def bokeh_histogram(x,
y,
pay_norm,
x_label: str,
y_label: str,
x_range: list,
title: str = '',
bc: str = "#f0f0f0",
bfc: str = "#fafafa"):
bin_size = x[1] - x[0]
s = bokeh_fig_init(x_range=x_range,
title=title,
x_label=x_label,
y_label=y_label,
bc=bc,
bfc=bfc)
# Add copyright
l1 = add_copyright()
s.add_layout(l1)
s.vbar(x=x,
top=y,
width=0.95 * bin_size,
fill_color="#f8b739",
fill_alpha=0.5,
line_color=None)
if CURRENCY_NORM and pay_norm == 1:
s.xaxis[0].formatter = PrintfTickFormatter(format="$%ik")
else:
s.xaxis[0].formatter = PrintfTickFormatter(format="$%i")
st.bokeh_chart(s, use_container_width=True)
def csd_base_figure(add_legend=True, add_custom_hover=True):
""" function to make a CSD plot dummy"""
fig = figure(width=800, height=600, sizing_mode='scale_both',
tools=['pan', 'box_zoom', 'reset', 'save', 'crosshair'],
toolbar_location='right', x_axis_type="log",
y_axis_type="linear", x_axis_label='time[s]',
y_axis_label='abundance')
# fig.y_range = Range1d(1E-3, 1)
fig.title.text = 'CSD Evolution' # diagram name
fig.title.align = 'left' # setting layout
fig.title.text_font_size = "14pt"
fig.xaxis.axis_label_text_font_size = "16pt"
fig.yaxis.axis_label_text_font_size = "16pt"
fig.yaxis.major_label_text_font_size = "14pt"
fig.xaxis.major_label_text_font_size = "14pt"
fig.xaxis[0].formatter = PrintfTickFormatter(format="%4.1e")
custom_hover = HoverTool()
custom_hover.tooltips = """
<style>
.bk-tooltip>div:not(:first-child) {display:none;}
</style>
<b>time [s] </b> @x <br>
<b>Relative abundance: </b> @y
"""
if add_custom_hover:
fig.add_tools(custom_hover)
if add_legend:
legend = Legend(items=[])
fig.add_layout(legend)
# fig.legend.title = 'charge states'
fig.legend.location = "top_left" # setup plot legend
fig.legend.click_policy = "mute"
return fig
def cs_base_figure():
""" function to make a CS plot dummy"""
fig = figure(width=800, height=600, sizing_mode='scale_both',
tools=['pan', 'box_zoom', 'reset', 'save', 'crosshair'],
toolbar_location='right', x_axis_type="linear",
y_axis_type="log", x_axis_label='charge state',
y_axis_label='reaction cross sections, cm²')
fig.title.text = 'Processes cross sections, cm²' # diagram name
fig.title.align = 'left' # setting layout
fig.title.text_font_size = "14pt"
fig.xaxis.axis_label_text_font_size = "16pt"
fig.yaxis.axis_label_text_font_size = "16pt"
fig.yaxis.major_label_text_font_size = "14pt"
fig.xaxis.major_label_text_font_size = "14pt"
fig.yaxis[0].formatter = PrintfTickFormatter(format="%4.1e")
custom_hover = HoverTool()
custom_hover.tooltips = """
<style>
.bk-tooltip>div:not(:first-child) {display:none;}
</style>
<b>charge state: </b> @x <br>
<b>process rate: </b> @y
"""
fig.add_tools(custom_hover)
legend = Legend(items=[])
fig.add_layout(legend)
fig.legend.location = "top_right" # setup plot legend
fig.legend.click_policy = "mute"
return fig
def create_map_plot(self):
lat = 39.8282
lng = -98.5795
zoom = 6
xr = Range1d()
yr = Range1d()
x_range = xr
y_range = yr
#map_options = GMapOptions(lat=39.8282, lng=-98.5795, zoom=6)
map_options = GMapOptions(lat=lat, lng=lng, zoom=zoom)
#map_options = GMapOptions(lat=30.2861, lng=-97.7394, zoom=15)
plot = GMapPlot(x_range=x_range,
y_range=y_range,
map_options=map_options,
plot_width=680,
plot_height=600,
title=" ")
plot.map_options.map_type = "hybrid"
xaxis = LinearAxis(axis_label="lon",
major_tick_in=0,
formatter=NumeralTickFormatter(format="0.000"))
plot.add_layout(xaxis, 'below')
yaxis = LinearAxis(axis_label="lat",
major_tick_in=0,
formatter=PrintfTickFormatter(format="%.3f"))
plot.add_layout(yaxis, 'left')
self.plot = plot
def make_lin():
#Create figure object.
p = figure(title='Lin. Plot of COVID-19 ' + plot_title[sel_var] + ' (' +
txt_src + ')',
toolbar_location='above',
plot_height=250,
plot_width=500,
x_axis_type='datetime',
tools='pan, wheel_zoom, box_zoom, reset',
sizing_mode="scale_width")
# Format your x-axis as datetime.
p.xaxis[0].formatter = DatetimeTickFormatter(days='%b %d')
p.yaxis[0].formatter = PrintfTickFormatter(format='%.1e')
p.circle(x='Date',
y='Selected',
source=source_grp,
fill_color='Color',
line_color='Color',
legend_field='Country')
p.legend.location = "top_left"
p.legend.click_policy = "mute"
p.add_layout(dt_span)
# Add your tooltips
p.add_tools(hover)
return p
def make_plot(src):
p1 = figure(title="Gasverbruik per uur",
x_range=(-0.5,23.5), y_range=(-0.5,6.5),
x_axis_location="above", plot_width=900, plot_height=400,
tools=TOOLS, toolbar_location='below',
tooltips=[('Verbruik', '@aantal'),('Dag', '@dayofweek'),('Uur','@hour')]
)
p1.grid.grid_line_color = None
p1.axis.axis_line_color = None
p1.axis.major_tick_line_color = None
p1.axis.major_label_text_font_size = "5pt"
p1.axis.major_label_standoff = 0
p1.xaxis.major_label_orientation = pi / 3
p1.xaxis.ticker = FixedTicker(ticks=[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23])
p1.yaxis.ticker = FixedTicker(ticks=[0,1,2,3,4,5,6])
p1.yaxis.major_label_overrides = {0:'Maandag', 1:'Dinsdag', 2:'Woensdag', 3:'Donderdag', 4:'Vrijdag', 5:'Zaterdag', 6:'Zondag'}
p1.rect(x="hour", y="dayofweek", width=1, height=1,
source=src,
fill_color={'field': 'aantal', 'transform': mapper1},
line_color=None)
color_bar1 = ColorBar(color_mapper=mapper1, major_label_text_font_size="5pt",
ticker=BasicTicker(desired_num_ticks=len(colors)),
formatter=PrintfTickFormatter(format="%d"),
label_standoff=6, border_line_color=None, location=(0, 0))
p1.add_layout(color_bar1, 'right')
return p1
def make_plot():
# prepare data stuff - take the keys from the data in order of likelihood
categories = list(reversed(probly.keys()))
palette = [cc.rainbow[i * 15] for i in range(17)]
x = linspace(-20, 110, 500)
source = ColumnDataSource(data=dict(x=x))
p = figure(y_range=categories, plot_width=900, x_range=(-5, 105), toolbar_location=None)
for i, cat in enumerate(reversed(categories)):
pdf = gaussian_kde(probly[cat])
y = ridge(cat, pdf(x))
source.add(y, cat)
p.patch('x', cat, color=palette[i], alpha=0.6, line_color="black", source=source)
p.outline_line_color = None
p.background_fill_color = "#efefef"
p.xaxis.ticker = FixedTicker(ticks=list(range(0, 101, 10)))
p.xaxis.formatter = PrintfTickFormatter(format="%d%%")
p.ygrid.grid_line_color = None
p.xgrid.grid_line_color = "#dddddd"
p.xgrid.ticker = p.xaxis[0].ticker
p.axis.minor_tick_line_color = None
p.axis.major_tick_line_color = None
p.axis.axis_line_color = None
p.y_range.range_padding = 0.12
return p
def bokeh_scatter_init(pay_norm: int,
x_label: str,
y_label: str,
title: str = '',
x_range: list = None,
bc: str = "#f0f0f0",
bfc: str = "#fafafa",
plot_constants: bool = False) -> figure:
x_buffer = 1000 / pay_norm
x_min = 10000 / pay_norm
x_limit = 500000 / pay_norm
if CURRENCY_NORM and pay_norm == 1:
x_buffer /= 1e3
x_min /= 1e3
x_limit /= 1e3
if pay_norm != 1:
x_label = 'Hourly Rate'
if x_range is None:
x_range = [x_min - x_buffer, x_limit + x_buffer]
s = bokeh_fig_init(x_range=x_range,
title=title,
x_label=x_label,
y_label=y_label,
bc=bc,
bfc=bfc,
tools="pan,wheel_zoom,box_zoom,hover,save,reset",
active_scroll='wheel_zoom',
tooltips=TOOLTIPS)
constants_level = [2500, 5000, 10000, 15000, 20000]
if CURRENCY_NORM and pay_norm == 1:
s.xaxis[0].formatter = PrintfTickFormatter(format="$%ik")
else:
if pay_norm > 1:
s.xaxis[0].formatter = PrintfTickFormatter(format="$%i")
else:
s.xaxis[0].formatter = PrintfTickFormatter(format="$%i")
if plot_constants:
s = draw_constant_salary_bump(s, constants_level, pay_norm)
return s
def plot_map(perf_map, res, title='performance map', colors=BLUE_COLORS, show=True, scale='default'):
ps = list(perf_map.values())
p_min, p_max = np.min(ps), np.max(ps)
if scale == 'log':
c_min, c_max = -math.log(-p_min+EPSILON), -math.log(-p_max+EPSILON)
else:
c_min, c_max = p_min, p_max
img = np.zeros((res, res), dtype=np.uint32)
view = img.view(dtype=np.uint8).reshape(img.shape + (4,))
for (i, j), p in perf_map.items():
if scale == 'log':
p = -math.log(-p+EPSILON)
c_idx = int(np.floor((len(colors)-1)*(p - c_min)/(c_max-c_min)))
r, g, b = colors[c_idx]
view[j, i, 0] = r
view[j, i, 1] = g
view[j, i, 2] = b
view[j, i, 3] = 255
plot = bkp.figure(width=SIZE, height=SIZE, x_range=(-0.7, 0.7), y_range=(-0.7, 0.7),
title=title, tools = "pan,box_zoom,reset,save")
plot.title.text_font_size = '12pt'
plot.image_rgba([img], x=[-0.7], y=[-0.7], dh=[1.4], dw=[1.4])
if scale == 'log':
cbar = colorbar(colors, inversed=True)
cb_plot = bkp.figure(width=100, height=SIZE, x_range=(0, 1.0), y_axis_type="log", y_range=(-p_max, -p_min))
cb_plot.image_rgba([cbar], x=[0.0], y=[-p_max], dw=[1.0], dh=[p_max-p_min])
else:
cbar = colorbar(colors)
cb_plot = bkp.figure(width=100, height=SIZE, x_range=(0, 1.0), y_range=(p_min, p_max))
cb_plot.image_rgba([cbar], x=[0.0], y=[p_min], dw=[1.0], dh=[p_max-p_min])
cb_plot.min_border_right = 25
cb_plot.xgrid.grid_line_color = None
cb_plot.xaxis.minor_tick_line_color = None
cb_plot.xaxis.major_tick_line_color = None
cb_plot.xaxis.axis_line_color = None
cb_plot.xaxis[0].formatter = PrintfTickFormatter(format="")
if scale == 'log':
cb_plot.yaxis.formatter = PrintfTickFormatter(format="-%1.0e")
if show:
bkp.show(bkp.gridplot([[plot, cb_plot]]))
return [plot, cb_plot]
def draw_book(df, params):
asks = df["ask"].dropna()
bids = df["bid"].dropna()
p = figure(plot_width=book_params['size']['width'],
plot_height=book_params['size']['height'],
title=book_params["title"] +
"\t\tBid : %1.8f \tAsk : %1.8f\tSpread : %1.8f" %
(bids.index[-1], asks.index[0], asks.index[0] - bids.index[-1]))
p.toolbar.logo = None
p.toolbar_location = None
p.title.text_color = "whitesmoke"
p.title.text_font = "noto"
p.background_fill_color = "#36393e"
p.border_fill_color = "#36393e"
p.grid.grid_line_color = "whitesmoke"
p.grid.grid_line_alpha = 0.4
p.grid.minor_grid_line_color = "whitesmoke"
p.grid.minor_grid_line_alpha = 0.2
p.outline_line_color = "whitesmoke"
p.outline_line_alpha = 0.3
p.xaxis.formatter = PrintfTickFormatter(format=params['tickFormat'])
p.yaxis.formatter = PrintfTickFormatter(format="%f")
p.axis.major_tick_line_color = "whitesmoke"
p.axis.minor_tick_line_color = "whitesmoke"
p.axis.axis_line_color = "whitesmoke"
p.axis.major_label_text_color = "whitesmoke"
p.axis.major_label_text_font = "noto"
p.axis.major_label_text_font_size = "10pt"
p.x_range = Range1d(bids.index.min(), asks.index.max())
p.y_range = Range1d(0, max(bids.max(), asks.max()))
p.patch(np.append(np.array(asks.index),
[np.array(asks.index).max(),
np.array(asks.index).min()]),
np.append(asks.values, [0, 0]),
alpha=0.7,
line_width=2,
color=book_params["colors"]["ask"])
p.patch(np.append(np.array(bids.index),
[np.array(bids.index).max(),
np.array(bids.index).min()]),
np.append(bids.values, [0, 0]),
alpha=0.7,
line_width=2,
color=book_params["colors"]["bid"])
return p
def make_plot(name, wave_list, spec_list, color_list, label_list, image,
sx, sy):
plot = figure(plot_height=300, plot_width=800,
toolbar_location=None, x_axis_location="above",
background_fill_color="#efefef", x_range=(3470., 5540.),
y_axis_type="linear")
imageplot = figure(plot_height=540, plot_width=430,
tools="crosshair, pan, reset, save, wheel_zoom",
y_axis_location="right",
tooltips=[("x", "$x"), ("y", "$y"),
("value", "@image")],
x_range=(image[1].min(), image[1].max()),
y_range=(image[2].min(), image[2].max()))
select = figure(title=("Drag the selection "
"box to change the range above"),
plot_height=240, plot_width=800, y_range=plot.y_range,
y_axis_type="linear",
tools="", toolbar_location=None,
background_fill_color="#efefef")
for wavelength, spectrum, color, label in zip(wave_list, spec_list,
color_list, label_list):
source = ColumnDataSource(data=dict(wavelength=wavelength,
spectrum=spectrum))
plot.line('wavelength', 'spectrum', source=source, line_width=3,
line_alpha=0.6, line_color=color,
legend=label)
select.line('wavelength', 'spectrum', source=source,
line_color=color)
plot.yaxis.axis_label = '1e-17 ergs/s/cm^2/A'
for p in [plot, select]:
p.xaxis.major_label_text_font_size = "14pt"
p.yaxis.major_label_text_font_size = "14pt"
p.xaxis.axis_label = 'Wavelength'
p.xaxis.axis_label_text_font_size = "16pt"
p.yaxis.axis_label_text_font_size = "16pt"
p.xaxis.major_tick_line_color = "firebrick"
p.xaxis.major_tick_line_width = 3
p.xaxis.minor_tick_line_color = "orange"
p.yaxis.major_tick_line_color = "firebrick"
p.yaxis.major_tick_line_width = 3
p.yaxis.minor_tick_line_color = "orange"
p.yaxis[0].formatter = PrintfTickFormatter(format="%3.1f")
select.ygrid.grid_line_color = None
range_tool = RangeTool(x_range=plot.x_range)
range_tool.overlay.fill_color = "navy"
range_tool.overlay.fill_alpha = 0.2
select.add_tools(range_tool)
select.toolbar.active_multi = range_tool
imageplot.image(image=[image[0]], x=image[1].min(), y=image[2].min(),
dw=image[1].max()-image[1].min(),
dh=image[2].max()-image[2].min())
imageplot.scatter(sx, sy, marker='circle_x', size=15,
line_color="orange", fill_color="red", alpha=0.75)
output_file(name+".html", title=name)
save(row(column(plot, select), imageplot))
def make_heatmap_object(df, years, months):
''' makes a bokeh figure '''
# this is the colormap from the original NYTimes plot
colors = [
"#75968f", "#a5bab7", "#c9d9d3", "#e2e2e2", "#dfccce", "#ddb7b1",
"#cc7878", "#933b41", "#550b1d"
]
mapper = LinearColorMapper(palette=colors,
low=df.rate.min(),
high=df.rate.max())
source = ColumnDataSource(df)
TOOLS = "hover,save,pan,box_zoom,reset,wheel_zoom"
# p is a bokeh figure object
p = figure(title="US Unemployment ({0} - {1})".format(years[0], years[-1]),
x_range=years,
y_range=list(reversed(months)),
x_axis_location="above",
plot_width=900,
plot_height=400,
tools=TOOLS,
toolbar_location='below')
p.grid.grid_line_color = None
p.axis.axis_line_color = None
p.axis.major_tick_line_color = None
p.axis.major_label_text_font_size = "5pt"
p.axis.major_label_standoff = 0
p.xaxis.major_label_orientation = pi / 3
p.rect(x="Year",
y="Month",
width=1,
height=1,
source=source,
fill_color={
'field': 'rate',
'transform': mapper
},
line_color=None)
color_bar = ColorBar(color_mapper=mapper,
major_label_text_font_size="5pt",
ticker=BasicTicker(desired_num_ticks=len(colors)),
formatter=PrintfTickFormatter(format="%d%%"),
label_standoff=6,
border_line_color=None,
location=(0, 0))
p.add_layout(color_bar, 'right')
p.select_one(HoverTool).tooltips = [
('date', '@Month @Year'),
('rate', '@rate%'),
]
return p
def _plot_wcorr(Wcorr, L):
f = figure(tools="box_select, pan, reset, save")
f.plot_width = 700
f.plot_height = 600
# Background settings
f.background_fill_color = '#859dcd'
f.background_fill_alpha = 0.05
# Title settings
f.title.text = "W-Correlation for L={}".format(L)
f.title.text_font = 'Helvetica'
f.title.text_font_size = '24px'
f.title.align = 'center'
f.title.text_font_style = "italic"
# Axis settings
f.xaxis.axis_label = 'Fⱼ'
f.yaxis.axis_label = 'Fᵢ'
f.axis.axis_label_text_font = 'Helvetica'
f.axis.axis_label_text_font_size = '24px'
f.axis.major_label_orientation = 0
f.x_range = Range1d(start=0.5, end=L + 0.5)
f.y_range = Range1d(start=L + 0.5, end=0.5)
f.axis[0].ticker.desired_num_ticks = L
f.axis[0].ticker.num_minor_ticks = 0
data = DataFrame(Wcorr)
axis = [i for i in range(1, Wcorr.shape[0] + 1)]
data['F_i'] = axis
data.set_index('F_i', inplace=True)
data.columns = axis
data.columns.name = 'F_j'
df = DataFrame(data.stack(), columns=['corr']).reset_index()
source = ColumnDataSource(df)
# this is the colormap from the original NYTimes plot
mapper = LinearColorMapper(palette=color.palettes['colors_2'], low=0, high=1)
f.rect(x="F_i", y="F_j", width=1, height=1, source=source,
line_color=None, fill_color=transform('corr', mapper))
color_bar = ColorBar(color_mapper=mapper, location=(0, 0),
ticker=BasicTicker(desired_num_ticks=len(color.palettes['colors_2'])),
formatter=PrintfTickFormatter(format="%.2f"))
f.add_layout(color_bar, 'right')
hover = HoverTool(tooltips=[("Components", "(@F_i, @F_j)"),
("Correlations", "@corr")])
f.add_tools(hover)
show(f)
def make_heatmap_object(df, sample_lst, gene_lst):
''' makes a bokeh figure '''
colors = [
"#75968f", "#a5bab7", "#c9d9d3", "#e2e2e2", "#dfccce", "#ddb7b1",
"#cc7878", "#933b41", "#550b1d"
]
mapper = LinearColorMapper(palette=colors,
low=df['counts'].min(),
high=df['counts'].max())
source = ColumnDataSource(df)
TOOLS = "hover,save,pan,box_zoom,reset,wheel_zoom"
# p is a bokeh figure object
p = figure(title="Gene Expression",
x_range=sample_lst,
y_range=gene_lst,
x_axis_location="above",
plot_width=600,
plot_height=900,
tools=TOOLS,
toolbar_location='below')
p.grid.grid_line_color = None
p.axis.axis_line_color = None
p.axis.major_tick_line_color = None
p.axis.major_label_text_font_size = "5pt"
p.axis.major_label_standoff = 0
p.xaxis.major_label_orientation = pi / 3
p.rect(x="Samples",
y="Gene_ID",
width=1,
height=1,
source=source,
fill_color={
'field': 'counts',
'transform': mapper
},
line_color=None)
color_bar = ColorBar(color_mapper=mapper,
major_label_text_font_size="5pt",
ticker=BasicTicker(desired_num_ticks=len(colors)),
formatter=PrintfTickFormatter(format="%d"),
label_standoff=6,
border_line_color=None,
location=(0, 0))
p.add_layout(color_bar, 'right')
p.select_one(HoverTool).tooltips = [
('coord', '@Samples @Gene_ID'),
('count', '@counts'),
]
return p
def get_heatmap(cls):
palette = palettes.Magma256
mapper = LinearColorMapper(palette=palette, low=0, high=1)
tools = "hover"
xy_range = list(Data.res_df.index)
# plot
hm = figure(
title="Heatmap",
tools=tools,
toolbar_location=None,
x_range=xy_range,
y_range=xy_range,
plot_width=PAGE_WIDTH // 2 - 50
)
src = ColumnDataSource(Data.hm_df)
hm.rect(
x="uid_x", # this needs to be column from DF
y="uid_y",
width=1,
height=1,
source=src,
fill_color={'field': 'similarity', 'transform': mapper}, # mapper gives the coloring
line_color='white'
)
hm.xaxis.major_label_orientation = 3.14 / 3 # rotation is in radians
# color bar
color_bar = ColorBar(
color_mapper=mapper,
major_label_text_font_size="8pt",
ticker=BasicTicker(desired_num_ticks=10),
formatter=PrintfTickFormatter(format="%0.1f"),
label_standoff=10,
border_line_color=None,
location=(0, 0)
)
hm.add_layout(color_bar, 'right')
hm.select_one(HoverTool).tooltips = App.format_tooltip_fields([
('Similarity', '@similarity'),
('X', '@uid_x'),
('Y', '@uid_y'),
('Survey X', '@survey_id_x (@survey_name_x)'),
('Form type X', '@form_type_x'),
('Tr. code X', '@tr_code_x'),
('Survey Y', '@survey_id_y (@survey_name_y)'),
('Form type Y', '@form_type_y'),
('Tr. code Y', '@tr_code_y'),
])
return components(hm)
def plot_heatmap(data):
index = list(data.index)
columns = list(data.columns)
import pandas as pd
# reshape to 1D array or rates with a month and year for each row.
df = pd.DataFrame(data.stack(), columns=['rate']).reset_index()
# this is the colormap from the original NYTimes plot
colors = [
"#75968f", "#a5bab7", "#c9d9d3", "#e2e2e2", "#dfccce", "#ddb7b1",
"#cc7878", "#933b41", "#550b1d"
]
mapper = LinearColorMapper(palette=colors,
low=df.rate.min(),
high=df.rate.max())
TOOLS = "hover,save,pan,box_zoom,reset,wheel_zoom"
p = figure(title="",
x_range=list(reversed(columns)),
y_range=index,
x_axis_location="above",
plot_width=550,
plot_height=390,
tools=TOOLS,
toolbar_location='below',
tooltips=[('date', '@columns @index'), ('rate', '@rate%')])
p.grid.grid_line_color = None
p.axis.axis_line_color = None
p.axis.major_tick_line_color = None
p.axis.major_label_standoff = 0
p.xaxis.major_label_orientation = math.pi / 3
p.rect(x="columns",
y="index",
width=1,
height=1,
source=df,
fill_color={
'field': 'rate',
'transform': mapper
},
line_color=None)
color_bar = ColorBar(color_mapper=mapper,
major_label_text_font_size="7px",
ticker=BasicTicker(desired_num_ticks=len(colors)),
formatter=PrintfTickFormatter(format="%d%%"),
label_standoff=6,
border_line_color=None,
location=(0, 0))
p.add_layout(color_bar, 'right')
script, div = components(p)
return script, div
def mat(self, output_name, nodesX, nodesY, counts):
xname = []
yname = []
for node1 in nodesX:
for node2 in nodesY:
xname.append(node1)
yname.append(node2)
source = ColumnDataSource(data=dict(
xnames=xname,
ynames=yname,
count=counts.flatten(),
))
colors = ["#FFFFFF", "#CCCCFF", "#9999FF", "#5555FF", "#0000FF", "#0000CC", "#0000AA", "#000088", "#000000"]
exp_cmap = LogColorMapper(palette=colors, low = 0, high = 10)
# exp_cmap = LogColorMapper(palette=colors, low = np.min(counts), high = np.max(counts))
p = figure(title="Matrix Figure",
x_axis_location="above", tools="hover,save",
x_range=nodesX, y_range=list(reversed(nodesY)))
p.rect(x='xnames', y='ynames', width=0.9, height=0.9,
source=source, line_color=None, fill_color=transform('count', exp_cmap))
p.plot_width = 800
p.plot_height = 800
p.grid.grid_line_color = None
p.axis.axis_line_color = None
p.axis.major_tick_line_color = None
p.axis.major_label_text_font_size = "5pt"
p.axis.major_label_standoff = 0
p.xaxis.major_label_orientation = np.pi / 3
#圖例
color_bar = ColorBar(color_mapper=exp_cmap, location=(0, 0),
ticker=BasicTicker(desired_num_ticks=len(colors)),
formatter=PrintfTickFormatter(format="%d"))
p.add_layout(color_bar, 'right')
#游標所指“點”的說明格式
p.select_one(HoverTool).tooltips = [
('names', '@ynames -> @xnames'),
('count', '@count'),
]
output_file(output_name + ".html", title=output_name)
show(p) # show the plot
def get_logloss_graph(self, n_every=1000, n_ave=10):
steps, ll = self.get_log_data('step', 'logloss', n_every)
ave_ll = self.get_average(ll, n_ave)
dct = {'title': 'Logloss',
'x': {'data': steps},
'y': {'data': ll, 'color': '#fed6e5'}, # Light pink
'ave y': {'data': ave_ll, 'color': '#FD367E'}, # Pink
'x axis': {'label': 'steps', 'formatter': NumeralTickFormatter(format='0,000')},
'y axis': {'label': 'logloss', 'formatter': PrintfTickFormatter(format='%.3f')}}
return self.get_graph(dct)
def _setup_glyph_coloring(_, color_high=0.3):
# colors = list(reversed(bokeh.palettes.Greens[9]))
mapper = LinearColorMapper(palette=COLORS, low=0.0, high=color_high)
color_transform = bokeh.transform.transform('weight', mapper)
color_bar = ColorBar(
color_mapper=mapper,
location=(0, 0),
ticker=BasicTicker(desired_num_ticks=len(COLORS)),
formatter=PrintfTickFormatter(format=" %5.2f"),
)
return color_transform, color_bar
def contourfPlot(Radar_var, name, minmax=None, steps=100, colormap="jet"):
# ========== get Colors =====================
if minmax == None:
max_dbz = np.nanmax(Radar_var)
min_dbz = np.nanmin(Radar_var)
else:
min_dbz, max_dbz = minmax
rangeGateHeight = Radar.range()[2] - Radar.range()[1]
rgb = getColormapAsList(steps, colormap)
# ========== prepare data for plotting ==============
df = pd.DataFrame(Radar_var, index=Radar.time(), columns=Radar.range())
df.columns.name = "range"
df.index.name = "time"
df1 = pd.DataFrame(df.stack(), columns=['data']).reset_index()
source = ColumnDataSource(df1)
mapper = LinearColorMapper(palette=rgb, low=min_dbz, high=max_dbz)
# ====================================
cf = p1.rect(x="time",
y="range",
height=rangeGateHeight,
width=timedelta(seconds=time_resolution),
source=source,
fill_color={
'field': 'data',
'transform': mapper
},
legend=name)
cf.glyph.line_color = cf.glyph.fill_color
cf.glyph.line_color = None
contourlist.append(cf)
p1.plot_width = 1300
p1.plot_height = 1000
p1.sizing_mode = "scale_width"
# Colorbar:
color_bar = ColorBar(
color_mapper=mapper,
major_label_text_font_size="10pt",
ticker=BasicTicker(desired_num_ticks=int(len(rgb) / 2)),
formatter=PrintfTickFormatter(format="%d"),
label_standoff=6,
border_line_color=None,
location=(0, 0))
colorbarlist.append(color_bar)
def plotMap(infileName, plot_title, dot_size, col_type, bounds):
#1. get list of X, Y, P values from dataframe
dX, dY, dP = file2Dataframe('main/sold-price-data.txt')
#2 assign colors to each P value using colorRange
colors = colorRange(dP)
#3 prepare for the plot, some tools can be removed
TOOLS = 'hover,crosshair,pan,wheel_zoom,zoom_in,zoom_out,box_zoom,undo,redo,reset,tap,save,box_select,poly_select,lasso_select,'
p = figure(title=plot_title, tools=TOOLS)
p.title.align = "center"
p.grid.grid_line_color = 'black'
p.axis.axis_line_color = 'black'
p.axis.major_tick_line_color = 'black'
p.axis.major_label_text_font_size = '10pt'
# 4 draw dots with color coresponding to the P value in X, and Y coordinates.
p.circle(x=dX, y=dY, size=dot_size, color=colors, line_color=None)
# 5 mapping color bar color same with the plot and fix the ticker boundaries.
palette_c = ["yellow"] * 5 + ["pink"] * 20 + ["red"] * 50 + [
"green"
] * 20 + ["cyan"] * 5
#Then this palette could be used with a LinearColorMapper with low, high = (0, 100).
mapper = LinearColorMapper(palette=palette_c, low=dP.min(), high=dP.max())
ticker = FixedTicker(ticks=bounds)
color_bar = ColorBar(color_mapper=mapper,
major_label_text_font_size='5pt',
ticker=ticker,
formatter=PrintfTickFormatter(format='%d%%'),
label_standoff=6,
border_line_color=None,
location=(0, 0))
# 6 give title to color bar
color_bar_p = figure(title='price %',
title_location="right",
width=110,
toolbar_location=None,
outline_line_color=None)
color_bar_p.add_layout(color_bar, 'right')
color_bar_p.title.align = "center"
layout = row(p, color_bar_p)
# 6 output the component of the plot to web
return components(layout)
def draw_class_count_bar_chart(self):
def check_for_undefined_classes():
obs_class_nums = [c.replace('class', '') for c in source.data['index']]
for ocn in obs_class_nums:
if ocn not in self.las_classes:
self.las_classes.update({ocn: 'UNDEFINED'})
self.class_counts['Expected'] = np.zeros(self.class_counts.index.size)
self.class_counts['Unexpected'] = np.zeros(self.class_counts.index.size)
for i, class_name in enumerate(self.class_counts.index):
class_num = int(class_name.replace('class', ''))
if class_num in self.config.exp_cls_key:
self.class_counts['Expected'][i] = self.class_counts['counts'][i]
else:
self.class_counts['Unexpected'][i] = self.class_counts['counts'][i]
source = ColumnDataSource(self.class_counts)
check_for_undefined_classes()
source.data.update({'labels': ['{} (Class {})'.format(
self.las_classes[c.replace('class', '').zfill(2)],
c.replace('class', '').zfill(2)) for c in source.data['index']]})
p2 = figure(y_range=source.data['labels'], plot_width=400, plot_height=400,
title="Class Counts", tools="")
p2.min_border_top = 100
p2.outline_line_color = None
p2.toolbar.logo = None
p2.toolbar_location = None
p2.xaxis[0].formatter = PrintfTickFormatter(format='%4.1e')
p2_expected = p2.hbar(y='labels', right='Expected',
height=0.9, color='#0074D9', source=source)
p2_unexpected = p2.hbar(y='labels', right='Unexpected',
height=0.9, color='#FF851B', source=source)
max_count = max(source.data['counts'])
self.class_counts = self.class_counts.drop(['counts'], axis=1)
p2.x_range = Range1d(0, max_count + 0.1 * max_count)
p2.xgrid.grid_line_color = None
p2.xaxis.major_label_orientation = "vertical"
p2.xaxis.minor_tick_line_color = None
legend = Legend(items=[('EXPECTED', [p2_expected]), ('UNEXPECTED', [p2_unexpected])], location=(0, 10))
p2.add_layout(legend, 'above')
return p2
def create_color_mapper(palette: Sequence[str]) -> Tuple[LinearColorMapper, ColorBar]:
"""
Create a color mapper and a colorbar for heatmap
"""
mapper = LinearColorMapper(palette=palette, low=-1, high=1)
colorbar = ColorBar(
color_mapper=mapper,
major_label_text_font_size="8pt",
ticker=BasicTicker(),
formatter=PrintfTickFormatter(format="%.2f"),
label_standoff=6,
border_line_color=None,
location=(0, 0),
)
return mapper, colorbar
def colorbar(self, mapper, title='(Val - Ref)'):
formatter = PrintfTickFormatter(format="%4.2f")
color_bar = ColorBar(color_mapper=mapper,
label_standoff=16,
title=title,
major_label_text_font_style='bold',
padding=26,
major_label_text_align='left',
major_label_text_font_size="10pt",
formatter=formatter,
title_text_baseline='alphabetic',
location=(0, 0))
self.plot.add_layout(color_bar, 'right')
return self
def create_map_plot(self):
lat = 39.8282
lng = -98.5795
zoom = 6
xr = Range1d()
yr = Range1d()
x_range = xr
y_range = yr
#map_options = GMapOptions(lat=39.8282, lng=-98.5795, zoom=6)
map_options = GMapOptions(lat=lat, lng=lng, zoom=zoom)
#map_options = GMapOptions(lat=30.2861, lng=-97.7394, zoom=15)
plot = GMapPlot(x_range=x_range,
y_range=y_range,
map_options=map_options,
title="Hotel Review Explorer",
plot_width=680,
plot_height=600)
plot.map_options.map_type = "hybrid"
xaxis = LinearAxis(axis_label="lon",
major_tick_in=0,
formatter=NumeralTickFormatter(format="0.000"))
plot.add_layout(xaxis, 'below')
yaxis = LinearAxis(axis_label="lat",
major_tick_in=0,
formatter=PrintfTickFormatter(format="%.3f"))
plot.add_layout(yaxis, 'left')
#pan = PanTool()
#wheel_zoom = WheelZoomTool()
#box_select = BoxSelectTool()
#box_select.renderers = [rndr]
#tooltips = "@name"
#tooltips = "<span class='tooltip-text'>@names</span>\n<br>"
#tooltips += "<span class='tooltip-text'>Reviews: @num_reviews</span>"
#hover = HoverTool(tooltips="@num_reviews")
#hover = HoverTool(tooltips="@names")
#hover = HoverTool(tooltips=tooltips)
#tap = TapTool()
#plot.add_tools(pan, wheel_zoom, box_select, hover, tap)
#plot.add_tools(hover, tap)
#overlay = BoxSelectionOverlay(tool=box_select)
#plot.add_layout(overlay)
#plot.add_glyph(self.source, circle)
#county_xs, county_ys = get_some_counties()
#apatch = Patch(x=county_xs, y=county_ys, fill_color=['white']*len(county_xs))
#plot.add_glyph(apatch)
self.plot = plot
def plot_centairo_sentiment(data):
x = np.linspace(0, 100, 100)
source = ColumnDataSource(data=dict(x=x))
categories = [x for x in data.keys()]
values = [y for y in data.values()]
p = figure(y_range=categories,
plot_width=800,
plot_height=300,
x_range=(0, 101),
toolbar_location=None,
title="Centairo Sentiment and Emotion Results")
p.square(values, categories, size=20, color="#b3de69")
p.xgrid.grid_line_color = None
p.ygrid.grid_line_color = "#000000"
p.xaxis.formatter = PrintfTickFormatter(format="%d%%")
p.xaxis.axis_label = "Confidence Score"
return p
def colorce():
import colorcet as cc
from numpy import linspace
from scipy.stats.kde import gaussian_kde
from bokeh.io import output_file, show
from bokeh.models import ColumnDataSource, FixedTicker, PrintfTickFormatter
from bokeh.plotting import figure
from bokeh.sampledata.perceptions import probly
def ridge(category, data, scale=20):
return list(zip([category]*len(data), scale*data))
cats = list(reversed(probly.keys()))
palette = [cc.rainbow[i*15] for i in range(17)]
x = linspace(-20,110, 500)
source = ColumnDataSource(data=dict(x=x))
p = figure(y_range=cats, plot_width=900, x_range=(-5, 105), toolbar_location=None)
for i, cat in enumerate(reversed(cats)):
pdf = gaussian_kde(probly[cat])
y = ridge(cat, pdf(x))
source.add(y, cat)
p.patch('x', cat, color=palette[i], alpha=0.6, line_color="black", source=source)
p.outline_line_color = None
p.background_fill_color = "#efefef"
p.xaxis.ticker = FixedTicker(ticks=list(range(0, 101, 10)))
p.xaxis.formatter = PrintfTickFormatter(format="%d%%")
p.ygrid.grid_line_color = None
p.xgrid.grid_line_color = "#dddddd"
p.xgrid.ticker = p.xaxis.ticker
p.axis.minor_tick_line_color = None
p.axis.major_tick_line_color = None
p.axis.axis_line_color = None
p.y_range.range_padding = 0.12
return p
def plot_heatmap(params):
params = np.array(params)
trainer1 = params[..., 0, :]
trainer2 = params[..., 1, :]
N = 500
x = np.linspace(0, 1, N)
y = np.linspace(0, 1, N)
xx, yy = np.meshgrid(x, y)
d = 1.2 - xx**2 - yy**2
mapper = LinearColorMapper(palette='Viridis256', low=-0.5, high=1.2)
p = figure(x_range=(0, 1),
y_range=(0, 1),
tooltips=[("theta_x", "$x"), ("theta_y", "$y"),
("value", "@image")])
p.xaxis.axis_label = 'theta_x'
p.yaxis.axis_label = 'theta_y'
# must give a vector of image data for image parameter
p.image(image=[d], x=0, y=0, dw=1., dh=1., palette='Viridis256')
levels = np.linspace(-0.5, 1.2, 12)
color_bar = ColorBar(color_mapper=mapper,
major_label_text_font_size="8pt",
ticker=FixedTicker(ticks=levels),
formatter=PrintfTickFormatter(format='%.2f'),
label_standoff=6,
border_line_color=None,
location=(0, 0))
p.circle(trainer1[..., 0],
trainer1[..., 1],
size=2,
color="black",
alpha=0.5)
p.circle(trainer2[..., 0],
trainer2[..., 1],
size=2,
color="red",
alpha=0.5)
p.add_layout(color_bar, 'right')
# show(p)
return p
def Render_dashboard():
# df_temp = pd.read_csv('BitBucketDashboardInput.csv')
df1 =df_temp.groupby(['author']).size().reset_index().rename(index=str,columns={0:'commits'}).sort_values(by=['commits'], ascending=False).head(10)
p1 = figure(x_range=list(df1['author']), plot_width=1200, plot_height=400, title="Top Committers of the Month",
toolbar_location=None, tools="")
p1.vbar(x=list(df1['author']), top=df1['commits'], width=0.9, color='#2171b5')
p1.xgrid.grid_line_color = None
p1.y_range.start = 0
p1.axis.major_label_text_font_size = "10pt"
print(df_temp.columns)
df2 =df_temp.groupby(['author','month-date']).size().reset_index().rename(index=str,columns={0:'commits'})
source = ColumnDataSource(df2)
colors = list(reversed(['#084594', '#2171b5', '#4292c6', '#6baed6', '#9ecae1', '#c6dbef']))
mapper = LinearColorMapper(palette=colors, low=df2.commits.min(), high=df2.commits.max())
p2 = figure(plot_width=1200, plot_height=400, title="Author Commits by Day",
x_range=sorted(list(df2['month-date'].unique())), y_range=sorted(list(df2['author'].unique())),
toolbar_location=None, tools="", x_axis_location="above")
p2.rect(x="month-date", y="author", width=1, height=1, source=source, line_color=None, fill_color=transform('commits', mapper))
color_bar = ColorBar(color_mapper=mapper, location=(0, 0),
ticker=BasicTicker(desired_num_ticks=len(colors)),
formatter=PrintfTickFormatter(format="%d"))
p2.add_layout(color_bar, 'right')
p2.axis.axis_line_color = None
p2.axis.major_tick_line_color = None
p2.axis.major_label_text_font_size = "10pt"
p2.axis.major_label_standoff = 0
p2.xaxis.major_label_orientation = 1.0
l = layout([[p1],[p2]], sizing_mode='stretch_both')
html = file_html(l , CDN, "dashboard.html")
Html_file= open("templates/dashboard.html","w")
Html_file.write(html)
Html_file.close()
def _settings(self, title, provinces):
"""
This function creates the scatter plot of the PCA analysis results. It loads data per province to split the
colors. The line colors are used to indicate province, with the inside colors showing the relative amounts of
deaths per 100.000 people per province. Range of the plot was set to get space for the legend.
:param title: Data frame containing the principal components and the provinces
:param provinces: The provinces used in the analysis to be added to the legend after plotting.
:return: returns the PCA scatter plot.
"""
plot = figure(plot_width=700, plot_height=550,
title=title, x_range=(-7, 5))
plot.xaxis.axis_label = "Principal Component 1"
plot.yaxis.axis_label = "Principal Component 2"
listofsources = []
dict_of_provinces = {"Groningen": "PV20 ", "Friesland": "PV21 ", "Drenthe": "PV22 ", "Overijssel": "PV23 ",
"Flevoland": "PV24 ", "Gelderland": "PV25 ", "Utrecht": "PV26 ",
"Noord-Holland": "PV27 ",
"Zuid-Holland": "PV28 ", "Zeeland": "PV29 ", "Noord-Brabant": "PV30 ",
"Limburg": "PV31 "}
for province in provinces:
listofsources.append(ColumnDataSource(self.data_frame[self.data_frame['RegioS'] == dict_of_provinces[province]]))
mapper = LinearColorMapper(
palette='Greys256',
low=self.data_frame["TotaalAlleOnderliggendeDoodsoorzaken_1"].min(),
high=self.data_frame["TotaalAlleOnderliggendeDoodsoorzaken_1"].max()
)
size = 10
for source, count in zip(listofsources, range(0, len(listofsources))):
plot.circle(x='principal component 1', y='principal component 2',
size=size,
source=source, line_color=Category20b[12][count], line_width=3,
fill_color=transform('TotaalAlleOnderliggendeDoodsoorzaken_1', mapper), fill_alpha=1.0,
legend_label=provinces[count])
color_bar = ColorBar(color_mapper=mapper, location=(0, 0),
ticker=BasicTicker(desired_num_ticks=10),
formatter=PrintfTickFormatter(format="%d"))
plot.add_layout(color_bar, 'right')
plot.legend.location = 'top_left'
return plot
