def main(root, pattern='68/*block03/*trial00', frames=10, start=27.5, stop=38.5):
_, jac = jacobian(root, pattern, frames)
n = int(np.sqrt(len(jac.columns) / 9))
pca = lmj.pca.PCA(filename=PCA_FILE)
for v in (0.5, 0.8, 0.9, 0.95, 0.98, 0.99, 0.995, 0.998, 0.999):
print('variance', v, 'components', pca.num_components(v))
if os.path.exists(MODEL):
dl = pickle.load(open(MODEL, 'rb'))
else:
dl = sklearn.decomposition.MiniBatchSparsePCA(
n_components=100, alpha=0.0001, verbose=10)
dl.fit(pca.encode(jac.dropna().values, retain=0.999))
pickle.dump(dl, open(MODEL, 'wb'), -1)
df = pd.DataFrame(pca.decode(dl.components_, retain=0.999), columns=jac.columns)
enc = sklearn.decomposition.sparse_encode(
pca.encode(jac.dropna().loc[start:stop, :], retain=0.999),
dl.components_,
algorithm='omp',
alpha=0.0001,
)
kw = dict(vmin=-10, vmax=10, cmap='RdBu')
lmj.plot.create_axes(111, spines=False).imshow(enc.T, **kw)
lmj.plot.show()
enc = sklearn.decomposition.sparse_encode(
jac.dropna().loc[start:stop, :],
pca.vecs.T[:len(dl.components_)],
algorithm='omp',
alpha=0.0001,
)
kw = dict(vmin=-10, vmax=10, cmap='RdBu')
lmj.plot.create_axes(111, spines=False).imshow(enc.T, **kw)
lmj.plot.show()
def find(i, g, b):
cs = [c for c in jac.columns if '{}/'.format(g) in c and c.endswith(b)]
return df[cs].loc[i, :].values.reshape((n, n))
def plot(where, i, g, b):
ax = lmj.plot.create_axes(where, spines=False)
ax.imshow(find(i, g, b), cmap='RdBu')
for i in range(10):
plot(331, i, 'x', 'x')
plot(332, i, 'x', 'y')
plot(333, i, 'x', 'z')
plot(334, i, 'y', 'x')
plot(335, i, 'y', 'y')
plot(336, i, 'y', 'z')
plot(337, i, 'z', 'x')
plot(338, i, 'z', 'y')
plot(339, i, 'z', 'z')
lmj.plot.gcf().subplots_adjust(left=0, right=1, bottom=0, top=1, wspace=0, hspace=0)
lmj.plot.show()
def fill(dfs, rank, window):
'''Complete missing marker data using linear interpolation.
This method alters the given `dfs` in-place.
Parameters
----------
dfs : list of pd.DataFrame
Frames of source data. The frames will be stacked into a single large
frame and interpolated linearly, either in the data space or (if rank is
not None) in principal component space.
rank : float
Number of principal components (if >1) or fraction of variance (if in
(0, 1)) to retain in the encoded data.
window : int
Model windows of this many consecutive frames.
'''
df = lmj.cubes.fill.stack(dfs, window)
centers = lmj.cubes.fill.center(df)
if rank is None:
prediction, _, _ = lmj.cubes.fill.window(df, window, True)
else:
if not 0 < rank < 1:
rank = int(rank)
pca = lmj.pca.PCA()
pos, _, _ = lmj.cubes.fill.window(df, window, None)
pca.fit(pos)
enc = pd.DataFrame(pca.encode(pos, retain=rank))
lin = enc.interpolate().ffill().bfill().values
prediction = pca.decode(lin, retain=rank)
lmj.cubes.fill.update(df, prediction, window)
lmj.cubes.fill.restore(df, centers)
lmj.cubes.fill.unstack(df, dfs)
def fill(dfs, rank, window):
"""Complete missing marker data using linear interpolation.
This method alters the given `dfs` in-place.
Parameters
----------
dfs : list of pd.DataFrame
Frames of source data. The frames will be stacked into a single large
frame and interpolated linearly, either in the data space or (if rank is
not None) in principal component space.
rank : float
Number of principal components (if >1) or fraction of variance (if in
(0, 1)) to retain in the encoded data.
window : int
Model windows of this many consecutive frames.
"""
df = lmj.cubes.fill.stack(dfs, window)
centers = lmj.cubes.fill.center(df)
if rank is None:
prediction, _, _ = lmj.cubes.fill.window(df, window, True)
else:
if not 0 < rank < 1:
rank = int(rank)
pca = lmj.pca.PCA()
pos, _, _ = lmj.cubes.fill.window(df, window, None)
pca.fit(pos)
enc = pd.DataFrame(pca.encode(pos, retain=rank))
lin = enc.interpolate().ffill().bfill().values
prediction = pca.decode(lin, retain=rank)
lmj.cubes.fill.update(df, prediction, window)
lmj.cubes.fill.restore(df, centers)
lmj.cubes.fill.unstack(df, dfs)
def main(root,
pattern='68/*block03/*trial00',
frames=10,
start=27.5,
stop=38.5):
_, jac = jacobian(root, pattern, frames)
n = int(np.sqrt(len(jac.columns) / 9))
pca = lmj.pca.PCA(filename=PCA_FILE)
for v in (0.5, 0.8, 0.9, 0.95, 0.98, 0.99, 0.995, 0.998, 0.999):
print('variance', v, 'components', pca.num_components(v))
if os.path.exists(MODEL):
dl = pickle.load(open(MODEL, 'rb'))
else:
dl = sklearn.decomposition.MiniBatchSparsePCA(n_components=100,
alpha=0.0001,
verbose=10)
dl.fit(pca.encode(jac.dropna().values, retain=0.999))
pickle.dump(dl, open(MODEL, 'wb'), -1)
df = pd.DataFrame(pca.decode(dl.components_, retain=0.999),
columns=jac.columns)
enc = sklearn.decomposition.sparse_encode(
pca.encode(jac.dropna().loc[start:stop, :], retain=0.999),
dl.components_,
algorithm='omp',
alpha=0.0001,
)
kw = dict(vmin=-10, vmax=10, cmap='RdBu')
lmj.plot.create_axes(111, spines=False).imshow(enc.T, **kw)
lmj.plot.show()
enc = sklearn.decomposition.sparse_encode(
jac.dropna().loc[start:stop, :],
pca.vecs.T[:len(dl.components_)],
algorithm='omp',
alpha=0.0001,
)
kw = dict(vmin=-10, vmax=10, cmap='RdBu')
lmj.plot.create_axes(111, spines=False).imshow(enc.T, **kw)
lmj.plot.show()
def find(i, g, b):
cs = [c for c in jac.columns if '{}/'.format(g) in c and c.endswith(b)]
return df[cs].loc[i, :].values.reshape((n, n))
def plot(where, i, g, b):
ax = lmj.plot.create_axes(where, spines=False)
ax.imshow(find(i, g, b), cmap='RdBu')
for i in range(10):
plot(331, i, 'x', 'x')
plot(332, i, 'x', 'y')
plot(333, i, 'x', 'z')
plot(334, i, 'y', 'x')
plot(335, i, 'y', 'y')
plot(336, i, 'y', 'z')
plot(337, i, 'z', 'x')
plot(338, i, 'z', 'y')
plot(339, i, 'z', 'z')
lmj.plot.gcf().subplots_adjust(left=0,
right=1,
bottom=0,
top=1,
wspace=0,
hspace=0)
lmj.plot.show()