stripping notebook info

d519057b · William Stonewall Monroe · cf3a83da · d519057b · d519057b
Commit d519057b authored 5 years ago by William Stonewall Monroe
--- a/importSACCTinfo.ipynb
+++ b/importSACCTinfo.ipynb
@@ -3,12 +3,7 @@
  {
   "cell_type": "code",
   "execution_count": null,
-   "metadata": {
+   "metadata": {},
-    "ExecuteTime": {
-     "end_time": "2020-03-16T20:57:10.405006Z",
-     "start_time": "2020-03-16T20:56:55.837670Z"
-    }
-   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
@@ -19,12 +14,7 @@
  {
   "cell_type": "code",
   "execution_count": null,
-   "metadata": {
+   "metadata": {},
-    "ExecuteTime": {
-     "end_time": "2020-03-16T20:57:11.865980Z",
-     "start_time": "2020-03-16T20:57:10.414986Z"
-    }
-   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('userusage.txt',delimiter='|')"
@@ -33,12 +23,7 @@
  {
   "cell_type": "code",
   "execution_count": null,
-   "metadata": {
+   "metadata": {},
-    "ExecuteTime": {
-     "end_time": "2020-03-16T20:57:11.932878Z",
-     "start_time": "2020-03-16T20:57:11.905219Z"
-    }
-   },
   "outputs": [],
   "source": [
    "df.head()"
@@ -47,12 +32,7 @@
  {
   "cell_type": "code",
   "execution_count": null,
-   "metadata": {
+   "metadata": {},
-    "ExecuteTime": {
-     "end_time": "2020-03-16T20:57:14.154962Z",
-     "start_time": "2020-03-16T20:57:11.967976Z"
-    }
-   },
   "outputs": [],
   "source": [
    "df[['jid','step']] = df.JobID.str.split(\".\",expand=True) \n",
@@ -62,11 +42,7 @@
  {
   "cell_type": "code",
   "execution_count": null,
-   "metadata": {
+   "metadata": {},
-    "ExecuteTime": {
-     "start_time": "2020-03-16T20:56:57.392Z"
-    }
-   },
   "outputs": [],
   "source": [
    "batchDF=df.dropna(subset=[\"MaxRSS\"])\n",
@@ -95,64 +71,9 @@
  }
 ],
 "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"
-   "pygments_lexer": "ipython3",
-   "version": "3.7.5"
-  },
-  "toc": {
-   "base_numbering": 1,
-   "nav_menu": {},
-   "number_sections": true,
-   "sideBar": true,
-   "skip_h1_title": false,
-   "title_cell": "Table of Contents",
-   "title_sidebar": "Contents",
-   "toc_cell": false,
-   "toc_position": {},
-   "toc_section_display": true,
-   "toc_window_display": false
-  },
-  "varInspector": {
-   "cols": {
-    "lenName": 16,
-    "lenType": 16,
-    "lenVar": 40
-   },
-   "kernels_config": {
-    "python": {
-     "delete_cmd_postfix": "",
-     "delete_cmd_prefix": "del ",
-     "library": "var_list.py",
-     "varRefreshCmd": "print(var_dic_list())"
-    },
-    "r": {
-     "delete_cmd_postfix": ") ",
-     "delete_cmd_prefix": "rm(",
-     "library": "var_list.r",
-     "varRefreshCmd": "cat(var_dic_list()) "
-    }
-   },
-   "types_to_exclude": [
-    "module",
-    "function",
-    "builtin_function_or_method",
-    "instance",
-    "_Feature"
-   ],
-   "window_display": false
  }
 },
 "nbformat": 4,

 %% Cell type:code id: tags:
-``` python
+``` 
 import numpy as np
 import pandas as pd
 import pandas_profiling
 ```
 %% Cell type:code id: tags:
-``` python
+``` 
 df = pd.read_csv('userusage.txt',delimiter='|')
 ```
 %% Cell type:code id: tags:
-``` python
+``` 
 df.head()
 ```
 %% Cell type:code id: tags:
-``` python
+``` 
 df[['jid','step']] = df.JobID.str.split(".",expand=True)
 df.Partition.values
 ```
 %% Cell type:code id: tags:
-``` python
+``` 
 batchDF=df.dropna(subset=["MaxRSS"])
 userDF=df.dropna(subset=["User"])
 for jid in df.jid.unique():
    userDF['MaxRSS'][userDF['jid'] == jid]=batchDF['MaxRSS'][batchDF['jid'] == jid]
    #print(userDF[userDF['jid'] == jid])
 userDF.head()
 ```
 %% Cell type:markdown id: tags:
 # add more graphs here
 %% Cell type:code id: tags:
-``` python
+``` 
 ```

--- a/slurm-2sql.ipynb
+++ b/slurm-2sql.ipynb
@@ -58,22 +58,9 @@
  }
 ],
 "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"
-   "pygments_lexer": "ipython3",
-   "version": "3.7.5"
  }
 },
 "nbformat": 4,

 %% Cell type:code id: tags:
-``` python
+``` 
 import sqlite3
 import slurm2sql
 import pandas as pd
 ```
 %% Cell type:code id: tags:
-``` python
+``` 
 db = sqlite3.connect('test.db')
 slurm2sql.slurm2sql(db, ['-S', '2020-03-18', '-a'])
 ```
 %% Cell type:code id: tags:
-``` python
+``` 
 # For example, you can then convert to a dataframe:
 df1 = pd.read_sql('SELECT * FROM slurm', db)
 ```
 %% Cell type:code id: tags:
-``` python
+``` 
 df1.head(5)
 ```
 %% Cell type:code id: tags:
-``` python
+``` 
 print("more plots to come")
 ```
 %% Cell type:code id: tags:
-``` python
+``` 
 ```