def plot_spectrogram(spectrogram, filename=None, freq_range=[],
clim=[], figsize=(8, 6), dpi=72):
""" Plot a spectrogram.
A simple plot of the spectrogram matrix `corr_mat` is generated and
saved under `filename` + '_spectrogram.png'. If filename is an empty
string (default) the figure is interactivly opened.
:type spectrogram: dictionary of the type spectrogram
:param spectrogram: spectrogram to be plotted
:type filename: string
:param filename: complete path the file under which the figure is to be
saved
:type freq_range: list like
:param freq_range: limits of frequency range to plot
:type clim: list
:param clim: lower and upper limit of the colorscale for plotting. Empty
list uses default color range
"""
check = spectrogram_check(spectrogram)
if not check['valid']:
print 'spectrogram is not a valid spectrogram dictionary'
return
frequency = spectrogram['frequency'].flatten()
if freq_range:
start = np.argmax(frequency[frequency <= freq_range[0]])
stop = np.argmax(frequency[frequency <= freq_range[1]])
X = np.array(spectrogram['spec_mat'])[:,start:stop]
frequency = frequency[start: stop]
else:
X = spectrogram['spec_mat']
extent=[frequency[0], frequency[-1], X.shape[0], 0]
if 'time' in spectrogram:
time = convert_time(spectrogram['time'])
else:
time = np.linspace(0, X.shape[0], X.shape[0])
plt.figure(figsize=figsize, dpi=dpi)
plt.gcf().subplots_adjust(left=0.35)
ax = plt.imshow(np.log(X),
aspect='auto',
origin='upper',
interpolation='none',
extent=[frequency[0], frequency[-1], X.shape[0], 0])
if clim:
plt.clim(clim)
plt.colorbar(format='%1.2g')
ax.axes.xaxis.set_major_locator(ticker.MaxNLocator(nbins=7,
integer=True,
symmetric=True))
try:
row, _ = X.shape
# number of ylabel from 2 to 15
ynbins = max(2, min(15, row // 15))
except Exception:
ynbins = 1
ax.axes.yaxis.set_major_locator(ticker.MaxNLocator(nbins=ynbins,
integer=True,
prune='upper'))
ytickpos = np.array(ax.axes.yaxis.get_ticklocs()).astype('i')
_ = ax.axes.yaxis.set_ticklabels(time[ytickpos])
plt.ylabel('Date')
plt.title('Spectrogram')
plt.xlabel('Frequency [Hz]')
if filename == None:
plt.show()
else:
plt.savefig(filename + '_spectrogram.png', dpi=dpi)
plt.close()
def plot_dv(dv,
save_dir='.',
figure_file_name=None,
mark_time=None,
normalize_simmat=False,
sim_mat_Clim=[],
figsize=(9, 11), dpi=72):
""" Plot the "extended" dv dictionary
This function is thought to plot the result of the velocity change estimate
as output by :class:`~miic.core.stretch_mod.multi_ref_vchange_and_align`
and successively "extended" to contain also the timing in the form
{'time': time_vect} where `time_vect` is a :class:`~numpy.ndarray` of
:class:`~datetime.datetime` objects.
This addition can be done, for example, using the function
:class:`~miic.core.miic_utils.add_var_to_dict`.
The produced figure is saved in `save_dir` that, if necessary, it is
created.
It is also possible to pass a "special" time value `mark_time` that will be
represented in the `dv/v` and `corr` plot as a vertical line; It can be
a string (i.e. YYYY-MM-DD) or directly a :class:`~datetime.datetime`
object.
if the `dv` dictionary also contains a 'comb_mseedid' keyword, its `value`
(i.e. MUST be a string) will be reported in the title.
In case of the chosen filename exist in the `save_dir` directory, a prefix
_<n> with n:0..+Inf, is added.
The aspect of the plot may change depending on the matplotlib version. The
recommended one is matplotlib 1.1.1
:type dv: dict
:param dv: velocity change estimate dictionary as output by
:class:`~miic.core.stretch_mod.multi_ref_vchange_and_align` and
successively "extended" to contain also the timing in the form
{'time': time_vect} where `time_vect` is a :class:`~numpy.ndarray` of
:class:`~datetime.datetime` objects.
:type save_dir: string
:param save_dir: Directory where to save the produced figure. It is created
if it doesn't exist.
:type figure_file_name: string
:param figure_file_name: filename to use for saving the figure. If None
the figure is displayed in interactive mode.
:type mark_time: string or :class:`~datetime.datetime` object
:param mark_time: It is a "special" time location that will be represented
in the `dv/v` and `corr` plot as a vertical line.
:type normalize_simmat: Bool
:param normalize_simmat: if True the simmat will be normalized to a maximum
of 1. Defaults to False
:type sim_mat_Clim: 2 element array_like
:param sim_mat_Clim: if non-empty it set the color scale limits of the
similarity matrix image
"""
check_state = dv_check(dv)
# Check if the dv dictionary is "correct"
if check_state['is_incomplete']:
print "Error: Incomplete dv"
print "Possible errors:"
for key in check_state:
if key is not 'is_incomplete':
print "%s: %s" % (key, check_state[key])
raise ValueError
# For compatibility with TraitsUI
if mark_time == '':
mark_time = None
if mark_time and type(mark_time) == str:
mark_time = from_str_to_datetime(mark_time, datetimefmt=True)
elif mark_time and type(mark_time) == datetime.datetime:
pass
elif mark_time:
print "Error: wrong mark_time format!"
mark_time = None
if not os.path.isdir(save_dir):
print "Warning: `save_dir` doesn't exist. Creating ..."
os.mkdir(save_dir)
print "Directory %s created" % save_dir
# Create a unique filename if TraitsUI-default is given
if figure_file_name == 'plot_default':
fname = figure_file_name + '_change.png'
exist = os.path.isfile(os.path.join(save_dir, fname))
i = 0
while exist:
fname = "%s_%i" % (figure_file_name, i)
exist = os.path.isfile(os.path.join(save_dir,
fname + '_change.png'))
i += 1
figure_file_name = fname
# Extract the data from the dictionary
value_type = dv['value_type'][0]
method = dv['method'][0]
corr = dv['corr']
dt = dv['value']
sim_mat = dv['sim_mat']
stretch_vect = dv['second_axis']
rtime = convert_time(dv['time'])
# normalize simmat if requested
if normalize_simmat:
sim_mat = sim_mat/np.tile(np.max(sim_mat,axis=1),(sim_mat.shape[1],1)).T
# if dv_type == 'single_ref':
# dt = 1 - dt
# stretch_vect = stretch_vect - 1
n_stretching = stretch_vect.shape[0]
stretching_amount = np.max(stretch_vect)
# Adapt plot details in agreement with the type of dictionary that
# has been passed
if (value_type == 'stretch') and (method == 'single_ref'):
tit = "Single reference dv/v"
dv_tick_delta = 0.01
dv_y_label = "dv/v"
elif (value_type == 'stretch') and (method == 'multi_ref'):
tit = "Multi reference dv/v"
dv_tick_delta = 0.01
dv_y_label = "dv/v"
elif (value_type == 'shift') and (method == 'time_shift'):
tit = "Time shift"
dv_tick_delta = 5
dv_y_label = "time shift (sample)"
else:
print "Error: Wrong dv type!"
return
f = plt.figure(figsize=figsize, dpi=dpi)
if matplotlib.__version__ >= '1.1.1':
gs = gridspec.GridSpec(3, 1, height_ratios=[3, 1, 1])
else:
gs = [311, 312, 313]
ax1 = f.add_subplot(gs[0])
imh = plt.imshow(sim_mat.T, interpolation='none', aspect='auto')
if sim_mat_Clim:
assert len(sim_mat_Clim) == 2, "sim_mat_Clim must be a two element list"
imh.set_clim(sim_mat_Clim[0], sim_mat_Clim[1])
plt.gca().get_xaxis().set_visible(False)
ax1.set_yticks(np.floor(np.linspace(0, n_stretching - 1, 7)).astype('int'))
ax1.set_yticklabels(["%4.3f" % x for x in
stretch_vect[np.floor(np.linspace(0,
n_stretching - 1,
7)).astype('int')][:-1]])
if 'stats' in dv:
stats = flatten_recarray(dv['stats'])
comb_mseedid = \
stats['network'] + '.' + stats['station'] + \
'.' + stats['location'] + '.' + stats['channel']
tit = "%s estimate (%s)" % (tit, comb_mseedid)
else:
tit = "%s estimate" % tit
ax1.set_title(tit)
ax1.yaxis.set_label_position('right')
ax1.yaxis.label.set_rotation(270)
ax1.set_xticklabels([])
ax1.set_ylabel(dv_y_label)
ax2 = f.add_subplot(gs[1])
plt.plot(rtime, -dt, '.')
plt.xlim([rtime[0], rtime[-1]])
plt.ylim((-stretching_amount, stretching_amount))
if mark_time and not np.all(rtime < mark_time) \
and not np.all(rtime > mark_time):
plt.axvline(mark_time, lw=1, color='r')
ax2.yaxis.set_ticks_position('left')
ax2.yaxis.set_label_position('right')
ax2.yaxis.label.set_rotation(270)
ax2.set_ylabel(dv_y_label)
ax2.yaxis.set_major_locator(plt.MultipleLocator(dv_tick_delta))
ax2.yaxis.grid(True, 'major', linewidth=1)
ax2.xaxis.grid(True, 'major', linewidth=1)
ax2.set_xticklabels([])
ax3 = f.add_subplot(gs[2])
plt.plot(rtime, corr, '.')
plt.xlim([rtime[0], rtime[-1]])
ax3.yaxis.set_ticks_position('right')
ax3.set_ylabel("Correlation")
plt.ylim((0, 1))
if mark_time and not np.all(rtime < mark_time)\
and not np.all(rtime > mark_time):
plt.axvline(mark_time, lw=1, color='r')
plt.setp(ax3.get_xticklabels(), rotation=45, ha='right')
ax3.yaxis.set_major_locator(plt.MultipleLocator(0.2))
ax3.yaxis.grid(True, 'major', linewidth=1)
ax3.xaxis.grid(True, 'major', linewidth=1)
plt.subplots_adjust(hspace=0, wspace=0)
if figure_file_name == None:
plt.show()
else:
print 'saving to %s' % figure_file_name
f.savefig(os.path.join(save_dir, figure_file_name + '_change.png'),
dpi=dpi)
plt.close()
def plot_single_corr_matrix(corr_mat, seconds=0, filename=None,
normalize=True, normtype='absmax', norm_time_win=[None, None],
clim=[], figsize=(8, 6), dpi=72):
""" Plot a single correlation matrix.
A simple plot of the correlation matrix `corr_mat` is generated and
saved under `filename` + '_corrMatrix.png'. If filename is an empty
string (default) the figure is saved under
'./plot_default_corrMatrix.png'. An optional parameter
`center_win_len` can be passed that restricts the length of the plotted
traces to the respective number of samples in the center.
:type corr_mat: dictionary of the type correlation matrix
:param corr_mat: correlation matrix to be plotted
:type seconds: int
:param seconds: How many seconds will be taken from the central part
of the correlation function (in a symmetric way respect to zero
time lag)
:type filename: string
:param filename: complete path the file under which the figure is to be
saved
:type normalize: bool
:param normalize: If True the matix will be normalized before plotting
:type normtype: string
:param normtype: one of the following 'energy', 'max', 'absmax', 'abssum'
to decide about the way to calculate the normalization.
:type norm_time_win: list
:param norm_time_win: list contaning start- and endtime in seconds of time
window used for normalization
:type clim: list
:param clim: lower and upper limit of the colorscale for plotting. Empty
list uses default color range
"""
zerotime = datetime.datetime(1971, 1, 1)
if normalize:
corr_mat = corr_mat_normalize(corr_mat, starttime=norm_time_win[0],
endtime=norm_time_win[1], normtype=normtype)
if seconds != 0:
corr_mat = corr_mat_trim(corr_mat, -seconds, +seconds)
X = corr_mat['corr_data']
if 'time' in corr_mat:
time = convert_time(corr_mat['time'])
else:
time = np.linspace(0, X.shape[0], X.shape[0])
tlag = np.linspace((convert_time([corr_mat['stats']
['starttime']])[0] - zerotime).total_seconds(),
(convert_time([corr_mat['stats']['endtime']])[0] -
zerotime).total_seconds(),
corr_mat['stats']['npts'])
plt.figure(figsize=figsize, dpi=dpi)
plt.gcf().subplots_adjust(left=0.35)
ax = plt.imshow(X,
aspect='auto',
origin='upper',
interpolation='none',
extent=(tlag[0], tlag[-1], X.shape[0], 0))
if clim:
plt.clim(clim)
plt.colorbar(format='%1.2g')
ax.axes.xaxis.set_major_locator(ticker.MaxNLocator(nbins=7,
integer=True,
symmetric=True))
try:
row, _ = X.shape
# number of ylabel from 2 to 15
ynbins = max(2, min(15, row // 15))
except Exception:
ynbins = 1
ax.axes.yaxis.set_major_locator(ticker.MaxNLocator(nbins=ynbins,
integer=True,
prune='upper'))
ytickpos = np.array(ax.axes.yaxis.get_ticklocs()).astype('i')
_ = ax.axes.yaxis.set_ticklabels(time[ytickpos])
plt.ylabel('Days')
plt.title('Correlation Matrix')
plt.xlabel('Correlation time [s]')
if filename == None:
plt.show()
else:
plt.savefig(filename + '_corrMatrix.png', dpi=dpi)
plt.close()
def _single_corr_trace_to_obspy_trace(trace):
""" Convert a correlation trace dictionary to an obspy trace.
Convert a single correlation trace in an
:class:`~obspy.core.trace.Trace` object.
:type corr_trace: dictionary of type correlation trace
:param corr_trace: input date to be converted
:rtype: :class:`~obspy.core.trace.Trace`
:return: **tr**: the obspy object containing the data
"""
tr = Trace(data=np.squeeze(trace['corr_trace']))
stats_keys = ['network', 'station', 'location',
'channel', 'npts', 'sampling_rate']
sac_keys = ['baz', 'az', 'stla', 'stlo', 'stel',
'evla', 'evlo', 'evel', 'dist']
# copy stats
for key in stats_keys:
try:
tr.stats[key] = trace['stats'][key]
except:
print 'Error copying key: %s' % key
raise
# special keys
tr.stats['starttime'] = UTCDateTime(
convert_time([trace['stats']['starttime']])[0])
# test for presence of geo information
flag = 0
for key in sac_keys:
if not key in trace['stats']:
flag += 1
if flag == 0: # geo information present
tr.stats['sac'] = {}
for key in sac_keys:
tr.stats['sac'][key] = trace['stats'][key]
# copy stats_tr1
if 'stats_tr1' in trace:
tr.stats_tr1 = Stats()
tr.stats_tr1['starttime'] = UTCDateTime(
convert_time([trace['stats_tr1']['starttime']])[0])
for key in stats_keys:
try:
tr.stats_tr1[key] = trace['stats_tr1'][key]
except:
print 'Error copying key: %s' % key
raise
for key in sac_keys:
try:
tr.stats_tr1[key] = trace['stats_tr1'][key]
except:
pass
# copy stats_tr2
if 'stats_tr2' in trace:
tr.stats_tr2 = Stats()
tr.stats_tr2['starttime'] = UTCDateTime(
convert_time([trace['stats_tr2']['starttime']])[0])
for key in stats_keys:
try:
tr.stats_tr2[key] = trace['stats_tr2'][key]
except:
print 'Error copying key: %s' % key
raise
for key in sac_keys:
try:
tr.stats_tr2[key] = trace['stats_tr2'][key]
except:
pass
return tr
def plot_spectrogram(spectrogram,
filename=None,
freq_range=[],
clim=[],
figsize=(8, 6),
dpi=72):
""" Plot a spectrogram.
A simple plot of the spectrogram matrix `corr_mat` is generated and
saved under `filename` + '_spectrogram.png'. If filename is an empty
string (default) the figure is interactivly opened.
:type spectrogram: dictionary of the type spectrogram
:param spectrogram: spectrogram to be plotted
:type filename: string
:param filename: complete path the file under which the figure is to be
saved
:type freq_range: list like
:param freq_range: limits of frequency range to plot
:type clim: list
:param clim: lower and upper limit of the colorscale for plotting. Empty
list uses default color range
"""
check = spectrogram_check(spectrogram)
if not check['valid']:
print 'spectrogram is not a valid spectrogram dictionary'
return
frequency = spectrogram['frequency'].flatten()
if freq_range:
start = np.argmax(frequency[frequency <= freq_range[0]])
stop = np.argmax(frequency[frequency <= freq_range[1]])
X = np.array(spectrogram['spec_mat'])[:, start:stop]
frequency = frequency[start:stop]
else:
X = spectrogram['spec_mat']
extent = [frequency[0], frequency[-1], X.shape[0], 0]
if 'time' in spectrogram:
time = convert_time(spectrogram['time'])
else:
time = np.linspace(0, X.shape[0], X.shape[0])
plt.figure(figsize=figsize, dpi=dpi)
plt.gcf().subplots_adjust(left=0.35)
ax = plt.imshow(np.log(X),
aspect='auto',
origin='upper',
interpolation='none',
extent=[frequency[0], frequency[-1], X.shape[0], 0])
if clim:
plt.clim(clim)
plt.colorbar(format='%1.2g')
ax.axes.xaxis.set_major_locator(
ticker.MaxNLocator(nbins=7, integer=True, symmetric=True))
try:
row, _ = X.shape
# number of ylabel from 2 to 15
ynbins = max(2, min(15, row // 15))
except Exception:
ynbins = 1
ax.axes.yaxis.set_major_locator(
ticker.MaxNLocator(nbins=ynbins, integer=True, prune='upper'))
ytickpos = np.array(ax.axes.yaxis.get_ticklocs()).astype('i')
_ = ax.axes.yaxis.set_ticklabels(time[ytickpos])
plt.ylabel('Date')
plt.title('Spectrogram')
plt.xlabel('Frequency [Hz]')
if filename == None:
plt.show()
else:
plt.savefig(filename + '_spectrogram.png', dpi=dpi)
plt.close()
def plot_single_corr_matrix(corr_mat,
seconds=0,
filename=None,
normalize=True,
normtype='absmax',
norm_time_win=[None, None],
clim=[],
figsize=(8, 6),
dpi=72):
""" Plot a single correlation matrix.
A simple plot of the correlation matrix `corr_mat` is generated and
saved under `filename` + '_corrMatrix.png'. If filename is an empty
string (default) the figure is saved under
'./plot_default_corrMatrix.png'. An optional parameter
`center_win_len` can be passed that restricts the length of the plotted
traces to the respective number of samples in the center.
:type corr_mat: dictionary of the type correlation matrix
:param corr_mat: correlation matrix to be plotted
:type seconds: int
:param seconds: How many seconds will be taken from the central part
of the correlation function (in a symmetric way respect to zero
time lag)
:type filename: string
:param filename: complete path the file under which the figure is to be
saved
:type normalize: bool
:param normalize: If True the matix will be normalized before plotting
:type normtype: string
:param normtype: one of the following 'energy', 'max', 'absmax', 'abssum'
to decide about the way to calculate the normalization.
:type norm_time_win: list
:param norm_time_win: list contaning start- and endtime in seconds of time
window used for normalization
:type clim: list
:param clim: lower and upper limit of the colorscale for plotting. Empty
list uses default color range
"""
zerotime = datetime.datetime(1971, 1, 1)
if normalize:
corr_mat = corr_mat_normalize(corr_mat,
starttime=norm_time_win[0],
endtime=norm_time_win[1],
normtype=normtype)
if seconds != 0:
corr_mat = corr_mat_trim(corr_mat, -seconds, +seconds)
X = corr_mat['corr_data']
if 'time' in corr_mat:
time = convert_time(corr_mat['time'])
else:
time = np.linspace(0, X.shape[0], X.shape[0])
tlag = np.linspace((convert_time([corr_mat['stats']['starttime']])[0] -
zerotime).total_seconds(),
(convert_time([corr_mat['stats']['endtime']])[0] -
zerotime).total_seconds(), corr_mat['stats']['npts'])
plt.figure(figsize=figsize, dpi=dpi)
plt.gcf().subplots_adjust(left=0.35)
ax = plt.imshow(X,
aspect='auto',
origin='upper',
interpolation='none',
extent=(tlag[0], tlag[-1], X.shape[0], 0))
if clim:
plt.clim(clim)
plt.colorbar(format='%1.2g')
ax.axes.xaxis.set_major_locator(
ticker.MaxNLocator(nbins=7, integer=True, symmetric=True))
try:
row, _ = X.shape
# number of ylabel from 2 to 15
ynbins = max(2, min(15, row // 15))
except Exception:
ynbins = 1
ax.axes.yaxis.set_major_locator(
ticker.MaxNLocator(nbins=ynbins, integer=True, prune='upper'))
ytickpos = np.array(ax.axes.yaxis.get_ticklocs()).astype('i')
_ = ax.axes.yaxis.set_ticklabels(time[ytickpos])
plt.ylabel('Days')
plt.title('Correlation Matrix')
plt.xlabel('Correlation time [s]')
if filename == None:
plt.show()
else:
plt.savefig(filename + '_corrMatrix.png', dpi=dpi)
plt.close()
def plot_dv(dv,
save_dir='.',
figure_file_name=None,
mark_time=None,
normalize_simmat=False,
sim_mat_Clim=[],
figsize=(9, 11),
dpi=72):
""" Plot the "extended" dv dictionary
This function is thought to plot the result of the velocity change estimate
as output by :class:`~miic.core.stretch_mod.multi_ref_vchange_and_align`
and successively "extended" to contain also the timing in the form
{'time': time_vect} where `time_vect` is a :class:`~numpy.ndarray` of
:class:`~datetime.datetime` objects.
This addition can be done, for example, using the function
:class:`~miic.core.miic_utils.add_var_to_dict`.
The produced figure is saved in `save_dir` that, if necessary, it is
created.
It is also possible to pass a "special" time value `mark_time` that will be
represented in the `dv/v` and `corr` plot as a vertical line; It can be
a string (i.e. YYYY-MM-DD) or directly a :class:`~datetime.datetime`
object.
if the `dv` dictionary also contains a 'comb_mseedid' keyword, its `value`
(i.e. MUST be a string) will be reported in the title.
In case of the chosen filename exist in the `save_dir` directory, a prefix
_<n> with n:0..+Inf, is added.
The aspect of the plot may change depending on the matplotlib version. The
recommended one is matplotlib 1.1.1
:type dv: dict
:param dv: velocity change estimate dictionary as output by
:class:`~miic.core.stretch_mod.multi_ref_vchange_and_align` and
successively "extended" to contain also the timing in the form
{'time': time_vect} where `time_vect` is a :class:`~numpy.ndarray` of
:class:`~datetime.datetime` objects.
:type save_dir: string
:param save_dir: Directory where to save the produced figure. It is created
if it doesn't exist.
:type figure_file_name: string
:param figure_file_name: filename to use for saving the figure. If None
the figure is displayed in interactive mode.
:type mark_time: string or :class:`~datetime.datetime` object
:param mark_time: It is a "special" time location that will be represented
in the `dv/v` and `corr` plot as a vertical line.
:type normalize_simmat: Bool
:param normalize_simmat: if True the simmat will be normalized to a maximum
of 1. Defaults to False
:type sim_mat_Clim: 2 element array_like
:param sim_mat_Clim: if non-empty it set the color scale limits of the
similarity matrix image
"""
check_state = dv_check(dv)
# Check if the dv dictionary is "correct"
if check_state['is_incomplete']:
print "Error: Incomplete dv"
print "Possible errors:"
for key in check_state:
if key is not 'is_incomplete':
print "%s: %s" % (key, check_state[key])
raise ValueError
# For compatibility with TraitsUI
if mark_time == '':
mark_time = None
if mark_time and type(mark_time) == str:
mark_time = from_str_to_datetime(mark_time, datetimefmt=True)
elif mark_time and type(mark_time) == datetime.datetime:
pass
elif mark_time:
print "Error: wrong mark_time format!"
mark_time = None
if not os.path.isdir(save_dir):
print "Warning: `save_dir` doesn't exist. Creating ..."
os.mkdir(save_dir)
print "Directory %s created" % save_dir
# Create a unique filename if TraitsUI-default is given
if figure_file_name == 'plot_default':
fname = figure_file_name + '_change.png'
exist = os.path.isfile(os.path.join(save_dir, fname))
i = 0
while exist:
fname = "%s_%i" % (figure_file_name, i)
exist = os.path.isfile(
os.path.join(save_dir, fname + '_change.png'))
i += 1
figure_file_name = fname
# Extract the data from the dictionary
value_type = dv['value_type'][0]
method = dv['method'][0]
corr = dv['corr']
dt = dv['value']
sim_mat = dv['sim_mat']
stretch_vect = dv['second_axis']
rtime = convert_time(dv['time'])
# normalize simmat if requested
if normalize_simmat:
sim_mat = sim_mat / np.tile(np.max(sim_mat, axis=1),
(sim_mat.shape[1], 1)).T
# if dv_type == 'single_ref':
# dt = 1 - dt
# stretch_vect = stretch_vect - 1
n_stretching = stretch_vect.shape[0]
stretching_amount = np.max(stretch_vect)
# Adapt plot details in agreement with the type of dictionary that
# has been passed
if (value_type == 'stretch') and (method == 'single_ref'):
tit = "Single reference dv/v"
dv_tick_delta = 0.01
dv_y_label = "dv/v"
elif (value_type == 'stretch') and (method == 'multi_ref'):
tit = "Multi reference dv/v"
dv_tick_delta = 0.01
dv_y_label = "dv/v"
elif (value_type == 'shift') and (method == 'time_shift'):
tit = "Time shift"
dv_tick_delta = 5
dv_y_label = "time shift (sample)"
else:
print "Error: Wrong dv type!"
return
f = plt.figure(figsize=figsize, dpi=dpi)
if matplotlib.__version__ >= '1.1.1':
gs = gridspec.GridSpec(3, 1, height_ratios=[3, 1, 1])
else:
gs = [311, 312, 313]
ax1 = f.add_subplot(gs[0])
imh = plt.imshow(sim_mat.T, interpolation='none', aspect='auto')
if sim_mat_Clim:
assert len(
sim_mat_Clim) == 2, "sim_mat_Clim must be a two element list"
imh.set_clim(sim_mat_Clim[0], sim_mat_Clim[1])
plt.gca().get_xaxis().set_visible(False)
ax1.set_yticks(np.floor(np.linspace(0, n_stretching - 1, 7)).astype('int'))
ax1.set_yticklabels([
"%4.3f" % x
for x in stretch_vect[np.floor(np.linspace(0, n_stretching -
1, 7)).astype('int')][:-1]
])
if 'stats' in dv:
stats = flatten_recarray(dv['stats'])
comb_mseedid = \
stats['network'] + '.' + stats['station'] + \
'.' + stats['location'] + '.' + stats['channel']
tit = "%s estimate (%s)" % (tit, comb_mseedid)
else:
tit = "%s estimate" % tit
ax1.set_title(tit)
ax1.yaxis.set_label_position('right')
ax1.yaxis.label.set_rotation(270)
ax1.set_xticklabels([])
ax1.set_ylabel(dv_y_label)
ax2 = f.add_subplot(gs[1])
plt.plot(rtime, -dt, '.')
plt.xlim([rtime[0], rtime[-1]])
plt.ylim((-stretching_amount, stretching_amount))
if mark_time and not np.all(rtime < mark_time) \
and not np.all(rtime > mark_time):
plt.axvline(mark_time, lw=1, color='r')
ax2.yaxis.set_ticks_position('left')
ax2.yaxis.set_label_position('right')
ax2.yaxis.label.set_rotation(270)
ax2.set_ylabel(dv_y_label)
ax2.yaxis.set_major_locator(plt.MultipleLocator(dv_tick_delta))
ax2.yaxis.grid(True, 'major', linewidth=1)
ax2.xaxis.grid(True, 'major', linewidth=1)
ax2.set_xticklabels([])
ax3 = f.add_subplot(gs[2])
plt.plot(rtime, corr, '.')
plt.xlim([rtime[0], rtime[-1]])
ax3.yaxis.set_ticks_position('right')
ax3.set_ylabel("Correlation")
plt.ylim((0, 1))
if mark_time and not np.all(rtime < mark_time)\
and not np.all(rtime > mark_time):
plt.axvline(mark_time, lw=1, color='r')
plt.setp(ax3.get_xticklabels(), rotation=45, ha='right')
ax3.yaxis.set_major_locator(plt.MultipleLocator(0.2))
ax3.yaxis.grid(True, 'major', linewidth=1)
ax3.xaxis.grid(True, 'major', linewidth=1)
plt.subplots_adjust(hspace=0, wspace=0)
if figure_file_name == None:
plt.show()
else:
print 'saving to %s' % figure_file_name
f.savefig(os.path.join(save_dir, figure_file_name + '_change.png'),
dpi=dpi)
plt.close()
def clock_offset_inversion(par):
"""Invert pairwise time differences for individual clock errors
A number of pairwise time difference measurements (shifts of noise
correlations) are used to estimate the individual clock errors that best
explain the time differences.
:type par: dict
:param par: project parameters
"""
logging.basicConfig(filename=os.path.join(par['log_dir'],'clock_offset_\
inversion.log'), level=logging.DEBUG, format='%(asctime)s %(message)s')
logger = logging.getLogger('clock_offset_inversion')
logger.info('Clock_offset_inversion.')
create_path(par['ce']['res_dir'])
create_path(par['ce']['fig_dir'])
# lists of times windows where changes should have been analyzed according to given parameters
start_time_list = datetime_list(par['dt']['start_date'], par['dt']['end_date'],
par['dt']['date_inc'])
DIFFS = {}
# loop over station combinations
for comb in par['net']['comb']:
station1 = par['net']['stations'][comb['sta'][0]]
station2 = par['net']['stations'][comb['sta'][1]]
print station1, station2
comb_key = station1+'-'+station2
# loop over channel combinations
for cha in comb['cha']:
comp = par['net']['channels'][cha[0]]+par['net']['channels'][cha[1]]
print comp
file_pattern = '*%s%s.%s%s.*.%s.mat' % (station1.split('.')[0],station2.split('.')[0],station1.split('.')[1],station2.split('.')[1],comp)
filenames = dir_read(par['dt']['res_dir'],file_pattern)
if len(filenames) != 1:
logging.info('%d files found for correlation matrix matching %s. No processing done.' % (len(filenames),file_pattern))
continue
filename = filenames[0]
dt = mat_to_ndarray(filename)
dt_bl = cpr.dt_baseline(dt)
# correct baseline
dt['value'] -= dt_bl
# check if time period match
flag = 0
if len(dt['time']) != len(start_time_list):
flag += 1
else:
for idx,tt in enumerate(convert_time(dt['time'])):
if tt != start_time_list[idx]:
flag += 1
# only if dt measurements span the same time
if flag == 0:
if comb_key not in DIFFS.keys():
DIFFS.update({comb_key:{'diff':[],'comp':[],'corr':[]}})
DIFFS[station1+'-'+station2]['diff'].append(dt['value']/dt['stats']['sampling_rate'])
DIFFS[station1+'-'+station2]['comp'].append(comp)
DIFFS[station1+'-'+station2]['corr'].append(dt['corr'])
#claculate averages over components for same station combinations
if comb_key in DIFFS.keys():
DIFFS[station1+'-'+station2]['diff'] = np.array(DIFFS[station1+'-'+station2]['diff'])
DIFFS[station1+'-'+station2]['corr'] = np.array(DIFFS[station1+'-'+station2]['corr'])
DIFFS[station1+'-'+station2].update({'mean_diff':np.mean(DIFFS[station1+'-'+station2]['diff'],axis=0),
'std':np.std(DIFFS[station1+'-'+station2]['diff'],axis=0)})
else:
DIFFS.update({comb_key:{'diff':[],'comp':[],'corr':[],'mean_diff':[],'std':[]}})
DIFFS[comb_key]['diff'] = np.zeros(len(start_time_list))*np.nan
DIFFS[comb_key]['corr'] = np.zeros(len(start_time_list))*np.nan
DIFFS[comb_key]['mean_diff'] = np.zeros(len(start_time_list))*np.nan
DIFFS[comb_key]['std'] = np.zeros(len(start_time_list))*np.nan
# create the matrix to invert
G = np.zeros([len(par['net']['comb']),len(par['net']['stations'])])
cnt = 0
for cnt,comb in enumerate(par['net']['comb']):
idx1 = comb['sta'][0]
idx2 = comb['sta'][1]
station1 = par['net']['stations'][idx1]
station2 = par['net']['stations'][idx2]
G[cnt,idx1] = 1
G[cnt,idx2] = -1
cnt +=1
aG = G[:,:-1]
# do the inversion for every measurement
co = np.zeros((len(start_time_list),len(par['net']['stations'])))
co[:] = np.nan
coe = np.zeros((len(start_time_list),len(par['net']['stations'])))
coe[:] = np.nan
R = np.zeros((len(start_time_list),len(par['net']['comb'])))
R[:] = np.nan
d = np.zeros([len(DIFFS),1])
for nd in range(len(start_time_list)):
d = np.zeros([len(par['net']['comb']),1])
for cnt,comb in enumerate(par['net']['comb']):
idx1 = comb['sta'][0]
idx2 = comb['sta'][1]
station1 = par['net']['stations'][idx1]
station2 = par['net']['stations'][idx2]
d[cnt,0] = DIFFS[station1+'-'+station2]['mean_diff'][nd]
# delete rows in case some measurements are missing
atG = deepcopy(aG)
tG = deepcopy(G)
nanind = np.where(np.isnan(d))[0]
tG = np.delete(tG,nanind,axis=0)
atG = np.delete(atG,nanind,axis=0)
td = np.delete(d,nanind,0)
# continue if no station combination is present at this time
if len(td)<1:
logging.info('No data for %s.' % start_time_list[nd])
continue
# delete columns that only contain zeros (unconstrained stations)
idy = np.where(np.sum(np.abs(tG),axis=0)==0)[0]
tG = np.delete(tG,idy,axis=1)
atG = np.delete(atG,idy,axis=1)
#tm = np.linalg.lstsq(atG,td,rcond=1e-5)[0]
# calc inverse of G in the least squares sense
itG = np.linalg.pinv(tG)
m = np.dot(itG,td)
# calculate residuals
tR = np.squeeze(np.dot(tG,m)) - np.squeeze(td)
tR = np.dot(tG,m) - td
# take resuduals as proxy of standard deviation and estimate
# the model variance
mvar = np.dot(itG**2,tR**2)
cnt = 0
for idx in range(len(par['net']['comb'])):
if idx not in nanind:
R[nd,idx] = tR[cnt]
cnt += 1
cnt = 0
for idx in range(len(par['net']['stations'])):
if idx not in idy:
co[nd,idx] = m[cnt]
coe[nd,idx] = np.sqrt(mvar[cnt])
cnt += 1
std_err = np.nanstd(R,axis=1)
# adjust for reference stations
for nd in range(len(start_time_list)):
mr = []
for rs in par['ce']['ref_stations']:
try:
mr.append(co[nd,par['net']['stations'].index(rs)])
except:
logging.info('Reference station % missing on %s.' % (rs, start_time_list[nd]))
pass
co[nd,:] -= np.nanmean(np.array(mr))
# create a data structure
ce = {'time':convert_time_to_string(start_time_list)}
ce.update({'clock_errors':{}})
ce.update({'std_err':std_err})
for ind,sta in enumerate(par['net']['stations']):
ce['clock_errors'].update({sta:{'clock_delay':co[:,ind],
'standard_error':coe[:,ind]}})
if par['ce']['plot_clock_error']:
tt = convert_time(ce['time'])
nsta = len(ce['clock_errors'].keys())
plt.figure()
for ind,sta in enumerate(ce['clock_errors'].keys()):
plt.subplot(nsta,1,ind+1)
lower = (ce['clock_errors'][sta]['clock_delay'] -
ce['clock_errors'][sta]['standard_error']*3)
upper = (ce['clock_errors'][sta]['clock_delay'] +
ce['clock_errors'][sta]['standard_error']*3)
plt.fill_between(tt,lower,upper,facecolor=np.array([1,1,1])*0.5,edgecolor=np.array([1,1,1])*0.5)
plt.plot(tt, ce['clock_errors'][sta]['clock_delay'],'r',lw=2,label=sta)
plt.ylabel('clock delay [s]')
plt.legend(loc=0)
plt.savefig(os.path.join(par['ce']['fig_dir'],'clock_delays.eps'))
f = open(os.path.join(par['ce']['res_dir'],'clock_delays.pkl'),'wb')
pickle.dump(ce,f)
f.close()
save_dict_to_matlab_file(os.path.join(par['ce']['res_dir'],'clock_delays.mat'),ce)
# write to text file
f = open(os.path.join(par['ce']['res_dir'],'clock_delays.txt'),'wb')
f.write('relative (up to an additive constant) errors of the station clocks\n')
f.write('time\tstd_err\t')
for sta in par['net']['stations']:
f.write(sta+'\t')
f.write('\n')
for ii,t in enumerate(ce['time']):
f.write('%s\t%e\t' % (t,ce['std_err'][ii]))
for sta in par['net']['stations']:
f.write('%e\t' % ce['clock_errors'][sta]['clock_delay'][ii])
f.write('\n')
f.close()
return co