def map_face2color(face, colormap, scale, vmin, vmax):
"""
Normalize facecolor values by vmin/vmax and return rgb-color strings
This function takes a tuple color along with a colormap and a minimum
(vmin) and maximum (vmax) range of possible mean distances for the
given parametrized surface. It returns an rgb color based on the mean
distance between vmin and vmax
"""
if vmin >= vmax:
raise exceptions.PlotlyError("Incorrect relation between vmin "
"and vmax. The vmin value cannot be "
"bigger than or equal to the value "
"of vmax.")
if len(colormap) == 1:
# color each triangle face with the same color in colormap
face_color = colormap[0]
face_color = colors.convert_to_RGB_255(face_color)
face_color = colors.label_rgb(face_color)
return face_color
if face == vmax:
# pick last color in colormap
face_color = colormap[-1]
face_color = colors.convert_to_RGB_255(face_color)
face_color = colors.label_rgb(face_color)
return face_color
else:
if scale is None:
# find the normalized distance t of a triangle face between
# vmin and vmax where the distance is between 0 and 1
t = (face - vmin) / float((vmax - vmin))
low_color_index = int(t / (1. / (len(colormap) - 1)))
face_color = colors.find_intermediate_color(
colormap[low_color_index], colormap[low_color_index + 1],
t * (len(colormap) - 1) - low_color_index)
face_color = colors.convert_to_RGB_255(face_color)
face_color = colors.label_rgb(face_color)
else:
# find the face color for a non-linearly interpolated scale
t = (face - vmin) / float((vmax - vmin))
low_color_index = 0
for k in range(len(scale) - 1):
if scale[k] <= t < scale[k + 1]:
break
low_color_index += 1
low_scale_val = scale[low_color_index]
high_scale_val = scale[low_color_index + 1]
face_color = colors.find_intermediate_color(
colormap[low_color_index], colormap[low_color_index + 1],
(t - low_scale_val) / (high_scale_val - low_scale_val))
face_color = colors.convert_to_RGB_255(face_color)
face_color = colors.label_rgb(face_color)
return face_color
def get_color(colorscale, nu, ex_=()):
if len(ex_) == 2:
mi, ma = ex_
if mi == nu == ma:
nu = 0
else:
nu = _scale(mi, nu, ma)
for ie in range(len(colorscale) - 1):
fr1, co1 = colorscale[ie]
fr2, co2 = colorscale[ie + 1]
if fr1 <= nu <= fr2:
return find_intermediate_color(
*convert_colors_to_same_type([co1, co2])[0],
_scale(fr1, nu, fr2),
colortype="rgb",
)
def _colors(ncontours, colormap=None):
"""
Return a list of ``ncontours`` colors from the ``colormap`` colorscale.
"""
if colormap in clrs.PLOTLY_SCALES.keys():
cmap = clrs.PLOTLY_SCALES[colormap]
else:
raise exceptions.PlotlyError(
"Colorscale must be a valid Plotly Colorscale."
"The available colorscale names are {}".format(
clrs.PLOTLY_SCALES.keys()))
values = np.linspace(0, 1, ncontours)
vals_cmap = np.array([pair[0] for pair in cmap])
cols = np.array([pair[1] for pair in cmap])
inds = np.searchsorted(vals_cmap, values)
if "#" in cols[0]: # for Viridis
cols = [clrs.label_rgb(clrs.hex_to_rgb(col)) for col in cols]
colors = [cols[0]]
for ind, val in zip(inds[1:], values[1:]):
val1, val2 = vals_cmap[ind - 1], vals_cmap[ind]
interm = (val - val1) / (val2 - val1)
col = clrs.find_intermediate_color(cols[ind - 1],
cols[ind],
interm,
colortype="rgb")
colors.append(col)
return colors
def _colors(ncontours, colormap=None):
"""
Return a list of ``ncontours`` colors from the ``colormap`` colorscale.
"""
if colormap in clrs.PLOTLY_SCALES.keys():
cmap = clrs.PLOTLY_SCALES[colormap]
else:
raise exceptions.PlotlyError(
"Colorscale must be a valid Plotly Colorscale."
"The available colorscale names are {}".format(
clrs.PLOTLY_SCALES.keys()))
values = np.linspace(0, 1, ncontours)
vals_cmap = np.array([pair[0] for pair in cmap])
cols = np.array([pair[1] for pair in cmap])
inds = np.searchsorted(vals_cmap, values)
if '#' in cols[0]: # for Viridis
cols = [clrs.label_rgb(clrs.hex_to_rgb(col)) for col in cols]
colors = [cols[0]]
for ind, val in zip(inds[1:], values[1:]):
val1, val2 = vals_cmap[ind - 1], vals_cmap[ind]
interm = (val - val1) / (val2 - val1)
col = clrs.find_intermediate_color(cols[ind - 1],
cols[ind], interm,
colortype='rgb')
colors.append(col)
return colors
def get_colors_from_colorscale(color_candidates: dict):
"""Assign a color to each dict entry"""
color_scale = PLOTLY_SCALES['Rainbow']
color_scale_floats = [i[0] for i in color_scale]
color_scale_rgbs = [i[1] for i in color_scale]
color_scale = {k: v for k, v in PLOTLY_SCALES['Rainbow']}
float_rgb_dict = dict()
for k, v in color_candidates.items():
if v in color_scale_floats:
float_rgb_dict[k] = color_scale[v]
else:
for i, s_v in enumerate(color_scale_floats):
if v < s_v:
float_rgb_dict[k] = find_intermediate_color(
color_scale_rgbs[i - 1], color_scale_rgbs[i], v, 'rgb')
break
return float_rgb_dict
def get_colors_from_name(colormapname, numbervalues, reverse=False):
plotly_colors, plotly_scale = colors.convert_colors_to_same_type(colormapname)
if reverse:
plotly_colors.reverse()
plotly_scale = np.array(plotly_scale)
plotly_colors = np.array(list(map(literal_eval, [color[3:] for color in plotly_colors])))/255.0
vmin = 0
vmax = numbervalues
values = np.arange(0, vmax)
v = (values - vmin)/(vmax - vmin)
closest_indices = [sorted(np.argsort(np.abs(plotly_scale - i))[0:2]) for i in v]
newcolors = [colors.find_intermediate_color(plotly_colors[indices[0]], plotly_colors[indices[1]], value)
for indices, value in zip(closest_indices, v)]
newcolors = [colors.label_rgb(colors.convert_to_RGB_255(i)) for i in newcolors]
return newcolors
def get_color_at(colorscale: ColorScaleTy, offset: float) -> str:
"""
Plotly continuous colorscales assign colors to the range [0, 1]. This
function computes the intermediate color for any value in that range.
Args:
colorscale:
a plotly continuous colorscale defined with RGB string colors
offset: value in the range [0, 1]
Returns:
color in rgb string format
"""
if len(colorscale) < 1:
raise ValueError("colorscale must have at least one color")
if offset <= 0 or len(colorscale) == 1:
return str(colors.convert_colors_to_same_type(colorscale[0][1])[0][0])
if offset >= 1:
return str(colors.convert_colors_to_same_type(colorscale[-1][1])[0][0])
low_color = high_color = ""
for cutoff, color in colorscale:
if offset > float(cutoff):
low_cutoff, low_color = float(cutoff), str(color)
else:
high_cutoff, high_color = float(cutoff), str(color)
break
low_color = colors.convert_colors_to_same_type(low_color)[0][0]
high_color = colors.convert_colors_to_same_type(high_color)[0][0]
# noinspection PyUnboundLocalVariable
return str(
colors.find_intermediate_color(
lowcolor=low_color,
highcolor=high_color,
intermed=((offset - low_cutoff) / (high_cutoff - low_cutoff)),
colortype="rgb"
)
)
def get_color(colorscale: ColorScaleType, midpoint: float) -> str:
"""Given a colorscale, it interpolates the expected color at a given
midpoint, on a scale from 0 to 1."""
if 0 > midpoint > 1:
raise ValueError(
f"The 'midpoint' should be a float value between 0 and 1, not {midpoint}."
)
scale = [s for s, _ in colorscale]
colors = [_any_to_rgb(c) for _, c in colorscale]
del colorscale
if midpoint in scale:
return colors[scale.index(midpoint)]
ceil = min(filter(lambda s: s > midpoint, scale))
floor = max(filter(lambda s: s < midpoint, scale))
midpoint_normalised = normalise(midpoint, min_=floor, max_=ceil)
color: str = find_intermediate_color(
lowcolor=colors[scale.index(floor)],
highcolor=colors[scale.index(ceil)],
intermed=midpoint_normalised,
colortype="rgb",
)
return color
def create_choropleth(fips,
values,
scope=["usa"],
binning_endpoints=None,
colorscale=None,
order=None,
simplify_county=0.02,
simplify_state=0.02,
asp=None,
show_hover=True,
show_state_data=True,
state_outline=None,
county_outline=None,
centroid_marker=None,
round_legend_values=False,
exponent_format=False,
legend_title="",
**layout_options):
"""
Returns figure for county choropleth. Uses data from package_data.
:param (list) fips: list of FIPS values which correspond to the con
catination of state and county ids. An example is '01001'.
:param (list) values: list of numbers/strings which correspond to the
fips list. These are the values that will determine how the counties
are colored.
:param (list) scope: list of states and/or states abbreviations. Fits
all states in the camera tightly. Selecting ['usa'] is the equivalent
of appending all 50 states into your scope list. Selecting only 'usa'
does not include 'Alaska', 'Puerto Rico', 'American Samoa',
'Commonwealth of the Northern Mariana Islands', 'Guam',
'United States Virgin Islands'. These must be added manually to the
list.
Default = ['usa']
:param (list) binning_endpoints: ascending numbers which implicitly define
real number intervals which are used as bins. The colorscale used must
have the same number of colors as the number of bins and this will
result in a categorical colormap.
:param (list) colorscale: a list of colors with length equal to the
number of categories of colors. The length must match either all
unique numbers in the 'values' list or if endpoints is being used, the
number of categories created by the endpoints.\n
For example, if binning_endpoints = [4, 6, 8], then there are 4 bins:
[-inf, 4), [4, 6), [6, 8), [8, inf)
:param (list) order: a list of the unique categories (numbers/bins) in any
desired order. This is helpful if you want to order string values to
a chosen colorscale.
:param (float) simplify_county: determines the simplification factor
for the counties. The larger the number, the fewer vertices and edges
each polygon has. See
http://toblerity.org/shapely/manual.html#object.simplify for more
information.
Default = 0.02
:param (float) simplify_state: simplifies the state outline polygon.
See http://toblerity.org/shapely/manual.html#object.simplify for more
information.
Default = 0.02
:param (float) asp: the width-to-height aspect ratio for the camera.
Default = 2.5
:param (bool) show_hover: show county hover and centroid info
:param (bool) show_state_data: reveals state boundary lines
:param (dict) state_outline: dict of attributes of the state outline
including width and color. See
https://plot.ly/python/reference/#scatter-marker-line for all valid
params
:param (dict) county_outline: dict of attributes of the county outline
including width and color. See
https://plot.ly/python/reference/#scatter-marker-line for all valid
params
:param (dict) centroid_marker: dict of attributes of the centroid marker.
The centroid markers are invisible by default and appear visible on
selection. See https://plot.ly/python/reference/#scatter-marker for
all valid params
:param (bool) round_legend_values: automatically round the numbers that
appear in the legend to the nearest integer.
Default = False
:param (bool) exponent_format: if set to True, puts numbers in the K, M,
B number format. For example 4000.0 becomes 4.0K
Default = False
:param (str) legend_title: title that appears above the legend
:param **layout_options: a **kwargs argument for all layout parameters
Example 1: Florida
```
import plotly.plotly as py
import plotly.figure_factory as ff
import numpy as np
import pandas as pd
df_sample = pd.read_csv(
'https://raw.githubusercontent.com/plotly/datasets/master/minoritymajority.csv'
)
df_sample_r = df_sample[df_sample['STNAME'] == 'Florida']
values = df_sample_r['TOT_POP'].tolist()
fips = df_sample_r['FIPS'].tolist()
binning_endpoints = list(np.mgrid[min(values):max(values):4j])
colorscale = ["#030512","#1d1d3b","#323268","#3d4b94","#3e6ab0",
"#4989bc","#60a7c7","#85c5d3","#b7e0e4","#eafcfd"]
fig = ff.create_choropleth(
fips=fips, values=values, scope=['Florida'], show_state_data=True,
colorscale=colorscale, binning_endpoints=binning_endpoints,
round_legend_values=True, plot_bgcolor='rgb(229,229,229)',
paper_bgcolor='rgb(229,229,229)', legend_title='Florida Population',
county_outline={'color': 'rgb(255,255,255)', 'width': 0.5},
exponent_format=True,
)
py.iplot(fig, filename='choropleth_florida')
```
Example 2: New England
```
import plotly.plotly as py
import plotly.figure_factory as ff
import pandas as pd
NE_states = ['Connecticut', 'Maine', 'Massachusetts',
'New Hampshire', 'Rhode Island']
df_sample = pd.read_csv(
'https://raw.githubusercontent.com/plotly/datasets/master/minoritymajority.csv'
)
df_sample_r = df_sample[df_sample['STNAME'].isin(NE_states)]
colorscale = ['rgb(68.0, 1.0, 84.0)',
'rgb(66.0, 64.0, 134.0)',
'rgb(38.0, 130.0, 142.0)',
'rgb(63.0, 188.0, 115.0)',
'rgb(216.0, 226.0, 25.0)']
values = df_sample_r['TOT_POP'].tolist()
fips = df_sample_r['FIPS'].tolist()
fig = ff.create_choropleth(
fips=fips, values=values, scope=NE_states, show_state_data=True
)
py.iplot(fig, filename='choropleth_new_england')
```
Example 3: California and Surrounding States
```
import plotly.plotly as py
import plotly.figure_factory as ff
import pandas as pd
df_sample = pd.read_csv(
'https://raw.githubusercontent.com/plotly/datasets/master/minoritymajority.csv'
)
df_sample_r = df_sample[df_sample['STNAME'] == 'California']
values = df_sample_r['TOT_POP'].tolist()
fips = df_sample_r['FIPS'].tolist()
colorscale = [
'rgb(193, 193, 193)',
'rgb(239,239,239)',
'rgb(195, 196, 222)',
'rgb(144,148,194)',
'rgb(101,104,168)',
'rgb(65, 53, 132)'
]
fig = ff.create_choropleth(
fips=fips, values=values, colorscale=colorscale,
scope=['CA', 'AZ', 'Nevada', 'Oregon', ' Idaho'],
binning_endpoints=[14348, 63983, 134827, 426762, 2081313],
county_outline={'color': 'rgb(255,255,255)', 'width': 0.5},
legend_title='California Counties',
title='California and Nearby States'
)
py.iplot(fig, filename='choropleth_california_and_surr_states_outlines')
```
Example 4: USA
```
import plotly.plotly as py
import plotly.figure_factory as ff
import numpy as np
import pandas as pd
df_sample = pd.read_csv(
'https://raw.githubusercontent.com/plotly/datasets/master/laucnty16.csv'
)
df_sample['State FIPS Code'] = df_sample['State FIPS Code'].apply(
lambda x: str(x).zfill(2)
)
df_sample['County FIPS Code'] = df_sample['County FIPS Code'].apply(
lambda x: str(x).zfill(3)
)
df_sample['FIPS'] = (
df_sample['State FIPS Code'] + df_sample['County FIPS Code']
)
binning_endpoints = list(np.linspace(1, 12, len(colorscale) - 1))
colorscale = ["#f7fbff", "#ebf3fb", "#deebf7", "#d2e3f3", "#c6dbef",
"#b3d2e9", "#9ecae1", "#85bcdb", "#6baed6", "#57a0ce",
"#4292c6", "#3082be", "#2171b5", "#1361a9", "#08519c",
"#0b4083","#08306b"]
fips = df_sample['FIPS']
values = df_sample['Unemployment Rate (%)']
fig = ff.create_choropleth(
fips=fips, values=values, scope=['usa'],
binning_endpoints=binning_endpoints, colorscale=colorscale,
show_hover=True, centroid_marker={'opacity': 0},
asp=2.9, title='USA by Unemployment %',
legend_title='Unemployment %'
)
py.iplot(fig, filename='choropleth_full_usa')
```
"""
# ensure optional modules imported
if not _plotly_geo:
raise ValueError("""
The create_choropleth figure factory requires the plotly-geo package.
Install using pip with:
$ pip install plotly-geo
Or, install using conda with
$ conda install -c plotly plotly-geo
""")
if not gp or not shapefile or not shapely:
raise ImportError(
"geopandas, pyshp and shapely must be installed for this figure "
"factory.\n\nRun the following commands to install the correct "
"versions of the following modules:\n\n"
"```\n"
"$ pip install geopandas==0.3.0\n"
"$ pip install pyshp==1.2.10\n"
"$ pip install shapely==1.6.3\n"
"```\n"
"If you are using Windows, follow this post to properly "
"install geopandas and dependencies:"
"http://geoffboeing.com/2014/09/using-geopandas-windows/\n\n"
"If you are using Anaconda, do not use PIP to install the "
"packages above. Instead use conda to install them:\n\n"
"```\n"
"$ conda install plotly\n"
"$ conda install geopandas\n"
"```")
df, df_state = _create_us_counties_df(st_to_state_name_dict,
state_to_st_dict)
fips_polygon_map = dict(zip(df["FIPS"].tolist(), df["geometry"].tolist()))
if not state_outline:
state_outline = {"color": "rgb(240, 240, 240)", "width": 1}
if not county_outline:
county_outline = {"color": "rgb(0, 0, 0)", "width": 0}
if not centroid_marker:
centroid_marker = {"size": 3, "color": "white", "opacity": 1}
# ensure centroid markers appear on selection
if "opacity" not in centroid_marker:
centroid_marker.update({"opacity": 1})
if len(fips) != len(values):
raise PlotlyError("fips and values must be the same length")
# make fips, values into lists
if isinstance(fips, pd.core.series.Series):
fips = fips.tolist()
if isinstance(values, pd.core.series.Series):
values = values.tolist()
# make fips numeric
fips = map(lambda x: int(x), fips)
if binning_endpoints:
intervals = utils.endpts_to_intervals(binning_endpoints)
LEVELS = _intervals_as_labels(intervals, round_legend_values,
exponent_format)
else:
if not order:
LEVELS = sorted(list(set(values)))
else:
# check if order is permutation
# of unique color col values
same_sets = sorted(list(set(values))) == set(order)
no_duplicates = not any(order.count(x) > 1 for x in order)
if same_sets and no_duplicates:
LEVELS = order
else:
raise PlotlyError(
"if you are using a custom order of unique values from "
"your color column, you must: have all the unique values "
"in your order and have no duplicate items")
if not colorscale:
colorscale = []
viridis_colors = clrs.colorscale_to_colors(
clrs.PLOTLY_SCALES["Viridis"])
viridis_colors = clrs.color_parser(viridis_colors, clrs.hex_to_rgb)
viridis_colors = clrs.color_parser(viridis_colors, clrs.label_rgb)
viri_len = len(viridis_colors) + 1
viri_intervals = utils.endpts_to_intervals(
list(np.linspace(0, 1, viri_len)))[1:-1]
for L in np.linspace(0, 1, len(LEVELS)):
for idx, inter in enumerate(viri_intervals):
if L == 0:
break
elif inter[0] < L <= inter[1]:
break
intermed = (L - viri_intervals[idx][0]) / (viri_intervals[idx][1] -
viri_intervals[idx][0])
float_color = clrs.find_intermediate_color(viridis_colors[idx],
viridis_colors[idx],
intermed,
colortype="rgb")
# make R,G,B into int values
float_color = clrs.unlabel_rgb(float_color)
float_color = clrs.unconvert_from_RGB_255(float_color)
int_rgb = clrs.convert_to_RGB_255(float_color)
int_rgb = clrs.label_rgb(int_rgb)
colorscale.append(int_rgb)
if len(colorscale) < len(LEVELS):
raise PlotlyError(
"You have {} LEVELS. Your number of colors in 'colorscale' must "
"be at least the number of LEVELS: {}. If you are "
"using 'binning_endpoints' then 'colorscale' must have at "
"least len(binning_endpoints) + 2 colors".format(
len(LEVELS), min(LEVELS, LEVELS[:20])))
color_lookup = dict(zip(LEVELS, colorscale))
x_traces = dict(zip(LEVELS, [[] for i in range(len(LEVELS))]))
y_traces = dict(zip(LEVELS, [[] for i in range(len(LEVELS))]))
# scope
if isinstance(scope, str):
raise PlotlyError("'scope' must be a list/tuple/sequence")
scope_names = []
extra_states = [
"Alaska",
"Commonwealth of the Northern Mariana Islands",
"Puerto Rico",
"Guam",
"United States Virgin Islands",
"American Samoa",
]
for state in scope:
if state.lower() == "usa":
scope_names = df["STATE_NAME"].unique()
scope_names = list(scope_names)
for ex_st in extra_states:
try:
scope_names.remove(ex_st)
except ValueError:
pass
else:
if state in st_to_state_name_dict.keys():
state = st_to_state_name_dict[state]
scope_names.append(state)
df_state = df_state[df_state["STATE_NAME"].isin(scope_names)]
plot_data = []
x_centroids = []
y_centroids = []
centroid_text = []
fips_not_in_shapefile = []
if not binning_endpoints:
for index, f in enumerate(fips):
level = values[index]
try:
fips_polygon_map[f].type
(
x_traces,
y_traces,
x_centroids,
y_centroids,
centroid_text,
) = _calculations(
df,
fips,
values,
index,
f,
simplify_county,
level,
x_centroids,
y_centroids,
centroid_text,
x_traces,
y_traces,
fips_polygon_map,
)
except KeyError:
fips_not_in_shapefile.append(f)
else:
for index, f in enumerate(fips):
for j, inter in enumerate(intervals):
if inter[0] < values[index] <= inter[1]:
break
level = LEVELS[j]
try:
fips_polygon_map[f].type
(
x_traces,
y_traces,
x_centroids,
y_centroids,
centroid_text,
) = _calculations(
df,
fips,
values,
index,
f,
simplify_county,
level,
x_centroids,
y_centroids,
centroid_text,
x_traces,
y_traces,
fips_polygon_map,
)
except KeyError:
fips_not_in_shapefile.append(f)
if len(fips_not_in_shapefile) > 0:
msg = ("Unrecognized FIPS Values\n\nWhoops! It looks like you are "
"trying to pass at least one FIPS value that is not in "
"our shapefile of FIPS and data for the counties. Your "
"choropleth will still show up but these counties cannot "
"be shown.\nUnrecognized FIPS are: {}".format(
fips_not_in_shapefile))
warnings.warn(msg)
x_states = []
y_states = []
for index, row in df_state.iterrows():
if df_state["geometry"][index].type == "Polygon":
x = row.geometry.simplify(simplify_state).exterior.xy[0].tolist()
y = row.geometry.simplify(simplify_state).exterior.xy[1].tolist()
x_states = x_states + x
y_states = y_states + y
elif df_state["geometry"][index].type == "MultiPolygon":
x = [
poly.simplify(simplify_state).exterior.xy[0].tolist()
for poly in df_state["geometry"][index]
]
y = [
poly.simplify(simplify_state).exterior.xy[1].tolist()
for poly in df_state["geometry"][index]
]
for segment in range(len(x)):
x_states = x_states + x[segment]
y_states = y_states + y[segment]
x_states.append(np.nan)
y_states.append(np.nan)
x_states.append(np.nan)
y_states.append(np.nan)
for lev in LEVELS:
county_data = dict(
type="scatter",
mode="lines",
x=x_traces[lev],
y=y_traces[lev],
line=county_outline,
fill="toself",
fillcolor=color_lookup[lev],
name=lev,
hoverinfo="none",
)
plot_data.append(county_data)
if show_hover:
hover_points = dict(
type="scatter",
showlegend=False,
legendgroup="centroids",
x=x_centroids,
y=y_centroids,
text=centroid_text,
name="US Counties",
mode="markers",
marker={
"color": "white",
"opacity": 0
},
hoverinfo="text",
)
centroids_on_select = dict(
selected=dict(marker=centroid_marker),
unselected=dict(marker=dict(opacity=0)),
)
hover_points.update(centroids_on_select)
plot_data.append(hover_points)
if show_state_data:
state_data = dict(
type="scatter",
legendgroup="States",
line=state_outline,
x=x_states,
y=y_states,
hoverinfo="text",
showlegend=False,
mode="lines",
)
plot_data.append(state_data)
DEFAULT_LAYOUT = dict(
hovermode="closest",
xaxis=dict(
autorange=False,
range=USA_XRANGE,
showgrid=False,
zeroline=False,
fixedrange=True,
showticklabels=False,
),
yaxis=dict(
autorange=False,
range=USA_YRANGE,
showgrid=False,
zeroline=False,
fixedrange=True,
showticklabels=False,
),
margin=dict(t=40, b=20, r=20, l=20),
width=900,
height=450,
dragmode="select",
legend=dict(traceorder="reversed",
xanchor="right",
yanchor="top",
x=1,
y=1),
annotations=[],
)
fig = dict(data=plot_data, layout=DEFAULT_LAYOUT)
fig["layout"].update(layout_options)
fig["layout"]["annotations"].append(
dict(
x=1,
y=1.05,
xref="paper",
yref="paper",
xanchor="right",
showarrow=False,
text="<b>" + legend_title + "</b>",
))
if len(scope) == 1 and scope[0].lower() == "usa":
xaxis_range_low = -125.0
xaxis_range_high = -55.0
yaxis_range_low = 25.0
yaxis_range_high = 49.0
else:
xaxis_range_low = float("inf")
xaxis_range_high = float("-inf")
yaxis_range_low = float("inf")
yaxis_range_high = float("-inf")
for trace in fig["data"]:
if all(isinstance(n, Number) for n in trace["x"]):
calc_x_min = min(trace["x"] or [float("inf")])
calc_x_max = max(trace["x"] or [float("-inf")])
if calc_x_min < xaxis_range_low:
xaxis_range_low = calc_x_min
if calc_x_max > xaxis_range_high:
xaxis_range_high = calc_x_max
if all(isinstance(n, Number) for n in trace["y"]):
calc_y_min = min(trace["y"] or [float("inf")])
calc_y_max = max(trace["y"] or [float("-inf")])
if calc_y_min < yaxis_range_low:
yaxis_range_low = calc_y_min
if calc_y_max > yaxis_range_high:
yaxis_range_high = calc_y_max
# camera zoom
fig["layout"]["xaxis"]["range"] = [xaxis_range_low, xaxis_range_high]
fig["layout"]["yaxis"]["range"] = [yaxis_range_low, yaxis_range_high]
# aspect ratio
if asp is None:
usa_x_range = USA_XRANGE[1] - USA_XRANGE[0]
usa_y_range = USA_YRANGE[1] - USA_YRANGE[0]
asp = usa_x_range / usa_y_range
# based on your figure
width = float(fig["layout"]["xaxis"]["range"][1] -
fig["layout"]["xaxis"]["range"][0])
height = float(fig["layout"]["yaxis"]["range"][1] -
fig["layout"]["yaxis"]["range"][0])
center = (
sum(fig["layout"]["xaxis"]["range"]) / 2.0,
sum(fig["layout"]["yaxis"]["range"]) / 2.0,
)
if height / width > (1 / asp):
new_width = asp * height
fig["layout"]["xaxis"]["range"][0] = center[0] - new_width * 0.5
fig["layout"]["xaxis"]["range"][1] = center[0] + new_width * 0.5
else:
new_height = (1 / asp) * width
fig["layout"]["yaxis"]["range"][0] = center[1] - new_height * 0.5
fig["layout"]["yaxis"]["range"][1] = center[1] + new_height * 0.5
return go.Figure(fig)
def colormap(i):
return label_rgb([
int(n)
for n in find_intermediate_color([0, 255., 255.], [255., 0., 0], i)
])
def violin_colorscale(
data,
data_header,
group_header,
colors,
use_colorscale,
group_stats,
rugplot,
sort,
height,
width,
title,
):
"""
Refer to FigureFactory.create_violin() for docstring.
Returns fig for violin plot with colorscale.
"""
# collect all group names
group_name = []
for name in data[group_header]:
if name not in group_name:
group_name.append(name)
if sort:
group_name.sort()
# make sure all group names are keys in group_stats
for group in group_name:
if group not in group_stats:
raise exceptions.PlotlyError("All values/groups in the index "
"column must be represented "
"as a key in group_stats.")
gb = data.groupby([group_header])
L = len(group_name)
fig = make_subplots(rows=1,
cols=L,
shared_yaxes=True,
horizontal_spacing=0.025,
print_grid=False)
# prepare low and high color for colorscale
lowcolor = clrs.color_parser(colors[0], clrs.unlabel_rgb)
highcolor = clrs.color_parser(colors[1], clrs.unlabel_rgb)
# find min and max values in group_stats
group_stats_values = []
for key in group_stats:
group_stats_values.append(group_stats[key])
max_value = max(group_stats_values)
min_value = min(group_stats_values)
for k, gr in enumerate(group_name):
vals = np.asarray(gb.get_group(gr)[data_header], np.float)
# find intermediate color from colorscale
intermed = (group_stats[gr] - min_value) / (max_value - min_value)
intermed_color = clrs.find_intermediate_color(lowcolor, highcolor,
intermed)
plot_data, plot_xrange = violinplot(
vals, fillcolor="rgb{}".format(intermed_color), rugplot=rugplot)
layout = graph_objs.Layout()
for item in plot_data:
fig.append_trace(item, 1, k + 1)
fig["layout"].update(
{"xaxis{}".format(k + 1): make_XAxis(group_name[k], plot_xrange)})
# add colorbar to plot
trace_dummy = graph_objs.Scatter(
x=[0],
y=[0],
mode="markers",
marker=dict(
size=2,
cmin=min_value,
cmax=max_value,
colorscale=[[0, colors[0]], [1, colors[1]]],
showscale=True,
),
showlegend=False,
)
fig.append_trace(trace_dummy, 1, L)
# set the sharey axis style
fig["layout"].update({"yaxis{}".format(1): make_YAxis("")})
fig["layout"].update(
title=title,
showlegend=False,
hovermode="closest",
autosize=False,
height=height,
width=width,
)
return fig
def gantt_colorscale(
chart,
colors,
title,
index_col,
show_colorbar,
bar_width,
showgrid_x,
showgrid_y,
height,
width,
tasks=None,
task_names=None,
data=None,
group_tasks=False,
show_hover_fill=True,
):
"""
Refer to FigureFactory.create_gantt() for docstring
"""
if tasks is None:
tasks = []
if task_names is None:
task_names = []
if data is None:
data = []
showlegend = False
for index in range(len(chart)):
task = dict(
x0=chart[index]["Start"],
x1=chart[index]["Finish"],
name=chart[index]["Task"],
)
if "Description" in chart[index]:
task["description"] = chart[index]["Description"]
tasks.append(task)
# create a scatter trace for every task group
scatter_data_dict = dict()
# create scatter traces for the start- and endpoints
marker_data_dict = dict()
if show_hover_fill:
hoverinfo = "name"
else:
hoverinfo = "skip"
scatter_data_template = {
"x": [],
"y": [],
"mode": "none",
"fill": "toself",
"showlegend": False,
"hoverinfo": hoverinfo,
"legendgroup": "",
}
marker_data_template = {
"x": [],
"y": [],
"mode": "markers",
"text": [],
"marker": dict(color="", size=1, opacity=0),
"name": "",
"showlegend": False,
"legendgroup": "",
}
index_vals = []
for row in range(len(tasks)):
if chart[row][index_col] not in index_vals:
index_vals.append(chart[row][index_col])
index_vals.sort()
# compute the color for task based on indexing column
if isinstance(chart[0][index_col], Number):
# check that colors has at least 2 colors
if len(colors) < 2:
raise exceptions.PlotlyError(
"You must use at least 2 colors in 'colors' if you "
"are using a colorscale. However only the first two "
"colors given will be used for the lower and upper "
"bounds on the colormap.")
# create the list of task names
for index in range(len(tasks)):
tn = tasks[index]["name"]
# Is added to task_names if group_tasks is set to False,
# or if the option is used (True) it only adds them if the
# name is not already in the list
if not group_tasks or tn not in task_names:
task_names.append(tn)
# Guarantees that for grouped tasks the tasks that are inserted
# first are shown at the top
if group_tasks:
task_names.reverse()
for index in range(len(tasks)):
tn = tasks[index]["name"]
del tasks[index]["name"]
# If group_tasks is True, all tasks with the same name belong
# to the same row.
groupID = index
if group_tasks:
groupID = task_names.index(tn)
tasks[index]["y0"] = groupID - bar_width
tasks[index]["y1"] = groupID + bar_width
# unlabel color
colors = clrs.color_parser(colors, clrs.unlabel_rgb)
lowcolor = colors[0]
highcolor = colors[1]
intermed = (chart[index][index_col]) / 100.0
intermed_color = clrs.find_intermediate_color(
lowcolor, highcolor, intermed)
intermed_color = clrs.color_parser(intermed_color, clrs.label_rgb)
tasks[index]["fillcolor"] = intermed_color
color_id = tasks[index]["fillcolor"]
if color_id not in scatter_data_dict:
scatter_data_dict[color_id] = copy.deepcopy(
scatter_data_template)
scatter_data_dict[color_id]["fillcolor"] = color_id
scatter_data_dict[color_id]["name"] = str(chart[index][index_col])
scatter_data_dict[color_id]["legendgroup"] = color_id
# relabel colors with 'rgb'
colors = clrs.color_parser(colors, clrs.label_rgb)
# if there are already values append the gap
if len(scatter_data_dict[color_id]["x"]) > 0:
# a gap on the scatterplot separates the rectangles from each other
scatter_data_dict[color_id]["x"].append(
scatter_data_dict[color_id]["x"][-1])
scatter_data_dict[color_id]["y"].append(None)
xs, ys = _get_corner_points(
tasks[index]["x0"],
tasks[index]["y0"],
tasks[index]["x1"],
tasks[index]["y1"],
)
scatter_data_dict[color_id]["x"] += xs
scatter_data_dict[color_id]["y"] += ys
# append dummy markers for showing start and end of interval
if color_id not in marker_data_dict:
marker_data_dict[color_id] = copy.deepcopy(
marker_data_template)
marker_data_dict[color_id]["marker"]["color"] = color_id
marker_data_dict[color_id]["legendgroup"] = color_id
marker_data_dict[color_id]["x"].append(tasks[index]["x0"])
marker_data_dict[color_id]["x"].append(tasks[index]["x1"])
marker_data_dict[color_id]["y"].append(groupID)
marker_data_dict[color_id]["y"].append(groupID)
if "description" in tasks[index]:
marker_data_dict[color_id]["text"].append(
tasks[index]["description"])
marker_data_dict[color_id]["text"].append(
tasks[index]["description"])
del tasks[index]["description"]
else:
marker_data_dict[color_id]["text"].append(None)
marker_data_dict[color_id]["text"].append(None)
# add colorbar to one of the traces randomly just for display
if show_colorbar is True:
k = list(marker_data_dict.keys())[0]
marker_data_dict[k]["marker"].update(
dict(
colorscale=[[0, colors[0]], [1, colors[1]]],
showscale=True,
cmax=100,
cmin=0,
))
if isinstance(chart[0][index_col], str):
index_vals = []
for row in range(len(tasks)):
if chart[row][index_col] not in index_vals:
index_vals.append(chart[row][index_col])
index_vals.sort()
if len(colors) < len(index_vals):
raise exceptions.PlotlyError(
"Error. The number of colors in 'colors' must be no less "
"than the number of unique index values in your group "
"column.")
# make a dictionary assignment to each index value
index_vals_dict = {}
# define color index
c_index = 0
for key in index_vals:
if c_index > len(colors) - 1:
c_index = 0
index_vals_dict[key] = colors[c_index]
c_index += 1
# create the list of task names
for index in range(len(tasks)):
tn = tasks[index]["name"]
# Is added to task_names if group_tasks is set to False,
# or if the option is used (True) it only adds them if the
# name is not already in the list
if not group_tasks or tn not in task_names:
task_names.append(tn)
# Guarantees that for grouped tasks the tasks that are inserted
# first are shown at the top
if group_tasks:
task_names.reverse()
for index in range(len(tasks)):
tn = tasks[index]["name"]
del tasks[index]["name"]
# If group_tasks is True, all tasks with the same name belong
# to the same row.
groupID = index
if group_tasks:
groupID = task_names.index(tn)
tasks[index]["y0"] = groupID - bar_width
tasks[index]["y1"] = groupID + bar_width
tasks[index]["fillcolor"] = index_vals_dict[chart[index]
[index_col]]
color_id = tasks[index]["fillcolor"]
if color_id not in scatter_data_dict:
scatter_data_dict[color_id] = copy.deepcopy(
scatter_data_template)
scatter_data_dict[color_id]["fillcolor"] = color_id
scatter_data_dict[color_id]["legendgroup"] = color_id
scatter_data_dict[color_id]["name"] = str(chart[index][index_col])
# relabel colors with 'rgb'
colors = clrs.color_parser(colors, clrs.label_rgb)
# if there are already values append the gap
if len(scatter_data_dict[color_id]["x"]) > 0:
# a gap on the scatterplot separates the rectangles from each other
scatter_data_dict[color_id]["x"].append(
scatter_data_dict[color_id]["x"][-1])
scatter_data_dict[color_id]["y"].append(None)
xs, ys = _get_corner_points(
tasks[index]["x0"],
tasks[index]["y0"],
tasks[index]["x1"],
tasks[index]["y1"],
)
scatter_data_dict[color_id]["x"] += xs
scatter_data_dict[color_id]["y"] += ys
# append dummy markers for showing start and end of interval
if color_id not in marker_data_dict:
marker_data_dict[color_id] = copy.deepcopy(
marker_data_template)
marker_data_dict[color_id]["marker"]["color"] = color_id
marker_data_dict[color_id]["legendgroup"] = color_id
marker_data_dict[color_id]["x"].append(tasks[index]["x0"])
marker_data_dict[color_id]["x"].append(tasks[index]["x1"])
marker_data_dict[color_id]["y"].append(groupID)
marker_data_dict[color_id]["y"].append(groupID)
if "description" in tasks[index]:
marker_data_dict[color_id]["text"].append(
tasks[index]["description"])
marker_data_dict[color_id]["text"].append(
tasks[index]["description"])
del tasks[index]["description"]
else:
marker_data_dict[color_id]["text"].append(None)
marker_data_dict[color_id]["text"].append(None)
if show_colorbar is True:
showlegend = True
for k in scatter_data_dict:
scatter_data_dict[k]["showlegend"] = showlegend
# add colorbar to one of the traces randomly just for display
# if show_colorbar is True:
# k = list(marker_data_dict.keys())[0]
# marker_data_dict[k]["marker"].update(
# dict(
# colorscale=[[0, colors[0]], [1, colors[1]]],
# showscale=True,
# cmax=100,
# cmin=0,
# )
# )
layout = dict(
title=title,
showlegend=showlegend,
height=height,
width=width,
shapes=[],
hovermode="closest",
yaxis=dict(
showgrid=showgrid_y,
ticktext=task_names,
tickvals=list(range(len(task_names))),
range=[-1, len(task_names) + 1],
autorange=False,
zeroline=False,
),
xaxis=dict(
showgrid=showgrid_x,
zeroline=False,
rangeselector=dict(buttons=list([
dict(count=7, label="1w", step="day", stepmode="backward"),
dict(count=1, label="1m", step="month", stepmode="backward"),
dict(count=6, label="6m", step="month", stepmode="backward"),
dict(count=1, label="YTD", step="year", stepmode="todate"),
dict(count=1, label="1y", step="year", stepmode="backward"),
dict(step="all"),
])),
type="date",
),
)
data = [scatter_data_dict[k] for k in sorted(scatter_data_dict)]
data += [marker_data_dict[k] for k in sorted(marker_data_dict)]
# fig = dict(
# data=data, layout=layout
# )
fig = go.Figure(data=data, layout=layout)
return fig
def map_face2color(face, colormap, scale, vmin, vmax):
"""
Normalize facecolor values by vmin/vmax and return rgb-color strings
This function takes a tuple color along with a colormap and a minimum
(vmin) and maximum (vmax) range of possible mean distances for the
given parametrized surface. It returns an rgb color based on the mean
distance between vmin and vmax
"""
if vmin >= vmax:
raise exceptions.PlotlyError("Incorrect relation between vmin "
"and vmax. The vmin value cannot be "
"bigger than or equal to the value "
"of vmax.")
if len(colormap) == 1:
# color each triangle face with the same color in colormap
face_color = colormap[0]
face_color = colors.convert_to_RGB_255(face_color)
face_color = colors.label_rgb(face_color)
return face_color
if face == vmax:
# pick last color in colormap
face_color = colormap[-1]
face_color = colors.convert_to_RGB_255(face_color)
face_color = colors.label_rgb(face_color)
return face_color
else:
if scale is None:
# find the normalized distance t of a triangle face between
# vmin and vmax where the distance is between 0 and 1
t = (face - vmin) / float((vmax - vmin))
low_color_index = int(t / (1./(len(colormap) - 1)))
face_color = colors.find_intermediate_color(
colormap[low_color_index],
colormap[low_color_index + 1],
t * (len(colormap) - 1) - low_color_index
)
face_color = colors.convert_to_RGB_255(face_color)
face_color = colors.label_rgb(face_color)
else:
# find the face color for a non-linearly interpolated scale
t = (face - vmin) / float((vmax - vmin))
low_color_index = 0
for k in range(len(scale) - 1):
if scale[k] <= t < scale[k+1]:
break
low_color_index += 1
low_scale_val = scale[low_color_index]
high_scale_val = scale[low_color_index + 1]
face_color = colors.find_intermediate_color(
colormap[low_color_index],
colormap[low_color_index + 1],
(t - low_scale_val)/(high_scale_val - low_scale_val)
)
face_color = colors.convert_to_RGB_255(face_color)
face_color = colors.label_rgb(face_color)
return face_color
def gantt_colorscale(chart, colors, title, index_col, show_colorbar, bar_width,
showgrid_x, showgrid_y, height, width, tasks=None,
task_names=None, data=None, group_tasks=False):
"""
Refer to FigureFactory.create_gantt() for docstring
"""
if tasks is None:
tasks = []
if task_names is None:
task_names = []
if data is None:
data = []
showlegend = False
for index in range(len(chart)):
task = dict(x0=chart[index]['Start'],
x1=chart[index]['Finish'],
name=chart[index]['Task'])
if 'Description' in chart[index]:
task['description'] = chart[index]['Description']
tasks.append(task)
shape_template = {
'type': 'rect',
'xref': 'x',
'yref': 'y',
'opacity': 1,
'line': {
'width': 0,
}
}
# compute the color for task based on indexing column
if isinstance(chart[0][index_col], Number):
# check that colors has at least 2 colors
if len(colors) < 2:
raise exceptions.PlotlyError(
"You must use at least 2 colors in 'colors' if you "
"are using a colorscale. However only the first two "
"colors given will be used for the lower and upper "
"bounds on the colormap."
)
# create the list of task names
for index in range(len(tasks)):
tn = tasks[index]['name']
# Is added to task_names if group_tasks is set to False,
# or if the option is used (True) it only adds them if the
# name is not already in the list
if not group_tasks or tn not in task_names:
task_names.append(tn)
# Guarantees that for grouped tasks the tasks that are inserted
# first are shown at the top
if group_tasks:
task_names.reverse()
for index in range(len(tasks)):
tn = tasks[index]['name']
del tasks[index]['name']
tasks[index].update(shape_template)
# If group_tasks is True, all tasks with the same name belong
# to the same row.
groupID = index
if group_tasks:
groupID = task_names.index(tn)
tasks[index]['y0'] = groupID - bar_width
tasks[index]['y1'] = groupID + bar_width
# unlabel color
colors = clrs.color_parser(colors, clrs.unlabel_rgb)
lowcolor = colors[0]
highcolor = colors[1]
intermed = (chart[index][index_col]) / 100.0
intermed_color = clrs.find_intermediate_color(
lowcolor, highcolor, intermed
)
intermed_color = clrs.color_parser(
intermed_color, clrs.label_rgb
)
tasks[index]['fillcolor'] = intermed_color
# relabel colors with 'rgb'
colors = clrs.color_parser(colors, clrs.label_rgb)
# add a line for hover text and autorange
entry = dict(
x=[tasks[index]['x0'], tasks[index]['x1']],
y=[groupID, groupID],
name='',
marker={'color': 'white'}
)
if "description" in tasks[index]:
entry['text'] = tasks[index]['description']
del tasks[index]['description']
data.append(entry)
if show_colorbar is True:
# generate dummy data for colorscale visibility
data.append(
dict(
x=[tasks[index]['x0'], tasks[index]['x0']],
y=[index, index],
name='',
marker={'color': 'white',
'colorscale': [[0, colors[0]], [1, colors[1]]],
'showscale': True,
'cmax': 100,
'cmin': 0}
)
)
if isinstance(chart[0][index_col], str):
index_vals = []
for row in range(len(tasks)):
if chart[row][index_col] not in index_vals:
index_vals.append(chart[row][index_col])
index_vals.sort()
if len(colors) < len(index_vals):
raise exceptions.PlotlyError(
"Error. The number of colors in 'colors' must be no less "
"than the number of unique index values in your group "
"column."
)
# make a dictionary assignment to each index value
index_vals_dict = {}
# define color index
c_index = 0
for key in index_vals:
if c_index > len(colors) - 1:
c_index = 0
index_vals_dict[key] = colors[c_index]
c_index += 1
# create the list of task names
for index in range(len(tasks)):
tn = tasks[index]['name']
# Is added to task_names if group_tasks is set to False,
# or if the option is used (True) it only adds them if the
# name is not already in the list
if not group_tasks or tn not in task_names:
task_names.append(tn)
# Guarantees that for grouped tasks the tasks that are inserted
# first are shown at the top
if group_tasks:
task_names.reverse()
for index in range(len(tasks)):
tn = tasks[index]['name']
del tasks[index]['name']
tasks[index].update(shape_template)
# If group_tasks is True, all tasks with the same name belong
# to the same row.
groupID = index
if group_tasks:
groupID = task_names.index(tn)
tasks[index]['y0'] = groupID - bar_width
tasks[index]['y1'] = groupID + bar_width
tasks[index]['fillcolor'] = index_vals_dict[
chart[index][index_col]
]
# add a line for hover text and autorange
entry = dict(
x=[tasks[index]['x0'], tasks[index]['x1']],
y=[groupID, groupID],
name='',
marker={'color': 'white'}
)
if "description" in tasks[index]:
entry['text'] = tasks[index]['description']
del tasks[index]['description']
data.append(entry)
if show_colorbar is True:
# generate dummy data to generate legend
showlegend = True
for k, index_value in enumerate(index_vals):
data.append(
dict(
x=[tasks[index]['x0'], tasks[index]['x0']],
y=[k, k],
showlegend=True,
name=str(index_value),
hoverinfo='none',
marker=dict(
color=colors[k],
size=1
)
)
)
layout = dict(
title=title,
showlegend=showlegend,
height=height,
width=width,
shapes=[],
hovermode='closest',
yaxis=dict(
showgrid=showgrid_y,
ticktext=task_names,
tickvals=list(range(len(task_names))),
range=[-1, len(task_names) + 1],
autorange=False,
zeroline=False,
),
xaxis=dict(
showgrid=showgrid_x,
zeroline=False,
rangeselector=dict(
buttons=list([
dict(count=7,
label='1w',
step='day',
stepmode='backward'),
dict(count=1,
label='1m',
step='month',
stepmode='backward'),
dict(count=6,
label='6m',
step='month',
stepmode='backward'),
dict(count=1,
label='YTD',
step='year',
stepmode='todate'),
dict(count=1,
label='1y',
step='year',
stepmode='backward'),
dict(step='all')
])
),
type='date'
)
)
layout['shapes'] = tasks
fig = dict(data=data, layout=layout)
return fig
def violin_colorscale(data, data_header, group_header, colors, use_colorscale,
group_stats, rugplot, sort, height, width,
title):
"""
Refer to FigureFactory.create_violin() for docstring.
Returns fig for violin plot with colorscale.
"""
# collect all group names
group_name = []
for name in data[group_header]:
if name not in group_name:
group_name.append(name)
if sort:
group_name.sort()
# make sure all group names are keys in group_stats
for group in group_name:
if group not in group_stats:
raise exceptions.PlotlyError("All values/groups in the index "
"column must be represented "
"as a key in group_stats.")
gb = data.groupby([group_header])
L = len(group_name)
fig = make_subplots(rows=1, cols=L,
shared_yaxes=True,
horizontal_spacing=0.025,
print_grid=False)
# prepare low and high color for colorscale
lowcolor = clrs.color_parser(colors[0], clrs.unlabel_rgb)
highcolor = clrs.color_parser(colors[1], clrs.unlabel_rgb)
# find min and max values in group_stats
group_stats_values = []
for key in group_stats:
group_stats_values.append(group_stats[key])
max_value = max(group_stats_values)
min_value = min(group_stats_values)
for k, gr in enumerate(group_name):
vals = np.asarray(gb.get_group(gr)[data_header], np.float)
# find intermediate color from colorscale
intermed = (group_stats[gr] - min_value) / (max_value - min_value)
intermed_color = clrs.find_intermediate_color(
lowcolor, highcolor, intermed
)
plot_data, plot_xrange = violinplot(
vals,
fillcolor='rgb{}'.format(intermed_color),
rugplot=rugplot
)
layout = graph_objs.Layout()
for item in plot_data:
fig.append_trace(item, 1, k + 1)
fig['layout'].update(
{'xaxis{}'.format(k + 1): make_XAxis(group_name[k], plot_xrange)}
)
# add colorbar to plot
trace_dummy = graph_objs.Scatter(
x=[0],
y=[0],
mode='markers',
marker=dict(
size=2,
cmin=min_value,
cmax=max_value,
colorscale=[[0, colors[0]],
[1, colors[1]]],
showscale=True),
showlegend=False,
)
fig.append_trace(trace_dummy, 1, L)
# set the sharey axis style
fig['layout'].update({'yaxis{}'.format(1): make_YAxis('')})
fig['layout'].update(
title=title,
showlegend=False,
hovermode='closest',
autosize=False,
height=height,
width=width
)
return fig
def gantt_colorscale(
chart,
colors,
title,
index_col,
show_colorbar,
bar_width,
showgrid_x,
showgrid_y,
height,
width,
tasks=None,
task_names=None,
data=None,
group_tasks=False,
):
"""
Refer to FigureFactory.create_gantt() for docstring
"""
if tasks is None:
tasks = []
if task_names is None:
task_names = []
if data is None:
data = []
showlegend = False
for index in range(len(chart)):
task = dict(
x0=chart[index]["Start"],
x1=chart[index]["Finish"],
name=chart[index]["Task"],
)
if "Description" in chart[index]:
task["description"] = chart[index]["Description"]
tasks.append(task)
shape_template = {
"type": "rect",
"xref": "x",
"yref": "y",
"opacity": 1,
"line": {
"width": 0
},
}
# compute the color for task based on indexing column
if isinstance(chart[0][index_col], Number):
# check that colors has at least 2 colors
if len(colors) < 2:
raise exceptions.PlotlyError(
"You must use at least 2 colors in 'colors' if you "
"are using a colorscale. However only the first two "
"colors given will be used for the lower and upper "
"bounds on the colormap.")
# create the list of task names
for index in range(len(tasks)):
tn = tasks[index]["name"]
# Is added to task_names if group_tasks is set to False,
# or if the option is used (True) it only adds them if the
# name is not already in the list
if not group_tasks or tn not in task_names:
task_names.append(tn)
# Guarantees that for grouped tasks the tasks that are inserted
# first are shown at the top
if group_tasks:
task_names.reverse()
for index in range(len(tasks)):
tn = tasks[index]["name"]
del tasks[index]["name"]
tasks[index].update(shape_template)
# If group_tasks is True, all tasks with the same name belong
# to the same row.
groupID = index
if group_tasks:
groupID = task_names.index(tn)
tasks[index]["y0"] = groupID - bar_width
tasks[index]["y1"] = groupID + bar_width
# unlabel color
colors = clrs.color_parser(colors, clrs.unlabel_rgb)
lowcolor = colors[0]
highcolor = colors[1]
intermed = (chart[index][index_col]) / 100.0
intermed_color = clrs.find_intermediate_color(
lowcolor, highcolor, intermed)
intermed_color = clrs.color_parser(intermed_color, clrs.label_rgb)
tasks[index]["fillcolor"] = intermed_color
# relabel colors with 'rgb'
colors = clrs.color_parser(colors, clrs.label_rgb)
# add a line for hover text and autorange
entry = dict(
x=[tasks[index]["x0"], tasks[index]["x1"]],
y=[groupID, groupID],
name="",
marker={"color": "white"},
)
if "description" in tasks[index]:
entry["text"] = tasks[index]["description"]
del tasks[index]["description"]
data.append(entry)
if show_colorbar is True:
# generate dummy data for colorscale visibility
data.append(
dict(
x=[tasks[index]["x0"], tasks[index]["x0"]],
y=[index, index],
name="",
marker={
"color": "white",
"colorscale": [[0, colors[0]], [1, colors[1]]],
"showscale": True,
"cmax": 100,
"cmin": 0,
},
))
if isinstance(chart[0][index_col], str):
index_vals = []
for row in range(len(tasks)):
if chart[row][index_col] not in index_vals:
index_vals.append(chart[row][index_col])
index_vals.sort()
if len(colors) < len(index_vals):
raise exceptions.PlotlyError(
"Error. The number of colors in 'colors' must be no less "
"than the number of unique index values in your group "
"column.")
# make a dictionary assignment to each index value
index_vals_dict = {}
# define color index
c_index = 0
for key in index_vals:
if c_index > len(colors) - 1:
c_index = 0
index_vals_dict[key] = colors[c_index]
c_index += 1
# create the list of task names
for index in range(len(tasks)):
tn = tasks[index]["name"]
# Is added to task_names if group_tasks is set to False,
# or if the option is used (True) it only adds them if the
# name is not already in the list
if not group_tasks or tn not in task_names:
task_names.append(tn)
# Guarantees that for grouped tasks the tasks that are inserted
# first are shown at the top
if group_tasks:
task_names.reverse()
for index in range(len(tasks)):
tn = tasks[index]["name"]
del tasks[index]["name"]
tasks[index].update(shape_template)
# If group_tasks is True, all tasks with the same name belong
# to the same row.
groupID = index
if group_tasks:
groupID = task_names.index(tn)
tasks[index]["y0"] = groupID - bar_width
tasks[index]["y1"] = groupID + bar_width
tasks[index]["fillcolor"] = index_vals_dict[chart[index]
[index_col]]
# add a line for hover text and autorange
entry = dict(
x=[tasks[index]["x0"], tasks[index]["x1"]],
y=[groupID, groupID],
name="",
marker={"color": "white"},
)
if "description" in tasks[index]:
entry["text"] = tasks[index]["description"]
del tasks[index]["description"]
data.append(entry)
if show_colorbar is True:
# generate dummy data to generate legend
showlegend = True
for k, index_value in enumerate(index_vals):
data.append(
dict(
x=[tasks[index]["x0"], tasks[index]["x0"]],
y=[k, k],
showlegend=True,
name=str(index_value),
hoverinfo="none",
marker=dict(color=colors[k], size=1),
))
layout = dict(
title=title,
showlegend=showlegend,
height=height,
width=width,
shapes=[],
hovermode="closest",
yaxis=dict(
showgrid=showgrid_y,
ticktext=task_names,
tickvals=list(range(len(task_names))),
range=[-1, len(task_names) + 1],
autorange=False,
zeroline=False,
),
xaxis=dict(
showgrid=showgrid_x,
zeroline=False,
rangeselector=dict(buttons=list([
dict(count=7, label="1w", step="day", stepmode="backward"),
dict(count=1, label="1m", step="month", stepmode="backward"),
dict(count=6, label="6m", step="month", stepmode="backward"),
dict(count=1, label="YTD", step="year", stepmode="todate"),
dict(count=1, label="1y", step="year", stepmode="backward"),
dict(step="all"),
])),
type="date",
),
)
layout["shapes"] = tasks
fig = graph_objs.Figure(data=data, layout=layout)
return fig
def create_choropleth(fips, values, scope=['usa'], binning_endpoints=None,
colorscale=None, order=None, simplify_county=0.02,
simplify_state=0.02, asp=None, show_hover=True,
show_state_data=True, state_outline=None,
county_outline=None, centroid_marker=None,
round_legend_values=False, exponent_format=False,
legend_title='', **layout_options):
"""
Returns figure for county choropleth. Uses data from package_data.
:param (list) fips: list of FIPS values which correspond to the con
catination of state and county ids. An example is '01001'.
:param (list) values: list of numbers/strings which correspond to the
fips list. These are the values that will determine how the counties
are colored.
:param (list) scope: list of states and/or states abbreviations. Fits
all states in the camera tightly. Selecting ['usa'] is the equivalent
of appending all 50 states into your scope list. Selecting only 'usa'
does not include 'Alaska', 'Puerto Rico', 'American Samoa',
'Commonwealth of the Northern Mariana Islands', 'Guam',
'United States Virgin Islands'. These must be added manually to the
list.
Default = ['usa']
:param (list) binning_endpoints: ascending numbers which implicitly define
real number intervals which are used as bins. The colorscale used must
have the same number of colors as the number of bins and this will
result in a categorical colormap.
:param (list) colorscale: a list of colors with length equal to the
number of categories of colors. The length must match either all
unique numbers in the 'values' list or if endpoints is being used, the
number of categories created by the endpoints.\n
For example, if binning_endpoints = [4, 6, 8], then there are 4 bins:
[-inf, 4), [4, 6), [6, 8), [8, inf)
:param (list) order: a list of the unique categories (numbers/bins) in any
desired order. This is helpful if you want to order string values to
a chosen colorscale.
:param (float) simplify_county: determines the simplification factor
for the counties. The larger the number, the fewer vertices and edges
each polygon has. See
http://toblerity.org/shapely/manual.html#object.simplify for more
information.
Default = 0.02
:param (float) simplify_state: simplifies the state outline polygon.
See http://toblerity.org/shapely/manual.html#object.simplify for more
information.
Default = 0.02
:param (float) asp: the width-to-height aspect ratio for the camera.
Default = 2.5
:param (bool) show_hover: show county hover and centroid info
:param (bool) show_state_data: reveals state boundary lines
:param (dict) state_outline: dict of attributes of the state outline
including width and color. See
https://plot.ly/python/reference/#scatter-marker-line for all valid
params
:param (dict) county_outline: dict of attributes of the county outline
including width and color. See
https://plot.ly/python/reference/#scatter-marker-line for all valid
params
:param (dict) centroid_marker: dict of attributes of the centroid marker.
The centroid markers are invisible by default and appear visible on
selection. See https://plot.ly/python/reference/#scatter-marker for
all valid params
:param (bool) round_legend_values: automatically round the numbers that
appear in the legend to the nearest integer.
Default = False
:param (bool) exponent_format: if set to True, puts numbers in the K, M,
B number format. For example 4000.0 becomes 4.0K
Default = False
:param (str) legend_title: title that appears above the legend
:param **layout_options: a **kwargs argument for all layout parameters
Example 1: Florida
```
import plotly.plotly as py
import plotly.figure_factory as ff
import numpy as np
import pandas as pd
df_sample = pd.read_csv(
'https://raw.githubusercontent.com/plotly/datasets/master/minoritymajority.csv'
)
df_sample_r = df_sample[df_sample['STNAME'] == 'Florida']
values = df_sample_r['TOT_POP'].tolist()
fips = df_sample_r['FIPS'].tolist()
binning_endpoints = list(np.mgrid[min(values):max(values):4j])
colorscale = ["#030512","#1d1d3b","#323268","#3d4b94","#3e6ab0",
"#4989bc","#60a7c7","#85c5d3","#b7e0e4","#eafcfd"]
fig = ff.create_choropleth(
fips=fips, values=values, scope=['Florida'], show_state_data=True,
colorscale=colorscale, binning_endpoints=binning_endpoints,
round_legend_values=True, plot_bgcolor='rgb(229,229,229)',
paper_bgcolor='rgb(229,229,229)', legend_title='Florida Population',
county_outline={'color': 'rgb(255,255,255)', 'width': 0.5},
exponent_format=True,
)
py.iplot(fig, filename='choropleth_florida')
```
Example 2: New England
```
import plotly.plotly as py
import plotly.figure_factory as ff
import pandas as pd
NE_states = ['Connecticut', 'Maine', 'Massachusetts',
'New Hampshire', 'Rhode Island']
df_sample = pd.read_csv(
'https://raw.githubusercontent.com/plotly/datasets/master/minoritymajority.csv'
)
df_sample_r = df_sample[df_sample['STNAME'].isin(NE_states)]
colorscale = ['rgb(68.0, 1.0, 84.0)',
'rgb(66.0, 64.0, 134.0)',
'rgb(38.0, 130.0, 142.0)',
'rgb(63.0, 188.0, 115.0)',
'rgb(216.0, 226.0, 25.0)']
values = df_sample_r['TOT_POP'].tolist()
fips = df_sample_r['FIPS'].tolist()
fig = ff.create_choropleth(
fips=fips, values=values, scope=NE_states, show_state_data=True
)
py.iplot(fig, filename='choropleth_new_england')
```
Example 3: California and Surrounding States
```
import plotly.plotly as py
import plotly.figure_factory as ff
import pandas as pd
df_sample = pd.read_csv(
'https://raw.githubusercontent.com/plotly/datasets/master/minoritymajority.csv'
)
df_sample_r = df_sample[df_sample['STNAME'] == 'California']
values = df_sample_r['TOT_POP'].tolist()
fips = df_sample_r['FIPS'].tolist()
colorscale = [
'rgb(193, 193, 193)',
'rgb(239,239,239)',
'rgb(195, 196, 222)',
'rgb(144,148,194)',
'rgb(101,104,168)',
'rgb(65, 53, 132)'
]
fig = ff.create_choropleth(
fips=fips, values=values, colorscale=colorscale,
scope=['CA', 'AZ', 'Nevada', 'Oregon', ' Idaho'],
binning_endpoints=[14348, 63983, 134827, 426762, 2081313],
county_outline={'color': 'rgb(255,255,255)', 'width': 0.5},
legend_title='California Counties',
title='California and Nearby States'
)
py.iplot(fig, filename='choropleth_california_and_surr_states_outlines')
```
Example 4: USA
```
import plotly.plotly as py
import plotly.figure_factory as ff
import numpy as np
import pandas as pd
df_sample = pd.read_csv(
'https://raw.githubusercontent.com/plotly/datasets/master/laucnty16.csv'
)
df_sample['State FIPS Code'] = df_sample['State FIPS Code'].apply(
lambda x: str(x).zfill(2)
)
df_sample['County FIPS Code'] = df_sample['County FIPS Code'].apply(
lambda x: str(x).zfill(3)
)
df_sample['FIPS'] = (
df_sample['State FIPS Code'] + df_sample['County FIPS Code']
)
binning_endpoints = list(np.linspace(1, 12, len(colorscale) - 1))
colorscale = ["#f7fbff", "#ebf3fb", "#deebf7", "#d2e3f3", "#c6dbef",
"#b3d2e9", "#9ecae1", "#85bcdb", "#6baed6", "#57a0ce",
"#4292c6", "#3082be", "#2171b5", "#1361a9", "#08519c",
"#0b4083","#08306b"]
fips = df_sample['FIPS']
values = df_sample['Unemployment Rate (%)']
fig = ff.create_choropleth(
fips=fips, values=values, scope=['usa'],
binning_endpoints=binning_endpoints, colorscale=colorscale,
show_hover=True, centroid_marker={'opacity': 0},
asp=2.9, title='USA by Unemployment %',
legend_title='Unemployment %'
)
py.iplot(fig, filename='choropleth_full_usa')
```
"""
# ensure optional modules imported
if not gp or not shapefile or not shapely:
raise ImportError(
"geopandas, pyshp and shapely must be installed for this figure "
"factory.\n\nRun the following commands to install the correct "
"versions of the following modules:\n\n"
"```\n"
"pip install geopandas==0.3.0\n"
"pip install pyshp==1.2.10\n"
"pip install shapely==1.6.3\n"
"```\n"
"If you are using Windows, follow this post to properly "
"install geopandas and dependencies:"
"http://geoffboeing.com/2014/09/using-geopandas-windows/\n\n"
"If you are using Anaconda, do not use PIP to install the "
"packages above. Instead use conda to install them:\n\n"
"```\n"
"conda install plotly\n"
"conda install geopandas\n"
"```"
)
df, df_state = _create_us_counties_df(st_to_state_name_dict,
state_to_st_dict)
fips_polygon_map = dict(
zip(
df['FIPS'].tolist(),
df['geometry'].tolist()
)
)
if not state_outline:
state_outline = {'color': 'rgb(240, 240, 240)',
'width': 1}
if not county_outline:
county_outline = {'color': 'rgb(0, 0, 0)',
'width': 0}
if not centroid_marker:
centroid_marker = {'size': 3, 'color': 'white', 'opacity': 1}
# ensure centroid markers appear on selection
if 'opacity' not in centroid_marker:
centroid_marker.update({'opacity': 1})
if len(fips) != len(values):
raise PlotlyError(
'fips and values must be the same length'
)
# make fips, values into lists
if isinstance(fips, pd.core.series.Series):
fips = fips.tolist()
if isinstance(values, pd.core.series.Series):
values = values.tolist()
# make fips numeric
fips = map(lambda x: int(x), fips)
if binning_endpoints:
intervals = utils.endpts_to_intervals(binning_endpoints)
LEVELS = _intervals_as_labels(intervals, round_legend_values,
exponent_format)
else:
if not order:
LEVELS = sorted(list(set(values)))
else:
# check if order is permutation
# of unique color col values
same_sets = sorted(list(set(values))) == set(order)
no_duplicates = not any(order.count(x) > 1 for x in order)
if same_sets and no_duplicates:
LEVELS = order
else:
raise PlotlyError(
'if you are using a custom order of unique values from '
'your color column, you must: have all the unique values '
'in your order and have no duplicate items'
)
if not colorscale:
colorscale = []
viridis_colors = clrs.colorscale_to_colors(
clrs.PLOTLY_SCALES['Viridis']
)
viridis_colors = clrs.color_parser(
viridis_colors, clrs.hex_to_rgb
)
viridis_colors = clrs.color_parser(
viridis_colors, clrs.label_rgb
)
viri_len = len(viridis_colors) + 1
viri_intervals = utils.endpts_to_intervals(
list(np.linspace(0, 1, viri_len))
)[1:-1]
for L in np.linspace(0, 1, len(LEVELS)):
for idx, inter in enumerate(viri_intervals):
if L == 0:
break
elif inter[0] < L <= inter[1]:
break
intermed = ((L - viri_intervals[idx][0]) /
(viri_intervals[idx][1] - viri_intervals[idx][0]))
float_color = clrs.find_intermediate_color(
viridis_colors[idx],
viridis_colors[idx],
intermed,
colortype='rgb'
)
# make R,G,B into int values
float_color = clrs.unlabel_rgb(float_color)
float_color = clrs.unconvert_from_RGB_255(float_color)
int_rgb = clrs.convert_to_RGB_255(float_color)
int_rgb = clrs.label_rgb(int_rgb)
colorscale.append(int_rgb)
if len(colorscale) < len(LEVELS):
raise PlotlyError(
"You have {} LEVELS. Your number of colors in 'colorscale' must "
"be at least the number of LEVELS: {}. If you are "
"using 'binning_endpoints' then 'colorscale' must have at "
"least len(binning_endpoints) + 2 colors".format(
len(LEVELS), min(LEVELS, LEVELS[:20])
)
)
color_lookup = dict(zip(LEVELS, colorscale))
x_traces = dict(zip(LEVELS, [[] for i in range(len(LEVELS))]))
y_traces = dict(zip(LEVELS, [[] for i in range(len(LEVELS))]))
# scope
if isinstance(scope, str):
raise PlotlyError(
"'scope' must be a list/tuple/sequence"
)
scope_names = []
extra_states = ['Alaska', 'Commonwealth of the Northern Mariana Islands',
'Puerto Rico', 'Guam', 'United States Virgin Islands',
'American Samoa']
for state in scope:
if state.lower() == 'usa':
scope_names = df['STATE_NAME'].unique()
scope_names = list(scope_names)
for ex_st in extra_states:
try:
scope_names.remove(ex_st)
except ValueError:
pass
else:
if state in st_to_state_name_dict.keys():
state = st_to_state_name_dict[state]
scope_names.append(state)
df_state = df_state[df_state['STATE_NAME'].isin(scope_names)]
plot_data = []
x_centroids = []
y_centroids = []
centroid_text = []
fips_not_in_shapefile = []
if not binning_endpoints:
for index, f in enumerate(fips):
level = values[index]
try:
fips_polygon_map[f].type
(x_traces, y_traces, x_centroids,
y_centroids, centroid_text) = _calculations(
df, fips, values, index, f, simplify_county, level,
x_centroids, y_centroids, centroid_text, x_traces,
y_traces, fips_polygon_map
)
except KeyError:
fips_not_in_shapefile.append(f)
else:
for index, f in enumerate(fips):
for j, inter in enumerate(intervals):
if inter[0] < values[index] <= inter[1]:
break
level = LEVELS[j]
try:
fips_polygon_map[f].type
(x_traces, y_traces, x_centroids,
y_centroids, centroid_text) = _calculations(
df, fips, values, index, f, simplify_county, level,
x_centroids, y_centroids, centroid_text, x_traces,
y_traces, fips_polygon_map
)
except KeyError:
fips_not_in_shapefile.append(f)
if len(fips_not_in_shapefile) > 0:
msg = (
'Unrecognized FIPS Values\n\nWhoops! It looks like you are '
'trying to pass at least one FIPS value that is not in '
'our shapefile of FIPS and data for the counties. Your '
'choropleth will still show up but these counties cannot '
'be shown.\nUnrecognized FIPS are: {}'.format(
fips_not_in_shapefile
)
)
warnings.warn(msg)
x_states = []
y_states = []
for index, row in df_state.iterrows():
if df_state['geometry'][index].type == 'Polygon':
x = row.geometry.simplify(simplify_state).exterior.xy[0].tolist()
y = row.geometry.simplify(simplify_state).exterior.xy[1].tolist()
x_states = x_states + x
y_states = y_states + y
elif df_state['geometry'][index].type == 'MultiPolygon':
x = ([poly.simplify(simplify_state).exterior.xy[0].tolist() for
poly in df_state['geometry'][index]])
y = ([poly.simplify(simplify_state).exterior.xy[1].tolist() for
poly in df_state['geometry'][index]])
for segment in range(len(x)):
x_states = x_states + x[segment]
y_states = y_states + y[segment]
x_states.append(np.nan)
y_states.append(np.nan)
x_states.append(np.nan)
y_states.append(np.nan)
for lev in LEVELS:
county_data = dict(
type='scatter',
mode='lines',
x=x_traces[lev],
y=y_traces[lev],
line=county_outline,
fill='toself',
fillcolor=color_lookup[lev],
name=lev,
hoverinfo='none',
)
plot_data.append(county_data)
if show_hover:
hover_points = dict(
type='scatter',
showlegend=False,
legendgroup='centroids',
x=x_centroids,
y=y_centroids,
text=centroid_text,
name='US Counties',
mode='markers',
marker={'color': 'white', 'opacity': 0},
hoverinfo='text'
)
centroids_on_select = dict(
selected=dict(marker=centroid_marker),
unselected=dict(marker=dict(opacity=0))
)
hover_points.update(centroids_on_select)
plot_data.append(hover_points)
if show_state_data:
state_data = dict(
type='scatter',
legendgroup='States',
line=state_outline,
x=x_states,
y=y_states,
hoverinfo='text',
showlegend=False,
mode='lines'
)
plot_data.append(state_data)
DEFAULT_LAYOUT = dict(
hovermode='closest',
xaxis=dict(
autorange=False,
range=USA_XRANGE,
showgrid=False,
zeroline=False,
fixedrange=True,
showticklabels=False
),
yaxis=dict(
autorange=False,
range=USA_YRANGE,
showgrid=False,
zeroline=False,
fixedrange=True,
showticklabels=False
),
margin=dict(t=40, b=20, r=20, l=20),
width=900,
height=450,
dragmode='select',
legend=dict(
traceorder='reversed',
xanchor='right',
yanchor='top',
x=1,
y=1
),
annotations=[]
)
fig = dict(data=plot_data, layout=DEFAULT_LAYOUT)
fig['layout'].update(layout_options)
fig['layout']['annotations'].append(
dict(
x=1,
y=1.05,
xref='paper',
yref='paper',
xanchor='right',
showarrow=False,
text='<b>' + legend_title + '</b>'
)
)
if len(scope) == 1 and scope[0].lower() == 'usa':
xaxis_range_low = -125.0
xaxis_range_high = -55.0
yaxis_range_low = 25.0
yaxis_range_high = 49.0
else:
xaxis_range_low = float('inf')
xaxis_range_high = float('-inf')
yaxis_range_low = float('inf')
yaxis_range_high = float('-inf')
for trace in fig['data']:
if all(isinstance(n, Number) for n in trace['x']):
calc_x_min = min(trace['x'] or [float('inf')])
calc_x_max = max(trace['x'] or [float('-inf')])
if calc_x_min < xaxis_range_low:
xaxis_range_low = calc_x_min
if calc_x_max > xaxis_range_high:
xaxis_range_high = calc_x_max
if all(isinstance(n, Number) for n in trace['y']):
calc_y_min = min(trace['y'] or [float('inf')])
calc_y_max = max(trace['y'] or [float('-inf')])
if calc_y_min < yaxis_range_low:
yaxis_range_low = calc_y_min
if calc_y_max > yaxis_range_high:
yaxis_range_high = calc_y_max
# camera zoom
fig['layout']['xaxis']['range'] = [xaxis_range_low, xaxis_range_high]
fig['layout']['yaxis']['range'] = [yaxis_range_low, yaxis_range_high]
# aspect ratio
if asp is None:
usa_x_range = USA_XRANGE[1] - USA_XRANGE[0]
usa_y_range = USA_YRANGE[1] - USA_YRANGE[0]
asp = usa_x_range / usa_y_range
# based on your figure
width = float(fig['layout']['xaxis']['range'][1] -
fig['layout']['xaxis']['range'][0])
height = float(fig['layout']['yaxis']['range'][1] -
fig['layout']['yaxis']['range'][0])
center = (sum(fig['layout']['xaxis']['range']) / 2.,
sum(fig['layout']['yaxis']['range']) / 2.)
if height / width > (1 / asp):
new_width = asp * height
fig['layout']['xaxis']['range'][0] = center[0] - new_width * 0.5
fig['layout']['xaxis']['range'][1] = center[0] + new_width * 0.5
else:
new_height = (1 / asp) * width
fig['layout']['yaxis']['range'][0] = center[1] - new_height * 0.5
fig['layout']['yaxis']['range'][1] = center[1] + new_height * 0.5
return fig
