Project Set 3

import numpy as np

x = np.arange(0.0,50.0,0.01)
y = np.sin(x) + np.cos(1.4*x) + 0.1*x
mean_y = y.mean()
min_index = np.argmin(y) #np.where(y==y.min())
max_index = np.argmax(y) #np.where(y==y.max())
print (f"mean y:{mean_y}")
print (f"max y:{y[min_index]} at x={x[min_index]}")
print (f"max y:{y[max_index]} at x={x[max_index]}")

"""inflation.py
   This program reads Consumer Price Index data and computes an approximation
   to the inflation rate
   @author: K. Holcomb
   @change: Modifications for Introduction to Python 20220202
   @change: Initial code 20130210
"""
import numpy as np
import matplotlib.pyplot as plt
import csv
import sys
import math

def prices(years,cpi,year1,year2):
    y1=math.nan
    y2=math.nan
    for y in range(len(years)):
        if years[y]==year1: y1=y
        if years[y]==year2: y2=y
    if math.isnan(y1) or math.isnan(y2):
        return None
    else:
       return cpi[y2]/cpi[y1]

def inflate(years,cpi):
    inflation=np.zeros(nyears-1)
    for n in range(nyears-1):
        inflation[n]=(cpi[n+1]-cpi[n])/12.
    return inflation

if len(sys.argv) < 2:
   sys.exit("No data file specified")


compare=int(float(input("Do you want to compare prices, 1 yes, 0 no:")))
if ( compare ):
    year1=int(float(input("Please enter the reference year:")))
    year2=int(float(input("Please enter the second year:")))
    amount=int(float(input("Please enter the amount to compare:")))

try:
   infile=sys.argv[1]
except (ValueError,TypeError):
   sys.exit("Invalid file name")

years_list=[]
cpi_list=[]

with open(infile,"r") as fin:
   #Skip header.
   fin.readline()
   for line in fin:
       (yr,factor)=line.rstrip("\r\n").split(",")
       years_list.append(float(yr))
       cpi_list.append(float(factor))

years =np.array(years_list)
cpi   =np.array(cpi_list)

nyears=np.size(years)

if ( compare ):
   ratio=prices(years,cpi,year1,year2)
   if ratio is None:
       print("One or more year specified is outside the data range")
       sys.exit()
   else:
       print("An item costing {:.2f} in {:} is about {:.2f} in {:}".format(amount,year1,ratio*amount,year2))

inflation=inflate(years,cpi)

fig, axs = plt.subplots(2, 1)
axs[0].plot(years, cpi)
axs[0].set_ylabel('CPI')

axs[1].plot(years[:-1], inflation)
axs[1].set_xlabel('year')
axs[1].set_ylabel('Inflation')

plt.show()

"""This program reads body-fat data, computes BMI (with appropriate units
   conversions), cleans the data, and plots the data before and after cleaning.
   Author:    K. Holcomb
   Changelog: Initial version 20130225
"""
import sys
import numpy as np
import matplotlib.pylab as plt
from scipy import stats

#Global constants
in2m =0.0254
oz2kg=0.0283495

def imp_ht_to_metric(ft,inch):
   """Converts imperial length units to meters"""
   return (ft*12.0+inch)*in2m

def imp_wt_to_metric(lb,oz):
   """Converts pounds and ounces to kg"""
   return (lb*16.0+oz)*oz2kg

def BMI(ht,wt):
   """Returns BMI from ht in meters and wt in kg"""
   return wt/ht**2

def reject_outliers(array):
   """Returns a mask of values of array below the outlier value."""
   """Kind of a dumb method.  Should use Chauvenet criterion at least."""
   cutoff=np.percentile(array,99)
   return array<cutoff

def main():
   #Get name of file from the command line
   if len(sys.argv) < 2:
      print("No file specified")
      sys.exit()

   infile=sys.argv[1]
   try:
       (bodyfat,weight,height)=np.loadtxt(infile,usecols=(0,2,3), \
                                           unpack=True,skiprows=1,delimiter=',')
   except IOError:
      print("Cannot read file")
      sys.exit()

   bmi=BMI(imp_ht_to_metric(0.,height),(imp_wt_to_metric(weight,0.)))

   plt.figure()
   plt.scatter(bodyfat,bmi)

   valid=reject_outliers(bmi)
   bodyfat=bodyfat[valid]
   bmi=bmi[valid]
   
   slope, intercept, r_value, p_value, std_err = stats.linregress(bodyfat,bmi)
   linear_fit=bodyfat*slope+intercept

   print("Mean BMI is %.2f, median is %.2f, and standard deviation is %.2f" % \
          (bmi.mean(),np.median(bmi),bmi.std()))

   print("The least-squares regression gives a r-value of %.2f" % r_value)

   plt.figure()
   plt.scatter(bodyfat,bmi)
   plt.plot(bodyfat,linear_fit)

   plt.figure()
   #Histogram using WHO BMI categories with somewhat arbitrary upper 
   #and lower bounds.
   bin_bounds=[5.,16.,18.5,25.,30.,35.,40.,60.]
   plt.hist(bmi,bins=bin_bounds)

   plt.show()

if __name__=='__main__':
   main()

import numpy as np
import matplotlib.pyplot as plt

date=[]
hi_temp_list=[]
lo_temp_list=[]
avg_wind_list=[]
wind_gust_list=[]
precip_list=[]
condition=[]
with open('cville_2017_april.csv', 'r') as file:
    file.readline()  #skip header
    for line in file:
        row=line.strip("\r\n").split(',')
        date.append(row[0])
        hi_temp_list.append(float(row[1]))
        lo_temp_list.append(float(row[2]))
        avg_wind_list.append(float(row[3]))
        wind_gust_list.append(float(row[4]))
        precip_list.append(float(row[5]))
        condition.append(row[6])

hi_temp=np.array(hi_temp_list)
lo_temp=np.array(lo_temp_list)
avg_wind=np.array(avg_wind_list)
wind_gust=np.array(wind_gust_list)
precip=np.array(precip_list)

xlabels=date

fig,axs1=plt.subplots()
axs1.set_title("Average Wind Speed")
axs1.set_xticklabels(rotation=45,labels=xlabels)
plt.xlabel("Date")
plt.ylabel("Speed in MPH")
plt.plot(avg_wind)
plt.show()

fig,axs2=plt.subplots()
axs2.set_title("Average Wind Speed and Gusts")
plt.plot(wind_gust,color='red')
plt.bar(xlabels,avg_wind)
axs2.xaxis.set_major_locator(plt.MaxNLocator(8))
axs2.set_xticklabels(rotation=45,labels=xlabels)
plt.xlabel("Date")
plt.ylabel("Speed in MPH")
axs2.legend()
plt.show()

#How to make a stacked chart if you want the values added
fig,axs3=plt.subplots()
axs3.set_title("Stacked High and Low Temperatures")
plt.bar(xlabels,lo_temp)
plt.bar(xlabels,hi_temp,bottom=lo_temp)
axs3.xaxis.set_major_locator(plt.MaxNLocator(8))
axs3.set_xticklabels(rotation=45,labels=xlabels)
plt.xlabel("Date")
plt.ylabel("Temperature (F)")
plt.show()

#What we really want
fig,axs4=plt.subplots()
x = np.arange(len(xlabels))  
#Calculate a reasonable width (found on StackOverflow)
width = np.min(np.diff(x))/3.
plt.bar(x-width/2,hi_temp,width=width,label="High",align="center")
plt.bar(x+width/2,lo_temp,width=width,label="Low",align="center")
axs4.xaxis.set_major_locator(plt.MaxNLocator(8))
axs4.set_xticklabels(rotation=45,labels=xlabels)
plt.xlabel("Date")
plt.ylabel("Temperature (F)")
plt.title("Daily High and Low Temperatures")
plt.legend(labels=["High","Low"])
plt.show()

days={}
for i in range(len(condition)):
    if condition[i] not in days:
        days[condition[i]]=1
    else:
        days[condition[i]]+=1
print(days)
   
fig,axs5=plt.subplots()
plt.title("Days Per Cloud Condition")
plt.ylabel("Count")
sky_condition=list(days.keys())
sky_count=days.values()
x=np.arange(len(days))
plt.bar(sky_condition,sky_count)
plt.show()