def generateHoloview(self, df, SSC, FSC, type, counter_max=50):
renderer = hv.renderer('bokeh')
body_points = hv.Scatter(df, SSC, FSC).opts(color='r', title='SSC vs FSC Default Gating')
body_hist = body_points.hist(num_bins=50, dimension=[SSC, FSC])
body = body_hist
counter = 0
for index in range(len(df.columns)):
for index2 in range(index+1, len(df.columns)):
col = df.columns[index]
col2 = df.columns[index2]
if col2 != col and col not in (SSC, FSC) and col2 not in (SSC, FSC) and counter < counter_max:
points = hv.Scatter(df, col, col2)
hist = points.hist(num_bins=50, dimension=[col, col2])
body += hist
counter += 1
print(counter)
try:
body = body.opts(
opts.Scatter(tools=['box_select', 'lasso_select']),
opts.Layout(shared_axes=True, shared_datasource=True)).cols(2)
except:
body = body.opts(
opts.Scatter(tools=['box_select', 'lasso_select']),
opts.Layout(shared_axes=True, shared_datasource=True))
renderer.save(body, os.path.join(self.directory, str(type)+"gating"))
def _getOptions(library):
if (library == 'bokeh'):
options = [
opts.Scatter(size='bSize', color='color', show_legend=False)
]
else:
options = [opts.Scatter(s='mSize', color='color', show_legend=True)]
return options
def load_indices(Index):
#scatter = hv.Scatter(multi_df[Index], kdims = ['JDK_RS_ratio', 'JDK_RS_momentum'])
scatter = hv.Scatter(multi_df[Index], kdims = ['JDK_RS_momentum'])
##Colors
explicit_mapping = {'Leading': 'green', 'Lagging': 'yellow', 'Weakening': 'red', 'Improving': 'blue'}
##Plot Joining all together
scatter = scatter.opts(opts.Scatter(tools=['hover'], height = 500, width=500, size = 10, xlim = x_range, ylim = y_range,
color = 'Quadrant', cmap=explicit_mapping,
))
##Line connecting the dots
#curve = hv.Curve(multi_df[Index], kdims = ['JDK_RS_ratio', 'JDK_RS_momentum'])
curve = hv.Curve(multi_df[Index], kdims = [ 'JDK_RS_momentum'])
curve = curve.opts(opts.Curve(color = 'black', line_width = 1))
##Vertical and Horizontal Lines
vline = hv.VLine(100).opts(color = 'black', line_width = 1)
hline = hv.HLine(100).opts(color = 'black', line_width = 1)
#All Together
full_scatter = scatter * vline * hline * curve
full_scatter = full_scatter.opts(legend_cols= True)
return full_scatter
def investigateOptimalAlgorithms(kmerId, kmerPca):
plot.setLibrary('bokeh')
pca = kmerPca.loc[:, PCA_DATA_COL_NAMES]
plots = {}
algos = (
('Elliptic', EllipticEnvelope()),
('SVM', OneClassSVM()),
('Forest', IsolationForest()),
('Local', LocalOutlierFactor()))
## Visualise data and manually determine which algorithm will be good
for i, (name, algo) in enumerate(algos, 1):
labels = _getLabels(algo, pca)
labels = pd.DataFrame(labels, columns=[OLABEL_COL_NAME])
kmerDf = pd.concat([kmerId, pca, labels], axis=1)
dataset = hv.Dataset(kmerDf, PCA_DATA_COL_NAMES)
scatter = dataset.to(hv.Scatter, PCA_DATA_COL_NAMES, groupby=OLABEL_COL_NAME).overlay()
scatter.opts(opts.Scatter(size=10, show_legend=True))
plots[name] = scatter
plots = hv.HoloMap(plots, kdims='algo')
plots = plots.collate()
return plots
def investigateOptimalAlgorithms(kmerId, kmerCount):
plot.setLibrary('bokeh')
plots = {}
params = {'n_components':N_PCA_COMPONENTS, 'random_state':42}
algos = (
('PCA', decomposition.PCA(**params)),
('LLE', manifold.LocallyLinearEmbedding(method='standard', **params)),
('LTSA', manifold.LocallyLinearEmbedding(method='ltsa', **params)),
('Hessian LLE', manifold.LocallyLinearEmbedding(method='hessian',
n_neighbors=10, **params)),
('Modified LLE', manifold.LocallyLinearEmbedding(method='modified',
**params)),
('tSNE', manifold.TSNE(**params)),
('Isomap', manifold.Isomap(n_components=N_PCA_COMPONENTS)),
('MDS', manifold.MDS(**params)),
('SE', manifold.SpectralEmbedding(**params)))
## Visualise data and manually determine which algorithm will be good
for i, (name, algo) in enumerate(algos, 1):
com = _getComponents(algo, kmerCount)
com = pd.DataFrame(com, columns=PCA_DATA_COL_NAMES)
kmerDf = pd.concat([kmerId, com], axis=1)
dataset = hv.Dataset(kmerDf, PCA_DATA_COL_NAMES)
scatter = dataset.to(hv.Scatter, PCA_DATA_COL_NAMES)
scatter.opts(opts.Scatter(size=10, show_legend=True))
plots[name] = scatter
plots = hv.HoloMap(plots, kdims='algo')
plots = plots.collate()
return plots
def _get_linked_plots(backend: str = "plotly") -> Tuple:
"""Returns a tuple (scatter, hist) of linked plots
Args:
backend (str, optional): "plotly" or "bokeh". Defaults to "plotly".
Returns:
[Tuple]: Returns a tuple (scatter, hist) of linked plots
"""
dataset = hv.Dataset(IRIS_DATASET)
scatter = hv.Scatter(dataset,
kdims=["sepal_length"],
vdims=["sepal_width"])
hist = hv.operation.histogram(dataset,
dimension="petal_width",
normed=False)
# pylint: disable=no-value-for-parameter
selection_linker = hv.selection.link_selections.instance()
scatter = selection_linker(scatter).opts(
opts.Scatter(**OPTS["all"]["scatter"], **OPTS[backend]["scatter"]))
hist = selection_linker(hist).opts(
opts.Histogram(**OPTS["all"]["hist"], **OPTS[backend]["hist"]))
return scatter, hist
def investigateOptimalAlgorithms(kmerId, kmerPca):
plot.setLibrary('bokeh')
pca = kmerPca.loc[:, PCA_DATA_COL_NAMES]
plots = {}
algos = (('KMeans', cluster.KMeans()), ('Affinity',
cluster.AffinityPropagation()),
('MeanShift',
cluster.MeanShift()), ('Spectral', cluster.SpectralClustering()),
('Agglomerative',
cluster.AgglomerativeClustering(linkage='average')),
('Agglomerative',
cluster.AgglomerativeClustering(linkage='ward')),
('DBSCAN', cluster.DBSCAN()), ('Gaussian', GaussianMixture()))
## Visualise data and manually determine which algorithm will be good
for i, (name, algo) in enumerate(algos, 1):
labels = _getLabels(algo, pca)
labels = pd.DataFrame(labels, columns=[CLABEL_COL_NAME])
kmerDf = pd.concat([kmerId, pca, labels], axis=1)
dataset = hv.Dataset(kmerDf, PCA_DATA_COL_NAMES)
scatter = dataset.to(hv.Scatter,
PCA_DATA_COL_NAMES,
groupby=CLABEL_COL_NAME).overlay()
scatter.opts(opts.Scatter(size=10, show_legend=True))
plots[name] = scatter
plots = hv.HoloMap(plots, kdims='algo')
plots = plots.collate()
return plots
def plot_curve():
df = download_data(index.value)
future_df = download_data_predicted(index.value)
title = index.value + " Exchange Rate"
# Create stock curve
past_label = "Past " + title
future_label = "Predicted Future " + title
df['label'] = past_label
future_df['label'] = future_label
new_df = pd.concat([df, future_df], axis=0)
curve = hv.Curve(df, 'Date', ('Close', 'label'))
curve_pred = hv.Curve(future_df, 'Date', ('Close', 'Price'))
# Labels and layout
tgt = curve.relabel("Past " + title).opts( #width=width,
height=600,
show_grid=True,
labelled=['y'],
default_tools=[hover],
hooks=[set_tools],
title=title,
responsive=True)
tgt_pred = curve_pred.relabel("Future " + title).opts( #width=width,
height=600,
show_grid=True,
labelled=['y'],
default_tools=[hover],
hooks=[set_tools],
title=title,
responsive=True)
src = curve.opts(height=100,
yaxis=None,
default_tools=[],
color='green',
responsive=True)
src_pred = curve_pred.opts(height=100,
yaxis=None,
default_tools=[],
color='green',
responsive=True)
circle = hv.Scatter(df, 'Date', ('Close', 'Price')).opts(color='green')
circle_pred = hv.Scatter(future_df, 'Date',
('Close', 'Price')).opts(color='blue')
RangeToolLink(src, tgt)
# Merge rangetool
layout = ((tgt * tgt_pred * circle * circle_pred) +
(src * src_pred)).cols(1)
layout.opts(opts.Layout(shared_axes=False, merge_tools=False),
opts.Curve(toolbar=None), opts.Scatter(size=3))
print("kepanggil nih viz")
print(df["Close"][0])
print(index.value)
return layout
def generateCombined(self, decimated, SSC, FSC, cachebust, counter_max=20):
renderer = hv.renderer('bokeh')
body = None
points = None
point_collect = []
for key in decimated.keys():
print(key)
point = hv.Scatter(decimated[key], SSC, FSC, label=key)
point_collect.append(point)
if points is None:
points = point
else:
points *= point
if body is None:
body = points.opts(title='Default {0}: SSC vs FSC'.format("Combined"), height=450, width=450)
else:
body += points.opts(title='Default {0}: SSC vs FSC'.format("Combined"))
for dim in (SSC, FSC):
hists = None
for point in point_collect:
hist = histogram(point, dimension=dim)
if hists is None:
hists = hist
else:
hists *= hist
body += hists
potentialCols = [c for c in decimated[list(decimated.keys())[0]].columns if c != SSC and c != FSC]
for i in range(len(potentialCols)):
for j in range(i+1, len(potentialCols)):
points = None
point_collect = []
for key in decimated.keys():
point = hv.Scatter(decimated[key], potentialCols[i], potentialCols[j], label=key)
point_collect.append(point)
if points is None:
points = point
else:
points *= point
body += points.opts(title='Combined: {0} vs {1}'.format(potentialCols[i], potentialCols[j]), height=450, width=450)
for dim in (potentialCols[i], potentialCols[j]):
hists = None
for point in point_collect:
hist = histogram(point, dimension=dim)
if hists is None:
hists = hist
else:
hists *= hist
body += hists
body = body.opts(
opts.Scatter(alpha=0.9),
opts.Histogram(alpha=0.9, height=450),
opts.Layout(shared_axes=True, shared_datasource=True)).cols(3)
renderer.save(body, os.path.join(self.directory, cachebust+"combined_gating"))
def concatenated_summary_curve_factory(
cdf,
kdims="Cycle_Index",
vdims="Charge_Capacity(mAh/g)",
title="Summary Curves",
fill_alpha=0.8,
size=12,
width=800,
legend_position="right",
colors=None,
markers=None,
):
# TODO: missing doc-string
if not hv_available:
print("This function uses holoviews. But could not import it."
"So I am aborting...")
return
if colors is None:
colors = hv.Cycle("Category10")
if markers is None:
markers = hv.Cycle(["circle", "square", "triangle", "diamond"])
groups = []
curves_opts = []
curves = {}
for indx, new_df in cdf.groupby(level=0, axis=1):
g = indx.split("_")[1]
groups.append(g)
n = hv.Scatter(data=new_df[indx],
kdims=kdims,
vdims=vdims,
group=g,
label=indx).opts(fill_alpha=fill_alpha, size=size)
curves[indx] = n
ugroups = set(groups)
max_sub_group = max([groups.count(x) for x in ugroups])
markers = markers[max_sub_group]
colors = colors[len(ugroups)]
for g, c in zip(ugroups, colors.values):
curves_opts.append(opts.Scatter(g, color=c, marker=markers))
curves_overlay = hv.NdOverlay(curves, kdims="cell id").opts(
opts.NdOverlay(width=800, legend_position=legend_position,
title=title),
*curves_opts,
)
return curves_overlay
def visualize_time_series(self, stats:List[str], rename_cols:Dict[str, str]=dict(), options:opts=opts()):
"""
Plots the given stats over a period of a time
:param stats: names of statistics to plot
:param rename_cols: any human readable names for the statistics
:param options: plotting options for holoviews
"""
df = pd.DataFrame(self.trend).fillna(0)
stats = list(map(lambda s: rename_cols.get(s, s), df.columns & stats))
df = df.rename(columns=rename_cols)
point_curves = [hv.Scatter(df[["date", statistic]], label=statistic) for statistic in stats]
line_curves = [hv.Curve(points) for points in point_curves]
return (hv.Overlay(line_curves + point_curves)). opts(opts.Scatter(tools=["hover"], size=6)). opts(padding=0.05, height=375, legend_position="bottom", title=""). opts(options)
def setLibrary(library='bokeh'):
## Use Bokeh by default
if (library == 'bokeh'):
hv.extension('bokeh')
hv.archive.auto(filename_formatter="{obj:.7}") ## For notebooks
opts.defaults(
opts.Scatter(tools=['hover'], width=700, height=700, padding=0.05),
opts.HeatMap(tools=['hover'],
width=700,
height=700,
labelled=[],
xrotation=45,
colorbar=True,
cmap=('Blues')))
# opts.HeatMap(tools=['hover'], width=700, height=700, labelled=[],
# xrotation=45, colorbar=True, cmap=('Blues')))
## The library that Bokeh uses to export to SVG is not longer supported
## and so cannot be exported to SVG
elif (library == 'matplotlib'):
hv.extension('matplotlib')
hv.output(fig='svg')
opts.defaults(
opts.Scatter(fig_size=300, padding=0.05),
opts.HeatMap(fig_size=300,
labelled=[],
xrotation=45,
colorbar=True,
cmap=('Blues')))
else:
raise NotImplementedError("Unknown plotting library.")
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'])
def view(self):
data = self.data
xmin, xmax = np.min(data), np.max(data)
x1_dim = hv.Dimension('x₁', range=(xmin, xmax))
x2_dim = hv.Dimension('x₂', range=(xmin, xmax))
samples_map = self.maps["samples"]
samples_map = samples_map.opts(width=600,
height=350,
show_grid=True,
padding=(0, 0.1),
toolbar=None)
samples_map = samples_map.opts(opts.Path(color=blue, framewise=True))
samples_map = samples_map.redim.label(y="f(x)", x="x")
control_vline_map = self.maps["vlines_control"]
control_vline_map = control_vline_map.redim(x=x1_dim, y=x2_dim)
control_vline_map = control_vline_map.opts(show_grid=True)
control_vline_map = control_vline_map.opts(
opts.HLine(line_width=2), opts.VLine(line_width=2),
opts.Points(color="white", marker="s", size=8),
opts.Image(cmap="viridis"))
vlines_map = self.maps["vlines"]
vlines_map = vlines_map.opts(toolbar=None)
vlines_map = vlines_map.opts(opts.VLine(line_width=2),
opts.Points(size=6))
scatter_map = self.maps["scatter"]
scatter_map = scatter_map.redim.label(y="f(x₂)", x="f(x₁)")
scatter_map = scatter_map.opts(padding=(0.5, 0.5),
show_grid=True,
toolbar=None)
scatter_map = scatter_map.opts(
opts.Scatter(size=7,
framewise=True,
fill_color=orange1,
line_color=orange2))
title = pn.pane.Markdown("## GP samples visualization", max_height=25)
descr = pn.pane.Markdown(
"_For moving x₁ and x₂ bars: 1. turn off **pan** tool, 2. click and move the orange squared point on the covariance matrix_"
)
row0 = pn.Row(pn.Spacer(width=25), self.kernels_controller.view())
row1 = samples_map * vlines_map
row2 = pn.Row(control_vline_map, scatter_map)
return pn.Column(title, descr, row0, row1, row2)
def scatter(data, x, y, hue=None):
key_dimensions = [(xi, xi) for xi in [x, hue]]
value_dimensions = [(y, y)]
macro = hv.Table(data, key_dimensions, value_dimensions)
scatter = macro.to.scatter(x, y).overlay(hue)
scatter.opts(
opts.Scatter(color=hv.Cycle('Category20'),
line_color='k',
size=10,
show_grid=True,
width=700,
height=400),
opts.NdOverlay(legend_position='left', show_frame=False))
return scatter
def generate_graph_ping_times_with_extreme_outliers(self):
fig_all_max_ping = hv.Curve((self.df_ping["date"], self.df_ping["max"]),
"Date",
"Ping in ms",
label="All messured pings")
fig_dot_over_upper_bound = hv.Scatter(
(self.df_ping["date"][self.df_ping["max"] > self.upper_acceptable_ping_bound],
self.df_ping["max"][self.df_ping["max"] > self.upper_acceptable_ping_bound]),
"Date",
"Max_Ping_Time",
label="Highlight pings over {} ms".format(
str(self.upper_acceptable_ping_bound))).opts(opts.Scatter(color="red", size=10))
fig_ping_times_with_extreme_outliers = (fig_all_max_ping *
fig_dot_over_upper_bound).opts(
legend_position="top_left",
title="All Max. Ping Times in ms", padding=0.05)
# Safe newly generated plot
hv.save(fig_ping_times_with_extreme_outliers,
os.path.join(self.path, "webpage", "figures",
"fig_ping_times_with_extreme_outliers.html"),
backend='bokeh')
def investigateOptimalParameters(kmerId, kmerCount):
## TO FIX
import holoviews as hv
from holoviews import dim ## Requires python 3.7; not 3.5
from holoviews import opts
hv.extension('bokeh')
kmerId = updateDuplicates(kmerId, kmer.ID_COL_NAME)
cond = ((kmerId[kmer.ID_COL_NAME].str.contains('Wuhan-Hu-1'))
| (kmerId[kmer.ID_COL_NAME].str.match('Australia'))
| (kmerId[kmer.ID_COL_NAME].str.match('Sydney'))
| (kmerId[kmer.ID_COL_NAME].str.match('C6')))
kmerId.loc[cond, kmer.FILE_COL_NAME] = kmerId.loc[cond, kmer.ID_COL_NAME]
labels = []
# algos = investigatePCAParameters(kmerId, kmerCount)
# algos = investigateMDSParameters(kmerId, kmerCount)
algos = investigateTSNEParameters(kmerId, kmerCount)
plots = {}
for p, a in algos:
print(p)
pca = getPca(a, kmerCount)
kmerPca = joinColumns(kmerId, PCA_DATA_COL_NAMES, pca)
dataset = hv.Dataset(kmerPca, PCA_DATA_COL_NAMES)
scatter = dataset.to(hv.Scatter, PCA_DATA_COL_NAMES,
groupby=kmer.FILE_COL_NAME) \
.overlay()
scatter.opts(opts.Scatter(tools=['hover'], height=700, width=700,
size=10, show_legend=True))
plots[tuple(p.values())] = scatter
labels = p.keys()
## Create the map of plots
plots = hv.HoloMap(plots, kdims=[*labels])
plots = plots.collate()
hv.save(plots, 'plot.html')
def config_layout(PlotItem, **kwargs):
"""Configs the layout of the output"""
for key, value in kwargs.items():
try:
getattr(PlotItem, key)(value)
except AttributeError as err:
log.warning(
"Option '{}' for plot not possible with error: {}".format(
key, err))
try:
TOOLTIPS = [("File", "@Name"), ("index", "$index"),
("(x,y)", "($x, $y)")]
hover = HoverTool(tooltips=TOOLTIPS)
PlotItem.opts(
opts.Curve(tools=[hover], toolbar="disable"),
opts.Scatter(tools=[hover], toolbar="disable"),
opts.Histogram(tools=[hover], toolbar="disable"),
opts.Points(tools=[hover], toolbar="disable"),
opts.BoxWhisker(tools=[hover], toolbar="disable"),
opts.Bars(tools=[
HoverTool(tooltips=[('Value of ID:', ' $x'), ('Value:', '$y')])
],
toolbar="disable"),
opts.Violin(tools=[hover], toolbar="disable"))
except AttributeError as err:
log.error(
"Nonetype object encountered while configuring final plots layout. This should not happen! Error: {}"
.format(err))
except ValueError as err:
if "unexpected option 'tools'" in str(err).lower(
) or "unexpected option 'toolbar'" in str(err).lower():
pass
else:
raise
return PlotItem
def plot_experiments(src,
experiments,
attr=None,
n_samples=10000,
single_mode=False,
width=550,
n_cols=3,
cmap=None):
if type(experiments) == int:
experiments = [experiments]
if src == 'mnist' or src == 'fmnist':
attr = 'labels'
cmap = 'Category10'
df = pd.read_feather(
os.path.join(META_PATH, src, src + '_meta.feather'))
df = df.loc[:n_samples]
elif src == 'cartoon' or src == 'cartoon10k':
n_file = int(np.ceil(n_samples / 10000))
df, attr_dict = load_cartoon_meta(n_file=n_file, src=src)
df = df.loc[:n_samples]
if attr is None:
print('TODO: implement DynamicMap for src=cartoon and attr=None')
attr = 'nhair'
if cmap is None:
if attr == 'nhair':
cmap = 'Category20'
else:
cmap = 'viridis'
df = add_experiments(experiments, src, df)
if n_samples > 10000:
scatter_options = opts.Scatter(width=width,
aspect=1,
xaxis=None,
yaxis=None,
colorbar=False,
cmap=cmap,
nonselection_alpha=0.01,
nonselection_color='black')
return hv.Layout([
dynspread(
datashade(hv.Scatter(df,
'x_' + str(e), ['y_' + str(e), attr],
label=get_label(
e, src)).opts(scatter_options),
aggregator=ds.count_cat(attr))) for e in experiments
])
else:
str_tooltips = "\
<div> \
<img src=\"@path\" width=\"100\" height=\"100\"></img> \
</div> \
<div> \
<span style=\"font-size: 12px;\">label: @labels</span> \
</div> \
"
hover = bokeh.models.HoverTool(tooltips=str_tooltips)
scatter_options = opts.Scatter(
width=width,
aspect=1,
xaxis=None,
yaxis=None,
colorbar=False,
cmap=cmap,
nonselection_alpha=0.01,
nonselection_color='black',
tools=[hover, 'box_select', 'lasso_select'])
if single_mode:
raise ValueError(
'Implement single_mode (no layout just DynamicMap)'
) # TODO: single_mode
scatter_list = [
hv.Scatter(df,
'x_' + str(e), ['y_' + str(e), attr, 'path', 'labels'],
label=get_label(
e,
src)).opts(scatter_options).opts(color=hv.dim(attr))
for e in experiments
]
if len(scatter_list) > 2:
[
DataLink(scatter1, scatter2)
for scatter1, scatter2 in combinations(scatter_list, 2)
]
DataLink(scatter_list[0], scatter_list[1])
return hv.Layout(scatter_list).cols(n_cols).relabel(src +
' plotting ' +
str(n_samples) +
' datapoints')
df = iris()
dataset = hv.Dataset(df)
# Build selection linking object
selection_linker = hv.selection.link_selections.instance()
scatter = selection_linker(
hv.Scatter(dataset, kdims=["sepal_length"], vdims=["sepal_width"]))
hist = selection_linker(
hv.operation.histogram(dataset, dimension="petal_width", normed=False))
# Use plot hook to set the default drag mode to box selection
def set_dragmode(plot, element):
fig = plot.state
fig['layout']['dragmode'] = "select"
if isinstance(element, hv.Histogram):
# Constrain histogram selection direction to horizontal
fig['layout']['selectdirection'] = "h"
scatter.opts(opts.Scatter(hooks=[set_dragmode]))
hist.opts(opts.Histogram(hooks=[set_dragmode]))
app = dash.Dash(__name__)
components = to_dash(app, [scatter, hist], reset_button=True)
app.layout = html.Div(components.children)
if __name__ == "__main__":
app.run_server(debug=True)
def plot_countries(df,
col,
round_val=1,
col_tooltip='',
nr_countries=10,
reverse=False):
'''
Plot the overview for the top x (default 10) countries and for the overall countries as well.
Returns a HoloViews plot layout.
Arguments:
df - Dataframe to process, must have the columns 'Country' and 'Year' within.
col - Column in Dataframe where values are evaluated for the plotting process
round_val (optional) - single numeric value to set the y axis limit on max found within col
col_tooltip (optional) - tooltip to be set in the plots for the col values
nr_countries (int) (optional) - number of countries to plot in the top views
reverse (bool) (optional) - if True the bottom countries are listed
'''
max_y = np.ceil(np.nanmax(df[col].values))
max_y = max_y - max_y % round_val + 2 * round_val
if col_tooltip == '':
col_tooltip = '@{' + col.replace(
" ", "_"
) + '}{0,0.000}' # Holoviews auto-replaces spaces with underscores
years_list = list(df['Year'].unique())
if reverse == True:
label = 'Bottom' + str(nr_countries)
plot_df = df[-nr_countries * len(years_list):]
else:
label = 'Top' + str(nr_countries)
plot_df = df[:nr_countries * len(years_list)][::-1]
plot_df_invert = plot_df[::-1].copy()
df_invert = df[::-1].copy()
# Plot settings and parameters
top_hover = HoverTool(tooltips=[('Country',
'@Country'), ('Year',
'@Year'), (col,
col_tooltip)])
country_hover = HoverTool(tooltips=[("Year", "@Year"), (col, col_tooltip)])
year_hover = HoverTool(tooltips=[("Country", "@Country"), (col,
col_tooltip)])
top_plot_arguments = dict(x='Year', y=col, by='Country', tools=[top_hover])
options_shared = dict(height=700,
ylim=(0, max_y),
hooks=[set_bokeh_plot],
active_tools=['wheel_zoom'],
padding=(0.1, 0.1))
options = [
opts.Bars(width=700, show_grid=True, **options_shared),
opts.Scatter(xticks=years_list, marker='o', size=10, **options_shared),
opts.NdOverlay(width=650, xticks=years_list, **options_shared),
opts.Layout(tabs=True)
]
# Create the multiplot
layout = (
plot_df_invert.hvplot(
kind='barh', label=label + 'BarPlot', **top_plot_arguments) +
plot_df.hvplot(
kind='line', label=label + 'LinePlot', **top_plot_arguments) *
plot_df.hvplot(
kind='scatter', label=label + 'LinePlot', **top_plot_arguments) +
df.hvplot(kind='bar',
x='Year',
y=col,
groupby='Country',
label='SingleCountryDropdown',
tools=[country_hover]) +
df_invert.hvplot(kind='barh',
x='Country',
y=col,
groupby='Year',
label='AllCountriesYearSlider',
tools=[year_hover])).opts(options)
return layout
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')
import pandas as pd
import sys
import os
import holoviews as hv
from holoviews import opts, dim, Palette
import configparser
# 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'))
# Initializes the figures path in webpage for the diagram output
if os.path.isdir(os.path.join("webpage","figures")) is False:
os.mkdir(os.path.join("webpage","figures"))
print("Path 'figures' created successfully")
else:
print("Path 'figures' initialized")
class InteractivePlots:
def __init__(self):
def plot_perforacion():
elementos_perforacion=serie_resumen[['dias_perforacion','Qi_hist','estado_actual']]
tabla_perforacion = hv.Table(elementos_perforacion,'pozo')
tabla_perforacion.opts(height=500,width=400,fontscale=20)
dist = hv.Distribution(serie_resumen.dias_perforacion,
label='Dias de perforacion - Función de Probabilidad')
hist=serie_resumen.dias_perforacion.dropna()
hist=np.histogram(hist)
plot_hist = hv.Histogram(hist)
#kde = univariate_kde(dist,
# bin_range=(0, serie_resumen.dias_perforacion.max()),
# bw_method='scott',
# n_samples=1000)
#kde
scatter = hv.Scatter(serie_resumen,
kdims=['dias_perforacion','profundidad_total'],
label='Dias de perforacion vs Profundidad total')
#dist = dists.redim.label(dias_perforacion='Dias de perforacion')
scatter = scatter.redim.label(dias_perforacion='Dias de perforacion', profundidad_total='Profundidad total')
tiempos = tabla_perforacion + dist + scatter
tiempos.opts(
opts.Distribution(height=500, width=700, xaxis=True,
xlabel='Dias de Perforacion',
xlim=(0,serie_resumen.dias_perforacion.max()),
line_width=1.00,
color='grey',
alpha=0.5,
fontscale=1.5,
tools=['hover']),
opts.Scatter(height=500,
width=700,
xaxis=True,
yaxis=True,
size=dim('Qi_hist')*50,
line_width=0.25,
color='estado_actual',
cmap='Set1',
fontscale=1.5,
legend_position='bottom_right'))
#fill_color=factor_cmap('estado_actual', palette=Spectral6, factors=elementos_tipos['tipo']))
tiempos
hv.output(tiempos, backend='bokeh', fig='html')
hv.save(tiempos, 'curvas_tipo.html')
#hv.save(tiempos, 'tiempos.html')
return
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
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
class Suptitle:
def __init__(self, suptitle, color, y=1.175):
self.suptitle = suptitle
self.color = color
self.y = y
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
tiempos
tiempos.opts(
opts.Distribution(height=400,
width=800,
xaxis=True,
xlabel='Dias de Perforacion',
xlim=(0, serie_resumen.dias_perforacion.max()),
line_width=1.00,
color='grey',
alpha=0.5,
tools=['hover']),
opts.Scatter(height=400,
width=800,
xaxis=True,
yaxis=True,
size=10,
line_width=0.25,
color='orange')).cols(1)
hv.save(tiempos, 'output_tiempos.html', backend='bokeh')
###### HV Plot
import panel as pn
tiempo_perf = serie_resumen.hvplot.scatter(x='dias_perforacion',
y='profundidad_total',
by='trayectoria',
alpha=0.5)
tiempo_perf
right_viterbi = 100 * np.count_nonzero(
gen_states == model.states(series)) / series.size
print(f"Right states with Viterbi: {right_states:.2f}%\n"
f"Right states with gamma argmax: {right_viterbi:.2f}%")
mt = hv.Scatter((model.matrix.flat, np.genfromtxt("gen_param/A.txt").flat))
avg = hv.Scatter(
(model.distr.mean, np.log(np.genfromtxt("gen_param/b.txt")[:, 0])))
std = hv.Scatter((model.distr.std, np.genfromtxt("gen_param/b.txt")[:, 1]))
pi = hv.Scatter((model.init_distr, np.genfromtxt("gen_param/pi.txt")))
ze = hv.Scatter(
(model.zero_distr, np.genfromtxt("gen_param/zero_distr.txt")[:, 0]))
param_max = (
np.array([model.matrix.max(), model.init_distr.max(), model.zero_distr.max()]).max()
+ 0.02
) # yapf: disable
fit = hv.Curve((*[[0, param_max]] * 2, ))
layout = (mt * std * pi * ze * fit).redim(x="Inferred", y="Simulated")
layout.opts(opts.Scatter(size=7, tools=["hover"]), opts.Curve(color="green"))
# %%
cProfile.run("model.baum_welch(series, 5)", sort="cumtime")
# %%
lp = line_profiler.LineProfiler(model.baum_welch)
lp.run("model.baum_welch(series, 3)").print_stats()
# %%
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()
