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improved further docstrings
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diffxpy/testing/det.py

Lines changed: 23 additions & 23 deletions
Original file line numberDiff line numberDiff line change
@@ -2587,8 +2587,8 @@ def summary_pair(
25872587
Summarize differential expression results of single pairwose comparison
25882588
into an output table.
25892589
2590-
:param groups0: First set of groups in pair-wise comparison.
2591-
:param groups1: Second set of groups in pair-wise comparison.
2590+
:param group0: First set of groups in pair-wise comparison.
2591+
:param group1: Second set of groups in pair-wise comparison.
25922592
:param qval_thres: Upper bound of corrected p-values for gene to be included.
25932593
:param fc_upper_thres: Upper bound of fold-change for gene to be included.
25942594
:param fc_lower_thres: Lower bound of fold-change p-values for gene to be included.
@@ -2970,7 +2970,7 @@ def log_fold_change(self, base=np.e, genes=None, nonnumeric=False):
29702970
else:
29712971
genes = self._idx_genes(genes)
29722972

2973-
fc = self.max(genes=genes, nonnumeric=nonnumeric) - self.min(genes=genes, nonnumeric=nonnumeric)
2973+
fc = self.max(genes=genes, non_numeric=nonnumeric) - self.min(genes=genes, non_numeric=nonnumeric)
29742974
fc = np.nextafter(0, 1, out=fc, where=fc == 0)
29752975

29762976
return np.log(fc) / np.log(base)
@@ -3031,75 +3031,75 @@ def _spline_par_loc_idx(self, intercept=True):
30313031
idx = np.concatenate([np.where([[x == 'Intercept' for x in par_loc_names]])[0], idx])
30323032
return idx
30333033

3034-
def _continuous_model(self, idx, nonnumeric=False):
3034+
def _continuous_model(self, idx, non_numeric=False):
30353035
"""
30363036
Recover continuous fit for a gene.
30373037
30383038
:param idx: Index of genes to recover fit for.
3039-
:param nonnumeric: Whether to include non-numeric covariates in fit.
3039+
:param non_numeric: Whether to include non-numeric covariates in fit.
30403040
:return: Continuuos fit for each cell for given gene.
30413041
"""
30423042
idx = np.asarray(idx)
3043-
if nonnumeric:
3043+
if non_numeric:
30443044
mu = np.matmul(self._model_estim.design_loc.values,
30453045
self._model_estim.par_link_loc[:, idx])
30463046
if self._size_factors is not None:
30473047
mu = mu + self._size_factors
30483048
else:
30493049
idx_basis = self._spline_par_loc_idx(intercept=True)
3050-
mu = np.matmul(self._model_estim.design_loc[:,idx_basis].values,
3050+
mu = np.matmul(self._model_estim.design_loc[:, idx_basis].values,
30513051
self._model_estim.par_link_loc[idx_basis, idx])
30523052

30533053
mu = np.exp(mu)
30543054
return mu
30553055

3056-
def max(self, genes, nonnumeric=False):
3056+
def max(self, genes, non_numeric=False):
30573057
"""
30583058
Return maximum fitted expression value by gene.
30593059
30603060
:param genes: Genes for which to return maximum fitted value.
3061-
:param nonnumeric: Whether to include non-numeric covariates in fit.
3061+
:param non_numeric: Whether to include non-numeric covariates in fit.
30623062
:return: Maximum fitted expression value by gene.
30633063
"""
30643064
genes = self._idx_genes(genes)
3065-
return np.array([np.max(self._continuous_model(idx=i, nonnumeric=nonnumeric))
3065+
return np.array([np.max(self._continuous_model(idx=i, non_numeric=non_numeric))
30663066
for i in genes])
30673067

3068-
def min(self, genes, nonnumeric=False):
3068+
def min(self, genes, non_numeric=False):
30693069
"""
30703070
Return minimum fitted expression value by gene.
30713071
30723072
:param genes: Genes for which to return maximum fitted value.
3073-
:param nonnumeric: Whether to include non-numeric covariates in fit.
3073+
:param non_numeric: Whether to include non-numeric covariates in fit.
30743074
:return: Maximum fitted expression value by gene.
30753075
"""
30763076
genes = self._idx_genes(genes)
3077-
return np.array([np.min(self._continuous_model(idx=i, nonnumeric=nonnumeric))
3077+
return np.array([np.min(self._continuous_model(idx=i, non_numeric=non_numeric))
30783078
for i in genes])
30793079

3080-
def argmax(self, genes, nonnumeric=False):
3080+
def argmax(self, genes, non_numeric=False):
30813081
"""
30823082
Return maximum fitted expression value by gene.
30833083
30843084
:param genes: Genes for which to return maximum fitted value.
3085-
:param nonnumeric: Whether to include non-numeric covariates in fit.
3085+
:param non_numeric: Whether to include non-numeric covariates in fit.
30863086
:return: Maximum fitted expression value by gene.
30873087
"""
30883088
genes = self._idx_genes(genes)
3089-
idx = np.array([np.argmax(self._continuous_model(idx=i, nonnumeric=nonnumeric))
3089+
idx = np.array([np.argmax(self._continuous_model(idx=i, non_numeric=non_numeric))
30903090
for i in genes])
30913091
return self._continuous_coords[idx]
30923092

3093-
def argmin(self, genes, nonnumeric=False):
3093+
def argmin(self, genes, non_numeric=False):
30943094
"""
30953095
Return minimum fitted expression value by gene.
30963096
30973097
:param genes: Genes for which to return maximum fitted value.
3098-
:param nonnumeric: Whether to include non-numeric covariates in fit.
3098+
:param non_numeric: Whether to include non-numeric covariates in fit.
30993099
:return: Maximum fitted expression value by gene.
31003100
"""
31013101
genes = self._idx_genes(genes)
3102-
idx = np.array([np.argmin(self._continuous_model(idx=i, nonnumeric=nonnumeric))
3102+
idx = np.array([np.argmin(self._continuous_model(idx=i, non_numeric=non_numeric))
31033103
for i in genes])
31043104
return self._continuous_coords[idx]
31053105

@@ -3109,7 +3109,7 @@ def plot_genes(
31093109
hue=None,
31103110
size=1,
31113111
log=True,
3112-
nonnumeric=False,
3112+
non_numeric=False,
31133113
save=None,
31143114
show=True,
31153115
ncols=2,
@@ -3124,7 +3124,7 @@ def plot_genes(
31243124
:param hue: Confounder to include in plot.
31253125
:param size: Point size.
31263126
:param log: Whether to log values.
3127-
:param nonnumeric:
3127+
:param non_numeric:
31283128
:param save: Path+file name stem to save plots to.
31293129
File will be save+"_genes.png". Does not save if save is None.
31303130
:param show: Whether to display plot.
@@ -3169,7 +3169,7 @@ def plot_genes(
31693169
axs.append(ax)
31703170

31713171
y = self.X[:, g]
3172-
yhat = self._continuous_model(idx=g, nonnumeric=nonnumeric)
3172+
yhat = self._continuous_model(idx=g, non_numeric=non_numeric)
31733173
if log:
31743174
y = np.log(y + 1)
31753175
yhat = np.log(yhat + 1)
@@ -3251,7 +3251,7 @@ def plot_heatmap(
32513251
# Build heatmap matrix.
32523252
# Add in data.
32533253
data = np.array([
3254-
self._continuous_model(idx=g, nonnumeric=False)
3254+
self._continuous_model(idx=g, non_numeric=False)
32553255
for i, g in enumerate(gene_idx)
32563256
])
32573257
# Order columns by continuous covariate.

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