def register_converter_cb(key):
from pandas.plotting import register_matplotlib_converters
from pandas.plotting import deregister_matplotlib_converters
if cf.get_option(key):
register_matplotlib_converters()
else:
deregister_matplotlib_converters()
def register_converter_cb(key):
from pandas.plotting import register_matplotlib_converters
from pandas.plotting import deregister_matplotlib_converters
if cf.get_option(key):
register_matplotlib_converters()
else:
deregister_matplotlib_converters()
def register_converter_cb(key) -> None:
from pandas.plotting import (
deregister_matplotlib_converters,
register_matplotlib_converters,
)
if cf.get_option(key):
register_matplotlib_converters()
else:
deregister_matplotlib_converters()
def _plot_network_relative_to_ref_station(df_plot, ref, target_stns,
batch_options, filter_options,
display_options):
"""
Compute relative residuals and send to plotting function.
:param df_plot: Pandas dataframe containing only events which are common to ref station and target stations.
:type df_plot: pandas.DataFrame
:param ref_stn: Network and station codes for reference network (expected to be just one entry)
:type ref_stn: dict of corresponding network and station codes under keys 'net' and 'sta'
(expected to be just one entry)
:param target_stns: Network and station codes for target network
:type target_stns: dict of corresponding network and station codes under keys 'net' and 'sta'
:param batch_options: Runtime options.
:type batch_options: class BatchOptions
:param filter_options: Filter options.
:type filter_options: class FilterOptions
:param display_options: Display options.
:type display_options: class DisplayOptions
"""
register_matplotlib_converters()
df_plot = df_plot.assign(relTtResidual=(df_plot['ttResidual'] -
df_plot['ttResidualRef']))
# Re-order columns
df_plot = df_plot[[
'#eventID', 'originTimestamp', 'mag', 'originLon', 'originLat',
'originDepthKm', 'net', 'sta', 'cha', 'pickTimestamp', 'phase',
'stationLon', 'stationLat', 'distance', 'snr', 'ttResidual',
'ttResidualRef', 'relTtResidual', 'qualityMeasureCWT',
'qualityMeasureSlope', 'nSigma'
]]
# Sort data by event origin time
df_plot = df_plot.sort_values(['#eventID', 'originTimestamp'])
def _plot_decorator(opts):
if opts.events is not None:
_add_event_marker_lines(opts.events)
if opts.deployments is not None:
_add_temporary_deployment_intervals(opts.deployments)
_plot_target_network_rel_residuals(
df_plot,
target_stns,
ref,
batch_options,
filter_options,
annotator=lambda: _plot_decorator(display_options))
deregister_matplotlib_converters()
def test_registry_resets(self):
units = pytest.importorskip("matplotlib.units")
dates = pytest.importorskip("matplotlib.dates")
# make a copy, to reset to
original = dict(units.registry)
try:
# get to a known state
units.registry.clear()
date_converter = dates.DateConverter()
units.registry[datetime] = date_converter
units.registry[date] = date_converter
register_matplotlib_converters()
assert units.registry[date] is not date_converter
deregister_matplotlib_converters()
assert units.registry[date] is date_converter
finally:
# restore original stater
units.registry.clear()
for k, v in original.items():
units.registry[k] = v
def test_registry_resets(self):
units = pytest.importorskip("matplotlib.units")
dates = pytest.importorskip("matplotlib.dates")
# make a copy, to reset to
original = dict(units.registry)
try:
# get to a known state
units.registry.clear()
date_converter = dates.DateConverter()
units.registry[datetime] = date_converter
units.registry[date] = date_converter
register_matplotlib_converters()
assert units.registry[date] is not date_converter
deregister_matplotlib_converters()
assert units.registry[date] is date_converter
finally:
# restore original stater
units.registry.clear()
for k, v in original.items():
units.registry[k] = v
from fbprophet.diagnostics import performance_metrics
logger = logging.getLogger('fbprophet')
try:
from matplotlib import pyplot as plt
from matplotlib.dates import (
MonthLocator,
num2date,
AutoDateLocator,
AutoDateFormatter,
)
from matplotlib.ticker import FuncFormatter
from pandas.plotting import deregister_matplotlib_converters
deregister_matplotlib_converters()
except ImportError:
logger.error('Importing matplotlib failed. Plotting will not work.')
try:
import plotly.graph_objs as go
except ImportError:
logger.error('Importing plotly failed. Interactive plots will not work.')
def plot(m,
fcst,
ax=None,
uncertainty=True,
plot_cap=True,
xlabel='ds',
def manage_matplotlib_context():
"""Return a context manager for temporarily changing matplotlib unit registries and rcParams.
"""
originalRcParams = matplotlib.rcParams.copy()
## Credits for this style go to the ggplot and seaborn packages.
## We copied the style file to remove dependencies on the Seaborn package.
## Check it out, it's an awesome library for plotting
customRcParams = {
"patch.facecolor": "#348ABD", # blue
"patch.antialiased": True,
"font.size": 10.0,
"figure.edgecolor": "0.50",
# Seaborn common parameters
"figure.facecolor": "white",
"text.color": ".15",
"axes.labelcolor": ".15",
# legend.frameon: False
"legend.numpoints": 1,
"legend.scatterpoints": 1,
"xtick.direction": "out",
"ytick.direction": "out",
"xtick.color": ".15",
"ytick.color": ".15",
"axes.axisbelow": True,
"image.cmap": "Greys",
"font.family": ["sans-serif"],
"font.sans-serif": [
"Arial",
"Liberation Sans",
"Bitstream Vera Sans",
"sans-serif",
],
"grid.linestyle": "-",
"lines.solid_capstyle": "round",
# Seaborn darkgrid parameters
# .15 = dark_gray
# .8 = light_gray
"axes.grid": True,
"axes.facecolor": "#EAEAF2",
"axes.edgecolor": "white",
"axes.linewidth": 0,
"grid.color": "white",
# Seaborn notebook context
"figure.figsize": [8.0, 5.5],
"axes.labelsize": 11,
"axes.titlesize": 12,
"xtick.labelsize": 10,
"ytick.labelsize": 10,
"legend.fontsize": 10,
"grid.linewidth": 1,
"lines.linewidth": 1.75,
"patch.linewidth": 0.3,
"lines.markersize": 7,
"lines.markeredgewidth": 0,
"xtick.major.width": 1,
"ytick.major.width": 1,
"xtick.minor.width": 0.5,
"ytick.minor.width": 0.5,
"xtick.major.pad": 7,
"ytick.major.pad": 7,
}
try:
register_matplotlib_converters()
matplotlib.rcParams.update(customRcParams)
sns.set_style(style="white")
yield
finally:
deregister_matplotlib_converters() # revert to original unit registries
with warnings.catch_warnings():
warnings.filterwarnings("ignore", category=matplotlib.cbook.mplDeprecation)
matplotlib.rcParams.update(originalRcParams) # revert to original rcParams
def batch_process_xcorr(src_files,
dataset,
time_window,
snr_threshold,
pearson_cutoff_factor=0.5,
save_plots=True,
underlay_rcf_xcorr=False,
force_save=False):
"""
Process a batch of .nc files to generate standard visualization graphics. PNG files are output alongside the
source .nc file. To suppress file output, set save_plots=False.
:param src_files: List of files to process
:type src_files: Iterable of str
:param dataset: Dataset to be used to ascertain the distance between stations.
:type dataset: FederatedASDFDataset
:param time_window: Lag time window to plot (plus or minus this value in seconds)
:type time_window: float
:param snr_threshold: Minimum signal to noise ratio for samples to be included into the clock lag estimate
:type snr_threshold: float
:param save_plots: Whether to save plots to file, defaults to True
:param save_plots: bool, optional
:param underlay_rcf_xcorr: Show the individual correlation of row sample with RCF beneath the computed time lag,
defaults to False
:param underlay_rcf_xcorr: bool, optional
:return: List of files for which processing failed, and associated error.
:rtype: list(tuple(str, str))
"""
PY2 = (sys.version_info[0] == 2)
register_matplotlib_converters()
pbar = tqdm(total=len(src_files), dynamic_ncols=True)
found_preexisting = False
failed_files = []
skipped_count = 0
success_count = 0
for src_file in src_files:
_, base_file = os.path.split(src_file)
pbar.set_description(base_file)
# Sleep to ensure progress bar is refreshed
time.sleep(0.2)
if not os.path.exists(src_file):
tqdm.write("ERROR! File {} not found!".format(src_file))
failed_files.append((src_file, "File not found!"))
continue
# Extract timestamp from nc filename if available
settings, title_tag = read_correlator_config(src_file)
try:
if save_plots:
basename, _ = os.path.splitext(src_file)
png_file = basename + ".png"
# If png file already exists and has later timestamp than src_file, then skip it.
if os.path.exists(png_file):
src_file_time = os.path.getmtime(src_file)
png_file_time = os.path.getmtime(png_file)
png_file_size = os.stat(png_file).st_size
if not force_save and (png_file_time > src_file_time) and (
png_file_size > 0):
tqdm.write(
"PNG file {} is more recent than source file {}, skipping!"
.format(
os.path.split(png_file)[1],
os.path.split(src_file)[1]))
found_preexisting = True
skipped_count += 1
pbar.update()
continue
plot_xcorr_file_clock_analysis(
src_file,
dataset,
time_window,
snr_threshold,
pearson_cutoff_factor,
png_file=png_file,
show=False,
underlay_rcf_xcorr=underlay_rcf_xcorr,
title_tag=title_tag,
settings=settings)
else:
plot_xcorr_file_clock_analysis(
src_file,
dataset,
time_window,
snr_threshold,
pearson_cutoff_factor,
underlay_rcf_xcorr=underlay_rcf_xcorr,
title_tag=title_tag,
settings=settings)
success_count += 1
pbar.update()
except Exception as e:
tqdm.write("ERROR processing file {}".format(src_file))
failed_files.append((src_file, str(e)))
# Python 2 does not handle circular references, so it helps to explicitly clean up.
if PY2:
gc.collect()
pbar.close()
if found_preexisting:
print(
"Some files were skipped because pre-existing matching png files were up-to-date.\n"
"Remove png files to force regeneration.")
deregister_matplotlib_converters()
return failed_files
