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
def pixel(ctx, config, px, py, band, plot, ylim, style, cmap,
embed, seed, algo_kw):
# Set seed
np.random.seed(seed)
# Convert band to index
band -= 1
# Get colormap
if hasattr(palettable.colorbrewer, cmap):
mpl_cmap = getattr(palettable.colorbrewer, cmap).mpl_colormap
elif hasattr(palettable.cubehelix, cmap):
mpl_cmap = getattr(palettable.cubehelix, cmap).mpl_colormap
elif hasattr(palettable.wesanderson, cmap):
mpl_cmap = getattr(palettable.wesanderson, cmap).mpl_colormap
else:
raise click.Abort('Cannot find specified colormap in `palettable`')
# Parse config
cfg = parse_config_file(config)
# Apply algorithm overrides
revalidate = False
for kw in algo_kw:
for cfg_key in cfg:
if kw in cfg[cfg_key]:
# Parse as YAML for type conversions used in config parser
value = yaml.load(algo_kw[kw])
print('Overriding cfg[%s][%s]=%s with %s' %
(cfg_key, kw, cfg[cfg_key][kw], value))
cfg[cfg_key][kw] = value
revalidate = True
if revalidate:
cfg = convert_config(cfg)
# Locate and fetch attributes from data
df = csvfile_to_dataframe(cfg['dataset']['input_file'],
date_format=cfg['dataset']['date_format'])
df['image_ID'] = get_image_IDs(df['filename'])
# Setup X/Y
kws = {'x': df['date']}
kws.update(df.to_dict())
X = patsy.dmatrix(cfg['YATSM']['design_matrix'], kws)
design_info = X.design_info
Y = read_pixel_timeseries(df['filename'], px, py)
fit_indices = np.arange(cfg['dataset']['n_bands'])
if cfg['dataset']['mask_band'] is not None:
fit_indices = fit_indices[:-1]
# Mask out of range data
idx_mask = cfg['dataset']['mask_band'] - 1
valid = cyprep.get_valid_mask(Y,
cfg['dataset']['min_values'],
cfg['dataset']['max_values']).astype(np.bool)
valid *= np.in1d(Y[idx_mask, :], cfg['dataset']['mask_values'],
invert=True).astype(np.bool)
# Apply mask
Y = np.delete(Y, idx_mask, axis=0)[:, valid]
X = X[valid, :]
dates = np.array([dt.datetime.fromordinal(d) for d in df['date'][valid]])
# Plot before fitting
with plt.xkcd() if style == 'xkcd' else mpl.style.context(style):
for _plot in plot:
if _plot == 'TS':
plot_TS(dates, Y[band, :])
elif _plot == 'DOY':
plot_DOY(dates, Y[band, :], mpl_cmap)
elif _plot == 'VAL':
plot_VAL(dates, Y[band, :], mpl_cmap)
if ylim:
plt.ylim(ylim)
plt.title('Timeseries: px={px} py={py}'.format(px=px, py=py))
plt.ylabel('Band {b}'.format(b=band + 1))
if embed and has_embed:
IPython_embed()
plt.tight_layout()
plt.show()
# Eliminate config parameters not algorithm and fit model
yatsm = cfg['YATSM']['algorithm_cls'](lm=cfg['YATSM']['prediction_object'],
**cfg[cfg['YATSM']['algorithm']])
yatsm.px = px
yatsm.py = py
yatsm.fit(X, Y, np.asarray(df['date'][valid]))
# Plot after predictions
with plt.xkcd() if style == 'xkcd' else mpl.style.context(style):
for _plot in plot:
if _plot == 'TS':
plot_TS(dates, Y[band, :])
elif _plot == 'DOY':
plot_DOY(dates, Y[band, :], mpl_cmap)
elif _plot == 'VAL':
plot_VAL(dates, Y[band, :], mpl_cmap)
if ylim:
plt.ylim(ylim)
plt.title('Timeseries: px={px} py={py}'.format(px=px, py=py))
plt.ylabel('Band {b}'.format(b=band + 1))
plot_results(band, cfg['YATSM'], yatsm, plot_type=_plot)
if embed and has_embed:
IPython_embed()
plt.tight_layout()
plt.show()
