JRN-EDI-diagnostics.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import requests\n",
    "import sys\n",
    "sys.path.append('/home/greg/GitHub/')\n",
    "import pyEDIutils.changes as edi\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Archive a request for earlier data\n",
    "\n",
    "Do this if needed to update the request archives for the current report ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#curl -i -X GET \"https://pasta.lternet.edu/package/changes/eml?fromDate=2017-02-01T12:00:00&toDate=2020-01-28&scope=knb-lter-jrn\"\n",
    "# This is from the \"List recent changes\" call  https://pastaplus-core.readthedocs.io/en/latest/doc_tree/pasta_api/data_package_manager_api.html#list-recent-changes\n",
    "# Only do this if it isn't archived yet!\n",
    "edi.archive_requested_changes('2023-06-02', '2023-07-15')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load archived requests and most recent data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading archived PASTA request knb-lter-jrn_20150417-20181231.xml\n",
      "Reading archived PASTA request knb-lter-jrn_20190101-20191231.xml\n",
      "Reading archived PASTA request knb-lter-jrn_20200101-20200601.xml\n",
      "Reading archived PASTA request knb-lter-jrn_20200602-20201201.xml\n",
      "Reading archived PASTA request knb-lter-jrn_20201202-20210601.xml\n",
      "Reading archived PASTA request knb-lter-jrn_20210602-20220101.xml\n",
      "Reading archived PASTA request knb-lter-jrn_20220102-20220531.xml\n",
      "Reading archived PASTA request knb-lter-jrn_20220601-20221101.xml\n",
      "Reading archived PASTA request knb-lter-jrn_20221102-20230315.xml\n",
      "Reading archived PASTA request knb-lter-jrn_20230316-20230601.xml\n",
      "Reading archived PASTA request knb-lter-jrn_20230602-20231102.xml\n",
      "9 duplicate records were removed.\n"
     ]
    }
   ],
   "source": [
    "archived = edi.load_archived_changes()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Find the last PASTA entry in the archive and convert to datetime\n",
    "last_archived = pd.to_datetime(archived.date.iloc[-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requesting PASTA changes for knb-lter-jrn from 2023-10-18T00:00:00 to None\n",
      "https://pasta.lternet.edu/package/changes/eml?fromDate=2023-10-18T00%3A00%3A00&toDate=2023-11-03&scope=knb-lter-jrn\n",
      "0 duplicate records were removed.\n"
     ]
    }
   ],
   "source": [
    "# If the last_archived  value is less than report endtime, get the most recent\n",
    "# changes (default is retrieve to today) and concatenate to archived requests\n",
    "if last_archived < pd.to_datetime('today'):\n",
    "    last_archived_plus1d = last_archived.date() + pd.DateOffset(1)\n",
    "    # Request changes from last archive to enddate\n",
    "    new = edi.request_changes(last_archived_plus1d.isoformat())\n",
    "    if new.empty:\n",
    "        df = archived.copy()\n",
    "    else:\n",
    "        # Concat\n",
    "        df = pd.concat([archived, new])\n",
    "else:\n",
    "    df = archived.copy()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
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       "                        date      pkgid             action\n",
       "725  2023-10-11T15:32:01.537  210126001  updateDataPackage\n",
       "726  2023-10-11T22:47:22.737  210413001  updateDataPackage\n",
       "727  2023-10-12T14:54:47.384  210413006  createDataPackage\n",
       "728  2023-10-17T03:30:59.482  210413005  createDataPackage\n",
       "729  2023-10-17T04:05:06.959  210413007  createDataPackage"
      ]
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   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": []
   },
   "outputs": [
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       "      <th>n_update</th>\n",
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       "      <th>n_delete</th>\n",
       "      <th>n_tot</th>\n",
       "      <th>studyid</th>\n",
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       "      <td>2015-04-17T14:42:05.936</td>\n",
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       "                      date       pkgid             action  n_update  n_create  \\\n",
       "0  2015-04-17T14:42:05.936  2100011003  createDataPackage         0         1   \n",
       "1  2015-04-17T15:43:26.172   210120002  createDataPackage         0         1   \n",
       "2  2015-04-17T15:43:26.771  2100392001  createDataPackage         0         1   \n",
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       "4   2015-04-17T15:43:45.55  2100010001  createDataPackage         0         1   \n",
       "\n",
       "   n_delete  n_tot studyid  \n",
       "0         0      1     011  \n",
       "1         0      1     120  \n",
       "2         0      1     392  \n",
       "3         0      1     169  \n",
       "4         0      1     010  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add method count columns\n",
    "df = edi.get_counts(df)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Get study ids\n",
    "df['studyid'] = df.pkgid.astype(str).str[-6:-3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "223    2100001002\n",
      "224    2100002001\n",
      "225    2100007001\n",
      "227    2100010001\n",
      "228    2100011001\n",
      "229    2100011003\n",
      "230    2100011004\n",
      "232    2100011005\n",
      "233    2100086001\n",
      "234    2100086002\n",
      "235    2100086003\n",
      "236    2100086004\n",
      "237    2100086005\n",
      "238    2100086006\n",
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      "240    2100086008\n",
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      "242    2100119001\n",
      "243    2100121004\n",
      "244    2100121005\n",
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      "246    2100126003\n",
      "247    2100127001\n",
      "249    2100169001\n",
      "250    2100169002\n",
      "251    2100278001\n",
      "252    2100278002\n",
      "254    2100287001\n",
      "255    2100288001\n",
      "257    2100351001\n",
      "260    2100365001\n",
      "261    2100379001\n",
      "262    2100379002\n",
      "263    2100379003\n",
      "264    2100380001\n",
      "265    2100392001\n",
      "266    2100409001\n",
      "267    2100409002\n",
      "276     210038001\n",
      "277     210338007\n",
      "278     210338008\n",
      "281     210169001\n",
      "283     210169002\n",
      "12       21038001\n",
      "456     210860002\n",
      "Name: pkgid, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Count deleted packages\n",
    "deleted_pkgs = df.loc[df.action=='deleteDataPackage','pkgid']\n",
    "print(deleted_pkgs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create daily data file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>n_update</th>\n",
       "      <th>n_create</th>\n",
       "      <th>n_tot</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-04-17</th>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-04-18</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-04-19</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-04-20</th>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-04-21</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
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      "text/plain": [
       "            n_update  n_create  n_tot\n",
       "date                                 \n",
       "2015-04-17         0        20     20\n",
       "2015-04-18         0         1      1\n",
       "2015-04-19         0         0      0\n",
       "2015-04-20         0        11     11\n",
       "2015-04-21         0         2      2"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sum to daily methods\n",
    "daily = edi.counts_to_daily(df)\n",
    "daily.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot activity since 2013\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "# Figure config\n",
    "#font = {'family' : 'normal',\n",
    "#        'weight' : 'normal',\n",
    "#        'size'   : 14}\n",
    "\n",
    "#matplotlib.rc('font', **font)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Get a datestamp for saving figures\n",
    "import datetime\n",
    "now = datetime.datetime.now().strftime(\"%Y%m%d\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 700x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# All time plot:\n",
    "fig, ax = plt.subplots(nrows=2, ncols=1, figsize=(7, 7), sharex=True)\n",
    "\n",
    "ax[0].plot(daily.index, daily.n_update, label='Update pkg')\n",
    "ax[0].plot(daily.index, daily.n_create, label='Create pkg')\n",
    "ax[0].set_ylabel('JRN events')\n",
    "\n",
    "ax[1].plot(daily.index, daily.n_update.cumsum(), label='Update pkg')\n",
    "ax[1].plot(daily.index, daily.n_create.cumsum(), label='Create pkg')\n",
    "ax[1].plot(daily.index, daily.n_tot.cumsum(), label='Total')\n",
    "\n",
    "plt.ylabel('JRN Cumulative')\n",
    "fig.autofmt_xdate()\n",
    "plt.suptitle('JRN activity on EDI (all time)')\n",
    "plt.legend()\n",
    "\n",
    "import os\n",
    "#fig.savefig(os.path.expanduser(\"~/data/GD_gmaurer@nmsu/IM/figures/JRN_EDI_all_{0}.png\".format(now)),\n",
    "#            dpi=150)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Total number of packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "385"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daily.n_tot.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Total number of non-met packages\n",
    "\n",
    "NPP met = 437, CSIS met = 548"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "162"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index = pd.to_datetime(df.date)\n",
    "nomet = np.logical_and(df.studyid!='437', df.studyid!='548')\n",
    "df.loc[nomet,['n_update', 'n_create', 'n_tot']].resample('D').sum().n_tot.sum()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### All changes excluding met"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>n_update</th>\n",
       "      <th>n_create</th>\n",
       "      <th>n_tot</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2023-10-13</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-10-14</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-10-15</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-10-16</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-10-17</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            n_update  n_create  n_tot\n",
       "date                                 \n",
       "2023-10-13         0         0      0\n",
       "2023-10-14         0         0      0\n",
       "2023-10-15         0         0      0\n",
       "2023-10-16         0         0      0\n",
       "2023-10-17         0         2      2"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daily_nomet = edi.counts_to_daily(df.loc[nomet,:])\n",
    "daily_nomet.head()\n",
    "daily_nomet.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f2df6b93760>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Recent plot:\n",
    "fig, ax = plt.subplots(nrows=2, ncols=1, figsize=(7, 7), sharex=True)\n",
    "\n",
    "ax[0].plot(daily_nomet.index, daily_nomet.n_update, label='Update pkg')\n",
    "ax[0].plot(daily_nomet.index, daily_nomet.n_create, label='Create pkg')\n",
    "ax[0].set_ylabel('JRN events')\n",
    "\n",
    "ax[1].plot(daily_nomet.index, daily_nomet.n_update.cumsum(), label='Update pkg')\n",
    "ax[1].plot(daily_nomet.index, daily_nomet.n_create.cumsum(), label='Create pkg')\n",
    "ax[1].plot(daily_nomet.index, daily_nomet.n_tot.cumsum(), label='$\\Delta$ Total')\n",
    "ax[1].hlines(y=100, xmin=daily_nomet.index.min(), xmax=daily_nomet.index.max(), ls=':', lw=1)\n",
    "\n",
    "plt.ylabel('JRN Cumulative')\n",
    "fig.autofmt_xdate()\n",
    "plt.suptitle('JRN activity on EDI (all time, excl. met)')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot activity since Sept2018 (excluding Met)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>n_update</th>\n",
       "      <th>n_create</th>\n",
       "      <th>n_tot</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-10-10</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-11</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-12</th>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-13</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-14</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            n_update  n_create  n_tot\n",
       "date                                 \n",
       "2018-10-10         0         1      1\n",
       "2018-10-11         0         1      1\n",
       "2018-10-12         0         4      4\n",
       "2018-10-13         0         0      0\n",
       "2018-10-14         0         0      0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This is after the LTER probabtion shock and after some big \n",
    "# delete operations on EDI\n",
    "startdt = \"2018-09-05\"\n",
    "daily_r_nomet = edi.counts_to_daily(df.loc[np.logical_and(nomet, df.index > startdt),:])\n",
    "daily_r_nomet.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Recent plot:\n",
    "fig, ax = plt.subplots(nrows=2, ncols=1, figsize=(7, 7), sharex=True)\n",
    "\n",
    "ax[0].plot(daily_r_nomet.index, daily_r_nomet.n_update, label='Update pkg')\n",
    "ax[0].plot(daily_r_nomet.index, daily_r_nomet.n_create, label='Create pkg')\n",
    "ax[0].set_ylabel('JRN events')\n",
    "\n",
    "ax[1].plot(daily_r_nomet.index, daily_r_nomet.n_update.cumsum(), label='Update pkg')\n",
    "ax[1].plot(daily_r_nomet.index, daily_r_nomet.n_create.cumsum(), label='Create pkg')\n",
    "ax[1].plot(daily_r_nomet.index, daily_r_nomet.n_tot.cumsum(), label='$\\Delta$ Total')\n",
    "#ax[1].axhline(y=63, xmin=0, xmax=1, ls=':', lw=1.5, color='k',\n",
    "#             label='Proposal goal')\n",
    "\n",
    "plt.ylabel('JRN Cumulative')\n",
    "fig.autofmt_xdate()\n",
    "plt.suptitle('JRN activity on EDI (recent, excl. met)')\n",
    "plt.legend()\n",
    "\n",
    "fig.savefig(os.path.expanduser(\n",
    "    \"~/data/GD_gmaurer@nmsu/IM/figures/JRN_EDI_recNoMet_{0}.png\".format(now)),\n",
    "            dpi=150)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary since Sept 2018 (after LTER7 probation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>n_update</th>\n",
       "      <th>n_create</th>\n",
       "      <th>n_tot</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-04-30</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-31</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-30</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-31</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-31</th>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-30</th>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-31</th>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-30</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-31</th>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-31</th>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-29</th>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-31</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-30</th>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-31</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-30</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-31</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-31</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-30</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-31</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-30</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-31</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-31</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-28</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-03-31</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-30</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-05-31</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-30</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-07-31</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-31</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-30</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-31</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-30</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-31</th>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-31</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-02-28</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-03-31</th>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            n_update  n_create  n_tot\n",
       "date                                 \n",
       "2019-04-30         4         4      4\n",
       "2019-05-31         0         0      0\n",
       "2019-06-30         4         1      0\n",
       "2019-07-31         3         0      0\n",
       "2019-08-31         8         0      0\n",
       "2019-09-30        15         1      0\n",
       "2019-10-31        13         1      1\n",
       "2019-11-30         3         1      1\n",
       "2019-12-31         9         0      0\n",
       "2020-01-31        13         0      0\n",
       "2020-02-29        21         0      0\n",
       "2020-03-31         0         0      0\n",
       "2020-04-30        14         0      0\n",
       "2020-05-31         2         2      2\n",
       "2020-06-30         3         0      0\n",
       "2020-07-31         0         0      0\n",
       "2020-08-31         1         0      0\n",
       "2020-09-30         2         2      2\n",
       "2020-10-31         2         3      3\n",
       "2020-11-30         3         2      2\n",
       "2020-12-31         1         1      1\n",
       "2021-01-31         0         1      1\n",
       "2021-02-28         2         1      1\n",
       "2021-03-31         0         0      0\n",
       "2021-04-30         0         0      0\n",
       "2021-05-31         0         1      1\n",
       "2021-06-30         0         0      0\n",
       "2021-07-31         0         1      1\n",
       "2021-08-31         0         2      2\n",
       "2021-09-30         0         0      0\n",
       "2021-10-31         2         2      2\n",
       "2021-11-30         2         2      2\n",
       "2021-12-31        16         0      0\n",
       "2022-01-31         2         0      0\n",
       "2022-02-28         5         0      0\n",
       "2022-03-31         7         3      3"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Monthly progress\n",
    "monthly_r_nomet = daily_r_nomet.resample('M').sum()\n",
    "monthly_r_nomet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([210351003, 210328003, 210328001, 210120001, 210127001, 210380001,\n",
       "       210288001, 210351001, 210351002, 210461001, 210086003, 210086008,\n",
       "       210002001, 210086009, 210086005, 210086004, 210086007, 210308004,\n",
       "       210228001, 210120002, 210013001, 210086002, 210086001, 210121005,\n",
       "       210121008, 210407001, 210086006, 210119001, 210011005, 210001002,\n",
       "       210011002, 210011003, 210011004, 210013002, 210013003, 210412001,\n",
       "       210012001, 210012002, 210010001, 210122001, 210328002, 210308001,\n",
       "       210308002, 210308003, 210308005, 210126003, 210262001, 210262004,\n",
       "       210262005, 210128001, 210128002, 210262008, 210392001, 210379001,\n",
       "       210379002, 210379003, 210287001, 210278002, 210278001, 210338006,\n",
       "       210338004, 210338002, 210425001, 210262010, 210365001, 210385001,\n",
       "       210385004, 210385005, 210385006, 210385008, 210385002, 210385003,\n",
       "       210385007,       100, 210001001, 210394009, 210395001, 210409002,\n",
       "       210008001, 210007001, 210008002, 210015001, 210351004, 210126001,\n",
       "       200021001, 210015002, 210015003, 210121001, 210368001, 210368002,\n",
       "       210368003, 210368004, 210121006, 210370001, 210370002, 210301001,\n",
       "       210301002, 210409001, 210389001, 210389002, 200045001, 210408005,\n",
       "       210398001, 210398002])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Unique packages updated (excluding met)\n",
    "recent = df.loc[df.index > startdt,]\n",
    "recent_nomet = np.logical_and(recent.studyid!='437', recent.studyid!='548')\n",
    "uniqueUp = recent.loc[np.logical_and(recent_nomet, recent.action=='updateDataPackage'),].pkgid.unique()\n",
    "#rEDI.loc[rEDI.action=='updateDataPackage',].pkgid.unique()\n",
    "uniqueUp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[210380001 210351004 210126001 200021001 210015002 210015003 210015001\n",
      " 210121001 210368001 210368002 210368003 210368004 210121006 210370001\n",
      " 210370002 210301001 210301002 210409001 210389001 210389002 210011004\n",
      " 210011002 200045001 210408005 210398001 210398002]\n",
      "[210351003 210328003 210328001 210120001 210127001 210380001 210288001\n",
      " 210351001 210351002 210461001 210086003 210086008 210002001 210086009\n",
      " 210086005 210086004 210086007 210308004 210228001 210120002 210013001\n",
      " 210086002 210086001 210121005 210121008 210407001 210086006 210119001\n",
      " 210011005 210001002 210011002 210011003 210011004 210013002 210013003\n",
      " 210412001 210012001 210012002 210010001 210122001 210328002 210308001\n",
      " 210308002 210308003 210308005 210126003 210262001 210262004 210262005\n",
      " 210128001 210128002 210262008 210392001 210379001 210379002 210379003\n",
      " 210287001 210278002 210278001 210338006 210338004 210338002 210425001\n",
      " 210262010 210365001 210548016 210548031 210548076 210548046 210548025\n",
      " 210548061 210548001 210548040 210548085 210548055 210548099 210548026\n",
      " 210548070 210548010 210548041 210548086 210548056 210548100 210548027\n",
      " 210548071 210548011 210548042 210548087 210548057 210548028 210548072\n",
      " 210548012 210548043 210548088 210548058 210548101 210548029 210548073\n",
      " 210548013 210548044 210548089 210548059 210548102 210548030 210548074\n",
      " 210548014 210548045 210548090 210548060 210548103 210548017 210548015\n",
      " 210548075 210548032 210548077 210548047 210548091 210548018 210548062\n",
      " 210548002 210548033 210548078 210548048 210548092 210548019 210548063\n",
      " 210548003 210548034 210548079 210548049 210548093 210548004 210548020\n",
      " 210548064 210548035 210548080 210548050 210548094 210548021 210548005\n",
      " 210548065 210548036 210548081 210548051 210548095 210548022 210548066\n",
      " 210548006 210548037 210548082 210548052 210548096 210548023 210548067\n",
      " 210548007 210548038 210548083 210548053 210548097 210548024 210548068\n",
      " 210548008 210548039 210548084 210548054 210548098 210548069 210548009\n",
      " 210385001 210385004 210385005 210385006 210385008 210385002 210385003\n",
      " 210385007       100 210001001 210394009 210395001 210409002 210008001\n",
      " 210007001 210008002 210015001]\n"
     ]
    }
   ],
   "source": [
    "# Unique updates since last meeting\n",
    "last_meeting = '2021-01-01'\n",
    "\n",
    "vrecent = df.loc[df.index >= last_meeting]\n",
    "vrecent_nomet = np.logical_and(vrecent.studyid!='437', vrecent.studyid!='548')\n",
    "uR = vrecent.loc[np.logical_and(vrecent_nomet, vrecent.action=='updateDataPackage'),].pkgid.unique()\n",
    "# Unique updates before last meeting (to startdt)\n",
    "oEDI = df.loc[np.logical_and(df.index > startdt, df.index < last_meeting),]\n",
    "uO = oEDI.loc[np.logical_and(oEDI.studyid!='437', oEDI.action=='updateDataPackage'),].pkgid.unique()\n",
    "print(uR)\n",
    "print(uO)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([210351004, 210126001, 200021001, 210015002, 210015003, 210121001,\n",
       "       210368001, 210368002, 210368003, 210368004, 210121006, 210370001,\n",
       "       210370002, 210301001, 210301002, 210409001])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uR[~np.in1d(uR, uO)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([210038001, 210338007, 210338008, 210169001, 210169002,  21038001,\n",
       "       210860002])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Unique packages deleted\n",
    "uniqueDel = recent.loc[np.logical_and(recent_nomet, recent.action=='deleteDataPackage'),].pkgid.unique()\n",
    "uniqueDel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([210520001, 210327001, 210338005, 210338006, 210338007, 210338008,\n",
       "       210472001, 210380001, 210128001, 210128002, 210351003, 210328003,\n",
       "       210328001, 210328002,  21038001, 210860002, 210011001,       100,\n",
       "       210420001, 210420002, 210394008, 210386002, 210008002, 210386009,\n",
       "       210386008, 210386011, 210493001, 210313001, 210408005, 210351004,\n",
       "       210228002,       101, 210389001, 210389002, 200021001, 200022001,\n",
       "       210500001, 210469001])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Unique packages created (See above)\n",
    "uniqueCreate = recent.loc[np.logical_and(recent_nomet, recent.action=='createDataPackage'),].pkgid.unique()\n",
    "uniqueCreate"
   ]
  }
 ],
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