Add 256 case

fftma_module/gen/analysis.ipynb

@@ -56,21 +56,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
-    "def analyze(file_name):\n",
+    "def get_data(file_name):\n",
     "    data = []\n",
     "\n",
     "    with open(file_name) as log_file:\n",
     "        lines = log_file.readlines()\n",
-    "        print(len(lines))\n",
     "        for line in lines:\n",
     "            row = {}\n",
     "            split_line = line.split()\n",
-    "            if \"USED\" not in split_line or \"ELAPSED\" not in split_line:\n",
-    "                continue\n",
+    "            if \"USED\" not in split_line or \"ELAPSED\" not in split_line: continue\n",
     "            idx_used_mem = split_line.index(\"USED\") + 4\n",
     "            idx_elapsed = split_line.index(\"ELAPSED\") + 2\n",
     "            \n",
@@ -84,8 +82,29 @@
     "            row[\"time\"] = elapsed\n",
     "            data.append(row)\n",
     "            \n",
+    "    return data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def create_df(file_name):\n",
+    "    data = get_data(file_name)\n",
     "    df = pd.DataFrame(data)\n",
-    "    df_grouped = df.groupby(['function']).agg({'time': ['min', 'max', 'mean', 'sum', 'count'], 'memory': ['min', 'max', 'median']})\n",
+    "    return df.groupby(['function']).agg({'time': ['min', 'max', 'mean', 'sum', 'count'], 'memory': ['min', 'max', 'median']})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def analyze(file_name):\n",
+    "    df_grouped = create_df(file_name)\n",
     "    return df_grouped.sort_values(by=('time', 'sum'), ascending=False)                "
    ]
   },
@@ -98,7 +117,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -349,7 +368,7 @@
        "clean_real         1  "
       ]
      },
-     "execution_count": 19,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1450,10 +1469,45 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0\n",
+      "1\n",
+      "2\n",
+      "3\n",
+      "4\n",
+      "5\n",
+      "6\n",
+      "7\n",
+      "8\n",
+      "9\n"
+     ]
+    }
+   ],
+   "source": [
+    "dfs = []\n",
+    "for i in range(10):\n",
+    "    print(i)\n",
+    "    df = create_df(\"log_256_{}.txt\".format(i+1))\n",
+    "    dfs.append(df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "functions = ['Py_kgeneration', 'generate', 'fftma2', 'covariance', 'gasdev', 'fourt', 'cov_value', 'ran2', 'build_real', 'prebuild_gwn', 'clean_real', 'cgrid', 'length', 'maxfactor']\n",
+    "\n",
+    "\n",
+    "#df_final = pd.concat(dfs).sort_values(by=('time', 'sum'), ascending=False)  "
+   ]
   }
  ],
  "metadata": {
@@ -1461,18 +1515,6 @@
    "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.18"
   }
  },
  "nbformat": 4,