def twi_fits(latitude, day, gen=False):
np.random.seed(0)
ES, td, tn, p = get_Eirr(latitude, day)
params = [latitude]
P = p[0][p[1] > 0]
t = p[1][p[1] > 0]
f = np.array([np.trapz(P[:i+1])*(t[0]-t[i])/i for i in
range(1, len(P)-1)])
ends = np.array([P[i]*(t[0]-t[i]) for i in range(1, len(P))][:-1])
Eday = np.array([P[i]*t[i] for i in range(1, len(P))][:-1])
C = ends - f
B = Eday + f
x = np.log(P[1:-15])
y = np.log(2*C[:-14])
cn, err = fit(x, y, 1, "MA")
rm = err
print("RMS error: %.4f" % rm)
dftw = cn.get_fitdata()
if not gen:
fig1, ax1 = plt.subplots()
fig2, ax2 = plt.subplots()
yfit = cn.evaluate(x)
ax1.plot(P[1:-15], 2*C[:-14], "o", c="g", markerfacecolor="none",
mew=1.5)
ax1.plot(P[1:-15], np.exp(yfit), c="g",
label="%dth Latitude" % latitude, lw=2)
ax1.set_xlabel("Minimum Power $(P/S)_{\mathrm{min}}$ [W/m$^2$]",
fontsize=19)
ax1.set_ylabel("Twilight Energy $(E/S)_{\mathrm{twilight}}}$ " +
"[Whr/m$^2$]", fontsize=19)
ax1.grid()
params.append(cn[0].right.c)
params.append(cn[0].right.exp[list(cn[0].varkeys["u_fit_(0,)"])[0]])
x = np.log(P[1:-15])
y = np.log(2*B[:-14])
cn, err = fit(x, y, 1, "MA")
rm = err
print("RMS error: %.4f" % rm)
dfday = cn.get_fitdata()
if not gen:
yfit = cn.evaluate(x)
ax2.plot(P[1:-15], 2*B[:-14], "o", c="g", markerfacecolor="none",
mew=1.5)
ax2.plot(P[1:-15], np.exp(yfit), c="g",
label="%dth Latitude" % latitude, lw=2)
ax2.grid()
ax2.set_xlabel("Minimum Necessary Power $(P/S)_{\mathrm{min}}$ " +
"[W/m$^2$]", fontsize=19)
ax2.set_ylabel("Daytime Energry $(E/S)_{\mathrm{day}}$ [Whr/m$^2$]",
fontsize=19)
fig1.savefig(path + "Cenergy.pdf")
fig2.savefig(path + "Benergy.pdf")
return dftw, dfday
def make_fit(naca_range, re_range, M_range):
#call the fit setup function
x, y = fit_setup(naca_range, re_range, M_range)
cstrt, rms = fit(x, y, 4, 'SMA')
print "RMS"
print rms
def arctanfit():
u = np.linspace(1e-15, 0.7, 100)
w = np.arctan(u)
x = np.log(u)
y = np.log(w)
cn, err = fit(x, y, 1, "MA")
rm = err
print "RMS error: %.4f" % rm
yfit = cn.evaluate(x)
df = cn.get_dataframe()
fig, ax = plt.subplots()
ax.plot(u, w, lw=2)
ax.plot(u, np.exp(yfit), "--", lw=2)
ax.set_xlim([0, 0.7])
ax.grid()
ax.set_xlabel("$V_{\\mathrm{gust}}/V$")
ax.set_ylabel("$\\alpha_{\\mathrm{gust}}$")
ax.legend([
"$\\arctan{(V_{\\mathrm{gust}}/V)}$",
"$0.905 (V_{\\mathrm{gust}}/V)^{0.961}$"
],
loc=2,
fontsize=15)
return df, fig, ax
def plot_BSFCtoPower(power_csv, bsfc_csv):
# Fitting BSFC vs. Power
df = pd.read_csv(power_csv)
p = df['P']
rpm = df["RPM"]
f = interpolate.interp1d(rpm, p)
df = pd.read_csv(bsfc_csv)
df = df[(df["RPM"] > min(rpm)) & (df["RPM"] < max(rpm))]
rpmnew = df["RPM"]
u = f(rpmnew) / max(p)
w = df['BSFC'] / min(df["BSFC"])
x = np.array(log(u))
y = np.array(log(w))
Type = 'SMA'
K = 2
cstrt, error = fit(x, y, K, Type)
print "RMS error = %.4f" % error
yfit = cstrt.evaluate(x)
dfc = cstrt.get_dataframe()
fig, ax = plt.subplots()
ax.plot(u * 100, w * min(df["BSFC"]), "o", mfc="None", ms=7, mew=1.5)
ax.plot(u * 100, np.exp(yfit) * min(df["BSFC"]), linewidth=2)
ax.set_xlabel("Percent Power")
ax.set_ylabel("$BSFC$ [kg/kW/hr]")
ax.legend(["RCV Engine Ltd. Data", "GP approximation"], fontsize=15)
ax.set_ylim([0, 1])
ax.set_xlim([0, 100])
ax.grid()
return dfc, fig, ax
def make_fit(thick_range, re_range, M_range):
#call the fit setup function
x, y = fit_setup(thick_range, re_range, M_range)
cstrt, rms = fit(x, y, 3, 'SMA')
print("RMS")
print(rms)
def test_rms_error(self):
np.random.seed(SEED)
Vdd = random_sample(1000,) + 1
Vth = 0.2*random_sample(1000,) + 0.2
P = Vdd**2 + 30*Vdd*exp(-(Vth-0.06*Vdd)/0.039)
u = vstack((Vdd, Vth))
x = log(u)
y = log(P)
K = 4
_, rms_error = fit(x, y, K, "MA")
self.assertTrue(rms_error < 1e-2)
def make_fits(day, latrange, month, gen=False, path="", plot=False):
for l in latrange:
print "Fitting for %d latitude" % l
altitudestart = range(43000, 50500, 500)
for j, a in enumerate(altitudestart):
print "Trying Altitude Range: %d-72000" % a
X, Y = fit_setup(altitude=(a, 72000), latitude=l, day=day)
rms_best = 1
cn_best = None
df_best = None
yfit = None
for K in range(2, 6):
for ftype in ["MA", "SMA"]:
try:
cns, err = fit(X, Y, K, ftype)
print "Lat %d; K = %d; ftype = %s; RMS = %.4f" % (
l, K, ftype, err)
except ValueError:
print "Fit failed: Lat %d; K = %d; ftype = %s;" % (
l, K, ftype)
err = 0.9
if err < rms_best:
df = cns.get_dataframe()
if "0.0" in df.values or "inf" in df.values:
continue
rms_best = err
cn_best, df_best = cns, df
if rms_best == 1:
print "Nothing worked... trying new altitude range"
continue
elif rms_best < 0.05:
print "success"
if gen:
df_best.to_csv("windfits" + month +
"/windaltfit_lat%d.csv" % l,
index=False)
if plot:
yfit = cn_best.evaluate(X)
break
else:
print "Lowest RMS: %.3f, trying new altitude range" % rms_best
if plot:
if not yfit is None:
fig, ax = plot_fits(X, Y, yfit, l, plot=plot)
fig.savefig(path + "windfits" + month + "/windfitl%d.pdf" % l,
bbox_inches="tight")
plt.close()
def plot_fit_1d(udata, wdata, K=1, fitclass='MA', plotspace='log'):
"Finds and plots a fit (MA or SMA) for 1D data"
cstrt, _ = fit(np.log(udata), np.log(wdata), K, fitclass)
uu = np.linspace(min(udata), max(udata), 1000)
if fitclass == 'MA':
uvarkey, = cstrt[0].left.varkeys
A = [c.left.exps[0][uvarkey] for c in cstrt]
B = np.log([c.left.cs[0] for c in cstrt])
WW = []
for k in range(K):
WW += [np.exp(B[k])*uu**A[k]]
stringlist = print_MA(A, B, 1, K)
if fitclass == 'SMA':
wexps, = cstrt.left.exps
alpha, = wexps.values()
uvarkey, = cstrt.right.varkeys
A = [d[uvarkey]/alpha for d in cstrt.right.exps]
B = np.log(cstrt.right.cs) / alpha
ww = 0
for k in range(K):
ww += (np.exp(alpha*B[k])*uu**(alpha*A[k]))
WW = [ww**(1./alpha)]
print_str = print_SMA(A, B, alpha, 1, K)
stringlist = [''.join(print_str)]
f, ax = plt.subplots()
if plotspace == 'log':
ax.loglog(udata, wdata, '+r')
for ww in WW:
ax.loglog(uu, ww)
elif plotspace == 'linear':
ax.plot(udata, wdata, '+r')
for ww in WW:
ax.plot(uu, ww)
ax.set_xlabel('u')
ax.legend(['Data'] + stringlist, loc='best')
plt.show()
return f, ax
def plot_powerlaw(csv):
df = pd.read_csv(csv)
u = np.array(df["lbs"] / WEIGHT)
w = np.array(df["hp"] / POWER)
x = np.log(u)
y = np.log(w)
np.random.seed(0)
cn, err = fit(x, y, 1, "MA")
print "RMS error: %.4f" % err
weight = np.linspace(min(df["lbs"]), max(df["lbs"]), 100)
yfit = cn.evaluate(np.log(weight / WEIGHT))
df = cn.get_dataframe()
fig, ax = plt.subplots()
ax.plot(u * WEIGHT, w * POWER, "o", mfc="None", mew=1.5)
ax.plot(weight, np.exp(yfit) * POWER, lw=2)
ax.set_xlabel("Engine Weight [lbs]")
ax.set_ylabel("Maximum SL Shaft Power [hp]")
ax.legend(["UND Engine Data", "Power Law Fit"], loc=2)
ax.grid()
return df, fig, ax
def plot_RPMtoPower():
# Fitting Power vs. RPM
df = pd.read_csv('Dataset_Power_Kw.csv')
RPM = df['RPM']
RPM_max = np.amax(RPM)
P = df['P']
P_max = np.amax(P)
y = np.array(log(P / P_max))
x = np.array(log(RPM / RPM_max))
Type = 'SMA'
K = 1
cstrt, rmserror = fit(x, y, K, Type)
print "RMS error = %.4f" % rmserror
yfit = cstrt.evaluate(x)
fig, ax = plt.subplots()
ax.plot(RPM, P, "o", markerfacecolor="None")
ax.plot(RPM, np.exp(yfit) * P_max)
ax.set_xlabel("$RPM$")
ax.set_ylabel("Shaft Power [kW]")
ax.legend(["Manufacture Data", "GP approximation"])
ax.grid()
return fig, ax
def plot_BSFCtoRPM():
# Fitting BSFC vs. RPM
df = pd.read_csv('Dataset_BSFC_kgKwh.csv')
RPM = df['RPM']
RPMmax = np.amax(RPM)
BSFC = df['BSFC']
BSFCmin = np.amin(BSFC)
x = np.array(log(RPM / RPMmax))
y = np.array(log(BSFC / BSFCmin))
Type = 'SMA'
K = 2
cstrt, rmserror = fit(x, y, K, Type)
print "RMS error = %.4f" % rmserror
yfit = cstrt.evaluate(x)
fig, ax = plt.subplots()
ax.plot(RPM, BSFC, "o", markerfacecolor="None")
ax.plot(RPM, np.exp(yfit) * BSFCmin)
ax.set_xlabel("$RPM$")
ax.set_ylabel("$BSFC$ [lb/hp/hr]")
ax.legend(["Manufacture Data", "GP approximation"])
ax.grid()
return fig, ax
ax.plot(re, cd, "o", mec=colors[i], mfc="none", mew=1.5)
ax.plot(re, fi, c=colors[i],
label="NACA" + naca_range[nacaint == na][0], lw=2)
i += 1
ax.legend(fontsize=15)
ax.set_xlabel("$Re$")
ax.set_ylabel("$c_{d_p}$")
ax.grid()
return fig, ax
if __name__ == "__main__":
Re = [20, 35] + range(50, 1050, 50)
NACA = np.array(["0005", "0008", "0009", "0010", "0015"])
X, Y = fit_setup(NACA, Re) # call fit(X, Y, 4, "SMA") to get fit
np.random.seed(0)
cn, err = fit(X, Y, 5, "MA")
print "RMS error: %.5f" % err
df = cn.get_dataframe()
if GENERATE:
path = os.path.dirname(TailAero.__file__)
df.to_csv(path + os.sep + "tail_dragfit.csv", index=False)
else:
df.to_csv("tail_dragfit.csv", index=False)
F, A = plot_fits(NACA, cn, X, Y)
if len(sys.argv) > 1:
path = sys.argv[1]
F.savefig(path + "taildragpolar.eps", bbox_inches="tight")
else:
F.savefig("taildragpolar.eps", bbox_inches="tight")
import unittest from numpy import log, exp, log10, vstack from gpfit.fit import fit from numpy.random import random_sample Vdd = random_sample(1000,) + 1 Vth = 0.2*random_sample(1000,) + 0.2 P = Vdd**2 + 30*Vdd*exp(-(Vth-0.06*Vdd)/0.039) u = vstack((Vdd,Vth)) x = log(u) y = log(P) K = 4 cstrt, rmsErr = fit(x,y,K,"ISMA")
import numpy as np
from gpfit.fit import fit
N = 100
A = np.linspace(0.5, 10, 100)
B = np.array([0.01, 0.05, 0.4])
x, y = np.meshgrid(A, B)
z = np.log(x/(x-y))
u1 = np.array(list(A)*len(B))
u2 = np.hstack([[v]*len(A) for v in B])
u = [u1, u2]
w = np.hstack(z)
x = np.log(u)
y = np.log(w)
cn, er = fit(x, y, 3, "SMA")
print "RMS error: %.5f" % er
df = cn.get_dataframe()
df.to_csv("logfit.csv", index=False)
cd0 = 0.020
cd1 = -0.004
cd2 = 0.020
cd8 = 1.0
cl0 = 0.8
cl_space = np.linspace(0, 1.5, 2)
cd = np.array([])
for cl in cl_space:
cd = np.append(
cd, cd0 + cd1 * (cl - cl0) + cd2 * (cl - cl0)**2 + cd8 * (cl - cl0)**8)
print(cl_space, cd)
K = 1
x = log(cl_space)
y = log(cd)
print len(x)
print len(y)
# plt.plot(mdot,delta_p)
# # plt.show()
cMA, errorMA = fit(x, y, K, "MA")
# cSMA, errorSMA = fit(x, y, K, "SMA")
# # cISMA, errorISMA = fit(x, y, K, "ISMA")
# print "MA RMS Error: %.5g" % errorMA
# print "SMA RMS Error: %.5g" % errorSMA
# # print "ISMA RMS Error: %.5g" % errorISMA
import pandas as pd
import numpy as np
from numpy import logspace, log, log10
import matplotlib.pyplot as plt
from gpfit.fit import fit
# Fitting for etah, effectiveness parameter
df = pd.read_csv('etahFit.csv')
Re = np.array(df['Re'])
etah = np.array(df['etah'])
Remax = np.amax(Re)
etahmax = np.amax(etah)
logRe = log(Re/Remax)
logetah = log(etah/etahmax)
K=1
Type = 'SMA'
cstrt, rms_error = fit(logRe,logetah,K,Type)
plt.plot(log(Re), log((0.799*(Re/Remax)**-0.0296)**10*etahmax))
plt.plot(log(Re),log(etah))
plt.show()
# Fitting BSFC vs. Power
df = pd.read_csv('Dataset_Power_Kw.csv')
p = df['P']
rpm = df["RPM"]
f = interpolate.interp1d(rpm, p)
df = pd.read_csv('Dataset_BSFC_kgKwh.csv')
df = df[(df["RPM"] > min(rpm)) & (df["RPM"] < max(rpm))]
rpmnew = df["RPM"]
u = f(rpmnew) / max(p)
w = df['BSFC'] / min(df["BSFC"])
x = np.array(log(u))
y = np.array(log(w))
Type = 'SMA'
K = 2
cstrt, rmserror = fit(x, y, K, Type)
print "RMS error = %.4f" % rmserror
yfit = cstrt.evaluate(x)
fig, ax = plt.subplots()
ax.plot(u, w * min(df["BSFC"]), "o", mfc="None", ms=7, mew=1.5)
ax.plot(u, np.exp(yfit) * min(df["BSFC"]), linewidth=2)
ax.set_xlabel("Percent Power")
ax.set_ylabel("$BSFC$ [kg/kW/hr]")
ax.legend(["RCV Engine Ltd. Data", "GP approximation"], fontsize=15)
ax.set_xlim([0, 1])
ax.set_ylim([0, 1])
ax.grid()
fig.savefig("powertobsfcfit.pdf", bbox_inches="tight")
# Fitting BSFC vs. RPM
def return_fit(cl, re):
"polar fit for the naca652 airfoil, K=3, SMA, RMS=0.0714"
cd = (2.44337e-49 * (cl)**25.264 * (re)**2.09928
+ 1.15012e+56 * (cl)**92.7062 * (re)**-14.1311
+ 1.56299e-10 * (cl)**0.0617453 * (re)**-5.1536)**(1./18.3487)
return cd
def plot_fits(re):
"plot fit compared to data"
colors = ["k", "m", "b", "g", "y"]
assert len(re) == len(colors)
fig, ax = plt.subplots()
cls = np.linspace(0.2, 1.3, 20)
for r, col in zip(re, colors):
dataf = text_to_df("naca652.ncrit09.Re%dk.pol" % r)
ax.plot(dataf["CL"], dataf["CD"], "o", c=col)
cd = return_fit(cls, r*1000.)
ax.plot(cls, cd, c=col, label="Re = %dk" % r)
ax.legend(loc=2)
ax.set_xlabel("$C_L$")
ax.set_ylabel("$C_D$")
ax.grid()
return fig, ax
if __name__ == "__main__":
Re = range(1000, 4500, 500)
X, Y = fit_setup(Re)
Cn, Rm = fit(X, Y, 3, "SMA")
F, A = plot_fits([1500, 2000, 2500, 3000, 3500])
F.savefig("naca652polarfit1.pdf")
import unittest from numpy import log, exp, log10, vstack from numpy import arccos, arange from gpfit.fit import fit from numpy.random import random_sample i = arange(0.0001, 3, .001) j = arccos(exp(-i)) x = log(i) y = log(j) K = 1 cstrt, rmsErr = fit(x, y, K, "SMA") print rmsErr
def fit_friction_factor_data(x, y, k, method):
c, error = fit(x, y, k, method)
return c, error
if roundre in re:
ax.plot(cl, cd, "o", mec=colors[i], mfc="none", mew=1.5)
ax.plot(cl, fi, c=colors[i], label="Re = %dk" % roundre, lw=2)
i += 1
ax.set_xlabel("$C_L$")
ax.set_ylabel("$c_{d_p}$")
ax.legend(loc=2)
ax.grid()
return fig, ax
if __name__ == "__main__":
Re = np.arange(150, 750, 50)
X, Y = fit_setup(Re) # call fit(X, Y, 4, "SMA") to get fit
np.random.seed(0)
cn, err = fit(X, Y, 4, "SMA")
print "RMS error: %.5f" % err
df = cn.get_dataframe()
if GENERATE:
path = os.path.dirname(inspect.getfile(Wing))
df.to_csv(path + os.sep + "jho_fitdata.csv", index=False)
else:
df.to_csv("jho_fitdata.csv", index=False)
# replot = np.array([150, 200, 300, 350, 400])
replot = np.array([300, 350, 400, 450, 500])
F, A = plot_fits(replot, cn, X, Y)
if len(sys.argv) > 1:
path = sys.argv[1]
F.savefig(path + "jho1polarfit1.eps", bbox_inches="tight")
else:
if roundre in re:
ax.plot(cl, cd, "o", mec=colors[i], mfc="none", mew=1.5)
ax.plot(cl, fi, c=colors[i], label="Re = %dk" % roundre, lw=2)
i += 1
ax.set_xlabel("$C_L$")
ax.set_ylabel("$c_{d_p}$")
ax.legend(loc=2)
ax.grid()
return fig, ax
if __name__ == "__main__":
Re = np.array([50, 75, 125, 150, 200, 300, 400, 500, 600, 700])
X, Y = fit_setup(Re) # call fit(X, Y, 4, "SMA") to get fit
np.random.seed(0)
cn, err = fit(X, Y, 3, "SMA")
print "RMS error: %.5f" % err
df = cn.get_dataframe()
if GENERATE:
path = os.path.dirname(inspect.getfile(ActuatorProp))
df.to_csv(path + os.sep + "dae51_fitdata.csv", index=False)
else:
df.to_csv("dae51fitdata.csv", index=False)
# replot = np.array([150, 200, 300, 350, 400])
replot = np.array([300, 350, 400, 450, 500])
F, A = plot_fits(replot, cn, X, Y)
if len(sys.argv) > 1:
path = sys.argv[1]
F.savefig(path + "dae51polarfit1.eps", bbox_inches="tight")
else:
from gpfit.fit import fit from numpy import logspace, log, log10 u = logspace(0, log10(3), 101) w = (u**2 + 3) / (u + 1)**2 x = log(u) y = log(w) K = 3 cMA, errorMA = fit(x, y, K, "MA") cSMA, errorSMA = fit(x, y, K, "SMA") cISMA, errorISMA = fit(x, y, K, "ISMA") print "MA RMS Error: ", errorMA print "SMA RMS Error: ", errorSMA print "ISMA RMS Error: ", errorISMA
from gpfit.fit import fit from numpy import logspace, log, log10, random import numpy as np import matplotlib.pyplot as plt # fixed initial guess for fitting # random.seed(33404) mdot = np.array([0.001411400768, 0.04734265813, 0.1014552215]) delta_p_inv = np.array([1121.643023, 908.6306982, 29.95485818]) K = 1 x = log(mdot) y = log(delta_p_inv) # plt.plot(mdot,delta_p) # plt.show() cMA, errorMA = fit(x, y, K, "MA") cSMA, errorSMA = fit(x, y, K, "SMA") # cISMA, errorISMA = fit(x, y, K, "ISMA") print "MA RMS Error: %.5g" % errorMA print "SMA RMS Error: %.5g" % errorSMA # print "ISMA RMS Error: %.5g" % errorISMA
from gpfit.fit import fit from numpy import logspace, log, log10 n = 501 u = logspace(0,log10(3),n) w = (u**2+3)/(u+1)**2 x = log(u) y = log(w) K = 3 Type = "ISMA" cstrt, rms_error = fit(x,y,K,Type)
