Notebooks/CometTS_Visualizer.ipynb

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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CometTS_Visualizer\n",
    "CometTS will run through and calculate the statistical central tendencies and amount of statistical spread present in the data over time. It will then plot this data and apply regression lines to it. Additionally, tabular data in a .csv format will be output in the same folder that contains your raster data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Imports\n",
    "import os\n",
    "from CometTS.CometTS import run_comet\n",
    "from CometTS.Plotting import run_plot, run_plot_arima\n",
    "from CometTS.ARIMA import run_arima, timeseries_trend\n",
    "import ipywidgets as widgets\n",
    "from IPython.display import Javascript, display, clear_output\n",
    "from ipywidgets import interact, interactive, fixed, interact_manual\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import shapely\n",
    "import pandas as pd\n",
    "import geopandas as gpd\n",
    "sns.set(color_codes=True)\n",
    "\n",
    "## Define default paths for sample data\n",
    "input_path=os.path.abspath('')\n",
    "input_path=os.path.join(input_path.split(\"Notebooks\")[0],\"CometTS/VIIRS_Sample\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create your polygons\n",
    "After the raster data are organized appropriately, you are ready to begin a workflow. The secondary input is simply a polygon vector file which will direct your analysis and calculate the relevant statistics and plots. Polygons can be created in QGIS, the GIS software of your choice, or can be done online. One such way is going to  http://geojson.io/ and using the tool. Ensure that the polygon is in the same projection as your raster data! Otherwise it will appear the data are not overlapping and no statistics will be calculated!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Some notes on VIIRS Nighttime Lights Imagery\n",
    "\n",
    "The example workflow continues below with default inputs including NPP VIIRS Nighttime lights monthly composite data and a polygon over San Juan, Puerto Rico.  VIIRS monthly composite data has two bands that can be useful for time series analysis. The first documents the night-time brightness on the ground (S* rade9.tif) and the second documents the number of observations that contribute to each monthly composite (S* cvg.tif). The observation band can be used to plot the number of observations AND as a mask band. If there are 0 observations in a pixel for a month, that does not mean that brightness is 0, simply that there are no observations typically due to cloud cover. As such, these data should be masked out."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initialize functions below for plotting, then scroll down to enter your inputs\n",
    "\n",
    "When your paths are set, click the “Execute…” button. This will then create the relevant statistics over time by iterating through your rasters. The amount of time this will take will depending upon the number of rasters and different AOIs you are analyzing at one time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create text widget for input\n",
    "def f(z):\n",
    "    return z\n",
    "\n",
    "print(\"Enter csv as created by CSV_Creator(ex: /Raster_List.csv)\")\n",
    "try:\n",
    "    directory_csv\n",
    "except NameError:\n",
    "    directory_csv = interactive(f, z=os.path.join(input_path,'Raster_List.csv'))\n",
    "    display(directory_csv)\n",
    "else:\n",
    "    directory_csv = str(directory_csv.children[0].value)\n",
    "    directory_csv = interactive(f, z=directory_csv)\n",
    "    display(directory_csv)\n",
    "\n",
    "print(\"Enter full path to vector polygon(ex: /NiameyPoly.shp)\")\n",
    "try:\n",
    "    zonalpoly\n",
    "except NameError:\n",
    "    zonalpoly = interactive(f, z=os.path.join(input_path,'San_Juan.shp'))\n",
    "    display(zonalpoly)\n",
    "else:\n",
    "    zonalpoly = str(zonalpoly.children[0].value)\n",
    "    zonalpoly = interactive(f, z=zonalpoly)\n",
    "    display(zonalpoly)\n",
    "\n",
    "print(\"Enter NoData Value for null space in imagery(ex: Landsat typically -9999, VIIRS Monthly Composites -1)\")\n",
    "try:\n",
    "    NoDataValue\n",
    "except NameError:\n",
    "    NoDataValue = interactive(f, z='-1')\n",
    "    display(NoDataValue)\n",
    "else:\n",
    "    NoDataValue = str(NoDataValue.children[0].value)\n",
    "    NoDataValue = interactive(f, z=NoDataValue)\n",
    "    display(NoDataValue)\n",
    "\n",
    "print(\"Enter mask pixel value(s).  (ex: Clouds/Cloud Shadow=1)\")\n",
    "print(\"If multiple values seperate with a comma, if no masking is required, leave blank\")\n",
    "try:\n",
    "    mask_value\n",
    "except NameError:\n",
    "    mask_value = interactive(f, z='0')\n",
    "    display(mask_value)\n",
    "else:\n",
    "    mask_value = str(mask_value.children[0].value)\n",
    "    mask_value = interactive(f, z=mask_value)\n",
    "    display(mask_value)\n",
    "\n",
    "\n",
    "def run_all(ev):\n",
    "    I = run_comet(\n",
    "        directory_csv.children[0].value.strip(),\n",
    "        zonalpoly.children[0].value.strip(),\n",
    "        NoDataValue.children[0].value.strip(),\n",
    "        mask_value.children[0].value.strip(),\n",
    "        maskit=checkbox.value)\n",
    "    %matplotlib inline\n",
    "    run_plot(I)\n",
    "\n",
    "\n",
    "checkbox = widgets.Checkbox(\n",
    "    value=True,\n",
    "    description='Mask Rasters',\n",
    "    disabled=False\n",
    ")\n",
    "\n",
    "\n",
    "button = widgets.Button(description=\"Execute...\")\n",
    "button.on_click(run_all)\n",
    "display(checkbox)\n",
    "display(button)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ARIMA Analysis\n",
    "CometTS has expanded its functionality and now uses auto-regressive integrated moving average (ARIMA) analysis to detect anomalies in a time-series of data.  We have a built in function to calculate this for you automatically.  If you choose to continue with the example workflow, you may recall that Hurricane Maria hit Puerto Rico in September of 2017.  We can track where the expected brightness levels should have been had the hurricane not hit.  \n",
    "\n",
    "Once initializing the functions and running the commands below we display the mean observed brightness level in San Juan in black and red dots, the number of observations caputured by the VIIRS satellite for the month in blue, and a few forecasts.  The forecasts use any observations before 8-15-2017 to calulate trends using ARIMA and linear analysis, and then apply these forecasts after this date to display where we expect brightness levels to be.  We then calculate the mean absolute error from the ARIMA trend before our date in question (8/15/2017) and multiply it by two to extend our error bars a bit (dark grey area).  We then can automatically flag observations as anamolies (red) if areas are brighter or darker than they should be. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
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   "source": [
    "print(\"Enter csv as output by CometTS(ex: /San_Juan_FullStats.csv)\")\n",
    "try:\n",
    "    comet_csv\n",
    "except NameError:\n",
    "    comet_csv = interactive(f, z=os.path.join(input_path,'San_Juan_FullStats.csv'))\n",
    "    display(comet_csv)\n",
    "else:\n",
    "    comet_csv = str(comet_csv.children[0].value)\n",
    "    comet_csv = interactive(f, z=comet_csv)\n",
    "    display(comet_csv)\n",
    "\n",
    "print(\"Enter the output path for all ARIMA statistics (ex: /San_Juan_FullStats_ARIMA.csv)\")\n",
    "try:\n",
    "    arima_output\n",
    "except NameError:\n",
    "    arima_output = interactive(f, z=os.path.join(input_path,'San_Juan_FullStats_ARIMA.csv'))\n",
    "    display(arima_output)\n",
    "else:\n",
    "    arima_output = str(arima_output.children[0].value)\n",
    "    arima_output = interactive(f, z=arima_output)\n",
    "    display(arima_output)\n",
    "\n",
    "print(\"Enter the centered moving average amount (typically 3, 5, or a larger odd number)\")\n",
    "try:\n",
    "    CMA\n",
    "except NameError:\n",
    "    CMA = interactive(f, z='3')\n",
    "    display(CMA)\n",
    "else:\n",
    "    CMA = str(CMA.children[0].value)\n",
    "    CMA = interactive(f, z=CMA)\n",
    "    display(CMA)\n",
    "\n",
    "print(\"Enter the cutoff date before or after a significant event in history.  If no event exists, use the final date in the time series. Format is YYYY/MM/DD.\")\n",
    "try:\n",
    "    Cutoff_Date\n",
    "except NameError:\n",
    "    Cutoff_Date = interactive(f, z=\"2017/08/15\")\n",
    "    display(Cutoff_Date)\n",
    "else:\n",
    "    Cutoff_Date = str(Cutoff_Date.children[0].value)\n",
    "    Cutoff_Date = interactive(f, z=Cutoff_Date)\n",
    "    display(Cutoff_Date)\n",
    "\n",
    "    \n",
    "print(\"Enter an uncertainty multiplier.  The mean absolute error of the ARIMA model vs. your data before the cutoff date will be used to set error bars and flag anomalies.  The uncertainty value is multipled by the MAE value to produce wider or tighter error bounds.\")\n",
    "try:\n",
    "    Uncertainty\n",
    "except NameError:\n",
    "    Uncertainty = interactive(f, z='3')\n",
    "    display(Uncertainty)\n",
    "else:\n",
    "    Uncertainty = str(Uncertainty.children[0].value)\n",
    "    Uncertainty = interactive(f, z=Uncertainty)\n",
    "    display(Uncertainty)\n",
    "    \n",
    "def gen_plots(input_csv):\n",
    "    df = pd.read_csv(input_csv)\n",
    "    df = df.sort_values(['date'])\n",
    "    geometry = df['geometry'].map(shapely.wkt.loads)\n",
    "    crs = {'init': 'epsg:2263'}\n",
    "    gdf = gpd.GeoDataFrame(df, crs=crs, geometry=geometry)\n",
    "    gdf['date'] = pd.to_datetime(gdf['date'], infer_datetime_format=True)\n",
    "    # run_plot(gdf, y_label=\"NDVI\", title_label= \"NDVI over time - ID: \")\n",
    "    run_plot_arima(gdf)  \n",
    "\n",
    "def arima_time(ev):\n",
    "    run_arima(\n",
    "        comet_csv.children[0].value.strip(),\n",
    "        arima_output.children[0].value.strip(),\n",
    "        int(CMA.children[0].value.strip()),\n",
    "        Cutoff_Date.children[0].value.strip(),\n",
    "        float(Uncertainty.children[0].value.strip()))\n",
    "    %matplotlib inline\n",
    "    gen_plots(arima_output.children[0].value.strip())\n",
    "\n",
    "\n",
    "checkbox = widgets.Checkbox(\n",
    "    value=True,\n",
    "    description='Mask Rasters',\n",
    "    disabled=False\n",
    ")\n",
    "\n",
    "\n",
    "button = widgets.Button(description=\"Execute...\")\n",
    "button.on_click(arima_time)\n",
    "display(button)"
   ]
  }
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