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Commit c579843a authored by Coda Phillips's avatar Coda Phillips
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add dcc notebook

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%% Cell type:code id: tags:
``` python
%pylab inline
```
%% Output
%pylab is deprecated, use %matplotlib inline and import the required libraries.
Populating the interactive namespace from numpy and matplotlib
%% Cell type:code id: tags:
``` python
import numpy as np
import pandas as pd
from collections import defaultdict
from pathlib import Path
from tqdm import tqdm
import netCDF4
import l2bc_utils
from l2bc_utils import PLATFORM_COLORS
```
%% Cell type:code id: tags:
``` python
# Read csv of file metadata
root = Path('/data/www/patmosx_l2bc')
_files = pd.read_csv(root / 'index.csv', parse_dates=['date'])
# Fast
_files['root'] = str(root) + '/'
_files['path'] = _files['root'].str.cat(_files['path'])
```
%% Cell type:code id: tags:
``` python
# pivot into variable columns
files = l2bc_utils.pivot_files(_files)
```
%% Cell type:code id: tags:
``` python
# Grab the first file to get the lat/lon
for f in files.values.ravel():
if f:
nc = netCDF4.Dataset(f)
lat = nc.variables['latitude'][:]
lon = nc.variables['longitude'][:]
nc.close()
break
```
%% Cell type:code id: tags:
``` python
lon, lat = np.meshgrid(lon, lat)
```
%% Cell type:code id: tags:
``` python
df = []
count = 0
for (platform, date, node), (sza, vza, t11, r065) in l2bc_utils.iter_nc_var(files, 'solar_zenith_angle', 'sensor_zenith_angle', 'temp_11_0um_nom', 'refl_0_65um_nom_counts'):
cold = t11 < 200
day = sza < 80
tropics = (lat > -20) & (lat < 20)
near_nadir = abs(vza) < 15
mask = cold & day & tropics & near_nadir
_df = (pd.DataFrame({
'solar_zenith_angle': sza[mask],
'sensor_zenith_angle': vza[mask],
'refl_0_65um_nom_counts': r065[mask],
'latitude': lat[mask],
'longitude': lon[mask],
}))
# subset
_df['platform'] = platform
_df['date'] = date
_df['node'] = node
df.append(_df)
count += len(_df)
```
%% Output
0%| | 0/79822 [00:00<?, ?it/s]
100%|██████████| 79822/79822 [25:33<00:00, 52.07it/s]
%% Cell type:code id: tags:
``` python
df = pd.concat(df)
```
%% Cell type:code id: tags:
``` python
k = 'refl_0_65um_nom_counts'
cos_sza = np.cos(np.deg2rad(df.solar_zenith_angle))
norm_counts = df[k] / cos_sza
norm_counts.index = pd.MultiIndex.from_frame(df[['platform', 'date', 'node']])
```
%% Cell type:code id: tags:
``` python
norm_counts_means = norm_counts.groupby('platform').apply(lambda x: x.droplevel(['platform','node']).resample('1MS').mean())
```
%% Cell type:code id: tags:
``` python
figure(figsize=(15,5))
for platform, s in norm_counts_means.groupby('platform'):
s = s.droplevel('platform')
s = s.groupby(lambda x: x.month, group_keys=False).apply(lambda x: x-x.mean()) + s.mean()
scatter(s.index, s.values, label=platform, color=PLATFORM_COLORS[platform.lower()], s=5)
ylabel(f'({k} / µ)')
title('Deseasonalized Normalized Counts')
legend(loc='center left', bbox_to_anchor=(1, .5))
ylim([400, 900])
grid()
```
%% Output
%% Cell type:code id: tags:
``` python
```
%% Cell type:code id: tags:
``` python
```
%% Cell type:code id: tags:
``` python
```
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