From 8ce3df158c966415c7a0027eebdae4d0801fa749 Mon Sep 17 00:00:00 2001
From: Janis Heuel <janis.heuel@ruhr-uni-bochum.de>
Date: Tue, 22 Jun 2021 09:43:21 +0200
Subject: [PATCH] Changed README and deleted one file.

---
 04-FFT_DFT_and_Applications/spectrogram.ipynb | 52 -------------------
 README.md                                     | 19 ++++++-
 2 files changed, 18 insertions(+), 53 deletions(-)
 delete mode 100644 04-FFT_DFT_and_Applications/spectrogram.ipynb

diff --git a/04-FFT_DFT_and_Applications/spectrogram.ipynb b/04-FFT_DFT_and_Applications/spectrogram.ipynb
deleted file mode 100644
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@@ -1,52 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Time-Frequency Analysis\n",
-    "## Moving window analysis\n",
-    "One way to analyse the time-varying frequency content of a signal is to\n",
-    "apply windows in the time domain to the signal and to calculate a Fourier spectrum\n",
-    "of the windowed part. The window marches along the signal with defined overlap creating\n",
-    "a series of Fourier spectra associated with the center times of the windows. The resulting amplitude\n",
-    "spectra are then plotted versus window center time. In more detail:\n",
-    "\n",
-    "1. Choose windowing functions: $w(t,t_m)$ with $t_m$ the center of the window.\n",
-    "2. Multiply windowing function with time series: $f_m(t) = f(t)w(t,t_m)$\n",
-    "3. Detrend the windowed signal.\n",
-    "4. Perform a DFT: $F_{km} = \\Delta t\\sum_{n=0}^N f_m(t)\\exp(-2\\pi i \\frac{kn}{N})$\n",
-    "    and calculate the absolute value, $|F_{km}|$.\n",
-    "5. Plot the resulting matrix: $|F_{km}|$ in the time-frequency plane."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.7"
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diff --git a/README.md b/README.md
index bb77044..d5e8fc5 100644
--- a/README.md
+++ b/README.md
@@ -18,4 +18,21 @@ This has been forked from [Seismo Live](http://seismo-live.org). The source code
 
 1. Simple Cross Correlation: Use the cross-correlation to detect and determine the time shift of two test events with one template event.
 2. Enhanced Picker: This has been taken from the [ObsPy Tutorial](https://docs.obspy.org/master/tutorial/code_snippets/xcorr_pick_correction.html).  ObsPy is licensed under the [LGPL v3.0](https://www.gnu.de/documents/lgpl-3.0.en.html)
-3. Ambient Seismic Noise Analysis from [Seismo Live](http://seismo-live.org). The source code is available at https://github.com/krischer/seismo_live (licensed under a ***CC BY-NC-SA 4.0 License***. &copy; 2015-2019 Lion Krischer).
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+3. Ambient Seismic Noise Analysis from [Seismo Live](http://seismo-live.org). The source code is available at https://github.com/krischer/seismo_live (licensed under a ***CC BY-NC-SA 4.0 License***. &copy; 2015-2019 Lion Krischer).
+
+### 04 - FFT, DFT and Applications
+
+Interpolation and Deciamtion of Time Series using the DFT
+
+### 05 - Spectrogram
+
+1. Analysing dispersive signals. How to visualize changes in frequency content over time (moving window analysis, mutiple filter technique, instantaneous frequency, continuous wavelet transform, the uncertainty problem).
+2. Filtering time series with non spectral filtering method.
+
+### 06 - Surface Waves
+
+Analysing phase and group velocity of recorded earthquake signals at one and two seismological stations.
+
+### 07 - Receiver Functions
+
+How to get receiver functions from seismograms.
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