def AR1(constrained=False):
g = .95
sn = .3
y, c, s = [a[0] for a in gen_data([g], sn, N=1)]
result = constrained_oasisAR1(y, g, sn) if constrained else oasisAR1(y, g, lam=2.4)
result_foopsi = constrained_foopsi(y, [g], sn) if constrained else foopsi(y, [g], lam=2.4)
npt.assert_allclose(np.corrcoef(result[0], result_foopsi[0])[0, 1], 1)
npt.assert_allclose(np.corrcoef(result[1], result_foopsi[1])[0, 1], 1)
npt.assert_allclose(np.corrcoef(result[0], c)[0, 1], 1, .03)
npt.assert_allclose(np.corrcoef(result[1], s)[0, 1], 1, .2)
def AR2(constrained=False):
g = [1.7, -.712]
sn = .3
y, c, s = [a[0] for a in gen_data(g, sn, N=1, seed=3)]
result = constrained_onnlsAR2(y, g, sn) if constrained else onnls(y, g, lam=25)
result_foopsi = constrained_foopsi(y, g, sn) if constrained else foopsi(y, g, lam=25)
npt.assert_allclose(np.corrcoef(result[0], result_foopsi[0])[0, 1], 1, 1e-3)
npt.assert_allclose(np.corrcoef(result[1], result_foopsi[1])[0, 1], 1, 1e-2)
npt.assert_allclose(np.corrcoef(result[0], c)[0, 1], 1, .03)
npt.assert_allclose(np.corrcoef(result[1], s)[0, 1], 1, .2)
result2 = constrained_oasisAR2(y, g[0], g[1], sn) if constrained \
else oasisAR2(y, g[0], g[1], lam=25)
npt.assert_allclose(np.corrcoef(result2[0], c)[0, 1], 1, .03)
npt.assert_allclose(np.corrcoef(result2[1], s)[0, 1], 1, .2)
'#0072B2', '#009E73', '#D55E00', '#E69F00', '#56B4E9', '#CC79A7',
'#F0E442', '#999999'
]
# real data from Chen et al 2013, available at following URL
# https://portal.nersc.gov/project/crcns/download/cai-1/GCaMP6s_9cells_Chen2013/processed_data.tar.gz
filename = "/Users/joe/Downloads/data_20120627_cell2_002.mat"
################
#### Traces ####
################
# AR(1)
g = .95
sn = .3
Y, trueC, trueSpikes = gen_data()
N, T = Y.shape
result_oasis = oasisAR1(Y[0], g=g, lam=2.4)
result_foopsi = foopsi(Y[0], g=[g], lam=2.4)
fig = plt.figure(figsize=(20, 5.5))
fig.add_axes([.038, .57, .96, .42])
plt.plot(result_oasis[0], c=col[0], label='OASIS')
plt.plot(result_foopsi[0], '--', c=col[6], label='CVXPY')
plt.plot(trueC[0], c=col[2], label='Truth', zorder=-5)
plt.plot(Y[0], c=col[7], alpha=.7, zorder=-10, lw=1, label='Data')
plt.legend(frameon=False, ncol=4, loc=(.275, .82))
plt.gca().set_xticklabels([])
simpleaxis(plt.gca())
plt.ylim(Y[0].min(), Y[0].max())
plt.yticks([0, int(Y[0].max())], [0, int(Y[0].max())])
"""Script illustrating the autoregressive calcium fluorescence model
for OASIS, an active set method for sparse nonnegative deconvolution
@author: Johannes Friedrich
"""
from matplotlib import pyplot as plt
from oasis.functions import gen_data
from oasis.plotting import init_fig, simpleaxis
init_fig()
# colors for colorblind from http://www.cookbook-r.com/Graphs/Colors_(ggplot2)/
col = ['#0072B2', '#009E73', '#D55E00']
Y, trueC, trueSpikes = gen_data([1.58, -.6],
.5,
T=455,
framerate=30,
firerate=2.,
seed=0)
plt.figure(figsize=(15, 2.5))
for i, t in enumerate(trueSpikes[0, 20:-1]):
if t:
plt.plot([i, i], [0, 1], c=col[2])
plt.plot([trueSpikes[0, -1], trueSpikes[0, -1]], [0, 1],
c=col[2],
label=r'$s$')
plt.plot(trueC[0, 20:] / 3., c=col[0], label=r'$c$', zorder=-11)
plt.scatter(range(435), Y[0, 20:] / 3., c=col[1], clip_on=False, label=r'$y$')
plt.legend(loc=(.38, .75), ncol=3)
plt.yticks([0, 1, 2], [0, 1, 2])
plt.xticks(*[[0, 150, 300]] * 2)
plt.xlim(0, 435)
import pytest
import numpy as np
import pandas as pd
import xarray as xr
import numpy.testing as npt
import xarray.testing as xrt
from sklearn.base import clone
try:
from neuroglia import calcium
from oasis.functions import gen_data
# Test functions perform as expected
true_b = 2
y, true_c, true_s = map(np.squeeze, gen_data(N=3, b=true_b, seed=0))
y = y.T
TIME = np.arange(0, len(y)/30, 1/30.)
LBL = ['a', 'b', 'c']
sin_scale = 5
# data = y
DFF = pd.DataFrame(y, TIME, LBL)
DFF_WITH_DRIFT = DFF.apply(lambda y: y + sin_scale*np.sin(.05*TIME),axis=0)
calcium_import_failed = False
except ImportError:
calcium_import_failed = True
@pytest.mark.skipif(
This is an example of how to infer spike events """ ####################################################### # First, we'll generate some fake data import numpy as np import pandas as pd from oasis.functions import gen_data neuron_ids = ['a', 'b', 'c'] sampling_rate = 30.0 traces, _, spikes = map(np.squeeze, gen_data(N=3, b=2, seed=0)) time = np.arange(0, traces.shape[1] / sampling_rate, 1 / sampling_rate) traces = pd.DataFrame(traces.T, index=time, columns=neuron_ids) spikes = pd.DataFrame(spikes.T, index=time, columns=neuron_ids) ######################################################## # let's plot the data import matplotlib.pyplot as plt traces.plot() plt.show() ########################################################## # Now, we'll deconvolve the data
active_set.pop(c + 1)
len_active_set -= 1
# update solution back to lag tau
v, w, f, l = active_set[c]
solution[max(f, f + l - tau - 1):f + l] = max(v, 0) / \
w * g**np.arange(max(0, l - tau - 1), l)
return solution
# correlation with ground truth spike train for OASIS with L1
tauls = [0, 1, 2, 5, 10, np.inf]
plt.figure(figsize=(7, 5))
for j, sn in enumerate([.1, .2, .3]):
Y, trueC, trueSpikes = gen_data(sn=sn)
N = len(Y)
C = np.asarray([
[
deconvolveAR1(
y, .95, tau=tau,
lam=(.2 + .25 * np.exp(-tau / 2.)
)) # lam=(.12 / (1 - (.8 if sn == .3 else .7)**(tau + 1))
for tau in tauls
] for y in Y
])
S = np.zeros_like(C)
S[:, :, 1:] = C[:, :, 1:] - .95 * C[:, :, :-1]
S[S < 0] = 0
corr = np.array([[np.corrcoef(ss, s[-1])[0, 1] for ss in s] for s in S])