diff --git a/notebooks/example-workflows/buoy-wave-height.ipynb b/notebooks/example-workflows/buoy-wave-height.ipynb index 837bc8dd..fcba24c5 100644 --- a/notebooks/example-workflows/buoy-wave-height.ipynb +++ b/notebooks/example-workflows/buoy-wave-height.ipynb @@ -5,7 +5,7 @@ "metadata": {}, "source": [ "

\n", - " \"Animation\n", + " \"Animation\n", "

" ] }, diff --git a/notebooks/example-workflows/nino3.ipynb b/notebooks/example-workflows/nino3.ipynb index ef43e47e..24062387 100644 --- a/notebooks/example-workflows/nino3.ipynb +++ b/notebooks/example-workflows/nino3.ipynb @@ -5,7 +5,7 @@ "metadata": {}, "source": [ "

\n", - " \"Weekly\n", + " \"Weekly\n", "

" ] }, diff --git a/notebooks/static/Animation_for_a_deep_water_wave.gif b/notebooks/static/Animation_for_a_deep_water_wave.gif new file mode 100644 index 00000000..bc4ecea2 Binary files /dev/null and b/notebooks/static/Animation_for_a_deep_water_wave.gif differ diff --git a/notebooks/static/Continuous_wavelet_transform.gif b/notebooks/static/Continuous_wavelet_transform.gif new file mode 100644 index 00000000..af9e3a4e Binary files /dev/null and b/notebooks/static/Continuous_wavelet_transform.gif differ diff --git a/notebooks/static/ENSOblog_animation_SSTA_anom_weekly_2023_Jun.gif b/notebooks/static/ENSOblog_animation_SSTA_anom_weekly_2023_Jun.gif new file mode 100644 index 00000000..309cb5b7 Binary files /dev/null and b/notebooks/static/ENSOblog_animation_SSTA_anom_weekly_2023_Jun.gif differ diff --git a/notebooks/static/MorletWaveletMathematica.png b/notebooks/static/MorletWaveletMathematica.png new file mode 100644 index 00000000..0fb82976 Binary files /dev/null and b/notebooks/static/MorletWaveletMathematica.png differ diff --git a/notebooks/wavelet-introduction/jingle-bells.ipynb b/notebooks/wavelet-introduction/jingle-bells.ipynb index 8ba8e883..2266d1fe 100644 --- a/notebooks/wavelet-introduction/jingle-bells.ipynb +++ b/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();" ] }, { diff --git a/notebooks/wavelet-introduction/spy-keypad.ipynb b/notebooks/wavelet-introduction/spy-keypad.ipynb index ea25e9df..f92e753d 100644 --- a/notebooks/wavelet-introduction/spy-keypad.ipynb +++ b/notebooks/wavelet-introduction/spy-keypad.ipynb @@ -5,7 +5,7 @@ "metadata": {}, "source": [ "

\n", - " \"Typing\n", + " \"Typing\n", "

" ] }, diff --git a/notebooks/wavelet-introduction/wavelet-basics.ipynb b/notebooks/wavelet-introduction/wavelet-basics.ipynb index a3a325f8..331cf3b8 100644 --- a/notebooks/wavelet-introduction/wavelet-basics.ipynb +++ b/notebooks/wavelet-introduction/wavelet-basics.ipynb @@ -5,7 +5,7 @@ "metadata": {}, "source": [ "

\n", - " \"Demo\n", + " \"Demo\n", "

" ] }, @@ -381,12 +381,12 @@ "In its simplest form, a wavelet is a short wave-like oscillation that averages out to zero.\n", "\n", "

\n", - " \"Morlet\n", + " \"Morlet\n", "

\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." ]