From 2c59a8b8712373d2fb7a4c08cfd1659e0da25c2f Mon Sep 17 00:00:00 2001 From: miguel5612 Date: Wed, 3 Jul 2019 20:49:40 -0500 Subject: [PATCH] Added MQ-6 --- .../MQ4_Regression-checkpoint.ipynb | 957 +++---- .../MQ5_Regression-checkpoint.ipynb | 1011 +++---- .../MQ6_Regression-checkpoint.ipynb | 2034 ++++++++++++++ Experiments/MQ4.eps | 1072 +++----- Experiments/MQ4.png | Bin 136766 -> 133190 bytes Experiments/MQ4.svg | 918 +++---- Experiments/MQ4_Regression.ipynb | 957 +++---- Experiments/MQ5.eps | 1132 ++------ Experiments/MQ5.png | Bin 135255 -> 116480 bytes Experiments/MQ5.svg | 979 +++---- Experiments/MQ5_Regression.ipynb | 1011 +++---- Experiments/MQ6.eps | 2441 +++++++++++++++++ Experiments/MQ6.png | Bin 0 -> 145395 bytes Experiments/MQ6.svg | 1537 +++++++++++ Experiments/MQ6_Regression.ipynb | 2059 ++++++++++++++ 15 files changed, 11224 insertions(+), 4884 deletions(-) create mode 100644 Experiments/.ipynb_checkpoints/MQ6_Regression-checkpoint.ipynb create mode 100644 Experiments/MQ6.eps create mode 100644 Experiments/MQ6.png create mode 100644 Experiments/MQ6.svg create mode 100644 Experiments/MQ6_Regression.ipynb diff --git a/Experiments/.ipynb_checkpoints/MQ4_Regression-checkpoint.ipynb b/Experiments/.ipynb_checkpoints/MQ4_Regression-checkpoint.ipynb index 5fcf942..728a44f 100644 --- a/Experiments/.ipynb_checkpoints/MQ4_Regression-checkpoint.ipynb +++ b/Experiments/.ipynb_checkpoints/MQ4_Regression-checkpoint.ipynb @@ -38,7 +38,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -60,7 +60,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -98,20 +98,20 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ - "x_MQ4 = sheetMQ3.col_values(0)[2:]\n", - "MQ4_CH4 = sheetMQ3.col_values(1)[2:]\n", - "MQ4_CO = sheetMQ3.col_values(2)[2:]\n", - "MQ4_H2 = sheetMQ3.col_values(3)[2:]\n", - "MQ4_Alcohol = sheetMQ3.col_values(4)[2:]" + "x_MQ4 = sheetMQ4.col_values(0)[2:]\n", + "MQ4_CH4 = sheetMQ4.col_values(1)[2:]\n", + "MQ4_CO = sheetMQ4.col_values(2)[2:]\n", + "MQ4_H2 = sheetMQ4.col_values(3)[2:]\n", + "MQ4_Alcohol = sheetMQ4.col_values(4)[2:]" ] }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -122,7 +122,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -134,7 +134,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 6, "metadata": { "scrolled": false }, @@ -174,7 +174,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -193,7 +193,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -212,7 +212,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -231,7 +231,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -250,7 +250,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -290,14 +290,14 @@ " \r\n", " \r\n", + "\" id=\"m9fe4efa17e\" style=\"stroke:#000000;stroke-width:0.8;\"/>\r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", - " \r\n", + "\" id=\"DejaVuSans-50\"/>\r\n", " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", - " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", - " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", + "\" id=\"ma09837bfe7\" style=\"stroke:#000000;stroke-width:0.6;\"/>\r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", @@ -894,19 +1000,19 @@ " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", + "\" id=\"m6572584403\" style=\"stroke:#000000;stroke-width:0.8;\"/>\r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", @@ -914,14 +1020,14 @@ " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", @@ -929,340 +1035,203 @@ " \r\n", " \r\n", " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", + " \r\n", + " \r\n", " \r\n", - " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", + " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " 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\r\n", + " \r\n", " \r\n", - " \r\n", - " \r\n", " \r\n", - " \r\n", - " \r\n", - " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", + "\" id=\"m0dd72c899e\" style=\"stroke:#1f77b4;\"/>\r\n", " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " 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\r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", " \r\n", " \r\n", - " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", + "\" id=\"m168ab7dfc1\" style=\"stroke:#d62728;\"/>\r\n", " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", @@ -1581,17 +1516,17 @@ "z\r\n", "\" style=\"fill:#ffffff;opacity:0.8;stroke:#cccccc;stroke-linejoin:miter;\"/>\r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", " \r\n", " \r\n", " \r\n", @@ -1632,17 +1550,17 @@ " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", @@ -1978,6 +1896,13 @@ "plt.savefig('MQ4.eps', format = 'eps', dpi = 1200)" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "code", "execution_count": null, diff --git a/Experiments/.ipynb_checkpoints/MQ5_Regression-checkpoint.ipynb b/Experiments/.ipynb_checkpoints/MQ5_Regression-checkpoint.ipynb index cf39570..8bf022f 100644 --- a/Experiments/.ipynb_checkpoints/MQ5_Regression-checkpoint.ipynb +++ b/Experiments/.ipynb_checkpoints/MQ5_Regression-checkpoint.ipynb @@ -38,7 +38,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -60,7 +60,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -98,20 +98,20 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ - "x_MQ5 = sheetMQ3.col_values(0)[2:]\n", - "MQ5_H2 = sheetMQ3.col_values(1)[2:]\n", - "MQ5_LPG = sheetMQ3.col_values(2)[2:]\n", - "MQ5_CH4 = sheetMQ3.col_values(3)[2:]\n", - "MQ5_CO = sheetMQ3.col_values(4)[2:]" + "x_MQ5 = sheetMQ5.col_values(0)[2:]\n", + "MQ5_H2 = sheetMQ5.col_values(1)[2:]\n", + "MQ5_LPG = sheetMQ5.col_values(2)[2:]\n", + "MQ5_CH4 = sheetMQ5.col_values(3)[2:]\n", + "MQ5_CO = sheetMQ5.col_values(4)[2:]" ] }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -122,7 +122,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -134,7 +134,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 7, "metadata": { "scrolled": false }, @@ -174,7 +174,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -193,7 +193,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -212,7 +212,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -231,7 +231,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -250,7 +250,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -290,14 +290,14 @@ " \r\n", " \r\n", + "\" id=\"mc8c2f9bc08\" style=\"stroke:#000000;stroke-width:0.8;\"/>\r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", - " \r\n", + "\" id=\"DejaVuSans-51\"/>\r\n", " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", - " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", - " \r\n", - " \r\n", " \r\n", - " \r\n", - " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", - " \r\n", " \r\n", - " \r\n", - " \r\n", - " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", + "\" id=\"m9818c08126\" style=\"stroke:#000000;stroke-width:0.8;\"/>\r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", @@ -914,14 +871,14 @@ " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", @@ -929,340 +886,203 @@ " \r\n", " \r\n", " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", + " \r\n", + " \r\n", " \r\n", - " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", + " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " 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+ " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", - " \r\n", - " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", diff --git a/Experiments/.ipynb_checkpoints/MQ6_Regression-checkpoint.ipynb b/Experiments/.ipynb_checkpoints/MQ6_Regression-checkpoint.ipynb new file mode 100644 index 0000000..0a3aa4a --- /dev/null +++ b/Experiments/.ipynb_checkpoints/MQ6_Regression-checkpoint.ipynb @@ -0,0 +1,2034 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: pandas in c:\\programdata\\anaconda3\\lib\\site-packages (0.24.2)\n", + "Requirement already satisfied: pytz>=2011k in c:\\programdata\\anaconda3\\lib\\site-packages (from pandas) (2018.9)\n", + "Requirement already satisfied: python-dateutil>=2.5.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from pandas) (2.8.0)\n", + "Requirement already satisfied: numpy>=1.12.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from pandas) (1.16.2)\n", + "Requirement already satisfied: six>=1.5 in c:\\programdata\\anaconda3\\lib\\site-packages (from python-dateutil>=2.5.0->pandas) (1.12.0)\n", + "Requirement already satisfied: xlrd in c:\\programdata\\anaconda3\\lib\\site-packages (1.2.0)\n", + "Requirement already satisfied: sklearn in c:\\programdata\\anaconda3\\lib\\site-packages (0.0)\n", + "Requirement already satisfied: scikit-learn in c:\\programdata\\anaconda3\\lib\\site-packages (from sklearn) (0.21.2)\n", + "Requirement already satisfied: scipy>=0.17.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from scikit-learn->sklearn) (1.2.1)\n", + "Requirement already satisfied: joblib>=0.11 in c:\\programdata\\anaconda3\\lib\\site-packages (from scikit-learn->sklearn) (0.13.2)\n", + "Requirement already satisfied: numpy>=1.11.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from scikit-learn->sklearn) (1.16.2)\n", + "Requirement already satisfied: imblearn in c:\\programdata\\anaconda3\\lib\\site-packages (0.0)\n", + "Requirement already satisfied: imbalanced-learn in c:\\programdata\\anaconda3\\lib\\site-packages (from imblearn) (0.5.0)\n", + "Requirement already satisfied: joblib>=0.11 in c:\\programdata\\anaconda3\\lib\\site-packages (from imbalanced-learn->imblearn) (0.13.2)\n", + "Requirement already satisfied: scipy>=0.17 in c:\\programdata\\anaconda3\\lib\\site-packages (from imbalanced-learn->imblearn) (1.2.1)\n", + "Requirement already satisfied: numpy>=1.11 in c:\\programdata\\anaconda3\\lib\\site-packages (from imbalanced-learn->imblearn) (1.16.2)\n", + "Requirement already satisfied: scikit-learn>=0.21 in c:\\programdata\\anaconda3\\lib\\site-packages (from imbalanced-learn->imblearn) (0.21.2)\n" + ] + } + ], + "source": [ + "!pip install pandas\n", + "!pip install xlrd\n", + "!pip install sklearn\n", + "!pip install imblearn" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import xlrd\n", + "book = xlrd.open_workbook(\"Datasheets info.xlsx\")\n", + "sheetMQ2 = book.sheet_by_name(\"MQ2 - Pololulu\")\n", + "sheetMQ3 = book.sheet_by_name(\"MQ3 - Sparkfun\")\n", + "sheetMQ4 = book.sheet_by_name(\"MQ4 - Sparkfun\")\n", + "sheetMQ5 = book.sheet_by_name(\"MQ5 - Sparkfun\")\n", + "sheetMQ6 = book.sheet_by_name(\"MQ6 - Sparkfun\")\n", + "sheetMQ7 = book.sheet_by_name(\"MQ7 - Sparkfun\")\n", + "sheetMQ8 = book.sheet_by_name(\"MQ8 - Sparkfun\")\n", + "sheetMQ9 = book.sheet_by_name(\"MQ9 - Haoyuelectronics\")\n", + "sheetMQ131 = book.sheet_by_name(\"MQ131- Sensorsportal\")\n", + "sheetMQ135 = book.sheet_by_name(\"MQ135 - HANWEI\")\n", + "sheetMQ303A = book.sheet_by_name(\"MQ303A - HANWEI\")\n", + "sheetMQ309A = book.sheet_by_name(\"MQ309A - HANWEI\")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.1 \t \t \t \t \t \n", + "0.2 \t \t \t \t \t \n", + "0.3 \t 5000.0 \t \t \t \t \n", + "0.4 \t \t \t 5000.0 \t \t \n", + "0.5 \t \t \t \t \t \n", + "0.6 \t \t \t \t \t \n", + "0.7 \t \t \t \t \t \n", + "0.8 \t \t \t \t \t \n", + "0.9 \t \t \t \t \t \n", + "1.0 \t 1000.0 \t \t 2000.0 \t \t \n", + "2.0 \t 200.0 \t 10000.0 \t \t \t \n", + "3.0 \t \t \t \t \t \n", + "4.0 \t \t 400.0 \t \t \t \n", + "5.0 \t \t \t \t \t 3000.0\n", + "6.0 \t \t 200.0 \t \t \t \n", + "7.0 \t \t \t \t 5000.0 \t 400.0\n", + "8.0 \t \t \t \t 1000.0 \t 200.0\n", + "9.0 \t \t \t \t 200.0 \t \n", + "10.0 \t \t \t \t \t \n" + ] + } + ], + "source": [ + "for row_index in range(1,20): #reading first columns\n", + " RsR0, LPG, H2, CH4, CO, Alcohol = sheetMQ6.row_values(row_index, start_colx=0, end_colx=6)\n", + " print(RsR0, \"\t\", LPG, \"\t\", H2, \"\t\", CH4, \"\t\", CO, \"\t\", Alcohol)\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "x_MQ6 = sheetMQ6.col_values(0)[2:]\n", + "MQ6_LPG = sheetMQ6.col_values(1)[2:]\n", + "MQ6_H2 = sheetMQ3.col_values(2)[2:]\n", + "MQ6_CH4 = sheetMQ3.col_values(3)[2:]\n", + "MQ6_CO = sheetMQ3.col_values(4)[2:]\n", + "MQ6_Alcohol = sheetMQ3.col_values(5)[2:]" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "def zero_to_nan(values):\n", + " \"\"\"Replace every 0 with 'nan' and return a copy.\"\"\"\n", + " return [float('nan') if x==0 else x for x in values]" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "MQ5_H2 =zero_to_nan(MQ5_H2)\n", + "MQ5_LPG =zero_to_nan(MQ5_LPG)\n", + "MQ5_CH4 =zero_to_nan(MQ5_CH4)\n", + "MQ5_CO =zero_to_nan(MQ5_CO)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "from sklearn.datasets import load_iris\n", + "#from sklearn.cross_validation import train_test_split\n", + "from sklearn.tree import DecisionTreeClassifier\n", + "from sklearn import datasets\n", + "from sklearn import linear_model\n", + "\n", + "dataH2 = {'RsRo': x_MQ5, 'H2': MQ5_H2}\n", + "dataLPG = {'RsRo': x_MQ5, 'LPG': MQ5_LPG}\n", + "dataCH4 = {'RsRo': x_MQ5, 'CH4': MQ5_CH4}\n", + "dataCO = {'RsRo': x_MQ5, 'CO': MQ5_CO}\n", + "\n", + "dfMQ5_H2 = pd.DataFrame(dataH2)\n", + "dfMQ5_LPG = pd.DataFrame(dataLPG)\n", + "dfMQ5_CH4 = pd.DataFrame(dataCH4)\n", + "dfMQ5_CO = pd.DataFrame(dataCO)\n", + "\n", + "dfMQ5_H2['H2'] = pd.to_numeric(dfMQ5_H2['H2'])\n", + "dfMQ5_LPG['LPG'] = pd.to_numeric(dfMQ5_LPG['LPG'])\n", + "dfMQ5_CH4['CH4'] = pd.to_numeric(dfMQ5_CH4['CH4'])\n", + "dfMQ5_CO['CO'] = pd.to_numeric(dfMQ5_CO['CO'])\n", + "\n", + "dfMQ5_H2['H2'] = dfMQ5_H2['H2'].replace('',None, regex=True)\n", + "dfMQ5_LPG['LPG'] = dfMQ5_LPG['LPG'].replace('',None, regex=True)\n", + "dfMQ5_CH4['CH4'] = dfMQ5_CH4['CH4'].replace('',None, regex=True)\n", + "dfMQ5_CO['CO'] = dfMQ5_CO['CO'].replace('',None, regex=True)\n", + "\n", + "#Global X_Predict variable\n", + "X_Predict = dfMQ5_LPG.RsRo.apply(lambda x: [x]).tolist()" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [], + "source": [ + "#Model and train H2\n", + "dataset2TrainH2 = dfMQ5_H2.copy()\n", + "dataset2TrainH2.dropna(inplace=True)\n", + "X_trainH2 = dataset2TrainH2.RsRo.apply(lambda x: [x]).tolist()\n", + "y_trainH2 = dataset2TrainH2['H2'].tolist()\n", + "model = linear_model.Lasso(alpha=0.1)\n", + "model.fit(X_trainH2, y_trainH2)\n", + "#Predict\n", + "H2_Predicted = model.predict(X_Predict)\n", + "#save into MQ2\n", + "MQ5_H2 = H2_Predicted\n" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [], + "source": [ + "#Model and train LPG\n", + "dataset2TrainLPG = dfMQ5_LPG.copy()\n", + "dataset2TrainLPG.dropna(inplace=True)\n", + "X_trainLPG = dataset2TrainLPG.RsRo.apply(lambda x: [x]).tolist()\n", + "y_trainLPG = dataset2TrainLPG['LPG'].tolist()\n", + "model = linear_model.Lasso(alpha=0.1)\n", + "model.fit(X_trainLPG, y_trainLPG)\n", + "#Predict\n", + "LPG_Predicted = model.predict(X_Predict)\n", + "#save into MQ2\n", + "MQ5_LPG = LPG_Predicted" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [], + "source": [ + "#Model and train CH4\n", + "dataset2TrainCH4 = dfMQ5_CH4.copy()\n", + "dataset2TrainCH4.dropna(inplace=True)\n", + "X_trainCH4 = dataset2TrainCH4.RsRo.apply(lambda x: [x]).tolist()\n", + "y_trainCH4 = dataset2TrainCH4['CH4'].tolist()\n", + "model = linear_model.Lasso(alpha=0.1)\n", + "model.fit(X_trainCH4, y_trainCH4)\n", + "#Predict\n", + "CH4_Predicted = model.predict(X_Predict)\n", + "#save into MQ2\n", + "MQ5_CH4 = CH4_Predicted" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [], + "source": [ + "#Model and train CO\n", + "dataset2TrainCO = dfMQ5_CO.copy()\n", + "dataset2TrainCO.dropna(inplace=True)\n", + "X_trainCO = dataset2TrainCO.RsRo.apply(lambda x: [x]).tolist()\n", + "y_trainCO = dataset2TrainCO['CO'].tolist()\n", + "model = linear_model.Lasso(alpha=0.1)\n", + "model.fit(X_trainCO, y_trainCO)\n", + "#Predict\n", + "CO_Predicted = model.predict(X_Predict)\n", + "#save into MQ2\n", + "MQ5_CO = CO_Predicted" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\r\n", + "\r\n", + "\r\n", + "\r\n", + " \r\n", + " \r\n", + " \r\n", + " 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "%config InlineBackend.figure_formats = ['svg']\n", + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.lines as mlines\n", + "import matplotlib.transforms as mtransforms\n", + "\n", + "fig, ax = plt.subplots()\n", + "\n", + "fig.set_size_inches(9, 5.5, forward=True)\n", + "fig.set_dpi(200)\n", + "\n", + "# only these two lines are calibration curves\n", + "plt.plot(MQ5_H2, x_MQ5, marker='o', linewidth=1, label='H2')\n", + "plt.plot(MQ5_LPG, x_MQ5, marker='o', linewidth=1, label='LPG')\n", + "plt.plot(MQ5_CH4, x_MQ5, marker='o', linewidth=1, label='CH4')\n", + "plt.plot(MQ5_CO, x_MQ5, marker='o', linewidth=1, label='CO')\n", + "\n", + "\n", + "# reference line, legends, and axis labels\n", + "#line = mlines.Line2D([0, 1], [0, 1], color='black')\n", + "#transform = ax.transAxes\n", + "#line.set_transform(transform)\n", + "#ax.add_line(line)\n", + "plt.yscale('log')\n", + "plt.xscale('log')\n", + "plt.legend()\n", + "\n", + "plt.grid(b=True, which='minor', color='lightgrey', linestyle='--')\n", + "\n", + "fig.suptitle('Calibration plot for MQ-5 data')\n", + "ax.set_xlabel('PPM Concentration')\n", + "ax.set_ylabel('Rs/Ro')\n", + "\n", + "\n", + "#Save image\n", + "plt.savefig('MQ5.svg', format = 'svg', dpi = 1200)\n", + "plt.savefig('MQ5.png')\n", + "plt.savefig('MQ5.eps', format = 'eps', dpi = 1200)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "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", + "version": "3.7.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Experiments/MQ4.eps b/Experiments/MQ4.eps index 9c19ddf..5e23852 100644 --- a/Experiments/MQ4.eps +++ b/Experiments/MQ4.eps @@ -1,7 +1,7 @@ %!PS-Adobe-3.0 EPSF-3.0 %%Title: MQ4.eps %%Creator: matplotlib version 3.0.3, http://matplotlib.org/ -%%CreationDate: Mon Jul 1 22:07:15 2019 +%%CreationDate: Wed Jul 3 20:40:18 2019 %%Orientation: portrait %%BoundingBox: -18 198 630 594 %%EndComments @@ -42,7 +42,7 @@ newpath /FontMatrix[.001 0 0 .001 0 0]def /FontBBox[-1021 -463 1793 1232]def /FontType 3 def -/Encoding [ /space /hyphen /slash /zero /one /two /four /A /C /H /M /O /P /Q /R /a /b /c /d /e /f /h /i /l /n /o /p /r /s /t /minus ] def +/Encoding [ /space /hyphen /slash /zero /one /two /three /four /A /C /H /M /O /P /Q /R /a /b /c /d /e /f /h /i /l /n /o /p /r /s /t ] def /FontInfo 10 dict dup begin /FamilyName (DejaVu Sans) def /FullName (DejaVu Sans) def @@ -130,6 +130,38 @@ _cl}_d 446 344 420 317 376 272 _c 332 227 271 164 192 83 _c _cl}_e}_d +/three{{636 0 76 -13 556 742 _sc +406 393 _m +453 383 490 362 516 330 _c +542 298 556 258 556 212 _c +556 140 531 84 482 45 _c +432 6 362 -13 271 -13 _c +240 -13 208 -10 176 -4 _c +144 1 110 10 76 22 _c +76 117 _l +103 101 133 89 166 81 _c +198 73 232 69 268 69 _c +330 69 377 81 409 105 _c +441 129 458 165 458 212 _c +458 254 443 288 413 312 _c +383 336 341 349 287 349 _c +}_e{202 349 _l +202 430 _l +291 430 _l +339 430 376 439 402 459 _c +428 478 441 506 441 543 _c +441 580 427 609 401 629 _c +374 649 336 659 287 659 _c +260 659 231 656 200 650 _c +169 644 135 635 98 623 _c +98 711 _l +135 721 170 729 203 734 _c +235 739 266 742 296 742 _c +370 742 429 725 473 691 _c +517 657 539 611 539 553 _c +539 513 527 479 504 451 _c +481 423 448 403 406 393 _c +_cl}_e}_d /four{636 0 49 0 580 729 _sc 378 643 _m 129 254 _l @@ -626,13 +658,6 @@ _cl}_d 93 702 _l 183 702 _l _cl}_d -/minus{838 0 106 272 732 355 _sc -106 355 _m -732 355 _l -732 272 _l -106 272 _l -106 355 _l -_cl}_d end readonly def 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b/Experiments/MQ4_Regression.ipynb index 5fcf942..728a44f 100644 --- a/Experiments/MQ4_Regression.ipynb +++ b/Experiments/MQ4_Regression.ipynb @@ -38,7 +38,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -60,7 +60,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -98,20 +98,20 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ - "x_MQ4 = sheetMQ3.col_values(0)[2:]\n", - "MQ4_CH4 = sheetMQ3.col_values(1)[2:]\n", - "MQ4_CO = sheetMQ3.col_values(2)[2:]\n", - "MQ4_H2 = sheetMQ3.col_values(3)[2:]\n", - "MQ4_Alcohol = sheetMQ3.col_values(4)[2:]" + "x_MQ4 = sheetMQ4.col_values(0)[2:]\n", + "MQ4_CH4 = sheetMQ4.col_values(1)[2:]\n", + "MQ4_CO = sheetMQ4.col_values(2)[2:]\n", + "MQ4_H2 = sheetMQ4.col_values(3)[2:]\n", + "MQ4_Alcohol = sheetMQ4.col_values(4)[2:]" ] }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -122,7 +122,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -134,7 +134,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 6, "metadata": { "scrolled": false }, @@ -174,7 +174,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -193,7 +193,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -212,7 +212,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -231,7 +231,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -250,7 +250,7 @@ }, { "cell_type": "code", - "execution_count": 35, + 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" \r\n", " \r\n", " \r\n", - " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", @@ -894,19 +1000,19 @@ " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", + "\" id=\"m6572584403\" style=\"stroke:#000000;stroke-width:0.8;\"/>\r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", @@ -914,14 +1020,14 @@ " \r\n", " \r\n", " \r\n", - " 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- " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", @@ -1581,17 +1516,17 @@ "z\r\n", "\" style=\"fill:#ffffff;opacity:0.8;stroke:#cccccc;stroke-linejoin:miter;\"/>\r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", " \r\n", " \r\n", " \r\n", @@ -1632,17 +1550,17 @@ " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " 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"MQ5_CO = sheetMQ3.col_values(4)[2:]" + "x_MQ5 = sheetMQ5.col_values(0)[2:]\n", + "MQ5_H2 = sheetMQ5.col_values(1)[2:]\n", + "MQ5_LPG = sheetMQ5.col_values(2)[2:]\n", + "MQ5_CH4 = sheetMQ5.col_values(3)[2:]\n", + "MQ5_CO = sheetMQ5.col_values(4)[2:]" ] }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -122,7 +122,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -134,7 +134,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 7, "metadata": { "scrolled": false }, @@ -174,7 +174,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -193,7 +193,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -212,7 +212,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -231,7 +231,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -250,7 +250,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -290,14 +290,14 @@ " \r\n", " \r\n", + "\" id=\"mc8c2f9bc08\" style=\"stroke:#000000;stroke-width:0.8;\"/>\r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", - " \r\n", + "\" id=\"DejaVuSans-51\"/>\r\n", " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", - " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", - " \r\n", - " \r\n", " \r\n", - " \r\n", - " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", - " \r\n", " \r\n", - " \r\n", - " \r\n", - " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", + "\" id=\"m9818c08126\" style=\"stroke:#000000;stroke-width:0.8;\"/>\r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", @@ -914,14 +871,14 @@ " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", @@ -929,340 +886,203 @@ " \r\n", " \r\n", " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", + " \r\n", + " \r\n", " \r\n", - " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", + " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", + "\" id=\"mad601a4878\" style=\"stroke:#1f77b4;\"/>\r\n", " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", " \r\n", " \r\n", - " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", + "\" id=\"m6980b74d7a\" style=\"stroke:#ff7f0e;\"/>\r\n", " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", " \r\n", " \r\n", - " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", + "\" id=\"m49f8dc263d\" style=\"stroke:#2ca02c;\"/>\r\n", " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", " \r\n", " \r\n", - " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", + "\" id=\"m2cfb26faca\" style=\"stroke:#d62728;\"/>\r\n", " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", - " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", @@ -1581,17 +1337,17 @@ "z\r\n", "\" style=\"fill:#ffffff;opacity:0.8;stroke:#cccccc;stroke-linejoin:miter;\"/>\r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", - " \r\n", - " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", " \r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", diff --git a/Experiments/MQ6.eps b/Experiments/MQ6.eps new file mode 100644 index 0000000..525c111 --- /dev/null +++ b/Experiments/MQ6.eps @@ -0,0 +1,2441 @@ +%!PS-Adobe-3.0 EPSF-3.0 +%%Title: MQ6.eps +%%Creator: matplotlib version 3.0.3, http://matplotlib.org/ +%%CreationDate: Wed Jul 3 20:49:28 2019 +%%Orientation: portrait +%%BoundingBox: -18 198 630 594 +%%EndComments +%%BeginProlog +/mpldict 8 dict def +mpldict begin +/m { moveto } bind def +/l { lineto } bind def +/r { rlineto } bind def +/c { curveto } bind def +/cl { closepath } bind def +/box { +m +1 index 0 r +0 exch r +neg 0 r +cl +} bind def +/clipbox { +box +clip +newpath +} bind def +%!PS-Adobe-3.0 Resource-Font +%%Title: DejaVu Sans +%%Copyright: Copyright (c) 2003 by Bitstream, Inc. All Rights Reserved. Copyright (c) 2006 by Tavmjong Bah. All Rights Reserved. DejaVu changes are in public domain +%%Creator: Converted from TrueType to type 3 by PPR +25 dict begin +/_d{bind def}bind def +/_m{moveto}_d +/_l{lineto}_d +/_cl{closepath eofill}_d +/_c{curveto}_d +/_sc{7 -1 roll{setcachedevice}{pop pop pop pop pop pop}ifelse}_d +/_e{exec}_d +/FontName /DejaVuSans def +/PaintType 0 def +/FontMatrix[.001 0 0 .001 0 0]def +/FontBBox[-1021 -463 1793 1232]def +/FontType 3 def +/Encoding [ /space /hyphen /slash /zero /one /two /three /four /six /A /C /G /H /L /M /O /P /Q /R /a /b /c /d /e /f /h /i /l /n /o /p /r /s /t ] def +/FontInfo 10 dict dup begin +/FamilyName (DejaVu Sans) def +/FullName (DejaVu Sans) def +/Notice (Copyright (c) 2003 by Bitstream, Inc. All Rights Reserved. Copyright (c) 2006 by Tavmjong Bah. All Rights Reserved. DejaVu changes are in public domain ) def +/Weight (Book) def +/Version (Version 2.35) def +/ItalicAngle 0.0 def +/isFixedPitch false def +/UnderlinePosition -130 def +/UnderlineThickness 90 def +end readonly def +/CharStrings 35 dict dup begin +/.notdef 0 def +/space{318 0 0 0 0 0 _sc +}_d +/hyphen{361 0 49 234 312 314 _sc +49 314 _m +312 314 _l +312 234 _l +49 234 _l +49 314 _l +_cl}_d +/slash{337 0 0 -92 337 729 _sc +254 729 _m +337 729 _l +83 -92 _l +0 -92 _l +254 729 _l +_cl}_d +/zero{636 0 66 -13 570 742 _sc +318 664 _m +267 664 229 639 203 589 _c +177 539 165 464 165 364 _c +165 264 177 189 203 139 _c +229 89 267 64 318 64 _c +369 64 407 89 433 139 _c +458 189 471 264 471 364 _c +471 464 458 539 433 589 _c +407 639 369 664 318 664 _c +318 742 _m +399 742 461 709 505 645 _c +548 580 570 486 570 364 _c +570 241 548 147 505 83 _c +461 19 399 -13 318 -13 _c +236 -13 173 19 130 83 _c +87 147 66 241 66 364 _c +66 486 87 580 130 645 _c +173 709 236 742 318 742 _c +_cl}_d +/one{636 0 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yxc;vH{}AKsAHd|IIAEa5g+vmC1{CBsw6sU@qvK&p&tV?*9Vd?Ccr< literal 0 HcmV?d00001 diff --git a/Experiments/MQ6.svg b/Experiments/MQ6.svg new file mode 100644 index 0000000..dc28fe1 --- /dev/null +++ b/Experiments/MQ6.svg @@ -0,0 +1,1537 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 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already satisfied: pandas in c:\\programdata\\anaconda3\\lib\\site-packages (0.24.2)\n", + "Requirement already satisfied: pytz>=2011k in c:\\programdata\\anaconda3\\lib\\site-packages (from pandas) (2018.9)\n", + "Requirement already satisfied: python-dateutil>=2.5.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from pandas) (2.8.0)\n", + "Requirement already satisfied: numpy>=1.12.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from pandas) (1.16.2)\n", + "Requirement already satisfied: six>=1.5 in c:\\programdata\\anaconda3\\lib\\site-packages (from python-dateutil>=2.5.0->pandas) (1.12.0)\n", + "Requirement already satisfied: xlrd in c:\\programdata\\anaconda3\\lib\\site-packages (1.2.0)\n", + "Requirement already satisfied: sklearn in c:\\programdata\\anaconda3\\lib\\site-packages (0.0)\n", + "Requirement already satisfied: scikit-learn in c:\\programdata\\anaconda3\\lib\\site-packages (from sklearn) (0.21.2)\n", + "Requirement already satisfied: scipy>=0.17.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from scikit-learn->sklearn) (1.2.1)\n", + "Requirement already satisfied: joblib>=0.11 in c:\\programdata\\anaconda3\\lib\\site-packages (from scikit-learn->sklearn) (0.13.2)\n", + "Requirement already satisfied: numpy>=1.11.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from scikit-learn->sklearn) (1.16.2)\n", + "Requirement already satisfied: imblearn in c:\\programdata\\anaconda3\\lib\\site-packages (0.0)\n", + "Requirement already satisfied: imbalanced-learn in c:\\programdata\\anaconda3\\lib\\site-packages (from imblearn) (0.5.0)\n", + "Requirement already satisfied: joblib>=0.11 in c:\\programdata\\anaconda3\\lib\\site-packages (from imbalanced-learn->imblearn) (0.13.2)\n", + "Requirement already satisfied: scipy>=0.17 in c:\\programdata\\anaconda3\\lib\\site-packages (from imbalanced-learn->imblearn) (1.2.1)\n", + "Requirement already satisfied: numpy>=1.11 in c:\\programdata\\anaconda3\\lib\\site-packages (from imbalanced-learn->imblearn) (1.16.2)\n", + "Requirement already satisfied: scikit-learn>=0.21 in c:\\programdata\\anaconda3\\lib\\site-packages (from imbalanced-learn->imblearn) (0.21.2)\n" + ] + } + ], + "source": [ + "!pip install pandas\n", + "!pip install xlrd\n", + "!pip install sklearn\n", + "!pip install imblearn" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import xlrd\n", + "book = xlrd.open_workbook(\"Datasheets info.xlsx\")\n", + "sheetMQ2 = book.sheet_by_name(\"MQ2 - Pololulu\")\n", + "sheetMQ3 = book.sheet_by_name(\"MQ3 - Sparkfun\")\n", + "sheetMQ4 = book.sheet_by_name(\"MQ4 - Sparkfun\")\n", + "sheetMQ5 = book.sheet_by_name(\"MQ5 - Sparkfun\")\n", + "sheetMQ6 = book.sheet_by_name(\"MQ6 - Sparkfun\")\n", + "sheetMQ7 = book.sheet_by_name(\"MQ7 - Sparkfun\")\n", + "sheetMQ8 = book.sheet_by_name(\"MQ8 - Sparkfun\")\n", + "sheetMQ9 = book.sheet_by_name(\"MQ9 - Haoyuelectronics\")\n", + "sheetMQ131 = book.sheet_by_name(\"MQ131- Sensorsportal\")\n", + "sheetMQ135 = book.sheet_by_name(\"MQ135 - HANWEI\")\n", + "sheetMQ303A = book.sheet_by_name(\"MQ303A - HANWEI\")\n", + "sheetMQ309A = book.sheet_by_name(\"MQ309A - HANWEI\")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.1 \t \t \t \t \t \n", + "0.2 \t \t \t \t \t \n", + "0.3 \t 5000.0 \t \t \t \t \n", + "0.4 \t \t \t 5000.0 \t \t \n", + "0.5 \t \t \t \t \t \n", + "0.6 \t \t \t \t \t \n", + "0.7 \t \t \t \t \t \n", + "0.8 \t \t \t \t \t \n", + "0.9 \t \t \t \t \t \n", + "1.0 \t 1000.0 \t \t 2000.0 \t \t \n", + "2.0 \t 200.0 \t 10000.0 \t \t \t \n", + "3.0 \t \t \t \t \t \n", + "4.0 \t \t 400.0 \t \t \t \n", + "5.0 \t \t \t \t \t 3000.0\n", + "6.0 \t \t 200.0 \t \t \t \n", + "7.0 \t \t \t \t 5000.0 \t 400.0\n", + "8.0 \t \t \t \t 1000.0 \t 200.0\n", + "9.0 \t \t \t \t 200.0 \t \n", + "10.0 \t \t \t \t \t \n" + ] + } + ], + "source": [ + "for row_index in range(1,20): #reading first columns\n", + " RsR0, LPG, H2, CH4, CO, Alcohol = sheetMQ6.row_values(row_index, start_colx=0, end_colx=6)\n", + " print(RsR0, \"\t\", LPG, \"\t\", H2, \"\t\", CH4, \"\t\", CO, \"\t\", Alcohol)\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "x_MQ6 = sheetMQ6.col_values(0)[2:]\n", + "MQ6_LPG = sheetMQ6.col_values(1)[2:]\n", + "MQ6_H2 = sheetMQ6.col_values(2)[2:]\n", + "MQ6_CH4 = sheetMQ6.col_values(3)[2:]\n", + "MQ6_CO = sheetMQ6.col_values(4)[2:]\n", + "MQ6_Alcohol = sheetMQ6.col_values(5)[2:]" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "def zero_to_nan(values):\n", + " \"\"\"Replace every 0 with 'nan' and return a copy.\"\"\"\n", + " return [float('nan') if x==0 else x for x in values]" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "MQ6_H2 =zero_to_nan(MQ6_H2)\n", + "MQ6_LPG =zero_to_nan(MQ6_LPG)\n", + "MQ6_CH4 =zero_to_nan(MQ6_CH4)\n", + "MQ6_CO =zero_to_nan(MQ6_CO)\n", + "MQ6_Alcohol =zero_to_nan(MQ6_Alcohol)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "from sklearn.datasets import load_iris\n", + "#from sklearn.cross_validation import train_test_split\n", + "from sklearn.tree import DecisionTreeClassifier\n", + "from sklearn import datasets\n", + "from sklearn import linear_model\n", + "\n", + "dataH2 = {'RsRo': x_MQ6, 'H2': MQ6_H2}\n", + "dataLPG = {'RsRo': x_MQ6, 'LPG': MQ6_LPG}\n", + "dataCH4 = {'RsRo': x_MQ6, 'CH4': MQ6_CH4}\n", + "dataCO = {'RsRo': x_MQ6, 'CO': MQ6_CO}\n", + "dataALcohol = {'RsRo': x_MQ6, 'Alcohol': MQ6_Alcohol}\n", + "\n", + "dfMQ6_H2 = pd.DataFrame(dataH2)\n", + "dfMQ6_LPG = pd.DataFrame(dataLPG)\n", + "dfMQ6_CH4 = pd.DataFrame(dataCH4)\n", + "dfMQ6_CO = pd.DataFrame(dataCO)\n", + "dfMQ6_Alcohol = pd.DataFrame(dataALcohol)\n", + "\n", + "dfMQ6_H2['H2'] = pd.to_numeric(dfMQ6_H2['H2'])\n", + "dfMQ6_LPG['LPG'] = pd.to_numeric(dfMQ6_LPG['LPG'])\n", + "dfMQ6_CH4['CH4'] = pd.to_numeric(dfMQ6_CH4['CH4'])\n", + "dfMQ6_CO['CO'] = pd.to_numeric(dfMQ6_CO['CO'])\n", + "dfMQ6_Alcohol['Alcohol'] = pd.to_numeric(dfMQ6_Alcohol['Alcohol'])\n", + "\n", + "dfMQ6_H2['H2'] = dfMQ6_H2['H2'].replace('',None, regex=True)\n", + "dfMQ6_LPG['LPG'] = dfMQ6_LPG['LPG'].replace('',None, regex=True)\n", + "dfMQ6_CH4['CH4'] = dfMQ6_CH4['CH4'].replace('',None, regex=True)\n", + "dfMQ6_CO['CO'] = dfMQ6_CO['CO'].replace('',None, regex=True)\n", + "dfMQ6_Alcohol['Alcohol'] = dfMQ6_Alcohol['Alcohol'].replace('',None, regex=True)\n", + "\n", + "#Global X_Predict variable\n", + "X_Predict = dfMQ6_LPG.RsRo.apply(lambda x: [x]).tolist()" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "#Model and train H2\n", + "dataset2TrainH2 = dfMQ6_H2.copy()\n", + "dataset2TrainH2.dropna(inplace=True)\n", + "X_trainH2 = dataset2TrainH2.RsRo.apply(lambda x: [x]).tolist()\n", + "y_trainH2 = dataset2TrainH2['H2'].tolist()\n", + "model = linear_model.Lasso(alpha=0.1)\n", + "model.fit(X_trainH2, y_trainH2)\n", + "#Predict\n", + "H2_Predicted = model.predict(X_Predict)\n", + "#save into MQ2\n", + "MQ6_H2 = H2_Predicted\n" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "#Model and train LPG\n", + "dataset2TrainLPG = dfMQ6_LPG.copy()\n", + "dataset2TrainLPG.dropna(inplace=True)\n", + "X_trainLPG = dataset2TrainLPG.RsRo.apply(lambda x: [x]).tolist()\n", + "y_trainLPG = dataset2TrainLPG['LPG'].tolist()\n", + "model = linear_model.Lasso(alpha=0.1)\n", + "model.fit(X_trainLPG, y_trainLPG)\n", + "#Predict\n", + "LPG_Predicted = model.predict(X_Predict)\n", + "#save into MQ2\n", + "MQ6_LPG = LPG_Predicted" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "#Model and train CH4\n", + "dataset2TrainCH4 = dfMQ6_CH4.copy()\n", + "dataset2TrainCH4.dropna(inplace=True)\n", + "X_trainCH4 = dataset2TrainCH4.RsRo.apply(lambda x: [x]).tolist()\n", + "y_trainCH4 = dataset2TrainCH4['CH4'].tolist()\n", + "model = linear_model.Lasso(alpha=0.1)\n", + "model.fit(X_trainCH4, y_trainCH4)\n", + "#Predict\n", + "CH4_Predicted = model.predict(X_Predict)\n", + "#save into MQ2\n", + "MQ6_CH4 = CH4_Predicted" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "#Model and train CO\n", + "dataset2TrainCO = dfMQ6_CO.copy()\n", + "dataset2TrainCO.dropna(inplace=True)\n", + "X_trainCO = dataset2TrainCO.RsRo.apply(lambda x: [x]).tolist()\n", + "y_trainCO = dataset2TrainCO['CO'].tolist()\n", + "model = linear_model.Lasso(alpha=0.1)\n", + "model.fit(X_trainCO, y_trainCO)\n", + "#Predict\n", + "CO_Predicted = model.predict(X_Predict)\n", + "#save into MQ2\n", + "MQ6_CO = CO_Predicted" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "#Model and train Alcohol\n", + "dataset2TrainAlcohol = dfMQ6_Alcohol.copy()\n", + "dataset2TrainAlcohol.dropna(inplace=True)\n", + "X_trainAlcohol = dataset2TrainAlcohol.RsRo.apply(lambda x: [x]).tolist()\n", + "y_trainAlcohol = dataset2TrainAlcohol['Alcohol'].tolist()\n", + "model = linear_model.Lasso(alpha=0.1)\n", + "model.fit(X_trainAlcohol, y_trainAlcohol)\n", + "#Predict\n", + "Alcohol_Predicted = model.predict(X_Predict)\n", + "#save into MQ2\n", + "MQ6_Alcohol = Alcohol_Predicted" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\r\n", + "\r\n", + "\r\n", + "\r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "%config InlineBackend.figure_formats = ['svg']\n", + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.lines as mlines\n", + "import matplotlib.transforms as mtransforms\n", + "\n", + "fig, ax = plt.subplots()\n", + "\n", + "fig.set_size_inches(9, 5.5, forward=True)\n", + "fig.set_dpi(200)\n", + "\n", + "# only these two lines are calibration curves\n", + "plt.plot(MQ6_H2, x_MQ6, marker='o', linewidth=1, label='H2')\n", + "plt.plot(MQ6_LPG, x_MQ6, marker='o', linewidth=1, label='LPG')\n", + "plt.plot(MQ6_CH4, x_MQ6, marker='o', linewidth=1, label='CH4')\n", + "plt.plot(MQ6_CO, x_MQ6, marker='o', linewidth=1, label='CO')\n", + "plt.plot(MQ6_Alcohol, x_MQ6, marker='o', linewidth=1, label='Alcohol')\n", + "\n", + "\n", + "# reference line, legends, and axis labels\n", + "#line = mlines.Line2D([0, 1], [0, 1], color='black')\n", + "#transform = ax.transAxes\n", + "#line.set_transform(transform)\n", + "#ax.add_line(line)\n", + "plt.yscale('log')\n", + "plt.xscale('log')\n", + "plt.legend()\n", + "\n", + "plt.grid(b=True, which='minor', color='lightgrey', linestyle='--')\n", + "\n", + "fig.suptitle('Calibration plot for MQ-5 data')\n", + "ax.set_xlabel('PPM Concentration')\n", + "ax.set_ylabel('Rs/Ro')\n", + "\n", + "\n", + "#Save image\n", + "plt.savefig('MQ5.svg', format = 'svg', dpi = 1200)\n", + "plt.savefig('MQ5.png')\n", + "plt.savefig('MQ5.eps', format = 'eps', dpi = 1200)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "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", + "version": "3.7.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}