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How to plot a map of a semi-sphere (eg northern hemisphere) using cartopy.
I'm trying to plot a map of the northern hemisphere using cartopy. But I don't understand how should I define the extent of the map so that only this region of interest is plotted. I would like the map to be cut off at 0° latitude. I would like to have code where I could easily define any subset of the glob using the ccrs.NearsidePerspective projection, or the ccrs.Orthographic projection.
Below I leave a code for reproduction.
import numpy as np
import cartopy.crs as ccrs
import matplotlib.pyplot as plt
# Creating fake data
x = np.linspace(-180, 180, 361)
y = np.linspace(-90, 90, 181)
lon, lat = np.meshgrid(x, y)
values = np.random.random(lon.shape)*20
fig = plt.figure(figsize=(15, 10))
proj = ccrs.NearsidePerspective(central_longitude=-45, central_latitude=21)
ax = fig.add_subplot(121, projection=proj)
ax.set_extent([-120, 40, 0, 60])
ax.pcolormesh(lon, lat, values, transform=ccrs.PlateCarree())
ax.coastlines(linewidth=2)
gl = ax.gridlines(draw_labels=True, linestyle='--')
The code generates the following figure:
Thank you very much in advance.
Robson
To plot only the upper hemisphere part of the map projection, a polygon of that part is needed to use as the projection boundary.
That polygon is created as a matplotlib-path object. It vertices' coordinates are data coordinates in my code, so that, no transformation is required when applied to the final plot.
This is a complete code:-
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
import matplotlib.path as mpath
import numpy as np
from geographiclib.geodesic import Geodesic
fig = plt.figure(figsize=[12, 12])
proj = ccrs.NearsidePerspective(central_longitude=-45, central_latitude=21, satellite_height=35785831)
ax = plt.subplot(projection=proj)
# The value of r is obtained by previous run of this code ...
# with the line .. #print(ax.get_xlim()) uncommented
r = 5476336.098
ax.set_xlim(-r, r)
ax.set_ylim(-r, r)
ax.stock_img()
ax.coastlines(lw=1, color="darkblue")
# Find the locations of points along the equatorial arc
# start location
lon_fr, lat_fr = 30, 0
# end location
lon_to, lat_to = -120, 0
# This gets geodesic between the two points, WGS84 ellipsoid is used
geodl = Geodesic.WGS84.InverseLine(lat_fr, lon_fr, lat_to, lon_to)
lonlist, latlist = [], []
num_points = 32 #for series of points on geodesic/equator
for ea in np.linspace(0, geodl.s13, num_points):
g = geodl.Position(ea, Geodesic.STANDARD | Geodesic.LONG_UNROLL)
#print("{:.0f} {:.5f} {:.5f} {:.5f}".format(g['s12'], g['lat2'], g['lon2'], g['azi2']))
lon2, lat2 = g['lon2'], g['lat2']
lonlist.append( g['lon2'] )
latlist.append( g['lat2'] )
# Get data-coords from (lonlist, latlist)
# .. as points along equatorial arc
dataxy = proj.transform_points(ccrs.PlateCarree(), np.array(lonlist), np.array(latlist))
# (Uncomment to) Plot equator line
#ax.plot(dataxy[:, 0:1], dataxy[:, 1:2], "go-", linewidth=2, markersize=5, zorder=10)
# Top semi-circle arc for map extent
theta = np.linspace(-0.5*np.pi, 0.5*np.pi, 64)
center, radius = [0, 0], r
verts = np.vstack([np.sin(theta), np.cos(theta)]).T
# Combine vertices of the semi-circle and equatorial arcs
# These points are in data coordinates, ready to plot on the axes.
verts = np.vstack([verts*r, dataxy[:, 0:2]])
polygon = mpath.Path(verts + center)
ax.set_boundary(polygon) #This masks-out unwanted part of the plot
gl = ax.gridlines(draw_labels=True, xlocs=range(-150,180,30), ylocs=range(0, 90, 15),
y_inline=True, linestyle='--', lw= 5, color= "w", )
# Get limits, the values are the radius of the circular map extent
# The values is then used as r = 5476336.09797 on top of the code
#print(ax.get_xlim())
#print(ax.get_ylim())
plt.show()
I'm currently trying to build an N-body simulation but I'm having a little trouble with plotting the results the way I'd like.
In the code below (with some example data for a few points in an orbit) I'm importing the position and time data and organizing it into a pandas dataframe. To create the 3D animation I use matplotlib's animation class, which works perfectly.
However, the usual way to set up an animation is limited in that you can't customize the points in each frame individually (please let me know if I'm wrong here :p). Since my animation is showing orbiting bodies I would like to vary their sizes and colors. To do that I essentially create a graph for each body and set it's color etc. When it gets to the update_graph function, I iterate over the n bodies, retrieve their individual (x,y,z) coordinates, and update their graphs.
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import cm
from mpl_toolkits.mplot3d.axes3d import get_test_data
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.animation as animation
import pandas as pd
nbodies = 2
x = np.array([[1.50000000e-10, 0.00000000e+00, 0.00000000e+00],
[9.99950000e-01, 1.00000000e-02, 0.00000000e+00],
[4.28093585e-06, 3.22964816e-06, 0.00000000e+00],
[-4.16142210e-01, 9.09335149e-01, 0.00000000e+00],
[5.10376489e-06, 1.42204430e-05, 0.00000000e+00],
[-6.53770813e-01, -7.56722445e-01, 0.00000000e+00]])
t = np.array([0.01, 0.01, 2.0, 2.0, 4.0, 4.0])
tt = np.array([0.01, 2.0, 4.0])
x = x.reshape((len(tt), nbodies, 3))
x_coords = x[:, :, 0].flatten()
y_coords = x[:, :, 1].flatten()
z_coords = x[:, :, 2].flatten()
df = pd.DataFrame({"time": t[:] ,"x" : x_coords, "y" : y_coords, "z" : z_coords})
print(df)
def update_graph(num):
data=df[df['time']==tt[num]] # x,y,z of all bodies at current time
for n in range(nbodies): # update graphs
data_n = data[data['x']==x_coords[int(num * nbodies) + n]] # x,y,z of body n
graph = graphs[n]
graph.set_data(data_n.x, data_n.y)
graph.set_3d_properties(data_n.z)
graphs[n] = graph
return graphs
plt.style.use('dark_background')
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.set_xlabel('x (AU)')
ax.set_ylabel('y (AU)')
ax.set_zlabel('z (AU)')
plt.xlim(-1.5,1.5)
plt.ylim(-1.5,1.5)
# initialize
data=df[df['time']==0]
ms_list = [5, 1]
c_list = ['yellow', 'blue']
graphs = []
for n in range(nbodies):
graphs.append(ax.plot([], [], [], linestyle="", marker=".",
markersize=ms_list[n], color=c_list[n])[0])
ani = animation.FuncAnimation(fig, update_graph, len(tt),
interval=400, blit=True, repeat=True)
plt.show()
However, doing this gives me the following error:
Traceback (most recent call last):
File "/home/kris/anaconda3/lib/python3.7/site-packages/matplotlib/backend_bases.py", line 1194, in _on_timer
ret = func(*args, **kwargs)
File "/home/kris/anaconda3/lib/python3.7/site-packages/matplotlib/animation.py", line 1447, in _step
still_going = Animation._step(self, *args)
File "/home/kris/anaconda3/lib/python3.7/site-packages/matplotlib/animation.py", line 1173, in _step
self._draw_next_frame(framedata, self._blit)
File "/home/kris/anaconda3/lib/python3.7/site-packages/matplotlib/animation.py", line 1193, in _draw_next_frame
self._post_draw(framedata, blit)
File "/home/kris/anaconda3/lib/python3.7/site-packages/matplotlib/animation.py", line 1216, in _post_draw
self._blit_draw(self._drawn_artists, self._blit_cache)
File "/home/kris/anaconda3/lib/python3.7/site-packages/matplotlib/animation.py", line 1231, in _blit_draw
a.axes.draw_artist(a)
File "/home/kris/anaconda3/lib/python3.7/site-packages/matplotlib/axes/_base.py", line 2661, in draw_artist
a.draw(self.figure._cachedRenderer)
File "/home/kris/anaconda3/lib/python3.7/site-packages/matplotlib/artist.py", line 38, in draw_wrapper
return draw(artist, renderer, *args, **kwargs)
File "/home/kris/anaconda3/lib/python3.7/site-packages/mpl_toolkits/mplot3d/art3d.py", line 202, in draw
xs, ys, zs = proj3d.proj_transform(xs3d, ys3d, zs3d, renderer.M)
File "/home/kris/anaconda3/lib/python3.7/site-packages/mpl_toolkits/mplot3d/proj3d.py", line 201, in proj_transform
vec = _vec_pad_ones(xs, ys, zs)
File "/home/kris/anaconda3/lib/python3.7/site-packages/mpl_toolkits/mplot3d/proj3d.py", line 189, in _vec_pad_ones
return np.array([xs, ys, zs, np.ones_like(xs)])
File "/home/kris/anaconda3/lib/python3.7/site-packages/pandas/core/series.py", line 871, in __getitem__
result = self.index.get_value(self, key)
File "/home/kris/anaconda3/lib/python3.7/site-packages/pandas/core/indexes/base.py", line 4405, in get_value
return self._engine.get_value(s, k, tz=getattr(series.dtype, "tz", None))
File "pandas/_libs/index.pyx", line 80, in pandas._libs.index.IndexEngine.get_value
File "pandas/_libs/index.pyx", line 90, in pandas._libs.index.IndexEngine.get_value
File "pandas/_libs/index.pyx", line 138, in pandas._libs.index.IndexEngine.get_loc
File "pandas/_libs/hashtable_class_helper.pxi", line 997, in pandas._libs.hashtable.Int64HashTable.get_item
File "pandas/_libs/hashtable_class_helper.pxi", line 1004, in pandas._libs.hashtable.Int64HashTable.get_item
KeyError: 0
Aborted (core dumped)
I'm not sure what this really means, but I do know the problem is something to do with updating the graphs with only one row of coordinates rather than all three. Because if I instead have
def update_graph(num):
data=df[df['time']==tt[num]] # x,y,z of all bodies at current time
for n in range(nbodies): # update graphs
#data_n = data[data['x']==x_coords[int(num * nbodies) + n]] # x,y,z of body n
graph = graphs[n]
graph.set_data(data.x, data.y) # using data rather than data_n here now
graph.set_3d_properties(data.z)
graphs[n] = graph
return graphs
it actually works, and plots three copies of the bodies with varying colors and sizes on top of each other as you would expect.
Any help would be much appreciated. Thanks!
I don't understand why you are going through a pandas DataFrame, when you seem to already have all the data you need in your numpy array. I couldn't reproduce the initial problem, by I propose this solution that uses pure numpy arrays, which may fix the problem:
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import cm
from mpl_toolkits.mplot3d.axes3d import get_test_data
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.animation as animation
import pandas as pd
nbodies = 2
x = np.array([[1.50000000e-10, 0.00000000e+00, 0.00000000e+00],
[9.99950000e-01, 1.00000000e-02, 0.00000000e+00],
[4.28093585e-06, 3.22964816e-06, 0.00000000e+00],
[-4.16142210e-01, 9.09335149e-01, 0.00000000e+00],
[5.10376489e-06, 1.42204430e-05, 0.00000000e+00],
[-6.53770813e-01, -7.56722445e-01, 0.00000000e+00]])
t = np.array([0.01, 0.01, 2.0, 2.0, 4.0, 4.0])
tt = np.array([0.01, 2.0, 4.0])
x = x.reshape((len(tt), nbodies, 3))
def update_graph(i):
data = x[i, :, :] # x,y,z of all bodies at current time
for body, graph in zip(data, graphs): # update graphs
graph.set_data(body[0], body[1])
graph.set_3d_properties(body[2])
return graphs
plt.style.use('dark_background')
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.set_xlabel('x (AU)')
ax.set_ylabel('y (AU)')
ax.set_zlabel('z (AU)')
plt.xlim(-1.5, 1.5)
plt.ylim(-1.5, 1.5)
# initialize
ms_list = [50, 10]
c_list = ['yellow', 'blue']
graphs = []
for n in range(nbodies):
graphs.append(ax.plot([], [], [], linestyle="", marker=".",
markersize=ms_list[n], color=c_list[n])[0])
ani = animation.FuncAnimation(fig, func=update_graph, frames=len(tt),
interval=400, blit=True, repeat=True)
plt.show()
I want to animate a point moving along a path from one location to another on the map.
For example, I drawn a path from New York to New Delhi, using Geodetic transform. Eg. taken from docs Adding data to the map
plt.plot([ny_lon, delhi_lon], [ny_lat, delhi_lat],
color='blue', linewidth=2, marker='o',
transform=ccrs.Geodetic(),
)
Now i want to move a point along this path.
My idea was to somehow get some (say 50) points, along the path and plot a marker on each point for each frame. But I am not able to find a way to get the points on the path.
I found a function transform_points under classCRS, but I am unable to use this, as this gives me the same number of points i have, not the points in between.
Thanks in advance!
There are a couple of approaches to this.
The matplotlib approach
I'll start with perhaps the most basic if you are familiar with matplotlib, but this approach suffers from indirectly using cartopy's functionality, and is therefore harder to configure/extend.
There is a private _get_transformed_path method on a Line2D object (the thing that is returned from plt.plot). The resulting TransformedPath object has a get_transformed_path_and_affine method, which basically will give us the projected line (in the coordinate system of the Axes being drawn).
In [1]: import cartopy.crs as ccrs
In [3]: import matplotlib.pyplot as plt
In [4]: ax = plt.axes(projection=ccrs.Robinson())
In [6]: ny_lon, ny_lat = -75, 43
In [7]: delhi_lon, delhi_lat = 77.23, 28.61
In [8]: [line] = plt.plot([ny_lon, delhi_lon], [ny_lat, delhi_lat],
...: color='blue', linewidth=2, marker='o',
...: transform=ccrs.Geodetic(),
...: )
In [9]: t_path = line._get_transformed_path()
In [10]: path_in_data_coords, _ = t_path.get_transformed_path_and_affine()
In [11]: path_in_data_coords.vertices
Out[11]:
array([[-6425061.82215208, 4594257.92617961],
[-5808923.84969279, 5250795.00604155],
[-5206753.88613758, 5777772.51828996],
[-4554622.94040482, 6244967.03723341],
[-3887558.58343227, 6627927.97123701],
[-3200922.19194864, 6932398.19937816],
[-2480001.76507805, 7165675.95095855],
[-1702269.5101901 , 7332885.72276795],
[ -859899.12295981, 7431215.78426759],
[ 23837.23431173, 7453455.61302756],
[ 889905.10635756, 7397128.77301289],
[ 1695586.66856764, 7268519.87627204],
[ 2434052.81300274, 7073912.54130764],
[ 3122221.22299409, 6812894.40443648],
[ 3782033.80448001, 6478364.28561403],
[ 4425266.18173684, 6062312.15662039],
[ 5049148.25986903, 5563097.6328901 ],
[ 5616318.74912886, 5008293.21452795],
[ 6213232.98764984, 4307186.23400115],
[ 6720608.93929235, 3584542.06839575],
[ 7034261.06659143, 3059873.62740856]])
We can pull this together with matplotlib's animation functionality to do as requested:
import cartopy.crs as ccrs
import matplotlib.animation as animation
import matplotlib.pyplot as plt
ax = plt.axes(projection=ccrs.Robinson())
ax.stock_img()
ny_lon, ny_lat = -75, 43
delhi_lon, delhi_lat = 77.23, 28.61
[line] = plt.plot([ny_lon, delhi_lon], [ny_lat, delhi_lat],
color='blue', linewidth=2, marker='o',
transform=ccrs.Geodetic(),
)
t_path = line._get_transformed_path()
path_in_data_coords, _ = t_path.get_transformed_path_and_affine()
# Draw the point that we want to animate.
[point] = plt.plot(ny_lon, ny_lat, marker='o', transform=ax.projection)
def animate_point(i):
verts = path_in_data_coords.vertices
i = i % verts.shape[0]
# Set the coordinates of the line to the coordinate of the path.
point.set_data(verts[i, 0], verts[i, 1])
ani = animation.FuncAnimation(
ax.figure, animate_point,
frames= path_in_data_coords.vertices.shape[0],
interval=125, repeat=True)
ani.save('point_ani.gif', writer='imagemagick')
plt.show()
The cartopy approach
Under the hood, cartopy's matplotlib implementation (as used above), is calling the project_geometry method. We may as well make use of this directly as it is often more convenient to be using Shapely geometries than it is matplotlib Paths.
With this approach, we simply define a shapely geometry, and then construct the source and target coordinate reference systems that we want to convert the geometry from/to:
target_cs.project_geometry(geometry, source_cs)
The only thing we have to watch out for is that the result can be a MultiLineString (or more generally, any Multi- geometry type). However, in our simple case, we don't need to deal with that (incidentally, the same was true of the simple Path returned in the first example).
The code to produce a similar plot to above:
import cartopy.crs as ccrs
import matplotlib.animation as animation
import matplotlib.pyplot as plt
import numpy as np
import shapely.geometry as sgeom
ax = plt.axes(projection=ccrs.Robinson())
ax.stock_img()
ny_lon, ny_lat = -75, 43
delhi_lon, delhi_lat = 77.23, 28.61
line = sgeom.LineString([[ny_lon, ny_lat], [delhi_lon, delhi_lat]])
projected_line = ccrs.PlateCarree().project_geometry(line, ccrs.Geodetic())
# We only animate along one of the projected lines.
if isinstance(projected_line, sgeom.MultiLineString):
projected_line = projected_line.geoms[0]
ax.add_geometries(
[projected_line], ccrs.PlateCarree(),
edgecolor='blue', facecolor='none')
[point] = plt.plot(ny_lon, ny_lat, marker='o', transform=ccrs.PlateCarree())
def animate_point(i):
verts = np.array(projected_line.coords)
i = i % verts.shape[0]
# Set the coordinates of the line to the coordinate of the path.
point.set_data(verts[i, 0], verts[i, 1])
ani = animation.FuncAnimation(
ax.figure, animate_point,
frames=len(projected_line.coords),
interval=125, repeat=True)
ani.save('projected_line_ani.gif', writer='imagemagick')
plt.show()
Final remaaaaarrrrrrks....
The approach naturally generalises to animating any type of matplotlib Arrrrtist.... in this case, I took a bit more control over the great circle resolution, and I animated an image along the great circle:
import cartopy.crs as ccrs
import matplotlib.animation as animation
import matplotlib.pyplot as plt
import numpy as np
import shapely.geometry as sgeom
ax = plt.axes(projection=ccrs.Mercator())
ax.stock_img()
line = sgeom.LineString([[-5.9845, 37.3891], [-82.3666, 23.1136]])
# Higher resolution version of Mercator. Same workaround as found in
# https://github.com/SciTools/cartopy/issues/8#issuecomment-326987465.
class HighRes(ax.projection.__class__):
#property
def threshold(self):
return super(HighRes, self).threshold / 100
projected_line = HighRes().project_geometry(line, ccrs.Geodetic())
# We only animate along one of the projected lines.
if isinstance(projected_line, sgeom.MultiLineString):
projected_line = projected_line.geoms[0]
# Add the projected line to the map.
ax.add_geometries(
[projected_line], ax.projection,
edgecolor='blue', facecolor='none')
def ll_to_extent(x, y, ax_size=(4000000, 4000000)):
"""
Return an image extent in centered on the given
point with the given width and height.
"""
return [x - ax_size[0] / 2, x + ax_size[0] / 2,
y - ax_size[1] / 2, y + ax_size[1] / 2]
# Image from https://pixabay.com/en/sailing-ship-boat-sail-pirate-28930/.
pirate = plt.imread('pirates.png')
img = ax.imshow(pirate, extent=ll_to_extent(0, 0), transform=ax.projection, origin='upper')
ax.set_global()
def animate_ship(i):
verts = np.array(projected_line.coords)
i = i % verts.shape[0]
# Set the extent of the image to the coordinate of the path.
img.set_extent(ll_to_extent(verts[i, 0], verts[i, 1]))
ani = animation.FuncAnimation(
ax.figure, animate_ship,
frames=len(projected_line.coords),
interval=125, repeat=False)
ani.save('arrrr.gif', writer='imagemagick')
plt.show()
All code and images for this answer can be found at https://gist.github.com/pelson/618a5f4ca003e56f06d43815b21848f6.
I'm attempting to make heat/intensity map using Basemap. My inputs are a set of lats, lons, and intensity at that point. The dataset looks like this:
lat[0], lon[0] = intensity[0]
lat[1], lon[1] = intensity[1]
...
lat[n], lon[n] = intensity[n]
At each index the lat and lon correspond to the correct sensor reading. My code looks something like this:
fig = plt.figure(figsize=(10, 8))
# Set title
fig.suptitle("Intensities {} {}".format(start_time, stop_time))
# US Centered Map
map_axis = fig.add_subplot(111)
map = Basemap(
ax = map_axis,
lat_0 = 40, lon_0 = -95,
width = 6500e3, height = 6500e3,
projection = 'stere',
resolution = 'l'
)
map.drawcoastlines()
lats = ...
lons = ...
intn = ...
# Convert coordinates
lons, lats = map(lons, lats)
LONS, LATS = np.meshgrid(lons, lats)
map.pcolormesh(
LONS, LATS,
intn,
vmin = 0, vmax = 100
)
fig.savefig(file_name)
plt.close(fig)
This code never completes. I've successfully plotted the Basemap by itself. The pcolormesh is what is failing. The program crashes with this error.
$ ./plot_intensities.py
Running 2013-04-10 00:02:30 2013-04-10 00:02:45
Traceback (most recent call last):
File "./plot_intensities.py", line 151, in <module>
make_maps(samples)
File "./plot_intensities.py", line 144, in make_maps
make_map(bin_samples, start, walk)
File "./plot_intensities.py", line 117, in make_map
vmin = 0, vmax = 100
File "/usr/lib/python3/dist-packages/mpl_toolkits/basemap/__init__.py", line 521, in with_transform
return plotfunc(self,x,y,data,*args,**kwargs)
File "/usr/lib/python3/dist-packages/mpl_toolkits/basemap/__init__.py", line 3418, in pcolormesh
ret = ax.pcolormesh(x,y,data,**kwargs)
File "/usr/lib/python3/dist-packages/matplotlib/__init__.py", line 1814, in inner
return func(ax, *args, **kwargs)
File "/usr/lib/python3/dist-packages/matplotlib/axes/_axes.py", line 5395, in pcolormesh
X, Y, C = self._pcolorargs('pcolormesh', *args, allmatch=allmatch)
File "/usr/lib/python3/dist-packages/matplotlib/axes/_axes.py", line 4995, in _pcolorargs
numRows, numCols = C.shape
ValueError: not enough values to unpack (expected 2, got 1)
I understand that my data, the third argument intn is not formatted correctly. I cannot find any documentation as to how I should shape that list. How do I format it to the correct shape?
Thanks.
As you know, pcolormesh is used to plot a quadrilateral mesh by creating a pseudocolor plot of a 2-D array. The error details indeed indicated that: at line numRows, numCols = C.shape, it expect C to be a 2-D array, while the C you provided seems to be a 1-D array, judging from ValueError: not enough values to unpack (expected 2, got 1). The dataset you introduced seems to me having only intensity values on the diagonal (where lat == lon). To get a colormesh, you need to at least extend intensity data into 2-D array and somehow fill in missing values. For example:
import matplotlib.pyplot as plt
from mpl_toolkits.basemap import Basemap
import numpy as np
fig = plt.figure(figsize=(10, 8))
# Set title
fig.suptitle("Intensities {} {}".format('start_time', 'stop_time'))
# US Centered Map
map_axis = fig.add_subplot(111)
map = Basemap(
ax = map_axis,
lat_0 = 40, lon_0 = -95,
width = 6500e3, height = 6500e3,
projection = 'stere',
resolution = 'l'
)
map.drawcoastlines()
# Tried my best to simulate your data example. Don't be surprise if the result is ugly ...
nstep = 1
lats = np.arange(map.latmin, map.latmax, nstep)
lons = np.arange(map.lonmin, map.lonmax, nstep)
l = min(len(lats), len(lons))
lats = lats[:l]
lons = lons[:l]
intn = np.random.randint(0, 100, size=l)
# Convert coordinates
lons, lats = map(lons, lats)
LONS, LATS = np.meshgrid(lons, lats)
# The following 3 lines are just an example of the minimum you got to do before it works.
intn_array = np.zeros(LONS.shape)
for i in range(l):
intn_array[i, i] = intn[i]
intn = intn_array
map.pcolormesh(
LONS, LATS,
intn_array,
vmin = 0, vmax = 100
)
plt.show()
I am trying to go a step further by creating a radar plot like this question states. I using the same source code that the previous question was using, except I'm trying to implement this using pandas dataframe and pivot tables.
import numpy as np
import pandas as pd
from StringIO import StringIO
import matplotlib.pyplot as plt
from matplotlib.projections.polar import PolarAxes
from matplotlib.projections import register_projection
def radar_factory(num_vars, frame='circle'):
"""Create a radar chart with `num_vars` axes."""
# calculate evenly-spaced axis angles
theta = 2 * np.pi * np.linspace(0, 1 - 1. / num_vars, num_vars)
# rotate theta such that the first axis is at the top
theta += np.pi / 2
def draw_poly_frame(self, x0, y0, r):
# TODO: use transforms to convert (x, y) to (r, theta)
verts = [(r * np.cos(t) + x0, r * np.sin(t) + y0) for t in theta]
return plt.Polygon(verts, closed=True, edgecolor='k')
def draw_circle_frame(self, x0, y0, r):
return plt.Circle((x0, y0), r)
frame_dict = {'polygon': draw_poly_frame, 'circle': draw_circle_frame}
if frame not in frame_dict:
raise ValueError, 'unknown value for `frame`: %s' % frame
class RadarAxes(PolarAxes):
"""Class for creating a radar chart (a.k.a. a spider or star chart)
http://en.wikipedia.org/wiki/Radar_chart
"""
name = 'radar'
# use 1 line segment to connect specified points
RESOLUTION = 1
# define draw_frame method
draw_frame = frame_dict[frame]
def fill(self, *args, **kwargs):
"""Override fill so that line is closed by default"""
closed = kwargs.pop('closed', True)
return super(RadarAxes, self).fill(closed=closed, *args, **kwargs)
def plot(self, *args, **kwargs):
"""Override plot so that line is closed by default"""
lines = super(RadarAxes, self).plot(*args, **kwargs)
for line in lines:
self._close_line(line)
def _close_line(self, line):
x, y = line.get_data()
# FIXME: markers at x[0], y[0] get doubled-up
if x[0] != x[-1]:
x = np.concatenate((x, [x[0]]))
y = np.concatenate((y, [y[0]]))
line.set_data(x, y)
def set_varlabels(self, labels):
self.set_thetagrids(theta * 180 / np.pi, labels)
def _gen_axes_patch(self):
x0, y0 = (0.5, 0.5)
r = 0.5
return self.draw_frame(x0, y0, r)
register_projection(RadarAxes)
return theta
def day_radar_plot(df):
fig = plt.figure(figsize=(6,6))
#adjust spacing around the subplots
fig.subplots_adjust(wspace=0.25,hspace=0.20,top=0.85,bottom=0.05)
ldo,rup = 0.1,0.8 #leftdown and right up normalized
ax = fig.add_axes([ldo,ldo,rup,rup],polar=True)
N = len(df['Group1'].unique())
theta = radar_factory(N)
polar_df = pd.DataFrame(df.groupby([df['Group1'],df['Type'],df['Vote']]).size())
polar_df.columns = ['Count']
radii = polar_df['Count'].get_values()
names = polar_df.index.get_values()
#get the number of unique colors needed
num_colors_needed = len(names)
#Create the list of unique colors needed for red and blue shades
Rcolors = []
Gcolors = []
for i in range(num_colors_needed):
ri=1-(float(i)/float(num_colors_needed))
gi=0.
bi=0.
Rcolors.append((ri,gi,bi))
for i in range(num_colors_needed):
ri=0.
gi=1-(float(i)/float(num_colors_needed))
bi=0.
Gcolors.append((ri,gi,bi))
from_x = np.linspace(0,0.95,num_colors_needed)
to_x = from_x + 0.05
i = 0
for d,f,R,G in zip(radii,polar_df.index,Rcolors,Gcolors):
i = i+1
if f[2].lower() == 'no':
ax.plot(theta,d,color=R)
ax.fill(theta,d,facecolor=R,alpha=0.25)
#this is where I think i have the issue
ax.axvspan(from_x[i],to_x[i],color=R)
elif f[2].lower() == 'yes':
ax.plot(theta,d,color=G)
ax.fill(theta,d,facecolor=G,alpha=0.25)
#this is where I think i have the issue
ax.axvspan(from_x[i],to_x[i],color=G)
plt.show()
So, let's say I have this StringIO that has a list of Group1 voting either yes or no and they are from a numbered type..these numbers are arbitrary in labeling but just as an example..
fakefile = StringIO("""\
Group1,Type,Vote
James,7,YES\nRachael,7,YES\nChris,2,YES\nRachael,9,NO
Chris,2,YES\nChris,7,NO\nRachael,9,NO\nJames,2,NO
James,7,NO\nJames,9,YES\nRachael,9,NO
Chris,2,YES\nChris,2,YES\nRachael,7,NO
Rachael,7,YES\nJames,9,YES\nJames,9,NO
Rachael,2,NO\nChris,2,YES\nRachael,7,YES
Rachael,9,NO\nChris,9,NO\nJames,7,NO
James,2,YES\nChris,2,NO\nRachael,9,YES
Rachael,9,YES\nRachael,2,NO\nChris,7,YES
James,7,YES\nChris,9,NO\nRachael,9,NO\n
Chris,9,YES
""")
record = pd.read_csv(fakefile, header=0)
day_radar_plot(record)
The error I get is Value Error: x and y must have same first dimension.
As I indicated in my script, I thought I had a solution for it but apparently I'm going by it the wrong way. Does anyone have any advice or guidance?
Since I'm completely lost in what you are trying to do, I will simply provide a solution on how to draw a radar chart from the given data.
It will answer the question how often have people voted Yes or No.
import pandas as pd
import numpy as np
from StringIO import StringIO
import matplotlib.pyplot as plt
fakefile = StringIO("""\
Group1,Type,Vote
James,7,YES\nRachael,7,YES\nChris,2,YES\nRachael,9,NO
Chris,2,YES\nChris,7,NO\nRachael,9,NO\nJames,2,NO
James,7,NO\nJames,9,YES\nRachael,9,NO
Chris,2,YES\nChris,2,YES\nRachael,7,NO
Rachael,7,YES\nJames,9,YES\nJames,9,NO
Rachael,2,NO\nChris,2,YES\nRachael,7,YES
Rachael,9,NO\nChris,9,NO\nJames,7,NO
James,2,YES\nChris,2,NO\nRachael,9,YES
Rachael,9,YES\nRachael,2,NO\nChris,7,YES
James,7,YES\nChris,9,NO\nRachael,9,NO\n
Chris,9,YES""")
df = pd.read_csv(fakefile, header=0)
df["cnt"] = np.ones(len(df))
pt = pd.pivot_table(df, values='cnt', index=['Group1'],
columns=['Vote'], aggfunc=np.sum)
fig = plt.figure()
ax = fig.add_subplot(111, projection="polar")
theta = np.arange(len(pt))/float(len(pt))*2.*np.pi
l1, = ax.plot(theta, pt["YES"], color="C2", marker="o", label="YES")
l2, = ax.plot(theta, pt["NO"], color="C3", marker="o", label="NO")
def _closeline(line):
x, y = line.get_data()
x = np.concatenate((x, [x[0]]))
y = np.concatenate((y, [y[0]]))
line.set_data(x, y)
[_closeline(l) for l in [l1,l2]]
ax.set_xticks(theta)
ax.set_xticklabels(pt.index)
plt.legend()
plt.title("How often have people votes Yes or No?")
plt.show()