# Design Example
# Exercise 2: Quantitative Analysis
# For demonstration purposes only

import matplotlib.pyplot as plt

def read_measurements(filename):
    """Return a dictionary mapping column names to a tuple of
    all of the values in filename corresponding to that column name. 
    Assumes the first line of the file is column names."""
    datafile = open(filename)
    row_list = [row.split() for row in datafile]
    datafile.close()
    print "row_list=", row_list
    rawcolumns = zip(*row_list)
    print "rawcols=", rawcolumns
    columns = dict([(col[0], col[1:]) for col in rawcolumns])
    print "read_measurements returns:", columns
    return columns

def tofloat(measurements, columnname):
    """measurements is a dictionary
    columnname is a key in measurements
    Extract the specified column of values from the given measurements
    Returns a list of values in that column, converted to floats"""
    float_list = [float(x) for x in measurements[columnname]]
    print "tofloat(", columnname, ") returns:", float_list
    return float_list

def STplot(measurements):
    """Plot salt vs. temp"""
    xs = tofloat(measurements, "salt")
    ys = tofloat(measurements, "temp")
    plt.plot(xs, ys)
    plt.xlabel("salt")
    plt.ylabel("temp")
    plt.show()

def minimumO2(measurements):
    """Return the minimum value of the oxygen measurement"""
    return min(tofloat(measurements, "o2"))

# Sample client calls:
col_to_data_dict = read_measurements("quant.txt")
STplot(col_to_data_dict)
print "minimum o2 in file:", minimumO2(col_to_data_dict)