#!/usr/bin/env python
# coding: utf-8

# # Backcasting Demo Notebook
# 
# _Loren Champlin_
# 
# Adapted from _Adarsh Pyarelal_'s WM 12 Month Evaluation Notebook 
# 
# As always, we begin with imports, and print out the commit hash for a rendered
# version of the notebook.

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get_ipython().run_line_magic('load_ext', 'autoreload')
get_ipython().run_line_magic('autoreload', '2')
get_ipython().run_line_magic('matplotlib', 'inline')
from IPython.display import set_matplotlib_formats
set_matplotlib_formats('retina')
import numpy as np
import pandas as pd
from delphi.db import engine
import random as rm
import delphi.evaluation_port as EN
import warnings
warnings.filterwarnings("ignore")
import logging
logging.getLogger().setLevel(logging.CRITICAL)
from delphi.cpp.DelphiPython import AnalysisGraph as AG, InitialBeta as IB, RNG
import time
import seaborn as sns
import matplotlib.pyplot as plt
from delphi.jupyter_tools import display
import seaborn as sns
from scipy import stats
import matplotlib.patches as mpatches


# Here I will set random seeds

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np.random.seed(87)
rm.seed(87)


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EN.get_data_value('Net migration',year=2012,month=2, use_heuristic=True)


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x = np.array([[1,2,2],[4,4,4],[8,3,4],[7,2,3]])
y = np.mean(x,axis=1)

ax = sns.lineplot(x=range(5),y=range(5))


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help(mpatches.Patch)


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query = " ".join(
        [
            f"select * from indicator",
        ]
    )

results = engine.execute(query)

list(results)


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query = " ".join(
        [
            f"select * from indicator",
            f"where `Variable` is 'Historical Production (Maize)'",
        ]
    )

results = engine.execute(query)

results_df2 = pd.DataFrame(results,columns=results.keys())


results_df2