Interpolates MRMS data to a different grid using cubic bivariate splines
507274
def interpolate_grid(self, in_lon, in_lat):
        
        out_data = np.zeros((self.data.shape[0], in_lon.shape[0], in_lon.shape[1]))
        for d in range(self.data.shape[0]):
            print("Loading ", d, self.variable, self.start_date)
            if self.data[d].max() > -999:
                step = self.data[d]
                step[step < 0] = 0
                if self.lat[-1] < self.lat[0]:
                    spline = RectBivariateSpline(self.lat[::-1], self.lon, step[::-1], kx=3, ky=3)
                else:
                    spline = RectBivariateSpline(self.lat, self.lon, step, kx=3, ky=3)
                print("Evaluating", d, self.variable, self.start_date)
                flat_data = spline.ev(in_lat.ravel(), in_lon.ravel())
                out_data[d] = flat_data.reshape(in_lon.shape)
                del spline
            else:
                print(d, " is missing")
                out_data[d] = -9999
        return out_data
719151
def background_rms_mesh_ma(self):
        

        if len(self._bkgrms1d) == self.nboxes:
            return self.background_rms_mesh
        else:
            return self._make_2d_array(self._bkgrms1d)
1240381
def q12d(grid, spacing=None, E=1e5, nu=0.3, dirichlet_boundary=True,
         format=None):
    
    X, Y = tuple(grid)

    if X < 1 or Y < 1:
        raise ValueError('invalid grid shape')

    if dirichlet_boundary:
        X += 1
        Y += 1

    pts = np.mgrid[0:X+1, 0:Y+1]
    pts = np.hstack((pts[0].T.reshape(-1, 1) - X / 2.0,
                     pts[1].T.reshape(-1, 1) - Y / 2.0))

    if spacing is None:
        DX, DY = 1, 1
    else:
        DX, DY = tuple(spacing)
        pts *= [DX, DY]

    # compute local stiffness matrix
    lame = E * nu / ((1 + nu) * (1 - 2*nu))  # Lame's first parameter
    mu = E / (2 + 2*nu)                   # shear modulus

    vertices = np.array([[0, 0], [DX, 0], [DX, DY], [0, DY]])
    K = q12d_local(vertices, lame, mu)

    nodes = np.arange((X+1)*(Y+1)).reshape(X+1, Y+1)
    LL = nodes[:-1, :-1]
    Id = (2*LL).repeat(K.size).reshape(-1, 8, 8)
    J = Id.copy()
    Id += np.tile([0, 1, 2, 3, 2*X + 4, 2*X + 5, 2*X + 2, 2*X + 3], (8, 1))
    J += np.tile([0, 1, 2, 3, 2*X + 4, 2*X + 5, 2*X + 2, 2*X + 3], (8, 1)).T
    V = np.tile(K, (X*Y, 1))

    Id = np.ravel(Id)
    J = np.ravel(J)
    V = np.ravel(V)

    # sum duplicates
    A = coo_matrix((V, (Id, J)), shape=(pts.size, pts.size)).tocsr()
    A = A.tobsr(blocksize=(2, 2))

    del Id, J, V, LL, nodes

    B = np.zeros((2 * (X+1)*(Y+1), 3))
    B[0::2, 0] = 1
    B[1::2, 1] = 1
    B[0::2, 2] = -pts[:, 1]
    B[1::2, 2] = pts[:, 0]

    if dirichlet_boundary:
        mask = np.zeros((X+1, Y+1), dtype='bool')
        mask[1:-1, 1:-1] = True
        mask = np.ravel(mask)
        data = np.zeros(((X-1)*(Y-1), 2, 2))
        data[:, 0, 0] = 1
        data[:, 1, 1] = 1
        indices = np.arange((X-1)*(Y-1))
        indptr = np.concatenate((np.array([0]), np.cumsum(mask)))
        P = bsr_matrix((data, indices, indptr),
                       shape=(2*(X+1)*(Y+1), 2*(X-1)*(Y-1)))
        Pt = P.T
        A = P.T * A * P

        B = Pt * B

    return A.asformat(format), B
731555
def make_gradients(dims=DEFAULT_DIMS):
    
    return np.meshgrid(
        np.linspace(0.0, 1.0, dims[0]),
        np.linspace(0.0, 1.0, dims[1])
    )
418326
def rigid_linear_interpolate(x_axis, y_axis, x_new_axis):
    
    f = interp1d(x_axis, y_axis)
    return f(x_new_axis)
996400
def adapt(self, d, x):
        
        y = np.dot(self.w, x)
        e = d - y
        nu = self.mu / (self.eps + np.dot(x, x))
        self.w += nu * x * e**3
791257
function CubicInterpolant( parameterPositions, sampleValues, sampleSize, resultBuffer ) {

	Interpolant.call( this, parameterPositions, sampleValues, sampleSize, resultBuffer );

	this._weightPrev = - 0;
	this._offsetPrev = - 0;
	this._weightNext = - 0;
	this._offsetNext = - 0;

}
152176
def _simulate_matern(D1, D2, D3, N, num_inducing, plot_sim=False):
    """"""
    Q_signal = 4
    import GPy
    import numpy as np
    np.random.seed(3000)

    k = GPy.kern.Matern32(Q_signal, 1., lengthscale=(np.random.uniform(1, 6, Q_signal)), ARD=1)
    for i in range(Q_signal):
        k += GPy.kern.PeriodicExponential(1, variance=1., active_dims=[i], period=3., lower=-2, upper=6)
    t = np.c_[[np.linspace(-1, 5, N) for _ in range(Q_signal)]].T
    K = k.K(t)
    s2, s1, s3, sS = np.random.multivariate_normal(np.zeros(K.shape[0]), K, size=(4))[:, :, None]

    Y1, Y2, Y3, S1, S2, S3 = _generate_high_dimensional_output(D1, D2, D3, s1, s2, s3, sS)

    slist = [sS, s1, s2, s3]
    slist_names = ["sS", "s1", "s2", "s3"]
    Ylist = [Y1, Y2, Y3]

    if plot_sim:
        from matplotlib import pyplot as plt
        import matplotlib.cm as cm
        import itertools
        fig = plt.figure("MRD Simulation Data", figsize=(8, 6))
        fig.clf()
        ax = fig.add_subplot(2, 1, 1)
        labls = slist_names
        for S, lab in zip(slist, labls):
            ax.plot(S, label=lab)
        ax.legend()
        for i, Y in enumerate(Ylist):
            ax = fig.add_subplot(2, len(Ylist), len(Ylist) + 1 + i)
            ax.imshow(Y, aspect='auto', cmap=cm.gray)  # @UndefinedVariable
            ax.set_title("Y{}".format(i + 1))
        plt.draw()
        plt.tight_layout()

    return slist, [S1, S2, S3], Ylist
127565
function (ctx, size, texMeters) {

    if (this.data.grid == 'none') return;

    // one grid feature each 2 meters

    var num = Math.floor(texMeters / 2);
    var step = size / (texMeters / 2); // 2 meters == <step> pixels
    var i, j, ii;

    ctx.fillStyle = this.data.gridColor;

    switch (this.data.grid) {
      case '1x1':
      case '2x2': {
        if (this.data.grid == '1x1') {
          num = num * 2;
          step = size / texMeters;
        }
        for (i = 0; i < num; i++) {
          ii = Math.floor(i * step);
          ctx.fillRect(0, ii, size, 1);
          ctx.fillRect(ii, 0, 1, size);
        }
        break;
      }
      case 'crosses': {
        var l = Math.floor(step / 20);
        for (i = 0; i < num + 1; i++) {
          ii = Math.floor(i * step);
          for (j = 0; j < num + 1; j++) {
            var jj = Math.floor(-l + j * step);
            ctx.fillRect(jj, ii, l * 2, 1);
            ctx.fillRect(ii, jj, 1, l * 2);
          }
        }
        break;
      }
      case 'dots': {
        for (i = 0; i < num + 1; i++) {
          for (j = 0; j < num + 1; j++) {
            ctx.beginPath(); ctx.arc(Math.floor(j * step), Math.floor(i * step), 4, 0, Math.PI * 2); ctx.fill();
          }
        }
        break;
      }
      case 'xlines': {
        for (i = 0; i < num; i++) {
          ctx.fillRect(Math.floor(i * step), 0, 1, size);
        }
        break;
      }
      case 'ylines': {
        for (i = 0; i < num; i++) {
          ctx.fillRect(0, Math.floor(i * step), size, 1);
        }
        break;
      }
    }
  }
905669
def reproject_to_grid_coordinates(self, grid_coordinates, interp=gdalconst.GRA_NearestNeighbour):
        
        source_dataset = self.grid_coordinates._as_gdal_dataset()
        dest_dataset = grid_coordinates._as_gdal_dataset()
        rb = source_dataset.GetRasterBand(1)
        rb.SetNoDataValue(NO_DATA_VALUE)
        rb.WriteArray(np.ma.filled(self.raster_data, NO_DATA_VALUE))

        gdal.ReprojectImage(source_dataset, dest_dataset,
                            source_dataset.GetProjection(),
                            dest_dataset.GetProjection(),
                            interp)
        dest_layer = self.clone_traits()
        dest_layer.grid_coordinates = grid_coordinates
        rb = dest_dataset.GetRasterBand(1)
        dest_layer.raster_data = np.ma.masked_values(rb.ReadAsArray(), NO_DATA_VALUE)
        return dest_layer
1287818
function interpolate(contours, accuracy) {
  return _.map(contours, function (contour) {
    var resContour = [];

    _.forEach(contour, function (point, idx) {
      // Never skip first and last points
      if (idx === 0 || idx === (contour.length - 1)) {
        resContour.push(point);
        return;
      }

      var prev = contour[idx - 1];
      var next = contour[idx + 1];

      var p, pPrev, pNext;

      // skip interpolateable oncurve points (if exactly between previous and next offcurves)
      if (!prev.onCurve && point.onCurve && !next.onCurve) {
        p = new math.Point(point.x, point.y);
        pPrev = new math.Point(prev.x, prev.y);
        pNext = new math.Point(next.x, next.y);
        if (pPrev.add(pNext).div(2).sub(p).dist() < accuracy) {
          return;
        }
      }
      // keep the rest
      resContour.push(point);
    });
    return resContour;
  });
}
641204
def multigrid(bounds, points_count):
    
    if len(bounds)==1:
        return np.linspace(bounds[0][0], bounds[0][1], points_count).reshape(points_count, 1)
    x_grid_rows = np.meshgrid(*[np.linspace(b[0], b[1], points_count) for b in bounds])
    x_grid_columns = np.vstack([x.flatten(order='F') for x in x_grid_rows]).T
    return x_grid_columns
1150485
def adapt(self, d, x):
        
        # create input matrix and target vector
        self.x_mem[:,1:] = self.x_mem[:,:-1]
        self.x_mem[:,0] = x
        self.d_mem[1:] = self.d_mem[:-1]
        self.d_mem[0] = d
        # estimate output and error
        self.y_mem = np.dot(self.x_mem.T, self.w)
        self.e_mem = self.d_mem - self.y_mem
        # update
        dw_part1 = np.dot(self.x_mem.T, self.x_mem) + self.ide_eps
        dw_part2 = np.linalg.solve(dw_part1, self.ide)
        dw = np.dot(self.x_mem, np.dot(dw_part2, self.e_mem))
        self.w += self.mu * dw
971225
def _flatten_mesh(self, Xs, term):
        """"""
        n = Xs[0].size

        if self.terms[term].istensor:
            terms = self.terms[term]
        else:
            terms = [self.terms[term]]

        X = np.zeros((n, self.statistics_['m_features']))
        for term_, x in zip(terms, Xs):
            X[:, term_.feature] = x.ravel()
        return X
397342
def interpolate_xarray_linear(xpoints, ypoints, values, shape, chunks=CHUNK_SIZE):
    """"""
    from scipy.interpolate.interpnd import (LinearNDInterpolator,
                                            _ndim_coords_from_arrays)

    if isinstance(chunks, (list, tuple)):
        vchunks, hchunks = chunks
    else:
        vchunks, hchunks = chunks, chunks

    points = _ndim_coords_from_arrays(np.vstack((np.asarray(ypoints),
                                                 np.asarray(xpoints))).T)

    interpolator = LinearNDInterpolator(points, values)

    grid_x, grid_y = da.meshgrid(da.arange(shape[1], chunks=hchunks),
                                 da.arange(shape[0], chunks=vchunks))

    # workaround for non-thread-safe first call of the interpolator:
    interpolator((0, 0))
    res = da.map_blocks(intp, grid_x, grid_y, interpolator=interpolator)

    return DataArray(res, dims=('y', 'x'))
1128870
def resample(grid, wl, flux):
    
    flux_rs = (interpolate.interp1d(wl, flux))(grid)
    return flux_rs
1202362
function cubicBezierAtTime(t,p1x,p1y,p2x,p2y,duration) {
      var ax = 0,bx = 0,cx = 0,ay = 0,by = 0,cy = 0;
      function sampleCurveX(t) {return ((ax * t + bx) * t + cx) * t;}
      function sampleCurveY(t) {return ((ay * t + by) * t + cy) * t;}
      function sampleCurveDerivativeX(t) {return (3.0 * ax * t + 2.0 * bx) * t + cx;}
      function solveEpsilon(duration) {return 1.0 / (200.0 * duration);}
      function solve(x,epsilon) {return sampleCurveY(solveCurveX(x,epsilon));}
      function fabs(n) {if (n >= 0) {return n;}else {return 0 - n;}}
      function solveCurveX(x,epsilon) {
        var t0,t1,t2,x2,d2,i;
        for (t2 = x, i = 0; i < 8; i++) {x2 = sampleCurveX(t2) - x; if (fabs(x2) < epsilon) {return t2;} d2 = sampleCurveDerivativeX(t2); if (fabs(d2) < 1e-6) {break;} t2 = t2 - x2 / d2;}
        t0 = 0.0; t1 = 1.0; t2 = x; if (t2 < t0) {return t0;} if (t2 > t1) {return t1;}
        while (t0 < t1) {x2 = sampleCurveX(t2); if (fabs(x2 - x) < epsilon) {return t2;} if (x > x2) {t0 = t2;}else {t1 = t2;} t2 = (t1 - t0) * 0.5 + t0;}
        return t2; // Failure.
      }
      cx = 3.0 * p1x; bx = 3.0 * (p2x - p1x) - cx; ax = 1.0 - cx - bx; cy = 3.0 * p1y; by = 3.0 * (p2y - p1y) - cy; ay = 1.0 - cy - by;
      return solve(t, solveEpsilon(duration));
    }