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Refactor mbon stats #81

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Refactor mbon stats #81

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72 changes: 30 additions & 42 deletions ioos_metrics/ioos_metrics.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3,10 +3,12 @@
import functools
import io
import logging
import urllib.parse

import httpx
import joblib
import pandas as pd
import pyobis
import requests
from bs4 import BeautifulSoup
from fake_useragent import UserAgent
Expand Down Expand Up @@ -563,62 +565,48 @@ def mbon_stats():
Information System (OBIS) and the Global Biodiversity Information Framework (GBIF). The function returns a
dataframe with rows corresponding to each paper citing a dataset.
"""
import urllib.parse

import pyobis
def _mapping(df) -> "pd.Dataframe":
return pd.DataFrame(
{
"gbif_uuid": df["results"].to_numpy()[0][0]["key"],
"title": [df["results"].to_numpy()[0][0]["title"]],
"doi": [df["results"].to_numpy()[0][0]["doi"]],
},
)

# collect dataset information from OBIS
institution_id = 23070
institution_id = 23070 # FIXME: Add a comment to explain the magic number.
query = pyobis.dataset.search(instituteid=institution_id)
df = pd.DataFrame(query.execute())
df_obis = pd.DataFrame.from_records(df["results"])
df_obis.columns = [f"obis_{col}" for col in df_obis.columns]

df_mapping = pd.DataFrame()
base_url = "https://api.gbif.org"
# iterate through each OBIS dataset to gather uuid from GBIF
# create a mapping table
for title in df_obis["obis_title"]:
string = title
query = f"{base_url}/v1/dataset/search?q={urllib.parse.quote(string)}"
df = pd.read_json(query, orient="index").T

# build a DataFrame with the info we need more accessible
df_mapping = pd.concat(
[
df_mapping,
pd.DataFrame(
{
"gbif_uuid": df["results"].to_numpy()[0][0]["key"],
"title": [df["results"].to_numpy()[0][0]["title"]],
"obis_id": [df_obis.loc[df_obis["obis_title"] == title, "obis_id"].to_string(index=False)],
"doi": [df["results"].to_numpy()[0][0]["doi"]],
},
),
],
ignore_index=True,
)

df_gbif = pd.DataFrame()
for key in df_mapping["gbif_uuid"]:
url = f"https://api.gbif.org/v1/literature/export?format=CSV&gbifDatasetKey={key}"
df2 = pd.read_csv(url) # collect literature cited information
df2.columns = ["literature_" + str(col) for col in df2.columns]
df2["gbif_uuid"] = key
# Query GBIF with OBIS titles and map responses to a table.
url = "https://api.gbif.org/v1/dataset/search?q={query}"
urls = [url.format(query=urllib.parse.quote(query)) for query in df_obis["obis_title"]]
read_json = functools.partial(pd.read_json, orient="index")
df_mapping = pd.concat([_mapping(df.T) for df in map(read_json, urls)], ignore_index=True)
df_mapping["obis_id"] = df_obis["obis_id"]

df_gbif = pd.concat([df2, df_gbif], ignore_index=True)
url = "https://api.gbif.org/v1/literature/export?format=CSV&gbifDatasetKey={key}"
urls = [url.format(key=key) for key in df_mapping["gbif_uuid"]]
df_gbif = pd.concat(map(pd.read_csv, urls), ignore_index=True)
df_gbif.columns = [f"literature_{col}" for col in df_gbif.columns]
# FIXME: Do we need this step? They are merged later.
df_gbif["gbif_uuid"] = df_mapping["gbif_uuid"]

# merge the OBIS and GBIF data frames together
df_obis = df_obis.merge(df_mapping, on="obis_id")

# add gbif download stats

for key in df_obis["gbif_uuid"]:
url = f"https://api.gbif.org/v1/occurrence/download/statistics/export?datasetKey={key}"
df2 = pd.read_csv(url, sep="\t")
df2_group = df2.groupby("year").agg({"number_downloads": "sum"})

df_obis.loc[df_obis["gbif_uuid"] == key, "gbif_downloads"] = str(df2_group.to_dict())
url = "https://api.gbif.org/v1/occurrence/download/statistics/export?datasetKey={key}"
urls = [url.format(key=key) for key in df_obis["gbif_uuid"]]
read_csv = functools.partial(pd.read_csv, sep="\t")
groups = [df.groupby("year").agg({"number_downloads": "sum"}).to_dict() for df in map(read_csv, urls)]
# FIXME: This was a string in the original but do we need it as a string or dict of dicts?
# It seems that it can be a flat dict instead like {year: number, ...}
df_obis["gbif_downloads"] = groups

return df_gbif.merge(df_obis, on="gbif_uuid")

Expand Down
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