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 swatches(*args, group=None, not_group=None, only_aliased=False, cols=None, **kwargs):
"""Show swatches for given names or names in group"""
args = args or all_original_names(group=group, not_group=not_group,
only_aliased=only_aliased)
if not cols:
cols = 3 if len(args) >= 3 else 1
backends = hv.Store.loaded_backends()
if 'matplotlib' in backends:
if 'aspect' not in kwargs:
kwargs['aspect'] = 12 // cols
if 'fig_size' not in kwargs:
kwargs['fig_size'] = 500 // cols
if 'bokeh' in backends:
if 'height' not in kwargs:
kwargs['height'] = 100
if 'width' not in kwargs:
kwargs['width'] = (9 * kwargs['height']) // cols
images = [swatch(arg, **kwargs) if isinstance(arg, str) else
swatch(*arg, **kwargs) for
arg in args]
plot = hv.Layout(images).opts(plot=dict(transpose=True)).cols(int(np.ceil(len(images)*1.0/cols)))
if 'matplotlib' in backends:
plot.opts(opts.Layout(backend='matplotlib', sublabel_format=None,
fig_size=kwargs.get('fig_size', 150)))
return plot
def swatches(names=None, cols=None, **kwargs):
"""Show swatches for all names or given names"""
if not names:
names = sorted([name for name in palette.keys()])
if not cols:
cols = 2 if len(names) >= 2 else 1
backends = hv.Store.loaded_backends()
if 'matplotlib' in backends:
if 'aspect' not in kwargs:
kwargs['aspect'] = 12 // cols
if 'fig_size' not in kwargs:
kwargs['fig_size'] = 500 // cols
if 'bokeh' in backends:
if 'height' not in kwargs:
kwargs['height'] = 100
if 'width' not in kwargs:
kwargs['width'] = (9 * kwargs['height']) // cols
images = [swatch(name, **kwargs) for name in names]
plot = hv.Layout(images).opts(plot=dict(transpose=True)).cols(int(np.ceil(len(images)*1.0/cols)))
if 'matplotlib' in backends:
plot.opts(opts.Layout(backend='matplotlib', sublabel_format=None,
fig_size=kwargs.get('fig_size', 150)))
return plot
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 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 hv_adjust_image_landmarks(image,
landmarks,
landmarks_match=None,
bregma_offset=None,
resolution=0.0194,
msize=40):
'''
TODO: merge part of this with the one for the landmarks
landmarks are in allen reference space
'''
h, w = image.shape
if bregma_offset is None:
# then it is the center of the image
bregma_offset = np.array([int(w / 2), int(h / 2)
]) # place bregma in the center of the image
landmarks_im = allen_landmarks_to_image_space(landmarks.copy(),
bregma_offset, resolution)
if landmarks_match is None:
landmarks_match = landmarks_im
import holoviews as hv
from holoviews import opts, streams
from holoviews.plotting.links import DataLink
bounds = np.array([0, 0, w, h])
im = hv.Image(image[::-1, :],
bounds=tuple(bounds.tolist())).opts(invert_yaxis=True,
cmap='gray')
points = hv.Points(landmarks_match, vdims='color').opts(marker='+',
size=msize)
point_stream = streams.PointDraw(data=points.columns(),
add=False,
num_objects=4,
source=points,
empty_value='black')
table = hv.Table(points, ['x', 'y', 'name'],
'color').opts(title='Annotation location')
DataLink(points, table)
from bokeh.models import HoverTool
hoverpts = HoverTool(tooltips=[("i", "$index")])
widget = (im * points + table).opts(
opts.Layout(merge_tools=False),
opts.Points(invert_yaxis=True,
active_tools=['point_draw'],
color='color',
tools=[hoverpts],
width=int(w),
height=int(h)), opts.Table(editable=True))
return widget, point_stream, landmarks_im
def sine_combs(*args, group=None, not_group=None, only_aliased=False, cols=1, **kwargs):
"""Show sine_combs for given names or names in group"""
args = args or all_original_names(group=group, not_group=not_group,
only_aliased=only_aliased)
images = [sine_comb(arg, **kwargs) if isinstance(arg, str) else
sine_comb(*arg, **kwargs) for
arg in args]
plot = hv.Layout(images).opts(plot=dict(transpose=True)).cols(int(np.ceil(len(images)*1.0/cols)))
backends = hv.Store.loaded_backends()
if 'matplotlib' in backends:
plot.opts(opts.Layout(backend='matplotlib', sublabel_format=None,
fig_size=kwargs.get('fig_size', 200)))
return plot
def hv_adjust_reference_landmarks(landmarks, ccf_regions, msize=40):
'''
landmarks = {'x': [-1.95, 0, 1.95, 0],
'y': [-3.45, -3.45, -3.45, 3.2],
'name': ['OB_left', 'OB_center', 'OB_right', 'RSP_base'],
'color': ['#fc9d03', '#0367fc', '#fc9d03', '#fc4103']}
landmarks = pd.DataFrame(landmarks)
# adjust landmarks
wid,landmark_wid = hv_adjust_reference_landmarks(landmarks,ccf_regions)
wid # to display
# use the following to retrieve (on another cell)
landmarks = pd.DataFrame(landmark_wid.data)[['x','y','name','color']]
'''
import holoviews as hv
from holoviews import opts, streams
from holoviews.plotting.links import DataLink
referenceplt = hv_plot_allen_regions(ccf_regions).options(
{'Curve': {
'color': 'black',
'width': 600
}})
points = hv.Points(landmarks, vdims='color').opts(marker='+', size=msize)
point_stream = streams.PointDraw(data=points.columns(),
add=False,
num_objects=4,
source=points,
empty_value='black')
table = hv.Table(points, ['x', 'y', 'name'],
'color').opts(title='Landmarks location')
DataLink(points, table)
widget = (referenceplt * points + table).opts(
opts.Layout(merge_tools=False),
opts.Points(invert_yaxis=True,
active_tools=['point_draw'],
color='color',
height=500,
tools=['hover'],
width=500), opts.Table(editable=True))
return widget, point_stream
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
opts.defaults(
opts.Curve(xaxis=None,
yaxis=None,
show_grid=False,
show_frame=False,
color='orangered',
framewise=True,
width=100),
opts.Image(width=800,
height=400,
shared_axes=False,
logz=True,
xaxis=None,
yaxis=None,
axiswise=True), opts.HLine(color='white', line_width=1),
opts.Layout(shared_axes=False), opts.VLine(color='white', line_width=1))
# Read the parquet file
df = dd.read_parquet('./data/nyc_taxi_wide.parq').persist()
# Declare points
points = hv.Points(df, kdims=['pickup_x', 'pickup_y'], vdims=[])
# Use datashader to rasterize and linked streams for interactivity
agg = aggregate(points, link_inputs=True, x_sampling=0.0001, y_sampling=0.0001)
pointerx = hv.streams.PointerX(x=np.mean(points.range('pickup_x')),
source=points)
pointery = hv.streams.PointerY(y=np.mean(points.range('pickup_y')),
source=points)
vline = hv.DynamicMap(lambda x: hv.VLine(x), streams=[pointerx])
hline = hv.DynamicMap(lambda y: hv.HLine(y), streams=[pointery])
hv.extension("bokeh")
pn.extension(sizing_mode="scale_width")
# Set some defaults for the visualization of the graphs
hvopts.defaults(
hvopts.Image( # pylint: disable=no-member
# Don't set both height and width, or the UI will not be responsive!
height=600,
# width=800,
responsive=True,
show_title=True,
tools=["hover"],
active_tools=["pan", "wheel_zoom"],
align="end",
),
hvopts.Layout(toolbar="right"), # pylint: disable=no-member
)
ui = thalassa.ThalassaUI(
display_variables=True,
display_stations=True,
)
# https://panel.holoviz.org/reference/templates/Bootstrap.html
bootstrap = pn.template.BootstrapTemplate(
site="example.com",
title="Thalassa",
logo="thalassa/static/logo.png",
favicon="thalassa/static/favicon.png",
sidebar=[ui.sidebar],
sidebar_width=350, # in pixels! must be an integer!
cmap_r = 'winter_r'
clr_cca = 'peru'
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),
]
