def test_annotate(self):
suppress_plotting()
f = DataFrame({
'x': [0, 1],
'y': [0, 1],
'frame': [0, 0],
'mass': [10, 20]
})
frame = np.random.randint(0, 255, (5, 5))
# Basic usage
plots.annotate(f, frame)
plots.annotate(f, frame, color='r')
# Coloring by threshold
plots.annotate(f,
frame,
split_category='mass',
split_thresh=15,
color=['r', 'g'])
plots.annotate(f,
frame,
split_category='mass',
split_thresh=[15],
color=['r', 'g'])
plots.annotate(f,
frame,
split_category='mass',
split_thresh=[15, 25],
color=['r', 'g', 'b'])
# Check that bad parameters raise an error.
# Too many colors
bad_call = lambda: plots.annotate(f,
frame,
split_category='mass',
split_thresh=15,
color=['r', 'g', 'b'])
self.assertRaises(ValueError, bad_call)
# Not enough colors
bad_call = lambda: plots.annotate(
f, frame, split_category='mass', split_thresh=15, color=['r'])
self.assertRaises(ValueError, bad_call)
bad_call = lambda: plots.annotate(
f, frame, split_category='mass', split_thresh=15, color='r')
self.assertRaises(ValueError, bad_call)
# Nonexistent column name for split_category
bad_call = lambda: plots.annotate(f,
frame,
split_category='not a column',
split_thresh=15,
color='r')
self.assertRaises(ValueError, bad_call)
# 3D image
bad_call = lambda: plots.annotate(f, frame[np.newaxis, :, :])
self.assertRaises(ValueError, bad_call)
def test_ptraj_t_column(self):
suppress_plotting()
df = self.sparse.copy()
cols = list(df.columns)
cols[cols.index('frame')] = 'arbitrary name'
df.columns = cols
plots.plot_traj(df, t_column='arbitrary name')
def test_ptraj_t_column(self):
suppress_plotting()
df = self.sparse.copy()
cols = list(df.columns)
cols[cols.index('frame')] = 'arbitrary name'
df.columns = cols
plots.plot_traj(df, t_column='arbitrary name')
def test_annotate3d(self):
suppress_plotting()
f = DataFrame({'x': [0, 1], 'y': [0, 1], 'z': [0, 1], 'frame': [0, 0],
'mass': [10, 20]})
frame = np.random.randint(0, 255, (5, 5, 5))
plots.annotate3d(f, frame)
plots.annotate3d(f, frame, color='r')
# 2D image
bad_call = lambda: plots.annotate3d(f, frame[0])
self.assertRaises(ValueError, bad_call)
def test_annotate3d(self):
_skip_if_no_pims()
suppress_plotting()
f = DataFrame({'x': [0, 1], 'y': [0, 1], 'z': [0, 1], 'frame': [0, 0],
'mass': [10, 20]})
frame = np.random.randint(0, 255, (5, 5, 5))
plots.annotate3d(f, frame)
plots.annotate3d(f, frame, color='r')
# 2D image
bad_call = lambda: plots.annotate3d(f, frame[0])
self.assertRaises(ValueError, bad_call)
def test_annotate(self):
suppress_plotting()
f = DataFrame({'x': [0, 1], 'y': [0, 1], 'frame': [0, 0],
'mass': [10, 20]})
frame = np.random.randint(0, 255, (5, 5))
# Basic usage
plots.annotate(f, frame)
plots.annotate(f, frame, color='r')
# Coloring by threshold
plots.annotate(f, frame, split_category='mass',
split_thresh=15, color=['r', 'g'])
plots.annotate(f, frame, split_category='mass',
split_thresh=[15], color=['r', 'g'])
plots.annotate(f, frame, split_category='mass',
split_thresh=[15, 25], color=['r', 'g', 'b'])
# Check that bad parameters raise an error.
# Too many colors
bad_call = lambda: plots.annotate(
f, frame, split_category='mass', split_thresh=15, color=['r', 'g', 'b'])
self.assertRaises(ValueError, bad_call)
# Not enough colors
bad_call = lambda: plots.annotate(
f, frame, split_category='mass', split_thresh=15, color=['r'])
self.assertRaises(ValueError, bad_call)
bad_call = lambda: plots.annotate(
f, frame, split_category='mass', split_thresh=15, color='r')
self.assertRaises(ValueError, bad_call)
# Nonexistent column name for split_category
bad_call = lambda: plots.annotate(
f, frame, split_category='not a column', split_thresh=15, color='r')
self.assertRaises(ValueError, bad_call)
# 3D image
bad_call = lambda: plots.annotate(f, frame[np.newaxis, :, :])
self.assertRaises(ValueError, bad_call)
def test_fit_powerlaw(self):
# smoke test
suppress_plotting()
em = Series([1, 2, 3], index=[1, 2, 3])
fit_powerlaw(em)
fit_powerlaw(em, plot=False)
def test_ptraj_empty(self):
suppress_plotting()
f = lambda: plots.plot_traj(DataFrame(columns=self.sparse.columns))
self.assertRaises(ValueError, f)
def test_labeling_sparse_trajectories(self):
suppress_plotting()
plots.plot_traj(self.sparse, label=True)
def test_labeling_sparse_trajectories(self):
suppress_plotting()
plots.plot_traj(self.sparse, label=True)
def test_labeling_sparse_trajectories(self):
suppress_plotting()
ptraj(self.sparse, label=True) # No errors?
def test_fit_powerlaw(self):
# smoke test
suppress_plotting()
em = Series([1, 2, 3], index=[1, 2, 3])
fit_powerlaw(em)
fit_powerlaw(em, plot=False)
def test_ptraj_empty(self):
suppress_plotting()
f = lambda: plots.plot_traj(DataFrame(columns=self.sparse.columns))
self.assertRaises(ValueError, f)
def test_labeling_sparse_trajectories(self):
suppress_plotting()
ptraj(self.sparse, label=True) # No errors?
