notebooks/wavelet.ipynb

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    "# Wavelet transform using `pywavelet`"
   ]
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   "source": [
    "## Introduction\n",
    "\n",
    "Wavelet transform is a powerful tool for analyzing localized variations of power within a time series or signals. Despite many existing applications, it is difficulte for beginners to use this technique. Here, wavelet transform is performed using [PyWavelet](https://github.com/PyWavelets/pywt) and an app is develped to facilitate the application.\n",
    "\n",
    "*Please note, part of the codes is shamelessly stolen from [geoscilabs](https://github.com/geoscixyz/geosci-labs) and [Wavelet analysis in Python](http://nicolasfauchereau.github.io/climatecode/posts/wavelet-analysis-in-python/).*\n",
    "\n",
    "## Getting started\n",
    "\n",
    "- **Load data**. User needs to provide a text file with two columns in the following format. User also has the option to detrend the signal before using wavelet transform.\n",
    "\n",
    "datetime|value\n",
    "---|---\n",
    "2018|0.43\n",
    "2018.25|9.0\n",
    "\n",
    "- **Choose parameters**. \n",
    "\n",
    "parameter|description\n",
    "---|---\n",
    "Scale_min|The smallest scale of wavelet. The default is $2*timestep$\n",
    "Scale_max|The largest scale of wavelet. This should be smaller than $len(data)/2$.\n",
    "Wavelet function|The wavelet family used. For continuous wavelet transform (CWT), `morl` is the default one. \n",
    "\n",
    "- **Launch the app**\n",
    "\n",
    "**References**\n",
    "- Torrence, C., and G. P. Compo, 1998: [A practical guide to wavelet analysis](http://paos.colorado.edu/research/wavelets/bams_79_01_0061.pdf). Bull. Amer. Meteor. Soc., 79, 6178."
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   "source": [
    "import sys\n",
    "sys.path.append(\"..\")\n",
    "from src.MyApp import wavelet_app\n",
    "from IPython.display import display"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib as mpl\n",
    "mpl.rcParams['figure.dpi'] = 100"
   ]
  },
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   "execution_count": 3,
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      "text/plain": [
       "MyApp(children=(Text(value='../data/sst_nino3.txt', description='File path:', placeholder='Type something'), C…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "app = wavelet_app()\n",
    "display(app)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!-- ![nino3](https://github.com/chris-torrence/wavelets/blob/master/images/nino3_wave.png?raw=true \"Torrence and Compo, 1998\") -->"
   ]
  }
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