else:
season = "fall"
root = "LE07_L1TP_047026_20141110_20160904_01_T1_"
prefix_remote = Path('landsat_scenes/l7_vancouver/fall')
prefix_local = Path("landsat7/vancouver/fall")
name_dict = dict()
for key, band_num in landsat_band_dict.items():
name_dict[key] = f"{root}B{band_num}.TIF"
dest_folder = a301.data_dir / prefix_local
dest_folder.mkdir(parents=True, exist_ok=True)
for key, the_file in name_dict.items():
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
# ## plotting Vancouver on a map
#
#
# ### Step 1: Use [cartopy](http://scitools.org.uk/cartopy/docs/latest/index.html) to make a LAEA map of BC including Vancouver.
#
# **The cell below uses [context.py](https://github.com/phaustin/a301_code/blob/master/notebooks/context.py) to find
# the path to data_dir. It is held in the variable context.data_dir**
# %%
from context import data_dir
from a301.utils.data_read import download
read_data = False
if read_data:
filename = "MYD021KM.A2013222.2105.061.2018047235850.hdf"
download(filename)
local_file = Path.cwd() / Path(filename)
to_file = data_dir / Path(filename)
local_file.rename(to_file)
# %% [markdown]
# **This cell sets up the datum and the LAEA projection, with the tangent point at the North Pole and the central meridian at -90 degrees west of Greenwich**
# %%
import cartopy.crs as ccrs
import matplotlib.pyplot as plt
import cartopy
from pathlib import Path
import pprint
import numpy as np
import a301
import json
from a301.utils.data_read import download
import a301
import pprint
import shutil
from pyhdf.SD import SD, SDC
import json
import pprint
import cartopy
read_data = False
if read_data:
filename_M3 = 'MYD03.A2013222.2105.006.2013223155808.hdf'
download(filename_M3)
for filename in [filename_M3, filename_M2]:
local_file = Path.cwd() / Path(filename)
to_file = a301.data_dir / Path(filename)
print(f'copy {local_file} to {to_file}')
shutil.copy(local_file, to_file)
# In[2]:
import cartopy.crs as ccrs
import matplotlib.pyplot as plt
import cartopy
from pathlib import Path
import pprint
import numpy as np
import pdb
# [matplotlib imshow](https://matplotlib.org/examples/images_contours_and_fields/image_demo.html) function to display the raw image
#
# If you don't have a MYD021KM file you can grab mine by changing
#
# get_data=False
#
# to True in the next cell.
#
#
# In[2]:
get_data = False
if get_data:
modis_file = "MYD021KM.A2013222.2105.061.2018047235850.hdf"
download(modis_file)
# # Reading modis data
# The general layout of a Modis data file is given in the [modis users guide](https://www.dropbox.com/s/ckd3dv4n7nxc9p0/modis_users_guide.pdf?dl=0) but we'll only need a fraction of the information in that manual. Modis data is written in [hdf4 format](https://portal.hdfgroup.org/display/HDF4/HDF4), which in python can be read with the [pyhdf module](http://hdfeos.github.io/pyhdf/modules/SD.html#sd-module-key-features).
#
#
# ## Installing pyhdf
#
# So far we have used the [anaconda default channel](https://conda.io/docs/user-guide/tasks/manage-channels.html) to install miniconda and other libraries. Not every conda package is in the default channel. For example, I have my own channel called [phaustin](https://anaconda.org/phaustin/repo) which I use to distribute software I write. Much of the conda software for the scientific community is distributed in the [conda-forge channel](https://conda-forge.org/) -- this includes the pyhdf module.
#
# To install pyhdf from the conda-forge channel, the conda command looks like:
#
# conda install -c conda-forge pyhdf
#
# On Macs, you will also need to update your jpeg library to match pyhdf:
