Plotting maps with a loop: Best Practices

Best practices for plotting data on a map with a loop...

If you want to plot many data sets with different times on a base map, only plot the base map once!

Some psudo code:

fig = plt.figure(1)
ax  = fig.add_subplot(111)

## Draw Background basemap
m = Basemap(resolution='i',projection='cyl',\
    llcrnrlon=bot_left_lon,llcrnrlat=bot_left_lat,\
    urcrnrlon=top_right_lon,urcrnrlat=top_right_lat,)
m.drawcoastlines()
m.drawcountries()
m.drawcounties()
m.drawstates()

# Brackground image from GIS
m.arcgisimage(service='NatGeo_World_Map', xpixels = 1000, dpi=100)

# Add a shape file for Fire perimiter
p4 = m.readshapefile('/uufs/chpc.utah.edu/common/home/u0553130/fire_shape/perim_'+perimiter,'perim',drawbounds=False)

# Plot station locations
m.scatter(lons,lats)
plt.text(lons[0],lats[0],stnid[0])
plt.text(lons[1],lats[1],stnid[1])

for analysis_h in range(1,5):

    # code to get lat/lon and data

    # plot pcolormesh, and colorbar
    PC = m.pcolormesh(lon,lat,data)
    cbar = plt.colorbar(orientation='horizontal',pad=0.05,shrink=.8,ticks=range(0,80,5))

    # plot contour
    CC = m.contour(lon,lat,data)

    # plot HRRR 10-m winds
    WB = m.barbs(lon,lat,u,v,)
    

    ### Before you plot the next loop, remove the data plots
    cbar.remove()                  # remove colorbar
    PC.remove()                    # remove pcolomesh
    WB[0].remove()              #remove wind barbs
    WB[1].remove()              #   (takes two steps)
    for cc in CC.collections:  #remove each line in the contour collection
        cc.remove()

Plot NEXRAD Level II Data with MetPy

Plotting NEXRAD Level II data is possible using MetPy.

View my script here: https://github.com/blaylockbk/Ute_other/blob/master/plot_NEXRAD_II_data.py

Example:

Plotting the fire perimeter from a shape file

There is very useful documentation related to plotting shape files on a basemap here: http://basemaptutorial.readthedocs.io/en/latest/shapefile.html

I needed to plot fire perimeter, plotted on this map of Idaho and shaded in red, on a basemap.

This is how I did it...

from matplotlib.patches import Polygon
from matplotlib.collections import PatchCollection
## My file 
# Plot Fire Perimeter                               
perimiter = '160806'
p4 = m.readshapefile('[PATH]/perim_'+perimiter,'perim',drawbounds=False)
                           
patches   = []
for info, shape in zip(m.perim_info, m.perim):
      if info['FIRENAME'] == 'PIONEER' and info['SHAPENUM']==1772:
             x, y = zip(*shape) 
              #print info
              #m.plot(x, y, marker=None,color='maroon',linewidth=2)
              patches.append(Polygon(np.array(shape), True) )
              ax.add_collection(PatchCollection(patches, facecolor= 'maroon', alpha=.65, edgecolor='k', linewidths=1.5, zorder=1))

Python Matplotlib subscript

This is how to add subscripts to a string in your matlab plot
See more here: http://matplotlib.org/users/mathtext.html

plt.scatter(cyr_no80,co2_no80,color='k',marker='+',label='points')
plt.title(r'CO$_2$ Concentration, no 1980s')
plt.ylabel(r'CO$_2$ (ppm)')
plt.xlabel('Year')

Sorting multiple vectors based on one vector sort order

That title was a mouthful. First, I want to sort an array vector. Then I want to sort other vectors with that same sort order.

I have a vector that contain heights, and with that two other vectors with the corresponding temperature and dew point. I want to sort the height array, and sort the temperature and dew point array in the same manner.

height = np.array([10, 50, 20, 30, 60, 20, 80])
temp = np.array([1, 4, 2, 7, 5, 3, 9])
dwpt = np.array([6, 3, 8, 5, 7, 4, 6])

First, use np.argsort to get the sort order.

sort_order = np.argsort(height)

[out] array([0, 2, 5, 3, 1, 4, 6], dtype=int64)

Now make new arrays of the sorted data with that sort order

sorted_height = np.array(height)[sort_order]
[out] array([10, 20, 20, 30, 50, 60, 80])

sorted_temp = np.array(temp)[sort_order]
[out] array([1, 2, 3, 7, 4, 5, 9])

sorted_dwpt = np.array(dwpt)[sort_order]
[out] array([6, 8, 4, 5, 3, 7, 6])

Thus, you see that we have sorted the height array from lowest to highest, and the temperature and dew point arrays have been sorted, preserving their order with the corresponding height.