Example 6 : TS with Geopsy Profiles

Time series analysis to compute surface response spectrum and site amplification functions using velocity profiles from geopsy.



In [1]:

    
import re

import matplotlib.pyplot as plt
import numpy as np

import pysra

%matplotlib inline



In [2]:

    
# Increased figure sizes
plt.rcParams['figure.dpi'] = 120

Function to parse the geospy files



In [3]:

    
def iter_geopsy_profiles(fname):
    """Read a Geopsy formatted text file created by gpdcreport."""
    with open(fname) as fp:
        next(fp)
        while True:
            try:
                line = next(fp)
            except StopIteration:
                break

            m = re.search(r'Layered model (\d+): value=([0-9.]+)', line)
            id, score = m.groups()
            count = int(next(fp))
            d = {
                'id': id,
                'score': score,
                'layers': [],
            }
            cols = ['thickness', 'vel_comp', 'vel_shear', 'density']
            for _ in range(count):
                values = [float(p) for p in next(fp).split()]
                d['layers'].append(dict(zip(cols, values)))

            yield d

Create the input motion



In [4]:

    
fname = 'data/NIS090.AT2'
ts = pysra.motion.TimeSeriesMotion.load_at2_file(fname)
ts.accels









    Out[4]:





array([2.33833e-07, 2.99033e-07, 5.15835e-07, ..., 4.90601e-05,
       4.94028e-05, 4.96963e-05])

Create the site response calculator



In [5]:

    
calc = pysra.propagation.LinearElasticCalculator()

Specify the output



In [6]:

    
freqs = np.logspace(-1, 2, num=500)

outputs = pysra.output.OutputCollection([
    pysra.output.ResponseSpectrumOutput(
        # Frequency
        freqs,
        # Location of the output
        pysra.output.OutputLocation('outcrop', index=0),
        # Damping
        0.05),
    pysra.output.ResponseSpectrumRatioOutput(
        # Frequency
        freqs,
        # Location in (denominator),
        pysra.output.OutputLocation('outcrop', index=-1),
        # Location out (numerator)
        pysra.output.OutputLocation('outcrop', index=0),
        # Damping
        0.05),
])

Create site profiles

Iterate over the geopsy profiles and create a site profile. For this example, we just use a linear elastic properties.



In [7]:

    
fname = 'data/best100_GM_linux.txt'

for geopsy_profile in iter_geopsy_profiles(fname):
    profile = pysra.site.Profile([
        pysra.site.Layer(
            pysra.site.SoilType(
                'soil-%d' % i, l['density'] / pysra.site.GRAVITY,
                damping=0.05), l['thickness'], l['vel_shear'])
        for i, l in enumerate(geopsy_profile['layers'])
    ])
    # Use 1% damping for the half-space
    profile[-1].soil_type.damping = 0.01
    # Compute the waves from the last layer
    calc(ts, profile, profile.location('outcrop', index=-1))
    # Compute the site amplification
    outputs(calc)

Plot the outputs

Create a few plots of the output.



In [8]:

    
for o in outputs:
    fig, ax = plt.subplots()
    ax.plot(o.refs, o.values, color='C0', alpha=0.6, linewidth=0.2)
    ax.set(xlabel=o.xlabel, xscale='log', ylabel=o.ylabel)
    fig.tight_layout();



In [ ]: