landsat_tif = prefix_remote / Path(the_file)
download(str(landsat_tif), dest_folder=dest_folder)
name_dict[key] = str(a301.data_dir / prefix_local / the_file)
the_file = Path(f"{root}MTL.txt")
mtl_file = prefix_remote / the_file
download(str(mtl_file), dest_folder=dest_folder)
name_dict['mtl'] = str(a301.data_dir / prefix_local / the_file)
# %% [markdown] {"nbgrader": {"grade": false, "grade_id": "cell-dd09edb695c92076", "locked": true, "schema_version": 1, "solution": false}}
# # This cell reads in your affine transform, metadata and profile
#
# Using red band file for transform/profile information (arbitrary)
# %% {"nbgrader": {"grade": false, "grade_id": "cell-daebc17375c70921", "locked": true, "schema_version": 1, "solution": false}}
metadata = landsat_metadata(name_dict['mtl'])
satellite = metadata.SPACECRAFT_ID
with rasterio.open(name_dict['red']) as raster:
big_transform = raster.transform
big_profile = raster.profile
zone = metadata.UTM_ZONE
crs = cartopy.crs.UTM(zone, southern_hemisphere=False)
p_utm = Proj(crs.proj4_init)
p_lonlat = Proj(proj='latlong', datum='WGS84')
# %% [markdown] {"nbgrader": {"grade": false, "grade_id": "cell-cef7282b3b40dd6f", "locked": true, "schema_version": 1, "solution": false}}
# # This cell gets the right reflection function for your satellite
# %% {"nbgrader": {"grade": false, "grade_id": "cell-df3ceb3d365ce669", "locked": true, "schema_version": 1, "solution": false}}
refl_dict = {'LANDSAT_7': calc_refl_457, 'LANDSAT_8': calc_reflc_8}
from matplotlib import pyplot as plt
from skimage import data, img_as_float
from skimage import exposure
june_2015 = a301.test_dir / Path('landsat8/LC80470262015165LGN02')
may_2015 = a301.test_dir / Path("landsat_2018_05_13")
data_dir = may_2015
data_dir = june_2015
tiff_file = list(data_dir.glob("*B1.TIF"))[0]
meta_file = list(data_dir.glob("*MTL.txt"))[0]
#jpeg_file=list(data_dir.glob("*T1.jpg"))[0]
help(img_as_float)
# In[2]:
out = landsat_metadata(meta_file)
meta_dict = out.__dict__
with pil_image.open(tiff_file) as img:
tiff_meta_dict = {TAGS[key]: img.tag[key] for key in img.tag.keys()}
ch1 = img_as_float(img)
img_eq = exposure.equalize_hist(ch1)
# In[3]:
plt.hist(ch1.ravel())
# In[4]:
plt.imshow(ch1)
# # Here is the correct orientation
# In[3]:
Image('images/LC08_L1TP_232091_20160108_20170405_01_T1.png',width=400)
# # Make two different crs versions (epsg and zone)
#
# Note the different lower limits on y
# In[4]:
meta_dict=landsat_metadata(mtl_file).__dict__
zone=meta_dict['UTM_ZONE']
epsg_code=crs.to_epsg()
print(f'we are in zone {zone} with epsg_code {epsg_code}')
cartopy_crs_code=cartopy.crs.epsg(epsg_code)
cartopy_crs_zone = cartopy.crs.UTM(zone, southern_hemisphere=False)
print(f"North zone y limits from the cartopy epsg constructor {cartopy_crs_code.y_limits}")
cartopy_crs=cartopy_crs_zone
print(f"North zone y limits from cartopy UTM constructor {cartopy_crs_zone.y_limits}")
# # Show that we can get the correct coastline
# In[5]:
# # Question A: define your own version of file_dict
#
# In the next cell create a version of file_dict with the same structure as mine, but with your filenames substituted
# for mine
# %% {"deletable": false, "nbgrader": {"checksum": "b996f3c4dda7d99b6eafc8eb406c6dbe", "grade": true, "grade_id": "cell-fc9e612b0aa125e8", "locked": false, "points": 4, "schema_version": 1, "solution": true}}
# YOUR CODE HERE
raise NotImplementedError()
# %% [markdown] {"deletable": false, "editable": false, "nbgrader": {"checksum": "9c203c221c48c22c6bae77781d739b39", "grade": false, "grade_id": "cell-dd09edb695c92076", "locked": true, "schema_version": 1, "solution": false}}
# # This cell reads in your affine transform, metadata and profile
#
# Using red band file for transform/profile information (arbitrary)
# %% {"deletable": false, "editable": false, "nbgrader": {"checksum": "df87b1b62c70bc362823c4adb4309837", "grade": false, "grade_id": "cell-daebc17375c70921", "locked": true, "schema_version": 1, "solution": false}}
metadata=landsat_metadata(file_dict['spring']['filenames']['mtl'])
zone = metadata.UTM_ZONE
crs = cartopy.crs.UTM(zone, southern_hemisphere=False)
p_utm=Proj(crs.proj4_init)
p_lonlat=Proj(proj='latlong',datum='WGS84')
file_dict['satellite']=metadata.SPACECRAFT_ID
# %% [markdown] {"deletable": false, "editable": false, "nbgrader": {"checksum": "92c3aa1b32a0b1f90621c3b0ef97b099", "grade": false, "grade_id": "cell-cef7282b3b40dd6f", "locked": true, "schema_version": 1, "solution": false}}
# # This cell gets the right reflection and radiance functions for your satellite
#
# See [the library listing](https://clouds.eos.ubc.ca/~phil/courses/atsc301/codedoc/full_listing.html) for the function signatures.
# %% {"deletable": false, "editable": false, "nbgrader": {"checksum": "57225c558d2128d4992783a5acd2f00f", "grade": false, "grade_id": "cell-df3ceb3d365ce669", "locked": true, "schema_version": 1, "solution": false}}
refl_dict={'LANDSAT_7':calc_refl_457,'LANDSAT_8':calc_reflc_8}
radiance_dict={'LANDSAT_7':calc_radiance_457,'LANDSAT_8':calc_radiance_8}
satellite=metadata.SPACECRAFT_ID