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show_contactmatrix.py
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show_contactmatrix.py
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import pandas as pd
import streamlit as st
import seaborn as sn
import matplotlib.pyplot as plt
from helpers import *
# contact matrix retrieved from
# https://www.medrxiv.org/content/10.1101/2020.05.18.20101501v1.full-text
# https://www.medrxiv.org/content/10.1101/2020.05.18.20101501v1.supplementary-material
# https://www.eurosurveillance.org/docserver/fulltext/eurosurveillance/26/8/20-00994_BACKER_Supplement2.pdf?expires=1622904589&id=id&accname=guest&checksum=D4271340B23924AA59899E444B283F63
def calculate_total(df, df_name, contact_type):
# 0-4 5-9 10-19 20-29 30-39 40-49 50-59 60-69 70-79 80+
#pop_ = [857000, 899000 , 1980000, 2245000, 2176000, 2164000, 2548000, 2141000, 1615000, 839000]
fraction = [ 0.04907, 0.05148, 0.11338, 0.12855, 0.12460, 0.12391, 0.14590, 0.12260, 0.09248, 0.04804]
all1 = df['All'].tolist()
total,total2 = 0,0
st.markdown (f"Gemiddeld aantal contacten per persoon _{contact_type}_ _{df_name}_ (gewogen naar populatiefractie)")
for n in range(len(all1)-1):
total += (all1[n]*fraction[n])
st.markdown (f"Van boven naar beneden __{round(total,2)}__")
all2 = df.loc[df.index == "All"].values.flatten().tolist()
for n in range(len(all2)-1):
total2 += (all2[n]*fraction[n])
st.markdown (f"Van links naar rechts __{round(total2,2)}__")
st.markdown (f"Gemiddeld van beide __{round(((total+total2)/2),2)}__")
def main():
# average number of contacts perday
st.header ("CONTACTMATRIXES")
contact_type = st.sidebar.selectbox("All, community or household", ["all", "community", "household"], index=0)
df1 = st.sidebar.selectbox("First dataframe", ["2016/-17", "April2020", "June2020"], index=0)
df2 = st.sidebar.selectbox("Second dataframe",["2016/-17", "April2020", "June2020"], index=1)
#test 13:40 14:49
df= pd.read_csv(
"https://raw.githubusercontent.com/rcsmit/COVIDcases/main/input/contactmatrix.tsv",
# "C:\\Users\\rcxsm\\Documents\\pyhton_scripts\\covid19_seir_models\\input\\contactmatrix.tsv",
comment="#",
delimiter="\t",
low_memory=False,
)
df = df.replace("[5,10)", "[05,10)")
df = df.replace ("baseline", "2016/-17")
df = df.rename(columns={'part_age':'participant_age'})
df = df.rename(columns={'cont_age':'contact_age'})
#contact_type = "community" #household" # community all
df_first = df[(df['survey'] == df1) & (df['contact_type'] == contact_type)]
df_second = df[(df['survey'] == df2) & (df['contact_type'] == contact_type)]
st.write ("Rijlabels = contact age / Kolomlabels = participant age")
df_first_pivot = df_first.pivot_table(index='contact_age', columns='participant_age', values="m_est", margins = True, aggfunc=sum)
st.subheader(f"Contactmatrix {df1}")
#st.write (df_first_pivot)
st.write (df_first_pivot.style.format(None, na_rep="-").applymap(lambda x: cell_background_helper(x,"lineair", 10, None)))#.set_precision(2))
# show_heatmap (df_first_pivot,"lineair", 10,None)
calculate_total (df_first_pivot, df1, contact_type)
df_second_pivot = df_second.pivot_table(index='contact_age', columns='participant_age', values="m_est", margins = True, aggfunc=sum)
st.subheader(f"Contactmatrix {df2}")
# show_heatmap(df_second_pivot,"lineair", 1.5,None)
st.write (df_second_pivot.style.format(None, na_rep="-").applymap(lambda x: cell_background_helper(x,"lineair", 1.5,None)))#.set_precision(2))
make_legenda("lineair", 1.5)
#st.write (df_second_pivot)
calculate_total (df_second_pivot, df2, contact_type)
st.subheader (f"Verschil als ratio -- ({df2}/{df1}")
df_difference_as_ratio = df_second_pivot / df_first_pivot
st.write (df_difference_as_ratio.style.format(None, na_rep="-").applymap(lambda x: cell_background_helper(x,"lineair", 1, None)))#.set_precision(2))
show_heatmap (df_difference_as_ratio,"lineair", 1,None)
#st.write (df_difference_as_ratio)
fig, ax = plt.subplots()
#max = result.to_numpy().mean() + ( 1* result.to_numpy().std())
max_value = st.sidebar.number_input("Max value heatmap", 0, None,2)
sn.heatmap(df_difference_as_ratio, ax=ax, vmax=max_value)
st.write(fig)
all_first = df_first_pivot['All'].tolist()
all_second = df_second_pivot['All'].tolist()
all_diff_ratio = df_difference_as_ratio['All'].tolist()
del all_first[-1]
del all_second[-1]
del all_diff_ratio[-1]
age_groups = ["0-4", "5-9", "10-19", "20-29", "30-39", "40-49", "50-59", "60-69", "70-79", "80+"]
relative_s_2_i = [ 1.000, 1.000, 3.051, 5.751, 3.538, 3.705, 4.365, 5.688, 5.324, 7.211]
s = sum(
round((all_diff_ratio[i] * relative_s_2_i[i]) / len(age_groups), 2)
for i in range(len(age_groups) - 1)
)
# st.write (f"Relative reduction of contacs = {s}")
st.subheader(f"Verschil als percentage -- ({df1}-{df2})/{df1} * 100" )
df_difference_as_perc = (df_first_pivot - df_second_pivot) / df_first_pivot*100
#st.write (df_difference_as_perc)
show_heatmap (df_difference_as_perc,"lineair", 100,None)
fig2a = plt.figure(facecolor='w')
ax = fig2a.add_subplot(111, axisbelow=True)
ax.plot (age_groups, all_first, label= df1)
ax.plot (age_groups, all_second, label = df2)
plt.legend()
plt.title("Average contacts per person per day")
st.pyplot(fig2a)
with st.sidebar.expander('Data sources', expanded=False):
#st.write ("Retrieved from https://www.medrxiv.org/content/10.1101/2020.05.18.20101501v1.full-text")
#st.write ("https://www.medrxiv.org/content/10.1101/2020.05.18.20101501v1.supplementary-material")
st.write ("Retrieved from https://www.eurosurveillance.org/content/10.2807/1560-7917.ES.2021.26.8.2000994#f4")
st.write ("https://www.eurosurveillance.org/docserver/fulltext/eurosurveillance/26/8/20-00994_BACKER_Supplement2.pdf?expires=1622904589&id=id&accname=guest&checksum=D4271340B23924AA59899E444B283F63")
if __name__ == "__main__":
main()