def Carbonatites():
'''
Load Carbonatite data from Humphreys-Williams and Zahirovic (2021)
'''
fname = _retrieve(
url="https://zenodo.org/record/5968095/files/1_CarbonatitesShapefile_WithAgeConstraints.zip?download=1",
known_hash="md5:7f219044c7a1ea9d81fc3410b64b2876",
downloader=_HTTPDownloader(progressbar=True),
path=_os_cache('gprm'),
processor=_Unzip(extract_dir='Carbonatites')
)
gdf = _gpd.read_file('{:s}/Carbonatites/1_CarbonatitesShapefile_WithAgeConstraints/carbonatites_gplates.shp'.format(str(_os_cache('gprm'))))
return gdf
def Kimberlites():
'''
Load the Kimberlite compilation from Faure (2010)
'''
fname = _retrieve(
url="https://consorem2.uqac.ca/production_scientifique/fiches_projets/world_kimberlites_and_lamproites_consorem_database_v2010.xls",
known_hash="sha256:8d9d8d89afa9304b6494ad32b8f66f6838de5631287c94155d498b7f6d413ac4",
downloader=_HTTPDownloader(progressbar=True),
path=_os_cache('gprm'),
)
df = _pd.read_excel(fname, skiprows=1)
gdf = _gpd.GeoDataFrame(df, geometry=_gpd.points_from_xy(df.Longitude, df.Latitude), crs=4326)
return gdf
def Metamorphism():
'''
Load the Metamorphims compilation from Brown and Johnson, as reported in the SM of Liu et al (2022)
'''
fname = _retrieve(
url="https://gsapubs.figshare.com/ndownloader/files/33947312",
known_hash="fa815bc1f07c347834dc4e9724285bfff12176bd42d5330cbc0cabeab1757fc4",
downloader=_HTTPDownloader(progressbar=True),
path=_os_cache('gprm'),
)
df = _pd.read_excel(fname, skiprows=1)
df = df.rename(columns={'LONGITUDE (˚E)':'Longitude', 'LATITUDE (˚N)':'Latitude'})
df['Age'] = df['AGE(Ga)']*1000.
gdf = _gpd.GeoDataFrame(df, geometry=_gpd.points_from_xy(df.Longitude, df.Latitude), crs=4326)
return gdf
def PacificSeamountAges(load=True):
'''
Pacific Seamount Age compilation from GMT website
'''
fname = _retrieve(
url="https://www.earthbyte.org/webdav/gmt_mirror/gmt/data/cache/Pacific_Ages.txt",
known_hash="sha256:8c5e57b478c2c2f5581527c7aea5ef282e976c36c5e00452210885a92e635021",
downloader=_HTTPDownloader(progressbar=True),
path=_os_cache('gprm'),
)
if load:
df = _pd.read_csv(fname, comment='#', delim_whitespace=True,
names=['Long', 'Lat', 'Average_Age_Ma', 'Average_Age_Error_Ma', 'Tag', 'SeamountName', 'SeamountChain'])
return _gpd.GeoDataFrame(df, geometry=_gpd.points_from_xy(df.Long, df.Lat))
else:
return fname
def MagneticPicks(load=True):
'''
Magnetic Picks from the 'Global Seafloor Fabric (and) Magnetic Linations'
database, returned as a geopandas dataframe
Alternatively, select 'load=False' to return filname of '.gmt' file in
cache folder
'''
fname = _retrieve(
url="http://www.soest.hawaii.edu/PT/GSFML/ML/DATA/GSFML.global.picks.gmt",
known_hash="sha256:0895b76597f600a6c6184a7bec0edc0df5ca9234255f3f7bac0fe944317caf65",
downloader=_HTTPDownloader(progressbar=True),
path=_os_cache('gprm'),
)
if load:
return _gpd.read_file(fname)
else:
return fname
def load_Hf():
fname = _retrieve(
url=
"https://ars.els-cdn.com/content/image/1-s2.0-S0012825221002464-mmc5.xlsx",
known_hash=
"sha256:7402470ca7e7319899d949207da3eae8c0ba20bdf21050a85ca70ff6c9be9b8c",
downloader=_HTTPDownloader(progressbar=True),
path=_os_cache('gprm'),
)
xls = _pd.ExcelFile(fname)
df = _pd.read_excel(xls, sheet_name='Hf_Data')
gdf = _gpd.GeoDataFrame(df,
geometry=_gpd.points_from_xy(
df.Longitude, df.Latitude),
crs=4326)
return gdf
def pbdb(path_to_pbdb_data=None):
"""
Load data from pbdb downloaded file (not retrieved by pooch)
The function assumes that a file has already been downloaded using
the pbdb navigator interface to the pbdb web service (version 1.2)
"""
if not path_to_pbdb_data:
path_to_pbdb_data = '{:s}/pbdb/pbdb_data.csv'.format(
str(_os_cache('gprm')))
df = _pd.read_csv(path_to_pbdb_data, delimiter=',', skiprows=14)
df.rename(columns={'lng': 'Longitude', 'lat': 'Latitude'}, inplace=True)
gdf = _gpd.GeoDataFrame(df,
geometry=_gpd.points_from_xy(
df.Longitude, df.Latitude),
crs=4326)
return gdf
def MarsTopo2600(lmax=2600):
'''
MarsTopo2600 is a 2600 degree and order spherical harmonic model of the
shape of the planet Mars. The coefficients are in units of meters.
Parameters
----------
lmax : int, optional
The maximum spherical harmonic degree to return.
Reference
---------
Wieczorek, M.A. (2015). Gravity and Topography of the Terrestrial Planets,
Treatise on Geophysics, 2nd edition, Oxford, 153-193,
doi:10.1016/B978-0-444-53802-4.00169-X.
'''
fname = _retrieve(
url="https://zenodo.org/record/3870922/files/MarsTopo2600.shape.gz",
known_hash="sha256:8882a9ee7ee405d971b752028409f69bd934ba5411f1c64eaacd149e3b8642af", # noqa: E501
downloader=_HTTPDownloader(progressbar=True),
path=_os_cache('pyshtools'),
)
return _SHCoeffs.from_file(fname, lmax=lmax, units='m')
def Geochem(usecols=None, return_column_names=False, remove_invalid_coordinates=True):
'''
Load the geochemistry database of Gard et al (2019)
doi: https://doi.org/10.5194/essd-11-1553-2019
Options:
usecols: optionally define a list of columns to load (rather than the full table) [default=None]
remove_invalid_coordinates: specify whether to remove rows from the table for which the latitude
and/or longitide are invalid [default=True]
return_column_names: instead of loading table into memory, return a list of column names
'''
fname = _retrieve(
url="https://zenodo.org/record/3359791/files/complete.zip",
known_hash="md5:9b97b54887ee7184c6650c845b4e92d4",
downloader=_HTTPDownloader(progressbar=True),
path=_os_cache('gprm'),
processor=_Unzip(extract_dir='GeochemDB'),
)[0]
if usecols:
# TODO if 'Long and Lat fields are not included, the attempt to create a gdf will throw an error - fix this
usecols = ['longitude' if x=='Longitude' else x for x in usecols]
usecols = ['latitude' if x=='Latitude' else x for x in usecols]
# TODO include some shorthands for the usecols, e.g. 'Major', 'REE', etc
if return_column_names:
return _pd.read_csv(fname, index_col=0, nrows=0, engine='python').columns.tolist()
else:
df = _pd.read_csv(fname, usecols=usecols, engine='python', encoding="ISO-8859-1")
if remove_invalid_coordinates:
df = df.dropna(subset=['longitude','latitude'])
df.reset_index(inplace=True)
df.rename(columns={'longitude':'Longitude', 'latitude':'Latitude'}, inplace=True)
return _gpd.GeoDataFrame(df, geometry=_gpd.points_from_xy(df.Longitude, df.Latitude), crs=4326)
def GRGM1200B_RM1_1E0(lmax=1200):
'''
GRGM1200B_RM1_1E0 is a GSFC 1200 degree and order spherical harmonic model
of the gravitational potential of the Moon. This model uses a rank-minus-1
constraint based on gravity from surface topography for degrees greater
than 600 with a value of lambda equal to 1.
Parameters
----------
lmax : int, optional
The maximum spherical harmonic degree to return.
Reference
---------
Goossens, S., Sabaka, T.J., Wieczorek, M.A., Neumann, G.A., Mazarico, E.,
Lemoine, F.G., Nicholas, J.B., Smith. D.E., Zuber M.T. (2020).
High-resolution gravity field models from GRAIL data and implications
for models of the density structure of the Moon's crust. Journal of
Geophysical Research Planets, 125, e2019JE006086,
doi:10.1029/2019JE006086.
'''
fname = _retrieve(
url=
"https://core2.gsfc.nasa.gov/PGDA/data/MoonRM1/sha.grgm1200b_rm1_1e0_sigma", # noqa: E501
known_hash=
"sha256:d42536cc716f5da8e067aa79a253c310e9d53d1d3b3ae7b43fa4517654d20d35", # noqa: E501
downloader=_HTTPDownloader(progressbar=True),
path=_os_cache('pyshtools'),
)
return _SHGravCoeffs.from_file(fname,
lmax=lmax,
header_units='m',
r0_index=1,
gm_index=0,
errors=True,
omega=_omega.value,
name='GRGM1200B_RM1_1E0')
def VenusTopo719(lmax=719):
'''
VenusTopo719 is a 719 degree and order spherical harmonic model of the
shape of the planet Venus. The coefficients are in units of meters.
Parameters
----------
lmax : int, optional
The maximum spherical harmonic degree to return.
Reference
---------
Wieczorek, M.A. (2015). Gravity and Topography of the Terrestrial Planets,
Treatise on Geophysics, 2nd edition, Oxford, 153-193,
doi:10.1016/B978-0-444-53802-4.00169-X.
'''
fname = _retrieve(
url="https://zenodo.org/record/3870926/files/VenusTopo719.shape.gz",
known_hash=
"sha256:9fcb04fb21eb7090df68e42458f6d7495a27ff62687b46534057ed608786cf3b", # noqa: E501
downloader=_HTTPDownloader(progressbar=True),
path=_os_cache('pyshtools'),
)
return _SHCoeffs.from_file(fname, lmax=lmax, units='m')
def T2015_449(lmax=449):
'''
T2015_449 is a 449 degree and order spherical harmonic model of the
magnetic potential of the Moon. This model was used in Wieczorek (2018) and
is a spherical harmonic expansion of the global magnetic field model of
Tsunakawa et al. (2015). The coefficients are output in units of nT.
Parameters
----------
lmax : int, optional
The maximum spherical harmonic degree to return.
References
----------
Tsunakawa, H., Takahashi, F., Shimizu, H., Shibuya, H., Matsushima, M.
(2015). Surface vector mapping of magnetic anomalies over the Moon
using Kaguya and Lunar Prospector observations, Journal of Geophysical
Research Planets, 120, 1160-1185, doi:10.1002/2014JE004785.
Wieczorek, M.A. (2018). Strength, depth, and geometry of magnetic sources
in the crust of the Moon from localized power spectrum analysis,
Journal of Geophysical Research Planets, 123, 291-316,
doi:10.1002/2017JE005418.
'''
fname = _retrieve(
url="https://zenodo.org/record/3873648/files/T2015_449.sh.gz",
known_hash=
"sha256:4db0b77b3863f38d6fb6e62c5c1116bf7123b77c5aad65df7dae598714edd655", # noqa: E501
downloader=_HTTPDownloader(progressbar=True),
path=_os_cache('pyshtools'),
)
return _SHMagCoeffs.from_file(fname,
lmax=lmax,
header=True,
file_units='T',
name='T2015_449',
units='nT')
def GRGM1200B(lmax=1200):
'''
GRGM1200B is a GSFC 1200 degree and order spherical harmonic model of the
gravitational potential of the Moon. This model applies a Kaula constraint
for degrees greater than 600.
Parameters
----------
lmax : int, optional
The maximum spherical harmonic degree to return.
Reference
---------
Goossens, S., Sabaka, T.J., Wieczorek, M.A., Neumann, G.A., Mazarico, E.,
Lemoine, F.G., Nicholas, J.B., Smith. D.E., Zuber M.T. (2020).
High-resolution gravity field models from GRAIL data and implications
for models of the density structure of the Moon's crust. Journal of
Geophysical Research Planets, 125, e2019JE006086,
doi:10.1029/2019JE006086.
'''
fname = _retrieve(
url=
"https://core2.gsfc.nasa.gov/PGDA/data/MoonRM1/sha.grgm1200b_sigma", # noqa: E501
known_hash=
"sha256:f08a988b43f3eaa5a2089045a9b7e41e02f16542c7912b87ea34366fafa39bc5", # noqa: E501
downloader=_HTTPDownloader(progressbar=True),
path=_os_cache('pyshtools'),
)
return _SHGravCoeffs.from_file(fname,
lmax=lmax,
header_units='m',
r0_index=1,
gm_index=0,
errors=True,
omega=_omega.value,
name='GRGM1200B')
def loadDB(version=2021):
'''
Load the Puetz (2018) database, returning two dataframes:
- the Sample Details
- the Data table
'''
if version == 2018:
fname = _retrieve(
url=
"https://ars.els-cdn.com/content/image/1-s2.0-S1674987117302141-mmc1.xlsx",
known_hash=
"sha256:8ea19b08d5c8d3c6e7f3239471d27b6da440fcfa40994e712ac9ae95642fe3d9",
downloader=_HTTPDownloader(progressbar=True),
path=_os_cache('gprm'),
)
# load the whole xls file once to avoid duplication
xls = _pd.ExcelFile(fname)
# Get dataframes for the sheets with information per site and per sample
df_Data = _pd.read_excel(xls, sheet_name='Data')
df_SampleDetails = _pd.read_excel(xls, sheet_name='Sample_Details')
# rename some fields for neatness
df_Data.rename(columns={
u'206Pb /\n238U\nAge\n(Ma)': '206Pb_238U_Age_Ma',
u'206Pb /\n238U\n2σ\nPrecis': '206Pb_238U_Precis',
u'207Pb /\n235U\nAge\n(Ma)': '207Pb_235U_Age_Ma',
u'207Pb /\n235U\n2σ\nPrecis': '207Pb_235U_Precis',
u'207Pb /\n206Pb\nAge\n(Ma)': '207Pb_206Pb_Age_Ma',
u'207Pb /\n206Pb\n2σ\nPrecis': '207Pb_206Pb_Precis'
},
inplace=True)
df_SampleDetails.rename(
columns={'Est. Depos. Age (Ma)': 'Est_Depos_Age_Ma'}, inplace=True)
df_SampleDetails = df_SampleDetails.dropna(subset=['Sample Key'])
return df_SampleDetails, df_Data
elif version == 2021:
fname = _retrieve(
url=
"https://ars.els-cdn.com/content/image/1-s2.0-S0012825221002464-mmc4.xlsx",
known_hash=
"sha256:afdcaa26698ba06f113f29fab311af0ec37f290db117fc85847966c28b78ff09",
downloader=_HTTPDownloader(progressbar=True),
path=_os_cache('gprm'),
)
xls = _pd.ExcelFile(fname)
df = _pd.read_excel(xls, sheet_name='UPb_Data')
gdf = _gpd.GeoDataFrame(df,
geometry=_gpd.points_from_xy(
df.Longitude, df.Latitude),
crs=4326)
return gdf
def fetch_Paleomap(resolution='01d'):
"""
PaleoDEM rasters from Scotese and Wright (2018)
resolution can be '01d' (default) or '06m'
"""
if resolution == '01d':
fnames = _retrieve(
url=
"https://zenodo.org/record/5460860/files/Scotese_Wright_2018_Maps_1-88_1degX1deg_PaleoDEMS_nc.zip?download=1",
known_hash="md5:77147998623ab039d86ff3e0b5e40344",
downloader=_HTTPDownloader(progressbar=True),
path=_os_cache('gprm'),
processor=_Unzip(extract_dir='Paleomap_01d'),
)
dirname = '{:s}/Paleomap_01d/Scotese_Wright_2018_Maps_1-88_1degX1deg_PaleoDEMS_nc_v2'.format(
fnames[0].split('Paleomap_01d')[0])
#dirname = '{:s}/Scotese_Wright_2018_Maps_1-88_1degX1deg_PaleoDEMS_nc_v2'.format(_os.path.split(fnames[0])[0])
# if downloading for first time, remove the unwanted cache files
for file in _os.listdir(dirname):
if file.endswith(".cache"):
_os.remove('{:s}/{:s}'.format(dirname, file))
raster_dict = {}
for file in _os.listdir(dirname):
if file.endswith(".nc"):
# Replace whitespace with underscore to help pygmt plotting
if ' ' in file:
_os.rename(
'{:s}/{:s}'.format(dirname, file),
'{:s}/{:s}'.format(dirname, file.replace(' ', '_')))
raster_dict[float(
file.split('_')[-1][:-5])] = '{:s}/{:s}'.format(
dirname, file.replace(' ', '_'))
ordered_raster_dict = collections.OrderedDict(
sorted(raster_dict.items()))
return ordered_raster_dict
elif resolution == '06m':
fnames = _retrieve(
url=
"https://zenodo.org/record/5460860/files/Scotese_Wright_2018_Maps_1-88_6minX6min_PaleoDEMS_nc.zip?download=1",
known_hash="md5:89eb50d8645707ab221b023078535bda",
downloader=_HTTPDownloader(progressbar=True),
path=_os_cache('gprm'),
processor=_Unzip(extract_dir='Paleomap_06m'),
)
dirname = '{:s}/Paleomap_06m/Scotese_Wright_2018_Maps_1-88_6minX6min_PaleoDEMS_nc'.format(
fnames[0].split('Paleomap_06m')[0])
#dirname = '{:s}/Scotese_Wright_2018_Maps_1-88_6minX6min_PaleoDEMS_nc'.format(_os.path.split(fnames[0])[0])
raster_dict = {}
for file in _os.listdir(dirname):
if file.endswith(".nc"):
# Replace whitespace with underscore to help pygmt plotting
if ' ' in file:
_os.rename(
'{:s}/{:s}'.format(dirname, file),
'{:s}/{:s}'.format(dirname, file.replace(' ', '_')))
raster_dict[float(
file.split('_')[-1][:-5])] = '{:s}/{:s}'.format(
dirname, file.replace(' ', '_'))
ordered_raster_dict = collections.OrderedDict(
sorted(raster_dict.items()))
return ordered_raster_dict
else:
ValueError(
'Spacing for source grids must be either 01d (for 1 degree version) or 06m (for 6 minute version)'
)