanning_motor",
axis_label="Scanning motor",
major_label_orientation="vertical",
),
place="right",
)
# ---- grid lines
overview_plot_y.add_layout(Grid(dimension=0, ticker=BasicTicker()))
overview_plot_y.add_layout(Grid(dimension=1, ticker=BasicTicker()))
# ---- rgba image glyph
overview_plot_y_image_source = ColumnDataSource(
dict(image=[np.zeros((1, 1), dtype="float32")],
x=[0],
y=[0],
dw=[IMAGE_H],
dh=[1]))
overview_plot_y_image_glyph = Image(image="image",
x="x",
y="y",
dw="dw",
dh="dh")
overview_plot_y.add_glyph(overview_plot_y_image_source,
overview_plot_y_image_glyph,
name="image_glyph")
roi_avg_plot = Plot(
x_range=DataRange1d(),
y_range=DataRange1d(),
plot_height=150,
plot_width=IMAGE_PLOT_W,
toolbar_location="left",
)
# ---- tools
roi_avg_plot.toolbar.logo = None
# ---- axes
roi_avg_plot.add_layout(LinearAxis(), place="below")
roi_avg_plot.add_layout(LinearAxis(major_label_orientation="vertical"),
place="left")
# ---- grid lines
roi_avg_plot.add_layout(Grid(dimension=0, ticker=BasicTicker()))
roi_avg_plot.add_layout(Grid(dimension=1, ticker=BasicTicker()))
roi_avg_plot_line_source = ColumnDataSource(dict(x=[], y=[]))
roi_avg_plot.add_glyph(roi_avg_plot_line_source,
Line(x="x", y="y", line_color="steelblue"))
cmap_dict = {
"gray": Greys256,
"gray_reversed": Greys256[::-1],
"plasma": Plasma256,
"cividis": Cividis256,
}
def colormap_callback(_attr, _old, new):
image_glyph.color_mapper = LinearColorMapper(palette=cmap_dict[new])
overview_plot_x_image_glyph.color_mapper = LinearColorMapper(
palette=cmap_dict[new])
overview_plot_y_image_glyph.color_mapper = LinearColorMapper(
palette=cmap_dict[new])
colormap = Select(title="Colormap:",
options=list(cmap_dict.keys()),
width=210)
colormap.on_change("value", colormap_callback)
colormap.value = "plasma"
STEP = 1
def main_auto_checkbox_callback(state):
if state:
display_min_spinner.disabled = True
display_max_spinner.disabled = True
else:
display_min_spinner.disabled = False
display_max_spinner.disabled = False
update_image()
main_auto_checkbox = CheckboxGroup(labels=["Main Auto Range"],
active=[0],
width=145,
margin=[10, 5, 0, 5])
main_auto_checkbox.on_click(main_auto_checkbox_callback)
def display_max_spinner_callback(_attr, _old_value, new_value):
display_min_spinner.high = new_value - STEP
image_glyph.color_mapper.high = new_value
display_max_spinner = Spinner(
low=0 + STEP,
value=1,
step=STEP,
disabled=bool(main_auto_checkbox.active),
width=100,
height=31,
)
display_max_spinner.on_change("value", display_max_spinner_callback)
def display_min_spinner_callback(_attr, _old_value, new_value):
display_max_spinner.low = new_value + STEP
image_glyph.color_mapper.low = new_value
display_min_spinner = Spinner(
low=0,
high=1 - STEP,
value=0,
step=STEP,
disabled=bool(main_auto_checkbox.active),
width=100,
height=31,
)
display_min_spinner.on_change("value", display_min_spinner_callback)
PROJ_STEP = 0.1
def proj_auto_checkbox_callback(state):
if state:
proj_display_min_spinner.disabled = True
proj_display_max_spinner.disabled = True
else:
proj_display_min_spinner.disabled = False
proj_display_max_spinner.disabled = False
update_overview_plot()
proj_auto_checkbox = CheckboxGroup(labels=["Projections Auto Range"],
active=[0],
width=145,
margin=[10, 5, 0, 5])
proj_auto_checkbox.on_click(proj_auto_checkbox_callback)
def proj_display_max_spinner_callback(_attr, _old_value, new_value):
proj_display_min_spinner.high = new_value - PROJ_STEP
overview_plot_x_image_glyph.color_mapper.high = new_value
overview_plot_y_image_glyph.color_mapper.high = new_value
proj_display_max_spinner = Spinner(
low=0 + PROJ_STEP,
value=1,
step=PROJ_STEP,
disabled=bool(proj_auto_checkbox.active),
width=100,
height=31,
)
proj_display_max_spinner.on_change("value",
proj_display_max_spinner_callback)
def proj_display_min_spinner_callback(_attr, _old_value, new_value):
proj_display_max_spinner.low = new_value + PROJ_STEP
overview_plot_x_image_glyph.color_mapper.low = new_value
overview_plot_y_image_glyph.color_mapper.low = new_value
proj_display_min_spinner = Spinner(
low=0,
high=1 - PROJ_STEP,
value=0,
step=PROJ_STEP,
disabled=bool(proj_auto_checkbox.active),
width=100,
height=31,
)
proj_display_min_spinner.on_change("value",
proj_display_min_spinner_callback)
def hkl_button_callback():
index = index_spinner.value
h, k, l = calculate_hkl(det_data, index)
image_source.data.update(h=[h], k=[k], l=[l])
hkl_button = Button(label="Calculate hkl (slow)", width=210)
hkl_button.on_click(hkl_button_callback)
def events_list_callback(_attr, _old, new):
doc.events_list_spind.value = new
events_list = TextAreaInput(rows=7, width=830)
events_list.on_change("value", events_list_callback)
doc.events_list_hdf_viewer = events_list
def add_event_button_callback():
diff_vec = []
p0 = [1.0, 0.0, 1.0]
maxfev = 100000
wave = det_data["wave"]
ddist = det_data["ddist"]
gamma = det_data["gamma"][0]
omega = det_data["omega"][0]
nu = det_data["nu"][0]
chi = det_data["chi"][0]
phi = det_data["phi"][0]
scan_motor = det_data["scan_motor"]
var_angle = det_data[scan_motor]
x0 = int(np.floor(det_x_range.start))
xN = int(np.ceil(det_x_range.end))
y0 = int(np.floor(det_y_range.start))
yN = int(np.ceil(det_y_range.end))
fr0 = int(np.floor(frame_range.start))
frN = int(np.ceil(frame_range.end))
data_roi = det_data["data"][fr0:frN, y0:yN, x0:xN]
cnts = np.sum(data_roi, axis=(1, 2))
coeff, _ = curve_fit(gauss,
range(len(cnts)),
cnts,
p0=p0,
maxfev=maxfev)
m = cnts.mean()
sd = cnts.std()
snr_cnts = np.where(sd == 0, 0, m / sd)
frC = fr0 + coeff[1]
var_F = var_angle[math.floor(frC)]
var_C = var_angle[math.ceil(frC)]
frStep = frC - math.floor(frC)
var_step = var_C - var_F
var_p = var_F + var_step * frStep
if scan_motor == "gamma":
gamma = var_p
elif scan_motor == "omega":
omega = var_p
elif scan_motor == "nu":
nu = var_p
elif scan_motor == "chi":
chi = var_p
elif scan_motor == "phi":
phi = var_p
intensity = coeff[1] * abs(
coeff[2] * var_step) * math.sqrt(2) * math.sqrt(np.pi)
projX = np.sum(data_roi, axis=(0, 1))
coeff, _ = curve_fit(gauss,
range(len(projX)),
projX,
p0=p0,
maxfev=maxfev)
x_pos = x0 + coeff[1]
projY = np.sum(data_roi, axis=(0, 2))
coeff, _ = curve_fit(gauss,
range(len(projY)),
projY,
p0=p0,
maxfev=maxfev)
y_pos = y0 + coeff[1]
ga, nu = pyzebra.det2pol(ddist, gamma, nu, x_pos, y_pos)
diff_vector = pyzebra.z1frmd(wave, ga, omega, chi, phi, nu)
d_spacing = float(pyzebra.dandth(wave, diff_vector)[0])
diff_vector = diff_vector.flatten() * 1e10
dv1, dv2, dv3 = diff_vector
diff_vec.append(diff_vector)
if events_list.value and not events_list.value.endswith("\n"):
events_list.value = events_list.value + "\n"
events_list.value = (
events_list.value +
f"{x_pos} {y_pos} {intensity} {snr_cnts} {dv1} {dv2} {dv3} {d_spacing}"
)
add_event_button = Button(label="Add spind event")
add_event_button.on_click(add_event_button_callback)
metadata_table_source = ColumnDataSource(
dict(geom=[""], temp=[None], mf=[None]))
num_formatter = NumberFormatter(format="0.00", nan_format="")
metadata_table = DataTable(
source=metadata_table_source,
columns=[
TableColumn(field="geom", title="Geometry", width=100),
TableColumn(field="temp",
title="Temperature",
formatter=num_formatter,
width=100),
TableColumn(field="mf",
title="Magnetic Field",
formatter=num_formatter,
width=100),
],
width=300,
height=50,
autosize_mode="none",
index_position=None,
)
# Final layout
import_layout = column(proposal_textinput, upload_div, upload_button,
file_select)
layout_image = column(
gridplot([[proj_v, None], [plot, proj_h]], merge_tools=False))
colormap_layout = column(
colormap,
main_auto_checkbox,
row(display_min_spinner, display_max_spinner),
proj_auto_checkbox,
row(proj_display_min_spinner, proj_display_max_spinner),
)
layout_controls = column(
row(metadata_table, index_spinner,
column(Spacer(height=25), index_slider)),
row(add_event_button, hkl_button),
row(events_list),
)
layout_overview = column(
gridplot(
[[overview_plot_x, overview_plot_y]],
toolbar_options=dict(logo=None),
merge_tools=True,
toolbar_location="left",
), )
tab_layout = row(
column(import_layout, colormap_layout),
column(layout_overview, layout_controls),
column(roi_avg_plot, layout_image),
)
return Panel(child=tab_layout, title="hdf viewer")
from bokeh.io import show
from bokeh.models import CustomJS, TextAreaInput
text_area_input = TextAreaInput(value="default", rows=6, title="Label:")
text_area_input.js_on_change(
"value",
CustomJS(code="""
console.log('text_area_input: value=' + this.value, this.toString())
"""))
show(text_area_input)
def create():
doc = curdoc()
def events_list_callback(_attr, _old, new):
doc.events_list_hdf_viewer.value = new
events_list = TextAreaInput(title="Spind events:", rows=7, width=1500)
events_list.on_change("value", events_list_callback)
doc.events_list_spind = events_list
lattice_const_textinput = TextInput(
title="Lattice constants:",
value="8.3211,8.3211,8.3211,90.00,90.00,90.00")
max_res_spinner = Spinner(title="max-res:", value=2, step=0.01, width=145)
seed_pool_size_spinner = Spinner(title="seed-pool-size:",
value=5,
step=0.01,
width=145)
seed_len_tol_spinner = Spinner(title="seed-len-tol:",
value=0.02,
step=0.01,
width=145)
seed_angle_tol_spinner = Spinner(title="seed-angle-tol:",
value=1,
step=0.01,
width=145)
eval_hkl_tol_spinner = Spinner(title="eval-hkl-tol:",
value=0.15,
step=0.01,
width=145)
diff_vec = []
ub_matrices = []
def process_button_callback():
# drop table selection to clear result fields
results_table_source.selected.indices = []
nonlocal diff_vec
with tempfile.TemporaryDirectory() as temp_dir:
temp_peak_list_dir = os.path.join(temp_dir, "peak_list")
os.mkdir(temp_peak_list_dir)
temp_event_file = os.path.join(temp_peak_list_dir, "event-0.txt")
temp_hkl_file = os.path.join(temp_dir, "hkl.h5")
comp_proc = subprocess.run(
[
"mpiexec",
"-n",
"2",
"python",
os.path.join(doc.spind_path, "gen_hkl_table.py"),
lattice_const_textinput.value,
"--max-res",
str(max_res_spinner.value),
"-o",
temp_hkl_file,
],
check=True,
stdout=subprocess.PIPE,
stderr=subprocess.STDOUT,
text=True,
)
print(" ".join(comp_proc.args))
print(comp_proc.stdout)
# prepare an event file
diff_vec = []
with open(temp_event_file, "w") as f:
for event in events_list.value.splitlines():
diff_vec.append(np.array(event.split()[4:7], dtype=float))
f.write(event + "\n")
print(f"Content of {temp_event_file}:")
with open(temp_event_file) as f:
print(f.read())
comp_proc = subprocess.run(
[
"mpiexec",
"-n",
"2",
"python",
os.path.join(doc.spind_path, "SPIND.py"),
temp_peak_list_dir,
temp_hkl_file,
"-o",
temp_dir,
"--seed-pool-size",
str(seed_pool_size_spinner.value),
"--seed-len-tol",
str(seed_len_tol_spinner.value),
"--seed-angle-tol",
str(seed_angle_tol_spinner.value),
"--eval-hkl-tol",
str(eval_hkl_tol_spinner.value),
],
check=True,
stdout=subprocess.PIPE,
stderr=subprocess.STDOUT,
text=True,
)
print(" ".join(comp_proc.args))
print(comp_proc.stdout)
spind_out_file = os.path.join(temp_dir, "spind.txt")
spind_res = dict(
label=[],
crystal_id=[],
match_rate=[],
matched_peaks=[],
column_5=[],
ub_matrix=[],
)
try:
with open(spind_out_file) as f_out:
for line in f_out:
c1, c2, c3, c4, c5, *c_rest = line.split()
spind_res["label"].append(c1)
spind_res["crystal_id"].append(c2)
spind_res["match_rate"].append(c3)
spind_res["matched_peaks"].append(c4)
spind_res["column_5"].append(c5)
# last digits are spind UB matrix
vals = list(map(float, c_rest))
ub_matrix_spind = np.transpose(
np.array(vals).reshape(3, 3))
ub_matrix = np.linalg.inv(ub_matrix_spind)
ub_matrices.append(ub_matrix)
spind_res["ub_matrix"].append(
str(ub_matrix_spind * 1e-10))
print(f"Content of {spind_out_file}:")
with open(spind_out_file) as f:
print(f.read())
except FileNotFoundError:
print("No results from spind")
results_table_source.data.update(spind_res)
process_button = Button(label="Process", button_type="primary")
process_button.on_click(process_button_callback)
if doc.spind_path is None:
process_button.disabled = True
ub_matrix_textareainput = TextAreaInput(title="UB matrix:",
rows=7,
width=400)
hkl_textareainput = TextAreaInput(title="hkl values:", rows=7, width=400)
def results_table_select_callback(_attr, old, new):
if new:
ind = new[0]
ub_matrix = ub_matrices[ind]
res = ""
for vec in diff_vec:
res += f"{ub_matrix @ vec}\n"
ub_matrix_textareainput.value = str(ub_matrix * 1e10)
hkl_textareainput.value = res
else:
ub_matrix_textareainput.value = ""
hkl_textareainput.value = ""
results_table_source = ColumnDataSource(
dict(label=[],
crystal_id=[],
match_rate=[],
matched_peaks=[],
column_5=[],
ub_matrix=[]))
results_table = DataTable(
source=results_table_source,
columns=[
TableColumn(field="label", title="Label", width=50),
TableColumn(field="crystal_id", title="Crystal ID", width=100),
TableColumn(field="match_rate", title="Match Rate", width=100),
TableColumn(field="matched_peaks",
title="Matched Peaks",
width=100),
TableColumn(field="column_5", title="", width=100),
TableColumn(field="ub_matrix", title="UB Matrix", width=700),
],
height=300,
width=1200,
autosize_mode="none",
index_position=None,
)
results_table_source.selected.on_change("indices",
results_table_select_callback)
tab_layout = column(
events_list,
row(
column(
lattice_const_textinput,
row(max_res_spinner, seed_pool_size_spinner),
row(seed_len_tol_spinner, seed_angle_tol_spinner),
row(eval_hkl_tol_spinner),
process_button,
),
column(results_table,
row(ub_matrix_textareainput, hkl_textareainput)),
),
)
return Panel(child=tab_layout, title="spind")
def get_layout(self):
self.contributer_list = TextAreaInput(
placeholder='Contributer names (include all)',
rows=2,
cols=3,
title='Names of all Contributers')
self.contributer_btn = Button(label='Update Contributer List',
css_classes=['add_button'],
width=300)
self.connect_hdr = Div(text="Connect to Existing Night Log",
css_classes=['subt-style'],
width=800)
self.init_hdr = Div(text="Update Tonight's Night Log",
css_classes=['subt-style'],
width=800)
self.init_btn = Button(label="Update Tonight's Log",
css_classes=['init_button'],
width=200)
self.os_name_1 = TextInput(title='Observing Scientist 1',
placeholder='Ruth Bader Ginsberg')
self.os_name_2 = TextInput(title='Observing Scientist 2',
placeholder="Sandra Day O'Connor")
self.dqs_name_1 = TextInput(title='Data Quality Scientist 1',
placeholder='Sally Ride')
self.dqs_name_2 = TextInput(title='Data Quality Scientist 2',
placeholder="Mae Jemison")
self.lo_names = [
'None ', 'Liz Buckley-Geer', 'Ann Elliott', 'Parker Fagrelius',
'Satya Gontcho A Gontcho', 'James Lasker', 'Martin Landriau',
'Claire Poppett', 'Michael Schubnell', 'Luke Tyas', 'Other '
]
self.oa_names = [
'None ', 'Karen Butler', 'Amy Robertson', 'Anthony Paat',
'Thaxton Smith', 'Dave Summers', 'Doug Williams', 'Other '
]
self.LO_1 = Select(title='Lead Observer 1',
value='None',
options=self.lo_names)
self.LO_2 = Select(title='Lead Observer 2',
value='None',
options=self.lo_names)
self.OA = Select(title='Observing Assistant',
value='Choose One',
options=self.oa_names)
self.get_intro_layout()
self.get_nl_layout()
self.get_milestone_layout()
self.get_plan_layout()
self.get_os_exp_layout()
self.get_prob_layout()
self.get_checklist_layout()
self.get_weather_layout()
self.get_ns_layout()
self.check_tab.title = 'OS Checklist'
intro_layout = layout([
self.buffer, self.title, [self.page_logo, self.instructions],
self.connect_hdr, [self.date_init, self.connect_bt
], self.connect_txt, self.line, self.init_hdr,
[[self.os_name_1, self.os_name_2],
[self.dqs_name_1, self.dqs_name_2], [self.LO_1, self.LO_2],
self.OA], self.init_btn, self.line2, self.contributer_list,
self.contributer_btn, self.nl_info, self.intro_txt
],
width=1000)
intro_tab = Panel(child=intro_layout, title="Initialization")
self.layout = Tabs(tabs=[
intro_tab, self.plan_tab, self.milestone_tab, self.exp_tab,
self.prob_tab, self.weather_tab, self.check_tab, self.nl_tab,
self.ns_tab
],
css_classes=['tabs-header'],
sizing_mode="scale_both")
def prepare_server(doc,
input_data,
cell_stack,
cell_markers=None,
default_cell_marker=None):
@lru_cache()
def image_markers(lower=False, mapping=False):
if mapping:
return {
y: j
for j, y in sorted(
((i, x) for i, x in enumerate(
image_markers(lower=lower, mapping=False))),
key=lambda x: x[1].lower(),
)
}
if lower:
return [
x.lower() for x in image_markers(lower=False, mapping=False)
]
return (cell_markers if cell_markers is not None else
[f"Marker {i + 1}" for i in range(cell_stack.shape[1])])
# Data sources
###########################################################################
def prepare_data(input_data):
data = input_data.copy()
if "contour" in data and not all(x in data
for x in ["contour_x", "contour_y"]):
contour = parse_contour(data["contour"])
data["contour_x"] = contour[0]
data["contour_y"] = contour[1]
if "marked" not in data:
data["marked"] = np.full(data.shape[0], "")
source.data = data
source = ColumnDataSource(data={})
prepare_data(input_data)
image_source = ColumnDataSource(
data=dict(image=[], dw=[], dh=[], contour_x=[], contour_y=[]))
# Cell picture plot
###########################################################################
def add_outline():
data = source.data
if all(x in data for x in ["contour_x", "contour_y"]):
cell_outline = cell_figure.patches(
xs="contour_x",
ys="contour_y",
fill_color=None,
line_color="red",
name="cell_outline",
source=image_source,
)
cell_outline.level = "overlay"
else:
cell_outline = cell_figure.select(name="cell_outline")
for x in cell_outline:
cell_figure.renderers.remove(x)
default_cell_marker = (0 if default_cell_marker is None else image_markers(
mapping=True)[default_cell_marker])
cell_markers_select = Select(
value=str(default_cell_marker),
options=list(
(str(i), x) for x, i in image_markers(mapping=True).items()),
title="Marker cell image",
)
cell_marker_input = AutocompleteInput(
completions=list(image_markers()) + list(image_markers(lower=True)),
min_characters=1,
placeholder="Search for marker",
)
cell_slider = RangeSlider(start=0,
end=1,
value=(0, 1),
orientation="vertical",
direction="rtl")
metric_select = RadioButtonGroup(active=0, labels=CELL_IMAGE_METRICS[0])
stats = PreText(text="", width=100)
cell_mapper = bokeh.models.mappers.LinearColorMapper(viridis(20),
low=0,
high=1000,
high_color=None)
cell_color_bar = ColorBar(color_mapper=cell_mapper,
width=12,
location=(0, 0))
cell_figure = figure(
plot_width=450,
plot_height=350,
tools="pan,wheel_zoom,reset",
toolbar_location="left",
)
cell_image = cell_figure.image(
image="image",
color_mapper=cell_mapper,
x=0,
y=0,
dw="dw",
dh="dh",
source=image_source,
)
add_outline()
cell_figure.add_layout(cell_color_bar, "right")
# Edit data of selected cells
###########################################################################
marker_edit_container = column()
marker_edit_instances = []
def add_marker_edit_callback():
editor = ColumnEditor(
source,
marker_edit_container,
log_widget=edit_selecton_log,
editor_delete_callback=delete_marker_edit_callback,
external_edit_callback=edit_selection_callback,
)
marker_edit_instances.append(editor)
def delete_marker_edit_callback(editor):
idx = next(i for i, x in enumerate(marker_edit_instances)
if x is editor)
del marker_edit_instances[idx]
file_name_text = Div()
add_marker_edit_button = Button(label="+",
button_type="success",
align=("start", "end"),
width=50)
add_marker_edit_button.on_click(add_marker_edit_callback)
edit_selection_submit = Button(label="Submit change",
button_type="primary",
align=("start", "end"))
download_button = Button(label="Download edited data",
button_type="success",
align=("start", "end"))
download_button.js_on_click(
CustomJS(args=dict(source=source), code=DOWNLOAD_JS))
edit_selecton_log = TextAreaInput(value="",
disabled=True,
css_classes=["edit_log"],
cols=30,
rows=10)
upload_file_input = FileInput(accept="text/csv", align=("end", "end"))
# Cell table
###########################################################################
default_data_table_cols = [
TableColumn(field="marked", title="Seen", width=20)
]
data_table = DataTable(source=source,
columns=default_data_table_cols,
width=800)
# Callbacks for buttons and widgets
###########################################################################
def cell_slider_change(attrname, old, new):
cell_mapper.low = new[0]
cell_mapper.high = new[1]
def selection_change(attrname, old, new):
selected = source.selected.indices
data = source.data
if not selected:
return
mean_image = CELL_IMAGE_METRICS[1][metric_select.active](
cell_stack[selected,
int(cell_markers_select.value), :, :], axis=0)
image_data = {
"image": [mean_image],
"dw": [cell_stack.shape[2]],
"dh": [cell_stack.shape[3]],
}
for coord in ["contour_x", "contour_y"]:
try:
image_data[coord] = list(data[coord][selected])
except KeyError:
pass
image_source.data = image_data
image_extr = round_signif(mean_image.min()), round_signif(
mean_image.max())
cell_slider.start = image_extr[0]
cell_slider.end = image_extr[1]
cell_slider.step = round_signif((image_extr[1] - image_extr[0]) / 50)
cell_slider.value = image_extr
stats.text = "n cells: " + str(len(selected))
def autocomplete_cell_change(attrname, old, new):
try:
idx = image_markers(mapping=True)[new]
except KeyError:
try:
idx = image_markers(lower=True, mapping=True)[new]
except KeyError:
return
cell_markers_select.value = str(idx)
cell_marker_input.value = None
def data_change(attrname, old, new):
new_keys = [n for n in new.keys() if n not in set(old.keys())]
for n in new_keys:
data_table.columns.append(TableColumn(field=n, title=n))
def edit_selection_submit_click():
for x in marker_edit_instances:
x.edit_callback()
def edit_selection_callback():
idx = source.selected.indices
try:
if len(idx) == 1 and all(
source.data[x.widgets["input_col"].value][idx] != "NA"
for x in marker_edit_instances):
source.selected.indices = [idx[0] + 1]
except KeyError:
pass
def upload_file_callback(attrname, old, new):
try:
data_text = b64decode(new)
data = pd.read_csv(BytesIO(data_text))
except Exception:
file_name_text.text = f"Error loading file {upload_file_input.filename}"
return
file_name_text.text = f"Editing file {upload_file_input.filename}"
# Have to regenerate contours
try:
del data["contour_x"]
del data["contour_y"]
except KeyError:
pass
data_table.columns = default_data_table_cols
prepare_data(data)
add_outline()
source.selected.on_change("indices", selection_change)
source.on_change("data", data_change)
cell_slider.on_change("value", cell_slider_change)
metric_select.on_change("active", selection_change)
cell_markers_select.on_change("value", selection_change)
cell_marker_input.on_change("value", autocomplete_cell_change)
edit_selection_submit.on_click(edit_selection_submit_click)
upload_file_input.on_change("value", upload_file_callback)
style_div = Div(text=CUSTOM_CSS)
# set up layout
layout = column(
row(
column(data_table),
column(
cell_markers_select,
cell_marker_input,
metric_select,
row(cell_figure, cell_slider),
stats,
),
),
file_name_text,
marker_edit_container,
add_marker_edit_button,
row(edit_selection_submit, download_button, upload_file_input),
edit_selecton_log,
style_div,
)
doc.add_root(layout)
doc.title = "Cell classifier"
def create():
det_data = {}
fit_params = {}
js_data = ColumnDataSource(
data=dict(content=["", ""], fname=["", ""], ext=["", ""]))
def proposal_textinput_callback(_attr, _old, new):
proposal = new.strip()
for zebra_proposals_path in pyzebra.ZEBRA_PROPOSALS_PATHS:
proposal_path = os.path.join(zebra_proposals_path, proposal)
if os.path.isdir(proposal_path):
# found it
break
else:
raise ValueError(f"Can not find data for proposal '{proposal}'.")
file_list = []
for file in os.listdir(proposal_path):
if file.endswith((".ccl", ".dat")):
file_list.append((os.path.join(proposal_path, file), file))
file_select.options = file_list
file_open_button.disabled = False
file_append_button.disabled = False
proposal_textinput = TextInput(title="Proposal number:", width=210)
proposal_textinput.on_change("value", proposal_textinput_callback)
def _init_datatable():
scan_list = [s["idx"] for s in det_data]
hkl = [f'{s["h"]} {s["k"]} {s["l"]}' for s in det_data]
export = [s.get("active", True) for s in det_data]
scan_table_source.data.update(
scan=scan_list,
hkl=hkl,
fit=[0] * len(scan_list),
export=export,
)
scan_table_source.selected.indices = []
scan_table_source.selected.indices = [0]
merge_options = [(str(i), f"{i} ({idx})")
for i, idx in enumerate(scan_list)]
merge_from_select.options = merge_options
merge_from_select.value = merge_options[0][0]
file_select = MultiSelect(title="Available .ccl/.dat files:",
width=210,
height=250)
def file_open_button_callback():
nonlocal det_data
det_data = []
for f_name in file_select.value:
with open(f_name) as file:
base, ext = os.path.splitext(f_name)
if det_data:
append_data = pyzebra.parse_1D(file, ext)
pyzebra.normalize_dataset(append_data,
monitor_spinner.value)
pyzebra.merge_datasets(det_data, append_data)
else:
det_data = pyzebra.parse_1D(file, ext)
pyzebra.normalize_dataset(det_data, monitor_spinner.value)
pyzebra.merge_duplicates(det_data)
js_data.data.update(fname=[base, base])
_init_datatable()
append_upload_button.disabled = False
file_open_button = Button(label="Open New", width=100, disabled=True)
file_open_button.on_click(file_open_button_callback)
def file_append_button_callback():
for f_name in file_select.value:
with open(f_name) as file:
_, ext = os.path.splitext(f_name)
append_data = pyzebra.parse_1D(file, ext)
pyzebra.normalize_dataset(append_data, monitor_spinner.value)
pyzebra.merge_datasets(det_data, append_data)
_init_datatable()
file_append_button = Button(label="Append", width=100, disabled=True)
file_append_button.on_click(file_append_button_callback)
def upload_button_callback(_attr, _old, new):
nonlocal det_data
det_data = []
for f_str, f_name in zip(new, upload_button.filename):
with io.StringIO(base64.b64decode(f_str).decode()) as file:
base, ext = os.path.splitext(f_name)
if det_data:
append_data = pyzebra.parse_1D(file, ext)
pyzebra.normalize_dataset(append_data,
monitor_spinner.value)
pyzebra.merge_datasets(det_data, append_data)
else:
det_data = pyzebra.parse_1D(file, ext)
pyzebra.normalize_dataset(det_data, monitor_spinner.value)
pyzebra.merge_duplicates(det_data)
js_data.data.update(fname=[base, base])
_init_datatable()
append_upload_button.disabled = False
upload_div = Div(text="or upload new .ccl/.dat files:",
margin=(5, 5, 0, 5))
upload_button = FileInput(accept=".ccl,.dat", multiple=True, width=200)
upload_button.on_change("value", upload_button_callback)
def append_upload_button_callback(_attr, _old, new):
for f_str, f_name in zip(new, append_upload_button.filename):
with io.StringIO(base64.b64decode(f_str).decode()) as file:
_, ext = os.path.splitext(f_name)
append_data = pyzebra.parse_1D(file, ext)
pyzebra.normalize_dataset(append_data, monitor_spinner.value)
pyzebra.merge_datasets(det_data, append_data)
_init_datatable()
append_upload_div = Div(text="append extra files:", margin=(5, 5, 0, 5))
append_upload_button = FileInput(accept=".ccl,.dat",
multiple=True,
width=200,
disabled=True)
append_upload_button.on_change("value", append_upload_button_callback)
def monitor_spinner_callback(_attr, old, new):
if det_data:
pyzebra.normalize_dataset(det_data, new)
_update_plot(_get_selected_scan())
monitor_spinner = Spinner(title="Monitor:",
mode="int",
value=100_000,
low=1,
width=145)
monitor_spinner.on_change("value", monitor_spinner_callback)
def _update_table():
fit_ok = [(1 if "fit" in scan else 0) for scan in det_data]
scan_table_source.data.update(fit=fit_ok)
def _update_plot(scan):
scan_motor = scan["scan_motor"]
y = scan["counts"]
x = scan[scan_motor]
plot.axis[0].axis_label = scan_motor
plot_scatter_source.data.update(x=x,
y=y,
y_upper=y + np.sqrt(y),
y_lower=y - np.sqrt(y))
fit = scan.get("fit")
if fit is not None:
x_fit = np.linspace(x[0], x[-1], 100)
plot_fit_source.data.update(x=x_fit, y=fit.eval(x=x_fit))
x_bkg = []
y_bkg = []
xs_peak = []
ys_peak = []
comps = fit.eval_components(x=x_fit)
for i, model in enumerate(fit_params):
if "linear" in model:
x_bkg = x_fit
y_bkg = comps[f"f{i}_"]
elif any(val in model
for val in ("gaussian", "voigt", "pvoigt")):
xs_peak.append(x_fit)
ys_peak.append(comps[f"f{i}_"])
plot_bkg_source.data.update(x=x_bkg, y=y_bkg)
plot_peak_source.data.update(xs=xs_peak, ys=ys_peak)
fit_output_textinput.value = fit.fit_report()
else:
plot_fit_source.data.update(x=[], y=[])
plot_bkg_source.data.update(x=[], y=[])
plot_peak_source.data.update(xs=[], ys=[])
fit_output_textinput.value = ""
# Main plot
plot = Plot(
x_range=DataRange1d(),
y_range=DataRange1d(only_visible=True),
plot_height=470,
plot_width=700,
)
plot.add_layout(LinearAxis(axis_label="Counts"), place="left")
plot.add_layout(LinearAxis(axis_label="Scan motor"), place="below")
plot.add_layout(Grid(dimension=0, ticker=BasicTicker()))
plot.add_layout(Grid(dimension=1, ticker=BasicTicker()))
plot_scatter_source = ColumnDataSource(
dict(x=[0], y=[0], y_upper=[0], y_lower=[0]))
plot_scatter = plot.add_glyph(
plot_scatter_source, Scatter(x="x", y="y", line_color="steelblue"))
plot.add_layout(
Whisker(source=plot_scatter_source,
base="x",
upper="y_upper",
lower="y_lower"))
plot_fit_source = ColumnDataSource(dict(x=[0], y=[0]))
plot_fit = plot.add_glyph(plot_fit_source, Line(x="x", y="y"))
plot_bkg_source = ColumnDataSource(dict(x=[0], y=[0]))
plot_bkg = plot.add_glyph(
plot_bkg_source,
Line(x="x", y="y", line_color="green", line_dash="dashed"))
plot_peak_source = ColumnDataSource(dict(xs=[[0]], ys=[[0]]))
plot_peak = plot.add_glyph(
plot_peak_source,
MultiLine(xs="xs", ys="ys", line_color="red", line_dash="dashed"))
fit_from_span = Span(location=None, dimension="height", line_dash="dashed")
plot.add_layout(fit_from_span)
fit_to_span = Span(location=None, dimension="height", line_dash="dashed")
plot.add_layout(fit_to_span)
plot.add_layout(
Legend(
items=[
("data", [plot_scatter]),
("best fit", [plot_fit]),
("peak", [plot_peak]),
("linear", [plot_bkg]),
],
location="top_left",
click_policy="hide",
))
plot.add_tools(PanTool(), WheelZoomTool(), ResetTool())
plot.toolbar.logo = None
# Scan select
def scan_table_select_callback(_attr, old, new):
if not new:
# skip empty selections
return
# Avoid selection of multiple indicies (via Shift+Click or Ctrl+Click)
if len(new) > 1:
# drop selection to the previous one
scan_table_source.selected.indices = old
return
if len(old) > 1:
# skip unnecessary update caused by selection drop
return
_update_plot(det_data[new[0]])
def scan_table_source_callback(_attr, _old, _new):
_update_preview()
scan_table_source = ColumnDataSource(
dict(scan=[], hkl=[], fit=[], export=[]))
scan_table_source.on_change("data", scan_table_source_callback)
scan_table = DataTable(
source=scan_table_source,
columns=[
TableColumn(field="scan", title="Scan", width=50),
TableColumn(field="hkl", title="hkl", width=100),
TableColumn(field="fit", title="Fit", width=50),
TableColumn(field="export",
title="Export",
editor=CheckboxEditor(),
width=50),
],
width=310, # +60 because of the index column
height=350,
autosize_mode="none",
editable=True,
)
scan_table_source.selected.on_change("indices", scan_table_select_callback)
def _get_selected_scan():
return det_data[scan_table_source.selected.indices[0]]
merge_from_select = Select(title="scan:", width=145)
def merge_button_callback():
scan_into = _get_selected_scan()
scan_from = det_data[int(merge_from_select.value)]
if scan_into is scan_from:
print("WARNING: Selected scans for merging are identical")
return
pyzebra.merge_scans(scan_into, scan_from)
_update_plot(_get_selected_scan())
merge_button = Button(label="Merge into current", width=145)
merge_button.on_click(merge_button_callback)
def restore_button_callback():
pyzebra.restore_scan(_get_selected_scan())
_update_plot(_get_selected_scan())
restore_button = Button(label="Restore scan", width=145)
restore_button.on_click(restore_button_callback)
def fit_from_spinner_callback(_attr, _old, new):
fit_from_span.location = new
fit_from_spinner = Spinner(title="Fit from:", width=145)
fit_from_spinner.on_change("value", fit_from_spinner_callback)
def fit_to_spinner_callback(_attr, _old, new):
fit_to_span.location = new
fit_to_spinner = Spinner(title="to:", width=145)
fit_to_spinner.on_change("value", fit_to_spinner_callback)
def fitparams_add_dropdown_callback(click):
# bokeh requires (str, str) for MultiSelect options
new_tag = f"{click.item}-{fitparams_select.tags[0]}"
fitparams_select.options.append((new_tag, click.item))
fit_params[new_tag] = fitparams_factory(click.item)
fitparams_select.tags[0] += 1
fitparams_add_dropdown = Dropdown(
label="Add fit function",
menu=[
("Linear", "linear"),
("Gaussian", "gaussian"),
("Voigt", "voigt"),
("Pseudo Voigt", "pvoigt"),
# ("Pseudo Voigt1", "pseudovoigt1"),
],
width=145,
)
fitparams_add_dropdown.on_click(fitparams_add_dropdown_callback)
def fitparams_select_callback(_attr, old, new):
# Avoid selection of multiple indicies (via Shift+Click or Ctrl+Click)
if len(new) > 1:
# drop selection to the previous one
fitparams_select.value = old
return
if len(old) > 1:
# skip unnecessary update caused by selection drop
return
if new:
fitparams_table_source.data.update(fit_params[new[0]])
else:
fitparams_table_source.data.update(
dict(param=[], value=[], vary=[], min=[], max=[]))
fitparams_select = MultiSelect(options=[], height=120, width=145)
fitparams_select.tags = [0]
fitparams_select.on_change("value", fitparams_select_callback)
def fitparams_remove_button_callback():
if fitparams_select.value:
sel_tag = fitparams_select.value[0]
del fit_params[sel_tag]
for elem in fitparams_select.options:
if elem[0] == sel_tag:
fitparams_select.options.remove(elem)
break
fitparams_select.value = []
fitparams_remove_button = Button(label="Remove fit function", width=145)
fitparams_remove_button.on_click(fitparams_remove_button_callback)
def fitparams_factory(function):
if function == "linear":
params = ["slope", "intercept"]
elif function == "gaussian":
params = ["amplitude", "center", "sigma"]
elif function == "voigt":
params = ["amplitude", "center", "sigma", "gamma"]
elif function == "pvoigt":
params = ["amplitude", "center", "sigma", "fraction"]
elif function == "pseudovoigt1":
params = ["amplitude", "center", "g_sigma", "l_sigma", "fraction"]
else:
raise ValueError("Unknown fit function")
n = len(params)
fitparams = dict(
param=params,
value=[None] * n,
vary=[True] * n,
min=[None] * n,
max=[None] * n,
)
if function == "linear":
fitparams["value"] = [0, 1]
fitparams["vary"] = [False, True]
fitparams["min"] = [None, 0]
elif function == "gaussian":
fitparams["min"] = [0, None, None]
return fitparams
fitparams_table_source = ColumnDataSource(
dict(param=[], value=[], vary=[], min=[], max=[]))
fitparams_table = DataTable(
source=fitparams_table_source,
columns=[
TableColumn(field="param", title="Parameter"),
TableColumn(field="value", title="Value", editor=NumberEditor()),
TableColumn(field="vary", title="Vary", editor=CheckboxEditor()),
TableColumn(field="min", title="Min", editor=NumberEditor()),
TableColumn(field="max", title="Max", editor=NumberEditor()),
],
height=200,
width=350,
index_position=None,
editable=True,
auto_edit=True,
)
# start with `background` and `gauss` fit functions added
fitparams_add_dropdown_callback(types.SimpleNamespace(item="linear"))
fitparams_add_dropdown_callback(types.SimpleNamespace(item="gaussian"))
fitparams_select.value = ["gaussian-1"] # add selection to gauss
fit_output_textinput = TextAreaInput(title="Fit results:",
width=750,
height=200)
def proc_all_button_callback():
for scan, export in zip(det_data, scan_table_source.data["export"]):
if export:
pyzebra.fit_scan(scan,
fit_params,
fit_from=fit_from_spinner.value,
fit_to=fit_to_spinner.value)
pyzebra.get_area(
scan,
area_method=AREA_METHODS[area_method_radiobutton.active],
lorentz=lorentz_checkbox.active,
)
_update_plot(_get_selected_scan())
_update_table()
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():
scan = _get_selected_scan()
pyzebra.fit_scan(scan,
fit_params,
fit_from=fit_from_spinner.value,
fit_to=fit_to_spinner.value)
pyzebra.get_area(
scan,
area_method=AREA_METHODS[area_method_radiobutton.active],
lorentz=lorentz_checkbox.active,
)
_update_plot(scan)
_update_table()
proc_button = Button(label="Process Current", width=145)
proc_button.on_click(proc_button_callback)
area_method_div = Div(text="Intensity:", margin=(5, 5, 0, 5))
area_method_radiobutton = RadioGroup(labels=["Function", "Area"],
active=0,
width=145)
lorentz_checkbox = CheckboxGroup(labels=["Lorentz Correction"],
width=145,
margin=(13, 5, 5, 5))
export_preview_textinput = TextAreaInput(title="Export file preview:",
width=500,
height=400)
def _update_preview():
with tempfile.TemporaryDirectory() as temp_dir:
temp_file = temp_dir + "/temp"
export_data = []
for s, export in zip(det_data, scan_table_source.data["export"]):
if export:
export_data.append(s)
pyzebra.export_1D(
export_data,
temp_file,
export_target_select.value,
hkl_precision=int(hkl_precision_select.value),
)
exported_content = ""
file_content = []
for ext in EXPORT_TARGETS[export_target_select.value]:
fname = temp_file + ext
if os.path.isfile(fname):
with open(fname) as f:
content = f.read()
exported_content += f"{ext} file:\n" + content
else:
content = ""
file_content.append(content)
js_data.data.update(content=file_content)
export_preview_textinput.value = exported_content
def export_target_select_callback(_attr, _old, new):
js_data.data.update(ext=EXPORT_TARGETS[new])
_update_preview()
export_target_select = Select(title="Export target:",
options=list(EXPORT_TARGETS.keys()),
value="fullprof",
width=80)
export_target_select.on_change("value", export_target_select_callback)
js_data.data.update(ext=EXPORT_TARGETS[export_target_select.value])
def hkl_precision_select_callback(_attr, _old, _new):
_update_preview()
hkl_precision_select = Select(title="hkl precision:",
options=["2", "3", "4"],
value="2",
width=80)
hkl_precision_select.on_change("value", hkl_precision_select_callback)
save_button = Button(label="Download File(s)",
button_type="success",
width=200)
save_button.js_on_click(
CustomJS(args={"js_data": js_data}, code=javaScript))
fitpeak_controls = row(
column(fitparams_add_dropdown, fitparams_select,
fitparams_remove_button),
fitparams_table,
Spacer(width=20),
column(fit_from_spinner, lorentz_checkbox, area_method_div,
area_method_radiobutton),
column(fit_to_spinner, proc_button, proc_all_button),
)
scan_layout = column(
scan_table,
row(monitor_spinner, column(Spacer(height=19), restore_button)),
row(column(Spacer(height=19), merge_button), merge_from_select),
)
import_layout = column(
proposal_textinput,
file_select,
row(file_open_button, file_append_button),
upload_div,
upload_button,
append_upload_div,
append_upload_button,
)
export_layout = column(
export_preview_textinput,
row(export_target_select, hkl_precision_select,
column(Spacer(height=19), row(save_button))),
)
tab_layout = column(
row(import_layout, scan_layout, plot, Spacer(width=30), export_layout),
row(fitpeak_controls, fit_output_textinput),
)
return Panel(child=tab_layout, title="ccl integrate")
sel_plot = Dropdown(label='Select plot type',
button_type='success',
menu=options,
value='line')
add_row = FileInput()
inp_id = TextInput(
placeholder='Insert ID',
value='1',
)
sh_ess = TextAreaInput(
title='Essay %s: 338 words' % inp_id.value,
rows=30,
cols=50,
value=
"""Dear local newspaper, I think effects computers have on people are great learning skills/affects because they give us time to chat with friends/new people, helps us learn about the globe(astronomy) and keeps us out of troble! Thing about! Dont you think so? How would you feel if your teenager is always on the phone with friends! Do you ever time to chat with your friends or buisness partner about things. Well now - there's a new way to chat the computer, theirs plenty of sites on the internet to do so: @ORGANIZATION1, @ORGANIZATION2, @CAPS1, facebook, myspace ect. Just think now while your setting up meeting with your boss on the computer, your teenager is having fun on the phone not rushing to get off cause you want to use it. How did you learn about other countrys/states outside of yours? Well I have by computer/internet, it's a new way to learn about what going on in our time! You might think your child spends a lot of time on the computer, but ask them so question about the economy, sea floor spreading or even about the @DATE1's you'll be surprise at how much he/she knows. Believe it or not the computer is much interesting then in class all day reading out of books. If your child is home on your computer or at a local library, it's better than being out with friends being fresh, or being perpressured to doing something they know isnt right. You might not know where your child is, @CAPS2 forbidde in a hospital bed because of a drive-by. Rather than your child on the computer learning, chatting or just playing games, safe and sound in your home or community place. Now I hope you have reached a point to understand and agree with me, because computers can have great effects on you or child because it gives us time to chat with friends/new people, helps us learn about the globe and believe or not keeps us out of troble. Thank you for listening."""
)
# ------------------------------------------------------------------------------
# Define functionality of controls
num_tokens.on_change('value', update)
sel_yaxis.on_change('value', update)
sel_xaxis.on_change('value', update)
sel_plot.on_change('value', update)
add_row.on_change('value', change_file)
inp_id.on_change('value', update_pie)
# ------------------------------------------------------------------------------
# Define variable which holds the different columns of our dataframe
#data source
from sklearn.datasets import load_iris
iris = load_iris()
df = pd.DataFrame(iris.data, columns=iris.feature_names)
df["Species"] = pd.DataFrame(iris.target)
df["Species"] = df["Species"].apply(lambda x: "A" if x == 0 else "B"
if x == 1 else "C")
#widgets for the model
select_default = "A"
select = Select(title="Product:",
value=select_default,
options=["A", "B", "C"])
select.max_width = 200
text_input = TextAreaInput(value='A', rows=6, title="Selected Product")
text_input.max_width = 200
text_input.max_height = 70
# text_input.background_fill_color = "beige"
colormap = {'A': 'red', 'B': 'green', 'C': 'blue'}
df['colors'] = [colormap[x] for x in df['Species']]
#dataource for the plots
df1 = df.copy()
df = df[df["Species"] == select_default]
source = ColumnDataSource(df)
group = df1.groupby("Species")
source1 = ColumnDataSource(group)
def create():
det_data = []
fit_params = {}
js_data = ColumnDataSource(data=dict(content=["", ""], fname=["", ""]))
def proposal_textinput_callback(_attr, _old, new):
proposal = new.strip()
for zebra_proposals_path in pyzebra.ZEBRA_PROPOSALS_PATHS:
proposal_path = os.path.join(zebra_proposals_path, proposal)
if os.path.isdir(proposal_path):
# found it
break
else:
raise ValueError(f"Can not find data for proposal '{proposal}'.")
file_list = []
for file in os.listdir(proposal_path):
if file.endswith((".ccl", ".dat")):
file_list.append((os.path.join(proposal_path, file), file))
file_select.options = file_list
file_open_button.disabled = False
file_append_button.disabled = False
proposal_textinput = TextInput(title="Proposal number:", width=210)
proposal_textinput.on_change("value", proposal_textinput_callback)
def _init_datatable():
scan_list = [s["idx"] for s in det_data]
file_list = []
for scan in det_data:
file_list.append(os.path.basename(scan["original_filename"]))
scan_table_source.data.update(
file=file_list,
scan=scan_list,
param=[None] * len(scan_list),
fit=[0] * len(scan_list),
export=[True] * len(scan_list),
)
scan_table_source.selected.indices = []
scan_table_source.selected.indices = [0]
scan_motor_select.options = det_data[0]["scan_motors"]
scan_motor_select.value = det_data[0]["scan_motor"]
param_select.value = "user defined"
file_select = MultiSelect(title="Available .ccl/.dat files:",
width=210,
height=250)
def file_open_button_callback():
nonlocal det_data
det_data = []
for f_name in file_select.value:
with open(f_name) as file:
base, ext = os.path.splitext(f_name)
if det_data:
append_data = pyzebra.parse_1D(file, ext)
pyzebra.normalize_dataset(append_data,
monitor_spinner.value)
det_data.extend(append_data)
else:
det_data = pyzebra.parse_1D(file, ext)
pyzebra.normalize_dataset(det_data, monitor_spinner.value)
js_data.data.update(
fname=[base + ".comm", base + ".incomm"])
_init_datatable()
append_upload_button.disabled = False
file_open_button = Button(label="Open New", width=100, disabled=True)
file_open_button.on_click(file_open_button_callback)
def file_append_button_callback():
for f_name in file_select.value:
with open(f_name) as file:
_, ext = os.path.splitext(f_name)
append_data = pyzebra.parse_1D(file, ext)
pyzebra.normalize_dataset(append_data, monitor_spinner.value)
det_data.extend(append_data)
_init_datatable()
file_append_button = Button(label="Append", width=100, disabled=True)
file_append_button.on_click(file_append_button_callback)
def upload_button_callback(_attr, _old, new):
nonlocal det_data
det_data = []
for f_str, f_name in zip(new, upload_button.filename):
with io.StringIO(base64.b64decode(f_str).decode()) as file:
base, ext = os.path.splitext(f_name)
if det_data:
append_data = pyzebra.parse_1D(file, ext)
pyzebra.normalize_dataset(append_data,
monitor_spinner.value)
det_data.extend(append_data)
else:
det_data = pyzebra.parse_1D(file, ext)
pyzebra.normalize_dataset(det_data, monitor_spinner.value)
js_data.data.update(
fname=[base + ".comm", base + ".incomm"])
_init_datatable()
append_upload_button.disabled = False
upload_div = Div(text="or upload new .ccl/.dat files:",
margin=(5, 5, 0, 5))
upload_button = FileInput(accept=".ccl,.dat", multiple=True, width=200)
upload_button.on_change("value", upload_button_callback)
def append_upload_button_callback(_attr, _old, new):
for f_str, f_name in zip(new, append_upload_button.filename):
with io.StringIO(base64.b64decode(f_str).decode()) as file:
_, ext = os.path.splitext(f_name)
append_data = pyzebra.parse_1D(file, ext)
pyzebra.normalize_dataset(append_data, monitor_spinner.value)
det_data.extend(append_data)
_init_datatable()
append_upload_div = Div(text="append extra files:", margin=(5, 5, 0, 5))
append_upload_button = FileInput(accept=".ccl,.dat",
multiple=True,
width=200,
disabled=True)
append_upload_button.on_change("value", append_upload_button_callback)
def monitor_spinner_callback(_attr, _old, new):
if det_data:
pyzebra.normalize_dataset(det_data, new)
_update_plot()
monitor_spinner = Spinner(title="Monitor:",
mode="int",
value=100_000,
low=1,
width=145)
monitor_spinner.on_change("value", monitor_spinner_callback)
def scan_motor_select_callback(_attr, _old, new):
if det_data:
for scan in det_data:
scan["scan_motor"] = new
_update_plot()
scan_motor_select = Select(title="Scan motor:", options=[], width=145)
scan_motor_select.on_change("value", scan_motor_select_callback)
def _update_table():
fit_ok = [(1 if "fit" in scan else 0) for scan in det_data]
scan_table_source.data.update(fit=fit_ok)
def _update_plot():
_update_single_scan_plot(_get_selected_scan())
_update_overview()
def _update_single_scan_plot(scan):
scan_motor = scan["scan_motor"]
y = scan["counts"]
x = scan[scan_motor]
plot.axis[0].axis_label = scan_motor
plot_scatter_source.data.update(x=x,
y=y,
y_upper=y + np.sqrt(y),
y_lower=y - np.sqrt(y))
fit = scan.get("fit")
if fit is not None:
x_fit = np.linspace(x[0], x[-1], 100)
plot_fit_source.data.update(x=x_fit, y=fit.eval(x=x_fit))
x_bkg = []
y_bkg = []
xs_peak = []
ys_peak = []
comps = fit.eval_components(x=x_fit)
for i, model in enumerate(fit_params):
if "linear" in model:
x_bkg = x_fit
y_bkg = comps[f"f{i}_"]
elif any(val in model
for val in ("gaussian", "voigt", "pvoigt")):
xs_peak.append(x_fit)
ys_peak.append(comps[f"f{i}_"])
plot_bkg_source.data.update(x=x_bkg, y=y_bkg)
plot_peak_source.data.update(xs=xs_peak, ys=ys_peak)
fit_output_textinput.value = fit.fit_report()
else:
plot_fit_source.data.update(x=[], y=[])
plot_bkg_source.data.update(x=[], y=[])
plot_peak_source.data.update(xs=[], ys=[])
fit_output_textinput.value = ""
def _update_overview():
xs = []
ys = []
param = []
x = []
y = []
par = []
for s, p in enumerate(scan_table_source.data["param"]):
if p is not None:
scan = det_data[s]
scan_motor = scan["scan_motor"]
xs.append(scan[scan_motor])
x.extend(scan[scan_motor])
ys.append(scan["counts"])
y.extend([float(p)] * len(scan[scan_motor]))
param.append(float(p))
par.extend(scan["counts"])
if det_data:
scan_motor = det_data[0]["scan_motor"]
ov_plot.axis[0].axis_label = scan_motor
ov_param_plot.axis[0].axis_label = scan_motor
ov_plot_mline_source.data.update(xs=xs,
ys=ys,
param=param,
color=color_palette(len(xs)))
if y:
mapper["transform"].low = np.min([np.min(y) for y in ys])
mapper["transform"].high = np.max([np.max(y) for y in ys])
ov_param_plot_scatter_source.data.update(x=x, y=y, param=par)
if y:
interp_f = interpolate.interp2d(x, y, par)
x1, x2 = min(x), max(x)
y1, y2 = min(y), max(y)
image = interp_f(
np.linspace(x1, x2, ov_param_plot.inner_width // 10),
np.linspace(y1, y2, ov_param_plot.inner_height // 10),
assume_sorted=True,
)
ov_param_plot_image_source.data.update(image=[image],
x=[x1],
y=[y1],
dw=[x2 - x1],
dh=[y2 - y1])
else:
ov_param_plot_image_source.data.update(image=[],
x=[],
y=[],
dw=[],
dh=[])
def _update_param_plot():
x = []
y = []
fit_param = fit_param_select.value
for s, p in zip(det_data, scan_table_source.data["param"]):
if "fit" in s and fit_param:
x.append(p)
y.append(s["fit"].values[fit_param])
param_plot_scatter_source.data.update(x=x, y=y)
# Main plot
plot = Plot(
x_range=DataRange1d(),
y_range=DataRange1d(only_visible=True),
plot_height=450,
plot_width=700,
)
plot.add_layout(LinearAxis(axis_label="Counts"), place="left")
plot.add_layout(LinearAxis(axis_label="Scan motor"), place="below")
plot.add_layout(Grid(dimension=0, ticker=BasicTicker()))
plot.add_layout(Grid(dimension=1, ticker=BasicTicker()))
plot_scatter_source = ColumnDataSource(
dict(x=[0], y=[0], y_upper=[0], y_lower=[0]))
plot_scatter = plot.add_glyph(
plot_scatter_source, Scatter(x="x", y="y", line_color="steelblue"))
plot.add_layout(
Whisker(source=plot_scatter_source,
base="x",
upper="y_upper",
lower="y_lower"))
plot_fit_source = ColumnDataSource(dict(x=[0], y=[0]))
plot_fit = plot.add_glyph(plot_fit_source, Line(x="x", y="y"))
plot_bkg_source = ColumnDataSource(dict(x=[0], y=[0]))
plot_bkg = plot.add_glyph(
plot_bkg_source,
Line(x="x", y="y", line_color="green", line_dash="dashed"))
plot_peak_source = ColumnDataSource(dict(xs=[[0]], ys=[[0]]))
plot_peak = plot.add_glyph(
plot_peak_source,
MultiLine(xs="xs", ys="ys", line_color="red", line_dash="dashed"))
fit_from_span = Span(location=None, dimension="height", line_dash="dashed")
plot.add_layout(fit_from_span)
fit_to_span = Span(location=None, dimension="height", line_dash="dashed")
plot.add_layout(fit_to_span)
plot.add_layout(
Legend(
items=[
("data", [plot_scatter]),
("best fit", [plot_fit]),
("peak", [plot_peak]),
("linear", [plot_bkg]),
],
location="top_left",
click_policy="hide",
))
plot.add_tools(PanTool(), WheelZoomTool(), ResetTool())
plot.toolbar.logo = None
# Overview multilines plot
ov_plot = Plot(x_range=DataRange1d(),
y_range=DataRange1d(),
plot_height=450,
plot_width=700)
ov_plot.add_layout(LinearAxis(axis_label="Counts"), place="left")
ov_plot.add_layout(LinearAxis(axis_label="Scan motor"), place="below")
ov_plot.add_layout(Grid(dimension=0, ticker=BasicTicker()))
ov_plot.add_layout(Grid(dimension=1, ticker=BasicTicker()))
ov_plot_mline_source = ColumnDataSource(
dict(xs=[], ys=[], param=[], color=[]))
ov_plot.add_glyph(ov_plot_mline_source,
MultiLine(xs="xs", ys="ys", line_color="color"))
hover_tool = HoverTool(tooltips=[("param", "@param")])
ov_plot.add_tools(PanTool(), WheelZoomTool(), hover_tool, ResetTool())
ov_plot.add_tools(PanTool(), WheelZoomTool(), ResetTool())
ov_plot.toolbar.logo = None
# Overview perams plot
ov_param_plot = Plot(x_range=DataRange1d(),
y_range=DataRange1d(),
plot_height=450,
plot_width=700)
ov_param_plot.add_layout(LinearAxis(axis_label="Param"), place="left")
ov_param_plot.add_layout(LinearAxis(axis_label="Scan motor"),
place="below")
ov_param_plot.add_layout(Grid(dimension=0, ticker=BasicTicker()))
ov_param_plot.add_layout(Grid(dimension=1, ticker=BasicTicker()))
ov_param_plot_image_source = ColumnDataSource(
dict(image=[], x=[], y=[], dw=[], dh=[]))
ov_param_plot.add_glyph(
ov_param_plot_image_source,
Image(image="image", x="x", y="y", dw="dw", dh="dh"))
ov_param_plot_scatter_source = ColumnDataSource(dict(x=[], y=[], param=[]))
mapper = linear_cmap(field_name="param", palette=Turbo256, low=0, high=50)
ov_param_plot.add_glyph(
ov_param_plot_scatter_source,
Scatter(x="x", y="y", line_color=mapper, fill_color=mapper, size=10),
)
ov_param_plot.add_tools(PanTool(), WheelZoomTool(), ResetTool())
ov_param_plot.toolbar.logo = None
# 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)
# Plot tabs
plots = Tabs(tabs=[
Panel(child=plot, title="single scan"),
Panel(child=ov_plot, title="overview"),
Panel(child=ov_param_plot, title="overview map"),
Panel(child=column(param_plot, row(fit_param_select)),
title="parameter plot"),
])
# Scan select
def scan_table_select_callback(_attr, old, new):
if not new:
# skip empty selections
return
# Avoid selection of multiple indicies (via Shift+Click or Ctrl+Click)
if len(new) > 1:
# drop selection to the previous one
scan_table_source.selected.indices = old
return
if len(old) > 1:
# skip unnecessary update caused by selection drop
return
_update_plot()
def scan_table_source_callback(_attr, _old, _new):
_update_preview()
scan_table_source = ColumnDataSource(
dict(file=[], scan=[], param=[], fit=[], export=[]))
scan_table_source.on_change("data", scan_table_source_callback)
scan_table = DataTable(
source=scan_table_source,
columns=[
TableColumn(field="file", title="file", width=150),
TableColumn(field="scan", title="scan", width=50),
TableColumn(field="param",
title="param",
editor=NumberEditor(),
width=50),
TableColumn(field="fit", title="Fit", width=50),
TableColumn(field="export",
title="Export",
editor=CheckboxEditor(),
width=50),
],
width=410, # +60 because of the index column
editable=True,
autosize_mode="none",
)
def scan_table_source_callback(_attr, _old, _new):
if scan_table_source.selected.indices:
_update_plot()
scan_table_source.selected.on_change("indices", scan_table_select_callback)
scan_table_source.on_change("data", scan_table_source_callback)
def _get_selected_scan():
return det_data[scan_table_source.selected.indices[0]]
def param_select_callback(_attr, _old, new):
if new == "user defined":
param = [None] * len(det_data)
else:
param = [scan[new] for scan in det_data]
scan_table_source.data["param"] = param
_update_param_plot()
param_select = Select(
title="Parameter:",
options=["user defined", "temp", "mf", "h", "k", "l"],
value="user defined",
width=145,
)
param_select.on_change("value", param_select_callback)
def fit_from_spinner_callback(_attr, _old, new):
fit_from_span.location = new
fit_from_spinner = Spinner(title="Fit from:", width=145)
fit_from_spinner.on_change("value", fit_from_spinner_callback)
def fit_to_spinner_callback(_attr, _old, new):
fit_to_span.location = new
fit_to_spinner = Spinner(title="to:", width=145)
fit_to_spinner.on_change("value", fit_to_spinner_callback)
def fitparams_add_dropdown_callback(click):
# bokeh requires (str, str) for MultiSelect options
new_tag = f"{click.item}-{fitparams_select.tags[0]}"
fitparams_select.options.append((new_tag, click.item))
fit_params[new_tag] = fitparams_factory(click.item)
fitparams_select.tags[0] += 1
fitparams_add_dropdown = Dropdown(
label="Add fit function",
menu=[
("Linear", "linear"),
("Gaussian", "gaussian"),
("Voigt", "voigt"),
("Pseudo Voigt", "pvoigt"),
# ("Pseudo Voigt1", "pseudovoigt1"),
],
width=145,
)
fitparams_add_dropdown.on_click(fitparams_add_dropdown_callback)
def fitparams_select_callback(_attr, old, new):
# Avoid selection of multiple indicies (via Shift+Click or Ctrl+Click)
if len(new) > 1:
# drop selection to the previous one
fitparams_select.value = old
return
if len(old) > 1:
# skip unnecessary update caused by selection drop
return
if new:
fitparams_table_source.data.update(fit_params[new[0]])
else:
fitparams_table_source.data.update(
dict(param=[], value=[], vary=[], min=[], max=[]))
fitparams_select = MultiSelect(options=[], height=120, width=145)
fitparams_select.tags = [0]
fitparams_select.on_change("value", fitparams_select_callback)
def fitparams_remove_button_callback():
if fitparams_select.value:
sel_tag = fitparams_select.value[0]
del fit_params[sel_tag]
for elem in fitparams_select.options:
if elem[0] == sel_tag:
fitparams_select.options.remove(elem)
break
fitparams_select.value = []
fitparams_remove_button = Button(label="Remove fit function", width=145)
fitparams_remove_button.on_click(fitparams_remove_button_callback)
def fitparams_factory(function):
if function == "linear":
params = ["slope", "intercept"]
elif function == "gaussian":
params = ["amplitude", "center", "sigma"]
elif function == "voigt":
params = ["amplitude", "center", "sigma", "gamma"]
elif function == "pvoigt":
params = ["amplitude", "center", "sigma", "fraction"]
elif function == "pseudovoigt1":
params = ["amplitude", "center", "g_sigma", "l_sigma", "fraction"]
else:
raise ValueError("Unknown fit function")
n = len(params)
fitparams = dict(
param=params,
value=[None] * n,
vary=[True] * n,
min=[None] * n,
max=[None] * n,
)
if function == "linear":
fitparams["value"] = [0, 1]
fitparams["vary"] = [False, True]
fitparams["min"] = [None, 0]
elif function == "gaussian":
fitparams["min"] = [0, None, None]
return fitparams
fitparams_table_source = ColumnDataSource(
dict(param=[], value=[], vary=[], min=[], max=[]))
fitparams_table = DataTable(
source=fitparams_table_source,
columns=[
TableColumn(field="param", title="Parameter"),
TableColumn(field="value", title="Value", editor=NumberEditor()),
TableColumn(field="vary", title="Vary", editor=CheckboxEditor()),
TableColumn(field="min", title="Min", editor=NumberEditor()),
TableColumn(field="max", title="Max", editor=NumberEditor()),
],
height=200,
width=350,
index_position=None,
editable=True,
auto_edit=True,
)
# start with `background` and `gauss` fit functions added
fitparams_add_dropdown_callback(types.SimpleNamespace(item="linear"))
fitparams_add_dropdown_callback(types.SimpleNamespace(item="gaussian"))
fitparams_select.value = ["gaussian-1"] # add selection to gauss
fit_output_textinput = TextAreaInput(title="Fit results:",
width=750,
height=200)
def proc_all_button_callback():
for scan, export in zip(det_data, scan_table_source.data["export"]):
if export:
pyzebra.fit_scan(scan,
fit_params,
fit_from=fit_from_spinner.value,
fit_to=fit_to_spinner.value)
pyzebra.get_area(
scan,
area_method=AREA_METHODS[area_method_radiobutton.active],
lorentz=lorentz_checkbox.active,
)
_update_plot()
_update_table()
for scan in det_data:
if "fit" in scan:
options = list(scan["fit"].params.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():
scan = _get_selected_scan()
pyzebra.fit_scan(scan,
fit_params,
fit_from=fit_from_spinner.value,
fit_to=fit_to_spinner.value)
pyzebra.get_area(
scan,
area_method=AREA_METHODS[area_method_radiobutton.active],
lorentz=lorentz_checkbox.active,
)
_update_plot()
_update_table()
for scan in det_data:
if "fit" in scan:
options = list(scan["fit"].params.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)
area_method_div = Div(text="Intensity:", margin=(5, 5, 0, 5))
area_method_radiobutton = RadioGroup(labels=["Function", "Area"],
active=0,
width=145)
lorentz_checkbox = CheckboxGroup(labels=["Lorentz Correction"],
width=145,
margin=(13, 5, 5, 5))
export_preview_textinput = TextAreaInput(title="Export file preview:",
width=450,
height=400)
def _update_preview():
with tempfile.TemporaryDirectory() as temp_dir:
temp_file = temp_dir + "/temp"
export_data = []
for s, export in zip(det_data, scan_table_source.data["export"]):
if export:
export_data.append(s)
# pyzebra.export_1D(export_data, temp_file, "fullprof")
exported_content = ""
file_content = []
for ext in (".comm", ".incomm"):
fname = temp_file + ext
if os.path.isfile(fname):
with open(fname) as f:
content = f.read()
exported_content += f"{ext} file:\n" + content
else:
content = ""
file_content.append(content)
js_data.data.update(content=file_content)
export_preview_textinput.value = exported_content
save_button = Button(label="Download File(s)",
button_type="success",
width=220)
save_button.js_on_click(
CustomJS(args={"js_data": js_data}, code=javaScript))
fitpeak_controls = row(
column(fitparams_add_dropdown, fitparams_select,
fitparams_remove_button),
fitparams_table,
Spacer(width=20),
column(fit_from_spinner, lorentz_checkbox, area_method_div,
area_method_radiobutton),
column(fit_to_spinner, proc_button, proc_all_button),
)
scan_layout = column(scan_table,
row(monitor_spinner, scan_motor_select, param_select))
import_layout = column(
proposal_textinput,
file_select,
row(file_open_button, file_append_button),
upload_div,
upload_button,
append_upload_div,
append_upload_button,
)
export_layout = column(export_preview_textinput, row(save_button))
tab_layout = column(
row(import_layout, scan_layout, plots, Spacer(width=30),
export_layout),
row(fitpeak_controls, fit_output_textinput),
)
return Panel(child=tab_layout, title="param study")
url1 = 'latimes-state-totals.csv'
g1_last_update = getDate(url1)
g1_source = 'aggregation of all local agency reports logged by Los Angeles Times. '+\
'URL: '+'https://github.com/datadesk/california-coronavirus-data/blob/master/latimes-state-totals.csv'
## define text for intro
pre = PreText(text="""
Use the dropdown menu below to select the option. You can choose the new confirmed case or new death case for California in August, 2020.
Your selection will be displayed in the box below. Hover on the line to see the details of a particular day. If the data is missing
for a particular day, this means that the data is not available in the dataset. The source and the date for last update are displayed
at the bottom of the plot.
""",
width=500,
height=120)
## define text box
textbox = TextAreaInput(value='New confirmed cases',
rows=1,
title='You are viewing:')
data = pd.read_csv(url1)
# select dates in August
data = data[data['date'] >= '2020-08-01']
data = data[data['date'] < '2020-09-01']
width = 1200 # plot width
data['date_time'] = pd.to_datetime(data['date'])
df = pd.DataFrame({'x': data['date_time'], 'y': data['new_confirmed_cases']})
source = ColumnDataSource(df)
p = figure(y_axis_label='Number of New Cases',
title='New Cases vs. Date',
x_axis_label='Date',
x_axis_type='datetime',
plot_width=width)
def create():
det_data = {}
roi_selection = {}
upload_div = Div(text="Open .cami file:")
def upload_button_callback(_attr, _old, new):
with io.StringIO(base64.b64decode(new).decode()) as file:
h5meta_list = pyzebra.parse_h5meta(file)
file_list = h5meta_list["filelist"]
filelist.options = file_list
filelist.value = file_list[0]
upload_button = FileInput(accept=".cami")
upload_button.on_change("value", upload_button_callback)
def update_image(index=None):
if index is None:
index = index_spinner.value
current_image = det_data["data"][index]
proj_v_line_source.data.update(x=np.arange(0, IMAGE_W) + 0.5,
y=np.mean(current_image, axis=0))
proj_h_line_source.data.update(x=np.mean(current_image, axis=1),
y=np.arange(0, IMAGE_H) + 0.5)
image_source.data.update(
h=[np.zeros((1, 1))],
k=[np.zeros((1, 1))],
l=[np.zeros((1, 1))],
)
image_source.data.update(image=[current_image])
if auto_toggle.active:
im_max = int(np.max(current_image))
im_min = int(np.min(current_image))
display_min_spinner.value = im_min
display_max_spinner.value = im_max
image_glyph.color_mapper.low = im_min
image_glyph.color_mapper.high = im_max
def update_overview_plot():
h5_data = det_data["data"]
n_im, n_y, n_x = h5_data.shape
overview_x = np.mean(h5_data, axis=1)
overview_y = np.mean(h5_data, axis=2)
overview_plot_x_image_source.data.update(image=[overview_x], dw=[n_x])
overview_plot_y_image_source.data.update(image=[overview_y], dw=[n_y])
if frame_button_group.active == 0: # Frame
overview_plot_x.axis[1].axis_label = "Frame"
overview_plot_y.axis[1].axis_label = "Frame"
overview_plot_x_image_source.data.update(y=[0], dh=[n_im])
overview_plot_y_image_source.data.update(y=[0], dh=[n_im])
elif frame_button_group.active == 1: # Omega
overview_plot_x.axis[1].axis_label = "Omega"
overview_plot_y.axis[1].axis_label = "Omega"
om = det_data["rot_angle"]
om_start = om[0]
om_end = (om[-1] - om[0]) * n_im / (n_im - 1)
overview_plot_x_image_source.data.update(y=[om_start], dh=[om_end])
overview_plot_y_image_source.data.update(y=[om_start], dh=[om_end])
def filelist_callback(_attr, _old, new):
nonlocal det_data
det_data = pyzebra.read_detector_data(new)
index_spinner.value = 0
index_spinner.high = det_data["data"].shape[0] - 1
update_image(0)
update_overview_plot()
filelist = Select()
filelist.on_change("value", filelist_callback)
def index_spinner_callback(_attr, _old, new):
update_image(new)
index_spinner = Spinner(title="Image index:", value=0, low=0)
index_spinner.on_change("value", index_spinner_callback)
plot = Plot(
x_range=Range1d(0, IMAGE_W, bounds=(0, IMAGE_W)),
y_range=Range1d(0, IMAGE_H, bounds=(0, IMAGE_H)),
plot_height=IMAGE_H * 3,
plot_width=IMAGE_W * 3,
toolbar_location="left",
)
# ---- tools
plot.toolbar.logo = None
# ---- axes
plot.add_layout(LinearAxis(), place="above")
plot.add_layout(LinearAxis(major_label_orientation="vertical"),
place="right")
# ---- grid lines
plot.add_layout(Grid(dimension=0, ticker=BasicTicker()))
plot.add_layout(Grid(dimension=1, ticker=BasicTicker()))
# ---- rgba image glyph
image_source = ColumnDataSource(
dict(
image=[np.zeros((IMAGE_H, IMAGE_W), dtype="float32")],
h=[np.zeros((1, 1))],
k=[np.zeros((1, 1))],
l=[np.zeros((1, 1))],
x=[0],
y=[0],
dw=[IMAGE_W],
dh=[IMAGE_H],
))
h_glyph = Image(image="h", x="x", y="y", dw="dw", dh="dh", global_alpha=0)
k_glyph = Image(image="k", x="x", y="y", dw="dw", dh="dh", global_alpha=0)
l_glyph = Image(image="l", x="x", y="y", dw="dw", dh="dh", global_alpha=0)
plot.add_glyph(image_source, h_glyph)
plot.add_glyph(image_source, k_glyph)
plot.add_glyph(image_source, l_glyph)
image_glyph = Image(image="image", x="x", y="y", dw="dw", dh="dh")
plot.add_glyph(image_source, image_glyph, name="image_glyph")
# ---- projections
proj_v = Plot(
x_range=plot.x_range,
y_range=DataRange1d(),
plot_height=200,
plot_width=IMAGE_W * 3,
toolbar_location=None,
)
proj_v.add_layout(LinearAxis(major_label_orientation="vertical"),
place="right")
proj_v.add_layout(LinearAxis(major_label_text_font_size="0pt"),
place="below")
proj_v.add_layout(Grid(dimension=0, ticker=BasicTicker()))
proj_v.add_layout(Grid(dimension=1, ticker=BasicTicker()))
proj_v_line_source = ColumnDataSource(dict(x=[], y=[]))
proj_v.add_glyph(proj_v_line_source,
Line(x="x", y="y", line_color="steelblue"))
proj_h = Plot(
x_range=DataRange1d(),
y_range=plot.y_range,
plot_height=IMAGE_H * 3,
plot_width=200,
toolbar_location=None,
)
proj_h.add_layout(LinearAxis(), place="above")
proj_h.add_layout(LinearAxis(major_label_text_font_size="0pt"),
place="left")
proj_h.add_layout(Grid(dimension=0, ticker=BasicTicker()))
proj_h.add_layout(Grid(dimension=1, ticker=BasicTicker()))
proj_h_line_source = ColumnDataSource(dict(x=[], y=[]))
proj_h.add_glyph(proj_h_line_source,
Line(x="x", y="y", line_color="steelblue"))
# add tools
hovertool = HoverTool(tooltips=[("intensity",
"@image"), ("h", "@h"), ("k",
"@k"), ("l",
"@l")])
box_edit_source = ColumnDataSource(dict(x=[], y=[], width=[], height=[]))
box_edit_glyph = Rect(x="x",
y="y",
width="width",
height="height",
fill_alpha=0,
line_color="red")
box_edit_renderer = plot.add_glyph(box_edit_source, box_edit_glyph)
boxedittool = BoxEditTool(renderers=[box_edit_renderer], num_objects=1)
def box_edit_callback(_attr, _old, new):
if new["x"]:
h5_data = det_data["data"]
x_val = np.arange(h5_data.shape[0])
left = int(np.floor(new["x"][0]))
right = int(np.ceil(new["x"][0] + new["width"][0]))
bottom = int(np.floor(new["y"][0]))
top = int(np.ceil(new["y"][0] + new["height"][0]))
y_val = np.sum(h5_data[:, bottom:top, left:right], axis=(1, 2))
else:
x_val = []
y_val = []
roi_avg_plot_line_source.data.update(x=x_val, y=y_val)
box_edit_source.on_change("data", box_edit_callback)
wheelzoomtool = WheelZoomTool(maintain_focus=False)
plot.add_tools(
PanTool(),
BoxZoomTool(),
wheelzoomtool,
ResetTool(),
hovertool,
boxedittool,
)
plot.toolbar.active_scroll = wheelzoomtool
# shared frame range
frame_range = DataRange1d()
det_x_range = DataRange1d()
overview_plot_x = Plot(
title=Title(text="Projections on X-axis"),
x_range=det_x_range,
y_range=frame_range,
plot_height=400,
plot_width=400,
toolbar_location="left",
)
# ---- tools
wheelzoomtool = WheelZoomTool(maintain_focus=False)
overview_plot_x.toolbar.logo = None
overview_plot_x.add_tools(
PanTool(),
BoxZoomTool(),
wheelzoomtool,
ResetTool(),
)
overview_plot_x.toolbar.active_scroll = wheelzoomtool
# ---- axes
overview_plot_x.add_layout(LinearAxis(axis_label="Coordinate X, pix"),
place="below")
overview_plot_x.add_layout(LinearAxis(axis_label="Frame",
major_label_orientation="vertical"),
place="left")
# ---- grid lines
overview_plot_x.add_layout(Grid(dimension=0, ticker=BasicTicker()))
overview_plot_x.add_layout(Grid(dimension=1, ticker=BasicTicker()))
# ---- rgba image glyph
overview_plot_x_image_source = ColumnDataSource(
dict(image=[np.zeros((1, 1), dtype="float32")],
x=[0],
y=[0],
dw=[1],
dh=[1]))
overview_plot_x_image_glyph = Image(image="image",
x="x",
y="y",
dw="dw",
dh="dh")
overview_plot_x.add_glyph(overview_plot_x_image_source,
overview_plot_x_image_glyph,
name="image_glyph")
det_y_range = DataRange1d()
overview_plot_y = Plot(
title=Title(text="Projections on Y-axis"),
x_range=det_y_range,
y_range=frame_range,
plot_height=400,
plot_width=400,
toolbar_location="left",
)
# ---- tools
wheelzoomtool = WheelZoomTool(maintain_focus=False)
overview_plot_y.toolbar.logo = None
overview_plot_y.add_tools(
PanTool(),
BoxZoomTool(),
wheelzoomtool,
ResetTool(),
)
overview_plot_y.toolbar.active_scroll = wheelzoomtool
# ---- axes
overview_plot_y.add_layout(LinearAxis(axis_label="Coordinate Y, pix"),
place="below")
overview_plot_y.add_layout(LinearAxis(axis_label="Frame",
major_label_orientation="vertical"),
place="left")
# ---- grid lines
overview_plot_y.add_layout(Grid(dimension=0, ticker=BasicTicker()))
overview_plot_y.add_layout(Grid(dimension=1, ticker=BasicTicker()))
# ---- rgba image glyph
overview_plot_y_image_source = ColumnDataSource(
dict(image=[np.zeros((1, 1), dtype="float32")],
x=[0],
y=[0],
dw=[1],
dh=[1]))
overview_plot_y_image_glyph = Image(image="image",
x="x",
y="y",
dw="dw",
dh="dh")
overview_plot_y.add_glyph(overview_plot_y_image_source,
overview_plot_y_image_glyph,
name="image_glyph")
def frame_button_group_callback(_active):
update_overview_plot()
frame_button_group = RadioButtonGroup(labels=["Frames", "Omega"], active=0)
frame_button_group.on_click(frame_button_group_callback)
roi_avg_plot = Plot(
x_range=DataRange1d(),
y_range=DataRange1d(),
plot_height=IMAGE_H * 3,
plot_width=IMAGE_W * 3,
toolbar_location="left",
)
# ---- tools
roi_avg_plot.toolbar.logo = None
# ---- axes
roi_avg_plot.add_layout(LinearAxis(), place="below")
roi_avg_plot.add_layout(LinearAxis(major_label_orientation="vertical"),
place="left")
# ---- grid lines
roi_avg_plot.add_layout(Grid(dimension=0, ticker=BasicTicker()))
roi_avg_plot.add_layout(Grid(dimension=1, ticker=BasicTicker()))
roi_avg_plot_line_source = ColumnDataSource(dict(x=[], y=[]))
roi_avg_plot.add_glyph(roi_avg_plot_line_source,
Line(x="x", y="y", line_color="steelblue"))
cmap_dict = {
"gray": Greys256,
"gray_reversed": Greys256[::-1],
"plasma": Plasma256,
"cividis": Cividis256,
}
def colormap_callback(_attr, _old, new):
image_glyph.color_mapper = LinearColorMapper(palette=cmap_dict[new])
overview_plot_x_image_glyph.color_mapper = LinearColorMapper(
palette=cmap_dict[new])
overview_plot_y_image_glyph.color_mapper = LinearColorMapper(
palette=cmap_dict[new])
colormap = Select(title="Colormap:", options=list(cmap_dict.keys()))
colormap.on_change("value", colormap_callback)
colormap.value = "plasma"
radio_button_group = RadioButtonGroup(labels=["nb", "nb_bi"], active=0)
STEP = 1
# ---- colormap auto toggle button
def auto_toggle_callback(state):
if state:
display_min_spinner.disabled = True
display_max_spinner.disabled = True
else:
display_min_spinner.disabled = False
display_max_spinner.disabled = False
update_image()
auto_toggle = Toggle(label="Auto Range",
active=True,
button_type="default")
auto_toggle.on_click(auto_toggle_callback)
# ---- colormap display max value
def display_max_spinner_callback(_attr, _old_value, new_value):
display_min_spinner.high = new_value - STEP
image_glyph.color_mapper.high = new_value
display_max_spinner = Spinner(
title="Maximal Display Value:",
low=0 + STEP,
value=1,
step=STEP,
disabled=auto_toggle.active,
)
display_max_spinner.on_change("value", display_max_spinner_callback)
# ---- colormap display min value
def display_min_spinner_callback(_attr, _old_value, new_value):
display_max_spinner.low = new_value + STEP
image_glyph.color_mapper.low = new_value
display_min_spinner = Spinner(
title="Minimal Display Value:",
high=1 - STEP,
value=0,
step=STEP,
disabled=auto_toggle.active,
)
display_min_spinner.on_change("value", display_min_spinner_callback)
def hkl_button_callback():
index = index_spinner.value
setup_type = "nb_bi" if radio_button_group.active else "nb"
h, k, l = calculate_hkl(det_data, index, setup_type)
image_source.data.update(h=[h], k=[k], l=[l])
hkl_button = Button(label="Calculate hkl (slow)")
hkl_button.on_click(hkl_button_callback)
selection_list = TextAreaInput(rows=7)
def selection_button_callback():
nonlocal roi_selection
selection = [
int(np.floor(det_x_range.start)),
int(np.ceil(det_x_range.end)),
int(np.floor(det_y_range.start)),
int(np.ceil(det_y_range.end)),
int(np.floor(frame_range.start)),
int(np.ceil(frame_range.end)),
]
filename_id = filelist.value[-8:-4]
if filename_id in roi_selection:
roi_selection[f"{filename_id}"].append(selection)
else:
roi_selection[f"{filename_id}"] = [selection]
selection_list.value = str(roi_selection)
selection_button = Button(label="Add selection")
selection_button.on_click(selection_button_callback)
# Final layout
layout_image = column(
gridplot([[proj_v, None], [plot, proj_h]], merge_tools=False),
row(index_spinner))
colormap_layout = column(colormap, auto_toggle, display_max_spinner,
display_min_spinner)
hkl_layout = column(radio_button_group, hkl_button)
layout_overview = column(
gridplot(
[[overview_plot_x, overview_plot_y]],
toolbar_options=dict(logo=None),
merge_tools=True,
),
frame_button_group,
)
tab_layout = row(
column(
upload_div,
upload_button,
filelist,
layout_image,
row(colormap_layout, hkl_layout),
),
column(
roi_avg_plot,
layout_overview,
row(selection_button, selection_list),
),
)
return Panel(child=tab_layout, title="Data Viewer")
def __init__(self, type):
self.test = False
self.report_type = type
self.utc = TimezoneInfo()
self.kp_zone = TimezoneInfo(utc_offset=-7*u.hour)
self.zones = [self.utc, self.kp_zone]
self.datefmt = DateFormatter(format="%m/%d/%Y %H:%M:%S")
self.inst_style = {'font-size':'150%'}
self.subt_style = {'font-size':'200%','font-style':'bold'}
self.title_style = {'font-size':'250%','font-style':'bold'}
self.alert_style = {'font-size':'150%','color':'red'}
self.nl_file = None
self.intro_subtitle = Div(text="Connect to Night Log",css_classes=['subt-style'])
self.time_note = Div(text="<b> Note: </b> Enter all times as HHMM (1818 = 18:18 = 6:18pm) in Kitt Peak local time. Either enter the time or hit the <b> Now </b> button if it just occured.", css_classes=['inst-style'])
hostname = socket.gethostname()
ip_address = socket.gethostbyname(hostname)
if 'desi' in hostname:
self.location = 'kpno'
#self.conn = psycopg2.connect(host="desi-db", port="5442", database="desi_dev", user="******", password="******")
elif 'app' in hostname: #this is not true. Needs to change.
self.location = 'nersc'
else:
self.location = 'other'
nw_dirs = {'nersc':'/global/cfs/cdirs/desi/spectro/nightwatch/nersc/', 'kpno':'/exposures/nightwatch/', 'other':None}
self.nw_dir = nw_dirs[self.location]
self.nl_dir = os.environ['NL_DIR']
self.your_name = TextInput(title ='Your Name', placeholder = 'John Smith')
self.os_name_1 = TextInput(title ='Observing Scientist 1', placeholder = 'Ruth Bader Ginsberg')
self.os_name_2 = TextInput(title ='Observing Scientist 2', placeholder = "Sandra Day O'Connor")
self.lo_names = ['None ','Liz Buckley-Geer','Ann Elliott','Parker Fagrelius','Satya Gontcho A Gontcho','James Lasker','Martin Landriau','Claire Poppett','Michael Schubnell','Luke Tyas','Other ']
self.oa_names = ['None ','Karen Butler','Amy Robertson','Anthony Paat','Dave Summers','Doug Williams','Other ']
self.intro_txt = Div(text=' ')
self.comment_txt = Div(text=" ", css_classes=['inst-style'], width=1000)
self.date_init = Select(title="Existing Night Logs")
self.time_title = Paragraph(text='Time* (Kitt Peak local time)', align='center')
self.now_btn = Button(label='Now', css_classes=['now_button'], width=50)
days = [d for d in os.listdir(self.nl_dir) if os.path.isdir(os.path.join(self.nl_dir, d))]
init_nl_list = np.sort([day for day in days if 'nightlog_meta.json' in os.listdir(os.path.join(self.nl_dir,day))])[::-1][0:10]
self.date_init.options = list(init_nl_list)
self.date_init.value = init_nl_list[0]
self.connect_txt = Div(text=' ', css_classes=['alert-style'])
self.connect_bt = Button(label="Connect to Existing Night Log", css_classes=['connect_button'])
self.exp_info = Div(text="Fill In Only Relevant Data. Mandatory fields have an asterisk*.", css_classes=['inst-style'],width=500)
self.exp_comment = TextAreaInput(title ='Comment/Remark', placeholder = 'Humidity high for calibration lamps',value=None,rows=10, cols=5,width=800,max_length=5000)
self.exp_time = TextInput(placeholder = '20:07',value=None, width=100) #title ='Time in Kitt Peak local time*',
self.exp_btn = Button(label='Add', css_classes=['add_button'])
self.exp_type = Select(title="Exposure Type", value = None, options=['None','Zero','Focus','Dark','Arc','FVC','DESI'])
self.exp_alert = Div(text=' ', css_classes=['alert-style'])
self.exp_exposure_start = TextInput(title='Exposure Number: First', placeholder='12345', value=None)
self.exp_exposure_finish = TextInput(title='Exposure Number: Last', placeholder='12346', value=None)
self.nl_subtitle = Div(text="Current DESI Night Log: {}".format(self.nl_file), css_classes=['subt-style'])
self.nl_btn = Button(label='Get Current DESI Night Log', css_classes=['connect_button'])
self.nl_text = Div(text=" ", css_classes=['inst-style'], width=1000)
self.nl_alert = Div(text='You must be connected to a Night Log', css_classes=['alert-style'], width=500)
self.nl_info = Div(text="Night Log Info:", css_classes=['inst-style'], width=500)
self.exptable_alert = Div(text=" ",css_classes=['alert-style'], width=500)
self.checklist = CheckboxGroup(labels=[])
self.check_time = TextInput(placeholder = '20:07', value=None) #title ='Time in Kitt Peak local time*',
self.check_alert = Div(text=" ", css_classes=['alert-style'])
self.check_btn = Button(label='Submit', css_classes=['add_button'])
self.check_comment = TextAreaInput(title='Comment', placeholder='comment if necessary', rows=3, cols=3)
self.prob_subtitle = Div(text="Problems", css_classes=['subt-style'])
self.prob_inst = Div(text="Describe problems as they come up and at what time they occur. If there is an Alarm ID associated with the problem, include it, but leave blank if not. If possible, include a description of the remedy.", css_classes=['inst-style'], width=1000)
self.prob_time = TextInput(placeholder = '20:07', value=None, width=100) #title ='Time in Kitt Peak local time*',
self.prob_input = TextAreaInput(placeholder="NightWatch not plotting raw data", rows=10, cols=3, title="Problem Description*:")
self.prob_alarm = TextInput(title='Alarm ID', placeholder='12', value=None, width=100)
self.prob_action = TextAreaInput(title='Resolution/Action',placeholder='description',rows=10, cols=3)
self.prob_btn = Button(label='Add', css_classes=['add_button'])
self.prob_alert = Div(text=' ', css_classes=['alert-style'])
self.img_subtitle = Div(text="Images", css_classes=['subt-style'])
self.img_upinst = Div(text="Include images in the Night Log by uploading a png image from your local computer. Select file, write a comment and click Add", css_classes=['inst-style'], width=1000)
self.img_upinst2 = Div(text=" Choose image to include with comment: ", css_classes=['inst-style'])
self.img_upload = FileInput(accept=".png")
self.img_upload.on_change('value', self.upload_image)
self.img_upload_comments = FileInput(accept=".png")
self.img_upload_comments.on_change('value', self.upload_image_comments)
self.img_upload_comments_os = FileInput(accept=".png")
self.img_upload_comments_os.on_change('value', self.upload_image_comments_os)
self.img_upload_problems = FileInput(accept=".png")
self.img_upload_problems.on_change('value', self.upload_image_problems)
self.img_inst = Div(text="Include images in the Night Log by entering the location of the images on the desi cluster", css_classes=['inst-style'], width=1000)
self.img_input = TextInput(title='image file location on desi cluster', placeholder='/n/home/desiobserver/image.png',value=None)
self.img_comment = TextAreaInput(placeholder='comment about image', rows=8, cols=3, title='Image caption')
self.img_btn = Button(label='Add', css_classes=['add_button'])
self.img_alert = Div(text=" ",width=1000)
self.plot_subtitle = Div(text="Telemetry Plots", css_classes=['subt-style'])
self.DESI_Log = None
self.save_telem_plots = False
from bokeh.io import output_file, show
from bokeh.models import TextAreaInput
output_file("text_input.html")
text_input = TextAreaInput(value="default", rows=6, title="Label:")
show(text_input)
def widgets():
from bokeh.io import show
from bokeh.models import Select, CheckboxButtonGroup, Button, CheckboxGroup, ColorPicker, Dropdown, \
FileInput, MultiSelect, RadioButtonGroup, RadioGroup, Slider, RangeSlider, TextAreaInput, TextInput, Toggle, \
Paragraph, PreText, Div
put_text('Button')
button = Button(label="Foo", button_type="success")
show(button)
put_text('CheckboxButtonGroup')
checkbox_button_group = CheckboxButtonGroup(
labels=["Option 1", "Option 2", "Option 3"], active=[0, 1])
show(checkbox_button_group)
put_text('CheckboxGroup')
checkbox_group = CheckboxGroup(labels=["Option 1", "Option 2", "Option 3"],
active=[0, 1])
show(checkbox_group)
put_text('ColorPicker')
color_picker = ColorPicker(color="#ff4466",
title="Choose color:",
width=200)
show(color_picker)
put_text('Dropdown')
menu = [("Item 1", "item_1"), ("Item 2", "item_2"), None,
("Item 3", "item_3")]
dropdown = Dropdown(label="Dropdown button",
button_type="warning",
menu=menu)
show(dropdown)
put_text('FileInput')
file_input = FileInput()
show(file_input)
put_text('MultiSelect')
multi_select = MultiSelect(title="Option:",
value=["foo", "quux"],
options=[("foo", "Foo"), ("bar", "BAR"),
("baz", "bAz"), ("quux", "quux")])
show(multi_select)
put_text('RadioButtonGroup')
radio_button_group = RadioButtonGroup(
labels=["Option 1", "Option 2", "Option 3"], active=0)
show(radio_button_group)
put_text('RadioGroup')
radio_group = RadioGroup(labels=["Option 1", "Option 2", "Option 3"],
active=0)
show(radio_group)
put_text('Select')
select = Select(title="Option:",
value="foo",
options=["foo", "bar", "baz", "quux"])
show(select)
put_text('Slider')
slider = Slider(start=0, end=10, value=1, step=.1, title="Stuff")
show(slider)
put_text('RangeSlider')
range_slider = RangeSlider(start=0,
end=10,
value=(1, 9),
step=.1,
title="Stuff")
show(range_slider)
put_text('TextAreaInput')
text_input = TextAreaInput(value="default", rows=6, title="Label:")
show(text_input)
put_text('TextInput')
text_input = TextInput(value="default", title="Label:")
show(text_input)
put_text('Toggle')
toggle = Toggle(label="Foo", button_type="success")
show(toggle)
put_text('Div')
div = Div(
text=
"""Your <a href="https://en.wikipedia.org/wiki/HTML">HTML</a>-supported text is initialized with the <b>text</b> argument. The
remaining div arguments are <b>width</b> and <b>height</b>. For this example, those values
are <i>200</i> and <i>100</i> respectively.""",
width=200,
height=100)
show(div)
put_text('Paragraph')
p = Paragraph(
text="""Your text is initialized with the 'text' argument. The
remaining Paragraph arguments are 'width' and 'height'. For this example, those values
are 200 and 100 respectively.""",
width=200,
height=100)
show(p)
put_text('PreText')
pre = PreText(text="""Your text is initialized with the 'text' argument.
The remaining Paragraph arguments are 'width' and 'height'. For this example,
those values are 500 and 100 respectively.""",
width=500,
height=100)
show(pre)
checkbox_group = CheckboxGroup(labels=["Option 1", "Option 2", "Option 3"], active=[0, 1]) radio_group = RadioGroup(labels=["Option 1", "Option 2", "Option 3"], active=0) checkbox_button_group = CheckboxButtonGroup(labels=["Option 1", "Option 2", "Option 3"], active=[0, 1]) radio_button_group = RadioButtonGroup(labels=["Option 1", "Option 2", "Option 3"], active=0) checkbox_button_group_vertical = CheckboxButtonGroup(labels=["Option 1", "Option 2", "Option 3"], active=[0, 1], orientation="vertical") radio_button_group_vertical = RadioButtonGroup(labels=["Option 1", "Option 2", "Option 3"], active=0, orientation="vertical") text_input = TextInput(placeholder="Enter value ...") completions = ["aaa", "aab", "aac", "baa", "caa"] autocomplete_input = AutocompleteInput(placeholder="Enter value (auto-complete) ...", completions=completions) text_area = TextAreaInput(placeholder="Enter text ...", cols=20, rows=10, value="uuu") select = Select(options=["Option 1", "Option 2", "Option 3"]) multi_select = MultiSelect(options=["Option %d" % (i+1) for i in range(16)], size=6) multi_choice = MultiChoice(options=["Option %d" % (i+1) for i in range(16)]) slider = Slider(value=10, start=0, end=100, step=0.5) range_slider = RangeSlider(value=[10, 90], start=0, end=100, step=0.5) date_slider = DateSlider(value=date(2016, 1, 1), start=date(2015, 1, 1), end=date(2017, 12, 31)) date_range_slider = DateRangeSlider(value=(date(2016, 1, 1), date(2016, 12, 31)), start=date(2015, 1, 1), end=date(2017, 12, 31))
from io import StringIO
from bokeh.io import curdoc
from bokeh.layouts import column, row
from bokeh.models import Tabs, TextAreaInput
import panel_ccl_integrate
import panel_hdf_anatric
import panel_hdf_viewer
import panel_param_study
import panel_spind
doc = curdoc()
sys.stdout = StringIO()
stdout_textareainput = TextAreaInput(title="print output:", height=150)
bokeh_stream = StringIO()
bokeh_handler = logging.StreamHandler(bokeh_stream)
bokeh_handler.setFormatter(logging.Formatter(logging.BASIC_FORMAT))
bokeh_logger = logging.getLogger("bokeh")
bokeh_logger.addHandler(bokeh_handler)
bokeh_log_textareainput = TextAreaInput(title="server output:", height=150)
# Final layout
tab_hdf_viewer = panel_hdf_viewer.create()
tab_hdf_anatric = panel_hdf_anatric.create()
tab_ccl_integrate = panel_ccl_integrate.create()
tab_param_study = panel_param_study.create()
tab_spind = panel_spind.create()
def create():
config = pyzebra.AnatricConfig()
def _load_config_file(file):
config.load_from_file(file)
logfile_textinput.value = config.logfile
logfile_verbosity_select.value = config.logfile_verbosity
filelist_type.value = config.filelist_type
filelist_format_textinput.value = config.filelist_format
filelist_datapath_textinput.value = config.filelist_datapath
filelist_ranges_textareainput.value = "\n".join(
map(str, config.filelist_ranges))
crystal_sample_textinput.value = config.crystal_sample
lambda_textinput.value = config.crystal_lambda
zeroOM_textinput.value = config.crystal_zeroOM
zeroSTT_textinput.value = config.crystal_zeroSTT
zeroCHI_textinput.value = config.crystal_zeroCHI
ub_textareainput.value = config.crystal_UB
dataFactory_implementation_select.value = config.dataFactory_implementation
dataFactory_dist1_textinput.value = config.dataFactory_dist1
reflectionPrinter_format_select.value = config.reflectionPrinter_format
set_active_widgets(config.algorithm)
if config.algorithm == "adaptivemaxcog":
threshold_textinput.value = config.threshold
shell_textinput.value = config.shell
steepness_textinput.value = config.steepness
duplicateDistance_textinput.value = config.duplicateDistance
maxequal_textinput.value = config.maxequal
aps_window_textinput.value = str(
tuple(map(int, config.aps_window.values())))
elif config.algorithm == "adaptivedynamic":
adm_window_textinput.value = str(
tuple(map(int, config.adm_window.values())))
border_textinput.value = str(
tuple(map(int, config.border.values())))
minWindow_textinput.value = str(
tuple(map(int, config.minWindow.values())))
reflectionFile_textinput.value = config.reflectionFile
targetMonitor_textinput.value = config.targetMonitor
smoothSize_textinput.value = config.smoothSize
loop_textinput.value = config.loop
minPeakCount_textinput.value = config.minPeakCount
displacementCurve_textinput.value = "\n".join(
map(str, config.displacementCurve))
else:
raise ValueError("Unknown processing mode.")
def set_active_widgets(implementation):
if implementation == "adaptivemaxcog":
mode_radio_button_group.active = 0
disable_adaptivemaxcog = False
disable_adaptivedynamic = True
elif implementation == "adaptivedynamic":
mode_radio_button_group.active = 1
disable_adaptivemaxcog = True
disable_adaptivedynamic = False
else:
raise ValueError(
"Implementation can be either 'adaptivemaxcog' or 'adaptivedynamic'"
)
threshold_textinput.disabled = disable_adaptivemaxcog
shell_textinput.disabled = disable_adaptivemaxcog
steepness_textinput.disabled = disable_adaptivemaxcog
duplicateDistance_textinput.disabled = disable_adaptivemaxcog
maxequal_textinput.disabled = disable_adaptivemaxcog
aps_window_textinput.disabled = disable_adaptivemaxcog
adm_window_textinput.disabled = disable_adaptivedynamic
border_textinput.disabled = disable_adaptivedynamic
minWindow_textinput.disabled = disable_adaptivedynamic
reflectionFile_textinput.disabled = disable_adaptivedynamic
targetMonitor_textinput.disabled = disable_adaptivedynamic
smoothSize_textinput.disabled = disable_adaptivedynamic
loop_textinput.disabled = disable_adaptivedynamic
minPeakCount_textinput.disabled = disable_adaptivedynamic
displacementCurve_textinput.disabled = disable_adaptivedynamic
upload_div = Div(text="Open XML configuration file:")
def upload_button_callback(_attr, _old, new):
with io.BytesIO(base64.b64decode(new)) as file:
_load_config_file(file)
upload_button = FileInput(accept=".xml")
upload_button.on_change("value", upload_button_callback)
# General parameters
# ---- logfile
def logfile_textinput_callback(_attr, _old, new):
config.logfile = new
logfile_textinput = TextInput(title="Logfile:",
value="logfile.log",
width=520)
logfile_textinput.on_change("value", logfile_textinput_callback)
def logfile_verbosity_select_callback(_attr, _old, new):
config.logfile_verbosity = new
logfile_verbosity_select = Select(title="verbosity:",
options=["0", "5", "10", "15", "30"],
width=70)
logfile_verbosity_select.on_change("value",
logfile_verbosity_select_callback)
# ---- FileList
def filelist_type_callback(_attr, _old, new):
config.filelist_type = new
filelist_type = Select(title="File List:",
options=["TRICS", "SINQ"],
width=100)
filelist_type.on_change("value", filelist_type_callback)
def filelist_format_textinput_callback(_attr, _old, new):
config.filelist_format = new
filelist_format_textinput = TextInput(title="format:", width=490)
filelist_format_textinput.on_change("value",
filelist_format_textinput_callback)
def filelist_datapath_textinput_callback(_attr, _old, new):
config.filelist_datapath = new
filelist_datapath_textinput = TextInput(title="datapath:")
filelist_datapath_textinput.on_change(
"value", filelist_datapath_textinput_callback)
def filelist_ranges_textareainput_callback(_attr, _old, new):
ranges = []
for line in new.splitlines():
ranges.append(re.findall(r"\b\d+\b", line))
config.filelist_ranges = ranges
filelist_ranges_textareainput = TextAreaInput(title="ranges:", height=100)
filelist_ranges_textareainput.on_change(
"value", filelist_ranges_textareainput_callback)
# ---- crystal
def crystal_sample_textinput_callback(_attr, _old, new):
config.crystal_sample = new
crystal_sample_textinput = TextInput(title="Sample Name:")
crystal_sample_textinput.on_change("value",
crystal_sample_textinput_callback)
def lambda_textinput_callback(_attr, _old, new):
config.crystal_lambda = new
lambda_textinput = TextInput(title="lambda:", width=140)
lambda_textinput.on_change("value", lambda_textinput_callback)
def ub_textareainput_callback(_attr, _old, new):
config.crystal_UB = new
ub_textareainput = TextAreaInput(title="UB matrix:", height=100)
ub_textareainput.on_change("value", ub_textareainput_callback)
def zeroOM_textinput_callback(_attr, _old, new):
config.crystal_zeroOM = new
zeroOM_textinput = TextInput(title="zeroOM:", width=140)
zeroOM_textinput.on_change("value", zeroOM_textinput_callback)
def zeroSTT_textinput_callback(_attr, _old, new):
config.crystal_zeroSTT = new
zeroSTT_textinput = TextInput(title="zeroSTT:", width=140)
zeroSTT_textinput.on_change("value", zeroSTT_textinput_callback)
def zeroCHI_textinput_callback(_attr, _old, new):
config.crystal_zeroCHI = new
zeroCHI_textinput = TextInput(title="zeroCHI:", width=140)
zeroCHI_textinput.on_change("value", zeroCHI_textinput_callback)
# ---- DataFactory
def dataFactory_implementation_select_callback(_attr, _old, new):
config.dataFactory_implementation = new
dataFactory_implementation_select = Select(
title="DataFactory implementation:",
options=DATA_FACTORY_IMPLEMENTATION,
width=300,
)
dataFactory_implementation_select.on_change(
"value", dataFactory_implementation_select_callback)
def dataFactory_dist1_textinput_callback(_attr, _old, new):
config.dataFactory_dist1 = new
dataFactory_dist1_textinput = TextInput(title="dist1:", width=290)
dataFactory_dist1_textinput.on_change(
"value", dataFactory_dist1_textinput_callback)
# ---- BackgroundProcessor
# ---- DetectorEfficency
# ---- ReflectionPrinter
def reflectionPrinter_format_select_callback(_attr, _old, new):
config.reflectionPrinter_format = new
reflectionPrinter_format_select = Select(
title="ReflectionPrinter format:",
options=REFLECTION_PRINTER_FORMATS,
width=300,
)
reflectionPrinter_format_select.on_change(
"value", reflectionPrinter_format_select_callback)
# Adaptive Peak Detection (adaptivemaxcog)
# ---- threshold
def threshold_textinput_callback(_attr, _old, new):
config.threshold = new
threshold_textinput = TextInput(title="Threshold:")
threshold_textinput.on_change("value", threshold_textinput_callback)
# ---- shell
def shell_textinput_callback(_attr, _old, new):
config.shell = new
shell_textinput = TextInput(title="Shell:")
shell_textinput.on_change("value", shell_textinput_callback)
# ---- steepness
def steepness_textinput_callback(_attr, _old, new):
config.steepness = new
steepness_textinput = TextInput(title="Steepness:")
steepness_textinput.on_change("value", steepness_textinput_callback)
# ---- duplicateDistance
def duplicateDistance_textinput_callback(_attr, _old, new):
config.duplicateDistance = new
duplicateDistance_textinput = TextInput(title="Duplicate Distance:")
duplicateDistance_textinput.on_change(
"value", duplicateDistance_textinput_callback)
# ---- maxequal
def maxequal_textinput_callback(_attr, _old, new):
config.maxequal = new
maxequal_textinput = TextInput(title="Max Equal:")
maxequal_textinput.on_change("value", maxequal_textinput_callback)
# ---- window
def aps_window_textinput_callback(_attr, _old, new):
config.aps_window = dict(
zip(("x", "y", "z"), re.findall(r"\b\d+\b", new)))
aps_window_textinput = TextInput(title="Window (x, y, z):")
aps_window_textinput.on_change("value", aps_window_textinput_callback)
# Adaptive Dynamic Mask Integration (adaptivedynamic)
# ---- window
def adm_window_textinput_callback(_attr, _old, new):
config.adm_window = dict(
zip(("x", "y", "z"), re.findall(r"\b\d+\b", new)))
adm_window_textinput = TextInput(title="Window (x, y, z):")
adm_window_textinput.on_change("value", adm_window_textinput_callback)
# ---- border
def border_textinput_callback(_attr, _old, new):
config.border = dict(zip(("x", "y", "z"), re.findall(r"\b\d+\b", new)))
border_textinput = TextInput(title="Border (x, y, z):")
border_textinput.on_change("value", border_textinput_callback)
# ---- minWindow
def minWindow_textinput_callback(_attr, _old, new):
config.minWindow = dict(
zip(("x", "y", "z"), re.findall(r"\b\d+\b", new)))
minWindow_textinput = TextInput(title="Min Window (x, y, z):")
minWindow_textinput.on_change("value", minWindow_textinput_callback)
# ---- reflectionFile
def reflectionFile_textinput_callback(_attr, _old, new):
config.reflectionFile = new
reflectionFile_textinput = TextInput(title="Reflection File:")
reflectionFile_textinput.on_change("value",
reflectionFile_textinput_callback)
# ---- targetMonitor
def targetMonitor_textinput_callback(_attr, _old, new):
config.targetMonitor = new
targetMonitor_textinput = TextInput(title="Target Monitor:")
targetMonitor_textinput.on_change("value",
targetMonitor_textinput_callback)
# ---- smoothSize
def smoothSize_textinput_callback(_attr, _old, new):
config.smoothSize = new
smoothSize_textinput = TextInput(title="Smooth Size:")
smoothSize_textinput.on_change("value", smoothSize_textinput_callback)
# ---- loop
def loop_textinput_callback(_attr, _old, new):
config.loop = new
loop_textinput = TextInput(title="Loop:")
loop_textinput.on_change("value", loop_textinput_callback)
# ---- minPeakCount
def minPeakCount_textinput_callback(_attr, _old, new):
config.minPeakCount = new
minPeakCount_textinput = TextInput(title="Min Peak Count:")
minPeakCount_textinput.on_change("value", minPeakCount_textinput_callback)
# ---- displacementCurve
def displacementCurve_textinput_callback(_attr, _old, new):
maps = []
for line in new.splitlines():
maps.append(re.findall(r"\d+(?:\.\d+)?", line))
config.displacementCurve = maps
displacementCurve_textinput = TextAreaInput(
title="Displacement Curve (twotheta, x, y):", height=100)
displacementCurve_textinput.on_change(
"value", displacementCurve_textinput_callback)
def mode_radio_button_group_callback(active):
if active == 0:
config.algorithm = "adaptivemaxcog"
set_active_widgets("adaptivemaxcog")
else:
config.algorithm = "adaptivedynamic"
set_active_widgets("adaptivedynamic")
mode_radio_button_group = RadioButtonGroup(
labels=["Adaptive Peak Detection", "Adaptive Dynamic Integration"],
active=0)
mode_radio_button_group.on_click(mode_radio_button_group_callback)
set_active_widgets("adaptivemaxcog")
def process_button_callback():
with tempfile.TemporaryDirectory() as temp_dir:
temp_file = temp_dir + "/temp.xml"
config.save_as(temp_file)
pyzebra.anatric(temp_file)
with open(config.logfile) as f_log:
output_log.value = f_log.read()
process_button = Button(label="Process", button_type="primary")
process_button.on_click(process_button_callback)
output_log = TextAreaInput(title="Logfile output:",
height=700,
disabled=True)
output_config = TextAreaInput(title="Current config:",
height=700,
width=400,
disabled=True)
tab_layout = row(
column(
upload_div,
upload_button,
row(logfile_textinput, logfile_verbosity_select),
row(filelist_type, filelist_format_textinput),
filelist_datapath_textinput,
filelist_ranges_textareainput,
crystal_sample_textinput,
row(lambda_textinput, zeroOM_textinput, zeroSTT_textinput,
zeroCHI_textinput),
ub_textareainput,
row(dataFactory_implementation_select,
dataFactory_dist1_textinput),
reflectionPrinter_format_select,
process_button,
),
column(
mode_radio_button_group,
row(
column(
threshold_textinput,
shell_textinput,
steepness_textinput,
duplicateDistance_textinput,
maxequal_textinput,
aps_window_textinput,
),
column(
adm_window_textinput,
border_textinput,
minWindow_textinput,
reflectionFile_textinput,
targetMonitor_textinput,
smoothSize_textinput,
loop_textinput,
minPeakCount_textinput,
displacementCurve_textinput,
),
),
),
output_config,
output_log,
)
async def update_config():
config.save_as("debug.xml")
with open("debug.xml") as f_config:
output_config.value = f_config.read()
curdoc().add_periodic_callback(update_config, 1000)
return Panel(child=tab_layout, title="Anatric")
