Example 1 : Time series SRA

Time series analysis to compute surface response spectrum and site amplification functions.



In [1]:

    
import matplotlib.pyplot as plt
import numpy as np

import pysra

%matplotlib inline



In [2]:

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

Load time series data



In [3]:

    
fname = 'data/NIS090.AT2'
with open(fname) as fp:
    next(fp)
    description = next(fp).strip()
    next(fp)
    parts = next(fp).split()
    time_step = float(parts[1])

    accels = [float(p) for l in fp for p in l.split()]

    ts = pysra.motion.TimeSeriesMotion(
        fname, description, time_step, accels)



In [4]:

    
ts.accels









    Out[4]:





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

There are a few supported file formats. AT2 files can be loaded as follows:



In [5]:

    
ts = pysra.motion.TimeSeriesMotion.load_at2_file(fname)
ts.accels









    Out[5]:





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



In [6]:

    
fig, ax = plt.subplots()
ax.plot(ts.times, ts.accels)
ax.set(xlabel='Time (sec)', ylabel='Accel (g)')
fig.tight_layout();

Create site profile

This is about the simplest profile that we can create. Linear-elastic soil and rock.



In [7]:

    
profile = pysra.site.Profile([
    pysra.site.Layer(
        pysra.site.SoilType(
            'Soil', 18., None, 0.05
        ),
        30, 400
    ),
    pysra.site.Layer(
        pysra.site.SoilType(
            'Rock', 24., None, 0.01
        ),
        0, 1200
    ),
])

Create the site response calculator



In [8]:

    
calc = pysra.propagation.LinearElasticCalculator()

Specify the output



In [9]:

    
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),
    pysra.output.FourierAmplitudeSpectrumOutput(
        # Frequency
        freqs,
        # Location of the output
        pysra.output.OutputLocation('outcrop', index=0),
        # Bandwidth for Konno-Omachi smoothing window
        ko_bandwidth=30)
])

Perform the calculation

Compute the response of the site, and store the state within the calculation object. Nothing is provided.



In [10]:

    
calc(ts, profile, profile.location('outcrop', index=-1))

Calculate all of the outputs from the calculation object.



In [11]:

    
outputs(calc)

Plot the outputs

Create a few plots of the output.



In [12]:

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



In [ ]: