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import datetime

import pandas as pd

import os

from pathlib import Path

def aggregate_by_month(df, col='T'):

"""Given a data frame of hourly data, compute statistics by month

Parameters

----------

df : DataFrame

Must have columns {'year', 'month'}

col : str, optional

The column to aggregate. Defaults to 'T'

Returns

-------

DataFrame

Indexed on the 15th of the month,

Has columns of `describe` + 'year' and 'month'

"""

gb = df.groupby(('year', 'month'))[col].describe()

new_index = [datetime.date(*m, *(15, )) for m in gb.index]

gb.reset_index(inplace=True)

gb.index = new_index

return gb

def aggregate_by_day(df, col='T'):

"""Given a data frame of hourly data, compute statistics by day

Parameters

----------

df : DataFrame

Must have columns {'year', 'month', 'day'}

col : str, optional

The column to aggregate. Defaults to 'T'

Returns

-------

DataFrame

Indexed by day.

Has columns of `describe` + 'year', 'month', and 'day'

"""

gb = df.groupby(('year', 'month', 'day'))[col].describe()

new_index = [datetime.date(*m) for m in gb.index]

gb.reset_index(inplace=True)

gb.index = new_index

return gb

def extract_month_of_daily(daily, year, month):

"""Given daily values, extract a given month

Parameters

----------

daily : DataFrame

must of columns {'year', 'month'}

year, month : int

The year and month of interest

Returns

-------

DataFrame

Indexed on days from start of month. Same columns as input

"""

ix = (daily['month'] == month) & (daily['year'] == year)

df = daily[ix]

idx = [(m - df.index[0]).days for m in df.index]

df.reset_index(inplace=True)

df.index = idx

return df

def extract_day_of_hourly(hourly_df, year, month, day):

"""Given a data frame with hourly data, extract data for year-month-day

Parameters

----------

hourly_df : DataFrame

Must have columns 'year', 'month', 'day' with the expected semantics and

a time index

year, month, day : int

The day to extract the data for

"""

ix = ((hourly_df['month'] == month) &

(hourly_df['year'] == year) &

(hourly_df['day'] == day))

df = hourly_df[ix]

midnight = datetime.datetime(year, month, day, 0, 0)

df.index = [(m - midnight).seconds / 3600 for m in df.index]

return df

def label_date(ax, label, date, df):

'''Helper function to annotate a date

``date`` is assumed to be in the index of ``df``

Parameters

----------

ax : Axes

The axes to draw to

label : str

The text of the label

date : object in index of df

The x coordinate

df : DataFrame

The data source

'''

y = df.loc[date]['mean']

return ax.annotate(label, (date, y),

ha='right',

xytext=(-10, -30),

textcoords='offset points',

arrowprops={'arrowstyle': '->'})

def load_data(dataset):

"""Load data from a given dataset

Parameters

----------

dataset : str

Searches from dataset.h5 in this file's directory

Returns

-------

DataFrame

Hourly temperature data

"""

p = Path(os.path.dirname(os.path.realpath(__file__))) / 'data'

fname = p / f'{dataset}.h5'

try:

return pd.read_hdf(str(fname))

except FileNotFoundError:

sources = {f.stem for f in p.iterdir() if

f.is_file() and f.name.endswith('h5')}

raise RuntimeError(f"Could not not find {dataset!r}. Existing "

f"datasets are {sources}")

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