Month: August 2020

{{This article was originally posted on December 22, 2016. My posts were lost and after being found I am re-posting them.}}

One day (in 2011) got a whim to try and write a Python program to that will try and find the best combinations of numbers (within given range and given tolerances) that will add up to a fixed target total. Didn’t think of much use for it then, but have used it in multiple instances to get some good results. The algorithm works as the diagram below :

The Python code for it :

import random
def generateBucketList():
    fixedTargetTotal = 94
    bucketMidPoints = [[10, 5], [5, 10], [6], [5], [5], [6], [6], [8], [6], [8], [5, 5, 5, 5]]
    bucketAssign = [1, 2, 3]
    rangeFromMidPoint = [2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 4]
    bucketMidPoints = sum(bucketMidPoints, [])
    finalAssign = []
    if sum(bucketMidPoints) > fixedTargetTotal:  # reduce(lambda x, y: x + y, bucketMidPoints) > fixedTargetTotal:
        while sum(bucketAssign) != fixedTargetTotal:  # reduce(lambda x, y: x + y, bucketAssign) != fixedTargetTotal:
            # bucketAssign = [random.randint(int(0.6*i),i) for i in bucketMidPoints]
            bucketAssign = []
            for i in bucketMidPoints:
                if i != 0:
                    bucketAssign.append(random.randint(int(0.6 * i), i))
                else:
                    bucketAssign.append(i)
    else:
        marks = "Bucket dispersal is not above given expected Target Total"
    # Print the list in given order from here
    startMarker = 0
    endMarker = 0
    for i in rangeFromMidPoint:
        endMarker = endMarker + i
        tempList = bucketAssign[startMarker: endMarker]
        finalAssign.append(tempList)
        startMarker = startMarker + i
    for i, j in enumerate(finalAssign):
        print i + 1, " -> ", j
    print "\n"
    print bucketAssign, sum(bucketAssign)  # reduce(lambda x, y: x + y, bucketAssign)

if __name__ == "__main__":
    generateBucketList()
    print "Demo!!!..."

{{This article was originally posted on December 22, 2016. My posts were lost and after being found I am re-posting them.}}

“””This is based on my binge study to learn about climate change and predicting weather at rural community levels using low power devices. Original thought was to try and do community level weather predicting so that locals are aware of the changes that are occuring around them and plan for agriculture better.“”” Hearing much about climate change and weather pattern changes got me thinking if I could make a simple weather prediction system. For hardware I had a raspberry pi that had temperature sensor, humidity sensor and pressure sensor. Then I didn’t have rain guage or wind direction sensor, but I wanted to suppose that if I had all that, could I use the raspberry pi to predict weather parameters at local level. My thought if I could have a lot of cheap weather monitoring stations at community schools in villages, then I could use my system to predict the weather at the station as well as pass on the data to other stations. So other stations could predict using their data and the one sent from the first one. slowly the data would be passed to each other resulting in more accurate predictions. Without bothering about how to pass the data I looked at ways of creating a simple wether prediction system that could easily work off raspberry pi as well as be able to scale up to parallelly be able to predict these parameters when used at aggregator levels. I settled on the windowing technique to do the prediction as described on the paper : http://www.hindawi.com/journals/isrn/2013/156540/ Weather data is available through a CSV file in following format:

Explanations : The way the algorithm is implemented right now is as follows: from the latest 7 days weather parameter data a 7 day dataframe is created this dataframe is compared against a running window of 7 days dataframe going through the whole year and then over years. the dataframe and testframe that creates the least delta is taken as similar weather predictions. The delta is added to dataframe and predictions given. My list of readings is below :Books :

• DETECTING TREND AND OTHER CHANGES IN HYDROLOGICAL DATA Zbigniew W. Kundzewicz and Alice Robson (Editors) WMO/TD-No. 1013 (Geneva, May 2000)
• Weather and Climate Extremes in a Changing Climate Regions of Focus: North America, Hawaii, Caribbean, and U.S. Pacific Islands U.S. Climate Change Science Program
• Synthesis and Assessment Product 3.3 June 2008
• Introduction to Time Series Analysis and Forecasting DOUGLAS C. MONTGOMERY, CHERYL L. JENNINGS, MURAT KULAHCI Wiley Publications 2008
• An Introductory Study on Time Series Modeling and Forecasting Ratnadip Adhikari R. K. Agrawal
• Python In Hydrology Sat Kumar Tomer Green Tea Press 2011

Articles :

• Trend analysis of rainfall and temperature data for India Sharad K. Jain and Vijay Kumar
• Detecting changes in rainfall pattern and seasonality index vis-`a-vis increasing water scarcity in Maharashtra Pulak Guhathakurta and Elizabeth Saji
• Trend Detection in the Temporal Pattern of the Precipitation at Different Timescales – Application to a Case Study: the Watershed of the Stream Gauging Station of Torrão Do Alentejo. Ana RAMALHEIRA, Maria Manuela PORTELA, Cristina FAEL
• Correlation and Regression without Sums of Squares (Kendall’s Tau) Rudy A. Gideon
• Change detection in hydrological records—a review of the methodology / Revue méthodologique de la détection de changements dans les chroniques hydrologiques Zbigniew W. Kundzewicz & Alice J. Robson
• Changes in the intra-annual rainfall pattern as an evidence of climate change Carlos Manuel Cotafo Martins
• Application of Data Mining Techniques in Weather Prediction and Climate Change Studies Folorunsho Olaiya

github repo : https://github.com/pravenj/weatherPredict

{{This article was originally posted on December 22, 2016. My posts were lost and after being found I am re-posting them.}}

Few years ago (I think in 2013), I read this article on Jump Search : http://www.stoimen.com/blog/2011/12/12/computer-algorithms-jump-search/ Since the code presented was in php, got the urge to try it in Python. So wrote the code, forgot about it and discovered it in my hard drive few months ago. So putting it up as an archive. The Jump Search works as in picture below:

My Python code is as given below:

import math

def jumpSearch(noToSearch, lis):
    lenOfList = len(lis)
    prev = 0
    counter = 0
    step = int(math.floor(math.sqrt(lenOfList)))
    while lis[step if step < lenOfList else (lenOfList-1)] < noToSearch:
        prev = step
        print "Inside while : ", prev
        step = step + int(math.floor(math.sqrt(lenOfList)))
        counter = counter + 1
        print "Count : ", counter
        if step >= lenOfList:
            break
    while lis[prev] < noToSearch:
        prev = prev + 1
        counter = counter + 1
        print "Count : ", counter
    return prev+1

if __name__ == "__main__":
    lis = range(1,1000)
    print jumpSearch(624, lis)