Code Updates -- Another update attempt

0_Introduction/1_glossary.ipynb

@@ -438,7 +438,7 @@
     "    * $\\mathbf{P}$ : parallactic angle\n",
     "* Jy : Jansky, Flux density unit, $1\\ \\mathrm{Jy} = 10^{-26} \\ \\mathrm{W\\, m^{-2}\\, Hz^{-1}}$\n",
     "* $\\kappa_{\\nu}$ : Radiative absorption coefficient\n",
-    "* $L$: Bolometric Luminosity ($\mathrm{W}$ or $\mathrm{erg\,s^{-1}}$)\n",
+    "* $L$: Bolometric Luminosity ($\\mathrm{W}$ or $\\mathrm{erg,s^{-1}}$)\n",
     "* $L_{\\nu}$: Spectral luminosity ($\\mathrm{W\\ s^{-1} \\ Hz^{-1}}$ or $\\mathrm{ergs\\, s^{-1} \\, Hz^{-1}}$)\n",
     "* $\\boldsymbol{\\mathcal{M}}$ : see $\\boldsymbol{\\mathcal{D}}$\n",
     "* $P$ : Power (W, ergs)\n",

1_Radio_Science/1_10_limits_of_single_dishes.ipynb

@@ -33,7 +33,7 @@
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
     "%matplotlib inline\n",
-    "from IPython.display import HTML \n",
+    "from IPython.display import HTML\n",
     "HTML('../style/course.css') #apply general CSS"
    ]
   },
@@ -50,8 +50,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import ipywidgets\n",
-    "from IPython.display import Image"
+    "from ipywidgets import interact\n",
+    "from IPython.display import Image\n",
+    "from textwrap import dedent"
    ]
   },
   {
@@ -144,7 +145,10 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "collapsed": true
+    "collapsed": true,
+    "jupyter": {
+     "outputs_hidden": true
+    }
    },
    "source": [
     "If we consider a generic radio telescope dish, ignoring blockage from feeds and structure and any practical issues, we can think of the dish as having a circular aperture. We will use the term 'primary beam' later in Chapter 7 to discuss this aperture in detail. Until then we can think of the dish aperture size as being the *collecting area*. The larger the aperture the more collecting area, thus the more *sensitive* (a measure of how well the telescope is able to measure a signal) the telescope. This is the same as in photography. Since we are modelling our simple telescope as a circle then the collection area $A$, or aperture size, is proportional to the square of the dish diameter $D$.\n",
@@ -508,7 +512,7 @@
    "lastKernelId": null
   },
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -522,9 +526,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.5"
+   "version": "3.12.3"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 4
 }

1_Radio_Science/1_9_a_brief_introduction_to_interferometry.ipynb

@@ -148,7 +148,7 @@
     "        to render fringes from multiple invocations of double_slit() into the\n",
     "        same intensity scale, i.e. for comparison.\n",
     "    \"\"\"\n",
-    "    \n",
+    "    positions = [positions] if isinstance(positions, float) else positions\n",
     "    p0 = np.atleast_1d(positions)\n",
     "    a0 = np.atleast_1d(intensities)\n",
     "    \n",
@@ -271,7 +271,7 @@
     "         **{**defaults,\n",
     "            **dict(baseline=(0.1, 2 , 0.01),\n",
     "                   wavelength=(0.05, 0.2, 0.01),\n",
-    "                   positions=FloatSlider(min=-1, max=1, step=0.01, description='position'))});"
+    "                   positions=(-1, 1, 0.01))});"
    ]
   },
   {
@@ -634,7 +634,7 @@
     "    ax.plot([0, 0], [-bl2, bl2], 'o', ms=10)\n",
     "    ax.plot([0, d2/2., d2/2., d2], [-bl2, -bl2, -d3/2., 0], '-k')\n",
     "    ax.plot([0, d2/2., d2/2., d2], [bl2, bl2, d3/2., 0], '-k')\n",
-    "    ax.text(0, 0, f'$b={baseline:.2f}\\lambda$', ha='right', va='bottom', size='xx-large')\n",
+    "    ax.text(0, 0, f'$b={baseline:.2f}\\\\lambda$', ha='right', va='bottom', size='xx-large')\n",
     "\n",
     "    # draw representation of sine wave from the central position\n",
     "    xw = np.arange(-d1, -d1 + (d1 + d2) / 4, 0.01)\n",
@@ -770,7 +770,7 @@
    "lastKernelId": null
   },
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -784,9 +784,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.5"
+   "version": "3.12.3"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 4
 }

1_Radio_Science/1_x_further_reading_and_references.ipynb

@@ -3,7 +3,10 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "collapsed": true
+    "collapsed": true,
+    "jupyter": {
+     "outputs_hidden": true
+    }
    },
    "source": [
     "***\n",
@@ -38,7 +41,7 @@
     "\n",
     "For technical aspects of this chapter, especially emission mechanisms, see [<cite data-cite='1981PEPI...25..432L'>Radiative Processes in Astrophysics</cite> &#10548;](http://adsabs.harvard.edu/abs/1985rpa..book.....R) and [<cite data-cite='2012tra..book.....W'>Tools of Radio Astronomy</cite> &#10548;](http://adsabs.harvard.edu/abs/2012tra..book.....W).\n",
     "\n",
-    "For an excellent account of the astronomical processes taking place in Cygnus A, see [<cite data-cite='1996A&ARv...7....1C'>Cygnus A</cite> &#10548;](https://ned.ipac.caltech.edu/level5/Carilli/Car_contents.html)."
+    "For an excellent account of the astronomical processes taking place in Cygnus A, see [<cite data-cite='1996A&ARv...7....1C'>Cygnus A</cite> &#10548;](https://ned.ipac.caltech.edu/level5/Carilli/Car_contents.html), and [<cite data-cite='10.3847/1538-4357/abb80e'>A Wideband Polarization Study of Cygnus A with the Jansky Very Large Array. I. The Observations and Data</cite>](https://ui.adsabs.harvard.edu/abs/2020ApJ...903...36S/abstract) for its radio spectropolarimetric properties."
    ]
   },
   {
@@ -57,7 +60,7 @@
    "lastKernelId": null
   },
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -71,9 +74,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.5"
+   "version": "3.12.3"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 4
 }

2_Mathematical_Groundwork/2_11_least_squares.ipynb

@@ -18,7 +18,10 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "collapsed": true
+    "collapsed": true,
+    "jupyter": {
+     "outputs_hidden": true
+    }
    },
    "source": [
     "Import standard modules:"
@@ -92,7 +95,10 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "collapsed": true
+    "collapsed": true,
+    "jupyter": {
+     "outputs_hidden": true
+    }
    },
    "source": [
     "$\\mathbf{r}$ is the residual vector and it is a measure of the difference between the values predicted by our model and the observed values."
@@ -465,9 +471,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "plt.plot(t, d, label=\"Data\")\n",
-    "plt.plot(t, sinusoid(solved_x, t), label=\"LM\")\n",
-    "plt.plot(t, sinusoid(true_x, t), label=\"Truth\")\n",
+    "plt.plot(t, d, label=\"Data\", lw=3, alpha=0.5)\n",
+    "plt.plot(t, sinusoid(true_x, t), label=\"Truth\", lw=3, alpha=1)\n",
+    "plt.plot(t, sinusoid(solved_x, t), label=\"LM\", lw=3, ls=\"--\")\n",
     "plt.xlabel(\"t\")\n",
     "plt.legend(loc='upper right')\n",
     "plt.show()"
@@ -476,22 +482,23 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "collapsed": true
+    "collapsed": true,
+    "jupyter": {
+     "outputs_hidden": true
+    }
    },
    "source": [
-    "The fitted values are so close to the true values that it is almost impossible to differentiate between the red and green lines in the above plot. The true values have been omitted from the following plot to make it clearer that the numerical solution does an excellent job of arriving at the correct parameter values."
+    "##### The fitted values are so close to the true values that it is almost impossible to differentiate between the red and green lines in the above plot. The true values have been omitted from the following plot to make it clearer that the numerical solution does an excellent job of arriving at the correct parameter values."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "scrolled": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
-    "plt.plot(t, d, label=\"Data\")\n",
-    "plt.plot(t, sinusoid(solved_x, t), label=\"LM\")\n",
+    "plt.plot(t, d, label=\"Data\", lw=3)\n",
+    "plt.plot(t, sinusoid(solved_x, t), label=\"LM\", lw=3)\n",
     "plt.xlabel(\"t\")\n",
     "plt.legend(loc='upper right')\n",
     "plt.show()"
@@ -517,8 +524,8 @@
     "print(\"scipy.optimize.leastsq: \", leastsq_x[0])\n",
     "print(\"Our LM: \", solved_x)\n",
     "\n",
-    "plt.plot(t, d, label=\"Data\")\n",
-    "plt.plot(t, sinusoid(leastsq_x[0], t), label=\"optimize.leastsq\")\n",
+    "plt.plot(t, d, label=\"Data\", lw=3)\n",
+    "plt.plot(t, sinusoid(leastsq_x[0], t), label=\"optimize.leastsq\", lw=3)\n",
     "plt.xlabel(\"t\")\n",
     "plt.legend(loc='upper right')\n",
     "plt.show()"
@@ -544,8 +551,8 @@
     "print(\"scipy.optimize.leastsq: \", leastsq_x[0])\n",
     "print(\"Our LM: \", solved_x)\n",
     "\n",
-    "plt.plot(t, d, label=\"Data\")\n",
-    "plt.plot(t, sinusoid(leastsq_x[0], t), label=\"optimize.leastsq\")\n",
+    "plt.plot(t, d, label=\"Data\", lw=3)\n",
+    "plt.plot(t, sinusoid(leastsq_x[0], t), label=\"optimize.leastsq\", lw=3)\n",
     "plt.xlabel(\"t\")\n",
     "plt.legend(loc='upper right')\n",
     "plt.show()"
@@ -573,7 +580,7 @@
    "lastKernelId": null
   },
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -587,9 +594,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.5"
+   "version": "3.12.3"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 4
 }

2_Mathematical_Groundwork/2_14_CLEAN_in_1D.ipynb

@@ -576,7 +576,8 @@
     "    ax[1].plot(u,Ws)\n",
     "    ax[1].set_xlim(-20,20)\n",
     "    ax[1].set_ylim(0,80)\n",
-    "    ax[0].set_title(r'$Visibility Weights$')"
+    "    ax[0].set_title(r'$Visibility Weights$')\n",
+    "    plt.show()"
    ]
   },
   {
@@ -605,7 +606,7 @@
    "lastKernelId": null
   },
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -619,9 +620,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.5"
+   "version": "3.12.3"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 4
 }

2_Mathematical_Groundwork/2_3_fourier_series.ipynb

@@ -313,7 +313,7 @@
     "    # Evaluate the integrand\n",
     "    am_int = func(x)*np.exp(-1j*2.0*m*np.pi*x/T)\n",
     "    # Use trapezoidal integration to get the coefficient\n",
-    "    am = np.trapz(am_int,x)\n",
+    "    am = np.trapezoid(am_int,x)\n",
     "    return am/T"
    ]
   },
@@ -402,14 +402,19 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "from ipywidgets import fixed\n",
+    "\n",
     "def inter_FS(x,m,func,T):\n",
     "    f_F = FS_sum(x, m, func, period=T)\n",
-    "    plt.plot(x,f_F.real,'b')\n",
-    "    plt.plot(x,func(x),'g')\n",
+    "\n",
+    "    fig, ax = plt.subplots(figsize=(16, 4))\n",
+    "    ax.plot(x,f_F.real,'b')\n",
+    "    ax.plot(x,func(x),'g')\n",
+    "    plt.show()\n",
     "    \n",
     "\n",
-    "interact(lambda m,T:inter_FS(x=np.linspace(-1.0,1.0,N),m=m,func=square_wave,T=T),\n",
-    "                m=(5,100,1),T=(0,2*np.pi,0.5)) and None\n",
+    "interact(inter_FS, x=fixed(np.linspace(-1.0,1.0,N)), func=fixed(square_wave),\n",
+    "            m=(5,100,1), T=(0,2*np.pi,0.5))\n",
     "# <a id='math:fig:fou_decomp_inter'></a><!--\\label{math:fig:fou_decomp_inter}-->"
    ]
   },
@@ -436,9 +441,9 @@
    "lastKernelId": null
   },
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "interferometry (local)",
    "language": "python",
-   "name": "python3"
+   "name": "interferometry"
   },
   "language_info": {
    "codemirror_mode": {
@@ -450,9 +455,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.5"
+   "version": "3.12.3"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 4
 }