Plot_ER_Data_Figure_2_3_4_5.py

# -*- coding: utf-8 -*-
"""
Created on Thu Jun 11 14:01:05 2015

@author: SWDG

"""
import matplotlib.pyplot as plt
from matplotlib import rcParams
from matplotlib import container
from matplotlib import collections
import numpy as np
import scipy.optimize as optimize
import bin_data as Bin
from scipy.stats import gaussian_kde
from scipy.stats import sem
from uncertainties import unumpy as unp

def LoadData(Path,Prefix,Order):
    """
    Loads topogrpahic data from the 3 datafiles generated by E_R_STAR.cpp.

    Path is the path where the files are stored, with a trailing slash.
    Prefix is the filename prefix used by E_R_STAR.cpp to denote a landscape.
    Order is the integer basin order used to extract basin average data in E_R_STAR.cpp.
    Returns 2D arrays of topographic data from the raw, patch average and basin average datasets.
    """

    #load the data from the raw file
    #not using genfromtext as I want access to individual elements
    #for debugging, may change in future
    with open(Path+Prefix+'_E_R_Star_Raw_Data.csv','r') as raw:
        no_of_cols = len(raw.readline().split(','))
        rawdata = raw.readlines()

    #want the data in a 2d array to make moving the values about simpler
    #dimensions will be 6Xlen(rawdata) no_of_cols = 6
    #and the row order will follow the header format in the input file:
    #i j LH CHT Relief Slope

    no_of_lines = len(rawdata)

    RawData = np.zeros((no_of_cols,no_of_lines),dtype='float64')

    for i,r in enumerate(rawdata):
        split = r.split(',')
        for a in range(no_of_cols):
            RawData[a][i] = split[a]
    #now we have a transformed 2d array of our raw data

    #Next, repeat the process for the patch data
    with open(Path+Prefix+'_E_R_Star_Patch_Data.csv','r') as patch:
        no_of_cols = len(patch.readline().split(','))
        patchdata = patch.readlines()

    #dimensions will be 18Xlen(patchdata) no_of_cols = 18
    #and the row order will follow the header format in the input file:
    #Final_ID lh_means lh_medians lh_std_devs lh_std_errs cht_means cht_medians cht_std_devs cht_std_errs r_means r_medians r_std_devs r_std_errs s_means s_medians s_std_devs s_std_errs patch_size

    no_of_lines = len(patchdata)

    PatchData = np.zeros((no_of_cols,no_of_lines),dtype='float64')

    for i,p in enumerate(patchdata):
        split = p.split(',')
        for a in range(no_of_cols):
            PatchData[a][i] = split[a]

    #Next, repeat the process for the Basin data
    with open(Path+Prefix+'_E_R_Star_Basin_'+str(Order)+'_Data.csv','r') as basin:
        no_of_cols = len(basin.readline().split(','))
        basindata = basin.readlines()

    #dimensions will be 11Xlen(basindata) no_of_cols = 19
    #and the row order will follow the header format in the input file:
    #Basin_ID,LH_mean,CHT_mean,Relief_mean,Slope_mean,LH_median,CHT_median,Relief_median,Slope_median,LH_StdDev,CHT_StdDev,Relief_StdDev,Slope_StdDev,LH_StdErr,CHT_StdErr,Relief_StdErr,Slope_StdErr,Area,Count

    no_of_lines = len(basindata)

    BasinData = np.zeros((no_of_cols,no_of_lines),dtype='float64')

    for i,d in enumerate(basindata):
        split = d.split(',')
        for a in range(no_of_cols):
            BasinData[a][i] = split[a]

    return RawData,PatchData,BasinData


def PropagateErrors(PatchData,BasinData):
    """
    Load the hillslope, Relief and hilltop curavture data from the basin and
    patch data files into the uncertainties package, so that we can propagate
    errors through our calculations.
    """

    #median, sem
    patchLH = unp.uarray(PatchData[2],PatchData[4])
    patchR = unp.uarray(PatchData[10],PatchData[12])
    patchCHT = unp.uarray(PatchData[6],PatchData[8])

    basinLH = unp.uarray(BasinData[5],BasinData[13])
    basinR = unp.uarray(BasinData[7],BasinData[15])
    basinCHT = unp.uarray(BasinData[6],BasinData[14])

    return (patchLH,patchR,patchCHT),(basinLH,basinR,basinCHT)

def SetUpPlot():
    """
    Configure the plotting environment.
    """
    rcParams['font.family'] = 'sans-serif'
    rcParams['font.sans-serif'] = ['arial']
    rcParams['font.size'] = 15

    ax = plt.gca()

    ax.set_yscale('log', nonposy='clip')
    ax.set_xscale('log', nonposx='clip')

    plt.xlabel('Dimensionless Erosion Rate, E*')
    plt.ylabel('Dimensionless Relief, R*')

    plt.ylim(0.05,1.1)
    plt.xlim(0.1,1000)

    return ax

def PlotRaw(Sc,RawData):
    """
    Plot the raw E*R* data as small grey points.
    """
    plt.plot(E_Star(Sc,RawData[3],RawData[2]),R_Star(Sc,RawData[4],RawData[2]),
    'k.',alpha=0.2,label='Raw Data')

def PlotRawDensity(Sc,RawData,Thin):
    """
    Plot the raw E*R* data as a density plot, computing the PDF as a gaussian
    and including a colorbar.

    Built around code from: http://stackoverflow.com/a/20107592/1627162
    """
    x = E_Star(Sc,RawData[3],RawData[2])
    y = R_Star(Sc,RawData[4],RawData[2])

    if Thin:
        x = x[::Thin]
        y = y[::Thin]

    xy = np.vstack([x,y])
    density = gaussian_kde(xy)(xy)

    #order the points by density so highest density is on top in the plot
    idx = density.argsort()
    x, y, density = x[idx], y[idx], density[idx]

    plt.scatter(x,y,c=density,edgecolor='',cmap=plt.get_cmap("autumn_r"))
    cbar = plt.colorbar()
    cbar.set_label('Probability Distribution Function')

def PlotRawBins(Sc,RawData,NumBins,MinimumBinSize=100,ErrorBars=True):
    """
    Plot E*R* data binned from the raw data.
    """
    E_s = E_Star(Sc, RawData[3], RawData[2])
    R_s = R_Star(Sc, RawData[4], RawData[2])

    bin_x, bin_std_x, bin_y, bin_std_y, std_err_x, std_err_y, count = Bin.bin_data_log10(E_s,R_s,NumBins)

    #filter bins based on the number of data points used in their calculation
    bin_x = np.ma.masked_where(count<MinimumBinSize, bin_x)
    bin_y = np.ma.masked_where(count<MinimumBinSize, bin_y)
    #these lines produce a meaningless warning - don't know how to solve it yet.

    if ErrorBars:
        #only plot errorbars for y as std dev of x is just the bin width == meaningless
        plt.scatter(bin_x,bin_y,c=count,s=50,edgecolor='',cmap=plt.get_cmap("autumn_r"),label='Binned Raw Data', zorder=100)
        plt.errorbar(bin_x, bin_y, yerr=std_err_y, fmt=None,ecolor='k', zorder=0)
        cbar = plt.colorbar()
        cbar.set_label('Number of values per bin')
    else:
        plt.errorbar(bin_x, bin_y, fmt='bo',label='Binned Raw Data')

def PlotPatchBins(Sc,PatchData,NumBins,MinimumBinSize=8,ErrorBars=True):
    """
    Plot E*R* data binned from the hilltop pacth data.
    """
    E_s = E_Star(Sc,PatchData[6],PatchData[2])
    R_s = R_Star(Sc,PatchData[10],PatchData[2])

    bin_x, bin_std_x, bin_y, bin_std_y, std_err_x, std_err_y, count = Bin.bin_data_log10(E_s,R_s,NumBins)

    #filter bins based on the number of data points used in their calculation
    bin_x = np.ma.masked_where(count<MinimumBinSize, bin_x)
    bin_y = np.ma.masked_where(count<MinimumBinSize, bin_y)
    #these lines produce a meaningless warning - don't know how to solve it yet.

    if ErrorBars:
        #only plot errorbars for y as std dev of x is just the bin width == meaningless
        plt.scatter(bin_x,bin_y,c=count,s=50,edgecolor='',cmap=plt.get_cmap("autumn_r"),label='Binned Patch Data', zorder=100)
        plt.errorbar(bin_x, bin_y, yerr=std_err_y, fmt=None,ecolor='k',elinewidth=2,capsize=3,zorder=0)
        cbar = plt.colorbar()
        cbar.set_label('Number of values per bin')

    else:
        plt.errorbar(bin_x, bin_y, fmt='bo',label='Binned Patch Data')

def PlotPatches(Sc,PatchData,ErrorBars):
    """
    Plot E*R* data binned from hilltop patches.
    """
    e_star = E_Star(Sc,PatchData[2],PatchData[0])
    r_star = R_Star(Sc,PatchData[1],PatchData[0])
    if ErrorBars:
        plt.errorbar(unp.nominal_values(e_star),unp.nominal_values(r_star),yerr=unp.std_devs(r_star),xerr=unp.std_devs(e_star),
                     fmt='ro',label='Hilltop Patch Data')
    else:
        plt.errorbar(unp.nominal_values(e_star),unp.nominal_values(r_star),
                     fmt='ro',label='Hilltop Patch Data')

def PlotPatchesArea(Sc,PatchData,thresh,alpha):
    """
    Plot patch average E*R* data filtered by a user defined patch area value.
    """
    e_star = E_Star(Sc,PatchData[6],PatchData[2])
    r_star = R_Star(Sc,PatchData[10],PatchData[2])
    area = PatchData[17]

    x = []
    y = []

    for a,b,s in zip(e_star,r_star,area):
        if s > thresh:
            x.append(a)
            y.append(b)

    plt.plot(x,y,color='k',alpha=alpha,marker='o',linestyle='',label='Min. Patch Area = '+str(thresh))

def PlotBasins(Sc,BasinData,ErrorBars):
    """
    Plot basin average E*R* data.
    """
    e_star = E_Star(Sc,BasinData[2],BasinData[0])
    r_star = R_Star(Sc,BasinData[1],BasinData[0])

    if ErrorBars:
        plt.errorbar(unp.nominal_values(e_star),unp.nominal_values(r_star),yerr=unp.std_devs(r_star),xerr=unp.std_devs(e_star),
                     fmt='go',label='Basin Data')
    else:
        plt.errorbar(unp.nominal_values(e_star),unp.nominal_values(r_star),
                     fmt='go',label='Basin Data')

def PlotBasinsArea(Sc,BasinData,thresh,alpha):
    """
    Plot basin average E*R* data filtered by a user defined basin area value.
    """
    e_star = E_Star(Sc,BasinData[6],BasinData[5])
    r_star = R_Star(Sc,BasinData[7],BasinData[5])
    area = BasinData[18]

    x = []
    y = []

    for a,b,s in zip(e_star,r_star,area):
        if s > thresh:
            x.append(a)
            y.append(b)

    plt.plot(x,y,color='k',alpha=alpha,marker='o',linestyle='',label='Min. Basin Data Points = '+str(thresh))

def PlotLandscapeAverage(Sc,RawData,ErrorBars):
    """
    Plot a landscape median data point, calculated using the raw data, and generating
    errorbars using the standard error.
    """
    E_Star_temp = E_Star(Sc,RawData[3],RawData[2])
    R_Star_temp = R_Star(Sc,RawData[4],RawData[2])
    E_Star_avg = np.median(E_Star_temp)
    R_Star_avg = np.median(R_Star_temp)

    if ErrorBars:
        E_Star_std = np.std(E_Star_temp)
        R_Star_std = np.std(R_Star_temp)
        E_Star_serr = sem(E_Star_temp)
        R_Star_serr = sem(R_Star_temp)
        plt.errorbar(E_Star_avg,R_Star_avg,yerr=R_Star_serr,xerr=E_Star_serr,
                     fmt='ko',label='Landscape Average')
    else:
        plt.errorbar(E_Star_avg,R_Star_avg, fmt='ko',label='Landscape Average')

def R_Star_Model(x):
    """
    Return the predicted R* value for a given value of E* using eq 10 in Roering
    et al. (2007) http://www.sciencedirect.com/science/article/pii/S0012821X07006061
    """
    return (1./x) * (np.sqrt(1.+(x*x)) - np.log(0.5*(1. + np.sqrt(1.+(x*x)))) - 1.)

def E_Star(Sc,CHT,LH):
    """
    Calculate the E* value from topographic data after Roering et al. (2007)
    http://www.sciencedirect.com/science/article/pii/S0012821X07006061
    """
    if type(LH[0]) == np.float64:
        return (2.*np.fabs(CHT)*LH)/Sc
    else:
        return (2.*unp.fabs(CHT)*LH)/Sc

def R_Star(Sc, R, LH):
    """
    Calculate the R* value from topographic data after Roering et al. (2007)
    http://www.sciencedirect.com/science/article/pii/S0012821X07006061
    """
    return R/(LH*Sc)

def Residuals(Sc, R, LH, CHT):
    """
    Calculate the residuals between the R* value computed using eq 10 in Roering
    et al. (2007) (http://www.sciencedirect.com/science/article/pii/S0012821X07006061)
    and the R* calculated from topographic data.
    """
    return R_Star_Model(E_Star(Sc,CHT,LH)) - R_Star(Sc, R, LH)

def reduced_chi_square(Residuals,Sc,DataErrs=None):
    """
    Compute a reduced chi square value for the best fit Sc value.
    """
    #if we are fitting from patches or basins, get the std err and include in the chi squared
    if DataErrs:
        r_star = R_Star(Sc,DataErrs[1],DataErrs[0])

        #get rid of any divide by zero errors
        temp = ((Residuals/unp.std_devs(r_star))**2)
        temp[np.isinf(temp)] = 0
        chi_square = np.sum(temp)

    else:
        chi_square = np.sum(Residuals**2)

    # degrees of freedom, as we have 1 free parameter, Sc
    d_o_f = Residuals.size-2

    return chi_square/d_o_f

def r_squared(Sc, R, LH, CHT, infodict):
    """
    Calculate the R squared value of the best fit Sc value, using the residuals
    from the infodict generated by the optimize package.
    """
    measured = R_Star(Sc, R, LH)
    mean_measured = np.mean(measured)

    sqr_err_w_line = np.square(infodict['fvec'])
    sqr_err_mean = np.square((measured - mean_measured))

    return 1.-(np.sum(sqr_err_w_line)/np.sum(sqr_err_mean))

def DrawCurve():
    """
    Plot the steady state curve in E*R* space.
    """
    #plot the e* r* curve from roering 2007
    x = np.arange(0.01, 1000, 0.1)
    plt.plot(x, R_Star_Model(x), 'k-', linewidth=2, label='Equation 5')

def GetBestFitSc(Method, Data, DataErrs=None):
    """
    Compute the best fit Sc value to the data using the scipy optimize.leassq
    package. Also returns the reduced chi squared as a measure of the goodness
    of fit.
    """
    ScInit = 0.8  # Need to have an initial for the optimizer, any valid Sc value can be used - will not impact the final value
    Fit_Sc = [] #Need to initialize this in case Method is incorrectly defined. Need some error handling!

    if Method == 'raw':
        Fit_Sc,_,infodict,_,_ = optimize.leastsq(Residuals, ScInit, args=(Data[4], Data[2], Data[3]),full_output=True)
        chi = reduced_chi_square(infodict['fvec'],Fit_Sc[0])
    elif Method == 'patches':
        Fit_Sc,_,infodict,_,_ = optimize.leastsq(Residuals, ScInit, args=(Data[10], Data[2], Data[6]),full_output=True)
        chi = reduced_chi_square(infodict['fvec'],Fit_Sc[0],DataErrs)
    elif Method == 'basins':
        Fit_Sc,_,infodict,_,_ = optimize.leastsq(Residuals, ScInit, args=(Data[7], Data[5], Data[6]),full_output=True)
        chi = reduced_chi_square(infodict['fvec'],Fit_Sc[0],DataErrs)

    return Fit_Sc[0],chi

def BootstrapSc(Method, Data, n=10000):
    """
    Bootstrap the calculation of the best fit Sc value n times to get the 95%
    confidence interval for the best fit Sc.

    Values of n larger than 10000 will take a long time to run.
    """
    tmp = []
    #need to convert the LH,R,CHT data into a serial 1D array before bootstrapping
    if Method == 'raw':
        for i in range(len(Data[0])):
            tmp.append(SerializeData(Data[2][i],Data[4][i],Data[3][i]))
    if Method == 'patches':
        for i in range(len(Data[0])):
            tmp.append(SerializeData(Data[2][i],Data[10][i],Data[6][i]))
    if Method == 'basins':
        for i in range(len(Data[0])):
            tmp.append(SerializeData(Data[5][i],Data[7][i],Data[6][i]))

    ToSample = np.array(tmp)

    Scs = []
    i=0
    while i < n:
        print i

        sample = np.random.choice(ToSample,len(ToSample),replace=True)
        LH,R,CHT = UnserializeList(sample)
        sc,_,_,_,_ = optimize.leastsq(Residuals, 0.8, args=(R, LH, CHT),full_output=True)
        if sc < 2.0:
            Scs.append(sc[0])
            i += 1

    #        mean          upper bound                               lower bound
    return np.mean(Scs),np.percentile(Scs,97.5)-np.mean(Scs), np.mean(Scs)-np.percentile(Scs,2.5)

def SerializeData(LH, R, CHT):
    """
    Convert the hillslope length, relief, and hilltop curvature data into a string,
    to facilitate the sampling by replacement of the data in the bootstrapping
    method.
    """
    return str([LH, R, CHT])[1:-1]

def UnserializeList(serial_list):
    """
    Convenicence function to unserialize a list of serialized data and return
    arrays of hillslope length, relief, and hilltop curvature.
    """
    LH=[]
    R=[]
    CHT=[]
    for s in serial_list:
       lh,r,cht = UnserializeData(s)
       LH.append(lh)
       R.append(r)
       CHT.append(cht)

    return np.array(LH),np.array(R),np.array(CHT)

def UnserializeData(serial):
    """
    Unpack the data serialized by SerializeData, returning the original values
    in their original format.
    """
    split = [float(s) for s in serial.split(',')]
    return split[0],split[1],split[2]

def Labels(Sc,Method,ax):
    """
    Method to handle the labelling of axes, generation of a legend and creation
    of a plot title.
    """
    #remove errorbars from the legend
    handles, labels = ax.get_legend_handles_labels()
    handles = [h[0] if isinstance(h, container.ErrorbarContainer) else h for h in handles]

    #color scatterplot symbols like colormap
    for h in handles:
        if isinstance(h, collections.PathCollection):
            h.set_color('r')
            h.set_edgecolor('')

    ax.legend(handles, labels, loc=4, numpoints=1,scatterpoints=1)

    #in case Method is invalid
    fit_description = ' = '

    if Method == 'raw':
        fit_description = ' from raw data = '

    elif Method == 'patches':
        fit_description = ' from hilltop patches = '

    elif Method == 'basins':
        fit_description = ' from basin average data = '

    if isinstance(Method,int) or isinstance(Method,float):
        plt.title('$\mathregular{S_c}$ = ' + str(round(Sc,2)), y = 1.02)
    else:
        plt.title('Best fit $\mathregular{S_c}$'+fit_description+str(round(Sc,2)), y = 1.02, fontsize=15)

def SavePlot(Path,Prefix,Format):
    """
    Wrapper function around the matplotlib savefig method, which save a high resolution
    copy of the final figure into the user supplied path with the user suppied
    file format.
    """
    plt.savefig(Path+'a'+Prefix+'_E_R_Star.'+Format,dpi=500)
    plt.clf()

def CRHurst():
    """
    Plots the E*R* data generated for the Cascade Ridge by Hurst et al. (2012)
    (http://onlinelibrary.wiley.com/doi/10.1029/2011JF002057/full) seen in
    figure 14.
    """

    x = [1.15541793184, 2.96599962747, 5.06753455114, 6.87537359947, 8.86462081724, 10.9425778888, 12.9426702489, 14.9866553641, 16.9785507349, 19.0034609662, 20.9560856862, 22.8577931724, 24.6085876779, 27.3044634219, 28.3873092441, 31.1978149101, 32.8625186998, 35.2335006909, 37.2282499959, 43.8911646306, 45.5936728215]
    y = [0.379133283693, 0.435531356239, 0.547479389809, 0.588874111323, 0.652649344881, 0.696659574468, 0.824275084903, 0.733856012658, 0.783243670886, 0.836195147679, 0.920291139241, 0.862545710267, 0.953440506329, 0.851824367089, 0.97046835443, 0.909219409283, 0.964772151899, 1.08295780591, 0.904050632911, 1.13525316456, 0.934139240506]

    yStdErr = [0.12913016, 0.03901928, 0.04112731, 0.02724568, 0.04694418,
               0.04026138, 0.03122017, 0.02083737, 0.01752255, 0.02029758,
               0.02403573, 0.02065953, 0.02382283, 0.021544, 0.0216427,
               0.02044206, 0.02158805, 0.02227467, 0.03237965, 0.04332041,
               0.09519842]

    plt.errorbar(x, y, yerr=yStdErr, fmt='k^', label='Hurst et al. (2012)',
                 elinewidth=2, capsize=3, markersize=6)

def GMRoering():
    """
    Plots the E*R* data for the Gabilan Mesa generated by Roering et al. (2007)
    http://www.sciencedirect.com/science/article/pii/S0012821X07006061
    """
    x = [1.68]*2
    y = [0.34,0.43]

    xerr = [0.7]*2
    yerr = [0.17,0.2]

    plt.errorbar(x,y,yerr,xerr,'k^',label='Roering et al. (2007)',elinewidth=2,capsize=3,markersize=12,zorder=1000)

def OCRRoering():
    """
    Plots the E*R* data for the Oregon Coast Range generated by Roering et al.
    (2007) http://www.sciencedirect.com/science/article/pii/S0012821X07006061
    """
    x = [6.3]*2
    y = [0.57,0.64]

    xerr = [2.1]*2
    yerr = [0.23,0.18]

    plt.errorbar(x,y,yerr,xerr,'k^',label='Roering et al. (2007)',elinewidth=2,capsize=3,markersize=12,zorder=1000)

def MakeThePlot(Path,Prefix,Sc_Method,RawFlag,DensityFlag,BinFlag,NumBins,PatchFlag,BasinFlag,LandscapeFlag,Order,ErrorBarFlag=True,Format='png',ComparisonData=(False,False,False),NumBootsraps=10000):
    """
    Method which controls the generation of E*R* data. Does not need to be
    interfaced with directly. Is called by the IngestSettings method using
    parameters in the Settings.py file.
    """
    RawData,PatchData,BasinData = LoadData(Path,Prefix,Order)

    PatchDataErrs, BasinDataErrs = PropagateErrors(PatchData,BasinData)

    ax = SetUpPlot()

    DrawCurve()

    if isinstance(Sc_Method,int) or isinstance(Sc_Method,float):
        Sc = Sc_Method
    else:
        if Sc_Method == 'raw':
            Sc,upper,lower = BootstrapSc(Sc_Method, RawData, NumBootsraps)
        if Sc_Method == 'patches':
            Sc,upper,lower = BootstrapSc(Sc_Method, PatchData, NumBootsraps)
        if Sc_Method == 'basins':
            Sc,upper,lower = BootstrapSc(Sc_Method, BasinData, NumBootsraps)

    if RawFlag:
        PlotRaw(Sc,RawData)
    if DensityFlag:
        PlotRawDensity(Sc,RawData,DensityFlag)
    if PatchFlag:
        PlotPatches(Sc,PatchDataErrs,ErrorBarFlag)
    if BinFlag == 'patches':
        PlotPatchBins(Sc,PatchData,NumBins,ErrorBars=ErrorBarFlag)
    elif BinFlag == 'raw':
        PlotRawBins(Sc,RawData,NumBins,ErrorBars=ErrorBarFlag)
    if BasinFlag:
        PlotBasins(Sc,BasinDataErrs,ErrorBarFlag)
    if LandscapeFlag:
        PlotLandscapeAverage(Sc,RawData,ErrorBarFlag)

    if ComparisonData[0]:
        GMRoering()
    if ComparisonData[1]:
        OCRRoering()
    if ComparisonData[2]:
        CRHurst()

    Labels(Sc,Sc_Method,ax)
    #plt.show()

    SavePlot(Path,Prefix+`Sc`,Format)

def IngestSettings():
    """
    Load the parameters from the Settings.py file, perform type and sanity checking
    and run the main code to generate E*R* data.
    """
    import Settings
    import sys

    #typecheck inputs
    if not isinstance(Settings.Path, str):
        sys.exit('Path=%s \nThis is not a valid string and so cannot be used as a path.\nExiting...' % Settings.Path)

    if not isinstance(Settings.Prefix, str):
        sys.exit('Prefix=%s \nThis is not a valid string and so cannot be used as a filename prefix.\nExiting...' % Settings.Prefix)

    if not isinstance(Settings.Sc_Method, str) and not isinstance(Settings.Sc_Method, float):
        sys.exit('Sc_Method=%s \nThis is not a valid string or floating point value.\nExiting...' % Settings.Sc_Method)
    else:
        if isinstance(Settings.Sc_Method, str) and (Settings.Sc_Method != 'raw' and Settings.Sc_Method != 'patches' and Settings.Sc_Method != 'basins'):
            sys.exit('Sc_Method=%s \nThis is not a valid method to fit a critical gradient. Valid options are \'raw\',\'patches\', or \'basins\'.\nExiting...' % Settings.Sc_Method)
        if isinstance(Settings.Sc_Method, float) and (Settings.Sc_Method <= 0 or Settings.Sc_Method > 3):
            sys.exit('Sc_Method=%s \nThis critical gradient not within a expected range of 0 to 3.\nExiting...' % Settings.Sc_Method)

    if not isinstance(Settings.RawFlag, int):
        sys.exit('RawFlag should be set to 1 to plot the raw data or 0 to exclude the raw data. You have entered %s\nExiting...' % Settings.RawFlag)

    if not isinstance(Settings.DensityFlag, int):
        sys.exit('DensityFlag should be set to 1 to produce a density plot or 0 to not plot a density plot. Integer values greater than 1 will thin the data before plotting. You have entered %s\nExiting...' % Settings.DensityFlag)

    if not isinstance(Settings.BinFlag, str):
        sys.exit('BinFlag=%s \nThis is not a valid string to select the binning method. If not performing binning, enter a blank string: \'\'.\nExiting...' % Settings.BinFlag)
    else:
        if Settings.BinFlag:
            if Settings.BinFlag.lower() != 'raw' and Settings.BinFlag.lower() != 'patches':
                sys.exit('BinFlag=%s \nSelect either \'raw\' or \'patches\' as the binning method. Enter a blank string: \'\' if no binning is required.\nExiting...' % Settings.BinFlag)

    if not isinstance(Settings.NumBins, int):
        sys.exit('NumBins should be set to the number of bins to be generated when binning the data. If no binning is to be performed, set the value to 0. You have entered %s\nExiting...' % Settings.NumBins)

    if not isinstance(Settings.PatchFlag, int):
        sys.exit('PatchFlag should be set to 1 to plot the patch data or 0 to exclude the patch data. You have entered %s\nExiting...' % Settings.PatchFlag)

    if not isinstance(Settings.BasinFlag, int):
        sys.exit('BasinFlag should be set to 1 to plot the basin data or 0 to exclude the basin data. You have entered %s\nExiting...' % Settings.BasinFlag)

    if not isinstance(Settings.LandscapeFlag, int):
        sys.exit('LandscapeFlag should be set to 1 to plot the landscape average data or 0 to exclude the landscape average data. You have entered %s\nExiting...' % Settings.LandscapeFlag)

    if not isinstance(Settings.Order, int):
        sys.exit('Order should be set to an integer (eg 1,2,3, etc) to load the basin average data generated for that order of basin. You have entered %s, of %s\nExiting...' % (Settings.Order, type(Settings.Order)))

    if not isinstance(Settings.ErrorBarFlag, bool):
        sys.exit('ErrorBarFlag should be set to either True or False. True will generate plots with errorbars, False will exclude them. You have entered %s\nExiting...' % Settings.ErrorBarFlag)

    ValidFormats = ['png', 'pdf','ps', 'eps','svg']
    if not isinstance(Settings.Format, str):
        sys.exit('Format=%s \nFile format must be a valid string.\nExiting...' % Settings.Format)
        if Settings.Format.lower() not in ValidFormats:
            sys.exit('Format=%s \nFile format must be one of: png, pdf, ps, eps or svg.\nExiting...' % Settings.Format)

    if not isinstance(Settings.GabilanMesa, bool):
        sys.exit('GabilanMesa should be set to either True or False. True will plot data from Roering et al. (2007), False will not. You have entered %s\nExiting...' % Settings.GabilanMesa)
    if not isinstance(Settings.OregonCoastRange, bool):
        sys.exit('OregonCoastRange should be set to either True or False. True will plot data from Roering et al. (2007), False will not. You have entered %s\nExiting...' % Settings.OregonCoastRange)
    if not isinstance(Settings.SierraNevada, bool):
        sys.exit('SierraNevada should be set to either True or False. True will plot data from Roering et al. (2007), False will not. You have entered %s\nExiting...' % Settings.SierraNevada)

    if not isinstance(Settings.NumBootsraps, int):
        sys.exit('NumBootsraps should be set to an integer (eg 10000) to select the number of iterations in the bootstrapping calculation.\n\nThis value is ignored if a value of Sc is supplied. Using a value > 10000 will take a long time on big datasets. You have entered %s, of %s\nExiting...' % (Settings.NumBootsraps, type(Settings.NumBootsraps)))

    MakeThePlot(Settings.Path,Settings.Prefix,Settings.Sc_Method,Settings.RawFlag,Settings.DensityFlag,Settings.BinFlag,Settings.NumBins,Settings.PatchFlag,Settings.BasinFlag,Settings.LandscapeFlag,Settings.Order,Settings.ErrorBarFlag,Settings.Format,(Settings.GabilanMesa,Settings.OregonCoastRange,Settings.SierraNevada),Settings.NumBootsraps)

IngestSettings()

#MakeThePlot('GM','patches',0,0,'',20,0,1,0,2,ErrorBarFlag=False,Format='png')


#for l in ['GM','OR','NC','CR']:
#    for m in ['raw','patches','basins']:
#        print l,m
#        MakeThePlot('C:\\Users\\Stuart\\Dropbox\\data\\final\\',l,m,0,0,'',20,0,1,0,2,ErrorBarFlag=False,Format='png')