def _plotly_color_map(names: List[str]) -> Mapping[str, str]:
"""
Private plot helper method for generating colors
for similar groups across subplots.
Parameters
----------
names:
List of levels in a group.
Returns
-------
Dictionary assigning group names to a color.
"""
if len(names) > 1:
linear_purple_scale = n_colors(lowcolor='rgb(218,183,193)',
highcolor='rgb(64,19,98)',
n_colors=len(names),
colortype='rgb')
hex_scale = [
rgb_to_hex(*unlabel_rgb(rgb)) for rgb in linear_purple_scale
]
# From https://github.com/plotly/plotly.py/issues/1026
color_generator = cycle(hex_scale)
return dict(zip(names, color_generator))
else:
return dict(zip(names, ['rgb(64,19,98)']))
def to_rgb_tuple(color):
from plotly.colors import unlabel_rgb
if isinstance(color, tuple):
pass
elif color.startswith("#"):
color = hex_to_rgb(color)
else:
color = unlabel_rgb(color)
return tuple(int(c) for c in color)
def add_alpha_channel(rgb_color: str, alpha: float) -> str:
"""
Add an alpha channel to a rgb color string.
Test:
>>> add_alpha_channel("rgb(0.0 ,0.0 ,0.0)", 0.0)
'rgba(0.0, 0.0, 0.0, 0.0)'
>>> add_alpha_channel("rgb(0.0 ,0.0 ,0.0)", 0.7)
'rgba(0.0, 0.0, 0.0, 0.7)'
"""
red, green, blue = colors.unlabel_rgb(rgb_color)
return f"rgba({red}, {green}, {blue}, {alpha})"
def plotly_color_map(names):
"""
Private plot helper method for generating colors
for similar groups across subplots.
Parameters
----------
names:
List of levels in a group.
Returns
-------
Dictionary assigning group names to a color.
"""
if len(names) > 1:
d3_category_20 = [
"#1f77b4", "#aec7e8", "#ff7f0e", "#ffbb78", "#2ca02c", "#98df8a",
"#d62728", "#ff9896", "#9467bd", "#c5b0d5", "#8c564b", "#c49c94",
"#e377c2", "#f7b6d2", "#7f7f7f", "#c7c7c7", "#bcbd22", "#dbdb8d",
"#17becf", "#9edae5"
]
color_generator = cycle(d3_category_20)
return dict(zip(names, color_generator))
else:
return dict(zip(names, [rgb_to_hex(*unlabel_rgb('rgb(64,19,98)'))]))
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 unlabel_rgbs(cmap):
from plotly import colors
return [colors.unlabel_rgb(c) for c in cmap]
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
def __init__(
self,
position: Position,
foreign_time_window_controller: StartAndDurationController = None,
):
super().__init__()
self.position = position
joint_keys = list(position.spatial_series.keys())
self.joint_colors = []
for (joint, c) in zip(joint_keys, DEFAULT_PLOTLY_COLORS):
self.joint_colors.append(c)
self.spatial_series = position.spatial_series[joint_keys[0]]
if foreign_time_window_controller is None:
self.time_window_controller = StartAndDurationController(
tmax=get_timeseries_maxt(self.spatial_series),
tmin=get_timeseries_mint(self.spatial_series),
start=0,
duration=5,
)
show_time_controller = True
else:
show_time_controller = False
self.time_window_controller = foreign_time_window_controller
frame_ind = timeseries_time_to_ind(
self.spatial_series, self.time_window_controller.value[0])
self.sample_period = 1 / list(
self.position.spatial_series.values())[0].rate
self.play = Play(
value=0,
min=0,
max=int(self.time_window_controller.duration.value /
self.sample_period),
step=1,
interval=self.sample_period * 1000,
)
joint_colors = [
to_hex(np.array(unlabel_rgb(x)) / 255)
for x in DEFAULT_PLOTLY_COLORS
]
self.joint_keys = POSITION_KEYS
self.joint_colors = [
joint_colors[0], # l_wrist
joint_colors[1], # l_elbow
joint_colors[2], # l_shoulder
joint_colors[9], # neck
joint_colors[4], # nose
joint_colors[3], # l_ear
joint_colors[5], # r_ear
joint_colors[4], # nose
joint_colors[9], # neck
joint_colors[6], # r_shoulder
joint_colors[7], # r_elbow
joint_colors[8], # r_wrist
]
self.skeleton_labels = [
"L_Wrist",
"L_Elbow",
"L_Shoulder",
"Neck",
"Nose",
"L_Ear",
"R_Ear",
"Nose",
"Neck",
"R_Shoulder",
"R_Elbow",
"R_Wrist",
]
self.fig = (go.FigureWidget()
) # animation_duration=int(1/spatial_series.rate*1000)
self.plot_skeleton(frame_ind)
self.updated_time_range({"new": None})
# Updates list of valid spike times at each change in time range
self.time_window_controller.observe(self.updated_time_range)
self.play.observe(self.animate_scatter_chart)
if show_time_controller:
self.children = [self.time_window_controller, self.fig, self.play]
else:
self.children = [self.fig, self.play]
