getting_started_0.ipynb

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   "source": [
    "# Code example: Wiener filter"
   ]
  },
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   "cell_type": "markdown",
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     "slide_type": "subslide"
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   "source": [
    "## Introduction\n",
    "IFT starting point:\n",
    "\n",
    "$$d = Rs+n$$\n",
    "\n",
    "Typically, $s$ is a continuous field, $d$ a discrete data vector. Particularly, $R$ is not invertible.\n",
    "\n",
    "IFT aims at **inverting** the above uninvertible problem in the **best possible way** using Bayesian statistics.\n",
    "\n",
    "NIFTy (Numerical Information Field Theory) is a Python framework in which IFT problems can be tackled easily.\n",
    "\n",
    "Main Interfaces:\n",
    "\n",
    "- **Spaces**: Cartesian, 2-Spheres (Healpix, Gauss-Legendre) and their respective harmonic spaces.\n",
    "- **Fields**: Defined on spaces.\n",
    "- **Operators**: Acting on fields."
   ]
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   "source": [
    "## Wiener filter on one-dimensional fields\n",
    "\n",
    "### Assumptions\n",
    "\n",
    "- $d=Rs+n$, $R$ linear operator.\n",
    "- $\\mathcal P (s) = \\mathcal G (s,S)$, $\\mathcal P (n) = \\mathcal G (n,N)$ where $S, N$ are positive definite matrices.\n",
    "\n",
    "### Posterior\n",
    "The Posterior is given by:\n",
    "\n",
    "$$\\mathcal P (s|d) \\propto P(s,d) = \\mathcal G(d-Rs,N) \\,\\mathcal G(s,S) \\propto \\mathcal G (s-m,D) $$\n",
    "\n",
    "where\n",
    "$$m = Dj$$\n",
    "with\n",
    "$$D = (S^{-1} +R^\\dagger N^{-1} R)^{-1} , \\quad j = R^\\dagger N^{-1} d.$$\n",
    "\n",
    "Let us implement this in NIFTy!"
   ]
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   "cell_type": "markdown",
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   "source": [
    "### In NIFTy\n",
    "\n",
    "- We assume statistical homogeneity and isotropy. Therefore the signal covariance $S$ is diagonal in harmonic space, and is described by a one-dimensional power spectrum, assumed here as $$P(k) = P_0\\,\\left(1+\\left(\\frac{k}{k_0}\\right)^2\\right)^{-\\gamma /2},$$\n",
    "with $P_0 = 0.2, k_0 = 5, \\gamma = 4$.\n",
    "- $N = 0.2 \\cdot \\mathbb{1}$.\n",
    "- Number of data points $N_{pix} = 512$.\n",
    "- reconstruction in harmonic space.\n",
    "- Response operator:\n",
    "$$R = FFT_{\\text{harmonic} \\rightarrow \\text{position}}$$\n"
   ]
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   "cell_type": "code",
   "execution_count": null,
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   "outputs": [],
   "source": [
    "N_pixels = 512     # Number of pixels\n",
    "\n",
    "def pow_spec(k):\n",
    "    P0, k0, gamma = [.2, 5, 4]\n",
    "    return P0 / ((1. + (k/k0)**2)**(gamma / 2))"
   ]
  },
  {
   "cell_type": "markdown",
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     "slide_type": "slide"
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   },
   "source": [
    "### Implementation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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     "slide_type": "-"
    }
   },
   "source": [
    "#### Import Modules"
   ]
  },
  {
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   "source": [
    "import numpy as np\n",
    "import nifty8 as ift\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['figure.dpi'] = 100\n",
    "plt.style.use(\"seaborn-notebook\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### Implement Propagator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "def Curvature(R, N, Sh):\n",
    "    IC = ift.GradientNormController(iteration_limit=50000,\n",
    "                                    tol_abs_gradnorm=0.1)\n",
    "    # WienerFilterCurvature is (R.adjoint*N.inverse*R + Sh.inverse) plus some handy\n",
    "    # helper methods.\n",
    "    return ift.WienerFilterCurvature(R,N,Sh,iteration_controller=IC,iteration_controller_sampling=IC)"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "#### Conjugate Gradient Preconditioning\n",
    "\n",
    "- $D$ is defined via:\n",
    "$$D^{-1} = \\mathcal S_h^{-1} + R^\\dagger N^{-1} R.$$\n",
    "In the end, we want to apply $D$ to $j$, i.e. we need the inverse action of $D^{-1}$. This is done numerically (algorithm: *Conjugate Gradient*). \n",
    "\n",
    "<!--\n",
    "- One can define the *condition number* of a non-singular and normal matrix $A$:\n",
    "$$\\kappa (A) := \\frac{|\\lambda_{\\text{max}}|}{|\\lambda_{\\text{min}}|},$$\n",
    "where $\\lambda_{\\text{max}}$ and $\\lambda_{\\text{min}}$ are the largest and smallest eigenvalue of $A$, respectively.\n",
    "\n",
    "- The larger $\\kappa$ the slower Conjugate Gradient.\n",
    "\n",
    "- By default, conjugate gradient solves: $D^{-1} m = j$ for $m$, where $D^{-1}$ can be badly conditioned. If one knows a non-singular matrix $T$ for which $TD^{-1}$ is better conditioned, one can solve the equivalent problem:\n",
    "$$\\tilde A m = \\tilde j,$$\n",
    "where $\\tilde A = T D^{-1}$ and $\\tilde j = Tj$.\n",
    "\n",
    "- In our case $S^{-1}$ is responsible for the bad conditioning of $D$ depending on the chosen power spectrum. Thus, we choose\n",
    "\n",
    "$$T = \\mathcal F^\\dagger S_h^{-1} \\mathcal F.$$\n",
    "-->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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    }
   },
   "source": [
    "#### Generate Mock data\n",
    "\n",
    "- Generate a field $s$ and $n$ with given covariances.\n",
    "- Calculate $d$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "s_space = ift.RGSpace(N_pixels)\n",
    "h_space = s_space.get_default_codomain()\n",
    "HT = ift.HarmonicTransformOperator(h_space, target=s_space)\n",
    "\n",
    "# Operators\n",
    "Sh = ift.create_power_operator(h_space, power_spectrum=pow_spec)\n",
    "R = HT # @ ift.create_harmonic_smoothing_operator((h_space,), 0, 0.02)\n",
    "\n",
    "# Fields and data\n",
    "sh = Sh.draw_sample_with_dtype(dtype=np.float64)\n",
    "noiseless_data=R(sh)\n",
    "noise_amplitude = np.sqrt(0.2)\n",
    "N = ift.ScalingOperator(s_space, noise_amplitude**2)\n",
    "\n",
    "n = ift.Field.from_random(domain=s_space, random_type='normal',\n",
    "                          std=noise_amplitude, mean=0)\n",
    "d = noiseless_data + n\n",
    "j = R.adjoint_times(N.inverse_times(d))\n",
    "curv = Curvature(R=R, N=N, Sh=Sh)\n",
    "D = curv.inverse"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### Run Wiener Filter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
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   "outputs": [],
   "source": [
    "m = D(j)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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   "outputs": [],
   "source": [
    "# Get signal data and reconstruction data\n",
    "s_data = HT(sh).val\n",
    "m_data = HT(m).val\n",
    "d_data = d.val\n",
    "\n",
    "plt.plot(s_data, 'r', label=\"Signal\", linewidth=2)\n",
    "plt.plot(d_data, 'k.', label=\"Data\")\n",
    "plt.plot(m_data, 'k', label=\"Reconstruction\",linewidth=2)\n",
    "plt.title(\"Reconstruction\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "outputs": [],
   "source": [
    "plt.plot(s_data - s_data, 'r', label=\"Signal\", linewidth=2)\n",
    "plt.plot(d_data - s_data, 'k.', label=\"Data\")\n",
    "plt.plot(m_data - s_data, 'k', label=\"Reconstruction\",linewidth=2)\n",
    "plt.axhspan(-noise_amplitude,noise_amplitude, facecolor='0.9', alpha=.5)\n",
    "plt.title(\"Residuals\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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     "slide_type": "subslide"
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   },
   "source": [
    "#### Power Spectrum"
   ]
  },
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   "cell_type": "code",
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   "source": [
    "s_power_data = ift.power_analyze(sh).val\n",
    "m_power_data = ift.power_analyze(m).val\n",
    "plt.loglog()\n",
    "plt.xlim(1, int(N_pixels/2))\n",
    "ymin = min(m_power_data)\n",
    "plt.ylim(ymin, 1)\n",
    "xs = np.arange(1,int(N_pixels/2),.1)\n",
    "plt.plot(xs, pow_spec(xs), label=\"True Power Spectrum\", color='k',alpha=0.5)\n",
    "plt.plot(s_power_data, 'r', label=\"Signal\")\n",
    "plt.plot(m_power_data, 'k', label=\"Reconstruction\")\n",
    "plt.axhline(noise_amplitude**2 / N_pixels, color=\"k\", linestyle='--', label=\"Noise level\", alpha=.5)\n",
    "plt.axhspan(noise_amplitude**2 / N_pixels, ymin, facecolor='0.9', alpha=.5)\n",
    "plt.title(\"Power Spectrum\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Wiener Filter on Incomplete Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
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   "outputs": [],
   "source": [
    "# Operators\n",
    "Sh = ift.create_power_operator(h_space, power_spectrum=pow_spec)\n",
    "N = ift.ScalingOperator(s_space, noise_amplitude**2)\n",
    "# R is defined below\n",
    "\n",
    "# Fields\n",
    "sh = Sh.draw_sample_with_dtype(dtype=np.float64)\n",
    "s = HT(sh)\n",
    "n = ift.Field.from_random(domain=s_space, random_type='normal',\n",
    "                      std=noise_amplitude, mean=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "### Partially Lose Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
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   "outputs": [],
   "source": [
    "l = int(N_pixels * 0.2)\n",
    "h = int(N_pixels * 0.2 * 2)\n",
    "\n",
    "mask = np.full(s_space.shape, 1.)\n",
    "mask[l:h] = 0\n",
    "mask = ift.Field.from_raw(s_space, mask)\n",
    "\n",
    "R = ift.DiagonalOperator(mask)(HT)\n",
    "n = n.val_rw()\n",
    "n[l:h] = 0\n",
    "n = ift.Field.from_raw(s_space, n)\n",
    "\n",
    "d = R(sh) + n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
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   },
   "outputs": [],
   "source": [
    "curv = Curvature(R=R, N=N, Sh=Sh)\n",
    "D = curv.inverse\n",
    "j = R.adjoint_times(N.inverse_times(d))\n",
    "m = D(j)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Compute Uncertainty\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "m_mean, m_var = ift.probe_with_posterior_samples(curv, HT, 200, np.float64)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "### Get data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
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   },
   "outputs": [],
   "source": [
    "# Get signal data and reconstruction data\n",
    "s_data = s.val\n",
    "m_data = HT(m).val\n",
    "m_var_data = m_var.val\n",
    "uncertainty = np.sqrt(m_var_data)\n",
    "d_data = d.val_rw()\n",
    "\n",
    "# Set lost data to NaN for proper plotting\n",
    "d_data[d_data == 0] = np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
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   },
   "outputs": [],
   "source": [
    "plt.axvspan(l, h, facecolor='0.8',alpha=0.5)\n",
    "plt.fill_between(range(N_pixels), m_data - uncertainty, m_data + uncertainty, facecolor='0.5', alpha=0.5)\n",
    "plt.plot(s_data, 'r', label=\"Signal\", alpha=1, linewidth=2)\n",
    "plt.plot(d_data, 'k.', label=\"Data\")\n",
    "plt.plot(m_data, 'k', label=\"Reconstruction\", linewidth=2)\n",
    "plt.title(\"Reconstruction of incomplete data\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Wiener filter on two-dimensional field"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "N_pixels = 256      # Number of pixels\n",
    "sigma2 = 2.         # Noise variance\n",
    "\n",
    "def pow_spec(k):\n",
    "    P0, k0, gamma = [.2, 2, 4]\n",
    "    return P0 * (1. + (k/k0)**2)**(-gamma/2)\n",
    "\n",
    "s_space = ift.RGSpace([N_pixels, N_pixels])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
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   },
   "outputs": [],
   "source": [
    "h_space = s_space.get_default_codomain()\n",
    "HT = ift.HarmonicTransformOperator(h_space,s_space)\n",
    "\n",
    "# Operators\n",
    "Sh = ift.create_power_operator(h_space, power_spectrum=pow_spec)\n",
    "N = ift.ScalingOperator(s_space, sigma2)\n",
    "\n",
    "# Fields and data\n",
    "sh = Sh.draw_sample_with_dtype(dtype=np.float64)\n",
    "n = ift.Field.from_random(domain=s_space, random_type='normal',\n",
    "                      std=np.sqrt(sigma2), mean=0)\n",
    "\n",
    "# Lose some data\n",
    "\n",
    "l = int(N_pixels * 0.33)\n",
    "h = int(N_pixels * 0.33 * 2)\n",
    "\n",
    "mask = np.full(s_space.shape, 1.)\n",
    "mask[l:h,l:h] = 0.\n",
    "mask = ift.Field.from_raw(s_space, mask)\n",
    "\n",
    "R = ift.DiagonalOperator(mask)(HT)\n",
    "n = n.val_rw()\n",
    "n[l:h, l:h] = 0\n",
    "n = ift.Field.from_raw(s_space, n)\n",
    "curv = Curvature(R=R, N=N, Sh=Sh)\n",
    "D = curv.inverse\n",
    "\n",
    "d = R(sh) + n\n",
    "j = R.adjoint_times(N.inverse_times(d))\n",
    "\n",
    "# Run Wiener filter\n",
    "m = D(j)\n",
    "\n",
    "# Uncertainty\n",
    "m_mean, m_var = ift.probe_with_posterior_samples(curv, HT, 20, np.float64)\n",
    "\n",
    "# Get data\n",
    "s_data = HT(sh).val\n",
    "m_data = HT(m).val\n",
    "m_var_data = m_var.val\n",
    "d_data = d.val\n",
    "uncertainty = np.sqrt(np.abs(m_var_data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
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   "outputs": [],
   "source": [
    "cmap = ['magma', 'inferno', 'plasma', 'viridis'][1]\n",
    "\n",
    "mi = np.min(s_data)\n",
    "ma = np.max(s_data)\n",
    "\n",
    "fig, axes = plt.subplots(1, 2)\n",
    "\n",
    "data = [s_data, d_data]\n",
    "caption = [\"Signal\", \"Data\"]\n",
    "\n",
    "for ax in axes.flat:\n",
    "    im = ax.imshow(data.pop(0), interpolation='nearest', cmap=cmap, vmin=mi,\n",
    "                   vmax=ma)\n",
    "    ax.set_title(caption.pop(0))\n",
    "\n",
    "fig.subplots_adjust(right=0.8)\n",
    "cbar_ax = fig.add_axes([0.85, 0.15, 0.05, 0.7])\n",
    "fig.colorbar(im, cax=cbar_ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "mi = np.min(s_data)\n",
    "ma = np.max(s_data)\n",
    "\n",
    "fig, axes = plt.subplots(3, 2, figsize=(10, 15))\n",
    "sample = HT(curv.draw_sample(from_inverse=True)+m).val\n",
    "post_mean = (m_mean + HT(m)).val\n",
    "\n",
    "data = [s_data, m_data, post_mean, sample, s_data - m_data, uncertainty]\n",
    "caption = [\"Signal\", \"Reconstruction\", \"Posterior mean\", \"Sample\", \"Residuals\", \"Uncertainty Map\"]\n",
    "\n",
    "for ax in axes.flat:\n",
    "    im = ax.imshow(data.pop(0), interpolation='nearest', cmap=cmap, vmin=mi, vmax=ma)\n",
    "    ax.set_title(caption.pop(0))\n",
    "\n",
    "fig.subplots_adjust(right=0.8)\n",
    "cbar_ax = fig.add_axes([.85, 0.15, 0.05, 0.7])\n",
    "fig.colorbar(im, cax=cbar_ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Is the uncertainty map reliable?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "precise = (np.abs(s_data-m_data) < uncertainty)\n",
    "print(\"Error within uncertainty map bounds: \" + str(np.sum(precise) * 100 / N_pixels**2) + \"%\")\n",
    "\n",
    "plt.imshow(precise.astype(float), cmap=\"brg\")\n",
    "plt.colorbar()"
   ]
  }
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