Temperature analysis

import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import datetime

# Download 5-min temperature data, for a nearby weather station. Look for the neareest 5-min weather stations in RI.
# These archives are available at:
# https://www1.ncdc.noaa.gov/pub/data/uscrn/products/subhourly01/2024/



# Use the pandas function pd.read_csv() to import the data.  Hint, you can indicate whitespace delimiters as '\s+'. 
# NOTE:  The NOAA file will take some special processing to create a datetime array.  See Pandas intro for an example.




#Kin_DF.info()

# Load the csv data you saved from your overnight data collection.



# Use the parse_dates option to ensure a datetime column is created.



# Use df.info() function to confirm it has all the correct columns and data types.

# Make it so that both time series use the datetime column as their row index.  
# Use the Pandas .set_index() function and apply your changes to the same dataframe using the inplace=True option.
# Must merge on index, so setting both timeseries to indices.


# Use the datetime column as the array index for the weather station data.


# Use the datetime colum as the array index for your home temp.

# The Kingston Weather data file covers the entire year up to the present.  We want to cut this down to the time period 
# that overlaps with your overnight temperature data.  
# Use boolean indices or the pandas .truncate() function to truncate the data to a time period that overlaps with
# your temperature data.

# Make a single panel plot with datetime on the x-axis and temperature on the y-axis.  Use this plot to confirm that
# the truncated weather station data overlaps with your overnight time series.

## Are there any bad data points?  If so, use boolean indices to remove them. 
# Hint: Don't make them NaNs or they will complicate the time series analysis.

# The time interval of the overnight temperature data has to match that of the NOAA weather station.  Use the Pandas 
# resample().mean() function to compute the running mean of your temperature time series at 5 minute intervals, to match the 
# NOAA data.

# Merge the two data sets together using the pandas .join() function.  

# Make a lag vector, k, that spans from -N to N-1, where N is the number of elements in Therm_5min.

# Plot the output from np.correlate() as a function of k to see how the correlation changes at negative lag (k < 0),
# zero lag (k = 0), and positive lag (k > 0).

# 1. Where are the two most correlated?
# 2. Are the series correlated or anti-correlated?
# 3. How long is the lag in minutes?  You can figure this out by multiplying k with your sampling interval.
# 4. Can you offer a theory why there is (or isn't) a lag between indoor and outdoor temperature?

# Subtract the mean from both time series to make both sets of measurements have zero mean.



# Use the function np.correlate(,,mode='full') in Python to compute the serial cross-correlation.
# Note, this Numpy function is fully compatible with Pandas dataframes as inputs.
# Use cf = ccf(T1,T2,type="correlation",plot=FALSE) in R to compute the serial cross-correlation.
#x = cf$lag; y = cf$acf


# Make the lag vector, k, that ranges from -N to N with steps of 1.



# Make a 2-panel plot.  In the first panel, plot the Overnight and Weather temperature data. In the second panel, plot the 
# cross-correlation as a function of lag.  Use these plots to answer the questions above.