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app.py
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app.py
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# Dependencies for data manipulation
import pandas as pd
import numpy as np
import simplejson
from flask import Flask, request, jsonify, render_template,json
#########################
# Flask set-up
app = Flask(__name__)
#########################
#########################
# Database set-up
#########################
# Dependencies for SQL
import sqlalchemy
from sqlalchemy.ext.automap import automap_base
from sqlalchemy.orm import Session
from sqlalchemy import create_engine, func, inspect, distinct
from flask_sqlalchemy import SQLAlchemy
app.config['SQLALCHEMY_DATABASE_URI'] = "sqlite:///db/bellybutton.sqlite"
db = SQLAlchemy(app)
# Create a database model
Base = automap_base() # Reflect an existing database into a new model
Base.prepare(db.engine, reflect = True) # Prepare the database
Base.classes.keys() # Find all the tables (classes) that automap found
# Save references for each table
Metadata = Base.classes.sample_metadata
Samples = Base.classes.samples
# Create session query that will load the whole Samples table (all columns)
qrySamples = db.session.query(Samples)
# Convert Samples to a pandas dataframe
df_Samples = pd.read_sql(qrySamples.statement, qrySamples.session.bind)
# Drop rows with at least one element missing
df_Samples = df_Samples.dropna()
df_Samples.head()
# Create a session query to get data for specific columns of the Metadata table
qryMeta = db.session.query(Metadata)
# Convert the query into a pandas dataframe
df_Meta = pd.read_sql(qryMeta.statement, qryMeta.session.bind)
df_Meta["EVENT"] = df_Meta["EVENT"].str.replace("BellyButtons", "") # remove BellyButtons prefix from events
df_Meta.head()
@app.route("/samples")
def samples():
# Calculate the abundance of each OTU_ID
df_Samples["abundance"] = df_Samples.sum(axis = 1)
df_Samples.head()
df_sorted = df_Samples.sort_values(by = ["abundance"], ascending = False)
x = df_sorted["otu_id"].values.tolist()
y = df_sorted["abundance"].values.tolist()
z = df_sorted["otu_label"].values.tolist()
y2 = [x/1000 for x in df_sorted["abundance"].values]
# Prepare data from Samples for graphs and for JSON
trace_Samples = {
"labels": z,
"values": y,
"labels2": x,
"marker_size": y2
}
return jsonify(trace_Samples)
@app.route("/samples/<sample>")
def samples1(sample):
# Retain only the column corresponding to the selected sample and information about it (for bubble chart)
df_sorted = df_Samples[["otu_id", "otu_label",str(sample)]].sort_values(by = [str(sample)], ascending = False)
x = df_sorted["otu_id"].values.tolist()
y = df_sorted[str(sample)].values.tolist()
z = df_sorted["otu_label"].values.tolist()
# Prepare data from Samples for graphs and for JSON
trace_Samples = {
"sample": sample,
"labels": z,
"values": y,
"labels2": x,
"marker_size": y
}
return jsonify(trace_Samples)
# Retain only the column corresponding to the selected sample and information about it (all otu_ids for pie chart)
df_sorted1 = df_Samples[["otu_id", "otu_label",str(sample)]].sort_values(by = [str(sample)], ascending = False)
# Prepare data from Samples for graphs and for JSON
trace_Samples2 = {
"sample": sample,
"labels": df_sorted1["otu_id"].values.tolist(),
"values": df_sorted1[str(sample)].values.tolist(),
"type": "pie",
}
return jsonify(trace_Samples2)
@app.route("/metadata")
def metadata():
# Prepare the trace from Metadata for graphs and for JSON
trace_Meta = {
"sample": df_Meta["sample"].values.tolist(),
"ethnicity": df_Meta["ETHNICITY"].values.tolist(),
"age": df_Meta["AGE"].values.tolist(),
"gender": df_Meta["GENDER"].values.tolist(),
"bbtype": df_Meta["BBTYPE"].values.tolist(),
"wfreq": df_Meta["WFREQ"].values.tolist(),
"sampling_event": df_Meta["EVENT"].values.tolist(),
"location": df_Meta["LOCATION"].values.tolist(),
}
# Convert dictionary to JSON string
jsonStr = simplejson.dumps(trace_Meta, ignore_nan = True)
# Parse JSON string to get JSON object
jsonObj = json.loads(jsonStr)
return jsonify(jsonObj)
@app.route("/metadata/<sample>")
def metadata1(sample):
# Create a session query to get data for specific columns of the Metadata table
sel = [Metadata.sample, Metadata.ETHNICITY, Metadata.AGE, Metadata.GENDER, Metadata.BBTYPE, Metadata.WFREQ, Metadata.EVENT, Metadata.LOCATION]
results = db.session.query(*sel).filter(Metadata.sample == sample).all()
# Prepare the trace from Metadata for graphs and for JSON
trace_Meta = {
"sample": results[0][0],
"ethnicity": results[0][1],
"age": results[0][2],
"gender": results[0][3],
"bbtype": results[0][4],
"wfreq": results[0][5],
"sampling_event": results[0][6].replace("BellyButtons", ""),
"location": results[0][7]
}
# Convert dictionary to JSON string
jsonStr = simplejson.dumps(trace_Meta, ignore_nan = True)
# Parse JSON string to get JSON object
jsonObj = json.loads(jsonStr)
return jsonify(jsonObj)
@app.route("/")
def home():
message = "Belly Button Biodiversity"
return render_template("index.html", message = message)
if __name__ == "__main__":
app.run()