timeseries_trends.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "abf859ee-ec25-42ba-a5d8-41d1b8f6dea8",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from mintpy.utils import readfile\n",
    "from mintpy.objects import timeseries\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "eca1ebcf-6270-40c2-a684-3da9827c8a45",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/u/sar-r2/fwilliam/Calif/UAVSAR/Stacks/Haywrd_05502_01-S2/mintpy\n"
     ]
    }
   ],
   "source": [
    "cd /u/sar-r2/fwilliam/Calif/UAVSAR/Stacks/Haywrd_05502_01-S2/mintpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3bf2cd5e-b796-46d4-a66a-895c3433b207",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "avgPhaseVelocity.h5      numTriNonzeroIntAmbiguity.h5\n",
      "avgSpatialCoh.h5         \u001b[0m\u001b[01;35mnumTriNonzeroIntAmbiguity.png\u001b[0m\n",
      "bperpHistory.pdf         \u001b[01;34mpic\u001b[0m/\n",
      "coherenceHistory.pdf     smallbaselineApp.cfg\n",
      "coherenceMatrix.pdf      \u001b[01;35mtcoherence_0550201_02.png\u001b[0m\n",
      "coherenceSpatialAvg.txt  temporalCoherence.h5\n",
      "\u001b[01;34mgeo\u001b[0m/                     timeseries.h5\n",
      "\u001b[01;35mifg_0550201_02.png\u001b[0m       timeseries_ramp.h5\n",
      "\u001b[01;34minputs\u001b[0m/                  \u001b[01;35mvelocity_0550201_02.png\u001b[0m\n",
      "invert.log               \u001b[01;35mvelocity_dem.png\u001b[0m\n",
      "maskConnComp.h5          velocity.h5\n",
      "maskTempCoh.h5           velocity.log\n",
      "network.pdf              waterMask.h5\n",
      "numInvIfgram.h5\n"
     ]
    }
   ],
   "source": [
    "ls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "01d3075b-2f7e-42d2-8915-381450ea94d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "ts = timeseries('timeseries_ramp.h5')\n",
    "# data = ts.read()\n",
    "dates = pd.to_datetime(ts.get_date_list())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "bdae24c8-9172-438f-b52f-4abaf04344c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "means = pd.Series(data.mean(axis=(1,2)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "bfdafa87-9956-4752-aa50-af72e2ee1d44",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame({'date':dates,'avg':means})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "1648edd5-350d-457c-8f49-037c7ab2541f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df.date,df.avg)\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Avg Timeseries Value')\n",
    "plt.savefig('mean_timeseries.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3b636f01-1b7f-4d7e-a49f-13352d56e086",
   "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": "3.6.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}