# ### Reading the Modis level 2 cloudmask
#
# http://modis-atmos.gsfc.nasa.gov/MOD35_L2/
# In[1]:
get_ipython().magic('matplotlib inline')
import h5py
import numpy as np
from matplotlib import pyplot as plt
from e582utils.data_read import download
mask_name='MYD35_L2.A2014125.2135.006.2014125184012.h5'
download(mask_name)
with h5py.File(mask_name,'r') as infile:
cloud_mask=infile['mod35/Data Fields/Cloud_Mask'][...]
maskVals=cloud_mask[0,...].astype(np.uint8) #get the first byte
# There are 6 bytes in the cloudmask:
# " \n",
# " Bit fields within each byte are numbered from the left: \n",
# " 7, 6, 5, 4, 3, 2, 1, 0. \n",
# " The left-most bit (bit 7) is the most significant bit. \n",
# " The right-most bit (bit 0) is the least significant bit. \n",
# " \n",
from e582utils.data_read import download
import numpy as np
import h5py
import sys
import warnings
from matplotlib import pyplot as plt
from IPython.display import Image
from mpl_toolkits.basemap import Basemap
from matplotlib.colors import Normalize
import matplotlib
import matplotlib.cm as cm
import seaborn as sns
get_ipython().magic('matplotlib inline')
myd02file = "MYD021KM.A2016224.2100.006.2016225153002.h5"
download(myd02file)
# In[2]:
Image('figures/MYBRGB.A2016224.2100.006.2016237025650.jpg')
# In[3]:
myd03file = "MYD03.A2016224.2100.006.2016225152335.h5"
download(myd03file)
# ### Calibrate and resample the channel 1 and channel 2 reflectivities
#
#
# In[4]:
# ## First, get channel 1 and the lat/lon files as usual
# In[1]:
import h5py
import numpy as np
import pyproj
import pyresample
from pyresample import kd_tree, geometry
from matplotlib import pyplot as plt
from e582utils.modismeta_read import parseMeta
from e582utils.data_read import download
from mpl_toolkits.basemap import Basemap
data_name = 'MYD021KM.A2016224.2100.006.2016225153002.h5'
download(data_name)
geom = 'MYD03.A2016224.2100.006.2016225152335.h5'
download(geom)
index = 0
my_name = 'EV_250_Aggr1km_RefSB'
with h5py.File(data_name, 'r') as h5_file:
chan1 = h5_file['MODIS_SWATH_Type_L1B']['Data Fields'][my_name][
index, :, :]
scale = h5_file['MODIS_SWATH_Type_L1B']['Data Fields'][my_name].attrs[
'reflectance_scales'][...]
offset = h5_file['MODIS_SWATH_Type_L1B']['Data Fields'][my_name].attrs[
'reflectance_offsets'][...]
chan1_calibrated = (chan1 - offset[index]) * scale[index]
with h5py.File(geom) as geo_file:
# In[13]:
from netCDF4 import Dataset
import numpy as np
import matplotlib
from matplotlib import pyplot as plt
from mpl_toolkits.basemap import Basemap
from e582utils.data_read import download
import warnings
warnings.filterwarnings("ignore")
import pyproj
from e582lib.map_slices import make_basemap_xy
from rasterio.transform import from_bounds
l3file='A2007008.L3m_DAY_PAR_par_9km.nc'
download(l3file)
# #### Extract the variables and the _FillValue and replace missing data with np.nan
#
# See http://unidata.github.io/netcdf4-python/
# In[2]:
with Dataset(l3file,'r') as ncdat:
ncdat.set_auto_mask(False)
par=ncdat.variables['par'][...]
lat=ncdat.variables['lat'][...]
lon=ncdat.variables['lon'][...]
fill_value=ncdat.variables['par']._FillValue
hit= par == fill_value
# and # # [Python for Data Analysis](http://shop.oreilly.com/product/0636920023784.do) # In[1]: from e582utils.data_read import download from contexttimer import Timer import h5py import pandas as pd import datetime as dt import numpy as np import time chlor_file='A20101522010181.L3b_MO_CHL.h5' download(chlor_file) # ### dump the monthly level3 binned chlorophyll metadata # In[2]: from e582utils.h5dump import dumph5 outstring=dumph5(chlor_file) print(outstring) # ### Structured arrays # # The hdf file stores the array chlor_a as a vector with 11384896 values, each with two fields: chlor_a_sum and chlor_a_sum_sq #
#
# 1) how to plot an image using imshow with a colorbar
#
# 2) how to find out which pixels are in a particular lon/lat grid cell so that we can
# regrid the radiances onto a uniform grid
#
# In[14]:
from e582utils.data_read import download
import numpy as np
import h5py
import sys
filename = 'MYD021KM.A2016136.2015.006.2016138123353.h5'
download(filename)
# Here is the corresponding red,green,blue color composite for the granule.
# In[15]:
from IPython.display import Image
Image(
url=
'http://clouds.eos.ubc.ca/~phil/courses/atsc301/downloads/aqua_136_2015.jpg',
width=600)
# In[16]:
### now use h5py to read some of the satellite channels
# ### Assignment -- for the following image, use [histogram2d](https://docs.scipy.org/doc/numpy/reference/generated/numpy.histogram2d.html) to make a 2-dimensional histogram
#
# Use the channel 1 reflectivity for your y-axis bins, and the channel 31 brightness temperature for your x-axis bins
# In[6]:
from IPython.display import Image
from e582utils.data_read import download
# In[5]:
Image('MOBRGB.A2012240.0235.006.2015311151021.jpg',width=500)
# In[9]:
l1b_file='MOD021KM.A2012240.0235.006.2014220124853.h5'
geom_file='MOD03.A2012240.0235.006.2012287184700.h5'
mask_file='MOD35_L2.A2012240.0235.006.2015059110241.h5'
cloud_file='MOD06_L2.A2012240.0235.006.2015062132158.h5'
files=[l1b_file,geom_file,mask_file,cloud_file]
for the_file in files:
download(the_file)
# In[ ]:
# unlike the file used in the calc_chlor and plot_chlor notebooks, downloads from the
# https://oceancolor.gsfc.nasa.gov/cgi/l3 have a nc suffix but don't open as nc files.
# Try as an h5 file instead.
#
# This notebook verifies that the data is the same format used in the calc_chlor notebook
# In[10]:
from e582utils.data_read import download
from e582utils import h5dump
import h5py
# In[3]:
infile = 'A2007010.L3b_DAY_PAR.nc'
download(infile)
# In[7]:
out = h5dump.dumph5(infile)
# In[25]:
root_key = 'level-3_binned_data'
with h5py.File(infile, 'r') as in_h5:
print('root keys: ', list(in_h5.keys()))
print('group keys: ', list(in_h5[root_key].keys()))
par = in_h5[root_key]['par'][...]
binlist = in_h5[root_key]['BinList'][...]
# In[26]:
dset[...] = the_chan[...]
dset.attrs['units'] = chan_dict[chan_name]['units']
dset.attrs['wavelength_um'] = chan_dict[chan_name]['wavelength_um']
dset.attrs['modis_chan'] = chan_name
f.attrs['geotiff_args'] = geotiff_string
f.attrs['basemap_args'] = basemap_string
f.attrs['area_def_args'] = area_def_string
f.attrs['fill_value'] = fill_value
f.attrs['level1b_file'] = in_file
return None
if __name__ == "__main__":
myd02_name = 'MYD021KM.A2016224.2100.006.2016225153002.h5'
download(myd02_name)
myd03_name = 'MYD03.A2016224.2100.006.2016225152335.h5'
download(myd03_name)
out_file = 'test.h5'
chan_list = ['1', '4', '31']
project_channels = modisl1b_resample(myd02_name, myd03_name, chan_list)
project_channels['out_file'] = 'test.h5'
project_channels['in_file'] = myd02_name
project_channels['chan_list'] = chan_list
write_h5(**project_channels)
from matplotlib import cm
cmap = cm.autumn #see http://wiki.scipy.org/Cookbook/Matplotlib/Show_colormaps
cmap.set_over('w')
cmap.set_under('b', alpha=0.2)
from matplotlib import pyplot as plt
import matplotlib.image as image
import matplotlib
import rasterio
from e582lib.map_slices import get_basemap
import numpy as np
from mpl_toolkits.basemap import Basemap
from e582utils.data_read import download
import warnings
warnings.filterwarnings("ignore")
# In[2]:
download('stretched_rgb.png')
download('stretched_rgb.tif')
# #### Read in the png 3-color image along with the tif version and save the projection and transform
# In[3]:
imfile = 'stretched_rgb.png'
im=image.imread(imfile)
#
# find all black pixels and turn them light grey
#
hit = np.logical_and(im[:,:,0]==0, im[:,:,1]==0,im[:,:,2]==0)
im[hit,:]=0.75
#
