Welcome to seismic-graph’s documentation!

seismic-graph is a plotting library for SEISMIC output.

Get started

Install SEISMOGRAPH

Install SEISMOGRAPH with pip. Use Python >= 3.10

pip install seismic-graph

Import your data into a study object

from seismic_graph import Study
import json

# Define the list of samples you want to load
my_samples = ['my_sample_1', 'my_sample_2', 'my_sample_3']

# Load the data as a list of dictionaries
data = []
for sample in my_samples:
    with open('{}.json'.format(sample), 'r') as f:
        data.append(json.load(f))

# Create a study object
my_study = Study(data)

# Print the list of available samples
print(my_study.get_samples())

Make a plot

study.mutation_fraction(
    sample='my_sample_1',
    reference = 'my_reference_1',
    section = 'my_section_1',
    cluster = 'pop_avg'
)

Note

We regularly update the list of available plots. Make sure that you have the last version of seismograph.

Outlier filtering

  • pearson_filter_gap: float, default=None

    This parameter allows filtering out outliers by setting a minimal gap between the Pearson correlation of the data with the outlier and the Pearson correlation of the data without the outlier. If the difference is higher than this value, the outlier is removed from the Pearson correlation calculation.

    If the parameter is set to None (default), no outlier filtering is applied.

    Example:

    reference_data = [1.1, 1.8, 2.9, 4.1, 5.2, 6.0]
data_with_outlier = [1., 2., 3., 4., 5., 100.]

    If outlier_filter_gap is set to 0.5, the outlier (100.) will be removed from the Pearson correlation calculation because the difference between the correlation with and without the outlier is greater than 0.5.

Normalize

  • normalize: bool, default=False

    If you have multiple samples and this parameter is set to True, the data of the samples are divided by the slope of the linear regression between the data of the first sample and the data of each other samples.

    Example:

    sample_1 = [1, 2, 3, 4, 5]
sample_2 = [2, 4, 6, 8, 10]

    If normalize is set to True, the data of sample_2 will be divided by 2.

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