def hvplot(self): # noqa F811
opts.defaults(
opts.Overlay(
width=self.width, height=self.height, active_tools=["wheel_zoom"]
)
)
return self._hvplot_trajectory(self.data)
def hvplot(self):
opts.defaults(opts.Overlay(width=self.width, height=self.height, active_tools=['wheel_zoom']))
for traj in self.data.trajectories:
overlay = self._hvplot_trajectory(traj)
if self.overlay:
self.overlay = self.overlay * overlay
else:
self.overlay = overlay
self.hvplot_tiles = False # has to be removed after the first iteration, otherwise tiles will cover trajectories!
return self.overlay
def run_experiment(n_iters,
model_gen,
dl,
loss_fn,
optim_gen,
lr_scheduler,
device,
print_every=None,
seed=1,
to_show=True):
"""
Args:
- model_generator (Callable): returns a new model
- Must accept two arguments, `device` and `seed`
- It returns a model object put in `device` with any weight initialization
random-seeded at `seed
- optim_gen (Callable): Must take in 'model.parameters()'
- eg: functools.partial(torch.optim.Adam, lr=1e-3)
- seed (None or int): random seed for clean model (model weights)
- None if randomness in initializing model weights is desired
- any other int to set the seed
- lr_scheduler: TriangleLR or ConstLR
"""
model = model_gen(device=device, seed=seed)
optimizer = optim_gen(model.parameters())
lrs, losses, avg_losses = lr_range_test(model,
dl,
loss_fn,
optimizer,
lr_scheduler,
device,
n_iters=n_iters,
print_every=print_every)
# Visualization
if to_show:
hv_lr = show_lr_generator(lr_scheduler, n_iters)
layout = (hv_lr.opts(color='red',
ylim=(lr_scheduler.min_lr, lr_scheduler.max_lr)) +
hv.Curve(losses, label='loss').opts(color='blue'))
display(
layout.opts(
opts.Overlay(shared_axes=False),
opts.Curve(padding=0.1,
width=800,
axiswise=True,
shared_axes=False)).cols(1))
return model, lrs, losses, avg_losses
def _plot_overlay(elevation, shadows):
'''
'''
shadows = hv.Dataset(
(np.arange(shadows.shape[2]), np.arange(
shadows.shape[0]), np.arange(shadows.shape[1]), shadows),
['Time', 'x', 'y'], 'Shadows')
elevation = hv.Dataset((np.arange(
elevation.shape[0]), np.arange(elevation.shape[1]), elevation),
['x', 'y'], 'Elevation')
opts.defaults(
opts.Image('elevation', cmap='viridis', invert_yaxis=True),
opts.Image('shadows', cmap='binary', invert_yaxis=True, alpha=0.7),
opts.Overlay(show_legend=False))
elevation = elevation.to(hv.Image, ['x', 'y'], group='elevation')
shadows = shadows.to(hv.Image, ['x', 'y'], group='shadows')
return elevation * shadows
import pandas as pd
import numpy as np
import holoviews as hv
from holoviews import opts, dim
hv.extension('bokeh')
macro_df = pd.read_csv('http://assets.holoviews.org/macro.csv', '\t')
key_dimensions = [('year', 'Year'), ('country', 'Country')]
value_dimensions = [('unem', 'Unemployment'), ('capmob', 'Capital Mobility'),
('gdp', 'GDP Growth'), ('trade', 'Trade')]
macro = hv.Table(macro_df, key_dimensions, value_dimensions)
gdp_curves = macro.to.curve('Year', 'GDP Growth')
gdp_unem_scatter = macro.to.scatter('Year', ['GDP Growth', 'Unemployment'])
annotations = hv.Arrow(1973, 8, 'Oil Crisis', 'v') * hv.Arrow(1975, 6, 'Stagflation', 'v') *\
hv.Arrow(1979, 8, 'Energy Crisis', 'v') * hv.Arrow(1981.9, 5, 'Early Eighties\n Recession', 'v')
composition=(gdp_curves * gdp_unem_scatter* annotations)
composition.opts(
opts.Curve(color='k'),
opts.Scatter(cmap='Blues', color='Unemployment',
line_color='k', size=dim('Unemployment')*1.5),
opts.Text(text_font_size='13px'),
opts.Overlay(height=400, show_frame=False, width=700))
hv.save(composition, 'holomap.html')
def __call__(self, dset, **params):
self.p = ParamOverrides(self, params)
if self.p.vdim is None:
vdim = dset.vdims[0].name
else:
vdim = self.p.vdim
ra_range = (ra0, ra1) = dset.range("ra")
if self.p.ra_sampling:
xsampling = (ra1 - ra0) / self.p.ra_sampling
else:
xsampling = None
dec_range = (dec0, dec1) = dset.range("dec")
if self.p.dec_sampling:
ysampling = (dec1 - dec0) / self.p.dec_sampling
else:
ysampling = None
if self.p.aggregator == "mean":
aggregator = ds.mean(vdim)
elif self.p.aggregator == "std":
aggregator = ds.std(vdim)
elif self.p.aggregator == "count":
aggregator = ds.count()
sky_range = RangeXY()
if self.p.range_stream:
def redim(dset, x_range, y_range):
ranges = {}
if x_range and all(isfinite(v) for v in x_range):
ranges["ra"] = x_range
if y_range and all(isfinite(v) for v in x_range):
ranges["dec"] = y_range
return dset.redim.range(**ranges) if ranges else dset
dset = dset.apply(redim, streams=[self.p.range_stream])
link_streams(self.p.range_stream, sky_range)
streams = [sky_range, PlotSize()]
pts = dset.apply(skypoints, streams=[self.p.filter_stream])
reset = PlotReset(source=pts)
reset.add_subscriber(partial(reset_stream, None,
[self.p.range_stream]))
rasterize_inst = rasterize.instance(aggregator=aggregator,
streams=streams,
x_sampling=xsampling,
y_sampling=ysampling)
raster_pts = apply_when(
pts,
operation=rasterize_inst,
predicate=lambda pts: len(pts) > self.p.max_points)
return raster_pts.opts(
opts.Image(
bgcolor="black",
colorbar=True,
cmap=self.p.cmap,
min_height=100,
responsive=True,
tools=["hover"],
symmetric=True,
),
opts.Points(
color=vdim,
cmap=self.p.cmap,
framewise=True,
size=self.p.decimate_size,
tools=["hover"],
symmetric=True,
),
opts.Overlay(hooks=[
partial(reset_hook, x_range=ra_range, y_range=dec_range)
]),
)
def __call__(self, dset, **params):
self.p = ParamOverrides(self, params)
if self.p.xdim not in dset.dimensions():
raise ValueError("{} not in Dataset.".format(self.p.xdim))
if self.p.ydim not in dset.dimensions():
raise ValueError("{} not in Dataset.".format(self.p.ydim))
if ("ra" not in dset.dimensions()) or ("dec" not in dset.dimensions()):
raise ValueError("ra and/or dec not in Dataset.")
# Compute sampling
ra_range = (ra0, ra1) = dset.range("ra")
if self.p.ra_sampling:
ra_sampling = (ra1 - ra0) / self.p.x_sampling
else:
ra_sampling = None
dec_range = (dec0, dec1) = dset.range("dec")
if self.p.dec_sampling:
dec_sampling = (dec1 - dec0) / self.p.y_sampling
else:
dec_sampling = None
x_range = (x0, x1) = dset.range(self.p.xdim)
if self.p.x_sampling:
x_sampling = (x1 - x0) / self.p.x_sampling
else:
x_sampling = None
y_range = (y0, y1) = dset.range(self.p.ydim)
if self.p.y_sampling:
y_sampling = (y1 - y0) / self.p.y_sampling
else:
y_sampling = None
# Set up scatter plot
scatter_range = RangeXY()
if self.p.scatter_range_stream:
def redim_scatter(dset, x_range, y_range):
ranges = {}
if x_range and all(isfinite(v) for v in x_range):
ranges[self.p.xdim] = x_range
if y_range and all(isfinite(v) for v in x_range):
ranges[self.p.ydim] = y_range
return dset.redim.range(**ranges) if ranges else dset
dset_scatter = dset.apply(redim_scatter,
streams=[self.p.scatter_range_stream])
link_streams(self.p.scatter_range_stream, scatter_range)
else:
dset_scatter = dset
scatter_pts = dset_scatter.apply(filterpoints,
streams=[self.p.filter_stream],
xdim=self.p.xdim,
ydim=self.p.ydim)
scatter_streams = [scatter_range, PlotSize()]
scatter_rasterize = rasterize.instance(streams=scatter_streams,
x_sampling=x_sampling,
y_sampling=y_sampling)
cmap = (process_cmap(self.p.scatter_cmap)[:250]
if self.p.scatter_cmap == "fire" else self.p.scatter_cmap)
scatter_rasterized = apply_when(
scatter_pts,
operation=scatter_rasterize,
predicate=lambda pts: len(pts) > self.p.max_points
).opts(
opts.Image(clim=(1, np.nan),
clipping_colors={"min": "transparent"},
cmap=cmap),
opts.Points(clim=(1, np.nan),
clipping_colors={"min": "transparent"},
cmap=cmap),
opts.Overlay(
hooks=[partial(reset_hook, x_range=x_range, y_range=y_range)]),
)
# Set up sky plot
sky_range = RangeXY()
if self.p.sky_range_stream:
def redim_sky(dset, x_range, y_range):
ranges = {}
if x_range and all(isfinite(v) for v in x_range):
ranges["ra"] = x_range
if y_range and all(isfinite(v) for v in x_range):
ranges["dec"] = y_range
return dset.redim.range(**ranges) if ranges else dset
dset_sky = dset.apply(redim_sky, streams=[self.p.sky_range_stream])
link_streams(self.p.sky_range_stream, sky_range)
else:
dset_sky = dset
sky_pts = dset_sky.apply(filterpoints,
xdim="ra",
ydim="dec",
set_title=False,
streams=[self.p.filter_stream])
skyplot_streams = [sky_range, PlotSize()]
sky_rasterize = rasterize.instance(
aggregator=ds.mean(self.p.ydim),
streams=skyplot_streams,
x_sampling=ra_sampling,
y_sampling=dec_sampling,
)
sky_rasterized = apply_when(
sky_pts,
operation=sky_rasterize,
predicate=lambda pts: len(pts) > self.p.max_points).opts(
opts.Image(bgcolor="black",
cmap=self.p.sky_cmap,
symmetric=True),
opts.Points(bgcolor="black",
cmap=self.p.sky_cmap,
symmetric=True),
opts.Overlay(hooks=[
partial(reset_hook, x_range=ra_range, y_range=dec_range)
]),
)
# Set up BoundsXY streams to listen to box_select events and notify FilterStream
scatter_select = BoundsXY(source=scatter_pts)
scatter_notifier = partial(notify_stream,
filter_stream=self.p.filter_stream,
xdim=self.p.xdim,
ydim=self.p.ydim)
scatter_select.add_subscriber(scatter_notifier)
sky_select = BoundsXY(source=sky_pts)
sky_notifier = partial(notify_stream,
filter_stream=self.p.filter_stream,
xdim="ra",
ydim="dec")
sky_select.add_subscriber(sky_notifier)
# Reset
reset = PlotReset(source=sky_pts)
reset.add_subscriber(
partial(reset_stream, self.p.filter_stream,
[self.p.sky_range_stream, self.p.scatter_range_stream]))
raw_scatterpts = filterpoints(dset, xdim=self.p.xdim, ydim=self.p.ydim)
raw_scatter = datashade(
raw_scatterpts,
cmap=list(Greys9[::-1][:5]),
streams=scatter_streams,
x_sampling=x_sampling,
y_sampling=y_sampling,
)
scatter_p = raw_scatter * scatter_rasterized
if self.p.show_rawsky:
raw_skypts = filterpoints(dset, xdim=self.p.xdim, ydim=self.p.ydim)
raw_sky = datashade(
rawskypts,
cmap=list(Greys9[::-1][:5]),
streams=skyplot_streams,
x_sampling=ra_sampling,
y_sampling=dec_sampling,
)
sky_p = raw_sky * sky_rasterized
else:
sky_p = sky_rasterized
if self.p.show_table:
table = dset.apply(summary_table,
ydim=self.p.ydim,
streams=[self.p.filter_stream])
table = table.opts()
layout = table + scatter_p + sky_p
else:
layout = (scatter_p + sky_p).opts(sizing_mode="stretch_width")
return layout.opts(
opts.Image(colorbar=True,
responsive=True,
tools=["box_select", "hover"]),
opts.Layout(sizing_mode="stretch_width"),
opts.Points(color=self.p.ydim, tools=["hover"]),
opts.RGB(alpha=0.5),
opts.Table(width=200),
)
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()
def plot_scatter(df,
x,
y,
x_round_val=1,
y_round_val=1,
x_tooltip='',
y_tooltip=''):
'''
Returns a HoloViews plot layout.
Arguments:
df - Dataframe to process, must have the column 'Country' adn the columns x and y within.
x - Column in Dataframe where values are evaluated for the x-axis
y - Column in Dataframe where values are evaluated for the y-axis
x_round_val (optional) - single numeric value to set the x axis limits on max found within x
y_round_val (optional) - single numeric value to set the y axis limits on max found within y
x_tooltip (optional) - tooltip to be set in the plots for the x values
y_tooltip (optional) - tooltip to be set in the plots for the y values
'''
max_y = np.ceil(np.nanmax(df[y].values))
max_y = max_y - max_y % y_round_val + 2 * y_round_val
max_x = np.ceil(np.nanmax(df[x].values))
max_x = max_x - max_x % x_round_val + 2 * x_round_val
'''
if max_x > max_y:
max_y = max_x
else:
max_x=max_y
'''
if x_tooltip == '':
x_tooltip = '@{' + x + '}{0,0.0}'
if y_tooltip == '':
y_tooltip = '@{' + y + '}{0,0.0}'
# Plot settings and parameters
hover = HoverTool(tooltips=[('Country',
'@Country'), (x, x_tooltip), (y, y_tooltip)])
padding = dict(x=(-1.2, 1.2), y=(-1.2, 1.2))
options_shared = dict(width=700,
height=700,
xlim=(0, max_x),
ylim=(0, max_y),
hooks=[axis_not_scientific],
active_tools=['wheel_zoom'],
padding=(0.1, 0.1),
show_grid=True,
show_legend=True,
legend_position='bottom',
legend_cols=3)
options = [
opts.Scatter(marker='o',
size=10,
fill_alpha=0.6,
tools=[hover],
color=hv.Palette('Set2'),
**options_shared),
opts.Points(color='Country',
cmap=cc.cm.fire,
size=8,
tools=[hover],
**options_shared),
opts.Labels(text_font_size='8pt', yoffset=y_round_val / 5),
opts.Overlay(**options_shared)
]
ds = hv.Table(df)
# Create the plot
layout = ( #hv.Scatter(df, kdims=[x], vdims=[y, 'Country'])*
ds.to(hv.Scatter, x, y, 'Country').overlay() *
hv.Labels(ds, kdims=[x, y], vdims=['Country'])).opts(options)
return layout
frame_width=600,
invert_yaxis=True,
xaxis='bare',
yaxis='bare',
bgcolor='black',
active_tools=['pan', 'wheel_zoom'],
show_title=False),
opts.HLine('orthoview',
line_dash='dashed',
line_width=1,
line_color='white'),
opts.VLine('orthoview',
line_dash='dashed',
line_width=1,
line_color='white'),
opts.Overlay('orthoview', shared_axes=False, show_title=False),
opts.Overlay('segmentation', show_title=False),
opts.Image('segmentation',
frame_width=600,
invert_yaxis=True,
xaxis='bare',
yaxis='bare',
bgcolor='black',
active_tools=['pan', 'wheel_zoom'],
show_title=False),
)
class BaseViewer(param.Parameterized):
return_panel = param.Boolean(
False,
clr_pls = 'lightseagreen'
clr_inSample = 'steelblue'
clr_5cv = 'tomato'
clr_20x5cv = 'gold'
clr_5cvMean = 'maroon'
clr_20x5cvMean = 'darkgoldenrod'
default_fontsizes = dict(title=8, labels=8, ticks=7, minor_ticks=7, legend=7)
fig_opts = [
opts.Layout(aspect_weight=1,
fig_inches=(3.42, None),
sublabel_size=10,
fontsize=8),
opts.Overlay(fontsize=default_fontsizes, ),
opts.Area(fontsize=default_fontsizes),
opts.Arrow(textsize=default_fontsizes),
opts.Curve(fontsize=default_fontsizes),
opts.HexTiles(fontsize=default_fontsizes),
opts.Histogram(fontsize=default_fontsizes),
opts.Raster(fontsize=default_fontsizes),
opts.Scatter(fontsize=default_fontsizes),
opts.Text(fontsize=default_fontsizes),
opts.QuadMesh(fontsize=default_fontsizes),
opts.Violin(fontsize=default_fontsizes),
opts.VLine(fontsize=default_fontsizes),
]
# hv hooks
nx, ny, nz = mat.shape
rock = hv.Dataset((np.arange(nx), np.arange(ny), np.arange(nz), mat),
["x", "y", "z"], ["value"])
rock = rock.clone(datatype=['xarray'])
rock.data
from holoviews import opts
opts.defaults(
opts.GridSpace(shared_xaxis=True, shared_yaxis=True),
opts.Image(cmap='Gray', width=400, height=400),
opts.Labels(text_color='white',
text_font_size='8pt',
text_align='left',
text_baseline='bottom'), opts.Path(color='white'),
opts.Spread(width=600), opts.Overlay(show_legend=False))
# Data has three dimensions. We can easily visualise two using an image, and use a slider to change the third dimension.
rock.to(hv.Image, ['x', 'y'], datatype=["xarray"], dynamic=True).hist()
# To make it more interesting we can create three plots and each having one of the dimensions on the slider.
#
# Here's we use the [Turbo](https://ai.googleblog.com/2019/08/turbo-improved-rainbow-colormap-for.html) colormap which is often better than viridis or plasma for highlighting differences
import bokeh.palettes as pal
# %%opts Image {+axiswise} [xaxis=None yaxis=None width=200 height=200]
cmap = pal.Turbo256
hmx = rock.to(hv.Image, ['z', 'y'], dynamic=True).opts(cmap=cmap)
hmy = rock.to(hv.Image, ['z', 'x'], dynamic=True).opts(cmap=cmap)
# %%
st, sc, sy, sx = xp.shape
# %%
ds = hv.Dataset((np.arange(sy),
np.arange(sx),
np.arange(st), xp[:,1,:,:]),
['x','y', 'Frame'], 'movie')
# %%
#ds.clone(datatype=['xarray']).data
# %%
opts.defaults(
opts.GridSpace(shared_xaxis=True, shared_yaxis=True),
opts.Image(cmap='viridis', width=400, height=400),
opts.Labels(text_color='white', text_font_size='8pt', text_align='left', text_baseline='bottom'),
opts.Path(color='white'),
opts.Spread(width=600),
opts.Overlay(show_legend=False))
# %%
ds.to(hv.Image, ['x', 'y']).hist()
# %%
#np.save('xp.npy', xp)
# %%
