def __init__(self, path, ping_file_path, speed_test_file_path):
self.path = path
self.ping_file_path = ping_file_path
self.speed_test_file_name = speed_test_file_path
# Define default layout of graphs
hv.extension('bokeh')
opts.defaults(
opts.Bars(xrotation=45, tools=['hover']),
opts.BoxWhisker(width=700, xrotation=30, box_fill_color=Palette('Category20')),
opts.Curve(width=700, tools=['hover']),
opts.GridSpace(shared_yaxis=True),
opts.Scatter(width=700, height=500, color=Palette('Category20'), size=dim('growth')+5, tools=['hover'],alpha=0.5, cmap='Set1'),
opts.NdOverlay(legend_position='left'))
if os.path.isdir(os.path.join(self.path, "webpage","figures")) is False:
os.mkdir(os.path.join(self.path, "webpage","figures"))
print("Path 'figures' created successfully")
else:
print("Path 'figures' initialized")
# Load basic configurations
config = configparser.ConfigParser()
try:
config.read('./modules/config_a.ini')
# Get values from configuration file
self.upper_acceptable_ping_bound = float(config['DEFAULT']['upper_acceptable_ping_bound'])
self.upper_ping_issue_bound = float(config['DEFAULT']['upper_ping_issue_bound'])
self.acceptable_network_speed = float(config['DEFAULT']['acceptable_network_speed'])
except:
# In case no config-file is found or another reading error occured
print("Configuration file not found/readable.")
print("Creating a new configuration file.")
# Creating new file with standard values
config['DEFAULT'] = {'upper_acceptable_ping_bound': '10',
'upper_ping_issue_bound': '99999',
'acceptable_network_speed': '16'}
with open('config_a.ini', 'w') as configfile:
config.write(configfile)
print("New configuration file was created. Running on default parameters, please restart for changes.")
#set default values to continue with program
self.upper_acceptable_ping_bound = float(config['DEFAULT']['upper_acceptable_ping_bound'])
self.upper_ping_issue_bound = float(config['DEFAULT']['upper_ping_issue_bound'])
self.acceptable_network_speed = float(config['DEFAULT']['acceptable_network_speed'])
class BasemapModule:
"""
NAME
----
BasemapModule
DESCRIPTION
-----------
Blueprint for Basemap objects.
Plots seismic survey elements such as polygon, wells, lines and the intersection of these
lines while providing interactive tools to improve the experience between data and users.
These plots are not images but objects that can be modified by the user and exported as
images.
ATTRIBUTES
----------
basemap_dataframe : (Pandas)DataFrame
Matrix compounded by the coordinates and lines of the seismic survey's corners.
Empty by default.
wells_dataframe : (Pandas)DataFrame
Matrix compounded by wells related information. Empty by default.
polygon : Holviews element [Curve]
Plot of the seismic survey polygon.
wells : Holviews element [Scatter]
Plot of the wells inside the seismic survey.
seismic_lines : Holviews element [Overlay]
Plot of the seismic lines (Inline referred as iline and Crossline referred as xline)
and its intersection.
basemap : Holviews element [Overlay]
Combination of the plots: polygon, wells and seismic_lines.
METHODS
-------
polygon_plot(**kwargs)
Constructs the polygon attribute.
wells_plot(**kwargs)
Constructs the wells attribute.
seismic_line_plot(**kwargS)
Constructs the seismic_lines attribute.
get_basemap(**kwargs)
Constructs the Basemap attribute and provides interactive methods to
inspect the plotted data.
LIBRARIES
---------
Holoviews: BSD open source Python library designed to simplify the visualization of data.
More information available at:
http://holoviews.org/
Numpy: BSD licensed package for scientific computing with Python. More information
available at:
https://numpy.org/
Pandas: BSD 3 licensed open source data analysis and manipulation tool, built on top of
the Python programming language. More information available at:
https://pandas.pydata.org/
Panel: BSD open source Python library that allows to create custom interactive dashboards
by connecting user defined widgets to plots. More information available at:
https://panel.holoviz.org/index.html
ON PROGRESS
-----------
Include a GIS element into plots.
"""
# Holoviews default config
plot_tools = ['pan', 'wheel_zoom', 'reset']
font_s = {"title": 16, "labels": 14, "xticks": 10, "yticks": 10}
opts.defaults(opts.Curve(tools=plot_tools,
default_tools=[],
xformatter='%.0f',
yformatter='%.0f',
fontsize=font_s,
height=400,
width=400,
padding=0.1,
toolbar='right'),
opts.Scatter(tools=plot_tools,
default_tools=[],
xformatter='%.0f',
yformatter='%.0f',
fontsize=font_s,
height=400,
width=400,
padding=0.1,
toolbar='right',
framewise=True,
show_grid=True),
opts.GridSpace(fontsize=font_s,
shared_yaxis=True,
plot_size=(120, 380),
toolbar="left"),
opts.Overlay(xformatter='%.0f',
yformatter='%.0f',
fontsize=font_s,
toolbar="left",
show_grid=True),
opts.Points(tools=['box_select', 'lasso_select'],
default_tools=[],
active_tools=["box_select"],
size=3,
width=500,
height=400,
padding=0.01,
fontsize={
'title': 16,
'ylabel': 14,
'xlabel': 14,
'ticks': 10
},
framewise=True,
show_grid=True),
toolbar="left")
def __init__(self, basemap_dataframe, wells_dataframe):
"""
DESCRIPTION
-----------
Instantiates BasemapModule's attributes. For more information, please refer to
BasemapModule's docstring.
"""
self.basemap_dataframe = basemap_dataframe
self.wells_dataframe = wells_dataframe
self.iline_step = 1
self.xline_step = 1
self.hover_format = [("Utmx", "$x{(0.00)}"), ("Utmy", "$y{(0.00)}")]
self.hover_attributes = {
"show_arrow": True,
"point_policy": "follow_mouse",
"anchor": "bottom_right",
"attachment": "above",
"line_policy": "none"
}
def polygon_plot(self):
"""
NAME
----
polygon_plot
DESCRIPTION
-----------
Constructs the polygon attribute.
Plots the boundaries of the seismic survey using Holoviews and bokeh as backend.
ARGUMENTS
---------
BasemapModule.basemap_dataframe : (Pandas)DataFrame
Matrix compounded by the coordinates and lines of the seismic survey's corners.
RETURN
------
BasemapModule.polygon : Holviews element [Curve] instance attribute
Plot of the seismic survey polygon.
"""
#Plotting the boundaries of the Seismic Survey. Holoviews Curve element
BasemapModule.polygon = hv.Curve(self.basemap_dataframe,
["utmx", "utmy"],
label="Polygon")
BasemapModule.polygon.opts(line_width=2, color="black")
return BasemapModule.polygon
def wells_plot(self):
"""
NAME
----
wells_plot
DESCRIPTION
-----------
Constructs the wells attribute
Plots the wells inside the Seismic Survey's polygon using Holoviews and bokeh as
backend.
ARGUMENTS
---------
BasemapModule.wells_dataframe : (Pandas)DataFrame
Matrix compounded by wells related information.
RETURN
------
BasemapModule.wells : Holviews element [Scatter] instance attribute
Plot of the wells inside the seismic survey.
"""
# Declaring the Hover tools (each line will use one)
wells_hover = HoverTool(tooltips=[("Well", "@name")] +
self.hover_format + [("Depth", "@depth{(0)}")])
# Plotting Wells. Holoviews Scatter element
BasemapModule.wells = hv.Scatter(
self.wells_dataframe, ["utmx", "utmy"],
["name", "cdp_iline", "cdp_xline", "depth"],
label="Wells")
BasemapModule.wells.opts(line_width=1,
color="green",
size=10,
marker="^")
return (BasemapModule.wells)
def seismic_line_plot(self, iline_number, xline_number):
"""
NAME
----
seismic_line_plot
DESCRIPTION
-----------
Constructs the seismic_lines attribute.
Plots seismic lines (given a set of inline and crossline numbers) and the intersection of
these using Holoviews and bokeh as backend.
ARGUMENTS
---------
iline_number : int
Number of the chosen inline. The value can be given manually or by Panel's slider
widget.
xline_number : int
Number of the chosen crossline. The value can be given manually or by Panel's slider
widget.
RETURN
------
BasemapModule.seismic_lines : Holviews element [Overlay] instance attribute
Plot of the seismic lines and its intersection.
FUNCTIONS
---------
seismic_lines_dataframe(**kwargs)
Builds a DataFrame for the first line either along inline or crossline direction.
seismic_intersection(**kwargs)
Computes the coordinates and tracf of the intersection between two seismic lines.
REFERENCES
----------
bokeh.pydata.org. Change the attributes of the hover tool. Online document:
https://bokeh.pydata.org/en/latest/docs/reference/models/tools.html#bokeh.models.tools.HoverTool.point_policy
"""
def seismic_lines_dataframe(line_direction, perpendicular_direction):
"""
NAME
----
seismic_lines_dataframe
DESCRIPTION
-----------
Builds a DataFrame for the first line either along inline or crossline direction.
The coordinates represent the lower end of a seismic line; therefore, these shall be used to
draft seismic lines after the computation of the higher end. If the users want to plot a line
along inline direction, the code will compute the coordinates of the traces within the first
crossline and vice versa.
ARGUMENTS
---------
basemap_dataframe : (Pandas)DataFrame
Matrix compounded by the coordinates and lines of the seismic survey's corners.
line_direction : str
Seismic line direction.
perpendicular_direction : str
Direction in which the points are going to be calculated. Is perpendicular to line_direction
argument.
RETURN
------
dlines : (Pandas)DataFrame
Contains the trace coordinates within the first seismic line.
"""
# Less stresful to read the code
df, ld, p_d = self.basemap_dataframe, line_direction, perpendicular_direction
#Measure the amount of perpendicular lines within line_direction
dif_lines = abs(
int(df[f"{perpendicular_direction}"].min() -
df[f"{perpendicular_direction}"].max())) + 1
#Computing the coordinates of each
utmx = np.linspace(
float(df[(df[ld] == df[ld].min())
& (df[p_d] == df[p_d].min())]["utmx"].unique()),
float(df[(df[ld] == df[ld].min())
& (df[p_d] == df[p_d].max())]["utmx"].unique()),
num=dif_lines,
endpoint=True)
utmy = np.linspace(
float(df[(df[ld] == df[ld].min())
& (df[p_d] == df[p_d].min())]["utmy"].unique()),
float(df[(df[ld] == df[ld].min())
& (df[p_d] == df[p_d].max())]["utmy"].unique()),
num=dif_lines,
endpoint=True)
#Array of perpendiculars
array = np.arange(df[f"{p_d}"].min(), df[f"{p_d}"].max() + 1, 1)
# Making dataframes to ease further calculations
dlines = pd.DataFrame({
ld: df[f"{ld}"].min(),
p_d: array,
"utmx": utmx,
"utmy": utmy
})
return (dlines)
def seismic_intersection(iline_df, xline_df, iline_number,
xline_number):
"""
NAME
----
seismic_intersection
DESCRIPTION
-----------
Computes the coordinates and tracf of the intersection between two seismic lines.
The computation of the intersection uses vector differences.
ARGUMENTS
---------
iline_df : (Pandas)DataFrame
Coordinates of the traces within the first crossline.
xline_df : (Pandas)DataFrame
Coordinates of the traces within the first inline.
iline_number : int
Number of the chosen inline.
xline_number : int
Number of the chosen crossline.
RETURN
------
list
List of tracf and coordinates of the intersection.
"""
# Amount of CDP within crosslines
dif_lines = abs(self.basemap_dataframe["xline"].max() -
self.basemap_dataframe["xline"].min()) + 1
# tracf
tracf = (iline_number -
self.basemap_dataframe["iline"].min()) * dif_lines + (
xline_number -
self.basemap_dataframe["xline"].min()) + 1
# vector diferences. Formula utm = b - a + c
tutmx = float(
xline_df[xline_df["xline"] == xline_number]
["utmx"]) - xline_df["utmx"].iloc[0] + float(
iline_df[iline_df["iline"] == iline_number]["utmx"])
tutmy = float(
xline_df[xline_df["xline"] == xline_number]
["utmy"]) - xline_df["utmy"].iloc[0] + float(
iline_df[iline_df["iline"] == iline_number]["utmy"])
return [int(tracf), tutmx, tutmy]
df = self.basemap_dataframe
# Assigning a variable for each dataframe in seismic_lines_dataframe
ilines, xlines = seismic_lines_dataframe(
df.keys()[1],
df.keys()[0]), seismic_lines_dataframe(df.keys()[0],
df.keys()[1])
# Extracting the intersection coordinates
intersection = seismic_intersection(ilines, xlines, iline_number,
xline_number)
# Computing the second point to plot the seismic lines (By using vector differences)
## This can be refactored
iutmx = float(xlines["utmx"].iloc[-1] - xlines["utmx"].iloc[0] +
ilines[ilines["iline"] == iline_number]["utmx"])
iutmy = float(xlines["utmy"].iloc[-1] - xlines["utmy"].iloc[0] +
ilines[ilines["iline"] == iline_number]["utmy"])
xutmx = float(ilines["utmx"].iloc[-1] - ilines["utmx"].iloc[0] +
xlines[xlines["xline"] == xline_number]["utmx"])
xutmy = float(ilines["utmy"].iloc[-1] - ilines["utmy"].iloc[0] +
xlines[xlines["xline"] == xline_number]["utmy"])
# hovers for lines and interception
iline_hover = HoverTool(tooltips=[("Inline", f"{iline_number}")] +
self.hover_format)
xline_hover = HoverTool(tooltips=[("Crossline", f"{xline_number}")] +
self.hover_format)
int_hover = HoverTool(
tooltips=[("Intersection", f"({iline_number}/{xline_number})")] +
self.hover_format)
#Updating hover attributes
for item in [iline_hover, xline_hover, int_hover]:
item._property_values.update(self.hover_attributes)
# Plotting the Inline. Holoviews Curve element
iline = hv.Curve(
[(float(ilines[ilines["iline"] == iline_number]["utmx"]),
float(ilines[ilines["iline"] == iline_number]["utmy"])),
(iutmx, iutmy)],
label="I-Line")
# Plotting the Crossline. Holoviews Curve element
xline = hv.Curve(
[(float(xlines[xlines["xline"] == xline_number]["utmx"]),
float(xlines[xlines["xline"] == xline_number]["utmy"])),
(xutmx, xutmy)],
label="C-Line")
# Plot the intersection. Holovies Scatter element.
intersection = hv.Scatter((intersection[1], intersection[2]),
label="Intersection")
# Adding the hover tool in to the plots
iline.opts(line_width=2,
color="red",
tools=self.plot_tools + [iline_hover])
xline.opts(line_width=2,
color="blue",
tools=self.plot_tools + [xline_hover])
intersection.opts(size=7,
line_color="black",
line_width=2,
color="yellow",
tools=self.plot_tools + [int_hover])
# Making the overlay of the seismic plot to deploy
BasemapModule.seismic_lines = iline * xline * intersection
return BasemapModule.seismic_lines
def get_basemap(self):
"""
NAME
----
get_basemap
DESCRIPTION
-----------
Constructs the basemap attribute and provides interactive methods to inspect the plotted
data.
Merges polygon, wells and seismic_lines attributes into one object using Holoviews and
bokeh as backend. It also includes Panel's widgets and methods to add elements that ease
data management.
ARGUMENTS
---------
BasemapModule.basemap_dataframe : (Pandas)DataFrame
Matrix compounded by the coordinates and lines of the seismic survey's corners.
Survey.survey_name : str
Name of the seismic survey.
RETURN
------
Panel Layout [Row]
Container of the following indexed elements:
[0] WidgetBox
[0] Markdown for Survey.survey_name
[1] IntSlider for inline number selection
[2] IntSlider for crossline number selection
[3] Select for well selection
[1] basemap attribute
FUNCTIONS
---------
basemap_plot(**kwargs)
Constructs the basemap attribute.
update_plot(**kwargs)
Links Panel's selection widgets to the basemap attribute.
"""
df = self.basemap_dataframe
# Widgets
iline_number = pn.widgets.IntSlider(name="Inline number",
start=int(df["iline"].min()),
end=int(df["iline"].max()),
step=self.iline_step,
value=int(df["iline"].min()))
xline_number = pn.widgets.IntSlider(name="Crossline number",
start=int(df["xline"].min()),
end=int(df["xline"].max()),
step=self.xline_step,
value=int(df["xline"].min()))
select_well = pn.widgets.Select(name="Select the well to inspect",
options=["None"] +
list(self.wells_dataframe["name"]),
value="None")
@pn.depends(iline_number.param.value, xline_number.param.value,
select_well.param.value)
def basemap_plot(iline_number, xline_number, select_well):
"""
NAME
----
basemap_plot
DESCRIPTION
-----------
Constructs the basemap attribute.
Merges seismic survey related plots using Holoviews and bokeh as backend.
ARGUMENTS
---------
Arguments are given by Panel's widgets through the panel's depend decorator:
iline_number : int
Number of the chosen inline.
xline_number : int
Number of the chosen crossline.
select_well : str
Automatically gives well's line numbers when selected.
RETURN
------
basemap : Holviews element [Overlay] instance attribute
Combination of the plots: polygon, wells and seismic_lines.
"""
#new attributes
WiggleModule.inline_number = iline_number
WiggleModule.crossline_number = xline_number
# First element
BasemapModule.polygon = BasemapModule.polygon_plot(self)
# Second element
BasemapModule.wells = BasemapModule.wells_plot(self)
# Third element
BasemapModule.seismic_lines = BasemapModule.seismic_line_plot(
self, iline_number, xline_number)
# Final Overlay
BasemapModule.basemap = BasemapModule.polygon * BasemapModule.wells * BasemapModule.seismic_lines
BasemapModule.basemap.opts(legend_position='top',
height=600,
width=600)
return (BasemapModule.basemap)
widgets = pn.WidgetBox(f"## {Survey.survey} Basemap", iline_number,
xline_number, select_well)
def update_plot(event):
"""
NAME
----
update_plot
DESCRIPTION
-----------
Links Panel's selection widgets to the basemap attribute.
Modifies the target plot when a well is selected through Panel's selector widget.
ARGUMENTS
---------
event : str
Panel's selector widget value.
RETURN
------
basemap : Holviews element [Overlay] instance attribute
Combination of the plots: polygon, wells and seismic_lines.
"""
if select_well.value != "None":
iline_number.value = int(
self.wells_dataframe["cdp_iline"].loc[str(
select_well.value)])
xline_number.value = int(
self.wells_dataframe["cdp_xline"].loc[str(
select_well.value)])
WiggleModule.inline_number = iline_number.value
WiggleModule.crossline_number = xline_number.value
select_well.param.watch(update_plot, 'value')
return pn.Row(widgets, basemap_plot).servable()
from .bokeh_plot_manager import BokehPlotManager
import math
from . import average_water_view
from rti_python.Post_Process.Average.AverageWaterColumn import AverageWaterColumn
import pandas as pd
import holoviews as hv
from holoviews import opts, dim, Palette
hv.extension('bokeh')
import panel as pn
pn.extension()
from bokeh.plotting import figure, ColumnDataSource
opts.defaults(
opts.Bars(xrotation=45, tools=['hover']),
opts.BoxWhisker(width=800, xrotation=30, box_fill_color=Palette('Category20')),
opts.Curve(width=600, tools=['hover']),
opts.GridSpace(shared_yaxis=True),
opts.Scatter(width=800, height=400, color=Palette('Category20'), size=dim('growth')+5, tools=['hover']),
opts.NdOverlay(legend_position='left'))
class AverageWaterVM(average_water_view.Ui_AvgWater, QWidget):
increment_ens_sig = pyqtSignal(int)
reset_avg_sig = pyqtSignal()
avg_taken_sig = pyqtSignal()
def __init__(self, parent, rti_config):
average_water_view.Ui_AvgWater.__init__(self)
QWidget.__init__(self, parent)
self.setupUi(self)
self.parent = parent
def view(self,
plane='axial',
three_planes=False,
image_size=300,
dynamic=True,
cmap='gray'):
# imopts = {'tools': ['hover'], 'width': 400, 'height': 400, 'cmap': 'gray'}
# imopts = {'tools': ['hover'], 'cmap': 'gray'}
opts.defaults(
opts.GridSpace(
shared_xaxis=False,
shared_yaxis=False,
fontsize={
'title': 16,
'labels': 16,
'xticks': 12,
'yticks': 12
},
),
# opts.Image(cmap='gray', tools=['hover'], xaxis=None,
# yaxis=None, shared_axes=False),
# opts.Overlay(tools=['hover']),
# opts.NdOverlay(tools=['hover']),
opts.Image(cmap=cmap, xaxis=None, yaxis=None, shared_axes=False),
)
self.is2d = False
if 'z' not in self.ds.dims:
self.is2d = True
self.set_size(image_size)
if self.is2d:
plane == '2d'
a1, a2 = 'x', 'y'
pane_width = self.pane_width
pane_height = self.pane_height
else:
if plane == 'axial':
a1, a2, a3 = 'x', 'y', 'z'
# invert = True
pane_width = self.axial_width
pane_height = self.axial_height
elif plane == 'coronal':
a1, a2, a3 = 'x', 'z', 'y'
pane_width = self.coronal_width
pane_height = self.coronal_height
# invert = False
elif plane == 'sagittal':
a1, a2, a3 = 'y', 'z', 'x'
# invert = False
pane_width = self.sagittal_width
pane_height = self.sagittal_height
contrast_start_min = np.asscalar(
self.ds.isel(subject_id=0, ).image.quantile(0.01).values) - 1e-6
contrast_start_max = np.asscalar(
self.ds.isel(subject_id=0, ).image.quantile(0.99).values) + 1e-6
contrast_min = np.asscalar(
self.ds.isel(subject_id=0).image.min().values)
contrast_max = np.asscalar(
self.ds.isel(subject_id=0).image.max().values)
ctotal = contrast_max - contrast_min
contrast_min -= ctotal * 0.1
contrast_max += ctotal * 0.1
cslider = pn.widgets.RangeSlider(start=contrast_min,
end=contrast_max,
value=(contrast_start_min,
contrast_start_max),
name='contrast')
if 'overlay' in self.ds.data_vars:
hv_ds_image = hv.Dataset(self.ds[['image', 'overlay']])
# hv_ds_image = hv.Dataset(self.ds['image'])
if self.verbose:
print(hv_ds_image)
hv_ds_overlay = hv.Dataset(self.ds['overlay'])
if self.verbose:
print(hv_ds_overlay)
# tooltips = [
# ('(x,y)', '($x, $y)'),
# ('image', '@image'),
# ('overlay', '@overlay')
# ]
# hover = HoverTool(tooltips=tooltips)
if self.verbose:
print('overlay_max_calc')
if self.is2d:
first_subj_max = self.ds.isel(subject_id=0).overlay.max(
dim=['x', 'y', 'label']).compute()
first_subj_min = self.ds.isel(subject_id=0).overlay.min(
dim=['x', 'y', 'label']).compute()
else:
first_subj_max = self.ds.isel(subject_id=0).overlay.max(
dim=['x', 'y', 'z', 'label']).compute()
first_subj_min = self.ds.isel(subject_id=0).overlay.min(
dim=['x', 'y', 'z', 'label']).compute()
if self.verbose:
print('overlay_max_calc ready')
print(first_subj_max)
overlay_max = first_subj_max.max()
alpha_slider = pn.widgets.FloatSlider(start=0,
end=1,
value=0.7,
name='overlay transparency')
cmap_select = pn.widgets.Select(name='Overlay Colormap',
options=['Discrete', 'Continuous'])
if self.verbose:
print('max thresh calc')
print(first_subj_max.max())
max_thresholds = first_subj_max.values
if max_thresholds.size != 1:
max_thresholds = sorted(set(max_thresholds))
else:
max_thresholds = [np.asscalar(max_thresholds)]
# max_thresholds = sorted(list(set([first_subj.overlay.sel(overlay_label=i).values.max()
# for i in first_subj.overlay_label])))
if self.verbose:
print('min thresh calc')
min_thresholds = first_subj_min.values + 1e-6
if min_thresholds.size != 1:
min_thresholds = sorted(set(min_thresholds))
else:
min_thresholds = [np.asscalar(min_thresholds)]
# min_thresholds = sorted(list(set(first_subj_min.min())))
# min_thresholds = sorted(list(set([first_subj.sel(overlay_label=i).min()+1e-6 for i in
# first_subj.overlay_label])))
# ocslider = pn.widgets.DiscreteSlider(name='overlay max threshold',
# options=max_thresholds,
# value=max_thresholds[-1])
if len(min_thresholds) == 1 and len(max_thresholds) == 1:
thresh_toggle = 0
oclim = (min_thresholds[0], max_thresholds[0])
elif len(min_thresholds) > 1 and len(max_thresholds) == 1:
thresh_toggle = 1
ocslider_min = pn.widgets.DiscreteSlider(
name='overlay min threshold',
options=min_thresholds,
value=min_thresholds[-1])
@pn.depends(ocslider_min)
def oclim(value):
return (value, max_thresholds[0])
elif len(min_thresholds) == 1 and len(max_thresholds) > 1:
thresh_toggle = 2
ocslider_max = pn.widgets.DiscreteSlider(
name='overlay max threshold',
options=max_thresholds,
value=max_thresholds[-1])
@pn.depends(ocslider_max)
def oclim(value):
return (min_thresholds[0], value)
else:
thresh_toggle = 3
ocslider_min = pn.widgets.DiscreteSlider(
name='overlay min threshold',
options=min_thresholds,
value=min_thresholds[-1])
ocslider_max = pn.widgets.DiscreteSlider(
name='overlay max threshold',
options=max_thresholds,
value=max_thresholds[-1])
@pn.depends(ocslider_min, ocslider_max)
def oclim(value_min, value_max):
return (value_min, value_max)
if self.verbose:
print(thresh_toggle)
@pn.depends(cmap_select)
def cmap_dict(value):
d = {'Discrete': 'glasbey_hv', 'Continuous': 'viridis'}
return d[value]
# subj_viewer = SubjectViewer(ds=self.ds,
# subject_id_sel=list(self.ds.subject_id.values))
if self.is2d:
gridspace = hv_ds_image.to(
hv.Image, [a1, a2],
vdims=['image', 'overlay'],
dynamic=dynamic).opts(
frame_width=pane_width,
frame_height=pane_height,
tools=['hover'],
).apply.opts(clim=cslider.param.value)
if self.verbose:
print(gridspace)
gridspace *= hv_ds_overlay.to(
hv.Image, [a1, a2], vdims='overlay', dynamic=dynamic).opts(
cmap='glasbey_hv',
clipping_colors={
'min': 'transparent',
'NaN': 'transparent'
},
).redim.range(overlay=(1e-6, overlay_max)).apply.opts(
alpha=alpha_slider.param.value,
cmap=cmap_dict,
clim=oclim)
# print(gridspace)
# print(gridspace)
# gridspace = hv.DynamicMap(subj_viewer.load_subject).grid('label')
gridspace = gridspace.layout('label')
elif three_planes:
# squish_height = int(max(image_size*(len(self.ds.z)/len(self.ds.x)), image_size/2))
# gridspace = hv.GridSpace(kdims=['plane', 'label'], label=f'{self.subject_id}')
gridspace = hv.GridSpace(kdims=['plane', 'label'])
for mod in self.ds.label.values:
gridspace['axial', mod] = hv_ds_image.select(label=mod).to(
hv.Image, ['x', 'y'],
groupby=['subject_id', 'z'],
vdims='image',
dynamic=dynamic).opts(
frame_width=self.axial_width,
frame_height=self.axial_height).apply.opts(
clim=cslider.param.value)
gridspace['coronal', mod] = hv_ds_image.select(
label=mod).to(
hv.Image, ['x', 'z'],
groupby=['subject_id', 'y'],
vdims='image',
dynamic=dynamic).opts(
frame_width=self.coronal_width,
frame_height=self.coronal_height).apply.opts(
clim=cslider.param.value)
gridspace['sagittal', mod] = hv_ds_image.select(
label=mod).to(
hv.Image, ['y', 'z'],
groupby=['subject_id', 'x'],
vdims='image',
dynamic=dynamic).opts(
frame_width=self.sagittal_width,
frame_height=self.sagittal_height).apply.opts(
clim=cslider.param.value)
gridspace['axial',
mod] *= hv_ds_overlay.select(label=mod).to(
hv.Image, ['x', 'y'],
groupby=['subject_id', 'z', 'overlay_label'],
vdims='overlay',
dynamic=dynamic).opts(
cmap='glasbey_hv',
clipping_colors={
'min': 'transparent',
'NaN': 'transparent'
},
).redim.range(
overlay=(0.1, overlay_max)).apply.opts(
alpha=alpha_slider.param.value,
cmap=cmap_dict,
clim=oclim)
gridspace['coronal',
mod] *= hv_ds_overlay.select(label=mod).to(
hv.Image, ['x', 'z'],
groupby=['subject_id', 'y', 'overlay_label'],
vdims='overlay',
dynamic=dynamic).opts(
cmap='glasbey_hv',
clipping_colors={
'min': 'transparent',
'NaN': 'transparent'
},
).redim.range(
overlay=(0.1, overlay_max)).apply.opts(
alpha=alpha_slider.param.value,
cmap=cmap_dict,
clim=oclim)
gridspace['sagittal',
mod] *= hv_ds_overlay.select(label=mod).to(
hv.Image, ['y', 'z'],
groupby=['subject_id', 'x', 'overlay_label'],
vdims='overlay',
dynamic=dynamic).opts(
cmap='glasbey_hv',
clipping_colors={
'min': 'transparent',
'NaN': 'transparent'
},
).redim.range(
overlay=(0.1, overlay_max)).apply.opts(
alpha=alpha_slider.param.value,
cmap=cmap_dict,
clim=oclim)
else:
# squish_height = int(max(image_size*(len(self.ds.z)/len(self.ds.x)), image_size/2))
# gridspace = hv.GridSpace(kdims=['label'], label=f'{self.subject_id}')
if self.verbose:
print('init gridspace')
# gridspace = hv.GridSpace(kdims=['label'])
# for mod in self.ds.label:
# gridspace[mod] = hv_ds_image.select(label=mod).to(
# hv.Image, [a1, a2], groupby=[a3], vdims='image',
# dynamic=dynamic).opts(frame_width=image_size, frame_height=image_size,
# ).apply.opts(clim=cslider.param.value)
# gridspace[mod] *= hv_ds_overlay.select(label=mod).to(
# hv.Image, [a1, a2], groupby=[a3, 'overlay_label'], vdims='overlay',
# dynamic=dynamic).opts(
# cmap='glasbey_hv', clipping_colors={'min': 'transparent'},
# ).redim.range(overlay=(1e-6, overlay_max)).apply.opts(
# alpha=alpha_slider.param.value, cmap=cmap_dict, clim=oclim)
# gridspace[mod] = gridspace[mod].opts(tools=['hover'])
# print(gridspace[mod])
gridspace = hv_ds_image.to(
hv.Image, [a1, a2],
vdims=['image', 'overlay'],
dynamic=dynamic).opts(
frame_width=pane_width,
frame_height=pane_height,
tools=['hover'],
).apply.opts(clim=cslider.param.value)
if self.verbose:
print(gridspace)
gridspace *= hv_ds_overlay.to(
hv.Image, [a1, a2], vdims='overlay', dynamic=dynamic).opts(
cmap='glasbey_hv',
clipping_colors={
'min': 'transparent',
'NaN': 'transparent'
},
).redim.range(overlay=(1e-6, overlay_max)).apply.opts(
alpha=alpha_slider.param.value,
cmap=cmap_dict,
clim=oclim)
# print(gridspace)
# print(gridspace)
# gridspace = hv.DynamicMap(subj_viewer.load_subject).grid('label')
gridspace = gridspace.layout('label')
else:
tooltips = [
('(x,y)', '($x, $y)'),
('image', '@image'),
]
hover = HoverTool(tooltips=tooltips)
hv_ds = hv.Dataset(self.ds['image'])
if self.is2d:
gridspace = hv.GridSpace(kdims=['label'])
for mod in self.ds.label.values:
gridspace[mod] = hv_ds.select(label=mod).to(
hv.Image, [a1, a2],
groupby=['subject_id'],
vdims='image',
dynamic=dynamic).opts(
frame_width=pane_width,
frame_height=pane_height,
shared_axes=False,
tools=[hover],
axiswise=True,
# ).apply.opts(clim=cslider.param.value)
)
elif three_planes:
# squish_height = int(max(image_size*(len(self.ds.z)/len(self.ds.x)), image_size/2))
# gridspace = hv.GridSpace(kdims=['plane', 'label'], label=f'{self.subject_id}')
gridspace = hv.GridSpace(kdims=['plane', 'label'])
for mod in self.ds.label.values:
gridspace['axial', mod] = hv_ds.select(label=mod).to(
hv.Image, ['x', 'y'],
groupby=['subject_id', 'z'],
vdims='image',
dynamic=dynamic).opts(frame_width=self.axial_width,
frame_height=self.axial_height,
invert_yaxis=False).apply.opts(
clim=cslider.param.value)
gridspace['coronal', mod] = hv_ds.select(label=mod).to(
hv.Image, ['x', 'z'],
groupby=['subject_id', 'y'],
vdims='image',
dynamic=dynamic).opts(
frame_width=self.coronal_width,
frame_height=self.coronal_height).apply.opts(
clim=cslider.param.value)
gridspace['sagittal', mod] = hv_ds.select(label=mod).to(
hv.Image, ['y', 'z'],
groupby=['subject_id', 'x'],
vdims='image',
dynamic=dynamic).opts(
frame_width=self.sagittal_width,
frame_height=self.sagittal_height).apply.opts(
clim=cslider.param.value)
else:
# squish_height = int(max(image_size*(len(self.ds.z)/len(self.ds.x)), image_size/2))
# gridspace = hv.GridSpace(kdims=['label'], label=f'{self.subject_id}')
gridspace = hv.GridSpace(kdims=['label'])
for mod in self.ds.label.values:
gridspace[mod] = hv_ds.select(label=mod).to(
hv.Image, [a1, a2],
groupby=['subject_id', a3],
vdims='image',
dynamic=dynamic).opts(
frame_width=pane_width,
frame_height=pane_height,
shared_axes=False,
tools=[hover],
).apply.opts(clim=cslider.param.value)
pn_layout = pn.pane.HoloViews(gridspace)
wb = pn_layout.widget_box
wb.append(cslider)
if 'overlay' in self.ds.data_vars:
wb.append(alpha_slider)
wb.append(cmap_select)
if thresh_toggle in [2, 3]:
wb.append(ocslider_max)
if thresh_toggle in [1, 3]:
wb.append(ocslider_min)
return pn.Row(wb, pn_layout)