diff --git a/last-access-per-user.py b/last-access-per-user.py
new file mode 100755
index 0000000000000000000000000000000000000000..c28167e969abe7dadea3af517d6eb3d7e5160b31
--- /dev/null
+++ b/last-access-per-user.py
@@ -0,0 +1,55 @@
+#!/usr/bin/env python
+
+import datetime
+import pandas as pd
+import matplotlib.pyplot as plt
+from urllib.parse import unquote
+import sys
+
+recs = []
+count=0
+progress=0
+
+file = sys.argv[1]
+
+with open(file) as gpfs_data:
+ for line in gpfs_data:
+ #print(unquote(line))
+ left, right = unquote(line).split(" -- ", 1)
+ fname = right.strip()
+ inode, meta = left.split('|', 1)
+ _, inode, _ = inode.split()
+ #print(meta)
+ meta, _ = meta.rsplit('|', 1)
+ #print(meta)
+ props = []
+ for prop in meta.split('|'):
+ props.append(prop.split('='))
+ #props.append(['path', fname])
+ #print(props)
+ props = dict(props)
+ props["inode"] = inode
+ for key in ["heat", "pool", "mode", "misc"]:
+ del props[key]
+ recs.append(props)
+ count += 1
+ progress += 1
+ if (progress // 20000000):
+ print(f"{datetime.datetime.now()} progress: {count}: {fname}")
+ progress = 0
+ if (count > 200000000):
+ break
+
+df = pd.DataFrame(recs)
+
+df = df.rename(columns={'access': 'atime', 'create': 'ctime', 'modify': 'mtime'})
+
+for intcol in ['size', 'kballoc', 'uid', 'gid']:
+ df[intcol] = df[intcol].astype("int")
+
+for intcol in ['atime', 'ctime', 'mtime']:
+ df[intcol] = df[intcol].astype('datetime64[ns]')
+
+print(df.groupby(["uid"], sort=False)["atime"].max())
+
+df.to_pickle(f"{file}.gz")
diff --git a/max-access-per-user-merged.ipynb b/max-access-per-user-merged.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..aa4e14a9fba804baba783b8fe14900201c766992
--- /dev/null
+++ b/max-access-per-user-merged.ipynb
@@ -0,0 +1,288 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "0f3842e0",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import datetime\n",
+ "import pandas as pd\n",
+ "import matplotlib.pyplot as plt\n",
+ "from urllib.parse import unquote\n",
+ "import sys\n",
+ "import os\n",
+ "import glob\n",
+ "\n",
+ "recs = []\n",
+ "count=0\n",
+ "progress=0\n",
+ "\n",
+ "report_name = \"temporary-scratch\"\n",
+ "dir = \"data/list-17075953.list.gather-info.d\"\n",
+ "#report_name = \"temporary-scratch\"\n",
+ "#dir = 'data/list-17094088.list.gather-info.d'\n",
+ "\n",
+ "os.chdir(dir)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "3b8c0042",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#os.chdir('../..')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "b379845f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "!pwd"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d3d9f107",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "!which python"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c2a94675",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "frames = []\n",
+ "\n",
+ "for file in glob.glob(\"*.gz\"):\n",
+ " print(f\"processing: {file}\")\n",
+ " # combine picked dfs into one df\n",
+ " # https://pandas.pydata.org/pandas-docs/stable/user_guide/merging.html\n",
+ " df = pd.read_pickle(file)\n",
+ " frames.append(df)\n",
+ "\n",
+ "df = pd.concat(frames)\n",
+ "del(frames)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a6a4e9bf",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df.info()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4ca6e641",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "print(f\"max atime: {df['atime'].max()}\\n\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "cc576380",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df.groupby([\"uid\"], sort=False)[\"atime\"].max().sort_values()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1a482ac6",
+ "metadata": {},
+ "source": [
+ "Get user names https://stackoverflow.com/a/421670/8928529"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7c4e531e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pwd\n",
+ "def getuser(uid):\n",
+ " return pwd.getpwuid(int(uid))[0].split(\":\")[0]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "6980fc30",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# set uname for uid\n",
+ "for uid in sorted(df[\"uid\"].unique()):\n",
+ " uname = pwd.getpwuid(int(uid))[0].split(\":\")[0]\n",
+ " print(\"uid: {} name: {}\".format(uid, uname))\n",
+ " df.loc[df[\"uid\"]==uid, [\"uname\"]] = uname"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "29af2c16",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "space=df.groupby([\"uname\"], sort=False)[\"size\"].sum().sort_values().to_frame()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "77b361b1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "space[\"GB\"]=space[\"size\"]/1000/1000/1000\n",
+ "space[\"GiB\"]=space[\"size\"]/1024/1024/1024"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "90b897a5",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "space=df.groupby([\"uname\"], sort=False)[\"size\"].sum().sort_values().to_frame()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5065d98e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "space"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1bf8adb7",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "atime=df.groupby([\"uname\"], sort=False)[\"atime\"].max().sort_values().to_frame()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "fb33c1de",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "atime.rename(columns={\"atime\": \"newest\"}, inplace=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ce376ec2",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "atime[\"oldest\"]=df.groupby([\"uname\"], sort=False)[\"atime\"].min().sort_values().to_frame()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "6fd5346a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "atime[\"newest_date\"] = pd.to_datetime(atime[\"newest\"].dt.strftime('%Y-%m-%d'))\n",
+ "atime[\"oldest_date\"] = pd.to_datetime(atime[\"oldest\"].dt.strftime('%Y-%m-%d'))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "422ecc0c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "report=pd.concat([atime, space], axis=1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1c979329",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "pd.set_option('display.max_rows', None)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "32139106",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# https://stackoverflow.com/a/20937592/8928529\n",
+ "pd.options.display.float_format = '{:,.2f}'.format"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8d40d60f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "report[[\"newest_date\", \"oldest_date\", \"GB\", \"GiB\"]]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "871995b1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "print(f\"report: {report_name}\")\n",
+ "print(report[[\"newest_date\", \"oldest_date\", \"GB\", \"GiB\"]])\n",
+ "print(\"--------\\ntotals: {0:,.2f}GB {1:,.2f}GiB\".format(report['GB'].sum(), report['GiB'].sum()))"
+ ]
+ }
+ ],
+ "metadata": {
+ "language_info": {
+ "name": "python",
+ "pygments_lexer": "ipython3"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/max-access-per-user.ipynb b/max-access-per-user.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..8424774058f4359aa0938867834e03e1b250673a
--- /dev/null
+++ b/max-access-per-user.ipynb
@@ -0,0 +1,124 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "63ee8026",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import datetime\n",
+ "import pandas as pd\n",
+ "import matplotlib.pyplot as plt\n",
+ "from urllib.parse import unquote\n",
+ "import sys\n",
+ "\n",
+ "recs = []\n",
+ "count=0\n",
+ "progress=0\n",
+ "\n",
+ "file = \"data/list-17075953.list.gather-info.d/list-000\"\n",
+ "\n",
+ "with open(file) as gpfs_data:\n",
+ " for line in gpfs_data:\n",
+ " #print(unquote(line))\n",
+ " left, right = unquote(line).split(\" -- \", 1)\n",
+ " fname = right.strip()\n",
+ " inode, meta = left.split('|', 1)\n",
+ " _, inode, _ = inode.split() \n",
+ " #print(meta)\n",
+ " meta, _ = meta.rsplit('|', 1)\n",
+ " #print(meta)\n",
+ " props = []\n",
+ " for prop in meta.split('|'):\n",
+ " props.append(prop.split('='))\n",
+ " #props.append(['path', fname])\n",
+ " #print(props)\n",
+ " props = dict(props)\n",
+ " props[\"inode\"] = inode\n",
+ " for key in [\"heat\", \"pool\", \"mode\", \"misc\"]:\n",
+ " del props[key] \n",
+ " recs.append(props)\n",
+ " count += 1\n",
+ " progress += 1\n",
+ " if (progress // 20000000):\n",
+ " print(f\"{datetime.datetime.now()} progress: {count}: {fname}\")\n",
+ " progress = 0\n",
+ " if (count > 200000000):\n",
+ " break"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "aa0f2ba7",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df = pd.DataFrame(recs)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d6308a64",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df = df.rename(columns={'access': 'atime', 'create': 'ctime', 'modify': 'mtime'})"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c60d8868",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "for intcol in ['size', 'kballoc', 'uid', 'gid']:\n",
+ " df[intcol] = df[intcol].astype(\"int\")\n",
+ "\n",
+ "for intcol in ['atime', 'ctime', 'mtime']:\n",
+ " df[intcol] = df[intcol].astype('datetime64[ns]')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4ca6e641",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "print(f\"max atime: {df['atime'].max()}\\n\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "71fd3b6f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df.info()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "cc576380",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "print(df.groupby([\"uid\"], sort=False)[\"atime\"].max())"
+ ]
+ }
+ ],
+ "metadata": {
+ "language_info": {
+ "name": "python",
+ "pygments_lexer": "ipython3"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/run-max-atime-per-user b/run-max-atime-per-user
new file mode 100755
index 0000000000000000000000000000000000000000..812698e6805c4f9d5ed1b19ff68e322c41655196
--- /dev/null
+++ b/run-max-atime-per-user
@@ -0,0 +1,10 @@
+#!/bin/bash
+
+suffix=`printf %.3d $SLURM_ARRAY_TASK_ID`
+
+#cd data/list-16144464.list.gather-info.d
+#cd data/list-17075953.list.gather-info.d
+cd data/list-17094088.list.gather-info.d
+
+echo -n "list-$suffix "
+../../last-access-per-user.py list-$suffix