Ejemplo n.º 1
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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
Ejemplo n.º 2
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    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)
Ejemplo n.º 3
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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
Ejemplo n.º 4
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    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]
Ejemplo n.º 5
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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
Ejemplo n.º 6
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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()  
Ejemplo n.º 7
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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))
Ejemplo n.º 8
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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
Ejemplo n.º 9
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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()
Ejemplo n.º 10
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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
Ejemplo n.º 11
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    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)
Ejemplo n.º 12
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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
Ejemplo n.º 14
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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
Ejemplo n.º 15
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 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]))
Ejemplo n.º 16
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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)
Ejemplo n.º 17
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    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])
Ejemplo n.º 18
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    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
Ejemplo n.º 19
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    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
Ejemplo n.º 20
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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
Ejemplo n.º 21
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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
Ejemplo n.º 22
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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
Ejemplo n.º 23
0
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
Ejemplo n.º 24
0
    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
Ejemplo n.º 25
0
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)
Ejemplo n.º 27
0
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
Ejemplo n.º 28
0
    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)
Ejemplo n.º 29
0
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)
Ejemplo n.º 30
0
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)))
# %%