def test_get_roi(self):
roi = CraterRoi(self.cds, 0, 0, 50)
arr = roi._get_roi()
self.assertIsNotNone(arr)
roi180 = CraterRoi(self.cds, 0, 180, 50)
arr180 = roi180.roi
self.assertIsNotNone(arr180)
def test_get_extent(self):
"""Test _get_extent"""
roi = CraterRoi(self.cds, 0, 0, 16)
actual = roi._get_extent()
expected = (-0.5278, 0.5278, -0.5278, 0.5278)
np.testing.assert_almost_equal(actual, expected, 4)
roi = CraterRoi(self.cds, 20, 20, 16)
actual = roi.extent
expected = (19.4384, 20.5616, 19.4722, 20.5278)
np.testing.assert_almost_equal(actual, expected, 4)
def ejecta_stats(df, cds, ejrad=2, stats=None, plot=False, vmin=float('-inf'),
vmax=float('inf'), strict=False):
"""Compute the specified stats from acestats.py on a circular ejecta ROI
extending ejsize crater radii from the crater center. Return results as
DataFrame with stats appended as extra columns.
Parameters
----------
df : pandas.DataFrame object
Contains the crater locations and sizes to locate ROIs in aceDS. Also
form the basis of the returned DataFrame
cds : CraterpyDataset object
CraterpyDataset of image data used to compute stats.
ejrad : int, float
The radius of ejecta blanket measured in crater radii from the
crater center to the ejecta extent.
stats : Array-like of str
Indicates the stat names to compute. Stat functions must be defined in
acestats.py. Default: all stats in acestats.py.
plot : bool
Plot the ejecta ROI.
vmin : int, float
The minimum valid image pixel value. Filter all values lower.
vmax : int, float
The maximum valid image pixel value. Filter all values higher.
strict : bool
How strict the (vmin, vmax) range is. If true, exclude values <= vmin
and values >= vmax, if they are specified.
Returns
-------
DataFrame
Copy of df with stats appended as new columns.
"""
# If stats and index not provided, assume use all stats and all rows in cdf
if stats is None:
stats = _list_quickstats()
# Initialize return Dataframe with stats as individual columns
ret_df = df.copy()
for stat in stats:
ret_df.loc[:, stat] = ret_df.index
latcol, loncol, radcol = ch.get_crater_cols(df)
# Main computation loop
for i in df.index:
# Get lat, lon, rad and compute roi for current crater
lat, lon, rad = df.loc[i, latcol], df.loc[i, loncol], df.loc[i, radcol]
croi = CraterRoi(cds, lat, lon, rad, ejrad)
mask = masking.crater_ring_mask(croi, rad, rad*ejrad)
croi.filter(vmin, vmax, strict)
croi.mask(~mask)
data_arr = croi.roi[~np.isnan(croi.roi)] # Collapses to 1D
for stat, function in _get_quickstats_functions(stats):
ret_df.loc[i, stat] = function(data_arr)
if plot: # plot filtered and masked roi
croi.plot()
return ret_df
class TestCraterRoi(unittest.TestCase):
"""Test CraterpyDataset object"""
def setUp(self):
import craterpy
self.data_path = p.join(craterpy.__path__[0], 'data')
self.moon_tif = p.join(self.data_path, 'moon.tif')
self.cds = CraterpyDataset(self.moon_tif, radius=1737)
self.roi = CraterRoi(self.cds, 0, 0, 100)
def test_roi_import(self):
"""Test import"""
roi = CraterRoi(self.cds, 0, 0, 100)
self.assertIsNotNone(roi)
def test_get_extent(self):
"""Test _get_extent"""
roi = CraterRoi(self.cds, 0, 0, 16)
actual = roi._get_extent()
expected = (-0.5278, 0.5278, -0.5278, 0.5278)
np.testing.assert_almost_equal(actual, expected, 4)
roi = CraterRoi(self.cds, 20, 20, 16)
actual = roi._get_extent()
expected = (19.4384, 20.5616, 19.4722, 20.5278)
np.testing.assert_almost_equal(actual, expected, 4)
def test_get_roi(self):
roi = CraterRoi(self.cds, 0, 0, 50)
arr = roi.get_roi()
self.assertIsNotNone(arr)
roi180 = CraterRoi(self.cds, 0, 180, 50)
arr180 = roi.get_roi()
self.assertIsNotNone(arr180)
def test_filter(self):
roi = self.roi
roi.filter(0, 100)
self.assertTrue(roi.roi.max() <= 100)
roi.filter(0, 50, strict=True)
self.assertTrue(roi.roi.max() < 50)
# TODO: fix this test
# def test_mask(self):
# shape, rad = self.roi.roi.shape, self.roi.rad
# mask = circle_mask(shape, rad)
# masked_roi = self.roi.mask(mask)
# self.assertTrue(np.isnan(self.roi.roi[*(shape/2)]))
def test_plot(self):
self.assertIsNotNone(self.roi.plot())
def compute_stats(df,
cds,
stats=[],
plot=False,
vmin=float('-inf'),
vmax=float('inf'),
strict=False,
maskfunc=None,
mask_out=False,
ejrad=None):
"""Computes stats on craters in df with image data from cds.
"""
# If stats and index not provided, assume use all stats and all rows in cdf
if not stats:
stats = _list_quickstats()
# Initialize return Dataframe with stats as individual columns
ret_df = df.copy()
for stat in stats:
ret_df.loc[:, stat] = ret_df.index
latcol, loncol, radcol = ch.get_crater_cols(df)
# Main computation loop
for i in df.index:
# Get lat, lon, rad and compute roi for current crater
lat, lon, rad = df.loc[i, latcol], df.loc[i, loncol], df.loc[i, radcol]
try:
print(lat, lon, rad)
croi = CraterRoi(cds, lat, lon, rad, ejrad)
croi.filter(vmin, vmax, strict)
if maskfunc:
if maskfunc == "crater":
mask = masking.crater_floor_mask(croi)
elif maskfunc == "ejecta":
mask = masking.crater_ring_mask(croi, rad, rad * ejrad)
croi.mask(mask, mask_out)
data_arr = croi.roi[~np.isnan(croi.roi)] # Collapses to 1D
for stat, function in _get_quickstats_functions(stats):
ret_df.loc[i, stat] = function(data_arr)
except Exception as e:
print(e)
print(lat, lon, rad)
break
if plot: # plot filtered and masked roi
croi.plot()
return ret_df
def ejecta_profile_stats(df, cds, ejrad=2, rspacing=0.25, stats=None,
plot_roi=False, vmin=None, vmax=None, strict=False,
plot_stats=False, savepath=None, timer=False, dt=60):
"""Compute stat profiles across ejecta blankets by computing stats in a
series of concentric rings beginning at the crater rim and extending to
ejrad crater radii from the crater centre. Each ring is rspacing thick and
the crater floor is excluded. Output is a dictionary of cdf.index values to
pandas.DataFrame of stats containing the computed stats as columns and
ring radius as rows.
Parameters
----------
Returns
-------
stat_df : dict of (index: pandas.DataFrame)
Dictionary of stats with cdf index as keys and pandas.DataFrame of
statistics as values (see example).
Example
-------
>>> compute_ejecta_profile_stats(my_df, my_cds, 2, 0.25, stats=['mean',
'median','maximum'],savepath='/tmp/')
/tmp/index1_ejecta_stats.csv
radius,mean,median,maximum
1, 55, 45, 70
1.25, 50, 40, 65
1.5, 35, 35, 45
1.75, 30, 33, 42
"""
if timer:
from timeit import default_timer as timer
Tstart, Tnow = timer(), timer()
count = 1 # count craters
if savepath:
import os.path as p
savename = p.join(savepath, '{}_ejpstats.csv')
if stats is None:
stats = _list_quickstats()
stat_dict = {}
latcol, loncol, radcol = ch.get_crater_cols(df)
for i in df.index: # Main DataFrame loop
lat, lon, rad = df.loc[i, latcol], df.loc[i, loncol], df.loc[i, radcol]
try:
croi = CraterRoi(cds, lat, lon, rad, ejrad)
ring_array = np.arange(1, ejrad+rspacing, rspacing) # inner radius
ring_darray = ring_array*rad # convert inner radius to km
stat_df = pd.DataFrame(index=ring_array[:-1], columns=stats)
for i in range(len(ring_array)-1): # Loop through radii
rad_inner = ring_darray[i]
rad_outer = ring_darray[i+1]
mask = masking.crater_ring_mask(cds, croi, lat, lon, rad_inner,
rad_outer)
ejecta_roi = croi.mask(~mask).filter(vmin, vmax, strict)
ejecta_roi = ejecta_roi[~np.isnan(ejecta_roi)]
for stat, function in _get_quickstats_functions(stats):
stat_df.loc[ring_array[i], stat] = function(ejecta_roi)
if plot_roi:
ejecta_roi.plot()
stat_dict[i] = stat_df
if plot_stats:
plotting.plot_ejecta_profile_stats(stat_df)
if savepath:
stat_df.to_csv(savename.format(i))
except ImportError as e: # Catches and prints out of bounds exceptions
print(e, ". Skipping....")
if timer: # Print a status update approx every dt seconds
if (timer() - Tnow > dt) or (count == len(df)):
update = 'Finished crater {} out of {}'.format(count, len(df))
elapsed = timer()-Tstart
update += '\n Time elapsed: \
{}h:{}m:{}s'.format(int(elapsed//3600),
int((elapsed % 3600)//60),
round((elapsed % 3600) % 60, 2))
print(update)
Tnow = timer()
count += 1
return stat_dict
def test_roi_import(self):
"""Test import"""
roi = CraterRoi(self.cds, 0, 0, 100)
self.assertIsNotNone(roi)
def setUp(self):
import craterpy
self.data_path = p.join(craterpy.__path__[0], 'data')
self.moon_tif = p.join(self.data_path, 'moon.tif')
self.cds = CraterpyDataset(self.moon_tif, radius=1737)
self.roi = CraterRoi(self.cds, 0, 0, 100)