Metpy NIDS Data Visualization

nids.py

import numpy as np
import matplotlib.pyplot as plt
from numpy import ma

from matplotlib.colors import ListedColormap
from metpy.cbook import get_test_data
from metpy.io.nexrad import Level3File
from metpy.plots import ctables
import matplotlib as mpl

%matplotlib inline

fig = plt.figure(figsize=(8, 6))
ax = plt.gca()

f = Level3File(
    "/tmp/KLIX_SDUS54_N0ULIX_201208301134")

# Pull the data out of the file object
datadict = f.sym_block[0][0]

# Turn into an array, then mask
data = ma.array(datadict['data'])

# Converting to knots according to Ryan's comments
data = (data / 10) * 1.94384

# IDV filters out data < 5
data[data < 5.0] = ma.masked

# Grab azimuths and calculate a range based on number of gates
az = np.array(datadict['start_az'] + [datadict['end_az'][-1]])
rng = np.linspace(0, f.max_range, data.shape[-1] + 1)

# Convert az,range to x,y
xlocs = rng * np.sin(np.deg2rad(az[:, np.newaxis]))
ylocs = rng * np.cos(np.deg2rad(az[:, np.newaxis]))

# IDV Color table
cmap = ListedColormap(
    [(0, 0, 0), (0.12, 0.97, 0.97), (0.12, 0.63, 0.63), (0.57, 0.91, 0.40),
     (0.47, 0.77, 0.35), (0.37, 0.63, 0.29), (0.27, 0.49, 0.23),
     (0.18, 0.35, 0.18), (0.97, 0.80, 0.18), (0.97, 0.57, 0.23),
     (0.97, 0.18, 0.23), (0.63, 0.12, 0.12), (0.74, 0.23, 0.57),
     (0.83, 0.35, 0.69), (1, 1, 1), (1, 0.57, 0.91)])

norm = mpl.colors.Normalize(vmin=0, vmax=80)

# Plot the data
im = ax.pcolormesh(xlocs, ylocs, data, norm=norm, cmap=cmap)
# im = ax.pcolormesh(xlocs, ylocs, data, cmap=cmap)
ax.set_aspect('equal', 'datalim')
ax.set_xlim(-300, 300)
ax.set_ylim(-300, 300)

cb = plt.colorbar(im, ticks=np.linspace(0, 80, 9))
cb.set_label('Knots')
plt.show()