#! /usr/bin/python3
# This script demonstrates the current state of the pyslat library.
# Import the pyslat library, and other libraries needed to plot
# the results:
import os
import pyslat
import matplotlib.pyplot as plt
import numpy as np
import math
# Create a non-linear hyperbolic function, as used in example 1, and use it as
# the base function for an IM-Rate relationship:
im = pyslat.factory(pyslat.FUNCTION_TYPE.NLH, [1221, 29.8, 62.2])
im_rate = pyslat.MakeIM(im)
old_slat_path = os.path.expanduser(
"~") + "/Downloads/SLATv1.15_Public/example1_output_gcc/"
old_slat_path = "paleo-SLAT-data/"
if True:
im_rate_GCC = np.loadtxt(old_slat_path + "example1_IM-rate-1",
skiprows=3,
unpack=True)
old_slat_line, = plt.loglog(im_rate_GCC[0], im_rate_GCC[1])
old_slat_line.set_label("Old SLAT")
old_slat_line.set_linewidth(1)
# Use the numpy and matplotlib libraries to reproduce the IM-Rate
# diagram from example 1:
t = np.arange(0.0, 1.001, 0.001)
# Import the pyslat library, and other libraries needed to plot
# the results:
import os
import pyslat
import matplotlib.pyplot as plt
import numpy as np
import math
# Create a an interpolated function from the data generated by the original SLAT
# code, and use it as the base function for an IM-Rate relationship:
old_slat_path = os.path.expanduser("~") + "/Downloads/SLATv1.15_Public/example1_output_gcc/"
im_rate_GCC = np.loadtxt(old_slat_path + "example1_IM-rate-1", skiprows=3, unpack=True)
im = pyslat.factory(pyslat.FUNCTION_TYPE.LOGLOG, [list(im_rate_GCC[0]), list(im_rate_GCC[1])])
im_rate = pyslat.MakeSimpleRelationship(im)
old_slat_line, = plt.loglog(im_rate_GCC[0], im_rate_GCC[1])
old_slat_line.set_label("Old SLAT")
old_slat_line.set_linewidth(1)
# Use the numpy and matplotlib libraries to reproduce the IM-Rate
# diagram from example 1:
t = np.arange(0.0, 1.001, 0.001)
s = list(map(im_rate.getlambda, t))
pyslat_line, = plt.loglog(t, s)
pyslat_line.set_label("pyslat")
pyslat_line.set_linewidth(6)
pyslat_line.set_linestyle(":")
plt.xlabel('IM (PGA)')
import os
import pyslat
import matplotlib.pyplot as plt
import numpy as np
import math
# Create a an interpolated function from the data generated by the original SLAT
# code, and use it as the base function for an IM-Rate relationship:
old_slat_path = os.path.expanduser(
"~") + "/Downloads/SLATv1.15_Public/example1_output_gcc/"
im_rate_GCC = np.loadtxt(old_slat_path + "example1_IM-rate-1",
skiprows=3,
unpack=True)
im = pyslat.factory(
pyslat.FUNCTION_TYPE.LOGLOG,
[list(im_rate_GCC[0]), list(im_rate_GCC[1])])
im_rate = pyslat.MakeSimpleRelationship(im)
old_slat_line, = plt.loglog(im_rate_GCC[0], im_rate_GCC[1])
old_slat_line.set_label("Old SLAT")
old_slat_line.set_linewidth(1)
# Use the numpy and matplotlib libraries to reproduce the IM-Rate
# diagram from example 1:
t = np.arange(0.0, 1.001, 0.001)
s = list(map(im_rate.getlambda, t))
pyslat_line, = plt.loglog(t, s)
pyslat_line.set_label("pyslat")
pyslat_line.set_linewidth(6)
pyslat_line.set_linestyle(":")
'NLH': pyslat.FUNCTION_TYPE.NLH,
'PLC': pyslat.FUNCTION_TYPE.PLC,
#'LIN': pyslat.FUNCTION_TYPE.LIN,
#'LOGLOG': pyslat.FUNCTION_TYPE.LOGLOG
}
id = command[1]
flavour = command[2]
params = list(map(lambda s: float(s), command[3:]))
if func_in_use(id):
raise ValueError("Function {0} already in use.".format(id))
flavour = flavours.get(flavour, False)
if type:
deter_funcs[id] = pyslat.factory(flavour, params)
else:
line_error(line)
print("Error creating function")
usage("FUNC")
else:
line_error(line)
print("Too few arguments")
usage("FUNC")
elif cmd=='PFUNC':
if len(command) == 5:
id = command[1]
flavour = command[2]
mu_func = command[3]
sigma_func = command[4]
'PLC': pyslat.FUNCTION_TYPE.PLC,
#'LIN': pyslat.FUNCTION_TYPE.LIN,
#'LOGLOG': pyslat.FUNCTION_TYPE.LOGLOG
}
id = command[1]
flavour = command[2]
params = list(map(lambda s: float(s), command[3:]))
if func_in_use(id):
raise ValueError(
"Function {0} already in use.".format(id))
flavour = flavours.get(flavour, False)
if type:
deter_funcs[id] = pyslat.factory(flavour, params)
else:
line_error(line)
print("Error creating function")
usage("FUNC")
else:
line_error(line)
print("Too few arguments")
usage("FUNC")
elif cmd == 'PFUNC':
if len(command) == 5:
id = command[1]
flavour = command[2]
mu_func = command[3]
sigma_func = command[4]
#! /usr/bin/python3
# This script demonstrates the current state of the pyslat library.
# Import the pyslat library, and other libraries needed to plot
# the results:
import os
import pyslat
import matplotlib.pyplot as plt
import numpy as np
import math
# Create a non-linear hyperbolic function, as used in example 1, and use it as
# the base function for an IM-Rate relationship:
im = pyslat.factory(pyslat.FUNCTION_TYPE.NLH, [1221, 29.8, 62.2])
im_rate = pyslat.MakeIM(im)
old_slat_path = os.path.expanduser("~") + "/Downloads/SLATv1.15_Public/example1_output_gcc/"
old_slat_path = "paleo-SLAT-data/"
if True:
im_rate_GCC = np.loadtxt(old_slat_path + "example1_IM-rate-1", skiprows=3, unpack=True)
old_slat_line, = plt.loglog(im_rate_GCC[0], im_rate_GCC[1])
old_slat_line.set_label("Old SLAT")
old_slat_line.set_linewidth(1)
# Use the numpy and matplotlib libraries to reproduce the IM-Rate
# diagram from example 1:
t = np.arange(0.0, 1.001, 0.001)
s = list(map(im_rate.getlambda, t))
pyslat_line, = plt.loglog(t, s)
pyslat_line.set_label("pyslat")
