ccplot

CloudSat and CALIPSO plotting tool

API (beta)

ccplot >= 1.5-rc5

If the command-line program does not fulfill your needs, you can use routines provided with ccplot to make custom plots in python. These include routines for reading HDF and HDF-EOS2 files, parsing time values and performing data interpolation. See API Reference for details.

Examples

Note about examples

The examples below differ from the ccplot program in a number of important details:

CALIPSO example

CALIPSO example

Input file: CAL_LID_L1-ValStage1-V3-01.2007-06-12T03-42-18ZN.hdf

Source: calipso-plot.py

#!/usr/bin/env python

import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
from ccplot.hdf import HDF
from ccplot.algorithms import interp2d_12
import ccplot.utils

filename = 'CAL_LID_L1-ValStage1-V3-01.2007-06-12T03-42-18ZN.hdf'
name = 'Total_Attenuated_Backscatter_532'
label = 'Total Attenuated Backscatter 532nm (km$^{-1}$ sr$^{-1}$)'
colormap = '/usr/local/share/ccplot/cmap/calipso-backscatter.cmap'
x1 = 0
x2 = 1000
h1 = 0  # km
h2 = 20  # km
nz = 500  # Number of pixels in the vertical.

if __name__ == '__main__':
    with HDF(filename) as product:
        # Import datasets.
        time = product['Profile_UTC_Time'][x1:x2, 0]
        height = product['metadata']['Lidar_Data_Altitudes']
        dataset = product[name][x1:x2]

        # Convert time to datetime.
        time = np.array([ccplot.utils.calipso_time2dt(t) for t in time])

        # Mask missing values.
        dataset = np.ma.masked_equal(dataset, -9999)

        # Interpolate data on a regular grid.
        X = np.arange(x1, x2, dtype=np.float32)
        Z, null = np.meshgrid(height, X)
        data = interp2d_12(
            dataset[::],
            X.astype(np.float32),
            Z.astype(np.float32),
            x1, x2, x2 - x1,
            h2, h1, nz,
        )

        # Import colormap.
        cmap = ccplot.utils.cmap(colormap)
        cm = mpl.colors.ListedColormap(cmap['colors']/255.0)
        cm.set_under(cmap['under']/255.0)
        cm.set_over(cmap['over']/255.0)
        cm.set_bad(cmap['bad']/255.0)
        norm = mpl.colors.BoundaryNorm(cmap['bounds'], cm.N)

        # Plot figure.
        fig = plt.figure(figsize=(12, 6))
        TIME_FORMAT = '%e %b %Y %H:%M:%S UTC'
        im = plt.imshow(
            data.T,
            extent=(mpl.dates.date2num(time[0]), mpl.dates.date2num(time[-1]), h1, h2),
            cmap=cm,
            norm=norm,
            aspect='auto',
            interpolation='nearest',
        )
        ax = im.axes
        ax.set_title('CALIPSO %s - %s' % (
            time[0].strftime(TIME_FORMAT),
            time[-1].strftime(TIME_FORMAT)
        ))
        ax.set_xlabel('Time')
        ax.set_ylabel('Altitude (km)')
        ax.xaxis.set_major_locator(mpl.dates.AutoDateLocator())
        ax.xaxis.set_major_formatter(mpl.dates.DateFormatter('%H:%M:%S'))
        cbar = plt.colorbar(
            extend='both',
            use_gridspec=True
        )
        cbar.set_label(label)
        fig.tight_layout()
        plt.savefig('calipso-plot.png')
        plt.show()

CloudSat example

CloudSat example

Input file: 2013119200420_37263_CS_2B-GEOPROF_GRANULE_P_R04_E06.hdf

Source: cloudsat-plot.py

#!/usr/bin/env python

import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
import datetime as dt
from ccplot.hdfeos import HDFEOS
from ccplot.algorithms import interp2d_12
import ccplot.utils

filename = '2013119200420_37263_CS_2B-GEOPROF_GRANULE_P_R04_E06.hdf'
swath = '2B-GEOPROF'
name = 'Radar_Reflectivity'
label = 'Reflectivity Factor (dBZe)'
colormap = '/usr/local/share/ccplot/cmap/cloudsat-reflectivity.cmap'
x1 = 1700
x2 = 2000
h1 = 0  # km
h2 = 10  # km
nz = 500  # Number of pixels in the vertical.

if __name__ == '__main__':
    with HDFEOS(filename) as product:
        # Import datasets.
        sw = product[swath]
        ds = sw[name]
        dataset = ds[x1:x2]
        time = sw['Profile_time'][x1:x2]
        height = sw['Height'][:]
        start_time = dt.datetime.strptime(
            sw.attributes['start_time'],
            '%Y%m%d%H%M%S'
        )

        # Convert time to datetime.
        time = np.array([
            ccplot.utils.cloudsat_time2dt(t, start_time)
            for t in time
        ])

        # Mask missing values.
        dataset = np.ma.masked_equal(dataset, ds.attributes["missing"])
        dataset = np.ma.masked_equal(dataset, ds.attributes["_FillValue"])

        # Transform data values to science values.
        factor = ds.attributes.get("factor", 1)
        offset = ds.attributes.get("offset", 0)
        dataset = 1.0/factor*(dataset - offset)

        # Interpolate data on a regular grid.
        X = np.arange(x1, x2, dtype=np.float32)
        Z = (height*0.001).astype(np.float32)
        data = interp2d_12(
            dataset.filled(np.nan), X, Z,
            x1, x2, x2 - x1,
            h2, h1, nz,
        )

        # Import colormap.
        cmap = ccplot.utils.cmap(colormap)
        cm = mpl.colors.ListedColormap(cmap['colors']/255.0)
        cm.set_under(cmap['under']/255.0)
        cm.set_over(cmap['over']/255.0)
        cm.set_bad(cmap['bad']/255.0)
        norm = mpl.colors.BoundaryNorm(cmap['bounds'], cm.N)

        # Plot figure.
        fig = plt.figure(figsize=(12, 6))
        TIME_FORMAT = '%e %b %Y %H:%M:%S UTC'
        im = plt.imshow(
            data.T,
            extent=(mpl.dates.date2num(time[0]), mpl.dates.date2num(time[-1]), h1, h2),
            cmap=cm,
            norm=norm,
            aspect='auto',
            interpolation='nearest',
        )
        ax = im.axes
        ax.set_title('CloudSat %s - %s' % (
            time[0].strftime(TIME_FORMAT),
            time[-1].strftime(TIME_FORMAT)
        ))
        ax.set_xlabel('Time')
        ax.set_ylabel('Altitude (km)')
        ax.xaxis.set_major_locator(mpl.dates.AutoDateLocator())
        ax.xaxis.set_major_formatter(mpl.dates.DateFormatter('%H:%M:%S'))
        cbar = plt.colorbar(
            extend='both',
            use_gridspec=True
        )
        cbar.set_label(label)
        fig.tight_layout()
        plt.savefig('cloudsat-plot.png')
        plt.show()