make draft app

constants.py

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+
+BIOSYN_CLASS_NAMES = ['Alkaloid', 'NRP', 'Polyketide', 'RiPP', 'Saccharide', 'Terpene', "Other"]
+
+SINGLE_DOMAINS_FILE = 'data/single_domains.csv.gz'
+PAIR_DOMAINS_FILE = 'data/pair_domains.csv.gz'
+

mpl_data_plotter.py

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+
+import matplotlib.pyplot as plt
+import plot_utils
+from constants import *
+
+
+class MatplotlibDataPlotter:
+    def __init__(self, single_df, pair_df, num_domains_in_region_df):
+        self.single_df = single_df
+        self.pair_df = pair_df
+
+        self.num_domains_in_region_df = num_domains_in_region_df
+
+        self.single_domains_fig = plt.figure(figsize=(5, 10))
+        self.pair_domains_fig = plt.figure(figsize=(5, 10))
+
+    def plot_single_domains(self, num_domains, split_name):
+        
+        #fig = plt.gcf()
+        #fig.set_size_inches(10, 5)
+        selected_region_ids = self.num_domains_in_region_df.loc[
+            self.num_domains_in_region_df.num_domains >= num_domains, 
+            'cds_region_id'].values
+        single_df_subset = self.single_df.loc[self.single_df.cds_region_id.isin(selected_region_ids)]
+        
+        split_name = 'stratified'
+        column_name = f'cosine_similarity_{split_name}'
+        # single_df_subset = single_df.loc[single_df.dom_location_len >= num_domains]
+        selected_keyword_index = single_df_subset.groupby('cds_region_id').agg(
+            {column_name: 'idxmax'}
+        ).values.flatten()
+        targets_list = single_df_subset.loc[selected_keyword_index, 'biosyn_class_index'].values
+        label_list = single_df_subset.loc[selected_keyword_index, 'profile_name'].values
+
+        top_n=5
+        bin_width=1
+        hue_group_offset=0.5
+        # hue_order=BIOSYN_CLASS_NAMES
+        hue2count={}
+        width=0.9
+
+        show_legend=True
+
+        fig = self.single_domains_fig
+        fig.clf()
+
+        ax = fig.gca()
+        plot_utils.draw_barplots(
+            targets_list, 
+            label_list=label_list,
+            top_n=5,
+            bin_width=1,
+            hue_group_offset=0.5,
+            hue_order=BIOSYN_CLASS_NAMES,
+            hue2count={},
+            width=0.9,
+            ax=ax, 
+            show_legend=True
+        )
+        plt.tight_layout()
+        return fig # plt.gcf()
+
+    def plot_pair_domains(self, num_domains, split_name):
+        selected_region_ids = self.num_domains_in_region_df.loc[
+            self.num_domains_in_region_df.num_domains >= num_domains, 
+            'cds_region_id'].values
+        pair_df_subset = self.pair_df.loc[self.pair_df.cds_region_id.isin(selected_region_ids)]
+        
+        split_name = 'stratified'
+        column_name = f'cosine_similarity_{split_name}'
+        # pair_df_subset = pair_df.loc[pair_df.dom_location_len >= num_domains]
+        selected_keyword_index = pair_df_subset.groupby('cds_region_id').agg(
+            {column_name: 'idxmax'}
+        ).values.flatten()
+        targets_list = pair_df_subset.loc[
+            selected_keyword_index, 'biosyn_class_index'].values
+        label_list=pair_df_subset.loc[
+            selected_keyword_index, 'profile_name'].values
+
+        top_n=5
+        bin_width=1
+        hue_group_offset=0.5
+        # hue_order=BIOSYN_CLASS_NAMES
+        hue2count={}
+        width=0.9
+
+        show_legend=True
+        # fig = plt.figure(figsize=(5, 10))
+        fig = self.pair_domains_fig
+        fig.clf()
+
+        ax = fig.gca()
+        plot_utils.draw_barplots(
+            targets_list, 
+            label_list=label_list,
+            top_n=5,
+            bin_width=1,
+            hue_group_offset=0.5,
+            hue_order=BIOSYN_CLASS_NAMES,
+            hue2count={},
+            width=0.9,
+            ax=ax, 
+            show_legend=True
+        )
+        plt.tight_layout()
+        return fig  #plt.gcf()
+
+
+

plot_utils.py

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+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+
+POSTER_BLUE = '#01589C'
+
+
+def groupby(array_like, hue_order=None):
+    idx = np.argsort(array_like, kind='stable')
+    values, indices, counts = np.unique(array_like[idx], return_counts=True, return_index=True)
+    split_idx = np.split(idx, indices[1:])
+    name2indices = {group_name: indices for group_name, indices in zip(values, split_idx)}
+    if hue_order is not None and isinstance(hue_order, list):
+        for k in sorted(hue_order):
+            if k in name2indices:
+                yield k, name2indices[k]
+        return
+    for k in sorted(name2indices):
+        yield k, name2indices[k]
+
+
+def draw_barplots(targets_list, label_list=None, top_n=5, bin_width=1, 
+                  hue_group_offset=0.5, hue_order=[], 
+                  hue2count={}, width=0.9, ax=None, show_legend=True, 
+                  palette='tab10'):
+    if isinstance(palette, str):
+        palette = sns.color_palette(palette)
+    if label_list is None:
+        label_list = np.asarray([hue_order[x] for x in targets_list])
+    hue_values, ucount = np.unique(targets_list, return_counts=True)
+    n_bins = max(len(hue_values), len(hue_values))
+    bin_size = top_n
+    
+    hue_offset = np.arange(n_bins)*(bin_size*bin_width + hue_group_offset) # 
+    hue_label2offset = {hue_order[k]: v for k, v in zip(hue_values, hue_offset)}
+    # print(hue_label2offset)
+    tick_positions = []
+    tick_labels = []
+    max_x_value = 0
+    
+    for idx, (hue_index, hue_indices) in enumerate(groupby(targets_list)):
+        hue_label = hue_order[hue_index]
+        #print(idx, hue_label, hue_indices)
+        bottom = np.zeros(n_bins*bin_size)
+        subset_y = label_list[hue_indices]
+        #print(subset_y)
+        bin_labels, bin_counts = np.unique(subset_y, return_counts=True)
+    
+        # if normalize:
+        denominator = hue2count.get(hue_label, 1)
+        bin_counts = bin_counts / denominator
+        max_x_value = max(max_x_value, bin_counts.max())
+        
+        if hue_label in hue_order:
+            color_index = hue_order.index(hue_label)
+        else:
+            color_index = idx
+        # new
+        top_indices = np.argsort(bin_counts)[::-1][:bin_size]
+        bin_labels = bin_labels[top_indices]
+        bin_counts = bin_counts[top_indices]
+        
+        bin_indices = np.asarray([hue_label2offset[hue_label] + i for i, label in enumerate(bin_labels)])
+        tick_positions.extend(bin_indices)
+        tick_labels.extend(bin_labels)
+        # old
+        #offset = hue_offsets.get(hue_label, 0)
+        
+        #bin_indices = np.asarray([label2tick[t]+offset for t in bin_labels])
+        
+        p = ax.barh(
+            bin_indices, bin_counts, width, label=hue_label, # left=bottom[bin_indices],
+            color=palette[color_index])
+        # if do_stack:
+        #     bottom[bin_indices] += bin_counts 
+        #     if not normalize:
+        #         bottom[bin_indices] += bar_offset
+        line_pos = bin_indices.max() + width/2 + hue_group_offset/2
+        plt.axhline(line_pos, linewidth=1, linestyle='dashed', color=POSTER_BLUE)
+    if show_legend:
+        ax.legend(
+            loc='upper center', bbox_to_anchor=(0.5, -0.05),
+            fancybox=True, shadow=True, 
+            ncol=4
+        )
+
+    ax.set_yticks(tick_positions)
+    ax.set_yticklabels(tick_labels)
+    if max_x_value <= 1:
+        ax.set_xlim(0, 1.)
+    ax.set_ylim(-0.5, np.max(tick_positions)+width/2+hue_group_offset/2)
+    ax.invert_yaxis()