def newCorrelation(self):
""" This method decides when a new correlation has to be created
:return:
"""
if len(self.correlations) == 0:
self.correlations.append(Correlations(None))
self.correlations[-1].figure.canvas.set_window_title(
'Correlation' + ' ' + str(len(self.correlations) - 1))
logging.info(
'New correlation. Number of existing correlations: %s',
len(self.correlations))
self.correlations[-1].i_reward_assigned = 1
if len(self.correlations) < 9: #6
if self.correlations[-1].established:
self.correlations.append(
Correlations(len(self.correlations) - 1))
#self.correlations.append(Correlations(0))
self.correlations[-1].figure.canvas.set_window_title(
'Correlation' + ' ' + str(len(self.correlations) - 1))
logging.info(
'New correlation. Number of existing correlations: %s',
len(self.correlations))
def newSUR(self, active_goal):
""" This method decides when a new SUR has to be created. Two conditions are considered to do it:
1- There are no SURs associated with the active goal.
2- All the SURs associated with the active goal are established.
:return:
"""
surs_asoc_active_goal = 0 #Used to count the number of SURs associated with the active goal. Condition 1
index_last_sur_asoc = None #Used to save the index of the last SUR associated wiht the active goal. Condition 2
for i in range(len(self.correlations)):
if self.correlations[i].goal == active_goal:
surs_asoc_active_goal += 1
index_last_sur_asoc = i
if surs_asoc_active_goal == 0: # Condition 1
self.correlations.append(Correlations(None, active_goal))
# self.correlations[-1].figure.canvas.set_window_title('SUR' + ' ' + str(len(self.correlations) - 1))
# logging.info('New correlation. Number of existing correlations: %s', len(self.correlations))
elif self.correlations[index_last_sur_asoc].established: # Condition 2
self.correlations.append(Correlations(None, active_goal))
def corr_one_d(self, quantity='redshift', end_s=0.02, ns=15, n_randoms=1, n_cores=1):
"""
Calculates the correlation function in 1d for the given quantity
"""
if quantity == 'c_dist' and end_s == 0.02:
end_s = 120
if quantity == 'redshift':
min_cut = self.sample_cuts['redshift'][0]
max_cut = self.sample_cuts['redshift'][1]
elif quantity == 'c_dist':
min_cut = cd.comoving_distance(self.sample_cuts['redshift'][0], **self.cosmology)
max_cut = cd.comoving_distance(self.sample_cuts['redshift'][1], **self.cosmology)
else:
raise ValueError("Quantity not supported: ", quantity)
s_bins, corr = cf._1d_correlation(self.survey_data[quantity], min_cut, max_cut, end_s, ns, n_randoms, n_cores)
print s_bins, corr
plt.clf()
plt.plot(s_bins, corr)
plt.title(r"Correlation Function for $" + quantity + "$ of sample " + self.sample_name.partition('.')[0] + \
" from " + self.survey_name)
print "made title"
if quantity == 'c_dist':
plt.xlabel(r'Separation, Mpc $h^{-1}$s')
elif quantity == "redshift":
plt.xlabel(r'Separation, [z]')
print "plotted"
plt.ylabel(r"$\xi(r)$")
plt.savefig(self._dirs["CorrelationFunctions"] + "CorrelationFunction_" + quantity + '_NS' + str(ns) + '_Nrand' + str(n_randoms) + '.eps')
print "saved first fig"
plt.clf()
plt.plot(s_bins, corr * s_bins ** 2)
plt.title(r"Correlation Function by $s^2$ for $" + quantity + "$ of sample " + self.sample_name.partition('.')[0] + \
" from " + self.survey_name)
if quantity == 'c_dist':
plt.xlabel(r'Separation, Mpc $h^{-1}$s')
elif quantity == "redshift":
plt.xlabel(r'Separation, [z]')
plt.ylabel(r"$r^2\xi(r)$")
plt.savefig(self._dirs["CorrelationFunctions"] + "CorrelationFunction_s2_" + quantity + '_NS' + str(ns) + '_Nrand' + str(n_randoms) + '.eps')
return s_bins, corr
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Feb 27 17:52:20 2020
@author: kellenbullock
"""
import pandas as pd
from pandas.plotting import scatter_matrix
import Correlations
Milkwaukee = pd.read_spss('/Users/kellenbullock/Desktop/Geographic Analysis II/Data/5303_EX_B.sav')
Milkwaukee = Milkwaukee.drop(columns=['SaleDate', 'AC', 'Garage', 'Attic', 'Ald', 'Basement', 'Record'])
df = Milkwaukee.corr()
df = Correlations.unstack_corr(df)
# t-test:
# Correaltions by month
def OBJECTIVE(x):
Tevap=x[0]
Tcond=x[1]
Tin_CC=x[2]
if Cycle.Mode=='AC':
#Condenser heat transfer rate
Qcond=epsilon*Cycle.Condenser.Fins.Air.Vdot_ha*rho_air*(Cycle.Condenser.Fins.Air.Tdb-Tcond)*1005 #updated
#Compressor power
pevap=PropsSI('P','T',Tevap,'Q',1.0,Cycle.Ref)
pcond=PropsSI('P','T',Tcond,'Q',1.0,Cycle.Ref)
Cycle.Compressor.pin_r=pevap
Cycle.Compressor.pout_r=pcond
Cycle.Compressor.Tin_r=Tevap+Cycle.Compressor.DT_sh
Cycle.Compressor.Ref=Cycle.Ref
Cycle.Compressor.Calculate()
W=Cycle.Compressor.W
Qcoolingcoil_dry=epsilon*Cycle.CoolingCoil.Fins.Air.Vdot_ha*rho_air*(Cycle.CoolingCoil.Fins.Air.Tdb-Tin_CC)*1005 #updated
# Air-side heat transfer UA
CC=Cycle.CoolingCoil
CC.Initialize()
UA_a=CC.Fins.h_a*CC.Fins.A_a*CC.Fins.eta_a
#Air outlet temp from dry analysis
Tout_a=CC.Tin_a-Qcoolingcoil_dry/(CC.Fins.mdot_a*CC.Fins.cp_a)
# Refrigerant side UA
f,h,Re=Correlations.f_h_1phase_Tube(Cycle.Pump.mdot_g/CC.Ncircuits, CC.ID, Tin_CC, CC.pin_g, CC.Ref_g)
UA_r=CC.A_g_wetted*h
#Refrigerant outlet temp
cp_g=PropsSI('C','T',Tin_CC,'P',Cycle.Pump.pin_g,Cycle.Pump.Ref_g) #*1000
Tout_CC=Tin_CC+Qcoolingcoil_dry/(Cycle.Pump.mdot_g*cp_g)
#Get wall temperatures at inlet and outlet from energy balance
T_so_a=(UA_a*CC.Tin_a+UA_r*Tout_CC)/(UA_a+UA_r)
T_so_b=(UA_a*Tout_a+UA_r*Tin_CC)/(UA_a+UA_r)
Tdewpoint=HAPropsSI('D','T',CC.Fins.Air.Tdb,'P',101325,'R',CC.Fins.Air.RH) #Updated from HumAir_Single(CC.Fins.Air.Tdb, 101325, 'RH',CC.Fins.Air.RH,'DewPoint')
#Now calculate the fully-wet analysis
#Evaporator is bounded by saturated air at the refrigerant temperature.
h_ai= HAPropsSI('H','T',CC.Fins.Air.Tdb,'P',101325,'R',CC.Fins.Air.RH) #Updated from HumAir_Single(CC.Fins.Air.Tdb, 101325, 'RH', CC.Fins.Air.RH,'Enthalpy')
h_s_w_o=HAPropsSI('H','T',Tin_CC,'P',101325,'R',1.0) #Updated from HumAir_Single(Tin_CC, 101325, 'RH', 1.0,'Enthalpy')
Qcoolingcoil_wet=epsilon*CC.Fins.Air.Vdot_ha*rho_air*(h_ai-h_s_w_o)
#Coil is either fully-wet, fully-dry or partially wet, partially dry
if T_so_a>Tdewpoint and T_so_b>Tdewpoint:
#Fully dry, use dry Q
f_dry=1.0
elif T_so_a<Tdewpoint and T_so_b<Tdewpoint:
#Fully wet, use wet Q
f_dry=0.0
else:
f_dry=1-(Tdewpoint-T_so_a)/(T_so_b-T_so_a)
Qcoolingcoil=f_dry*Qcoolingcoil_dry+(1-f_dry)*Qcoolingcoil_wet
Tin_IHX=Tin_CC+Qcoolingcoil/(Cycle.Pump.mdot_g*cp_g)
QIHX=epsilon*Cycle.Pump.mdot_g*cp_g*(Tin_IHX-Tevap)
Qcond_enthalpy=Cycle.Compressor.mdot_r*(Cycle.Compressor.hout_r-PropsSI('H','T',Tcond-Cycle.DT_sc_target,'P',pcond,Cycle.Ref)) #*1000)
resids=[QIHX+W+Qcond,Qcond+Qcond_enthalpy,Qcoolingcoil-QIHX]
return resids
elif Cycle.Mode=='HP':
#Evaporator heat transfer rate
Qevap=epsilon*Cycle.Evaporator.Fins.Air.Vdot_ha*rho_air*(Cycle.Evaporator.Fins.Air.Tdb-Tevap)*1005 #updated
#Compressor power
pevap=PropsSI('P','T',Tevap,'Q',1.0,Cycle.Ref)
pcond=PropsSI('P','T',Tcond,'Q',1.0,Cycle.Ref)
Cycle.Compressor.pin_r=pevap
Cycle.Compressor.pout_r=pcond
Cycle.Compressor.Tin_r=Tevap+Cycle.Evaporator.DT_sh
Cycle.Compressor.Ref=Cycle.Ref
Cycle.Compressor.Calculate()
W=Cycle.Compressor.W
#Evaporator will be dry
Qcoolingcoil=epsilon*Cycle.CoolingCoil.Fins.Air.Vdot_ha*rho_air*(Tin_CC-Cycle.CoolingCoil.Fins.Air.Tdb)*1005 #updated
cp_g=PropsSI('C','T',Tin_CC,'P',Cycle.Pump.pin_g,Cycle.Pump.Ref_g) #*1000
Tin_IHX=Tin_CC-Qcoolingcoil/(Cycle.Pump.mdot_g*cp_g)
QIHX=epsilon*Cycle.Pump.mdot_g*cp_g*(Tin_IHX-Tcond)
QIHX_enthalpy=Cycle.Compressor.mdot_r*(Cycle.Compressor.hout_r-PropsSI('H','T',Tcond-Cycle.DT_sc_target,'P',pcond,Cycle.Ref)) #*1000)
resids=[QIHX+W+Qevap,QIHX+QIHX_enthalpy,Qcoolingcoil+QIHX]
return resids
def OBJECTIVE(x):
Tevap = x[0]
Tcond = x[1]
Tin_CC = x[2]
if Cycle.Mode == 'AC':
#Condenser heat transfer rate
Qcond = epsilon * Cycle.Condenser.Fins.Air.Vdot_ha * rho_air * (
Cycle.Condenser.Fins.Air.Tdb - Tcond) * 1005 #updated
#Compressor power
pevap = PropsSI('P', 'T', Tevap, 'Q', 1.0, Cycle.Ref)
pcond = PropsSI('P', 'T', Tcond, 'Q', 1.0, Cycle.Ref)
Cycle.Compressor.pin_r = pevap
Cycle.Compressor.pout_r = pcond
Cycle.Compressor.Tin_r = Tevap + Cycle.Compressor.DT_sh
Cycle.Compressor.Ref = Cycle.Ref
Cycle.Compressor.Calculate()
W = Cycle.Compressor.W
Qcoolingcoil_dry = epsilon * Cycle.CoolingCoil.Fins.Air.Vdot_ha * rho_air * (
Cycle.CoolingCoil.Fins.Air.Tdb - Tin_CC) * 1005 #updated
# Air-side heat transfer UA
CC = Cycle.CoolingCoil
CC.Initialize()
UA_a = CC.Fins.h_a * CC.Fins.A_a * CC.Fins.eta_a
#Air outlet temp from dry analysis
Tout_a = CC.Tin_a - Qcoolingcoil_dry / (CC.Fins.mdot_a *
CC.Fins.cp_a)
# Refrigerant side UA
f, h, Re = Correlations.f_h_1phase_Tube(
Cycle.Pump.mdot_g / CC.Ncircuits, CC.ID, Tin_CC, CC.pin_g,
CC.Ref_g)
UA_r = CC.A_g_wetted * h
#Refrigerant outlet temp
cp_g = PropsSI('C', 'T', Tin_CC, 'P', Cycle.Pump.pin_g,
Cycle.Pump.Ref_g) #*1000
Tout_CC = Tin_CC + Qcoolingcoil_dry / (Cycle.Pump.mdot_g * cp_g)
#Get wall temperatures at inlet and outlet from energy balance
T_so_a = (UA_a * CC.Tin_a + UA_r * Tout_CC) / (UA_a + UA_r)
T_so_b = (UA_a * Tout_a + UA_r * Tin_CC) / (UA_a + UA_r)
Tdewpoint = HAPropsSI(
'D', 'T', CC.Fins.Air.Tdb, 'P', 101325, 'R', CC.Fins.Air.RH
) #Updated from HumAir_Single(CC.Fins.Air.Tdb, 101325, 'RH',CC.Fins.Air.RH,'DewPoint')
#Now calculate the fully-wet analysis
#Evaporator is bounded by saturated air at the refrigerant temperature.
h_ai = HAPropsSI(
'H', 'T', CC.Fins.Air.Tdb, 'P', 101325, 'R', CC.Fins.Air.RH
) #Updated from HumAir_Single(CC.Fins.Air.Tdb, 101325, 'RH', CC.Fins.Air.RH,'Enthalpy')
h_s_w_o = HAPropsSI(
'H', 'T', Tin_CC, 'P', 101325, 'R', 1.0
) #Updated from HumAir_Single(Tin_CC, 101325, 'RH', 1.0,'Enthalpy')
Qcoolingcoil_wet = epsilon * CC.Fins.Air.Vdot_ha * rho_air * (
h_ai - h_s_w_o)
#Coil is either fully-wet, fully-dry or partially wet, partially dry
if T_so_a > Tdewpoint and T_so_b > Tdewpoint:
#Fully dry, use dry Q
f_dry = 1.0
elif T_so_a < Tdewpoint and T_so_b < Tdewpoint:
#Fully wet, use wet Q
f_dry = 0.0
else:
f_dry = 1 - (Tdewpoint - T_so_a) / (T_so_b - T_so_a)
Qcoolingcoil = f_dry * Qcoolingcoil_dry + (
1 - f_dry) * Qcoolingcoil_wet
Tin_IHX = Tin_CC + Qcoolingcoil / (Cycle.Pump.mdot_g * cp_g)
QIHX = epsilon * Cycle.Pump.mdot_g * cp_g * (Tin_IHX - Tevap)
Qcond_enthalpy = Cycle.Compressor.mdot_r * (
Cycle.Compressor.hout_r -
PropsSI('H', 'T', Tcond - Cycle.DT_sc_target, 'P', pcond,
Cycle.Ref)) #*1000)
resids = [
QIHX + W + Qcond, Qcond + Qcond_enthalpy, Qcoolingcoil - QIHX
]
return resids
elif Cycle.Mode == 'HP':
#Evaporator heat transfer rate
Qevap = epsilon * Cycle.Evaporator.Fins.Air.Vdot_ha * rho_air * (
Cycle.Evaporator.Fins.Air.Tdb - Tevap) * 1005 #updated
#Compressor power
pevap = PropsSI('P', 'T', Tevap, 'Q', 1.0, Cycle.Ref)
pcond = PropsSI('P', 'T', Tcond, 'Q', 1.0, Cycle.Ref)
Cycle.Compressor.pin_r = pevap
Cycle.Compressor.pout_r = pcond
Cycle.Compressor.Tin_r = Tevap + Cycle.Evaporator.DT_sh
Cycle.Compressor.Ref = Cycle.Ref
Cycle.Compressor.Calculate()
W = Cycle.Compressor.W
#Evaporator will be dry
Qcoolingcoil = epsilon * Cycle.CoolingCoil.Fins.Air.Vdot_ha * rho_air * (
Tin_CC - Cycle.CoolingCoil.Fins.Air.Tdb) * 1005 #updated
cp_g = PropsSI('C', 'T', Tin_CC, 'P', Cycle.Pump.pin_g,
Cycle.Pump.Ref_g) #*1000
Tin_IHX = Tin_CC - Qcoolingcoil / (Cycle.Pump.mdot_g * cp_g)
QIHX = epsilon * Cycle.Pump.mdot_g * cp_g * (Tin_IHX - Tcond)
QIHX_enthalpy = Cycle.Compressor.mdot_r * (
Cycle.Compressor.hout_r -
PropsSI('H', 'T', Tcond - Cycle.DT_sc_target, 'P', pcond,
Cycle.Ref)) #*1000)
resids = [
QIHX + W + Qevap, QIHX + QIHX_enthalpy, Qcoolingcoil + QIHX
]
return resids
def OBJECTIVE(x):
Tevap = x[0]
Tcond = x[1]
#Condensing heat transfer rate from enthalpies
rho_air = 1.1
#Use fixed effectiveness to get a guess for the condenser capacity
Qcond = epsilon * Cycle.Condenser.Fins.Air.Vdot_ha * rho_air * (
Cycle.Condenser.Fins.Air.Tdb - Tcond
) * 1005 #Cp_air =1005J/kg/K #Cycle.Condenser.Fins.Air.Vdot_ha/rho_air, division is updated with *
pevap = PropsSI('P', 'T', Tevap, 'Q', 1.0, Cycle.Ref)
pcond = PropsSI('P', 'T', Tcond, 'Q', 1.0, Cycle.Ref)
Cycle.Compressor.pin_r = pevap
Cycle.Compressor.pout_r = pcond
Cycle.Compressor.Tin_r = Tevap + Cycle.Evaporator.DT_sh
Cycle.Compressor.Ref = Cycle.Ref
Cycle.Compressor.Calculate()
W = Cycle.Compressor.W
# Evaporator fully-dry analysis
Qevap_dry = epsilon * Cycle.Evaporator.Fins.Air.Vdot_ha * rho_air * (
Cycle.Evaporator.Fins.Air.Tdb - Tevap) * 1005 #updated
#Air-side heat transfer UA
Evap = Cycle.Evaporator
Evap.mdot_r = Cycle.Compressor.mdot_r
Evap.psat_r = pevap
Evap.Ref = Cycle.Ref
Evap.Initialize()
UA_a = Evap.Fins.h_a * Evap.Fins.A_a * Evap.Fins.eta_a
Tin_a = Evap.Fins.Air.Tdb
Tout_a = Tin_a + Qevap_dry / (Evap.Fins.mdot_da * Evap.Fins.cp_da)
#Refrigerant-side heat transfer UA
UA_r = Evap.A_r_wetted * Correlations.ShahEvaporation_Average(
0.5, 0.5, Cycle.Ref, Evap.G_r, Evap.ID, Evap.psat_r,
Qevap_dry / Evap.A_r_wetted, Evap.Tbubble_r, Evap.Tdew_r)
#Get wall temperatures at inlet and outlet from energy balance
T_so_a = (UA_a * Evap.Tin_a + UA_r * Tevap) / (UA_a + UA_r)
T_so_b = (UA_a * Tout_a + UA_r * Tevap) / (UA_a + UA_r)
Tdewpoint = HAPropsSI('D', 'T', Cycle.Evaporator.Fins.Air.Tdb, 'P',
101325, 'R', Evap.Fins.Air.RH)
#Now calculate the fully-wet analysis
#Evaporator is bounded by saturated air at the refrigerant temperature.
h_ai = HAPropsSI('H', 'T', Cycle.Evaporator.Fins.Air.Tdb, 'P', 101325,
'R', Cycle.Evaporator.Fins.Air.RH) #*1000 #[J/kg_da]
h_s_w_o = HAPropsSI('H', 'T', Tevap, 'P', 101325, 'R',
1.0) #*1000 #[J/kg_da]
Qevap_wet = epsilon * Cycle.Evaporator.Fins.Air.Vdot_ha * rho_air * (
h_ai - h_s_w_o)
#Coil is either fully-wet, fully-dry or partially wet, partially dry
if T_so_a > Tdewpoint and T_so_b > Tdewpoint:
#Fully dry, use dry Q
f_dry = 1.0
elif T_so_a < Tdewpoint and T_so_b < Tdewpoint:
#Fully wet, use wet Q
f_dry = 0.0
else:
f_dry = 1 - (Tdewpoint - T_so_a) / (T_so_b - T_so_a)
Qevap = f_dry * Qevap_dry + (1 - f_dry) * Qevap_wet
Qcond_enthalpy = Cycle.Compressor.mdot_r * (
Cycle.Compressor.hout_r - PropsSI(
'H', 'T', Tcond - Cycle.DT_sc_target, 'P', pcond, Cycle.Ref)
) #*1000)
resids = [Qevap + W + Qcond, Qcond + Qcond_enthalpy] #,Qevap,f_dry]
return resids
elif series == 'NEAR OCEAN':
return 4
elif series == 'ISLAND':
return 5
else:
return 0
# Univariate
df = pd.read_excel('/Users/kellenbullock/Desktop/Cali_housing/Bullock_BM2.xlsx')
df = df.loc[df['median_house_value'] < 500000]
df = df.loc[df['housing_median_age'] < 52]
df['ocean_proximity'] = df['ocean_proximity'].apply(ocean_to_num)
df = df.drop(columns=['Unnamed: 0'])
df = df.dropna()
df.to_excel('FINAL.xlsx')
EDA.Descrptives(df, df)
EDA.figures(df, '')
# Bivar
Correlations.corr_w_sig(df, df['median_house_value'])
x = ['longitude','latitude','housing_median_age','total_rooms','total_bedrooms','population','households','median_income','ocean_proximity']
y = 'median_house_value'
Correlations.corr_scatter(df, x, y)
EDA.merge_pdfs()
# corr.style.background_gradient(cmap='coolwarm')
# df.corr(method='spearman')
# corr.style.background_gradient(cmap='coolwarm')