def plot_results():
# Add in deleted obs
deleted = np.in1d(X[:, 1], yatsm.X[:, 1], invert=True)
plot_dataset()
plt.plot(dates[deleted], Y[plot_index, deleted], 'ro')
# Get qualitative color map for model segment lines
# Color map ncolors goes from 3 - 9
ncolors = min(9, max(3, len(yatsm.record)))
# Repeat if number of segments > 9
repeat = int(math.ceil(len(yatsm.record) / 9.0))
fit_colors = brewer2mpl.get_map('set1',
'qualitative',
ncolors).hex_colors * repeat
# Direction for prediction x
step = -1 if reverse else 1
for i, r in enumerate(yatsm.record):
# Predict
mx = np.arange(r['start'], r['end'], step)
my = np.dot(r['coef'][:, plot_index],
make_X(mx, freq))
mx_date = np.array([dt.fromordinal(int(_x)) for _x in mx])
plt.plot(mx_date, my, fit_colors[i])
idx = np.where((yatsm.X[:, 1] >= r['start']) & (yatsm.X[:, 1] <= r['end']))[0]
sklearn_lasso = sklearn.linear_model.Lasso(alpha=20).fit(yatsm.X[idx, :], yatsm.Y[plot_index, idx])
plt.plot(mx_date, sklearn_lasso.predict(make_X(mx, freq).T), fit_colors[i], ls='dashed', lw=3)
# from IPython.core.debugger import Pdb
# Pdb().set_trace()
if r['break'] != 0:
break_date = dt.fromordinal(int(r['break']))
break_i = np.where(X[:, 1] == r['break'])[0]
if not plot_ylim:
_plot_ylim = (Y[plot_index, :].min(), Y[plot_index, :].max())
else:
_plot_ylim = plot_ylim
plt.vlines(break_date, _plot_ylim[0], _plot_ylim[1], 'r')
plt.plot(break_date, Y[plot_index, break_i],
'ro', mec='r', mfc='none', ms=10, mew=5)
def preprocess(location, px, py, freq,
image_pattern='L*stack'):
""" Read and preprocess Landsat data before analysis """
dates, files = find_stack_images(location,
image_pattern=image_pattern)
x = np.array(dates)
Y = read_pixel_timeseries(files, px, py)
# Filter out time series and remove Fmask
clear = np.logical_and(Y[fmask, :] <= 1,
np.all(Y <= 10000, axis=0))
Y = Y[:, clear][:fmask, :]
x = x[clear]
# Ordinal date
ord_x = np.array(map(dt.toordinal, x))
# Make X matrix
X = make_X(ord_x, freq).T
return X, Y, clear
