def transform_points(src, dst, pts, steps):
surfaces = []
surf_array = np.zeros((1280, 720, 3))
first_line = poly.fit([src[0][0], dst[0][0]], [src[0][1], dst[0][1]], 1)
second_line = poly.fit([src[1][0], dst[1][0]], [src[1][1], dst[1][1]], 1)
third_line = poly.fit([src[2][0], dst[2][0]], [src[2][1], dst[2][1]], 1)
fourth_line = poly.fit([src[3][0], dst[3][0]], [src[3][1], dst[3][1]], 1)
first_points = first_line.linspace(steps)
second_points = second_line.linspace(steps)
third_points = third_line.linspace(steps)
fourth_points = fourth_line.linspace(steps)
for i in range(1, steps):
dst = np.float32([[first_points[0][-i], first_points[1][-i]],
[second_points[0][i], second_points[1][i]],
[third_points[0][i], third_points[1][i]],
[fourth_points[0][-i], fourth_points[1][-i]]])
M = cv2.getPerspectiveTransform(src, dst)
transformed_points = cv2.perspectiveTransform(pts, M)
surface = pygame.Surface((1280, 720))
pygame.draw.polygon(surface, (0, 130, 0),
np.squeeze(transformed_points, axis=0))
surfaces.append(surface)
return surfaces
def experiment(self):
self.leg = self.genlegpoly()
self.dataset = self.gendataset()
self.g2 = Polynomial.fit(self.dataset[0], self.dataset[1], 2,
[-1.0, 1.0])
self.g10 = Polynomial.fit(self.dataset[0], self.dataset[1], 10,
[-1.0, 1.0])
self.eoutg2 = self.mse4(self.g2, self.leg)
self.eoutg10 = self.mse4(self.g10, self.leg)
def momentum_analysis_global_chi2(meas_solver,
analysis_B_func,
B_name='Mau 13 (nominal)',
sigma=1.,
delta=.2,
N_hyp=7,
plot_chi2=False):
# use circle fit analysis mean to determine test_ps
meas_solver.analyze_trajectory(step=25,
stride=1,
query="z >= 8.41 & z <= 11.66",
B=analysis_B_func)
circle_fit_mom = meas_solver.df_reco.p.mean()
pmin = circle_fit_mom - delta
pmax = circle_fit_mom + delta
ps_test = np.linspace(pmin, pmax, N_hyp)
# run particles for each hypothesis momentum
sum_of_impacts2 = global_chi2_ptests(test_ps=ps_test,
test_B_func=analysis_B_func,
meas_solver=meas_solver,
sigma=sigma)
# fit quadratic to calculated chi2 values
c, b, a = Polynomial.fit(ps_test, sum_of_impacts2, 2).convert().coef
# use quadratic parameters to get momentum estimate
chi2_fit_mom = -b / (2 * a)
# plot (if selected)
if plot_chi2:
ptrue = meas_solver.init_conds.p0
ps_quad = np.linspace(pmin, pmax, 50)
y_quad = a * ps_quad**2 + b * ps_quad + c
fig = plt.figure()
plt.plot(ps_quad, y_quad, c='gray', label='Quadratic Fit')
plt.scatter(ps_test,
sum_of_impacts2,
c='black',
label='Calculated ' + r"$\chi^2$",
zorder=100)
ymax = np.max(sum_of_impacts2)
plt.plot([ptrue, ptrue], [0., ymax],
'r-',
linewidth=2,
label=f'True Momentum ={ptrue:.3f} MeV/c',
zorder=90)
plt.plot([chi2_fit_mom, chi2_fit_mom], [0., ymax],
'g--',
label='Momentum ' + r'$(\chi^2) =$' +
f'{chi2_fit_mom:.3f} MeV/c',
zorder=92)
plt.plot([circle_fit_mom, circle_fit_mom], [0., ymax],
'b--',
label=f'Momentum (circle) = {circle_fit_mom:.3f} MeV/c',
zorder=91)
plt.xlabel(r"$p$" + " [MeV/c]")
plt.ylabel(r"$\chi^2$")
plt.title('Global ' + r'$\chi^2$' + f' Momentum Fit: {B_name}')
l = plt.legend(loc='upper right')
l.set_zorder(110)
return chi2_fit_mom, circle_fit_mom, fig
# return results
return chi2_fit_mom, circle_fit_mom
def measure(self):
"""Compute some metrics that give an idea of how well each area is doing."""
for aoi in self.aoi.values():
if 'population' not in aoi:
print("No population info for", aoi['name'])
continue
if 'cases' not in aoi['data']:
print("No cases for", aoi['name'])
continue
if len(aoi['data']['cases']) < 2:
print("Insufficient cases for", aoi['name'])
continue
distance = []
pop = aoi['population']
distance = [(v or 0) / pop for v in aoi['data']['cases']]
window = min(14, len(distance))
polynomial = Polynomial.fit(range(window), distance[-window:],
min(3, window))
coefficients = list(polynomial.convert().coef[1:])
# If the trailing coefficients are 0, they're not included in coef.
# Add them back.
while len(coefficients) < 3:
coefficients.append(0)
aoi['velocity'] = coefficients[0]
aoi['acceleration'] = coefficients[1]
aoi['jerk'] = coefficients[2]
def polyDataFor(arr):
''' t -> vel fit
func = Poly.fit(arr[:, 0], arr[:, 1], 1)
data = np.empty_like(arr)
for i in range(len(arr)):
data[i] = [arr[i,0], func(arr[i,0])]'''
# before -> vel fit
func = Poly.fit(arr[:-1, 1],
(arr[1:, 1] - arr[:-1, 1]) / (arr[1:, 0] - arr[:-1, 0]),
1,
domain=[])
print("Poly fitted:", func, func.domain, func.window)
data = np.empty((arr.shape[0] - 1, arr.shape[1]))
prevV = arr[0, 1]
for i in range(len(arr) - 1):
prevT = arr[i, 0]
nextT = arr[i + 1, 0]
#nextV = prevV + func(prevV) * (nextT - prevT)
#fac = - 0.0305 * prevV # free in air -0.4690
#fac = -227.20539704480407 - 0.03047 * prevV # rollin on ground > 550 vel
#fac = func(prevV)
nextV = prevV + fac * (nextT - prevT)
data[i] = [nextT, nextV] #func(prevV)
prevV = nextV
return data
def poly_fit_eta():
from numpy.polynomial.polynomial import Polynomial
x = np.linspace(1,0,2000)
for i in range(10):
U1, U2 = [unitary_group.rvs(4) for _ in range(2)]
A1,A2 = [np.random.rand(16).reshape(4,4) + 1j*np.random.rand(16).reshape(4,4) for _ in range(2)]
H1 = 0.5*(A1 + A1.conj().T)
H2 = 0.5*(A2 + A2.conj().T)
te1 = expm(1j*H1*0.1)
te2 = expm(1j*H2*0.1)
U1_ = (te1 @ U1).conj().T
U2_ = (te2 @ U2).conj().T
RE = RightEnvironment()
params = [np.pi/4,0,0,0,0,0] # np.random.rand(6)
C = CircuitSolver()
M = C.M(params)
M_ = RE.circuit(r(U1), r(U2), r(U1_), r(U2_), M)
scores = []
for p in x:
scores.append(np.linalg.norm(p * M - M_))
coefs = Polynomial.fit(x[:10],scores[:10],deg = 2, domain = (1,0.9))
new_vals = [coefs(a) for a in x]
plt.plot(x, scores, label = "Exact")
plt.plot(x, new_vals, label = "Poly Fit")
plt.legend()
plt.show()
def test_poly1D_fit_weighted():
polyOrder = np.random.randint(2, 5)
numMeasurements = np.random.randint(50, 100)
xValues = np.random.rand(numMeasurements) * 100 - 50
coefficients = np.random.rand(polyOrder + 1) * 5 - 10
yValues = []
for x in xValues:
y = 0
for order in range(polyOrder + 1):
y += coefficients[order] * x**order
yValues.append(y + np.random.randn(1).item())
yValues = np.array(yValues)
yValues = yValues.reshape(yValues.size, 1)
weights = np.random.rand(numMeasurements)
cX = NumCpp.NdArray(1, xValues.size)
cY = NumCpp.NdArray(yValues.size, 1)
cWeights = NumCpp.NdArray(1, xValues.size)
cX.setArray(xValues)
cY.setArray(yValues)
cWeights.setArray(weights)
poly = Polynomial.fit(xValues, yValues.flatten(), polyOrder,
w=weights).convert().coef
polyC = NumCpp.Poly1d.fitWeighted(
cX, cY, cWeights, polyOrder).coefficients().getNumpyArray().flatten()
assert np.array_equal(np.round(poly, 1), np.round(polyC, 1))
def fit(x, y, deg, regularization=0):
# order = polyfit1d(y, x, deg, regularization)
if deg == "best":
order = best_fit(x, y)
else:
order = Polynomial.fit(y, x, deg=deg, domain=[]).coef[::-1]
return order
def weighted_linregress(x,
y,
degree=1,
weights=None) -> Tuple[np.ndarray, float]:
"""
https://en.wikipedia.org/wiki/Coefficient_of_determination#Definitions
:param x:
:param y:
:param degree:
:param weights:
:return:
- coefficients in increasing order of power of x (intercept, linear, quadratic etc.)
- coefficient of determination (R^2)
"""
series, (weighted_sum_of_squares_of_residuals, _, _,
_) = Polynomial.fit(x, y, degree, full=True, w=weights)
y_predicted = series(x)
mean_observed = np.mean(y) # \bar{y}
total_sum_of_squares = np.sum((y - mean_observed)**2) # SS_{tot}
# variance = total_sum_of_squares / (len(x) - 1)
# std_dev = np.sqrt(variance)
# std_err = std_dev / np.sqrt(len(x))
# regression_sum_of_squares = np.sum((y_predicted - mean_observed)**2)
sum_of_squares_of_residuals = np.sum((y - y_predicted)**2)
coeff_determination = 1 - (sum_of_squares_of_residuals /
total_sum_of_squares)
return series.convert().coef, coeff_determination.squeeze()
def plot_speed(data):
speed = (1.0 / 5.0) * data['length'] / (data['time'] / 1000 / 60)
x = np.arange(len(speed))
y = speed
fig, ax = plt.subplots(2, 1,
figsize=(20, 10)) #plt.figure(figsize = (20,10))
# ax[0].set_title("Words per minute", fontsize=24)
fit = Polynomial.fit(x, y, deg=12)
ax[0].scatter(x,
y,
linewidth=2,
color='limegreen',
label='speed (raw data)')
ax[0].plot(x,
fit(np.array(x).astype(float)),
linewidth=4,
color='forestgreen',
label='speed (polynomial fit)')
ax[0].legend(fontsize=24)
# ax[0].set_xlabel("sample num", fontsize=24)
# ax[0].set_xlabel()
ax[0].set_xticklabels([])
ax[0].set_ylabel("Speed, words per min", fontsize=24)
ax[0].set_ylim([0, 1.1 * np.max(y)])
ax[0].grid(True)
errors = data['errors']
x = np.arange(len(errors))
y = errors
fit = Polynomial.fit(x, y, deg=12)
ax[1].scatter(x, y, s=2, color='red', label='errors (raw data)')
ax[1].plot(x,
fit(np.array(x).astype(float)),
linewidth=4,
color='crimson',
label='errors (polynomial fit)')
ax[1].legend(fontsize=24)
ax[1].set_xlabel("sample num", fontsize=24)
ax[1].set_ylabel("Errors", fontsize=24)
ax[1].set_ylim([0, 1.1 * np.max(y)])
ax[1].grid(True)
plt.subplots_adjust(wspace=None, hspace=None)
plt.tight_layout()
plt.show(block=True)
return fig
def __init__(self, x, y, degree=1, weights=None):
self.x = x
self.y = y
self.degree = degree
self.w = weights
self.series, (weighted_sum_of_squares_of_residuals, _, _,
_) = Polynomial.fit(x, y, degree, full=True, w=weights)
def op(seq):
if seq == [1]:
return [1]
return [
int(i) for i in (rint(
Polynomial.fit(range(1,
len(seq) + 1), seq,
len(seq) - 1).convert().coef[::-1]).tolist())
]
def getSyncInfo(self):
"""
unpack everything
return: dictionary indexed by channel number
"""
if self.syncInfo is None:
l_allSyncTimes = []
l_allSyncChannels = []
l_allPhaseTimes = []
l_allPhaseChannels = []
l_allPhases = []
for iFile, fileName in enumerate(self.binFnList):
print iFile, len(self.binFnList),
rv = unpackOneFramesFile.unpackOneFramesFile(fileName)
l_allSyncTimes.append(rv['syncTimes'])
l_allSyncChannels.append(rv['syncChannels'])
l_allPhaseTimes.append(rv['times'])
l_allPhaseChannels.append(rv['channels'])
l_allPhases.append(rv['phases'])
print "now call _ltoa on lists"
allSyncTimes = _ltoa(l_allSyncTimes)
allSyncChannels = _ltoa(l_allSyncChannels).astype(int)
allPhaseTimes = _ltoa(l_allPhaseTimes)
allPhaseChannels = _ltoa(l_allPhaseChannels)
allPhases = _ltoa(l_allPhases)
self.syncInfo = {}
st0 = allSyncTimes[0]
st = np.unique(allSyncTimes) - st0
self.syncInfo['syncTimes'] = st
x = np.arange(len(st))
fit = Polynomial.fit(x, st, 1)
print("fit.coef=", fit.coef)
n = fit.domain[1]
x, yFit = fit.linspace(n=n + 1)
resid = st - yFit
syncFreq = 1 / fit.convert().coef[1]
self.syncInfo['fit'] = fit
self.syncInfo['syncTimesFit'] = yFit
self.syncInfo['syncFreq'] = syncFreq
print "sort by channel"
for channel in np.unique(allSyncChannels):
if channel > 0:
print "channel =", channel
sic = {}
self.syncInfo[channel] = sic
inds = np.where(allPhaseChannels == channel)[0]
sic['phaseTimes'] = allPhaseTimes[inds] - st0
sic['phases'] = allPhases[inds]
print "write", self.syncInfoFn
pickle.dump(self.syncInfo, open(self.syncInfoFn, 'wb'))
return self.syncInfo
def image_transition(image, src, dst, steps):
surfaces = []
first_line = poly.fit([src[0][0], dst[0][0]], [src[0][1], dst[0][1]], 1)
second_line = poly.fit([src[1][0], dst[1][0]], [src[1][1], dst[1][1]], 1)
third_line = poly.fit([src[2][0], dst[2][0]], [src[2][1], dst[2][1]], 1)
fourth_line = poly.fit([src[3][0], dst[3][0]], [src[3][1], dst[3][1]], 1)
first_points = first_line.linspace(steps)
second_points = second_line.linspace(steps)
third_points = third_line.linspace(steps)
fourth_points = fourth_line.linspace(steps)
for i in range(1, steps):
dst = np.float32([[first_points[0][i], first_points[1][i]],
[second_points[0][-i], second_points[1][-i]],
[third_points[0][-i], third_points[1][-i]],
[fourth_points[0][i], fourth_points[1][i]]])
warped_image = transform_view(image, src, dst)
image_surface = pygame.surfarray.make_surface(
warped_image.swapaxes(0, 1))
surfaces.append(image_surface)
return surfaces
def peak_and_std_via_resample(self, x_start, N=10, factor=0.75):
x_peak = self.find_peak(x_start)
h = self.dx * factor
x_around = np.array([x_peak - h, x_peak, x_peak + h]).reshape(-1, 1)
# collect the new posterior mean and variance
mu, var = self.predict(x_around, grads=False)
fs = np.random.multivariate_normal(mu.flatten(), var, N)
ds = []
for ii in range(N):
c = Polynomial.fit(x_around.flatten() - x_peak, fs[ii, :], deg=2)
c = c.coef
dx = -0.5 * c[1] / c[2]
ds.append(dx)
return x_peak, np.std(ds), mu[1], np.sqrt(np.abs(var[1][1]))
def extrapolate_to_N_inf():
MD_data = get_meta_MD_isotherms()
keys = ["N=4000", "N=2048", "N=500", "N=256"]
x = np.array([1.0 / 4000.0, 1.0 / 2048.0, 1.0 / 500.0, 1.0 / 256.0])
rho = []
p = []
for i in range(9):
j = -5 - i
rho.append(MD_data[keys[0]]["rho"][j])
p_of_x = []
for key in keys:
p_of_x.append(MD_data[key]["p"][j])
line = Poly.fit(x, p_of_x, deg=1)
p.append(line(0.0))
return np.array(rho), np.array(p)
def set_parameters(self,
q=0.5,
delta=6,
gamma=10,
death_rate=0.01,
Eo_frac=0.00001,
coefs=None,
beta_0=0.08,
degree=6):
"""
This simple routine simply sets the parameters for the model.
Note they are not all unity, I want you to figure out the
appropriate parameter values.
q - the attenuation of the infectivity, gamma in the population that is E
delta - the length of time of asymptomatic exposure
gamma - the length of time of infection
death_rate - the fraction of infected dying
Eo_frac - a small number that is multiplied by the population to give the initially exposed
degree - degree of polynomial used for beta(t)
coeffs - the set of initial coefficients for the polynomial, None in many cases
beta_o - a constant initial value of beta, will be changed in the optimization
"""
# Set beta as a polynomial object
if coefs is None:
beta_t = Polynomial.fit(self.days_from_zero,
beta_0 * np.ones(self.days_from_zero.size),
deg=degree)
else:
beta_t = Polynomial.basis(
degree,
domain=[self.days_from_zero[0], self.days_from_zero[-1]])
beta_t.coef = coefs
self.p['beta'] = beta_t
# Fill out the rest of the parameters from function arguments
self.p['q'] = q
self.p['delta'] = delta
self.p['gamma'] = gamma
self.p['d'] = death_rate
# Fill out the state vector y
S = self.p['N']
E = S * Eo_frac
I = 0
R = 0
D = 0
self.y_init = np.array([S, E, I, R, D])
def _fit_polyfit(self, time_series: np.ndarray) -> np.ndarray:
"""
Utility method which takes in a NumPy array representing a time-series,
fit a polynomial of order 'poly_degree' given at construction,
and returns the polynomial over the same x-axis range.
:param time_series: (NumPy array) The time-series to smooth
:return: (NumPy array) The smoothed time-series
"""
x = np.arange(len(time_series))
poly = Polynomial.fit(x, time_series, deg=self.poly_degree)
self.poly = poly
fitted_time_series = poly(x)
return fitted_time_series
def fit_peaks(self, f, verbose=False):
self.fit = Poly.fit(self.peaks[sclen], self.peaks[prof], 2)
if verbose:
print(f'Fitted 2nd order to measured peaks:\n {self.fit}')
x = np.arange(0, 10000)
y = self.fit(x)
yargmax = y.argmax()
self.fit_max = [x[yargmax], y[yargmax]]
if f:
mask = y > 0
f.add_trace(go.Scatter(x=x[mask], y=y[mask],
name='Polynomial Fit'))
return f
def scatterplot(x, y, x_label, y_label, x_lim, y_lim, title, fit_poly, phi_insp=None, deg=None):
fig = plt.figure()
plt.title(title)
plt.scatter(x, y)
if fit_poly:
p = Polynomial.fit(x, y, deg=deg)
plt.plot(np.sort(x), p(np.sort(x)), color='r', linewidth=3)
if not phi_insp is None:
plt.axvline(phi_insp, color='k', linestyle='--')
plt.grid(True)
plt.xlabel(x_label)
plt.ylabel(y_label)
if not y_lim is None:
plt.ylim(y_lim)
if not x_lim is None:
plt.xlim(x_lim)
# plt.show(block=True)
# plt.savefig(path_save_to)
return fig
def calculate_midline(
rot_seg_img: Union[np.ndarray, xr.DataArray], degree: int = 4, pad: int = 10
) -> Polynomial:
"""
Calculate a the midline for a single image by fitting a polynomial to the segmented
pharynx
Parameters
----------
rot_seg_img: Union[np.ndarray, xr.DataArray]
The rotated masked pharynx image
degree
the degree of the polynomial
pad
the number of pixels to "pad" the domain of the midline with respect to the
boundaries of the segmentation mask
Returns
-------
Polynomial
the estimated midline
Notes
-----
Right now this only works for images that this have been centered and aligned with their
anterior-posterior along the horizontal.
"""
import warnings
with warnings.catch_warnings():
warnings.simplefilter("ignore")
try:
rp = measure.regionprops(measure.label(rot_seg_img))[0]
xs, ys = rp.coords[:, 1], rp.coords[:, 0]
left_bound, _, _, right_bound = rp.bbox
return Polynomial.fit(
xs, ys, degree, domain=[left_bound - pad, right_bound + pad]
)
except IndexError:
# Indicates trying to measure on TL for example
return None
def get_spec_arc(x):
"""
Fits an arc to a set of wavelengths including increasingly high order terms
until satisfactory convergence. If an 8th order polynomial still fails, a
ValueError is raised.
"""
#Check px scale is smooth
npx = len(x)
px = 1 + np.arange(npx)
for deg in range(1, 9):
arc = Polynomial.fit(px / npx, x, deg=deg)
c = arc(px / npx)
diff = (c - x)[1:] / np.diff(x)
maxdiff = np.max(abs(diff))
#poly fitted pixels within 1% of pixel width from data
if maxdiff < 0.01:
break
else:
raise ValueError("Could not adequately fit wavelengths(pixels)")
return arc, npx
def plot_prc(phi, delta_phi, model_name, signal=None, scatter=True):
fig = plt.figure(figsize=(20, 10))
tmp = list(zip(phi, delta_phi))
tmp.sort(key=lambda a: a[0])
phi, delta_phi = zip(*tmp)
p = Polynomial.fit(phi, delta_phi, deg=30)
if scatter == True:
plt.scatter(np.array(phi),
np.array(delta_phi),
s=0.5,
color='k',
label="Integral Phase Change")
else:
plt.plot(np.array(phi),
np.array(delta_phi),
linewidth=3,
color='k',
label="Integral Phase Change")
if not signal is None:
plt.plot((np.array(phi)),
np.pi * scale(signal),
ls='-',
color='b',
linewidth=2,
label="signal")
plt.xlabel(r"$\phi$, rad", fontsize=24)
plt.ylabel(r"$Z(\phi)$, rad", fontsize=24)
tcks = [0, np.pi / 2, np.pi, 3 * np.pi / 2, 2 * np.pi]
lbls = ["0", r"$\frac{\pi}{2}$", r"$\pi$", r"$\frac{3\pi}{2}$", r"$2\pi$"]
plt.xticks(ticks=tcks, labels=lbls, fontsize=20)
tcks = [-np.pi, -np.pi / 2, 0, np.pi / 2, np.pi]
lbls = [r"$-\pi$", r"$-\frac{\pi}{2}$", "0", r"$\frac{\pi}{2}$", r"$\pi$"]
plt.yticks(ticks=tcks, labels=lbls, fontsize=20)
plt.legend(fontsize=24)
ttl = model_name.replace("_", " ")
plt.title(f"PRC {ttl}", fontdict={"size": 24})
plt.grid(True)
return fig
def frequency_comb(self, comb, wave, lines=None):
self.nord, self.ncol = comb.shape
# TODO give everything better names
pixel, order, wavelengths = [], [], []
n_all, f_all = [], []
comb = np.ma.masked_array(comb, mask=comb <= 0)
for i in range(self.nord):
# Find Peak positions in current order
n, peaks = self._find_peaks(comb[i])
# Determine the n-offset of this order, relative to the anchor frequency
# Use the existing absolute wavelength calibration as reference
y_ord = np.full(len(peaks), i)
w_old = interp1d(np.arange(len(wave[i])), wave[i],
kind="cubic")(peaks)
# w_old = np.interp(peaks, np.arange(len(wave[i])), wave[i])
# w_old = self.evaluate_solution(peaks, y_ord, wave_solution)
f_old = speed_of_light / w_old
# fr: repeating frequency
# fd: anchor frequency of this order, needs to be shifted to the absolute reference frame
res = Polynomial.fit(n, f_old, deg=1, domain=[])
fd, fr = res.coef
n = np.round((f_old - fd) / fr)
res = Polynomial.fit(n, f_old, deg=1, domain=[])
fd, fr = res.coef
# The first order is used as the baseline for all other orders
# The choice is arbitrary and doesn't matter
if i == 0:
f0 = fd
# n0: shift in n, relative to the absolute reference
# shift n to the absolute grid, so that all peaks are given by the same f0
n_offset = (f0 - fd) / fr
n_offset = int(round(n_offset))
n -= n_offset
n_all += [n]
f_all += [f_old]
pixel += [peaks]
order += [y_ord]
fd += n_offset * fr
logger.debug("LFC Order: %i, f0: %.3f, fr: %.5f, n0: %.2f", i, fd,
fr, n_offset)
# Merge Data
n_all = np.concatenate(n_all)
f_all = np.concatenate(f_all)
# Fit f0 and fr to all data
# (fr, f0), cov = np.polyfit(n_all, f_all, deg=1, cov=True)
res = Polynomial.fit(n_all, f_all, deg=1, domain=[])
f0, fr = res.coef
logger.debug("Laser Frequency Comb Anchor Frequency: %.3f 10**10 Hz",
f0)
logger.debug(
"Laser Frequency Comb Repeating Frequency: %.5f 10**10 Hz", fr)
# All peaks are then given by f0 + n * fr
wavelengths = speed_of_light / (f0 + n_all * fr)
pixel = np.concatenate(pixel)
order = np.concatenate(order)
flag = np.full(len(wavelengths), True)
laser_lines = np.rec.fromarrays(
(wavelengths, pixel, pixel, order, flag),
names=("wll", "posm", "posc", "order", "flag"),
)
# Use now better resolution to find the new solution
# A single pass of discarding outliers should be enough
coef = self.build_2d_solution(laser_lines)
resid = self.calculate_residual(coef, laser_lines)
laser_lines["flag"] = np.abs(resid) < self.threshold
# laser_lines["flag"] = np.abs(resid) < resid.std() * 5
coef = self.build_2d_solution(laser_lines)
new_wave = self.make_wave(coef)
aic = self.calculate_AIC(laser_lines, coef)
ngood = np.count_nonzero(laser_lines["flag"])
logger.info(f"Laser Frequency Comb solution based on {ngood} lines.")
if self.plot:
residual = wave - new_wave
residual = residual.ravel()
area = np.percentile(residual, (32, 50, 68))
area = area[0] - 5 * (area[1] - area[0]), area[0] + 5 * (area[2] -
area[1])
plt.hist(residual, bins=100, range=area)
plt.title("ThAr - LFC")
plt.xlabel(r"$\Delta\lambda$ [Å]")
plt.ylabel("N")
plt.show()
if self.plot:
if lines is not None:
self.plot_residuals(
lines,
coef,
title=
"GasLamp Line Residuals in the Laser Frequency Comb Solution",
)
self.plot_residuals(
laser_lines,
coef,
title="Laser Frequency Comb Peak Residuals in the LFC Solution",
)
if self.plot:
wave_img = wave
plt.suptitle(
"Difference between GasLamp Solution and Laser Frequency Comb solution\nEach plot shows one order."
)
for i in range(len(new_wave)):
plt.subplot(len(new_wave) // 4 + 1, 4, i + 1)
plt.plot(wave_img[i] - new_wave[i])
plt.show()
if self.plot:
self.plot_results(new_wave, comb)
return new_wave
def plot_prc_experimental(phi,
delta_phi,
model_name,
stim_duration,
stim_amp,
signal=None,
phase=None,
scatter=True,
fit=False):
fig = plt.figure(figsize=(20, 10))
tmp = list(zip(phi, delta_phi))
tmp.sort(key=lambda a: a[0])
phi, delta_phi = zip(*tmp)
delta_phi = np.array(delta_phi) / (stim_amp * stim_duration)
p = Polynomial.fit(phi, delta_phi, deg=50)
# delta_phi = savgol_filter(np.array(delta_phi), 11,5)
phi = np.array(phi)
if scatter == True:
plt.scatter(np.array(phi),
np.array(delta_phi),
s=0.5,
color='k',
label="Integral Phase Change")
else:
plt.plot(np.array(phi),
np.array(delta_phi),
linewidth=3,
color='k',
label="Integral Phase Change")
if fit == True:
plt.plot((np.array(phi)),
p(np.sort(np.array(phi))),
ls='dashed',
color='r',
linewidth=2,
label="Polynomial Fit")
if not signal is None:
plt.plot((np.array(phase)),
np.pi * scale(signal),
ls='-',
color='b',
linewidth=2,
label="signal")
plt.xlabel(r"$\phi$, rad", fontsize=24)
plt.ylabel(r"$Z(\phi)$, rad", fontsize=24)
tcks = [0, np.pi / 2, np.pi, 3 * np.pi / 2, 2 * np.pi]
lbls = ["0", r"$\frac{\pi}{2}$", r"$\pi$", r"$\frac{3\pi}{2}$", r"$2\pi$"]
plt.xticks(ticks=tcks, labels=lbls, fontsize=20)
tcks = [-np.pi, -np.pi / 2, 0, np.pi / 2, np.pi]
lbls = [r"$-\pi$", r"$-\frac{\pi}{2}$", "0", r"$\frac{\pi}{2}$", r"$\pi$"]
plt.yticks(ticks=tcks, labels=lbls, fontsize=20)
plt.legend(fontsize=24)
ttl = model_name.replace("_", " ")
plt.title(
f"PRC {ttl}, stim_duration = {stim_duration}, stim_amp = {stim_amp}",
fontdict={"size": 24})
plt.grid(True)
return fig
#print(f"Type: {type(image_copy)}, and dimensions: {image_copy.shape}")
thresh_idx = (image[:, :, 0] < 200) | (image[:, :, 1] < 200) | (image[:, :, 2]
< 200)
image_copy[thresh_idx] = [0, 0, 0]
plt.imshow(image_copy)
#mpimg.imsave("image_color_selection.jpg",image_copy)
#%% Region mask
image_region = image_copy.copy()
xsize = image_region.shape[0]
ysize = image_region.shape[1]
left_buttom = [xsize, 0]
right_buttom = [xsize, ysize]
apex = [4.2 * xsize / 7, ysize / 2]
# Fit lines using Polynomial.fit
left_fit = poly.fit((left_buttom[0], apex[0]), (left_buttom[1], apex[1]), 1,
[])
right_fit = poly.fit((right_buttom[0], apex[0]), (right_buttom[1], apex[1]), 1,
[])
#buttom_fit = poly.fit((left_buttom[0], right_buttom[0]), (left_buttom[1], right_buttom[1]), 0, [])
X, Y = np.meshgrid(
np.arange(0, xsize), np.arange(0, ysize), sparse=False, indexing='ij'
) # xy indexing by default means you should make it ij to get X = len(x)*len(y) otherwise you get X = len(y)*len(x)
region_mask = (Y > left_fit(X)) & (Y < right_fit(X)) & (X < left_buttom[0])
image_region[~region_mask] = [50, 50, 250]
plt.imshow(image_region)
#mpimg.imsave("image_region_mask.jpg",image_region)
#%%
image_lines = image.copy()
image_lines[~thresh_idx & region_mask] = [255, 0, 0]
plt.imshow(image_lines)
def fit(x, y, deg, regularization=0):
# order = polyfit1d(y, x, deg, regularization)
order = Polynomial.fit(y, x, deg=deg, domain=[]).coef[::-1]
return order
def fit(points):
if type(points) == list: points = np.array(points)
poly = P.fit(points[:, 0], points[:, 1], 1, domain=[-1, 1])
return Line.from_polynomial(poly)
def get_calibration_polynomial(calibration_table):
u"""From calibration points, computes a best-fit quadratic equation."""
return Polynomial.fit(
[x['reading'] for x in calibration_table],
[y['diameter'] for y in calibration_table],
2)
import plotly.graph_objects as go
import plotly.express as px
#https://repository.upenn.edu/cgi/viewcontent.cgi?article=1000&context=scn_tooldata
s1805 = [[500, 1.287], [1000, 0.914], [1500, 0.742], [2000, 0.644],
[2500, 0.569], [3000, 0.524], [3500, 0.490], [4000, 0.459],
[4500, 0.438], [5000, 0.420], [5500, 0.409], [6000, 0.399]]
s1805 = pd.DataFrame(s1805, columns=['Speed', 'Thickness'])
from scipy.optimize import curve_fit
# %%
def spin_asymptote(x, a, b, c, d):
return (a / (((x * c) + b)**2))
# %%
s1805_fit = Poly.fit(s1805['Speed'], s1805['Thickness'], 2)
x = np.linspace(500, 6000)
y = s1805_fit(x)
f = px.scatter(s1805, 'Speed', 'Thickness')
f.add_trace(go.Scatter(x=x, y=y))
f
# %%
popt, pcov = curve_fit(spin_asymptote, s1805['Speed'], s1805['Thickness'])
# %%
f.add_trace(go.Scatter(x=x, y=spin_asymptote(x, *popt)))
# %%