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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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36
client/src/components/graph/graph.js
View File

@@ -112,24 +112,20 @@ class Graph extends React.Component {
return !shallowEqual(props.watchProps, prevProps.watchProps);
}
computePointPositions = memoize(
(X, Y, modelTF, spatialMetadata, imageUnderlay) => {
/*
computePointPositions = memoize((X, Y, modelTF) => {
/*
compute the model coordinate for each point
*/
console.log({ X }, { Y });
const positions = new Float32Array(2 * X.length);
for (let i = 0, len = X.length; i < len; i += 1) {
const p = imageUnderlay?.isActive
? this.rescalePointForSpatial(X[i], Y[i], spatialMetadata)
: vec2.fromValues(X[i], Y[i]);
vec2.transformMat3(p, p, modelTF);
positions[2 * i] = p[0];
positions[2 * i + 1] = p[1];
}
return positions;
console.log({ X }, { Y });
const positions = new Float32Array(2 * X.length);
for (let i = 0, len = X.length; i < len; i += 1) {
const p = vec2.fromValues(X[i], Y[i]);
vec2.transformMat3(p, p, modelTF);
positions[2 * i] = p[0];
positions[2 * i + 1] = p[1];
}
);
return positions;
});
computePointColors = memoize((rgb) => {
/*
@@ -585,13 +581,7 @@ class Graph extends React.Component {
const { currentDimNames } = layoutChoice;
const X = layoutDf.col(currentDimNames[0]).asArray();
const Y = layoutDf.col(currentDimNames[1]).asArray();
const positions = this.computePointPositions(
X,
Y,
modelTF,
spatial.data,
imageUnderlay
);
const positions = this.computePointPositions(X, Y, modelTF);
const colorTable = this.updateColorTable(colorsProp, colorDf);
const colors = this.computePointColors(colorTable.rgb);
@@ -809,8 +799,6 @@ class Graph extends React.Component {
const { pointBuffer, colorBuffer, flagBuffer } = this.state;
let needToRenderCanvas = false;
console.log("updateReglAndRender");
if (height !== prevAsyncProps?.height || width !== prevAsyncProps?.width) {
needToRenderCanvas = true;
}

2
server/common/rest.py
View File

@@ -294,7 +294,7 @@ def layout_obs_get(request, data_adaptor):
try:
return make_response(
data_adaptor.layout_to_fbs_matrix(fields), HTTPStatus.OK, {"Content-Type": "application/octet-stream"}
data_adaptor.layout_to_fbs_matrix(fields, data_adaptor.get_spatial()), HTTPStatus.OK, {"Content-Type": "application/octet-stream"}
)
except (KeyError, DatasetAccessError) as e:
return abort_and_log(HTTPStatus.BAD_REQUEST, str(e), include_exc_info=True)

66
server/data_common/data_adaptor.py
View File

@@ -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