rstoolbox.plot.positional_structural_similarity_plot¶

rstoolbox.plot.positional_structural_similarity_plot(df, ax, alpha_color='royalblue', beta_color='tomato', identity_color='black', identity_width=3)¶

Generates a bar plot for positional prevalence of secondary structure elements.

Input data can/should be generated with positional_structural_count().

If there is a identity_perc column present, which can be obtained by running positional_structural_identity(), it will also print a line showing how often the secondary structure matches the expected/reference one.

Both DataFrame obtained through the two functions can be simply merged with:

pd.concat([df1, df2], axis=1)

Parameters:

df (DataFrame) – Data container, where rows are sequence positions and columns represent the secondary structure type: H, E and L ax (Axes) – matplotlib axis to which we will plot. alpha_color (Union[int, str]) – Color to represent helices. beta_color (Union[int, str]) – Color to represent beta strands. identity_color (Union[int, str]) – Color to represent the secondary structure identity with the expected reference_structure. identity_width (int) – Width of the line representing the identity with the expected reference_structure.

See also

positional_structural_count() positional_structural_identity()

Example

In [1]: from rstoolbox.io import parse_rosetta_file
   ...: from rstoolbox.analysis import positional_structural_count
   ...: from rstoolbox.analysis import positional_structural_identity
   ...: from rstoolbox.plot import positional_structural_similarity_plot
   ...: import pandas as pd
   ...: pd.set_option('display.width', 1000)
   ...: pd.set_option('display.max_columns', 500)
   ...: df = parse_rosetta_file("../rstoolbox/tests/data/input_ssebig.minisilent.gz",
   ...:                         {'scores': ['score'], 'structure': 'C'})
   ...: df.add_reference_structure('C', df.get_structure('C').values[0])
   ...: df1 = positional_structural_identity(df.iloc[1:], 'C')
   ...: df2 = positional_structural_count(df.iloc[1:], 'C')
   ...: fig = plt.figure(figsize=(35, 10))
   ...: ax00 = plt.subplot2grid((1, 1), (0, 0))
   ...: positional_structural_similarity_plot(pd.concat([df1, df2], axis=1), ax00)
   ...: plt.tight_layout()
   ...: 

In [2]: plt.show()

In [3]: plt.close()