def load_nn(path):
"""
load NN object from file
path: str, PATH to the loaded file
return NN object
"""
if path[-3:] == "csv":
nn_obj = prn.loadNN(path)
elif path[-2:] == "pt":
nn_obj = tr.load(path)
nn_obj.eval()
else:
print("canceled")
return
return nn_obj
def LM_predic(LM_para_file, LM_para_normalfile, fileName, DI1, DI2):
#load NN method for feature work
net = prn.loadNN(LM_para_file)
#Nomalized parameter
df_normalize = pd.read_csv(LM_para_normalfile, header=None)
normalize_factor = np.array(df_normalize.iloc[:, 0].tolist())
#Read file
df_test = pd.read_excel(fileName, 'Summary').T
#移除不需要的參數
columnDrop = [
'1300 AC',
'1300 DC',
'1300 HR',
'1300 Area',
'1300 PWTT',
'1300 BVI value',
'1300 BVI amp',
'1300 BVI time',
'1300 BVA value',
]
df_test = df_test.drop(columnDrop, axis=1)
input_x = df_test.iloc[0, :].tolist()
#Diabetes index
input_x.extend([DI1, DI2])
input_f = np.array(input_x)
#參數正規化
input_x_normalized = input_f / normalize_factor
input_x_pre = input_x_normalized.reshape(29, 1)
#輸出讀值
glucose = prn.NNOut(input_x_pre, net) * 600
Glu = float(glucose[0]) #將array 轉成float
Glu = round(Glu, 1) # 轉成float後才可以進行round指令
print(Glu)
return Glu
def load_pyrenn(filename='narxNet'):
return pr.loadNN(filename)
# example_compair.py
# This is an example of a dynamic system with 2 outputs and 3 inputs
print("compair")
iteration = 0
while iteration < iterations:
# Read Example Data
df = genfromtxt('example_data_compressed_air.csv', delimiter=',')
df = df.transpose()
P = np.array([df[1][1:-1], df[2][1:-1], df[3][1:-1]])
Y = np.array([df[4][1:-1], df[5][1:-1]])
Ptest = np.array([df[6][1:-1], df[7][1:-1], df[8][1:-1]])
Ytest = np.array([df[9][1:-1], df[10][1:-1]])
# Load saved NN from file
net = prn.loadNN("./SavedNN/compair.csv")
psutil.cpu_percent(interval=None, percpu=True)
time_start = time.time()
# Calculate outputs of the trained NN for train and test data
y = prn.NNOut(P, net)
ytest = prn.NNOut(Ptest, net)
res = nvidia_smi.nvmlDeviceGetUtilizationRates(handle)
print(f'gpu: {res.gpu}%, gpu-mem: {res.memory}%')
time_stop = (time.time())
cores = psutil.cpu_percent(interval=None, percpu=True)
if (mean(cores) != 0.0) and (time_stop - time_start != 0):
logging_data(0, time_stop, time_start, cores)
file = 'building_with_storage_data.xlsx'
#define constants, timeseries, list of possible decisions U and
#timesteps, which are needed for the model
cst, srs, U, states = model.read_data(file)
#add some data to timeseries
srs['massflow'] = 0
srs['P_th'] = 0
srs['T_room'] = 20
#define timesteps, on which optimization will be realized
timesteps = np.arange(cst['t_start'], cst['t_end'])
#define NN
net = prn.loadNN('NN_building.csv') #use pre-trained NN
cst['net'] = net
#creating array of initial terminal costs J0
xsteps = np.prod(states['xsteps'].values)
J0 = np.zeros(xsteps)
idx = prd.find_index(np.array([20, 0]), states)
J0[idx] = -9999.9
#define function for simulation that calculates costs and next state
system = model.building_with_storage
#optimize with DP forward algorithm
result = prd.DP_forward(states,
U,
timesteps,
import librosa
import pyrenn
from scipy.io import wavfile
sourcefile = 'test_in.wav'
# Parameters.
frameLength = 1024
overlap = 0.25
hop_length = frameLength * overlap
order = 25
alpha = 0.42
gamma = -0.35
# Loading pyrenn Model
net = pyrenn.loadNN('pyrennweights_2.csv')
# Input
sr, sx = wavfile.read(sourcefile)
l = len(sx)
# framing
sourceframes = librosa.util.frame(sx, frame_length=frameLength, hop_length=hop_length).astype(np.float64).T
# Windowing
sourceframes *= pysptk.blackman(frameLength)
# extract MCEPs
sourcemcepvectors = np.apply_along_axis(pysptk.mcep, 1, sourceframes, order, alpha)
# provide the source MCEPs as input to the trained neural network which gives the target MCEPs
mgc = pyrenn.NNOut(sourcemcepvectors.transpose(), net).transpose()
# -*- coding: utf-8 -*-
"""
Created on Fri Jan 25 14:29:49 2019
@author: RICHARD.WENG
"""
#導入函數庫
import numpy as np
import pandas as pd
import pyrenn as prn
#load NN method for feature work
LM_para_file = 'ANN_training_data_AC_combination_0122_LM_parameter.csv'
net = prn.loadNN(LM_para_file)
#Nomalized parameter
LM_para_normalfile = 'ANN_training_data_AC_combination_0122_LM_normalize_index.csv'
df_normalize = pd.read_csv(LM_para_normalfile,header = None)
normalize_factor = np.array(df_normalize.iloc[:,0].tolist())
#Read file
fileName = '0905-1_Volts_20190122_090921.txt_new.xlsx'
df_test = pd.read_excel(fileName,'Summary').T
#移除不需要的參數
columnDrop = [
'1300 AC',
'1300 DC',
'1300 HR',
'1300 Area',
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import pyrenn as prn
import mpl_settings as ms
ms.set_style()
file = 'building_data.xlsx'
Srs = pd.read_excel(file, sheetname='Time-Series', index_col=[0])
net = prn.loadNN('NN_building.csv')
Srs['massflow'] = 200
Srs['T_room'] = 20
Srs['P_th'] = 1
delay = 4
timesteps = np.arange(4, 287)
for t in timesteps:
hour0 = Srs.loc[t - delay:t - 1]['hour'].values.copy()
solar0 = Srs.loc[t - delay:t - 1]['solar'].values.copy()
T_amb0 = Srs.loc[t - delay:t - 1]['T_amb'].values.copy()
user0 = Srs.loc[t - delay:t - 1]['use_room'].values.copy()
T_inlet0 = Srs.loc[t - delay:t - 1]['T_inlet'].values.copy()
massflow0 = Srs.loc[t - delay:t - 1]['massflow'].values.copy()
T_room0 = Srs.loc[t - delay:t - 1]['T_room'].values.copy()
P_th0 = Srs.loc[t - delay:t - 1]['P_th'].values.copy()
def finddisease(request):
del list[:]
if request.method == 'POST':
q1 = request.POST.get("symptom1")
q2 = request.POST.get("symptom2")
q3 = request.POST.get("symptom3")
q4 = request.POST.get("symptom4")
q5 = request.POST.get("symptom5")
q6 = request.POST.get("symptom6")
q7 = request.POST.get("symptom7")
q8 = request.POST.get("symptom8")
q9 = request.POST.get("symptom9")
q10 = request.POST.get("symptom10")
q11 = request.POST.get("symptom11")
q12 = request.POST.get("symptom12")
q13 = request.POST.get("symptom13")
q14 = request.POST.get("symptom14")
q15 = request.POST.get("symptom15")
q16 = request.POST.get("symptom16")
q17 = request.POST.get("symptom17")
q18 = request.POST.get("symptom18")
q19 = request.POST.get("symptom19")
q20 = request.POST.get("symptom20")
q21 = request.POST.get("symptom21")
q22 = request.POST.get("symptom22")
q23 = request.POST.get("symptom23")
q24 = request.POST.get("symptom24")
q25 = request.POST.get("symptom25")
q26 = request.POST.get("symptom26")
q27 = request.POST.get("symptom27")
q28 = request.POST.get("symptom28")
q29 = request.POST.get("symptom29")
q30 = request.POST.get("symptom30")
q31 = request.POST.get("symptom31")
q32 = request.POST.get("symptom32")
q33 = request.POST.get("symptom33")
q34 = request.POST.get("symptom34")
q35 = request.POST.get("symptom35")
q36 = request.POST.get("symptom36")
q37 = request.POST.get("symptom37")
q38 = request.POST.get("symptom38")
q39 = request.POST.get("symptom39")
q40 = request.POST.get("symptom40")
q41 = request.POST.get("symptom41")
q42 = request.POST.get("symptom42")
q43 = request.POST.get("symptom43")
q44 = request.POST.get("symptom44")
q45 = request.POST.get("symptom45")
q46 = request.POST.get("symptom46")
q47 = request.POST.get("symptom47")
q48 = request.POST.get("symptom48")
q49 = request.POST.get("symptom49")
q50 = request.POST.get("symptom50")
q = [
q1, q2, q3, q4, q5, q6, q7, q8, q9, q10, q11, q12, q13, q14, q15,
q16, q17, q18, q19, q20, q21, q22, q23, q24, q25, q26, q27, q28,
q29, q30, q31, q32, q33, q34, q35, q36, q37, q38, q39, q40, q41,
q42, q43, q44, q45, q46, q47, q48, q49, q50
]
s = [
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0
]
for i in range(len(q)):
#for j in range(len(s)):
if q[i] == 'on':
s[i] = 1
wb = load_workbook('test.xlsx', data_only=True)
#wb = load_workbook('dataset.xlsx', data_only=True)
ws = wb.active
ws['CZ2'].value = s[0]
ws['DA2'].value = s[1]
ws['DB2'].value = s[2]
ws['DC2'].value = s[3]
ws['DD2'].value = s[4]
ws['DE2'].value = s[5]
ws['DF2'].value = s[6]
ws['DG2'].value = s[7]
ws['DH2'].value = s[8]
ws['DI2'].value = s[9]
ws['DJ2'].value = s[10]
ws['DK2'].value = s[11]
ws['DL2'].value = s[12]
ws['DM2'].value = s[13]
ws['DN2'].value = s[14]
ws['DO2'].value = s[15]
ws['DP2'].value = s[16]
ws['DQ2'].value = s[17]
ws['DR2'].value = s[18]
ws['DS2'].value = s[19]
ws['DT2'].value = s[20]
ws['DU2'].value = s[21]
ws['DV2'].value = s[22]
ws['DW2'].value = s[23]
ws['DX2'].value = s[24]
ws['DY2'].value = s[25]
ws['DZ2'].value = s[26]
ws['EA2'].value = s[27]
ws['EB2'].value = s[28]
ws['EC2'].value = s[29]
ws['ED2'].value = s[30]
ws['EE2'].value = s[31]
ws['EF2'].value = s[32]
ws['EG2'].value = s[33]
ws['EH2'].value = s[34]
ws['EI2'].value = s[35]
ws['EJ2'].value = s[36]
ws['EK2'].value = s[37]
ws['EL2'].value = s[38]
ws['EM2'].value = s[39]
ws['EN2'].value = s[40]
ws['EO2'].value = s[41]
ws['EP2'].value = s[42]
ws['EQ2'].value = s[43]
ws['ER2'].value = s[44]
ws['ES2'].value = s[45]
ws['ET2'].value = s[46]
ws['EU2'].value = s[47]
ws['EV2'].value = s[48]
ws['EW2'].value = s[49]
wb.save('test.xlsx')
df = pd.ExcelFile('test.xlsx').parse('Sheet')
P0 = np.array([
df['s1'].values, df['s2'].values, df['s3'].values, df['s4'].values,
df['s5'].values, df['s6'].values, df['s7'].values, df['s8'].values,
df['s9'].values, df['s10'].values, df['s11'].values,
df['s12'].values, df['s13'].values, df['s14'].values,
df['s15'].values, df['s16'].values, df['s17'].values,
df['s18'].values, df['s19'].values, df['s20'].values,
df['s21'].values, df['s22'].values, df['s23'].values,
df['s24'].values, df['s25'].values, df['s26'].values,
df['s27'].values, df['s28'].values, df['s29'].values,
df['s30'].values, df['s31'].values, df['s32'].values,
df['s33'].values, df['s34'].values, df['s35'].values,
df['s36'].values, df['s37'].values, df['s38'].values,
df['s39'].values, df['s40'].values, df['s41'].values,
df['s42'].values, df['s43'].values, df['s44'].values,
df['s45'].values, df['s46'].values, df['s47'].values,
df['s48'].values, df['s49'].values, df['s50'].values
])
net = prn.loadNN('C:\Users\STUTI\Desktop\minor_final.csv')
diseases = [
'AIDS (acquired immuno-deficiency syndrome)', 'Adhesion',
'Affect labile', 'Alzheimers disease', 'Anemia', 'Aphasia',
'Asthma', 'Biliary calculus', 'Bipolar disorder',
'Carcinoma prostate', 'Cholecystitis',
'Chronic alcoholic intoxication', 'Chronic kidney failure',
'Chronic obstructive airway disease', 'Coronary arteriosclerosis',
'Decubitus ulcer', 'Degenerative polyarthritis',
'Deglutition disorder', 'Dehydration', 'Depressive disorder',
'Diverticulosis', 'Pulmonary Embolism', 'Encephalopathy',
'Endocarditis', 'Epilepsy', 'Heart Failure', 'Kidney Failure',
'Fibroid tumor', 'Gastritis', 'Gout', 'Hepatitis', 'Hernia hiatal',
'Hyperbilirubinemia', 'Hypercholesterolemia', 'Hyperglycemia',
'Hypothyroidism', 'Ileus', 'Incontinence',
'Infection urinary tract', 'Influenza', 'Insufficiency renal',
'Lymphoma', 'Malignant neoplasm of breast',
'Malignant neoplasm of prostate', 'Malignant tumor of colon',
'Myocardial infarction', 'Neoplasm', 'Neoplasm Metastasis',
'Obesity', 'Obesity Morbid'
]
y = prn.NNOut(P0, net)
z = y
np.around(y, 0, z)
for i in range(len(z)):
if z[i][0] == 1:
list.append(diseases[i])
if request.session['first_name'] is None:
template = loader.get_template('symptomchecker/index.html')
context = RequestContext(request, {
'checked': list,
'checked1': ws
})
return HttpResponse(template.render(context))
else:
template = loader.get_template('symptomchecker/indexloggedin.html')
context = RequestContext(
request, {
'checked': list,
'checked1': ws,
'name': request.session['first_name']
})
return HttpResponse(template.render(context))
else:
if request.session['first_name'] is None:
sform = symform()
template = loader.get_template('symptomchecker/symtest.html')
context = RequestContext(request, {'symform': sform})
return HttpResponse(template.render(context))
else:
sform = symform()
template = loader.get_template('symptomchecker/symptomc.html')
context = RequestContext(request, {
'symform': sform,
'name': request.session['first_name']
})
return HttpResponse(template.render(context))
dtminL[indxV,nn+ant], \
# dwspdL[indxV,nn+ant], \
xQV, bQV,\
))
xV = np.transpose(xV)
mdl_name = datdir + 'Bin/pyrennNet4_' + basin + '_L' + str(nn) + '.csv'
# #Create and train NN
# print 'Training NN..'
# net = prn.CreateNN([x.shape[0],7,1])
# net = prn.train_LM(x,t,net,verbose=True,k_max=500,E_stop=1e-5)
# prn.saveNN(net,mdl_name)
#Load saved NN
net = prn.loadNN(mdl_name)
#Calculate outputs of the trained NN for train and test data
yV[:, nn] = prn.NNOut(xV, net)
# tmp=y[:,nn]
# tmp[tmp<0]=0
# tmp[tmp>400000]=0
# y[:,nn]=tmp
# tmp=yV[:,nn]
# tmp[tmp<0]=0
# tmp[tmp>400000]=0
# yV[:,nn]=tmp
byV[0, nn] = yV[0, nn]
for k in range(1, len(yV)):
def outputNetwork(f):
img=cv2.imread(f+"image.png",0)
process_image(0,f+"image.png",f,f)
make_square(f+"/0.png")
resize_image(f+"/0.png")
features_list=[]
img = cv2.imread(f+"/0.png",0)
image_features =hog(img, block_norm='L2-Hys', pixels_per_cell=(16, 16))
features_list.append(image_features)
feature_matrix=np.array(features_list)
ss = StandardScaler()
# run this on our feature matrix
fracture_stand = ss.fit_transform(feature_matrix)
pca = PCA(n_components=500)
# use fit_transform to run PCA on our standardized matrix
fracture_pca = ss.fit_transform(fracture_stand)
# look at new shape
#print('PCA matrix shape is: ', fracture_pca.shape)
X = pd.DataFrame(fracture_pca)
svm= load("C:/TrainedModels/svm_model_PI12_68.36%.csv")
y_pred = svm.predict(X)
svm_prob=svm.predict_proba(X)[0]
print(type(svm_prob))
valid_data=glcmNN(f,1)
net=pyrenn.loadNN("C:/TrainedModels/glcm_model_1.csv")
y = pyrenn.NNOut(valid_data.transpose(),net)
final=0
glcmOutput = final_OP(y)[0][0]
SVMOutput = y_pred[0]
glcm_prob = [0.0, 0.0]
if glcmOutput == 1:
glcm_prob = [0.25, 0.75]
else:
glcm_prob = [0.75, 0.25]
'''
for i,j in zip(final_OP(y),y_pred):
if i[0]==j==0:
final=0
elif i[0]==j==1:
final=0
elif j==1:
final=1
elif i[0]==0:
final=0
else:
final=0
'''
print("GCLM output:",glcmOutput)
print("SVM output:",SVMOutput)
print("GCLM prob:",glcm_prob)
print("SVM prob:",svm_prob)
final_prob = [(svm_prob[0]+glcm_prob[0])/2, (svm_prob[1]+glcm_prob[1])/2]
print('Final prob: ',final_prob)
if final_prob[0] > 0.5:
final = 0
else:
final = 1
return final, final_prob[1]
dato = 2
dato -= 1
datos_size = 200
while i < datos_size:
X_test = Entradas[0:7, dato - 1:dato]
X_test = np.array(X_test)
datos_test = X_test.reshape(1, 7)
print("Datos tested: ")
print(datos_test)
index = X_test.item((2, 0))
filename = "nnBP.csv"
net = prn.loadNN(filename)
edad = X_test.item((0, 0))
sexo = X_test.item((1, 0))
bmi = X_test.item((2, 0))
sys = X_test.item((3, 0))
dia = X_test.item((4, 0))
fuma = X_test.item((5, 0))
padres = X_test.item((6, 0))
agedb = edad * dia
target = 1 - math.exp(-math.exp((math.log(4) -
(22.949536 + (-0.156412 * edad) +
(-0.202933 * sexo) + (-0.033881 * bmi) +
(-0.05933 * sys) + (-0.128468 * dia) +
(-0.190731 * fuma) +
])
#P = np.array([df['agitation'].values,df['apyrexial'].values,df['ascites'].values,df['asthenia'].values,df['blackout'].values,df['bradycardia'].values,df['breath sounds decreased'].values,df['chest tightness'].values,df['chill'].values,df['consciousness clear'].values,df['cough'].values,df['decreased body weight'].values,df['diarrhea'].values,df['difficulty'].values,df['distress respiratory'].values,df['drowsiness'].values,df['dyspnea'].values,df['facial paresis'].values,df['fatigue'].values,df['feeling hopeless'].values,df['feeling suicidal'].values,df['fever'].values,df['guaiac positive'].values,df['haemorrhage'].values,df['hallucinations auditory'].values,df['hallucinations visual'].values,df['headache'].values,df['hematuria'].values,df['hemodynamically stable'].values,df['homelessness'].values,df['hypokinesia'].values,df['hypotension'].values,df['intoxication'].values,df['irritable mood'].values,df['lesion'].values,df['mass of body structure'].values,df['mental status changes'].values,df['mood depressed'].values,df['nausea'].values,df['orthopnea'].values,df['pain'].values,df['pain abdominal'].values,df['pain chest'].values,df['patient non compliance'].values,df['pleuritic pain'].values,df['prostatism'].values,df['rale'].values,df['shortness of breath'].values,df['sleeplessness'].values,df['sore to touch'].values])
#Y = np.array([df['acquired immuno-deficiency syndrome'].values,df['adhesion'].values,df['affect labile'].values,df['Alzheimers disease'].values,df['anemia'].values,df['aphasia'].values,df['asthma'].values,df['biliary calculus'].values,df['bipolar disorder'].values,df['carcinoma prostate'].values,df['cholecystitis'].values,df['chronic alcoholic intoxication'].values,df['chronic kidney failure'].values,df['chronic obstructive airway disease'].values,df['chronic obstructive airway disease'].values,df['coronary arteriosclerosis'].values,df['decubitus ulcer'].values,df['degenerative polyarthritis'].values,df['deglutition disorder'].values,df['dehydration'].values,df['depressive disorder'].values,df['diverticulosis'].values,df['embolism pulmonary'].values,df['encephalopathy'].values,df['endocarditis'].values,df['epilepsy'].values,df['failure heart'].values,df['failure kidney'].values,df['fibroid tumor'].values,df['gastritis'].values,df['gout'].values,df['hepatitis'].values,df['hernia hiatal'].values,df['hyperbilirubinemia'].values,df['hypercholesterolemia'].values,df['hyperglycemia'].values,df['hypothyroidism'].values,df['ileus'].values,df['incontinence'].values,df['infection urinary tract'].values,df['influenza'].values,df['insufficiency renal'].values,df['lymphoma'].values,df['malignant neoplasm of breast'].values,df['malignant neoplasm of prostate'].values,df['malignant tumor of colon'].values,df['myocardial infarction'].values,df['neoplasm'].values,df['neoplasm metastasis'].values,df['obesity'].values])
#Ptest = np.array([df['CT1'].values,df['CT2'].values,df['CT3'].values,df['CT4'].values,df['CT5'].values,df['CT6'].values,df['CT7'].values,df['CT8'].values,df['CT9'].values,df['CT10'].values,df['CT11'].values,df['CT12'].values,df['CT13'].values,df['CT14'].values,df['CT15'].values,df['CT16'].values])
#Ytest = np.array([df['CT17'].values,df['CT18'].values,df['CT19'].values,df['CT20'].values,df['CT21'].values,df['CT22'].values,df['CT23'].values])
print len(P0)
###
#Create and train NN
#create feed forward neural network with 1 input, 2 hidden layers with
#4 neurons each and 1 output
#the NN has a recurrent connection with delay of 1 timesteps from the output
# to the first layer
#print len(P),len(Y)
#net = prn.CreateNN([50,50,50])
net = prn.loadNN(
'/usr/local/lib/python2.7/site-packages/examples/minor_final.csv')
#Train NN with training data P=input and Y=target
#Set maximum number of iterations k_max to 500
#Set termination condition for Error E_stop to 1e-5
#The Training will stop after 500 iterations or when the Error <=E_stop
#net = prn.train_LM(P,Y,net,verbose=True,k_max=50,E_stop=1e-5)
###
#Calculate outputs of the trained NN for train and test data
j = 0
diseases = [
'AIDS (acquired immuno-deficiency syndrome)', 'Adhesion', 'Affect labile',
'Alzheimers disease', 'Anemia', 'Aphasia', 'Asthma', 'Biliary calculus',
'Bipolar disorder', 'Carcinoma prostate', 'Cholecystitis',
'Chronic alcoholic intoxication', 'Chronic kidney failure',
time_total_start = time.time()
# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
# example_compair.py
for i in range(iterations):
# Read Example Data
df = genfromtxt('example_data_compressed_air.csv', delimiter=',')
P = np.array([df[1], df[2], df[3]])
Y = np.array([df[4], df[5]])
Ptest = np.array([df[6], df[7], df[8]])
Ytest = np.array([df[9], df[10]])
# Load saved NN from file
net = prn.loadNN("D:/School/Masterproef/Python/pyrenn/SavedNN/compair.csv")
psutil.cpu_percent(interval=None, percpu=True)
time_start.append(time.time())
# Calculate outputs of the trained NN for train and test data
y = prn.NNOut(P, net)
ytest = prn.NNOut(Ptest, net)
time_stop.append(time.time())
cores.append(psutil.cpu_percent(interval=None, percpu=True))
print(cores)
virtual_mem.append(psutil.virtual_memory())
# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
# example_friction.py