mirror of
https://github.com/CoolProp/CoolProp.git
synced 2026-04-01 03:00:13 -04:00
Merge branch 'master' of https://github.com/coolprop/coolprop
This commit is contained in:
@@ -13,12 +13,12 @@ Superpacks are available at XXXXXXX which include all the code for a given opera
|
||||
======================= =========================== =======================================
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||||
Language Operating Systems Notes
|
||||
======================= =========================== =======================================
|
||||
:ref:`Python <Python>` linux, OSX, win
|
||||
:ref:`Octave <Octave>` linux, OSX, win Wrapper is SWIG based
|
||||
:ref:`C# <Csharp>` linux, OSX, win Wrapper is SWIG based
|
||||
Java linux, OSX, win Wrapper is SWIG based
|
||||
:ref:`MATLAB <MATLAB>` linux, OSX, win Wrapper is SWIG based (VERY experimental)
|
||||
:ref:`Java <Java>` linux, OSX, win Wrapper is SWIG based
|
||||
Scilab linux, OSX, win Wrapper is SWIG based (experimental)
|
||||
MATLAB linux, OSX, win Wrapper is SWIG based (VERY experimental)
|
||||
Python linux, OSX, win
|
||||
Modelica linux, OSX, win
|
||||
Javascript linux, OSX, win Also works in all internet browsers
|
||||
Maple
|
||||
@@ -77,4 +77,4 @@ OSX should come with a c++ compiler (clang), for git and cmake your best bet is
|
||||
:hidden:
|
||||
|
||||
Octave/index.rst
|
||||
Csharp/index.rst
|
||||
Csharp/index.rst
|
||||
|
||||
@@ -205,7 +205,7 @@ def combine_json(root_dir):
|
||||
|
||||
master = []
|
||||
|
||||
for file in glob.glob(os.path.join(root_dir,'dev','incompressible_liquids','*.json')):
|
||||
for file in glob.glob(os.path.join(root_dir,'dev','incompressible_liquids','json','*.json')):
|
||||
|
||||
path, file_name = os.path.split(file)
|
||||
fluid_name = file_name.split('.')[0]
|
||||
|
||||
2
dev/incompressible_liquids/.gitignore
vendored
Normal file
2
dev/incompressible_liquids/.gitignore
vendored
Normal file
@@ -0,0 +1,2 @@
|
||||
/report/
|
||||
/.ipynb_checkpoints/
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|
||||
},
|
||||
"mole2input": {
|
||||
"coeffs": "null",
|
||||
"type": "notdefined"
|
||||
},
|
||||
"name": "AS10",
|
||||
"reference": "Aspen Petroleum AB, SecCool software",
|
||||
"saturation_pressure": {
|
||||
"coeffs": "null",
|
||||
"type": "notdefined"
|
||||
},
|
||||
"specific_heat": {
|
||||
"coeffs": [
|
||||
[
|
||||
3.54183e+03
|
||||
],
|
||||
[
|
||||
2.01000e+00
|
||||
],
|
||||
[
|
||||
-1.32589e-02
|
||||
]
|
||||
],
|
||||
"type": "polynomial"
|
||||
},
|
||||
"viscosity": {
|
||||
"coeffs": [
|
||||
[
|
||||
2.80155e+00
|
||||
],
|
||||
[
|
||||
-1.06314e-01
|
||||
],
|
||||
[
|
||||
2.35381e-03
|
||||
],
|
||||
[
|
||||
-2.11481e-05
|
||||
]
|
||||
],
|
||||
"type": "polynomial"
|
||||
},
|
||||
"volume2input": {
|
||||
"coeffs": "null",
|
||||
"type": "notdefined"
|
||||
},
|
||||
"xbase": 0.00000e+00,
|
||||
"xid": 3,
|
||||
"xmax": 1.00000e+00,
|
||||
"xmin": 0.00000e+00
|
||||
}
|
||||
@@ -1,92 +0,0 @@
|
||||
{
|
||||
"T_freeze": {
|
||||
"coeffs": "null",
|
||||
"type": "notdefined"
|
||||
},
|
||||
"Tbase": 2.73150e+02,
|
||||
"Tmax": 3.03150e+02,
|
||||
"Tmin": -20,
|
||||
"TminPsat": 3.03150e+02,
|
||||
"conductivity": {
|
||||
"coeffs": [
|
||||
[
|
||||
4.80766e-01
|
||||
],
|
||||
[
|
||||
1.34200e-03
|
||||
]
|
||||
],
|
||||
"type": "polynomial"
|
||||
},
|
||||
"density": {
|
||||
"coeffs": [
|
||||
[
|
||||
1.14700e+03
|
||||
],
|
||||
[
|
||||
-2.21429e-01
|
||||
],
|
||||
[
|
||||
-1.42857e-03
|
||||
],
|
||||
[
|
||||
2.98156e-19
|
||||
]
|
||||
],
|
||||
"type": "polynomial"
|
||||
},
|
||||
"description": "Aspen Temper -20, Potassium acetate/formate",
|
||||
"mass2input": {
|
||||
"coeffs": "null",
|
||||
"type": "notdefined"
|
||||
},
|
||||
"mole2input": {
|
||||
"coeffs": "null",
|
||||
"type": "notdefined"
|
||||
},
|
||||
"name": "AS20",
|
||||
"reference": "Aspen Petroleum AB, SecCool software",
|
||||
"saturation_pressure": {
|
||||
"coeffs": "null",
|
||||
"type": "notdefined"
|
||||
},
|
||||
"specific_heat": {
|
||||
"coeffs": [
|
||||
[
|
||||
3.26252e+03
|
||||
],
|
||||
[
|
||||
2.86000e+00
|
||||
],
|
||||
[
|
||||
-1.22673e-02
|
||||
]
|
||||
],
|
||||
"type": "polynomial"
|
||||
},
|
||||
"viscosity": {
|
||||
"coeffs": [
|
||||
[
|
||||
1.35858e+00
|
||||
],
|
||||
[
|
||||
-4.17551e-02
|
||||
],
|
||||
[
|
||||
8.14818e-05
|
||||
],
|
||||
[
|
||||
1.60342e-07
|
||||
]
|
||||
],
|
||||
"type": "exppolynomial"
|
||||
},
|
||||
"volume2input": {
|
||||
"coeffs": "null",
|
||||
"type": "notdefined"
|
||||
},
|
||||
"xbase": 0.00000e+00,
|
||||
"xid": 3,
|
||||
"xmax": 1.00000e+00,
|
||||
"xmin": 0.00000e+00
|
||||
}
|
||||
@@ -1,92 +0,0 @@
|
||||
{
|
||||
"T_freeze": {
|
||||
"coeffs": "null",
|
||||
"type": "notdefined"
|
||||
},
|
||||
"Tbase": 2.73150e+02,
|
||||
"Tmax": 3.03150e+02,
|
||||
"Tmin": -30,
|
||||
"TminPsat": 3.03150e+02,
|
||||
"conductivity": {
|
||||
"coeffs": [
|
||||
[
|
||||
4.60388e-01
|
||||
],
|
||||
[
|
||||
1.25600e-03
|
||||
]
|
||||
],
|
||||
"type": "polynomial"
|
||||
},
|
||||
"density": {
|
||||
"coeffs": [
|
||||
[
|
||||
1.18386e+03
|
||||
],
|
||||
[
|
||||
-3.10859e-01
|
||||
],
|
||||
[
|
||||
-1.03896e-03
|
||||
],
|
||||
[
|
||||
1.76768e-05
|
||||
]
|
||||
],
|
||||
"type": "polynomial"
|
||||
},
|
||||
"description": "Aspen Temper -30, Potassium acetate/formate",
|
||||
"mass2input": {
|
||||
"coeffs": "null",
|
||||
"type": "notdefined"
|
||||
},
|
||||
"mole2input": {
|
||||
"coeffs": "null",
|
||||
"type": "notdefined"
|
||||
},
|
||||
"name": "AS30",
|
||||
"reference": "Aspen Petroleum AB, SecCool software",
|
||||
"saturation_pressure": {
|
||||
"coeffs": "null",
|
||||
"type": "notdefined"
|
||||
},
|
||||
"specific_heat": {
|
||||
"coeffs": [
|
||||
[
|
||||
3.07504e+03
|
||||
],
|
||||
[
|
||||
2.99000e+00
|
||||
],
|
||||
[
|
||||
-2.70232e-02
|
||||
]
|
||||
],
|
||||
"type": "polynomial"
|
||||
},
|
||||
"viscosity": {
|
||||
"coeffs": [
|
||||
[
|
||||
1.55518e+00
|
||||
],
|
||||
[
|
||||
-4.51874e-02
|
||||
],
|
||||
[
|
||||
9.74090e-05
|
||||
],
|
||||
[
|
||||
1.86342e-07
|
||||
]
|
||||
],
|
||||
"type": "exppolynomial"
|
||||
},
|
||||
"volume2input": {
|
||||
"coeffs": "null",
|
||||
"type": "notdefined"
|
||||
},
|
||||
"xbase": 0.00000e+00,
|
||||
"xid": 3,
|
||||
"xmax": 1.00000e+00,
|
||||
"xmin": 0.00000e+00
|
||||
}
|
||||
@@ -1,92 +0,0 @@
|
||||
{
|
||||
"T_freeze": {
|
||||
"coeffs": "null",
|
||||
"type": "notdefined"
|
||||
},
|
||||
"Tbase": 2.73150e+02,
|
||||
"Tmax": 3.03150e+02,
|
||||
"Tmin": -40,
|
||||
"TminPsat": 3.03150e+02,
|
||||
"conductivity": {
|
||||
"coeffs": [
|
||||
[
|
||||
4.43327e-01
|
||||
],
|
||||
[
|
||||
1.09900e-03
|
||||
]
|
||||
],
|
||||
"type": "polynomial"
|
||||
},
|
||||
"density": {
|
||||
"coeffs": [
|
||||
[
|
||||
1.21484e+03
|
||||
],
|
||||
[
|
||||
-3.78199e-01
|
||||
],
|
||||
[
|
||||
-1.09307e-03
|
||||
],
|
||||
[
|
||||
1.68350e-05
|
||||
]
|
||||
],
|
||||
"type": "polynomial"
|
||||
},
|
||||
"description": "Aspen Temper -40, Potassium acetate/formate",
|
||||
"mass2input": {
|
||||
"coeffs": "null",
|
||||
"type": "notdefined"
|
||||
},
|
||||
"mole2input": {
|
||||
"coeffs": "null",
|
||||
"type": "notdefined"
|
||||
},
|
||||
"name": "AS40",
|
||||
"reference": "Aspen Petroleum AB, SecCool software",
|
||||
"saturation_pressure": {
|
||||
"coeffs": "null",
|
||||
"type": "notdefined"
|
||||
},
|
||||
"specific_heat": {
|
||||
"coeffs": [
|
||||
[
|
||||
2.97788e+03
|
||||
],
|
||||
[
|
||||
2.28000e+00
|
||||
],
|
||||
[
|
||||
-3.87227e-02
|
||||
]
|
||||
],
|
||||
"type": "polynomial"
|
||||
},
|
||||
"viscosity": {
|
||||
"coeffs": [
|
||||
[
|
||||
1.73307e+00
|
||||
],
|
||||
[
|
||||
-5.42737e-02
|
||||
],
|
||||
[
|
||||
2.78602e-04
|
||||
],
|
||||
[
|
||||
4.99909e-07
|
||||
]
|
||||
],
|
||||
"type": "exppolynomial"
|
||||
},
|
||||
"volume2input": {
|
||||
"coeffs": "null",
|
||||
"type": "notdefined"
|
||||
},
|
||||
"xbase": 0.00000e+00,
|
||||
"xid": 3,
|
||||
"xmax": 1.00000e+00,
|
||||
"xmin": 0.00000e+00
|
||||
}
|
||||
@@ -1,95 +0,0 @@
|
||||
{
|
||||
"T_freeze": {
|
||||
"coeffs": "null",
|
||||
"type": "notdefined"
|
||||
},
|
||||
"Tbase": 2.73150e+02,
|
||||
"Tmax": 3.03150e+02,
|
||||
"Tmin": -55,
|
||||
"TminPsat": 3.03150e+02,
|
||||
"conductivity": {
|
||||
"coeffs": [
|
||||
[
|
||||
4.25799e-01
|
||||
],
|
||||
[
|
||||
9.37902e-04
|
||||
],
|
||||
[
|
||||
2.28700e-06
|
||||
]
|
||||
],
|
||||
"type": "polynomial"
|
||||
},
|
||||
"density": {
|
||||
"coeffs": [
|
||||
[
|
||||
1.24975e+03
|
||||
],
|
||||
[
|
||||
-4.76296e-01
|
||||
],
|
||||
[
|
||||
-1.17189e-03
|
||||
],
|
||||
[
|
||||
1.98824e-05
|
||||
]
|
||||
],
|
||||
"type": "polynomial"
|
||||
},
|
||||
"description": "Aspen Temper -55, Potassium acetate/formate",
|
||||
"mass2input": {
|
||||
"coeffs": "null",
|
||||
"type": "notdefined"
|
||||
},
|
||||
"mole2input": {
|
||||
"coeffs": "null",
|
||||
"type": "notdefined"
|
||||
},
|
||||
"name": "AS55",
|
||||
"reference": "Aspen Petroleum AB, SecCool software",
|
||||
"saturation_pressure": {
|
||||
"coeffs": "null",
|
||||
"type": "notdefined"
|
||||
},
|
||||
"specific_heat": {
|
||||
"coeffs": [
|
||||
[
|
||||
2.83985e+03
|
||||
],
|
||||
[
|
||||
2.29000e+00
|
||||
],
|
||||
[
|
||||
-2.48618e-02
|
||||
]
|
||||
],
|
||||
"type": "polynomial"
|
||||
},
|
||||
"viscosity": {
|
||||
"coeffs": [
|
||||
[
|
||||
1.79891e+00
|
||||
],
|
||||
[
|
||||
-6.30089e-02
|
||||
],
|
||||
[
|
||||
4.30552e-04
|
||||
],
|
||||
[
|
||||
7.31131e-07
|
||||
]
|
||||
],
|
||||
"type": "exppolynomial"
|
||||
},
|
||||
"volume2input": {
|
||||
"coeffs": "null",
|
||||
"type": "notdefined"
|
||||
},
|
||||
"xbase": 0.00000e+00,
|
||||
"xid": 3,
|
||||
"xmax": 1.00000e+00,
|
||||
"xmin": 0.00000e+00
|
||||
}
|
||||
@@ -2,6 +2,7 @@ from __future__ import division, print_function
|
||||
import numpy as np
|
||||
from scipy.optimize._minimize import minimize
|
||||
from scipy.optimize.minpack import curve_fit
|
||||
import sys
|
||||
|
||||
# Here we define the types. This is done to keep the definitions at one
|
||||
# central place instead of hiding them somewhere in the data.
|
||||
@@ -12,77 +13,209 @@ class IncompressibleData(object):
|
||||
Implements both data structures and fitting
|
||||
procedures.
|
||||
"""
|
||||
def __init__(self):
|
||||
self.INCOMPRESSIBLE_NOT_SET = 'notdefined'
|
||||
self.INCOMPRESSIBLE_POLYNOMIAL = 'polynomial'
|
||||
self.INCOMPRESSIBLE_EXPONENTIAL = 'exponential'
|
||||
self.INCOMPRESSIBLE_EXPPOLYNOMIAL = 'exppolynomial'
|
||||
self.INCOMPRESSIBLE_EXPOFFSET = 'expoffset'
|
||||
self.INCOMPRESSIBLE_POLYOFFSET = 'polyoffset'
|
||||
self.INCOMPRESSIBLE_CHEBYSHEV = 'chebyshev'
|
||||
INCOMPRESSIBLE_NOT_SET = 'notdefined'
|
||||
INCOMPRESSIBLE_POLYNOMIAL = 'polynomial'
|
||||
INCOMPRESSIBLE_EXPPOLYNOMIAL = 'exppolynomial'
|
||||
INCOMPRESSIBLE_EXPONENTIAL = 'exponential'
|
||||
INCOMPRESSIBLE_LOGEXPONENTIAL= 'logexponential'
|
||||
INCOMPRESSIBLE_POLYOFFSET = 'polyoffset'
|
||||
INCOMPRESSIBLE_CHEBYSHEV = 'chebyshev'
|
||||
|
||||
SOURCE_DATA = 'data'
|
||||
SOURCE_EQUATION = 'equation'
|
||||
SOURCE_COEFFS = 'coefficients'
|
||||
SOURCE_NOT_SET = 'notdefined'
|
||||
|
||||
maxLin = np.finfo(np.float64).max-1
|
||||
minLin = -maxLin
|
||||
|
||||
maxLog = np.log(maxLin)
|
||||
minLog = -maxLog
|
||||
|
||||
def __init__(self):
|
||||
self.source = self.SOURCE_NOT_SET
|
||||
self.type = self.INCOMPRESSIBLE_NOT_SET
|
||||
self.coeffs = None #np.zeros((4,4))
|
||||
self.data = None # None #np.zeros((10,10))
|
||||
self.xData = None # In case you need a customised first data set (temperature?)
|
||||
self.yData = None # In case you need a customised second data set (concentration?)
|
||||
self.sErr = None # Coefficient of determination
|
||||
self.DEBUG = False
|
||||
|
||||
self.maxLog = np.log(np.finfo(np.float64).max-1)
|
||||
self.minLog = -self.maxLog
|
||||
@staticmethod
|
||||
def baseFunc(x, y=0.0, xbase=0.0, ybase=0.0, eqnType=None, c=None):
|
||||
|
||||
self.DEBUG = False
|
||||
if eqnType==None: raise ValueError("You did not provide data for eqnType.")
|
||||
if c==None: raise ValueError("You did not provide data for the coefficients.")
|
||||
|
||||
|
||||
### Base functions that handle the custom data type, just a place holder to show the structure.
|
||||
def baseFunction(self, x, y=0.0, xbase=0.0, ybase=0.0, c=None):
|
||||
if c==None:
|
||||
c = self.coeffs
|
||||
|
||||
if self.type==self.INCOMPRESSIBLE_POLYNOMIAL:
|
||||
if eqnType==IncompressibleData.INCOMPRESSIBLE_POLYNOMIAL:
|
||||
return np.polynomial.polynomial.polyval2d(x-xbase, y-ybase, c)
|
||||
|
||||
elif self.type==self.INCOMPRESSIBLE_POLYOFFSET:
|
||||
if y!=0.0: raise ValueError("This is 1D only, use x not y.")
|
||||
return self.basePolyOffset(c, x) # offset included in coeffs
|
||||
elif eqnType==IncompressibleData.INCOMPRESSIBLE_POLYOFFSET:
|
||||
#if y!=0.0: raise ValueError("This is 1D only, use x not y.")
|
||||
return IncompressibleData.basePolyOffset(c, x) # offset included in coeffs
|
||||
|
||||
elif self.type==self.INCOMPRESSIBLE_EXPONENTIAL:
|
||||
if y!=0.0: raise ValueError("This is 1D only, use x not y.")
|
||||
return self.baseExponential(c, x-xbase)
|
||||
elif eqnType==IncompressibleData.INCOMPRESSIBLE_EXPONENTIAL:
|
||||
#if y!=0.0: raise ValueError("This is 1D only, use x not y.")
|
||||
return IncompressibleData.baseExponential(c, x)
|
||||
|
||||
elif eqnType==IncompressibleData.INCOMPRESSIBLE_LOGEXPONENTIAL:
|
||||
#if y!=0.0: raise ValueError("This is 1D only, use x not y.")
|
||||
return IncompressibleData.baseLogexponential(c, x)
|
||||
|
||||
elif self.type==self.INCOMPRESSIBLE_EXPPOLYNOMIAL:
|
||||
elif eqnType==IncompressibleData.INCOMPRESSIBLE_EXPPOLYNOMIAL:
|
||||
return np.exp(np.polynomial.polynomial.polyval2d(x-xbase, y-ybase, c))
|
||||
|
||||
elif self.type==self.INCOMPRESSIBLE_EXPOFFSET:
|
||||
if y!=0.0: raise ValueError("This is 1D only, use x not y.")
|
||||
return self.baseExponentialOffset(c, x-xbase)
|
||||
|
||||
else:
|
||||
raise ValueError("Unknown function: {0}.".format(self.type))
|
||||
|
||||
|
||||
def baseExponential(self, co, x):
|
||||
r,c,coeffs = self.shapeArray(co)
|
||||
raise ValueError("Unknown function: {0}.".format(eqnType))
|
||||
|
||||
@staticmethod
|
||||
def baseExponential(co, x):
|
||||
r,c,coeffs = IncompressibleFitter.shapeArray(co)
|
||||
if not ( (r==3 and c==1) or (r==1 and c==3) ):
|
||||
raise ValueError("You have to provide a 3,1 matrix of coefficients, not ({0},{1}).".format(r,c))
|
||||
coeffs_tmp = np.array(coeffs.flat)
|
||||
return np.exp(np.clip( (coeffs_tmp[0]/ ( x+coeffs_tmp[1] ) - coeffs_tmp[2]),self.minLog,self.maxLog))
|
||||
|
||||
|
||||
def baseExponentialOffset(self, c, x):
|
||||
raise ValueError("Function not implemented.")
|
||||
r,c,coeffs = self.shapeArray(c)
|
||||
if not ( (r==4 and c==1) or (r==1 and c==4) ):
|
||||
return np.exp(np.clip( (coeffs_tmp[0]/ ( x+coeffs_tmp[1] ) - coeffs_tmp[2]),IncompressibleData.minLog,IncompressibleData.maxLog))
|
||||
|
||||
@staticmethod
|
||||
def baseLogexponential(co, x):
|
||||
r,c,coeffs = IncompressibleFitter.shapeArray(co)
|
||||
if not ( (r==3 and c==1) or (r==1 and c==3) ):
|
||||
raise ValueError("You have to provide a 4,1 matrix of coefficients, not ({0},{1}).".format(r,c))
|
||||
coeffs_tmp = np.array(coeffs.flat)
|
||||
return np.exp(np.clip( (coeffs_tmp[1]/ ( x-coeffs_tmp[0]+coeffs_tmp[2]) - coeffs_tmp[3]),self.minLog,self.maxLog))
|
||||
return np.exp(np.clip(np.log(np.clip(1.0/(x+coeffs_tmp[0]) + 1.0/(x+coeffs_tmp[0])**2,1e-10,IncompressibleData.maxLin))*coeffs_tmp[1]+coeffs_tmp[2],IncompressibleData.minLog,IncompressibleData.maxLog))
|
||||
|
||||
|
||||
def basePolyOffset(self, co, x):
|
||||
r,c,coeffs = self.shapeArray(co)
|
||||
@staticmethod
|
||||
def basePolyOffset(co, x):
|
||||
r,c,coeffs = IncompressibleFitter.shapeArray(co)
|
||||
if not ( c==1 or r==1 ):
|
||||
raise ValueError("You have to provide a 1D vector of coefficients, not ({0},{1}).".format(r,c))
|
||||
offset = coeffs[0][0]
|
||||
coeffs = np.array(coeffs.flat)[1:c]
|
||||
coeffs = np.array(coeffs.flat)[1:]
|
||||
return np.polynomial.polynomial.polyval(x-offset, coeffs)
|
||||
|
||||
|
||||
### Base functions that handle the custom data type, just a place holder to show the structure.
|
||||
def baseFunction(self, x, y=0.0, xbase=0.0, ybase=0.0, c=None):
|
||||
if c==None: c = self.coeffs
|
||||
return self.baseFunc(x, y, xbase, ybase, self.type, c)
|
||||
|
||||
def fitCoeffs(self, xbase, ybase, x=None, y=None):
|
||||
|
||||
if (x!=None and self.xData!=None and not IncompressibleFitter.allClose(x, self.xData)) \
|
||||
or (x==None and self.xData==None): raise ValueError("I do not know which x-value you would like to use. Define either x or self.xData.")
|
||||
if (y!=None and self.yData!=None and not IncompressibleFitter.allClose(y, self.yData)) \
|
||||
or (y==None and self.yData==None): raise ValueError("I do not know which y-value you would like to use. Define either y or self.yData.")
|
||||
|
||||
if x==None and self.xData!=None: x=self.xData
|
||||
if y==None and self.yData!=None: y=self.yData
|
||||
|
||||
#res = None
|
||||
#r2 = None
|
||||
|
||||
res,sErr = IncompressibleFitter.fitter(x=x, y=y, z=self.data, \
|
||||
xbase=xbase, ybase=ybase, \
|
||||
eqnType=self.type, \
|
||||
coeffs=self.coeffs, DEBUG=self.DEBUG)
|
||||
|
||||
bestCoeffs = np.copy(res)
|
||||
bestType = self.type
|
||||
bestsErr = np.copy(sErr)
|
||||
bestRMS = np.sqrt(np.square(bestsErr).mean()).sum()
|
||||
|
||||
count = 0
|
||||
while bestRMS>0.01 and count<2:
|
||||
#if self.DEBUG: print("Poor solution found, trying once more with more coefficients.")
|
||||
if self.type==IncompressibleData.INCOMPRESSIBLE_EXPONENTIAL:
|
||||
if self.DEBUG: print("Poor solution found with exponential, trying once more with log exponential.")
|
||||
self.type=IncompressibleData.INCOMPRESSIBLE_LOGEXPONENTIAL
|
||||
self.coeffs = np.array([-250,1.5,10])
|
||||
res,sErr = IncompressibleFitter.fitter(x=x, y=y, z=self.data, \
|
||||
xbase=xbase, ybase=ybase, \
|
||||
eqnType=self.type, \
|
||||
coeffs=self.coeffs, DEBUG=self.DEBUG)
|
||||
|
||||
elif self.type==IncompressibleData.INCOMPRESSIBLE_LOGEXPONENTIAL:
|
||||
if self.DEBUG: print("Poor solution found with log exponential, trying once more with exponential polynomial.")
|
||||
self.type=IncompressibleData.INCOMPRESSIBLE_EXPPOLYNOMIAL
|
||||
self.coeffs = np.zeros((4,6))
|
||||
res,sErr = IncompressibleFitter.fitter(x=x, y=y, z=self.data, \
|
||||
xbase=xbase, ybase=ybase, \
|
||||
eqnType=self.type, \
|
||||
coeffs=self.coeffs, DEBUG=self.DEBUG)
|
||||
|
||||
# elif self.type==IncompressibleData.INCOMPRESSIBLE_EXPPOLYNOMIAL:
|
||||
# if self.DEBUG: print("Poor solution found with exponential polynomial, trying once more with normal polynomial.")
|
||||
# self.type=IncompressibleData.INCOMPRESSIBLE_POLYNOMIAL
|
||||
# self.coeffs = np.zeros((4,6))
|
||||
# res,sErr = IncompressibleFitter.fitter(x=x, y=y, z=self.data, \
|
||||
# xbase=xbase, ybase=ybase, \
|
||||
# eqnType=self.type, \
|
||||
# coeffs=self.coeffs, DEBUG=self.DEBUG)
|
||||
|
||||
RMS = np.sqrt(np.square(sErr).mean()).sum()
|
||||
if RMS<bestRMS: # Better fit
|
||||
bestCoeffs = np.copy(res)
|
||||
bestType = self.type
|
||||
bestsErr = np.copy(sErr)
|
||||
bestRMS = RMS
|
||||
|
||||
count += 1
|
||||
|
||||
if bestCoeffs==None:
|
||||
if self.DEBUG: print("There was a fitting error, no solution found.")
|
||||
elif IncompressibleFitter.allClose(bestCoeffs, self.coeffs):
|
||||
if self.DEBUG: print("Coefficients did not change.")
|
||||
else:
|
||||
if self.DEBUG: print("Best fit for: {0}".format(bestType))
|
||||
self.coeffs = bestCoeffs
|
||||
self.type = bestType
|
||||
self.sErr = bestsErr
|
||||
|
||||
#if self.DEBUG: print("Fitting statistics:")
|
||||
#SSE = np.square(self.sErr).sum() # Sum of squares due to error
|
||||
#SST = ((zData-zData.mean())**2).sum()
|
||||
#R2 = 1-(ssErr/ssTot )
|
||||
|
||||
|
||||
def setxData(self, xData):
|
||||
if self.xData==None:
|
||||
self.xData = xData
|
||||
else:
|
||||
if self.DEBUG: print("Cannot update xData, value is set already.")
|
||||
|
||||
|
||||
def setyData(self, yData):
|
||||
if self.yData==None:
|
||||
self.yData = yData
|
||||
else:
|
||||
if self.DEBUG: print("Cannot update yData, value is set already.")
|
||||
|
||||
def setxyData(self, xData, yData):
|
||||
self.setxData(xData)
|
||||
self.setyData(yData)
|
||||
|
||||
|
||||
def toJSON(self):
|
||||
j = {}
|
||||
try:
|
||||
j['coeffs'] = self.coeffs.tolist()
|
||||
except:
|
||||
j['coeffs'] = 'null'
|
||||
|
||||
j['type'] = self.type
|
||||
return j
|
||||
|
||||
|
||||
|
||||
class IncompressibleFitter(object):
|
||||
|
||||
@staticmethod
|
||||
def allClose(a,b):
|
||||
if np.array(a).shape==np.array(b).shape:
|
||||
return np.allclose(a, b)
|
||||
else: return False
|
||||
|
||||
@staticmethod
|
||||
def shapeArray(array, axs=0):
|
||||
"""
|
||||
@@ -113,75 +246,116 @@ class IncompressibleData(object):
|
||||
raise ValueError("You have to provide a 1D-vector or a 2D-matrix.")
|
||||
return (r,c,np.reshape(array,(r,c)))
|
||||
|
||||
@staticmethod
|
||||
def fitter(x=None, y=None, z=None, \
|
||||
xbase=0.0, ybase=0.0, \
|
||||
eqnType=None, \
|
||||
coeffs=None, DEBUG=False):
|
||||
""" The entry point to the fitting routines
|
||||
|
||||
:param x : a 1D array in x direction or 2D with one column, most likely temperature
|
||||
:param y : a 1D array in y direction or 2D with one row, most likely cocentration
|
||||
:param z : a 2D array of data, rows = len(x[:,0]) and cols = len(y[0])
|
||||
:param xbase : a value to be subtracted from x, might not be used
|
||||
:param ybase : a value to be subtracted from y, might not be used
|
||||
:param eqnType : an instance of IncompressibleData.INCOMPRESSIBLE_ ...
|
||||
:param coeffs : the initial guess and shape (!) for the desired coefficients, can be zeros
|
||||
:param DEBUG : message to display
|
||||
|
||||
:returns : None if failed or coeffs filled with the right values
|
||||
|
||||
|
||||
def fitCoeffs(self, x=0.0, y=0.0, xbase=0.0, ybase=0.0):
|
||||
"""
|
||||
A function that selects the correct equations and
|
||||
fits coefficients. Some functions require a start
|
||||
guess for the coefficients to work properly.
|
||||
"""
|
||||
dr,dc,_ = self.shapeArray(self.data)
|
||||
xr,xc,x = self.shapeArray(x)
|
||||
yr,yc,y = self.shapeArray(y, axs=1)
|
||||
"""
|
||||
|
||||
if self.DEBUG: print("Data : ({0},{1})".format(dr,dc))
|
||||
if self.DEBUG: print("x-axis : ({0},{1})".format(xr,xc))
|
||||
if self.DEBUG: print("y-axis : ({0},{1})".format(yr,yc))
|
||||
if x==None: raise ValueError("You did not provide data for the x-values.")
|
||||
if y==None: raise ValueError("You did not provide data for the y-values.")
|
||||
if z==None: raise ValueError("You did not provide data for the z-values.")
|
||||
if xbase==None: raise ValueError("You did not provide data for xbase.")
|
||||
if ybase==None: raise ValueError("You did not provide data for ybase.")
|
||||
if eqnType==None: raise ValueError("You did not provide data for eqnType.")
|
||||
if coeffs==None: raise ValueError("You did not provide data for the coefficients.")
|
||||
if DEBUG==None: raise ValueError("You did not provide data for DEBUG.")
|
||||
|
||||
if dr==1 and dc==1: #
|
||||
if self.DEBUG: print("Data no set, we cannot fit the coefficients")
|
||||
self.coeffs = None
|
||||
self.type = self.INCOMPRESSIBLE_NOT_SET
|
||||
return
|
||||
zr,zc,_ = IncompressibleFitter.shapeArray(z)
|
||||
xr,xc,x = IncompressibleFitter.shapeArray(x)
|
||||
yr,yc,y = IncompressibleFitter.shapeArray(y, axs=1)
|
||||
|
||||
if DEBUG: print("Data : ({0},{1})".format(zr,zc))
|
||||
if DEBUG: print("x-axis : ({0},{1})".format(xr,xc))
|
||||
if DEBUG: print("y-axis : ({0},{1})".format(yr,yc))
|
||||
|
||||
if zr==1 and zc==1: #
|
||||
if DEBUG: print("Data no set, we cannot fit the coefficients")
|
||||
return None,None
|
||||
|
||||
if (xc!=1): raise ValueError("The first input has to be a 2D array with one column.")
|
||||
if (yr!=1): raise ValueError("The second input has to be a 2D array with one row.")
|
||||
if (xr!=dr): raise ValueError("First independent vector and result vector have to have the same number of rows, {0} is not {1}.".format(xr,dr))
|
||||
if (yc!=dc): raise ValueError("Second independent vector and result vector have to have the same number of columns, {0} is not {1}.".format(yc,dc))
|
||||
|
||||
cr,cc,_ = self.shapeArray(self.coeffs)
|
||||
if self.DEBUG: print("Coefficients: ({0},{1})".format(cr,cc))
|
||||
if (xr==1 and xc==1 and cr>1):
|
||||
if self.DEBUG: print("Discarding coefficient rows, {0} -> {1}".format(cr,xr))
|
||||
self.coeffs = self.coeffs[0]
|
||||
self.coeffs = self.coeffs.reshape((1,cc))
|
||||
if (yr==1 and yc==1 and cc>1):
|
||||
if self.DEBUG: print("Discarding coefficient columns, {0} -> {1}".format(cc,yc))
|
||||
self.coeffs = self.coeffs.T[0]
|
||||
self.coeffs = self.coeffs.reshape((cr,1))
|
||||
cr,cc,_ = self.shapeArray(self.coeffs)
|
||||
|
||||
if self.DEBUG: print("Coefficients before fitting: \n{0}".format(self.coeffs))
|
||||
if (xr!=zr): raise ValueError("First independent vector and result vector have to have the same number of rows, {0} is not {1}.".format(xr,zr))
|
||||
if (yc!=zc): raise ValueError("Second independent vector and result vector have to have the same number of columns, {0} is not {1}.".format(yc,zc))
|
||||
|
||||
if DEBUG: print("Coefficients before fitting: \n{0}".format(coeffs))
|
||||
|
||||
# Polynomial fitting works for both 1D and 2D functions
|
||||
if self.type==self.INCOMPRESSIBLE_POLYNOMIAL or self.type==self.INCOMPRESSIBLE_EXPPOLYNOMIAL:
|
||||
if self.DEBUG: print("polynomial detected, fitting {0}".format(self.type))
|
||||
if eqnType==IncompressibleData.INCOMPRESSIBLE_POLYNOMIAL or eqnType==IncompressibleData.INCOMPRESSIBLE_EXPPOLYNOMIAL:
|
||||
|
||||
cr,cc,_ = IncompressibleFitter.shapeArray(coeffs)
|
||||
if DEBUG: print("Coefficients: ({0},{1})".format(cr,cc))
|
||||
if (xr==1 and xc==1 and cr>1):
|
||||
if DEBUG: print("Discarding coefficient rows, {0} -> {1}".format(cr,xr))
|
||||
coeffs = coeffs[0]
|
||||
coeffs = coeffs.reshape((1,cc))
|
||||
if (yr==1 and yc==1 and cc>1):
|
||||
if DEBUG: print("Discarding coefficient columns, {0} -> {1}".format(cc,yc))
|
||||
coeffs = coeffs.T[0]
|
||||
coeffs = coeffs.reshape((cr,1))
|
||||
cr,cc,_ = IncompressibleFitter.shapeArray(coeffs)
|
||||
|
||||
if DEBUG: print("polynomial detected, fitting {0}".format(eqnType))
|
||||
if cr==1 and cc==1:
|
||||
if self.DEBUG: print("No coefficients left to fit, aborting procedure.")
|
||||
self.coeffs = np.array([[0.0]])
|
||||
return
|
||||
if (xr<cr): raise ValueError("Less data points than coefficients in first dimension ({0} < {1}), aborting.".format(xr,cr))
|
||||
if (yc<cc): raise ValueError("Less data points than coefficients in second dimension ({0} < {1}), aborting.".format(yc,cc))
|
||||
if DEBUG: print("No coefficients left to fit, aborting procedure.")
|
||||
coeffs = np.array([[0.0]])
|
||||
return coeffs, None
|
||||
if (xr<cr):
|
||||
if DEBUG: print("Less data points than coefficients in first dimension ({0} < {1}), reducing coefficient matrix.".format(xr,cr))
|
||||
coeffs = coeffs[:xr,:]
|
||||
if (yc<cc):
|
||||
if DEBUG: print("Less data points than coefficients in second dimension ({0} < {1}), reducing coefficient matrix.".format(yc,cc))
|
||||
coeffs = coeffs[:,:yc]
|
||||
cr,cc,_ = IncompressibleFitter.shapeArray(coeffs)
|
||||
x_input = np.array(x.flat)-xbase
|
||||
y_input = np.array(y.flat)-ybase
|
||||
z_input = np.copy(self.data)
|
||||
if self.type==self.INCOMPRESSIBLE_EXPPOLYNOMIAL:
|
||||
z_input = np.copy(z)
|
||||
if eqnType==IncompressibleData.INCOMPRESSIBLE_EXPPOLYNOMIAL:
|
||||
z_input = np.log(z_input)
|
||||
self.coeffs = self.getCoeffs2d(x_input, y_input, z_input, cr-1, cc-1)
|
||||
if self.DEBUG: print("Coefficients after fitting: \n{0}".format(self.coeffs))
|
||||
return
|
||||
coeffs,sErr = IncompressibleFitter.getCoeffs2d(x_input, y_input, z_input, cr-1, cc-1, DEBUG=DEBUG)
|
||||
if DEBUG: print("Coefficients after fitting: \n{0}".format(coeffs))
|
||||
if DEBUG: print("Standard deviation: {0}".format(np.nanstd(sErr)))
|
||||
if DEBUG: print("Sum of squared errors: {0}".format(np.square(sErr).sum()))
|
||||
if DEBUG: print("Root mean squared errors: {0}".format(np.sqrt(np.square(sErr).mean()).sum()))
|
||||
return coeffs,sErr
|
||||
|
||||
|
||||
# Select if 1D or 2D fitting
|
||||
if yc==1: # 1D fitting, only one input
|
||||
if self.DEBUG: print("1D function detected, fitting {0}".format(self.type))
|
||||
x_input = x-xbase
|
||||
self.coeffs = self.getCoeffsIterative1D(x_input, coeffs_start=None)
|
||||
if self.DEBUG: print("Coefficients after fitting: \n{0}".format(self.coeffs))
|
||||
return
|
||||
if yc==1 or xr==1: # 1D fitting, only one input
|
||||
if DEBUG: print("1D function detected.")
|
||||
if yc==1:
|
||||
if DEBUG: print("Fitting {0} in x-direction.".format(eqnType))
|
||||
coeffs,sErr = IncompressibleFitter.getCoeffsIterative1D(x, z, eqnType=eqnType, coeffs=coeffs, DEBUG=DEBUG)
|
||||
elif xr==1:
|
||||
if DEBUG: print("Fitting {0} in y-direction.".format(eqnType))
|
||||
coeffs,sErr = IncompressibleFitter.getCoeffsIterative1D(y, z, eqnType=eqnType, coeffs=coeffs, DEBUG=DEBUG)
|
||||
else: raise ValueError("Unknown error in matrix shapes.")
|
||||
if DEBUG: print("Coefficients after fitting: \n{0}".format(coeffs))
|
||||
if DEBUG: print("Standard deviation: {0}".format(np.nanstd(sErr)))
|
||||
if DEBUG: print("Sum of squared errors: {0}".format(np.square(sErr).sum()))
|
||||
if DEBUG: print("Root mean squared errors: {0}".format(np.sqrt(np.square(sErr).mean()).sum()))
|
||||
return coeffs, sErr
|
||||
|
||||
elif yc>1: # 2D fitting
|
||||
raise ValueError("There are no other 2D fitting functions than polynomials, cannot use {0}.".format(self.type))
|
||||
raise ValueError("There are no other 2D fitting functions than polynomials, cannot use {0}.".format(eqnType))
|
||||
|
||||
else:
|
||||
raise ValueError("Unknown function.")
|
||||
@@ -197,8 +371,16 @@ class IncompressibleData(object):
|
||||
# coeffs, resids, rank, singulars = np.linalg.lstsq(A, z)
|
||||
# return np.reshape(coeffs, (len(x),1))
|
||||
|
||||
|
||||
def getCoeffs2d(self, x_in, y_in, z_in, x_order, y_order):
|
||||
@staticmethod
|
||||
def getCoeffs2d(x_in, y_in, z_in, x_order, y_order, DEBUG=False):
|
||||
|
||||
if x_in==None: raise ValueError("You did not provide data for the x-values.")
|
||||
if y_in==None: raise ValueError("You did not provide data for the y-values.")
|
||||
if z_in==None: raise ValueError("You did not provide data for the z-values.")
|
||||
if x_order==None: raise ValueError("You did not provide data for x_order.")
|
||||
if y_order==None: raise ValueError("You did not provide data for y_order.")
|
||||
if DEBUG==None: raise ValueError("You did not provide data for DEBUG.")
|
||||
|
||||
x_order += 1
|
||||
y_order += 1
|
||||
#To avoid overfitting, we only use the upper left triangle of the coefficient matrix
|
||||
@@ -239,56 +421,68 @@ class IncompressibleData(object):
|
||||
# Remove np.nan elements
|
||||
mask = np.isfinite(zz)
|
||||
A = A[mask]
|
||||
xx = xx[mask]
|
||||
yy = yy[mask]
|
||||
zz = zz[mask]
|
||||
|
||||
if (len(A) < cols):
|
||||
raise ValueError("Your matrix has only {0} valid rows and you try to fit {1} coefficients, please reduce the order.".format(len(A),cols))
|
||||
|
||||
coeffs, resids, rank, singulars = np.linalg.lstsq(A, zz)
|
||||
if self.DEBUG: print("Linear algebra solver returned:")
|
||||
if self.DEBUG: print(coeffs)
|
||||
if self.DEBUG: print(resids)
|
||||
if self.DEBUG: print(rank)
|
||||
if self.DEBUG: print(singulars)
|
||||
if DEBUG: print("Linear algebra solver returned:")
|
||||
if DEBUG: print(coeffs)
|
||||
if DEBUG: print(resids)
|
||||
if DEBUG: print(rank)
|
||||
if DEBUG: print(singulars)
|
||||
|
||||
#if resids.size>0:
|
||||
# r2 = 1 - resids / (zz.size * zz.var())
|
||||
#else:
|
||||
# r2 = 0
|
||||
#print("\n r2 2d: ",r2.shape,r2,"\n")
|
||||
|
||||
#Rearrange coefficients to a matrix shape
|
||||
C = np.zeros((x_order,y_order))
|
||||
for i, (xi,yi) in enumerate(xy_exp): # makes columns
|
||||
C[xi][yi] = coeffs[i]
|
||||
|
||||
return C
|
||||
|
||||
sErr = zz - np.polynomial.polynomial.polyval2d(xx, yy, C)
|
||||
return C, sErr
|
||||
|
||||
|
||||
def getCoeffsIterative1D(self, xData, coeffs_start=None):
|
||||
@staticmethod
|
||||
def getCoeffsIterative1D(x_in, z_in, eqnType, coeffs, DEBUG=False):
|
||||
|
||||
if x_in==None: raise ValueError("You did not provide data for the x-values.")
|
||||
if z_in==None: raise ValueError("You did not provide data for the z-values.")
|
||||
if eqnType==None: raise ValueError("You did not provide data for eqnType.")
|
||||
if coeffs==None: raise ValueError("You did not provide data for the coefficients.")
|
||||
if DEBUG==None: raise ValueError("You did not provide data for DEBUG.")
|
||||
|
||||
sErr = None
|
||||
|
||||
#fit = "Powell" # use Powell's algorithm
|
||||
#fit = "BFGS" # use Broyden-Fletcher-Goldfarb-Shanno
|
||||
#fit = "LMA" # use the Levenberg-Marquardt algorithm from curve_fit
|
||||
fit = ["LMA","Powell","BFGS"] # First try LMA, use others as fall-back
|
||||
|
||||
# Basic settings to keep track of our efforts
|
||||
success = False
|
||||
counter = -1
|
||||
|
||||
if coeffs_start==None and \
|
||||
self.coeffs!=None:
|
||||
coeffs_start = self.coeffs
|
||||
|
||||
|
||||
# make sure that we use other routines for polynomials
|
||||
if (self.type==self.INCOMPRESSIBLE_POLYNOMIAL) or \
|
||||
(self.type==self.INCOMPRESSIBLE_EXPPOLYNOMIAL) :
|
||||
if (eqnType==IncompressibleData.INCOMPRESSIBLE_POLYNOMIAL) or \
|
||||
(eqnType==IncompressibleData.INCOMPRESSIBLE_EXPPOLYNOMIAL) :
|
||||
raise ValueError("Please use the specific polynomial functions, they are much better.")
|
||||
|
||||
expLog = False
|
||||
# Preprocess the exponential data
|
||||
if (self.type==self.INCOMPRESSIBLE_EXPONENTIAL) or \
|
||||
(self.type==self.INCOMPRESSIBLE_EXPOFFSET) :
|
||||
# Fitting the logarithm of z_in?
|
||||
if (eqnType==IncompressibleData.INCOMPRESSIBLE_EXPONENTIAL or eqnType==IncompressibleData.INCOMPRESSIBLE_LOGEXPONENTIAL):
|
||||
expLog = True
|
||||
|
||||
xData = np.array(xData.flat)
|
||||
if expLog:
|
||||
yData = np.log(self.data.flat)
|
||||
else:
|
||||
yData = np.array(self.data.flat)
|
||||
xData = np.array(x_in.flat)
|
||||
if expLog: zData = np.log(z_in.flat)
|
||||
else: zData = np.array(z_in.flat)
|
||||
|
||||
# Remove np.nan elements
|
||||
mask = np.isfinite(zData)
|
||||
xData = xData[mask]
|
||||
zData = zData[mask]
|
||||
|
||||
# The residual function
|
||||
def fun(coefficients,xArray,yArray):
|
||||
@@ -298,19 +492,22 @@ class IncompressibleData(object):
|
||||
the sum of the squared residuals if yArray is not
|
||||
equal to None
|
||||
"""
|
||||
calculated = self.baseFunction(xArray, 0.0, 0.0, 0.0, coefficients)
|
||||
# No offset and no Tbase etc for 1D functions!
|
||||
calculated = IncompressibleData.baseFunc(xArray, y=0.0, xbase=0.0, ybase=0.0, eqnType=eqnType, c=coefficients)
|
||||
if expLog: calculated = np.log(calculated)
|
||||
if yArray==None: return calculated
|
||||
data = yArray
|
||||
res = np.sum(np.square(calculated-data))
|
||||
return res
|
||||
|
||||
# Loop through the list of algorithms
|
||||
# Loop through the list of algorithms with basic settings to keep track of our efforts
|
||||
success = False
|
||||
counter = 0
|
||||
tolerance = 1e-16
|
||||
while (not success):
|
||||
counter += 1
|
||||
|
||||
algorithm = fit[counter]
|
||||
#fit = "LMA" # use the Levenberg-Marquardt algorithm from curve_fit
|
||||
if fit[counter]=="LMA":
|
||||
if algorithm=="LMA":
|
||||
|
||||
def func(xVector, *coefficients):
|
||||
return fun(np.array(coefficients), xVector, None)
|
||||
@@ -319,68 +516,78 @@ class IncompressibleData(object):
|
||||
try:
|
||||
#print func(xData, coeffs_start)
|
||||
# Do the actual fitting
|
||||
popt, pcov = curve_fit(func, xData, yData, p0=coeffs_start)
|
||||
if np.any(popt!=coeffs_start):
|
||||
popt, pcov = curve_fit(func, xData, zData, p0=coeffs, ftol=tolerance)
|
||||
if np.any(popt!=coeffs):
|
||||
success = True
|
||||
# print "Fit succeeded for "+fit[counter]+": "
|
||||
# print "data: {0}, func: {1}".format(yData[ 2],func(xData[ 2], popt))
|
||||
# print "data: {0}, func: {1}".format(yData[ 6],func(xData[ 6], popt))
|
||||
# print "data: {0}, func: {1}".format(yData[-1],func(xData[-1], popt))
|
||||
if self.DEBUG: print("Estimated covariance of parameters: ".format(pcov))
|
||||
return popt
|
||||
if DEBUG: print("Fit succeeded with: {0}".format(algorithm))
|
||||
sErr = zData - func(xData, popt)
|
||||
#print "Fit succeeded for "+fit[counter]+": "
|
||||
#print "data: {0}, func: {1}".format(yData[ 2],func(xData[ 2], popt))
|
||||
#print "data: {0}, func: {1}".format(yData[ 6],func(xData[ 6], popt))
|
||||
#print "data: {0}, func: {1}".format(yData[-1],func(xData[-1], popt))
|
||||
#if DEBUG: print("Estimated covariance of parameters: {0}".format(pcov))
|
||||
#ssErr = np.sqrt(np.diag(pcov)).sum()
|
||||
#ssTot = ((zData-zData.mean())**2).sum()
|
||||
#r2 = 1-(ssErr/ssTot )
|
||||
#print("\n r2 FMA: ",r2.shape,r2,"\n")
|
||||
return popt,sErr
|
||||
else:
|
||||
print("Fit failed for "+fit[counter]+": ")
|
||||
if DEBUG: print("Fit failed for {0}.".format(algorithm))
|
||||
if DEBUG: sys.stdout.flush()
|
||||
success = False
|
||||
|
||||
except RuntimeError as e:
|
||||
print("Exception using "+fit[counter]+": "+str(e))
|
||||
print("Using "+str(fit[counter+1])+" as a fall-back.")
|
||||
if DEBUG: print("Exception using "+algorithm+": "+str(e))
|
||||
if DEBUG: sys.stdout.flush()
|
||||
success = False
|
||||
|
||||
#fit = "MIN" # use a home-made minimisation with Powell and Broyden-Fletcher-Goldfarb-Shanno
|
||||
elif fit[counter]=="Powell" or fit[counter]=="BFGS":
|
||||
elif algorithm=="Powell" or algorithm=="BFGS":
|
||||
|
||||
arguments = (xData,yData)
|
||||
arguments = (xData,zData)
|
||||
#options = {'maxiter': 1e2, 'maxfev': 1e5}
|
||||
|
||||
tolStart = 1e-13
|
||||
tol = tolStart
|
||||
res = minimize(fun, coeffs_start, method=fit[counter], args=arguments, tol=tol)
|
||||
|
||||
while ((not res.success) and tol<1e-6):
|
||||
tol *= 1e2
|
||||
print("Fit did not succeed, reducing tolerance to "+str(tol))
|
||||
res = minimize(fun, coeffs_start, method=fit[counter], args=arguments, tol=tol)
|
||||
|
||||
# Include these lines for an additional fit with new guess values.
|
||||
#if res.success and tol>tolStart:
|
||||
# print "Refitting with new guesses and original tolerance of "+str(tolStart)
|
||||
# res = minimize(fun, res.x, method=method, args=arguments, tol=tolStart)
|
||||
|
||||
if res.success:
|
||||
success = True
|
||||
return res.x
|
||||
else:
|
||||
print("Fit failed for "+fit[counter]+": ")
|
||||
print("Using "+str(fit[counter+1])+" as a fall-back.")
|
||||
print(res)
|
||||
try:
|
||||
res = minimize(fun, coeffs, method=algorithm, args=arguments, tol=tolerance)
|
||||
if res.success:
|
||||
success = True
|
||||
if DEBUG: print("Fit succeeded with: {0}".format(algorithm))
|
||||
sErr = zData - fun(np.array(res.x), xData)
|
||||
#if res.has_key('fvec'):
|
||||
#ssErr = (res['fvec']**2).sum()
|
||||
#ssTot = ((zData-zData.mean())**2).sum()
|
||||
#r2 = 1-(ssErr/ssTot )
|
||||
#print("\n r2 : ",r2.shape,r2,algorithm,"\n")
|
||||
return res.x,sErr
|
||||
else:
|
||||
if DEBUG: print("Fit failed for {0}.".format(algorithm))
|
||||
if DEBUG: sys.stdout.flush()
|
||||
success = False
|
||||
except RuntimeError as e:
|
||||
if DEBUG: print("Exception using "+algorithm+": "+str(e))
|
||||
if DEBUG: sys.stdout.flush()
|
||||
success = False
|
||||
|
||||
|
||||
# Something went wrong, probably a typo in the algorithm selector
|
||||
else:
|
||||
raise (ValueError("Error: You used an unknown fit method."))
|
||||
|
||||
|
||||
|
||||
def toJSON(self):
|
||||
j = {}
|
||||
try:
|
||||
j['coeffs'] = self.coeffs.tolist()
|
||||
except:
|
||||
j['coeffs'] = 'null'
|
||||
|
||||
if counter<len(fit)-1:
|
||||
#print("Fit did not succeed with {0}, reducing tolerance to {1}.".format(algorithm,tol))
|
||||
success = False
|
||||
counter += 1
|
||||
elif tolerance<1e-3:
|
||||
tolerance *= 1e2
|
||||
if DEBUG: print("Fit did not succeed, reducing tolerance to {0}.".format(tolerance))
|
||||
success = False
|
||||
counter = 0
|
||||
else:
|
||||
if DEBUG: print("--------------------------------------------------------------")
|
||||
if DEBUG: print("Fit failed for {0}. ".format(fit))
|
||||
if DEBUG: print("--------------------------------------------------------------")
|
||||
return coeffs,1
|
||||
|
||||
j['type'] = self.type
|
||||
return j
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
@@ -8,8 +8,8 @@ class NitrateSalt(PureData,CoefficientData):
|
||||
Heat transfer fluid based on 60% NaNO3 and 40% KNO3
|
||||
"""
|
||||
def __init__(self):
|
||||
PureData.__init__(self)
|
||||
CoefficientData.__init__(self)
|
||||
CoefficientData.__init__(self)
|
||||
PureData.__init__(self)
|
||||
self.name = "NaK"
|
||||
self.description = "NitrateSalt"
|
||||
self.reference = "Solar Power Tower Design Basis Document, Alexis B. Zavoico, Sandia Labs, USA"
|
||||
@@ -24,13 +24,19 @@ class NitrateSalt(PureData,CoefficientData):
|
||||
#self.concentration.data = np.array([ 0 ]) # mass fraction
|
||||
|
||||
self.density.type = self.density.INCOMPRESSIBLE_POLYNOMIAL
|
||||
self.density.source = self.density.SOURCE_COEFFS
|
||||
self.density.coeffs = np.array([[2090],[-0.636]])
|
||||
|
||||
self.specific_heat.type = self.specific_heat.INCOMPRESSIBLE_POLYNOMIAL
|
||||
self.specific_heat.source = self.specific_heat.SOURCE_COEFFS
|
||||
self.specific_heat.coeffs = np.array([[1443],[+0.172]])
|
||||
|
||||
self.conductivity.type = self.conductivity.INCOMPRESSIBLE_POLYNOMIAL
|
||||
self.conductivity.source = self.conductivity.SOURCE_COEFFS
|
||||
self.conductivity.coeffs = np.array([[0.443],[+1.9e-4]])
|
||||
|
||||
self.viscosity.type = self.viscosity.INCOMPRESSIBLE_POLYNOMIAL
|
||||
self.viscosity.coeffs = np.array([[22.714],[-0.120],[2.281 * 1e-4],[-1.474 * 1e-7]])/1e3
|
||||
self.viscosity.source = self.viscosity.SOURCE_COEFFS
|
||||
self.viscosity.coeffs = np.array([[22.714],[-0.120],[2.281 * 1e-4],[-1.474 * 1e-7]])/1e3
|
||||
|
||||
|
||||
@@ -3,6 +3,7 @@ import numpy as np
|
||||
import os, math
|
||||
from BaseObjects import IncompressibleData
|
||||
from abc import ABCMeta
|
||||
from CPIncomp.BaseObjects import IncompressibleFitter
|
||||
|
||||
class SolutionData(object):
|
||||
"""
|
||||
@@ -11,13 +12,15 @@ class SolutionData(object):
|
||||
put in your data and add some documentation for where the
|
||||
information came from.
|
||||
"""
|
||||
ifrac_mass = "mass"
|
||||
ifrac_mole = "mole"
|
||||
ifrac_volume = "volume"
|
||||
ifrac_undefined = "not defined"
|
||||
ifrac_pure = "pure"
|
||||
|
||||
__metaclass__ = ABCMeta
|
||||
def __init__(self):
|
||||
self.ifrac_mass = 0
|
||||
self.ifrac_mole = 1
|
||||
self.ifrac_volume = 2
|
||||
self.ifrac_undefined = 3
|
||||
self.ifrac_pure = 4
|
||||
|
||||
|
||||
self.significantDigits = 6
|
||||
|
||||
@@ -77,10 +80,10 @@ class SolutionData(object):
|
||||
|
||||
def roundSingle(self,x):
|
||||
if x==0.0: return 0.0
|
||||
return round(x, self.significantDigits-int(math.floor(math.log10(abs(x))))-1)
|
||||
return round(x, self.significantDigits-int(math.floor(math.log10(abs(x))))-1)
|
||||
|
||||
def round(self, x):
|
||||
r,c,res = IncompressibleData.shapeArray(x)
|
||||
r,c,res = IncompressibleFitter.shapeArray(x)
|
||||
#digits = -1*np.floor(np.log10(res))+self.significantDigits-1
|
||||
for i in range(r):
|
||||
for j in range(c):
|
||||
@@ -105,9 +108,45 @@ class SolutionData(object):
|
||||
# objList["viscosity"] = self.viscosity
|
||||
# objList["saturation pressure"] = self.saturation_pressure
|
||||
# return objList
|
||||
|
||||
|
||||
def checkT(self, T, p, x):
|
||||
if self.Tmin <= 0.: raise ValueError("Please specify the minimum temperature.")
|
||||
if self.Tmax <= 0.: raise ValueError("Please specify the maximum temperature.");
|
||||
if ((self.Tmin > T) or (T > self.Tmax)): raise ValueError("Your temperature {0} is not between {1} and {2}.".format(T, self.Tmin, self.Tmax))
|
||||
TF = 0.0
|
||||
if (self.T_freeze.type!=IncompressibleData.INCOMPRESSIBLE_NOT_SET): TF = self.Tfreeze(T, p, x)
|
||||
if ( T<TF ): raise ValueError("Your temperature {0} is below the freezing point of {1}.".format(T, TF))
|
||||
else: return True
|
||||
return False
|
||||
|
||||
def checkP(self, T, p, x):
|
||||
ps = 0.0
|
||||
if (self.saturation_pressure.type!=IncompressibleData.INCOMPRESSIBLE_NOT_SET): ps = self.psat(T, p, x)
|
||||
if (p < 0.0): raise ValueError("You cannot use negative pressures: {0} < {1}. ".format(p, 0.0))
|
||||
if (p < ps) : raise ValueError("Equations are valid for liquid phase only: {0} < {1}. ".format(p, ps))
|
||||
else : return True
|
||||
return False
|
||||
|
||||
|
||||
def checkX(self, x):
|
||||
if (self.xmin < 0.0 or self.xmin > 1.0): raise ValueError("Please specify the minimum concentration between 0 and 1.");
|
||||
if (self.xmax < 0.0 or self.xmax > 1.0): raise ValueError("Please specify the maximum concentration between 0 and 1.");
|
||||
if ((self.xmin > x) or (x > self.xmax)): raise ValueError("Your composition {0} is not between {1} and {2}.".format(x, self.xmin, self.xmax))
|
||||
else: return True
|
||||
return False
|
||||
|
||||
def checkTPX(self, T, p, x):
|
||||
try:
|
||||
return (self.checkT(T,p,x) and self.checkP(T,p,x) and self.checkX(x))
|
||||
except ValueError as ve:
|
||||
#print("Check failed: {0}".format(ve))
|
||||
pass
|
||||
return False
|
||||
|
||||
|
||||
def rho (self, T, p=0.0, x=0.0, c=None):
|
||||
if not self.checkTPX(T, p, x): return np.NAN
|
||||
if c==None:
|
||||
c=self.density.coeffs
|
||||
if self.density.type==self.density.INCOMPRESSIBLE_POLYNOMIAL:
|
||||
@@ -115,6 +154,7 @@ class SolutionData(object):
|
||||
else: raise ValueError("Unknown function.")
|
||||
|
||||
def c (self, T, p=0.0, x=0.0, c=None):
|
||||
if not self.checkTPX(T, p, x): return np.NAN
|
||||
if c==None:
|
||||
c = self.specific_heat.coeffs
|
||||
if self.specific_heat.type==self.specific_heat.INCOMPRESSIBLE_POLYNOMIAL:
|
||||
@@ -128,6 +168,7 @@ class SolutionData(object):
|
||||
return self.c(T,p,x,c)
|
||||
|
||||
def u (self, T, p=0.0, x=0.0, c=None):
|
||||
if not self.checkTPX(T, p, x): return np.NAN
|
||||
if c==None:
|
||||
c = self.specific_heat.coeffs
|
||||
if self.specific_heat.type==self.specific_heat.INCOMPRESSIBLE_POLYNOMIAL:
|
||||
@@ -140,16 +181,44 @@ class SolutionData(object):
|
||||
return self.h_u(T,p,x)
|
||||
|
||||
def visc(self, T, p=0.0, x=0.0, c=None):
|
||||
if not self.checkTPX(T, p, x): return np.NAN
|
||||
return self.viscosity.baseFunction(T, x, self.Tbase, self.xbase, c=c)
|
||||
|
||||
def cond(self, T, p=0.0, x=0.0, c=None):
|
||||
if not self.checkTPX(T, p, x): return np.NAN
|
||||
return self.conductivity.baseFunction(T, x, self.Tbase, self.xbase, c=c)
|
||||
|
||||
def psat(self, T, x=0.0, c=None):
|
||||
def psat(self, T, p=0.0, x=0.0, c=None):
|
||||
if (T<=self.TminPsat): return 0.0
|
||||
return self.saturation_pressure.baseFunction(T, x, self.Tbase, self.xbase, c=c)
|
||||
|
||||
def Tfreeze(self, p=0.0, x=0.0, c=None):
|
||||
return self.T_freeze.baseFunction(x, 0.0, self.xbase, 0.0, c=c)
|
||||
def Tfreeze(self, T, p=0.0, x=0.0, c=None):
|
||||
if c==None:
|
||||
c = self.T_freeze.coeffs
|
||||
|
||||
if self.T_freeze.type==self.T_freeze.INCOMPRESSIBLE_POLYNOMIAL:
|
||||
return np.polynomial.polynomial.polyval2d(p-0.0, x-self.xbase, c)
|
||||
|
||||
elif self.T_freeze.type==self.T_freeze.INCOMPRESSIBLE_POLYOFFSET:
|
||||
#if y!=0.0: raise ValueError("This is 1D only, use x not y.")
|
||||
return self.T_freeze.basePolyOffset(c, x) # offset included in coeffs
|
||||
|
||||
elif self.T_freeze.type==self.T_freeze.INCOMPRESSIBLE_EXPONENTIAL:
|
||||
#if y!=0.0: raise ValueError("This is 1D only, use x not y.")
|
||||
return self.T_freeze.baseExponential(c, x)
|
||||
|
||||
elif self.T_freeze.type==IncompressibleData.INCOMPRESSIBLE_LOGEXPONENTIAL:
|
||||
#if y!=0.0: raise ValueError("This is 1D only, use x not y.")
|
||||
return self.T_freeze.baseLogexponential(c, x)
|
||||
|
||||
elif self.T_freeze.type==self.T_freeze.INCOMPRESSIBLE_EXPPOLYNOMIAL:
|
||||
return np.exp(np.polynomial.polynomial.polyval2d(p-0.0, x-self.xbase, c))
|
||||
|
||||
else:
|
||||
raise ValueError("Unknown function: {0}.".format(self.T_freeze.type))
|
||||
|
||||
|
||||
|
||||
|
||||
#def V2M (T, y);
|
||||
#def M2M (T, x);
|
||||
@@ -222,7 +291,7 @@ class DigitalData(SolutionData):
|
||||
|
||||
def getFromFile(self, data):
|
||||
fullPath = self.getFile(data)
|
||||
_,_,res = IncompressibleData.shapeArray(np.loadtxt(fullPath))
|
||||
_,_,res = IncompressibleFitter.shapeArray(np.loadtxt(fullPath))
|
||||
return res
|
||||
|
||||
def writeToFile(self, data, array):
|
||||
@@ -237,91 +306,94 @@ class DigitalData(SolutionData):
|
||||
return np.array([0.0])
|
||||
|
||||
def getxrange(self):
|
||||
if self.xmin<self.xmax:
|
||||
if self.xmin<self.xmax and self.xid!=self.ifrac_pure:
|
||||
return np.linspace(self.xmin, self.xmax, 20)
|
||||
else:
|
||||
return np.array([0.0])
|
||||
|
||||
def getArray(self, func=None, data=None):
|
||||
"""
|
||||
func is a callable object that takes T,x as inputs
|
||||
and data is the file name for the data.
|
||||
We try to read the file and if unsuccessful, we
|
||||
generate the data and write it.
|
||||
def getArray(self, dataID=None, func=None, x_in=None, y_in=None, DEBUG=False):
|
||||
""" Tries to read a data file, overwrites it if x or y do not match
|
||||
|
||||
:param dataID : ID to contruct the path to the data file
|
||||
:param func : Callable object that can take x_in and y_in
|
||||
:param x_in : a 1D array in x direction or 2D with one column, most likely temperature
|
||||
:param y_in : a 1D array in y direction or 2D with one row, most likely cocentration
|
||||
:param DEBUG : a boolean that controls verbosity
|
||||
|
||||
:returns : Returns a tuple with three entries: x(1D),y(1D),data(2D)
|
||||
"""
|
||||
|
||||
forceUpdate = False
|
||||
readFromFile = False
|
||||
fileArray = None
|
||||
x = None
|
||||
y = None
|
||||
z = None
|
||||
|
||||
# First we try to read the file
|
||||
if (dataID!=None and os.path.isfile(self.getFile(dataID))): # File found
|
||||
fileArray = self.getFromFile(dataID)
|
||||
x = np.copy(fileArray[1:,0 ])
|
||||
y = np.copy(fileArray[0 ,1:])
|
||||
z = np.copy(fileArray[1:,1:])
|
||||
else:
|
||||
if DEBUG: print("No readable file found for {0}: {1}".format(dataID,self.getFile(dataID)))
|
||||
|
||||
if self.temperature.data==None or self.concentration.data==None: # no data set, try to get it from file
|
||||
if self.temperature.data!=None: raise ValueError("Temperature is not None, but concentration is.")
|
||||
if self.concentration.data!=None: raise ValueError("Concentration is not None, but temperature is.")
|
||||
if (data!=None and os.path.isfile(self.getFile(data))): # File found
|
||||
fileArray = self.getFromFile(data)
|
||||
self.temperature.data = np.copy(fileArray[1:,0])
|
||||
self.concentration.data = np.copy(fileArray[0,1:])
|
||||
readFromFile = True
|
||||
else:
|
||||
raise ValueError("No temperature and concentration data given and no readable file found for {0}".format(data))
|
||||
|
||||
tData = self.round(self.temperature.data)[:,0]
|
||||
xData = self.round(self.concentration.data)[:,0]
|
||||
updateFile = DEBUG
|
||||
|
||||
baseArray = np.zeros( (len(tData)+1,len(xData)+1) )
|
||||
if x_in!=None: # Might need update
|
||||
if x!=None: # Both given, check if different
|
||||
mask = np.isfinite(x)
|
||||
if IncompressibleFitter.allClose(x[mask], x_in[mask]):
|
||||
if DEBUG: print("Both x-arrays are the same, no action required.")
|
||||
updateFile = (updateFile or False) # Do not change a True value to False
|
||||
else:
|
||||
updateFile = True
|
||||
if DEBUG: print("x-arrays do not match. {0} contains \n {1} \n and will be updated with \n {2}".format(self.getFile(dataID),x,x_in))
|
||||
else: updateFile = True
|
||||
elif x==None:
|
||||
if DEBUG: print("Could not load x from file {0} and no x_in provided, aborting.".format(self.getFile(dataID)))
|
||||
return None,None,None
|
||||
else: updateFile = (updateFile or False) # Do not change a True value to False
|
||||
|
||||
if (data!=None and os.path.isfile(self.getFile(data)) and not forceUpdate): # File found and no update wanted
|
||||
if fileArray==None: fileArray = self.getFromFile(data)
|
||||
if y_in!=None: # Might need update
|
||||
if y!=None: # Both given, check if different
|
||||
mask = np.isfinite(y)
|
||||
if IncompressibleFitter.allClose(y[mask], y_in[mask]):
|
||||
if DEBUG: print("Both y-arrays are the same, no action required.")
|
||||
updateFile = (updateFile or False) # Do not change a True value to False
|
||||
else:
|
||||
updateFile = True
|
||||
if DEBUG: print("y-arrays do not match. {0} contains \n {1} \n and will be updated with \n {2}".format(self.getFile(dataID),y,y_in))
|
||||
else: updateFile = True
|
||||
elif y==None:
|
||||
if DEBUG: print("Could not load y from file {0} and no y_in provided, aborting.".format(self.getFile(dataID)))
|
||||
return None,None,None
|
||||
else: updateFile = (updateFile or False) # Do not change a True value to False
|
||||
|
||||
# tFile = fileArray[1:,0]
|
||||
# xFile = fileArray[0,1:]
|
||||
# curDataLim = np.array([np.min(tData),np.max(tData),np.min(xData),np.max(xData)])
|
||||
# curFileLim = np.array([np.min(tFile),np.max(tFile),np.min(xFile),np.max(xFile)])
|
||||
# if np.allclose(curDataLim, curFileLim, rtol=1e-2) and fileArray.shape!=baseArray.shape: # We might have to interpolate
|
||||
# if len(tData)<len(tFile) or len(xData)<len(xFile): # OK, we can interpolate
|
||||
# data = fileArray[1:,1:]
|
||||
# if len(tFile)==1: # 1d in concentration
|
||||
# f = interpolate.interp1d(xFile, data.flat)#, kind='cubic')
|
||||
# dataNew = f(xData).reshape((1,len(xData)))
|
||||
# elif len(xFile)==1: # 1d in temperature
|
||||
# f = interpolate.interp1d(tFile, data.flat)#, kind='cubic')
|
||||
# dataNew = f(tData).reshape((len(tData),1))
|
||||
# else: # 2d
|
||||
# f = interpolate.interp2d(xFile, tFile, data)#, kind='cubic')
|
||||
# dataNew = f(xData,tData)
|
||||
# fileArray = np.copy(baseArray)
|
||||
# fileArray[1:,1:] = dataNew
|
||||
# fileArray[1:,0] = tData
|
||||
# fileArray[0,1:] = xData
|
||||
# else:
|
||||
# raise ValueError("Less points in file, not enough data for interpolation.")
|
||||
# elif fileArray.shape==baseArray.shape:
|
||||
# pass
|
||||
# else:
|
||||
# raise ValueError("The array shapes do not match. Check {0}".format(self.getFile(data)))
|
||||
|
||||
if readFromFile or fileArray.shape==baseArray.shape: # Shapes match
|
||||
if readFromFile or np.allclose(tData, fileArray[1:,0]): # Temperature data matches
|
||||
if readFromFile or np.allclose(xData, fileArray[0,1:]): # Concentration data matches
|
||||
baseArray = fileArray
|
||||
else:
|
||||
raise ValueError("Concentration arrays do not match. Check {0} \n and have a look at \n {1} \n vs \n {2} \n yielding \n {3}".format(self.getFile(data),xData,fileArray[0,1:],(xData==fileArray[0,1:])))
|
||||
else:
|
||||
raise ValueError("Temperature arrays do not match. Check {0} \n and have a look at \n {1} \n vs \n {2} \n yielding \n {3}".format(self.getFile(data),tData,fileArray[1:,0],(tData==fileArray[1:,0])))
|
||||
else:
|
||||
raise ValueError("The array shapes do not match. Check {0}".format(self.getFile(data)))
|
||||
else: # file not found or update forced
|
||||
if func==None and forceUpdate:
|
||||
raise ValueError("Need a function to update the data file.")
|
||||
for cT,T in enumerate(self.temperature.data):
|
||||
for cx,x in enumerate(self.concentration.data):
|
||||
baseArray[cT+1][cx+1] = func(T,x)
|
||||
baseArray[0,0] = np.NaN
|
||||
baseArray[1:,0] = np.copy(self.temperature.data)
|
||||
baseArray[0,1:] = np.copy(self.concentration.data)
|
||||
if data!=None: self.writeToFile(data, baseArray)
|
||||
|
||||
return np.copy(baseArray.T[1:].T[1:]) # Remove the first column and row and return
|
||||
if DEBUG: print("Updating data file {0}".format(updateFile))
|
||||
|
||||
if not updateFile: return x,y,z # Done, data read from file
|
||||
|
||||
# Overwrite inputs
|
||||
x = x_in
|
||||
y = y_in
|
||||
z = np.zeros( (len(x)+1,len(y)+1) )
|
||||
r,c = z.shape
|
||||
|
||||
if func==None: raise ValueError("Need a function to update the data file.")
|
||||
|
||||
for i in range(r-1):
|
||||
for j in range(c-1):
|
||||
z[i+1,j+1] = func(x[i],y[j])
|
||||
z[0,0 ] = np.NaN
|
||||
z[1:,0] = x
|
||||
z[0,1:] = y
|
||||
|
||||
if dataID!=None:
|
||||
self.writeToFile(dataID, z)
|
||||
else:
|
||||
if DEBUG: print("Not updating data file, dataID is missing.")
|
||||
|
||||
return x,y,z[1:,1:]
|
||||
|
||||
|
||||
|
||||
class CoefficientData(SolutionData):
|
||||
@@ -371,7 +443,7 @@ class CoefficientData(SolutionData):
|
||||
|
||||
# Concentration is no longer handled in per cent!
|
||||
for i in range(6):
|
||||
tmp.T[i] *= 100.0**i
|
||||
tmp.T[i] *= 100.0**i
|
||||
|
||||
return tmp
|
||||
|
||||
@@ -487,20 +559,34 @@ class CoefficientData(SolutionData):
|
||||
|
||||
coeffs = self.convertMelinderMatrix(matrix).T
|
||||
|
||||
self.T_freeze.type = self.T_freeze.INCOMPRESSIBLE_POLYNOMIAL
|
||||
self.T_freeze.source = self.T_freeze.SOURCE_COEFFS
|
||||
self.T_freeze.type = self.T_freeze.INCOMPRESSIBLE_POLYNOMIAL
|
||||
self.T_freeze.coeffs = self.convertMelinderArray(coeffs[0])
|
||||
self.T_freeze.coeffs[0,0] += 273.15
|
||||
self.T_freeze.coeffs = np.array([self.T_freeze.coeffs[0]])
|
||||
#print(self.T_freeze.coeffs)
|
||||
|
||||
self.density.source = self.density.SOURCE_COEFFS
|
||||
self.density.type = self.density.INCOMPRESSIBLE_POLYNOMIAL
|
||||
self.density.coeffs = self.convertMelinderArray(coeffs[1])
|
||||
|
||||
self.specific_heat.source = self.specific_heat.SOURCE_COEFFS
|
||||
self.specific_heat.type = self.specific_heat.INCOMPRESSIBLE_POLYNOMIAL
|
||||
self.specific_heat.coeffs = self.convertMelinderArray(coeffs[2])
|
||||
|
||||
self.conductivity.source = self.conductivity.SOURCE_COEFFS
|
||||
self.conductivity.type = self.conductivity.INCOMPRESSIBLE_POLYNOMIAL
|
||||
self.conductivity.coeffs = self.convertMelinderArray(coeffs[3])
|
||||
|
||||
self.viscosity.source = self.viscosity.SOURCE_COEFFS
|
||||
self.viscosity.type = self.viscosity.INCOMPRESSIBLE_POLYNOMIAL
|
||||
self.viscosity.coeffs = self.convertMelinderArray(coeffs[4])
|
||||
self.viscosity.coeffs = self.convertMelinderArray(coeffs[4])/1e3
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
@@ -10,7 +10,8 @@ parameter form.
|
||||
|
||||
"""
|
||||
from __future__ import division, print_function
|
||||
from CPIncomp.DataObjects import DigitalData
|
||||
import numpy as np
|
||||
from CPIncomp.DataObjects import DigitalData, PureData
|
||||
|
||||
class LiBrData(DigitalData):
|
||||
"""
|
||||
@@ -48,15 +49,213 @@ class LiBrData(DigitalData):
|
||||
key = 'D'
|
||||
def funcD(T,x):
|
||||
return CP.PropsSI(key,'T',T,'P',1e8,self.name+"-{0:.4f}%".format(x*100.0))
|
||||
self.density.data = self.getArray(funcD,key)
|
||||
self.density.xData,self.density.yData,self.density.data = self.getArray(dataID=key, func=funcD, x_in=self.temperature.data, y_in=self.concentration.data,DEBUG=self.density.DEBUG)
|
||||
self.density.source = self.density.SOURCE_EQUATION
|
||||
|
||||
key = 'C'
|
||||
def funcC(T,x):
|
||||
return CP.PropsSI(key,'T',T,'P',1e8,self.name+"-{0:.4f}%".format(x*100.0))
|
||||
self.specific_heat.data = self.getArray(funcC,key)
|
||||
self.specific_heat.xData,self.specific_heat.yData,self.specific_heat.data = self.getArray(dataID=key, func=funcC, x_in=self.temperature.data, y_in=self.concentration.data,DEBUG=self.specific_heat.DEBUG)
|
||||
self.specific_heat.source = self.specific_heat.SOURCE_EQUATION
|
||||
|
||||
key = 'Psat'
|
||||
def funcP(T,x):
|
||||
return CP.PropsSI(key,'T',T,'P',1e8,self.name+"-{0:.4f}%".format(x*100.0))
|
||||
self.saturation_pressure.data = self.getArray(funcP,key)
|
||||
self.saturation_pressure.xData,self.saturation_pressure.yData,self.saturation_pressure.data = self.getArray(dataID=key, func=funcP, x_in=self.temperature.data, y_in=self.concentration.data,DEBUG=self.saturation_pressure.DEBUG)
|
||||
self.saturation_pressure.source = self.saturation_pressure.SOURCE_EQUATION
|
||||
|
||||
|
||||
|
||||
class HyCool20(PureData,DigitalData):
|
||||
def __init__(self):
|
||||
DigitalData.__init__(self)
|
||||
PureData.__init__(self)
|
||||
|
||||
self.name = "HY20"
|
||||
self.description = "HYCOOL 20, Potassium formate"
|
||||
self.reference = "Hydro Chemicals"
|
||||
|
||||
self.Tmax = 50 + 273.15
|
||||
self.Tmin = -20 + 273.15
|
||||
self.TminPsat = self.Tmax
|
||||
self.Tbase = 0.00 + 273.15
|
||||
self.temperature.data = self.getTrange()
|
||||
|
||||
self.density.source = self.density.SOURCE_COEFFS
|
||||
self.density.type = self.density.INCOMPRESSIBLE_POLYNOMIAL
|
||||
self.density.coeffs = np.array([[1202.2],[-0.42918]])
|
||||
|
||||
self.specific_heat.source = self.specific_heat.SOURCE_COEFFS
|
||||
self.specific_heat.type = self.specific_heat.INCOMPRESSIBLE_POLYNOMIAL
|
||||
self.specific_heat.coeffs = np.array([[2.955],[0.0023]])*1e3
|
||||
|
||||
key = 'Cond'
|
||||
def funcCond(T,x):
|
||||
T = (T-self.Tbase)
|
||||
if T <= 20: return 0.001978*T+0.5172
|
||||
else: return 0.001005*T+0.5368
|
||||
self.conductivity.xData,self.conductivity.yData,self.conductivity.data = self.getArray(dataID=key,func=funcCond,x_in=self.temperature.data,y_in=self.concentration.data,DEBUG=self.conductivity.DEBUG)
|
||||
self.conductivity.source = self.conductivity.SOURCE_EQUATION
|
||||
funcCond = None
|
||||
|
||||
key = 'Mu'
|
||||
def funcMu(T,x):
|
||||
T = (T-self.Tbase)
|
||||
if T <= 20: return 0.07190*np.exp(524.75/(T+142.05))
|
||||
else: return T*(0.0005524*T - 0.06281)+2.8536
|
||||
self.viscosity.xData,self.viscosity.yData,self.viscosity.data = self.getArray(dataID=key,func=funcMu,x_in=self.temperature.data,y_in=self.concentration.data,DEBUG=self.viscosity.DEBUG)
|
||||
self.viscosity.source = self.viscosity.SOURCE_EQUATION
|
||||
funcMu = None
|
||||
|
||||
|
||||
class HyCool30(PureData,DigitalData):
|
||||
def __init__(self):
|
||||
DigitalData.__init__(self)
|
||||
PureData.__init__(self)
|
||||
|
||||
self.name = "HY30"
|
||||
self.description = "HYCOOL 30, Potassium formate"
|
||||
self.reference = "Hydro Chemicals"
|
||||
|
||||
self.Tmax = 50 + 273.15
|
||||
self.Tmin = -30 + 273.15
|
||||
self.TminPsat = self.Tmax
|
||||
self.Tbase = 0.00 + 273.15
|
||||
self.temperature.data = self.getTrange()
|
||||
|
||||
self.density.source = self.density.SOURCE_COEFFS
|
||||
self.density.type = self.density.INCOMPRESSIBLE_POLYNOMIAL
|
||||
self.density.coeffs = np.array([[1257.5],[-0.475350]])
|
||||
|
||||
self.specific_heat.source = self.specific_heat.SOURCE_COEFFS
|
||||
self.specific_heat.type = self.specific_heat.INCOMPRESSIBLE_POLYNOMIAL
|
||||
self.specific_heat.coeffs = np.array([[2.783],[0.0023]])*1e3
|
||||
|
||||
key = 'Cond'
|
||||
def funcCond(T,x):
|
||||
T = (T-self.Tbase)
|
||||
if T <= 20: return 0.001840*T+0.4980
|
||||
else: return 0.001000*T+0.5140
|
||||
self.conductivity.xData,self.conductivity.yData,self.conductivity.data = self.getArray(dataID=key,func=funcCond,x_in=self.temperature.data,y_in=self.concentration.data,DEBUG=self.conductivity.DEBUG)
|
||||
self.conductivity.source = self.conductivity.SOURCE_EQUATION
|
||||
funcCond = None
|
||||
|
||||
key = 'Mu'
|
||||
def funcMu(T,x):
|
||||
T = (T-self.Tbase)
|
||||
if T <= 20: return 0.11100*np.exp(408.17/(T+123.15))
|
||||
else: return T*(0.000295*T - 0.0441)+2.6836
|
||||
self.viscosity.xData,self.viscosity.yData,self.viscosity.data = self.getArray(dataID=key,func=funcMu,x_in=self.temperature.data,y_in=self.concentration.data,DEBUG=self.viscosity.DEBUG)
|
||||
self.viscosity.source = self.viscosity.SOURCE_EQUATION
|
||||
funcMu = None
|
||||
|
||||
|
||||
class HyCool40(PureData,DigitalData):
|
||||
def __init__(self):
|
||||
DigitalData.__init__(self)
|
||||
PureData.__init__(self)
|
||||
|
||||
self.name = "HY40"
|
||||
self.description = "HYCOOL 40, Potassium formate"
|
||||
self.reference = "Hydro Chemicals"
|
||||
|
||||
self.Tmax = 20 + 273.15
|
||||
self.Tmin = -40 + 273.15
|
||||
self.TminPsat = self.Tmax
|
||||
self.Tbase = 0.00 + 273.15
|
||||
self.temperature.data = self.getTrange()
|
||||
|
||||
self.density.source = self.density.SOURCE_COEFFS
|
||||
self.density.type = self.density.INCOMPRESSIBLE_POLYNOMIAL
|
||||
self.density.coeffs = np.array([[1304.5],[-0.512290]])
|
||||
|
||||
self.specific_heat.source = self.specific_heat.SOURCE_COEFFS
|
||||
self.specific_heat.type = self.specific_heat.INCOMPRESSIBLE_POLYNOMIAL
|
||||
self.specific_heat.coeffs = np.array([[2.646],[0.0023]])*1e3
|
||||
|
||||
self.conductivity.source = self.conductivity.SOURCE_COEFFS
|
||||
self.conductivity.type = self.conductivity.INCOMPRESSIBLE_POLYNOMIAL
|
||||
self.conductivity.coeffs = np.array([[0.4826],[0.001730]])
|
||||
|
||||
key = 'Mu'
|
||||
def funcMu(T,x):
|
||||
T = (T-self.Tbase)
|
||||
return 0.07830*np.exp(498.13/(T+130.25))
|
||||
self.viscosity.xData,self.viscosity.yData,self.viscosity.data = self.getArray(dataID=key,func=funcMu,x_in=self.temperature.data,y_in=self.concentration.data,DEBUG=self.viscosity.DEBUG)
|
||||
self.viscosity.source = self.viscosity.SOURCE_EQUATION
|
||||
funcMu = None
|
||||
|
||||
|
||||
class HyCool45(PureData,DigitalData):
|
||||
def __init__(self):
|
||||
DigitalData.__init__(self)
|
||||
PureData.__init__(self)
|
||||
|
||||
self.name = "HY45"
|
||||
self.description = "HYCOOL 45, Potassium formate"
|
||||
self.reference = "Hydro Chemicals"
|
||||
|
||||
self.Tmax = 20 + 273.15
|
||||
self.Tmin = -45 + 273.15
|
||||
self.TminPsat = self.Tmax
|
||||
self.Tbase = 0.00 + 273.15
|
||||
self.temperature.data = self.getTrange()
|
||||
|
||||
self.density.source = self.density.SOURCE_COEFFS
|
||||
self.density.type = self.density.INCOMPRESSIBLE_POLYNOMIAL
|
||||
self.density.coeffs = np.array([[1328.7],[-0.530754]])
|
||||
|
||||
self.specific_heat.source = self.specific_heat.SOURCE_COEFFS
|
||||
self.specific_heat.type = self.specific_heat.INCOMPRESSIBLE_POLYNOMIAL
|
||||
self.specific_heat.coeffs = np.array([[2.578],[0.0023]])*1e3
|
||||
|
||||
self.conductivity.source = self.conductivity.SOURCE_COEFFS
|
||||
self.conductivity.type = self.conductivity.INCOMPRESSIBLE_POLYNOMIAL
|
||||
self.conductivity.coeffs = np.array([[0.4750],[0.001674]])
|
||||
|
||||
key = 'Mu'
|
||||
def funcMu(T,x):
|
||||
T = (T-self.Tbase)
|
||||
return 0.08990*np.exp(479.09/(T+126.55))
|
||||
self.viscosity.xData,self.viscosity.yData,self.viscosity.data = self.getArray(dataID=key,func=funcMu,x_in=self.temperature.data,y_in=self.concentration.data,DEBUG=self.viscosity.DEBUG)
|
||||
self.viscosity.source = self.viscosity.SOURCE_EQUATION
|
||||
funcMu = None
|
||||
|
||||
|
||||
class HyCool50(PureData,DigitalData):
|
||||
def __init__(self):
|
||||
DigitalData.__init__(self)
|
||||
PureData.__init__(self)
|
||||
|
||||
self.name = "HY50"
|
||||
self.description = "HYCOOL 50, Potassium formate"
|
||||
self.reference = "Hydro Chemicals"
|
||||
|
||||
self.Tmax = 20 + 273.15
|
||||
self.Tmin = -50 + 273.15
|
||||
self.TminPsat = self.Tmax
|
||||
self.Tbase = 0.00 + 273.15
|
||||
self.temperature.data = self.getTrange()
|
||||
|
||||
self.density.source = self.density.SOURCE_COEFFS
|
||||
self.density.type = self.density.INCOMPRESSIBLE_POLYNOMIAL
|
||||
self.density.coeffs = np.array([[1359.0],[-0.552300]])
|
||||
|
||||
self.specific_heat.source = self.specific_heat.SOURCE_COEFFS
|
||||
self.specific_heat.type = self.specific_heat.INCOMPRESSIBLE_POLYNOMIAL
|
||||
self.specific_heat.coeffs = np.array([[2.498],[0.0023]])*1e3
|
||||
|
||||
self.conductivity.source = self.conductivity.SOURCE_COEFFS
|
||||
self.conductivity.type = self.conductivity.INCOMPRESSIBLE_POLYNOMIAL
|
||||
self.conductivity.coeffs = np.array([[0.4660],[0.001610]])
|
||||
|
||||
key = 'Mu'
|
||||
def funcMu(T,x):
|
||||
T = (T-self.Tbase)
|
||||
res = 0.0491*np.exp(581.12/(T+129.05))
|
||||
if T > -10: return res + 0.2
|
||||
else: return res
|
||||
self.viscosity.xData,self.viscosity.yData,self.viscosity.data = self.getArray(dataID=key,func=funcMu,x_in=self.temperature.data,y_in=self.concentration.data,DEBUG=self.viscosity.DEBUG)
|
||||
self.viscosity.source = self.viscosity.SOURCE_EQUATION
|
||||
funcMu = None
|
||||
|
||||
|
||||
@@ -13,6 +13,12 @@ class PureExample(PureData):
|
||||
self.Tmin = 50 + 273.15
|
||||
self.TminPsat = self.Tmax
|
||||
|
||||
self.density.source = self.density.SOURCE_DATA
|
||||
self.specific_heat.source = self.specific_heat.SOURCE_DATA
|
||||
self.conductivity.source = self.conductivity.SOURCE_DATA
|
||||
self.viscosity.source = self.viscosity.SOURCE_DATA
|
||||
self.saturation_pressure.source = self.saturation_pressure.SOURCE_DATA
|
||||
|
||||
self.temperature.data = np.array([ 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150])+273.15 # Kelvin
|
||||
self.density.data = np.array([ 740, 733, 726, 717, 710, 702, 695, 687, 679, 670, 662]) # kg/m3
|
||||
self.specific_heat.data = np.array([ 2235, 2280, 2326, 2361, 2406, 2445, 2485, 2528, 2571, 2607, 2645]) # J/kg-K
|
||||
@@ -44,6 +50,9 @@ class SolutionExample(SolutionData):
|
||||
|
||||
self.specific_heat.data = np.copy(self.density.data)
|
||||
|
||||
self.density.source = self.density.SOURCE_DATA
|
||||
self.specific_heat.source = self.specific_heat.SOURCE_DATA
|
||||
|
||||
self.Tmax = np.max(self.temperature.data)
|
||||
self.Tmin = np.min(self.temperature.data)
|
||||
self.xmax = np.max(self.concentration.data)
|
||||
@@ -72,11 +81,62 @@ class DigitalExample(DigitalData):
|
||||
|
||||
def funcRho(T,x):
|
||||
return T + x*100.0 + T*(x+0.5)
|
||||
self.density.data = self.getArray(funcRho,"rho")
|
||||
self.density.xData,self.density.yData,self.density.data = self.getArray(dataID="D", func=funcRho, x_in=self.temperature.data, y_in=self.concentration.data,DEBUG=self.density.DEBUG)
|
||||
self.density.source = self.density.SOURCE_EQUATION
|
||||
|
||||
def funcCp(T,x):
|
||||
return T + x*50.0 + T*(x+0.6)
|
||||
self.specific_heat.data = self.getArray(funcCp,"cp")
|
||||
self.specific_heat.xData,self.specific_heat.yData,self.specific_heat.data = self.getArray(dataID="C", func=funcCp, x_in=self.temperature.data, y_in=self.concentration.data,DEBUG=self.specific_heat.DEBUG)
|
||||
self.specific_heat.source = self.specific_heat.SOURCE_EQUATION
|
||||
|
||||
class DigitalExamplePure(PureData,DigitalData):
|
||||
def __init__(self):
|
||||
DigitalData.__init__(self)
|
||||
PureData.__init__(self)
|
||||
|
||||
self.name = "ExampleDigitalPure"
|
||||
self.description = "water at 100 bar"
|
||||
self.reference = "none"
|
||||
|
||||
self.Tmin = 280.00;
|
||||
self.Tmax = 500.00;
|
||||
|
||||
self.TminPsat = self.Tmin;
|
||||
|
||||
self.temperature.data = self.getTrange()
|
||||
self.concentration.data = self.getxrange()
|
||||
|
||||
import CoolProp.CoolProp as CP
|
||||
|
||||
def funcD(T,x):
|
||||
return CP.PropsSI('D','T',T,'P',1e7,'water')
|
||||
def funcC(T,x):
|
||||
return CP.PropsSI('C','T',T,'P',1e7,'water')
|
||||
def funcL(T,x):
|
||||
return CP.PropsSI('L','T',T,'P',1e7,'water')
|
||||
def funcV(T,x):
|
||||
return CP.PropsSI('V','T',T,'P',1e7,'water')
|
||||
def funcP(T,x):
|
||||
return CP.PropsSI('P','T',T,'Q',0.0,'water')
|
||||
|
||||
self.density.xData,self.density.yData,self.density.data = self.getArray(dataID="D", func=funcD, x_in=self.temperature.data, y_in=self.concentration.data,DEBUG=self.density.DEBUG)
|
||||
self.density.source = self.density.SOURCE_EQUATION
|
||||
|
||||
self.specific_heat.xData,self.specific_heat.yData,self.specific_heat.data = self.getArray(dataID="C", func=funcC, x_in=self.temperature.data, y_in=self.concentration.data,DEBUG=self.specific_heat.DEBUG)
|
||||
self.specific_heat.source = self.specific_heat.SOURCE_EQUATION
|
||||
|
||||
self.conductivity.xData,self.conductivity.yData,self.conductivity.data = self.getArray(dataID="L", func=funcL, x_in=self.temperature.data, y_in=self.concentration.data,DEBUG=self.conductivity.DEBUG)
|
||||
self.conductivity.source = self.conductivity.SOURCE_EQUATION
|
||||
|
||||
self.viscosity.xData,self.viscosity.yData,self.viscosity.data = self.getArray(dataID="V", func=funcV, x_in=self.temperature.data, y_in=self.concentration.data,DEBUG=self.viscosity.DEBUG)
|
||||
self.viscosity.source = self.viscosity.SOURCE_EQUATION
|
||||
|
||||
self.saturation_pressure.xData,self.saturation_pressure.yData,self.saturation_pressure.data = self.getArray(dataID="P", func=funcP, x_in=self.temperature.data, y_in=self.concentration.data,DEBUG=self.saturation_pressure.DEBUG)
|
||||
self.saturation_pressure.source = self.saturation_pressure.SOURCE_EQUATION
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
class SecCoolExample(CoefficientData):
|
||||
@@ -186,12 +246,18 @@ class SecCoolExample(CoefficientData):
|
||||
4.482000E-09]))
|
||||
|
||||
self.T_freeze.type = self.T_freeze.INCOMPRESSIBLE_POLYOFFSET
|
||||
self.T_freeze.coeffs = self.convertSecCoolTfreeze(np.array([
|
||||
27.755555600,
|
||||
-22.973221700,
|
||||
-1.1040507200,
|
||||
-0.0120762281,
|
||||
-9.343458E-05]))
|
||||
self.T_freeze.coeffs = np.array([
|
||||
27.755555600/100.0,
|
||||
-22.973221700+273.15,
|
||||
-1.1040507200*100.0,
|
||||
-0.0120762281*100.0*100.0,
|
||||
-9.343458E-05*100.0*100.0*100.0])
|
||||
|
||||
self.density.source = self.density.SOURCE_COEFFS
|
||||
self.specific_heat.source = self.specific_heat.SOURCE_COEFFS
|
||||
self.conductivity.source = self.conductivity.SOURCE_COEFFS
|
||||
self.viscosity.source = self.viscosity.SOURCE_COEFFS
|
||||
self.T_freeze.source = self.T_freeze.SOURCE_COEFFS
|
||||
|
||||
|
||||
class MelinderExample(CoefficientData):
|
||||
|
||||
@@ -1,6 +1,7 @@
|
||||
from __future__ import division, print_function
|
||||
import numpy as np
|
||||
from CPIncomp.DataObjects import PureData,CoefficientData
|
||||
from CPIncomp.BaseObjects import IncompressibleFitter
|
||||
|
||||
|
||||
class DEBLiquidClass(CoefficientData,PureData):
|
||||
@@ -19,16 +20,21 @@ class DEBLiquidClass(CoefficientData,PureData):
|
||||
self.TminPsat = self.Tmax
|
||||
|
||||
self.density.type = self.density.INCOMPRESSIBLE_POLYNOMIAL
|
||||
_,_,self.density.coeffs = self.density.shapeArray(np.array([1076.5,-0.731182]))
|
||||
_,_,self.density.coeffs = IncompressibleFitter.shapeArray(np.array([1076.5,-0.731182]))
|
||||
|
||||
self.specific_heat.type = self.specific_heat.INCOMPRESSIBLE_POLYNOMIAL
|
||||
_,_,self.specific_heat.coeffs = self.specific_heat.shapeArray(np.array([999.729,2.87576]))
|
||||
_,_,self.specific_heat.coeffs = IncompressibleFitter.shapeArray(np.array([999.729,2.87576]))
|
||||
|
||||
self.viscosity.type = self.viscosity.INCOMPRESSIBLE_EXPPOLYNOMIAL
|
||||
_,_,self.viscosity.coeffs = self.viscosity.shapeArray(np.array([3.5503,-0.0566396,7.03331e-05]))
|
||||
_,_,self.viscosity.coeffs = IncompressibleFitter.shapeArray(np.array([3.5503,-0.0566396,7.03331e-05]))
|
||||
|
||||
self.conductivity.type = self.conductivity.INCOMPRESSIBLE_POLYNOMIAL
|
||||
_,_,self.conductivity.coeffs = self.conductivity.shapeArray(np.array([0.000189132,-2.06364e-07]))
|
||||
_,_,self.conductivity.coeffs = IncompressibleFitter.shapeArray(np.array([0.000189132,-2.06364e-07]))
|
||||
|
||||
self.density.source = self.density.SOURCE_COEFFS
|
||||
self.specific_heat.source = self.specific_heat.SOURCE_COEFFS
|
||||
self.conductivity.source = self.conductivity.SOURCE_COEFFS
|
||||
self.viscosity.source = self.viscosity.SOURCE_COEFFS
|
||||
|
||||
|
||||
class HCMLiquidClass(CoefficientData,PureData):
|
||||
@@ -47,16 +53,21 @@ class HCMLiquidClass(CoefficientData,PureData):
|
||||
self.TminPsat = self.Tmax
|
||||
|
||||
self.density.type = self.density.INCOMPRESSIBLE_POLYNOMIAL
|
||||
_,_,self.density.coeffs = self.density.shapeArray(np.array([971.725,-0.718788]))
|
||||
_,_,self.density.coeffs = IncompressibleFitter.shapeArray(np.array([971.725,-0.718788]))
|
||||
|
||||
self.specific_heat.type = self.specific_heat.INCOMPRESSIBLE_POLYNOMIAL
|
||||
_,_,self.specific_heat.coeffs = self.specific_heat.shapeArray(np.array([844.023,4.31212]))
|
||||
_,_,self.specific_heat.coeffs = IncompressibleFitter.shapeArray(np.array([844.023,4.31212]))
|
||||
|
||||
self.viscosity.type = self.viscosity.INCOMPRESSIBLE_EXPPOLYNOMIAL
|
||||
_,_,self.viscosity.coeffs = self.viscosity.shapeArray(np.array([18.3237,-0.14706,0.000209096]))
|
||||
_,_,self.viscosity.coeffs = IncompressibleFitter.shapeArray(np.array([18.3237,-0.14706,0.000209096]))
|
||||
|
||||
self.conductivity.type = self.conductivity.INCOMPRESSIBLE_POLYNOMIAL
|
||||
_,_,self.conductivity.coeffs = self.conductivity.shapeArray(np.array([0.000153716,-1.51212e-07]))
|
||||
_,_,self.conductivity.coeffs = IncompressibleFitter.shapeArray(np.array([0.000153716,-1.51212e-07]))
|
||||
|
||||
self.density.source = self.density.SOURCE_COEFFS
|
||||
self.specific_heat.source = self.specific_heat.SOURCE_COEFFS
|
||||
self.conductivity.source = self.conductivity.SOURCE_COEFFS
|
||||
self.viscosity.source = self.viscosity.SOURCE_COEFFS
|
||||
|
||||
|
||||
class HFELiquidClass(CoefficientData,PureData):
|
||||
@@ -75,16 +86,21 @@ class HFELiquidClass(CoefficientData,PureData):
|
||||
self.TminPsat = self.Tmax
|
||||
|
||||
self.density.type = self.density.INCOMPRESSIBLE_POLYNOMIAL
|
||||
_,_,self.density.coeffs = self.density.shapeArray(np.array([1822.37,-0.918485]))
|
||||
_,_,self.density.coeffs = IncompressibleFitter.shapeArray(np.array([1822.37,-0.918485]))
|
||||
|
||||
self.specific_heat.type = self.specific_heat.INCOMPRESSIBLE_POLYNOMIAL
|
||||
_,_,self.specific_heat.coeffs = self.specific_heat.shapeArray(np.array([871.834,858788]))
|
||||
_,_,self.specific_heat.coeffs = IncompressibleFitter.shapeArray(np.array([871.834,858788]))
|
||||
|
||||
self.viscosity.type = self.viscosity.INCOMPRESSIBLE_EXPPOLYNOMIAL
|
||||
_,_,self.viscosity.coeffs = self.viscosity.shapeArray(np.array([-4.22878,-0.0114765,7.39823e-06]))
|
||||
_,_,self.viscosity.coeffs = IncompressibleFitter.shapeArray(np.array([-4.22878,-0.0114765,7.39823e-06]))
|
||||
|
||||
self.conductivity.type = self.conductivity.INCOMPRESSIBLE_POLYNOMIAL
|
||||
_,_,self.conductivity.coeffs = self.conductivity.shapeArray(np.array([9.92958e-05,-8.33333e-08]))
|
||||
_,_,self.conductivity.coeffs = IncompressibleFitter.shapeArray(np.array([9.92958e-05,-8.33333e-08]))
|
||||
|
||||
self.density.source = self.density.SOURCE_COEFFS
|
||||
self.specific_heat.source = self.specific_heat.SOURCE_COEFFS
|
||||
self.conductivity.source = self.conductivity.SOURCE_COEFFS
|
||||
self.viscosity.source = self.viscosity.SOURCE_COEFFS
|
||||
|
||||
|
||||
class PMS1LiquidClass(CoefficientData,PureData):
|
||||
@@ -103,16 +119,21 @@ class PMS1LiquidClass(CoefficientData,PureData):
|
||||
self.TminPsat = self.Tmax
|
||||
|
||||
self.density.type = self.density.INCOMPRESSIBLE_POLYNOMIAL
|
||||
_,_,self.density.coeffs = self.density.shapeArray(np.array([1172.35,-0.9025]))
|
||||
_,_,self.density.coeffs = IncompressibleFitter.shapeArray(np.array([1172.35,-0.9025]))
|
||||
|
||||
self.specific_heat.type = self.specific_heat.INCOMPRESSIBLE_POLYNOMIAL
|
||||
_,_,self.specific_heat.coeffs = self.specific_heat.shapeArray(np.array([1223.69,1.48417]))
|
||||
_,_,self.specific_heat.coeffs = IncompressibleFitter.shapeArray(np.array([1223.69,1.48417]))
|
||||
|
||||
self.viscosity.type = self.viscosity.INCOMPRESSIBLE_EXPPOLYNOMIAL
|
||||
_,_,self.viscosity.coeffs = self.viscosity.shapeArray(np.array([6.36183,-0.0636352,7.51428e-05]))
|
||||
_,_,self.viscosity.coeffs = IncompressibleFitter.shapeArray(np.array([6.36183,-0.0636352,7.51428e-05]))
|
||||
|
||||
self.conductivity.type = self.conductivity.INCOMPRESSIBLE_POLYNOMIAL
|
||||
_,_,self.conductivity.coeffs = self.conductivity.shapeArray(np.array([0.000207526,-2.84167e-07]))
|
||||
_,_,self.conductivity.coeffs = IncompressibleFitter.shapeArray(np.array([0.000207526,-2.84167e-07]))
|
||||
|
||||
self.density.source = self.density.SOURCE_COEFFS
|
||||
self.specific_heat.source = self.specific_heat.SOURCE_COEFFS
|
||||
self.conductivity.source = self.conductivity.SOURCE_COEFFS
|
||||
self.viscosity.source = self.viscosity.SOURCE_COEFFS
|
||||
|
||||
|
||||
class PMS2LiquidClass(CoefficientData,PureData):
|
||||
@@ -131,16 +152,21 @@ class PMS2LiquidClass(CoefficientData,PureData):
|
||||
self.TminPsat = self.Tmax
|
||||
|
||||
self.density.type = self.density.INCOMPRESSIBLE_POLYNOMIAL
|
||||
_,_,self.density.coeffs = self.density.shapeArray(np.array([1155.94,-1.02576]))
|
||||
_,_,self.density.coeffs = IncompressibleFitter.shapeArray(np.array([1155.94,-1.02576]))
|
||||
|
||||
self.specific_heat.type = self.specific_heat.INCOMPRESSIBLE_POLYNOMIAL
|
||||
_,_,self.specific_heat.coeffs = self.specific_heat.shapeArray(np.array([1153.55,2.10788]))
|
||||
_,_,self.specific_heat.coeffs = IncompressibleFitter.shapeArray(np.array([1153.55,2.10788]))
|
||||
|
||||
self.viscosity.type = self.viscosity.INCOMPRESSIBLE_EXPPOLYNOMIAL
|
||||
_,_,self.viscosity.coeffs = self.viscosity.shapeArray(np.array([5.66926,-0.065582,8.09988e-05]))
|
||||
_,_,self.viscosity.coeffs = IncompressibleFitter.shapeArray(np.array([5.66926,-0.065582,8.09988e-05]))
|
||||
|
||||
self.conductivity.type = self.conductivity.INCOMPRESSIBLE_POLYNOMIAL
|
||||
_,_,self.conductivity.coeffs = self.conductivity.shapeArray(np.array([0.000172305,-2.11212e-07]))
|
||||
_,_,self.conductivity.coeffs = IncompressibleFitter.shapeArray(np.array([0.000172305,-2.11212e-07]))
|
||||
|
||||
self.density.source = self.density.SOURCE_COEFFS
|
||||
self.specific_heat.source = self.specific_heat.SOURCE_COEFFS
|
||||
self.conductivity.source = self.conductivity.SOURCE_COEFFS
|
||||
self.viscosity.source = self.viscosity.SOURCE_COEFFS
|
||||
|
||||
|
||||
class SABLiquidClass(CoefficientData,PureData):
|
||||
@@ -159,16 +185,21 @@ class SABLiquidClass(CoefficientData,PureData):
|
||||
self.TminPsat = self.Tmax
|
||||
|
||||
self.density.type = self.density.INCOMPRESSIBLE_POLYNOMIAL
|
||||
_,_,self.density.coeffs = self.density.shapeArray(np.array([1102.34,-0.801667]))
|
||||
_,_,self.density.coeffs = IncompressibleFitter.shapeArray(np.array([1102.34,-0.801667]))
|
||||
|
||||
self.specific_heat.type = self.specific_heat.INCOMPRESSIBLE_POLYNOMIAL
|
||||
_,_,self.specific_heat.coeffs = self.specific_heat.shapeArray(np.array([1360.94,1.51667]))
|
||||
_,_,self.specific_heat.coeffs = IncompressibleFitter.shapeArray(np.array([1360.94,1.51667]))
|
||||
|
||||
self.viscosity.type = self.viscosity.INCOMPRESSIBLE_EXPPOLYNOMIAL
|
||||
_,_,self.viscosity.coeffs = self.viscosity.shapeArray(np.array([5.21288,-0.0665792,8.5066e-05]))
|
||||
_,_,self.viscosity.coeffs = IncompressibleFitter.shapeArray(np.array([5.21288,-0.0665792,8.5066e-05]))
|
||||
|
||||
self.conductivity.type = self.conductivity.INCOMPRESSIBLE_POLYNOMIAL
|
||||
_,_,self.conductivity.coeffs = self.conductivity.shapeArray(np.array([0.000208374,-2.61667e-07]))
|
||||
_,_,self.conductivity.coeffs = IncompressibleFitter.shapeArray(np.array([0.000208374,-2.61667e-07]))
|
||||
|
||||
self.density.source = self.density.SOURCE_COEFFS
|
||||
self.specific_heat.source = self.specific_heat.SOURCE_COEFFS
|
||||
self.conductivity.source = self.conductivity.SOURCE_COEFFS
|
||||
self.viscosity.source = self.viscosity.SOURCE_COEFFS
|
||||
|
||||
|
||||
class HCBLiquidClass(CoefficientData,PureData):
|
||||
@@ -187,16 +218,21 @@ class HCBLiquidClass(CoefficientData,PureData):
|
||||
self.TminPsat = self.Tmax
|
||||
|
||||
self.density.type = self.density.INCOMPRESSIBLE_POLYNOMIAL
|
||||
_,_,self.density.coeffs = self.density.shapeArray(np.array([1071.78,-0.772024]))
|
||||
_,_,self.density.coeffs = IncompressibleFitter.shapeArray(np.array([1071.78,-0.772024]))
|
||||
|
||||
self.specific_heat.type = self.specific_heat.INCOMPRESSIBLE_POLYNOMIAL
|
||||
_,_,self.specific_heat.coeffs = self.specific_heat.shapeArray(np.array([761.393,3.52976]))
|
||||
_,_,self.specific_heat.coeffs = IncompressibleFitter.shapeArray(np.array([761.393,3.52976]))
|
||||
|
||||
self.viscosity.type = self.viscosity.INCOMPRESSIBLE_EXPPOLYNOMIAL
|
||||
_,_,self.viscosity.coeffs = self.viscosity.shapeArray(np.array([7.16819,-0.0863212,0.000130604]))
|
||||
_,_,self.viscosity.coeffs = IncompressibleFitter.shapeArray(np.array([7.16819,-0.0863212,0.000130604]))
|
||||
|
||||
self.conductivity.type = self.conductivity.INCOMPRESSIBLE_POLYNOMIAL
|
||||
_,_,self.conductivity.coeffs = self.conductivity.shapeArray(np.array([0.000203186,-2.3869e-07]))
|
||||
_,_,self.conductivity.coeffs = IncompressibleFitter.shapeArray(np.array([0.000203186,-2.3869e-07]))
|
||||
|
||||
self.density.source = self.density.SOURCE_COEFFS
|
||||
self.specific_heat.source = self.specific_heat.SOURCE_COEFFS
|
||||
self.conductivity.source = self.conductivity.SOURCE_COEFFS
|
||||
self.viscosity.source = self.viscosity.SOURCE_COEFFS
|
||||
|
||||
|
||||
class TCOLiquidClass(CoefficientData,PureData):
|
||||
@@ -215,16 +251,21 @@ class TCOLiquidClass(CoefficientData,PureData):
|
||||
self.TminPsat = self.Tmax
|
||||
|
||||
self.density.type = self.density.INCOMPRESSIBLE_POLYNOMIAL
|
||||
_,_,self.density.coeffs = self.density.shapeArray(np.array([1071.02,-0.778166]))
|
||||
_,_,self.density.coeffs = IncompressibleFitter.shapeArray(np.array([1071.02,-0.778166]))
|
||||
|
||||
self.specific_heat.type = self.specific_heat.INCOMPRESSIBLE_POLYNOMIAL
|
||||
_,_,self.specific_heat.coeffs = self.specific_heat.shapeArray(np.array([223.775,5.2159]))
|
||||
_,_,self.specific_heat.coeffs = IncompressibleFitter.shapeArray(np.array([223.775,5.2159]))
|
||||
|
||||
self.viscosity.type = self.viscosity.INCOMPRESSIBLE_EXPPOLYNOMIAL
|
||||
_,_,self.viscosity.coeffs = self.viscosity.shapeArray(np.array([-3.47971,-0.0107031,1.14086e-06]))
|
||||
_,_,self.viscosity.coeffs = IncompressibleFitter.shapeArray(np.array([-3.47971,-0.0107031,1.14086e-06]))
|
||||
|
||||
self.conductivity.type = self.conductivity.INCOMPRESSIBLE_POLYNOMIAL
|
||||
_,_,self.conductivity.coeffs = self.conductivity.shapeArray(np.array([0.000174156,-1.85052e-07]))
|
||||
_,_,self.conductivity.coeffs = IncompressibleFitter.shapeArray(np.array([0.000174156,-1.85052e-07]))
|
||||
|
||||
self.density.source = self.density.SOURCE_COEFFS
|
||||
self.specific_heat.source = self.specific_heat.SOURCE_COEFFS
|
||||
self.conductivity.source = self.conductivity.SOURCE_COEFFS
|
||||
self.viscosity.source = self.viscosity.SOURCE_COEFFS
|
||||
|
||||
|
||||
|
||||
|
||||
@@ -1,6 +1,6 @@
|
||||
from __future__ import division, print_function
|
||||
import numpy as np
|
||||
from CPIncomp.DataObjects import PureData
|
||||
from CPIncomp.DataObjects import PureData , DigitalData
|
||||
|
||||
class TherminolD12(PureData):
|
||||
"""
|
||||
@@ -8,6 +8,11 @@ class TherminolD12(PureData):
|
||||
"""
|
||||
def __init__(self):
|
||||
PureData.__init__(self)
|
||||
self.density.source = self.density.SOURCE_DATA
|
||||
self.specific_heat.source = self.specific_heat.SOURCE_DATA
|
||||
self.conductivity.source = self.conductivity.SOURCE_DATA
|
||||
self.viscosity.source = self.viscosity.SOURCE_DATA
|
||||
self.saturation_pressure.source = self.saturation_pressure.SOURCE_DATA
|
||||
self.temperature.data = np.array([-8.50000E+1, -8.00000E+1, -7.50000E+1, -7.00000E+1, -6.50000E+1, -6.00000E+1, -5.50000E+1, -5.00000E+1, -4.50000E+1, -4.00000E+1, -3.50000E+1, -3.00000E+1, -2.50000E+1, -2.00000E+1, -1.50000E+1, -1.00000E+1, -5.00000E+0, +0.00000E+0, +5.00000E+0, +1.00000E+1, +1.50000E+1, +2.00000E+1, +2.50000E+1, +3.00000E+1, +3.50000E+1, +4.00000E+1, +4.50000E+1, +5.00000E+1, +5.50000E+1, +6.00000E+1, +6.50000E+1, +7.00000E+1, +7.50000E+1, +8.00000E+1, +8.50000E+1, +9.00000E+1, +9.50000E+1, +1.00000E+2, +1.05000E+2, +1.10000E+2, +1.15000E+2, +1.20000E+2, +1.25000E+2, +1.30000E+2, +1.35000E+2, +1.40000E+2, +1.45000E+2, +1.50000E+2, +1.55000E+2, +1.60000E+2, +1.65000E+2, +1.70000E+2, +1.75000E+2, +1.80000E+2, +1.85000E+2, +1.90000E+2, +1.95000E+2, +2.00000E+2, +2.05000E+2, +2.10000E+2, +2.15000E+2, +2.20000E+2, +2.25000E+2, +2.30000E+2])+273.15 # Kelvin
|
||||
self.density.data = np.array([+8.35000E+2, +8.32000E+2, +8.28000E+2, +8.25000E+2, +8.22000E+2, +8.18000E+2, +8.15000E+2, +8.11000E+2, +8.08000E+2, +8.05000E+2, +8.01000E+2, +7.98000E+2, +7.94000E+2, +7.91000E+2, +7.87000E+2, +7.84000E+2, +7.80000E+2, +7.77000E+2, +7.73000E+2, +7.70000E+2, +7.66000E+2, +7.62000E+2, +7.59000E+2, +7.55000E+2, +7.52000E+2, +7.48000E+2, +7.44000E+2, +7.41000E+2, +7.37000E+2, +7.33000E+2, +7.29000E+2, +7.26000E+2, +7.22000E+2, +7.18000E+2, +7.14000E+2, +7.10000E+2, +7.06000E+2, +7.03000E+2, +6.99000E+2, +6.95000E+2, +6.91000E+2, +6.87000E+2, +6.82000E+2, +6.78000E+2, +6.74000E+2, +6.70000E+2, +6.66000E+2, +6.61000E+2, +6.57000E+2, +6.53000E+2, +6.48000E+2, +6.44000E+2, +6.39000E+2, +6.35000E+2, +6.30000E+2, +6.25000E+2, +6.20000E+2, +6.16000E+2, +6.11000E+2, +6.06000E+2, +6.00000E+2, +5.95000E+2, +5.90000E+2, +5.84000E+2]) # kg/m3
|
||||
self.specific_heat.data = np.array([+1.69400E+0, +1.71200E+0, +1.73100E+0, +1.75000E+0, +1.76800E+0, +1.78700E+0, +1.80600E+0, +1.82400E+0, +1.84300E+0, +1.86200E+0, +1.88100E+0, +1.90000E+0, +1.91900E+0, +1.93800E+0, +1.95700E+0, +1.97700E+0, +1.99600E+0, +2.01500E+0, +2.03500E+0, +2.05400E+0, +2.07300E+0, +2.09300E+0, +2.11300E+0, +2.13200E+0, +2.15200E+0, +2.17200E+0, +2.19100E+0, +2.21100E+0, +2.23100E+0, +2.25100E+0, +2.27100E+0, +2.29100E+0, +2.31200E+0, +2.33200E+0, +2.35200E+0, +2.37300E+0, +2.39300E+0, +2.41400E+0, +2.43400E+0, +2.45500E+0, +2.47600E+0, +2.49600E+0, +2.51700E+0, +2.53800E+0, +2.55900E+0, +2.58000E+0, +2.60200E+0, +2.62300E+0, +2.64400E+0, +2.66600E+0, +2.68700E+0, +2.70900E+0, +2.73100E+0, +2.75300E+0, +2.77500E+0, +2.79700E+0, +2.82000E+0, +2.84200E+0, +2.86500E+0, +2.88800E+0, +2.91100E+0, +2.93500E+0, +2.95900E+0, +2.98300E+0])*1000. # J/kg-K
|
||||
@@ -28,6 +33,11 @@ class TherminolVP1(PureData):
|
||||
"""
|
||||
def __init__(self):
|
||||
PureData.__init__(self)
|
||||
self.density.source = self.density.SOURCE_DATA
|
||||
self.specific_heat.source = self.specific_heat.SOURCE_DATA
|
||||
self.conductivity.source = self.conductivity.SOURCE_DATA
|
||||
self.viscosity.source = self.viscosity.SOURCE_DATA
|
||||
self.saturation_pressure.source = self.saturation_pressure.SOURCE_DATA
|
||||
self.temperature.data = np.array([+1.20000E+1, +1.70000E+1, +2.20000E+1, +2.70000E+1, +3.20000E+1, +3.70000E+1, +4.20000E+1, +4.70000E+1, +5.20000E+1, +5.70000E+1, +6.20000E+1, +6.70000E+1, +7.20000E+1, +7.70000E+1, +8.20000E+1, +8.70000E+1, +9.20000E+1, +9.70000E+1, +1.02000E+2, +1.07000E+2, +1.12000E+2, +1.17000E+2, +1.22000E+2, +1.27000E+2, +1.32000E+2, +1.37000E+2, +1.42000E+2, +1.47000E+2, +1.52000E+2, +1.57000E+2, +1.62000E+2, +1.67000E+2, +1.72000E+2, +1.77000E+2, +1.82000E+2, +1.87000E+2, +1.92000E+2, +1.97000E+2, +2.02000E+2, +2.07000E+2, +2.12000E+2, +2.17000E+2, +2.22000E+2, +2.27000E+2, +2.32000E+2, +2.37000E+2, +2.42000E+2, +2.47000E+2, +2.52000E+2, +2.57000E+2, +2.62000E+2, +2.67000E+2, +2.72000E+2, +2.77000E+2, +2.82000E+2, +2.87000E+2, +2.92000E+2, +2.97000E+2, +3.02000E+2, +3.07000E+2, +3.12000E+2, +3.17000E+2, +3.22000E+2, +3.27000E+2, +3.32000E+2, +3.37000E+2, +3.42000E+2, +3.47000E+2, +3.52000E+2, +3.57000E+2, +3.62000E+2, +3.67000E+2, +3.72000E+2, +3.77000E+2, +3.82000E+2, +3.87000E+2, +3.92000E+2, +3.97000E+2])+273.15 # Kelvin
|
||||
self.density.data = np.array([+1.07000E+3, +1.07000E+3, +1.06000E+3, +1.06000E+3, +1.05000E+3, +1.05000E+3, +1.05000E+3, +1.04000E+3, +1.04000E+3, +1.03000E+3, +1.03000E+3, +1.03000E+3, +1.02000E+3, +1.02000E+3, +1.01000E+3, +1.01000E+3, +1.01000E+3, +1.00000E+3, +9.97000E+2, +9.93000E+2, +9.88000E+2, +9.84000E+2, +9.80000E+2, +9.76000E+2, +9.72000E+2, +9.67000E+2, +9.63000E+2, +9.59000E+2, +9.55000E+2, +9.50000E+2, +9.46000E+2, +9.42000E+2, +9.37000E+2, +9.33000E+2, +9.29000E+2, +9.24000E+2, +9.20000E+2, +9.15000E+2, +9.11000E+2, +9.06000E+2, +9.02000E+2, +8.98000E+2, +8.93000E+2, +8.89000E+2, +8.84000E+2, +8.79000E+2, +8.75000E+2, +8.70000E+2, +8.65000E+2, +8.60000E+2, +8.56000E+2, +8.51000E+2, +8.46000E+2, +8.41000E+2, +8.36000E+2, +8.31000E+2, +8.25000E+2, +8.20000E+2, +8.15000E+2, +8.10000E+2, +8.04000E+2, +7.99000E+2, +7.93000E+2, +7.88000E+2, +7.82000E+2, +7.76000E+2, +7.70000E+2, +7.65000E+2, +7.59000E+2, +7.52000E+2, +7.46000E+2, +7.40000E+2, +7.33000E+2, +7.27000E+2, +7.20000E+2, +7.13000E+2, +7.06000E+2, +6.99000E+2]) # kg/m3
|
||||
self.specific_heat.data = np.array([+1.52300E+0, +1.53700E+0, +1.55200E+0, +1.56600E+0, +1.58100E+0, +1.59600E+0, +1.61000E+0, +1.62400E+0, +1.63900E+0, +1.65300E+0, +1.66800E+0, +1.68200E+0, +1.69600E+0, +1.71000E+0, +1.72400E+0, +1.73900E+0, +1.75300E+0, +1.76700E+0, +1.78100E+0, +1.79500E+0, +1.80900E+0, +1.82200E+0, +1.83600E+0, +1.85000E+0, +1.86400E+0, +1.87800E+0, +1.89100E+0, +1.90500E+0, +1.91900E+0, +1.93200E+0, +1.94600E+0, +1.95900E+0, +1.97300E+0, +1.98600E+0, +2.00000E+0, +2.01300E+0, +2.02700E+0, +2.04000E+0, +2.05400E+0, +2.06700E+0, +2.08000E+0, +2.09300E+0, +2.10700E+0, +2.12000E+0, +2.13300E+0, +2.14700E+0, +2.16000E+0, +2.17300E+0, +2.18600E+0, +2.19900E+0, +2.21300E+0, +2.22600E+0, +2.23900E+0, +2.25200E+0, +2.26600E+0, +2.27900E+0, +2.29300E+0, +2.30600E+0, +2.31900E+0, +2.33300E+0, +2.34700E+0, +2.36000E+0, +2.37400E+0, +2.38800E+0, +2.40200E+0, +2.41600E+0, +2.43100E+0, +2.44600E+0, +2.46000E+0, +2.47600E+0, +2.49100E+0, +2.50700E+0, +2.52300E+0, +2.54000E+0, +2.55800E+0, +2.57600E+0, +2.59500E+0, +2.61500E+0])*1000. # J/kg-K
|
||||
@@ -49,6 +59,11 @@ class Therminol66(PureData):
|
||||
"""
|
||||
def __init__(self):
|
||||
PureData.__init__(self)
|
||||
self.density.source = self.density.SOURCE_DATA
|
||||
self.specific_heat.source = self.specific_heat.SOURCE_DATA
|
||||
self.conductivity.source = self.conductivity.SOURCE_DATA
|
||||
self.viscosity.source = self.viscosity.SOURCE_DATA
|
||||
self.saturation_pressure.source = self.saturation_pressure.SOURCE_DATA
|
||||
self.temperature.data = np.array([2.7315E+02, 2.8315E+02, 2.9315E+02, 3.0315E+02, 3.1315E+02, 3.2315E+02, 3.3315E+02, 3.4315E+02, 3.5315E+02, 3.6315E+02, 3.7315E+02, 3.8315E+02, 3.9315E+02, 4.0315E+02, 4.1315E+02, 4.2315E+02, 4.3315E+02, 4.4315E+02, 4.5315E+02, 4.6315E+02, 4.7315E+02, 4.8315E+02, 4.9315E+02, 5.0315E+02, 5.1315E+02, 5.2315E+02, 5.3315E+02, 5.4315E+02, 5.5315E+02, 5.6315E+02, 5.7315E+02, 5.8315E+02, 5.9315E+02, 6.0315E+02, 6.1315E+02, 6.2315E+02, 6.3315E+02, 6.4315E+02, 6.5315E+02])
|
||||
self.density.data = np.array([1.0215E+03, 1.0149E+03, 1.0084E+03, 1.0018E+03, 9.9520E+02, 9.8860E+02, 9.8190E+02, 9.7520E+02, 9.6850E+02, 9.6180E+02, 9.5500E+02, 9.4820E+02, 9.4140E+02, 9.3450E+02, 9.2760E+02, 9.2060E+02, 9.1360E+02, 9.0660E+02, 8.9950E+02, 8.9230E+02, 8.8510E+02, 8.7780E+02, 8.7040E+02, 8.6300E+02, 8.5550E+02, 8.4790E+02, 8.4030E+02, 8.3250E+02, 8.2460E+02, 8.1660E+02, 8.0850E+02, 8.0030E+02, 7.9200E+02, 7.8350E+02, 7.7480E+02, 7.6590E+02, 7.5690E+02, 7.4770E+02, 7.3820E+02])
|
||||
self.specific_heat.data = np.array([1.4950E+03, 1.5290E+03, 1.5620E+03, 1.5960E+03, 1.6300E+03, 1.6650E+03, 1.6990E+03, 1.7330E+03, 1.7680E+03, 1.8030E+03, 1.8370E+03, 1.8730E+03, 1.9080E+03, 1.9430E+03, 1.9780E+03, 2.0140E+03, 2.0500E+03, 2.0860E+03, 2.1220E+03, 2.1580E+03, 2.1950E+03, 2.2310E+03, 2.2680E+03, 2.3050E+03, 2.3420E+03, 2.3790E+03, 2.4170E+03, 2.4550E+03, 2.4920E+03, 2.5310E+03, 2.5690E+03, 2.6080E+03, 2.6470E+03, 2.6860E+03, 2.7260E+03, 2.7660E+03, 2.8060E+03, 2.8470E+03, 2.8890E+03])
|
||||
@@ -70,6 +85,11 @@ class Therminol72(PureData):
|
||||
"""
|
||||
def __init__(self):
|
||||
PureData.__init__(self)
|
||||
self.density.source = self.density.SOURCE_DATA
|
||||
self.specific_heat.source = self.specific_heat.SOURCE_DATA
|
||||
self.conductivity.source = self.conductivity.SOURCE_DATA
|
||||
self.viscosity.source = self.viscosity.SOURCE_DATA
|
||||
self.saturation_pressure.source = self.saturation_pressure.SOURCE_DATA
|
||||
self.temperature.data = np.array([-1.00000E+1, -5.00000E+0, +0.00000E+0, +5.00000E+0, +1.00000E+1, +1.50000E+1, +2.00000E+1, +2.50000E+1, +3.00000E+1, +3.50000E+1, +4.00000E+1, +4.50000E+1, +5.00000E+1, +5.50000E+1, +6.00000E+1, +6.50000E+1, +7.00000E+1, +7.50000E+1, +8.00000E+1, +8.50000E+1, +9.00000E+1, +9.50000E+1, +1.00000E+2, +1.05000E+2, +1.10000E+2, +1.15000E+2, +1.20000E+2, +1.25000E+2, +1.30000E+2, +1.35000E+2, +1.40000E+2, +1.45000E+2, +1.50000E+2, +1.55000E+2, +1.60000E+2, +1.65000E+2, +1.70000E+2, +1.75000E+2, +1.80000E+2, +1.85000E+2, +1.90000E+2, +1.95000E+2, +2.00000E+2, +2.05000E+2, +2.10000E+2, +2.15000E+2, +2.20000E+2, +2.25000E+2, +2.30000E+2, +2.35000E+2, +2.40000E+2, +2.45000E+2, +2.50000E+2, +2.55000E+2, +2.60000E+2, +2.65000E+2, +2.70000E+2, +2.75000E+2, +2.80000E+2, +2.85000E+2, +2.90000E+2, +2.95000E+2, +3.00000E+2, +3.05000E+2, +3.10000E+2, +3.15000E+2, +3.20000E+2, +3.25000E+2, +3.30000E+2, +3.35000E+2, +3.40000E+2, +3.45000E+2, +3.50000E+2, +3.55000E+2, +3.60000E+2, +3.65000E+2, +3.70000E+2, +3.75000E+2, +3.80000E+2])+273.15 # Kelvin
|
||||
self.density.data = np.array([+1.11000E+3, +1.10000E+3, +1.10000E+3, +1.09000E+3, +1.09000E+3, +1.08000E+3, +1.08000E+3, +1.07000E+3, +1.07000E+3, +1.07000E+3, +1.06000E+3, +1.06000E+3, +1.05000E+3, +1.05000E+3, +1.04000E+3, +1.04000E+3, +1.03000E+3, +1.03000E+3, +1.02000E+3, +1.02000E+3, +1.02000E+3, +1.01000E+3, +1.01000E+3, +1.00000E+3, +9.97000E+2, +9.93000E+2, +9.88000E+2, +9.84000E+2, +9.79000E+2, +9.74000E+2, +9.70000E+2, +9.65000E+2, +9.61000E+2, +9.56000E+2, +9.52000E+2, +9.47000E+2, +9.43000E+2, +9.38000E+2, +9.34000E+2, +9.29000E+2, +9.25000E+2, +9.20000E+2, +9.16000E+2, +9.11000E+2, +9.06000E+2, +9.02000E+2, +8.98000E+2, +8.93000E+2, +8.89000E+2, +8.84000E+2, +8.80000E+2, +8.75000E+2, +8.71000E+2, +8.66000E+2, +8.62000E+2, +8.57000E+2, +8.53000E+2, +8.48000E+2, +8.44000E+2, +8.39000E+2, +8.34000E+2, +8.30000E+2, +8.25000E+2, +8.21000E+2, +8.16000E+2, +8.12000E+2, +8.07000E+2, +8.03000E+2, +7.98000E+2, +7.94000E+2, +7.89000E+2, +7.85000E+2, +7.80000E+2, +7.76000E+2, +7.71000E+2, +7.66000E+2, +7.62000E+2, +7.57000E+2, +7.53000E+2]) # kg/m3
|
||||
self.specific_heat.data = np.array([+1.47100E+0, +1.48400E+0, +1.49800E+0, +1.51200E+0, +1.52500E+0, +1.53900E+0, +1.55200E+0, +1.56600E+0, +1.57900E+0, +1.59300E+0, +1.60600E+0, +1.62000E+0, +1.63400E+0, +1.64700E+0, +1.66100E+0, +1.67400E+0, +1.68800E+0, +1.70100E+0, +1.71500E+0, +1.72800E+0, +1.74200E+0, +1.75500E+0, +1.76900E+0, +1.78300E+0, +1.79600E+0, +1.81000E+0, +1.82300E+0, +1.83700E+0, +1.85000E+0, +1.86400E+0, +1.87700E+0, +1.89100E+0, +1.90500E+0, +1.91800E+0, +1.93200E+0, +1.94500E+0, +1.95900E+0, +1.97200E+0, +1.98600E+0, +1.99900E+0, +2.01300E+0, +2.02600E+0, +2.04000E+0, +2.05400E+0, +2.06700E+0, +2.08100E+0, +2.09400E+0, +2.10800E+0, +2.12100E+0, +2.13500E+0, +2.14800E+0, +2.16200E+0, +2.17600E+0, +2.18900E+0, +2.20300E+0, +2.21600E+0, +2.23000E+0, +2.24300E+0, +2.25700E+0, +2.27000E+0, +2.28400E+0, +2.29700E+0, +2.31100E+0, +2.32500E+0, +2.33800E+0, +2.35200E+0, +2.36500E+0, +2.37900E+0, +2.39200E+0, +2.40600E+0, +2.41900E+0, +2.43300E+0, +2.44600E+0, +2.46000E+0, +2.47400E+0, +2.48700E+0, +2.50100E+0, +2.51400E+0, +2.52800E+0])*1000. # J/kg-K
|
||||
@@ -92,6 +112,11 @@ class DowthermJ(PureData):
|
||||
"""
|
||||
def __init__(self):
|
||||
PureData.__init__(self)
|
||||
self.density.source = self.density.SOURCE_DATA
|
||||
self.specific_heat.source = self.specific_heat.SOURCE_DATA
|
||||
self.conductivity.source = self.conductivity.SOURCE_DATA
|
||||
self.viscosity.source = self.viscosity.SOURCE_DATA
|
||||
self.saturation_pressure.source = self.saturation_pressure.SOURCE_DATA
|
||||
self.temperature.data = np.array([-8.00000E+1, -7.00000E+1, -6.00000E+1, -5.00000E+1, -4.00000E+1, -3.00000E+1, -2.00000E+1, -1.00000E+1, +0.00000E+0, +1.00000E+1, +2.00000E+1, +3.00000E+1, +4.00000E+1, +5.00000E+1, +6.00000E+1, +7.00000E+1, +8.00000E+1, +9.00000E+1, +1.00000E+2, +1.10000E+2, +1.20000E+2, +1.30000E+2, +1.40000E+2, +1.50000E+2, +1.60000E+2, +1.70000E+2, +1.80000E+2, +1.81300E+2, +1.90000E+2, +2.00000E+2, +2.10000E+2, +2.20000E+2, +2.30000E+2, +2.40000E+2, +2.50000E+2, +2.60000E+2, +2.70000E+2, +2.80000E+2, +2.90000E+2, +3.00000E+2, +3.10000E+2, +3.20000E+2, +3.30000E+2, +3.40000E+2, +3.45000E+2])+273.15 # Kelvin
|
||||
self.density.data = np.array([+9.31300E+2, +9.27900E+2, +9.21000E+2, +9.14100E+2, +9.07100E+2, +9.00000E+2, +8.92900E+2, +8.85700E+2, +8.78500E+2, +8.71100E+2, +8.63700E+2, +8.56200E+2, +8.48700E+2, +8.41000E+2, +8.33200E+2, +8.25400E+2, +8.17400E+2, +8.09400E+2, +8.01200E+2, +7.92900E+2, +7.84400E+2, +7.75900E+2, +7.67100E+2, +7.58300E+2, +7.49200E+2, +7.40000E+2, +7.30600E+2, +7.29300E+2, +7.20900E+2, +7.11000E+2, +7.00900E+2, +6.90500E+2, +6.79800E+2, +6.68800E+2, +6.57300E+2, +6.45500E+2, +6.33100E+2, +6.20200E+2, +6.06600E+2, +5.92200E+2, +5.76900E+2, +5.60400E+2, +5.42400E+2, +5.22400E+2, +5.11400E+2]) # kg/m3
|
||||
self.specific_heat.data = np.array([+1.58400E+0, +1.59400E+0, +1.61600E+0, +1.63900E+0, +1.66300E+0, +1.68800E+0, +1.71400E+0, +1.74100E+0, +1.76900E+0, +1.79800E+0, +1.82800E+0, +1.85900E+0, +1.89000E+0, +1.92300E+0, +1.95500E+0, +1.98900E+0, +2.02300E+0, +2.05800E+0, +2.09300E+0, +2.12900E+0, +2.16500E+0, +2.20200E+0, +2.23900E+0, +2.27700E+0, +2.31500E+0, +2.35300E+0, +2.39200E+0, +2.39700E+0, +2.43200E+0, +2.47200E+0, +2.51200E+0, +2.55300E+0, +2.59400E+0, +2.63600E+0, +2.68000E+0, +2.72400E+0, +2.76900E+0, +2.81600E+0, +2.86600E+0, +2.91900E+0, +2.97600E+0, +3.04000E+0, +3.11500E+0, +3.20800E+0, +3.26500E+0])*1000. # J/kg-K
|
||||
@@ -111,7 +136,12 @@ class DowthermQ(PureData):
|
||||
Heat transfer fluid Dowtherm Q by Dow Chemicals
|
||||
"""
|
||||
def __init__(self):
|
||||
PureData.__init__(self)
|
||||
PureData.__init__(self)
|
||||
self.density.source = self.density.SOURCE_DATA
|
||||
self.specific_heat.source = self.specific_heat.SOURCE_DATA
|
||||
self.conductivity.source = self.conductivity.SOURCE_DATA
|
||||
self.viscosity.source = self.viscosity.SOURCE_DATA
|
||||
self.saturation_pressure.source = self.saturation_pressure.SOURCE_DATA
|
||||
self.temperature.data = np.array([-3.50000E+1, -3.00000E+1, -2.00000E+1, -1.00000E+1, +0.00000E+0, +1.00000E+1, +2.00000E+1, +3.00000E+1, +4.00000E+1, +5.00000E+1, +6.00000E+1, +7.00000E+1, +8.00000E+1, +9.00000E+1, +1.00000E+2, +1.10000E+2, +1.20000E+2, +1.30000E+2, +1.40000E+2, +1.50000E+2, +1.60000E+2, +1.70000E+2, +1.80000E+2, +1.90000E+2, +2.00000E+2, +2.10000E+2, +2.20000E+2, +2.30000E+2, +2.40000E+2, +2.50000E+2, +2.60000E+2, +2.70000E+2, +2.80000E+2, +2.90000E+2, +3.00000E+2, +3.10000E+2, +3.20000E+2, +3.30000E+2, +3.40000E+2, +3.50000E+2, +3.60000E+2])+273.15 # Kelvin
|
||||
self.density.data = np.array([+1.01140E+3, +1.00320E+3, +9.95600E+2, +9.88000E+2, +9.80500E+2, +9.72900E+2, +9.65400E+2, +9.57800E+2, +9.50200E+2, +9.42700E+2, +9.35100E+2, +9.27600E+2, +9.20000E+2, +9.12400E+2, +9.04900E+2, +8.97300E+2, +8.89800E+2, +8.82200E+2, +8.74600E+2, +8.67100E+2, +8.59500E+2, +8.52000E+2, +8.44400E+2, +8.36800E+2, +8.29300E+2, +8.21700E+2, +8.14200E+2, +8.06600E+2, +7.99000E+2, +7.91500E+2, +7.83900E+2, +7.76400E+2, +7.68800E+2, +7.61200E+2, +7.53700E+2, +7.46100E+2, +7.38600E+2, +7.31000E+2, +7.23400E+2, +7.15900E+2, +7.08300E+2]) # kg/m3
|
||||
self.specific_heat.data = np.array([+1.47800E+0, +1.49200E+0, +1.52500E+0, +1.55700E+0, +1.58900E+0, +1.62100E+0, +1.65300E+0, +1.68500E+0, +1.71600E+0, +1.74800E+0, +1.77900E+0, +1.81100E+0, +1.84200E+0, +1.87300E+0, +1.90400E+0, +1.93500E+0, +1.96600E+0, +1.99700E+0, +2.02700E+0, +2.05800E+0, +2.08800E+0, +2.11800E+0, +2.14800E+0, +2.17800E+0, +2.20800E+0, +2.23800E+0, +2.26800E+0, +2.29700E+0, +2.32700E+0, +2.35600E+0, +2.38600E+0, +2.41500E+0, +2.44400E+0, +2.47300E+0, +2.50200E+0, +2.53000E+0, +2.55900E+0, +2.58700E+0, +2.61600E+0, +2.64400E+0, +2.67200E+0])*1000. # J/kg-K
|
||||
@@ -133,6 +163,11 @@ class Texatherm22(PureData):
|
||||
"""
|
||||
def __init__(self):
|
||||
PureData.__init__(self)
|
||||
self.density.source = self.density.SOURCE_DATA
|
||||
self.specific_heat.source = self.specific_heat.SOURCE_DATA
|
||||
self.conductivity.source = self.conductivity.SOURCE_DATA
|
||||
self.viscosity.source = self.viscosity.SOURCE_DATA
|
||||
self.saturation_pressure.source = self.saturation_pressure.SOURCE_DATA
|
||||
self.temperature.data = np.array([+0.00000E+0, +4.00000E+1, +5.00000E+1, +1.00000E+2, +1.50000E+2, +2.00000E+2, +2.50000E+2, +3.00000E+2, +3.50000E+2])+273.15 # Kelvin
|
||||
self.density.data = np.array([+8.74500E+2, +8.47300E+2, +8.42500E+2, +8.10500E+2, +7.76300E+2, +7.41600E+2, +7.03200E+2, +6.68000E+2, +6.21500E+2]) # kg/m3
|
||||
self.specific_heat.data = np.array([+1.81000E+0, +1.95000E+0, +1.99000E+0, +2.18000E+0, +2.36000E+0, +2.54000E+0, +2.72000E+0, +2.90000E+0, +3.08000E+0])*1e3 # J/kg-K
|
||||
@@ -153,7 +188,11 @@ class SylthermXLT(PureData):
|
||||
Heat transfer fluid Syltherm XLT by Dow Chemicals
|
||||
"""
|
||||
def __init__(self):
|
||||
PureData.__init__(self)
|
||||
PureData.__init__(self)
|
||||
self.density.source = self.density.SOURCE_DATA
|
||||
self.specific_heat.source = self.specific_heat.SOURCE_DATA
|
||||
self.conductivity.source = self.conductivity.SOURCE_DATA
|
||||
self.viscosity.source = self.viscosity.SOURCE_DATA
|
||||
self.temperature.data = np.array([-1.00000E+2, -9.50000E+1, -9.00000E+1, -8.50000E+1, -8.00000E+1, -7.50000E+1, -7.00000E+1, -6.50000E+1, -6.00000E+1, -5.50000E+1, -5.00000E+1, -4.50000E+1, -4.00000E+1, -3.50000E+1, -3.00000E+1, -2.50000E+1, -2.00000E+1, -1.50000E+1, -1.00000E+1, -5.00000E+0, +0.00000E+0, +5.00000E+0, +1.00000E+1, +1.50000E+1, +2.00000E+1, +2.50000E+1, +3.00000E+1, +3.50000E+1, +4.00000E+1, +4.50000E+1, +5.00000E+1, +5.50000E+1, +6.00000E+1, +6.50000E+1, +7.00000E+1, +7.50000E+1, +8.00000E+1, +8.50000E+1, +9.00000E+1, +9.50000E+1, +1.00000E+2, +1.05000E+2, +1.10000E+2, +1.15000E+2, +1.20000E+2, +1.25000E+2, +1.30000E+2, +1.35000E+2, +1.40000E+2, +1.45000E+2, +1.50000E+2, +1.55000E+2, +1.60000E+2, +1.65000E+2, +1.70000E+2, +1.75000E+2, +1.80000E+2, +1.85000E+2, +1.90000E+2, +1.95000E+2, +2.00000E+2, +2.05000E+2, +2.10000E+2, +2.15000E+2, +2.20000E+2, +2.25000E+2, +2.30000E+2, +2.35000E+2, +2.40000E+2, +2.45000E+2, +2.50000E+2, +2.55000E+2, +2.60000E+2])+273.15 # Kelvin
|
||||
self.density.data = np.array([+9.78500E+2, +9.73400E+2, +9.68300E+2, +9.63100E+2, +9.58000E+2, +9.52900E+2, +9.47700E+2, +9.42600E+2, +9.37500E+2, +9.32300E+2, +9.27200E+2, +9.22000E+2, +9.16900E+2, +9.11800E+2, +9.06600E+2, +9.01500E+2, +8.96400E+2, +8.91200E+2, +8.86100E+2, +8.81000E+2, +8.75800E+2, +8.70700E+2, +8.65500E+2, +8.60400E+2, +8.55300E+2, +8.50100E+2, +8.45000E+2, +8.39900E+2, +8.34700E+2, +8.29600E+2, +8.24500E+2, +8.19300E+2, +8.14200E+2, +8.09100E+2, +8.03900E+2, +7.98800E+2, +7.93600E+2, +7.88500E+2, +7.83400E+2, +7.78200E+2, +7.73100E+2, +7.68000E+2, +7.62800E+2, +7.57700E+2, +7.52600E+2, +7.47400E+2, +7.42300E+2, +7.37200E+2, +7.32000E+2, +7.26900E+2, +7.21700E+2, +7.16600E+2, +7.11500E+2, +7.06300E+2, +7.01200E+2, +6.96100E+2, +6.90900E+2, +6.85800E+2, +6.80700E+2, +6.75500E+2, +6.70400E+2, +6.65300E+2, +6.60100E+2, +6.55000E+2, +6.49800E+2, +6.44700E+2, +6.39600E+2, +6.34400E+2, +6.29300E+2, +6.24200E+2, +6.19000E+2, +6.13900E+2, +6.08800E+2]) # kg/m3
|
||||
self.specific_heat.data = np.array([+1.52000E+0, +1.53000E+0, +1.54100E+0, +1.55100E+0, +1.56200E+0, +1.57200E+0, +1.58300E+0, +1.59300E+0, +1.60400E+0, +1.61400E+0, +1.62500E+0, +1.63500E+0, +1.64600E+0, +1.65600E+0, +1.66700E+0, +1.67700E+0, +1.68800E+0, +1.69800E+0, +1.70900E+0, +1.71900E+0, +1.73000E+0, +1.74000E+0, +1.75100E+0, +1.76100E+0, +1.77200E+0, +1.78200E+0, +1.79300E+0, +1.80300E+0, +1.81400E+0, +1.82400E+0, +1.83500E+0, +1.84500E+0, +1.85600E+0, +1.86600E+0, +1.87700E+0, +1.88700E+0, +1.89800E+0, +1.90800E+0, +1.91900E+0, +1.92900E+0, +1.94000E+0, +1.95000E+0, +1.96100E+0, +1.97100E+0, +1.98200E+0, +1.99200E+0, +2.00300E+0, +2.01300E+0, +2.02400E+0, +2.03400E+0, +2.04500E+0, +2.05500E+0, +2.06600E+0, +2.07600E+0, +2.08700E+0, +2.09700E+0, +2.10800E+0, +2.11800E+0, +2.12900E+0, +2.13900E+0, +2.15000E+0, +2.16000E+0, +2.17100E+0, +2.18100E+0, +2.19200E+0, +2.20200E+0, +2.21300E+0, +2.22300E+0, +2.23400E+0, +2.24400E+0, +2.25500E+0, +2.26500E+0, +2.27600E+0])*1e3 # J/kg-K
|
||||
@@ -174,6 +213,11 @@ class HC50(PureData):
|
||||
"""
|
||||
def __init__(self):
|
||||
PureData.__init__(self)
|
||||
self.density.source = self.density.SOURCE_DATA
|
||||
self.specific_heat.source = self.specific_heat.SOURCE_DATA
|
||||
self.conductivity.source = self.conductivity.SOURCE_DATA
|
||||
self.viscosity.source = self.viscosity.SOURCE_DATA
|
||||
self.saturation_pressure.source = self.saturation_pressure.SOURCE_DATA
|
||||
self.temperature.data = np.array([+2.23150E+2,+2.33150E+2,+2.43150E+2,+2.53150E+2,+2.63150E+2,+2.73150E+2,+2.83150E+2,+2.93150E+2,+3.03150E+2,+3.13150E+2,+3.23150E+2,+3.33150E+2,+3.43150E+2,+3.53150E+2,+3.63150E+2,+3.73150E+2,+3.83150E+2,+3.93150E+2,+4.03150E+2,+4.13150E+2,+4.23150E+2,+4.33150E+2,+4.43150E+2,+4.53150E+2,+4.63150E+2,+4.73150E+2,+4.83150E+2]) # Kelvin
|
||||
self.density.data = np.array([+1.37800E+3,+1.37300E+3,+1.36700E+3,+1.36200E+3,+1.35600E+3,+1.35100E+3,+1.34500E+3,+1.34000E+3,+1.33400E+3,+1.32800E+3,+1.32300E+3,+1.31700E+3,+1.31200E+3,+1.30600E+3,+1.30100E+3,+1.29500E+3,+1.29000E+3,+1.28400E+3,+1.27900E+3,+1.27300E+3,+1.26700E+3,+1.26200E+3,+1.25600E+3,+1.25100E+3,+1.24500E+3,+1.24000E+3,+1.23400E+3]) # kg/m3
|
||||
self.specific_heat.data = np.array([+2.56300E+3,+2.58300E+3,+2.60200E+3,+2.62200E+3,+2.64200E+3,+2.66100E+3,+2.68100E+3,+2.70100E+3,+2.72000E+3,+2.74000E+3,+2.76000E+3,+2.78000E+3,+2.79900E+3,+2.81900E+3,+2.83900E+3,+2.85800E+3,+2.87800E+3,+2.89800E+3,+2.91700E+3,+2.93700E+3,+2.95700E+3,+2.97700E+3,+2.99600E+3,+3.01600E+3,+3.03600E+3,+3.05500E+3,+3.07500E+3]) # J/kg-K
|
||||
@@ -195,6 +239,11 @@ class HC40(PureData):
|
||||
"""
|
||||
def __init__(self):
|
||||
PureData.__init__(self)
|
||||
self.density.source = self.density.SOURCE_DATA
|
||||
self.specific_heat.source = self.specific_heat.SOURCE_DATA
|
||||
self.conductivity.source = self.conductivity.SOURCE_DATA
|
||||
self.viscosity.source = self.viscosity.SOURCE_DATA
|
||||
self.saturation_pressure.source = self.saturation_pressure.SOURCE_DATA
|
||||
self.temperature.data = np.array([+2.33150E+2,+2.43150E+2,+2.53150E+2,+2.63150E+2,+2.73150E+2,+2.83150E+2,+2.93150E+2,+3.03150E+2,+3.13150E+2,+3.23150E+2,+3.33150E+2,+3.43150E+2,+3.53150E+2,+3.63150E+2,+3.73150E+2,+3.83150E+2,+3.93150E+2,+4.03150E+2,+4.13150E+2,+4.23150E+2,+4.33150E+2,+4.38150E+2,+4.43150E+2,+4.53150E+2,+4.63150E+2,+4.73150E+2]) # Kelvin
|
||||
self.density.data = np.array([+1.34800E+3,+1.34300E+3,+1.33700E+3,+1.33200E+3,+1.32600E+3,+1.32100E+3,+1.31500E+3,+1.30900E+3,+1.30400E+3,+1.29800E+3,+1.29300E+3,+1.28700E+3,+1.28100E+3,+1.27600E+3,+1.27000E+3,+1.26500E+3,+1.25900E+3,+1.25300E+3,+1.24800E+3,+1.24200E+3,+1.23700E+3,+1.23400E+3,+1.23100E+3,+1.22500E+3,+1.22000E+3,+1.21400E+3]) # kg/m3
|
||||
self.specific_heat.data = np.array([+2.80000E+3,+2.82000E+3,+2.84000E+3,+2.87000E+3,+2.89000E+3,+2.91000E+3,+2.93000E+3,+2.96000E+3,+2.98000E+3,+3.00000E+3,+3.03000E+3,+3.05000E+3,+3.07000E+3,+3.09000E+3,+3.12000E+3,+3.14000E+3,+3.16000E+3,+3.19000E+3,+3.21000E+3,+3.23000E+3,+3.25000E+3,+3.27000E+3,+3.28000E+3,+3.30000E+3,+3.32000E+3,+3.35000E+3]) # J/kg-K
|
||||
@@ -216,6 +265,11 @@ class HC30(PureData):
|
||||
"""
|
||||
def __init__(self):
|
||||
PureData.__init__(self)
|
||||
self.density.source = self.density.SOURCE_DATA
|
||||
self.specific_heat.source = self.specific_heat.SOURCE_DATA
|
||||
self.conductivity.source = self.conductivity.SOURCE_DATA
|
||||
self.viscosity.source = self.viscosity.SOURCE_DATA
|
||||
self.saturation_pressure.source = self.saturation_pressure.SOURCE_DATA
|
||||
self.temperature.data = np.array([+2.43150E+2,+2.53150E+2,+2.63150E+2,+2.73150E+2,+2.83150E+2,+2.93150E+2,+3.03150E+2,+3.13150E+2,+3.23150E+2,+3.33150E+2,+3.43150E+2,+3.53150E+2,+3.63150E+2,+3.73150E+2,+3.83150E+2,+3.93150E+2,+4.03150E+2,+4.13150E+2,+4.23150E+2,+4.33150E+2,+4.43150E+2,+4.53150E+2,+4.63150E+2,+4.73150E+2,+4.83150E+2]) # Kelvin
|
||||
self.density.data = np.array([+1.30000E+3,+1.29500E+3,+1.29000E+3,+1.28500E+3,+1.28000E+3,+1.27500E+3,+1.27000E+3,+1.26500E+3,+1.26000E+3,+1.25500E+3,+1.25000E+3,+1.24400E+3,+1.23900E+3,+1.23400E+3,+1.22900E+3,+1.22400E+3,+1.21900E+3,+1.21400E+3,+1.20900E+3,+1.20400E+3,+1.19900E+3,+1.19300E+3,+1.18800E+3,+1.18300E+3,+1.17800E+3]) # kg/m3
|
||||
self.specific_heat.data = np.array([+2.96100E+3,+2.98400E+3,+3.00700E+3,+3.03100E+3,+3.05400E+3,+3.07700E+3,+3.10000E+3,+3.12300E+3,+3.14600E+3,+3.16900E+3,+3.19200E+3,+3.21500E+3,+3.23800E+3,+3.26200E+3,+3.28500E+3,+3.30800E+3,+3.33100E+3,+3.35400E+3,+3.37700E+3,+3.40000E+3,+3.42300E+3,+3.44600E+3,+3.46900E+3,+3.49300E+3,+3.51600E+3]) # J/kg-K
|
||||
@@ -237,6 +291,11 @@ class HC20(PureData):
|
||||
"""
|
||||
def __init__(self):
|
||||
PureData.__init__(self)
|
||||
self.density.source = self.density.SOURCE_DATA
|
||||
self.specific_heat.source = self.specific_heat.SOURCE_DATA
|
||||
self.conductivity.source = self.conductivity.SOURCE_DATA
|
||||
self.viscosity.source = self.viscosity.SOURCE_DATA
|
||||
self.saturation_pressure.source = self.saturation_pressure.SOURCE_DATA
|
||||
self.temperature.data = np.array([+2.53150E+2,+2.63150E+2,+2.73150E+2,+2.83150E+2,+2.93150E+2,+3.03150E+2,+3.13150E+2,+3.23150E+2,+3.33150E+2,+3.43150E+2,+3.53150E+2,+3.63150E+2,+3.73150E+2,+3.83150E+2,+3.93150E+2,+4.03150E+2,+4.13150E+2,+4.23150E+2,+4.33150E+2,+4.43150E+2,+4.53150E+2,+4.63150E+2,+4.73150E+2,+4.83150E+2]) # Kelvin
|
||||
self.density.data = np.array([+1.25800E+3,+1.25300E+3,+1.24800E+3,+1.24200E+3,+1.23700E+3,+1.23200E+3,+1.22700E+3,+1.22200E+3,+1.21600E+3,+1.21100E+3,+1.20600E+3,+1.20100E+3,+1.19600E+3,+1.19100E+3,+1.18500E+3,+1.18000E+3,+1.17500E+3,+1.17000E+3,+1.16500E+3,+1.15900E+3,+1.15400E+3,+1.14900E+3,+1.14400E+3,+1.13900E+3]) # kg/m3
|
||||
self.specific_heat.data = np.array([+3.11700E+3,+3.14100E+3,+3.16400E+3,+3.18800E+3,+3.21200E+3,+3.23500E+3,+3.25900E+3,+3.28200E+3,+3.30600E+3,+3.33000E+3,+3.35300E+3,+3.37700E+3,+3.40000E+3,+3.42400E+3,+3.44800E+3,+3.47100E+3,+3.49500E+3,+3.51800E+3,+3.54200E+3,+3.56600E+3,+3.58900E+3,+3.61300E+3,+3.63600E+3,+3.66000E+3]) # J/kg-K
|
||||
@@ -258,6 +317,11 @@ class HC10(PureData):
|
||||
"""
|
||||
def __init__(self):
|
||||
PureData.__init__(self)
|
||||
self.density.source = self.density.SOURCE_DATA
|
||||
self.specific_heat.source = self.specific_heat.SOURCE_DATA
|
||||
self.conductivity.source = self.conductivity.SOURCE_DATA
|
||||
self.viscosity.source = self.viscosity.SOURCE_DATA
|
||||
self.saturation_pressure.source = self.saturation_pressure.SOURCE_DATA
|
||||
self.temperature.data = np.array([+2.63150E+2,+2.73150E+2,+2.83150E+2,+2.93150E+2,+3.03150E+2,+3.13150E+2,+3.23150E+2,+3.33150E+2,+3.43150E+2,+3.53150E+2,+3.63150E+2,+3.73150E+2,+3.83150E+2,+3.93150E+2,+4.03150E+2,+4.13150E+2,+4.23150E+2,+4.33150E+2,+4.43150E+2,+4.53150E+2,+4.63150E+2,+4.73150E+2,+4.83150E+2,+4.91150E+2]) # Kelvin
|
||||
self.density.data = np.array([+1.20400E+3,+1.19900E+3,+1.19500E+3,+1.19000E+3,+1.18600E+3,+1.18100E+3,+1.17700E+3,+1.17200E+3,+1.16700E+3,+1.16300E+3,+1.15800E+3,+1.15400E+3,+1.14900E+3,+1.14500E+3,+1.14000E+3,+1.13600E+3,+1.13100E+3,+1.12700E+3,+1.12200E+3,+1.11800E+3,+1.11300E+3,+1.10900E+3,+1.10400E+3,+1.10100E+3]) # kg/m3
|
||||
self.specific_heat.data = np.array([+3.24600E+3,+3.27100E+3,+3.29600E+3,+3.32000E+3,+3.34500E+3,+3.37000E+3,+3.39500E+3,+3.42000E+3,+3.44400E+3,+3.46900E+3,+3.49400E+3,+3.51900E+3,+3.54400E+3,+3.56800E+3,+3.59300E+3,+3.61800E+3,+3.64300E+3,+3.66800E+3,+3.69200E+3,+3.71700E+3,+3.74200E+3,+3.76700E+3,+3.79200E+3,+3.81100E+3]) # J/kg-K
|
||||
@@ -272,3 +336,4 @@ class HC10(PureData):
|
||||
self.reference = "Dynalene data sheet"
|
||||
self.reshapeAll()
|
||||
|
||||
|
||||
File diff suppressed because it is too large
Load Diff
@@ -1,7 +1,14 @@
|
||||
from __future__ import division, print_function
|
||||
import numpy as np
|
||||
|
||||
import hashlib, os, json
|
||||
import hashlib, os, json, sys
|
||||
import matplotlib.pyplot as plt
|
||||
import matplotlib.gridspec as gridspec
|
||||
from CPIncomp.DataObjects import SolutionData
|
||||
from CPIncomp.BaseObjects import IncompressibleData, IncompressibleFitter
|
||||
from matplotlib.patches import Rectangle
|
||||
from matplotlib.ticker import MaxNLocator
|
||||
from matplotlib.backends.backend_pdf import PdfPages
|
||||
|
||||
class SolutionDataWriter(object):
|
||||
"""
|
||||
@@ -13,15 +20,18 @@ class SolutionDataWriter(object):
|
||||
def __init__(self):
|
||||
pass
|
||||
|
||||
def fitAll(self, fluidObject):
|
||||
def fitAll(self, fluidObject=SolutionData()):
|
||||
|
||||
tempData = fluidObject.temperature.data
|
||||
concData = fluidObject.concentration.data
|
||||
|
||||
if fluidObject.Tbase==0.0 or fluidObject.Tbase==None:
|
||||
fluidObject.Tbase = (np.min(tempData) + np.max(tempData)) / 2.0
|
||||
if fluidObject.xbase==0.0 or fluidObject.xbase==None:
|
||||
fluidObject.xbase = (np.min(concData) + np.max(concData)) / 2.0
|
||||
if fluidObject.Tbase==None:
|
||||
fluidObject.Tbase = (fluidObject.Tmin + fluidObject.Tmax) / 2.0
|
||||
|
||||
if fluidObject.xbase==None:
|
||||
fluidObject.xbase = (fluidObject.xmin + fluidObject.xmax) / 2.0
|
||||
|
||||
tData = fluidObject.temperature.data
|
||||
xData = fluidObject.concentration.data
|
||||
tBase = fluidObject.Tbase
|
||||
xBase = fluidObject.xbase
|
||||
|
||||
# Set the standard order for polynomials
|
||||
std_xorder = 3+1
|
||||
@@ -31,43 +41,79 @@ class SolutionDataWriter(object):
|
||||
errList = (ValueError, AttributeError, TypeError, RuntimeError)
|
||||
|
||||
try:
|
||||
fluidObject.density.setxyData(tData,xData)
|
||||
fluidObject.density.coeffs = np.copy(std_coeffs)
|
||||
fluidObject.density.type = fluidObject.density.INCOMPRESSIBLE_POLYNOMIAL
|
||||
fluidObject.density.fitCoeffs(tempData,concData,fluidObject.Tbase,fluidObject.xbase)
|
||||
fluidObject.density.type = IncompressibleData.INCOMPRESSIBLE_POLYNOMIAL
|
||||
fluidObject.density.fitCoeffs(tBase,xBase)
|
||||
except errList as ve:
|
||||
if fluidObject.density.DEBUG: print("{0}: Could not fit polynomial {1} coefficients: {2}".format(fluidObject.name,'density',ve))
|
||||
pass
|
||||
|
||||
try:
|
||||
fluidObject.specific_heat.setxyData(tData,xData)
|
||||
fluidObject.specific_heat.coeffs = np.copy(std_coeffs)
|
||||
fluidObject.specific_heat.type = fluidObject.specific_heat.INCOMPRESSIBLE_POLYNOMIAL
|
||||
fluidObject.specific_heat.fitCoeffs(tempData,concData,fluidObject.Tbase,fluidObject.xbase)
|
||||
fluidObject.specific_heat.type = IncompressibleData.INCOMPRESSIBLE_POLYNOMIAL
|
||||
fluidObject.specific_heat.fitCoeffs(tBase,xBase)
|
||||
except errList as ve:
|
||||
if fluidObject.specific_heat.DEBUG: print("{0}: Could not fit polynomial {1} coefficients: {2}".format(fluidObject.name,'specific heat',ve))
|
||||
pass
|
||||
|
||||
try:
|
||||
fluidObject.conductivity.setxyData(tData,xData)
|
||||
fluidObject.conductivity.coeffs = np.copy(std_coeffs)
|
||||
fluidObject.conductivity.type = fluidObject.conductivity.INCOMPRESSIBLE_POLYNOMIAL
|
||||
fluidObject.conductivity.fitCoeffs(tempData,concData,fluidObject.Tbase,fluidObject.xbase)
|
||||
fluidObject.conductivity.type = IncompressibleData.INCOMPRESSIBLE_POLYNOMIAL
|
||||
fluidObject.conductivity.fitCoeffs(tBase,xBase)
|
||||
except errList as ve:
|
||||
if fluidObject.conductivity.DEBUG: print("{0}: Could not fit polynomial {1} coefficients: {2}".format(fluidObject.name,'conductivity',ve))
|
||||
pass
|
||||
|
||||
try:
|
||||
fluidObject.viscosity.coeffs = np.copy(std_coeffs)
|
||||
fluidObject.viscosity.type = fluidObject.viscosity.INCOMPRESSIBLE_EXPPOLYNOMIAL
|
||||
fluidObject.viscosity.fitCoeffs(tempData,concData,fluidObject.Tbase,fluidObject.xbase)
|
||||
fluidObject.viscosity.setxyData(tData,xData)
|
||||
tried = False
|
||||
if len(fluidObject.viscosity.yData)==1:# and np.isfinite(fluidObject.viscosity.data).sum()<10:
|
||||
fluidObject.viscosity.coeffs = np.array([+5e+2, -6e+1, +1e+1])
|
||||
fluidObject.viscosity.type = IncompressibleData.INCOMPRESSIBLE_EXPONENTIAL
|
||||
fluidObject.viscosity.fitCoeffs(tBase,xBase)
|
||||
if fluidObject.viscosity.coeffs==None or IncompressibleFitter.allClose(fluidObject.viscosity.coeffs, np.array([+5e+2, -6e+1, +1e+1])): # Fit failed
|
||||
tried = True
|
||||
if len(fluidObject.viscosity.yData)>1 or tried:
|
||||
#fluidObject.viscosity.coeffs = np.zeros(np.round(np.array(std_coeffs.shape) * 1.5))
|
||||
fluidObject.viscosity.coeffs = np.copy(std_coeffs)
|
||||
fluidObject.viscosity.type = IncompressibleData.INCOMPRESSIBLE_EXPPOLYNOMIAL
|
||||
fluidObject.viscosity.fitCoeffs(tBase,xBase)
|
||||
except errList as ve:
|
||||
if fluidObject.viscosity.DEBUG: print("{0}: Could not fit polynomial {1} coefficients: {2}".format(fluidObject.name,'viscosity',ve))
|
||||
pass
|
||||
|
||||
try:
|
||||
fluidObject.saturation_pressure.setxyData(tData,xData)
|
||||
tried = False
|
||||
if len(fluidObject.saturation_pressure.yData)==1:# and np.isfinite(fluidObject.saturation_pressure.data).sum()<10:
|
||||
fluidObject.saturation_pressure.coeffs = np.array([-5e+3, +6e+1, -1e+1])
|
||||
fluidObject.saturation_pressure.type = IncompressibleData.INCOMPRESSIBLE_EXPONENTIAL
|
||||
fluidObject.saturation_pressure.fitCoeffs(tBase,xBase)
|
||||
if fluidObject.saturation_pressure.coeffs==None or IncompressibleFitter.allClose(fluidObject.saturation_pressure.coeffs, np.array([-5e+3, +6e+1, -1e+1])): # Fit failed
|
||||
tried = True
|
||||
if len(fluidObject.saturation_pressure.yData)>1 or tried:
|
||||
#fluidObject.saturation_pressure.coeffs = np.zeros(np.round(np.array(std_coeffs.shape) * 1.5))
|
||||
fluidObject.saturation_pressure.coeffs = np.copy(std_coeffs)
|
||||
fluidObject.saturation_pressure.type = IncompressibleData.INCOMPRESSIBLE_EXPPOLYNOMIAL
|
||||
fluidObject.saturation_pressure.fitCoeffs(tBase,xBase)
|
||||
except errList as ve:
|
||||
if fluidObject.saturation_pressure.DEBUG: print("{0}: Could not fit polynomial {1} coefficients: {2}".format(fluidObject.name,'saturation pressure',ve))
|
||||
pass
|
||||
|
||||
# reset data for getArray and read special files
|
||||
if fluidObject.xid!=fluidObject.ifrac_pure and fluidObject.xid!=fluidObject.ifrac_undefined:
|
||||
try:
|
||||
fluidObject.T_freeze.coeffs = np.copy(std_coeffs)
|
||||
fluidObject.T_freeze.type = fluidObject.T_freeze.INCOMPRESSIBLE_POLYNOMIAL
|
||||
fluidObject.T_freeze.fitCoeffs(concData,0.0,fluidObject.xbase,0.0)
|
||||
fluidObject.T_freeze.setxyData([0.0],xData)
|
||||
try:
|
||||
if len(fluidObject.T_freeze.xData)==1:# and np.isfinite(fluidObject.T_freeze.data).sum()<10:
|
||||
fluidObject.T_freeze.coeffs = np.array([+7e+2, -6e+1, +1e+1])
|
||||
fluidObject.T_freeze.type = IncompressibleData.INCOMPRESSIBLE_EXPONENTIAL
|
||||
else:
|
||||
fluidObject.specific_heat.coeffs = np.copy(std_coeffs)
|
||||
fluidObject.T_freeze.type = IncompressibleData.INCOMPRESSIBLE_EXPPOLYNOMIAL
|
||||
fluidObject.T_freeze.fitCoeffs(tBase,xBase)
|
||||
except errList as ve:
|
||||
if fluidObject.T_freeze.DEBUG: print("{0}: Could not fit {1} coefficients: {2}".format(fluidObject.name,"T_freeze",ve))
|
||||
pass
|
||||
@@ -147,7 +193,7 @@ class SolutionDataWriter(object):
|
||||
|
||||
original_float_repr = json.encoder.FLOAT_REPR
|
||||
#print json.dumps(1.0001)
|
||||
stdFmt = " .{0}e".format(int(data.significantDigits-1))
|
||||
stdFmt = "1.{0}e".format(int(data.significantDigits-1))
|
||||
#pr = np.finfo(float).eps * 10.0
|
||||
#pr = np.finfo(float).precision - 2 # stay away from numerical precision
|
||||
#json.encoder.FLOAT_REPR = lambda o: format(np.around(o,decimals=pr), stdFmt)
|
||||
@@ -165,12 +211,11 @@ class SolutionDataWriter(object):
|
||||
hashes[name] != hash: # update hashes and write file
|
||||
|
||||
hashes[name] = hash
|
||||
self.write_hashes(hashes)
|
||||
|
||||
fp = open(name+'.json', 'w')
|
||||
self.write_hashes(hashes)
|
||||
path = os.path.join("json",name+'.json')
|
||||
fp = open(path, 'w')
|
||||
fp.write(dump)
|
||||
fp.close()
|
||||
|
||||
fp.close()
|
||||
if not quiet: print(" ({0})".format("w"), end="")
|
||||
else:
|
||||
if not quiet: print(" ({0})".format("i"), end="")
|
||||
@@ -184,7 +229,8 @@ class SolutionDataWriter(object):
|
||||
print(", {0}".format(obj.name), end="")
|
||||
else:
|
||||
print(", {0}".format(obj.name), end="")
|
||||
|
||||
|
||||
sys.stdout.flush()
|
||||
return
|
||||
|
||||
|
||||
@@ -213,7 +259,7 @@ class SolutionDataWriter(object):
|
||||
print("An error occurred for fluid: {0}".format(obj.name))
|
||||
print(obj)
|
||||
print(e)
|
||||
pass
|
||||
pass
|
||||
print(" ... done")
|
||||
return
|
||||
|
||||
@@ -232,6 +278,623 @@ class SolutionDataWriter(object):
|
||||
pass
|
||||
print(" ... done")
|
||||
return
|
||||
|
||||
def writeReportList(self, fluidObjs, pdfFile=None):
|
||||
print("Writing fitting reports:", end="")
|
||||
pdfObj = None
|
||||
if pdfFile!=None: pdfObj = PdfPages(pdfFile)
|
||||
for obj in fluidObjs:
|
||||
self.printStatusID(fluidObjs, obj)
|
||||
self.makeFitReportPage(obj,pdfObj=pdfObj)
|
||||
try:
|
||||
self.makeFitReportPage(obj)
|
||||
except (TypeError, ValueError) as e:
|
||||
print("An error occurred for fluid: {0}".format(obj.name))
|
||||
print(obj)
|
||||
print(e)
|
||||
pass
|
||||
if pdfFile!=None: pdfObj.close()
|
||||
print(" ... done")
|
||||
return
|
||||
|
||||
|
||||
#####################################
|
||||
# Plotting routines
|
||||
#####################################
|
||||
def relError(self, A=[], B=[], PCT=False):
|
||||
"""
|
||||
Returns the absolute relative Error from either
|
||||
(B-A)/B or (A-B)/A for abs(B)>abs(A) and abs(A)>abs(B),
|
||||
respectively. If PCT is True, it returns it in percent.
|
||||
"""
|
||||
A_a = np.array(A)
|
||||
B_a = np.array(B)
|
||||
|
||||
abl = np.absolute(B_a)
|
||||
eps = np.ones_like(abl) * np.finfo(float).eps
|
||||
#div = np.amax(np.hstack((abl,eps)), axis=1)*np.sign(B_a)
|
||||
|
||||
pos = np.isfinite(B_a)
|
||||
pos2 = (B_a>eps)
|
||||
result = np.ones_like(A_a)*np.NAN
|
||||
|
||||
result[pos & pos2] = (A_a[pos & pos2]-B_a[pos & pos2])/B_a[pos & pos2]
|
||||
|
||||
if PCT:
|
||||
return result * 100.
|
||||
else:
|
||||
return result
|
||||
|
||||
############################################################
|
||||
# Define the general purpose routines for plotting
|
||||
############################################################
|
||||
def wireFrame2D(self,xz,yz,linesX=5,linesY=None,color='black',ax=None,plot=False):
|
||||
"""
|
||||
xz is a 2D array that holds x-values for constant z1 and z2.
|
||||
yz is a 2D array that holds y-values for the same z1 and z2.
|
||||
xz and yz have to have the same size.
|
||||
The first dimension of xz should be greater than
|
||||
or equal to the lines input.
|
||||
"""
|
||||
if xz.ndim!=2:
|
||||
raise ValueError("xz has to be a 2D array.")
|
||||
if yz.ndim!=2:
|
||||
raise ValueError("yz has to be a 2D array.")
|
||||
if xz.shape!=yz.shape:
|
||||
raise ValueError("xz and yz have to have the same shape: {0} != {1}".format(xz.shape,yz.shape))
|
||||
if linesY==None and linesX!=None:
|
||||
linesY = linesX
|
||||
if linesX==None and linesY!=None:
|
||||
linesX = linesY
|
||||
if linesY==None and linesX==None:
|
||||
raise ValueError("You have to provide linesX or linesY")
|
||||
|
||||
xl,yl = xz.shape
|
||||
x_index = np.round(np.linspace(0, xl-1, linesX))
|
||||
y_index = np.round(np.linspace(0, yl-1, linesY))
|
||||
x_toPlot = []
|
||||
y_toPlot = []
|
||||
for i in x_index:
|
||||
x_toPlot += [xz.T[i]]
|
||||
y_toPlot += [yz.T[i]]
|
||||
for i in y_index:
|
||||
x_toPlot += [xz[i]]
|
||||
y_toPlot += [yz[i]]
|
||||
if plot==False:
|
||||
return x_toPlot,y_toPlot
|
||||
if ax==None:
|
||||
raise ValueError("You have to give an axis to plot.")
|
||||
for i in range(len(x_toPlot)):
|
||||
ax.plot(x_toPlot[i],y_toPlot[i],color=color)
|
||||
return x_toPlot,y_toPlot
|
||||
|
||||
|
||||
def plotValues(self,axVal,axErr,solObj=SolutionData(),dataObj=IncompressibleData(),func=None,old=None):
|
||||
"""
|
||||
Plots two data series using the same axis. You can
|
||||
choose if you prefer points or a line for the reference
|
||||
data. This can be used to show that we have experimental
|
||||
data or a reference equation.
|
||||
You can use the old input to call CoolProp with this ID.
|
||||
You can use this feature to visualise changes for new
|
||||
data fits. Primarily intended to highlight changes from
|
||||
v4 to v5 of CoolProp.
|
||||
"""
|
||||
|
||||
if dataObj.type==dataObj.INCOMPRESSIBLE_NOT_SET \
|
||||
or dataObj.source==dataObj.SOURCE_NOT_SET:
|
||||
return
|
||||
|
||||
# TODO: Improve this work-around
|
||||
xFunction = False
|
||||
try:
|
||||
if solObj.T_freeze.coeffs.shape==dataObj.coeffs.shape:
|
||||
if np.all(solObj.T_freeze.coeffs==dataObj.coeffs):
|
||||
xFunction = True
|
||||
except AttributeError as ae:
|
||||
if False: print(ae)
|
||||
pass
|
||||
|
||||
points = 30
|
||||
|
||||
dataFormatter = {}
|
||||
tData = None
|
||||
xData = None
|
||||
pData = None
|
||||
zData = None
|
||||
zError= None
|
||||
|
||||
if dataObj.source==dataObj.SOURCE_DATA or dataObj.source==dataObj.SOURCE_EQUATION:
|
||||
dataFormatter['color'] = 'blue'
|
||||
dataFormatter['marker'] = 'o'
|
||||
dataFormatter['ls'] = 'none'
|
||||
|
||||
|
||||
dataObj.setxyData(solObj.temperature.data,solObj.concentration.data)
|
||||
|
||||
tData = dataObj.xData
|
||||
xData = dataObj.yData
|
||||
pData = 1e7 # 100 bar
|
||||
zData = dataObj.data
|
||||
|
||||
if func!=None and zData!=None:
|
||||
r,c = zData.shape
|
||||
zError= np.zeros((r,c))
|
||||
for i in range(r):
|
||||
for j in range(c):
|
||||
zError[i,j]= func(tData[i],pData,xData[j])
|
||||
|
||||
zError = self.relError(zData, zError) * 1e2
|
||||
|
||||
|
||||
## Find the column with the largest single error
|
||||
#maxVal = np.amax(zError, axis=0) # largest error per column
|
||||
#col2plot = np.argmax(maxVal) # largest error in row
|
||||
## Find the column with the largest total error
|
||||
#totVal = np.sum(zError, axis=0) # summed error per column
|
||||
#col2plot = np.argmax(totVal) # largest error in row
|
||||
# Find the column with the largest average error
|
||||
if xFunction:
|
||||
#avgVal = np.average(zError, axis=1) # summed error per column
|
||||
#set2plot = np.argmax(avgVal) # largest error in row
|
||||
set2plot = int(np.round(r/2.0))
|
||||
tData = np.array([tData[set2plot]])
|
||||
zData = zData[set2plot]
|
||||
zError= zError[set2plot]
|
||||
else:
|
||||
#avgVal = np.average(zError, axis=0) # summed error per column
|
||||
#set2plot = np.argmax(avgVal) # largest error in row
|
||||
set2plot = int(np.round(c/2.0))
|
||||
xData = np.array([xData[set2plot]])
|
||||
zData = zData.T[set2plot]
|
||||
zError= zError.T[set2plot]
|
||||
else:
|
||||
raise ValueError("You have to provide data and a fitted function.")
|
||||
|
||||
elif dataObj.source==dataObj.SOURCE_COEFFS:
|
||||
dataFormatter['color'] = 'blue'
|
||||
#dataFormatter['marker'] = 'o'
|
||||
dataFormatter['ls'] = 'solid'
|
||||
|
||||
if xFunction:
|
||||
xData = np.linspace(solObj.xmin, solObj.xmax, num=points)
|
||||
if solObj.xid==solObj.ifrac_pure: tData = np.array([0.0])
|
||||
else: tData = np.array([solObj.Tmin+solObj.Tmax])/2.0
|
||||
else:
|
||||
tData = np.linspace(solObj.Tmin, solObj.Tmax, num=points)
|
||||
if solObj.xid==solObj.ifrac_pure: xData = np.array([0.0])
|
||||
else: xData = np.array([solObj.xmin+solObj.xmax])/2.0
|
||||
|
||||
pData = 1e7 # 100 bar
|
||||
|
||||
#zData= np.zeros((len(tData),len(xData)))
|
||||
#for i in range(len(tData)):
|
||||
# for j in range(len(xData)):
|
||||
# zData[i,j] = func(tData[i],pData,xData[j])
|
||||
#r,c = zData.shape
|
||||
#if r==1 or c==1:
|
||||
# zData = np.array(zData.flat)
|
||||
#else:
|
||||
# raise ValueError("Cannot plot non-flat arrays!")
|
||||
|
||||
|
||||
# Copy the arrays
|
||||
tFunc = tData
|
||||
xFunc = xData
|
||||
pFunc = pData
|
||||
zFunc = None
|
||||
zMiMa = None
|
||||
xFree = xData
|
||||
tFree = tData
|
||||
zFree = None
|
||||
|
||||
if func!=None:
|
||||
if len(tFunc)<points and len(tFunc)>1:
|
||||
tFunc = np.linspace(solObj.Tmin, solObj.Tmax, num=points)
|
||||
if len(xFunc)<points and len(xFunc)>1:
|
||||
xFunc = np.linspace(solObj.xmin, solObj.xmax, num=points)
|
||||
|
||||
zFunc = np.zeros((len(tFunc),len(xFunc)))
|
||||
for i in range(len(tFunc)):
|
||||
for j in range(len(xFunc)):
|
||||
zFunc[i,j] = func(tFunc[i],pFunc,xFunc[j])
|
||||
r,c = zFunc.shape
|
||||
if r==1 or c==1:
|
||||
zFunc = np.array(zFunc.flat)
|
||||
else:
|
||||
raise ValueError("Cannot plot non-flat arrays!")
|
||||
|
||||
if xFunction:
|
||||
tMiMa = np.array([solObj.Tmin, solObj.Tmax])
|
||||
xMiMa = xFunc
|
||||
else:
|
||||
tMiMa = tFunc
|
||||
xMiMa = np.array([solObj.xmin, solObj.xmax])
|
||||
|
||||
zMiMa = np.zeros((len(tMiMa),len(xMiMa)))
|
||||
for i in range(len(tMiMa)):
|
||||
for j in range(len(xMiMa)):
|
||||
zMiMa[i,j] = func(tMiMa[i],pFunc,xMiMa[j])
|
||||
|
||||
if not xFunction: # add the freezing front
|
||||
if solObj.T_freeze.type!=IncompressibleData.INCOMPRESSIBLE_NOT_SET:
|
||||
cols = len(tMiMa)
|
||||
conc = np.linspace(solObj.xmin, solObj.xmax, num=cols)
|
||||
tFree = np.zeros_like(conc)
|
||||
zFree = np.zeros_like(conc)
|
||||
for i in range(cols):
|
||||
tFree[i] = solObj.Tfreeze(10.0, p=pFunc, x=conc[i])
|
||||
zFree[i] = func(tFree[i],pFunc,conc[i])
|
||||
#zMiMa = np.hstack((zMiMa,temp.reshape((len(conc),1))))
|
||||
|
||||
fitFormatter = {}
|
||||
fitFormatter['color'] = 'red'
|
||||
fitFormatter['ls'] = 'solid'
|
||||
|
||||
errorFormatter = {}
|
||||
errorFormatter['color'] = dataFormatter['color']
|
||||
errorFormatter['marker'] = 'o'
|
||||
errorFormatter['ls'] = 'none'
|
||||
errorFormatter['alpha'] = 0.25
|
||||
|
||||
pData = None
|
||||
pFree = None
|
||||
if xFunction:
|
||||
pData = xData
|
||||
pFunc = xFunc
|
||||
pMiMa = xMiMa
|
||||
zMiMa = zMiMa.T
|
||||
#pFree = xFree
|
||||
#zFree = zFree.T
|
||||
else:
|
||||
pData = tData - 273.15
|
||||
pFunc = tFunc - 273.15
|
||||
pMiMa = tMiMa - 273.15
|
||||
#zMiMa = zMiMa
|
||||
pFree = tFree - 273.15
|
||||
|
||||
if zData!=None and axVal!=None:
|
||||
axVal.plot(pData, zData, label='data', **dataFormatter)
|
||||
|
||||
if zFunc!=None and axVal!=None:
|
||||
axVal.plot(pFunc, zFunc, label='function' , **fitFormatter)
|
||||
if solObj.xid!=solObj.ifrac_pure and not xFunction:
|
||||
axVal.set_title("showing x={0:3.2f}".format(xFunc[0]))
|
||||
else:
|
||||
axVal.set_title(" ")
|
||||
|
||||
if zMiMa!=None and axVal!=None:
|
||||
axVal.plot(pMiMa, zMiMa, alpha=0.25, ls=':', color=fitFormatter["color"])
|
||||
|
||||
if zFree!=None and axVal!=None:
|
||||
axVal.plot(pFree, zFree, alpha=0.25, ls=':', color=fitFormatter["color"])
|
||||
|
||||
if zError!=None and axErr!=None:
|
||||
axErr.plot(pData, zError, label='error' , **errorFormatter)
|
||||
|
||||
elif axErr!=None:
|
||||
errorFormatter['alpha'] = 0.00
|
||||
axErr.plot([pData[0],pData[-1]], [0,0], **errorFormatter)
|
||||
#axErr.xaxis.set_visible(False)
|
||||
#axErr.yaxis.set_visible(False)
|
||||
#axErr.plot(pData, zFunc, label='function' , **fitFormatter)
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
#else:
|
||||
# plt.setp(axErr.get_yticklabels(), visible=False)
|
||||
# plt.setp(axErr.yaxis.get_label(), visible=False)
|
||||
|
||||
|
||||
def printFluidInfo(self,ax,solObj=SolutionData()):
|
||||
"""
|
||||
Prints some fluid information on top of the fitting report.
|
||||
"""
|
||||
#ax = subplot(111, frame_on=False)
|
||||
ax.xaxis.set_visible(False)
|
||||
ax.yaxis.set_visible(False)
|
||||
|
||||
#cellText = [["1","2"],["3","4"]]
|
||||
#rowLabels = ["Name","Source"]
|
||||
## Add a table at the bottom of the axes
|
||||
#the_table = ax.table(cellText=cellText)
|
||||
|
||||
annotateSettingsTitle = {}
|
||||
#annotateSettingsLabel['xycoords']=('figure fraction', 'figure fraction')
|
||||
annotateSettingsTitle['ha'] = 'center'
|
||||
annotateSettingsTitle['va'] = 'baseline'
|
||||
annotateSettingsTitle['fontsize'] = 'xx-large'
|
||||
annotateSettingsTitle['fontweight'] = 'bold'
|
||||
|
||||
annotateSettingsLabel = {}
|
||||
#annotateSettingsLabel['xycoords']=('figure fraction', 'figure fraction')
|
||||
annotateSettingsLabel['ha'] = 'right'
|
||||
annotateSettingsLabel['va'] = 'baseline'
|
||||
#annotateSettingsLabel['fontsize'] = 'large'
|
||||
annotateSettingsLabel['fontweight'] = 'semibold'
|
||||
|
||||
annotateSettingsText = {}
|
||||
#annotateSettingsText['xycoords']=('figure fraction', 'figure fraction')
|
||||
annotateSettingsText['ha'] = 'left'
|
||||
annotateSettingsText['va'] = 'baseline'
|
||||
#annotateSettingsText['fontsize'] = 'large'
|
||||
annotateSettingsText['fontweight'] = 'medium'
|
||||
|
||||
|
||||
#ax.set_title('Fitting Report for {0}'.format(solObj.name))
|
||||
ax.text(0.5, 0.8, 'Fitting Report for {0}'.format(solObj.name), **annotateSettingsTitle)
|
||||
|
||||
def myAnnotate(label,text,x=0.0,y=0.0):
|
||||
dx = 0.005
|
||||
ax.text(x-dx,y, label, **annotateSettingsLabel)
|
||||
ax.text(x+dx,y, text, **annotateSettingsText)
|
||||
|
||||
#ax.annotate(r'Enthalpy [$\mathdefault{10^5\!J/kg}$]', xy=(0.25, 0.03), **annotateSettings)
|
||||
#ax.annotate(r'Enthalpy [$\mathdefault{10^5\!J/kg}$]', xy=(0.25, 0.03), **annotateSettings)
|
||||
|
||||
dx = 0.50
|
||||
dy = 0.10
|
||||
|
||||
xStart = 0.175 ; x = xStart
|
||||
yStart = 0.575; y = yStart
|
||||
|
||||
#myAnnotate('Name: ',solObj.name,x=x,y=y); x += .0; y -= dy
|
||||
myAnnotate('Description: ',solObj.description,x=x,y=y); x += .0; y -= dy
|
||||
myAnnotate('Source: ',solObj.reference,x=x,y=y); x += .0; y -= dy
|
||||
myAnnotate('Temperature: ',u'{0} \u00B0C to {1} \u00B0C'.format(solObj.Tmin-273.15, solObj.Tmax-273.15),x=x,y=y); x += .0; y -= dy
|
||||
conc = False
|
||||
if solObj.xid==solObj.ifrac_mass: conc=True
|
||||
if solObj.xid==solObj.ifrac_volume: conc=True
|
||||
if solObj.xid==solObj.ifrac_mole: conc=True
|
||||
if conc==True:
|
||||
myAnnotate('Composition: ',u'{0} % to {1} %, {2}'.format(solObj.xmin*100., solObj.xmax*100., solObj.xid),x=x,y=y)
|
||||
else:
|
||||
myAnnotate('Composition: ','pure fluid',x=x,y=y)
|
||||
x += .0; y -= dy
|
||||
|
||||
if solObj.density.source!=solObj.density.SOURCE_NOT_SET:
|
||||
myAnnotate('Density: ',u'{0} to {1} {2}'.format(solObj.density.source, solObj.density.type, solObj.density.coeffs.shape),x=x,y=y)
|
||||
else:
|
||||
myAnnotate('Density: ','no information',x=x,y=y)
|
||||
x += .0; y -= dy
|
||||
if solObj.specific_heat.source!=solObj.specific_heat.SOURCE_NOT_SET:
|
||||
myAnnotate('Spec. Heat: ',u'{0} to {1} {2}'.format(solObj.specific_heat.source, solObj.specific_heat.type, solObj.specific_heat.coeffs.shape),x=x,y=y)
|
||||
else:
|
||||
myAnnotate('Spec. Heat: ','no information',x=x,y=y)
|
||||
x += .0; y -= dy
|
||||
|
||||
x = xStart + dx; y = yStart-dy-dy
|
||||
if solObj.conductivity.source!=solObj.conductivity.SOURCE_NOT_SET:
|
||||
myAnnotate('Th. Cond.: ',u'{0} to {1} {2}'.format(solObj.conductivity.source, solObj.conductivity.type, solObj.conductivity.coeffs.shape),x=x,y=y)
|
||||
else:
|
||||
myAnnotate('Th. Cond.: ','no information',x=x,y=y)
|
||||
x += .0; y -= dy
|
||||
if solObj.viscosity.source!=solObj.viscosity.SOURCE_NOT_SET:
|
||||
myAnnotate('Viscosity: ',u'{0} to {1} {2}'.format(solObj.viscosity.source, solObj.viscosity.type, solObj.viscosity.coeffs.shape),x=x,y=y)
|
||||
else:
|
||||
myAnnotate('Viscosity: ','no information',x=x,y=y)
|
||||
x += .0; y -= dy
|
||||
if solObj.saturation_pressure.source!=solObj.saturation_pressure.SOURCE_NOT_SET:
|
||||
myAnnotate('Psat: ',u'{0} to {1} {2}'.format(solObj.saturation_pressure.source, solObj.saturation_pressure.type, solObj.saturation_pressure.coeffs.shape),x=x,y=y)
|
||||
else:
|
||||
myAnnotate('Psat: ','no information',x=x,y=y)
|
||||
x += .0; y -= dy
|
||||
if solObj.T_freeze.source!=solObj.T_freeze.SOURCE_NOT_SET:
|
||||
myAnnotate('Tfreeze: ',u'{0} to {1} {2}'.format(solObj.T_freeze.source, solObj.T_freeze.type, solObj.T_freeze.coeffs.shape),x=x,y=y)
|
||||
else:
|
||||
myAnnotate('Tfreeze: ','no information',x=x,y=y)
|
||||
x += .0; y -= dy
|
||||
|
||||
|
||||
#ax5.set_xlabel(ur'$\mathregular{Temperature\/(\u00B0C)}$')
|
||||
|
||||
#x += dx; y = yStart
|
||||
#myAnnotate('Name: ',solObj.name,x=x,y=y); x += .0; y -= dy
|
||||
|
||||
ax.set_xlim((0,1))
|
||||
ax.set_ylim((0,1))
|
||||
|
||||
|
||||
def printFitDetails(self):
|
||||
pass
|
||||
|
||||
|
||||
|
||||
def makeFitReportPage(self, solObj=SolutionData(), pdfObj=None):
|
||||
"""
|
||||
Creates a whole page with some plots and basic information
|
||||
for both fit quality, reference data, data sources and
|
||||
more.
|
||||
"""
|
||||
|
||||
# First we determine some basic settings
|
||||
|
||||
gs = gridspec.GridSpec(4, 2, wspace=None, hspace=None, height_ratios=[2.5,3,3,3])
|
||||
#gs.update(top=0.75, hspace=0.05)
|
||||
div = 22
|
||||
fig = plt.figure(figsize=(210/div,297/div))
|
||||
table_axis = plt.subplot(gs[0,:], frame_on=False)
|
||||
|
||||
|
||||
|
||||
|
||||
# Info text settings
|
||||
infoText = {}
|
||||
infoText['ha'] = 'center'
|
||||
infoText['va'] = 'baseline'
|
||||
infoText['fontsize'] = 'smaller'
|
||||
infoText['xycoords'] =('axes fraction', 'axes fraction')
|
||||
|
||||
# Setting the labels
|
||||
#errLabel = ur'$\mathdefault{rel.\/Error\/[\u2030]}$'
|
||||
errLabel = r'$\mathdefault{rel.\/Error\/[\%]}$'
|
||||
tempLabel = ur'$\mathdefault{Temperature\/(\u00B0C)}$'
|
||||
|
||||
density_axis = plt.subplot(gs[1,0])
|
||||
density_error = density_axis.twinx()
|
||||
density_axis.set_ylabel(r'Density [$\mathdefault{kg/m^3\!}$]')
|
||||
density_axis.set_xlabel(tempLabel)
|
||||
density_error.set_ylabel(errLabel)
|
||||
if solObj.density.source!=solObj.density.SOURCE_NOT_SET:
|
||||
self.plotValues(density_axis,density_error,solObj=solObj,dataObj=solObj.density,func=solObj.rho)
|
||||
else:
|
||||
raise ValueError("Density data has to be provided!")
|
||||
|
||||
capacity_axis = plt.subplot(gs[1,1])
|
||||
capacity_error = capacity_axis.twinx()
|
||||
capacity_axis.set_ylabel(r'Heat Capacity [$\mathdefault{J/kg/K}$]')
|
||||
capacity_axis.set_xlabel(tempLabel)
|
||||
capacity_error.set_ylabel(errLabel)
|
||||
if solObj.specific_heat.source!=solObj.specific_heat.SOURCE_NOT_SET:
|
||||
self.plotValues(capacity_axis,capacity_error,solObj=solObj,dataObj=solObj.specific_heat,func=solObj.c)
|
||||
else:
|
||||
raise ValueError("Specific heat data has to be provided!")
|
||||
|
||||
# Optional plots, might not all be shown
|
||||
conductivity_axis = plt.subplot(gs[2,0])#, sharex=density_axis)
|
||||
conductivity_error = conductivity_axis.twinx()
|
||||
conductivity_axis.set_ylabel(r'Thermal Conductivity [$\mathdefault{W/m/K}$]')
|
||||
conductivity_axis.set_xlabel(tempLabel)
|
||||
conductivity_error.set_ylabel(errLabel)
|
||||
if solObj.conductivity.source!=solObj.conductivity.SOURCE_NOT_SET:
|
||||
self.plotValues(conductivity_axis,conductivity_error,solObj=solObj,dataObj=solObj.conductivity,func=solObj.cond)
|
||||
else:
|
||||
#conductivity_axis.xaxis.set_visible(False)
|
||||
#conductivity_axis.yaxis.set_visible(False)
|
||||
#conductivity_error.xaxis.set_visible(False)
|
||||
#conductivity_error.yaxis.set_visible(False)
|
||||
conductivity_axis.annotate("No conductivity information",xy=(0.5,0.5),**infoText)
|
||||
|
||||
viscosity_axis = plt.subplot(gs[2,1])#, sharex=capacity_axis)
|
||||
viscosity_error = viscosity_axis.twinx()
|
||||
viscosity_axis.set_yscale('log')
|
||||
viscosity_axis.set_ylabel(r'Dynamic Viscosity [$\mathdefault{Pa\/s}$]')
|
||||
viscosity_axis.set_xlabel(tempLabel)
|
||||
viscosity_error.set_ylabel(errLabel)
|
||||
if solObj.viscosity.source!=solObj.viscosity.SOURCE_NOT_SET:
|
||||
self.plotValues(viscosity_axis,viscosity_error,solObj=solObj,dataObj=solObj.viscosity,func=solObj.visc)
|
||||
else:
|
||||
#viscosity_axis.xaxis.set_visible(False)
|
||||
#viscosity_axis.yaxis.set_visible(False)
|
||||
#viscosity_error.xaxis.set_visible(False)
|
||||
#viscosity_error.yaxis.set_visible(False)
|
||||
viscosity_axis.annotate("No viscosity information",xy=(0.5,0.5),**infoText)
|
||||
|
||||
saturation_axis = plt.subplot(gs[3,0])#, sharex=density_axis)
|
||||
saturation_error = saturation_axis.twinx()
|
||||
saturation_axis.set_yscale('log')
|
||||
saturation_axis.set_ylabel(r'Saturation Pressure [$\mathdefault{Pa}$]')
|
||||
saturation_axis.set_xlabel(tempLabel)
|
||||
saturation_error.set_ylabel(errLabel)
|
||||
if solObj.saturation_pressure.source != solObj.saturation_pressure.SOURCE_NOT_SET: # exists
|
||||
self.plotValues(saturation_axis,saturation_error,solObj=solObj,dataObj=solObj.saturation_pressure,func=solObj.psat)
|
||||
else:
|
||||
#saturation_axis.xaxis.set_visible(False)
|
||||
#saturation_axis.yaxis.set_visible(False)
|
||||
#saturation_error.xaxis.set_visible(False)
|
||||
#saturation_error.yaxis.set_visible(False)
|
||||
saturation_axis.annotate("No saturation state information",xy=(0.5,0.5),**infoText)
|
||||
|
||||
Tfreeze_axis = plt.subplot(gs[3,1])#, sharex=capacity_axis)
|
||||
Tfreeze_error = Tfreeze_axis.twinx()
|
||||
Tfreeze_axis.set_ylabel(r'Freezing Temperature [$\mathdefault{K}$]')
|
||||
Tfreeze_axis.set_xlabel("{0} fraction".format(solObj.xid.title()))
|
||||
Tfreeze_error.set_ylabel(errLabel)
|
||||
if solObj.T_freeze.source != solObj.T_freeze.SOURCE_NOT_SET: # exists
|
||||
self.plotValues(Tfreeze_axis,Tfreeze_error,solObj=solObj,dataObj=solObj.T_freeze,func=solObj.Tfreeze)
|
||||
else:
|
||||
#Tfreeze_axis.xaxis.set_visible(False)
|
||||
#Tfreeze_axis.yaxis.set_visible(False)
|
||||
#Tfreeze_error.xaxis.set_visible(False)
|
||||
#Tfreeze_error.yaxis.set_visible(False)
|
||||
Tfreeze_axis.annotate("No freezing point information",xy=(0.5,0.5),**infoText)
|
||||
Tfreeze_axis.set_xlabel("Fraction")
|
||||
|
||||
#saturation_axis = plt.subplot2grid((3,2), (2,0))
|
||||
#Tfreeze_axis = plt.subplot2grid((3,2), (2,0))
|
||||
#mass2input_axis = plt.subplot2grid((3,2), (2,0))
|
||||
#volume2input_axis = plt.subplot2grid((3,2), (2,0))
|
||||
|
||||
# Set a minimum error level and do some more formatting
|
||||
minAbsErrorScale = 0.05 # in per cent
|
||||
for a in fig.axes:
|
||||
if a.get_ylabel()==errLabel:
|
||||
mi,ma = a.get_ylim()
|
||||
if mi>-minAbsErrorScale: a.set_ylim(bottom=-minAbsErrorScale)
|
||||
if ma< minAbsErrorScale: a.set_ylim( top= minAbsErrorScale)
|
||||
a.xaxis.set_major_locator(MaxNLocator(5))
|
||||
#a.yaxis.set_major_locator(MaxNLocator(7))
|
||||
|
||||
|
||||
|
||||
# print headlines etc.
|
||||
self.printFluidInfo(table_axis, solObj)
|
||||
# Prepare the legend
|
||||
legenddict = {}
|
||||
for a in fig.axes:
|
||||
handles, labels = a.get_legend_handles_labels()
|
||||
for i in range(len(labels)):
|
||||
legenddict[labels[i]] = handles[i]
|
||||
|
||||
legKey = ["Legend: "]
|
||||
legVal = [Rectangle((0, 0), 1, 1, alpha=0.0)]
|
||||
legKey += legenddict.keys()
|
||||
legVal += legenddict.values()
|
||||
legKey += [" "]
|
||||
legVal += [Rectangle((0, 0), 1, 1, alpha=0.0)]
|
||||
|
||||
table_axis.legend(
|
||||
legVal, legKey,
|
||||
bbox_to_anchor=(0.0, -0.025, 1., -0.025),
|
||||
ncol=len(legKey), mode="expand", borderaxespad=0.,
|
||||
numpoints=1)
|
||||
#table_axis.legend(handles, labels, bbox_to_anchor=(0.0, -0.1), loc=2, ncol=3)
|
||||
|
||||
#
|
||||
#
|
||||
#
|
||||
#
|
||||
#
|
||||
#
|
||||
#
|
||||
# t = arange(0.01, 5.0, 0.01)
|
||||
#s1 = sin(2*pi*t)
|
||||
#s2 = exp(-t)
|
||||
#s3 = sin(4*pi*t)
|
||||
#ax1 = subplot(311)
|
||||
#plot(t,s1)
|
||||
#setp( ax1.get_xticklabels(), fontsize=6)
|
||||
#
|
||||
### share x only
|
||||
#ax2 = subplot(312, sharex=ax1)
|
||||
#plot(t, s2)
|
||||
## make these tick labels invisible
|
||||
#setp( ax2.get_xticklabels(), visible=False)
|
||||
#
|
||||
## share x and y
|
||||
#ax3 = subplot(313, sharex=ax1, sharey=ax1)
|
||||
#plot(t, s3)
|
||||
#xlim(0.01,5.0)
|
||||
#show()
|
||||
|
||||
gs.tight_layout(fig)#, rect=[0, 0, 1, 0.75])
|
||||
# Fine-tune figure; make subplots close to each other
|
||||
# and hide x ticks for all but bottom plot.
|
||||
#fig.subplots_adjust(wspace=0)
|
||||
#plt.setp([a.get_xticklabels() for a in fig.axes[:-1]], visible=False)
|
||||
|
||||
plt.savefig(os.path.join("report","{0}_fitreport.pdf".format(solObj.name)))
|
||||
if pdfObj!=None: pdfObj.savefig(fig)
|
||||
plt.close()
|
||||
pass
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
#class FitGraphWriter(object):
|
||||
@@ -266,15 +929,7 @@ class SolutionDataWriter(object):
|
||||
## containerList += [ZS10(), ZS25(), ZS40(), ZS45(), ZS55()]
|
||||
# return containerList
|
||||
#
|
||||
# def relError(A=[],B=[],PCT=False):
|
||||
# """
|
||||
# Returns the relative Error from (A-B)/B, if PCT is True, it returns percent.
|
||||
# """
|
||||
# result = (np.array(A)-np.array(B))/np.array(B);
|
||||
# if PCT:
|
||||
# return result * 100.
|
||||
# else:
|
||||
# return result
|
||||
|
||||
#
|
||||
#
|
||||
# def makePlots(self, fluid):
|
||||
|
||||
@@ -1,80 +0,0 @@
|
||||
{
|
||||
"T_freeze": {
|
||||
"coeffs": "null",
|
||||
"type": "notdefined"
|
||||
},
|
||||
"Tbase": 0.00000e+00,
|
||||
"Tmax": 3.73150e+02,
|
||||
"Tmin": 1.93150e+02,
|
||||
"TminPsat": 3.73150e+02,
|
||||
"conductivity": {
|
||||
"coeffs": [
|
||||
[
|
||||
1.89132e-04
|
||||
],
|
||||
[
|
||||
-2.06364e-07
|
||||
]
|
||||
],
|
||||
"type": "polynomial"
|
||||
},
|
||||
"density": {
|
||||
"coeffs": [
|
||||
[
|
||||
1.07650e+03
|
||||
],
|
||||
[
|
||||
-7.31182e-01
|
||||
]
|
||||
],
|
||||
"type": "polynomial"
|
||||
},
|
||||
"description": "Diethylbenzene mixture - Dowtherm J Dow Chemical Co.",
|
||||
"mass2input": {
|
||||
"coeffs": "null",
|
||||
"type": "notdefined"
|
||||
},
|
||||
"mole2input": {
|
||||
"coeffs": "null",
|
||||
"type": "notdefined"
|
||||
},
|
||||
"name": "DEB",
|
||||
"reference": "Melinder-BOOK-2010",
|
||||
"saturation_pressure": {
|
||||
"coeffs": "null",
|
||||
"type": "notdefined"
|
||||
},
|
||||
"specific_heat": {
|
||||
"coeffs": [
|
||||
[
|
||||
9.99729e+02
|
||||
],
|
||||
[
|
||||
2.87576e+00
|
||||
]
|
||||
],
|
||||
"type": "polynomial"
|
||||
},
|
||||
"viscosity": {
|
||||
"coeffs": [
|
||||
[
|
||||
3.55030e+00
|
||||
],
|
||||
[
|
||||
-5.66396e-02
|
||||
],
|
||||
[
|
||||
7.03331e-05
|
||||
]
|
||||
],
|
||||
"type": "exppolynomial"
|
||||
},
|
||||
"volume2input": {
|
||||
"coeffs": "null",
|
||||
"type": "notdefined"
|
||||
},
|
||||
"xbase": 0.00000e+00,
|
||||
"xid": 4,
|
||||
"xmax": 1.00000e+00,
|
||||
"xmin": 0.00000e+00
|
||||
}
|
||||
@@ -1,101 +0,0 @@
|
||||
{
|
||||
"T_freeze": {
|
||||
"coeffs": "null",
|
||||
"type": "notdefined"
|
||||
},
|
||||
"Tbase": 4.05650e+02,
|
||||
"Tmax": 6.18150e+02,
|
||||
"Tmin": 1.93150e+02,
|
||||
"TminPsat": 3.23150e+02,
|
||||
"conductivity": {
|
||||
"coeffs": [
|
||||
[
|
||||
1.04518e-01
|
||||
],
|
||||
[
|
||||
-2.12843e-04
|
||||
],
|
||||
[
|
||||
-3.45327e-09
|
||||
],
|
||||
[
|
||||
3.58008e-11
|
||||
]
|
||||
],
|
||||
"type": "polynomial"
|
||||
},
|
||||
"density": {
|
||||
"coeffs": [
|
||||
[
|
||||
7.75459e+02
|
||||
],
|
||||
[
|
||||
-8.53428e-01
|
||||
],
|
||||
[
|
||||
-1.06580e-03
|
||||
],
|
||||
[
|
||||
-2.92227e-06
|
||||
]
|
||||
],
|
||||
"type": "polynomial"
|
||||
},
|
||||
"description": "DowthermJ",
|
||||
"mass2input": {
|
||||
"coeffs": "null",
|
||||
"type": "notdefined"
|
||||
},
|
||||
"mole2input": {
|
||||
"coeffs": "null",
|
||||
"type": "notdefined"
|
||||
},
|
||||
"name": "DowJ",
|
||||
"reference": "Dow Chemicals data sheet",
|
||||
"saturation_pressure": {
|
||||
"coeffs": "null",
|
||||
"type": "notdefined"
|
||||
},
|
||||
"specific_heat": {
|
||||
"coeffs": [
|
||||
[
|
||||
2.20030e+03
|
||||
],
|
||||
[
|
||||
3.58155e+00
|
||||
],
|
||||
[
|
||||
4.04739e-03
|
||||
],
|
||||
[
|
||||
6.78271e-06
|
||||
]
|
||||
],
|
||||
"type": "polynomial"
|
||||
},
|
||||
"viscosity": {
|
||||
"coeffs": [
|
||||
[
|
||||
-8.08082e+00
|
||||
],
|
||||
[
|
||||
-5.67888e-03
|
||||
],
|
||||
[
|
||||
2.80778e-05
|
||||
],
|
||||
[
|
||||
-8.60957e-08
|
||||
]
|
||||
],
|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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0.00000e+00
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||||
],
|
||||
[
|
||||
0.00000e+00,
|
||||
0.00000e+00,
|
||||
0.00000e+00,
|
||||
0.00000e+00,
|
||||
0.00000e+00,
|
||||
0.00000e+00
|
||||
],
|
||||
[
|
||||
0.00000e+00,
|
||||
0.00000e+00,
|
||||
0.00000e+00,
|
||||
0.00000e+00,
|
||||
0.00000e+00,
|
||||
0.00000e+00
|
||||
],
|
||||
[
|
||||
0.00000e+00,
|
||||
0.00000e+00,
|
||||
0.00000e+00,
|
||||
0.00000e+00,
|
||||
0.00000e+00,
|
||||
0.00000e+00
|
||||
]
|
||||
],
|
||||
"type": "polynomial"
|
||||
},
|
||||
"viscosity": {
|
||||
"coeffs": [
|
||||
[
|
||||
0.00000e+00,
|
||||
0.00000e+00,
|
||||
0.00000e+00,
|
||||
0.00000e+00,
|
||||
0.00000e+00,
|
||||
0.00000e+00
|
||||
],
|
||||
[
|
||||
0.00000e+00,
|
||||
0.00000e+00,
|
||||
0.00000e+00,
|
||||
0.00000e+00,
|
||||
0.00000e+00,
|
||||
0.00000e+00
|
||||
],
|
||||
[
|
||||
0.00000e+00,
|
||||
0.00000e+00,
|
||||
0.00000e+00,
|
||||
0.00000e+00,
|
||||
0.00000e+00,
|
||||
0.00000e+00
|
||||
],
|
||||
[
|
||||
0.00000e+00,
|
||||
0.00000e+00,
|
||||
0.00000e+00,
|
||||
0.00000e+00,
|
||||
0.00000e+00,
|
||||
0.00000e+00
|
||||
]
|
||||
],
|
||||
"type": "exppolynomial"
|
||||
},
|
||||
"volume2input": {
|
||||
"coeffs": "null",
|
||||
"type": "notdefined"
|
||||
},
|
||||
"xbase": 5.00000e-01,
|
||||
"xid": 2,
|
||||
"xmax": 7.00000e-01,
|
||||
"xmin": 3.00000e-01
|
||||
}
|
||||
Some files were not shown because too many files have changed in this diff Show More
Reference in New Issue
Block a user