Granules#

[1]:

import starepandas
import geopandas
import pystare
import matplotlib.pyplot as plt
import importlib
import copy

[2]:

fname = '../tests/data/granules/MOD05_L2.A2019336.0000.061.2019336211522.hdf'
modis = starepandas.read_granule(fname, latlon=True, sidecar=True, xy=True)

[3]:

modis = copy.copy(modis[(modis.y>250) & (modis.x<150)])

[4]:

modis

[4]:

	lat	lon	sids	x	y	Scan_Start_Time	Solar_Zenith	Solar_Azimuth	Sensor_Zenith	Sensor_Azimuth	Water_Vapor_Infrared
67770	64.289093	-16.075922	3662037656027565065	0	251	8.493986e+08	136.769997	-17.160000	65.359999	-87.399998	NaN
67771	64.298607	-16.575207	3662124605389551017	1	251	8.493986e+08	136.689997	-17.820000	64.739999	-87.849998	NaN
67772	64.305969	-17.040155	3662126430541234153	2	251	8.493986e+08	136.619997	-18.440000	64.129999	-88.269998	NaN
67773	64.311798	-17.501287	3662105186047635465	3	251	8.493986e+08	136.549997	-19.060000	63.529999	-88.689998	NaN
67774	64.315926	-17.928263	3732481344770526057	4	251	8.493986e+08	136.489997	-19.620000	62.929999	-89.079998	NaN
...	...	...	...	...	...	...	...	...	...	...	...
109495	69.114990	-47.227417	3744993157413528587	145	405	8.493987e+08	126.089997	-51.889999	4.470000	69.109998	NaN
109496	69.095970	-47.343407	3744993282781123915	146	405	8.493987e+08	126.069997	-52.029999	4.920000	68.649998	NaN
109497	69.076836	-47.459351	3744993705556370667	147	405	8.493987e+08	126.049997	-52.169999	5.370000	68.239998	NaN
109498	69.057602	-47.575237	3744992437391885067	148	405	8.493987e+08	126.019997	-52.309999	5.820000	67.879998	NaN
109499	69.038269	-47.691101	3745022036721761931	149	405	8.493987e+08	125.999997	-52.439999	6.270000	67.559998	NaN

23250 rows × 11 columns

[5]:

trixels = modis.make_trixels()
modis.set_trixels(trixels, inplace=True)

[6]:

geom = geopandas.points_from_xy(modis.lon, modis.lat)
modis.set_geometry(geom, inplace=True)

[7]:

fig, ax = plt.subplots(figsize=(10, 10), dpi=100)
ax.grid(True)

modis.plot(trixels=False, ax=ax, marker='.', markersize=20)
modis.plot(trixels=True, color='r', ax=ax, lw=0.5)

#plt.savefig('modis.png')

[7]:

<AxesSubplot:>

../../_images/examples_notebooks_granules_7_1.png

Plotting footprints#

[8]:

fname = '../tests/data/granules/MOD05_L2.A2019336.0000.061.2019336211522.hdf'
mod05 = starepandas.io.granules.Mod05(fname)

[9]:

mod05.read_sidecar_cover()
mod05.stare_cover

[9]:

masked_array(data=[3614138700964823044, 3616390500778508292,
                   3618642300592193540, ..., 4298608878511194121,
                   4298613276557705225, 4298668252139094025],
             mask=False,
       fill_value=999999)

Manually getting the 2D STARE array#

[10]:

import netCDF4
from pyhdf.SD import SD
import numpy
import pystare
import datetime

[11]:

hdf = SD(fname)
lon = hdf.select('Longitude').get().astype(numpy.double)
lat = hdf.select('Latitude').get().astype(numpy.double)

[12]:

start = datetime.datetime.now()
sids = pystare.from_latlon_2d(lat=lat, lon=lon, adapt_level=True)
datetime.datetime.now()-start

[12]:

datetime.timedelta(microseconds=985640)

[13]:

print(pystare.spatial_resolution(sids).min())
print(pystare.spatial_resolution(sids).max())

9
11

Intersecting Data#

[14]:

world = geopandas.read_file(geopandas.datasets.get_path('naturalearth_lowres'))
world.sort_values(by='name', inplace=True)
world = starepandas.STAREDataFrame(world)
sids = world.make_sids(level=6)
world.set_sids(sids, inplace=True)
trixels = world.make_trixels()
world.set_trixels(trixels, inplace=True)

[15]:

fig, ax = plt.subplots(figsize=(4,4), dpi=200)
ax.grid(True)

country = world[world.name=='Iceland']
country.plot(ax=ax, trixels=True, boundary=True, color='y', zorder=1)
country.plot(ax=ax, trixels=False, facecolor="none", edgecolor='blue', zorder=1)
modis.plot(ax=ax, color='red', trixels=False, zorder=0, linewidth=0.1)

[15]:

<AxesSubplot:>

../../_images/examples_notebooks_granules_18_1.png

[16]:

a = modis.stare_intersects(country['sids'].iloc[0])
a.any()

[16]:

True

[17]:

len(country['sids'].iloc[0])

[17]:

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