Skip to content

Commit

Permalink
updated main
Browse files Browse the repository at this point in the history 
change ui, added std to dataframe
  • Loading branch information
@CV-Neurognos
CV-Neurognos authored Jan 26, 2022
1 parent d1437a8 commit 42e7cc1
Showing 1 changed file with 104 additions and 43 deletions.
147 changes: 104 additions & 43 deletions frontend/main.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -4,44 +4,110 @@
from numpy import random
import matplotlib.pyplot as plt
import SessionState
import scipy.stats

###### CONSTANTS#####

SIMULATIONS = 250
################### CONFIGURATION ###########################

st.set_page_config(layout="wide")

################### CONSTANTS & Functions ###############################

###### FUNCTIONS #####
SIMULATIONS = 250

def check_positive(input_array):
if 1 in input_array:
return True
else:
return False

#############################################################################
st.title('Distribucion de extracciones segun numero de muestras por Pool')
def mean_confidence_interval(data, confidence=0.95):
a = 1.0 * np.array(data)
n = len(a)
m, se = np.mean(a), scipy.stats.sem(a)
h = se * scipy.stats.t.ppf((1 + confidence) / 2., n-1)
return m, m-h, m+h

################### WEBSITE DESIGN ##########################


################## TITLE ##################

col1, col2 = st.columns((1,2))

with col1:
new_title = '<p style="font-family:sans-serif; font-weight: bold; color:#050a30; font-size:5rem;">POOL-SIZE IMPACT ON <p style="font-family:sans-serif; font-weight: bold; color:Red; font-size:5rem;">qPCR </p> <p style="font-family:sans-serif; font-weight: bold; color:#050a30; font-size:5rem;"> WORKFLOW</p>'
st.markdown(new_title, unsafe_allow_html=True)
#st.title("Pool-Size impact on qPCR Workflow")

with col2:
col2_text = '<p style="font-family:sans-serif; font-weight: bold; text-align: right; vertical-align: text-bottom; color:Blue; font-size:1rem;"> <a href="https://www.w3schools.com">Author: Cristian Valls </a></p>'
st.markdown(col2_text, unsafe_allow_html=True)
#st.title("Pool-Size impact on qPCR Workflow")

################## DESCRIPTION ##################

#with st.container():
# st.write("This is inside the container")

col1, col2 = st.columns((1,1))

with col1:
text1 = '<p style="font-family:sans-serif; font-weight: bold; color:#050a30;font-size:2rem; margin-top: 2rem; "> Rationale</p>'
st.markdown(text1, unsafe_allow_html=True)
text1 = '<p style="font-family:sans-serif; font-weight: bold; color:#050a30;font-size:1rem;margin-left: 0.5rem;margin-right: 0.5rem;text-align: justify;"> RT-PCR is regarded as the gold-standard laboratory technique for the identification of SARS-CoV-2 in a clinical setting (and this can be extrapolated to other pathogen identification). However the increase in demand for this technique produce a deficit in the supplychain. One method to maximize the reagents for qPCR is by Pooling samples . This has been used in different screening protocols and also been proposed for the detection of SARS-CoV-2. </p>'
st.markdown(text1, unsafe_allow_html=True)
text2 = '<p style="font-family:sans-serif; font-weight: bold; color:#050a30;font-size:1rem; margin-left: 0.5rem;margin-right: 0.5rem;text-align: justify;"> The assumption was to pool multiple specimens and process them with the one RNA isolation and a single PCR test without significant loss of test sensitivity.</p>'
st.markdown(text2, unsafe_allow_html=True)
text3 = '<p style="font-family:sans-serif; font-weight: bold; color:Red ;font-size:1rem; margin-left: 0.5rem;margin-right: 0.5rem;text-align: justify;"> This Streamlit App shows how many pools per sample are ideal to maximize reagents in a one-stage pool strategies (i.e., pool samples, if positive, run samples individually) </p>'
st.markdown(text3, unsafe_allow_html=True)
text4 = '<p style="font-family:sans-serif; font-weight: bold; color:#050a30;font-size:2rem; margin-top: 1rem; "> References</p>'
st.markdown(text4, unsafe_allow_html=True)

reference_1 = '<p style="font-family:sans-serif; font-weight: bold; color:#050a30; ;font-size:1rem; margin-left: 0.5rem;margin-right: 0.5rem;text-align: justify;"> 2021 Apr 17;21(1):360. doi: 10.1186/s12879-021-06061-3.</p>'
st.markdown(reference_1, unsafe_allow_html=True)



with col2:
text2 = '<p style="font-family:sans-serif; font-weight: bold; color:#050a30;font-size:2rem;margin-top: 2rem; "> How it Works:</p>'
st.markdown(text2, unsafe_allow_html=True)
text1 = '<p style="font-family:sans-serif; font-weight: bold; color:#050a30;font-size:1rem; margin-left: 0.5rem;margin-right: 0.5rem;text-align: justify;"> We generate a population with a predefined number of positive cases. Population Size (number of samples), Positivity (positive rate for SARS-CoV-2 detection) and Pool-size(number of samples per pool) are defined by the user. according to the positivity that is inputted by users. Then a population is randomly generated with the positivity defined. </p>'
st.markdown(text1, unsafe_allow_html=True)
text2 = '<p style="font-family:sans-serif; font-weight: bold; color:#050a30;font-size:1rem; margin-left: 0.5rem;margin-right: 0.5rem;text-align: justify;"> FIRST: select - Population Size (number of samples), Positivity (positive rate for SARS-CoV-2 detection) and Pool-size(number of samples per pool). according to the positivity that is inputted by users. Then a population is randomly generated with the positivity defined. </p>'
st.markdown(text2, unsafe_allow_html=True)
text3 = '<p style="font-family:sans-serif; font-weight: bold; color:#050a30;font-size:1rem; margin-left: 0.5rem;margin-right: 0.5rem;text-align: justify;"> SECOND: According to the positivity that is inputted by users, a population is randomly generated. Then a population is randomly generated with the positivity defined. </p>'
st.markdown(text3, unsafe_allow_html=True)
text4 = '<p style="font-family:sans-serif; font-weight: bold; color:#050a30;font-size:1rem; margin-left: 0.5rem;margin-right: 0.5rem;text-align: justify;"> THIRD: Using the population generated, we pool samples by the size determined by the user </p>'
st.markdown(text4, unsafe_allow_html=True)
text5 = '<p style="font-family:sans-serif; font-weight: bold; color:#050a30;font-size:1rem; margin-left: 0.5rem;margin-right: 0.5rem;text-align: justify;"> FOURTH: Pools are analyzed; if it contains a positive sample , that pool is proccesed as individually</p>'
st.markdown(text5, unsafe_allow_html=True)
text6 = '<p style="font-family:sans-serif; font-weight: bold; color:#050a30;font-size:1rem; margin-left: 0.5rem;margin-right: 0.5rem;text-align: justify;"> COUNT: we count every pool created and every individual reaction derived from a positive pool and register the number of total theoric reactions performed. This process is repeated 250 times, and a frecuency histogram is plotted</p>'
st.markdown(text6, unsafe_allow_html=True)

header = "Esta webApp simula la cantidad de extracciones que se realizaran considerando una determinada cantidad de muestras por pool y una determinada positividad"
st.header(header)
#############################################################################

label = "Escoger muestras por Pool"
label = "Samples per Pool"
POOL_SIZE = st.slider(label, min_value=2, max_value=10,value=5, step=1)

label_2 = "Escoger Numero de muestras por simulacion"
label_2 = "Population Size (Total number of samples to process) "
SAMPLE_SIZE = st.slider(label_2, min_value=100, max_value=100000,value=5000, step=100)

label_3 = "Seleccionar Positividad"
label_3 = "Positivity Rate"
POSITIVITY = st.slider(label_3, min_value=1.0, max_value=25.0, value=5.0,step=0.1)

POSITIVITY = POSITIVITY/100

# Execute Button
button = st.button('Registrar')
button = st.button('Register')

# Create populations

sample_distribution = np.random.binomial(SAMPLE_SIZE , POSITIVITY , SIMULATIONS)


############### Population analysis ####################

trial = 0
total_extractions = []
while trial < len(sample_distribution):
Expand Down Expand Up @@ -75,54 +141,49 @@ def check_positive(input_array):

#print("number of extractions: {ext} in a total of {smp} samples , pooling with {pool} samples per pool".format(ext = extractions , smp = SAMPLE_SIZE, pool= POOL_SIZE ))

print("mean extractions in {SIMULATIONS} simulations: {count}".format(SIMULATIONS = SIMULATIONS, count = sum(total_extractions)/SIMULATIONS))
#print("mean extractions in {SIMULATIONS} simulations: {count}".format(SIMULATIONS = SIMULATIONS, count = sum(total_extractions)/SIMULATIONS))

mean_extractions = round(sum(total_extractions)/SIMULATIONS,0)
mean,h_lower,h_high = mean_confidence_interval(total_extractions)
print(h_lower, h_high)

#Plot Construction
#figure = plt.hist(total_extractions, density=False,color ='green',alpha = 0.7)
#plt.axvline(mean_extractions, color='k', linestyle='dashed', linewidth=1)

header2 = "El grafico inferior muestra la cantidad de extracciones obtenidas en 250 simulaciones. Eje X corresponde a la cantidad de extracciones que se hicieron en cada Trial. Eje Y corresponde a la frecuencia correspondiente"
header2 = "Plot interpretation: X-axis corresponds to number of RNA extractions, y-axis corresponds to number of simulations. Hence, distribution of extractions in the populations simulated "
st.header(header2)

fig, ax = plt.subplots()
ax.hist(total_extractions, density=False,color ='red',alpha = 0.5)
plt.xlabel("Numero de extracciones por simulacion")
plt.ylabel("Frequencia")
plt.axvline(mean_extractions, color='k', linestyle='dashed', linewidth=1)

st.pyplot(fig)
col3, col4 = st.columns((1,1))

with col3:
fig, ax = plt.subplots()
ax.hist(total_extractions, density=False,color ='red',alpha = 0.5)
plt.xlabel("Num of Extraccion")
plt.ylabel("Num of Populations")
plt.axvline(mean_extractions, color='k', linestyle='dashed', linewidth=1)

# data = {'Pool Size':[POOL_SIZE],'Sample Size':[SAMPLE_SIZE],'Positivity':[POSITIVITY],"average extractions":[mean_extractions]}
#
# df = pd.DataFrame(data)
#
# # persist state of dataframe
# session_state = SessionState.get(df=data)
#
# if button:
# # update dataframe state
# session_state.df = session_state.df.append({'Pool Size': POOL_SIZE , 'Sample Size':SAMPLE_SIZE,'Positivity':POSITIVITY,"average extractions":mean_extractions}, ignore_index=True)
# st.dataframe(session_state.df)
st.pyplot(fig)

# Create an empty dataframe
data = pd.DataFrame(columns=["Pool Size" , "SAMPLE_SIZE" , "POSITIVITY", "Number Of Extractions"])
# Create an empty dataframe
data = pd.DataFrame(columns=["Pool Size" , "SAMPLE_SIZE" , "POSITIVITY", "Number Of Extractions" , "simulation's std"])

# with every interaction, the script runs from top to bottom
# resulting in the empty dataframe
# st.dataframe(data)
# with every interaction, the script runs from top to bottom
# resulting in the empty dataframe
# st.dataframe(data)

# persist state of dataframe
session_state = SessionState.get(df=data)
# persist state of dataframe
session_state = SessionState.get(df=data)

if button:
# update dataframe state
session_state.df = session_state.df.append({"Pool Size": POOL_SIZE , "SAMPLE_SIZE" : SAMPLE_SIZE,"POSITIVITY" : POSITIVITY , "Number Of Extractions" : mean_extractions}, ignore_index=True)
st.dataframe(session_state.df)
with col4:

if button:
# update dataframe state
session_state.df = session_state.df.append({"Pool Size": round(int(POOL_SIZE),0) , "SAMPLE_SIZE" : round(int(SAMPLE_SIZE),0),"POSITIVITY" : POSITIVITY , "Number Of Extractions" : round(mean_extractions,0) , "simulation's std" : np.std(total_extractions)}, ignore_index=True)
st.dataframe(session_state.df)


#st.table(df)

author = "Author: Cristian Valls"
st.header(author)

0 comments on commit 42e7cc1

Please sign in to comment.