I need to fill my polygon using a heatmap. For source of polygon I've use shapefile.
This is my code:
import shapefile
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.patches as patches
import matplotlib.cm as mcm
import matplotlib.image as mpimg
from matplotlib.patches import Polygon
from matplotlib.collections import PatchCollection
import pylab as plb
fig = plt.figure()
ax = fig.add_subplot(111)
ax.set_frame_on(False)
sf = shapefile.Reader("./data/boundary-polygon")
recs = sf.records()
shapes = sf.shapes()
print shapes[1].__dict__
Nshp = len(shapes)
cns = []
for nshp in xrange(Nshp):
cns.append(recs[nshp][1])
cns = np.array(cns)
cm = mcm.get_cmap('Dark2')
cccol = cm(1.*np.arange(Nshp)/Nshp)
# facecolor=cccol[nshp,:],
for nshp in xrange(Nshp):
ptchs = []
pts = np.array(shapes[nshp].points)
prt = shapes[nshp].parts
par = list(prt) + [pts.shape[0]]
for pij in xrange(len(prt)):
ptchs.append(Polygon(pts[par[pij]:par[pij+1]], alpha=1))
ax.add_collection(PatchCollection(ptchs,facecolors=((1, 1, 1, 1),),alpha=0.1 ,linewidths=1))
ax.set_xlim(54,67)
ax.set_ylim(50,57)
I want to change facecolors=((1, 1, 1, 1),) to facecolors=<image_of_my_heat_map>. Any help regarding this would be deeply appreciated.
Just set the polygons to be whatever color you want them to be:
ptchs=[]
for pij in xrange(len(prt)):
ptchs.append(Polygon(pts[par[pij]:par[pij+1]], alpha=1, color=your_color))
and then create the PatchCollection with the warg match_orginal:
ax.add_collection(PatchCollection(ptchs, match_orginal=True, alpha=0.1 ,linewidths=1))
Also see Why is matplotlib.PatchCollection messing with color of the patches?
I am not familiar with the shapefile API for reading the vector data/polygons. I typically use OGR to read GIS vector data. The colour per polygon can be stored as an attribute per feature or just as a scalar which is assigned a colour using the colourmap as you have done here.
Related
I'm trying to plot a series of frequency spectra in a 3D space using PolyCollection. My goal is to set "facecolors" as a gradient, i.e., the higher the magnitude, the lighter the color.
Please see this image for reference (I am not looking for the fancy design, just the gradients).
I tried to use the cmap argument of the PollyCollection, but I was unsuccessful.
I came this far with the following code adapted from here:
import matplotlib.pyplot as plt
from matplotlib.collections import PolyCollection
from mpl_toolkits.mplot3d import axes3d
import numpy as np
from scipy.ndimage import gaussian_filter1d
def plot_poly(magnitudes):
freq_data = np.arange(magnitudes.shape[0])[:,None]*np.ones(magnitudes.shape[1])[None,:]
mag_data = magnitudes
rad_data = np.linspace(1,magnitudes.shape[1],magnitudes.shape[1])
verts = []
for irad in range(len(rad_data)):
xs = np.concatenate([[freq_data[0,irad]], freq_data[:,irad], [freq_data[-1,irad]]])
ys = np.concatenate([[0],mag_data[:,irad],[0]])
verts.append(list(zip(xs, ys)))
poly = PolyCollection(verts, edgecolor='white', linewidths=0.5, cmap='Greys')
poly.set_alpha(.7)
fig = plt.figure(figsize=(24, 16))
ax = fig.add_subplot(111, projection='3d', proj_type = 'ortho')
ax.add_collection3d(poly, zs=rad_data, zdir='y')
ax.set_xlim3d(freq_data.min(), freq_data.max())
ax.set_xlabel('Frequency')
ax.set_ylim3d(rad_data.min(), rad_data.max())
ax.set_ylabel('Measurement')
ax.set_zlabel('Magnitude')
# Remove gray panes and axis grid
ax.xaxis.pane.fill = False
ax.xaxis.pane.set_edgecolor('white')
ax.yaxis.pane.fill = False
ax.yaxis.pane.set_edgecolor('white')
ax.zaxis.pane.fill = False
ax.zaxis.pane.set_edgecolor('white')
ax.view_init(50,-60)
plt.show()
sample_data = np.random.rand(2205, 4)
sample_data = gaussian_filter1d(sample_data, sigma=10, axis=0) # Just to smoothe the curves
plot_poly(sample_data)
Besides the missing gradients I am happy with the output of the code above.
I have a data with coordinates X,Y similar to a Vertical Sine function, I want to fill the area between left edge and the curve generated using variable color with colormap on matplot, changes in color whith X value as the image (From Blue to Red). I've tried and get this result where start point and final point are conected by a line. I need to fill the left area.
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.path import Path
from matplotlib.patches import PathPatch
#Data
y=np.arange(0,10,0.01)
x=np.sin(y)*y+2
#Set Array
xx=np.asarray(x)
yy=np.asarray(y)
path = Path(np.array([xx,yy]).transpose())
patch = PathPatch(path, facecolor='none')
plt.gca().add_patch(patch)
im = plt.imshow(xx.reshape(yy.size,1), cmap=plt.cm.coolwarm,interpolation="nearest",
origin='left',extent=[-5,10,0,10],aspect="auto", clip_path=patch, clip_on=True)
im.set_clip_path(patch)
You could add two additional points lying on the y-axis to create the desired shape:
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.path import Path
from matplotlib.patches import PathPatch
y = np.linspace(0, 10, 200)
x = np.sin(y) * y + 2
path = Path(np.array([np.append(x, [-5, -5]), np.append(y, [y[-1], y[0]])]).T)
patch = PathPatch(path, facecolor='none')
plt.gca().add_patch(patch)
im = plt.imshow(x.reshape(y.size, 1), cmap=plt.cm.coolwarm, interpolation="nearest",
origin='lower', extent=[-5, 10, 0, 10], aspect="auto", clip_path=patch, clip_on=True)
plt.show()
I am attempting to plot an ellipse surrounding scattered data points, but when the pyplot displays my plot only the data points are showing up and ellipse isn't. Do I need to install the patch somehow?
Here is my code:
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as pp
import numpy as np
from matplotlib import cm
import pandas as pd
from matplotlib.patches import Ellipse
data_to_plot = pd.read_csv("positions.csv", sep = ",", index_col=False)
xs = data_to_plot['x']
ys = data_to_plot['y']
zs = data_to_plot['z']
covxy = np.cov(xs, ys)
lambda_xy, vxy = np.linalg.eig(covxy)
lambda_xy = np.sqrt(lambda_xy)
ax1 = pp.subplot(221)
for j in xrange(1, 4):
ell = Ellipse(xy=(xav, yav),
width=lambda_xy[0]*j*2, height=lambda_xy[1]*j*2,
angle=-np.rad2deg(np.arccos(vxy[0, 0])))
ell.set_facecolor('none')
ax1.add_artist(ell)
ax1.set_xlabel("x distance from the sun / AU")
ax1.set_ylabel("y distance from the sun / AU")
pp.scatter(xs, ys)
pp.xlim(xav-0.001,xav+0.0013)
pp.ylim(yav-0.001,yav+0.001)
ell.set_facecolor('none') seems counterproductive. If the intention is to show the ellipse, it should not have 'none' as color. Or it should have at least an edgecolor being set, e.g.
ell.set_edgecolor("limegreen")
Or consider giving the colors as arguments to the ellipse
Ellipse(..., fc="none", ec="limegreen")
I have the nice hexbin plot below, but I'm wondering if there is any way to get hexbin into an Aitoff projection? The salient code is:
import numpy as np
import math
import matplotlib.pyplot as plt
from astropy.io import ascii
filename = 'WISE_W4SNRge3_and_W4MPRO_lt_6.0_RADecl_nohdr.dat'
datafile= path+filename
data = ascii.read(datafile)
points = np.array([data['ra'], data['dec']])
color_map = plt.cm.Spectral_r
points = np.array([data['ra'], data['dec']])
xbnds = np.array([ 0.0,360.0])
ybnds = np.array([-90.0,90.0])
extent = [xbnds[0],xbnds[1],ybnds[0],ybnds[1]]
fig = plt.figure(figsize=(6, 4))
ax = fig.add_subplot(111)
x, y = points
gsize = 45
image = plt.hexbin(x,y,cmap=color_map,
gridsize=gsize,extent=extent,mincnt=1,bins='log')
counts = image.get_array()
ncnts = np.count_nonzero(np.power(10,counts))
verts = image.get_offsets()
ax.set_xlim(xbnds)
ax.set_ylim(ybnds)
plt.xlabel('R.A.')
plt.ylabel(r'Decl.')
plt.grid(True)
cb = plt.colorbar(image, spacing='uniform', extend='max')
plt.show()
and I've tried:
plt.subplot(111, projection="aitoff")
before doing the plt.hexbin command, but which only gives a nice, but blank, Aitoff grid.
The problem is that the Aitoff projection uses radians, from -π to +π. Not degrees from 0 to 360. I use the Angle.wrap_at function to achieve this, as per this Astropy example (which essentially tells you how to create a proper Aitoff projection plot).
In addition, you can't change the axis limits (that'll lead to an error), and shouldn't use extent (as ImportanceOfBeingErnest's answer also states).
You can change your code as follows to get what you want:
import numpy as np
import matplotlib.pyplot as plt
from astropy.io import ascii
from astropy.coordinates import SkyCoord
from astropy import units
filename = 'WISE_W4SNRge3_and_W4MPRO_lt_6.0_RADecl_nohdr.dat'
data = ascii.read(filename)
coords = SkyCoord(ra=data['ra'], dec=data['dec'], unit='degree')
ra = coords.ra.wrap_at(180 * units.deg).radian
dec = coords.dec.radian
color_map = plt.cm.Spectral_r
fig = plt.figure(figsize=(6, 4))
fig.add_subplot(111, projection='aitoff')
image = plt.hexbin(ra, dec, cmap=color_map,
gridsize=45, mincnt=1, bins='log')
plt.xlabel('R.A.')
plt.ylabel('Decl.')
plt.grid(True)
plt.colorbar(image, spacing='uniform', extend='max')
plt.show()
Which gives
I guess your problem lies in the use of the extent which is set to something other than the range of the spherical coordinate system.
The following works fine:
import matplotlib.pyplot as plt
import numpy as np
ra = np.linspace(-np.pi/2.,np.pi/2.,1000)
dec = np.sin(ra)*np.pi/2./2.
points = np.array([ra, dec])
plt.subplot(111, projection="aitoff")
color_map = plt.cm.Spectral_r
x, y = points
gsize = 45
image = plt.hexbin(x,y,cmap=color_map,
gridsize=45,mincnt=1,bins='log')
plt.xlabel('R.A.')
plt.ylabel(r'Decl.')
plt.grid(True)
cb = plt.colorbar(image, spacing='uniform', extend='max')
plt.show()
I am trying to plot a contour and quiver plot over a basemap. When I plot, I get no errors, but only the basemap will show. The netcdf file only has one point in it for lat and long, so I had to create a range of coordinates. Any ideas why this is happening?
import netCDF4
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import pylab
from mpl_toolkits.basemap import Basemap
from matplotlib.patches import Polygon
ncfile = netCDF4.Dataset('30JUNE2012_0400UTC.cdf', 'r')
dbZ = ncfile.variables['MAXDBZF']
u = ncfile.variables['UNEW']
v = ncfile.variables['VNEW']
#print u
#print v
#print dbZ
data = dbZ[0,0]
data.shape
#print data.shape
z_index = 0 # z-level you want to plot (0-19)
U = u[0,z_index, :,:] #[time,z,x,y]
V = v[0,z_index, :,:]
lats = np.linspace(35.0, 41.0, data.shape[0])
lons = np.linspace(-81.0,-73.0, data.shape[1])
# create the map
map = Basemap(llcrnrlat=36,urcrnrlat=40,\
llcrnrlon=-80,urcrnrlon=-74,lat_ts=20,resolution='c')
# load the shapefile, use the name 'states'
map.readshapefile('st99_d00', name='states', drawbounds=True)
# collect the state names from the shapefile attributes so we can
# look up the shape obect for a state by it's name
state_names = []
for shape_dict in map.states_info:
state_names.append(shape_dict['NAME'])
ax = plt.gca() # get current axes instance
x,y = map(*np.meshgrid(lats,lons))
levels = np.arange(5,60,3)
c = map.contourf(x,y,data, levels, cmap='jet')
plt.colorbar()
q=plt.quiver(U,V,width=0.002, scale_units='xy',scale=10)
qk= plt.quiverkey (q,0.95, 1.02, 20, '20m/s', labelpos='N')
plt.show()