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_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 multi_ref_creation(corr_mat,
rtime,
freq=30,
use_break_point=False,
break_point=None):
""" Create the multi-reference traces
This function creates multi-reference traces according with the given
frequency.
In case of a break-point is passed, the intervals to consider for the
reference traces creation are symmetric respect to it.
:type corr_mat: :class:`~numpy.ndarray`
:param corr_mat: Correlation matrix with one correlation function on each
row
:type rtime: :class:`~numpy.array` of :class:`~datetime.datetime` objects
:param rtime: Time vector associated to the given correlation matrix. Its
lenght must be equal to the numer of columns of the `corr_mat`
parameter
:type freq: int
:param freq: One reference trace every `freq` days
:type use_break_point: bool
:param use_break_point: If `True` the reference traces are calculated in
different intervals of `freq` days symmetric respect to the
`break_point`
:type break_point: string
:param break_point: Brake point expressed as "YYYY-MM-DD"
"""
if use_break_point and break_point == None:
print "Error: A break point must be passed!"
return None
if use_break_point and type(break_point) != str:
print "Error: wrong break_point format!\nCheck the docs:\n\n"
print __doc__
return None
if use_break_point:
bp = from_str_to_datetime(break_point, datetimefmt=True)
f_frw = "%iD" % int(freq)
f_bck = "-%iD" % int(freq)
# Time "backward" intervals starting from the break_point and going
# back to the first day available in the data.
dr = date_range(rtime[rtime <= bp].max(),
rtime[rtime < bp].min(),
freq=f_bck)[::-1]
# Time "forward" intervals starting from the break_point and going
# ahead to the last day available in the data.
dr1 = date_range(bp, rtime.max(), freq=f_frw)[1:] # break_point must
# be removed
dr = dr.append(dr1)
else:
f = "%iD" % int(freq)
dr = date_range(np.min(rtime), np.max(rtime), freq=f)
# DataFrame creation
df = DataFrame(corr_mat, index=rtime)
# GroupBy the given intervals
dfg = df.groupby(lambda x: np.sum(np.ones(dr.shape)[dr <= x]))
# Calculate the reference traces averaging the traces on each interval
df_ref = dfg.mean()
# Take the raw output
ref_mat = df_ref.values
return ref_mat
def multi_ref_creation(corr_mat, rtime, freq=30, use_break_point=False, break_point=None):
""" Create the multi-reference traces
This function creates multi-reference traces according with the given
frequency.
In case of a break-point is passed, the intervals to consider for the
reference traces creation are symmetric respect to it.
:type corr_mat: :class:`~numpy.ndarray`
:param corr_mat: Correlation matrix with one correlation function on each
row
:type rtime: :class:`~numpy.array` of :class:`~datetime.datetime` objects
:param rtime: Time vector associated to the given correlation matrix. Its
lenght must be equal to the numer of columns of the `corr_mat`
parameter
:type freq: int
:param freq: One reference trace every `freq` days
:type use_break_point: bool
:param use_break_point: If `True` the reference traces are calculated in
different intervals of `freq` days symmetric respect to the
`break_point`
:type break_point: string
:param break_point: Brake point expressed as "YYYY-MM-DD"
"""
if use_break_point and break_point == None:
print "Error: A break point must be passed!"
return None
if use_break_point and type(break_point) != str:
print "Error: wrong break_point format!\nCheck the docs:\n\n"
print __doc__
return None
if use_break_point:
bp = from_str_to_datetime(break_point, datetimefmt=True)
f_frw = "%iD" % int(freq)
f_bck = "-%iD" % int(freq)
# Time "backward" intervals starting from the break_point and going
# back to the first day available in the data.
dr = date_range(rtime[rtime <= bp].max(), rtime[rtime < bp].min(), freq=f_bck)[::-1]
# Time "forward" intervals starting from the break_point and going
# ahead to the last day available in the data.
dr1 = date_range(bp, rtime.max(), freq=f_frw)[1:] # break_point must
# be removed
dr = dr.append(dr1)
else:
f = "%iD" % int(freq)
dr = date_range(np.min(rtime), np.max(rtime), freq=f)
# DataFrame creation
df = DataFrame(corr_mat, index=rtime)
# GroupBy the given intervals
dfg = df.groupby(lambda x: np.sum(np.ones(dr.shape)[dr <= x]))
# Calculate the reference traces averaging the traces on each interval
df_ref = dfg.mean()
# Take the raw output
ref_mat = df_ref.values
return ref_mat
