CPPPlot.heatmap

CPPPlot.heatmap(df_feat, shap_plot=False, col_cat='subcategory', col_val='mean_dif', name_test='TEST', name_ref='REF', figsize=None, cell_size=None, start=1, tmd_len=20, tmd_seq=None, jmd_n_seq=None, jmd_c_seq=None, tmd_color='mediumspringgreen', jmd_color='blue', tmd_seq_color='black', jmd_seq_color='white', seq_size='auto', seq_char_fill=None, fontsize_tmd_jmd=None, weight_tmd_jmd='normal', fontsize_labels=12, add_xticks_pos=False, grid_linewidth=0.01, grid_linecolor=None, border_linewidth=2, facecolor_dark=None, vmin=None, vmax=None, cmap=None, cmap_n_colors=101, cbar_pct=True, cbar_kws=None, cbar_xywh=(0.7, None, 0.2, None), dict_color=None, legend_kws=None, legend_xy=(-0.1, -0.01), xtick_size=11.0, xtick_width=2.0, xtick_length=5.0, sample_kws=None)

Plot a CPP/-SHAP heatmap showing the feature value mean difference/feature impact per scale subcategory (y-axis) and residue position (x-axis).

For each (subcategory, position) cell the chosen col_val from df_feat is colour-coded, giving a two-dimensional view of the physicochemical signature produced by CPP.run(). At sample level (shap_plot=True) the same layout visualises per-residue SHAP feature impact.

Added in version 0.1.0.

Parameters:

• df_feat (pd.DataFrame, shape (n_feature, n_feature_info)) – Feature DataFrame with a unique identifier, scale information, statistics, and positions for each feature. Can also include feature impact (feat_impact) column.

• shap_plot (bool, default=False) –

  Set the analysis type: CPP Analysis (if False) for group-level or CPP-SHAP Analysis for sample-level (or subgroup-level) results:

  CPP Analysis

  • col_val: Displays typically the difference of feature values, either at group-level when the mean_dif column is selected or at sample-level (group-level) when a mean_dif_’name’ column is provided.

  CPP-SHAP Analysis

  • col_val: Enables typically the selection of specific feature impacts from a feat_impact_’name’ column for an individual sample, where positive (red) and negative (blue) feature impacts are indicated.

• col_cat ({'category', 'subcategory', 'scale_name'}, default='subcategory') – Column name in df_feat for scale classification (y-axis).

• col_val ({‘mean_dif’, ‘abs_mean_dif’, ‘abs_auc’, ‘feat_importance’, mean_dif_'name', feat_impact_'name'}, default=’mean_dif’) – Column name in df_feat for numerical values to display. Must match with the shap_plot setting.

• name_test (str, default="TEST") – Name for the test dataset.

• name_ref (str, default="REF") – Name for the reference dataset.

• figsize (tuple, optional) – Figure dimensions (width, height) in inches. When None (default) and the global auto_font option is enabled, the size is derived from the grid shape so cells stay a constant size (the figure shrinks for a small grid and grows for a large one); any explicit figsize (including (8, 8)) is honored as a fixed size. With auto_font disabled, None falls back to (8, 8).

• cell_size (tuple, optional) –

  Target physical size (width, height) in inches of one grid cell. When given, the figure is sized so every cell renders at this exact size (shrinking or growing as needed, nothing clipping) regardless of auto_font; None (default) uses a calibrated default cell on the auto_font path. The standalone heatmap’s default cell is taller than the feature map’s so row labels do not crowd.

  Added in version 1.1.0.

  Changed in version 1.1.0: Defaults to None and participates in auto_font; explicit figsize wins.

• start (int, default=1) – Position label of first residue position (starting at N-terminus).

• tmd_len (int, default=20) – Length of target middle domain (TMD) to be depicted (>0). Must match with all feature from df_feat.

• tmd_seq (str, optional) – TMD sequence for specific sample.

• jmd_n_seq (str, optional) – Juxta middle domain (JMD) N-terminal sequence for specific sample. Length must match with ‘jmd_n_len’ attribute.

• jmd_c_seq (str, optional) – JMD C-terminal sequence for specific sample. Length must match with ‘jmd_c_len’ attribute.

• tmd_color (str, default='mediumspringgreen') – Color for TMD.

• jmd_color (str, default='blue') – Color for JMDs.

• tmd_seq_color (str, default='black') – Color for TMD sequence.

• jmd_seq_color (str, default='white') – Color for JMD sequence.

• seq_size (str, int, or float, optional) – Residue-letter size. "auto" (default) fits the letters to the grid cell and steps the size down for short TMDs. A value in (0, 1] sets the letter height to that fraction of the cell height (e.g. 0.9); a value > 1 is an absolute font size in points. The "auto" and fractional modes keep the letters from overlapping; an absolute point size is used as given.

• seq_char_fill (bool, optional) –

  If True, the sequence renders as a continuous, gap-free colored band (one full-width cell per residue) with the letters drawn on top. If False, each residue gets its own glyph-sized colored box. If None (default), follows the auto_font option (on when auto-sizing is enabled, off otherwise).

  Added in version 1.1.0.

• fontsize_tmd_jmd (int or float, optional) – Font size (>=0) for the part labels: ‘JMD-N’, ‘TMD’, ‘JMD-C’. If None, optimized automatically.

• weight_tmd_jmd ({'normal', 'bold'}, default='normal') – Font weight for the part labels: ‘JMD-N’, ‘TMD’, ‘JMD-C’.

• fontsize_labels (str, int, or float, default=12) –

  Font size (>= 0) for the figure labels (the scale-subcategory row labels, the scale-category legend, and the colorbar). A number sets the size directly (the default 12 leaves the output unchanged). "auto" scales the size with the plot_settings font scale, caps it at about 13 pt, and shrinks it further if the subcategory rows would overlap, so the rows never collide.

  Changed in version 1.1.0: Accepts "auto" to track the plot_settings font scale without row overlap.

• add_xticks_pos (bool, default=False) – If True, include x-tick positions when TMD-JMD sequence is given.

• grid_linewidth (int or float, default=0.01) – Line width for the grid.

• grid_linecolor (str, optional) – Color for the grid lines. If None, automatically determined based on facecolor_dark.

• border_linewidth (int or float, default=2) – Line width for the TMD-JMD border.

• facecolor_dark (bool, optional) – Sets background of heatmap to black (if True) or white. If None, automatically determined from shap_plot setting. Affects grid cells for missing or near-zero data based on col_val.

• vmin (int or float, optional) – Minimum col_val value setting the lower end of the colormap. If None, determined automatically.

• vmax (int or float, optional) – Maximum col_val value setting the upper end of the colormap. If None, determined automatically.

• cmap (matplotlib colormap name or object, optional) – Name of the colormap to use. If None, automatically determined col_val data and ‘shap_plot’ setting.

• cmap_n_colors (int, default=101) – Number of discrete steps (>1) in diverging or sequential colormap.

• cbar_pct (bool, default=True) – If True, colorbar is represented in percentage and the col_val values are converted accordingly by multiplying with 100 if necessary.

• cbar_kws (dict of key, value mappings, optional) – Keyword arguments for colorbar passed to matplotlib.figure.Figure.colorbar().

• cbar_xywh (tuple, default=(0.7, None, 0.2, None)) – Colorbar position and size: x-axis (left), y-axis (bottom), width, height. Values are optimized if None.

• dict_color (dict, optional) – Color dictionary of scale categories classifying scales shown on y-axis. Default from plot_get_cdict() with name='DICT_CAT'.

• legend_kws (dict, optional) – Keyword arguments for the legend passed to plot_legend().

• legend_xy (tuple, default=(-0.1, -0.01)) – Position for scale category legend: x- and y-axis coordinates. Values are set to default if None.

• xtick_size (int or float, default=11.0) – Size of x-tick labels (>0).

• xtick_width (int or float, default=2.0) – Width of the x-ticks (>0).

• xtick_length (int or float, default=5.0) – Length of the x-ticks (>0).

• sample_kws (dict, optional) – Structured bundle selecting one sample to draw its TMD-JMD sequence band — the bundled alternative to providing the sequences directly. Fixed keys: sample (an entry name or name-column value str, or a row-position int), df_seq and df_parts. The TMD-JMD parts are read from df_parts via SequenceFeature.get_seq_kws() and override any explicitly passed tmd_seq / jmd_n_seq / jmd_c_seq. Because the displayed sequence must stay faithful to the df_parts the features map to, the sequence’s own lengths set the grid geometry; tmd_len / jmd_n_len / jmd_c_len apply only when no sequence is shown. See the keyword-dict parameters overview.

Returns:

• fig (Figure) – The Figure object for the CPP heatmap.

• ax (Axes) – Axes object for the CPP heatmap.

Notes

tmd_seq_color and jmd_seq_color are applicable only when tmd_seq, jmd_n_seq, and jmd_c_seq are provided.

The returned figure is self-contained: the scale-category legend and the “Feature value” colorbar are arranged automatically below the grid. The method manages its own layout, so calling plt.tight_layout() afterwards is unnecessary (it is neutralized on the returned figure to keep this furniture from being pulled back onto the heatmap); fig.savefig(..., bbox_inches="tight") and plt.show() work as usual.

When no sequence is supplied (tmd_seq is None), the TMD and JMD regions are drawn as a thin solid colored bar below the grid, kept at a constant height (a fixed fraction of a grid cell-row) at any figure size.

See also

• CPP.run() for details on CPP statistical measures of the df_feat DataFrame.

• SequenceFeature for definition of sequence Parts.

• CPPPlot.feature() for visualization of mean differences for specific features.

• seaborn.heatmap() for seaborn heatmap.

• matplotlib.figure.Figure.colorbar() for colorbar arguments.

• Matplotlib Colormaps for further cmap options.

• plot_legend() used for setting scale category legend.

Examples

To demonstrate the CPPPlot().heatmap() method, we first load the example DOM_GSEC dataset and its respective features (see [Breimann25]):

import matplotlib.pyplot as plt
import aaanalysis as aa
aa.options["verbose"] = False

df_seq = aa.load_dataset(name="DOM_GSEC")
df_feat = aa.load_features(name="DOM_GSEC")
df_feat = df_feat.sort_values(by="feat_importance", ascending=False).reset_index(drop=True)
aa.display_df(df_feat, show_shape=True, n_rows=7)

DataFrame shape: (150, 15)

	feature	category	subcategory	scale_name	scale_description	abs_auc	abs_mean_dif	mean_dif	std_test	std_ref	p_val_mann_whitney	p_val_fdr_bh	positions	feat_importance	feat_importance_std
1	TMD_C_JMD_C-Seg...,11)-LIFS790102	Conformation	β-strand	β-strand	Conformational ...n-Sander, 1979)	0.189000	0.125674	0.125674	0.183876	0.218813	0.000001	0.000039	28,29	4.729200	4.776785
2	TMD_C_JMD_C-Seg...2,3)-CHOP780212	Conformation	β-sheet (C-term)	β-turn (1st residue)	Frequency of th...-Fasman, 1978b)	0.199000	0.065983	-0.065983	0.087814	0.105835	0.000000	0.000016	27,28,29,30,31,32,33	4.106000	5.236574
3	TMD_C_JMD_C-Seg...3,4)-HUTJ700102	Energy	Entropy	Entropy	Absolute entrop...Hutchens, 1970)	0.229000	0.098224	0.098224	0.106865	0.124608	0.000000	0.000001	31,32,33,34,35	3.111200	3.109955
4	TMD_C_JMD_C-Seg...2,3)-AURR980110	Conformation	α-helix	α-helix (middle)	Normalized posi...ora-Rose, 1998)	0.211000	0.077355	0.077355	0.102965	0.107453	0.000000	0.000005	27,28,29,30,31,32,33	3.048800	3.623912
5	TMD_C_JMD_C-Pat...4,8)-JANJ790102	Energy	Free energy (unfolding)	Transfer free e...(TFE) to inside	Transfer free e...y (Janin, 1979)	0.187000	0.144354	-0.144354	0.181777	0.233103	0.000001	0.000049	33,37	2.833600	3.640617
6	TMD_C_JMD_C-Pat...4,8)-KANM800103	Conformation	α-helix	α-helix	Average relativ...sa-Tsong, 1980)	0.176000	0.087846	0.087846	0.140464	0.157561	0.000004	0.000113	24,28	2.704000	4.076269
7	TMD_C_JMD_C-Pat...,10)-LEVM760105	Shape	Side chain length	Side chain length	Radius of gyrat... (Levitt, 1976)	0.149000	0.073526	0.073526	0.133612	0.157088	0.000090	0.000714	31,34,38	2.050800	2.338278

CPP Analysis (group-level)

The group-level feature value difference per scale subcategory (y-axis) and residue position (x-axis) can be visualized by providing the df_feat DataFrame:

# Plot CPP heatmap at group-level
cpp_plot = aa.CPPPlot()
aa.plot_settings(font_scale=0.7, weight_bold=False)
cpp_plot.heatmap(df_feat=df_feat)
plt.show()

You can select a subset of features by filtering df_feat:

# Plot top 15 features
df_top15 = df_feat.head(15)
cpp_plot.heatmap(df_feat=df_top15)
plt.show()

Adjust the scale classification level (y-axis) using the col_cat parameter. Choose from the ‘category’, ‘subcategory’ (default), and ‘scale_name’ columns from the df_feat:

# Show heatmap with scales classified by categories
cpp_plot.heatmap(df_feat=df_feat, col_cat="category")
plt.show()

The numerical value shown in the heatmap can be adjusted by the col_val parameter, which specifies one of the following df_feat columns: ‘mean_dif’ (default), ‘abs_mean_dif’, ‘abs_auc’, or ‘feat_importance’:

# Show heatmap with absolute feature value difference
cpp_plot.heatmap(df_feat=df_feat, col_val="abs_mean_dif")
plt.show()

Adjust the names of the test and reference datasets using the name_test (default=‘TEST’) and name_ref (default=‘REF’) parameters:

# Adjust dataset names shown in colorbar
cpp_plot.heatmap(df_feat=df_feat, name_test="Target group", name_ref="Control group")
plt.show()

You can adjust the figsize (default=(8, 8)), useful if only a subset of features is shown:

# Show only top 15 features
df_top15 = df_feat.head(15)
cpp_plot.heatmap(df_feat=df_top15, figsize=(8, 4))
plt.show()

Hold each grid cell at a fixed physical size with cell_size (width, height in inches); the figure resizes to fit the grid at any density.

# Fix the grid cell size (width, height in inches)
cpp_plot.heatmap(df_feat=df_feat, cell_size=(0.16, 0.3))
plt.tight_layout()
plt.show()

You can adjust the start position and the tmd_len (default=20) by providing them as parameters. Change the length of the jmd_n and jmd_c using the CPPPlot object.

# Start at residue position 10 and adjust the length each part
cpp_plot = aa.CPPPlot(jmd_n_len=15, jmd_c_len=15)
cpp_plot.heatmap(df_feat=df_feat, start=10, tmd_len=30)
plt.show()

CPP Analysis (sample-level)

You can visualize how the general feature value difference is translated onto the sequence of a specific sample. To this end, you need to provide the corresponding sequence parameters: jmd_n_seq, tmd_seq, and jmd_c_seq:

# Get sequence parts of first sample
cpp_plot = aa.CPPPlot()
sf = aa.SequenceFeature()
df_parts = sf.get_df_parts(df_seq=df_seq)
seq_kws = sf.get_seq_kws(df_seq=df_seq, df_parts=df_parts, sample=0)
jmd_n_seq, tmd_seq, jmd_c_seq = seq_kws["jmd_n_seq"], seq_kws["tmd_seq"], seq_kws["jmd_c_seq"]
print("Sequence parts of first sample")
print(seq_kws)

# Plot CPP profile for first sample
cpp_plot.heatmap(df_feat=df_feat, **seq_kws)
plt.show()

Sequence parts of first sample
{'jmd_n_seq': 'FAEDVGSNKG', 'tmd_seq': 'AIIGLMVGGVVIATVIVITLVML', 'jmd_c_seq': 'KKKQYTSIHH'}

# 'sample_kws' bundles the same per-sample lookup into one dict -- the alternative to passing
# the TMD-JMD sequences directly. 'sample' accepts an entry name (or a 'name'-column value in
# df_seq) or a row position; df_seq and df_parts resolve its sequence (faithful to the df_parts
# the features map to).
cpp_plot.heatmap(df_feat=df_feat, sample_kws=dict(sample=0, df_seq=df_seq, df_parts=df_parts))
plt.show()

You can customize the following color parameters: tmd_color (default=‘mediumspringgreen’), jmd_color (default=‘blue’), tmd_seq_color (default=‘black’), and jmd_seq_color (default=‘white’):

# Change default TMD-JMD colors
cpp_plot.heatmap(df_feat=df_feat, **seq_kws, tmd_color="orange", jmd_color="white", tmd_seq_color="blue", jmd_seq_color="blue")
plt.show()

By default (seq_size="auto") the residue letters are fit to the grid cell and stepped down for a short TMD; set verbose=True to see the chosen size. You can override it: a value in (0, 1] sets the letter height to that fraction of the cell (e.g. 0.9), and a value > 1 is an absolute font size in points.

# seq_size accepts a cell-height fraction (<= 1) or an absolute point size (> 1)
cpp_plot.heatmap(df_feat=df_feat, **seq_kws, seq_size=0.9)  # 90% of the cell height
plt.show()

cpp_plot.heatmap(df_feat=df_feat, **seq_kws, seq_size=8)    # 8 pt (absolute)
plt.show()

This might result in suboptimal spacing among sequence characters. Adjust the font size of the part labels (‘JMD-N’, ‘TMD’, ‘JMD-C’) using fontsize_tmd_jmd, which is set by default to the optimized sequence size. Change its weight using weight_tmd_jmd (default=‘normal’)

# Adjust the fontsize of the TMD-JMD characters
cpp_plot.heatmap(df_feat=df_feat, **seq_kws, fontsize_tmd_jmd=16, weight_tmd_jmd="bold")
plt.show()

Display the xtick positions in addition to the sequence by setting add_xticks_pos=True (default=False):

# Add the xticks indicating the sequence positions
cpp_plot.heatmap(df_feat=df_feat, **seq_kws, add_xticks_pos=True)
plt.show()

CPP Analysis

Use fontsize_labels (default 12) to set the size of the figure labels: the scale-subcategory row labels, the scale-category legend, and the colorbar. Pass a number, or "auto" to track the plot_settings font scale (capped so the subcategory rows never overlap):

# Modify label size of legend and colorbar
cpp_plot.heatmap(df_feat=df_feat, fontsize_labels=16)
plt.show()

Adjust the heatmap grid using the grid_linewidth (default=0.01) and grid_linecolor (set by default based on facecolor_dark) parameters:

# Adjust heatmap grid
cpp_plot.heatmap(df_feat=df_feat, grid_linewidth=1, grid_linecolor="orange")
plt.show()

The TMD part borders are highlighted by an extra line, which width can be customized by border_linewidth (default=2):

# Increase width of TMD border
cpp_plot.heatmap(df_feat=df_feat, border_linewidth=5)
plt.show()

The background is set automatically basd on shap_plot. You can set it to black by facecolor_dark=True:

# Set background to black
cpp_plot.heatmap(df_feat=df_feat, facecolor_dark=True)
plt.show()

Adjust the lower and upper end of the colormap using the vmin and vmax parameters:

# Change minimum and maximum values
cpp_plot.heatmap(df_feat=df_feat, vmin=-10, vmax=20)
plt.show()

You can provide any colormap from Matplotlib Colormaps using the cmap parameter. The number of discrete steps can be adjusted by cmap_n_colors (default=101):

# Use matplotlib color map with 7 color steps
cpp_plot.heatmap(df_feat=df_feat, cmap="viridis", cmap_n_colors=7)
plt.show()

Customize the colorbar using cbar_kws. You can adjust its position (x-axis, y-axis), width, and height by cbar_xywh (default=(0.7, None, 0.2, None)), where default values are adopted if None is provided.

# Change colorbar title, position, width and height
cbar_kws = dict(orientation="vertical")
fig, ax = cpp_plot.heatmap(df_feat=df_feat, cbar_kws=cbar_kws,
                           cbar_xywh=(0.86, 0.25, 0.01, 0.5))
# Plot must be adjusted by plt.subplots_adjust and not by plt.tight_layout
plt.subplots_adjust(right=0.84)
plt.show()

Adjust the scale legend by the legend_kws parameter and its position using legend_xy (default=(-0.1, -0.01)):

# Adjust legend, colors can be changed by 'dict_color'
legend_kws = dict(n_cols=3, fontsize=13, fontsize_title=15, weight_title="bold")
cpp_plot.heatmap(df_feat=df_feat, legend_kws=legend_kws, legend_xy=(None, 0.05))
plt.show()

Following x-tick parameters can be adjusted: xtick_size (default=11.0), xtick_width (default=2.0), and xtick_length (default=5.0):

# Adjust x-ticks
cpp_plot.heatmap(df_feat=df_feat, xtick_size=16, xtick_width=5, xtick_length=10)
plt.show()

CPP-SHAP analysis

Set shap_plot=True for visualizing the sample-specific feature impact instead of the overall feature importance. To demonstrate this, we create the feature matrix for the DOM_GSEC example dataset (see [Breimann25]) using the SequenceFeature().feature_matrix() method:

# Create feature matrix
sf = aa.SequenceFeature()
df_parts = sf.get_df_parts(df_seq=df_seq)
X = sf.feature_matrix(features=df_feat["feature"], df_parts=df_parts)

Next, we must include the feature impact into the df_feat for all samples using the ShapModel model:

labels = df_seq["label"].to_list()

# Fit SHAP explainer to obtain SHAP values
sm = aa.ShapModel()
sm.fit(X, labels=labels)

# Include feature value difference and feature impact for all samples
df_feat = sm.add_sample_mean_dif(X, labels=labels, df_feat=df_feat, drop=True)
df_feat = sm.add_feat_impact(df_feat=df_feat, drop=True)
aa.display_df(df_feat, n_rows=5, n_cols=15, show_shape=True)

DataFrame shape: (150, 265)

	feature	category	subcategory	scale_name	scale_description	abs_auc	abs_mean_dif	mean_dif	std_test	std_ref	p_val_mann_whitney	p_val_fdr_bh	positions	mean_dif_Protein0	mean_dif_Protein1
1	TMD_C_JMD_C-Seg...,11)-LIFS790102	Conformation	β-strand	β-strand	Conformational ...n-Sander, 1979)	0.189000	0.125674	0.125674	0.183876	0.218813	0.000001	0.000039	28,29	0.364754	0.379754
2	TMD_C_JMD_C-Seg...2,3)-CHOP780212	Conformation	β-sheet (C-term)	β-turn (1st residue)	Frequency of th...-Fasman, 1978b)	0.199000	0.065983	-0.065983	0.087814	0.105835	0.000000	0.000016	27,28,29,30,31,32,33	-0.244818	-0.224388
3	TMD_C_JMD_C-Seg...3,4)-HUTJ700102	Energy	Entropy	Entropy	Absolute entrop...Hutchens, 1970)	0.229000	0.098224	0.098224	0.106865	0.124608	0.000000	0.000001	31,32,33,34,35	0.162838	0.243838
4	TMD_C_JMD_C-Seg...2,3)-AURR980110	Conformation	α-helix	α-helix (middle)	Normalized posi...ora-Rose, 1998)	0.211000	0.077355	0.077355	0.102965	0.107453	0.000000	0.000005	27,28,29,30,31,32,33	0.203609	0.120469
5	TMD_C_JMD_C-Pat...4,8)-JANJ790102	Energy	Free energy (unfolding)	Transfer free e...(TFE) to inside	Transfer free e...y (Janin, 1979)	0.187000	0.144354	-0.144354	0.181777	0.233103	0.000001	0.000049	33,37	-0.254103	-0.180103

Finally, we can visualize the feature impact for a selected sample by providing the respective column name in col_val and its sequence parameters together with setting shap_plot=True:

# Plot CPP heatmap for selected protein (use similar value range for comparability)
cpp_plot.heatmap(df_feat=df_feat, shap_plot=True,
                 col_val="mean_dif_Protein0", name_test="Protein0",
                 tmd_seq=tmd_seq, jmd_n_seq=jmd_n_seq, jmd_c_seq=jmd_c_seq,
                 vmin=-15, vmax=15)
plt.show()

# Plot CPP-SHAP heatmap for selected protein
cpp_plot.heatmap(df_feat=df_feat, shap_plot=True,
                 col_val="feat_impact_Protein0",
                 tmd_seq=tmd_seq, jmd_n_seq=jmd_n_seq, jmd_c_seq=jmd_c_seq)
plt.show()

Further parameters. CPPPlot.heatmap also accepts: cbar_pct — If True, colorbar is represented in percentage and the col_val values are converted accordingly by multiplying with 100 if necessary.

# Further parameters: cbar_pct renders the colorbar in percent (values scaled x100 as needed)
import matplotlib.pyplot as plt
cpp_plot = aa.CPPPlot()
df_feat_hm = aa.load_features(name="DOM_GSEC")
cpp_plot.heatmap(df_feat=df_feat_hm, cbar_pct=True)
plt.show()

# seq_char_fill controls how the residue sequence is drawn below the heatmap
cpp_plot.heatmap(df_feat=df_feat_hm, **seq_kws, seq_char_fill=False)  # per-residue box, small gap
plt.tight_layout()
plt.show()

cpp_plot.heatmap(df_feat=df_feat_hm, **seq_kws, seq_char_fill=True)  # continuous gap-free colored band
plt.tight_layout()
plt.show()