I am trying to plot azimuth and altitude points in a polar chart in Python using matplolib, basemap and numpy.
winterAzi = 81.67440007, 75.55094006, 67.57616189, 55.73337697
winterAlt = 11.28088118, 25.0837551, 38.44986883, 50.8769649
#create instance of basemap, note we want a south polar projection to 90 = E
myMap = Basemap(projection='spstere',boundinglat=0,lon_0=180,resolution='l',round=True,suppress_ticks=True)
# set the grid up
gridX,gridY = 10.0,15.0
parallelGrid = np.arange(-90.0,90.0,gridX)
meridianGrid = np.arange(-180.0,180.0,gridY)
# draw parallel and meridian grid, not labels are off. We have to manually create these.
myMap.drawparallels(parallelGrid,labels=[False,False,False,False])
myMap.drawmeridians(meridianGrid,labels=[False,False,False,False],labelstyle='+/-',fmt='%i')
# we have to send our values through basemap to convert coordinates, note -winterAlt
winterX,winterY = myMap(winterAzi,-winterAlt)
# plot azimuth labels, with a North label.
ax = plt.gca()
ax.text(0.5,1.025,'N',transform=ax.transAxes,horizontalalignment='center',verticalalignment='bottom',size=25)
for para in np.arange(gridY,360,gridY):
x= (1.1*0.5*np.sin(np.deg2rad(para)))+0.5
y= (1.1*0.5*np.cos(np.deg2rad(para)))+0.5
ax.text(x,y,u'%i\N{DEGREE SIGN}'%para,transform=ax.transAxes,horizontalalignment='center',verticalalignment='center')
# plot the winter values
myMap.plot(winterX,winterY ,'bo')
plt.show()
However it is not plotting the data points at the beginning of the code!
How can I plot them?
You probably want to put the variables in a numpy array:
winterAzi = np.array([81.67440007, 75.55094006, 67.57616189, 55.73337697])
winterAlt = np.array([11.28088118, 25.0837551, 38.44986883, 50.8769649])
Related
I need to draw a xarray.DataArray object onto a shapefile with Basemap.
The source data contains a few days' data.
I would like each dataset to be drawn on the shapefile with Basemap.
... ...
shapefile1="./shp/CFA_DISTRICT_BODY"
# Select 3 days' datasets
da_criteria_1or0_hourly_rolled_resampled_sel_slice = da_criteria_1or0_hourly_rolled_resampled.sel(time=slice('2017-01-01', '2017-01-03'))
# Draw each day's dataset and set them drawn horizontally
p = da_criteria_1or0_hourly_rolled_resampled_sel_slice.plot(levels=[0,1,2], x='longitude', y='latitude', col='time', col_wrap=3)
# Draw the shapefile
map = Basemap(llcrnrlat=-39.2,urcrnrlat=-33.9,llcrnrlon=140.8,urcrnrlon=150.0,resolution='i')
map.readshapefile(shapefile1, 'CFA_DISTRICT_BODY', linewidth=0.5)
plt.show()
The issue with the code above is only the Day 3's dataset is drawn on the shapefile.
You only define a single Basemap. That'll apply to the last active axes.
Instead you would create a Basemap for each axes in the FacetGrid.
The idea would be along the lines of
grid = data.plot(...)
for ax in grid.axes.flatten():
map = Basemap(..., ax=ax)
map.readshapefile(...)
plt.show()
I'm testing geopandas to make something quite simple : use the difference method to delete some points of a GeoDataFrame that are inside a circle.
Here's the begining of my script :
%matplotlib inline
# previous line is because I used ipynb
import pandas as pd
import geopandas as gp
from shapely.geometry import Point
[...]
points_df = gp.GeoDataFrame(csv_file, crs=None, geometry=geometry)
Here's the first rows of points_df :
Name Adress geometry
0 place1 street1 POINT (6.182674 48.694416)
1 place2 street2 POINT (6.177306 48.689889)
2 place3 street3 POINT (6.18 48.69600000000001)
3 place4 street4 POINT (6.1819 48.6938)
4 place5 street5 POINT (6.175694 48.690833)
Then, I add a point that will contain several points of the first GeoDF :
base = points_df.plot(marker='o', color='red', markersize=5)
center_coord = [Point(6.18, 48.689900)]
center = gp.GeoDataFrame(crs=None, geometry=center_coord)
center.plot(ax=base, color = 'blue',markersize=5)
circle = center.buffer(0.015)
circle.plot(ax=base, color = 'green')
Here's the result displayed by the iPython notebook :
Now, the goal is to delete red points inside the green circle. To do that, I thought that difference method will be enough. But when I write :
selection = points_df['geometry'].difference(circle)
selection.plot(color = 'green', markersize=5)
The result is that... nothing changed with points_df :
I guess that the difference() method works only with polygons GeoDataFrames and the mix between points and polygons is not posible. But maybe I missed something !
Will a function to test the presence of a point in the circle be better than the difference method in this case ?
I guess that the difference() method works only with polygons
GeoDataFrames and the mix between points and polygons is not posible.
That seems to be the issue, you cant use the overlay with points.
And also for that kind of spatial operation a simple spatial join seems to be the easiest solution.
Starting with the last example ;):
%matplotlib inline
import pandas as pd
import geopandas as gp
import numpy as np
import matplotlib.pyplot as plt
from shapely.geometry import Point
# Create Fake Data
df = pd.DataFrame(np.random.randint(10,20,size=(35, 3)), columns=['Longitude','Latitude','data'])
# create Geometry series with lat / longitude
geometry = [Point(xy) for xy in zip(df.Longitude, df.Latitude)]
df = df.drop(['Longitude', 'Latitude'], axis = 1)
# Create GeoDataFrame
points = gp.GeoDataFrame(df, crs=None, geometry=geometry)
# Create Matplotlib figure
fig, ax = plt.subplots()
# Set Axes to equal (otherwise plot looks weird)
ax.set_aspect('equal')
# Plot GeoDataFrame on Axis ax
points.plot(ax=ax,marker='o', color='red', markersize=5)
# Create new point
center_coord = [Point(15, 13)]
center = gp.GeoDataFrame(crs=None, geometry=center_coord)
# Plot new point
center.plot(ax=ax,color = 'blue',markersize=5)
# Buffer point and plot it
circle = gp.GeoDataFrame(crs=None, geometry=center.buffer(2.5))
circle.plot(color = 'white',ax=ax)
Leaves us with the problem on how to determine if a point is inside or outside of the polygon... one way of achieving that is to Join all points inside the polygon, and create a DataFrame with the difference between all points and points within the circle:
# Calculate the points inside the circle
pointsinside = gp.sjoin(points,circle,how="inner")
# Now the points outside the circle is just the difference
# between points and points inside (see the ~)
pointsoutside = points[~points.index.isin(pointsinside.index)]
# Create a nice plot
fig, ax = plt.subplots()
ax.set_aspect('equal')
circle.plot(color = 'white',ax=ax)
center.plot(ax=ax,color = 'blue',markersize=5)
pointsinside.plot(ax=ax,marker='o', color='green', markersize=5)
pointsoutside.plot(ax=ax,marker='o', color='yellow', markersize=5)
print('Total points:' ,len(points))
print('Points inside circle:' ,len(pointsinside))
print('Points outside circle:' ,len(pointsoutside))
Total points: 35
Points inside circle: 10
Points outside circle: 25
I'm trying to Plot a high resolution surface_plot, but I would also really like some nice grid lines on top of it. If i use the gridlines in the same argument
ax.plot_surface(x_itp, y_itp, z_itp, rstride=1, cstride=1, facecolors=facecolors, linewidth=0.1)
I get a LOT of grid lines. If I, on the other hand, set "rstride" and "cstride" to higher values, my sphere will become ugly.
I then tried to smash a
ax.plot_wireframe(x_itp, y_itp, z_itp, rstride=3, cstride=3)
in afterwards, but it just lies on top of the colored sphere.. meaning that I can see the backside of the wireframe and then the surface_plot behind it all.
Have anyone tried this?
Another option was to use "Basemap" which can create a nice grid, but then I will have to adapt my colored surface to that.?!
My plot looks like this:
If I add edges to the map with a higher "rstride" and "cstride" then it looks like this:
code :
norm = plt.Normalize()
facecolors = plt.cm.jet(norm(d_itp))
# surface plot
fig, ax = plt.subplots(1, 1, subplot_kw={'projection':'3d', 'aspect':'equal'})
ax.hold(True)
surf = ax.plot_surface(x_itp, y_itp, z_itp, rstride=4, cstride=4, facecolors=facecolors)
surf.set_edgecolors("black")
I want to show the \theta and \phi angles around the sphere.. maybe with 30 degrees apart.
Cheers!
Morten
It looks like you may need to use basemap. With plot_surface() you can either have high resolution plot or low resolution with good grid on top. But not both. I just made a simple basemap with contour plot. I think you can do easily apply pcolor on it. Just do not draw continent and country boundary. Then, you have a nice sphere which gives more control. After making your plot, you can easily add grid on it.
from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
import numpy as np
map = Basemap(projection='ortho',lat_0=45,lon_0=-150)
map.drawmapboundary(fill_color='aquamarine')
map.drawmeridians(np.arange(0,360,30)) # grid every 30 deg
map.drawparallels(np.arange(-90,90,30))
nlats = 73; nlons = 145; delta = 2.*np.pi/(nlons-1)
lats = (0.5*np.pi-delta*np.indices((nlats,nlons))[0,:,:])
lons = (delta*np.indices((nlats,nlons))[1,:,:])
wave = 0.6*(np.sin(2.*lats)**6*np.cos(4.*lons))
mean = 0.5*np.cos(2.*lats)*((np.sin(2.*lats))**2 + 2.)
x, y = map(lons*180./np.pi, lats*180./np.pi) # projection from lat, lon to sphere
cs = map.contour(x,y,wave+mean,15,linewidths=1.5) # contour data. You can use pcolor() for your project
plt.title('test1')
plt.show()
I am trying to use streamplot function to plot wind field with basemap, projection "ortho". My test code is mainly based on this example:
Plotting wind vectors and wind barbs
Here is my code:
import numpy as np
import matplotlib.pyplot as plt
import datetime
from mpl_toolkits.basemap import Basemap, shiftgrid
from Scientific.IO.NetCDF import NetCDFFile as Dataset
# specify date to plot.
yyyy=1993; mm=03; dd=14; hh=00
date = datetime.datetime(yyyy,mm,dd,hh)
# set OpenDAP server URL.
URLbase="http://nomads.ncdc.noaa.gov/thredds/dodsC/modeldata/cmd_pgbh/"
URL=URLbase+"%04i/%04i%02i/%04i%02i%02i/pgbh00.gdas.%04i%02i%02i%02i.grb2" %\
(yyyy,yyyy,mm,yyyy,mm,dd,yyyy,mm,dd,hh)
data = Dataset(URL)
#data = netcdf.netcdf_file(URL)
# read lats,lons
# reverse latitudes so they go from south to north.
latitudes = data.variables['lat'][:][::-1]
longitudes = data.variables['lon'][:].tolist()
# get wind data
uin = data.variables['U-component_of_wind_height_above_ground'][:].squeeze()
vin = data.variables['V-component_of_wind_height_above_ground'][:].squeeze()
# add cyclic points manually (could use addcyclic function)
u = np.zeros((uin.shape[0],uin.shape[1]+1),np.float64)
u[:,0:-1] = uin[::-1]; u[:,-1] = uin[::-1,0]
v = np.zeros((vin.shape[0],vin.shape[1]+1),np.float64)
v[:,0:-1] = vin[::-1]; v[:,-1] = vin[::-1,0]
longitudes.append(360.); longitudes = np.array(longitudes)
# make 2-d grid of lons, lats
lons, lats = np.meshgrid(longitudes,latitudes)
# make orthographic basemap.
m = Basemap(resolution='c',projection='ortho',lat_0=60.,lon_0=-60.)
# create figure, add axes
fig1 = plt.figure(figsize=(8,10))
ax = fig1.add_axes([0.1,0.1,0.8,0.8])
# define parallels and meridians to draw.
parallels = np.arange(-80.,90,20.)
meridians = np.arange(0.,360.,20.)
# first, shift grid so it goes from -180 to 180 (instead of 0 to 360
# in longitude). Otherwise, interpolation is messed up.
ugrid,newlons = shiftgrid(180.,u,longitudes,start=False)
vgrid,newlons = shiftgrid(180.,v,longitudes,start=False)
# now plot.
lonn, latt = np.meshgrid(newlons, latitudes)
x, y = m(lonn, latt)
st = plt.streamplot(x, y, ugrid, vgrid, color='r', latlon='True')
# draw coastlines, parallels, meridians.
m.drawcoastlines(linewidth=1.5)
m.drawparallels(parallels)
m.drawmeridians(meridians)
# set plot title
ax.set_title('SLP and Wind Vectors '+str(date))
plt.show()
After running the code, I got a blank map with a red smear in the lower left corner (please see the figure). After zoom this smear out, I can see the wind stream in a flat projection (not in "ortho" projection) So I guess this is the problem of data projection on the map. I did tried function transform_vector but it does not solve the problem Can anybody tell me, what did I do wrong, please! Thank you.
A new map after updating code:
You are plotting lat/lon coordinates on a map with an orthographic projection. Normally you can fix this by changing your plotting command to:
m.streamplot(mapx, mapy, ugrid, vgrid, color='r', latlon=True)
But your coordinate arrays don't have the same dimensions, that needs to be fixed as well.
I am trying to create a simple stereographic sun path diagram similar to these:
http://wiki.naturalfrequency.com/wiki/Sun-Path_Diagram
I am able to rotate a polar plot and set the scale to 90. How do I go about reversing the y-axis?
Currently the axis goes from 0>90, how do I reverse the axis to 90>0 to represent the azimuth?
I have tried:
ax.invert_yaxis()
ax.yaxis_inverted()
Further, how would I go about creating a stereographic projection as opposed to a equidistant?
My code:
import matplotlib.pylab as plt
testFig = plt.figure(1, figsize=(8,8))
rect = [0.1,0.1,0.8,0.8]
testAx = testFig.add_axes(rect,polar=True)
testAx.invert_yaxis()
testAx.set_theta_zero_location('N')
testAx.set_theta_direction(-1)
Azi = [90,180,270]
Alt= [0,42,0]
testAx.plot(Azi,Alt)
plt.show()
Currently my code doesn't seem to even plot the lines correctly, do I need need to convert the angle or degrees into something else?
Any help is greatly appreciated.
I finally had time to play around with matplotlib. After much searching, the correct way as Joe Kington points out is to subclass the Axes. I found a much quicker way utilising the excellent basemap module.
Below is some code I have adapted for stackoverflow. The sun altitude and azimuth were calculated with Pysolar with a set of timeseries stamps created in pandas.
import matplotlib.pylab as plt
from mpl_toolkits.basemap import Basemap
import numpy as np
winterAzi = datafomPySolarAzi
winterAlt = datafromPySolarAlt
# create instance of basemap, note we want a south polar projection to 90 = E
myMap = Basemap(projection='spstere',boundinglat=0,lon_0=180,resolution='l',round=True,suppress_ticks=True)
# set the grid up
gridX,gridY = 10.0,15.0
parallelGrid = np.arange(-90.0,90.0,gridX)
meridianGrid = np.arange(-180.0,180.0,gridY)
# draw parallel and meridian grid, not labels are off. We have to manually create these.
myMap.drawparallels(parallelGrid,labels=[False,False,False,False])
myMap.drawmeridians(meridianGrid,labels=[False,False,False,False],labelstyle='+/-',fmt='%i')
# we have to send our values through basemap to convert coordinates, note -winterAlt
winterX,winterY = myMap(winterAzi,-winterAlt)
# plot azimuth labels, with a North label.
ax = plt.gca()
ax.text(0.5,1.025,'N',transform=ax.transAxes,horizontalalignment='center',verticalalignment='bottom',size=25)
for para in np.arange(gridY,360,gridY):
x= (1.1*0.5*np.sin(np.deg2rad(para)))+0.5
y= (1.1*0.5*np.cos(np.deg2rad(para)))+0.5
ax.text(x,y,u'%i\N{DEGREE SIGN}'%para,transform=ax.transAxes,horizontalalignment='center',verticalalignment='center')
# plot the winter values
myMap.plot(winterX,winterY ,'bo')
Note that currently I am only plotting points, you will have to make sure that line points have a point at alt 0 at sunrise/sunset.