def parse_spectra(self):
"""
Parses the response spectra - 5 % damping is assumed
"""
sm_record = OrderedDict([("X", {
"Scalar": {},
"Spectra": {
"Response": {}
}
}), ("Y", {
"Scalar": {},
"Spectra": {
"Response": {}
}
}), ("V", {
"Scalar": {},
"Spectra": {
"Response": {}
}
})])
target_names = list(sm_record)
for iloc, ifile in enumerate(self.input_files):
if not os.path.exists(ifile):
continue
metadata = _get_metadata_from_file(ifile)
data = np.genfromtxt(ifile, skip_header=64)
units = metadata["UNITS"]
if "s^2" in units:
units = units.replace("s^2", "s/s")
pga = convert_accel_units(
_to_float(metadata["PGA_" + metadata["UNITS"].upper()]), units)
periods = data[:, 0]
s_a = convert_accel_units(data[:, 1], units)
sm_record[target_names[iloc]]["Spectra"]["Response"] = {
"Periods": periods,
"Number Periods": len(periods),
"Acceleration": {
"Units": "cm/s/s"
},
"Velocity": None,
"Displacement": None,
"PSA": None,
"PSV": None
}
sm_record[target_names[iloc]]["Spectra"]["Response"]\
["Acceleration"]["damping_05"] = s_a
# If the displacement file exists - get the data from that directly
sd_file = ifile.replace("SA.ASC", "SD.ASC")
if os.path.exists(sd_file):
# SD data
sd_data = np.genfromtxt(sd_file, skip_header=64)
# Units should be cm
sm_record[target_names[iloc]]["Spectra"]["Response"]\
["Displacement"] = {"damping_05": sd_data[:, 1],
"Units": "cm"}
return sm_record
def build_time_series_hdf5(self, record, sm_data, record_dir):
"""
Constructs the hdf5 file for storing the strong motion record
:param record:
Strong motion record as instance of :class: GroundMotionRecord
:param dict sm_data:
Data dictionary for the strong motion record
:param str record_dir:
Directory in which to save the record
"""
output_file = os.path.join(record_dir, record.id + ".hdf5")
fle = h5py.File(output_file, "w-")
grp = fle.create_group("Time Series")
for key in sm_data.keys():
if not sm_data[key]["Original"]:
continue
grp_comp = grp.create_group(key)
grp_orig = grp_comp.create_group("Original Record")
for attribute in self.TS_ATTRIBUTE_LIST:
if attribute in sm_data[key]["Original"].keys():
grp_orig.attrs[attribute] =\
sm_data[key]["Original"][attribute]
ts_dset = grp_orig.create_dataset(
"Acceleration", (sm_data[key]["Original"]["Number Steps"], ),
dtype="f")
ts_dset.attrs["Units"] = "cm/s/s"
time_step = sm_data[key]["Original"]["Time-step"]
ts_dset.attrs["Time-step"] = time_step
number_steps = sm_data[key]["Original"]["Number Steps"]
ts_dset.attrs["Number Steps"] = number_steps
ts_dset.attrs["PGA"] = utils.convert_accel_units(
sm_data[key]["Original"]["PGA"],
sm_data[key]["Original"]["Units"])
# Store acceleration as cm/s/s
ts_dset[:] = utils.convert_accel_units(
sm_data[key]["Original"]["Acceleration"],
sm_data[key]["Original"]["Units"])
# Get velocity and displacement
vel, dis = utils.get_velocity_displacement(time_step, ts_dset[:],
"cm/s/s")
# Build velocity data set
v_dset = grp_orig.create_dataset("Velocity", (number_steps, ),
dtype="f")
v_dset.attrs["Units"] = "cm/s"
v_dset.attrs["Time-step"] = time_step
v_dset.attrs["Number Steps"] = number_steps
v_dset[:] = vel
# Build displacement data set
d_dset = grp_orig.create_dataset("Displacement", (number_steps, ),
dtype="f")
d_dset.attrs["Units"] = "cm"
d_dset.attrs["Time-step"] = time_step
d_dset.attrs["Number Steps"] = number_steps
d_dset[:] = dis
# Get the velocity and displacement time series and build scalar IMS
return fle, output_file
def parse_row(cls, rowdict, sa_colnames):
'''This method is intended to be overridden by subclasses (by default
is no-op) to perform any further operation on the given csv row
`rowdict` before writing it to the GM databse file. Please note that:
1. This method should process `rowdict` in place, the returned value is
ignored. Any exception raised here is hanlded in the caller method.
2. `rowdict` keys might not be the same as the csv
field names (first csv row). See `mappings` class attribute
3. The values of `rowdict` are all strings and they will be casted
later according to the column type. However, if a cast is needed
here for some custom operation, in order to convert strings to
floats or timestamps (floats denoting date-times) you can use the
static methods `timestamp` and `float`. Both methods accept also
lists or tuples to convert arrays and silenttly coerce unparsable
values to nan (Note that nan represents a missing value for any
numeric or timestamp column).
4. the `rowdict` keys 'event_id', 'station_id' and 'record_id' are
reserved and their values will be overridden anyway
:param rowdict: a row of the csv flatfile, as Python dict
:param sa_colnames: a list of strings of the column
names denoting the SA values. The list is sorted ascending
according to the relative numeric period defined in
:method:`get_sa_periods`
'''
# replace non lower case keys with their lower case counterpart:
for key in cls._non_lcase_fieldnames:
rowdict[key.lower()] = rowdict.pop(key)
# assign values (sa, event time, pga):
tofloat = cls.float
sa_ = tofloat([rowdict[p] for p in sa_colnames])
sa_unit = rowdict[cls._acc_unit_col] if cls._acc_unit_col else 'g'
sa_ = convert_accel_units(sa_, sa_unit)
rowdict['sa'] = sa_
# assign event time:
evtime_fieldnames = cls._evtime_fieldnames
dtime = ""
if len(evtime_fieldnames) == 6:
# use zfill to account for '934' formatted as '0934' for years,
# and '5' formatted as '05' for all other fields:
dtime = "{}-{}-{}T{}:{}:{}".\
format(*(rowdict[c].zfill(4 if i == 0 else 2)
for i, c in enumerate(evtime_fieldnames)))
else:
dtime = rowdict[evtime_fieldnames[0]]
rowdict['event_time'] = cls.timestamp(dtime)
# assign pga:
pga_col, pga_unit = cls._pga_col, cls._pga_unit
if not pga_unit:
pga_unit = cls._acc_unit_col if cls._acc_unit_col else 'g'
rowdict['pga'] = \
convert_accel_units(tofloat(rowdict[pga_col]), pga_unit)
def parse_row(cls, rowdict, sa_colnames):
'''This method is intended to be overridden by subclasses (by default
is no-op) to perform any further operation on the given csv row
`rowdict` before writing it to the GM databse file. Please note that:
1. This method should process `rowdict` in place, the returned value is
ignored. Any exception raised here is hanlded in the caller method.
2. `rowdict` keys might not be the same as the csv
field names (first csv row). See `mappings` class attribute
3. The values of `rowdict` are all strings and they will be casted
later according to the column type. However, if a cast is needed
here for some custom operation, in order to convert strings to
floats or timestamps (floats denoting date-times) you can use the
static methods `timestamp` and `float`. Both methods accept also
lists or tuples to convert arrays and silenttly coerce unparsable
values to nan (Note that nan represents a missing value for any
numeric or timestamp column).
4. the `rowdict` keys 'event_id', 'station_id' and 'record_id' are
reserved and their values will be overridden anyway
:param rowdict: a row of the csv flatfile, as Python dict
:param sa_colnames: a list of strings of the column
names denoting the SA values. The list is sorted ascending
according to the relative numeric period defined in
:method:`get_sa_periods`
'''
# replace non lower case keys with their lower case counterpart:
for key in cls._non_lcase_fieldnames:
rowdict[key.lower()] = rowdict.pop(key)
# assign values (sa, event time, pga):
tofloat = cls.float
sa_ = tofloat([rowdict[p] for p in sa_colnames])
sa_unit = rowdict[cls._acc_unit_col] if cls._acc_unit_col else 'g'
sa_ = convert_accel_units(sa_, sa_unit)
rowdict['sa'] = sa_
# assign event time:
evtime_fieldnames = cls._evtime_fieldnames
dtime = ""
if len(evtime_fieldnames) == 6:
# use zfill to account for '934' formatted as '0934' for years,
# and '5' formatted as '05' for all other fields:
dtime = "{}-{}-{}T{}:{}:{}".\
format(*(rowdict[c].zfill(4 if i == 0 else 2)
for i, c in enumerate(evtime_fieldnames)))
else:
dtime = rowdict[evtime_fieldnames[0]]
rowdict['event_time'] = cls.timestamp(dtime)
# assign pga:
pga_col, pga_unit = cls._pga_col, cls._pga_unit
if not pga_unit:
pga_unit = cls._acc_unit_col if cls._acc_unit_col else 'g'
rowdict['pga'] = \
convert_accel_units(tofloat(rowdict[pga_col]), pga_unit)
def build_time_series_hdf5(self, record, sm_data, record_dir):
"""
Constructs the hdf5 file for storing the strong motion record
:param record:
Strong motion record as instance of :class: GroundMotionRecord
:param dict sm_data:
Data dictionary for the strong motion record
:param str record_dir:
Directory in which to save the record
"""
output_file = os.path.join(record_dir, record.id + ".hdf5")
fle = h5py.File(output_file, "w-")
grp = fle.create_group("Time Series")
for key in sm_data.keys():
if not sm_data[key]["Original"]:
continue
grp_comp = grp.create_group(key)
grp_orig = grp_comp.create_group("Original Record")
for attribute in self.TS_ATTRIBUTE_LIST:
if attribute in sm_data[key]["Original"].keys():
grp_orig.attrs[attribute] = sm_data[key]["Original"][attribute]
ts_dset = grp_orig.create_dataset("Acceleration", (sm_data[key]["Original"]["Number Steps"],), dtype="f")
ts_dset.attrs["Units"] = "cm/s/s"
time_step = sm_data[key]["Original"]["Time-step"]
ts_dset.attrs["Time-step"] = time_step
number_steps = sm_data[key]["Original"]["Number Steps"]
ts_dset.attrs["Number Steps"] = number_steps
ts_dset.attrs["PGA"] = utils.convert_accel_units(
sm_data[key]["Original"]["PGA"], sm_data[key]["Original"]["Units"]
)
# Store acceleration as cm/s/s
ts_dset[:] = utils.convert_accel_units(
sm_data[key]["Original"]["Acceleration"], sm_data[key]["Original"]["Units"]
)
# Get velocity and displacement
vel, dis = utils.get_velocity_displacement(time_step, ts_dset[:], "cm/s/s")
# Build velocity data set
v_dset = grp_orig.create_dataset("Velocity", (number_steps,), dtype="f")
v_dset.attrs["Units"] = "cm/s"
v_dset.attrs["Time-step"] = time_step
v_dset.attrs["Number Steps"] = number_steps
v_dset[:] = vel
# Build displacement data set
d_dset = grp_orig.create_dataset("Displacement", (number_steps,), dtype="f")
d_dset.attrs["Units"] = "cm"
d_dset.attrs["Time-step"] = time_step
d_dset.attrs["Number Steps"] = number_steps
d_dset[:] = dis
# Get the velocity and displacement time series and build scalar IMS
return fle, output_file
def _build_spectra_hdf5_from_row(self, output_file, row, periods,
scalar_fields, spectra_fields, component,
damping, units):
"""
"""
fle = h5py.File(output_file, "w-")
ts_grp = fle.create_group("Time Series")
ims_grp = fle.create_group("IMS")
h_grp = ims_grp.create_group("H")
scalar_grp = h_grp.create_group("Scalar")
# Create Scalar values
for f_attr, imt in scalar_fields:
dset = scalar_grp.create_dataset(imt, (1,), dtype="f")
dset.attrs["Component"] = component
input_units = re.search('\((.*?)\)', f_attr).group(1)
if imt == "PGA":
# Convert acceleration from reported units to cm/s/s
dset.attrs["Units"] = "cm/s/s"
dset[:] = utils.convert_accel_units(get_float(row[f_attr]),
input_units)
else:
# For other values take direct from spreadsheet
# Units should be given in parenthesis from fieldname
dset.attrs["Units"] = input_units
dset[:] = get_float(row[f_attr])
spectra_grp = h_grp.create_group("Spectra")
rsp_grp = spectra_grp.create_group("Response")
# Setup periods dataset
per_dset = rsp_grp.create_dataset("Periods",
(len(periods),),
dtype="f")
per_dset.attrs["High Period"] = np.max(periods)
per_dset.attrs["Low Period"] = np.min(periods)
per_dset.attrs["Number Periods"] = len(periods)
per_dset[:] = periods
# Get response spectra
spectra = np.array([get_float(row[f_attr])
for f_attr in spectra_fields])
acc_grp = rsp_grp.create_group("Acceleration")
comp_grp = acc_grp.create_group(component)
spectra_dset = comp_grp.create_dataset("damping_{:s}".format(damping),
(len(spectra),),
dtype="f")
spectra_dset.attrs["Units"] = "cm/s/s"
spectra_dset[:] = utils.convert_accel_units(spectra, units)
fle.close()
def __init__(self,
acceleration,
time_step,
periods,
damping=0.05,
units="cm/s/s"):
'''
Setup the response spectrum calculator
:param numpy.ndarray time_hist:
Acceleration time history [Time, Acceleration]
:param numpy.ndarray periods:
Spectral periods (s) for calculation
:param float damping:
Fractional coefficient of damping
:param str units:
Units of the acceleration time history {"g", "m/s", "cm/s/s"}
'''
self.periods = periods
self.num_per = len(periods)
self.acceleration = convert_accel_units(acceleration, units)
self.damping = damping
self.d_t = time_step
self.velocity, self.displacement = get_velocity_displacement(
self.d_t, self.acceleration)
self.num_steps = len(self.acceleration)
self.omega = (2. * np.pi) / self.periods
self.response_spectrum = None
def _parse_time_history(self, ifile):
"""
Parses the time history
"""
# Build the metadata dictionary again
metadata = _get_metadata_from_file(ifile)
self.number_steps = _to_int(metadata["NDATA"])
self.time_step = _to_float(metadata["SAMPLING_INTERVAL_S"])
self.units = metadata["UNITS"]
# Get acceleration data
accel = np.genfromtxt(ifile, skip_header=64)
if "DIS" in ifile:
pga = None
pgd = np.fabs(_to_float(metadata["PGD_" +
metadata["UNITS"].upper()]))
else:
pga = np.fabs(_to_float(
metadata["PGA_" + metadata["UNITS"].upper()]))
pgd = None
if "s^2" in self.units:
self.units = self.units.replace("s^2", "s/s")
output = {
# Although the data will be converted to cm/s/s internally we can
# do it here too
"Acceleration": convert_accel_units(accel, self.units),
"Time": get_time_vector(self.time_step, self.number_steps),
"Time-step": self.time_step,
"Number Steps": self.number_steps,
"Units": self.units,
"PGA": pga,
"PGD": pgd
}
return output
def _parse_time_history(self, ifile):
"""
Parses the time history
"""
# Build the metadata dictionary again
metadata = _get_metadata_from_file(ifile)
self.number_steps = _to_int(metadata["NDATA"])
self.time_step = _to_float(metadata["SAMPLING_INTERVAL_S"])
self.units = metadata["UNITS"]
# Get acceleration data
accel = np.genfromtxt(ifile, skip_header=64)
if "DIS" in ifile:
pga = None
pgd = np.fabs(
_to_float(metadata["PGD_" + metadata["UNITS"].upper()]))
else:
pga = np.fabs(
_to_float(metadata["PGA_" + metadata["UNITS"].upper()]))
pgd = None
if "s^2" in self.units:
self.units = self.units.replace("s^2", "s/s")
output = {
# Although the data will be converted to cm/s/s internally we can
# do it here too
"Acceleration": convert_accel_units(accel, self.units),
"Time": get_time_vector(self.time_step, self.number_steps),
"Time-step": self.time_step,
"Number Steps": self.number_steps,
"Units": self.units,
"PGA": pga,
"PGD": pgd
}
return output
def _build_spectra_hdf5_from_row(self, output_file, row, periods,
scalar_fields, spectra_fields, component,
damping, units):
"""
"""
fle = h5py.File(output_file, "w-")
ts_grp = fle.create_group("Time Series")
ims_grp = fle.create_group("IMS")
h_grp = ims_grp.create_group("H")
scalar_grp = h_grp.create_group("Scalar")
# Create Scalar values
for f_attr, imt in scalar_fields:
dset = scalar_grp.create_dataset(imt, (1, ), dtype="f")
dset.attrs["Component"] = component
input_units = re.search('\((.*?)\)', f_attr).group(1)
if imt == "PGA":
# Convert acceleration from reported units to cm/s/s
dset.attrs["Units"] = "cm/s/s"
dset[:] = utils.convert_accel_units(get_float(row[f_attr]),
input_units)
else:
# For other values take direct from spreadsheet
# Units should be given in parenthesis from fieldname
dset.attrs["Units"] = input_units
dset[:] = get_float(row[f_attr])
spectra_grp = h_grp.create_group("Spectra")
rsp_grp = spectra_grp.create_group("Response")
# Setup periods dataset
per_dset = rsp_grp.create_dataset("Periods", (len(periods), ),
dtype="f")
per_dset.attrs["High Period"] = np.max(periods)
per_dset.attrs["Low Period"] = np.min(periods)
per_dset.attrs["Number Periods"] = len(periods)
per_dset[:] = periods
# Get response spectra
spectra = np.array(
[get_float(row[f_attr]) for f_attr in spectra_fields])
acc_grp = rsp_grp.create_group("Acceleration")
comp_grp = acc_grp.create_group(component)
spectra_dset = comp_grp.create_dataset("damping_{:s}".format(damping),
(len(spectra), ),
dtype="f")
spectra_dset.attrs["Units"] = "cm/s/s"
spectra_dset[:] = utils.convert_accel_units(spectra, units)
fle.close()
def parse_spectra(self):
"""
Parses the response spectra - 5 % damping is assumed
"""
sm_record = OrderedDict([
("X", {"Scalar": {}, "Spectra": {"Response": {}}}),
("Y", {"Scalar": {}, "Spectra": {"Response": {}}}),
("V", {"Scalar": {}, "Spectra": {"Response": {}}})])
target_names = list(sm_record)
for iloc, ifile in enumerate(self.input_files):
if not os.path.exists(ifile):
continue
metadata = _get_metadata_from_file(ifile)
data = np.genfromtxt(ifile, skip_header=64)
units = metadata["UNITS"]
if "s^2" in units:
units = units.replace("s^2", "s/s")
pga = convert_accel_units(
_to_float(metadata["PGA_" + metadata["UNITS"].upper()]),
units)
periods = data[:, 0]
s_a = convert_accel_units(data[:, 1], units)
sm_record[target_names[iloc]]["Spectra"]["Response"] = {
"Periods": periods,
"Number Periods" : len(periods),
"Acceleration" : {"Units": "cm/s/s"},
"Velocity" : None,
"Displacement" : None,
"PSA" : None,
"PSV" : None}
sm_record[target_names[iloc]]["Spectra"]["Response"]\
["Acceleration"]["damping_05"] = s_a
# If the displacement file exists - get the data from that directly
sd_file = ifile.replace("SA.ASC", "SD.ASC")
if os.path.exists(sd_file):
# SD data
sd_data = np.genfromtxt(sd_file, skip_header=64)
# Units should be cm
sm_record[target_names[iloc]]["Spectra"]["Response"]\
["Displacement"] = {"damping_05": sd_data[:, 1],
"Units": "cm"}
return sm_record
def plot_time_series(acceleration,
time_step,
velocity=[],
displacement=[],
units="cm/s/s",
figure_size=(8, 6),
filename=None,
filetype="png",
dpi=300,
linewidth=1.5):
"""
Creates a plot of acceleration, velocity and displacement for a specific
ground motion record
"""
acceleration = convert_accel_units(acceleration, units)
accel_time = get_time_vector(time_step, len(acceleration))
if not len(velocity):
velocity, dspl = get_velocity_displacement(time_step, acceleration)
vel_time = get_time_vector(time_step, len(velocity))
if not len(displacement):
displacement = dspl
disp_time = get_time_vector(time_step, len(displacement))
fig = plt.figure(figsize=figure_size)
fig.set_tight_layout(True)
ax = plt.subplot(3, 1, 1)
# Accleration
ax.plot(accel_time, acceleration, 'k-', linewidth=linewidth)
ax.set_xlabel("Time (s)", fontsize=12)
ax.set_ylabel("Acceleration (cm/s/s)", fontsize=12)
end_time = np.max(np.array([accel_time[-1], vel_time[-1], disp_time[-1]]))
pga = np.max(np.fabs(acceleration))
ax.set_xlim(0, end_time)
ax.set_ylim(-1.1 * pga, 1.1 * pga)
ax.grid()
# Velocity
ax = plt.subplot(3, 1, 2)
ax.plot(vel_time, velocity, 'b-', linewidth=linewidth)
ax.set_xlabel("Time (s)", fontsize=12)
ax.set_ylabel("Velocity (cm/s)", fontsize=12)
pgv = np.max(np.fabs(velocity))
ax.set_xlim(0, end_time)
ax.set_ylim(-1.1 * pgv, 1.1 * pgv)
ax.grid()
# Displacement
ax = plt.subplot(3, 1, 3)
ax.plot(disp_time, displacement, 'r-', linewidth=linewidth)
ax.set_xlabel("Time (s)", fontsize=12)
ax.set_ylabel("Displacement (cm)", fontsize=12)
pgd = np.max(np.fabs(displacement))
ax.set_xlim(0, end_time)
ax.set_ylim(-1.1 * pgd, 1.1 * pgd)
ax.grid()
_save_image(filename, filetype, dpi)
plt.show()
def _parse_time_history(self, ifile):
"""
Parses the time history from the file and returns a dictionary of
time-series properties
"""
output = {}
accel = np.genfromtxt(ifile, skip_header=1)
output["Acceleration"] = convert_accel_units(accel, self.units)
nvals, time_step = (getline(ifile, 1).rstrip("\n")).split()
output["Time-step"] = float(time_step)
output["Number Steps"] = int(nvals)
output["Units"] = "cm/s/s"
output["PGA"] = np.max(np.fabs(output["Acceleration"]))
return output
def _parse_time_history(self, ifile, units="cm/s/s"):
"""
Parses the time history from the file and returns a dictionary of
time-series properties
"""
output = {}
accel = np.genfromtxt(ifile, skip_header=1)
output["Acceleration"] = convert_accel_units(accel, self.units)
nvals, time_step = (getline(ifile, 1).rstrip("\n")).split()
output["Time-step"] = float(time_step)
output["Number Steps"] = int(nvals)
output["Units"] = units
output["PGA"] = np.max(np.fabs(output["Acceleration"]))
return output
def plot_time_series(acceleration, time_step, velocity=[], displacement=[],
units="cm/s/s", figure_size=(8, 6), filename=None, filetype="png",
dpi=300, linewidth=1.5):
"""
Creates a plot of acceleration, velocity and displacement for a specific
ground motion record
"""
acceleration = convert_accel_units(acceleration, units)
accel_time = get_time_vector(time_step, len(acceleration))
if not len(velocity):
velocity, dspl = get_velocity_displacement(time_step, acceleration)
vel_time = get_time_vector(time_step, len(velocity))
if not len(displacement):
displacement = dspl
disp_time = get_time_vector(time_step, len(displacement))
fig = plt.figure(figsize=figure_size)
fig.set_tight_layout(True)
ax = plt.subplot(3, 1, 1)
# Accleration
ax.plot(accel_time, acceleration, 'k-', linewidth=linewidth)
ax.set_xlabel("Time (s)", fontsize=12)
ax.set_ylabel("Acceleration (cm/s/s)", fontsize=12)
end_time = np.max(np.array([accel_time[-1], vel_time[-1], disp_time[-1]]))
pga = np.max(np.fabs(acceleration))
ax.set_xlim(0, end_time)
ax.set_ylim(-1.1 * pga, 1.1 * pga)
ax.grid()
# Velocity
ax = plt.subplot(3, 1, 2)
ax.plot(vel_time, velocity, 'b-', linewidth=linewidth)
ax.set_xlabel("Time (s)", fontsize=12)
ax.set_ylabel("Velocity (cm/s)", fontsize=12)
pgv = np.max(np.fabs(velocity))
ax.set_xlim(0, end_time)
ax.set_ylim(-1.1 * pgv, 1.1 * pgv)
ax.grid()
# Displacement
ax = plt.subplot(3, 1, 3)
ax.plot(disp_time, displacement, 'r-', linewidth=linewidth)
ax.set_xlabel("Time (s)", fontsize=12)
ax.set_ylabel("Displacement (cm)", fontsize=12)
pgd = np.max(np.fabs(displacement))
ax.set_xlim(0, end_time)
ax.set_ylim(-1.1 * pgd, 1.1 * pgd)
ax.grid()
_save_image(filename, filetype, dpi)
plt.show()
def __init__(self, acceleration, time_step, periods, damping=0.05,
units="cm/s/s"):
'''
Setup the response spectrum calculator
:param numpy.ndarray time_hist:
Acceleration time history [Time, Acceleration]
:param numpy.ndarray periods:
Spectral periods (s) for calculation
:param float damping:
Fractional coefficient of damping
:param str units:
Units of the acceleration time history {"g", "m/s", "cm/s/s"}
'''
self.periods = periods
self.num_per = len(periods)
self.acceleration = convert_accel_units(acceleration, units)
self.damping = damping
self.d_t = time_step
self.velocity, self.displacement = get_velocity_displacement(
self.d_t, self.acceleration)
self.num_steps = len(self.acceleration)
self.omega = (2. * np.pi) / self.periods
self.response_spectrum = None
def get_residuals(self,
ctx_database,
nodal_plane_index=1,
component="Geometric",
normalise=True):
"""
Calculate the residuals for a set of ground motion records
:param ctx_database: a :class:`context_db.ContextDB`, i.e. a database of
records capable of returning dicts of earthquake-based Contexts and
observed IMTs.
See e.g., :class:`smtk.sm_database.GroundMotionDatabase` for an
example
"""
contexts = ctx_database.get_contexts(nodal_plane_index, self.imts,
component)
# Fetch now outside the loop for efficiency the IMTs which need
# acceleration units conversion from cm/s/s to g. Conversion will be
# done inside the loop:
accel_imts = tuple(
[imtx for imtx in self.imts if (imtx == "PGA" or "SA(" in imtx)])
# Contexts is in either case a list of dictionaries
self.contexts = []
for context in contexts:
# convert all IMTS with acceleration units, which are supposed to
# be in cm/s/s, to g:
for a_imt in accel_imts:
context['Observations'][a_imt] = \
convert_accel_units(context['Observations'][a_imt],
'cm/s/s', 'g')
# Get the expected ground motions
context = self.get_expected_motions(context)
context = self.calculate_residuals(context, normalise)
for gmpe in self.residuals.keys():
for imtx in self.residuals[gmpe].keys():
if not context["Residual"][gmpe][imtx]:
continue
for res_type in self.residuals[gmpe][imtx].keys():
if res_type == "Inter event":
inter_ev = \
context["Residual"][gmpe][imtx][res_type]
if np.all(
np.fabs(inter_ev - inter_ev[0]) < 1.0E-12):
# Single inter-event residual
self.residuals[gmpe][imtx][res_type].append(
inter_ev[0])
# Append indices
self.unique_indices[gmpe][imtx].append(
np.array([0]))
else:
# Inter event residuals per-site e.g. Chiou
# & Youngs (2008; 2014) case
self.residuals[gmpe][imtx][res_type].extend(
inter_ev)
self.unique_indices[gmpe][imtx].append(
np.arange(len(inter_ev)))
else:
self.residuals[gmpe][imtx][res_type].extend(
context["Residual"][gmpe][imtx][res_type])
self.modelled[gmpe][imtx][res_type].extend(
context["Expected"][gmpe][imtx][res_type])
self.modelled[gmpe][imtx]["Mean"].extend(
context["Expected"][gmpe][imtx]["Mean"])
self.contexts.append(context)
for gmpe in self.residuals.keys():
for imtx in self.residuals[gmpe].keys():
if not self.residuals[gmpe][imtx]:
continue
for res_type in self.residuals[gmpe][imtx].keys():
self.residuals[gmpe][imtx][res_type] = np.array(
self.residuals[gmpe][imtx][res_type])
self.modelled[gmpe][imtx][res_type] = np.array(
self.modelled[gmpe][imtx][res_type])
self.modelled[gmpe][imtx]["Mean"] = np.array(
self.modelled[gmpe][imtx]["Mean"])
def _parse_time_history(self, ifile, component2parse):
"""
Parses the time history and returns the time history of the specified
component. All 3 components are provided in every ASA file. Note that
components are defined with various names, and are not always
given in the same order
"""
# The components are definied using the following names
comp_names = {'X': ['ENE', 'N90E', 'N90E;', 'N90W', 'N90W;',
'S90E', 'S90W', 'E--W', 'S9OE'],
'Y': ['ENN', 'N00E', 'N00E;', 'NOOE;', 'N00W',
'NOOW;', 'S00E', 'S00W', 'N--S', 'NOOE'],
'V': ['ENZ', 'V', 'V;+', '+V', 'Z', 'VERT']}
# Read component names, which are given on line 107
o = open(ifile, "r", encoding='iso-8859-1')
r = o.readlines()
components = list(r[107].split())
# Check if any component names are repeated
if any(components.count(x) > 1 for x in components):
raise ValueError(
"Some components %s in record %s have the same name"
% (components, ifile))
# Check if more than 3 components are given
if len(components) > 3:
raise ValueError(
"More than 3 components %s in record %s"
% (components, ifile))
# Get acceleration data from correct column
column = None
for i in comp_names[component2parse]:
if i == components[0]:
column = 0
try:
accel = np.genfromtxt(ifile, skip_header=109,
usecols=column, delimiter='', encoding='iso-8859-1')
except:
raise ValueError(
"Check %s has 3 equal length time-series columns"
% ifile)
break
elif i == components[1]:
column = 1
try:
accel = np.genfromtxt(ifile, skip_header=109,
usecols=column, delimiter='', encoding='iso-8859-1')
except:
raise ValueError(
"Check %s has 3 equal length time-series columns"
% ifile)
break
elif i == components[2]:
column = 2
try:
accel = np.genfromtxt(ifile, skip_header=109,
usecols=column, delimiter='', encoding='iso-8859-1')
except:
raise ValueError(
"Check %s has 3 equal length time-series columns"
% ifile)
break
if column is None:
raise ValueError(
"None of the components %s were found to be \n\
the %s component of file %s" %
(components, component2parse, ifile))
# Build the metadata dictionary again
metadata = _get_metadata_from_file(ifile)
# Get units
units_provided = metadata["UNIDADES DE LOS DATOS"]
units = units_provided[units_provided.find("(") + 1:
units_provided.find(")")]
# Get time step, naming is not consistent so allow for variation
for i in metadata:
if 'INTERVALO DE MUESTREO, C1' in i:
self.time_step = get_float(metadata[i].split("/")[1])
# Get number of time steps, use len(accel) because
# sometimes "NUM. TOTAL DE MUESTRAS, C1-C6" is wrong
self.number_steps = len(accel)
output = {
"Acceleration": convert_accel_units(accel, self.units),
"Time": get_time_vector(self.time_step, self.number_steps),
"Time-step": self.time_step,
"Number Steps": self.number_steps,
"Units": self.units,
"PGA": max(abs(accel)),
"PGD": None
}
return output
def _parse_time_history(self, ifile, component2parse):
"""
Parses the time history and returns the time history of the specified
component. All 3 components are provided in every ASA file. Note that
components are defined with various names, and are not always
given in the same order
"""
# The components are definied using the following names
comp_names = {
'X':
['N90E', 'N90E;', 'N90W', 'N90W;', 'S90E', 'S90W', 'E--W', 'S9OE'],
'Y':
['N00E', 'N00E;', 'N00W', 'N00W;', 'S00E', 'S00W', 'N--S', 'NOOE'],
'V': ['V', 'V;+', '+V', 'Z', 'VERT']
}
# Read component names, which are given on line 107
o = open(ifile, "r")
r = o.readlines()
components = list(r[107].split())
# Check if any component names are repeated
if any(components.count(x) > 1 for x in components):
raise ValueError(
"Some components %s in record %s have the same name" %
(components, ifile))
# Check if more than 3 components are given
if len(components) > 3:
raise ValueError("More than 3 components %s in record %s" %
(components, ifile))
# Get acceleration data from correct column
column = None
for i in comp_names[component2parse]:
if i == components[0]:
column = 0
try:
accel = np.genfromtxt(ifile,
skip_header=109,
usecols=column,
delimiter='')
except:
raise ValueError(
"Check %s has 3 equal length time-series columns" %
ifile)
break
elif i == components[1]:
column = 1
try:
accel = np.genfromtxt(ifile,
skip_header=109,
usecols=column,
delimiter='')
except:
raise ValueError(
"Check %s has 3 equal length time-series columns" %
ifile)
break
elif i == components[2]:
column = 2
try:
accel = np.genfromtxt(ifile,
skip_header=109,
usecols=column,
delimiter='')
except:
raise ValueError(
"Check %s has 3 equal length time-series columns" %
ifile)
break
if column is None:
raise ValueError("None of the components %s were found to be \n\
the %s component of file %s" %
(components, component2parse, ifile))
# Build the metadata dictionary again
metadata = _get_metadata_from_file(ifile)
# Get units
units_provided = metadata["UNIDADES DE LOS DATOS"]
units = units_provided[units_provided.find("(") +
1:units_provided.find(")")]
# Get time step, naming is not consistent so allow for variation
for i in metadata:
if 'INTERVALO DE MUESTREO, C1' in i:
self.time_step = get_float(metadata[i].split("/")[1])
# Get number of time steps, use len(accel) because
# sometimes "NUM. TOTAL DE MUESTRAS, C1-C6" is wrong
self.number_steps = len(accel)
output = {
"Acceleration": convert_accel_units(accel, self.units),
"Time": get_time_vector(self.time_step, self.number_steps),
"Time-step": self.time_step,
"Number Steps": self.number_steps,
"Units": self.units,
"PGA": max(abs(accel)),
"PGD": None
}
return output
