Python numpy.polynomial.polynomial.polyfit() Examples
The following are 5
code examples of numpy.polynomial.polynomial.polyfit().
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numpy.polynomial.polynomial
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Example #1
| Source File: computer.py From allesfitter with MIT License | 6 votes |
|
def baseline_hybrid_poly(*args):
x, y, yerr_w, xx, params, inst, key = args
polyorder = int(config.BASEMENT.settings['baseline_'+key+'_'+inst][-1])
xx = (xx - x[0])/x[-1] #polyfit needs the xx-axis scaled to [0,1], otherwise it goes nuts
x = (x - x[0])/x[-1] #polyfit needs the x-axis scaled to [0,1], otherwise it goes nuts
if polyorder>=0:
yerr_weights = yerr_w/np.nanmean(yerr_w)
weights = 1./yerr_weights
ind = np.isfinite(y) #polyfit can't handle NaN
params_poly = poly.polyfit(x[ind],y[ind],polyorder,w=weights[ind]) #WARNING: returns params in reverse order than np.polyfit!!!
baseline = poly.polyval(xx, params_poly) #evaluate on xx (!)
else:
raise ValueError("'polyorder' has to be > 0.")
return baseline
#==============================================================================
#::: calculate baseline: hybrid_spline (like Gillon+2012, but with a cubic spline)
#==============================================================================
Example #2
| Source File: FitDisp.py From altanalyze with Apache License 2.0 | 6 votes |
|
def disp(hgvfile):
lab={}
me=[]
std=[]
for line in open(hgvfile,'rU').xreadlines():
data = line.rstrip()
t = string.split(data,'\t')
me.append(float(t[2]))
std.append(float(t[1]))
me=np.asarray(me)
std=np.asarray(std)
coefs = poly.polyfit(me, std, 2)
ffit = poly.Polynomial(coefs)
print len(ffit(me))
np.savetxt("fitted.txt",ffit(me),delimiter="\t")
fig1 = plt.figure()
ax1 = fig1.add_subplot(111)
ax1.scatter(me, std, facecolors='None')
ax1.plot(me, ffit(me))
plt.show()
Example #3
| Source File: rplot.py From Computable with MIT License | 5 votes |
|
def work(self, fig=None, ax=None):
"""Draw the polynomial fit on matplotlib figure or axis
Parameters:
-----------
fig: matplotlib figure
ax: matplotlib axis
Returns:
--------
a tuple with figure and axis objects
"""
if ax is None:
if fig is None:
return fig, ax
else:
ax = fig.gca()
from numpy.polynomial.polynomial import polyfit
from numpy.polynomial.polynomial import polyval
x = self.data[self.aes['x']]
y = self.data[self.aes['y']]
min_x = min(x)
max_x = max(x)
c = polyfit(x, y, self.degree)
x_ = np.linspace(min_x, max_x, len(x))
y_ = polyval(x_, c)
ax.plot(x_, y_, lw=self.lw, c=self.colour)
return fig, ax
Example #4
| Source File: test_polynomial.py From Computable with MIT License | 4 votes |
|
def test_polyfit(self) :
def f(x) :
return x*(x - 1)*(x - 2)
# Test exceptions
assert_raises(ValueError, poly.polyfit, [1], [1], -1)
assert_raises(TypeError, poly.polyfit, [[1]], [1], 0)
assert_raises(TypeError, poly.polyfit, [], [1], 0)
assert_raises(TypeError, poly.polyfit, [1], [[[1]]], 0)
assert_raises(TypeError, poly.polyfit, [1, 2], [1], 0)
assert_raises(TypeError, poly.polyfit, [1], [1, 2], 0)
assert_raises(TypeError, poly.polyfit, [1], [1], 0, w=[[1]])
assert_raises(TypeError, poly.polyfit, [1], [1], 0, w=[1, 1])
# Test fit
x = np.linspace(0, 2)
y = f(x)
#
coef3 = poly.polyfit(x, y, 3)
assert_equal(len(coef3), 4)
assert_almost_equal(poly.polyval(x, coef3), y)
#
coef4 = poly.polyfit(x, y, 4)
assert_equal(len(coef4), 5)
assert_almost_equal(poly.polyval(x, coef4), y)
#
coef2d = poly.polyfit(x, np.array([y, y]).T, 3)
assert_almost_equal(coef2d, np.array([coef3, coef3]).T)
# test weighting
w = np.zeros_like(x)
yw = y.copy()
w[1::2] = 1
yw[0::2] = 0
wcoef3 = poly.polyfit(x, yw, 3, w=w)
assert_almost_equal(wcoef3, coef3)
#
wcoef2d = poly.polyfit(x, np.array([yw, yw]).T, 3, w=w)
assert_almost_equal(wcoef2d, np.array([coef3, coef3]).T)
# test scaling with complex values x points whose square
# is zero when summed.
x = [1, 1j, -1, -1j]
assert_almost_equal(poly.polyfit(x, x, 1), [0, 1])
Example #5
| Source File: test_polynomial.py From ImageFusion with MIT License | 4 votes |
|
def test_polyfit(self):
def f(x):
return x*(x - 1)*(x - 2)
# Test exceptions
assert_raises(ValueError, poly.polyfit, [1], [1], -1)
assert_raises(TypeError, poly.polyfit, [[1]], [1], 0)
assert_raises(TypeError, poly.polyfit, [], [1], 0)
assert_raises(TypeError, poly.polyfit, [1], [[[1]]], 0)
assert_raises(TypeError, poly.polyfit, [1, 2], [1], 0)
assert_raises(TypeError, poly.polyfit, [1], [1, 2], 0)
assert_raises(TypeError, poly.polyfit, [1], [1], 0, w=[[1]])
assert_raises(TypeError, poly.polyfit, [1], [1], 0, w=[1, 1])
# Test fit
x = np.linspace(0, 2)
y = f(x)
#
coef3 = poly.polyfit(x, y, 3)
assert_equal(len(coef3), 4)
assert_almost_equal(poly.polyval(x, coef3), y)
#
coef4 = poly.polyfit(x, y, 4)
assert_equal(len(coef4), 5)
assert_almost_equal(poly.polyval(x, coef4), y)
#
coef2d = poly.polyfit(x, np.array([y, y]).T, 3)
assert_almost_equal(coef2d, np.array([coef3, coef3]).T)
# test weighting
w = np.zeros_like(x)
yw = y.copy()
w[1::2] = 1
yw[0::2] = 0
wcoef3 = poly.polyfit(x, yw, 3, w=w)
assert_almost_equal(wcoef3, coef3)
#
wcoef2d = poly.polyfit(x, np.array([yw, yw]).T, 3, w=w)
assert_almost_equal(wcoef2d, np.array([coef3, coef3]).T)
# test scaling with complex values x points whose square
# is zero when summed.
x = [1, 1j, -1, -1j]
assert_almost_equal(poly.polyfit(x, x, 1), [0, 1])
