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import os
import scholarpy
import pandas as pd
import streamlit as st
import leafmap.foliumap as leafmap
import plotly.express as px
import datetime
current_year = datetime.datetime.now().year
if "dsl" not in st.session_state:
st.session_state["dsl"] = scholarpy.Dsl()
def app():
st.title("Search Organizations")
dsl = st.session_state["dsl"]
row1_col1, row1_col2 = st.columns([1, 1])
with row1_col1:
name = st.text_input("Enter an organization name:", "")
if name:
orgs = dsl.search_org_by_name(name, exact_match=False, return_list=True)
if orgs is not None:
with row1_col1:
selected_org = st.selectbox("Select a organization id:", orgs)
org_id = selected_org.split("|")[0].strip()
id_info = dsl.search_org_by_id(org_id)
info_df = scholarpy.json_to_df(id_info, transpose=True)
info_df.rename(
columns={info_df.columns[0]: "Type", info_df.columns[1]: "Value"},
inplace=True,
)
with row1_col1:
st.header("Organization Information")
if not info_df.empty:
st.dataframe(info_df)
leafmap.st_download_button(
"Download data", info_df, csv_sep="\t"
)
else:
st.text("No information found")
with row1_col2:
years = st.slider(
"Select the start and end year:",
1950,
current_year + 6,
(1980, current_year),
)
st.header("Publications by year")
pubs, fig = dsl.org_pubs_annual_stats(
org_id, start_year=years[0], end_year=years[1], return_plot=True
)
st.text(f'Total number of publications: {pubs["count"].sum():,}')
if fig is not None:
st.plotly_chart(fig)
leafmap.st_download_button(
"Download data",
pubs,
file_name="data.csv",
csv_sep="\t",
)
else:
st.text("No publications found")
with row1_col1:
st.header("Top funders")
funder_count = st.slider(
"Select the number of funders:", 1, 100, 20
)
funders, fig = dsl.org_grant_funders(
org_id, limit=funder_count, return_plot=True
)
st.text(f'Total funding amount: ${funders["funding"].sum():,}')
if fig is not None:
st.plotly_chart(fig)
leafmap.st_download_button(
"Download data",
funders,
file_name="data.csv",
csv_sep="\t",
)
else:
st.text("No funders found")
with row1_col2:
st.header("The number of grants by year")
grants, fig_count, fig_amount = dsl.org_grants_annual_stats(
org_id, start_year=years[0], end_year=years[1], return_plot=True
)
st.plotly_chart(fig_count)
st.plotly_chart(fig_amount)
leafmap.st_download_button(
"Download data",
grants,
file_name="data.csv",
csv_sep="\t",
)
with row1_col1:
st.header("List of grants")
st.text("Only the first 1000 grants are shown")
result = dsl.search_grants_by_org(
org_id, start_year=years[0], end_year=years[1]
)
df = result.as_dataframe()
if not df.empty:
st.dataframe(df)
leafmap.st_download_button(
"Download data", df, file_name="data.csv", csv_sep="\t"
)
with row1_col1:
st.header("Publications most cited in last 2 years")
result = dsl.org_pubs_most_cited(org_id, recent=True, limit=100)
df = scholarpy.json_to_df(result, transpose=False)
if not df.empty:
st.dataframe(df)
leafmap.st_download_button(
"Download data", df, file_name="data.csv", csv_sep="\t"
)
with row1_col2:
st.header("Publications most cited - all time")
result = dsl.org_pubs_most_cited(org_id, recent=False, limit=100)
df = scholarpy.json_to_df(result, transpose=False)
if not df.empty:
st.dataframe(df)
leafmap.st_download_button(
"Download data", df, file_name="data.csv", csv_sep="\t"
)
df, area_fig, journal_fig = dsl.org_pubs_top_areas(
org_id, return_plot=True
)
if not df.empty:
with row1_col1:
st.header("Research areas of most cited publications")
st.plotly_chart(area_fig)
# leafmap.st_download_button(
# "Download data", df, file_name="data.csv", csv_sep="\t"
# )
with row1_col2:
st.header("Journals of most cited publications")
st.plotly_chart(journal_fig)
leafmap.st_download_button(
"Download data", df, file_name="data.csv", csv_sep="\t"
)
else:
st.text("No organizations found")
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