CHEM455/CHEM555: Chemometrics

print(4 + 4)  # this command prints the results of 4+4 to the screen
4+4           #this one does not (unless it is the last command in the cell)
print(4 - 4)  #What does this do?
print(4 * 4 ) #and this
print(4 / 4)
print(4**2)

x =5      #this command assigns the value 5 to the variable x
          # but will not print the contents of x
print(x)  #this command prints the contents of x

x =  4+1   # this command assigns the results of the operation 4+1 to x 
           # but will not print the contents of x
print(x)   # ths command prints the value contained in x

x = 5
print(x*5)  ##this command prints x times 5 to the screen

x = 5
u = x*5

print(x,u)  #this command prints x and u to the screen

x = 5
u = x*5

print(x,u)    #this command prints x and u to the screen
print('x*5 = ',x)  #this command prints x to the screen with some text showing how it was calculated

# 
pi = 3.141592653589793238462643383279502884197169399375105820


print('pi = ',pi)  # this prints an undefined by you amount of digits
print('pi = {:.2f}'.format(pi))  # in this case you choose the significant digits to print

# many functions are avaible in modules or libraries
# in this example we will load the numpy module of functions
import numpy as np     #this command loads all of the functions in numpy and labels them np
a = 3-1
b = 2-4
r = np.sqrt( a**2 + b**2 )  ## note the np.sqrt, note the ** for power
print('np.sqrt( a**2 + b**2 ) = ',r)
print('np.sqrt( a**2 + b**2)= {:2.2f}'.format(r))

# many functions are avaible in modules or libraries
# in this example we will load the numpy module of functions

import numpy as np     #this command loads all of the functions in numpy and labels them np

pi = np.pi                   ## notice that we also get pi from the numpy (np) module
xrounded  = np.round(pi, 5)  ### round pi to 5 decimal places 
print(xrounded)

x = 1

who     ##this command prints the variables currently in memory

y = 100


whos   # this is a "magic" command and prints variables and their types

# many functions are avaible in modules or libraries
# in this example we will load the numpy module of functions
import numpy as np                 #this command loads all of the functions in numpy and labels them np


a = 3-1
b = 2-4
Q = np.arctan(a/b)
print('the angle Q is equal to {:2.3f}'.format(Q))

def function_name( inputvariable1, inputvariable2):          ## end the first line of a code block with a colon
    '''
    this is a comment section or help section that prints when you type ? before a function in jupyter
    notice the indentation.  this is important in python
    '''
    import numpy as np

    ### this is where you type the code for your calculations

    return outputvariable  ##return stuff back to the caller

def findq():  ## end the first line of a code block with a colon
    '''
    for now let's just have the function make the calculation and print it out.  
    Later we will make it more flexable
    '''
    import numpy as np                 #this command loads all of the functions in numpy and labels them np


    a = 3-1                  #Coordinates hard coded into the function 
    b = 2-4                  #Coordinates hard coded into the function
    Q = np.arctan(a/b)
    print('the angle Q is equal to {:.3f}'.format(Q))
    return
#notice the indentation is back to the first column.  So the above code block is ended
findq()     # you need the () they are part of the function.

def findq():  ## end the first line of a code block with a colon
    '''
        this function calculates the angle between to line segments.
        Usage:  angle = findq() prints the answer to the screen and returns the angle 
        The angle has units of radians
        '''
        import numpy as np                 #this command loads all of the functions in numpy and labels them np


        a = 3-1
        b = 2-4
        Q = np.arctan(a/b)
        print('the angle Q is equal to {:2.3f}'.format(Q))
        return  Q    #return angle Q 

angle = findq()                               ##store q in the variable angle
print('The square of the angle is ',angle**2, 'and the angle is',angle) #now we can use this angle for something else 

def sameasfindq():
    '''
    same behavior as findq
    '''
    print('the angle Q is equal to -0.785')
    return -0.785398163397
angle = sameasfindq()
print('The square of the angle is ',angle**2, 'and the angle is',angle)

def findq(a,b):   ## end the first line of a code block with a colon
        '''
        this function calculates the angle between to sides of a triangle.
        Usage:  Q = findq(a,b) prints the answer to the screen and returns the angle 
        The angle given has units of radians
        a is length of the opposite  side
        b  is the length of the adjacent side
        '''
        import numpy as np                 #this command loads all of the functions in numpy and labels them np


        #a = 3-1    #commmented out so you can see the change
        #b = 2-4    #commendted out so you can see the change
        Q = np.arctan(a/b)
        print('the angle Q is equal to {:2.3f}'.format(Q))
        return  Q    #return angle Q 
angle = findq(3-1,2-4)                               ##store q in the variable angle
a = 3-1
b = 2-4
angle=findq(a,b)
print('The square of the angle is ',angle**2, 'and the angle is',angle) #now we can use this angle for something else 

x = 1+2+3+4+5+6+7+8+9+10+11+12+13+14+15+16+17+18+19+20+21+22+23+24+25+26+27+28+29+30+31+32+33+34+35+36+37+38+39+40+41+42+43+44+45+46+47+48+49+50+51+52+53+54+55+56+57+58+59+60+61+62+63+64+65+66+67+68+69+70+71+72+73+74+75+76+77+78+79+80+81+82+83+84+85+86+87+88+89+90+91+92+93+94+95+96+97+98+99+100
print(x)

x = 4
if x == 3:                   #in python the conditional if line ends in a colon (:)  
    print('x was 3')
    x = x**2
    print('now it is nine')
else:                       # there is also a colon after else: 
    print('x was not 3')   

                                  # in python a code block (the whole if block) is "ended" by returning back to the 
                            # same column number as the i in if
print('Thanks for playing!')

refletters = 'abcdefg'      #this is a string 
questionletter = 'g'        #here is another one

if questionletter in refletters:                            #remember to end with a :
    print(questionletter, 'was in the set',refletters)
else:                                                      #a new code block begins so remember to end with a :
    print(questionletter, 'was NOT in the set',refletters)

print('Thanks for playing')

#this program tests if the number stored in  x to be greater than 3
# and if it is then set it to a maximun value of 3
x = 3.9
if  float(x)  == 3:         # the colon tells python that a code block has begun
    print('x was 3')
elif  x > 3:                      # another code block so you need another colon at the end of the line
    print('x was greater than 3')
    x = 3.0                         ## redefine x  as 3
elif x < 3:                       # another code block so you need another colon at the end of the line
    print('x was less than 3. So I letter it pass.')
else:                            # another code block so you need another colon at the end of the line
  pass                      #this command is only requred if this else or elif choise is empty

msg = 'x is now {:.1f}'.format(x)   ## some pretty printing of the result
print(msg)

#this program checks to see if x to be greater than 3 and  less than 1
x = 5
if x <= 3 & x >= 1: #both must be true to for the  conditional action to be executed
    print('x was between 1 and 3') #conditional action
else:
    print('x was outside of the range 1 and 3')

msg = 'x is  {:.1f}'.format(x)
print(msg)

def numtest(x):
    if x < 0:
        msg = '-'
    elif x > 0:
        msg = '+'
    elif x == 0:
        msg = 'nil'
    print(msg)
    return


## test the function
numtest(75)

words = 'This is a string in \t \t python. \n\t Isn\'t it awesome!'
print(words)

path = 'C:\some\name_of_directory'
print(path)

betterpath = r'C:\some\name_of_directory'
print(betterpath)

## un un un + ium
name = 3 * 'un' + 'ium'  # NOTICE the + between the un and ium + means concatenate in strings
print(name)

word = 'Python'
print(word[0])  # print the character in position 0

print(word[5])  # print character in position 5

##extraction of subsets of the string
x = word[1]   #extract the second letter in the string or charactor as index = 1
print('the string\'s second letter or index 1 is',x)

x = word[-1]  # extract the last character in the string 
print('last is ',x)

x = word[-2]  # extract the second-last character in the string 
print('second to last is',x)

word = 'Python'
### substrings
x = word[0:2]  # characters from position 0 (included) up to 2 (excluded)
print('substring',x)

x = word[2:5]  # characters from position 2 (included) up to 5 (excluded)
print('substring',x)

print(word[:3] + word[3:])  ## see note below

word = 'Python'
### substrings concatenation
x = word[:2] + word[2:]  
print('notice that ',x,' is the same as ',word)

x = word[:4] + word[4:]  
print('Also, notice that ',x,' is the same as ',word)

word = 'Python'
### substring slicing
x = word[:2]   # characters from the beginning to position 2 (excluded)
print('characters from the beginning to position 2 (excluded) are => ',x)

x = word[4:]   # characters from position 4 (included) to the end
print('characters from position 4 (included) to the end are => ',x)

x = word[-2:]  # characters from the second-to-last (included) to the end
print('characters from the second to last (included) to the end are => ',x)

word = 'Python'
x = word[4:42]
print(x)
print(word[42:])    ##what is this doing prints an empty string

word = 'Python'
print(word[3:])
print(word[:4])
print(word[:5])
print(word[-1])
print(word[2:5])
print(word[1:5])

word = 'Python'
print('p' + word[1:])

word = 'supercalifragilisticexpialidocious'
length = len(word)
print('the word ', word,' has ',length,' characters.')

squares = [1, 4, 9, 16, 25]  ## define a list by enclosing csv in []
print(squares)
letters = ['A','B','C','D']
print(letters)

squares = [1, 4, 9, 16, 25]  ## define a list by enclosing csv in []


x = squares[0]  # indexing returns the item
print('x =', x)

y = squares[-1]
print('y =', y)

z = squares[-3:]  # slicing returns a new list
print('z = ',z)

squares = [1, 4, 9, 16, 25]  ## define a list by enclosing csv in []

x = squares[:]
print('x = ',x)

squares = [1, 4, 9, 16, 25]  ## define a list by enclosing csv in []

x = squares + [36, 49, 64, 81, 100]  # concatentation with +
print('now x = ',x)

squares = [1, 4, 9, 16, 25]  ## define a list by enclosing csv in []

x = 3* squares  # repeated concatentation with *
print('now x = ',x)

squares = [1, 4, 9, 16, 25]  ## define a list by enclosing csv in []

cubes = [1, 8, 27, 65, 125]  # something's wrong here
print('something is wrong here:  ',cubes)
print('but ',4 ** 3, ' is the cube of 4')  # the cube of 4 is 64, not 65!

cubes[3] = 64  # replace the wrong value



print('now cubes  = ',cubes)

squares = [1, 4, 9, 16, 25]  ## define a list by enclosing csv in []

cubes = [1, 8, 27, 65, 125]  # something's wrong here
print('cubes was initially defined as ',cubes)

cubes.append(216)  # add the cube of 6
cubes.append(7 ** 3)  # and the cube of 7
print('appending 2 new values to cubes.  now cubes is ',cubes)

letters = ['a', 'b', 'c', 'd', 'e', 'f', 'g']
print('letters is ',letters)

# replace some values
letters[2:5] = ['C', 'D', 'E']
print('replacing indices 2-5 yields',letters)

# now remove them
letters[2:5] = []
print('droping the indeces 2-5 yeilds',letters)


# clear the list by replacing all the elements with an empty list
letters[:] = []
print('clearing the whole list yeilds and empty list',letters)

letters = ['a', 'b', 'c', 'd']
length = len(letters)
print('The length of this list is ',length)

### hand coding a list of lists

LL = [['I','think', 'we'],['are','here']]
print('hand coded list of lists ',LL)

## or more programatically
a = ['a', 'b', 'c']    #make 1 list
n = [1, 2, 3]          #make another list
x = [a, n]             # now make a list of lists   or nested lists
print('look at what x is ',x)

##indexing a nested list
b = x[0]
print('the 0th index in x is ',b, ' and is the sublist a =>',a)

## indexing a nested list
deepinside = x[0][1]  ## note the 2 sets of square brackets 
print('here is an item within the 0 index nested list = >',deepinside)
print('here is an item within the 1 index nested list = >',x[1][2])

import numpy as np                 #this command loads all of the functions in numpy and labels them np

from numpy.random import randint  ## this command loads the function randint in to memory for later use

subjects = ['I', 'You']
idx = randint(0,high=2)       # the 0 is the lowest integer (0th index)
                                  # ths high=3 is one count above the highest integer that you need
                                  # to make this flexible maybe don't hard code the high = 2
                                  # but get it from the list     

print('idx is ',idx)

for i in range(10):
    print(i)

start = 1
stop = 101
step = 1
count = 0                           ## this is the seed of the count
for i in range(start,stop,step):
    count =  count + i
    print(i, count)

start = 1
stop = 101
step = 1
L = []                         ## this starts a list to put the numbers in
for i in range(start,stop,step):
    L.append(i)
print(i, L)
Ls = sum(L) 
print('The sum is ', Ls)

L = ['The','cat', 'has','a','scary','smell']
for i in range(len(L)):
  print(L[i])

  L = ['The','cat', 'has','a','scary','smell']
  for word in L:
    print(word)
#Notice how there is no math here at all

L = ['The','cat', 'has','a','scary','smell']
P = ['THE','CAT', 'HAS','A','SCARY','SMELL']
for i,word in enumerate(L):  ##use the enumerate around a list to create an index generator
    print(i,word,P[i])       # i is the index of the list and word is the item in the list

# learning review check add up some stuff
#first do it not in a function
start = 12
stop = 115 
step = 1
count = 0
for i in range(start, stop + 1, step):
    count = count + i
print(i,count)

## then make a function
def addmup(start,stop, step):  #put the parts defined by the user as inputs
    '''
    Adds up all the numbers from start to stop 
    step is the step size between the start and stop, 1,2,3, etc.
    '''
    count = 0                 ## you start adding at zero
    for i in range(start, stop + 1, step):  ## notice stop + 1, range before the value in stop
        count = count + i       
    print('The sum of all values in the range (',start,' to ',stop,') is',count)
    return count

## test out your function
addmup(12,115,1)

def fibonacci(n):
    '''
    function to generate a list of the first nth numbers in the Fibonacci sequence
    n is the integer number in the fibonacci sequence
    n must be larger than 1
    '''

    F1 = 1              #first number in the Fibonacci sequence
    F2 = 1              # second number in the Fibonacci sequence
    fiblist = [F1,F2]        # seed the list with the first two fibonacci numbers
    for i in range(1,n-1):      # start loop up to n, note:  start with 1 because that is how most people count, n-1 already have fisrt 2 in list, remember in range(stop) the stop number is 1< stop
        F = F1+ F2       # calculate the next number
        fiblist.append(F)  #store the F in running list
        F1= F2              # Shift current F1 to F2
        F2 = F              # shift current F to F1   
    return fiblist          #return the list 

print(fibonacci(12))

if 'cat' in ['cat','dog','cow']:
    print('yes')

# many functions are avaible in modules or libraries
# in this example we will load the numpy module of functions

import numpy as np                 #this command loads all of the functions in numpy and labels them np


A = np.array([4,5,6,7])            #turn a list into an array
print(type(A))                     ## print the type of object 


print('the shape of the array is ',A.shape)               # print the shape of the array.

# many functions are avaible in modules or libraries
# in this example we will load the numpy module of functions

import numpy as np                 #this command loads all of the functions in numpy and labels them np
datalist = [15.2, 16.7, 17.3, 14.6, 13.9]       ## the org data
data = np.array(datalist)                     ## first make the list into an array 
sqdata = data**2                 ## square the data
print(datalist)
print(sqdata)
print(type(sqdata))

# many functions are avaible in modules or libraries
# in this example we will load the numpy module of functions

import numpy as np                 #this command loads all of the functions in numpy and labels them np

data = np.array([15.2, 16.7, 17.3, 14.6, 13.9] )  # onestep create of array

print('The zeroth index of the array is ', data[0])    #indexing returns a number
print('The first 3 items in the array are ', data[:3])  ## slicing returns an array

# many functions are avaible in modules or libraries
# in this example we will load the numpy module of functions

import numpy as np                 #this command loads all of the functions in numpy and labels them np

data = np.array([15.2, 16.7, 17.3, 14.6, 13.9] )  # onestep create of array

print('The zeroth index of the array is ', data[0])    #indexing returns a number

data[0] = 55.5                     ## replace this zeroth indexed number

print('The zeroth index of the array is now', data[0])    #indexing returns a number

# many functions are avaible in modules or libraries
# in this example we will load the numpy module of functions

import numpy as np                 #this command loads all of the functions in numpy and labels them np

## all ones
A = np.ones( (3,3) )  ## the (3,3) is the shape that you want

print('A is \n', A)  ## the \n prints a new line
print(A.shape)

## all zeros
B = np.ones( (4,4) )  ## the (4,4) is the shape

print('B is \n', B)    ## the \n prints a new line
print(B.shape)

## normally distributed random numbers
center = 3       ##center of random numbers
spread = 2       ## standard deivation of the random numbers 
shape = (2,4)    ## shape of the array created.  this is called a tuple
C = np.random.normal(loc=center,scale=spread,size=shape)
print('C is \n',C)  ## the \n prints a new line
print(C.shape)

## uniformly distributed random numbers
D = np.random.rand(3,5)                ## random numbers are spread out evenly between 0 and 1
print('D is \n ', D)         ## the \n prints a new line
print(D.shape)

# many functions are avaible in modules or libraries
# in this example we will load the numpy module of functions

import numpy as np                 #this command loads all of the functions in numpy and labels them np


data = np.array([15.2, 16.7, 17.3, 14.6, 13.9] )  # onestep create of array
print(data)
print(np.sin( data ))  
print(np.sum(data))
print(np.median(data))
print(np.min(data))         ## determines the miniumn value in an array
print(np.max(data))         ## determines the maximun value in an array
print(np.std(data,ddof=1))  ## determine the standard deviation "ddof = 1"  means use degree of freedom = 1, which is the words for n-1 in the bottom of the std. dev. equation.

import numpy as np  #this command loads all of the functions in numpy and labels them np

data = np.array([15.2, 16.7, 17.3, 14.6, 13.9] )  # onestep create of array

print( np.sum(data) ) ## print the np.sum of the data ndarray

print( data.sum() )  ## same as np.sum( )  but as a method: the dot .sum() makes it a method

import numpy as np  #this command loads all of the functions in numpy and labels them np

data = np.array([15.2, 16.7, 17.3, 14.6, 13.9] )  # onestep create of array

# then type data. and depress the tab button, and wait,  you will see a menu pop-up of all the methods

import numpy as np  #this command loads all of the functions in numpy and labels them np

## generic example:
#      name = {key:value,key:value}

hr = {'Aaron':47,
      'Ruth':60,
      'Bonds':73,
      'McGwire':70,
      'Sosa':66
     }

print( hr['Bonds'] )   

### a simplified spectroscopic example
wavelengths = np.array([200, 250, 300, 350, 400])
spec = np.array([0, 0.2,     0.5, 0.3,  0.001])

d = {'x':wavelengths, 'y':spec}

y = d['y']
print(y)

import numpy as np  #this command loads all of the functions in numpy and labels them np
import pandas as pd   ## this command loads the pandas module

hr = {'Aaron':47,
      'Ruth':60,
      'Bonds':73,
      'McGwire':70,
      'Sosa':66
     }

df = pd.DataFrame.from_dict(hr,orient='index',columns=['HR'])

print(df)  #the dataframe presents itself as a table with the rows labeled (called the index) and the column(s) labeled
print('')
print('****************** spacer *********************')
print('Average Homeruns \n',df.mean())  ## you can operate on the dataframe, NOTE:  the output of these operations is another dataframe


print('****************** spacer *********************')
#to get the values out of the dataframe use the .values method


hrdf = df.mean()
print(hrdf)
print(hrdf.values)  ## output as an array

import numpy as np  #this command loads all of the functions in numpy and labels them np
import pandas as pd   ## this command loads the pandas module


wavelengths = np.array([200, 250, 300, 350, 400])  ## wavelength labels for absorbance values
spec1 = np.array([0, 0.2,     0.5, 0.3,  0.001])  ## absorbance values
spec2 = np.array([0.2,0.5,0.1,0.0,0.0])           ## aborbance values


d = {'spectrum 1':spec1,
    'spectrum 2':spec2}                      ## make a dictionary

df = pd.DataFrame.from_dict(d,orient='index',columns=wavelengths)   ## make a dataframe

print('Type for spec',type(spec1))
print('Type for d',type(d))

print('Type for df',type(df))
print('****************** spacer *********************')
## as a dataframe you can take the average spectrum

print(df.mean())

print('****************** spacer *********************')
## or you can get the absorbacne values at a specific wavelength
print(df[300])

# many functions are avaible in modules or libraries
# in this example we will load the numpy module of functions
# you only need to load the modules once per file
import numpy as np                 #this command loads all of the functions in numpy and labels them np
import pandas as pd                # data organization module 
import matplotlib.pyplot as plt    # plotting module


## get/make the data
x  = np.arange(200,400)    # these will be the x values in the graph for a simulated  UV spectrum
xc = 250                   # this is the center of the band
sigma = 10                 # %sigma*2*sqrt(2*ln(2)) is the width at half height of the lambda max of the curve

spec =  np.exp(-1*((x-xc)**2)/(2*sigma**2))   ##this will make a Gaussian curve (idea shape of lots of data)

## process data


## plot data
fig, ax = plt.subplots(figsize=(5,4))   ## figsize is the aspect ratio of the figure
                                         ## ax is the variable for the axis object where you want to work   
ax.plot(x,spec)                          ## .plot(x,y) method to make a plot on the ax object

ax.set_xlim((200,400))                   ## set x dim edges of the plot
ax.set_xlabel('Wavelength /nm')          ## LaTeXworks here
ax.set_ylabel('Absorbance')              ## and here
ofile = 'figures/examplespec.png'        ## name of the output file
fig.savefig(ofile,dpi=100)      ## dpi is dots per inch and is a figure resolution thing

### This part is for making this website, so you don't need it
return ofile

# many functions are avaible in modules or libraries
# in this example we will load the numpy module of functions
import numpy as np                 #this command loads all of the functions in numpy and labels them np
import pandas as pd                # data organization module  
import matplotlib.pyplot as plt    # plotting module


## get data
C = [1.0, 2.0, 3.0, 4.0, 5.0]          #  these will be the x values in the graph they are simulating concentration in ppm
A = [0.1, 0.2, 0.3, 0.4, 0.5]    #this is the simulated Aborbance values
## process data


## plot data
fig, ax = plt.subplots(figsize=(5,4)) ## figsize is the aspect ratio of the figure
ax.scatter(C,A, s=55,marker='.')   ## ax is the variable for the axis where you want to work
                                   ## s is the size of the marker
                                   ## marker is the marker some choices: ('.','o', 'v', '^', '<', '>', '8', 's', 'p', '*', 'h', 'H', 'D', 'd', 'P', 'X')

ax.set_xlabel('Concentration /ppm')      ## latex works here
ax.set_ylabel('Absorbance')              ## and here
ofile = 'figures/examplescatter.png'
fig.savefig(ofile,dpi=100)      ## dpi is dots per inch and is a figure resotution thing

### This part is for making this website, so you don't need it
return ofile

# many functions are avaible in modules or libraries
# in this example we will load the numpy module of functions
import numpy as np                 #this command loads all of the functions in numpy and labels them np
import pandas as pd                # data organization module  
import matplotlib.pyplot as plt    # plotting module


## get data
mass = 10+np.random.randn(200)*0.29    ## these will be 200 simulated mass measurements normally distributed around 10 g

## process data


## plot data
fig, ax = plt.subplots(figsize=(5,4))              ## figsize is the aspect ratio of the figure
ax.hist(mass)                                     ## ax is the variable for the axis where you want to work

ax.set_xlabel('Mass /g')               ## latex works here
ax.set_ylabel('Count')
ofile = 'figures/examplehist.png'   ## the output filename
fig.savefig(ofile,dpi=100)      ## dpi is dots per inch and is a figure resolution thing

### This part is for making this website, so you don't need it
return ofile

# many functions are avaible in modules or libraries
# in this example we will load the numpy module of functions
import numpy as np                 #this command loads all of the functions in numpy and labels them np
import pandas as pd                # data organization module  
import matplotlib.pyplot as plt    # plotting module


## get data
ifile = 'data/sample1.csv'           ## the name and relative path of the data file
rawdata = np.genfromtxt(ifile,delimiter=",")         ## read the file output into an array

print('the shape of the d is ',rawdata.shape)    ## inspect the data size

x = rawdata[:,0]                                    ## extract/slice the wavenumbers (x values)
spec = rawdata[:,1]                                 ## extract/slice the absorbance values (y-values)

## process data


## plot data
fig, ax = plt.subplots(figsize=(5,4))    ## figsize is the aspect ratio of the figure
ax.plot(x,spec)                           ## ax is the variable for the axis where you want to work


ax.set_xlim((650,4000))            ## set graph's limits
ax.invert_xaxis()                  ## vibrational spectra need this

ax.set_xlabel('Wavenumber /cm$^{-1}$')      ## latex works here
ax.set_ylabel('Absorbance')                 ## latex works here
fig.tight_layout()                   ## removes a bunch of white space around figure
ofile = 'figures/dataspec.png'
fig.savefig(ofile,dpi=100)      ## dpi is dots per inch and is a figure resotution thing

### This part is for making this website, so you don't need it
return ofile

# many functions are avaible in modules or libraries
# in this example we will load the numpy module of functions
import numpy as np                 #this command loads all of the functions in numpy and labels them np
import pandas as pd                # data organization module  
import matplotlib.pyplot as plt    # plotting module

import scipy.io as sio             #open matlab files module

## get data
ifile = 'data/tolpxyl.mat'       ## the path and name of the datafile

mat_contents = sio.loadmat(ifile)     ## read the file output into a dictionary

print('The contents of the mat file is ',mat_contents.keys()) ##print the names of the contents of the matfile

Aorg = mat_contents['Aorg']                        ## retrieve the Spectra from the dictionary
xorg = mat_contents['xorg']                        ## retrieve the x values from the dictionary

print('the shape of the Aorg is ',Aorg.shape)     ## inspect the variable's size


x = xorg.T                                    ## xorg is stored funny in the mat file so we transpose it
A = Aorg                               

print('x and A shapes are ',x.shape,A.shape)    
## process data

# many functions are avaible in modules or libraries
# in this example we will load the numpy module of functions
import numpy as np                 #this command loads all of the functions in numpy and labels them np
import pandas as pd                # data organization module  
import matplotlib.pyplot as plt    # plotting module

import scipy.io as sio             #open matlab files module

## get data
ifile = 'data/glass_elemental_conc.xlsx'       ## the path and name of the datafile
df = pd.read_excel(ifile,index_col=0)


print('df \n',df)    
## process data

# many functions are avaible in modules or libraries
# in this example we will load the numpy module of functions
import numpy as np                 #this command loads all of the functions in numpy and labels them np
import pandas as pd                # data organization module  
import matplotlib.pyplot as plt    # plotting module


## get data
## one datafile
ifileA = 'data/sample1.csv'
rawdataA = np.genfromtxt(ifileA,delimiter=",")         ## read the file output into an array
print('the shape of the d is ',rawdataA.shape)
xA = rawdataA[:,0]                                    ## extract the wavenumbers
specA = rawdataA[:,1]                                 ## extract the absorbance values

## second datafile
ifileB = 'data/sample2.csv'
rawdataB = np.genfromtxt(ifileB,delimiter=",")         ## read the file output into an array
print('the shape of the d is ',rawdataB.shape)
xB = rawdataB[:,0]                                    ## extract the wavenumbers
specB = rawdataB[:,1]                                 ## extract the absorbance values


## process data


## plot data
fig, ax = plt.subplots(figsize=(12,9))  ## figsize is the aspect ratio of the figure
ax.plot(xA,specA,label='spec 1')        ## ax is the variable for the axis where you want to work
ax.plot(xB,specB,label='spec 2')        # do it again to plot the second spectrum 

ax.set_xlim((650,4000))                 ## set graph's limits
ax.invert_xaxis()                       ## vibrational spectra need this
ax.legend()                             ## call the legond for the axis to show it

ax.set_xlabel('Wavenumber /cm$^{-1}$')  ## latex works here
ax.set_ylabel('Absorbance')             ## latex works here
ofile = 'figures/dataspecoverlay2.png'
fig.savefig(ofile,dpi=100)      ## dpi is dots per inch and is a figure resotution thing


### This part is for making this website, so you don't need it
return ofile

# many functions are avaible in modules or libraries
# in this example we will load the numpy module of functions
import numpy as np                 #this command loads all of the functions in numpy and labels them np
import pandas as pd                # data organization module  
import matplotlib.pyplot as plt    # plotting module

## define some custom functions
def dataloader(ifile):
    rawdata = np.genfromtxt(ifile,delimiter=",")         ## read the file output into an array

    x = rawdata[:,0]                                    ## extract/slice the wavenumbers (x values)
    spec = rawdata[:,1]   
    return x,spec

## get data file list
filelist = ['specfile1.csv', 'specfile2.csv', 'specfile3.csv', 'specfile4.csv', 'specfile5.csv']  ## list of files
datahole = 'data/'



## load and plot data
fig, ax = plt.subplots(figsize=(12,9))  ## figsize is the aspect ratio of the figure

for ifile in filelist:                        ## loop through filelist
    name = ifile[:-4]                        ## create a legion name from file list
    x,spec = dataloader(datahole+ifile)    ## use you new function to load the data;notice the concatantion of filename and path
    ax.plot(x,spec,label=name)         ## plot each spectrum; add the label 



ax.set_xlim((650,4000))                 ## set graph's limits
ax.invert_xaxis()                       ## vibrational spectra need this
ax.legend()                             ## call the legond for the axis to show it

ax.set_xlabel('Wavenumber /cm$^{-1}$')  ## latex works here
ax.set_ylabel('Absorbance')             ## latex works here
ofile = 'figures/dataspecoverlayforloop.png'
fig.savefig(ofile,dpi=100)      ## dpi is dots per inch and is a figure resotution thing


### This part is for making this website, so you don't need it
return ofile

# many functions are avaible in modules or libraries
# in this example we will load the numpy module of functions
import numpy as np                 #this command loads all of the functions in numpy and labels them np
import pandas as pd                # data organization module  
import matplotlib.pyplot as plt    # plotting module

## define some custom functions
def dataloader(ifile):
    rawdata = np.genfromtxt(ifile,delimiter=",")         ## read the file output into an array

    x = rawdata[:,0]                                    ## extract/slice the wavenumbers (x values)
    spec = rawdata[:,1]   
    return x,spec

## get data file list
filelist = ['specfile1.csv', 'specfile2.csv', 'specfile3.csv', 'specfile4.csv', 'specfile5.csv']  ## list of files
datahole = 'data/'



## load and plot data
fig, ax = plt.subplots(figsize=(12,9))  ## figsize is the aspect ratio of the figure

A = np.zeros((5,3351))                 ## pre populate the array with zeros

for n,ifile in enumerate(filelist):                        ## loop through filelist; enumerate to create a counter
    name = ifile[:-4]                        ## create a legion name from file list
    x,spec = dataloader(datahole+ifile)    ## use you new function to load the data;notice the concatantion of filename and path
    ax.plot(x,spec,label=name)         ## plot each spectrum; add the label 
    A[n,:] = spec                ## store each spectrum in an array as they are loaded in the for loop


ax.set_xlim((650,4000))                 ## set graph's limits
ax.invert_xaxis()                       ## vibrational spectra need this
ax.legend()                             ## call the legond for the axis to show it

ax.set_xlabel('Wavenumber /cm$^{-1}$')  ## latex works here
ax.set_ylabel('Absorbance')             ## latex works here
ofile = 'figures/dataspecoverlayforlooparray.png'
fig.savefig(ofile,dpi=100)      ## dpi is dots per inch and is a figure resotution thing


### This part is for making this website, so you don't need it
return ofile

# many functions are avaible in modules or libraries
# in this example we will load the numpy module of functions
import numpy as np                 #this command loads all of the functions in numpy and labels them np
import pandas as pd                # data organization module  
import matplotlib.pyplot as plt    # plotting module

## define some custom functions
def dataloader(ifile):
    rawdata = np.genfromtxt(ifile,delimiter=",")         ## read the file output into an array

    x = rawdata[:,0]                                    ## extract/slice the wavenumbers (x values)
    spec = rawdata[:,1]   
    return x,spec

## multiple files from an excel file
datahole = 'data/'
filelist = ['specfile1.csv', 'specfile2.csv', 'specfile3.csv', 'specfile4.csv', 'specfile5.csv']

## load the data
datadict = {}            ## create an empty dictionary to store the data


for ifile in filelist:                    ## for loop through the filelist
    x,spec = dataloader(datahole+ifile)
    key = ifile[:-4]                       ## get dictionary key from ifile
    datadict[key] = spec                   ## store spectrum in dictiorary 
df = pd.DataFrame.from_dict(datadict,orient='index',columns=x)   ## convert dictionary to dataframe


fig, ax = plt.subplots(figsize=(12,9))    ## figsize is the aspect ratio of the figure
#for key in df.index:                      ## loop through dataframe one index at a time  
#    x = df.columns                        ## extract the xvalues from the columns of the dataframe 
#    sp = df[df.index == key].values.T    ## get the spectrum that correponds to the indexed spectrum (key)
#    ax.plot(x,sp,label=key)              ##plot that spectrum
df.T.plot(ax=ax)                 ## plot the dataframe with index as labels on the axis ax




ax.set_xlim((650,4000))                 ## set graph's limits
ax.invert_xaxis()                       ## vibrational spectra need this
ax.legend()                             ## call the legond for the axis to show it

ax.set_xlabel('Wavenumber /cm$^{-1}$')  ## latex works here
ax.set_ylabel('Absorbance')             ## latex works here
ofile = 'figures/dataspecoverlayforloopdict.png'
fig.savefig(ofile,dpi=100)      ## dpi is dots per inch and is a figure resotution thing


### This part is for making this website, so you don't need it
return ofile

# many functions are avaible in modules or libraries
# in this example we will load the numpy module of functions
import numpy as np                 #this command loads all of the functions in numpy and labels them np
import pandas as pd                # data organization module  
import matplotlib.pyplot as plt    # plotting module

## define some custom functions
def dataloader(ifile):
    rawdata = np.genfromtxt(ifile,delimiter=",")         ## read the file output into an array

    x = rawdata[:,0]                                    ## extract/slice the wavenumbers (x values)
    spec = rawdata[:,1]   
    return x,spec

## multiple files from an excel file
datahole = 'data/'
mdf = pd.read_excel('data/metadatafile.xlsx',index_col=0)  ## read file info from Excel meta data file
#mdf

## load the data
datadict = {}            ## create an empty dictionary to store the data
for ifile in mdf.index:       ## for loop through the mdf.index
    x,spec = dataloader(datahole+ifile)   ## load data
    key = ifile[:-4]    ## extract key and legend name from the mdf
    datadict[ifile] = spec            ## store the spectrum in the dictionary under the key

# the indices of the dataframe are the keys in the dicitonary (or part of the filenames)
# the columns of the df are the wavenumbers of the spectra
## all spectra must be the same size for this to work
df = pd.DataFrame.from_dict(datadict,orient='index',columns=x)     ## convert the dictionary to a dataframe



# plot the data
fig, ax = plt.subplots(figsize=(12,9))    ## figsize is the aspect ratio of the figure
#for ifile in df.index:                      ## for loop through each index
#    x = df.columns                        ## if you already have the x-values in memory then you don't need to do this step  
#    sp = df[df.index == ifile].values.T     # get the spectra out one index at a time
#    name = mdf[mdf.index == ifile].values[0][0]   ## get the legend name from mdf
#    ax.plot(x,sp,label=name)              ## plot each spectrum labeled with its name

leglist = mdf.key.values.tolist()            ## extract  the legend labels from the mdf
fig,ax = plt.subplots(figsize= (12,4))     ## create an axis and figure
df.T.plot(ax=ax)                    ## plot the dataframe with index as labels
ax.legend(leglist)                  ## call the legond for the axis to show it


ax.set_xlim((650,4000))                 ## set graph's limits
ax.invert_xaxis()                       ## vibrational spectra need this
#ax.legend()                             ## call the legond for the axis to show it

ax.set_xlabel('Wavenumber /cm$^{-1}$')  ## latex works here
ax.set_ylabel('Absorbance')             ## latex works here
ofile = 'figures/dataspecoverlayforloopdict_fromexcel.png'
fig.savefig(ofile,dpi=100)      ## dpi is dots per inch and is a figure resotution thing


### This part is for making this website, so you don't need it
return ofile

# many functions are avaible in modules or libraries
# in this example we will load the numpy module of functions
import numpy as np                 #this command loads all of the functions in numpy and labels them np
import pandas as pd                # data organization module  
import matplotlib.pyplot as plt    # plotting module
import scipy.io as sio             #module for opening matlab files

## get data
ifile = 'data/tolpxyl.mat'

mat_contents = sio.loadmat(ifile)  ## read the file output into a dictionary
print(mat_contents.keys())         ##print the names of the contents of the matfile

Aorg = mat_contents['Aorg']        ## retrieve the Spectra
xorg = mat_contents['xorg']        ## retrieve the x values
print('the shape of the Aorg is ',Aorg.shape)
x = xorg.T                                    
A = Aorg                               

## process data


## plot data
fig, ax = plt.subplots(figsize=(9,6)) ## figsize is the aspect ratio of the figure
ax.plot(x,A.T)                         ## ax is the variable for the axis where you want to work


ax.set_xlim((200,340))                 ## set graph's limits
#ax.invert_xaxis()                     ## vibrational spectra need this

ax.set_xlabel('Wavelength /nm')        ## latex works here
ax.set_ylabel('Absorbance')            ## latex works here
ofile = 'figures/multidataspecoverlay.png'
fig.savefig(ofile,dpi=100)      ## dpi is dots per inch and is a figure resotution thing
### This part is for making this website, so you don't need it
return ofile

# many functions are avaible in modules or libraries
# in this example we will load the numpy module of functions
import numpy as np                 #this command loads all of the functions in numpy and labels them np
import pandas as pd                # data organization module  
import matplotlib.pyplot as plt    # plotting module


## get data
def dataloader(ifile):
    import numpy as np
    rawdata = np.genfromtxt(ifile, delimiter=',')  ## read the file output into an array
    x = rawdata[:,0]                                 ## extract the wavenumbers
    spec = rawdata[:,1]                             ## extract the absorbance values
    return x, spec                              ## return the wavenumbers and absorbance values

ifile1 = 'sample2.csv'                  ## datafile named stored in variable
ifile2 = 'specfile1.csv'                ## datafile named stored in variable
datahole = 'data/'                         #location of datafiles (path)
x,spec1 = dataloader(datahole + ifile1)    ## load data file notice the concatenation of the path and datfile 
x,spec2 = dataloader(datahole + ifile2)



fig, ax = plt.subplots(figsize=(9,6))    ## create a figure and axis to plot onto
ax.plot(x,spec1,label='spec 1')            ## plot one file with label
ax.plot(x,spec2 ,label='spec 2')    ## plot another file with label  

ax.set_xlim((700,2000))                      ## change the view of the graph
ax.invert_xaxis()                             ##important for IR
ax.legend()                         ## show the legend
ax.set_ylabel('Absorbance')               ## set the y axis label
ax.set_xlabel('Wavenumber /cm$^{-1}$')    ## set the x axis label
ax.grid()                                ## turn on a basic grid to help see which bands lineup



# put a vertical line on the ax axis
# linestyles [‘solid’ | ‘dashed’, ‘dashdot’, ‘dotted’ | (offset, on-off-dash-seq) | '-' | '--' | '-.' | ':' | 'None' | ' ' | '']
# alpha is transparency 0 - 1 
ax.axvline(x=837,linestyle='--',color='g',alpha=0.5)

ofile = 'figures/dataspecoverlay.png'
fig.savefig(ofile,dpi=100)      ## dpi is dots per inch and is a figure resotution thing
### This part is for making this website, so you don't need it
return ofile

# many functions are avaible in modules or libraries
# in this example we will load the numpy module of functions
import numpy as np                 #this command loads all of the functions in numpy and labels them np
import pandas as pd                # data organization module  
import matplotlib.pyplot as plt    # plotting module


## get data
def dataloader(ifile):
    import numpy as np
    rawdata = np.genfromtxt(ifile, delimiter=',')  ## read the file output into an array
    x = rawdata[:,0]                                 ## extract the wavenumbers
    spec = rawdata[:,1]                             ## extract the absorbance values
    return x, spec                              ## return the wavenumbers and absorbance values

ifile1 = 'sample2.csv'                  ## datafile named stored in variable
ifile2 = 'specfile1.csv'                ## datafile named stored in variable
datahole = 'data/'                         #location of datafiles (path)
x,spec1 = dataloader(datahole + ifile1)    ## load data file notice the concatenation of the path and datfile 
x,spec2 = dataloader(datahole + ifile2)


offset = 0.2
fig, ax = plt.subplots(figsize=(9,6))    ## create a figure and axis to plot onto
ax.plot(x,spec1,label='spec 1')            ## plot one file with label
ax.plot(x,spec2 + offset ,label='spec 2')    ## plot another file with label  notice the offset

ax.set_xlim((700,2000))                      ## change the view of the graph
ax.invert_xaxis()                             ##important for IR
ax.legend()                         ## show the legend
ax.set_ylabel('Absorbance')               ## set the y axis label
ax.set_xlabel('Wavenumber /cm$^{-1}$')    ## set the x axis label
ax.grid()                                ## turn on a basic grid to help see which bands lineup

ax.set_yticklabels('')   # absorbance is no longer correct so turn it off

# add a bar that indicates the absorbance scale for the two spectra
ax.annotate('0.1 absorbance',(1800,0.20),(1800,0.3),ha='center',arrowprops={'arrowstyle':'|-|'})



# put a vertical line on the ax axis
# linestyles [‘solid’ | ‘dashed’, ‘dashdot’, ‘dotted’ | (offset, on-off-dash-seq) | '-' | '--' | '-.' | ':' | 'None' | ' ' | '']
# alpha is transparency 0 - 1 
ax.axvline(x=837,linestyle='--',color='g',alpha=0.5)

ofile = 'figures/dataspecstack.png'
fig.savefig(ofile,dpi=100)      ## dpi is dots per inch and is a figure resotution thing
### This part is for making this website, so you don't need it
return ofile

# many functions are avaible in modules or libraries
# in this example we will load the numpy module of functions
import numpy as np                 #this command loads all of the functions in numpy and labels them np
import pandas as pd                # data organization module  
import matplotlib.pyplot as plt    # plotting module


## get data
C = [1, 2, 3, 4, 5]          #  these will be the x values in the graph they are simulating concentration in ppm
A = [0.1, 0.2, 0.3, 0.4, 0.5]    #this is the simulated Aborbance values
## process data


## plot data
fig, ax = plt.subplots(figsize=(5,4))     ## figsize is the aspect ratio of the figure
ax.scatter(C,A)                            ## ax is the variable for the axis where you want to work

ax.set_xlabel('Concentration /ppm')               ## latex works here
ax.set_ylabel('Absorbance')
ofile = 'figures/examplescatter.png'
fig.savefig(ofile,dpi=100)      ## dpi is dots per inch and is a figure resotution thing

### This part is for making this website, so you don't need it
return ofile

# many functions are avaible in modules or libraries
# in this example we will load the numpy module of functions
import numpy as np                 #this command loads all of the functions in numpy and labels them np
import pandas as pd                # data organization module  
import matplotlib.pyplot as plt    # plotting module


## get data toluene and xylone
C1 = [1, 2, 3, 4, 5]          #  these will be the x values in the graph they are simulating concentration in ppm
A1 = [0.1, 0.2, 0.3, 0.4, 0.5]    #this is the simulated Aborbance values
C2 = [0.98, 2.10, 3.05, 4.09, 5.01]    # toluene
A2 = [0.11, 0.25, 0.39, 0.51, 0.691]    # toluene
## process data


## plot data
fig, ax = plt.subplots(figsize=(5,4))              ## figsize is the aspect ratio of the figure
ax.scatter(C1,A1,label='p-Xylene')                  ## ax is the variable for the axis where you want to work
ax.scatter(C2,A2,label='toluene')

ax.legend()                                          ## turn the legend on

ax.set_xlabel('Concentration of analyte in water /ppm')               ## latex works here
ax.set_ylabel('Absorbance at 252 nm')
ofile = 'figures/examplescattermulti.png'
fig.savefig(ofile,dpi=100)      ## dpi is dots per inch and is a figure resotution thing

### This part is for making this website, so you don't need it
return ofile

# many functions are avaible in modules or libraries
# in this example we will load the numpy module of functions
import numpy as np                 #this command loads all of the functions in numpy and labels them np
import pandas as pd                # data organization module  
import matplotlib.pyplot as plt    # plotting module


## get data
x  = np.arange(200,400)     # these will be the x values in the graph they are simulating a UV spectrum
xc = 250                         # this is the center of the band
sigma = 10                       # %sigma*2*sqrt(2*ln(2)) is the width at half height  of the Y max of the curve
spec =  np.exp(-1*((x-xc)**2)/(2*sigma**2))   ##this will make a Gaussian curve

## process data


## plot data
fig, ax = plt.subplots(figsize=(12,9))              ## figsize is the aspect ratio of the figure
ax.plot(x,spec)                                     ## ax is the variable for the axis where you want to work

ax.set_xlabel('Wavelength /nm')               ## latex works here
ax.set_ylabel('Absorbance')
ofile = 'figures/examplespec.png'
fig.savefig(ofile,dpi=100)      ## dpi is dots per inch and is a figure resotution thing
### This part is for making this website, so you don't need it
return ofile

# many functions are avaible in modules or libraries
# in this example we will load the numpy module of functions
import numpy as np                 #this command loads all of the functions in numpy and labels them np
import pandas as pd                # data organization module  
import matplotlib.pyplot as plt    # plotting module


## get data
x  = np.arange(200,400)                    # these will be the x values in the graph they are simulating a UV spectrum
xc = 250                         # this is the center of the band
sigma = 10                       # %sigma*2*sqrt(2*ln(2)) is the width at half height  of the Y max of the curve
spec =  np.exp(-1*((x-xc)**2)/(2*sigma**2))   ##this will make a Gaussian curve

## process data


## plot data
fig, ax = plt.subplots(figsize=(12,9))              ## figsize is the aspect ratio of the figure
ax.plot(x,spec)                                     ## ax is the variable for the axis where you want to work

ax.set_xlabel('Wavelength /nm')               ## latex works here
ax.set_ylabel('Absorbance')
ofile = 'figures/examplespecpub.png'
fig.savefig(ofile,dpi=300)      ## dpi is dots per inch and is a figure resotution thing
### This part is for making this website, so you don't need it
return ofile

# many functions are avaible in modules or libraries
# in this example we will load the numpy module of functions
import numpy as np                 #this command loads all of the functions in numpy and labels them np
import pandas as pd                # data organization module  
import matplotlib.pyplot as plt    # plotting module


## get data
x  = np.arange(200,400)                    # these will be the x values in the graph they are simulating a UV spectrum
xc = 250                         # this is the center of the band
sigma = 10                       # %sigma*2*sqrt(2*ln(2)) is the width at half height  of the Y max of the curve
spec =  np.exp(-1*((x-xc)**2)/(2*sigma**2))   ##this will make a Gaussian curve

## process data


## plot data
fig, ax = plt.subplots(figsize=(12,9))              ## figsize is the aspect ratio of the figure
ax.plot(x,spec)                                     ## ax is the variable for the axis where you want to work

ax.set_xlabel('Wavelength /nm')               ## latex works here
ax.set_ylabel('Absorbance')
ofile = 'figures/examplespecthumb.png'
fig.savefig(ofile,dpi=75)      ## dpi is dots per inch and is a figure resotution thing
### This part is for making this website, so you don't need it
return ofile

# many functions are avaible in modules or libraries
# in this example we will load the numpy module of functions
import numpy as np                 #this command loads all of the functions in numpy and labels them np
import pandas as pd                # data organization module  
import matplotlib.pyplot as plt    # plotting module


## get data
x  = np.arange(200,400)                    # these will be the x values in the graph they are simulating a UV spectrum
xc = 250                         # this is the center of the band
sigma = 10                       # %sigma*2*sqrt(2*ln(2)) is the width at half height  of the Y max of the curve
spec =  np.exp(-1*((x-xc)**2)/(2*sigma**2))   ##this will make a Gaussian curve

## process data


## plot data
fig, ax = plt.subplots(figsize=(12,9))              ## figsize is the aspect ratio of the figure
ax.plot(x,spec)                                     ## ax is the variable for the axis where you want to work

ax.set_xlabel('Wavelength /nm')               ## latex works here
ax.set_ylabel('Absorbance')
ofile = 'figures/examplespecbeamer.png'
fig.savefig(ofile,dpi=150)      ## dpi is dots per inch and is a figure resotution thing
### This part is for making this website, so you don't need it
return ofile

# many functions are avaible in modules or libraries
# in this example we will load the numpy module of functions
import numpy as np                 #this command loads all of the functions in numpy and labels them np
import pandas as pd                # data organization module  
import matplotlib.pyplot as plt    # plotting module


## get data
x  = np.arange(1600,1800)    # these will be the x values in the graph they are simulating a UV spectrum
xc = 1656                         # this is the center of the band
sigma = 50                       # %sigma*2*sqrt(2*ln(2)) is the width at half height  of the Y max of the curve
spec =  np.exp(-1*((x-xc)**2)/(2*sigma**2))   ##this will make a Gaussian curve

## process data


## plot data
fig, ax = plt.subplots(figsize=(12,9))              ## figsize is the aspect ratio of the figure
ax.plot(x,spec)                                     ## ax is the variable for the axis where you want to work

ax.invert_xaxis()                                   ## vibrational spectra need this

ax.set_xlabel('Wavenumber /cm$^{-1}$')               ## latex works here
ax.set_ylabel('Absorbance')
ofile = 'figures/examplespecreversex.png'
fig.savefig(ofile,dpi=75)      ## dpi is dots per inch and is a figure resotution thing
### This part is for making this website, so you don't need it
return ofile

# many functions are avaible in modules or libraries
# in this example we will load the numpy module of functions
import numpy as np                 #this command loads all of the functions in numpy and labels them np
import pandas as pd                # data organization module  
import matplotlib.pyplot as plt    # plotting module

lw = 5                            # use a variable to control graphs at once

## get data
x  = np.arange(1600,1800)           # these will be the x values in the graph they are simulating a UV spectrum
xc = 1656                         # this is the center of the band
sigma = 50                       # %sigma*2*sqrt(2*ln(2)) is the width at half height  of the Y max of the curve
spec =  np.exp(-1*((x-xc)**2)/(2*sigma**2))   ##this will make a Gaussian curve

## process data


## plot data
fig, ax = plt.subplots(figsize=(5,4))              ## figsize is the aspect ratio of the figure
ax.plot(x,spec, linewidth=lw)                                     ## ax is the variable for the axis where you want to work

ax.invert_xaxis()                                   ## vibrational spectra need this

ax.set_xlabel('Wavenumber /cm$^{-1}$')               ## latex works here
ax.set_ylabel('Absorbance')
ofile = 'figures/examplespeclinewidth.png'
fig.savefig(ofile,dpi=75)      ## dpi is dots per inch and is a figure resotution thing
### This part is for making this website, so you don't need it
return ofile

# many functions are avaible in modules or libraries
# in this example we will load the numpy module of functions
import numpy as np                 #this command loads all of the functions in numpy and labels them np
import pandas as pd                # data organization module  
import matplotlib.pyplot as plt    # plotting module

###these next lines write the parameters lw,lsz to plotting parameters
import matplotlib as mpl           ## this loads the moduule matplotlib as mpl
lw = 5           #this is the linewidth
lsz = 12         #this is the font size 
mpl.rcParams['xtick.labelsize'] = lsz
mpl.rcParams['ytick.labelsize'] = lsz
mpl.rcParams['font.size'] = lsz
mpl.rcParams['lines.linewidth'] = lw

## get data
x  = np.arange(1600,1800)           # these will be the x values in the graph they are simulating a UV spectrum
xc = 1656                         # this is the center of the band
sigma = 50                       # %sigma*2*sqrt(2*ln(2)) is the width at half height  of the Y max of the curve
spec =  np.exp(-1*((x-xc)**2)/(2*sigma**2))   ##this will make a Gaussian curve

## process data


## plot data
fig, ax = plt.subplots(figsize=(5,4))              ## figsize is the aspect ratio of the figure
ax.plot(x,spec)                                     ## ax is the variable for the axis where you want to work

ax.invert_xaxis()                                   ## vibrational spectra need this

ax.set_xlabel('Wavenumber /cm$^{-1}$')               ## latex works here
ax.set_ylabel('Absorbance')
ofile = 'figures/examplespeclinewidth2.png'
fig.savefig(ofile,dpi=75)      ## dpi is dots per inch and is a figure resotution thing
### This part is for making this website, so you don't need it
return ofile

# many functions are avaible in modules or libraries
# in this example we will load the numpy module of functions
import numpy as np                 #this command loads all of the functions in numpy and labels them np
import pandas as pd                # data organization module  
import matplotlib.pyplot as plt    # plotting module


## get data
x  = np.arange(200,400)                    # these will be the x values in the graph they are simulating a UV spectrum
xc = 350                         # this is the center of the band
sigma = 50                       # %sigma*2*sqrt(2*ln(2)) is the width at half height  of the Y max of the curve
spec =  np.exp(-1*((x-xc)**2)/(2*sigma**2))   ##this will make a Gaussian curve

## process data


## plot data
fig, ax = plt.subplots(figsize=(6,5))              ## figsize is the aspect ratio of the figure
ax.plot(x,spec,'r')                                     ## ax is the variable for the axis where you want to work
ax.plot(x,spec+0.5,'k')

ax.set_xlabel('Wavelength /nm')               ## latex works here
ax.set_ylabel('Absorbance')
ofile = 'figures/examplespeccolor1.png'
fig.savefig(ofile,dpi=75)      ## dpi is dots per inch and is a figure resotution thing
### This part is for making this website, so you don't need it
return ofile

# many functions are avaible in modules or libraries
# in this example we will load the numpy module of functions
import numpy as np                 #this command loads all of the functions in numpy and labels them np
import pandas as pd                # data organization module  
import matplotlib.pyplot as plt    # plotting module

# use a color tuple
# this is a dictionary of colors usage cl[0] yields blue (0,0,1)
# you don't have to use this dictionary, I just like it.
# you could also redefine the keys to something more memorable
cl = {0:(0, 0, 1),#;  %blue
      4:(0, 1, 0),#;  %  bright green
      6:(0, 1, 1),#;  % cyan
      3:(0, 0, 0),#;  %black
      1:(1, 0, 0),#;  % red
      7:(1, 1, 0),#  % yellow
      2:(1, 0, 1),#;  % magenta
      5:(1, 0.5, 0.5),#;  % pink brown
      8:(0.75, 1, 0.75),#  % dull light green
      9:(0.5, 0.5, 1),#  % greyblue
      10:(1, 0.5, 0),#;  % orange
      11:(0, 0.8, 0.5),#  % bluegreen
      12:(0.5, 0, 0.6),#  % purple  
      13:(1,0.75,0),## orange
      14:(0.5,0,1),  ##also purple, dark
      15:(0.78,1,0), ##ugly green
      16:(0,0.5,1),   #blue ski
      17:(1,0,0.25),  #pink red
      18:(0.39,0.39,0.49),  #grey blue
      19:(0.02,0.42,0.1),   #forest green
      20:(0.75,0.26,0.36)  #dark red 
}


## get data
x  = np.arange(200,400)                    # these will be the x values in the graph they are simulating a UV spectrum
xc = 350                         # this is the center of the band
sigma = 50                       # %sigma*2*sqrt(2*ln(2)) is the width at half height  of the Y max of the curve
spec =  np.exp(-1*((x-xc)**2)/(2*sigma**2))   ##this will make a Gaussian curve

## process data


## plot data
color1 = cl[0]   ## get the 0th color tuple 
color2 = cl[1]   ## get the 1st color tuple

print(color1, color2)
fig, ax = plt.subplots(figsize=(6,4))              ## figsize is the aspect ratio of the figure
ax.plot(x,spec,color=color1)                                     ## ax is the variable for the axis where you want to work
ax.plot(x,spec+0.5,color=color2)

ax.set_xlabel('Wavenumber /cm$^{-1}$')               ## latex works here
ax.set_ylabel('Absorbance')
ofile = 'figures/examplespeccolor1.png'
fig.savefig(ofile,dpi=75)      ## dpi is dots per inch and is a figure resotution thing
### This part is for making this website, so you don't need it
return ofile

# many functions are avaible in modules or libraries
# in this example we will load the numpy module of functions
import numpy as np                 #this command loads all of the functions in numpy and labels them np
import pandas as pd                # data organization module  
import matplotlib.pyplot as plt    # plotting module


## get data
C = [1, 2, 3, 4, 5]          #  these will be the x values in the graph they are simulating concentration in ppm
A = np.array([0.1, 0.2, 0.3, 0.4, 0.5])    #this is the simulated Aborbance values
## process data


## plot data
fig, ax = plt.subplots(figsize=(6,5))              ## figsize is the aspect ratio of the figure
ax.scatter(C,A,marker='o',color='r',label='data 1')   ## ax is the variable for the axis where you want to work
ax.scatter(C, A+0.5,marker='+',color='r',label='data 2') ## ax is the variable for the axis where you want to work
ax.scatter(C, A+0.3,marker='x',color='g',label='data 3')           

ax.legend()
ax.set_xlabel('Concentration /ppm')               ## latex works here
ax.set_ylabel('Absorbance')
ofile = 'figures/examplescattermakers.png'
fig.savefig(ofile,dpi=100)      ## dpi is dots per inch and is a figure resolution thing

### This part is for making this website, so you don't need it
return ofile

# many functions are avaible in modules or libraries
# in this example we will load the numpy module of functions
import numpy as np                 #this command loads all of the functions in numpy and labels them np
import pandas as pd                # data organization module  
import matplotlib.pyplot as plt    # plotting module


## get data
C = [1, 2, 3, 4, 5]          #  these will be the x values in the graph they are simulating concentration in ppm
A = np.array([0.1, 0.2, 0.3, 0.4, 0.5])    #this is the simulated Aborbance values
## process data


## plot data
fig, ax = plt.subplots(figsize=(6,4))              ## figsize is the aspect ratio of the figure
ax.scatter(C,A,marker='o',color='r')                 ## ax is the variable for the axis where you want to work



ax.set_xlabel('Concentration /M')               ## latex works here
ax.set_ylabel('Absorbance')
ofile = 'figures/exampleaxislabels.png'
fig.savefig(ofile,dpi=100)      ## dpi is dots per inch and is a figure resotution thing

### This part is for making this website, so you don't need it
return ofile

# many functions are avaible in modules or libraries
# in this example we will load the numpy module of functions
import numpy as np                 #this command loads all of the functions in numpy and labels them np
import pandas as pd                # data organization module  
import matplotlib.pyplot as plt    # plotting module

###these next lines write the parameters lw,lsz to plotting parameters for the whole jupyter/python session
import matplotlib as mpl           ## this loads the module matplotlib as mpl
lw = 5           #this is the linewidth
lsz = 50         #this is the font size, 50 is probably WAAAAAYYYYY too big.
mpl.rcParams['xtick.labelsize'] = lsz
mpl.rcParams['ytick.labelsize'] = lsz
mpl.rcParams['font.size'] = lsz
mpl.rcParams['lines.linewidth'] = lw

## get data
x  = np.arange(1600,1800)           # these will be the x values in the graph they are simulating a UV spectrum
xc = 1656                         # this is the center of the band
sigma = 50                       # %sigma*2*sqrt(2*ln(2)) is the width at half height  of the Y max of the curve
spec =  np.exp(-1*((x-xc)**2)/(2*sigma**2))   ##this will make a Gaussian curve

## process data


## plot data
fig, ax = plt.subplots(figsize=(12,9))              ## figsize is the aspect ratio of the figure
ax.plot(x,spec)                                     ## ax is the variable for the axis where you want to work

ax.invert_xaxis()                                   ## vibrational spectra need this

ax.set_xlabel('Wavenumber /cm$^{-1}$')               ## latex works here
ax.set_ylabel('Absorbance')
ofile = 'figures/examplesfont1.png'
fig.savefig(ofile,dpi=75)      ## dpi is dots per inch and is a figure resotution thing
### This part is for making this website, so you don't need it
return ofile

# many functions are avaible in modules or libraries
# in this example we will load the numpy module of functions
import numpy as np                 #this command loads all of the functions in numpy and labels them np
import pandas as pd                # data organization module  
import matplotlib.pyplot as plt    # plotting module


## get data
x  = np.arange(200,400)           # these will be the x values in the graph they are simulating a UV spectrum
xc = 250                         # this is the center of the band
sigma = 30                       # %sigma*2*sqrt(2*ln(2)) is the width at half height  of the Y max of the curve
spec =  np.exp(-1*((x-xc)**2)/(2*sigma**2))   ##this will make a Gaussian curve

## process data


## plot data
fig = plt.figure(figsize=(12,8))
ax1 = fig.add_subplot(211)      ## axis 1 for subplot 1
ax1.plot(x,spec)
ax1.set_xlabel('Wavelength /nm')               ## latex works here
ax1.set_ylabel('Absorbance')

ax2 = fig.add_subplot(212)    ## axis 2 for subplot 2
ax2.plot(x,np.sin(x))
ax2.set_xlabel('Time /min')
ax2.set_ylabel('Aplitude /V')

ofile = 'figures/examplesuplots.png'
fig.savefig(ofile,dpi=75)      ## dpi is dots per inch and is a figure resotution thing
### This part is for making this website, so you don't need it
return ofile

# many functions are avaible in modules or libraries
# in this example we will load the numpy module of functions
import numpy as np                 #this command loads all of the functions in numpy and labels them np
import pandas as pd                # data organization module  
import matplotlib.pyplot as plt    # plotting module

from matplotlib.patches import Rectangle ## for drawing the rectangle

## get data
x  = np.arange(200,400,0.1)           # these will be the x values in the graph they are simulating a UV spectrum
xc = 250                         # this is the center of the band
sigma = 30                       # %sigma*2*sqrt(2*ln(2)) is the width at half height  of the Y max of the curve
spec =  np.exp(-1*((x-xc)**2)/(2*sigma**2))   ##this will make a Gaussian curve

## process data


## plot data
fig = plt.figure(figsize=(12,8))
ax0 = fig.add_subplot(211)             ## whole specturm
ax0.plot(x,np.sin(x))
ax0.set_xlabel('Wavelength /nm')               ## latex works here
ax0.set_ylabel('Absorbance')
ax0.add_patch(Rectangle((225,0.4),width=50, height=0.6,alpha=0.5))

ax1 = fig.add_subplot(212)
ax1.plot(x,np.sin(x))
ax1.set_xlabel('Wavelength /nm')               ## latex works here
ax1.set_ylabel('Absorbance')

ax1.set_xlim(225,275)          ### control the view of lower graph
ax1.set_ylim(0.4, 1)           ### control the view


ofile = 'figures/examplezoom.png'
fig.savefig(ofile,dpi=75)      ## dpi is dots per inch and is a figure resotution thing
### This part is for making this website, so you don't need it
return ofile

# many functions are avaible in modules or libraries
# in this example we will load the numpy module of functions
import numpy as np                 #this command loads all of the functions in numpy and labels them np
import pandas as pd                # data organization module  
import matplotlib.pyplot as plt    # plotting module
import scipy.io as sio             #open matlab files

## get data
ifile = 'data/tolpxyl.mat'

mat_contents = sio.loadmat(ifile)                ## read the file output into a dictionary
print(mat_contents.keys())                          ##print the names of the contents of the matfile

Aorg = mat_contents['Aorg']                        ## retrieve the Spectra
xorg = mat_contents['xorg']                        ## retrieve the x values
print('the shape of the Aorg is ',Aorg.shape)
x = xorg.T                                    
A = Aorg                               
C = mat_contents['Ctrue']    ## concentration matrix
## process data


## plot data
fig = plt.figure(figsize=(12,8))  ## figsize is the aspect ratio of the figure
ax1 = fig.add_subplot(211)       ## ax1 is the variable for the axis where you want to work      
ax1.plot(x,A.T)                                     

ax1.set_xlim((200,340))                             ## set graph's limits

# put a vertical line on the ax axis
# linestyles [‘solid’ | ‘dashed’, ‘dashdot’, ‘dotted’ | (offset, on-off-dash-seq) | '-' | '--' | '-.' | ':' | 'None' | ' ' | '']
# alpha is transparency 0 - 1 
ax1.axvline(x=222.5,linestyle='--',color='k',alpha=0.9)
ax1.axvline(x=274,linestyle='--',color='r',alpha=0.9)

ax1.text(224, 2.62, ' this band is not linear with concentration')  ## text annotations
ax1.text(280,1.3, '$\leftarrow$ this band  is linear with concentration')  ##latex annotations

ax1.set_xlabel('Wavelength /nm')               ## latex works here
ax1.set_ylabel('Absorbance')

ax2 = fig.add_subplot(212)
ax2.scatter(C[:,0],A[:,304],color='r',label='274 nm')
ax2.scatter(C[:,0],A[:,509],color='k',label='222 nm')
ax2.set_xlabel('Analyte Concentration v/v /%')               ## latex works here
ax2.set_ylabel('Absorbance')
ax2.legend()

ofile = 'figures/exampleverticalline.png'
fig.savefig(ofile,dpi=100)      ## dpi is dots per inch and is a figure resotution thing
### This part is for making this website, so you don't need it
return ofile

# many functions are avaible in modules or libraries
# in this example we will load the numpy module of functions
import numpy as np                 #this command loads all of the functions in numpy and labels them np
import pandas as pd                # data organization module  
import matplotlib.pyplot as plt    # plotting module
import scipy.io as sio             #open matlab files

## get data
ifile = 'data/tolpxyl.mat'

mat_contents = sio.loadmat(ifile)                ## read the file output into a dictionary
print(mat_contents.keys())                          ##print the names of the contents of the matfile

Aorg = mat_contents['Aorg']                        ## retrieve the Spectra
xorg = mat_contents['xorg']                        ## retrieve the x values
print('the shape of the Aorg is ',Aorg.shape)
x = xorg.T                                    
A = Aorg                               

## process data


## plot data
fig, ax = plt.subplots(figsize=(12,9))              ## figsize is the aspect ratio of the figure
ax.plot(x,A.T)                                     ## ax is the variable for the axis where you want to work


ax.set_xlim((200,340))                             ## set graph's limits

# which ---> major, minor, both
# Turn on the minor TICKS, which are required for the minor GRID
ax.minorticks_on()

ax.grid(True, which='major')                ##select which to show, 'major' grid lines or 'minor'

ax.set_xlabel('Wavelength /nm')               ## latex works here
ax.set_ylabel('Absorbance')
ofile = 'figures/examplegrid.png'
fig.savefig(ofile,dpi=300)      ## dpi is dots per inch and is a figure resotution thing
### This part is for making this website, so you don't need it
return ofile

# many functions are avaible in modules or libraries
# in this example we will load the numpy module of functions
import numpy as np                 #this command loads all of the functions in numpy and labels them np
import pandas as pd                # data organization module  
import matplotlib.pyplot as plt    # plotting module
import scipy.io as sio             #open matlab files

## get data
ifile = 'data/tolpxyl.mat'

mat_contents = sio.loadmat(ifile)                ## read the file output into a dictionary
print(mat_contents.keys())                          ##print the names of the contents of the matfile

Aorg = mat_contents['Aorg']                        ## retrieve the Spectra
xorg = mat_contents['xorg']                        ## retrieve the x values
print('the shape of the Aorg is ',Aorg.shape)
x = xorg.T                                    
A = Aorg                               

## process data


## plot data
fig, ax = plt.subplots(figsize=(12,9))              ## figsize is the aspect ratio of the figure
ax.plot(x,A.T)                                     ## ax is the variable for the axis where you want to work


ax.set_xlim((200,340))                             ## set graph's limits

ax.text(275,1.2, '(a)',fontsize=25)  ## this fontsize is too big 

ax.set_xlabel('Wavelength /nm')               ## latex works here
ax.set_ylabel('Absorbance')
ofile = 'figures/exampletext.png'
fig.savefig(ofile,dpi=100)      ## dpi is dots per inch and is a figure resotution thing
### This part is for making this website, so you don't need it
return ofile