seismic-graph: Python Package Documentation

Install seismic-graph

Install seismic-graph with pip. Use Python >= 3.10

pip install seismic-graph

Import your data into a study object

from seismic_graph import Study
import json

# List the paths to the samples you want to load
paths = [
    '/path/to/sample/data/my_sample_1__webapp.json',
    '/path/to/sample/data/my_sample_2__webapp.json'
]

# Load the data as a list of dictionaries
data = []
for path in paths:
    with open (path, 'r') as file:
        json_data = json.load(file)
        data.append(json_data)

# 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 latest version of seismic-graph.

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.