lstm_2_rtu_model

lstm.ipynb

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -22,7 +22,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -434,7 +434,7 @@
        "[2072154 rows x 30 columns]"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1135,7 +1135,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 325,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -1272,7 +1272,7 @@
        "[2 rows x 65 columns]"
       ]
      },
-     "execution_count": 325,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1294,9 +1294,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 363,
+   "execution_count": 7,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "There are NA values in the DataFrame columns. [0, 0, 0, 0, 0, 0, 27872, 36554, 15482, 39222, 30748, 37389, 18791, 36175, 31546, 36586, 16271, 36346, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 23177, 23177, 0, 0, 0, 0, 0]\n"
+     ]
+    }
+   ],
    "source": [
     "df_filtered['date'] = pd.to_datetime(df_filtered['date'], format = \"%Y-%m-%d %H:%M:%S\")\n",
     "df_filtered = df_filtered[ (df_filtered.date.dt.date >date(2018, 5, 1)) & (df_filtered.date.dt.date< date(2020, 5, 1))] #(2018, 5, 1)\n",
@@ -1307,7 +1315,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 364,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1340,20 +1348,22 @@
     " 'rtu_004_ma_temp',\n",
     " 'rtu_004_sf_vfd_spd_fbk_tn',\n",
     " 'rtu_004_rf_vfd_spd_fbk_tn',\n",
-    " 'rtu_001_sa_temp',\n",
-    " 'rtu_001_oadmpr_pct',\n",
-    " 'rtu_001_ra_temp',\n",
-    " 'rtu_001_oa_temp',\n",
-    " 'rtu_001_ma_temp',\n",
-    " 'rtu_001_sf_vfd_spd_fbk_tn',\n",
-    " 'rtu_001_rf_vfd_spd_fbk_tn',\n",
-    " 'rtu_002_sa_temp',\n",
-    " 'rtu_002_oadmpr_pct',\n",
-    " 'rtu_002_ra_temp',\n",
-    " 'rtu_002_oa_temp',\n",
-    " 'rtu_002_ma_temp',\n",
-    " 'rtu_002_sf_vfd_spd_fbk_tn',\n",
-    " 'rtu_002_rf_vfd_spd_fbk_tn',\n",
+    "#  'rtu_001_sa_temp',\n",
+    "#  'rtu_001_oadmpr_pct',\n",
+    "#  'rtu_001_ra_temp',\n",
+    "#  'rtu_001_oa_temp',\n",
+    "#  'rtu_001_ma_temp',\n",
+    "#  'rtu_001_sf_vfd_spd_fbk_tn',\n",
+    "#  'rtu_001_rf_vfd_spd_fbk_tn',\n",
+    "#  'rtu_002_sa_temp',\n",
+    "#  'rtu_002_oadmpr_pct',\n",
+    "#  'rtu_002_ra_temp',\n",
+    "#  'rtu_002_oa_temp',\n",
+    "#  'rtu_002_ma_temp',\n",
+    "#  'rtu_002_sf_vfd_spd_fbk_tn',\n",
+    "#  'rtu_002_rf_vfd_spd_fbk_tn',\n",
+    " 'rtu_003_sat_sp_tn',\n",
+    " 'rtu_004_sat_sp_tn',\n",
     " 'air_temp_set_1',\n",
     " 'air_temp_set_2',\n",
     " 'dew_point_temperature_set_1d',\n",
@@ -1363,7 +1373,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 365,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
@@ -1380,13 +1390,13 @@
     "# df_filtered.to_csv(\"sample_data.csv\",index=False)\n",
     "\n",
     "#----------------------------------------------------------------------------------\n",
-    "# testdataset_df = df_filtered[(df_filtered.date.dt.date >date(2019, 7, 21))]\n",
+    "testdataset_df = df_filtered[(df_filtered.date.dt.date >date(2019, 7, 21))]\n",
     "\n",
-    "# traindataset_df = df_filtered[(df_filtered.date.dt.date <date(2019, 7, 21))]\n",
+    "traindataset_df = df_filtered[(df_filtered.date.dt.date <date(2019, 7, 21))]\n",
     "\n",
-    "testdataset_df = df_filtered[(df_filtered.date.dt.date >date(2020, 1, 1))]\n",
+    "# testdataset_df = df_filtered[(df_filtered.date.dt.date >date(2020, 1, 1))]\n",
     "\n",
-    "traindataset_df = df_filtered[(df_filtered.date.dt.date <date(2020, 1, 1))]\n",
+    "# traindataset_df = df_filtered[(df_filtered.date.dt.date <date(2020, 1, 1))]\n",
     "\n",
     "\n",
     "# testdataset_df.to_csv(\"sample_data_02.csv\",index=False)\n",
@@ -1403,7 +1413,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 366,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
@@ -1449,7 +1459,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 201,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
@@ -1464,35 +1474,23 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 1/5\n",
-      "\u001b[1m8067/8067\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 0.0568\n",
-      "Epoch 1: val_loss improved from inf to 0.08613, saving model to lstm_2rtu_smooth_02.keras\n",
-      "\u001b[1m8067/8067\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━��━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m198s\u001b[0m 24ms/step - loss: 0.0568 - val_loss: 0.0861\n",
-      "Epoch 2/5\n",
-      "\u001b[1m8066/8067\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 9.6620e-04\n",
-      "Epoch 2: val_loss improved from 0.08613 to 0.07149, saving model to lstm_2rtu_smooth_02.keras\n",
-      "\u001b[1m8067/8067\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m189s\u001b[0m 23ms/step - loss: 9.6615e-04 - val_loss: 0.0715\n",
-      "Epoch 3/5\n",
-      "\u001b[1m8067/8067\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 5.0913e-04\n",
-      "Epoch 3: val_loss improved from 0.07149 to 0.06955, saving model to lstm_2rtu_smooth_02.keras\n",
-      "\u001b[1m8067/8067\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m187s\u001b[0m 23ms/step - loss: 5.0913e-04 - val_loss: 0.0695\n",
-      "Epoch 4/5\n",
-      "\u001b[1m8066/8067\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 3.9422e-04\n",
-      "Epoch 4: val_loss improved from 0.06955 to 0.06820, saving model to lstm_2rtu_smooth_02.keras\n",
-      "\u001b[1m8067/8067\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m185s\u001b[0m 23ms/step - loss: 3.9421e-04 - val_loss: 0.0682\n",
-      "Epoch 5/5\n",
-      "\u001b[1m8066/8067\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 3.6433e-04\n",
-      "Epoch 5: val_loss improved from 0.06820 to 0.06542, saving model to lstm_2rtu_smooth_02.keras\n",
-      "\u001b[1m8067/8067\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m188s\u001b[0m 23ms/step - loss: 3.6432e-04 - val_loss: 0.0654\n"
+      "Epoch 1/2\n",
+      "\u001b[1m8067/8067\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step - loss: 0.0577\n",
+      "Epoch 1: val_loss improved from inf to 0.08234, saving model to lstm_2rtu_smooth_03.keras\n",
+      "\u001b[1m8067/8067\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m234s\u001b[0m 29ms/step - loss: 0.0577 - val_loss: 0.0823\n",
+      "Epoch 2/2\n",
+      "\u001b[1m8067/8067\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step - loss: 8.6307e-04\n",
+      "Epoch 2: val_loss improved from 0.08234 to 0.07465, saving model to lstm_2rtu_smooth_03.keras\n",
+      "\u001b[1m8067/8067\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m232s\u001b[0m 29ms/step - loss: 8.6305e-04 - val_loss: 0.0747\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "<keras.src.callbacks.history.History at 0x15088066a50>"
+       "<keras.src.callbacks.history.History at 0x207d62cebd0>"
       ]
      },
-     "execution_count": 201,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1500,40 +1498,40 @@
    "source": [
     "#2 rtu model\n",
     "\n",
-    "# train,test = traindataset,testdataset\n",
-    "\n",
-    "# def create_dataset(dataset,time_step):\n",
-    "#     x = [[] for _ in range(20)] \n",
-    "#     Y = []\n",
-    "#     for i in range(len(dataset) - time_step - 1):\n",
-    "#         for j in range(20):\n",
-    "#             x[j].append(dataset[i:(i + time_step), j])\n",
-    "#         Y.append([dataset[i + time_step, 0],dataset[i + time_step, 1],dataset[i + time_step, 2],dataset[i + time_step, 3],\n",
-    "#                   dataset[i + time_step, 4],dataset[i + time_step, 5],\n",
-    "#                   dataset[i + time_step, 6],dataset[i + time_step, 7],\n",
-    "#                   dataset[i + time_step, 8],dataset[i + time_step, 9],dataset[i + time_step, 10],\n",
-    "#                   dataset[i + time_step, 11],dataset[i + time_step, 12],\n",
-    "#                   dataset[i + time_step, 13],dataset[i + time_step, 14]])\n",
-    "#     x= [np.array(feature_list) for feature_list in x]\n",
-    "#     Y = np.reshape(Y,(len(Y),15))\n",
-    "#     return np.stack(x,axis=2),Y\n",
-    "\n",
-    "# time_step = 30\n",
-    "# X_train, y_train = create_dataset(train, time_step)\n",
-    "# X_test, y_test = create_dataset(test, time_step)\n",
-    "\n",
-    "\n",
-    "# model = Sequential()\n",
-    "# model.add(LSTM(units=50, return_sequences=True, input_shape=(X_train.shape[1], X_train.shape[2])))\n",
-    "# model.add(LSTM(units=50, return_sequences=True))\n",
-    "# model.add(LSTM(units=30))\n",
-    "# model.add(Dense(units=15))\n",
-    "\n",
-    "# model.compile(optimizer='adam', loss='mean_squared_error')\n",
-    "\n",
-    "# checkpoint_path = \"lstm_2rtu_smooth_02.keras\"\n",
-    "# checkpoint_callback = ModelCheckpoint(filepath=checkpoint_path, monitor='val_loss', verbose=1, save_best_only=True, mode='min')\n",
-    "# model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=5, batch_size=64, verbose=1, callbacks=[checkpoint_callback])"
+    "train,test = traindataset,testdataset\n",
+    "\n",
+    "def create_dataset(dataset,time_step):\n",
+    "    x = [[] for _ in range(22)] \n",
+    "    Y = []\n",
+    "    for i in range(len(dataset) - time_step - 1):\n",
+    "        for j in range(22):\n",
+    "            x[j].append(dataset[i:(i + time_step), j])\n",
+    "        Y.append([dataset[i + time_step, 0],dataset[i + time_step, 1],dataset[i + time_step, 2],dataset[i + time_step, 3],\n",
+    "                  dataset[i + time_step, 4],dataset[i + time_step, 5],\n",
+    "                  dataset[i + time_step, 6],dataset[i + time_step, 7],\n",
+    "                  dataset[i + time_step, 8],dataset[i + time_step, 9],dataset[i + time_step, 10],\n",
+    "                  dataset[i + time_step, 11],dataset[i + time_step, 12],\n",
+    "                  dataset[i + time_step, 13],dataset[i + time_step, 14]])\n",
+    "    x= [np.array(feature_list) for feature_list in x]\n",
+    "    Y = np.reshape(Y,(len(Y),15))\n",
+    "    return np.stack(x,axis=2),Y\n",
+    "\n",
+    "time_step = 30\n",
+    "X_train, y_train = create_dataset(train, time_step)\n",
+    "X_test, y_test = create_dataset(test, time_step)\n",
+    "\n",
+    "\n",
+    "model = Sequential()\n",
+    "model.add(LSTM(units=50, return_sequences=True, input_shape=(X_train.shape[1], X_train.shape[2])))\n",
+    "model.add(LSTM(units=50, return_sequences=True))\n",
+    "model.add(LSTM(units=30))\n",
+    "model.add(Dense(units=15))\n",
+    "\n",
+    "model.compile(optimizer='adam', loss='mean_squared_error')\n",
+    "\n",
+    "checkpoint_path = \"lstm_2rtu_smooth_03.keras\"\n",
+    "checkpoint_callback = ModelCheckpoint(filepath=checkpoint_path, monitor='val_loss', verbose=1, save_best_only=True, mode='min')\n",
+    "model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=2, batch_size=64, verbose=1, callbacks=[checkpoint_callback])"
    ]
   },
   {
@@ -1695,32 +1693,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 341,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\u001b[1m11765/11765\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m84s\u001b[0m 7ms/step\n"
+      "\u001b[1m19190/19190\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m95s\u001b[0m 5ms/step\n",
+      "\u001b[1m16134/16134\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m83s\u001b[0m 5ms/step\n"
      ]
     }
    ],
    "source": [
     "test_predict1 = model.predict(X_test)\n",
-    "# train_predict1 = model.predict(X_train)"
+    "train_predict1 = model.predict(X_train)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 362,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [],
    "source": [
     "%matplotlib qt\n",
-    "var = 13\n",
+    "var = 7\n",
     "plt.plot(y_test[:,var], label='Original Testing Data', color='blue')\n",
-    "plt.plot(test_predict2[:,var], label='Predicted Testing Data', color='red',alpha=0.8)\n",
+    "plt.plot(test_predict1[:,var], label='Predicted Testing Data', color='red',alpha=0.8)\n",
     "# anomalies = np.where(abs(test_predict1[:,var] - y_test[:,var]) > 0.38)\n",
     "# plt.scatter(anomalies,test_predict1[anomalies,var], color='black',marker =\"o\",s=100 )\n",
     "\n",
@@ -1787,425 +1786,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 195,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/html": [
-       "<style>#sk-container-id-3 {\n",
-       "  /* Definition of color scheme common for light and dark mode */\n",
-       "  --sklearn-color-text: black;\n",
-       "  --sklearn-color-line: gray;\n",
-       "  /* Definition of color scheme for unfitted estimators */\n",
-       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
-       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
-       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
-       "  --sklearn-color-unfitted-level-3: chocolate;\n",
-       "  /* Definition of color scheme for fitted estimators */\n",
-       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
-       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
-       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
-       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
-       "\n",
-       "  /* Specific color for light theme */\n",
-       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
-       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-icon: #696969;\n",
-       "\n",
-       "  @media (prefers-color-scheme: dark) {\n",
-       "    /* Redefinition of color scheme for dark theme */\n",
-       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
-       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-icon: #878787;\n",
-       "  }\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 pre {\n",
-       "  padding: 0;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 input.sk-hidden--visually {\n",
-       "  border: 0;\n",
-       "  clip: rect(1px 1px 1px 1px);\n",
-       "  clip: rect(1px, 1px, 1px, 1px);\n",
-       "  height: 1px;\n",
-       "  margin: -1px;\n",
-       "  overflow: hidden;\n",
-       "  padding: 0;\n",
-       "  position: absolute;\n",
-       "  width: 1px;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-dashed-wrapped {\n",
-       "  border: 1px dashed var(--sklearn-color-line);\n",
-       "  margin: 0 0.4em 0.5em 0.4em;\n",
-       "  box-sizing: border-box;\n",
-       "  padding-bottom: 0.4em;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-container {\n",
-       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
-       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
-       "     so we also need the `!important` here to be able to override the\n",
-       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
-       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
-       "  display: inline-block !important;\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-text-repr-fallback {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-parallel-item,\n",
-       "div.sk-serial,\n",
-       "div.sk-item {\n",
-       "  /* draw centered vertical line to link estimators */\n",
-       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
-       "  background-size: 2px 100%;\n",
-       "  background-repeat: no-repeat;\n",
-       "  background-position: center center;\n",
-       "}\n",
-       "\n",
-       "/* Parallel-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-3 div.sk-parallel-item::after {\n",
-       "  content: \"\";\n",
-       "  width: 100%;\n",
-       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
-       "  flex-grow: 1;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-parallel {\n",
-       "  display: flex;\n",
-       "  align-items: stretch;\n",
-       "  justify-content: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-parallel-item {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
-       "  align-self: flex-end;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
-       "  align-self: flex-start;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
-       "  width: 0;\n",
-       "}\n",
-       "\n",
-       "/* Serial-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-3 div.sk-serial {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "  align-items: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  padding-right: 1em;\n",
-       "  padding-left: 1em;\n",
-       "}\n",
-       "\n",
-       "\n",
-       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
-       "clickable and can be expanded/collapsed.\n",
-       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
-       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
-       "*/\n",
-       "\n",
-       "/* Pipeline and ColumnTransformer style (default) */\n",
-       "\n",
-       "#sk-container-id-3 div.sk-toggleable {\n",
-       "  /* Default theme specific background. It is overwritten whether we have a\n",
-       "  specific estimator or a Pipeline/ColumnTransformer */\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable label */\n",
-       "#sk-container-id-3 label.sk-toggleable__label {\n",
-       "  cursor: pointer;\n",
-       "  display: block;\n",
-       "  width: 100%;\n",
-       "  margin-bottom: 0;\n",
-       "  padding: 0.5em;\n",
-       "  box-sizing: border-box;\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
-       "  /* Arrow on the left of the label */\n",
-       "  content: \"▸\";\n",
-       "  float: left;\n",
-       "  margin-right: 0.25em;\n",
-       "  color: var(--sklearn-color-icon);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable content - dropdown */\n",
-       "\n",
-       "#sk-container-id-3 div.sk-toggleable__content {\n",
-       "  max-height: 0;\n",
-       "  max-width: 0;\n",
-       "  overflow: hidden;\n",
-       "  text-align: left;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-toggleable__content pre {\n",
-       "  margin: 0.2em;\n",
-       "  border-radius: 0.25em;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
-       "  /* Expand drop-down */\n",
-       "  max-height: 200px;\n",
-       "  max-width: 100%;\n",
-       "  overflow: auto;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
-       "  content: \"▾\";\n",
-       "}\n",
-       "\n",
-       "/* Pipeline/ColumnTransformer-specific style */\n",
-       "\n",
-       "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific style */\n",
-       "\n",
-       "/* Colorize estimator box */\n",
-       "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-3 div.sk-label label {\n",
-       "  /* The background is the default theme color */\n",
-       "  color: var(--sklearn-color-text-on-default-background);\n",
-       "}\n",
-       "\n",
-       "/* On hover, darken the color of the background */\n",
-       "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Label box, darken color on hover, fitted */\n",
-       "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator label */\n",
-       "\n",
-       "#sk-container-id-3 div.sk-label label {\n",
-       "  font-family: monospace;\n",
-       "  font-weight: bold;\n",
-       "  display: inline-block;\n",
-       "  line-height: 1.2em;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-label-container {\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific */\n",
-       "#sk-container-id-3 div.sk-estimator {\n",
-       "  font-family: monospace;\n",
-       "  border: 1px dotted var(--sklearn-color-border-box);\n",
-       "  border-radius: 0.25em;\n",
-       "  box-sizing: border-box;\n",
-       "  margin-bottom: 0.5em;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-estimator.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "/* on hover */\n",
-       "#sk-container-id-3 div.sk-estimator:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
-       "\n",
-       "/* Common style for \"i\" and \"?\" */\n",
-       "\n",
-       ".sk-estimator-doc-link,\n",
-       "a:link.sk-estimator-doc-link,\n",
-       "a:visited.sk-estimator-doc-link {\n",
-       "  float: right;\n",
-       "  font-size: smaller;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1em;\n",
-       "  height: 1em;\n",
-       "  width: 1em;\n",
-       "  text-decoration: none !important;\n",
-       "  margin-left: 1ex;\n",
-       "  /* unfitted */\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted,\n",
-       "a:link.sk-estimator-doc-link.fitted,\n",
-       "a:visited.sk-estimator-doc-link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "/* Span, style for the box shown on hovering the info icon */\n",
-       ".sk-estimator-doc-link span {\n",
-       "  display: none;\n",
-       "  z-index: 9999;\n",
-       "  position: relative;\n",
-       "  font-weight: normal;\n",
-       "  right: .2ex;\n",
-       "  padding: .5ex;\n",
-       "  margin: .5ex;\n",
-       "  width: min-content;\n",
-       "  min-width: 20ex;\n",
-       "  max-width: 50ex;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  box-shadow: 2pt 2pt 4pt #999;\n",
-       "  /* unfitted */\n",
-       "  background: var(--sklearn-color-unfitted-level-0);\n",
-       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted span {\n",
-       "  /* fitted */\n",
-       "  background: var(--sklearn-color-fitted-level-0);\n",
-       "  border: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link:hover span {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
-       "\n",
-       "#sk-container-id-3 a.estimator_doc_link {\n",
-       "  float: right;\n",
-       "  font-size: 1rem;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1rem;\n",
-       "  height: 1rem;\n",
-       "  width: 1rem;\n",
-       "  text-decoration: none;\n",
-       "  /* unfitted */\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "#sk-container-id-3 a.estimator_doc_link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KMeans(n_clusters=1, random_state=10)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;KMeans<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.cluster.KMeans.html\">?<span>Documentation for KMeans</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>KMeans(n_clusters=1, random_state=10)</pre></div> </div></div></div></div>"
-      ],
-      "text/plain": [
-       "KMeans(n_clusters=1, random_state=10)"
-      ]
-     },
-     "execution_count": 195,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[ 0.00183319  0.01064136  0.01099219  0.01235903  0.01590783  0.0086837\n",
+      "  -0.00136298]]\n"
+     ]
     }
    ],
    "source": [
@@ -2217,8 +1807,8 @@
     "np.random.seed(0)\n",
     "X1 = train_predict1[:,1:8] - y_train[:,1:8]\n",
     "X2 = train_predict1[:,8:15] - y_train[:,8:15]\n",
-    "X3 = train_predict1[:,15:22] - y_train[:,15:22]\n",
-    "X4 = train_predict1[:,22:29] - y_train[:,22:29]\n",
+    "# X3 = train_predict1[:,15:22] - y_train[:,15:22]\n",
+    "# X4 = train_predict1[:,22:29] - y_train[:,22:29]\n",
     "\n",
     "k = 1\n",
     "\n",
@@ -2226,30 +1816,30 @@
     "kmeans1.fit(X1)\n",
     "kmeans2 = KMeans(n_clusters=k, random_state=10) #2 for without smoothing\n",
     "kmeans2.fit(X2)\n",
-    "kmeans3 = KMeans(n_clusters=k, random_state=10) #2 for without smoothing\n",
-    "kmeans3.fit(X3)\n",
-    "kmeans4 = KMeans(n_clusters=k, random_state=10) #2 for without smoothing\n",
-    "kmeans4.fit(X4)\n",
+    "# kmeans3 = KMeans(n_clusters=k, random_state=10) #2 for without smoothing\n",
+    "# kmeans3.fit(X3)\n",
+    "# kmeans4 = KMeans(n_clusters=k, random_state=10) #2 for without smoothing\n",
+    "# kmeans4.fit(X4)\n",
     "\n",
     "\n",
     "\n",
-    "# pca = PCA(n_components=2)\n",
-    "# X = pca.fit_transform(X2)\n",
+    "pca = PCA(n_components=2)\n",
+    "X = pca.fit_transform(X1)\n",
     "\n",
     "\n",
     "\n",
     "\n",
     "# # Getting the cluster centers and labels\n",
-    "# centroids = pca.transform(kmeans.cluster_centers_)\n",
-    "# labels = kmeans.labels_\n",
-    "# print(kmeans.cluster_centers_)\n",
+    "centroids = pca.transform(kmeans1.cluster_centers_)\n",
+    "labels = kmeans1.labels_\n",
+    "print(kmeans1.cluster_centers_)\n",
     "# Plotting the data points and cluster centers\n",
-    "# plt.scatter(X[:, 0], X[:, 1], c=labels, cmap='viridis', alpha=0.5)\n",
-    "# plt.scatter(centroids[:, 0], centroids[:, 1], marker='x', c='red', s=200, linewidths=2)\n",
-    "# plt.title('KMeans Clustering')\n",
-    "# plt.xlabel('Feature 1')\n",
-    "# plt.ylabel('Feature 2')\n",
-    "# plt.show()\n"
+    "plt.scatter(X[:, 0], X[:, 1], c=labels, cmap='viridis', alpha=0.5)\n",
+    "plt.scatter(centroids[:, 0], centroids[:, 1], marker='x', c='red', s=200, linewidths=2)\n",
+    "plt.title('KMeans Clustering')\n",
+    "plt.xlabel('Feature 1')\n",
+    "plt.ylabel('Feature 2')\n",
+    "plt.show()\n"
    ]
   },
   {
@@ -2302,16 +1892,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 323,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x228993bb690>]"
+       "[<matplotlib.lines.Line2D at 0x207c1f8ce90>]"
       ]
      },
-     "execution_count": 323,
+     "execution_count": 41,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2321,15 +1911,15 @@
     "\n",
     "distance1 = np.linalg.norm((test_predict1[:,1:8]-y_test[:,1:8])-kmeans1.cluster_centers_[0], ord=2, axis = 1)\n",
     "distance2 = np.linalg.norm((test_predict1[:,8:15]-y_test[:,8:15])-kmeans2.cluster_centers_[0], ord=2, axis = 1)\n",
-    "distance3 = np.linalg.norm((test_predict1[:,15:22]-y_test[:,15:22])-kmeans3.cluster_centers_[0], ord=2, axis = 1)\n",
-    "distance4 = np.linalg.norm((test_predict1[:,22:29]-y_test[:,22:29])-kmeans4.cluster_centers_[0], ord=2, axis = 1)\n",
+    "# distance3 = np.linalg.norm((test_predict1[:,15:22]-y_test[:,15:22])-kmeans3.cluster_centers_[0], ord=2, axis = 1)\n",
+    "# distance4 = np.linalg.norm((test_predict1[:,22:29]-y_test[:,22:29])-kmeans4.cluster_centers_[0], ord=2, axis = 1)\n",
     "# distance_a = np.linalg.norm(test_predict1[:,8:]-a, ord=2, axis = 1)\n",
     "# plt.plot(y_test[:,23],alpha=0.6)\n",
-    "plt.plot(y_test[:,3],alpha=0.6)\n",
+    "# plt.plot(y_test[:,3],alpha=0.6)\n",
     "# plt.plot(y_test[:,6],alpha=0.6)\n",
-    "# plt.plot(y_test[:,7],alpha=0.6)\n",
-    "plt.plot(test_predict1[:,3],alpha=0.6)\n",
-    "plt.plot(abs(distance1)>2)\n",
+    "plt.plot(y_test[:,7],alpha=0.6)\n",
+    "# plt.plot(test_predict1[:,3],alpha=0.6)\n",
+    "plt.plot(abs(distance1)>2.5)\n",
     "# plt.plot(abs(distance2)>3.5)\n",
     "# plt.plot(abs(distance3)>5)\n",
     "# plt.plot(abs(distance4)>5)\n",

src/main.py

@@ -23,7 +23,8 @@ def main():
         # print(json.loads(message.payload.decode()))
         df_new, df_trans = rtu_data_pipeline.fit(message)
         if not df_new is None and not df_trans is None:
-            out = rtu_anomalizer.pipeline(df_new, df_trans, rtu_data_pipeline.scaler)
+            out1,out2,out3,out4 = rtu_anomalizer.pipeline(df_new, df_trans, rtu_data_pipeline.scaler)
+            print(out3)
 
     broker_address = "localhost"
     broker_port = 1883

streamlit.py

@@ -12,14 +12,14 @@ import plotly.graph_objects as go
 warnings.filterwarnings('ignore')
 
 
-model = load_model("lstm_4rtu_smooth_02.keras")
-scaler = joblib.load('scaler_1.pkl')
+model = load_model(r"src\rtu\models\lstm_4rtu_smooth_02.keras")
+scaler = joblib.load(r'src\rtu\models\scaler_1.pkl')
 # kmeans = joblib.load('kmeans_model.pkl')
-kmeans1 = joblib.load('kmeans_model1.pkl')
-kmeans2 = joblib.load('kmeans_model2.pkl')
-kmeans3 = joblib.load('kmeans_model3.pkl')
-kmeans4 = joblib.load('kmeans_model4.pkl')
-pca = joblib.load('pca_model.pkl')
+kmeans1 = joblib.load(r'src\rtu\models\kmeans_model1.pkl')
+kmeans2 = joblib.load(r'src\rtu\models\kmeans_model2.pkl')
+kmeans3 = joblib.load(r'src\rtu\models\kmeans_model3.pkl')
+kmeans4 = joblib.load(r'src\rtu\models\kmeans_model4.pkl')
+# pca = joblib.load('pca_model.pkl')
 
 st.set_page_config(
     page_title = 'Real-Time Data Buliding 59',
@@ -63,10 +63,10 @@ df = pd.DataFrame(columns=['hp_hws_temp',
  'rtu_002_ma_temp',
  'rtu_002_sf_vfd_spd_fbk_tn',
  'rtu_002_rf_vfd_spd_fbk_tn',
- 'rtu_004_sat_sp_tn',
- 'rtu_003_sat_sp_tn',
- 'rtu_001_sat_sp_tn',
- 'rtu_002_sat_sp_tn',
+#  'rtu_004_sat_sp_tn',
+#  'rtu_003_sat_sp_tn',
+#  'rtu_001_sat_sp_tn',
+#  'rtu_002_sat_sp_tn',
  'air_temp_set_1',
  'air_temp_set_2',
  'dew_point_temperature_set_1d',
@@ -112,10 +112,10 @@ def on_message(client, userdata, message):
     rtu_002_ma_temp = payload['rtu_002_ma_temp']
     rtu_002_sf_vfd_spd_fbk_tn = payload['rtu_002_sf_vfd_spd_fbk_tn']
     rtu_002_rf_vfd_spd_fbk_tn = payload['rtu_002_rf_vfd_spd_fbk_tn']
-    rtu_004_sat_sp_tn = payload['rtu_004_sat_sp_tn']
-    rtu_003_sat_sp_tn = payload['rtu_003_sat_sp_tn']
-    rtu_001_sat_sp_tn = payload['rtu_001_sat_sp_tn']
-    rtu_002_sat_sp_tn = payload['rtu_002_sat_sp_tn']
+    # rtu_004_sat_sp_tn = payload['rtu_004_sat_sp_tn']
+    # rtu_003_sat_sp_tn = payload['rtu_003_sat_sp_tn']
+    # rtu_001_sat_sp_tn = payload['rtu_001_sat_sp_tn']
+    # rtu_002_sat_sp_tn = payload['rtu_002_sat_sp_tn']
     air_temp_set_1 = payload['air_temp_set_1']
     air_temp_set_2 = payload['air_temp_set_2']
     dew_point_temperature_set_1d = payload['dew_point_temperature_set_1d']
@@ -152,10 +152,10 @@ def on_message(client, userdata, message):
     'rtu_002_ma_temp':rtu_002_ma_temp,
     'rtu_002_sf_vfd_spd_fbk_tn':rtu_002_sf_vfd_spd_fbk_tn,
     'rtu_002_rf_vfd_spd_fbk_tn':rtu_002_rf_vfd_spd_fbk_tn,
-    'rtu_004_sat_sp_tn':rtu_004_sat_sp_tn,
-    'rtu_003_sat_sp_tn' :rtu_003_sat_sp_tn,
-    'rtu_001_sat_sp_tn':rtu_001_sat_sp_tn,
-    'rtu_002_sat_sp_tn':rtu_002_sat_sp_tn,
+    # 'rtu_004_sat_sp_tn':rtu_004_sat_sp_tn,
+    # 'rtu_003_sat_sp_tn' :rtu_003_sat_sp_tn,
+    # 'rtu_001_sat_sp_tn':rtu_001_sat_sp_tn,
+    # 'rtu_002_sat_sp_tn':rtu_002_sat_sp_tn,
     'air_temp_set_1':air_temp_set_1,
     'air_temp_set_2':air_temp_set_2,
     'dew_point_temperature_set_1d':dew_point_temperature_set_1d,