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

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import pandas as pd
import matplotlib.pyplot as plt
import scipy
from scipy import integrate
import numpy as np
from .uniqueFinder import uniqueFinder
def velocityProfile(dataOrig, VelAll=None):
    '''
    This function is to plot the velocity profile using the measured data from a single file obtained from DasyLAB.

    Input:
    -----
    data : pandas dataframe
    VelAll : [optional] if True, it returns instantaneous velocity components.

    Return:
    ------
    velocity profiles
    Depth-averaged velocity
    '''
    data = dataOrig.copy()
    data = data[data['DisplacementLDA [V]'] != 0.00]
    data.reset_index(drop=True, inplace=True)

    data = data.sort_values('DisplacementLDA [V]').reset_index(drop=True)
    data = uniqueFinder(data)
    data['depth[cm]'] = data['uniqueDisplacement'].groupby(data['uniqueDisplacement']).transform(lambda x:np.round(-2.4953*x+25.753,2))

    ## Velocity components calcuation
    data['U'] = data['Vel1 [V]'].groupby(data['depth[cm]']).transform(lambda x: 0.096*(data['Vel2 [V]']-x))
    data['V'] = data['Vel1 [V]'].groupby(data['depth[cm]']).transform(lambda x: 0.096*(x+data['Vel2 [V]']))

#     ## Scatter plots
#     fig, ax = plt.subplots(1,2, figsize=(10,5))

#     ax[0].scatter(data['U'].groupby(data['depth[cm]']).mean(), data['depth[cm]'].groupby(data['depth[cm]']).mean(), color='k')
#     ax[0].set_xlabel('$U$ [cm/s]')
#     ax[0].set_ylabel('$y$ [cm]')
#     ax[0].set_xlim([0,np.max(data['U'])])
#     ax[0].set_ylim(bottom=0)
#     ax[0].grid(True)

#     ax[1].scatter(data['V'].groupby(data['depth[cm]']).mean(), data['depth[cm]'].groupby(data['depth[cm]']).mean(), color='r')
#     ax[1].set_xlabel('$V$ [cm/s]')
#     ax[1].grid(True)
#     ax[1].set_xlim([0,np.max(data['U'])])
#     ax[1].set_ylim(bottom=0)

    '''
        To calculate the average velocity
    '''
    totalDepth = data['depth[cm]'].groupby(data['depth[cm]']).mean().iloc[-1] - data['depth[cm]'].groupby(data['depth[cm]']).mean().iloc[0]
    u_avg = scipy.integrate.simps(np.abs(data['U'].groupby(data['depth[cm]']).mean()), data['depth[cm]'].groupby(data['depth[cm]']).mean()) / totalDepth
    print('Depth-averaged velocity is = ', np.round(u_avg,3), 'm/sec.')


    '''
        To calculate friction velocity
    '''
    upstreamWL = np.mean(38.01-3.76*data['WaterLevel1 [v]'])
    #print('upStreamWL', upstreamWL)
    downstreamWL = np.mean(36.42-3.8*data['WaterLevel3 [V]'])
    #print('downStreamWL', downstreamWL)
    diffWL = np.abs(upstreamWL - downstreamWL)
    waterDepth = (upstreamWL + downstreamWL)/2
    dx = 6  # horizontal length between two water levels
    dHdx = (diffWL/100)/dx # energy slope : water level is in centimeter, therefore need to change in meter
    #print('energy gradient', dHdx)
    R = (waterDepth/100 * 0.4)/(2*waterDepth/100 + 0.04) # hydraulic radius R = Area/Perimeter
    Ustar = np.sqrt(9.81*(waterDepth/100)*dHdx)

    '''
        To insert the line for depth-averaged velocity
    '''
    #ax[0].axvline(np.round(u_avg,3), color ='k', lw = 4, ls='--', label='$U_{avg}$ = %.3f m/sec' %np.round(u_avg,3))
    #ax[0].legend()

    if VelAll == True:
        return data['U'].groupby(data['depth[cm]'])

    return data['U'].groupby(data['depth[cm]']).mean(), upstreamWL, u_avg