'''
:copyright:
The SeisMIC development team (makus@gfz-potsdam.de).
:license:
EUROPEAN UNION PUBLIC LICENCE v. 1.2
(https://joinup.ec.europa.eu/collection/eupl/eupl-text-eupl-12)
:author:
Peter Makus (makus@gfz-potsdam.de)
Created: Tuesday, 15th June 2021 04:12:18 pm
Last Modified: Tuesday, 5th March 2024 01:59:30 pm
'''
from datetime import datetime
from glob import glob
from typing import List, Tuple, Optional
import warnings
from zipfile import BadZipFile
import matplotlib.pyplot as plt
import numpy as np
from seismic.plot.plot_dv import plot_dv
from seismic.utils import miic_utils as mu
from seismic.correlate.stats import CorrStats
[docs]class DV(object):
"""
A simple object to save velocity changes.
"""
def __init__(
self, corr: np.ndarray, value: np.ndarray, value_type: str,
sim_mat: np.ndarray, second_axis: np.ndarray, method: str,
stats: CorrStats, stretches: np.ndarray = None,
corrs: np.ndarray = None, n_stat: np.ndarray = None,
dv_processing: dict = {}):
"""
Creates an object designed to hold and process velocity changes.
:param corr: Array with maximum Correlation coefficients.
:type corr: np.ndarray
:param value: Array with decimal velocity changes / stretches.
:type value: np.ndarray
:param value_type: Type of value (e.g., stretch)
:type value_type: str
:param sim_mat: Similarity Matrix with the dimensions time and stretch.
:type sim_mat: np.ndarray
:param second_axis: second axis (e.g., the stretch axis)
:type second_axis: np.ndarray
:param method: Stretching method that was used
:type method: str
:param stats: Stats of the correlation object that was used
:type stats: CorrStats
:param stretches: Scatter of the value (e.g., stretch).
In case this dv object holds an average
of several dvs, the user can choose to keep all dvs' stretches.
defaults to None
:type stretches: np.ndarray, optional
:param corrs: Scatter of the correlation coefficient (e.g., stretch).
In case this dv object holds an average
of several dvs, the user can choose to keep all dvs' corrs.
defaults to None
:type corrs: np.ndarray, optional
:param n_stat: Number of stations used for the stack at corr_start t.
Has the same shape as `value` and `corr`. Defaults to None
:type n_stat: np.ndarray, optional
"""
# Allocate attributes
self.value_type = value_type
self.corr = corr
self.value = value
self.sim_mat = sim_mat
self.corrs = corrs
self.stretches = stretches
self.n_stat = n_stat
self.second_axis = second_axis
self.method = method
self.stats = stats
self.dv_processing = dv_processing
# Available indices
self.avail = ~np.isnan(self.corr)
def __str__(self):
"""
Print a prettier string.
"""
code = f'{self.stats.network}.{self.stats.station}.'\
+ self.stats.channel
out = f'{self.method} {self.value_type} velocity change estimate of '\
+ f'{code}.\nstarttdate: {min(self.stats.corr_start).ctime()}\n'\
+ f'enddate: {max(self.stats.corr_end).ctime()}\n\n'\
+ f'processed with the following parameters: {self.dv_processing}'
if self.method == np.array(['time_shift']):
out = f'Time shift estimate of {code}.\nstarttdate: '\
+ f'{min(self.stats.corr_start).ctime()}\nenddate: '\
+ f'{max(self.stats.corr_end).ctime()}'
return out
[docs] def save(self, path: str):
"""
Saves the dv object to a compressed numpy binary format. The DV can
later be read using :func:`~seismic.monitor.dv.read_dv`.
:param path: output path
:type path: str
"""
kwargs = mu.save_header_to_np_array(self.stats)
if self.dv_processing is not None and len(self.dv_processing):
kwargs.update({
'freq_min': self.dv_processing['freq_min'],
'freq_max': self.dv_processing['freq_max'],
'tw_len': self.dv_processing['tw_len'],
'tw_start': self.dv_processing['tw_start'],
'sides': self.dv_processing.get('sides', 'unknown'),
'aligned': self.dv_processing.get('aligned', False)
})
kwargs.update({
'method_array': np.array([self.method]),
'vt_array': np.array([self.value_type]),
'corr': self.corr,
'value': self.value,
'sim_mat': self.sim_mat,
'second_axis': self.second_axis,
'stretches': self.stretches,
'corrs': self.corrs,
'n_stat': self.n_stat
})
# Np load will otherwise be annoying
to_save = {k: v for k, v in kwargs.items() if v is not None}
np.savez_compressed(
path, **to_save)
[docs] def plot(
self, save_dir: str = '.', figure_file_name: str = None,
mark_time: datetime = None, normalize_simmat: bool = False,
sim_mat_Clim: List[float] = [], xlim: Tuple[datetime, datetime] = None,
ylim: Tuple[int, int] = None, plot_scatter: bool = False,
figsize: Tuple[float, float] = (9, 11), dpi: int = 144,
title: str = None, return_ax=False, style: str = 'technical',
dateformat: str = '%d %b %y', ax: plt.Axes = None) -> Tuple[
plt.figure, List[plt.axis]]:
r"""
Plots the dv object into a *multi-panel-view* of `similarity matrix`
`dv-value`, and `correlation coefficient`.
:param save_dir: Directory to save the figure to, defaults to '.'
:type save_dir: str, optional
:param figure_file_name: Filename of the figure. If None, the figure
will not be save, defaults to None.
:type figure_file_name: str, optional
:param mark_time: times to put ticks to. If not set, they will be
determined automatically, defaults to None.
:type mark_time: datetime, optional
:param normalize_simmat: Normalise the similarity matrix?
Defaults to False.
:type normalize_simmat: bool, optional
:param sim_mat_Clim: Color limits for the similarity matrix
, defaults to []. Format is [low_limit, upper_limit]
:type sim_mat_Clim: List[float], optional
:param xlim: Time Limits to plot, defaults to None.
:type xlim: Tuple[datetime, datetime], optional
:param ylim: Y-limits (e.g., stretch) of the plot, defaults to None
:type ylim: Tuple[int, int], optional
:param plot_scatter: If set to True, the scatter of all used
combinations is plotted in form of a histogram.
Defaults to False.
:type plot_scatter: bool, optional
:param figsize: Size of the figure/canvas, defaults to (9, 11)
:type figsize: Tuple[float, float], optional
:param dpi: Pixels per inch, defaults to 144
:type dpi: int, optional
:param title: Define a custom title. Otherwise, an automatic title
will be created, defaults to None
:type title: str, optional
:param return_ax: Return plt.figure and list of axes.
Defaults to False.
This overwrites any choice to save the figure.
:type return_ax: bool, optional
:param style: Style of the plot. Defaults to 'technical'. The other
option would be 'publication' (looks better but is less
informative).
:type style: str, optional
:param dateformat: Format of the date on the x-axis. Defaults to
'%d %b %y'.
:type dateformat: str, optional
:returns: If `return_ax` is set to True it returns fig and axes.
"""
return plot_dv(
style, self, save_dir=save_dir,
figure_file_name=figure_file_name, mark_time=mark_time,
normalize_simmat=normalize_simmat, sim_mat_Clim=sim_mat_Clim,
figsize=figsize, dpi=dpi, xlim=xlim, ylim=ylim,
title=title, plot_scatter=plot_scatter, return_ax=return_ax,
dateformat=dateformat, ax=ax)
[docs] def smooth_sim_mat(
self, win_len: int, exclude_corr_below: Optional[float] = None):
"""
Smoothes the similarity matrix along the correlation time axis.
:param win_len: Length of the window in number of samples.
:type win_len: int
:param exclude_corr_below: Exclude data points with a correlation
coefficient below the chosen threshold. Defaults to None (i.e.,
include all)
:type exclude_corr_below: float
:note::
This action is perfomed in-place and irreversible as the original
data are not accesible any more.
"""
if exclude_corr_below is not None:
self.sim_mat[self.sim_mat < exclude_corr_below] = np.nan
self.sim_mat = mu.nan_moving_av(self.sim_mat, int(win_len/2), axis=0)
# Compute the dependencies again
self.corr = np.nanmax(self.sim_mat, axis=1)
self.value = self.second_axis[
np.argmax(np.nan_to_num(self.sim_mat), axis=1)]
return self
[docs]def read_dv(path: str) -> DV:
"""
Reads a saved velocity change object from an **.npz** file.
:param path: Path to file, wildcards allowed
:type path: str
:return: the corresponding and converted DV object
:rtype: DV, List[DV] if path contains wildcards
"""
if '*' in path or '?' in path:
dvl = []
for p in glob(path):
try:
dvl.append(read_dv(p))
except BadZipFile:
warnings.warn(f'File {p} corrupt, skipping..')
if not len(dvl):
raise FileNotFoundError(
f'No files that adhere the pattern {path} found.')
return dvl
loaded = np.load(path)
stats = CorrStats(mu.load_header_from_np_array(loaded))
# to check that compatibility works
vt = loaded['vt_array']
while not isinstance(vt, str):
vt = vt[0]
method = loaded['method_array']
while not isinstance(method, str):
method = method[0]
try:
dv_processing = dict(
freq_min=float(loaded['freq_min']),
freq_max=float(loaded['freq_max']),
tw_start=float(loaded['tw_start']),
tw_len=float(loaded['tw_len']))
dv_processing['sides'] = loaded.get('sides', 'unknown')
dv_processing['aligned'] = loaded.get('aligned', False)
except KeyError:
dv_processing = {}
return DV(
loaded['corr'], loaded['value'], vt, loaded['sim_mat'],
loaded['second_axis'], method, stats=stats,
stretches=loaded.get('stretches', None),
corrs=loaded.get('corrs', None), n_stat=loaded.get('n_stat', None),
dv_processing=dv_processing)
