In [2]:

    

# import some modules using standard naming conventions
from pylab import *
import pandas as pd

# find the .csv files and read them into pandas data sets
import glob
flist = glob.glob('/usgs/data1/csherwood/poking_eyeball/*.csv')


    

In [3]:

    

def Phi(mm):
    return -1.44296504*log(mm)


    

In [4]:

    

def my_read(csv):
    phi = float(csv[-6:-4])/10. # extract phi size from CSV file name
    df = pd.read_csv(csv)     # create Pandas DataFrame from each CSV file
    mg = df.geom_mean.mean()  # mean of geometric means
    m  = df.arith_mean       
    ma = m.mean()             # mean of arithmetic means
    sa = m.std()              # std of arithmetic means
    return phi,ma,sa,mg


    

In [5]:

    

# list of stats for each file
stats = [my_read(f) for f in flist]
# convert to array
stats = array(stats)


    

In [6]:

    

# create a data frame from the stats array
df_stats = pd.DataFrame(stats,columns=['Phi','Ma','Sa','Mg'])
df_stats


    

    
Out[6]:






  

    

      

      
Phi
      
Ma
      
Sa
      
Mg
    
  
  

    

      
0
      
 0.0
      
 0.557322
      
 0.067476
      
 0.468722
    
    

      
1
      
 0.5
      
 0.543682
      
 0.134539
      
 0.430470
    
    

      
2
      
 1.0
      
 0.487743
      
 0.082410
      
 0.381277
    
    

      
3
      
 1.5
      
 0.373238
      
 0.048243
      
 0.267245
    
    

      
4
      
 2.0
      
 0.326144
      
 0.031638
      
 0.209683
    
    

      
5
      
 2.5
      
 0.301667
      
 0.034709
      
 0.199168
    
    

      
6
      
 3.0
      
 0.235943
      
 0.032090
      
 0.134345
    
    

      
7
      
 3.5
      
 0.238555
      
 0.015709
      
 0.135005
    
    

      
8
      
 4.0
      
 0.204032
      
 0.015977
      
 0.106308
    
  

In [7]:

    

# list of full DataFrames
df_list = [pd.read_csv(f) for f in flist]


    

In [8]:

    

# 1st column of stats array is the phi size
phi = stats[:,0]


    

In [9]:

    

# make a dictionary of phi_size: full data_frame
d = dict(zip(phi, df_list))


    

In [14]:

    

plot(phi,phi,'-k')
plot(phi-0.5,phi,'--k') # one sieve larger
for p,df in d.iteritems():
    plot(p*ones_like(df.geom_mean),Phi(df.geom_mean),'o',color='0.8')
plot(phi,Phi(df_stats.Mg),'or')
xlim([-.5,4.5])
xlabel('Sieve size (phi)')
ylabel('DGS geometric mean (phi)')
title('MOF Poking Eyeball Tests, May 3, 2013')
grid()


    

In [ ]: