Plotting

This notebook demonstrates all gcmprocpy plotting functions, including projections, wind vector overlays, coastlines, and height-mode plotting.

Note: This notebook requires TIE-GCM or WACCM-X model output files.

[1]:

import warnings
warnings.filterwarnings('ignore')

%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import gcmprocpy as gy

directory = '/glade/work/nikhilr/tiegcm3.0/benchmarks/2.5/seasons/decsol_smin/hist'
datasets = gy.load_datasets(directory, dataset_filter='sech')
times = gy.time_list(datasets)
t_val = times[0]
print(f'Using time: {t_val}')

Using time: 2002-12-21T01:00:00.000000000

Latitude vs Longitude Contour Plot

[2]:

fig = gy.plt_lat_lon(datasets, 'TN', time=t_val, level=5.0)
plt.show()
../../_images/gcmprocpy_notebooks_03_plotting_3_0.png

Using Height (level_type='height')

[3]:

fig = gy.plt_lat_lon(datasets, 'TN', time=t_val, level=300.0, level_type='height')
plt.show()
../../_images/gcmprocpy_notebooks_03_plotting_5_0.png

Polar Projections

The projection parameter supports 'north_polar', 'south_polar', and 'polar' (both hemispheres).

[4]:

fig = gy.plt_lat_lon(datasets, 'TN', time=t_val, level=5.0, projection='north_polar')
plt.show()
../../_images/gcmprocpy_notebooks_03_plotting_7_0.png 
[5]:

fig = gy.plt_lat_lon(datasets, 'TN', time=t_val, level=5.0, projection='south_polar')
plt.show()
../../_images/gcmprocpy_notebooks_03_plotting_8_0.png 
[6]:

fig = gy.plt_lat_lon(datasets, 'TN', time=t_val, level=5.0, projection='polar')
plt.show()
../../_images/gcmprocpy_notebooks_03_plotting_9_0.png

Orthographic and Mollweide Projections

[7]:

fig = gy.plt_lat_lon(datasets, 'TN', time=t_val, level=5.0, projection='orthographic', coastlines=True)
plt.show()
../../_images/gcmprocpy_notebooks_03_plotting_11_0.png 
[8]:

fig = gy.plt_lat_lon(datasets, 'TN', time=t_val, level=5.0, projection='mollweide', coastlines=True)
plt.show()
../../_images/gcmprocpy_notebooks_03_plotting_12_0.png

Wind Vector Overlays

Set wind=True to overlay wind vectors. Wind variable names are automatically selected based on model type.

[9]:

fig = gy.plt_lat_lon(datasets, 'TN', time=t_val, level=5.0, wind=True, wind_density=3)
plt.show()
../../_images/gcmprocpy_notebooks_03_plotting_14_0.png 
[10]:

fig = gy.plt_lat_lon(datasets, 'TN', time=t_val, level=5.0,
                     projection='orthographic', central_latitude=0,
                     wind=True, wind_density=3, coastlines=True)
plt.show()
../../_images/gcmprocpy_notebooks_03_plotting_15_0.png

Coastlines

[11]:

fig = gy.plt_lat_lon(datasets, 'TN', time=t_val, level=5.0, coastlines=True)
plt.show()
../../_images/gcmprocpy_notebooks_03_plotting_17_0.png

Pressure Level / Height vs Variable Line Plot

[12]:

fig = gy.plt_lev_var(datasets, 'NO', latitude=0.0, time=t_val, longitude=0.0)
plt.show()
../../_images/gcmprocpy_notebooks_03_plotting_19_0.png

Using y_axis='height' to display the vertical axis in km:

[13]:

fig = gy.plt_lev_var(datasets, 'NO', latitude=0.0, time=t_val, longitude=0.0, y_axis='height')
plt.show()
../../_images/gcmprocpy_notebooks_03_plotting_21_0.png

Variable vs Latitude Line Plot

1D meridional cut at a fixed longitude and pressure level. Pass longitude='mean' (or omit it) to compute a zonal mean.

[14]:

fig = gy.plt_var_lat(datasets, 'TN', level=5.0, time=t_val, longitude=0.0)
plt.show()
../../_images/gcmprocpy_notebooks_03_plotting_23_0.png

Zonal mean (averaged over all longitudes):

[15]:

fig = gy.plt_var_lat(datasets, 'TN', level=5.0, time=t_val)
plt.show()
../../_images/gcmprocpy_notebooks_03_plotting_25_0.png

Variable vs Longitude Line Plot

1D zonal cut at a fixed latitude and pressure level. Pass latitude='mean' (or omit it) to compute a meridional mean.

[16]:

fig = gy.plt_var_lon(datasets, 'TN', level=5.0, time=t_val, latitude=0.0)
plt.show()
../../_images/gcmprocpy_notebooks_03_plotting_27_0.png

Meridional mean (averaged over all latitudes):

[17]:

fig = gy.plt_var_lon(datasets, 'TN', level=5.0, time=t_val)
plt.show()
../../_images/gcmprocpy_notebooks_03_plotting_29_0.png

Pressure Level / Height vs Longitude Contour Plot

[18]:

fig = gy.plt_lev_lon(datasets, 'WN', symmetric_interval=True,    latitude=0.0, time=t_val)
plt.show()
../../_images/gcmprocpy_notebooks_03_plotting_31_0.png

Pressure Level / Height vs Latitude Contour Plot

[19]:

fig = gy.plt_lev_lat(datasets, 'O1', time=t_val, longitude=0.0)
plt.show()
../../_images/gcmprocpy_notebooks_03_plotting_33_0.png

Using y_axis='height':

[20]:

fig = gy.plt_lev_lat(datasets, 'WN', time=t_val, longitude=0.0, y_axis='height')
plt.show()
../../_images/gcmprocpy_notebooks_03_plotting_35_0.png

Pressure Level / Height vs Time Contour Plot

[21]:

fig = gy.plt_lev_time(datasets, 'O2', latitude=0.0, longitude=0.0)
plt.show()
../../_images/gcmprocpy_notebooks_03_plotting_37_0.png

Using y_axis='height':

[22]:

fig = gy.plt_lev_time(datasets, 'O2', latitude=0.0, longitude=0.0, y_axis='height')
plt.show()
../../_images/gcmprocpy_notebooks_03_plotting_39_0.png

Latitude vs Time Contour Plot

[23]:

fig = gy.plt_lat_time(datasets, 'TN', level=5.0, longitude=0.0)
plt.show()
../../_images/gcmprocpy_notebooks_03_plotting_41_0.png

Using level_type='height':

[24]:

fig = gy.plt_lat_time(datasets, 'TN', level=300.0, longitude=0.0, level_type='height')
plt.show()
../../_images/gcmprocpy_notebooks_03_plotting_43_0.png

Longitude vs Time Contour Plot

[25]:

fig = gy.plt_lon_time(datasets, 'TN', latitude=0.0, level=5.0)
plt.show()
../../_images/gcmprocpy_notebooks_03_plotting_45_0.png

Variable vs Time Line Plot

[26]:

fig = gy.plt_var_time(datasets, 'TN', latitude=0.0, longitude=0.0, level=5.0)
plt.show()
../../_images/gcmprocpy_notebooks_03_plotting_47_0.png

Satellite Track Interpolation Plot

Interpolate model data along a satellite trajectory. With a level specified: 1D line plot. Without: 2D contour plot.

[27]:

# Simulate a LEO ascending pass
sat_time = np.array([times[0] + np.timedelta64(i * 6, 'm') for i in range(20)])
sat_lat = np.linspace(-60, 60, 20)
sat_lon = np.linspace(-120, 120, 20)

# Line plot at a fixed level
fig = gy.plt_sat_track(datasets, 'NO', sat_time, sat_lat, sat_lon, level=5.0)
plt.show()
../../_images/gcmprocpy_notebooks_03_plotting_49_0.png 
[28]:

# Contour plot across all levels
fig = gy.plt_sat_track(datasets, 'NO', sat_time, sat_lat, sat_lon)
plt.show()
../../_images/gcmprocpy_notebooks_03_plotting_50_0.png

Cleanup

[29]:

gy.close_datasets(datasets)
print('Datasets closed.')

Datasets closed.