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jtrive84/pinmap.py

Created August 21, 2024 14:05
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Simple Dash pinmap application
Raw
pinmap.py
import datetime
import io
import os
import os.path
import sys
import time
import dash
from dash.dependencies import Input, Output
# import dash_auth
from dash import dcc
from dash import html
from dash import dash_table
import dash.dash_table.FormatTemplate as FormatTemplate
import numpy as np
import pandas as pd
import plotly.graph_objs as go
import pygeohash
VALID_AUTH_PAIRS = {
"skydog": "skydog",
}
__file__ = "C:/Users/i103455/repos/pinmap/app.py"
add2path = os.path.dirname(__file__)
sys.path.append(add2path)
# from prepdata import *
# excl_ = ["ATTAINED_AGE", "POLICY_NUMBER", "CLAIM_NUMBER", "FRAUD_INDICATIOR",
# "RESIDENT_STATE", "score", "priority", "DAILY_BENEFIT_INFL_BANDED",
# "SITUS_CURRENT", "PREMIUM_PAYMENT_MODE",]
# dfinput = dfall
# dfinput = dfinput.drop("FRAUD_INDICATOR", axis=1) # imported from prepdata
# claim_numbers = sorted(dfinput["CLAIM_NUMBER"].unique().tolist())
# dftypes = dfinput.dtypes
# dftypes = dftypes[~dftypes.index.isin(excl_)].reset_index(drop=False)
# dftypes.columns = ["fieldname", "datatype"]
# dftypes["plot"] = dftypes["datatype"].map(lambda v: "hist" if v==np.float_ else "bar")
# dftypes = dftypes.sort_values("fieldname").reset_index(drop=True)
# dftypes["varid"] = ["var" + "{}".format(i).zfill(2) for i in range(dftypes.shape[0])]
def load_data():
df = pd.read_csv("C:/Users/i103455/repos/pinmap/customers.csv")
# Get geohash for each lat lon pair.
df["Geohash"] = df.apply(lambda r: pygeohash.encode(latitude=r.Lat, longitude=r.Lon, precision=10), axis=1)
return df
dfinput = load_data()
# Dashboard initialization -----------------------------------------------------
app = dash.Dash(__name__, external_stylesheets=["assets/css/bWLwgP.css"])
# auth = dash_auth.BasicAuth(app, VALID_AUTH_PAIRS)
app.config["suppress_callback_exceptions"] = True
# Layout -----------------------------------------------------------------------
layout_init = [
html.Div([
html.Div([
html.H2("Policy Map"),
], style={"textAlign":"left", "width":"65%", "display":"inline-block"}
),
html.Div([
html.Img(src="assets/logo2.jpg", style={"height":"70%", "width":"70%"}),
], style={"float":"right", "width":"35%", "display":"inline-block", "padding":2}
)
]),
]
# Hidden div to facilitate data update.
app.layout = html.Div([
dcc.Tabs(id="tabs", children=[
# dcc.Tab(label="Policy View", children=[
# html.Div(layout_init),
# html.Div(id="intermediate", style={"display": "none"}),
# html.Div(id="ph", style={"display": "none"})
# ]),
dcc.Tab(label="Tabular Summary", children=[
html.Div(id="intermediate", style={"display": "none"}),
html.Div(id="ph", style={"display": "none"}),
html.Div([
html.H2("Policy Viewer"),
], style={"textAlign":"left", "width":"65%", "display":"inline-block"}
),
html.Div([
dash_table.DataTable(
# columns=[{"name": i, "id": i} for i in ["CLAIM_NUMBER", "POLICY_NUMBER", "priority"]],
# style_data={"border": "1px solid blue"},
# style_table={"overflowX":"scroll"},
# style_cell={'width': '150px'},
# style_data_conditional=[{"if": {"row_index": 4}, "backgroundColor": "#3D9970", 'color': 'white'}]
# fixed_rows={"headers":True, "data":0},
data=dfinput[["AccountNo", "Lat", "Lon", "Premium", "Geohash"]].to_dict("records"),
columns=[
{"id": "AccountNo", "name": "AccountNo", "type": "text"},
{"id": "Lat", "name": "Latitude", "type": "numeric"},
{"id": "Lon", "name": "Longitude", "type": "numeric"},
{"id": "Premium", "name": "Premium", "type": "numeric"},
{"id": "Geohash", "name": "Geohash", "type": "text"}
],
style_header={"border":"1px solid pink", "textAlign":"center"},
style_data_conditional=[
{"if": {"row_index":"odd"}, "backgroundColor":"rgb(248, 248, 248)"},
],
style_cell_conditional=[
{"if": {"column_id": "AccountNo"}, "textAlign": "left", "width": "10px", "maxWidth": "10px", "minWidth": "10px"},
{"if": {"column_id": "Lat"}, "textAlign": "right", "width": "10px", "maxWidth": "10px", "minWidth": "10px"},
{"if": {"column_id": "Lon"}, "textAlign": "right", "width": "10px", "maxWidth": "10px", "minWidth": "10px"},
{"if": {"column_id": "Premium"}, "textAlign": "right", "width": "10px", "maxWidth": "10px", "minWidth": "10px"},
{"if": {"column_id": "Geohash"}, "textAlign": "right", "width": "10px", "maxWidth": "10px", "minWidth": "10px"},
]
)], style={"padding":25, "width":"50%", "margin-left":"25%", "margin-right":"25%"}
)
]),
dcc.Tab(label="Map View", children=[
# html.Div([
# html.Label("Select Priority Threshold:"),
# html.Div(dcc.Dropdown(
# id="threshold", options=[{"label":i, "value":i} for i in np.arange(50, 101, 1)],
# value="90", placeholder="")),
# ], style={"width":200, "vertical-align":"middle", "padding":8},
# ),
html.Div([
dcc.Graph(id="mapview")
], style={"padding":0})
]),
])
])
# ------------------------------------------------------------------------------
# @app.callback(
# dash.dependencies.Output("claim_number", "options"),
# [dash.dependencies.Input("which_subset", "value")])
# def update_claim_numbers(which_subset):
# """
# Update options available in dropdown based on selcted radioitem.
# """
# if which_subset=="both":
# claim_numbers_ = sorted(np.unique(dfinput["CLAIM_NUMBER"].values))
# elif which_subset=="high":
# claim_numbers_ = sorted(np.unique(dfinput[dfinput.priority>50].CLAIM_NUMBER.values))
# elif which_subset=="low":
# claim_numbers_ = sorted(np.unique(dfinput[dfinput.priority<=50].CLAIM_NUMBER.values))
# return([{"label":i, "value":i} for i in claim_numbers_])
# @app.callback(
# dash.dependencies.Output("mdsumm", "children"),
# [dash.dependencies.Input("intermediate", "children"),
# dash.dependencies.Input("claim_number", "value")]
# )
# def update_md(_json, claim_number):
# """
# Update markdown summary of given claim's values per attribute.
# """
# # claim_number = "4301064194"
# # dfsubset_ = dfinput[dfinput["CLAIM_NUMBER"]==claim_number].reset_index(drop=True)
# dfsubset_ = pd.read_json(_json, orient="split").reset_index(drop=True)
# priority_ = dfsubset_.priority.values[0]
# md_str_ = "#### Summary for **{}** (priority score: {:.2f}): \n```".format(claim_number, priority_)
# vardict = {}
# for indx_ in range(dftypes.shape[0]):
# var_ = dftypes.loc[indx_, "fieldname"]
# val_ = dfsubset_.loc[0, var_]
# plot_ = dftypes.loc[indx_, "plot"]
# if plot_=="bar":
# # Determine which proportion of variable equal var_.
# dfvar_ = dfinput[var_].value_counts().sort_index().reset_index(drop=False)
# dfvar_["ratio"] = dfvar_[var_] / dfvar_[var_].sum()
# prop_ = dfvar_[dfvar_["index"]==val_]["ratio"].values[0]
# summstr_ = "* Proportion of records with {} = {}: {:.3f} \n".format(var_, val_, prop_)
# elif plot_=="hist":
# dfvar_ = dfinput[[var_]].sort_values(var_).reset_index(drop=True)
# dfvar_["cdf"] = dfvar_[var_] / dfvar_[var_].max()
# dfvar_["ecdf"] = (1 / dfvar_.shape[0])
# dfvar_["ecdf"] = np.cumsum(dfvar_["ecdf"])
# prop_ = dfvar_[dfvar_[var_]==val_]["ecdf"].values[0]
# summstr_ = "* Proportion of records with {} <= {}: {:.3f} \n".format(var_, val_, prop_)
# vardict[prop_] = summstr_
# _, varstrs_ = zip(*sorted(vardict.items(), key=lambda v: abs(.5 - v[0]), reverse=True))
# for mds_ in varstrs_: md_str_+=mds_
# md_str_+="```"
# return(md_str_)
@app.callback(
dash.dependencies.Output("intermediate", "children"),
[dash.dependencies.Input("ph", "children")]
)
def filter_data(ph):
"""
Filter dfinput to selected claim_number.
"""
return dfinput.to_json(orient="split")
# @app.callback(
# dash.dependencies.Output("table", "children"),
# [dash.dependencies.Input("ph", "value")]
# )
# def update_table(_json):
# """
# Update rendered HTML table. Uses dash_table.DataTable.
# """
# df = pd.read_json(_json, orient="split").reset_index(drop=True)
# data = df.to_dict("records")
# columns = [{"name": ii, "id": ii} for ii in df.columns]
# table = dash_table.DataTable(
# data=data, columns=columns, style_table={"overflowX": "scroll"},
# # style_cell={"minWidth":"0px", "maxWidth":"250px",},
# )
# return table
@app.callback(
dash.dependencies.Output("mapview", "figure"),
[dash.dependencies.Input("intermediate", "children")]
)
def update_map(_json):
"""
Update rendered dash_table.DataTable.
"""
mapbox_token = "pk.eyJ1IjoianRyaXZlIiwiYSI6ImNseTYwdzJsZzAwbXQydnByZm9yMXZyOHAifQ.pKr-NmN905Wyi9xmjR25hQ"
df = pd.read_json(io.StringIO(_json), orient="split")
lats = df["Lat"].tolist()
lons = df["Lon"].tolist()
acct = df["AccountNo"].tolist()
prem = df["Premium"].tolist()
desc = [f"{ii}: {jj}" for ii, jj in zip(acct, prem)]
mid_lat = sum(lats) / len(lats)
mid_lon = sum(lons) / len(lons)
trace = go.Scattermapbox(
lat=lats,
lon=lons,
mode='markers',
marker=go.scattermapbox.Marker(size=20, color="red"),
text=desc
)
layout = go.Layout(
title=None,
autosize=True,
hovermode='closest',
height=825,
width=1200,
margin=dict(l=20, r=20, b=20, t=20, pad=4),
mapbox=dict(
accesstoken=mapbox_token,
bearing=0,
center=dict(lat=mid_lat, lon=mid_lon),
pitch=0,
zoom=6
),
)
return({"data": [trace], "layout": layout})
# @app.callback(
# dash.dependencies.Output("mapping", "figure"),
# [dash.dependencies.Input("intermediate", "children"),
# dash.dependencies.Input("threshold", "value")]
# )
# def update_map(_json, threshold):
# """
# Update rendered dash_table.DataTable.
# """
# # dfsubset_ = dfinput[dfinput["CLAIM_NUMBER"]=="4301064194"].reset_index(drop=True)
# dfsubset_ = pd.read_json(_json, orient="split").reset_index(drop=True)
# dfmap_ = dfinput[dfinput.priority>=int(threshold)].reset_index(drop=True)
# dfmap_["count"] = 1
# dfmap_["avg_priority"] = dfmap_.groupby("RESIDENT_STATE", as_index=False)["priority"].transform("mean")
# keepcols_ = ["RESIDENT_STATE", "count", "avg_priority"]
# dfmap_ = dfmap_[keepcols_].groupby(["RESIDENT_STATE", "avg_priority"], as_index=False).sum()
# dfmap_["text"] = dfmap_["RESIDENT_STATE"] + "<br>" + " Count " + dfmap_["count"].astype(np.str) + \
# "<br>" + " Average Priority " + dfmap_["avg_priority"].astype(np.str)
# scl = [
# [0.0, 'rgb(242,240,247)'], [0.2, 'rgb(218,218,235)'], [0.4, 'rgb(188,189,220)'],
# [0.6, 'rgb(158,154,200)'], [0.8, 'rgb(117,107,177)'], [1.0, 'rgb(84,39,143)']
# ]
# trace_ = go.Choropleth(
# colorscale=scl,
# autocolorscale=False,
# locations=dfmap_["RESIDENT_STATE"],
# z=dfmap_["count"],
# locationmode="USA-states",
# text=dfmap_["text"],
# marker=go.choropleth.Marker(line=go.choropleth.marker.Line(color="rgb(255,255,255)", width=1)),
# # colorbar=go.choropleth.ColorBar(title="Frequency"),
# )
# layout_ = go.Layout(
# title=go.layout.Title(text="Distribution of Priority Scores by State"),
# geo = go.layout.Geo(
# scope="usa",
# projection=go.layout.geo.Projection(type="albers usa"),
# showlakes=True,
# lakecolor='rgb(255, 255, 255)'
# ),
# )
# return({"data":[trace_], "layout":layout_})
# @app.callback(
# dash.dependencies.Output("var00", "figure"),
# [dash.dependencies.Input("intermediate", "children"),
# dash.dependencies.Input("claim_number", "value"),]
# )
# def update_var00(_json, claim_number):
# """
# Update visualizations based on attributes of selected CLAIM_NUMBER.
# Note that a figure represents a dictionary containing two keys:
# {"data":trace, "layout":layout}.
# """
# # dfsubset_ = dfinput[dfinput["CLAIM_NUMBER"]=="4301064194"].reset_index(drop=True)
# dfsubset_ = pd.read_json(_json, orient="split")
# fieldname_ = dftypes.loc[0, "fieldname"]
# fieldval_ = dfsubset_.loc[0, fieldname_]
# plot_ = dftypes.loc[0, "plot"]
# if plot_=="bar":
# dexhibit_ = update_categorical(
# dfsubset=dfsubset_, fieldname=fieldname_, claim_number=claim_number,
# )
# elif plot_=="hist":
# dexhibit_ = update_continuous(
# dfsubset=dfsubset_, fieldname=fieldname_, claim_number=claim_number,
# )
# return(dexhibit_)
# @app.callback(
# dash.dependencies.Output("var01", "figure"),
# [dash.dependencies.Input("intermediate", "children"),
# dash.dependencies.Input("claim_number", "value"),]
# )
# def update_var01(_json, claim_number):
# """
# Update visualizations based on attributes of selected CLAIM_NUMBER.
# Note that a figure represents a dictionary containing two keys:
# {"data":trace, "layout":layout}.
# """
# # dfsubset_ = dfinput[dfinput["CLAIM_NUMBER"]=="4301064194"].reset_index(drop=True)
# dfsubset_ = pd.read_json(_json, orient="split")
# fieldname_ = dftypes.loc[1, "fieldname"]
# fieldval_ = dfsubset_.loc[0, fieldname_]
# plot_ = dftypes.loc[1, "plot"]
# if plot_=="bar":
# dexhibit_ = update_categorical(
# dfsubset=dfsubset_, fieldname=fieldname_, claim_number=claim_number,
# )
# elif plot_=="hist":
# dexhibit_ = update_continuous(
# dfsubset=dfsubset_, fieldname=fieldname_, claim_number=claim_number,
# )
# return(dexhibit_)
# @app.callback(
# dash.dependencies.Output("var02", "figure"),
# [dash.dependencies.Input("intermediate", "children"),
# dash.dependencies.Input("claim_number", "value"),]
# )
# def update_var02(_json, claim_number):
# """
# Update visualizations based on attributes of selected CLAIM_NUMBER.
# Note that a figure represents a dictionary containing two keys:
# {"data":trace, "layout":layout}.
# """
# # dfsubset_ = dfinput[dfinput["CLAIM_NUMBER"]=="4301064194"].reset_index(drop=True)
# dfsubset_ = pd.read_json(_json, orient="split")
# fieldname_ = dftypes.loc[2, "fieldname"]
# fieldval_ = dfsubset_.loc[0, fieldname_]
# plot_ = dftypes.loc[2, "plot"]
# if plot_=="bar":
# dexhibit_ = update_categorical(
# dfsubset=dfsubset_, fieldname=fieldname_, claim_number=claim_number,
# )
# elif plot_=="hist":
# dexhibit_ = update_continuous(
# dfsubset=dfsubset_, fieldname=fieldname_, claim_number=claim_number,
# )
# return(dexhibit_)
# @app.callback(
# dash.dependencies.Output("var03", "figure"),
# [dash.dependencies.Input("intermediate", "children"),
# dash.dependencies.Input("claim_number", "value"),]
# )
# def update_var03(_json, claim_number):
# """
# Update visualizations based on attributes of selected CLAIM_NUMBER.
# Note that a figure represents a dictionary containing two keys:
# {"data":trace, "layout":layout}.
# """
# # dfsubset_ = dfinput[dfinput["CLAIM_NUMBER"]=="4301064194"].reset_index(drop=True)
# dfsubset_ = pd.read_json(_json, orient="split")
# fieldname_ = dftypes.loc[3, "fieldname"]
# fieldval_ = dfsubset_.loc[0, fieldname_]
# plot_ = dftypes.loc[3, "plot"]
# if plot_=="bar":
# dexhibit_ = update_categorical(
# dfsubset=dfsubset_, fieldname=fieldname_, claim_number=claim_number,
# )
# elif plot_=="hist":
# dexhibit_ = update_continuous(
# dfsubset=dfsubset_, fieldname=fieldname_, claim_number=claim_number,
# )
# return(dexhibit_)
# @app.callback(
# dash.dependencies.Output("var04", "figure"),
# [dash.dependencies.Input("intermediate", "children"),
# dash.dependencies.Input("claim_number", "value"),]
# )
# def update_var04(_json, claim_number):
# """
# Update visualizations based on attributes of selected CLAIM_NUMBER.
# Note that a figure represents a dictionary containing two keys:
# {"data":trace, "layout":layout}.
# """
# # dfsubset_ = dfinput[dfinput["CLAIM_NUMBER"]=="4301064194"].reset_index(drop=True)
# dfsubset_ = pd.read_json(_json, orient="split")
# fieldname_ = dftypes.loc[4, "fieldname"]
# fieldval_ = dfsubset_.loc[0, fieldname_]
# plot_ = dftypes.loc[4, "plot"]
# if plot_=="bar":
# dexhibit_ = update_categorical(
# dfsubset=dfsubset_, fieldname=fieldname_, claim_number=claim_number,
# )
# elif plot_=="hist":
# dexhibit_ = update_continuous(
# dfsubset=dfsubset_, fieldname=fieldname_, claim_number=claim_number,
# )
# return(dexhibit_)
# @app.callback(
# dash.dependencies.Output("var05", "figure"),
# [dash.dependencies.Input("intermediate", "children"),
# dash.dependencies.Input("claim_number", "value"),]
# )
# def update_var05(_json, claim_number):
# """
# Update visualizations based on attributes of selected CLAIM_NUMBER.
# Note that a figure represents a dictionary containing two keys:
# {"data":trace, "layout":layout}.
# """
# # dfsubset_ = dfinput[dfinput["CLAIM_NUMBER"]=="4301064194"].reset_index(drop=True)
# dfsubset_ = pd.read_json(_json, orient="split")
# fieldname_ = dftypes.loc[5, "fieldname"]
# fieldval_ = dfsubset_.loc[0, fieldname_]
# plot_ = dftypes.loc[5, "plot"]
# if plot_=="bar":
# dexhibit_ = update_categorical(
# dfsubset=dfsubset_, fieldname=fieldname_, claim_number=claim_number,
# )
# elif plot_=="hist":
# dexhibit_ = update_continuous(
# dfsubset=dfsubset_, fieldname=fieldname_, claim_number=claim_number,
# )
# return(dexhibit_)
# @app.callback(
# dash.dependencies.Output("var06", "figure"),
# [dash.dependencies.Input("intermediate", "children"),
# dash.dependencies.Input("claim_number", "value"),]
# )
# def update_var06(_json, claim_number):
# """
# Update visualizations based on attributes of selected CLAIM_NUMBER.
# Note that a figure represents a dictionary containing two keys:
# {"data":trace, "layout":layout}.
# """
# # dfsubset_ = dfinput[dfinput["CLAIM_NUMBER"]=="4301064194"].reset_index(drop=True)
# dfsubset_ = pd.read_json(_json, orient="split")
# fieldname_ = dftypes.loc[6, "fieldname"]
# fieldval_ = dfsubset_.loc[0, fieldname_]
# plot_ = dftypes.loc[6, "plot"]
# if plot_=="bar":
# dexhibit_ = update_categorical(
# dfsubset=dfsubset_, fieldname=fieldname_, claim_number=claim_number,
# )
# elif plot_=="hist":
# dexhibit_ = update_continuous(
# dfsubset=dfsubset_, fieldname=fieldname_, claim_number=claim_number,
# )
# return(dexhibit_)
# @app.callback(
# dash.dependencies.Output("var07", "figure"),
# [dash.dependencies.Input("intermediate", "children"),
# dash.dependencies.Input("claim_number", "value"),]
# )
# def update_var07(_json, claim_number):
# """
# Update visualizations based on attributes of selected CLAIM_NUMBER.
# Note that a figure represents a dictionary containing two keys:
# {"data":trace, "layout":layout}.
# """
# # dfsubset_ = dfinput[dfinput["CLAIM_NUMBER"]=="4301064194"].reset_index(drop=True)
# dfsubset_ = pd.read_json(_json, orient="split")
# fieldname_ = dftypes.loc[7, "fieldname"]
# fieldval_ = dfsubset_.loc[0, fieldname_]
# plot_ = dftypes.loc[7, "plot"]
# if plot_=="bar":
# dexhibit_ = update_categorical(
# dfsubset=dfsubset_, fieldname=fieldname_, claim_number=claim_number,
# )
# elif plot_=="hist":
# dexhibit_ = update_continuous(
# dfsubset=dfsubset_, fieldname=fieldname_, claim_number=claim_number,
# )
# return(dexhibit_)
# @app.callback(
# dash.dependencies.Output("var08", "figure"),
# [dash.dependencies.Input("intermediate", "children"),
# dash.dependencies.Input("claim_number", "value"),]
# )
# def update_var08(_json, claim_number):
# """
# Update visualizations based on attributes of selected CLAIM_NUMBER.
# Note that a figure represents a dictionary containing two keys:
# {"data":trace, "layout":layout}.
# """
# # dfsubset_ = dfinput[dfinput["CLAIM_NUMBER"]=="4301064194"].reset_index(drop=True)
# dfsubset_ = pd.read_json(_json, orient="split")
# fieldname_ = dftypes.loc[8, "fieldname"]
# fieldval_ = dfsubset_.loc[0, fieldname_]
# plot_ = dftypes.loc[8, "plot"]
# if plot_=="bar":
# dexhibit_ = update_categorical(
# dfsubset=dfsubset_, fieldname=fieldname_, claim_number=claim_number,
# )
# elif plot_=="hist":
# dexhibit_ = update_continuous(
# dfsubset=dfsubset_, fieldname=fieldname_, claim_number=claim_number,
# )
# return(dexhibit_)
# @app.callback(
# dash.dependencies.Output("var09", "figure"),
# [dash.dependencies.Input("intermediate", "children"),
# dash.dependencies.Input("claim_number", "value"),]
# )
# def update_var09(_json, claim_number):
# """
# Update visualizations based on attributes of selected CLAIM_NUMBER.
# Note that a figure represents a dictionary containing two keys:
# {"data":trace, "layout":layout}.
# """
# # dfsubset_ = dfinput[dfinput["CLAIM_NUMBER"]=="4301064194"].reset_index(drop=True)
# dfsubset_ = pd.read_json(_json, orient="split")
# fieldname_ = dftypes.loc[9, "fieldname"]
# fieldval_ = dfsubset_.loc[0, fieldname_]
# plot_ = dftypes.loc[9, "plot"]
# if plot_=="bar":
# dexhibit_ = update_categorical(
# dfsubset=dfsubset_, fieldname=fieldname_, claim_number=claim_number,
# )
# elif plot_=="hist":
# dexhibit_ = update_continuous(
# dfsubset=dfsubset_, fieldname=fieldname_, claim_number=claim_number,
# )
# return(dexhibit_)
# @app.callback(
# dash.dependencies.Output("var10", "figure"),
# [dash.dependencies.Input("intermediate", "children"),
# dash.dependencies.Input("claim_number", "value"),]
# )
# def update_var10(_json, claim_number):
# """
# Update visualizations based on attributes of selected CLAIM_NUMBER.
# Note that a figure represents a dictionary containing two keys:
# {"data":trace, "layout":layout}.
# """
# # dfsubset_ = dfinput[dfinput["CLAIM_NUMBER"]=="4301064194"].reset_index(drop=True)
# dfsubset_ = pd.read_json(_json, orient="split")
# fieldname_ = dftypes.loc[10, "fieldname"]
# fieldval_ = dfsubset_.loc[0, fieldname_]
# plot_ = dftypes.loc[10, "plot"]
# if plot_=="bar":
# dexhibit_ = update_categorical(
# dfsubset=dfsubset_, fieldname=fieldname_, claim_number=claim_number,
# )
# elif plot_=="hist":
# dexhibit_ = update_continuous(
# dfsubset=dfsubset_, fieldname=fieldname_, claim_number=claim_number,
# )
# return(dexhibit_)
# @app.callback(
# dash.dependencies.Output("var11", "figure"),
# [dash.dependencies.Input("intermediate", "children"),
# dash.dependencies.Input("claim_number", "value"),]
# )
# def update_var11(_json, claim_number):
# """
# Update visualizations based on attributes of selected CLAIM_NUMBER.
# Note that a figure represents a dictionary containing two keys:
# {"data":trace, "layout":layout}.
# """
# # dfsubset_ = dfinput[dfinput["CLAIM_NUMBER"]=="4301064194"].reset_index(drop=True)
# dfsubset_ = pd.read_json(_json, orient="split")
# fieldname_ = dftypes.loc[11, "fieldname"]
# fieldval_ = dfsubset_.loc[0, fieldname_]
# plot_ = dftypes.loc[11, "plot"]
# if plot_=="bar":
# dexhibit_ = update_categorical(
# dfsubset=dfsubset_, fieldname=fieldname_, claim_number=claim_number,
# )
# elif plot_=="hist":
# dexhibit_ = update_continuous(
# dfsubset=dfsubset_, fieldname=fieldname_, claim_number=claim_number,
# )
# return(dexhibit_)
# @app.callback(
# dash.dependencies.Output("var12", "figure"),
# [dash.dependencies.Input("intermediate", "children"),
# dash.dependencies.Input("claim_number", "value"),]
# )
# def update_var12(_json, claim_number):
# """
# Update visualizations based on attributes of selected CLAIM_NUMBER.
# Note that a figure represents a dictionary containing two keys:
# {"data":trace, "layout":layout}.
# """
# # dfsubset_ = dfinput[dfinput["CLAIM_NUMBER"]=="4301064194"].reset_index(drop=True)
# dfsubset_ = pd.read_json(_json, orient="split")
# fieldname_ = dftypes.loc[12, "fieldname"]
# fieldval_ = dfsubset_.loc[0, fieldname_]
# plot_ = dftypes.loc[12, "plot"]
# if plot_=="bar":
# dexhibit_ = update_categorical(
# dfsubset=dfsubset_, fieldname=fieldname_, claim_number=claim_number,
# )
# elif plot_=="hist":
# dexhibit_ = update_continuous(
# dfsubset=dfsubset_, fieldname=fieldname_, claim_number=claim_number,
# )
# return(dexhibit_)
# @app.callback(
# dash.dependencies.Output("var13", "figure"),
# [dash.dependencies.Input("intermediate", "children"),
# dash.dependencies.Input("claim_number", "value"),]
# )
# def update_var13(_json, claim_number):
# """
# Update visualizations based on attributes of selected CLAIM_NUMBER.
# Note that a figure represents a dictionary containing two keys:
# {"data":trace, "layout":layout}.
# """
# # dfsubset_ = dfinput[dfinput["CLAIM_NUMBER"]=="4301064194"].reset_index(drop=True)
# dfsubset_ = pd.read_json(_json, orient="split")
# fieldname_ = dftypes.loc[13, "fieldname"]
# fieldval_ = dfsubset_.loc[0, fieldname_]
# plot_ = dftypes.loc[13, "plot"]
# if plot_=="bar":
# dexhibit_ = update_categorical(
# dfsubset=dfsubset_, fieldname=fieldname_, claim_number=claim_number,
# )
# elif plot_=="hist":
# dexhibit_ = update_continuous(
# dfsubset=dfsubset_, fieldname=fieldname_, claim_number=claim_number,
# )
# return(dexhibit_)
# @app.callback(
# dash.dependencies.Output("var14", "figure"),
# [dash.dependencies.Input("intermediate", "children"),
# dash.dependencies.Input("claim_number", "value"),]
# )
# def update_var14(_json, claim_number):
# """
# Update visualizations based on attributes of selected CLAIM_NUMBER.
# Note that a figure represents a dictionary containing two keys:
# {"data":trace, "layout":layout}.
# """
# # dfsubset_ = dfinput[dfinput["CLAIM_NUMBER"]=="4301064194"].reset_index(drop=True)
# dfsubset_ = pd.read_json(_json, orient="split")
# fieldname_ = dftypes.loc[14, "fieldname"]
# fieldval_ = dfsubset_.loc[0, fieldname_]
# plot_ = dftypes.loc[14, "plot"]
# if plot_=="bar":
# dexhibit_ = update_categorical(
# dfsubset=dfsubset_, fieldname=fieldname_, claim_number=claim_number,
# )
# elif plot_=="hist":
# dexhibit_ = update_continuous(
# dfsubset=dfsubset_, fieldname=fieldname_, claim_number=claim_number,
# )
# return(dexhibit_)
# @app.callback(
# dash.dependencies.Output("var15", "figure"),
# [dash.dependencies.Input("intermediate", "children"),
# dash.dependencies.Input("claim_number", "value"),]
# )
# def update_var15(_json, claim_number):
# """
# Update visualizations based on attributes of selected CLAIM_NUMBER.
# Note that a figure represents a dictionary containing two keys:
# {"data":trace, "layout":layout}.
# """
# # dfsubset_ = dfinput[dfinput["CLAIM_NUMBER"]=="4301064194"].reset_index(drop=True)
# dfsubset_ = pd.read_json(_json, orient="split")
# fieldname_ = dftypes.loc[15, "fieldname"]
# fieldval_ = dfsubset_.loc[0, fieldname_]
# plot_ = dftypes.loc[15, "plot"]
# if plot_=="bar":
# dexhibit_ = update_categorical(
# dfsubset=dfsubset_, fieldname=fieldname_, claim_number=claim_number,
# )
# elif plot_=="hist":
# dexhibit_ = update_continuous(
# dfsubset=dfsubset_, fieldname=fieldname_, claim_number=claim_number,
# )
# return(dexhibit_)
if __name__ == "__main__":
app.run_server(host='0.0.0.0', port=9999, debug=True, threaded=False)
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