The ChangeMonitor

Date Published: 21/10/2019

This project is currently my lovejoy, I am working on getting a bit of code and AI working. Its mainly also an excuse to do some AI pratice for my algebra engine

Date Published: 26/11/2019

It has been a month and the project has changed drastically over the past month, the project now follows as this:

To start with, the project will now use arduino ESP8266 to collect and push data to the cloud.
I am going to use the service ThingSpeak with MatLab, instead of python for data analysis
The end aim of the project to predict big weather changes, instead of finding the source of pollution, although I still aim to do that
To reiterate, the project now has two parts: to predict weather events and to show trends in pollution. I shall firstly focus on the weather prediction.

I came up with a quick, and very dirty, script to plot datasets:

 # -*- coding: utf-8 -*-
"""
Created on Fri Nov 22 11:16:20 2019

@author: james
"""

import numpy as np
import matplotlib.pyplot as plt
import pylab


def Fig(src, name):
    """
    A function that plots data from a csv and outputs a graph
    Input: src: A CSV file that is in the working directory
           name: The name of the output file
    Output: The figure, also saved to your working directory
    """
    data = np.genfromtxt(src, delimiter=',', dtype=None, skip_header=1, names=('year', 'month', 'tmax', 'tmin', 'AirFrost'))
        
    plt.title('Averaged Minimum Temperature per year from 1978 to 2019')
    plt.xlabel('Month')
    plt.ylabel('degrees')
    plt.bar(data['month'], data['tmin'], color='c')
    plt.savefig(name + ".png")
    
    x = data['month']
    y = data['tmin']
    # calc the trendline
    z = np.polyfit(x, y, 1)
    p = np.poly1d(z)
    pylab.plot(x,p(x),"r--")
    # the line equation:
    print("y=%.6fx+(%.6f)"%(z[0],z[1]))
    
Fig('CBNdata.csv', 'tmin')