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# -*- coding: utf-8 -*-
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import warnings
import numpy as np
import pandas as pd
import scipy.ndimage as ndi
from matplotlib import cm
from matplotlib import pyplot as plt
from matplotlib.collections import PatchCollection
from matplotlib.patches import Polygon
from matplotlib.patches import Rectangle
from scipy.spatial import ConvexHull
from .google_static_maps_api import GoogleStaticMapsAPI
from .google_static_maps_api import MAPTYPE
from .google_static_maps_api import MAX_SIZE
from .google_static_maps_api import SCALE
BLANK_THRESH = 2 * 1e-3 # Value below which point in a heatmap should be blank
def register_api_key(api_key):
"""Register a Google Static Maps API key to enable queries to Google.
Create your own Google Static Maps API key on https://console.developers.google.com.
:param str api_key: the API key
:return: None
"""
GoogleStaticMapsAPI.register_api_key(api_key)
def background_and_pixels(latitudes, longitudes, size, maptype):
"""Queries the proper background map and translate geo coordinated into pixel locations on this map.
:param pandas.Series latitudes: series of sample latitudes
:param pandas.Series longitudes: series of sample longitudes
:param int size: target size of the map, in pixels
:param string maptype: type of maps, see GoogleStaticMapsAPI docs for more info
:return: map and pixels
:rtype: (PIL.Image, pandas.DataFrame)
"""
# From lat/long to pixels, zoom and position in the tile
center_lat = (latitudes.max() + latitudes.min()) / 2
center_long = (longitudes.max() + longitudes.min()) / 2
zoom = GoogleStaticMapsAPI.get_zoom(latitudes, longitudes, size, SCALE)
pixels = GoogleStaticMapsAPI.to_tile_coordinates(latitudes, longitudes, center_lat, center_long, zoom, size, SCALE)
# Google Map
img = GoogleStaticMapsAPI.map(
center=(center_lat, center_long),
zoom=zoom,
scale=SCALE,
size=(size, size),
maptype=maptype,
)
return img, pixels
def scatter(latitudes, longitudes, colors=None, maptype=MAPTYPE):
"""Scatter plot over a map. Can be used to visualize clusters by providing the marker colors.
:param pandas.Series latitudes: series of sample latitudes
:param pandas.Series longitudes: series of sample longitudes
:param pandas.Series colors: marker colors, as integers
:param string maptype: type of maps, see GoogleStaticMapsAPI docs for more info
:return: None
"""
width = SCALE * MAX_SIZE
colors = pd.Series(0, index=latitudes.index) if colors is None else colors
img, pixels = background_and_pixels(latitudes, longitudes, MAX_SIZE, maptype)
plt.figure(figsize=(10, 10))
plt.imshow(np.array(img)) # Background map
plt.scatter( # Scatter plot
pixels['x_pixel'],
pixels['y_pixel'],
c=colors,
s=width / 40,
linewidth=0,
alpha=0.5,
)
plt.gca().invert_yaxis() # Origin of map is upper left
plt.axis([0, width, width, 0]) # Remove margin
plt.axis('off')
plt.tight_layout()
plt.show()
def plot_markers(markers, maptype=MAPTYPE):
"""Plot markers on a map.
:param pandas.DataFrame markers: DataFrame with at least 'latitude' and 'longitude' columns, and optionally
* 'color' column, see GoogleStaticMapsAPI docs for more info
* 'label' column, see GoogleStaticMapsAPI docs for more info
* 'size' column, see GoogleStaticMapsAPI docs for more info
:param string maptype: type of maps, see GoogleStaticMapsAPI docs for more info
:return: None
"""
# Checking input columns
fields = markers.columns.intersection(['latitude', 'longitude', 'color', 'label', 'size'])
if len(fields) == 0 or 'latitude' not in fields or 'longitude' not in fields:
msg = 'Input dataframe should contain at least colums \'latitude\' and \'longitude\' '
msg += '(and columns \'color\', \'label\', \'size\' optionally).'
raise KeyError(msg)
# Checking NaN input
nans = (markers.latitude.isnull() | markers.longitude.isnull())
if nans.sum() > 0:
warnings.warn('Ignoring {} example(s) containing NaN latitude or longitude.'.format(nans.sum()))
# Querying map
img = GoogleStaticMapsAPI.map(
scale=SCALE,
markers=markers[fields].loc[~nans].T.to_dict().values(),
maptype=maptype,
)
plt.figure(figsize=(10, 10))
plt.imshow(np.array(img))
plt.tight_layout()
plt.axis('off')
plt.show()
def heatmap(latitudes, longitudes, values, resolution=None, maptype=MAPTYPE):
"""Plot a geographical heatmap of the given metric.
:param pandas.Series latitudes: series of sample latitudes
:param pandas.Series longitudes: series of sample longitudes
:param pandas.Series values: series of sample values
:param int resolution: resolution (in pixels) for the heatmap
:param string maptype: type of maps, see GoogleStaticMapsAPI docs for more info
:return: None
"""
img, pixels = background_and_pixels(latitudes, longitudes, MAX_SIZE, maptype)
# Smooth metric
z = grid_density_gaussian_filter(
zip(pixels['x_pixel'], pixels['y_pixel'], values),
MAX_SIZE * SCALE,
resolution=resolution if resolution else MAX_SIZE * SCALE, # Heuristic for pretty plots
)
# Plot
width = SCALE * MAX_SIZE
plt.figure(figsize=(10, 10))
plt.imshow(np.array(img)) # Background map
plt.imshow(z, origin='lower', extent=[0, width, 0, width], alpha=0.15) # Foreground, transparent heatmap
plt.scatter(pixels['x_pixel'], pixels['y_pixel'], s=1) # Markers of all points
plt.gca().invert_yaxis() # Origin of map is upper left
plt.axis([0, width, width, 0]) # Remove margin
plt.axis('off')
plt.tight_layout()
plt.show()
def density_plot(latitudes, longitudes, resolution=None, maptype=MAPTYPE):
"""Given a set of geo coordinates, draw a density plot on a map.
:param pandas.Series latitudes: series of sample latitudes
:param pandas.Series longitudes: series of sample longitudes
:param int resolution: resolution (in pixels) for the heatmap
:param string maptype: type of maps, see GoogleStaticMapsAPI docs for more info
:return: None
"""
heatmap(latitudes, longitudes, np.ones(latitudes.shape[0]), resolution=resolution, maptype=maptype)
def grid_density_gaussian_filter(data, size, resolution=None, smoothing_window=None):
"""Smoothing grid values with a Gaussian filter.
:param [(float, float, float)] data: list of 3-dimensional grid coordinates
:param int size: grid size
:param int resolution: desired grid resolution
:param int smoothing_window: size of the gaussian kernels for smoothing
:return: smoothed grid values
:rtype: numpy.ndarray
"""
resolution = resolution if resolution else size
k = (resolution - 1) / size
w = smoothing_window if smoothing_window else int(0.01 * resolution) # Heuristic
imgw = (resolution + 2 * w)
img = np.zeros((imgw, imgw))
for x, y, z in data:
ix = int(x * k) + w
iy = int(y * k) + w
if 0 <= ix < imgw and 0 <= iy < imgw:
img[iy][ix] += z
z = ndi.gaussian_filter(img, (w, w)) # Gaussian convolution
z[z <= BLANK_THRESH] = np.nan # Making low values blank
return z[w:-w, w:-w]
def polygons(latitudes, longitudes, clusters, maptype=MAPTYPE):
"""Plot clusters of points on map, including them in a polygon defining their convex hull.
:param pandas.Series latitudes: series of sample latitudes
:param pandas.Series longitudes: series of sample longitudes
:param pandas.Series clusters: marker clusters
:param string maptype: type of maps, see GoogleStaticMapsAPI docs for more info
:return: None
"""
width = SCALE * MAX_SIZE
img, pixels = background_and_pixels(latitudes, longitudes, MAX_SIZE, maptype)
# Building collection of polygons
polygon_list = []
unique_clusters = clusters.unique()
cmap = pd.Series(np.arange(unique_clusters.shape[0] - 1, -1, -1), index=unique_clusters)
for c in unique_clusters:
in_polygon = clusters == c
if in_polygon.sum() < 3:
print('[WARN] Cannot draw polygon for cluster {} - only {} samples.'.format(c, in_polygon.sum()))
continue
cluster_pixels = pixels.loc[clusters == c]
polygon_list.append(Polygon(cluster_pixels.iloc[ConvexHull(cluster_pixels).vertices], closed=True))
# Background map
plt.figure(figsize=(10, 10))
ax = plt.subplot(111)
plt.imshow(np.array(img))
# Collection of polygons
p = PatchCollection(polygon_list, cmap='jet', alpha=0.25)
p.set_array(cmap.values)
ax.add_collection(p)
# Scatter plot
plt.scatter(
pixels['x_pixel'],
pixels['y_pixel'],
c=cmap.loc[clusters],
cmap='jet',
s=width / 40,
linewidth=0,
alpha=0.25,
)
# Axis options
plt.gca().invert_yaxis() # Origin of map is upper left
plt.axis([0, width, width, 0]) # Remove margin
plt.axis('off')
plt.tight_layout()
# Building legend box
jet_cmap = cm.get_cmap('jet')
plt.legend(
[Rectangle((0, 0), 1, 1, fc=jet_cmap(i / (cmap.shape[0] - 1)), alpha=0.25) for i in cmap.values],
cmap.index,
loc=4,
bbox_to_anchor=(1.1, 0),
)
plt.show()
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