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Put scaling back in the backend

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This commit is contained in:
Emanuele Bezzi
2021-12-09 14:12:16 -05:00
parent 2fe9cc4aac
commit cbf3ba240a
3 changed files with 57 additions and 47 deletions
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server/data_common/data_adaptor.py
+44 -22
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@@ -340,41 +340,65 @@ class DataAdaptor(metaclass=ABCMeta):
pass
@staticmethod
def normalize_embedding(embedding):
def normalize_embedding(embedding, spatial = None):
"""Normalize embedding layout to meet client assumptions.
Embedding is an ndarray, shape (n_obs, n)., where n is normally 2
Embedding is an ndarray, shape (n_obs, n)., where n is normally 2.
Note: if spatial data is available, the normalization will be done
according to the size of the underlying image
"""
if spatial is not None:
# TODO: sync with the code in spatial_data_get
resolution = "hires"
if len(list(spatial)) == 0:
raise Exception("uns does not have spatial information")
library_id = list(spatial)[0]
if "images" not in spatial[library_id]:
raise Exception("spatial information does not contain images")
if resolution not in spatial[library_id]["images"]:
raise Exception(f"spatial information does not contain requested resolution '{resolution}'")
scaleref = spatial[library_id]["scalefactors"][f"tissue_{resolution}_scalef"]
(h, w, _) = spatial[library_id]["images"][resolution].shape
A = embedding * scaleref
A = np.column_stack([A[:, 0] / w, A[:, 1] / h])
normalized_layout = A.astype(dtype=np.float32)
else:
# scale isotropically
try:
min = np.nanmin(embedding, axis=0)
max = np.nanmax(embedding, axis=0)
except RuntimeError:
# indicates entire array was NaN, which should propagate
min = np.NaN
max = np.NaN
try:
min = np.nanmin(embedding, axis=0)
max = np.nanmax(embedding, axis=0)
except RuntimeError:
# indicates entire array was NaN, which should propagate
min = np.NaN
max = np.NaN
scale = np.amax(max - min)
normalized_layout = (embedding - min) / scale
scale = np.amax(max - min)
normalized_layout = (embedding - min) / scale
# translate to center on both axis
translate = 0.5 - ((max - min) / scale / 2)
normalized_layout = normalized_layout + translate
# translate to center on both axis
translate = 0.5 - ((max - min) / scale / 2)
normalized_layout = normalized_layout + translate
print(f"scale {scale}, translate {translate}")
# print(f"scale {scale}, translate {translate}")
# if True: # if visium
# self.data.uns["spatial"]
# adata.uns["spatial"]['V1_Adult_Mouse_Brain']["scalefactors"]["tissue_hires_scalef"]
# A = embedding * 0.17011142
# A = np.column_stack([A[:, 0] / 1921, A[:, 1] / 2000])
# normalized_layout = A.astype(dtype=np.float32)
return normalized_layout
def layout_to_fbs_matrix(self, fields):
def layout_to_fbs_matrix(self, fields, spatial = None):
"""
return specified embeddings as a flatbuffer, using the cellxgene matrix fbs encoding.
@@ -390,7 +414,7 @@ class DataAdaptor(metaclass=ABCMeta):
with ServerTiming.time("layout.query"):
for ename in embeddings:
embedding = self.get_embedding_array(ename, 2)
normalized_layout = DataAdaptor.normalize_embedding(embedding)
normalized_layout = DataAdaptor.normalize_embedding(embedding, ename == "spatial" and spatial)
layout_data.append(pd.DataFrame(normalized_layout, columns=[f"{ename}_0", f"{ename}_1"]))
with ServerTiming.time("layout.encode"):
@@ -398,8 +422,6 @@ class DataAdaptor(metaclass=ABCMeta):
df = pd.concat(layout_data, axis=1, copy=False)
else:
df = pd.DataFrame()
# print("##########DF")
# print(df)
fbs = encode_matrix_fbs(df, col_idx=df.columns, row_idx=None)
return fbs