def classify(ctx, config, algo, job_number, total_jobs, resume):
cfg = parse_config_file(config)
df = csvfile_to_dataframe(cfg['dataset']['input_file'],
cfg['dataset']['date_format'])
nrow = get_image_attribute(df['filename'][0])[0]
classifier = joblib.load(algo)
# Split into lines and classify
job_lines = distribute_jobs(job_number, total_jobs, nrow)
logger.debug('Responsible for lines: {l}'.format(l=job_lines))
start_time = time.time()
logger.info('Starting to run lines')
for job_line in job_lines:
filename = get_output_name(cfg['dataset'], job_line)
if not os.path.exists(filename):
logger.warning('No model result found for line {l} '
'(file {f})'.format(l=job_line, f=filename))
pass
if resume and try_resume(filename):
logger.debug('Already processed line {l}'.format(l=job_line))
continue
logger.debug('Classifying line {l}'.format(l=job_line))
classify_line(filename, classifier)
logger.debug('Completed {n} lines in {m} minutes'.format(
n=len(job_lines),
m=round((time.time() - start_time) / 60.0, 2))
)
def line(ctx, config, job_number, total_jobs,
resume, check_cache, do_not_run, verbose_yatsm):
if verbose_yatsm:
logger_algo.setLevel(logging.DEBUG)
# Parse config
cfg = parse_config_file(config)
if ('phenology' in cfg and cfg['phenology'].get('enable')) and not pheno:
click.secho('Could not import yatsm.phenology but phenology metrics '
'are requested', fg='red')
click.secho('Error: %s' % pheno_exception, fg='red')
raise click.Abort()
# Make sure output directory exists and is writable
output_dir = cfg['dataset']['output']
try:
os.makedirs(output_dir)
except OSError as e:
# File exists
if e.errno == 17:
pass
elif e.errno == 13:
click.secho('Cannot create output directory %s' % output_dir,
fg='red')
raise click.Abort()
if not os.access(output_dir, os.W_OK):
click.secho('Cannot write to output directory %s' % output_dir,
fg='red')
raise click.Abort()
# Test existence of cache directory
read_cache, write_cache = test_cache(cfg['dataset'])
logger.info('Job {i} of {n} - using config file {f}'.format(i=job_number,
n=total_jobs,
f=config))
df = csvfile_to_dataframe(cfg['dataset']['input_file'],
cfg['dataset']['date_format'])
df['image_ID'] = get_image_IDs(df['filename'])
# Get attributes of one of the images
nrow, ncol, nband, dtype = get_image_attribute(df['filename'][0])
# Calculate the lines this job ID works on
job_lines = distribute_jobs(job_number, total_jobs, nrow)
logger.debug('Responsible for lines: {l}'.format(l=job_lines))
# Calculate X feature input
dates = np.asarray(df['date'])
kws = {'x': dates}
kws.update(df.to_dict())
X = patsy.dmatrix(cfg['YATSM']['design_matrix'], kws)
cfg['YATSM']['design'] = X.design_info.column_name_indexes
# Form YATSM class arguments
fit_indices = np.arange(cfg['dataset']['n_bands'])
if cfg['dataset']['mask_band'] is not None:
fit_indices = fit_indices[:-1]
if cfg['YATSM']['reverse']:
X = np.flipud(X)
# Create output metadata to save
md = {
'YATSM': cfg['YATSM'],
cfg['YATSM']['algorithm']: cfg[cfg['YATSM']['algorithm']]
}
if cfg['phenology']['enable']:
md.update({'phenology': cfg['phenology']})
# Begin process
start_time_all = time.time()
for line in job_lines:
out = get_output_name(cfg['dataset'], line)
if resume:
try:
np.load(out)
except:
pass
else:
logger.debug('Already processed line %s' % line)
continue
logger.debug('Running line %s' % line)
start_time = time.time()
Y = read_line(line, df['filename'], df['image_ID'], cfg['dataset'],
ncol, nband, dtype,
read_cache=read_cache, write_cache=write_cache,
validate_cache=False)
if do_not_run:
continue
if cfg['YATSM']['reverse']:
Y = np.fliplr(Y)
output = []
for col in np.arange(Y.shape[-1]):
_Y = Y.take(col, axis=2)
# Mask
idx_mask = cfg['dataset']['mask_band'] - 1
valid = cyprep.get_valid_mask(
_Y,
cfg['dataset']['min_values'],
cfg['dataset']['max_values']).astype(bool)
valid *= np.in1d(_Y.take(idx_mask, axis=0),
cfg['dataset']['mask_values'],
invert=True).astype(np.bool)
_Y = np.delete(_Y, idx_mask, axis=0)[:, valid]
_X = X[valid, :]
_dates = dates[valid]
# Run model
cls = cfg['YATSM']['algorithm_cls']
algo_cfg = cfg[cfg['YATSM']['algorithm']]
yatsm = cls(lm=cfg['YATSM']['prediction_object'],
**algo_cfg.get('init', {}))
yatsm.px = col
yatsm.py = line
try:
yatsm.fit(_X, _Y, _dates, **algo_cfg.get('fit', {}))
except TSLengthException:
continue
if yatsm.record is None or len(yatsm.record) == 0:
continue
# Postprocess
if cfg['YATSM'].get('commission_alpha'):
yatsm.record = postprocess.commission_test(
yatsm, cfg['YATSM']['commission_alpha'])
for prefix, lm in zip(cfg['YATSM']['refit']['prefix'],
cfg['YATSM']['refit']['prediction_object']):
yatsm.record = postprocess.refit_record(yatsm, prefix, lm,
keep_regularized=True)
if cfg['phenology']['enable']:
pcfg = cfg['phenology']
ltm = pheno.LongTermMeanPhenology(**pcfg.get('init', {}))
yatsm.record = ltm.fit(yatsm, **pcfg.get('fit', {}))
output.extend(yatsm.record)
logger.debug(' Saving YATSM output to %s' % out)
np.savez(out,
record=np.array(output),
version=__version__,
metadata=md)
run_time = time.time() - start_time
logger.debug('Line %s took %ss to run' % (line, run_time))
logger.info('Completed {n} lines in {m} minutes'.format(
n=len(job_lines),
m=round((time.time() - start_time_all) / 60.0, 2)))
def annual(row1, row2, pct):
NDV = -9999
# EXAMPLE IMAGE for dimensions, map creation
#example_img_fn = '/projectnb/landsat/users/valpasq/LCMS/stacks/p035r032/images/example_img'
example_img_fn = '/projectnb/landsat/projects/Massachusetts/p012r031/images/example_img'
# YATSM CONFIG FILE
#config_file = '/projectnb/landsat/users/valpasq/LCMS/stacks/p035r032/p035r032_config_LCMS.yaml'
config_file = '/projectnb/landsat/projects/Massachusetts/p012r031/p012r031_config_pixel.yaml'
#WRS2 = 'p027r027'
WRS2 = 'p012r031'
# Up front -- declare hard coded dataset attributes (for now)
BAND_NAMES = [
'blue', 'green', 'red', 'nir', 'swir1', 'swir2', 'therm', 'tcb', 'tcg',
'tcw', 'fmask'
]
n_band = len(BAND_NAMES) - 1
col_names = [
'date', 'blue', 'green', 'red', 'nir', 'swir1', 'swir2', 'therm',
'tcb', 'tcg', 'tcw'
]
dtype = np.int16
years = range(1985, 2016, 1)
length = 33 # number of years
# Read in example image
example_img = read_image(example_img_fn)
py_dim = example_img.shape[0]
px_dim = example_img.shape[1]
print('Shape of example image:')
print(example_img.shape)
# Read in and parse config file
cfg = yaml.load(open(config_file))
# List to np.ndarray so it works with cyprep.get_valid_mask
cfg['dataset']['min_values'] = np.asarray(cfg['dataset']['min_values'])
cfg['dataset']['max_values'] = np.asarray(cfg['dataset']['max_values'])
# Get files list
df = csvfile_to_dataframe(cfg['dataset']['input_file'], \
date_format=cfg['dataset']['date_format'])
# Get dates for image stack
df['image_ID'] = get_image_IDs(df['filename'])
df['x'] = df['date']
dates = df['date'].values
# Initialize arrays for storing stats
mean_TCB = np.zeros((py_dim, px_dim, length))
mean_TCG = np.zeros((py_dim, px_dim, length))
mean_TCW = np.zeros((py_dim, px_dim, length))
min_val_TCB = np.zeros((py_dim, px_dim, length))
min_val_TCG = np.zeros((py_dim, px_dim, length))
min_val_TCW = np.zeros((py_dim, px_dim, length))
min_idx_TCB = np.zeros((py_dim, px_dim, length))
min_idx_TCG = np.zeros((py_dim, px_dim, length))
min_idx_TCW = np.zeros((py_dim, px_dim, length))
max_val_TCB = np.zeros((py_dim, px_dim, length))
max_val_TCG = np.zeros((py_dim, px_dim, length))
max_val_TCW = np.zeros((py_dim, px_dim, length))
max_idx_TCB = np.zeros((py_dim, px_dim, length))
max_idx_TCG = np.zeros((py_dim, px_dim, length))
max_idx_TCW = np.zeros((py_dim, px_dim, length))
for py in range(row1, row2): # row iterator
print('Working on row {py}'.format(py=py))
sys.stdout.flush()
start_time = time.time()
Y_row = read_line(
py,
df['filename'],
df['image_ID'],
cfg['dataset'],
px_dim,
n_band + 1,
dtype, # +1 for now for Fmask
read_cache=False,
write_cache=False,
validate_cache=False)
for px in range(0, px_dim): # column iterator
Y = Y_row.take(px, axis=2)
if (Y[0:6] == NDV).mean() > 0.3:
continue
else: # process time series for disturbance events
# Mask based on physical constraints and Fmask
valid = cyprep.get_valid_mask( \
Y, \
cfg['dataset']['min_values'], \
cfg['dataset']['max_values']).astype(bool)
# Apply mask band
idx_mask = cfg['dataset']['mask_band'] - 1
valid *= np.in1d(Y.take(idx_mask, axis=0), \
cfg['dataset']['mask_values'], \
invert=True).astype(np.bool)
# Mask time series using fmask result
Y_fmask = np.delete(Y, idx_mask, axis=0)[:, valid]
dates_fmask = dates[valid]
# Apply multi-temporal mask (modified tmask)
# Step 1. mask where green > 3 stddev from mean (fmasked) green
multitemp1_fmask = np.where(
Y_fmask[1] < (np.mean(Y_fmask[1]) +
np.std(Y_fmask[1]) * 3))
dates_fmask = dates_fmask[multitemp1_fmask[0]]
Y_fmask = Y_fmask[:, multitemp1_fmask[0]]
# Step 2. mask where swir < 3 std devfrom mean (fmasked) SWIR
multitemp2_fmask = np.where(
Y_fmask[4] > (np.mean(Y_fmask[4]) -
np.std(Y_fmask[4]) * 3))
dates_fmask = dates_fmask[multitemp2_fmask[0]]
Y_fmask = Y_fmask[:, multitemp2_fmask[0]]
# convert time from ordinal to dates
dt_dates_fmask = np.array(
[dt.datetime.fromordinal(d) for d in dates_fmask])
# Create dataframes for analysis
# Step 1. reshape data
shp_ = dt_dates_fmask.shape[0]
dt_dates_fmask_csv = dt_dates_fmask.reshape(shp_, 1)
Y_fmask_csv = np.transpose(Y_fmask)
data_fmask = np.concatenate([dt_dates_fmask_csv, Y_fmask_csv],
axis=1)
# Step 2. create dataframe
data_fmask_df = pd.DataFrame(data_fmask, columns=col_names)
# convert reflectance to numeric type
data_fmask_df[BAND_NAMES[0:10]] = data_fmask_df[
BAND_NAMES[0:10]].astype(int)
# Group observations by year to generate annual TS
year_group_fmask = data_fmask_df.groupby(
data_fmask_df.date.dt.year)
# get years in time series
years_fmask = np.asarray(year_group_fmask.groups.keys())
years_fmask = years_fmask.astype(int)
# TODO: FIX THIS!!!!!!!
#import pdb; pdb.set_trace()
month_group_fmask = data_fmask_df.groupby(
[data_fmask_df.date.dt.year,
data_fmask_df.date.dt.month]).max()
month_groups = month_group_fmask.groupby(
month_group_fmask.date.dt.year)
# Calculate number of observations
nobs = year_group_fmask['tcb'].count()
### TC Brightness
# Calculate mean annual TCB
TCB_mean = year_group_fmask['tcb'].mean()
if pct == False:
TCB_max_val = month_groups['tcb'].max()
TCB_max_idx = month_groups['tcb'].idxmax()
TCB_min_val = month_groups['tcb'].min()
TCB_min_idx = month_groups['tcb'].idxmin()
else:
# percentile clip
TCB_max = year_group_fmask['tcb'].quantile([pct2])
TCB_min = year_group_fmask['tcb'].quantile([pct1])
### TC Greenness
# Calculate mean annual TCG
TCG_mean = year_group_fmask['tcg'].mean()
if pct == False:
TCG_max_val = month_groups['tcg'].max()
TCG_max_idx = month_groups['tcg'].idxmax()
TCG_min_val = month_groups['tcg'].min()
TCG_min_idx = month_groups['tcg'].idxmin()
else:
# percentile clip
TCG_max = year_group_fmask['tcg'].quantile([pct2])
TCG_min = year_group_fmask['tcg'].quantile([pct1])
### TC Wetness
# Calculate mean annual TCW
TCW_mean = year_group_fmask['tcw'].mean()
if pct == False:
TCW_max_val = month_groups['tcw'].max()
TCW_max_idx = month_groups['tcw'].idxmax()
TCW_min_val = month_groups['tcw'].min()
TCW_min_idx = month_groups['tcw'].idxmin()
else:
# percentile clip
TCW_max = year_group_fmask['tcw'].quantile([pct2])
TCW_min = year_group_fmask['tcw'].quantile([pct1])
for index, year in enumerate(years):
if year in TCB_mean.index:
mean_TCB[py, px, index] = TCB_mean[year]
mean_TCG[py, px, index] = TCG_mean[year]
mean_TCW[py, px, index] = TCW_mean[year]
min_val_TCB[py, px, index] = TCB_min_val[year]
min_val_TCG[py, px, index] = TCG_min_val[year]
min_val_TCW[py, px, index] = TCW_min_val[year]
max_val_TCB[py, px, index] = TCB_max_val[year]
max_val_TCG[py, px, index] = TCG_max_val[year]
max_val_TCW[py, px, index] = TCW_max_val[year]
min_idx_TCB[py, px, index] = TCB_min_idx[year][1]
min_idx_TCG[py, px, index] = TCG_min_idx[year][1]
min_idx_TCW[py, px, index] = TCW_min_idx[year][1]
max_idx_TCB[py, px, index] = TCB_max_idx[year][1]
max_idx_TCG[py, px, index] = TCG_max_idx[year][1]
max_idx_TCW[py, px, index] = TCW_max_idx[year][1]
run_time = time.time() - start_time
print('Line {line} took {run_time}s to run'.format(line=py,
run_time=run_time))
sys.stdout.flush()
print('Statistics complete')
print('Writing results to raster...')
start_time = time.time()
# Output map for each year
in_ds = gdal.Open(example_img_fn, gdal.GA_ReadOnly)
for index, year in enumerate(years):
condition_fn = '/projectnb/landsat/users/valpasq/LCMS/dataviz/results/{WRS2}/mean/{WRS2}_ST-BGW_mean_{year}_{row1}-{row2}.tif'.format(
WRS2=WRS2, year=year, row1=row1, row2=row2)
out_driver = gdal.GetDriverByName("GTiff")
out_ds = out_driver.Create(
condition_fn,
example_img.shape[1], # x size
example_img.shape[0], # y size
3, # number of bands
gdal.GDT_Int32)
out_ds.SetProjection(in_ds.GetProjection())
out_ds.SetGeoTransform(in_ds.GetGeoTransform())
out_ds.GetRasterBand(1).WriteArray(mean_TCB[:, :, index])
out_ds.GetRasterBand(1).SetNoDataValue(0)
out_ds.GetRasterBand(1).SetDescription('Mean Annual TC Brightness')
out_ds.GetRasterBand(2).WriteArray(mean_TCG[:, :, index])
out_ds.GetRasterBand(2).SetNoDataValue(0)
out_ds.GetRasterBand(2).SetDescription('Mean Annual TC Greenness')
out_ds.GetRasterBand(3).WriteArray(mean_TCW[:, :, index])
out_ds.GetRasterBand(3).SetNoDataValue(0)
out_ds.GetRasterBand(3).SetDescription('Mean Annual TC Wetness')
out_ds = None
condition_fn = '/projectnb/landsat/users/valpasq/LCMS/dataviz/results/{WRS2}/min/{WRS2}_ST-BGW_min_val_{year}_{row1}-{row2}.tif'.format(
WRS2=WRS2, year=year, row1=row1, row2=row2)
out_driver = gdal.GetDriverByName("GTiff")
out_ds = out_driver.Create(
condition_fn,
example_img.shape[1], # x size
example_img.shape[0], # y size
3, # number of bands
gdal.GDT_Int32)
out_ds.SetProjection(in_ds.GetProjection())
out_ds.SetGeoTransform(in_ds.GetGeoTransform())
out_ds.GetRasterBand(1).WriteArray(min_val_TCB[:, :, index])
out_ds.GetRasterBand(1).SetNoDataValue(0)
out_ds.GetRasterBand(1).SetDescription('Minimum Annual TC Brightness')
out_ds.GetRasterBand(2).WriteArray(min_val_TCG[:, :, index])
out_ds.GetRasterBand(2).SetNoDataValue(0)
out_ds.GetRasterBand(2).SetDescription('Minimum Annual TC Greenness')
out_ds.GetRasterBand(3).WriteArray(min_val_TCW[:, :, index])
out_ds.GetRasterBand(3).SetNoDataValue(0)
out_ds.GetRasterBand(3).SetDescription('Minimum Annual TC Wetness')
out_ds = None
condition_fn = '/projectnb/landsat/users/valpasq/LCMS/dataviz/results/{WRS2}/max/{WRS2}_ST-BGW_max_val_{year}_{row1}-{row2}.tif'.format(
WRS2=WRS2, year=year, row1=row1, row2=row2)
out_driver = gdal.GetDriverByName("GTiff")
out_ds = out_driver.Create(
condition_fn,
example_img.shape[1], # x size
example_img.shape[0], # y size
3, # number of bands
gdal.GDT_Int32)
out_ds.SetProjection(in_ds.GetProjection())
out_ds.SetGeoTransform(in_ds.GetGeoTransform())
out_ds.GetRasterBand(1).WriteArray(max_val_TCB[:, :, index])
out_ds.GetRasterBand(1).SetNoDataValue(0)
out_ds.GetRasterBand(1).SetDescription('Maximum Annual TC Brightness')
out_ds.GetRasterBand(2).WriteArray(max_val_TCG[:, :, index])
out_ds.GetRasterBand(2).SetNoDataValue(0)
out_ds.GetRasterBand(2).SetDescription('Maximum Annual TC Greenness')
out_ds.GetRasterBand(3).WriteArray(max_val_TCW[:, :, index])
out_ds.GetRasterBand(3).SetNoDataValue(0)
out_ds.GetRasterBand(3).SetDescription('Maximum Annual TC Wetness')
out_ds = None
condition_fn = '/projectnb/landsat/users/valpasq/LCMS/dataviz/results/{WRS2}/min/{WRS2}_ST-BGW_min_mon_{year}_{row1}-{row2}.tif'.format(
WRS2=WRS2, year=year, row1=row1, row2=row2)
out_driver = gdal.GetDriverByName("GTiff")
out_ds = out_driver.Create(
condition_fn,
example_img.shape[1], # x size
example_img.shape[0], # y size
3, # number of bands
gdal.GDT_Int32)
out_ds.SetProjection(in_ds.GetProjection())
out_ds.SetGeoTransform(in_ds.GetGeoTransform())
out_ds.GetRasterBand(1).WriteArray(min_idx_TCB[:, :, index])
out_ds.GetRasterBand(1).SetNoDataValue(0)
out_ds.GetRasterBand(1).SetDescription('Minimum Annual TC Brightness')
out_ds.GetRasterBand(2).WriteArray(min_idx_TCG[:, :, index])
out_ds.GetRasterBand(2).SetNoDataValue(0)
out_ds.GetRasterBand(2).SetDescription('Minimum Annual TC Greenness')
out_ds.GetRasterBand(3).WriteArray(min_idx_TCW[:, :, index])
out_ds.GetRasterBand(3).SetNoDataValue(0)
out_ds.GetRasterBand(3).SetDescription('Minimum Annual TC Wetness')
out_ds = None
condition_fn = '/projectnb/landsat/users/valpasq/LCMS/dataviz/results/{WRS2}/max/{WRS2}_ST-BGW_max_mon_{year}_{row1}-{row2}.tif'.format(
WRS2=WRS2, year=year, row1=row1, row2=row2)
out_driver = gdal.GetDriverByName("GTiff")
out_ds = out_driver.Create(
condition_fn,
example_img.shape[1], # x size
example_img.shape[0], # y size
3, # number of bands
gdal.GDT_Int32)
out_ds.SetProjection(in_ds.GetProjection())
out_ds.SetGeoTransform(in_ds.GetGeoTransform())
out_ds.GetRasterBand(1).WriteArray(max_idx_TCB[:, :, index])
out_ds.GetRasterBand(1).SetNoDataValue(0)
out_ds.GetRasterBand(1).SetDescription('Maximum Annual TC Brightness')
out_ds.GetRasterBand(2).WriteArray(max_idx_TCG[:, :, index])
out_ds.GetRasterBand(2).SetNoDataValue(0)
out_ds.GetRasterBand(2).SetDescription('Maximum Annual TC Greenness')
out_ds.GetRasterBand(3).WriteArray(max_idx_TCW[:, :, index])
out_ds.GetRasterBand(3).SetNoDataValue(0)
out_ds.GetRasterBand(3).SetDescription('Maximum Annual TC Wetness')
out_ds = None
run_time = time.time() - start_time
print('Rasters took {run_time}s to export'.format(run_time=run_time))
sys.stdout.flush()
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()
def cache(ctx, config, job_number, total_jobs, update_pattern, interlace):
cfg = parse_config_file(config)
if not os.path.isdir(cfg['dataset']['cache_line_dir']):
os.makedirs(cfg['dataset']['cache_line_dir'])
df = csvfile_to_dataframe(cfg['dataset']['input_file'],
cfg['dataset']['date_format'])
df['image_IDs'] = get_image_IDs(df['filename'])
nrow, ncol, nband, dtype = reader.get_image_attribute(df['filename'][0])
# Determine lines to work on
job_lines = distribute_jobs(job_number, total_jobs, nrow,
interlaced=interlace)
logger.debug('Responsible for lines: {l}'.format(l=job_lines))
# Determine file reader
if cfg['dataset']['use_bip_reader']:
logger.debug('Reading in data from disk using BIP reader')
image_reader = reader.read_row_BIP
image_reader_kwargs = {'size': (ncol, nband),
'dtype': dtype}
else:
logger.debug('Reading in data from disk using GDAL')
image_reader = reader.read_row_GDAL
image_reader_kwargs = {}
# Attempt to update cache files
previous_cache = None
if update_pattern:
previous_cache = fnmatch.filter(
os.listdir(cfg['dataset']['cache_line_dir']), update_pattern)
if not previous_cache:
logger.warning('Could not find cache files to update with pattern '
'%s' % update_pattern)
else:
logger.debug('Found %s previously cached files to update' %
len(previous_cache))
for job_line in job_lines:
cache_filename = get_line_cache_name(cfg['dataset'], len(df),
job_line, nband)
logger.debug('Caching line {l} to {f}'.format(
l=job_line, f=cache_filename))
start_time = time.time()
# Find matching cache file
update = False
if previous_cache:
pattern = get_line_cache_pattern(job_line, nband, regex=False)
potential = fnmatch.filter(previous_cache, pattern)
if not potential:
logger.info('Found zero previous cache files for '
'line {l}'.format(l=job_line))
elif len(potential) > 1:
logger.info('Found more than one previous cache file for '
'line {l}. Keeping first'.format(l=job_line))
update = os.path.join(cfg['dataset']['cache_line_dir'],
potential[0])
else:
update = os.path.join(cfg['dataset']['cache_line_dir'],
potential[0])
logger.info('Updating from cache file {f}'.format(f=update))
if update:
update_cache_file(df['filename'], df['image_IDs'],
update, cache_filename,
job_line, image_reader, image_reader_kwargs)
else:
if cfg['dataset']['use_bip_reader']:
# Use BIP reader
logger.debug('Reading in data from disk using BIP reader')
Y = reader.read_row_BIP(df['filename'], job_line,
(ncol, nband), dtype)
else:
# Read in data just using GDAL
logger.debug('Reading in data from disk using GDAL')
Y = reader.read_row_GDAL(df['filename'], job_line)
write_cache_file(cache_filename, Y, df['image_IDs'])
logger.debug('Took {s}s to cache the data'.format(
s=round(time.time() - start_time, 2)))
