def make_plot(dataframe=pd.DataFrame(),
highlight=[],
top=100,
minvalues=0.01,
stacked=True,
lgaxis=True,
errorbar=True,
showS1=True,
showST=True):
"""
Basic method to plot first and total order sensitivity indices.
This is the method to generate a Bokeh plot similar to the burtin example
template at the Bokeh website. For clarification, parameters refer to an
input being measured (Tmax, C, k2, etc.) and stats refer to the 1st or
total order sensitivity index.
Parameters
-----------
dataframe : pandas dataframe
Dataframe containing sensitivity analysis results to be
plotted.
highlight : lst, optional
List of strings indicating which parameter wedges will be
highlighted.
top : int, optional
Integer indicating the number of parameters to display
(highest sensitivity values) (after minimum cutoff is
applied).
minvalues : float, optional
Cutoff minimum for which parameters should be plotted.
Applies to total order only.
stacked : bool, optional
Boolean indicating in bars should be stacked for each
parameter (True) or unstacked (False).
lgaxis : bool, optional
Boolean indicating if log axis should be used (True) or if a
linear axis should be used (False).
errorbar : bool, optional
Boolean indicating if error bars are shown (True) or are
omitted (False).
showS1 : bool, optional
Boolean indicating whether 1st order sensitivity indices
will be plotted (True) or omitted (False).
showST : bool, optional
Boolean indicating whether total order sensitivity indices
will be plotted (True) or omitted (False).
**Note if showS1 and showST are both false, the plot will
default to showing ST data only instead of a blank plot**
Returns
--------
p : bokeh figure
A Bokeh figure of the data to be plotted
"""
df = dataframe
top = int(top)
# Initialize boolean checks and check dataframe structure
if (('S1' not in df) or ('ST' not in df) or ('Parameter' not in df)
or ('ST_conf' not in df) or ('S1_conf' not in df)):
raise Exception('Dataframe not formatted correctly')
# Remove rows which have values less than cutoff values
df = df[df['ST'] > minvalues]
df = df.dropna()
# Only keep top values indicated by variable top
df = df.sort_values('ST', ascending=False)
df = df.head(top)
df = df.reset_index(drop=True)
# Create arrays of colors and order labels for plotting
colors = ["#a1d99b", "#31a354", "#546775", "#225ea8"]
s1color = np.array(["#31a354"] * df.S1.size)
sTcolor = np.array(["#a1d99b"] * df.ST.size)
errs1color = np.array(["#225ea8"] * df.S1.size)
errsTcolor = np.array(["#546775"] * df.ST.size)
firstorder = np.array(["1st (S1)"] * df.S1.size)
totalorder = np.array(["Total (ST)"] * df.S1.size)
# Add column indicating which parameters should be highlighted
tohighlight = df.Parameter.isin(highlight)
df['highlighted'] = tohighlight
back_color = {
True: "#aeaeb8",
False: "#e6e6e6",
}
# Switch to bar chart if dataframe shrinks below 5 parameters
if len(df) <= 5:
if stacked is False:
data = {
'Sensitivity':
pd.Series.append(df.ST, df.S1),
'Parameter':
pd.Series.append(df.Parameter, df.Parameter),
'Order':
np.append(np.array(['ST'] * len(df)),
np.array(['S1'] * len(df))),
'Confidence':
pd.Series.append(df.ST_conf, df.S1_conf)
}
p = Bar(data,
values='Sensitivity',
label='Parameter',
group='Order',
legend='top_right',
color=["#31a354", "#a1d99b"],
ylabel='Sensitivity Indices')
else:
data = {
'Sensitivity':
pd.Series.append(df.S1, (df.ST - df.S1)),
'Parameter':
pd.Series.append(df.Parameter, df.Parameter),
'Order':
np.append(np.array(['S1'] * len(df)),
np.array(['ST'] * len(df))),
'Confidence':
pd.Series.append(df.S1_conf, df.ST_conf)
}
p = Bar(data,
values='Sensitivity',
label='Parameter',
color='Order',
legend='top_right',
stack='Order',
palette=["#31a354", "#a1d99b"],
ylabel='Sensitivity Indices')
return p
# Create Dictionary of colors
stat_color = OrderedDict()
error_color = OrderedDict()
for i in range(0, 2):
stat_color[i] = colors[i]
# Reset index of dataframe.
for i in range(2, 4):
error_color[i] = colors[i]
# Sizing parameters
width = 800
height = 800
inner_radius = 90
outer_radius = 300 - 10
# Determine wedge size based off number of parameters
big_angle = 2.0 * np.pi / (len(df) + 1)
# Determine division of wedges for plotting bars based on # stats plotted
# for stacked or unstacked bars
if stacked is False:
small_angle = big_angle / 5
else:
small_angle = big_angle / 3
# tools enabled for bokeh figure
plottools = "hover, wheel_zoom, save, reset, resize" # , tap"
# Initialize figure with tools, coloring, etc.
p = figure(plot_width=width,
plot_height=height,
title="",
x_axis_type=None,
y_axis_type=None,
x_range=(-350, 350),
y_range=(-350, 350),
min_border=0,
outline_line_color="#e6e6e6",
background_fill_color="#e6e6e6",
border_fill_color="#e6e6e6",
tools=plottools)
# Specify labels for hover tool
hover = p.select(dict(type=HoverTool))
hover.tooltips = [("Order", "@Order"), ("Parameter", "@Param"),
("Sensitivity", "@Sens"), ("Confidence", "@Conf")]
hover.point_policy = "follow_mouse"
p.xgrid.grid_line_color = None
p.ygrid.grid_line_color = None
# annular wedges divided into smaller sections for bars
# Angles for axial line placement
num_lines = np.arange(0, len(df) + 1, 1)
line_angles = np.pi / 2 - big_angle / 2 - num_lines * big_angle
# Angles for data placement
angles = np.pi / 2 - big_angle / 2 - df.index.to_series() * big_angle
# circular axes and labels
minlabel = min(round(np.log10(min(df.ST))), round(np.log10(min(df.S1))))
labels = np.power(10.0, np.arange(0, minlabel - 1, -1))
# Set max radial line to correspond to 1.1 * maximum value + error
maxvalST = max(df.ST + df.ST_conf)
maxvalS1 = max(df.S1 + df.S1_conf)
maxval = max(maxvalST, maxvalS1)
labels = np.append(labels, 0.0)
labels[0] = round(1.1 * maxval, 1)
# Determine if radial axis are log or linearly scaled
if lgaxis is True:
radii = (((np.log10(labels / labels[0])) + labels.size) *
(outer_radius - inner_radius) / labels.size + inner_radius)
radii[-1] = inner_radius
else:
labels = np.delete(labels, -2)
radii = (outer_radius -
inner_radius) * labels / labels[0] + inner_radius
# Convert sensitivity values to the plotted values
# Same conversion as for the labels above
# Also calculate the angle to which the bars are placed
# Add values to the dataframe for future reference
cols = np.array(['S1', 'ST'])
for statistic in range(0, 2):
if lgaxis is True:
radius_of_stat = (
((np.log10(df[cols[statistic]] / labels[0])) + labels.size) *
(outer_radius - inner_radius) / labels.size + inner_radius)
lower_of_stat = (((np.log10(
(df[cols[statistic]] - df[cols[statistic] + '_conf']) /
labels[0])) + labels.size) *
(outer_radius - inner_radius) / labels.size +
inner_radius)
higher_of_stat = (((np.log10(
(df[cols[statistic]] + df[cols[statistic] + '_conf']) /
labels[0])) + labels.size) *
(outer_radius - inner_radius) / labels.size +
inner_radius)
else:
radius_of_stat = ((outer_radius - inner_radius) *
df[cols[statistic]] / labels[0] + inner_radius)
lower_of_stat = (
(outer_radius - inner_radius) *
(df[cols[statistic]] - df[cols[statistic] + '_conf']) /
labels[0] + inner_radius)
higher_of_stat = ((outer_radius - inner_radius) * (
(df[cols[statistic]] + df[cols[statistic] + '_conf']) /
labels[0]) + inner_radius)
if stacked is False:
startA = -big_angle + angles + (2 * statistic + 1) * small_angle
stopA = -big_angle + angles + (2 * statistic + 2) * small_angle
df[cols[statistic] + '_err_angle'] = pd.Series(
(startA + stopA) / 2, index=df.index)
else:
startA = -big_angle + angles + (1) * small_angle
stopA = -big_angle + angles + (2) * small_angle
if statistic == 0:
df[cols[statistic] + '_err_angle'] = pd.Series(
(startA * 2 + stopA) / 3, index=df.index)
if statistic == 1:
df[cols[statistic] + '_err_angle'] = pd.Series(
(startA + stopA * 2) / 3, index=df.index)
df[cols[statistic] + 'radial'] = pd.Series(radius_of_stat,
index=df.index)
df[cols[statistic] + 'upper'] = pd.Series(higher_of_stat,
index=df.index)
df[cols[statistic] + 'lower'] = pd.Series(lower_of_stat,
index=df.index)
df[cols[statistic] + '_start_angle'] = pd.Series(startA,
index=df.index)
df[cols[statistic] + '_stop_angle'] = pd.Series(stopA, index=df.index)
# df[cols[statistic]+'_err_angle'] = pd.Series((startA+stopA)/2,
# index=df.index)
inner_rad = np.ones_like(angles) * inner_radius
df[cols[statistic] + 'lower'] = df[cols[statistic] +
'lower'].fillna(90)
# Store plotted values into dictionary to be add glyphs
pdata = pd.DataFrame({
'x':
np.append(np.zeros_like(inner_rad), np.zeros_like(inner_rad)),
'y':
np.append(np.zeros_like(inner_rad), np.zeros_like(inner_rad)),
'ymin':
np.append(inner_rad, inner_rad),
'ymax':
pd.Series.append(df[cols[1] + 'radial'],
df[cols[0] + 'radial']).reset_index(drop=True),
'starts':
pd.Series.append(df[cols[1] + '_start_angle'],
df[cols[0] + '_start_angle']).reset_index(drop=True),
'stops':
pd.Series.append(df[cols[1] + '_stop_angle'],
df[cols[0] + '_stop_angle']).reset_index(drop=True),
'Param':
pd.Series.append(df.Parameter, df.Parameter).reset_index(drop=True),
'Colors':
np.append(sTcolor, s1color),
'Error Colors':
np.append(errsTcolor, errs1color),
'Conf':
pd.Series.append(df.ST_conf, df.S1_conf).reset_index(drop=True),
'Order':
np.append(totalorder, firstorder),
'Sens':
pd.Series.append(df.ST, df.S1).reset_index(drop=True),
'Lower':
pd.Series.append(df.STlower, df.S1lower).reset_index(drop=True),
'Upper':
pd.Series.append(
df.STupper,
df.S1upper,
).reset_index(drop=True),
'Err_Angle':
pd.Series.append(
df.ST_err_angle,
df.S1_err_angle,
).reset_index(drop=True)
})
# removed S1 or ST values if indicated by input
if showS1 is False:
pdata = pdata.head(len(df))
if showST is False:
pdata = pdata.tail(len(df))
# convert dataframe to ColumnDataSource for glyphs
pdata_s = ColumnDataSource(pdata)
colors = [back_color[highl] for highl in df.highlighted]
p.annular_wedge(
0,
0,
inner_radius,
outer_radius,
-big_angle + angles,
angles,
color=colors,
)
# Adding axis lines and labels
p.circle(0, 0, radius=radii, fill_color=None, line_color="white")
p.text(0,
radii[:], [str(r) for r in labels[:]],
text_font_size="8pt",
text_align="center",
text_baseline="middle")
# Specify that the plotted bars are the only thing to activate hovertool
hoverable = p.annular_wedge(x='x',
y='y',
inner_radius='ymin',
outer_radius='ymax',
start_angle='starts',
end_angle='stops',
color='Colors',
source=pdata_s)
hover.renderers = [hoverable]
# Add error bars
if errorbar is True:
p.annular_wedge(0,
0,
pdata['Lower'],
pdata['Upper'],
pdata['Err_Angle'],
pdata['Err_Angle'],
color=pdata['Error Colors'],
line_width=1.0)
p.annular_wedge(0,
0,
pdata['Lower'],
pdata['Lower'],
pdata['starts'],
pdata['stops'],
color=pdata['Error Colors'],
line_width=2.0)
p.annular_wedge(0,
0,
pdata['Upper'],
pdata['Upper'],
pdata['starts'],
pdata['stops'],
color=pdata['Error Colors'],
line_width=2.0)
# Placement of parameter labels
xr = (radii[0] * 1.1) * np.cos(np.array(-big_angle / 2 + angles))
yr = (radii[0] * 1.1) * np.sin(np.array(-big_angle / 2 + angles))
label_angle = np.array(-big_angle / 2 + angles)
label_angle[label_angle < -np.pi / 2] += np.pi
# Placing Labels and Legend
legend_text = ['ST', 'ST Conf', 'S1', 'S1 Conf']
p.text(xr,
yr,
df.Parameter,
angle=label_angle,
text_font_size="9pt",
text_align="center",
text_baseline="middle")
p.rect([-40, -40], [30, -10],
width=30,
height=13,
color=list(stat_color.values()))
p.rect([-40, -40], [10, -30],
width=30,
height=1,
color=list(error_color.values()))
p.text([-15, -15, -15, -15], [30, 10, -10, -30],
text=legend_text,
text_font_size="9pt",
text_align="left",
text_baseline="middle")
p.annular_wedge(0,
0,
inner_radius - 10,
outer_radius + 10,
-big_angle + line_angles,
-big_angle + line_angles,
color="#999999")
return p
def make_plot(dataframe=pd.DataFrame(), highlight=[],
top=100, minvalues=0.01, stacked=True, lgaxis=True,
errorbar=True, showS1=True, showST=True):
"""
Basic method to plot first and total order sensitivity indices.
This is the method to generate a Bokeh plot similar to the burtin example
template at the Bokeh website. For clarification, parameters refer to an
input being measured (Tmax, C, k2, etc.) and stats refer to the 1st or
total order sensitivity index.
Parameters
-----------
dataframe : pandas dataframe
Dataframe containing sensitivity analysis results to be
plotted.
highlight : lst, optional
List of strings indicating which parameter wedges will be
highlighted.
top : int, optional
Integer indicating the number of parameters to display
(highest sensitivity values) (after minimum cutoff is
applied).
minvalues : float, optional
Cutoff minimum for which parameters should be plotted.
Applies to total order only.
stacked : bool, optional
Boolean indicating in bars should be stacked for each
parameter (True) or unstacked (False).
lgaxis : bool, optional
Boolean indicating if log axis should be used (True) or if a
linear axis should be used (False).
errorbar : bool, optional
Boolean indicating if error bars are shown (True) or are
omitted (False).
showS1 : bool, optional
Boolean indicating whether 1st order sensitivity indices
will be plotted (True) or omitted (False).
showST : bool, optional
Boolean indicating whether total order sensitivity indices
will be plotted (True) or omitted (False).
**Note if showS1 and showST are both false, the plot will
default to showing ST data only instead of a blank plot**
Returns
--------
p : bokeh figure
A Bokeh figure of the data to be plotted
"""
df = dataframe
top = int(top)
# Initialize boolean checks and check dataframe structure
if (('S1' not in df) or ('ST' not in df) or ('Parameter' not in df) or
('ST_conf' not in df) or ('S1_conf' not in df)):
raise Exception('Dataframe not formatted correctly')
# Remove rows which have values less than cutoff values
df = df[df['ST'] > minvalues]
df = df.dropna()
# Only keep top values indicated by variable top
df = df.sort_values('ST', ascending=False)
df = df.head(top)
df = df.reset_index(drop=True)
# Create arrays of colors and order labels for plotting
colors = ["#a1d99b", "#31a354", "#546775", "#225ea8"]
s1color = np.array(["#31a354"]*df.S1.size)
sTcolor = np.array(["#a1d99b"]*df.ST.size)
errs1color = np.array(["#225ea8"]*df.S1.size)
errsTcolor = np.array(["#546775"]*df.ST.size)
firstorder = np.array(["1st (S1)"]*df.S1.size)
totalorder = np.array(["Total (ST)"]*df.S1.size)
# Add column indicating which parameters should be highlighted
tohighlight = df.Parameter.isin(highlight)
df['highlighted'] = tohighlight
back_color = {
True: "#aeaeb8",
False: "#e6e6e6",
}
# Switch to bar chart if dataframe shrinks below 5 parameters
if len(df) <= 5:
if stacked is False:
data = {
'Sensitivity': pd.Series.append(df.ST, df.S1),
'Parameter': pd.Series.append(df.Parameter, df.Parameter),
'Order': np.append(np.array(['ST']*len(df)),
np.array(['S1']*len(df))),
'Confidence': pd.Series.append(df.ST_conf,
df.S1_conf)
}
p = Bar(data, values='Sensitivity', label='Parameter',
group='Order', legend='top_right',
color=["#31a354", "#a1d99b"], ylabel='Sensitivity Indices')
else:
data = {
'Sensitivity': pd.Series.append(df.S1, (df.ST-df.S1)),
'Parameter': pd.Series.append(df.Parameter, df.Parameter),
'Order': np.append(np.array(['S1']*len(df)),
np.array(['ST']*len(df))),
'Confidence': pd.Series.append(df.S1_conf,
df.ST_conf)
}
p = Bar(data, values='Sensitivity', label='Parameter',
color='Order', legend='top_right',
stack='Order', palette=["#31a354", "#a1d99b"],
ylabel='Sensitivity Indices')
return p
# Create Dictionary of colors
stat_color = OrderedDict()
error_color = OrderedDict()
for i in range(0, 2):
stat_color[i] = colors[i]
# Reset index of dataframe.
for i in range(2, 4):
error_color[i] = colors[i]
# Sizing parameters
width = 800
height = 800
inner_radius = 90
outer_radius = 300 - 10
# Determine wedge size based off number of parameters
big_angle = 2.0 * np.pi / (len(df)+1)
# Determine division of wedges for plotting bars based on # stats plotted
# for stacked or unstacked bars
if stacked is False:
small_angle = big_angle / 5
else:
small_angle = big_angle / 3
# tools enabled for bokeh figure
plottools = "hover, wheel_zoom, save, reset, resize" # , tap"
# Initialize figure with tools, coloring, etc.
p = figure(plot_width=width, plot_height=height, title="",
x_axis_type=None, y_axis_type=None,
x_range=(-350, 350), y_range=(-350, 350),
min_border=0, outline_line_color="#e6e6e6",
background_fill_color="#e6e6e6", border_fill_color="#e6e6e6",
tools=plottools)
# Specify labels for hover tool
hover = p.select(dict(type=HoverTool))
hover.tooltips = [("Order", "@Order"), ("Parameter", "@Param"),
("Sensitivity", "@Sens"), ("Confidence", "@Conf")]
hover.point_policy = "follow_mouse"
p.xgrid.grid_line_color = None
p.ygrid.grid_line_color = None
# annular wedges divided into smaller sections for bars
# Angles for axial line placement
num_lines = np.arange(0, len(df)+1, 1)
line_angles = np.pi/2 - big_angle/2 - num_lines*big_angle
# Angles for data placement
angles = np.pi/2 - big_angle/2 - df.index.to_series()*big_angle
# circular axes and labels
minlabel = min(round(np.log10(min(df.ST))), round(np.log10(min(df.S1))))
labels = np.power(10.0, np.arange(0, minlabel-1, -1))
# Set max radial line to correspond to 1.1 * maximum value + error
maxvalST = max(df.ST+df.ST_conf)
maxvalS1 = max(df.S1+df.S1_conf)
maxval = max(maxvalST, maxvalS1)
labels = np.append(labels, 0.0)
labels[0] = round(1.1*maxval, 1)
# Determine if radial axis are log or linearly scaled
if lgaxis is True:
radii = (((np.log10(labels / labels[0])) +
labels.size) * (outer_radius - inner_radius) /
labels.size + inner_radius)
radii[-1] = inner_radius
else:
labels = np.delete(labels, -2)
radii = (outer_radius - inner_radius)*labels/labels[0] + inner_radius
# Convert sensitivity values to the plotted values
# Same conversion as for the labels above
# Also calculate the angle to which the bars are placed
# Add values to the dataframe for future reference
cols = np.array(['S1', 'ST'])
for statistic in range(0, 2):
if lgaxis is True:
radius_of_stat = (((np.log10(df[cols[statistic]] / labels[0])) +
labels.size) * (outer_radius - inner_radius) /
labels.size + inner_radius)
lower_of_stat = (((np.log10((df[cols[statistic]] -
df[cols[statistic]+'_conf']) / labels[0])) +
labels.size) * (outer_radius - inner_radius) /
labels.size + inner_radius)
higher_of_stat = (((np.log10((df[cols[statistic]] +
df[cols[statistic]+'_conf']) / labels[0])) +
labels.size) * (outer_radius - inner_radius) /
labels.size + inner_radius)
else:
radius_of_stat = ((outer_radius - inner_radius) *
df[cols[statistic]]/labels[0] + inner_radius)
lower_of_stat = ((outer_radius - inner_radius) *
(df[cols[statistic]] -
df[cols[statistic]+'_conf'])/labels[0] +
inner_radius)
higher_of_stat = ((outer_radius - inner_radius) *
((df[cols[statistic]] +
df[cols[statistic]+'_conf'])/labels[0]) +
inner_radius)
if stacked is False:
startA = -big_angle + angles + (2*statistic + 1)*small_angle
stopA = -big_angle + angles + (2*statistic + 2)*small_angle
df[cols[statistic]+'_err_angle'] = pd.Series((startA+stopA)/2,
index=df.index)
else:
startA = -big_angle + angles + (1)*small_angle
stopA = -big_angle + angles + (2)*small_angle
if statistic == 0:
df[cols[statistic]+'_err_angle'] = pd.Series((startA*2 +
stopA)/3,
index=df.index)
if statistic == 1:
df[cols[statistic]+'_err_angle'] = pd.Series((startA +
stopA*2)/3,
index=df.index)
df[cols[statistic]+'radial'] = pd.Series(radius_of_stat,
index=df.index)
df[cols[statistic]+'upper'] = pd.Series(higher_of_stat,
index=df.index)
df[cols[statistic]+'lower'] = pd.Series(lower_of_stat,
index=df.index)
df[cols[statistic]+'_start_angle'] = pd.Series(startA,
index=df.index)
df[cols[statistic]+'_stop_angle'] = pd.Series(stopA,
index=df.index)
# df[cols[statistic]+'_err_angle'] = pd.Series((startA+stopA)/2,
# index=df.index)
inner_rad = np.ones_like(angles)*inner_radius
df[cols[statistic]+'lower'] = df[cols[statistic]+'lower'].fillna(90)
# Store plotted values into dictionary to be add glyphs
pdata = pd.DataFrame({
'x': np.append(np.zeros_like(inner_rad),
np.zeros_like(inner_rad)),
'y': np.append(np.zeros_like(inner_rad),
np.zeros_like(inner_rad)),
'ymin': np.append(inner_rad, inner_rad),
'ymax': pd.Series.append(df[cols[1]+'radial'],
df[cols[0]+'radial']
).reset_index(drop=True),
'starts': pd.Series.append(df[cols[1] +
'_start_angle'],
df[cols[0] +
'_start_angle']
).reset_index(drop=True),
'stops': pd.Series.append(df[cols[1] +
'_stop_angle'],
df[cols[0] +
'_stop_angle']
).reset_index(drop=True),
'Param': pd.Series.append(df.Parameter,
df.Parameter
).reset_index(drop=True),
'Colors': np.append(sTcolor, s1color),
'Error Colors': np.append(errsTcolor, errs1color),
'Conf': pd.Series.append(df.ST_conf,
df.S1_conf
).reset_index(drop=True),
'Order': np.append(totalorder, firstorder),
'Sens': pd.Series.append(df.ST, df.S1
).reset_index(drop=True),
'Lower': pd.Series.append(df.STlower,
df.S1lower
).reset_index(drop=True),
'Upper': pd.Series.append(df.STupper,
df.S1upper,
).reset_index(drop=True),
'Err_Angle': pd.Series.append(df.ST_err_angle,
df.S1_err_angle,
).reset_index(drop=True)
})
# removed S1 or ST values if indicated by input
if showS1 is False:
pdata = pdata.head(len(df))
if showST is False:
pdata = pdata.tail(len(df))
# convert dataframe to ColumnDataSource for glyphs
pdata_s = ColumnDataSource(pdata)
colors = [back_color[highl] for highl in df.highlighted]
p.annular_wedge(
0, 0, inner_radius, outer_radius, -big_angle+angles,
angles, color=colors,
)
# Adding axis lines and labels
p.circle(0, 0, radius=radii, fill_color=None, line_color="white")
p.text(0, radii[:], [str(r) for r in labels[:]],
text_font_size="8pt", text_align="center", text_baseline="middle")
# Specify that the plotted bars are the only thing to activate hovertool
hoverable = p.annular_wedge(x='x', y='y', inner_radius='ymin',
outer_radius='ymax',
start_angle='starts',
end_angle='stops',
color='Colors',
source=pdata_s
)
hover.renderers = [hoverable]
# Add error bars
if errorbar is True:
p.annular_wedge(0, 0, pdata['Lower'], pdata['Upper'],
pdata['Err_Angle'],
pdata['Err_Angle'],
color=pdata['Error Colors'], line_width=1.0)
p.annular_wedge(0, 0, pdata['Lower'], pdata['Lower'],
pdata['starts'],
pdata['stops'],
color=pdata['Error Colors'], line_width=2.0)
p.annular_wedge(0, 0, pdata['Upper'], pdata['Upper'],
pdata['starts'],
pdata['stops'],
color=pdata['Error Colors'], line_width=2.0)
# Placement of parameter labels
xr = (radii[0]*1.1)*np.cos(np.array(-big_angle/2 + angles))
yr = (radii[0]*1.1)*np.sin(np.array(-big_angle/2 + angles))
label_angle = np.array(-big_angle/2+angles)
label_angle[label_angle < -np.pi/2] += np.pi
# Placing Labels and Legend
legend_text = ['ST', 'ST Conf', 'S1', 'S1 Conf']
p.text(xr, yr, df.Parameter, angle=label_angle,
text_font_size="9pt", text_align="center", text_baseline="middle")
p.rect([-40, -40], [30, -10], width=30, height=13,
color=list(stat_color.values()))
p.rect([-40, -40], [10, -30], width=30, height=1,
color=list(error_color.values()))
p.text([-15, -15, -15, -15], [30, 10, -10, -30], text=legend_text,
text_font_size="9pt", text_align="left", text_baseline="middle")
p.annular_wedge(0, 0, inner_radius-10, outer_radius+10,
-big_angle+line_angles, -big_angle+line_angles,
color="#999999")
return p
mp = Bar(subjects, label='DistrictCode', values='CompositeIndex', agg='mean', group = 'shp3_cashSavings',
title="Median CompositeIndex by District Code grouped by Cash Savings", legend='top_right')
show(mp)
# In[60]:
from bokeh.plotting import figure, show
mp = figure(title="CompositeIndex and CompositeIndicator",
x_axis_label='CompositeIndex',
y_axis_label='CompositeIndicator')
mp.circle(subjects['CompositeIndex'], subjects['CompositeIndicator'], size = 5, alpha = 0.8,
fill_color="red", line_color="navy", line_width=3)
show(mp)
# In[36]:
cIdic = subjects.loc[:,"CompositeIndicator"].values
print(len(cIdic))
# In[37]:
cIdex = subjects.loc[:,"CompositeIndex"].values
print(len(cIdex))