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Nikuradse Model (Comparison).py
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Nikuradse Model (Comparison).py
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from Functions import *
from MainConfigurationFile import experiments, experimentConfig
#Creating list of all experiments based on configuration in experimentConfig dictionary
for k, v in experimentConfig.items():
for exp in v.get('list'):
experiments.append(f'{k}_rescan{exp}')
#Creating dictionary to search for zeroshift experiments
ZeroshiftSearch = dict()
location = r"C:\Users\PipeFlow\Desktop\Experiments\Data\New\Valley"
for experiment in experiments:
ZeroshiftSearch[experiment] = (f"{location}\{experiment[:2]}\{experiment[3:]}\{'before'}.tdms")
#putting zeroshift experiments in a list and creating dictionary to search for excel experiments
ExcelSearch = dict()
Zeroshifts = []
for k, v in ZeroshiftSearch.items():
if exists(v):
Zeroshifts.append(k)
else:
for experiment in experiments:
ExcelSearch[experiment] = f'{location}\{experiment[:2]}\{experiment[3:]}\{experiment[3:]}.xlsx'
#putting excel experiments in a list
Excels = []
for k, v in ExcelSearch.items():
if exists(v):
Excels.append(k)
else:
continue
#putting remaining experiments in a list
Remainder = [x for x in experiments if x not in Zeroshifts]
experiments = [x for x in Remainder if x not in Excels]
#------------------------------64/re & Blasius
#reynolds range
num = np.arange(100,31600,100)
num1 = np.arange(1200,31600,100)
#64/re line
lam = pd.DataFrame(num, columns=['Reynolds Number'])
lam['64/re'] = 64/lam['Reynolds Number']
lam = np.log10(lam)
lam = lam.set_index('Reynolds Number')
#Blasius line
tur = pd.DataFrame(num1, columns=['Reynolds Number'])
tur['blasius'] = .316/(tur['Reynolds Number']**.25)
tur = np.log10(tur)
tur = tur.set_index('Reynolds Number')
legendEntries = []
legendEntries.append('64/Re')
legendEntries.append('Blasius')
plt.plot(lam)
plt.plot(tur)
#-----------------------------------------------------Graph
#Plots for zeroshift experiments
if Zeroshifts:
ZeroShiftResults = dict()
for Zeroshift in Zeroshifts:
ZeroShiftResults[Zeroshift] = Process_ZeroShift_Experiment(Zeroshift[:2], Zeroshift[3:])
print(ZeroShiftResults)
for Zeroshift, ZeroShiftResults in ZeroShiftResults.items():
before, actual, after = [ZeroShiftResults[x] for x in ['before', 'actual', 'after']]
before = before.iloc[1:]
#plt.plot(before.index, before['Friction Factor'])
plt.errorbar(before.index, before['Friction Factor'], yerr = before['Error'])
legendEntries.append('Transducer (%s)' % Zeroshift)
else:
print("No zeroshift experiments found.")
#Plots for excel experiments
if Excels:
ExcelResults = dict()
for Excel in Excels:
ExcelResults[Excel] = Process_Excel_Experiemnt(Excel[:2], Excel[3:])
print(ExcelResults)
for Excel, ExcelResults in ExcelResults.items():
Friction = [ExcelResults[x] for x in ['Friction']]
plt.plot(ExcelResults.index, ExcelResults['Friction'])
legendEntries.append('Monometer (%s)' % Excel)
else:
print("No excel experiments found.")
# if Excels:
# ExcelResults = dict()
# for Excel in Excels:
# ExcelResults[Excel] = Process_Excel_Experiemnt(Excel[:2], Excel[3:])
# for Excel, ExcelResults in ExcelResults.items():
# Friction = [ExcelResults[x] for x in ['Friction']]
# plt.scatter(ExcelResults.index, ExcelResults, s=1)
#Plots for regular experiments
results = dict()
for experiment in experiments:
results[experiment] = process_experiment(experiment)
#print(results)
for experiment, result in results.items():
smooth, rough = [result[x] for x in ['smooth', 'rough']]
# plt.plot(smooth)
# legendEntries.append("Smooth (%s)" % experiment)
plt.plot(rough)
legendEntries.append('Actual (%s)' % experiment)
# for experiment, result in results.items():
# smooth, rough = [result[x] for x in ['smooth', 'rough']]
# #plt.scatter(smooth.index, smooth, s=1,label='_Hidden label')
# plt.scatter(rough.index, rough, s=1)
plt.xlabel('log Re')
plt.ylabel('log f')
plt.legend(legendEntries)
plt.grid()
plt.xlim(3.4, 4.2)
plt.ylim(-3, 0)
plt.show()