plotSeriesCyt/plotSeriesNonDim.py at main · ChitYanToe/plotSeriesCyt · GitHub

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import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
from .uniqueFinder import uniqueFinder
from .velocityProfile import velocityProfile
from .Rxx import Rxx
from .Ryy import Ryy
from .Rxy import Rxy
from .outlierRemove import outlierRemove
from .calcUstar import calcUstar
from .TKE import TKE
import pickle

def plotSeriesNonDim(dataPickle, positionList, function, plotTitle):

    '''
    Input : dataPickle - a pickle containing different measurement data (.pkl format)
            function - function name for required fluid properties
                        available functions' keywords - "U", "Rxy", "Rxx", "Ryy"

    Output: A series of subplots
    '''
    dataPickle = dataPickle
    positionList = positionList
    function = function
    plotTitle = plotTitle

    with open(dataPickle, 'rb') as f:
        dataList = pickle.load(f)

    yList = [] # a list to store the depth values and finally to pick up the max; depth

    # parameter for Subplots #
    num_cols = 5
    # num_cols = min(len(dataList),5)
    num_rows = -(-len(dataList)//num_cols)


    fig, axes = plt.subplots(nrows=num_rows, ncols=num_cols, layout='constrained',sharey=True, sharex=True)

    axes_flat = axes.flatten()

    file = open(plotTitle, 'w')  ## Create a file to store results
    file.write("Position\tU*\tUavg\tCf\n")

    for i, data in reversed(list(enumerate(dataList))):
        print('----Program Executing----')
        row = i // num_cols ## commented for subplots array
        col = i % num_cols

        if function == 'Rxy':  # Function calling
            # if i == len(dataList) - 1: # We want to calculate U* only for free channel section i.e., the first dataset
            #     Ustar = calcUstar(data) ## Calculate U* by calling calcUstar function
            Ustar = calcUstar(data) ## Calculate U* by calling calcUstar function
            data = Rxy(data)
            # Remove outlier
            dataWithoutOutliers = outlierRemove(data,function)
            # Fitting line
            #slope, intercept = np.polyfit(dataWithoutOutliers['depth[cm]'], dataWithoutOutliers['uv_prime'], 1)
            # if i != len(dataList)-1:
            #     slope, intercept = np.polyfit(dataWithoutOutliers['depth[cm]'], dataWithoutOutliers['uv_prime'], 1)
            #
            # if i == len(dataList)-1: # to remove the outliers in the data (near the free surface)
            #     slope, intercept = np.polyfit(dataWithoutOutliers['depth[cm]'][:-1:], dataWithoutOutliers['uv_prime'][:-1:], 1)

            y=np.linspace(0,data[-2])
            # Ustar = np.sqrt(np.abs(slope)*data[-2])
            # print('U* = ', Ustar)

            ### Plotting
            axes[row, col].scatter(dataWithoutOutliers['uv_prime']/Ustar**2, dataWithoutOutliers['depth[cm]']/data[-2], facecolors='None', s=4, color='k')
            axes[row, col].ticklabel_format(style='sci',scilimits=(-3,4),axis='both', useLocale=True)
            #axes[row, col].tick_params(axis='x', rotation=45)
            axes[row, col].set_title(str(positionList[i])+' m', fontsize=10)
            axes[row, col].axline((0, 1), slope=-1, color="gray", ls='-.')
            #axes[row, col].set_xlim(right=1)
            axes[row, col].set_ylim([0, 1])
            #axes[row, col].plot(slope*y+intercept, y, 'k')
            fig.supxlabel(r"$\overline{u'v'}/U_{*b}^2$")

            #### Storing in a file
            Cf = 2*(Ustar/data[-1])**2  # Ref; Wim's lecture notes - data[-1] is depth-averaged velocity
            file.write(f"at {positionList[i]}\t{Ustar}\t{data[-1]}\t{Cf}\n")

        if function == 'Rxx':  # Function calling

            Ustar = calcUstar(data) ## Calculate U* by calling calcUstar function
            data = Rxx(data)
            # Remove outlier
            dataWithoutOutliers = outlierRemove(data,function)

            # Fitting line
            #slope, intercept = np.polyfit(dataWithoutOutliers['depth[cm]'], dataWithoutOutliers['uu_prime'], 1)

            #y=np.linspace(0,data[-2])
            #Ustar = np.sqrt(np.abs(slope)*data[-2])
            #print('U* = ', Ustar)

            ### Plotting
            axes[row, col].scatter(dataWithoutOutliers['uu_prime']/Ustar**2, dataWithoutOutliers['depth[cm]']/data[-2], facecolors='None', s=4, color='k')
            axes[row, col].set_ylim([0, 1])
            #axes[row, col].plot(slope*y+intercept, y, 'k')
            axes[row, col].ticklabel_format(style='sci',scilimits=(-3,4),axis='both', useLocale=True)
            axes[row, col].set_title(str(positionList[i])+' m', fontsize=10)

            #### Storing in a file
            Cf = 'N/A'  # Ref; Wim's lecture notes - data[-1] is depth-averaged velocity
            #file.write(f"{positionList[i]}\t{Ustar}\t{data[-1]}\t{Cf}\n")

            fig.supxlabel(r"$\overline{u'u'}/U_*^2$")

        if function == 'Ryy':  # Function calling

            Ustar = calcUstar(data) ## Calculate U* by calling calcUstar function
            data = Ryy(data)
            # Remove outlier
            dataWithoutOutliers = outlierRemove(data,function)

            # Fitting line
            #slope, intercept = np.polyfit(dataWithoutOutliers['depth[cm]'], dataWithoutOutliers['uu_prime'], 1)

            #y=np.linspace(0,data[-2])
            #Ustar = np.sqrt(np.abs(slope)*data[-2])
            #print('U* = ', Ustar)

            ### Plotting
            axes[row, col].scatter(dataWithoutOutliers['vv_prime']/Ustar**2, dataWithoutOutliers['depth[cm]']/data[-2], facecolors='None', s=4, color='k')
            axes[row, col].set_ylim([0, 1])
            #axes[row, col].plot(slope*y+intercept, y, 'k')
            axes[row, col].ticklabel_format(style='sci',scilimits=(-3,4),axis='both', useLocale=True)
            axes[row, col].set_title(str(positionList[i])+' m', fontsize=10)

            #### Storing in a file
            Cf = 'N/A'  # Ref; Wim's lecture notes - data[-1] is depth-averaged velocity
            #file.write(f"{positionList[i]}\t{Ustar}\t{data[-1]}\t{Cf}\n")

            fig.supxlabel(r"$\overline{v'v'}/U_*^2$")

        if function == 'TKE': ## Function calling
            Ustar = calcUstar(data) ## Calculate U* by calling calcUstar function
            data = TKE(data)
            #plotting
            axes[row, col].scatter(data[0].reset_index()[0]/Ustar**2, data[0].reset_index()['depth[cm]']/data[1], s=4, facecolors='None', color='k')
            axes[row, col].ticklabel_format(style='sci',scilimits=(-3,4),axis='both', useLocale=True)
            axes[row, col].set_ylim([0, 1])
            axes[row, col].set_xlim(left=0)
            axes[row, col].tick_params(axis='x', rotation=45)
            axes[row, col].set_title('at '+str(positionList[i])+' m', fontsize=10)
            fig.supxlabel('$TKE/U_{*b}^2$')

        if function == 'U':
            data = velocityProfile(data)
            axes[row, col].scatter(data[0].reset_index()['U']/data[-1], data[0].reset_index()['depth[cm]']/data[-2], facecolors='None', s=4, color='k')
            axes[row, col].set_title(str(positionList[i])+' m', fontsize=10)
            axes[row, col].set_xlim(left=0)
            axes[row, col].set_ylim([0, 1])
            fig.supxlabel(r"$U/\overline{U}$")

        fig.supylabel('$d/H$')
        yList.append(data[-2]) # storing depth values for all positions
        #axes[row, col].set_ylim([0, np.max(yList)]) # limiting the y-lim using max; depth
        axes[row, col].grid()
        axes[row, col].set_box_aspect(2) # aspect ratio for subplot

    file.close()

    # # Add empty subplots as placeholders for the last row
    num_empty = num_cols * num_rows - len(dataList)
    for i in range(num_empty):
        ax = axes_flat[-1]
        ax.axis('off')
        axes_flat = axes_flat[:-1]

    #plt.tight_layout()
    plt.suptitle(function+plotTitle)
    plt.savefig(function+plotTitle+'.pdf')