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import dimcli |
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import pandas as pd |
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import sys |
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import os |
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import streamlit as st |
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import scholarpy |
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if "dsl" not in st.session_state: |
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st.session_state["dsl"] = scholarpy.Dsl() |
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@st.cache_data |
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def the_H_function(sorted_citations_list, n=1): |
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"""from a list of integers [n1, n2 ..] representing publications citations, |
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return the max list-position which is >= integer |
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eg |
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>>> the_H_function([10, 8, 5, 4, 3]) => 4 |
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>>> the_H_function([25, 8, 5, 3, 3]) => 3 |
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>>> the_H_function([1000, 20]) => 2 |
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""" |
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if sorted_citations_list and sorted_citations_list[0] >= n: |
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return the_H_function(sorted_citations_list[1:], n + 1) |
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else: |
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return n - 1 |
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def dim_login(key=None, endpoint=None): |
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if key is None: |
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KEY = os.environ.get("DIM_TOKEN") |
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if endpoint is None: |
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ENDPOINT = "https://app.dimensions.ai" |
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try: |
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dimcli.login(key=KEY, endpoint=ENDPOINT) |
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dsl = dimcli.Dsl() |
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return dsl |
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except: |
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raise Exception("Failed to login to Dimensions") |
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@st.cache_data |
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def get_pubs_df(dsl, researcher_id): |
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q = """search publications where researchers.id = "{}" return publications[id+title+doi+times_cited] sort by times_cited limit 1000""" |
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pubs = dsl.query(q.format(researcher_id)) |
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return pubs.as_dataframe() |
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@st.cache_data |
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def get_citations(df): |
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return list(df.fillna(0)["times_cited"]) |
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def app(): |
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dsl = st.session_state["dsl"] |
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researchER_id = st.text_input("Enter researcher ID:", "ur.013632443777.66") |
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df = get_pubs_df(dsl, researchER_id) |
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st.dataframe(df) |
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citations = get_citations(df) |
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h_index = the_H_function(citations) |
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st.write(f"H-index: {h_index}") |
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