seiscod 3.3.3

No project description provided

SEISCOD

Tools to manage seismic traces

• Store, filter, manipulate seismic waveforms
• Visualise array data based on matplotlib collections
• Save and load seismic data using numpy npz format (system independent)
• No dependency to obspy, although conversion from obspy is handled

This program was originally named seispy, renamed to seiscod
because the name was already used.

Install with anaconda3

Change the current directory to the installation path

cd ./to/installation/path

Now get the seiscod package from gitlab

git clone https://gitlab.com/mlehujeur/seiscod.git

Ceate a dedicated conda environment, and install seiscod in it

conda create --name seiscodenv python=3.9 --yes
conda activate seiscodenv

cd seiscod
python -m pip install lib/*.whl
python -m pip install -e .

Optional dependencies
obspy is not a dependency of this package (and I don't want it to be!!)
but might be used in some optional functions

conda config --add channels conda-forge  # ignore the warning if any
conda install obspy

Test

viz -m 1 -d -ns ./seiscod/readwrite/filesamples/segdfile.segd

How to ...

Create a stream of traces from scratch, and display it

from seiscod import *

# create an empty stream object
st = Stream()

# append traces to it
for i in range(12):
    # generate a waveform array (whatever)
    data_array = np.zeros(256, float)
    data_array[5 * i + 30] = 1.0
    
    # create the trace object
    tr = Trace(
        seedid=f"trace#{i}", # name of the trace
        starttime=0.,  # time of first sample
        delta=0.000001,  # sampling interv in sec
        data=data_array)  # numpy.ndarray, 1d
    
    # process it (methods can be queued as follow)
    tr.detrend().taperwidth(width=10e-6)
    tr.gaussbandpass(freq0=100000., alpha=5.0)
    
    # add it to the current stream
    st.append(tr)

fig = plt.figure(figsize=(12, 6))
axt = plt.subplot(121, xlabel="time")
axf = plt.subplot(122, xlabel="frequency", sharey=axt)

# colormap display
st.shade(ax=axt, 
        powergain=0.8,
        cmap=plt.get_cmap('gray'),
        seedticks=True,  # display trace names
        timeticks=False,  # True for calendar data
        )

# wiggle display (on top of it)
st.show(ax=axt, 
        facealpha=0.5,
        facecolor="br",
        seedticks=True,  # display trace names
        timeticks=False,  # True for calendar data
        )
microtimetick(axt)

# display in Fourier domain
st.fft()
st.show(axf, 
        facealpha=0.5,
        facecolor="br",
        seedticks=True,  # display trace names
        timeticks=False,  # True for calendar data
        )

plt.show()