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Kernel: Python 3 (Ubuntu Linux)

Data Access and Manipulation Using iPython Notebooks

OFAM and Himawari 8

In this notebook:

The following will go through how to: <br >

Access netCDF data locally from /g/data
Extract/view data
Combine data from different datasets

The following material uses the CSIRO Ocean Forcasting Australian Model (OFAM) and the Bureau of Meteorology Himawari 8 Data Collections. For more information on the collection and licensing, please click here for OFAM and here for Himawari 8.

Prerequisites

A Python 3 environment

Setup instructions for python 3 virtual environments can be found here.

Ensure that the VDI modules listed below are loaded in the order listed. If you're uncertain, purge modules and start over (you can do this from inside your Python virtual environment).

    $ module load proj/4.8.0
    $ module load geos/3.5.0
    $ module load python3/3.5.2
    $ module load python3/3.5.2-matplotlib
    $ module load ipython/4.2.0-py3.5

The following Python modules need to be installed in your virtual environment:

Numpy
Cython
Cartopy 0.13

Check loaded modules using pip list

   $ source /path/to/your_virtualenv/bin/activate
   (your_venv)$ pip list

If you don't see any of the modules listed, then:

    (your_venv)$ pip3 install numpy
    (your_venv)$ pip3 install cython
    (your_venv)$ pip3 install cartopy==0.13

Cartopy needs a version flag (==0.13) because later iterations require Proj4.9, which is not available on the VDI at the time of writing this material.

   (your_venv)$ pip list

...should now show the required modules.

Launch the Jupyter Notebook application: ``` $ jupyter notebook ```

NOTE: This will launch the Notebook Dashboard within a new web browser window.

Import python libraries

In [1]:

from netCDF4 import Dataset
import matplotlib.pyplot as plt 
import numpy as np
%matplotlib inline

Ocean Forcasting Australian Model (OFAM v3.0) Data

Geonetwork URL: http://geonetwork.nci.org.au/geonetwork/srv/eng/catalog.search#/metadata/f9007_2850_9392_7175

NCI THREDDS Data Server: http://dapds00.nci.org.au/thredds/catalogs/gb6/catalog.html

Open dataset

In [2]:

ofam_path = '/g/data3/gb6/BRAN/BRAN_2015/OFAM/ocean_temp_2016_02.nc'

In [3]:

ofam = Dataset(ofam_path)

Take a look at the file contents

In [4]:

for item in ofam.dimensions:
    print("name: {}, size: {}\n".format(ofam.dimensions[item].name, ofam.dimensions[item].size))


print("\n")
vars = ofam.variables.keys()
for item in vars:
    print('Variable:\t{}'.format(item))
    print('Dimensions:\t{}'.format(ofam[item].dimensions))
    print('Shape:\t{}\n'.format(ofam[item].shape))

Out[4]:

name: xt_ocean, size: 3600

name: yt_ocean, size: 1500

name: st_ocean, size: 51

name: Time, size: 29

name: nv, size: 2

name: st_edges_ocean, size: 52



Variable:	xt_ocean
Dimensions:	('xt_ocean',)
Shape:	(3600,)

Variable:	yt_ocean
Dimensions:	('yt_ocean',)
Shape:	(1500,)

Variable:	st_ocean
Dimensions:	('st_ocean',)
Shape:	(51,)

Variable:	Time
Dimensions:	('Time',)
Shape:	(29,)

Variable:	nv
Dimensions:	('nv',)
Shape:	(2,)

Variable:	st_edges_ocean
Dimensions:	('st_edges_ocean',)
Shape:	(52,)

Variable:	average_T1
Dimensions:	('Time',)
Shape:	(29,)

Variable:	average_T2
Dimensions:	('Time',)
Shape:	(29,)

Variable:	average_DT
Dimensions:	('Time',)
Shape:	(29,)

Variable:	Time_bounds
Dimensions:	('Time', 'nv')
Shape:	(29, 2)

Variable:	temp
Dimensions:	('Time', 'st_ocean', 'yt_ocean', 'xt_ocean')
Shape:	(29, 51, 1500, 3600)

Extract and plot global data from single time and depth

In [5]:

lat = ofam.variables['yt_ocean'][:]
lon = ofam.variables['xt_ocean'][:]
T = ofam.variables['temp'][22,0,:,:]
time = ofam.variables['Time'][:]

In [8]:

plt.figure(figsize=(12,5))
plt.pcolormesh(lon, lat, T)

Out[8]:

<matplotlib.collections.QuadMesh at 0x7ff200c03630>

Do the same for a smaller subset

In [9]:

# time slice
i = 22

T_s = ofam.variables['temp'][i, 0, 200:800, 1400:2100]
lon_s = lon[1400:2100]
lat_s = lat[200:800]

plt.figure(figsize=(12,12))
plt.pcolormesh(lon_s, lat_s, T_s)

plt.clim(vmin=18, vmax=30)

Out[9]:

Himawari 8

Geonetwork URL: http://geonetwork.nci.org.au/geonetwork/srv/eng/catalog.search#/metadata/f9385_6463_3730_3415

NCI THREDDS Data Server: http://dapds00.nci.org.au/thredds/catalog/rr5/satellite/obs/himawari8/FLDK/catalog.html

This example is from 23-Feb-16 at 0400.

Open dataset

In [10]:

# Data paths
h8b1_path = '/g/data2/rr5/satellite/obs/himawari8/FLDK/2016/02/23/0400/20160223040000-P1S-ABOM_OBS_B01-PRJ_GEOS141_2000-HIMAWARI8-AHI.nc'
h8b2_path = '/g/data2/rr5/satellite/obs/himawari8/FLDK/2016/02/23/0400/20160223040000-P1S-ABOM_OBS_B02-PRJ_GEOS141_2000-HIMAWARI8-AHI.nc'
h8b3_path = '/g/data2/rr5/satellite/obs/himawari8/FLDK/2016/02/23/0400/20160223040000-P1S-ABOM_OBS_B03-PRJ_GEOS141_2000-HIMAWARI8-AHI.nc'

# Open data
h8b1 = Dataset(h8b1_path)
h8b2 = Dataset(h8b2_path)
h8b3 = Dataset(h8b3_path)

Take a look at the file contents¶

In [11]:

for item in h8b1.dimensions:
    print("dimensions: {}, name: {}\n".format(h8b1.dimensions[item].name,  h8b1.dimensions[item].size))

vars = h8b1.variables.keys()
for item in vars:
    print('Variable:\t{}'.format(item))
    print('Dimensions:\t{}'.format(h8b1[item].dimensions))
    print('Shape:\t{}\n'.format(h8b1[item].shape))

Out[11]:

dimensions: time, name: 1

dimensions: y, name: 5500

dimensions: x, name: 5500

Variable:	time
Dimensions:	('time',)
Shape:	(1,)

Variable:	y
Dimensions:	('y',)
Shape:	(5500,)

Variable:	x
Dimensions:	('x',)
Shape:	(5500,)

Variable:	geostationary
Dimensions:	()
Shape:	()

Variable:	scan_line_time
Dimensions:	('y',)
Shape:	(5500,)

Variable:	channel_0001_scaled_radiance
Dimensions:	('time', 'y', 'x')
Shape:	(1, 5500, 5500)

Extract and plot global data

In [12]:

b1 = h8b1.variables['channel_0001_scaled_radiance'][0,:,:]
b2 = h8b2.variables['channel_0002_scaled_radiance'][0,:,:]
b3 = h8b3.variables['channel_0003_scaled_radiance'][0,:,:]

x = h8b1.variables['x'][:]
y = h8b1.variables['y'][:]

In [13]:

plt.figure(figsize=(6,6))
plt.imshow(b1, extent=[x[0], x[-1], y[-1], y[0]])

Out[13]:

<matplotlib.image.AxesImage at 0x7ff200adcc18>

Instead of looking at single band, let's make an RGB image

In [14]:

vmin = 0
vmax = .5
B1 = b1.clip(vmin, vmax) / vmax * 255
B2 = b2.clip(vmin, vmax) / vmax * 255
B3 = b3.clip(vmin, vmax) / vmax * 255

rgb = np.stack((B3, B2, B1), axis=2).astype('uint8')

In [15]:

# Plot image
plt.figure(figsize=(12,12))
plt.imshow(rgb, extent=[x[0], x[-1], y[-1], y[0]])

# Add labels
plt.title('Himawari 8 \n', fontsize=20)

# Adjust tick mark size
plt.tick_params(labelsize=16)

Out[15]:

Do the same for a smaller subset (let's choose roughly the same region as the OFAM subset)

Note: In these examples, the subsets are specified directly by the index value but you could also query based on lat/lon values.

In [16]:

vmin = 0
vmax = .5
B1 = b1[3300:4500,3000:5000].clip(vmin, vmax) / vmax * 255
B2 = b2[3300:4500,3000:5000].clip(vmin, vmax) / vmax * 255
B3 = b3[3300:4500,3000:5000].clip(vmin, vmax) / vmax * 255

X = x[3000:5000]
Y = y[3300:4500]
rgb = np.stack((B3, B2, B1), axis=2).astype('uint8')


# Plot image
plt.figure(figsize=(12,12))
plt.imshow(rgb , extent=[X[0], X[-1], Y[-1], Y[0]])

# Add labels
plt.title('Himawari 8 \n', fontsize=20)

# Adjust tick mark size
plt.tick_params(labelsize=16)

Out[16]:

Now let's try and plot both datasets in the same plot...

Cartopy (based on Matplotlib but includes support for different coordinate reference systems)

Note: On the VDI, the Cartopy package will have to be installed locally. Instructions at the end of this notebook if you do not already have Cartopy installed.

Let's first replot the previous Himawari subset

In [17]:

import cartopy.crs as ccrs

plt.figure(figsize=(12,12))

# Define the projection information of the image
img_proj = ccrs.Geostationary(central_longitude=h8b1['geostationary'].longitude_of_projection_origin, 
                                  satellite_height=h8b1['geostationary'].satellite_height)
# The extents of the image we are plotting
img_extent = (X[0], X[-1], Y[-1], Y[0])


# Setup the axes projection
ax = plt.axes(projection=ccrs.Miller(central_longitude=float(h8b1['geostationary'].longitude_of_projection_origin)))

# Extent of the axes in the plot
ax.set_extent([150, 190, -35, -5])

# Plot image
plt.imshow(rgb, transform=img_proj, extent=img_extent, origin='upper')

Out[17]:

<matplotlib.image.AxesImage at 0x7ff216305278>

Now let's do the same for the OFAM data

In [18]:

plt.figure(figsize=(12,12))

# Setup the axes projection
ax = plt.axes(projection=ccrs.Miller(central_longitude=float(h8b1['geostationary'].longitude_of_projection_origin)))

# Define the projection information of the image
img_proj = ccrs.PlateCarree()

# The extents of the image we are plotting
img_extent = (lon_s[0], lon_s[-1], lat_s[-1], lat_s[0])

# Extent of the axes in the plot
ax.set_extent([150, 190, -35, -5])

plt.imshow(T_s, transform=img_proj, extent=img_extent, origin='upper')
plt.clim(vmin=18, vmax=30)

Out[18]:

Now combine...

In [19]:

plt.figure(figsize=(12,12))

# Setup the axes projection
ax = plt.axes(projection=ccrs.Miller(central_longitude=float(h8b1['geostationary'].longitude_of_projection_origin)))


### OFAM ###

img_proj = ccrs.PlateCarree()
img_extent = (lon_s[0], lon_s[-1], lat_s[-1], lat_s[0])
ax.set_extent([150, 190, -35, -5])
plt.imshow(T_s, transform=img_proj, extent=img_extent, origin='upper', alpha=.75)
plt.clim(vmin=18, vmax=30)


### HIMAWARI ###
img_proj = ccrs.Geostationary(central_longitude=h8b1['geostationary'].longitude_of_projection_origin, 
                                  satellite_height=h8b1['geostationary'].satellite_height)
img_extent = (X[0], X[-1], Y[-1], Y[0])
ax.set_extent([150, 190, -35, -5])
plt.imshow(rgb, transform=img_proj, extent=img_extent, origin='upper', alpha=.5)

Out[19]:

<matplotlib.image.AxesImage at 0x7ff1aaf79e10>

EXTRA:
(1) Try playing around with different plotting options. For example, plotting contours instead of using "imshow".
(2) Try merging another dataset of interest with either of the ones above.

Close files

In [20]:

ofam.close()
h8b1.close()
h8b2.close()
h8b3.close()

Data Access and Manipulation Using iPython Notebooks

OFAM and Himawari 8

In this notebook:

The following material uses the CSIRO Ocean Forcasting Australian Model (OFAM) and the Bureau of Meteorology Himawari 8 Data Collections. For more information on the collection and licensing, please click here for OFAM and here for Himawari 8.

Prerequisites

A Python 3 environment

Import python libraries

Ocean Forcasting Australian Model (OFAM v3.0) Data

Open dataset

Take a look at the file contents

Extract and plot global data from single time and depth

Do the same for a smaller subset

Himawari 8

Open dataset

Take a look at the file contents¶

Extract and plot global data

Instead of looking at single band, let's make an RGB image

Do the same for a smaller subset (let's choose roughly the same region as the OFAM subset)

Now let's try and plot both datasets in the same plot...

Cartopy (based on Matplotlib but includes support for different coordinate reference systems)

Let's first replot the previous Himawari subset

Now let's do the same for the OFAM data

Now combine...

Close files

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