def plot_bokeh_history(solvers, x, y, x_arrays, y_arrays, mins, legends,
log_scale, show):
import bokeh.plotting as bk
min_x, max_x, min_y, max_y = mins
if log_scale:
# Bokeh has a weird behaviour when using logscale with 0 entries...
# We use the difference between smallest value of second small
# to set the range of y
all_ys = np.hstack(y_arrays)
y_range_min = np.min(all_ys[all_ys != 0])
if y_range_min < 0:
raise ValueError("Cannot plot negative values on a log scale")
fig = bk.Figure(plot_height=300,
y_axis_type="log",
y_range=[y_range_min, max_y])
else:
fig = bk.Figure(plot_height=300,
x_range=[min_x, max_x],
y_range=[min_y, max_y])
for i, (solver, x_array, y_array,
legend) in enumerate(zip(solvers, x_arrays, y_arrays, legends)):
color = get_plot_color(i)
fig.line(x_array, y_array, line_width=3, legend=legend, color=color)
fig.xaxis.axis_label = x
fig.yaxis.axis_label = y
fig.xaxis.axis_label_text_font_size = "12pt"
fig.yaxis.axis_label_text_font_size = "12pt"
if show:
bk.show(fig)
return None
else:
return fig
def initialize():
for scenario_name in scenarios:
#build plots
plot = {
'figure':
bp.Figure(toolbar_location='right',
tools='save,pan,box_zoom,reset',
width=250,
height=225),
'series': [],
'xmin':
0,
'xmax':
0,
'ymin':
0,
'ymax':
0,
}
plot['figure'].title.text = scenario_name
plot['figure'].xaxis.major_label_orientation = 'vertical'
plot['figure'].xaxis.major_label_standoff = 25
plot_list['scenarios'][scenario_name] = plot
#build maps
map_plot = bm.Plot(x_range=bm.DataRange1d(),
y_range=bm.DataRange1d(),
plot_width=450,
plot_height=250,
toolbar_location=None)
map_plot.title.text = scenario_name
map_list[scenario_name]['plot'] = map_plot
combined_plot = {
'figure':
bp.Figure(toolbar_location='right',
tools='save,pan,box_zoom,reset',
width=250,
height=225),
'series': [],
}
combined_plot['figure'].title.text = 'Combined'
combined_plot['figure'].xaxis.major_label_orientation = 'vertical'
combined_plot['figure'].xaxis.major_label_standoff = 25
plot_list['combined'] = combined_plot
build_plots()
if showMaps: build_maps()
def make_figure(output_backend='webgl',
tooltips=default_tooltips,
tools=default_tools,
**kwargs) -> plotting.Figure:
"""Wrapper for creating bokeh figures that enforces customized default arguments"""
return plotting.Figure(output_backend=output_backend,
tooltips=tooltips,
tools=tools,
**kwargs)
def line(self,
width=DEFAULT_WIDTH,
height=DEFAULT_HEIGHT,
fig=None,
**kwds):
"""Line plot for series data.
Parameters
----------
width : int, optional
The width of the plot in pixels.
height : int, optional
The height of the plot in pixels.
fig : Figure, optional
If specified, the line is added to the existing figure.
**kwds :
Extra keywords passed to ``line``.
Examples
--------
>>> series.plot.line(color='blue', alpha=0.5) # doctest: +SKIP
Returns
-------
fig : bokeh.plotting.Figure
"""
df = self._data.reset_index()
x, y = df.columns = [str(c) for c in df.columns]
x_data = df[x]
y_data = df[y]
axis_kwargs = {}
for axis, data in [('x', x_data), ('y', y_data)]:
if is_datetime64_any_dtype(data.dtype):
axis_kwargs['%s_axis_type' % axis] = 'datetime'
elif is_categorical_dtype(data.dtype):
raise ValueError("Categorical dtype not supported for line")
source = bp.ColumnDataSource({x: x_data, y: y_data})
if fig is None:
fig = bp.Figure(plot_height=height,
plot_width=width,
**axis_kwargs)
fig.line(x, y, source=source, **kwds)
return fig
content_pdf = content_list + [
(co.FigureMpl(plt.Figure())),
(co.FigureMpl(plt.Figure(), output_format="svg")),
(co.FigureMpl(plt.Figure(), output_format="svg", pdf_figsize=0.9)),
(co.FigureMpl(plt.Figure(), output_format="svg", pdf_figsize=(8, 5))),
]
adaptors_extra = [
(_irises_path, co.Image),
(Path(_irises_path), co.Image),
(pd.DataFrame({
"a": range(1, 11),
"b": range(11, 21)
}), co.DataFramePd),
(plt.figure(), co.FigureMpl),
(bkp.Figure(), co.FigureBokeh),
(bkl.column(bkp.figure()), co.FigureBokeh),
(px.line(x=range(10), y=range(10)), co.FigurePlotly),
]
adaptor_list += adaptors_extra
@pytest.fixture
def content_list_fn():
return content_list
@pytest.fixture
def layout_list_fn():
return layout_list
bkp.output_file('waveletDemo.html', mode='inline')
wave1 = structWave('Ricker25', 'red', 512, 0.5)
wave1.typeRicker(25)
wave1.calcAmpSpec()
wave2 = structWave('BP 5,10,50,70', 'green', 512, 0.5)
wave2.typeBandPass(5, 10, 50, 70)
wave2.calcAmpSpec()
wave3 = structWave('Ormsby 5,10,50,70', 'blue', 512, 0.5)
wave3.typeOrmsby(5, 10, 50, 70)
wave3.calcAmpSpec()
pwave1 = bkp.Figure(title='Time Amplitude', x_axis_label='Time', webgl=True)
pwave2 = bkp.Figure(title='Amplitude Spectrum',
x_axis_label='Freq',
webgl=True)
sw1 = bkm.ColumnDataSource(data=dict(time=wave1.timeseries, B=wave1.timeAmp))
sw2 = bkm.ColumnDataSource(data=dict(time=wave2.timeseries, B=wave2.timeAmp))
sw3 = bkm.ColumnDataSource(data=dict(time=wave3.timeseries, B=wave3.timeAmp))
sf1 = bkm.ColumnDataSource(data=dict(freq=wave1.freqseries, B=wave1.ampSpec))
sf2 = bkm.ColumnDataSource(data=dict(freq=wave2.freqseries, B=wave2.ampSpec))
sf3 = bkm.ColumnDataSource(data=dict(freq=wave3.freqseries, B=wave3.ampSpec))
#pwave1.line('time','B',color=wave1.colour,source=sw1)
pwave1.line('time', 'B', color=wave2.colour, source=sw2)
#pwave1.line('time','B',color=wave3.colour,source=sw3)
pwave2.line('freq', 'B', color=wave1.colour, source=sf1)
settingsOutputSize = Slider(start=50,
end=500,
step=10,
value=image['outputSize'],
title="Resolution of output image")
settingsAlgorithm = Select(options=[
'roberts', 'sobel', 'scharr', 'prewitt', 'canny-1', 'canny-2', 'canny-3'
],
value=image['edgeAlgorithm'])
chooseFileLabel = Div(text="""""")
chooseFileButton = upload_button()
applyChangesButton = Button(label="Apply changes", button_type="primary")
applyChangesButton.on_click(callback_modify_output_image)
showImageOrig = bp.Figure(plot_width=int(simulation['sizeX']),
plot_height=int(simulation['sizeY']),
title="Original image",
name="original")
showImageOrig.toolbar.logo = None
showImageOrig.toolbar_location = None
showImageOrig.toolbar.active_inspect = None
showImageOrig.toolbar.active_scroll = None
showImageOrig.toolbar.active_tap = None
showImageOrig.toolbar.active_drag = None
showImageOrig.x_range = Range1d(start=0, end=simulation['sizeX'])
showImageOrig.y_range = Range1d(start=0, end=simulation['sizeY'])
showImageOrig.xaxis.visible = None
showImageOrig.yaxis.visible = None
showImageOrig.xgrid.grid_line_color = None
showImageOrig.ygrid.grid_line_color = None
showImageResize = bp.Figure(plot_width=int(simulation['sizeX']),
import notebook
import numpy as np
import pandas as pd
from bokeh import plotting
with notebook.Notebook() as print:
print('Playing with timeseries', fmt='header')
dates = pd.date_range('1/1/2018', periods=365)
df = pd.DataFrame(np.random.randn(365), index=dates, columns=['deltas'])
df['cumsum'] = df.cumsum()
print(df)
fig = plotting.Figure()
fig.line(np.arange(10), np.arange(10), color='blue')
# fig.line(df.index.values, df['deltas'].values)
print('the figure:', fig)