Add notebook to generate visuals plus paired py file for diff record.

notebooks/BioCLIP_taxa_viz.ipynb

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+version https://git-lfs.github.com/spec/v1
+oid sha256:bf708489236ae18c886a7485df8627ad24b237a9bd61b4e0ee28a6500b403676
+size 237174723

notebooks/BioCLIP_taxa_viz.py

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+# ---
+# jupyter:
+#   jupytext:
+#     formats: ipynb,py:percent
+#     text_representation:
+#       extension: .py
+#       format_name: percent
+#       format_version: '1.3'
+#       jupytext_version: 1.15.2
+#   kernelspec:
+#     display_name: viz
+#     language: python
+#     name: python3
+# ---
+
+# %%
+import pandas as pd
+import seaborn as sns
+import plotly.express as px
+
+sns.set_style("whitegrid")
+sns.set(rc = {'figure.figsize': (10,10)})
+
+# %% [markdown]
+# # Number of Images by Taxonomic Rank
+
+# %%
+df = pd.read_csv("../data/catalog.csv")
+
+# %%
+# Add data_source column for easier slicing
+df.loc[df['inat21_filename'].notna(), 'data_source'] = 'iNat21'
+df.loc[df['bioscan_filename'].notna(), 'data_source'] = 'BIOSCAN'
+df.loc[df['eol_content_id'].notna(), 'data_source'] = 'EOL'
+
+# %%
+taxa = list(df.columns[8:15])
+taxa
+
+# %% [markdown]
+# Shrink down to just columns we may want for graphing.
+
+# %%
+columns = taxa.copy()
+columns.insert(0, 'data_source')
+columns.append('common')
+
+# %%
+df_taxa = df[columns]
+df_taxa.head()
+
+# %% [markdown]
+# Since the pie charts didn't show much change for phylum, let's try a treemap so we also get a sense of all the diversity inside.
+
+# %%
+# Drop null phylum values
+df_phylum = df_taxa.loc[df_taxa.phylum.notna()]
+
+# %%
+# Fill null lower ranks with 'unknown' for graphing purposes
+df_phylum = df_phylum.fillna('unknown')
+
+# %% [markdown]
+# Get list of all phyla and set color scheme. We'll then assign a color to each phylum so they're consistent across the two charts.
+
+# %%
+phyla = list(df_phylum.phylum.unique())
+colors = px.colors.qualitative.Bold
+
+# %%
+color_map = {}
+i = 0
+for phylum in phyla:
+    # There are only 10 colors in the sequence, so we'll need to loop through it a few times to assign all 49 phyla
+    i = i%10 
+    color_map[phylum] = colors[i]
+    i += 1
+
+# %%
+# Distribution of Phyla and Lower Taxa (to family) in TreeOfLife10M
+# Minimize margins, set aspect ratio to 2:1
+
+fig_phyla = px.treemap(df_phylum, path = ['phylum', 'class', 'order', 'family'],
+                 color = 'phylum',
+                 color_discrete_map = color_map)
+
+fig_phyla.update_scenes(aspectratio = {'x': 2, 'y': 1})
+fig_phyla.update_layout(font = {'size': 14},
+                    margin = {
+                        'l': 0,
+                        'r': 0,
+                        't': 0,
+                        'b': 0
+                    })
+fig_phyla.show()
+
+# %%
+fig_phyla.write_html("../visuals/phyla_ToL_tree.html")
+
+# %% [markdown]
+# Aspect ratio set in the plot doesn't work for export (unless using the png export on the graph itself), so we'll set the size manually.
+
+# %%
+fig_phyla.write_image("../visuals/phyla_ToL_tree.pdf", width = 900, height = 450)
+
+# %% [markdown]
+# ## Images by Kingdom
+
+# %%
+df_kingdom = df_taxa.loc[df_taxa.kingdom.notna()]
+df_kingdom.head()
+
+# %%
+# Drop null kingdom values
+df_kingdom = df_taxa.loc[df_taxa.kingdom.notna()]
+
+# %%
+# Fill null lower ranks with 'unknown' for graphing purposes
+df_kingdom = df_kingdom.fillna('unknown')
+
+# %% [markdown]
+# Get list of all kingdoms and set color scheme. We'll then assign a color to each kingdom so they're consistent across the two charts.
+
+# %%
+kingdoms = list(df_kingdom.kingdom.unique())
+#colors = px.colors.qualitative.Bold
+
+# %%
+king_color_map = {}
+i = 0
+for kingdom in kingdoms:
+    # There are only 10 colors in the sequence, so we'll need to loop through it once to assign all 12 kingdoms
+    i = i%10 
+    king_color_map[kingdom] = colors[i]
+    i += 1
+
+# %%
+# Distribution of Kingdoms and Lower Taxa in TreeOfLife10M
+# Minimize margins, set aspect ratio to 2:1
+
+fig_king = px.treemap(df_kingdom, path = ['kingdom', 'phylum', 'class', 'order', 'family'],
+                 color = 'kingdom',
+                 color_discrete_map = king_color_map)
+
+fig_king.update_scenes(aspectratio = {'x': 2, 'y': 1})
+fig_king.update_layout(font = {'size': 14},
+                    margin = {
+                        'l': 0,
+                        'r': 0,
+                        't': 0,
+                        'b': 0
+                    })
+fig_king.show()
+
+# %%
+fig_king.write_html("../visuals/kingdom_ToL_tree.html")
+
+# %% [markdown]
+# Aspect ratio set in the plot doesn't work for export (unless using the png export on the graph itself), so we'll set the size manually.
+
+# %%
+fig_king.write_image("../visuals/kingdom_ToL_tree.pdf", width = 900, height = 450)
+
+# %% [markdown]
+# ### Histograms for Kingdom
+
+# %%
+fig = px.histogram(df_kingdom, 
+             x = 'kingdom',
+             #y = 'num_species',
+             color = 'kingdom',
+             color_discrete_sequence = px.colors.qualitative.Bold,
+             labels = {
+                 'kingdom': "Kingdom",
+                 #'num_phyla' : "Number of distinct species"
+             },
+             #text_auto=False
+             )
+fig.update_layout(title = "Number of Images by Kingdom",
+                    yaxis_title = "Number of Images")
+
+fig.show()
+
+# %%