def fetch_Landsat8_scene_list():
"""
Simple downloads and extracts the most recent version of the scene_list
text file for reference
http://landsat-pds.s3.amazonaws.com/scene_list.gz
:return scene_list_text_data: returns a text data object with all
the data on scene inventory on amazon WS.
"""
print("Updating scene list")
# define save path for new scene list
directory = site.getsitepackages()[1]
gz_path = "{0}/dnppy/landsat/metadata/scene_list.gz".format(directory)
txt_path = "{0}/dnppy/landsat/metadata/scene_list.txt".format(directory)
# download then extract the gz file to a txt file.
download_url("http://landsat-pds.s3.amazonaws.com/scene_list.gz", gz_path)
with gzip.open(gz_path, 'rb') as gz:
content = gz.read()
with open(txt_path, 'wb+') as f:
f.writelines(content)
# build a new text data object from the fresh scene list
scene_list_text_data = textio.text_data()
scene_list_text_data.read_csv(txt_path, delim=",", has_headers=True)
return scene_list_text_data
def fetch_Landsat8_scene_list():
"""
Simple downloads and extracts the most recent version of the scene_list
text file for reference
http://landsat-pds.s3.amazonaws.com/scene_list.gz
:return scene_list_text_data: returns a text data object with all
the data on scene inventory on amazon WS.
"""
print("Updating scene list")
# define save path for new scene list
directory = site.getsitepackages()[1]
gz_path = "{0}/dnppy/landsat/metadata/scene_list.gz".format(directory)
txt_path = "{0}/dnppy/landsat/metadata/scene_list.txt".format(directory)
# download then extract the gz file to a txt file.
download_url("http://landsat-pds.s3.amazonaws.com/scene_list.gz", gz_path)
with gzip.open(gz_path,'rb') as gz:
content = gz.read()
with open(txt_path, 'wb+') as f:
f.writelines(content)
# build a new text data object from the fresh scene list
scene_list_text_data = textio.text_data()
scene_list_text_data.read_csv(txt_path, delim = ",", has_headers = True)
return scene_list_text_data
def to_csv(self, csv_path):
"""
Writes the row data of this time_series to a csv file.
"""
# disallow overwriting the csv used as input. Added by request
if os.path.abspath(self.infilepath) == os.path.abspath(csv_path):
csv_path = csv_path.replace(".csv", "_out.csv")
print("Saved time series '{0}' with {1} rows and {2} columns".format(
self.name, len(self.row_data), len(self.col_data)))
tdo = textio.text_data( text_filepath = csv_path,
headers = self.headers,
row_data = self.row_data)
tdo.write_csv()
return
def datatype_library():
"""
This function builds the datatype_library dict out of file datatype_library.csv
and returns it. Adding to this datatype library should be done by editing
the csv file, not this code. Note that the standard format for names is
``<product short name>_<resolution identifier>_<coverage area>``, such as
"TRMM_1.0_GLOBAL", or "GPM_IMERG_0.1_GLOBAL".
Geotransform math used to create the array [A, B, C, D, E, F]
.. code-block:: python
x = A + iB + jC
y = D + iE + jF
Where x,y are real spatial coordinates, and i,j are matrix indices.
A, B, C, D, E, F and are coefficients that make up the geotransformation array.
:return datatype_library_dict: A dictionary
"""
# empty dict
datatype_dict = {}
# find path of this installation
dirname = os.path.dirname(__file__)
# read in the library
lib_path = os.path.join(dirname,"lib","datatype_library.csv")
tdat = textio.text_data()
tdat.read_csv(lib_path)
rows = tdat.row_data
for row in rows:
# interpret text file rows
name = str(row[0].replace(" ",""))
proj = str(row[1].replace(" ","").split("-")[-1])
proj_lib = str("-".join(row[1].replace(" ","").split("-")[:-1]))
A = float(row[2])
B = float(row[3])
C = float(row[4])
D = float(row[5])
E = float(row[6])
F = float(row[7])
dls = str(row[8])
# build projection text from osr library
if proj_lib == "EPSG":
srs = osr.SpatialReference()
srs.ImportFromEPSG(int(proj))
proj_text = srs.ExportToWkt()
# read projection from SR-ORG prj file.
else:
proj_fname = os.path.join(dirname, "lib","prj","{0}.prj".format(proj))
with open(proj_fname, "r") as f:
proj_text = f.read()
# assemble the geotransform
geotrans = (A, B, C, D, E, F)
# create the datatype instance
datatype_dict[name] = datatype(name = name,
projectionID = "-".join([proj_lib, proj]),
geotransform = geotrans,
projectionTXT = proj_text,
downloadSource = dls)
return datatype_dict
def datatype_library():
"""
This function builds the datatype_library dict out of file datatype_library.csv
and returns it. Adding to this datatype library should be done by editing
the csv file, not this code. Note that the standard format for names is
``<product short name>_<resolution identifier>_<coverage area>``, such as
"TRMM_1.0_GLOBAL", or "GPM_IMERG_0.1_GLOBAL".
Geotransform math used to create the array [A, B, C, D, E, F]
.. code-block:: python
x = A + iB + jC
y = D + iE + jF
Where x,y are real spatial coordinates, and i,j are matrix indices.
A, B, C, D, E, F and are coefficients that make up the geotransformation array.
:return datatype_library_dict: A dictionary
"""
# empty dict
datatype_dict = {}
# find path of this installation
dirname = os.path.dirname(__file__)
# read in the library
lib_path = os.path.join(dirname, "lib", "datatype_library.csv")
tdat = textio.text_data()
tdat.read_csv(lib_path)
rows = tdat.row_data
for row in rows:
# interpret text file rows
name = str(row[0].replace(" ", ""))
proj = str(row[1].replace(" ", "").split("-")[-1])
proj_lib = str("-".join(row[1].replace(" ", "").split("-")[:-1]))
A = float(row[2])
B = float(row[3])
C = float(row[4])
D = float(row[5])
E = float(row[6])
F = float(row[7])
dls = str(row[8])
# build projection text from osr library
if proj_lib == "EPSG":
srs = osr.SpatialReference()
srs.ImportFromEPSG(int(proj))
proj_text = srs.ExportToWkt()
# read projection from SR-ORG prj file.
else:
proj_fname = os.path.join(dirname, "lib", "prj",
"{0}.prj".format(proj))
with open(proj_fname, "r") as f:
proj_text = f.read()
# assemble the geotransform
geotrans = (A, B, C, D, E, F)
# create the datatype instance
datatype_dict[name] = datatype(name=name,
projectionID="-".join([proj_lib, proj]),
geotransform=geotrans,
projectionTXT=proj_text,
downloadSource=dls)
return datatype_dict
