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Black -- formatter for python (#508)

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* Add black

* use black to format code

* Black version
This commit is contained in:
Charlotte Weaver
2018-12-12 09:44:47 -08:00
committed by GitHub
parent a847951658
commit 83154577e4
18 changed files with 636 additions and 791 deletions
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server/app/scanpy_engine/scanpy_engine.py
+53 -62
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@@ -22,7 +22,6 @@ Sort order for methods
class ScanpyEngine(CXGDriver):
def __init__(self, data, args):
super().__init__(data, args)
self._alias_annotation_names(Axis.OBS, args["obs_names"])
@@ -36,7 +35,7 @@ class ScanpyEngine(CXGDriver):
self._create_schema()
# TODO: temporary work-arounds
if args['nan_to_num']:
if args["nan_to_num"]:
self._IEEE754_special_values_workaround()
def _alias_annotation_names(self, axis, name):
@@ -61,8 +60,9 @@ class ScanpyEngine(CXGDriver):
if name not in df_axis.columns:
raise KeyError(f"Annotation name {name}, specified in --{ax_name}-name does not exist.")
if not df_axis[name].is_unique:
raise KeyError(f"Values in -{ax_name}-name must be unique. "
"Please prepare data to contain unique values.")
raise KeyError(
f"Values in -{ax_name}-name must be unique. " "Please prepare data to contain unique values."
)
# reset index to simple range; alias user-specified annotation to "name"
df_axis.reset_index(drop=True, inplace=True)
df_axis.rename(inplace=True, columns={name: "name"})
@@ -89,15 +89,8 @@ class ScanpyEngine(CXGDriver):
def _create_schema(self):
self.schema = {
"dataframe": {
"nObs": self.cell_count,
"nVar": self.gene_count,
"type": str(self.data.X.dtype)
},
"annotations": {
"obs": [],
"var": []
}
"dataframe": {"nObs": self.cell_count, "nVar": self.gene_count, "type": str(self.data.X.dtype)},
"annotations": {"obs": [], "var": []},
}
for ax in Axis:
curr_axis = getattr(self.data, str(ax))
@@ -129,32 +122,35 @@ class ScanpyEngine(CXGDriver):
try:
result = sc.read(data, cache=True)
except ValueError:
raise ScanpyFileError("File must be in the .h5ad format. Please read "
"https://github.com/theislab/scanpy_usage/blob/master/170505_seurat/info_h5ad.md to "
"learn more about this format. You may be able to convert your file into this format "
"using `cellxgene prepare`, please run `cellxgene prepare --help` for more "
"information.")
raise ScanpyFileError(
"File must be in the .h5ad format. Please read "
"https://github.com/theislab/scanpy_usage/blob/master/170505_seurat/info_h5ad.md to "
"learn more about this format. You may be able to convert your file into this format "
"using `cellxgene prepare`, please run `cellxgene prepare --help` for more "
"information."
)
except Exception as e:
raise ScanpyFileError(f"Error while loading file: {e}, File must be in the .h5ad format, please check "
f"that your input and try again.")
raise ScanpyFileError(
f"Error while loading file: {e}, File must be in the .h5ad format, please check "
f"that your input and try again."
)
return result
def _validate_data_types(self):
if self.data.X.dtype != "float32":
warnings.warn(f"Scanpy data matrix is in {self.data.X.dtype} format not float32. "
f"Precision may be truncated.")
warnings.warn(
f"Scanpy data matrix is in {self.data.X.dtype} format not float32. " f"Precision may be truncated."
)
for ax in Axis:
curr_axis = getattr(self.data, str(ax))
for ann in curr_axis:
datatype = curr_axis[ann].dtype
downcast_map = {"int64": "int32",
"uint32": "int32",
"uint64": "int32",
"float64": "float32",
}
downcast_map = {"int64": "int32", "uint32": "int32", "uint64": "int32", "float64": "float32"}
if datatype in downcast_map:
warnings.warn(f"Scanpy annotation {ax}:{ann} is in unsupported format: {datatype}. "
f"Data will be downcast to {downcast_map[datatype]}.")
warnings.warn(
f"Scanpy annotation {ax}:{ann} is in unsupported format: {datatype}. "
f"Data will be downcast to {downcast_map[datatype]}."
)
if isinstance(datatype, CategoricalDtype):
category_num = len(curr_axis[ann].dtype.categories)
if category_num > 500 and category_num > self.max_category_items:
@@ -162,7 +158,8 @@ class ScanpyEngine(CXGDriver):
f"{str(ax).title()} annotation '{ann}' has {category_num} categories, this may be "
f"cumbersome or slow to display. We recommend setting the "
f"--max-category-items option to 500, this will hide categorical "
f"annotations with more than 500 categories in the UI")
f"annotations with more than 500 categories in the UI"
)
def _validate_data_calculations(self):
layout_key = f"X_{self.layout_method}"
@@ -174,7 +171,8 @@ class ScanpyEngine(CXGDriver):
f" layout may have been computed. The requested layout must be pre-calculated and saved "
f"back in the h5ad file. You can run "
f"`cellxgene prepare --layout {self.layout_method} <datafile>` "
f"to solve this problem. ")
f"to solve this problem. "
)
def _IEEE754_special_values_workaround(self):
"""
@@ -196,7 +194,7 @@ class ScanpyEngine(CXGDriver):
curr_axis = getattr(self.data, str(ax))
for ann in curr_axis:
dtype = curr_axis[ann].dtype
if dtype.kind == 'f':
if dtype.kind == "f":
finite_idx = np.isfinite(curr_axis[ann])
if not finite_idx.all():
curr_axis.loc[np.isnan(curr_axis[ann]), ann] = 0
@@ -233,8 +231,7 @@ class ScanpyEngine(CXGDriver):
if non_finite_X_found:
warnings.warn(
"Dataframe X contains floating point NaN or Infinities. "
"These will be converted to finite values."
"Dataframe X contains floating point NaN or Infinities. " "These will be converted to finite values."
)
def filter_dataframe(self, filter):
@@ -256,7 +253,7 @@ class ScanpyEngine(CXGDriver):
@staticmethod
def _annotation_filter_to_mask(filter, d_axis, count):
mask = np.ones((count, ), dtype=bool)
mask = np.ones((count,), dtype=bool)
for v in filter:
if d_axis[v["name"]].dtype.name in ["boolean", "category", "object"]:
key_idx = np.in1d(getattr(d_axis, v["name"]), v["values"])
@@ -274,24 +271,23 @@ class ScanpyEngine(CXGDriver):
@staticmethod
def _index_filter_to_mask(filter, count):
mask = np.zeros((count, ), dtype=bool)
mask = np.zeros((count,), dtype=bool)
for i in filter:
if type(i) == list:
mask[i[0]:i[1]] = True
mask[i[0] : i[1]] = True
else:
mask[i] = True
return mask
@staticmethod
def _axis_filter_to_mask(filter, d_axis, count):
mask = np.ones((count, ), dtype=bool)
mask = np.ones((count,), dtype=bool)
if "index" in filter:
mask = np.logical_and(mask, ScanpyEngine._index_filter_to_mask(filter["index"], count))
if "annotation_value" in filter:
mask = np.logical_and(mask,
ScanpyEngine._annotation_filter_to_mask(filter["annotation_value"],
d_axis,
count))
mask = np.logical_and(
mask, ScanpyEngine._annotation_filter_to_mask(filter["annotation_value"], d_axis, count)
)
return mask
def _filter_to_mask(self, filter, use_slices=True):
@@ -321,8 +317,9 @@ class ScanpyEngine(CXGDriver):
https://docs.scipy.org/doc/scipy/reference/sparse.html
"""
prefer_row_access = sparse.isspmatrix_csr(data._X) or sparse.isspmatrix_lil(data._X) \
or sparse.isspmatrix_bsr(data._X)
prefer_row_access = (
sparse.isspmatrix_csr(data._X) or sparse.isspmatrix_lil(data._X) or sparse.isspmatrix_bsr(data._X)
)
if prefer_row_access:
# Row-major slicing
if obs_selector is not None:
@@ -355,18 +352,12 @@ class ScanpyEngine(CXGDriver):
obs = self.data.obs[obs_selector]
if not fields:
fields = obs.columns.tolist()
result = {
"names": fields,
"data": DataFrame(obs[fields]).to_records(index=True).tolist()
}
result = {"names": fields, "data": DataFrame(obs[fields]).to_records(index=True).tolist()}
else:
var = self.data.var[var_selector]
if not fields:
fields = var.columns.tolist()
result = {
"names": fields,
"data": DataFrame(var[fields]).to_records(index=True).tolist()
}
result = {"names": fields, "data": DataFrame(var[fields]).to_records(index=True).tolist()}
return result
def data_frame(self, filter, axis):
@@ -391,12 +382,12 @@ class ScanpyEngine(CXGDriver):
if axis == Axis.OBS:
result = {
"var": var_index_sliced.tolist(),
"obs": DataFrame(_X, index=obs_index_sliced).to_records(index=True).tolist()
"obs": DataFrame(_X, index=obs_index_sliced).to_records(index=True).tolist(),
}
else:
result = {
"obs": obs_index_sliced.tolist(),
"var": DataFrame(_X.T, index=var_index_sliced).to_records(index=True).tolist()
"var": DataFrame(_X.T, index=var_index_sliced).to_records(index=True).tolist(),
}
return result
@@ -435,11 +426,11 @@ class ScanpyEngine(CXGDriver):
try:
df_layout = df.obsm[f"X_{self.layout_method}"]
except ValueError as e:
raise PrepareError(f"Layout has not been calculated using {self.layout_method}, "
f"please prepare your datafile and relaunch cellxgene") from e
normalized_layout = DataFrame((df_layout - df_layout.min()) / (df_layout.max() - df_layout.min()),
index=df.obs.index)
return {
"ndims": normalized_layout.shape[1],
"coordinates": normalized_layout.to_records(index=True).tolist()
}
raise PrepareError(
f"Layout has not been calculated using {self.layout_method}, "
f"please prepare your datafile and relaunch cellxgene"
) from e
normalized_layout = DataFrame(
(df_layout - df_layout.min()) / (df_layout.max() - df_layout.min()), index=df.obs.index
)
return {"ndims": normalized_layout.shape[1], "coordinates": normalized_layout.to_records(index=True).tolist()}