def exceedence(file, content, plot=False):
#takes in the raw data file as input
#content : string : the signal for which exceedence is needed
#plot : string : Boolean. if true, plot is also displayed
#outputs the exceedence curve : array
#outputs the array for which the exceedence is calculated : array
df, _ = access_file(file)
mean = df.mean(axis=0)
maxima = df.max(axis=0) / mean #needed to define the upper limit of array
minima = df.min(
axis=0) / mean #defines lower limit for which exceedence is calculated
mean_array = np.linspace(minima[content], maxima[content], 100)
exceedence = []
for i in range(len(mean_array)):
d = df[content] / mean[content] < mean_array[
i] #checks which value is above or below the value in the array
x = 1 - sum(d) / len(
df
) #first fins total number of values in the df which exceed the stated value in the array. then finds the probability of the true values. then subtracts from 1 to find the exceedence
exceedence.append(x) #push it into the array which is then returned
if plot == True:
#fig = plt.figure(figsize = (20,10))
plt.semilogy(mean_array, exceedence)
plt.xlabel(r'X/$\bar{X}$')
plt.ylabel('Exceedence Probability')
plt.grid('both')
plt.title(content)
return mean_array, exceedence
#creaet a data frame with average of the thrust and RPM from the data files
force_df = pd.DataFrame({'Original Name':dfs['Original'], 'Force Name':dfs['Force File Name'], \
'RPM' : None, 'Thrust_mean' : None, 'Torque_mean' : None, \
'Thrust sigma' : None, 'Torque sigma' : None, 'Cp mean' : None, \
'Cp sigma' : None, 'Ct mean' : None, 'Ct sigma' : None, 'My1 Mean' : None, 'My1 sigma' : None})
#Cp curve
#Eq: Cp = Torque x RPM / 0.5 x rho x A x u^3_mean
rho = 1000 #water density in kg/m3
rotor_dia = 0.724
rotor_area = math.pi * rotor_dia**2 / 4
for file in force_files:
#reading the entire data file can slow down the process
#in future, this functionality can be adjusted
#units are being returned as well which we will be dumping
df, _ = access_file(path_data_files + file)
rpm_mean = np.mean(df['RPM'])
thrust_mean = np.mean(df['Force'])
torque_mean = np.mean(df['Couple'])
thrust_std = np.std(df['Force'])
torque_std = np.std(df['Couple'])
My1_mean = np.mean(df['My1'])
My1_std = np.std(df['My1'])
My2_mean = np.mean(df['My2'])
My2_std = np.std(df['My2'])
My3_mean = np.mean(df['My3'])
My3_std = np.std(df['My3'])
force_df.at[force_df[force_df['Force Name'] == file[:-4]].index[0],
'RPM'] = rpm_mean
force_df.at[force_df[force_df['Force Name'] == file[:-4]].index[0],
'Thrust_mean'] = thrust_mean
#for binned and normalised.
out_direc_bin_norm = '/Users/goharshoukat/Documents/GitHub/Thesis_Tidal_Turbine/Results/Angular_Variation/Binned_normalised_azimuthal/'
#binned and non-normalised
out_direc_bin_nnorm = '/Users/goharshoukat/Documents/GitHub/Thesis_Tidal_Turbine/Results/Angular_Variation/Binned_nonnormalised_azimuthal/'
#spline and normalised
out_direc_spl_norm = '/Users/goharshoukat/Documents/GitHub/Thesis_Tidal_Turbine/Results/Angular_Variation/Spline_normalised_azimuthal/'
#spline and non-normalised
out_direc_spl_nnorm = '/Users/goharshoukat/Documents/GitHub/Thesis_Tidal_Turbine/Results/Angular_Variation/Spline_nonnormalised_azimuthal/'
for r,d in zip(runs,dist):
df2, units = access_file(direc + r)
for col in df2.columns[1:]:
if not os.path.isdir(out_direc_bin_norm+col):
os.makedirs(out_direc_bin_norm + col)
new_dir = out_direc_bin_norm + col
polar_chart_binning(df2, col, True)
plt.title('Distance = {} mm, TSR = 4, Vel = 1.0 m/s'.format(d))
plt.savefig(new_dir +'/' + str(d) + '.png', dpi = 300)
plt.close()
for col in df2.columns[1:]:
if not os.path.isdir(out_direc_bin_nnorm+col):
os.makedirs(out_direc_bin_nnorm + col)
# -*- coding: utf-8 -*-
"""
Created on Tue Jul 6 19:23:47 2021
@author: goharshoukat
This sript analyses the dry tests to identify the cause of the noise in the fft signal
"""
import numpy as np
import pandas as pd
from Dashboard.data_import_func import access_file, spectral_analysis, rotor_freq
import matplotlib.pyplot as plt
direc = 'Dry_tests/run005.txt'
df, units = access_file(direc)
column = ['Fx1', 'Thrust', 'Torque', 'My1']
for col in column:
fig = plt.figure(figsize=(60, 60))
ax = fig.add_subplot(111)
spec_s, f_s = spectral_analysis(df, col, bins='Default')
rfreq = rotor_freq(df['#RPM'])
freq_s = f_s / rfreq
ax.loglog(freq_s, spec_s[1:], linewidth=0.8)
ax.minorticks_on()
ax.grid(which='minor', linestyle='--')
ax.grid(which='major', linestyle='-')
#plt.title('{}, TSR = 4.0, Dry Test'.format(col))
ax.set_xlabel(r'$\frac{F}{f_0}$ [Hz/Hz]')
ax.set_ylabel('Magnitude ({})'.format(units[col]))
plt.savefig('Results/fft/Dry_tests/' + col + '.pdf')
# %%
#for binned and normalised.
out_direc_bin_norm = '/Users/goharshoukat/Documents/GitHub/Thesis_Tidal_Turbine/Results/Angular_Variation/Binned_normalised_azimuthal/'
#binned and non-normalised
out_direc_bin_nnorm = '/Users/goharshoukat/Documents/GitHub/Thesis_Tidal_Turbine/Results/Angular_Variation/Binned_nonnormalised_azimuthal/'
#spline and normalised
out_direc_spl_norm = '/Users/goharshoukat/Documents/GitHub/Thesis_Tidal_Turbine/Results/Angular_Variation/Spline_normalised_azimuthal/'
#spline and non-normalised
out_direc_spl_nnorm = '/Users/goharshoukat/Documents/GitHub/Thesis_Tidal_Turbine/Results/Angular_Variation/Spline_nonnormalised_azimuthal/'
for r, d in zip(runs, dist):
df2, units = access_file(direc + r)
for col in df2.columns[1:]:
if not os.path.isdir(out_direc_bin_norm + col):
os.makedirs(out_direc_bin_norm + col)
new_dir = out_direc_bin_norm + col
polar_chart_binning(df2, col, True)
plt.title('Distance = {} mm, TSR = 4, Vel = 1.0 m/s'.format(d))
plt.savefig(new_dir + '/' + str(d) + '.png', dpi=300)
plt.close()
for col in df2.columns[1:]:
if not os.path.isdir(out_direc_bin_nnorm + col):
os.makedirs(out_direc_bin_nnorm + col)
# %% For loop
out_direc = '/Users/goharshoukat/Documents/GitHub/Thesis_Tidal_Turbine/Results/fft/'
column = ['Fx1', 'Force', 'Couple', 'My1']
list_files = np.array_split(runs, 5)[:3]
list_dist = np.array_split(dist, 5)[:3]
for col in column:
fig, ax = plt.subplots(3, 1, figsize=(60, 60), sharex=True)
for i in range(len(ax)):
for r, d in zip(list_files[i], list_dist[i]):
df, units = access_file(direc + r)
spec_s, f_s = spectral_analysis(df, col, bins='Default')
rfreq = rotor_freq(df['RPM'])
freq_s = f_s / rfreq
ax[i].loglog(freq_s,
spec_s[1:],
linewidth=0.8,
label='{} mnm'.format(d))
ax[i].minorticks_on()
ax[i].grid(which='minor', linestyle='--')
ax[i].grid(which='major', linestyle='-')
ax[i].legend()
ax[i].set_ylabel('Magnitude ({})'.format(units[col]))
ax[2].set_xlabel(r'$\frac{F}{f_0}$ [Hz/Hz]')
plt.savefig(out_direc + col + '_1.pdf')