|
|
# Creating an environment
|
|
|
```bash
|
|
|
module load miniconda || module load anaconda27 # SSEC-specific
|
|
|
conda create -n himawari python=2.7 netcdf4 cffi anaconda # only need to do this once
|
|
|
source activate himawari # switch to himawari environment
|
|
|
export PYTHONPATH=/path/to/himawari/py
|
|
|
ipython --pylab
|
|
|
```
|
|
|
|
|
|
# Attach a scene and load raw counts, radiances, brightness temps
|
|
|
```python
|
|
|
import HimawariScene as hsd
|
|
|
b07 = hsd.HimawariScene('input/HS_H08_20161010_1900_B07_FLDK')
|
|
|
b07raw = b07.counts()
|
|
|
b07rad = b07.radiances()
|
|
|
b07bt = b07.brightnessTemps()
|
|
|
```
|
|
|
|
|
|
# slice a section out and look at data marked missing, or having BT<100K
|
|
|
```python
|
|
|
q=b07bt[2000:2400,2000:2400]
|
|
|
c=b07cnt[2000:2400,2000:2400]
|
|
|
r=b07rad[2000:2400,2000:2400]
|
|
|
sum(q.ravel().mask), sum(q.ravel() < 100.0)
|
|
|
```
|
|
|
|
|
|
# examine radiance slope/intercept constants and their relation to counts/radiance
|
|
|
```python
|
|
|
cal = b07.calibration
|
|
|
cal.rad_m
|
|
|
cal.rad_b
|
|
|
cal.rad_m * 16350 + cal.rad_b
|
|
|
cal.rad_m * 16349 + cal.rad_b
|
|
|
cal.rad_m * 16351 + cal.rad_b
|
|
|
```
|
|
|
|
|
|
# find coordinates of masked data, and make a list of radiances at those coordinates
|
|
|
```python
|
|
|
dex = np.argwhere(q.mask)
|
|
|
rmasked = [r[a[0],a[1]] for a in dex]
|
|
|
rmasked
|
|
|
```
|
|
|
|
|
|
# likewise with low radiances
|
|
|
```python
|
|
|
dex = np.argwhere(q<100)
|
|
|
rlow = [r[a[0],a[1]] for a in dex]
|
|
|
rlow
|
|
|
``` |
|
|
\ No newline at end of file |