# Individual case's name
case_names = []
case_names.append('PH-Breuer-10595')
case_names.append('PH-Breuer-5600')
case_names.append('PH-Breuer-2800')
case_names.append('PH-Breuer-1400')
case_names.append('PH-Breuer-700')
# Instantiate cases
hifi_data = []
for case_name in case_names:
hifi_data.append(flowCase(case_name))
#####################################################################
### Figures
#####################################################################
# Fig 21 (b) pressure distribution
# compare_profiles('pm', 'x', 0, *hifi_data)
#
# # Fig 27 (a), (c)
# compare_profiles('um', 'y', 4, *hifi_data, xlim=[-0.2, 1.2], ylim=[-0.1, 3.1])
# compare_profiles('uu', 'y', 4, *hifi_data, xlim=[0, 0.1], ylim=[-0.1, 3.1])
#
# # Fig 23, tiny separation bubble
# hifi_data[3].show_flow('um', contour_level=[-0.1, 0, 0.1, 0.2, 0.3], xlim=[0.55, 0.93], ylim=[0.58, 0.82])
#
import numpy as np
import matplotlib.pyplot as plt
from uncert_ident.data_handling.flowcase import flowCase
case = flowCase("PH-Breuer-10595")
data = case.flow_dict
x = data['x']
y = data['y']
nx = data['nx']
ny = data['ny']
pos = np.linspace(0, 210, 22)
for p in pos:
rng = range(int(p * ny), int((p + 1) * ny))
yi = y[rng]
ui = data['um'][rng]
# dyi = np.abs(yi[1:] - yi[0:-1])
# dui = np.mean([ui[1:], ui[:-1]], axis=0)
# plt.plot(dui, np.cumsum(dyi))
# plt.plot(ui, yi)
# volflow = np.sum(dui*dyi)
volflow = np.trapz(ui, yi)
mflow = volflow * data['rho']
labl_test = labl_tests[-1]
qualitative_eval(model,
test_case,
feat_test,
labl_test,
model_idx,
result_name,
zoom_data=zoom_data)
show()
close_all()
# True error metric
if not exist_eval_file(
result_name,
"/figures/true_" + emetric + "_" + test_case + ".jpg"):
case = flowCase(test_case)
case.get_labels()
fig, ax = case.show_label(
emetric, show_background=True,
labelpos='upper right') #, zoom_box=zoom_data)
# if zoom_data:
# set_limits(ax, zoom_data[0][0], zoom_data[1][0])
save("./results/" + result_name + "/figures/true_" + emetric + "_" +
test_case + ".jpg") #_zoom#_zoom_window
show()
close_all()
# Model matrix
if algo == "spaRTA" and not exist_eval_file(result_name,
"model_matrix.pdf"):
# get_model_matrix(result_name)
# ###################################################################
# test line_label
#
# Description
# Test appending line labels to lining plots.
#
# ###################################################################
# Author: hw
# created: 26. Apr. 2020
# ###################################################################
import matplotlib.pyplot as plt
from uncert_ident.data_handling.flowcase import flowCase, compare_profiles
case_name1 = 'Breuer_PH_LES_DNS_Re10595'
case_name2 = 'Breuer_PH_LES_DNS_Re5600'
case_name3 = 'Breuer_PH_LES_DNS_Re1400'
case1 = flowCase(case_name1, dimension=2)
case2 = flowCase(case_name2, dimension=2)
case3 = flowCase(case_name3, dimension=2)
compare_profiles('um', 'y', 2.0, case1, case2, case3)
plt.show()
# ################################################################### # test lumley_triangle # # Description # Test for the lumley triangle plot. # # ################################################################### # Author: hw # created: 24. Apr. 2020 # ################################################################### import matplotlib.pyplot as plt from uncert_ident.visualisation import plotter as plot from uncert_ident.data_handling.flowcase import flowCase # case_name = 'Breuer_PH_LES_DNS_Re10595' case_name = 'Xiao_PH_DNS_case_1p0_refined_XYZ' case = flowCase(case_name, dimension=2) case.show_anisotropy(lumley=False) case.show_anisotropy(lumley=False, loc_key='x', loc_value=2) case.show_anisotropy(lumley=True) case.show_anisotropy(lumley=True, loc_key='x', loc_value=2) plt.show()
### Import
#####################################################################
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.lines import Line2D
from uncert_ident.data_handling.flowcase import flowCase
from uncert_ident.methods.labels import compute_all_labels
import uncert_ident.visualisation.plotter as plot
#####################################################################
### Test
#####################################################################
# Choose case
case = flowCase('PH-Breuer-10595')
# case = flowCase('PH-Xiao-15')
# case = flowCase('CBFS-Bentaleb')
# case = flowCase('NACA4412-Vinuesa-top-10')
# case = flowCase('TBL-APG-Bobke-m13')
# Choose label
label = 'anisotropic'
# label = 'non_negative'
# Get flow data
dit = case.flow_dict
x, y = dit['x'], dit['y']
nx, ny = dit['nx'], dit['ny']
# Get labels without filter
#####################################################################
# Load data
# case_name = 'PH-Breuer-5600'
# case_name = 'PH-Xiao-15'
# case_name = 'CBFS-Bentaleb'
# case_name = 'NACA4412-Vinuesa-bottom-1'
# case_name = 'NACA4412-Vinuesa-top-1'
# case_name = 'NACA0012-Tanarro-top-4'
case_name = 'TBL-APG-Bobke-m13'
case_names = [case_name]
# case_names = ['PH-Breuer-10595', 'PH-Xiao-15', 'CBFS-Bentaleb', 'NACA4412-Vinuesa-top-4', 'TBL-APG-Bobke-b1']
for case_name in case_names:
case = flowCase(case_name)
data = case.flow_dict
nx, ny = case.nx, case.ny
X, Y = data['x'].reshape(nx, ny), data['y'].reshape(nx, ny)
U, V = data['um'].reshape(nx, ny), data['vm'].reshape(nx, ny)
spd = np.sqrt(U**2 + V**2)
# Boundaries, seeding points, mask and axis label
if "PH" in case_name:
boundaries = get_boundaries(case)
boundaries = list(boundaries)
# boundaries.append([np.array([0.05, 8.9]), np.array([0.05, 0.05])])
# boundaries.append([np.array([0.1, 0.1]), np.array([0.05, 2.985])])
import uncert_ident.visualisation.plotter as plot
from uncert_ident.methods.gradient import gradient_2d_with_transformation
from uncert_ident.data_handling.flowcase import flowCase
#####################################################################
### Tests
#####################################################################
print("TEST OF GRADIENT COMPUTATION RUNNING...")
# Read any flowcase data
# case_name = 'Breuer_PH_LES_DNS_Re10595'
case_name = 'Noorani_bent_pipe_DNS_R11700k001'
if 'Noorani' in case_name:
case = flowCase(case_name, dimension=2, coord_sys='polar')
else:
case = flowCase(case_name, dimension=2, coord_sys='cartesian')
dictionary = case.flow_dict
# Read non-uniform grid coordinates
nx = dictionary['nx']
ny = dictionary['ny']
x = dictionary['x'].reshape(nx, ny)
y = dictionary['y'].reshape(nx, ny)
# Compute test function and exact gradient in both coordinate systems
f = np.sin(x) * np.cos(y)
exact_gradient = np.array([np.cos(x) * np.cos(y),
-np.sin(x) * np.sin(y)]).reshape(2, -1)
from uncert_ident.data_handling.flowcase import flowCase from uncert_ident.data_handling.data_import import find_case_names from uncert_ident.methods.classification import get_databasis_frames, confusion_matrix, TRUE_POSITIVE, TRUE_NEGATIVE, FALSE_POSITIVE, FALSE_NEGATIVE from uncert_ident.utilities import LABEL_KEYS from uncert_ident.visualisation import plotter as plot ##################################################################### ### Test ##################################################################### df_data, df_features, df_labels = get_databasis_frames(get_features=True, get_labels=True) pred_case = 'PH-Breuer-10595' # Get test case physical data pred_data = flowCase(pred_case).flow_dict stat_x, stat_y = pred_data['x'], pred_data['y'] nx, ny = pred_data['nx'], pred_data['ny'] # Build confusion matrix from predicted and true labels pred_labl = np.random.choice([0, 1], size=nx*ny) true_labl = df_labels.loc[df_labels['case'] == pred_case, LABEL_KEYS[1]].to_numpy() confusion = confusion_matrix(pred_labl, true_labl) pred_deci = np.random.randint(-100, 100, nx*ny) # Plot random predictions plot.physical_confusion(pred_data, confusion, show_background=False) plot.physical_decision(pred_data, pred_deci, show_background=False)
#####################################################################
### Import
#####################################################################
import pandas as pd
from uncert_ident.data_handling.flowcase import flowCase
from uncert_ident.data_handling.data_import import find_case_names
#####################################################################
### Tests
#####################################################################
# Get cases
case_names = find_case_names()
hifi_data = list()
for case_name in case_names:
hifi_data.append(flowCase(case_name, get_features=True, get_labels=True))
print("No of cases: " + str(len(hifi_data)))
# Get metadata
nams, npts, dims, geos, feat, labl = list(), list(), list(), list(), list(), list()
for case in hifi_data:
nams.append(case.case_name)
npts.append(case.num_of_points)
dims.append(case.dimension)
geos.append(case.geometry)
case.feature_dict.update({'case': case.case_name})
case.label_dict.update({'case': case.case_name})
feat.append(pd.DataFrame(case.feature_dict))
labl.append(pd.DataFrame(case.label_dict))
assert len(vector.shape) == 2
assert vector.shape[0] < vector.shape[1]
for i in range(vector.shape[0]):
maxabs = np.nanmax(abs(vector[i]))
vector[i] = safe_divide(vector[i], maxabs)
return vector
#####################################################################
### Test
#####################################################################
# Load data
# case = flowCase('PH-Breuer-10595')
case = flowCase('TBL-APG-Bobke-b2')
# Build anti-symmetric pseudotensors Pij, Kij
construct_antisymmetric_grad_p_grad_k(case.flow_dict)
# Normalise all tensors
normalise_Sij_Wij_Pij_Kij(case.flow_dict)
# Compute invariants by hand
inv = get_inv(case.flow_dict)
inv_norm = inv.copy()
# Normalise invariants
inv_norm = normalise_max_abs(inv_norm)
# Safe invariants to flow_dict (Only for visualisation, safe to feature_dict!)
import pandas as pd
import numpy as np
from uncert_ident.utilities import feature_to_q_keys
from uncert_ident.data_handling.flowcase import flowCase
from uncert_ident.visualisation.plotter import *
# case = flowCase("NACA4412-Vinuesa-top-1")
case = flowCase("PH-Xiao-10")
case.get_features()
case.get_labels()
a = 500
# idx = range(a*52, (a+1)*52)
idx = range(a * 385, (a + 1) * 385)
data = case.flow_dict
x = data['x'][idx]
y = data['y'][idx]
labl = case.label_dict['anisotropic'][idx]
feat = case.feature_dict
tke = feat['tke'][idx]
red = feat['Re_d'][idx]
keps = feat["k_eps_Sij"][idx]
orthogonal = feat["orthogonal"][idx]
stream_curv = feat["stream_curv"][idx]
pm_normal_shear_ratio = feat["pm_normal_shear_ratio"][idx]
inv02 = feat['inv02'][idx]
inv03 = feat['inv03'][idx]
