Updates to meet myst --strict building requirements

notebooks/example-workflows/buoy-wave-height.ipynb

@@ -5,7 +5,7 @@
    "metadata": {},
    "source": [
     "<p align=\"center\">\n",
-    "  <img src=\"https://upload.wikimedia.org/wikipedia/commons/7/74/Animation_for_a_deep_water_wave.gif\" alt=\"Animation for a deep water wave\" />\n",
+    "  <img src=\"../static/Animation_for_a_deep_water_wave.gif\" alt=\"Animation for a deep water wave\" />\n",
     "</p>"
    ]
   },

notebooks/example-workflows/nino3.ipynb

@@ -5,7 +5,7 @@
    "metadata": {},
    "source": [
     "<p align=\"center\">\n",
-    "  <img src=\"https://www.climate.gov/sites/default/files/2023-06/ENSOblog_animation_SSTA_anom_weekly_2023_Jun.gif\" alt=\"Weekly Sea Surface Temperature Patterns from NOAA\" />\n",
+    "  <img src=\"../static/ENSOblog_animation_SSTA_anom_weekly_2023_Jun.gif\" alt=\"Weekly Sea Surface Temperature Patterns from NOAA\" />\n",
     "</p>"
    ]
   },

notebooks/wavelet-introduction/jingle-bells.ipynb

@@ -242,7 +242,7 @@
     "ax.set_xlim(signal_df[\"time (seconds)\"][2000], signal_df[\"time (seconds)\"][3000])\n",
     "plt.xlabel(\"Time (seconds)\")\n",
     "plt.ylabel(\"Amplitude\")\n",
-    "plt.show()"
+    "plt.show();"
    ]
   },
   {
@@ -309,7 +309,7 @@
     "plt.xlabel(\"Scale\")\n",
     "plt.ylabel(\"Frequency (Hz)\")\n",
     "print(f\"Frequency in Hz:\\n{freqs*sampleRate}\")\n",
-    "plt.plot(freqs*sampleRate)"
+    "plt.plot(freqs*sampleRate);"
    ]
   },
   {
@@ -335,7 +335,7 @@
     "           vmax=(data).max(), vmin=(data).min(),\n",
     "           cmap=\"coolwarm\", aspect=\"auto\")\n",
     "plt.colorbar()\n",
-    "plt.show()"
+    "plt.show();"
    ]
   },
   {
@@ -385,7 +385,7 @@
     "           vmax=(power).max(), vmin=(power).min(),\n",
     "           cmap=\"coolwarm\", aspect=\"auto\")\n",
     "plt.legend()\n",
-    "plt.show()"
+    "plt.show();"
    ]
   },
   {
@@ -437,7 +437,7 @@
     "plt.xlabel('Frequency (Hz)')\n",
     "plt.ylabel('Amplitude')\n",
     "plt.legend()\n",
-    "plt.show()"
+    "plt.show();"
    ]
   },
   {
@@ -490,7 +490,7 @@
     "           vmax=(power).max(), vmin=(power).min(),\n",
     "           cmap=\"coolwarm\", aspect=\"auto\")\n",
     "plt.legend()\n",
-    "plt.show()"
+    "plt.show();"
    ]
   },
   {

notebooks/wavelet-introduction/spy-keypad.ipynb

@@ -5,7 +5,7 @@
    "metadata": {},
    "source": [
     "<p align=\"center\">\n",
-    "  <img src=\"https://upload.wikimedia.org/wikipedia/commons/6/6f/Programming123najra.gif\" alt=\"Typing gif from Wikipedia\" width=\"500px\" />\n",
+    "  <img src=\"../static/Programming123najra.gif\" alt=\"Typing gif from Wikipedia\" width=500px/>\n",
     "</p>"
    ]
   },

notebooks/wavelet-introduction/wavelet-basics.ipynb

@@ -5,7 +5,7 @@
    "metadata": {},
    "source": [
     "<p align=\"center\">\n",
-    "  <img src=\"https://upload.wikimedia.org/wikipedia/commons/9/95/Continuous_wavelet_transform.gif\" alt=\"Demo of different wavelets matching signals on Wikipedia\" />\n",
+    "  <img src=\"../static/Continuous_wavelet_transform.gif\" alt=\"Demo of different wavelets matching signals on Wikipedia\" />\n",
     "</p>"
    ]
   },
@@ -381,12 +381,12 @@
     "In its simplest form, a wavelet is a short wave-like oscillation that averages out to zero.\n",
     "\n",
     "<p align=\"center\">\n",
-    "  <img src=\"https://upload.wikimedia.org/wikipedia/commons/thumb/0/0a/MorletWaveletMathematica.svg/1024px-MorletWaveletMathematica.svg.png\" alt=\"Morlet Wavelet on Wikipedia\" width=350/>\n",
+    "  <img src=\"../static/MorletWaveletMathematica.png\" alt=\"Morlet Wavelet on Wikipedia\" width=350/>\n",
     "</p>\n",
     "\n",
     "> Many signals and images of interest exhibit piecewise smooth behavior punctuated by transients. Speech signals are characterized by short bursts encoding consonants followed by steady-state oscillations indicative of vowels. Natural images have edges. Financial time series exhibit transient behavior, which characterize rapid upturns and downturns in economic conditions. Unlike the Fourier basis, wavelet bases are adept at sparsely representing piecewise regular signals and images, which include transient behavior.\n",
     "> \n",
-    "> [Mathworks: \"What is a Wavelet\"]((https://www.mathworks.com/help/wavelet/gs/what-is-a-wavelet.html))\n",
+    "> [Mathworks: \"What is a Wavelet\"](https://www.mathworks.com/help/wavelet/gs/what-is-a-wavelet.html)\n",
     "\n",
     "Fourier transforms are made up of multiple sine waves of different phases and frequencies to best match a signal. However, while Fourier transforms can be used to match frequency, information about **when** each frequency occurs in the signal is lost. This can be overcome with wavelet analysis. Like Fourier transforms, wavelet analysis works with multiple different wavelets that will be scaled up or down to produce different shaped wavelets that can shifted along the signal. Because the signal is matched by different scaled wavelets at different points along the signal, both the signal's frequency and the time at which the signal's frequency occurs can be determined."
    ]